From a7c337041ea238c90fa2de071dd113c2c76708fe Mon Sep 17 00:00:00 2001
From: anton-climate <anton.steketee@anu.edu.au>
Date: Mon, 11 Dec 2023 11:27:20 +1100
Subject: [PATCH 1/4] sea ice comparison to observations

---
 .../SeaIce_Obs_Model_Compare.ipynb            | 4442 +++++++++++++++++
 1 file changed, 4442 insertions(+)
 create mode 100644 DocumentedExamples/SeaIce_Obs_Model_Compare.ipynb

diff --git a/DocumentedExamples/SeaIce_Obs_Model_Compare.ipynb b/DocumentedExamples/SeaIce_Obs_Model_Compare.ipynb
new file mode 100644
index 00000000..68be7cb5
--- /dev/null
+++ b/DocumentedExamples/SeaIce_Obs_Model_Compare.ipynb
@@ -0,0 +1,4442 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Sea Ice Comparisons to Observations\n",
+    "\n",
+    "This script shows how to load and plot sea ice concentration from CICE output and compare it to the NSIDC CDR (National Snow and Ice Data Centre, Climate Data Record) dataset\n",
+    "\n",
+    "This notebook uses the _ACCESS-NRI Intake Catalog_ following the examples in [Tutorails/Using the Intake Catalog](https://cosima-recipes.readthedocs.io/en/latest/Tutorials/Using_Intake_Catalog.html). \n",
+    "\n",
+    "Requirements: The runs analysed here are only in access-nri-intake-catalog version 0.1.0 or newer. This is included in `conda/analysis3-23.10` or newer modules from `/g/data/hh5/public/modules`. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**OM2 Expreiments:**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These are the ACCESS-OM2 runs we are going to use, we can compare results from prototype runs forced with ERA5 against normal runs using JRA55do, as [described on the ACCESS_HIVE](https://forum.access-hive.org.au/t/era-5-forced-access-om2-simulations/1103/5). To compare against the observational datasets, we use IAF (Inter-Annual Forcing)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-12-11T00:02:09.729461Z",
+     "iopub.status.busy": "2023-12-11T00:02:09.728970Z",
+     "iopub.status.idle": "2023-12-11T00:02:09.742751Z",
+     "shell.execute_reply": "2023-12-11T00:02:09.741622Z",
+     "shell.execute_reply.started": "2023-12-11T00:02:09.729409Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "RUNS={\n",
+    "    '025deg_era5':['025deg_era5_iaf'], #(our name: run name(s))\n",
+    "    '025deg_jra55': ['025deg_jra55_iaf_era5comparison'],\n",
+    "    '1deg_era5':['1deg_era5_iaf'],\n",
+    "    '1deg_jra55':['1deg_jra55_iaf_era5comparison']\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We are going to look at Sea Ice Concentration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-12-11T00:02:09.745974Z",
+     "iopub.status.busy": "2023-12-11T00:02:09.745264Z",
+     "iopub.status.idle": "2023-12-11T00:02:09.759216Z",
+     "shell.execute_reply": "2023-12-11T00:02:09.758030Z",
+     "shell.execute_reply.started": "2023-12-11T00:02:09.745935Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "VARS=['aice_m'] # ice area fraction or sea ice concentration\n",
+    "VARS_2D=['area_t','geolat_t','geolon_t']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observational Data:**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sea Ice concentration is measured through passice microwave remote sensing. We are going to used the NSIDC CDR Dataset (descibed at [nsidc.org](https://nsidc.org/data/g02202/versions/4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-12-11T00:02:09.761693Z",
+     "iopub.status.busy": "2023-12-11T00:02:09.760805Z",
+     "iopub.status.idle": "2023-12-11T00:02:09.768264Z",
+     "shell.execute_reply": "2023-12-11T00:02:09.767094Z",
+     "shell.execute_reply.started": "2023-12-11T00:02:09.761645Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ST='1979' #start date\n",
+    "ET='2022' #end date\n",
+    "OBS_TIME_SLICE=slice(ST,ET)\n",
+    "sh_obs_url='https://polarwatch.noaa.gov/erddap/griddap/nsidcG02202v4shmday'\n",
+    "nh_obs_url='https://polarwatch.noaa.gov/erddap/griddap/nsidcG02202v4nhmday'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load packages:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-12-11T00:13:31.647986Z",
+     "iopub.status.busy": "2023-12-11T00:13:31.646840Z",
+     "iopub.status.idle": "2023-12-11T00:13:40.635744Z",
+     "shell.execute_reply": "2023-12-11T00:13:40.634429Z",
+     "shell.execute_reply.started": "2023-12-11T00:13:31.647934Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from intake import cat\n",
+    "from datatree import DataTree,map_over_subtree\n",
+    "\n",
+    "from dask.distributed import Client\n",
+    "\n",
+    "import xarray as xr\n",
+    "import numpy as np\n",
+    "from datetime import timedelta\n",
+    "import cf_xarray as cfxr\n",
+    "import xesmf\n",
+    "\n",
+    "#plotting\n",
+    "import matplotlib.pyplot as plt\n",
+    "import cartopy.crs as ccrs\n",
+    "import cmocean.cm as cmo\n",
+    "import matplotlib.lines as mlines"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Start a dask client"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-12-11T00:13:42.824181Z",
+     "iopub.status.busy": "2023-12-11T00:13:42.821833Z",
+     "iopub.status.idle": "2023-12-11T00:13:46.076825Z",
+     "shell.execute_reply": "2023-12-11T00:13:46.075376Z",
+     "shell.execute_reply.started": "2023-12-11T00:13:42.824117Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "client = Client()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-12-11T00:13:46.080216Z",
+     "iopub.status.busy": "2023-12-11T00:13:46.079404Z",
+     "iopub.status.idle": "2023-12-11T00:13:46.158044Z",
+     "shell.execute_reply": "2023-12-11T00:13:46.157101Z",
+     "shell.execute_reply.started": "2023-12-11T00:13:46.080164Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
+       "    <div style=\"margin-left: 48px;\">\n",
+       "        <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
+       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-249117de-97ba-11ee-9559-00000088fe80</p>\n",
+       "        <table style=\"width: 100%; text-align: left;\">\n",
+       "\n",
+       "        <tr>\n",
+       "        \n",
+       "            <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
+       "            <td style=\"text-align: left;\"><strong>Cluster type:</strong> distributed.LocalCluster</td>\n",
+       "        \n",
+       "        </tr>\n",
+       "\n",
+       "        \n",
+       "            <tr>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Dashboard: </strong> <a href=\"/proxy/8787/status\" target=\"_blank\">/proxy/8787/status</a>\n",
+       "                </td>\n",
+       "                <td style=\"text-align: left;\"></td>\n",
+       "            </tr>\n",
+       "        \n",
+       "\n",
+       "        </table>\n",
+       "\n",
+       "        \n",
+       "            <button style=\"margin-bottom: 12px;\" data-commandlinker-command=\"dask:populate-and-launch-layout\" data-commandlinker-args='{\"url\": \"/proxy/8787/status\" }'>\n",
+       "                Launch dashboard in JupyterLab\n",
+       "            </button>\n",
+       "        \n",
+       "\n",
+       "        \n",
+       "            <details>\n",
+       "            <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
+       "            <div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
+       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\">\n",
+       "    </div>\n",
+       "    <div style=\"margin-left: 48px;\">\n",
+       "        <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">LocalCluster</h3>\n",
+       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">859c7974</p>\n",
+       "        <table style=\"width: 100%; text-align: left;\">\n",
+       "            <tr>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Dashboard:</strong> <a href=\"/proxy/8787/status\" target=\"_blank\">/proxy/8787/status</a>\n",
+       "                </td>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Workers:</strong> 4\n",
+       "                </td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Total threads:</strong> 12\n",
+       "                </td>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Total memory:</strong> 46.00 GiB\n",
+       "                </td>\n",
+       "            </tr>\n",
+       "            \n",
+       "            <tr>\n",
+       "    <td style=\"text-align: left;\"><strong>Status:</strong> running</td>\n",
+       "    <td style=\"text-align: left;\"><strong>Using processes:</strong> True</td>\n",
+       "</tr>\n",
+       "\n",
+       "            \n",
+       "        </table>\n",
+       "\n",
+       "        <details>\n",
+       "            <summary style=\"margin-bottom: 20px;\">\n",
+       "                <h3 style=\"display: inline;\">Scheduler Info</h3>\n",
+       "            </summary>\n",
+       "\n",
+       "            <div style=\"\">\n",
+       "    <div>\n",
+       "        <div style=\"width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;\"> </div>\n",
+       "        <div style=\"margin-left: 48px;\">\n",
+       "            <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n",
+       "            <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-6ecf1630-5135-4f01-a4e8-53c403b18224</p>\n",
+       "            <table style=\"width: 100%; text-align: left;\">\n",
+       "                <tr>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Comm:</strong> tcp://127.0.0.1:45613\n",
+       "                    </td>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Workers:</strong> 4\n",
+       "                    </td>\n",
+       "                </tr>\n",
+       "                <tr>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Dashboard:</strong> <a href=\"/proxy/8787/status\" target=\"_blank\">/proxy/8787/status</a>\n",
+       "                    </td>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Total threads:</strong> 12\n",
+       "                    </td>\n",
+       "                </tr>\n",
+       "                <tr>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Started:</strong> Just now\n",
+       "                    </td>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Total memory:</strong> 46.00 GiB\n",
+       "                    </td>\n",
+       "                </tr>\n",
+       "            </table>\n",
+       "        </div>\n",
+       "    </div>\n",
+       "\n",
+       "    <details style=\"margin-left: 48px;\">\n",
+       "        <summary style=\"margin-bottom: 20px;\">\n",
+       "            <h3 style=\"display: inline;\">Workers</h3>\n",
+       "        </summary>\n",
+       "\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 0</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:43581\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 3\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/34869/status\" target=\"_blank\">/proxy/34869/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 11.50 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:33579\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /jobfs/103751882.gadi-pbs/dask-scratch-space/worker-h4hltsrq\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 1</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:38457\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 3\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/46157/status\" target=\"_blank\">/proxy/46157/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 11.50 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:46027\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /jobfs/103751882.gadi-pbs/dask-scratch-space/worker-zc4vgj6q\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 2</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:33087\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 3\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/35035/status\" target=\"_blank\">/proxy/35035/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 11.50 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:42471\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /jobfs/103751882.gadi-pbs/dask-scratch-space/worker-30cgwyb7\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 3</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:33171\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 3\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/35237/status\" target=\"_blank\">/proxy/35237/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 11.50 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:40003\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /jobfs/103751882.gadi-pbs/dask-scratch-space/worker-jh_ng_zt\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "\n",
+       "    </details>\n",
+       "</div>\n",
+       "\n",
+       "        </details>\n",
+       "    </div>\n",
+       "</div>\n",
+       "            </details>\n",
+       "        \n",
+       "\n",
+       "    </div>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "<Client: 'tcp://127.0.0.1:45613' processes=4 threads=12, memory=46.00 GiB>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "client"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Open the catalog"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-12-11T00:13:46.160118Z",
+     "iopub.status.busy": "2023-12-11T00:13:46.159747Z",
+     "iopub.status.idle": "2023-12-11T00:13:46.552070Z",
+     "shell.execute_reply": "2023-12-11T00:13:46.551171Z",
+     "shell.execute_reply.started": "2023-12-11T00:13:46.160086Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "catalog = cat.access_nri"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Load the ACCESS-OM2 results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For CICE data in OM2, we need to do some wrangling to make it easier to deal with. This is described in more detail in [DocumentedExamples/SeaIce_Plot_Example](https://cosima-recipes.readthedocs.io/en/latest/DocumentedExamples/SeaIce_Plot_Example.html). Its included in this function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-12-11T00:13:46.553629Z",
+     "iopub.status.busy": "2023-12-11T00:13:46.553357Z",
+     "iopub.status.idle": "2023-12-11T00:13:46.563904Z",
+     "shell.execute_reply": "2023-12-11T00:13:46.562914Z",
+     "shell.execute_reply.started": "2023-12-11T00:13:46.553602Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def open_by_experiment(exp_name, vars):\n",
+    "    \"\"\" Concatenate any datasets provided for this experiment into one ds, and add area and geo coordinates \"\"\"\n",
+    "\n",
+    "    import xarray as xr\n",
+    "    import cf_xarray as cfxr\n",
+    "    \n",
+    "    open_by_name = lambda name : catalog[name].search(\n",
+    "        variable=vars\n",
+    "    ).to_dask(\n",
+    "        xarray_open_kwargs={\"chunks\":{'time':'auto','ni':-1,'nj':-1},\"decode_coords\":False},\n",
+    "        xarray_combine_by_coords_kwargs={'compat':'override','data_vars':'minimal', 'coords':'minimal'}\n",
+    "    )\n",
+    "\n",
+    "    #get the data for each run of this config \n",
+    "    cice_ds=xr.concat([\n",
+    "        open_by_name(iName) for iName in RUNS[exp_name]\n",
+    "    ], dim='time'\n",
+    "    )\n",
+    "    \n",
+    "    #We also want the area/lat/lom fields, but these are not time dependent.\n",
+    "    area_ds=xr.merge([\n",
+    "        xr.open_dataset(\n",
+    "            catalog[RUNS[exp_name][0]].search(variable=iVar).df.path[0] \n",
+    "            # path of the first file with the area field, the geolon field and the geolat field \n",
+    "        ).drop('time')\n",
+    "    for iVar in VARS_2D])\n",
+    "\n",
+    "    #Label the lats and lons\n",
+    "    cice_ds.coords['ni'] = area_ds['xt_ocean'].values\n",
+    "    cice_ds.coords['nj'] = area_ds['yt_ocean'].values\n",
+    "    \n",
+    "    #Copy attributes for cf compliance\n",
+    "    cice_ds.ni.attrs=area_ds.xt_ocean.attrs\n",
+    "    cice_ds.nj.attrs=area_ds.yt_ocean.attrs\n",
+    "    \n",
+    "    cice_ds = cice_ds.rename(({'ni':'xt_ocean', 'nj':'yt_ocean'}))\n",
+    "    \n",
+    "    # Add the geolon, geolat, and area as extra co-ordinates fields from area_t\n",
+    "    \n",
+    "    cice_ds=cice_ds.assign_coords({\n",
+    "        'geolat_t':area_ds.geolat_t,\n",
+    "        'geolon_t':area_ds.geolon_t,\n",
+    "        'area_t':area_ds.area_t\n",
+    "    })\n",
+    "\n",
+    "    #cice timestamps are also misleading:\n",
+    "    cice_ds['time'] = cice_ds.time.to_pandas() - timedelta(days = 1)\n",
+    "       \n",
+    "    return cice_ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because the dimensions are different for different experiments, they would not fit in a Dataset, a DataTree is required. The DataTree has a group for each experiment, which contains a xarray dataset with the data for that experiment. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-12-11T00:13:46.566441Z",
+     "iopub.status.busy": "2023-12-11T00:13:46.566175Z",
+     "iopub.status.idle": "2023-12-11T00:17:57.974904Z",
+     "shell.execute_reply": "2023-12-11T00:17:57.973388Z",
+     "shell.execute_reply.started": "2023-12-11T00:13:46.566419Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.3.0\n",
+      "CPU times: user 46.2 s, sys: 6.47 s, total: 52.7 s\n",
+      "Wall time: 4min 11s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "si_name_ds_pairs = [(iRun, open_by_experiment(iRun, VARS)) for iRun in RUNS.keys()]\n",
+    "\n",
+    "si_dt = DataTree.from_dict(\n",
+    "    dict(si_name_ds_pairs)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-12-11T00:17:57.981890Z",
+     "iopub.status.busy": "2023-12-11T00:17:57.981600Z",
+     "iopub.status.idle": "2023-12-11T00:17:57.996827Z",
+     "shell.execute_reply": "2023-12-11T00:17:57.996147Z",
+     "shell.execute_reply.started": "2023-12-11T00:17:57.981867Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "@map_over_subtree\n",
+    "def match_timestamps_to_NSIDC(ds):\n",
+    "\n",
+    "    cice_ds = ds.copy()\n",
+    "\n",
+    "    # we are going to use the same timestamps as NSIDC\n",
+    "    cice_ds['time'] = [np.datetime64(str(i)[0:7]+'-01T00:00:00.000000000') for i in cice_ds.time.values]\n",
+    "\n",
+    "    return cice_ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-12-11T00:17:57.998442Z",
+     "iopub.status.busy": "2023-12-11T00:17:57.997928Z",
+     "iopub.status.idle": "2023-12-11T00:17:58.051695Z",
+     "shell.execute_reply": "2023-12-11T00:17:58.050736Z",
+     "shell.execute_reply.started": "2023-12-11T00:17:57.998408Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "si_dt = match_timestamps_to_NSIDC(si_dt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a datatree, with a dataset for each experiment and timestamps which align with the observational timestamps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-12-11T00:17:58.053122Z",
+     "iopub.status.busy": "2023-12-11T00:17:58.052737Z",
+     "iopub.status.idle": "2023-12-11T00:17:58.727843Z",
+     "shell.execute_reply": "2023-12-11T00:17:58.719424Z",
+     "shell.execute_reply.started": "2023-12-11T00:17:58.053096Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DatasetView&gt;\n",
+       "Dimensions:  ()\n",
+       "Data variables:\n",
+       "    *empty*</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>datatree.DataTree</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-de6013d5-87e1-4af9-af0b-167b837bd2ee' class='xr-section-summary-in' type='checkbox'  ><label for='section-de6013d5-87e1-4af9-af0b-167b837bd2ee' class='xr-section-summary' >Groups: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%'><div style='display: inline-grid;'><div style='grid-column-start: 1;border-right: 0.2em solid;border-color: var(--xr-border-color);height: 100%;width: 0px;'></div><div style='grid-column-start: 2;grid-row-start: 1;height: 1em;width: 20px;border-bottom: 0.2em solid;border-color: var(--xr-border-color);'></div><div style='grid-column-start: 3;'><ul class='xr-sections'><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DatasetView&gt;\n",
+       "Dimensions:   (time: 523, yt_ocean: 1080, xt_ocean: 1440)\n",
+       "Coordinates:\n",
+       "  * time      (time) datetime64[ns] 1980-01-01 1980-02-01 ... 2023-07-01\n",
+       "  * xt_ocean  (xt_ocean) float64 -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n",
+       "  * yt_ocean  (yt_ocean) float64 -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n",
+       "    geolat_t  (yt_ocean, xt_ocean) float32 ...\n",
+       "    geolon_t  (yt_ocean, xt_ocean) float32 ...\n",
+       "    area_t    (yt_ocean, xt_ocean) float32 ...\n",
+       "Data variables:\n",
+       "    aice_m    (time, yt_ocean, xt_ocean) float32 dask.array&lt;chunksize=(1, 1080, 1440), meta=np.ndarray&gt;\n",
+       "Attributes: (12/18)\n",
+       "    title:                                    sea ice model output for CICE\n",
+       "    contents:                                 Diagnostic and Prognostic Varia...\n",
+       "    source:                                   Los Alamos Sea Ice Model (CICE)...\n",
+       "    time_period_freq:                         month_1\n",
+       "    comment3:                                 seconds elapsed into model date...\n",
+       "    conventions:                              CF-1.0\n",
+       "    ...                                       ...\n",
+       "    intake_esm_attrs:variable_standard_name:  ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,...\n",
+       "    intake_esm_attrs:variable_cell_methods:   ,,,,,,,,,,,,,,,,,,time: mean,ti...\n",
+       "    intake_esm_attrs:variable_units:          days since 1980-01-01 00:00:00,...\n",
+       "    intake_esm_attrs:file_id:                 iceh_XXXX_XX\n",
+       "    intake_esm_attrs:_data_format_:           netcdf\n",
+       "    intake_esm_dataset_key:                   iceh_XXXX_XX.1mon</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>025deg_era5</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-e9afb33a-7fb8-47f9-80b7-9850209dd6e3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e9afb33a-7fb8-47f9-80b7-9850209dd6e3' class='xr-section-summary'  title='Expand/collapse section'>Groups: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%'></div></div></li><li class='xr-section-item'><input id='section-66cedc11-3af3-4a1f-b0f3-eb8cfb45557f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-66cedc11-3af3-4a1f-b0f3-eb8cfb45557f' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 523</li><li><span class='xr-has-index'>yt_ocean</span>: 1080</li><li><span class='xr-has-index'>xt_ocean</span>: 1440</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1e806c0f-e813-448b-a5bf-658528f73952' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1e806c0f-e813-448b-a5bf-658528f73952' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1980-01-01 ... 2023-07-01</div><input id='attrs-005e0a38-eca2-410d-974d-ca00038c1fb6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-005e0a38-eca2-410d-974d-ca00038c1fb6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e2eac8c-77fd-48a4-952b-3bb17985604b' class='xr-var-data-in' type='checkbox'><label for='data-1e2eac8c-77fd-48a4-952b-3bb17985604b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1980-01-01T00:00:00.000000000&#x27;, &#x27;1980-02-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;1980-03-01T00:00:00.000000000&#x27;, ..., &#x27;2023-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2023-06-01T00:00:00.000000000&#x27;, &#x27;2023-07-01T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.9 -279.6 ... 79.62 79.88</div><input id='attrs-e30a4250-c01a-4a83-ba7b-06ed444b3ba2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e30a4250-c01a-4a83-ba7b-06ed444b3ba2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c5d0d66-0d73-4100-a4d8-4cd415bc5313' class='xr-var-data-in' type='checkbox'><label for='data-7c5d0d66-0d73-4100-a4d8-4cd415bc5313' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-279.875, -279.625, -279.375, ...,   79.375,   79.625,   79.875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-81.08 -80.97 ... 89.84 89.95</div><input id='attrs-d5ece997-40c3-49b1-98ef-b8b5159831e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5ece997-40c3-49b1-98ef-b8b5159831e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72a39d4e-6498-4872-8871-be27187a3725' class='xr-var-data-in' type='checkbox'><label for='data-72a39d4e-6498-4872-8871-be27187a3725' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-81.077001, -80.971402, -80.865804, ...,  89.736085,  89.841684,\n",
+       "        89.947282])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolat_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-16c308e0-06bf-49c0-9ba9-9a604e6d8c9a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-16c308e0-06bf-49c0-9ba9-9a604e6d8c9a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3cd5c558-76e7-4027-94d6-0f78ec219994' class='xr-var-data-in' type='checkbox'><label for='data-3cd5c558-76e7-4027-94d6-0f78ec219994' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>valid_range :</span></dt><dd>[-91.  91.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[1555200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolon_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dbc4e968-7363-4bdb-a0bd-e5ddb4c0a6eb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dbc4e968-7363-4bdb-a0bd-e5ddb4c0a6eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd1905c1-df39-444d-9220-f314b663db11' class='xr-var-data-in' type='checkbox'><label for='data-bd1905c1-df39-444d-9220-f314b663db11' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>valid_range :</span></dt><dd>[-281.  361.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[1555200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>area_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-496ece30-48de-412b-afd6-68c136e957fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-496ece30-48de-412b-afd6-68c136e957fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99535faf-066a-4928-811a-0b3aab0c78c1' class='xr-var-data-in' type='checkbox'><label for='data-99535faf-066a-4928-811a-0b3aab0c78c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer cell area</dd><dt><span>units :</span></dt><dd>m^2</dd><dt><span>valid_range :</span></dt><dd>[0.e+00 1.e+15]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[1555200 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-54908232-d294-4df4-aaa0-72b1cfb012ac' class='xr-section-summary-in' type='checkbox'  checked><label for='section-54908232-d294-4df4-aaa0-72b1cfb012ac' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>aice_m</span></div><div class='xr-var-dims'>(time, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1440), meta=np.ndarray&gt;</div><input id='attrs-d88d6440-087a-4eb7-818c-b58a18a1c618' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d88d6440-087a-4eb7-818c-b58a18a1c618' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5abb3419-3fee-4f10-89e2-05dffa539ac8' class='xr-var-data-in' type='checkbox'><label for='data-5abb3419-3fee-4f10-89e2-05dffa539ac8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>ice area  (aggregate)</dd><dt><span>coordinates :</span></dt><dd>TLON TLAT time</dd><dt><span>cell_measures :</span></dt><dd>area: tarea</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_rep :</span></dt><dd>averaged</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 3.03 GiB </td>\n",
+       "                        <td> 5.93 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (523, 1080, 1440) </td>\n",
+       "                        <td> (1, 1080, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 523 chunks in 1047 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"205\" height=\"165\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"90\" x2=\"35\" y2=\"115\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"90\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"91\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"92\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"94\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"95\" />\n",
+       "  <line x1=\"16\" y1=\"6\" x2=\"16\" y2=\"96\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"98\" />\n",
+       "  <line x1=\"19\" y1=\"9\" x2=\"19\" y2=\"99\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"100\" />\n",
+       "  <line x1=\"22\" y1=\"12\" x2=\"22\" y2=\"102\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"103\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"104\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"106\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"107\" />\n",
+       "  <line x1=\"29\" y1=\"19\" x2=\"29\" y2=\"109\" />\n",
+       "  <line x1=\"30\" y1=\"20\" x2=\"30\" y2=\"110\" />\n",
+       "  <line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"111\" />\n",
+       "  <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"113\" />\n",
+       "  <line x1=\"34\" y1=\"24\" x2=\"34\" y2=\"114\" />\n",
+       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"115\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 35.96853812142349,25.968538121423492 35.96853812142349,115.96853812142349 10.0,90.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"16\" y1=\"6\" x2=\"136\" y2=\"6\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"19\" y1=\"9\" x2=\"139\" y2=\"9\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"22\" y1=\"12\" x2=\"142\" y2=\"12\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" />\n",
+       "  <line x1=\"29\" y1=\"19\" x2=\"149\" y2=\"19\" />\n",
+       "  <line x1=\"30\" y1=\"20\" x2=\"150\" y2=\"20\" />\n",
+       "  <line x1=\"31\" y1=\"21\" x2=\"151\" y2=\"21\" />\n",
+       "  <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" />\n",
+       "  <line x1=\"34\" y1=\"24\" x2=\"154\" y2=\"24\" />\n",
+       "  <line x1=\"35\" y1=\"25\" x2=\"155\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"130\" y1=\"0\" x2=\"155\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 130.0,0.0 155.96853812142348,25.968538121423492 35.96853812142349,25.968538121423492\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"35\" y1=\"25\" x2=\"155\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"115\" x2=\"155\" y2=\"115\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"115\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"155\" y1=\"25\" x2=\"155\" y2=\"115\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"35.96853812142349,25.968538121423492 155.96853812142348,25.968538121423492 155.96853812142348,115.96853812142349 35.96853812142349,115.96853812142349\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"95.968538\" y=\"135.968538\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"175.968538\" y=\"70.968538\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,175.968538,70.968538)\">1080</text>\n",
+       "  <text x=\"12.984269\" y=\"122.984269\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,12.984269,122.984269)\">523</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-fdee836e-8c9b-47b6-a202-f37bddf28472' class='xr-section-summary-in' type='checkbox'  ><label for='section-fdee836e-8c9b-47b6-a202-f37bddf28472' class='xr-section-summary' >Attributes: <span>(18)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>sea ice model output for CICE</dd><dt><span>contents :</span></dt><dd>Diagnostic and Prognostic Variables</dd><dt><span>source :</span></dt><dd>Los Alamos Sea Ice Model (CICE) Version 5</dd><dt><span>time_period_freq :</span></dt><dd>month_1</dd><dt><span>comment3 :</span></dt><dd>seconds elapsed into model date:      0</dd><dt><span>conventions :</span></dt><dd>CF-1.0</dd><dt><span>io_flavor :</span></dt><dd>io_pio</dd><dt><span>intake_esm_vars :</span></dt><dd>[&#x27;aice_m&#x27;]</dd><dt><span>intake_esm_attrs:realm :</span></dt><dd>seaIce</dd><dt><span>intake_esm_attrs:variable :</span></dt><dd>time_bounds,TLON,TLAT,ULON,ULAT,NCAT,tmask,blkmask,tarea,uarea,dxt,dyt,dxu,dyu,HTN,HTE,ANGLE,ANGLET,hi_m,hs_m,Tsfc_m,aice_m,uvel_m,vvel_m,uatm_m,vatm_m,sice_m,fswdn_m,fswup_m,flwdn_m,snow_ai_m,rain_ai_m,frzmlt_m,fswfac_m,fswabs_ai_m,albsni_m,alvdr_ai_m,alidr_ai_m,alvdf_ai_m,alidf_ai_m,albice_m,albsno_m,flat_ai_m,fsens_ai_m,flwup_ai_m,evap_ai_m,Tair_m,congel_m,frazil_m,snoice_m,meltt_m,melts_m,meltb_m,meltl_m,fresh_ai_m,fsalt_m,fsalt_ai_m,fhocn_ai_m,fswthru_ai_m,strairx_m,strairy_m,strtltx_m,strtlty_m,strcorx_m,strcory_m,strocnx_m,strocny_m,strintx_m,strinty_m,strength_m,divu_m,shear_m,dvidtt_m,dvidtd_m,daidtt_m,daidtd_m,trsig_m,ice_present_m,fcondtop_ai_m,fmeltt_ai_m,opening_m,aicen_m,vicen_m,fsurfn_ai_m,fcondtopn_ai_m,fmelttn_ai_m,flatn_ai_m</dd><dt><span>intake_esm_attrs:frequency :</span></dt><dd>1mon</dd><dt><span>intake_esm_attrs:variable_long_name :</span></dt><dd>boundaries for time-averaging interval,T grid center longitude,T grid center latitude,U grid center longitude,U grid center latitude,category maximum thickness,ocean grid mask,ice grid block mask,area of T grid cells,area of U grid cells,T cell width through middle,T cell height through middle,U cell width through middle,U cell height through middle,T cell width on North side,T cell width on East side,angle grid makes with latitude line on U grid,angle grid makes with latitude line on T grid,grid cell mean ice thickness,grid cell mean snow thickness,snow/ice surface temperature,ice area  (aggregate),ice velocity (x),ice velocity (y),atm velocity (x),atm velocity (y),bulk ice salinity,down solar flux,upward solar flux,down longwave flux,snowfall rate,rainfall rate,freeze/melt potential,shortwave scaling factor,snow/ice/ocn absorbed solar flux,snow/ice broad band albedo,visible direct albedo,near IR direct albedo,visible diffuse albedo,near IR diffuse albedo,bare ice albedo,snow albedo,latent heat flux,sensible heat flux,upward longwave flux,evaporative water flux,air temperature,congelation ice growth,frazil ice growth,snow-ice formation,top ice melt,top snow melt,basal ice melt,lateral ice melt,freshwtr flx ice to ocn,salt flux ice to ocn (cpl),salt flux ice to ocean,heat flux ice to ocean,SW flux thru ice to ocean,atm/ice stress (x),atm/ice stress (y),sea sfc tilt stress (x),sea sfc tilt stress (y),coriolis stress (x),coriolis stress (y),ocean/ice stress (x),ocean/ice stress (y),internal ice stress (x),internal ice stress (y),compressive ice strength,strain rate (divergence),strain rate (shear),volume tendency thermo,volume tendency dynamics,area tendency thermo,area tendency dynamics,internal stress tensor trace,fraction of time-avg interval that ice is present,top surface conductive heat flux,net surface heat flux causing melt,lead area opening rate,ice area, categories,ice volume, categories,net surface heat flux, categories,top sfc conductive heat flux, cat,net sfc heat flux causing melt, cat,latent heat flux, category</dd><dt><span>intake_esm_attrs:variable_standard_name :</span></dt><dd>,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,</dd><dt><span>intake_esm_attrs:variable_cell_methods :</span></dt><dd>,,,,,,,,,,,,,,,,,,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean</dd><dt><span>intake_esm_attrs:variable_units :</span></dt><dd>days since 1980-01-01 00:00:00,degrees_east,degrees_north,degrees_east,degrees_north,m,,,m^2,m^2,m,m,m,m,m,m,radians,radians,m,m,C,1,m/s,m/s,m/s,m/s,ppt,W/m^2,W/m^2,W/m^2,cm/day,cm/day,W/m^2,1,W/m^2,%,%,%,%,%,%,%,W/m^2,W/m^2,W/m^2,cm/day,C,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,kg/m^2/s,kg/m^2/s,W/m^2,W/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m,%/day,%/day,cm/day,cm/day,%/day,%/day,N/m^2,1,W/m^2,W/m^2,%/day,1,m,W/m^2,W/m^2,W/m^2,W/m^2</dd><dt><span>intake_esm_attrs:file_id :</span></dt><dd>iceh_XXXX_XX</dd><dt><span>intake_esm_attrs:_data_format_ :</span></dt><dd>netcdf</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>iceh_XXXX_XX.1mon</dd></dl></div></li></ul></div></div></ul></div></div><div style='display: inline-grid;'><div style='grid-column-start: 1;border-right: 0.2em solid;border-color: var(--xr-border-color);height: 100%;width: 0px;'></div><div style='grid-column-start: 2;grid-row-start: 1;height: 1em;width: 20px;border-bottom: 0.2em solid;border-color: var(--xr-border-color);'></div><div style='grid-column-start: 3;'><ul class='xr-sections'><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DatasetView&gt;\n",
+       "Dimensions:   (time: 523, yt_ocean: 1080, xt_ocean: 1440)\n",
+       "Coordinates:\n",
+       "  * time      (time) datetime64[ns] 1980-01-01 1980-02-01 ... 2023-07-01\n",
+       "  * xt_ocean  (xt_ocean) float64 -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n",
+       "  * yt_ocean  (yt_ocean) float64 -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n",
+       "    geolat_t  (yt_ocean, xt_ocean) float32 ...\n",
+       "    geolon_t  (yt_ocean, xt_ocean) float32 ...\n",
+       "    area_t    (yt_ocean, xt_ocean) float32 ...\n",
+       "Data variables:\n",
+       "    aice_m    (time, yt_ocean, xt_ocean) float32 dask.array&lt;chunksize=(1, 1080, 1440), meta=np.ndarray&gt;\n",
+       "Attributes: (12/18)\n",
+       "    title:                                    sea ice model output for CICE\n",
+       "    contents:                                 Diagnostic and Prognostic Varia...\n",
+       "    source:                                   Los Alamos Sea Ice Model (CICE)...\n",
+       "    time_period_freq:                         month_1\n",
+       "    comment3:                                 seconds elapsed into model date...\n",
+       "    conventions:                              CF-1.0\n",
+       "    ...                                       ...\n",
+       "    intake_esm_attrs:variable_standard_name:  ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,...\n",
+       "    intake_esm_attrs:variable_cell_methods:   ,,,,,,,,,,,,,,,,,,time: mean,ti...\n",
+       "    intake_esm_attrs:variable_units:          days since 1980-01-01 00:00:00,...\n",
+       "    intake_esm_attrs:file_id:                 iceh_XXXX_XX\n",
+       "    intake_esm_attrs:_data_format_:           netcdf\n",
+       "    intake_esm_dataset_key:                   iceh_XXXX_XX.1mon</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>025deg_jra55</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-ae921da2-7c86-4756-9819-4c4a156e3a7a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ae921da2-7c86-4756-9819-4c4a156e3a7a' class='xr-section-summary'  title='Expand/collapse section'>Groups: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%'></div></div></li><li class='xr-section-item'><input id='section-ae90a198-3748-4584-8de6-cdeb552b981d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ae90a198-3748-4584-8de6-cdeb552b981d' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 523</li><li><span class='xr-has-index'>yt_ocean</span>: 1080</li><li><span class='xr-has-index'>xt_ocean</span>: 1440</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-dd2e9227-c81b-44a8-9fb6-2f389d57951c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dd2e9227-c81b-44a8-9fb6-2f389d57951c' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1980-01-01 ... 2023-07-01</div><input id='attrs-cde922fc-cc71-49e4-9bcf-1557c1ca6ca4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cde922fc-cc71-49e4-9bcf-1557c1ca6ca4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e83841f4-7ebe-443a-861a-4397ec5c8163' class='xr-var-data-in' type='checkbox'><label for='data-e83841f4-7ebe-443a-861a-4397ec5c8163' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1980-01-01T00:00:00.000000000&#x27;, &#x27;1980-02-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;1980-03-01T00:00:00.000000000&#x27;, ..., &#x27;2023-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2023-06-01T00:00:00.000000000&#x27;, &#x27;2023-07-01T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.9 -279.6 ... 79.62 79.88</div><input id='attrs-d43e0727-82e7-4a3f-83aa-f21a6037629c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d43e0727-82e7-4a3f-83aa-f21a6037629c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a70c3b03-2cb0-4c0e-9226-512237336e4c' class='xr-var-data-in' type='checkbox'><label for='data-a70c3b03-2cb0-4c0e-9226-512237336e4c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-279.875, -279.625, -279.375, ...,   79.375,   79.625,   79.875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-81.08 -80.97 ... 89.84 89.95</div><input id='attrs-7c69eb63-1eed-4423-bfa2-663b008723d8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c69eb63-1eed-4423-bfa2-663b008723d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7046c210-1066-457b-bf5e-5eae36f21551' class='xr-var-data-in' type='checkbox'><label for='data-7046c210-1066-457b-bf5e-5eae36f21551' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-81.077001, -80.971402, -80.865804, ...,  89.736085,  89.841684,\n",
+       "        89.947282])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolat_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bb60ad10-db16-4660-865b-21e782c89f10' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb60ad10-db16-4660-865b-21e782c89f10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1671b555-ec5b-4d1e-b432-8c1b3f360b3f' class='xr-var-data-in' type='checkbox'><label for='data-1671b555-ec5b-4d1e-b432-8c1b3f360b3f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>valid_range :</span></dt><dd>[-91.  91.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[1555200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolon_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dd88d5a0-d2e8-4b53-9ec8-69bf71354261' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd88d5a0-d2e8-4b53-9ec8-69bf71354261' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ac54e00-03c7-467e-9f0c-94b4ecc3079b' class='xr-var-data-in' type='checkbox'><label for='data-6ac54e00-03c7-467e-9f0c-94b4ecc3079b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>valid_range :</span></dt><dd>[-281.  361.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[1555200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>area_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f5725fa9-9bfa-4953-9d6b-bcf958077a03' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f5725fa9-9bfa-4953-9d6b-bcf958077a03' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-590e189f-7746-44ea-b58c-164ad97fa0a5' class='xr-var-data-in' type='checkbox'><label for='data-590e189f-7746-44ea-b58c-164ad97fa0a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer cell area</dd><dt><span>units :</span></dt><dd>m^2</dd><dt><span>valid_range :</span></dt><dd>[0.e+00 1.e+15]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[1555200 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4591087b-2088-4995-a825-5152dfad9b09' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4591087b-2088-4995-a825-5152dfad9b09' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>aice_m</span></div><div class='xr-var-dims'>(time, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1440), meta=np.ndarray&gt;</div><input id='attrs-6587f948-bba0-4f4e-a4f4-9461ee019c54' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6587f948-bba0-4f4e-a4f4-9461ee019c54' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7be31ec0-fe5a-43e7-a341-49f4f78f7cb0' class='xr-var-data-in' type='checkbox'><label for='data-7be31ec0-fe5a-43e7-a341-49f4f78f7cb0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>ice area  (aggregate)</dd><dt><span>coordinates :</span></dt><dd>TLON TLAT time</dd><dt><span>cell_measures :</span></dt><dd>area: tarea</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_rep :</span></dt><dd>averaged</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 3.03 GiB </td>\n",
+       "                        <td> 5.93 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (523, 1080, 1440) </td>\n",
+       "                        <td> (1, 1080, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 523 chunks in 1047 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"205\" height=\"165\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"90\" x2=\"35\" y2=\"115\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"90\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"91\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"92\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"94\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"95\" />\n",
+       "  <line x1=\"16\" y1=\"6\" x2=\"16\" y2=\"96\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"98\" />\n",
+       "  <line x1=\"19\" y1=\"9\" x2=\"19\" y2=\"99\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"100\" />\n",
+       "  <line x1=\"22\" y1=\"12\" x2=\"22\" y2=\"102\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"103\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"104\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"106\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"107\" />\n",
+       "  <line x1=\"29\" y1=\"19\" x2=\"29\" y2=\"109\" />\n",
+       "  <line x1=\"30\" y1=\"20\" x2=\"30\" y2=\"110\" />\n",
+       "  <line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"111\" />\n",
+       "  <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"113\" />\n",
+       "  <line x1=\"34\" y1=\"24\" x2=\"34\" y2=\"114\" />\n",
+       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"115\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 35.96853812142349,25.968538121423492 35.96853812142349,115.96853812142349 10.0,90.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"16\" y1=\"6\" x2=\"136\" y2=\"6\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"19\" y1=\"9\" x2=\"139\" y2=\"9\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"22\" y1=\"12\" x2=\"142\" y2=\"12\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" />\n",
+       "  <line x1=\"29\" y1=\"19\" x2=\"149\" y2=\"19\" />\n",
+       "  <line x1=\"30\" y1=\"20\" x2=\"150\" y2=\"20\" />\n",
+       "  <line x1=\"31\" y1=\"21\" x2=\"151\" y2=\"21\" />\n",
+       "  <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" />\n",
+       "  <line x1=\"34\" y1=\"24\" x2=\"154\" y2=\"24\" />\n",
+       "  <line x1=\"35\" y1=\"25\" x2=\"155\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"130\" y1=\"0\" x2=\"155\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 130.0,0.0 155.96853812142348,25.968538121423492 35.96853812142349,25.968538121423492\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"35\" y1=\"25\" x2=\"155\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"115\" x2=\"155\" y2=\"115\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"115\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"155\" y1=\"25\" x2=\"155\" y2=\"115\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"35.96853812142349,25.968538121423492 155.96853812142348,25.968538121423492 155.96853812142348,115.96853812142349 35.96853812142349,115.96853812142349\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"95.968538\" y=\"135.968538\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"175.968538\" y=\"70.968538\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,175.968538,70.968538)\">1080</text>\n",
+       "  <text x=\"12.984269\" y=\"122.984269\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,12.984269,122.984269)\">523</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-b2e4b938-3ddc-4248-b7a5-1b1647f542be' class='xr-section-summary-in' type='checkbox'  ><label for='section-b2e4b938-3ddc-4248-b7a5-1b1647f542be' class='xr-section-summary' >Attributes: <span>(18)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>sea ice model output for CICE</dd><dt><span>contents :</span></dt><dd>Diagnostic and Prognostic Variables</dd><dt><span>source :</span></dt><dd>Los Alamos Sea Ice Model (CICE) Version 5</dd><dt><span>time_period_freq :</span></dt><dd>month_1</dd><dt><span>comment3 :</span></dt><dd>seconds elapsed into model date:      0</dd><dt><span>conventions :</span></dt><dd>CF-1.0</dd><dt><span>io_flavor :</span></dt><dd>io_pio</dd><dt><span>intake_esm_vars :</span></dt><dd>[&#x27;aice_m&#x27;]</dd><dt><span>intake_esm_attrs:realm :</span></dt><dd>seaIce</dd><dt><span>intake_esm_attrs:variable :</span></dt><dd>time_bounds,TLON,TLAT,ULON,ULAT,NCAT,tmask,blkmask,tarea,uarea,dxt,dyt,dxu,dyu,HTN,HTE,ANGLE,ANGLET,hi_m,hs_m,Tsfc_m,aice_m,uvel_m,vvel_m,sice_m,fswdn_m,fswup_m,flwdn_m,snow_ai_m,rain_ai_m,frzmlt_m,fswfac_m,fswabs_ai_m,albsni_m,albice_m,albsno_m,flat_ai_m,fsens_ai_m,flwup_ai_m,evap_ai_m,Tair_m,congel_m,frazil_m,snoice_m,meltt_m,melts_m,meltb_m,meltl_m,fresh_ai_m,fsalt_m,fsalt_ai_m,fhocn_ai_m,fswthru_ai_m,strairx_m,strairy_m,strtltx_m,strtlty_m,strcorx_m,strcory_m,strocnx_m,strocny_m,strintx_m,strinty_m,strength_m,divu_m,shear_m,dvidtt_m,dvidtd_m,daidtt_m,daidtd_m,trsig_m,ice_present_m,fcondtop_ai_m,fmeltt_ai_m,opening_m,aicen_m,vicen_m,fsurfn_ai_m,fcondtopn_ai_m,fmelttn_ai_m,flatn_ai_m</dd><dt><span>intake_esm_attrs:frequency :</span></dt><dd>1mon</dd><dt><span>intake_esm_attrs:variable_long_name :</span></dt><dd>boundaries for time-averaging interval,T grid center longitude,T grid center latitude,U grid center longitude,U grid center latitude,category maximum thickness,ocean grid mask,ice grid block mask,area of T grid cells,area of U grid cells,T cell width through middle,T cell height through middle,U cell width through middle,U cell height through middle,T cell width on North side,T cell width on East side,angle grid makes with latitude line on U grid,angle grid makes with latitude line on T grid,grid cell mean ice thickness,grid cell mean snow thickness,snow/ice surface temperature,ice area  (aggregate),ice velocity (x),ice velocity (y),bulk ice salinity,down solar flux,upward solar flux,down longwave flux,snowfall rate,rainfall rate,freeze/melt potential,shortwave scaling factor,snow/ice/ocn absorbed solar flux,snow/ice broad band albedo,bare ice albedo,snow albedo,latent heat flux,sensible heat flux,upward longwave flux,evaporative water flux,air temperature,congelation ice growth,frazil ice growth,snow-ice formation,top ice melt,top snow melt,basal ice melt,lateral ice melt,freshwtr flx ice to ocn,salt flux ice to ocn (cpl),salt flux ice to ocean,heat flux ice to ocean,SW flux thru ice to ocean,atm/ice stress (x),atm/ice stress (y),sea sfc tilt stress (x),sea sfc tilt stress (y),coriolis stress (x),coriolis stress (y),ocean/ice stress (x),ocean/ice stress (y),internal ice stress (x),internal ice stress (y),compressive ice strength,strain rate (divergence),strain rate (shear),volume tendency thermo,volume tendency dynamics,area tendency thermo,area tendency dynamics,internal stress tensor trace,fraction of time-avg interval that ice is present,top surface conductive heat flux,net surface heat flux causing melt,lead area opening rate,ice area, categories,ice volume, categories,net surface heat flux, categories,top sfc conductive heat flux, cat,net sfc heat flux causing melt, cat,latent heat flux, category</dd><dt><span>intake_esm_attrs:variable_standard_name :</span></dt><dd>,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,</dd><dt><span>intake_esm_attrs:variable_cell_methods :</span></dt><dd>,,,,,,,,,,,,,,,,,,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean</dd><dt><span>intake_esm_attrs:variable_units :</span></dt><dd>days since 1980-01-01 00:00:00,degrees_east,degrees_north,degrees_east,degrees_north,m,,,m^2,m^2,m,m,m,m,m,m,radians,radians,m,m,C,1,m/s,m/s,ppt,W/m^2,W/m^2,W/m^2,cm/day,cm/day,W/m^2,1,W/m^2,%,%,%,W/m^2,W/m^2,W/m^2,cm/day,C,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,kg/m^2/s,kg/m^2/s,W/m^2,W/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m,%/day,%/day,cm/day,cm/day,%/day,%/day,N/m^2,1,W/m^2,W/m^2,%/day,1,m,W/m^2,W/m^2,W/m^2,W/m^2</dd><dt><span>intake_esm_attrs:file_id :</span></dt><dd>iceh_XXXX_XX</dd><dt><span>intake_esm_attrs:_data_format_ :</span></dt><dd>netcdf</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>iceh_XXXX_XX.1mon</dd></dl></div></li></ul></div></div></ul></div></div><div style='display: inline-grid;'><div style='grid-column-start: 1;border-right: 0.2em solid;border-color: var(--xr-border-color);height: 100%;width: 0px;'></div><div style='grid-column-start: 2;grid-row-start: 1;height: 1em;width: 20px;border-bottom: 0.2em solid;border-color: var(--xr-border-color);'></div><div style='grid-column-start: 3;'><ul class='xr-sections'><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DatasetView&gt;\n",
+       "Dimensions:   (time: 708, yt_ocean: 300, xt_ocean: 360)\n",
+       "Coordinates:\n",
+       "  * time      (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n",
+       "  * xt_ocean  (xt_ocean) float64 -279.5 -278.5 -277.5 -276.5 ... 77.5 78.5 79.5\n",
+       "  * yt_ocean  (yt_ocean) float64 -77.88 -77.63 -77.38 ... 88.87 89.32 89.77\n",
+       "    geolat_t  (yt_ocean, xt_ocean) float32 ...\n",
+       "    geolon_t  (yt_ocean, xt_ocean) float32 ...\n",
+       "    area_t    (yt_ocean, xt_ocean) float32 ...\n",
+       "Data variables:\n",
+       "    aice_m    (time, yt_ocean, xt_ocean) float32 dask.array&lt;chunksize=(1, 300, 360), meta=np.ndarray&gt;\n",
+       "Attributes: (12/18)\n",
+       "    title:                                    sea ice model output for CICE\n",
+       "    contents:                                 Diagnostic and Prognostic Varia...\n",
+       "    source:                                   Los Alamos Sea Ice Model (CICE)...\n",
+       "    time_period_freq:                         month_1\n",
+       "    comment3:                                 seconds elapsed into model date...\n",
+       "    conventions:                              CF-1.0\n",
+       "    ...                                       ...\n",
+       "    intake_esm_attrs:variable_standard_name:  ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,...\n",
+       "    intake_esm_attrs:variable_cell_methods:   ,,,,,,,,,,,,,,,,,,time: mean,ti...\n",
+       "    intake_esm_attrs:variable_units:          days since 1960-01-01 00:00:00,...\n",
+       "    intake_esm_attrs:file_id:                 iceh_XXXX_XX\n",
+       "    intake_esm_attrs:_data_format_:           netcdf\n",
+       "    intake_esm_dataset_key:                   iceh_XXXX_XX.1mon</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>1deg_era5</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-3688a02c-8e18-49c8-8f1e-bdf840e0dd33' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3688a02c-8e18-49c8-8f1e-bdf840e0dd33' class='xr-section-summary'  title='Expand/collapse section'>Groups: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%'></div></div></li><li class='xr-section-item'><input id='section-abacfedd-01fc-4b83-adb3-f7e7b593c23d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-abacfedd-01fc-4b83-adb3-f7e7b593c23d' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 708</li><li><span class='xr-has-index'>yt_ocean</span>: 300</li><li><span class='xr-has-index'>xt_ocean</span>: 360</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-940b6587-27ec-4f1a-9b1b-881bcec73af1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-940b6587-27ec-4f1a-9b1b-881bcec73af1' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1960-01-01 ... 2018-12-01</div><input id='attrs-2a92f754-5b7a-4a9b-8886-571abfd7c6ae' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2a92f754-5b7a-4a9b-8886-571abfd7c6ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1ad5aef-ea10-4154-9d22-62923e58be03' class='xr-var-data-in' type='checkbox'><label for='data-b1ad5aef-ea10-4154-9d22-62923e58be03' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1960-01-01T00:00:00.000000000&#x27;, &#x27;1960-02-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;1960-03-01T00:00:00.000000000&#x27;, ..., &#x27;2018-10-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-11-01T00:00:00.000000000&#x27;, &#x27;2018-12-01T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.5 -278.5 -277.5 ... 78.5 79.5</div><input id='attrs-1c125723-5a56-4d63-8127-d79b8b5570a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c125723-5a56-4d63-8127-d79b8b5570a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2df6ed90-a657-4b69-a6eb-9a13fd769f7a' class='xr-var-data-in' type='checkbox'><label for='data-2df6ed90-a657-4b69-a6eb-9a13fd769f7a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-279.5, -278.5, -277.5, ...,   77.5,   78.5,   79.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-77.88 -77.63 ... 89.32 89.77</div><input id='attrs-29b322ab-8eff-46b7-bb9e-e48468eaa6f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29b322ab-8eff-46b7-bb9e-e48468eaa6f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-951766f7-6161-41a6-901f-5779503c3613' class='xr-var-data-in' type='checkbox'><label for='data-951766f7-6161-41a6-901f-5779503c3613' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-77.876623, -77.629713, -77.381707, ...,  88.872933,  89.324006,\n",
+       "        89.774476])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolat_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bcb7e46d-4b02-4cb5-ac83-dae0facafc63' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bcb7e46d-4b02-4cb5-ac83-dae0facafc63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-972904b7-6ba6-46fc-bdc4-5fde37664f85' class='xr-var-data-in' type='checkbox'><label for='data-972904b7-6ba6-46fc-bdc4-5fde37664f85' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>valid_range :</span></dt><dd>[-91.  91.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[108000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolon_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-32378b27-d293-4dfa-a406-f7e33b3e647c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32378b27-d293-4dfa-a406-f7e33b3e647c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d6dd471-dd8b-4867-b81e-5af3ec137fa0' class='xr-var-data-in' type='checkbox'><label for='data-1d6dd471-dd8b-4867-b81e-5af3ec137fa0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>valid_range :</span></dt><dd>[-281.  361.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[108000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>area_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1952082e-dc54-47ea-b1a2-96ad793a047b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1952082e-dc54-47ea-b1a2-96ad793a047b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77e9ef71-9ccc-4cc5-9ab7-108d31b7c958' class='xr-var-data-in' type='checkbox'><label for='data-77e9ef71-9ccc-4cc5-9ab7-108d31b7c958' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer cell area</dd><dt><span>units :</span></dt><dd>m^2</dd><dt><span>valid_range :</span></dt><dd>[0.e+00 1.e+15]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[108000 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-24a171e5-42ad-4f2c-b61a-8352540a89ab' class='xr-section-summary-in' type='checkbox'  checked><label for='section-24a171e5-42ad-4f2c-b61a-8352540a89ab' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>aice_m</span></div><div class='xr-var-dims'>(time, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 300, 360), meta=np.ndarray&gt;</div><input id='attrs-e03b53dc-0680-4e3c-b23a-2ec7c37cf9d9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e03b53dc-0680-4e3c-b23a-2ec7c37cf9d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d25663c4-a1d0-483a-b906-4529cba71f02' class='xr-var-data-in' type='checkbox'><label for='data-d25663c4-a1d0-483a-b906-4529cba71f02' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>ice area  (aggregate)</dd><dt><span>coordinates :</span></dt><dd>TLON TLAT time</dd><dt><span>cell_measures :</span></dt><dd>area: tarea</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_rep :</span></dt><dd>averaged</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 291.69 MiB </td>\n",
+       "                        <td> 421.88 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (708, 300, 360) </td>\n",
+       "                        <td> (1, 300, 360) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 708 chunks in 1417 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"191\" height=\"171\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"50\" x2=\"80\" y2=\"121\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"50\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"54\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"58\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"61\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"65\" />\n",
+       "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"69\" />\n",
+       "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"73\" />\n",
+       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"76\" />\n",
+       "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"80\" />\n",
+       "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"84\" />\n",
+       "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"87\" />\n",
+       "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"91\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"95\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"99\" />\n",
+       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"102\" />\n",
+       "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"106\" />\n",
+       "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"110\" />\n",
+       "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"113\" />\n",
+       "  <line x1=\"76\" y1=\"66\" x2=\"76\" y2=\"117\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"121\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,121.4356929212363 10.0,50.847457627118644\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"71\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"13\" y1=\"3\" x2=\"74\" y2=\"3\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"78\" y2=\"7\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"82\" y2=\"11\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"85\" y2=\"14\" />\n",
+       "  <line x1=\"28\" y1=\"18\" x2=\"89\" y2=\"18\" />\n",
+       "  <line x1=\"32\" y1=\"22\" x2=\"93\" y2=\"22\" />\n",
+       "  <line x1=\"35\" y1=\"25\" x2=\"96\" y2=\"25\" />\n",
+       "  <line x1=\"39\" y1=\"29\" x2=\"100\" y2=\"29\" />\n",
+       "  <line x1=\"43\" y1=\"33\" x2=\"104\" y2=\"33\" />\n",
+       "  <line x1=\"47\" y1=\"37\" x2=\"108\" y2=\"37\" />\n",
+       "  <line x1=\"50\" y1=\"40\" x2=\"111\" y2=\"40\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"115\" y2=\"44\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"119\" y2=\"48\" />\n",
+       "  <line x1=\"61\" y1=\"51\" x2=\"122\" y2=\"51\" />\n",
+       "  <line x1=\"65\" y1=\"55\" x2=\"126\" y2=\"55\" />\n",
+       "  <line x1=\"69\" y1=\"59\" x2=\"130\" y2=\"59\" />\n",
+       "  <line x1=\"73\" y1=\"63\" x2=\"134\" y2=\"63\" />\n",
+       "  <line x1=\"76\" y1=\"66\" x2=\"137\" y2=\"66\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"141\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"71\" y1=\"0\" x2=\"141\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 71.01694915254237,0.0 141.60518444666002,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"141\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"121\" x2=\"141\" y2=\"121\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"121\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"141\" y1=\"70\" x2=\"141\" y2=\"121\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 141.60518444666002,70.58823529411765 141.60518444666002,121.4356929212363 80.58823529411765,121.4356929212363\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"111.096710\" y=\"141.435693\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >360</text>\n",
+       "  <text x=\"161.605184\" y=\"96.011964\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,161.605184,96.011964)\">300</text>\n",
+       "  <text x=\"35.294118\" y=\"106.141575\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,106.141575)\">708</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-9e7803ff-bae2-4412-9101-650eb7f00715' class='xr-section-summary-in' type='checkbox'  ><label for='section-9e7803ff-bae2-4412-9101-650eb7f00715' class='xr-section-summary' >Attributes: <span>(18)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>sea ice model output for CICE</dd><dt><span>contents :</span></dt><dd>Diagnostic and Prognostic Variables</dd><dt><span>source :</span></dt><dd>Los Alamos Sea Ice Model (CICE) Version 5</dd><dt><span>time_period_freq :</span></dt><dd>month_1</dd><dt><span>comment3 :</span></dt><dd>seconds elapsed into model date:      0</dd><dt><span>conventions :</span></dt><dd>CF-1.0</dd><dt><span>io_flavor :</span></dt><dd>io_pio</dd><dt><span>intake_esm_vars :</span></dt><dd>[&#x27;aice_m&#x27;]</dd><dt><span>intake_esm_attrs:realm :</span></dt><dd>seaIce</dd><dt><span>intake_esm_attrs:variable :</span></dt><dd>time_bounds,TLON,TLAT,ULON,ULAT,NCAT,tmask,blkmask,tarea,uarea,dxt,dyt,dxu,dyu,HTN,HTE,ANGLE,ANGLET,hi_m,hs_m,Tsfc_m,aice_m,uvel_m,vvel_m,uatm_m,vatm_m,sice_m,fswdn_m,fswup_m,flwdn_m,snow_ai_m,rain_ai_m,frzmlt_m,fswfac_m,fswabs_ai_m,albsni_m,alvdr_ai_m,alidr_ai_m,alvdf_ai_m,alidf_ai_m,albice_m,albsno_m,flat_ai_m,fsens_ai_m,flwup_ai_m,evap_ai_m,Tair_m,congel_m,frazil_m,snoice_m,meltt_m,melts_m,meltb_m,meltl_m,fresh_ai_m,fsalt_m,fsalt_ai_m,fhocn_ai_m,fswthru_ai_m,strairx_m,strairy_m,strtltx_m,strtlty_m,strcorx_m,strcory_m,strocnx_m,strocny_m,strintx_m,strinty_m,strength_m,divu_m,shear_m,dvidtt_m,dvidtd_m,daidtt_m,daidtd_m,trsig_m,ice_present_m,fcondtop_ai_m,fmeltt_ai_m,opening_m,aicen_m,vicen_m,fsurfn_ai_m,fcondtopn_ai_m,fmelttn_ai_m,flatn_ai_m</dd><dt><span>intake_esm_attrs:frequency :</span></dt><dd>1mon</dd><dt><span>intake_esm_attrs:variable_long_name :</span></dt><dd>boundaries for time-averaging interval,T grid center longitude,T grid center latitude,U grid center longitude,U grid center latitude,category maximum thickness,ocean grid mask,ice grid block mask,area of T grid cells,area of U grid cells,T cell width through middle,T cell height through middle,U cell width through middle,U cell height through middle,T cell width on North side,T cell width on East side,angle grid makes with latitude line on U grid,angle grid makes with latitude line on T grid,grid cell mean ice thickness,grid cell mean snow thickness,snow/ice surface temperature,ice area  (aggregate),ice velocity (x),ice velocity (y),atm velocity (x),atm velocity (y),bulk ice salinity,down solar flux,upward solar flux,down longwave flux,snowfall rate,rainfall rate,freeze/melt potential,shortwave scaling factor,snow/ice/ocn absorbed solar flux,snow/ice broad band albedo,visible direct albedo,near IR direct albedo,visible diffuse albedo,near IR diffuse albedo,bare ice albedo,snow albedo,latent heat flux,sensible heat flux,upward longwave flux,evaporative water flux,air temperature,congelation ice growth,frazil ice growth,snow-ice formation,top ice melt,top snow melt,basal ice melt,lateral ice melt,freshwtr flx ice to ocn,salt flux ice to ocn (cpl),salt flux ice to ocean,heat flux ice to ocean,SW flux thru ice to ocean,atm/ice stress (x),atm/ice stress (y),sea sfc tilt stress (x),sea sfc tilt stress (y),coriolis stress (x),coriolis stress (y),ocean/ice stress (x),ocean/ice stress (y),internal ice stress (x),internal ice stress (y),compressive ice strength,strain rate (divergence),strain rate (shear),volume tendency thermo,volume tendency dynamics,area tendency thermo,area tendency dynamics,internal stress tensor trace,fraction of time-avg interval that ice is present,top surface conductive heat flux,net surface heat flux causing melt,lead area opening rate,ice area, categories,ice volume, categories,net surface heat flux, categories,top sfc conductive heat flux, cat,net sfc heat flux causing melt, cat,latent heat flux, category</dd><dt><span>intake_esm_attrs:variable_standard_name :</span></dt><dd>,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,</dd><dt><span>intake_esm_attrs:variable_cell_methods :</span></dt><dd>,,,,,,,,,,,,,,,,,,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean</dd><dt><span>intake_esm_attrs:variable_units :</span></dt><dd>days since 1960-01-01 00:00:00,degrees_east,degrees_north,degrees_east,degrees_north,m,,,m^2,m^2,m,m,m,m,m,m,radians,radians,m,m,C,1,m/s,m/s,m/s,m/s,ppt,W/m^2,W/m^2,W/m^2,cm/day,cm/day,W/m^2,1,W/m^2,%,%,%,%,%,%,%,W/m^2,W/m^2,W/m^2,cm/day,C,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,kg/m^2/s,kg/m^2/s,W/m^2,W/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m,%/day,%/day,cm/day,cm/day,%/day,%/day,N/m^2,1,W/m^2,W/m^2,%/day,1,m,W/m^2,W/m^2,W/m^2,W/m^2</dd><dt><span>intake_esm_attrs:file_id :</span></dt><dd>iceh_XXXX_XX</dd><dt><span>intake_esm_attrs:_data_format_ :</span></dt><dd>netcdf</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>iceh_XXXX_XX.1mon</dd></dl></div></li></ul></div></div></ul></div></div><div style='display: inline-grid;'><div style='grid-column-start: 1;border-right: 0.2em solid;border-color: var(--xr-border-color);height: 1.2em;width: 0px;'></div><div style='grid-column-start: 2;grid-row-start: 1;height: 1em;width: 20px;border-bottom: 0.2em solid;border-color: var(--xr-border-color);'></div><div style='grid-column-start: 3;'><ul class='xr-sections'><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DatasetView&gt;\n",
+       "Dimensions:   (time: 720, yt_ocean: 300, xt_ocean: 360)\n",
+       "Coordinates:\n",
+       "  * time      (time) datetime64[ns] 1959-01-01 1959-02-01 ... 2019-12-01\n",
+       "  * xt_ocean  (xt_ocean) float64 -279.5 -278.5 -277.5 -276.5 ... 77.5 78.5 79.5\n",
+       "  * yt_ocean  (yt_ocean) float64 -77.88 -77.63 -77.38 ... 88.87 89.32 89.77\n",
+       "    geolat_t  (yt_ocean, xt_ocean) float32 ...\n",
+       "    geolon_t  (yt_ocean, xt_ocean) float32 ...\n",
+       "    area_t    (yt_ocean, xt_ocean) float32 ...\n",
+       "Data variables:\n",
+       "    aice_m    (time, yt_ocean, xt_ocean) float32 dask.array&lt;chunksize=(1, 300, 360), meta=np.ndarray&gt;\n",
+       "Attributes: (12/17)\n",
+       "    title:                                    sea ice model output for CICE\n",
+       "    contents:                                 Diagnostic and Prognostic Varia...\n",
+       "    source:                                   Los Alamos Sea Ice Model (CICE)...\n",
+       "    time_period_freq:                         month_1\n",
+       "    comment3:                                 seconds elapsed into model date...\n",
+       "    conventions:                              CF-1.0\n",
+       "    ...                                       ...\n",
+       "    intake_esm_attrs:variable_long_name:      boundaries for time-averaging i...\n",
+       "    intake_esm_attrs:variable_standard_name:  ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,...\n",
+       "    intake_esm_attrs:variable_cell_methods:   ,,,,,,,,,,,,,,,,,,time: mean,ti...\n",
+       "    intake_esm_attrs:file_id:                 iceh_XXXX_XX\n",
+       "    intake_esm_attrs:_data_format_:           netcdf\n",
+       "    intake_esm_dataset_key:                   iceh_XXXX_XX.1mon</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>1deg_jra55</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-6f20df52-1122-47c5-a213-7344174ad87c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6f20df52-1122-47c5-a213-7344174ad87c' class='xr-section-summary'  title='Expand/collapse section'>Groups: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%'></div></div></li><li class='xr-section-item'><input id='section-ae82c9f5-7fcb-4458-9969-cb6033d41ad0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ae82c9f5-7fcb-4458-9969-cb6033d41ad0' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 720</li><li><span class='xr-has-index'>yt_ocean</span>: 300</li><li><span class='xr-has-index'>xt_ocean</span>: 360</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e04e3869-cd3f-43b0-a71b-c9b840e08fe9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e04e3869-cd3f-43b0-a71b-c9b840e08fe9' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1959-01-01 ... 2019-12-01</div><input id='attrs-0b9c705b-c392-481d-8bcc-e318b6ba42a9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0b9c705b-c392-481d-8bcc-e318b6ba42a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9409bd6d-956d-479d-be64-ddf332c70757' class='xr-var-data-in' type='checkbox'><label for='data-9409bd6d-956d-479d-be64-ddf332c70757' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1959-01-01T00:00:00.000000000&#x27;, &#x27;1959-02-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;1959-03-01T00:00:00.000000000&#x27;, ..., &#x27;2019-10-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2019-11-01T00:00:00.000000000&#x27;, &#x27;2019-12-01T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.5 -278.5 -277.5 ... 78.5 79.5</div><input id='attrs-343e995c-986e-478e-bcd5-58807ccadba4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-343e995c-986e-478e-bcd5-58807ccadba4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b3e166b-a1ff-46d1-ad90-417e3498636a' class='xr-var-data-in' type='checkbox'><label for='data-1b3e166b-a1ff-46d1-ad90-417e3498636a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-279.5, -278.5, -277.5, ...,   77.5,   78.5,   79.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-77.88 -77.63 ... 89.32 89.77</div><input id='attrs-f588c2a1-a1b9-4869-9757-fc864e6c8380' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f588c2a1-a1b9-4869-9757-fc864e6c8380' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea35a730-11fe-4ab5-b948-db81f632e7e1' class='xr-var-data-in' type='checkbox'><label for='data-ea35a730-11fe-4ab5-b948-db81f632e7e1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-77.876623, -77.629713, -77.381707, ...,  88.872933,  89.324006,\n",
+       "        89.774476])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolat_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cb4edcc1-5666-4c5c-8f6d-7bda9349eee4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb4edcc1-5666-4c5c-8f6d-7bda9349eee4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6bd3f375-3f89-4712-9032-c437db49d511' class='xr-var-data-in' type='checkbox'><label for='data-6bd3f375-3f89-4712-9032-c437db49d511' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>valid_range :</span></dt><dd>[-91.  91.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[108000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolon_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7dbac691-c68e-4f1a-9fd1-b10fa2b0a2d8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7dbac691-c68e-4f1a-9fd1-b10fa2b0a2d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64f2171a-8383-4e3a-a29a-7d9f88efbb7a' class='xr-var-data-in' type='checkbox'><label for='data-64f2171a-8383-4e3a-a29a-7d9f88efbb7a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>valid_range :</span></dt><dd>[-281.  361.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[108000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>area_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f67583cb-08cb-4aff-9660-770c6e91aff2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f67583cb-08cb-4aff-9660-770c6e91aff2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8697a6bc-d397-4aa0-8c78-6c0dc46b22ff' class='xr-var-data-in' type='checkbox'><label for='data-8697a6bc-d397-4aa0-8c78-6c0dc46b22ff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer cell area</dd><dt><span>units :</span></dt><dd>m^2</dd><dt><span>valid_range :</span></dt><dd>[0.e+00 1.e+15]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[108000 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-480f586b-2bdf-4bb0-ac18-5b2133574be3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-480f586b-2bdf-4bb0-ac18-5b2133574be3' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>aice_m</span></div><div class='xr-var-dims'>(time, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 300, 360), meta=np.ndarray&gt;</div><input id='attrs-6966c779-07b0-45fd-8c50-62dff2402577' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6966c779-07b0-45fd-8c50-62dff2402577' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8bd7d2c-85c6-48f1-a415-e8bee683bcb5' class='xr-var-data-in' type='checkbox'><label for='data-e8bd7d2c-85c6-48f1-a415-e8bee683bcb5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>ice area  (aggregate)</dd><dt><span>coordinates :</span></dt><dd>TLON TLAT time</dd><dt><span>cell_measures :</span></dt><dd>area: tarea</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_rep :</span></dt><dd>averaged</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 296.63 MiB </td>\n",
+       "                        <td> 421.88 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (720, 300, 360) </td>\n",
+       "                        <td> (1, 300, 360) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 720 chunks in 1441 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"190\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"50\" x2=\"80\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"50\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"53\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"57\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"61\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"64\" />\n",
+       "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"68\" />\n",
+       "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"72\" />\n",
+       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"75\" />\n",
+       "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"79\" />\n",
+       "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"83\" />\n",
+       "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"87\" />\n",
+       "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"90\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"94\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"98\" />\n",
+       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"101\" />\n",
+       "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"105\" />\n",
+       "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"109\" />\n",
+       "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"113\" />\n",
+       "  <line x1=\"76\" y1=\"66\" x2=\"76\" y2=\"116\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,120.58823529411765 10.0,50.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"70\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"13\" y1=\"3\" x2=\"73\" y2=\"3\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"77\" y2=\"7\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"81\" y2=\"11\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"84\" y2=\"14\" />\n",
+       "  <line x1=\"28\" y1=\"18\" x2=\"88\" y2=\"18\" />\n",
+       "  <line x1=\"32\" y1=\"22\" x2=\"92\" y2=\"22\" />\n",
+       "  <line x1=\"35\" y1=\"25\" x2=\"95\" y2=\"25\" />\n",
+       "  <line x1=\"39\" y1=\"29\" x2=\"99\" y2=\"29\" />\n",
+       "  <line x1=\"43\" y1=\"33\" x2=\"103\" y2=\"33\" />\n",
+       "  <line x1=\"47\" y1=\"37\" x2=\"107\" y2=\"37\" />\n",
+       "  <line x1=\"50\" y1=\"40\" x2=\"110\" y2=\"40\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"114\" y2=\"44\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"118\" y2=\"48\" />\n",
+       "  <line x1=\"61\" y1=\"51\" x2=\"121\" y2=\"51\" />\n",
+       "  <line x1=\"65\" y1=\"55\" x2=\"125\" y2=\"55\" />\n",
+       "  <line x1=\"69\" y1=\"59\" x2=\"129\" y2=\"59\" />\n",
+       "  <line x1=\"73\" y1=\"63\" x2=\"133\" y2=\"63\" />\n",
+       "  <line x1=\"76\" y1=\"66\" x2=\"136\" y2=\"66\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"140\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"70\" y1=\"0\" x2=\"140\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 70.0,0.0 140.58823529411765,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"140\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"120\" x2=\"140\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"140\" y1=\"70\" x2=\"140\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 140.58823529411765,70.58823529411765 140.58823529411765,120.58823529411765 80.58823529411765,120.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"110.588235\" y=\"140.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >360</text>\n",
+       "  <text x=\"160.588235\" y=\"95.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,160.588235,95.588235)\">300</text>\n",
+       "  <text x=\"35.294118\" y=\"105.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,105.294118)\">720</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-a11bea96-18af-4c1e-a43b-951265a463b0' class='xr-section-summary-in' type='checkbox'  ><label for='section-a11bea96-18af-4c1e-a43b-951265a463b0' class='xr-section-summary' >Attributes: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>sea ice model output for CICE</dd><dt><span>contents :</span></dt><dd>Diagnostic and Prognostic Variables</dd><dt><span>source :</span></dt><dd>Los Alamos Sea Ice Model (CICE) Version 5</dd><dt><span>time_period_freq :</span></dt><dd>month_1</dd><dt><span>comment3 :</span></dt><dd>seconds elapsed into model date:      0</dd><dt><span>conventions :</span></dt><dd>CF-1.0</dd><dt><span>io_flavor :</span></dt><dd>io_pio</dd><dt><span>intake_esm_vars :</span></dt><dd>[&#x27;aice_m&#x27;]</dd><dt><span>intake_esm_attrs:realm :</span></dt><dd>seaIce</dd><dt><span>intake_esm_attrs:variable :</span></dt><dd>time_bounds,TLON,TLAT,ULON,ULAT,NCAT,tmask,blkmask,tarea,uarea,dxt,dyt,dxu,dyu,HTN,HTE,ANGLE,ANGLET,hi_m,hs_m,Tsfc_m,aice_m,uvel_m,vvel_m,uatm_m,vatm_m,sice_m,fswdn_m,fswup_m,flwdn_m,snow_ai_m,rain_ai_m,frzmlt_m,fswfac_m,fswabs_ai_m,albsni_m,alvdr_ai_m,alidr_ai_m,alvdf_ai_m,alidf_ai_m,albice_m,albsno_m,flat_ai_m,fsens_ai_m,flwup_ai_m,evap_ai_m,Tair_m,congel_m,frazil_m,snoice_m,meltt_m,melts_m,meltb_m,meltl_m,fresh_ai_m,fsalt_m,fsalt_ai_m,fhocn_ai_m,fswthru_ai_m,strairx_m,strairy_m,strtltx_m,strtlty_m,strcorx_m,strcory_m,strocnx_m,strocny_m,strintx_m,strinty_m,strength_m,divu_m,shear_m,dvidtt_m,dvidtd_m,daidtt_m,daidtd_m,trsig_m,ice_present_m,fcondtop_ai_m,fmeltt_ai_m,opening_m,aicen_m,vicen_m,fsurfn_ai_m,fcondtopn_ai_m,fmelttn_ai_m,flatn_ai_m</dd><dt><span>intake_esm_attrs:frequency :</span></dt><dd>1mon</dd><dt><span>intake_esm_attrs:variable_long_name :</span></dt><dd>boundaries for time-averaging interval,T grid center longitude,T grid center latitude,U grid center longitude,U grid center latitude,category maximum thickness,ocean grid mask,ice grid block mask,area of T grid cells,area of U grid cells,T cell width through middle,T cell height through middle,U cell width through middle,U cell height through middle,T cell width on North side,T cell width on East side,angle grid makes with latitude line on U grid,angle grid makes with latitude line on T grid,grid cell mean ice thickness,grid cell mean snow thickness,snow/ice surface temperature,ice area  (aggregate),ice velocity (x),ice velocity (y),atm velocity (x),atm velocity (y),bulk ice salinity,down solar flux,upward solar flux,down longwave flux,snowfall rate,rainfall rate,freeze/melt potential,shortwave scaling factor,snow/ice/ocn absorbed solar flux,snow/ice broad band albedo,visible direct albedo,near IR direct albedo,visible diffuse albedo,near IR diffuse albedo,bare ice albedo,snow albedo,latent heat flux,sensible heat flux,upward longwave flux,evaporative water flux,air temperature,congelation ice growth,frazil ice growth,snow-ice formation,top ice melt,top snow melt,basal ice melt,lateral ice melt,freshwtr flx ice to ocn,salt flux ice to ocn (cpl),salt flux ice to ocean,heat flux ice to ocean,SW flux thru ice to ocean,atm/ice stress (x),atm/ice stress (y),sea sfc tilt stress (x),sea sfc tilt stress (y),coriolis stress (x),coriolis stress (y),ocean/ice stress (x),ocean/ice stress (y),internal ice stress (x),internal ice stress (y),compressive ice strength,strain rate (divergence),strain rate (shear),volume tendency thermo,volume tendency dynamics,area tendency thermo,area tendency dynamics,internal stress tensor trace,fraction of time-avg interval that ice is present,top surface conductive heat flux,net surface heat flux causing melt,lead area opening rate,ice area, categories,ice volume, categories,net surface heat flux, categories,top sfc conductive heat flux, cat,net sfc heat flux causing melt, cat,latent heat flux, category</dd><dt><span>intake_esm_attrs:variable_standard_name :</span></dt><dd>,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,</dd><dt><span>intake_esm_attrs:variable_cell_methods :</span></dt><dd>,,,,,,,,,,,,,,,,,,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean</dd><dt><span>intake_esm_attrs:file_id :</span></dt><dd>iceh_XXXX_XX</dd><dt><span>intake_esm_attrs:_data_format_ :</span></dt><dd>netcdf</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>iceh_XXXX_XX.1mon</dd></dl></div></li></ul></div></div></ul></div></div></div></div></li><li class='xr-section-item'><input id='section-2c783c48-2845-4011-a294-da49f9b9017d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2c783c48-2845-4011-a294-da49f9b9017d' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-bc5ca0f5-845a-40d8-8a22-dac724e8e674' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bc5ca0f5-845a-40d8-8a22-dac724e8e674' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-b0de26e2-2664-4f5c-89d4-f99cb830349f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b0de26e2-2664-4f5c-89d4-f99cb830349f' class='xr-section-summary'  title='Expand/collapse section'>Data variables: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-29946245-0465-4256-a088-d2300fe7d24a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-29946245-0465-4256-a088-d2300fe7d24a' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "DataTree('None', parent=None)\n",
+       "├── DataTree('025deg_era5')\n",
+       "│       Dimensions:   (time: 523, yt_ocean: 1080, xt_ocean: 1440)\n",
+       "│       Coordinates:\n",
+       "│         * time      (time) datetime64[ns] 1980-01-01 1980-02-01 ... 2023-07-01\n",
+       "│         * xt_ocean  (xt_ocean) float64 -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n",
+       "│         * yt_ocean  (yt_ocean) float64 -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n",
+       "│           geolat_t  (yt_ocean, xt_ocean) float32 ...\n",
+       "│           geolon_t  (yt_ocean, xt_ocean) float32 ...\n",
+       "│           area_t    (yt_ocean, xt_ocean) float32 ...\n",
+       "│       Data variables:\n",
+       "│           aice_m    (time, yt_ocean, xt_ocean) float32 dask.array<chunksize=(1, 1080, 1440), meta=np.ndarray>\n",
+       "│       Attributes: (12/18)\n",
+       "│           title:                                    sea ice model output for CICE\n",
+       "│           contents:                                 Diagnostic and Prognostic Varia...\n",
+       "│           source:                                   Los Alamos Sea Ice Model (CICE)...\n",
+       "│           time_period_freq:                         month_1\n",
+       "│           comment3:                                 seconds elapsed into model date...\n",
+       "│           conventions:                              CF-1.0\n",
+       "│           ...                                       ...\n",
+       "│           intake_esm_attrs:variable_standard_name:  ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,...\n",
+       "│           intake_esm_attrs:variable_cell_methods:   ,,,,,,,,,,,,,,,,,,time: mean,ti...\n",
+       "│           intake_esm_attrs:variable_units:          days since 1980-01-01 00:00:00,...\n",
+       "│           intake_esm_attrs:file_id:                 iceh_XXXX_XX\n",
+       "│           intake_esm_attrs:_data_format_:           netcdf\n",
+       "│           intake_esm_dataset_key:                   iceh_XXXX_XX.1mon\n",
+       "├── DataTree('025deg_jra55')\n",
+       "│       Dimensions:   (time: 523, yt_ocean: 1080, xt_ocean: 1440)\n",
+       "│       Coordinates:\n",
+       "│         * time      (time) datetime64[ns] 1980-01-01 1980-02-01 ... 2023-07-01\n",
+       "│         * xt_ocean  (xt_ocean) float64 -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n",
+       "│         * yt_ocean  (yt_ocean) float64 -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n",
+       "│           geolat_t  (yt_ocean, xt_ocean) float32 ...\n",
+       "│           geolon_t  (yt_ocean, xt_ocean) float32 ...\n",
+       "│           area_t    (yt_ocean, xt_ocean) float32 ...\n",
+       "│       Data variables:\n",
+       "│           aice_m    (time, yt_ocean, xt_ocean) float32 dask.array<chunksize=(1, 1080, 1440), meta=np.ndarray>\n",
+       "│       Attributes: (12/18)\n",
+       "│           title:                                    sea ice model output for CICE\n",
+       "│           contents:                                 Diagnostic and Prognostic Varia...\n",
+       "│           source:                                   Los Alamos Sea Ice Model (CICE)...\n",
+       "│           time_period_freq:                         month_1\n",
+       "│           comment3:                                 seconds elapsed into model date...\n",
+       "│           conventions:                              CF-1.0\n",
+       "│           ...                                       ...\n",
+       "│           intake_esm_attrs:variable_standard_name:  ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,...\n",
+       "│           intake_esm_attrs:variable_cell_methods:   ,,,,,,,,,,,,,,,,,,time: mean,ti...\n",
+       "│           intake_esm_attrs:variable_units:          days since 1980-01-01 00:00:00,...\n",
+       "│           intake_esm_attrs:file_id:                 iceh_XXXX_XX\n",
+       "│           intake_esm_attrs:_data_format_:           netcdf\n",
+       "│           intake_esm_dataset_key:                   iceh_XXXX_XX.1mon\n",
+       "├── DataTree('1deg_era5')\n",
+       "│       Dimensions:   (time: 708, yt_ocean: 300, xt_ocean: 360)\n",
+       "│       Coordinates:\n",
+       "│         * time      (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n",
+       "│         * xt_ocean  (xt_ocean) float64 -279.5 -278.5 -277.5 -276.5 ... 77.5 78.5 79.5\n",
+       "│         * yt_ocean  (yt_ocean) float64 -77.88 -77.63 -77.38 ... 88.87 89.32 89.77\n",
+       "│           geolat_t  (yt_ocean, xt_ocean) float32 ...\n",
+       "│           geolon_t  (yt_ocean, xt_ocean) float32 ...\n",
+       "│           area_t    (yt_ocean, xt_ocean) float32 ...\n",
+       "│       Data variables:\n",
+       "│           aice_m    (time, yt_ocean, xt_ocean) float32 dask.array<chunksize=(1, 300, 360), meta=np.ndarray>\n",
+       "│       Attributes: (12/18)\n",
+       "│           title:                                    sea ice model output for CICE\n",
+       "│           contents:                                 Diagnostic and Prognostic Varia...\n",
+       "│           source:                                   Los Alamos Sea Ice Model (CICE)...\n",
+       "│           time_period_freq:                         month_1\n",
+       "│           comment3:                                 seconds elapsed into model date...\n",
+       "│           conventions:                              CF-1.0\n",
+       "│           ...                                       ...\n",
+       "│           intake_esm_attrs:variable_standard_name:  ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,...\n",
+       "│           intake_esm_attrs:variable_cell_methods:   ,,,,,,,,,,,,,,,,,,time: mean,ti...\n",
+       "│           intake_esm_attrs:variable_units:          days since 1960-01-01 00:00:00,...\n",
+       "│           intake_esm_attrs:file_id:                 iceh_XXXX_XX\n",
+       "│           intake_esm_attrs:_data_format_:           netcdf\n",
+       "│           intake_esm_dataset_key:                   iceh_XXXX_XX.1mon\n",
+       "└── DataTree('1deg_jra55')\n",
+       "        Dimensions:   (time: 720, yt_ocean: 300, xt_ocean: 360)\n",
+       "        Coordinates:\n",
+       "          * time      (time) datetime64[ns] 1959-01-01 1959-02-01 ... 2019-12-01\n",
+       "          * xt_ocean  (xt_ocean) float64 -279.5 -278.5 -277.5 -276.5 ... 77.5 78.5 79.5\n",
+       "          * yt_ocean  (yt_ocean) float64 -77.88 -77.63 -77.38 ... 88.87 89.32 89.77\n",
+       "            geolat_t  (yt_ocean, xt_ocean) float32 ...\n",
+       "            geolon_t  (yt_ocean, xt_ocean) float32 ...\n",
+       "            area_t    (yt_ocean, xt_ocean) float32 ...\n",
+       "        Data variables:\n",
+       "            aice_m    (time, yt_ocean, xt_ocean) float32 dask.array<chunksize=(1, 300, 360), meta=np.ndarray>\n",
+       "        Attributes: (12/17)\n",
+       "            title:                                    sea ice model output for CICE\n",
+       "            contents:                                 Diagnostic and Prognostic Varia...\n",
+       "            source:                                   Los Alamos Sea Ice Model (CICE)...\n",
+       "            time_period_freq:                         month_1\n",
+       "            comment3:                                 seconds elapsed into model date...\n",
+       "            conventions:                              CF-1.0\n",
+       "            ...                                       ...\n",
+       "            intake_esm_attrs:variable_long_name:      boundaries for time-averaging i...\n",
+       "            intake_esm_attrs:variable_standard_name:  ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,...\n",
+       "            intake_esm_attrs:variable_cell_methods:   ,,,,,,,,,,,,,,,,,,time: mean,ti...\n",
+       "            intake_esm_attrs:file_id:                 iceh_XXXX_XX\n",
+       "            intake_esm_attrs:_data_format_:           netcdf\n",
+       "            intake_esm_dataset_key:                   iceh_XXXX_XX.1mon"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "si_dt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Load the observational dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-12-11T00:17:58.735821Z",
+     "iopub.status.busy": "2023-12-11T00:17:58.732387Z",
+     "iopub.status.idle": "2023-12-11T00:17:58.816501Z",
+     "shell.execute_reply": "2023-12-11T00:17:58.800999Z",
+     "shell.execute_reply.started": "2023-12-11T00:17:58.735791Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def open_cdr_dataset(path, area_file):\n",
+    "    ds=xr.open_dataset(path).rename(\n",
+    "        {'cdr_seaice_conc_monthly':'cdr_seaice_conc','xgrid':'x','ygrid':'y'}\n",
+    "    )#.swap_dims({'tdim':'time','x':'xgrid','y':'ygrid'})\n",
+    "\n",
+    "    #we also need the area of each gridcell\n",
+    "    datFile=open(area_file, 'rb')\n",
+    "    #pss25area_v3.dat: 316 columns x 332 rows\n",
+    "    areasDmNd=np.fromfile(datFile, dtype=np.int32).reshape(ds.cdr_seaice_conc.isel(time=0).shape)\n",
+    "    #Divide by 1000 to get km2\n",
+    "    areasKmNd_sh=areasDmNd/1000\n",
+    "\n",
+    "    ds['area']=xr.DataArray(areasKmNd_sh, dims=['y','x'])\n",
+    "    ds=ds.set_coords('area')\n",
+    "\n",
+    "    ds=ds.cdr_seaice_conc\n",
+    "    \n",
+    "    ds=ds.where(ds<=1) #convert error codes to Nan\n",
+    "\n",
+    "    return ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can pull the datasets direct from the url, however the cell area needs to be downloaded seperately:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-12-11T00:17:58.855242Z",
+     "iopub.status.busy": "2023-12-11T00:17:58.848852Z",
+     "iopub.status.idle": "2023-12-11T00:17:59.399533Z",
+     "shell.execute_reply": "2023-12-11T00:17:59.397040Z",
+     "shell.execute_reply.started": "2023-12-11T00:17:58.855209Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "File ‘pss25area_v3.dat’ already there; not retrieving.\n",
+      "File ‘psn25area_v3.dat’ already there; not retrieving.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!wget --ftp-user=anonymous -nc ftp://sidads.colorado.edu/DATASETS/seaice/polar-stereo/tools/pss25area_v3.dat ftp://sidads.colorado.edu/DATASETS/seaice/polar-stereo/tools/psn25area_v3.dat "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-12-11T00:17:59.406056Z",
+     "iopub.status.busy": "2023-12-11T00:17:59.403692Z",
+     "iopub.status.idle": "2023-12-11T00:21:40.273734Z",
+     "shell.execute_reply": "2023-12-11T00:21:40.272403Z",
+     "shell.execute_reply.started": "2023-12-11T00:17:59.405995Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "sh_cdr_xr = open_cdr_dataset(\n",
+    "    sh_obs_url,\n",
+    "    'pss25area_v3.dat'\n",
+    ")\n",
+    "\n",
+    "# nh_cdr_xr = open_cdr_dataset(\n",
+    "#     nh_obs_url,\n",
+    "#     'psn25area_v3.dat'\n",
+    "# )\n",
+    "\n",
+    "cdr_dt = DataTree.from_dict({\n",
+    "    'cdr_sh':sh_cdr_xr,\n",
+    "    # 'cdr_nh':nh_cdr_xr\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a datatree, with seperate datasets for each hemisphere"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Calculate Sea Ice Area"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sea ice area is the circumpolar sum of sea ice concentration multiplied by the area of each grid cell. By convention, and because lower concentrations are not accurate when measured through remote sensing, concentrations below 0.15 are not included"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-12-11T00:21:40.278975Z",
+     "iopub.status.busy": "2023-12-11T00:21:40.278691Z",
+     "iopub.status.idle": "2023-12-11T00:21:40.285323Z",
+     "shell.execute_reply": "2023-12-11T00:21:40.284189Z",
+     "shell.execute_reply.started": "2023-12-11T00:21:40.278941Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def sea_ice_area(sic,area, range=[0.15,1]):\n",
+    "    return (sic*area).where((sic>=range[0])*(sic<=range[1])).cf.sum(['X','Y'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculate for observational data, and remove gaps with missing data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-12-11T00:21:40.287957Z",
+     "iopub.status.busy": "2023-12-11T00:21:40.287015Z",
+     "iopub.status.idle": "2023-12-11T00:21:40.295736Z",
+     "shell.execute_reply": "2023-12-11T00:21:40.294636Z",
+     "shell.execute_reply.started": "2023-12-11T00:21:40.287909Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "@map_over_subtree\n",
+    "def sea_ice_area_obs(ds):\n",
+    "    sic=ds.cdr_seaice_conc\n",
+    "    result=sea_ice_area(sic,sic.area).to_dataset(name='cdr_area')\n",
+    "\n",
+    "    #Theres a couple of data gaps which should be nan\n",
+    "    result.loc[{'time':'1988-01-01'}]=np.nan\n",
+    "    result.loc[{'time':'1987-12'}]=np.nan\n",
+    "\n",
+    "    return result.sel(time=OBS_TIME_SLICE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-12-11T00:21:40.304187Z",
+     "iopub.status.busy": "2023-12-11T00:21:40.303259Z",
+     "iopub.status.idle": "2023-12-11T00:21:41.072155Z",
+     "shell.execute_reply": "2023-12-11T00:21:41.070727Z",
+     "shell.execute_reply.started": "2023-12-11T00:21:40.304142Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "obs_area_dt=sea_ice_area_obs(cdr_dt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-12-11T00:21:41.074041Z",
+     "iopub.status.busy": "2023-12-11T00:21:41.073652Z",
+     "iopub.status.idle": "2023-12-11T00:21:41.078981Z",
+     "shell.execute_reply": "2023-12-11T00:21:41.077810Z",
+     "shell.execute_reply.started": "2023-12-11T00:21:41.074003Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "#Theres another gap which should be nan\n",
+    "#obs_area_dt['cdr_nh'].to_dataset().loc[{'time':'1984-07'}]=np.nan"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculate for model data, limit to southern hemisphere / Antarctica"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-12-11T00:21:41.081869Z",
+     "iopub.status.busy": "2023-12-11T00:21:41.080837Z",
+     "iopub.status.idle": "2023-12-11T00:21:41.095718Z",
+     "shell.execute_reply": "2023-12-11T00:21:41.094577Z",
+     "shell.execute_reply.started": "2023-12-11T00:21:41.081822Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "@map_over_subtree\n",
+    "def sea_ice_area_model_sh(ds):\n",
+    "    sic=ds.aice_m.cf.sel(Y=slice(-90,0))   \n",
+    "    area_km2=ds.area_t/1e6\n",
+    "\n",
+    "    return sea_ice_area(sic,area_km2).to_dataset(name='si_area').load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-12-11T00:21:41.098195Z",
+     "iopub.status.busy": "2023-12-11T00:21:41.097372Z",
+     "iopub.status.idle": "2023-12-11T00:23:10.571063Z",
+     "shell.execute_reply": "2023-12-11T00:23:10.563689Z",
+     "shell.execute_reply.started": "2023-12-11T00:21:41.098148Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "model_area_dt=sea_ice_area_model_sh(si_dt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Sea Ice Area Trends"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We are going to compare the trends in the minima and maxima over time, and the climatology"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-12-11T00:23:10.580762Z",
+     "iopub.status.busy": "2023-12-11T00:23:10.579515Z",
+     "iopub.status.idle": "2023-12-11T00:23:10.900272Z",
+     "shell.execute_reply": "2023-12-11T00:23:10.899225Z",
+     "shell.execute_reply.started": "2023-12-11T00:23:10.580710Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "@map_over_subtree\n",
+    "def min_and_max(ds):\n",
+    "    def min_and_max_year(da):\n",
+    "        result = xr.Dataset()\n",
+    "        result['min']=da.min()\n",
+    "        result['max']=da.max()\n",
+    "        return result\n",
+    "    annual_min_max_ds=ds.si_area.groupby('time.year').apply(min_and_max_year)\n",
+    "    return annual_min_max_ds\n",
+    "    \n",
+    "model_min_max_dt=min_and_max(model_area_dt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-12-11T00:23:10.903018Z",
+     "iopub.status.busy": "2023-12-11T00:23:10.902778Z",
+     "iopub.status.idle": "2023-12-11T00:23:12.077225Z",
+     "shell.execute_reply": "2023-12-11T00:23:12.076476Z",
+     "shell.execute_reply.started": "2023-12-11T00:23:10.902995Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for iGroup in model_min_max_dt.groups[1:]:\n",
+    "    ds=model_min_max_dt[iGroup].ds\n",
+    "    ds['min'].plot(label=iGroup[1:])\n",
+    "\n",
+    "obs_area_dt['cdr_sh'].ds.cdr_area.groupby('time.year').min().plot(label='CDR')\n",
+    "\n",
+    "plt.title('Trends in Sea-Ice Minima')\n",
+    "plt.ylabel('Sea-Ice Area (km2)')\n",
+    "\n",
+    "_ = plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that all models have sea ice area which is too low in summer. Model runs forced by JRA have more variability than those forced by ERA5 and are slightly closer to the measured sea ice area from observations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-12-11T00:23:12.078306Z",
+     "iopub.status.busy": "2023-12-11T00:23:12.078021Z",
+     "iopub.status.idle": "2023-12-11T00:23:12.335569Z",
+     "shell.execute_reply": "2023-12-11T00:23:12.334731Z",
+     "shell.execute_reply.started": "2023-12-11T00:23:12.078289Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for iGroup in model_min_max_dt.groups[1:]:\n",
+    "    ds=model_min_max_dt[iGroup].ds\n",
+    "    ds['max'].plot(label=iGroup[1:])\n",
+    "\n",
+    "obs_area_dt['cdr_sh'].ds.cdr_area.groupby('time.year').max().plot(label='CDR')\n",
+    "\n",
+    "plt.title('Trends in Sea-Ice Maxima')\n",
+    "plt.ylabel('Sea-Ice Area (km2)')\n",
+    "\n",
+    "_ = plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-12-08T03:00:12.062280Z",
+     "iopub.status.busy": "2023-12-08T03:00:12.060174Z",
+     "iopub.status.idle": "2023-12-08T03:00:12.075164Z",
+     "shell.execute_reply": "2023-12-08T03:00:12.073582Z",
+     "shell.execute_reply.started": "2023-12-08T03:00:12.062204Z"
+    }
+   },
+   "source": [
+    "We see that the closest runs are those forced by ERA5, and the runs forced by JRA55 are under representing Winter sea ice."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-12-11T00:23:12.336398Z",
+     "iopub.status.busy": "2023-12-11T00:23:12.336227Z",
+     "iopub.status.idle": "2023-12-11T00:23:12.599445Z",
+     "shell.execute_reply": "2023-12-11T00:23:12.598342Z",
+     "shell.execute_reply.started": "2023-12-11T00:23:12.336382Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x148c31cb0970>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for iGroup in model_area_dt.groups[1:]:\n",
+    "    model_area_dt[iGroup].ds.si_area.groupby('time.month').mean('time').plot(label=iGroup[1:])\n",
+    "\n",
+    "obs_area_dt['cdr_sh'].ds.cdr_area.groupby('time.month').mean('time').plot(label='CDR')\n",
+    "\n",
+    "plt.title('Climatology of Sea-Ice Area')\n",
+    "plt.ylabel('Sea-Ice Area (km2)')\n",
+    "\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-12-08T03:01:35.360367Z",
+     "iopub.status.busy": "2023-12-08T03:01:35.359044Z",
+     "iopub.status.idle": "2023-12-08T03:01:35.370817Z",
+     "shell.execute_reply": "2023-12-08T03:01:35.369364Z",
+     "shell.execute_reply.started": "2023-12-08T03:01:35.360297Z"
+    }
+   },
+   "source": [
+    "We see all model runs have too low sea ice in summer, and grow faster than observations in autumn and earlier that the observed maximum."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Sea Ice Area differences"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To examine the differences between the model results and observations, we calculate difference in each grid cell between observations and each experiment\n",
+    "\n",
+    "As that data are on different grids, we need to regrid to compare the datasets\n",
+    "\n",
+    "Lets simplify a little to only look at 0.25"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-12-11T00:23:12.600429Z",
+     "iopub.status.busy": "2023-12-11T00:23:12.600253Z",
+     "iopub.status.idle": "2023-12-11T00:23:12.604007Z",
+     "shell.execute_reply": "2023-12-11T00:23:12.603332Z",
+     "shell.execute_reply.started": "2023-12-11T00:23:12.600412Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "groups=('/025deg_era5', '/025deg_jra55')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-12-11T00:23:12.604871Z",
+     "iopub.status.busy": "2023-12-11T00:23:12.604709Z",
+     "iopub.status.idle": "2023-12-11T00:23:12.648801Z",
+     "shell.execute_reply": "2023-12-11T00:23:12.647929Z",
+     "shell.execute_reply.started": "2023-12-11T00:23:12.604856Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DatasetView&gt;\n",
+       "Dimensions:          (time: 536, y: 332, x: 316)\n",
+       "Coordinates:\n",
+       "  * time             (time) datetime64[ns] 1978-11-01 1978-12-01 ... 2023-06-01\n",
+       "  * y                (y) float32 4.338e+06 4.312e+06 ... -3.912e+06 -3.938e+06\n",
+       "  * x                (x) float32 -3.938e+06 -3.912e+06 ... 3.912e+06 3.938e+06\n",
+       "    area             (y, x) float64 444.1 445.0 445.9 ... 462.1 461.1 460.1\n",
+       "Data variables:\n",
+       "    cdr_seaice_conc  (time, y, x) float32 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>datatree.DataTree</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-7873b895-b588-4892-9594-dec217400676' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7873b895-b588-4892-9594-dec217400676' class='xr-section-summary'  title='Expand/collapse section'>Groups: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%'></div></div></li><li class='xr-section-item'><input id='section-0efcfb20-8d6c-4b2b-9c5e-b00f4a9d290a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0efcfb20-8d6c-4b2b-9c5e-b00f4a9d290a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 536</li><li><span class='xr-has-index'>y</span>: 332</li><li><span class='xr-has-index'>x</span>: 316</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-804e0b9f-b280-4916-8164-a933bb299416' class='xr-section-summary-in' type='checkbox'  checked><label for='section-804e0b9f-b280-4916-8164-a933bb299416' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1978-11-01 ... 2023-06-01</div><input id='attrs-436166c7-ceeb-4251-b350-d0aba408c3eb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-436166c7-ceeb-4251-b350-d0aba408c3eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67532312-88f9-4f14-b456-0bd02818b143' class='xr-var-data-in' type='checkbox'><label for='data-67532312-88f9-4f14-b456-0bd02818b143' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>1024</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Time</dd><dt><span>actual_range :</span></dt><dd>[2.7872640e+08 1.6855776e+09]</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>ioos_category :</span></dt><dd>Time</dd><dt><span>long_name :</span></dt><dd>ANSI date</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>01-JAN-1970 00:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1978-11-01T00:00:00.000000000&#x27;, &#x27;1978-12-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;1979-01-01T00:00:00.000000000&#x27;, ..., &#x27;2023-04-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2023-05-01T00:00:00.000000000&#x27;, &#x27;2023-06-01T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>4.338e+06 4.312e+06 ... -3.938e+06</div><input id='attrs-4cf4085e-2eed-447d-a233-cbd646815a85' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4cf4085e-2eed-447d-a233-cbd646815a85' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65e02f95-6b4e-4609-82f5-661542f198f1' class='xr-var-data-in' type='checkbox'><label for='data-65e02f95-6b4e-4609-82f5-661542f198f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>332</dd><dt><span>actual_range :</span></dt><dd>[-3937500.  4337500.]</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>ioos_category :</span></dt><dd>Location</dd><dt><span>long_name :</span></dt><dd>projection_grid_y_centers</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>meters</dd><dt><span>valid_range :</span></dt><dd>[-3950000.  4350000.]</dd></dl></div><div class='xr-var-data'><pre>array([ 4337500.,  4312500.,  4287500., ..., -3887500., -3912500., -3937500.],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-3.938e+06 -3.912e+06 ... 3.938e+06</div><input id='attrs-b3158920-20fc-4cf1-9eb9-653ceb0faa0c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3158920-20fc-4cf1-9eb9-653ceb0faa0c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-801180d9-1773-41e9-9d3f-3ffdf75a3e6f' class='xr-var-data-in' type='checkbox'><label for='data-801180d9-1773-41e9-9d3f-3ffdf75a3e6f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>316</dd><dt><span>actual_range :</span></dt><dd>[-3937500.  3937500.]</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>ioos_category :</span></dt><dd>Location</dd><dt><span>long_name :</span></dt><dd>projection_grid_x_centers</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>meters</dd><dt><span>valid_range :</span></dt><dd>[-3950000.  3950000.]</dd></dl></div><div class='xr-var-data'><pre>array([-3937500., -3912500., -3887500., ...,  3887500.,  3912500.,  3937500.],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>area</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>444.1 445.0 445.9 ... 461.1 460.1</div><input id='attrs-baf2a960-67c3-46b0-a23c-0a732cc505b3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-baf2a960-67c3-46b0-a23c-0a732cc505b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1be9e16a-7309-4fac-ae9a-4960051bb623' class='xr-var-data-in' type='checkbox'><label for='data-1be9e16a-7309-4fac-ae9a-4960051bb623' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[444.053, 444.983, 445.91 , ..., 445.91 , 444.983, 444.053],\n",
+       "       [445.078, 446.011, 446.942, ..., 446.942, 446.011, 445.078],\n",
+       "       [446.101, 447.037, 447.971, ..., 447.971, 447.037, 446.101],\n",
+       "       ...,\n",
+       "       [462.098, 463.085, 464.069, ..., 464.069, 463.085, 462.098],\n",
+       "       [461.12 , 462.104, 463.085, ..., 463.085, 462.104, 461.12 ],\n",
+       "       [460.139, 461.12 , 462.098, ..., 462.098, 461.12 , 460.139]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8baa8b77-e6a8-4b78-aeb4-e9c8be1ae7ce' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8baa8b77-e6a8-4b78-aeb4-e9c8be1ae7ce' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>cdr_seaice_conc</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-e80de25c-353e-4cae-b91a-3b561260e4fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e80de25c-353e-4cae-b91a-3b561260e4fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36f31937-28d5-46f7-9cdf-f8dc7c66aa54' class='xr-var-data-in' type='checkbox'><label for='data-36f31937-28d5-46f7-9cdf-f8dc7c66aa54' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>ancillary_variables :</span></dt><dd>stdev_of_cdr_seaice_conc_monthly qa_of_cdr_seaice_conc_monthly</dd><dt><span>colorBarMaximum :</span></dt><dd>1.0</dd><dt><span>colorBarMinimum :</span></dt><dd>0.0</dd><dt><span>colorBarPalette :</span></dt><dd>KT_ice</dd><dt><span>datum :</span></dt><dd>+ellps=urn:ogc:def:crs:EPSG::4326</dd><dt><span>flag_meanings :</span></dt><dd>pole_hole lakes coastal land_mask missing_data</dd><dt><span>flag_values :</span></dt><dd>[-5 -4 -3 -2 -1]</dd><dt><span>ioos_category :</span></dt><dd>Ice Distribution</dd><dt><span>long_name :</span></dt><dd>NOAA/NSIDC Climate Data Record of Passive Microwave Monthly Southern Hemisphere Sea Ice Concentration</dd><dt><span>references :</span></dt><dd>https://nsidc.org/data/g02202/versions/4/</dd><dt><span>standard_name :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_range :</span></dt><dd>[0. 1.]</dd></dl></div><div class='xr-var-data'><pre>array([[[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "...\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-066660ba-6824-4983-a243-2c92f4b065e0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-066660ba-6824-4983-a243-2c92f4b065e0' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "DataTree('cdr_sh', parent=\"None\")\n",
+       "    Dimensions:          (time: 536, y: 332, x: 316)\n",
+       "    Coordinates:\n",
+       "      * time             (time) datetime64[ns] 1978-11-01 1978-12-01 ... 2023-06-01\n",
+       "      * y                (y) float32 4.338e+06 4.312e+06 ... -3.912e+06 -3.938e+06\n",
+       "      * x                (x) float32 -3.938e+06 -3.912e+06 ... 3.912e+06 3.938e+06\n",
+       "        area             (y, x) float64 444.1 445.0 445.9 ... 462.1 461.1 460.1\n",
+       "    Data variables:\n",
+       "        cdr_seaice_conc  (time, y, x) float32 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cdr_dt['cdr_sh']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-12-08T03:05:37.422498Z",
+     "iopub.status.busy": "2023-12-08T03:05:37.421532Z",
+     "iopub.status.idle": "2023-12-08T03:05:37.432650Z",
+     "shell.execute_reply": "2023-12-08T03:05:37.431031Z",
+     "shell.execute_reply.started": "2023-12-08T03:05:37.422430Z"
+    }
+   },
+   "source": [
+    "The lat/lon of of each cell in the observational dataset are in a different file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-12-11T00:23:12.650117Z",
+     "iopub.status.busy": "2023-12-11T00:23:12.649768Z",
+     "iopub.status.idle": "2023-12-11T00:23:13.020585Z",
+     "shell.execute_reply": "2023-12-11T00:23:13.019084Z",
+     "shell.execute_reply.started": "2023-12-11T00:23:12.650091Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "File ‘G02202-cdr-ancillary-sh.nc’ already there; not retrieving.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "! wget -nc https://noaadata.apps.nsidc.org/NOAA/G02202_V4/ancillary/G02202-cdr-ancillary-sh.nc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-12-11T00:23:13.024733Z",
+     "iopub.status.busy": "2023-12-11T00:23:13.024079Z",
+     "iopub.status.idle": "2023-12-11T00:23:13.099519Z",
+     "shell.execute_reply": "2023-12-11T00:23:13.098740Z",
+     "shell.execute_reply.started": "2023-12-11T00:23:13.024680Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "cdr_sps_ds=xr.open_dataset('G02202-cdr-ancillary-sh.nc')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now build the re-gridder. This is described in detail in [DocumentedExamples/Regridding](https://cosima-recipes.readthedocs.io/en/latest/DocumentedExamples/Regridding.html)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-12-11T00:23:13.100733Z",
+     "iopub.status.busy": "2023-12-11T00:23:13.100561Z",
+     "iopub.status.idle": "2023-12-11T00:23:31.520172Z",
+     "shell.execute_reply": "2023-12-11T00:23:31.518607Z",
+     "shell.execute_reply.started": "2023-12-11T00:23:13.100717Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "regridder_ACCESSOM2_025deg_sh = xesmf.Regridder(\n",
+    "    si_dt['025deg_era5'].ds.isel(time=0).drop(['xt_ocean','yt_ocean']), \n",
+    "    cdr_sps_ds, \n",
+    "    'bilinear', \n",
+    "    periodic=True,\n",
+    "    unmapped_to_nan=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-12-11T00:23:31.527651Z",
+     "iopub.status.busy": "2023-12-11T00:23:31.527177Z",
+     "iopub.status.idle": "2023-12-11T00:24:37.575311Z",
+     "shell.execute_reply": "2023-12-11T00:24:37.571354Z",
+     "shell.execute_reply.started": "2023-12-11T00:23:31.527623Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "aice_sh_3976_ds=xr.Dataset()\n",
+    "aice_sh_diff_ds=xr.Dataset()\n",
+    "\n",
+    "for iG in groups:\n",
+    "    aice_sh_3976_ds[iG]=regridder_ACCESSOM2_025deg_sh(\n",
+    "        si_dt[iG].ds.aice_m\n",
+    "    ).load()\n",
+    "    aice_sh_diff_ds[iG]=aice_sh_3976_ds[iG]-cdr_dt['cdr_sh'].ds.cdr_seaice_conc.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now plot the difference between modelled and observed sea ice concentration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-12-11T00:24:48.229582Z",
+     "iopub.status.busy": "2023-12-11T00:24:48.228372Z",
+     "iopub.status.idle": "2023-12-11T00:24:50.017347Z",
+     "shell.execute_reply": "2023-12-11T00:24:50.016509Z",
+     "shell.execute_reply.started": "2023-12-11T00:24:48.229528Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 900x800 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "months=[2,9] #february, september\n",
+    "month_names=['Feb','Sep']\n",
+    "n_months=len(months)\n",
+    "\n",
+    "plt.figure(figsize=(9,n_months*4))\n",
+    "j=0\n",
+    "\n",
+    "cdr=cdr_dt['cdr_sh'].ds.cdr_seaice_conc\n",
+    "\n",
+    "for iMonth in np.arange(0,n_months):\n",
+    "\n",
+    "    i=1\n",
+    "\n",
+    "    for iGroup in aice_sh_diff_ds.data_vars:\n",
+    "        ds=aice_sh_diff_ds[iGroup]\n",
+    "        \n",
+    "        ds=ds.sel(time=ds.time.dt.month.isin(months[iMonth])).mean('time')\n",
+    "        \n",
+    "        ax = plt.subplot(n_months,3,i+j*3,projection=ccrs.SouthPolarStereo(true_scale_latitude=-70))\n",
+    "            \n",
+    "        plt.contourf(\n",
+    "            ds.x, ds.y, ds,\n",
+    "            levels=np.arange(-0.9,0.91,.2), cmap=cmo.balance_r\n",
+    "        )\n",
+    "\n",
+    "        cs_cdr=cdr.sel(time=cdr.time.dt.month.isin(months[iMonth])).mean('time').plot.contour(levels=[0.15])\n",
+    "        \n",
+    "        da=aice_sh_3976_ds[iGroup]\n",
+    "        cs_mod=da.sel(time=da.time.dt.month.isin(months[iMonth])).mean('time').plot.contour(levels=[0.15], colors=['black'])\n",
+    "\n",
+    "        plt.title(month_names[iMonth]+'_'+iGroup[1:])\n",
+    "        i+=1\n",
+    "\n",
+    "    j+=1\n",
+    "\n",
+    "\n",
+    "color_cdr = cs_cdr.collections[0].get_edgecolor()\n",
+    "line_cdr = mlines.Line2D([], [], color=color_cdr, label=\"Observed Extent\")\n",
+    "\n",
+    "color_mod = cs_mod.collections[0].get_edgecolor()\n",
+    "line_mod = mlines.Line2D([], [], color=color_mod, label=\"Modelled Extent\")\n",
+    "\n",
+    "# cax = plt.subplot(3,12,34)\n",
+    "plt.legend(handles=[line_cdr,line_mod], loc='center left', bbox_to_anchor=(1.2,0.5))\n",
+    "\n",
+    "cax = plt.axes([0.7,0.6,0.05,0.2])\n",
+    "_ = plt.colorbar(cax=cax, label='Difference in \\nSea Ice Concentration')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that OM2 under-represents sea ice in Summer, particularly in the Weddell Sea. In Winter, trends are less clear, although ERA5 forced sea ice concentration is too high at the northern boundary."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-12-11T00:24:56.014716Z",
+     "iopub.status.busy": "2023-12-11T00:24:56.013726Z",
+     "iopub.status.idle": "2023-12-11T00:24:59.394830Z",
+     "shell.execute_reply": "2023-12-11T00:24:59.393645Z",
+     "shell.execute_reply.started": "2023-12-11T00:24:56.014661Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.3.0\n",
+      "0.3.0\n",
+      "0.3.0\n",
+      "0.3.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-12-11 11:24:59,223 - distributed.nanny - WARNING - Worker process still alive after 3.199998474121094 seconds, killing\n",
+      "2023-12-11 11:24:59,227 - distributed.nanny - WARNING - Worker process still alive after 3.199998474121094 seconds, killing\n",
+      "2023-12-11 11:24:59,228 - distributed.nanny - WARNING - Worker process still alive after 3.1999992370605472 seconds, killing\n",
+      "2023-12-11 11:24:59,229 - distributed.nanny - WARNING - Worker process still alive after 3.1999992370605472 seconds, killing\n"
+     ]
+    }
+   ],
+   "source": [
+    "client.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 63d33d8de07f030e6ffdd03368e3bc61bd45263a Mon Sep 17 00:00:00 2001
From: anton-climate <anton.steketee@anu.edu.au>
Date: Mon, 11 Dec 2023 11:39:30 +1100
Subject: [PATCH 2/4] Add notebook to tests

---
 test/test_notebooks.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/test/test_notebooks.py b/test/test_notebooks.py
index 79615c97..74e0caf3 100644
--- a/test/test_notebooks.py
+++ b/test/test_notebooks.py
@@ -66,6 +66,7 @@ def test_Tutorials(notebook_filename):
         ("Querying_Scalar_Quantities_and_Annually_Averaged_Timeseries.ipynb"),
         ("Regridding.ipynb"),
         ("RelativeVorticity.ipynb"),
+        ("SeaIce_Obs_Model_Compare.ipynb"),
         ("SeaIce_Plot_Example.ipynb"),
         #("SeaIceSeasonality_DFA.ipynb"), # Does not run
         ("Surface_Water_Mass_Transformation.ipynb"),

From 1fccef30337a29cc22fde5d57ed27028f6f0236c Mon Sep 17 00:00:00 2001
From: anton-climate <anton.steketee@anu.edu.au>
Date: Mon, 8 Jan 2024 14:18:47 +1100
Subject: [PATCH 3/4] Add sea ice volume

---
 .../SeaIce_Obs_Model_Compare.ipynb            | 1311 +++++++++++++----
 1 file changed, 1027 insertions(+), 284 deletions(-)

diff --git a/DocumentedExamples/SeaIce_Obs_Model_Compare.ipynb b/DocumentedExamples/SeaIce_Obs_Model_Compare.ipynb
index 68be7cb5..4993c161 100644
--- a/DocumentedExamples/SeaIce_Obs_Model_Compare.ipynb
+++ b/DocumentedExamples/SeaIce_Obs_Model_Compare.ipynb
@@ -8,7 +8,7 @@
     "\n",
     "This script shows how to load and plot sea ice concentration from CICE output and compare it to the NSIDC CDR (National Snow and Ice Data Centre, Climate Data Record) dataset\n",
     "\n",
-    "This notebook uses the _ACCESS-NRI Intake Catalog_ following the examples in [Tutorails/Using the Intake Catalog](https://cosima-recipes.readthedocs.io/en/latest/Tutorials/Using_Intake_Catalog.html). \n",
+    "This notebook uses the _ACCESS-NRI Intake Catalog_ following the examples in [Tutorials/Using the Intake Catalog](https://cosima-recipes.readthedocs.io/en/latest/Tutorials/Using_Intake_Catalog.html). \n",
     "\n",
     "Requirements: The runs analysed here are only in access-nri-intake-catalog version 0.1.0 or newer. This is included in `conda/analysis3-23.10` or newer modules from `/g/data/hh5/public/modules`. "
    ]
@@ -17,7 +17,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**OM2 Expreiments:**"
+    "**OM2 Experiments:**"
    ]
   },
   {
@@ -32,11 +32,11 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-11T00:02:09.729461Z",
-     "iopub.status.busy": "2023-12-11T00:02:09.728970Z",
-     "iopub.status.idle": "2023-12-11T00:02:09.742751Z",
-     "shell.execute_reply": "2023-12-11T00:02:09.741622Z",
-     "shell.execute_reply.started": "2023-12-11T00:02:09.729409Z"
+     "iopub.execute_input": "2024-01-08T03:04:29.465533Z",
+     "iopub.status.busy": "2024-01-08T03:04:29.465106Z",
+     "iopub.status.idle": "2024-01-08T03:04:29.479853Z",
+     "shell.execute_reply": "2024-01-08T03:04:29.478557Z",
+     "shell.execute_reply.started": "2024-01-08T03:04:29.465479Z"
     }
    },
    "outputs": [],
@@ -53,7 +53,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We are going to look at Sea Ice Concentration"
+    "We are going to look at Sea Ice Concentration and Sea Ice Volume"
    ]
   },
   {
@@ -61,16 +61,16 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-11T00:02:09.745974Z",
-     "iopub.status.busy": "2023-12-11T00:02:09.745264Z",
-     "iopub.status.idle": "2023-12-11T00:02:09.759216Z",
-     "shell.execute_reply": "2023-12-11T00:02:09.758030Z",
-     "shell.execute_reply.started": "2023-12-11T00:02:09.745935Z"
+     "iopub.execute_input": "2024-01-08T03:04:29.482723Z",
+     "iopub.status.busy": "2024-01-08T03:04:29.482042Z",
+     "iopub.status.idle": "2024-01-08T03:04:29.492234Z",
+     "shell.execute_reply": "2024-01-08T03:04:29.491211Z",
+     "shell.execute_reply.started": "2024-01-08T03:04:29.482678Z"
     }
    },
    "outputs": [],
    "source": [
-    "VARS=['aice_m'] # ice area fraction or sea ice concentration\n",
+    "VARS=['aice_m', 'vicen_m'] # ice area fraction or sea ice concentration\n",
     "VARS_2D=['area_t','geolat_t','geolon_t']"
    ]
   },
@@ -93,11 +93,11 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-11T00:02:09.761693Z",
-     "iopub.status.busy": "2023-12-11T00:02:09.760805Z",
-     "iopub.status.idle": "2023-12-11T00:02:09.768264Z",
-     "shell.execute_reply": "2023-12-11T00:02:09.767094Z",
-     "shell.execute_reply.started": "2023-12-11T00:02:09.761645Z"
+     "iopub.execute_input": "2024-01-08T03:04:29.493997Z",
+     "iopub.status.busy": "2024-01-08T03:04:29.493577Z",
+     "iopub.status.idle": "2024-01-08T03:04:29.501149Z",
+     "shell.execute_reply": "2024-01-08T03:04:29.500096Z",
+     "shell.execute_reply.started": "2024-01-08T03:04:29.493953Z"
     }
    },
    "outputs": [],
@@ -118,14 +118,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-11T00:13:31.647986Z",
-     "iopub.status.busy": "2023-12-11T00:13:31.646840Z",
-     "iopub.status.idle": "2023-12-11T00:13:40.635744Z",
-     "shell.execute_reply": "2023-12-11T00:13:40.634429Z",
-     "shell.execute_reply.started": "2023-12-11T00:13:31.647934Z"
+     "iopub.execute_input": "2024-01-08T03:04:29.503170Z",
+     "iopub.status.busy": "2024-01-08T03:04:29.502560Z",
+     "iopub.status.idle": "2024-01-08T03:04:46.875562Z",
+     "shell.execute_reply": "2024-01-08T03:04:46.871803Z",
+     "shell.execute_reply.started": "2024-01-08T03:04:29.503125Z"
     }
    },
    "outputs": [],
@@ -157,14 +157,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-11T00:13:42.824181Z",
-     "iopub.status.busy": "2023-12-11T00:13:42.821833Z",
-     "iopub.status.idle": "2023-12-11T00:13:46.076825Z",
-     "shell.execute_reply": "2023-12-11T00:13:46.075376Z",
-     "shell.execute_reply.started": "2023-12-11T00:13:42.824117Z"
+     "iopub.execute_input": "2024-01-08T03:04:46.882392Z",
+     "iopub.status.busy": "2024-01-08T03:04:46.881957Z",
+     "iopub.status.idle": "2024-01-08T03:04:50.450062Z",
+     "shell.execute_reply": "2024-01-08T03:04:50.448558Z",
+     "shell.execute_reply.started": "2024-01-08T03:04:46.882373Z"
     }
    },
    "outputs": [],
@@ -174,14 +174,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-11T00:13:46.080216Z",
-     "iopub.status.busy": "2023-12-11T00:13:46.079404Z",
-     "iopub.status.idle": "2023-12-11T00:13:46.158044Z",
-     "shell.execute_reply": "2023-12-11T00:13:46.157101Z",
-     "shell.execute_reply.started": "2023-12-11T00:13:46.080164Z"
+     "iopub.execute_input": "2024-01-08T03:04:50.453447Z",
+     "iopub.status.busy": "2024-01-08T03:04:50.452942Z",
+     "iopub.status.idle": "2024-01-08T03:04:50.544152Z",
+     "shell.execute_reply": "2024-01-08T03:04:50.542926Z",
+     "shell.execute_reply.started": "2024-01-08T03:04:50.453399Z"
     }
    },
    "outputs": [
@@ -192,7 +192,7 @@
        "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
        "    <div style=\"margin-left: 48px;\">\n",
        "        <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
-       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-249117de-97ba-11ee-9559-00000088fe80</p>\n",
+       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-adfcdc04-add2-11ee-975c-00000183fe80</p>\n",
        "        <table style=\"width: 100%; text-align: left;\">\n",
        "\n",
        "        <tr>\n",
@@ -227,7 +227,7 @@
        "    </div>\n",
        "    <div style=\"margin-left: 48px;\">\n",
        "        <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">LocalCluster</h3>\n",
-       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">859c7974</p>\n",
+       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">5d0a3821</p>\n",
        "        <table style=\"width: 100%; text-align: left;\">\n",
        "            <tr>\n",
        "                <td style=\"text-align: left;\">\n",
@@ -264,11 +264,11 @@
        "        <div style=\"width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;\"> </div>\n",
        "        <div style=\"margin-left: 48px;\">\n",
        "            <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n",
-       "            <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-6ecf1630-5135-4f01-a4e8-53c403b18224</p>\n",
+       "            <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-ddd7101b-1410-4637-9589-cba912a601a9</p>\n",
        "            <table style=\"width: 100%; text-align: left;\">\n",
        "                <tr>\n",
        "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Comm:</strong> tcp://127.0.0.1:45613\n",
+       "                        <strong>Comm:</strong> tcp://127.0.0.1:40921\n",
        "                    </td>\n",
        "                    <td style=\"text-align: left;\">\n",
        "                        <strong>Workers:</strong> 4\n",
@@ -310,7 +310,7 @@
        "                <table style=\"width: 100%; text-align: left;\">\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:43581\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:34723\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
        "                            <strong>Total threads: </strong> 3\n",
@@ -318,7 +318,7 @@
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"/proxy/34869/status\" target=\"_blank\">/proxy/34869/status</a>\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/42735/status\" target=\"_blank\">/proxy/42735/status</a>\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
        "                            <strong>Memory: </strong> 11.50 GiB\n",
@@ -326,13 +326,13 @@
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:33579\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:37735\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\"></td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> /jobfs/103751882.gadi-pbs/dask-scratch-space/worker-h4hltsrq\n",
+       "                            <strong>Local directory: </strong> /jobfs/105674656.gadi-pbs/dask-scratch-space/worker-fd3ezkbc\n",
        "                        </td>\n",
        "                    </tr>\n",
        "\n",
@@ -355,7 +355,7 @@
        "                <table style=\"width: 100%; text-align: left;\">\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:38457\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:45459\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
        "                            <strong>Total threads: </strong> 3\n",
@@ -363,7 +363,7 @@
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"/proxy/46157/status\" target=\"_blank\">/proxy/46157/status</a>\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/39269/status\" target=\"_blank\">/proxy/39269/status</a>\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
        "                            <strong>Memory: </strong> 11.50 GiB\n",
@@ -371,13 +371,13 @@
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:46027\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:39831\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\"></td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> /jobfs/103751882.gadi-pbs/dask-scratch-space/worker-zc4vgj6q\n",
+       "                            <strong>Local directory: </strong> /jobfs/105674656.gadi-pbs/dask-scratch-space/worker-s9itsvld\n",
        "                        </td>\n",
        "                    </tr>\n",
        "\n",
@@ -400,7 +400,7 @@
        "                <table style=\"width: 100%; text-align: left;\">\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:33087\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:41387\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
        "                            <strong>Total threads: </strong> 3\n",
@@ -408,7 +408,7 @@
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"/proxy/35035/status\" target=\"_blank\">/proxy/35035/status</a>\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/41653/status\" target=\"_blank\">/proxy/41653/status</a>\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
        "                            <strong>Memory: </strong> 11.50 GiB\n",
@@ -416,13 +416,13 @@
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:42471\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:33555\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\"></td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> /jobfs/103751882.gadi-pbs/dask-scratch-space/worker-30cgwyb7\n",
+       "                            <strong>Local directory: </strong> /jobfs/105674656.gadi-pbs/dask-scratch-space/worker-idm357v8\n",
        "                        </td>\n",
        "                    </tr>\n",
        "\n",
@@ -445,7 +445,7 @@
        "                <table style=\"width: 100%; text-align: left;\">\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:33171\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:45141\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
        "                            <strong>Total threads: </strong> 3\n",
@@ -453,7 +453,7 @@
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"/proxy/35237/status\" target=\"_blank\">/proxy/35237/status</a>\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/39915/status\" target=\"_blank\">/proxy/39915/status</a>\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
        "                            <strong>Memory: </strong> 11.50 GiB\n",
@@ -461,13 +461,13 @@
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:40003\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:36921\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\"></td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> /jobfs/103751882.gadi-pbs/dask-scratch-space/worker-jh_ng_zt\n",
+       "                            <strong>Local directory: </strong> /jobfs/105674656.gadi-pbs/dask-scratch-space/worker-wcjlbs40\n",
        "                        </td>\n",
        "                    </tr>\n",
        "\n",
@@ -494,10 +494,10 @@
        "</div>"
       ],
       "text/plain": [
-       "<Client: 'tcp://127.0.0.1:45613' processes=4 threads=12, memory=46.00 GiB>"
+       "<Client: 'tcp://127.0.0.1:40921' processes=4 threads=12, memory=46.00 GiB>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -510,19 +510,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Open the catalog"
+    "Open the _ACCESS-NRI Intake Catalog_"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-11T00:13:46.160118Z",
-     "iopub.status.busy": "2023-12-11T00:13:46.159747Z",
-     "iopub.status.idle": "2023-12-11T00:13:46.552070Z",
-     "shell.execute_reply": "2023-12-11T00:13:46.551171Z",
-     "shell.execute_reply.started": "2023-12-11T00:13:46.160086Z"
+     "iopub.execute_input": "2024-01-08T03:04:50.552154Z",
+     "iopub.status.busy": "2024-01-08T03:04:50.551797Z",
+     "iopub.status.idle": "2024-01-08T03:04:51.237424Z",
+     "shell.execute_reply": "2024-01-08T03:04:51.231153Z",
+     "shell.execute_reply.started": "2024-01-08T03:04:50.552122Z"
     }
    },
    "outputs": [],
@@ -546,14 +546,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-11T00:13:46.553629Z",
-     "iopub.status.busy": "2023-12-11T00:13:46.553357Z",
-     "iopub.status.idle": "2023-12-11T00:13:46.563904Z",
-     "shell.execute_reply": "2023-12-11T00:13:46.562914Z",
-     "shell.execute_reply.started": "2023-12-11T00:13:46.553602Z"
+     "iopub.execute_input": "2024-01-08T03:04:51.246049Z",
+     "iopub.status.busy": "2024-01-08T03:04:51.245738Z",
+     "iopub.status.idle": "2024-01-08T03:04:51.260159Z",
+     "shell.execute_reply": "2024-01-08T03:04:51.259333Z",
+     "shell.execute_reply.started": "2024-01-08T03:04:51.246028Z"
     }
    },
    "outputs": [],
@@ -577,7 +577,7 @@
     "    ], dim='time'\n",
     "    )\n",
     "    \n",
-    "    #We also want the area/lat/lom fields, but these are not time dependent.\n",
+    "    #We also want the area/lat/lon fields, but these are not time dependent.\n",
     "    area_ds=xr.merge([\n",
     "        xr.open_dataset(\n",
     "            catalog[RUNS[exp_name][0]].search(variable=iVar).df.path[0] \n",
@@ -618,14 +618,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-11T00:13:46.566441Z",
-     "iopub.status.busy": "2023-12-11T00:13:46.566175Z",
-     "iopub.status.idle": "2023-12-11T00:17:57.974904Z",
-     "shell.execute_reply": "2023-12-11T00:17:57.973388Z",
-     "shell.execute_reply.started": "2023-12-11T00:13:46.566419Z"
+     "iopub.execute_input": "2024-01-08T03:04:51.262147Z",
+     "iopub.status.busy": "2024-01-08T03:04:51.261856Z",
+     "iopub.status.idle": "2024-01-08T03:07:42.249461Z",
+     "shell.execute_reply": "2024-01-08T03:07:42.247261Z",
+     "shell.execute_reply.started": "2024-01-08T03:04:51.262119Z"
     },
     "scrolled": true
    },
@@ -635,8 +635,8 @@
      "output_type": "stream",
      "text": [
       "0.3.0\n",
-      "CPU times: user 46.2 s, sys: 6.47 s, total: 52.7 s\n",
-      "Wall time: 4min 11s\n"
+      "CPU times: user 51.2 s, sys: 8.23 s, total: 59.4 s\n",
+      "Wall time: 2min 50s\n"
      ]
     }
    ],
@@ -652,14 +652,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-11T00:17:57.981890Z",
-     "iopub.status.busy": "2023-12-11T00:17:57.981600Z",
-     "iopub.status.idle": "2023-12-11T00:17:57.996827Z",
-     "shell.execute_reply": "2023-12-11T00:17:57.996147Z",
-     "shell.execute_reply.started": "2023-12-11T00:17:57.981867Z"
+     "iopub.execute_input": "2024-01-08T03:07:42.264050Z",
+     "iopub.status.busy": "2024-01-08T03:07:42.262435Z",
+     "iopub.status.idle": "2024-01-08T03:07:42.280237Z",
+     "shell.execute_reply": "2024-01-08T03:07:42.278906Z",
+     "shell.execute_reply.started": "2024-01-08T03:07:42.263994Z"
     }
    },
    "outputs": [],
@@ -677,14 +677,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-11T00:17:57.998442Z",
-     "iopub.status.busy": "2023-12-11T00:17:57.997928Z",
-     "iopub.status.idle": "2023-12-11T00:17:58.051695Z",
-     "shell.execute_reply": "2023-12-11T00:17:58.050736Z",
-     "shell.execute_reply.started": "2023-12-11T00:17:57.998408Z"
+     "iopub.execute_input": "2024-01-08T03:07:42.282362Z",
+     "iopub.status.busy": "2024-01-08T03:07:42.281852Z",
+     "iopub.status.idle": "2024-01-08T03:07:42.335507Z",
+     "shell.execute_reply": "2024-01-08T03:07:42.334432Z",
+     "shell.execute_reply.started": "2024-01-08T03:07:42.282315Z"
     }
    },
    "outputs": [],
@@ -701,14 +701,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-11T00:17:58.053122Z",
-     "iopub.status.busy": "2023-12-11T00:17:58.052737Z",
-     "iopub.status.idle": "2023-12-11T00:17:58.727843Z",
-     "shell.execute_reply": "2023-12-11T00:17:58.719424Z",
-     "shell.execute_reply.started": "2023-12-11T00:17:58.053096Z"
+     "iopub.execute_input": "2024-01-08T03:07:42.336544Z",
+     "iopub.status.busy": "2024-01-08T03:07:42.336304Z",
+     "iopub.status.idle": "2024-01-08T03:07:42.564314Z",
+     "shell.execute_reply": "2024-01-08T03:07:42.562888Z",
+     "shell.execute_reply.started": "2024-01-08T03:07:42.336522Z"
     },
     "scrolled": true
    },
@@ -1082,7 +1082,7 @@
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DatasetView&gt;\n",
        "Dimensions:  ()\n",
        "Data variables:\n",
-       "    *empty*</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>datatree.DataTree</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-de6013d5-87e1-4af9-af0b-167b837bd2ee' class='xr-section-summary-in' type='checkbox'  ><label for='section-de6013d5-87e1-4af9-af0b-167b837bd2ee' class='xr-section-summary' >Groups: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%'><div style='display: inline-grid;'><div style='grid-column-start: 1;border-right: 0.2em solid;border-color: var(--xr-border-color);height: 100%;width: 0px;'></div><div style='grid-column-start: 2;grid-row-start: 1;height: 1em;width: 20px;border-bottom: 0.2em solid;border-color: var(--xr-border-color);'></div><div style='grid-column-start: 3;'><ul class='xr-sections'><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "    *empty*</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>datatree.DataTree</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b74acdf8-99c8-4184-a01a-f8422bcd0621' class='xr-section-summary-in' type='checkbox'  ><label for='section-b74acdf8-99c8-4184-a01a-f8422bcd0621' class='xr-section-summary' >Groups: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%'><div style='display: inline-grid;'><div style='grid-column-start: 1;border-right: 0.2em solid;border-color: var(--xr-border-color);height: 100%;width: 0px;'></div><div style='grid-column-start: 2;grid-row-start: 1;height: 1em;width: 20px;border-bottom: 0.2em solid;border-color: var(--xr-border-color);'></div><div style='grid-column-start: 3;'><ul class='xr-sections'><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
        "<defs>\n",
        "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
        "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
@@ -1446,7 +1446,7 @@
        "  fill: currentColor;\n",
        "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DatasetView&gt;\n",
-       "Dimensions:   (time: 523, yt_ocean: 1080, xt_ocean: 1440)\n",
+       "Dimensions:   (time: 523, nc: 5, yt_ocean: 1080, xt_ocean: 1440)\n",
        "Coordinates:\n",
        "  * time      (time) datetime64[ns] 1980-01-01 1980-02-01 ... 2023-07-01\n",
        "  * xt_ocean  (xt_ocean) float64 -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n",
@@ -1454,9 +1454,11 @@
        "    geolat_t  (yt_ocean, xt_ocean) float32 ...\n",
        "    geolon_t  (yt_ocean, xt_ocean) float32 ...\n",
        "    area_t    (yt_ocean, xt_ocean) float32 ...\n",
+       "Dimensions without coordinates: nc\n",
        "Data variables:\n",
+       "    vicen_m   (time, nc, yt_ocean, xt_ocean) float32 dask.array&lt;chunksize=(1, 1, 1080, 1440), meta=np.ndarray&gt;\n",
        "    aice_m    (time, yt_ocean, xt_ocean) float32 dask.array&lt;chunksize=(1, 1080, 1440), meta=np.ndarray&gt;\n",
-       "Attributes: (12/18)\n",
+       "Attributes: (12/17)\n",
        "    title:                                    sea ice model output for CICE\n",
        "    contents:                                 Diagnostic and Prognostic Varia...\n",
        "    source:                                   Los Alamos Sea Ice Model (CICE)...\n",
@@ -1469,11 +1471,144 @@
        "    intake_esm_attrs:variable_units:          days since 1980-01-01 00:00:00,...\n",
        "    intake_esm_attrs:file_id:                 iceh_XXXX_XX\n",
        "    intake_esm_attrs:_data_format_:           netcdf\n",
-       "    intake_esm_dataset_key:                   iceh_XXXX_XX.1mon</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>025deg_era5</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-e9afb33a-7fb8-47f9-80b7-9850209dd6e3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e9afb33a-7fb8-47f9-80b7-9850209dd6e3' class='xr-section-summary'  title='Expand/collapse section'>Groups: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%'></div></div></li><li class='xr-section-item'><input id='section-66cedc11-3af3-4a1f-b0f3-eb8cfb45557f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-66cedc11-3af3-4a1f-b0f3-eb8cfb45557f' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 523</li><li><span class='xr-has-index'>yt_ocean</span>: 1080</li><li><span class='xr-has-index'>xt_ocean</span>: 1440</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1e806c0f-e813-448b-a5bf-658528f73952' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1e806c0f-e813-448b-a5bf-658528f73952' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1980-01-01 ... 2023-07-01</div><input id='attrs-005e0a38-eca2-410d-974d-ca00038c1fb6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-005e0a38-eca2-410d-974d-ca00038c1fb6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e2eac8c-77fd-48a4-952b-3bb17985604b' class='xr-var-data-in' type='checkbox'><label for='data-1e2eac8c-77fd-48a4-952b-3bb17985604b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1980-01-01T00:00:00.000000000&#x27;, &#x27;1980-02-01T00:00:00.000000000&#x27;,\n",
+       "    intake_esm_dataset_key:                   iceh_XXXX_XX.1mon</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>025deg_era5</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f5df2624-2970-4983-80b4-9ba073787a45' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f5df2624-2970-4983-80b4-9ba073787a45' class='xr-section-summary'  title='Expand/collapse section'>Groups: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%'></div></div></li><li class='xr-section-item'><input id='section-3f3ca772-5e73-43c2-bc7d-a5aca4054aae' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3f3ca772-5e73-43c2-bc7d-a5aca4054aae' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 523</li><li><span>nc</span>: 5</li><li><span class='xr-has-index'>yt_ocean</span>: 1080</li><li><span class='xr-has-index'>xt_ocean</span>: 1440</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-cb257768-9600-4fd8-a05c-84b45ef486f9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cb257768-9600-4fd8-a05c-84b45ef486f9' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1980-01-01 ... 2023-07-01</div><input id='attrs-c3824c80-c497-4eb3-872a-d8c701cd78c3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c3824c80-c497-4eb3-872a-d8c701cd78c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25d5f14e-21e4-42a0-a206-f3368b4195f5' class='xr-var-data-in' type='checkbox'><label for='data-25d5f14e-21e4-42a0-a206-f3368b4195f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1980-01-01T00:00:00.000000000&#x27;, &#x27;1980-02-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;1980-03-01T00:00:00.000000000&#x27;, ..., &#x27;2023-05-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;2023-06-01T00:00:00.000000000&#x27;, &#x27;2023-07-01T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.9 -279.6 ... 79.62 79.88</div><input id='attrs-e30a4250-c01a-4a83-ba7b-06ed444b3ba2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e30a4250-c01a-4a83-ba7b-06ed444b3ba2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c5d0d66-0d73-4100-a4d8-4cd415bc5313' class='xr-var-data-in' type='checkbox'><label for='data-7c5d0d66-0d73-4100-a4d8-4cd415bc5313' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-279.875, -279.625, -279.375, ...,   79.375,   79.625,   79.875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-81.08 -80.97 ... 89.84 89.95</div><input id='attrs-d5ece997-40c3-49b1-98ef-b8b5159831e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5ece997-40c3-49b1-98ef-b8b5159831e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72a39d4e-6498-4872-8871-be27187a3725' class='xr-var-data-in' type='checkbox'><label for='data-72a39d4e-6498-4872-8871-be27187a3725' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-81.077001, -80.971402, -80.865804, ...,  89.736085,  89.841684,\n",
-       "        89.947282])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolat_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-16c308e0-06bf-49c0-9ba9-9a604e6d8c9a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-16c308e0-06bf-49c0-9ba9-9a604e6d8c9a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3cd5c558-76e7-4027-94d6-0f78ec219994' class='xr-var-data-in' type='checkbox'><label for='data-3cd5c558-76e7-4027-94d6-0f78ec219994' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>valid_range :</span></dt><dd>[-91.  91.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[1555200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolon_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dbc4e968-7363-4bdb-a0bd-e5ddb4c0a6eb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dbc4e968-7363-4bdb-a0bd-e5ddb4c0a6eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd1905c1-df39-444d-9220-f314b663db11' class='xr-var-data-in' type='checkbox'><label for='data-bd1905c1-df39-444d-9220-f314b663db11' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>valid_range :</span></dt><dd>[-281.  361.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[1555200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>area_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-496ece30-48de-412b-afd6-68c136e957fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-496ece30-48de-412b-afd6-68c136e957fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99535faf-066a-4928-811a-0b3aab0c78c1' class='xr-var-data-in' type='checkbox'><label for='data-99535faf-066a-4928-811a-0b3aab0c78c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer cell area</dd><dt><span>units :</span></dt><dd>m^2</dd><dt><span>valid_range :</span></dt><dd>[0.e+00 1.e+15]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[1555200 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-54908232-d294-4df4-aaa0-72b1cfb012ac' class='xr-section-summary-in' type='checkbox'  checked><label for='section-54908232-d294-4df4-aaa0-72b1cfb012ac' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>aice_m</span></div><div class='xr-var-dims'>(time, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1440), meta=np.ndarray&gt;</div><input id='attrs-d88d6440-087a-4eb7-818c-b58a18a1c618' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d88d6440-087a-4eb7-818c-b58a18a1c618' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5abb3419-3fee-4f10-89e2-05dffa539ac8' class='xr-var-data-in' type='checkbox'><label for='data-5abb3419-3fee-4f10-89e2-05dffa539ac8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>ice area  (aggregate)</dd><dt><span>coordinates :</span></dt><dd>TLON TLAT time</dd><dt><span>cell_measures :</span></dt><dd>area: tarea</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_rep :</span></dt><dd>averaged</dd></dl></div><div class='xr-var-data'><table>\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.9 -279.6 ... 79.62 79.88</div><input id='attrs-f9050e59-a636-4f68-8e79-5fee8864e594' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f9050e59-a636-4f68-8e79-5fee8864e594' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c0d0dcb-3825-47b1-86ab-780447416f99' class='xr-var-data-in' type='checkbox'><label for='data-6c0d0dcb-3825-47b1-86ab-780447416f99' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-279.875, -279.625, -279.375, ...,   79.375,   79.625,   79.875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-81.08 -80.97 ... 89.84 89.95</div><input id='attrs-a4f0a0db-599d-433f-98c9-aec2d363a168' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a4f0a0db-599d-433f-98c9-aec2d363a168' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9a8b87c-93a6-4b71-9993-1a424d20f221' class='xr-var-data-in' type='checkbox'><label for='data-b9a8b87c-93a6-4b71-9993-1a424d20f221' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-81.077001, -80.971402, -80.865804, ...,  89.736085,  89.841684,\n",
+       "        89.947282])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolat_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-29a6bb3b-2301-4a0b-b8ef-660ce81b2a43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29a6bb3b-2301-4a0b-b8ef-660ce81b2a43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac28bcd4-2af8-4d51-be5a-0235558223ed' class='xr-var-data-in' type='checkbox'><label for='data-ac28bcd4-2af8-4d51-be5a-0235558223ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>valid_range :</span></dt><dd>[-91.  91.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[1555200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolon_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-18a0385f-85f0-48aa-af54-2fde03690905' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-18a0385f-85f0-48aa-af54-2fde03690905' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2bbab1e-c086-4d24-b386-cec084309ef9' class='xr-var-data-in' type='checkbox'><label for='data-f2bbab1e-c086-4d24-b386-cec084309ef9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>valid_range :</span></dt><dd>[-281.  361.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[1555200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>area_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ab1ba62b-bcc6-4d19-bed8-47aa942a6e80' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab1ba62b-bcc6-4d19-bed8-47aa942a6e80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-968cffcb-09b3-46d2-8e00-7b8e1690fcd3' class='xr-var-data-in' type='checkbox'><label for='data-968cffcb-09b3-46d2-8e00-7b8e1690fcd3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer cell area</dd><dt><span>units :</span></dt><dd>m^2</dd><dt><span>valid_range :</span></dt><dd>[0.e+00 1.e+15]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[1555200 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cc150ed8-6cdc-4d59-9f9e-1ef201a25587' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cc150ed8-6cdc-4d59-9f9e-1ef201a25587' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>vicen_m</span></div><div class='xr-var-dims'>(time, nc, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 1080, 1440), meta=np.ndarray&gt;</div><input id='attrs-ce9c7996-5bcd-445f-a680-a26a5f32c8e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce9c7996-5bcd-445f-a680-a26a5f32c8e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8cb83f3-f28e-43db-9e50-699ac52d180b' class='xr-var-data-in' type='checkbox'><label for='data-e8cb83f3-f28e-43db-9e50-699ac52d180b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>ice volume, categories</dd><dt><span>coordinates :</span></dt><dd>TLON TLAT NCAT  time</dd><dt><span>cell_measures :</span></dt><dd>area: tarea</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_rep :</span></dt><dd>averaged</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 15.15 GiB </td>\n",
+       "                        <td> 5.93 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (523, 5, 1080, 1440) </td>\n",
+       "                        <td> (1, 1, 1080, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2615 chunks in 1047 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"412\" height=\"154\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"44\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"44\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"1\" y1=\"0\" x2=\"1\" y2=\"25\" />\n",
+       "  <line x1=\"2\" y1=\"0\" x2=\"2\" y2=\"25\" />\n",
+       "  <line x1=\"4\" y1=\"0\" x2=\"4\" y2=\"25\" />\n",
+       "  <line x1=\"5\" y1=\"0\" x2=\"5\" y2=\"25\" />\n",
+       "  <line x1=\"6\" y1=\"0\" x2=\"6\" y2=\"25\" />\n",
+       "  <line x1=\"8\" y1=\"0\" x2=\"8\" y2=\"25\" />\n",
+       "  <line x1=\"9\" y1=\"0\" x2=\"9\" y2=\"25\" />\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"25\" />\n",
+       "  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n",
+       "  <line x1=\"13\" y1=\"0\" x2=\"13\" y2=\"25\" />\n",
+       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n",
+       "  <line x1=\"16\" y1=\"0\" x2=\"16\" y2=\"25\" />\n",
+       "  <line x1=\"17\" y1=\"0\" x2=\"17\" y2=\"25\" />\n",
+       "  <line x1=\"19\" y1=\"0\" x2=\"19\" y2=\"25\" />\n",
+       "  <line x1=\"20\" y1=\"0\" x2=\"20\" y2=\"25\" />\n",
+       "  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n",
+       "  <line x1=\"23\" y1=\"0\" x2=\"23\" y2=\"25\" />\n",
+       "  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"25\" />\n",
+       "  <line x1=\"26\" y1=\"0\" x2=\"26\" y2=\"25\" />\n",
+       "  <line x1=\"27\" y1=\"0\" x2=\"27\" y2=\"25\" />\n",
+       "  <line x1=\"28\" y1=\"0\" x2=\"28\" y2=\"25\" />\n",
+       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n",
+       "  <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"25\" />\n",
+       "  <line x1=\"33\" y1=\"0\" x2=\"33\" y2=\"25\" />\n",
+       "  <line x1=\"34\" y1=\"0\" x2=\"34\" y2=\"25\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"35\" y2=\"25\" />\n",
+       "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
+       "  <line x1=\"38\" y1=\"0\" x2=\"38\" y2=\"25\" />\n",
+       "  <line x1=\"39\" y1=\"0\" x2=\"39\" y2=\"25\" />\n",
+       "  <line x1=\"41\" y1=\"0\" x2=\"41\" y2=\"25\" />\n",
+       "  <line x1=\"42\" y1=\"0\" x2=\"42\" y2=\"25\" />\n",
+       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 44.14651480641994,0.0 44.14651480641994,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"22.073257\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >523</text>\n",
+       "  <text x=\"64.146515\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,64.146515,12.706308)\">1</text>\n",
+       "\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"114\" y1=\"0\" x2=\"128\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"114\" y1=\"90\" x2=\"128\" y2=\"104\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"114\" y1=\"0\" x2=\"114\" y2=\"90\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"116\" y1=\"2\" x2=\"116\" y2=\"92\" />\n",
+       "  <line x1=\"119\" y1=\"5\" x2=\"119\" y2=\"95\" />\n",
+       "  <line x1=\"122\" y1=\"8\" x2=\"122\" y2=\"98\" />\n",
+       "  <line x1=\"125\" y1=\"11\" x2=\"125\" y2=\"101\" />\n",
+       "  <line x1=\"128\" y1=\"14\" x2=\"128\" y2=\"104\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"114.0,0.0 128.9485979497544,14.948597949754403 128.9485979497544,104.9485979497544 114.0,90.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"114\" y1=\"0\" x2=\"234\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"116\" y1=\"2\" x2=\"236\" y2=\"2\" />\n",
+       "  <line x1=\"119\" y1=\"5\" x2=\"239\" y2=\"5\" />\n",
+       "  <line x1=\"122\" y1=\"8\" x2=\"242\" y2=\"8\" />\n",
+       "  <line x1=\"125\" y1=\"11\" x2=\"245\" y2=\"11\" />\n",
+       "  <line x1=\"128\" y1=\"14\" x2=\"248\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"114\" y1=\"0\" x2=\"128\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"234\" y1=\"0\" x2=\"248\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"114.0,0.0 234.0,0.0 248.9485979497544,14.948597949754403 128.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"128\" y1=\"14\" x2=\"248\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"128\" y1=\"104\" x2=\"248\" y2=\"104\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"128\" y1=\"14\" x2=\"128\" y2=\"104\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"248\" y1=\"14\" x2=\"248\" y2=\"104\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"128.9485979497544,14.948597949754403 248.9485979497544,14.948597949754403 248.9485979497544,104.9485979497544 128.9485979497544,104.9485979497544\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"188.948598\" y=\"124.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"268.948598\" y=\"59.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,268.948598,59.948598)\">1080</text>\n",
+       "  <text x=\"111.474299\" y=\"117.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,111.474299,117.474299)\">5</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aice_m</span></div><div class='xr-var-dims'>(time, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1440), meta=np.ndarray&gt;</div><input id='attrs-84a7cc15-685c-40be-ba5e-6330af40f32d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84a7cc15-685c-40be-ba5e-6330af40f32d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1d0dd2a-bc32-40af-abd6-561705a4f2db' class='xr-var-data-in' type='checkbox'><label for='data-d1d0dd2a-bc32-40af-abd6-561705a4f2db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>ice area  (aggregate)</dd><dt><span>coordinates :</span></dt><dd>TLON TLAT time</dd><dt><span>cell_measures :</span></dt><dd>area: tarea</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_rep :</span></dt><dd>averaged</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1587,7 +1722,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-fdee836e-8c9b-47b6-a202-f37bddf28472' class='xr-section-summary-in' type='checkbox'  ><label for='section-fdee836e-8c9b-47b6-a202-f37bddf28472' class='xr-section-summary' >Attributes: <span>(18)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>sea ice model output for CICE</dd><dt><span>contents :</span></dt><dd>Diagnostic and Prognostic Variables</dd><dt><span>source :</span></dt><dd>Los Alamos Sea Ice Model (CICE) Version 5</dd><dt><span>time_period_freq :</span></dt><dd>month_1</dd><dt><span>comment3 :</span></dt><dd>seconds elapsed into model date:      0</dd><dt><span>conventions :</span></dt><dd>CF-1.0</dd><dt><span>io_flavor :</span></dt><dd>io_pio</dd><dt><span>intake_esm_vars :</span></dt><dd>[&#x27;aice_m&#x27;]</dd><dt><span>intake_esm_attrs:realm :</span></dt><dd>seaIce</dd><dt><span>intake_esm_attrs:variable :</span></dt><dd>time_bounds,TLON,TLAT,ULON,ULAT,NCAT,tmask,blkmask,tarea,uarea,dxt,dyt,dxu,dyu,HTN,HTE,ANGLE,ANGLET,hi_m,hs_m,Tsfc_m,aice_m,uvel_m,vvel_m,uatm_m,vatm_m,sice_m,fswdn_m,fswup_m,flwdn_m,snow_ai_m,rain_ai_m,frzmlt_m,fswfac_m,fswabs_ai_m,albsni_m,alvdr_ai_m,alidr_ai_m,alvdf_ai_m,alidf_ai_m,albice_m,albsno_m,flat_ai_m,fsens_ai_m,flwup_ai_m,evap_ai_m,Tair_m,congel_m,frazil_m,snoice_m,meltt_m,melts_m,meltb_m,meltl_m,fresh_ai_m,fsalt_m,fsalt_ai_m,fhocn_ai_m,fswthru_ai_m,strairx_m,strairy_m,strtltx_m,strtlty_m,strcorx_m,strcory_m,strocnx_m,strocny_m,strintx_m,strinty_m,strength_m,divu_m,shear_m,dvidtt_m,dvidtd_m,daidtt_m,daidtd_m,trsig_m,ice_present_m,fcondtop_ai_m,fmeltt_ai_m,opening_m,aicen_m,vicen_m,fsurfn_ai_m,fcondtopn_ai_m,fmelttn_ai_m,flatn_ai_m</dd><dt><span>intake_esm_attrs:frequency :</span></dt><dd>1mon</dd><dt><span>intake_esm_attrs:variable_long_name :</span></dt><dd>boundaries for time-averaging interval,T grid center longitude,T grid center latitude,U grid center longitude,U grid center latitude,category maximum thickness,ocean grid mask,ice grid block mask,area of T grid cells,area of U grid cells,T cell width through middle,T cell height through middle,U cell width through middle,U cell height through middle,T cell width on North side,T cell width on East side,angle grid makes with latitude line on U grid,angle grid makes with latitude line on T grid,grid cell mean ice thickness,grid cell mean snow thickness,snow/ice surface temperature,ice area  (aggregate),ice velocity (x),ice velocity (y),atm velocity (x),atm velocity (y),bulk ice salinity,down solar flux,upward solar flux,down longwave flux,snowfall rate,rainfall rate,freeze/melt potential,shortwave scaling factor,snow/ice/ocn absorbed solar flux,snow/ice broad band albedo,visible direct albedo,near IR direct albedo,visible diffuse albedo,near IR diffuse albedo,bare ice albedo,snow albedo,latent heat flux,sensible heat flux,upward longwave flux,evaporative water flux,air temperature,congelation ice growth,frazil ice growth,snow-ice formation,top ice melt,top snow melt,basal ice melt,lateral ice melt,freshwtr flx ice to ocn,salt flux ice to ocn (cpl),salt flux ice to ocean,heat flux ice to ocean,SW flux thru ice to ocean,atm/ice stress (x),atm/ice stress (y),sea sfc tilt stress (x),sea sfc tilt stress (y),coriolis stress (x),coriolis stress (y),ocean/ice stress (x),ocean/ice stress (y),internal ice stress (x),internal ice stress (y),compressive ice strength,strain rate (divergence),strain rate (shear),volume tendency thermo,volume tendency dynamics,area tendency thermo,area tendency dynamics,internal stress tensor trace,fraction of time-avg interval that ice is present,top surface conductive heat flux,net surface heat flux causing melt,lead area opening rate,ice area, categories,ice volume, categories,net surface heat flux, categories,top sfc conductive heat flux, cat,net sfc heat flux causing melt, cat,latent heat flux, category</dd><dt><span>intake_esm_attrs:variable_standard_name :</span></dt><dd>,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,</dd><dt><span>intake_esm_attrs:variable_cell_methods :</span></dt><dd>,,,,,,,,,,,,,,,,,,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean</dd><dt><span>intake_esm_attrs:variable_units :</span></dt><dd>days since 1980-01-01 00:00:00,degrees_east,degrees_north,degrees_east,degrees_north,m,,,m^2,m^2,m,m,m,m,m,m,radians,radians,m,m,C,1,m/s,m/s,m/s,m/s,ppt,W/m^2,W/m^2,W/m^2,cm/day,cm/day,W/m^2,1,W/m^2,%,%,%,%,%,%,%,W/m^2,W/m^2,W/m^2,cm/day,C,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,kg/m^2/s,kg/m^2/s,W/m^2,W/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m,%/day,%/day,cm/day,cm/day,%/day,%/day,N/m^2,1,W/m^2,W/m^2,%/day,1,m,W/m^2,W/m^2,W/m^2,W/m^2</dd><dt><span>intake_esm_attrs:file_id :</span></dt><dd>iceh_XXXX_XX</dd><dt><span>intake_esm_attrs:_data_format_ :</span></dt><dd>netcdf</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>iceh_XXXX_XX.1mon</dd></dl></div></li></ul></div></div></ul></div></div><div style='display: inline-grid;'><div style='grid-column-start: 1;border-right: 0.2em solid;border-color: var(--xr-border-color);height: 100%;width: 0px;'></div><div style='grid-column-start: 2;grid-row-start: 1;height: 1em;width: 20px;border-bottom: 0.2em solid;border-color: var(--xr-border-color);'></div><div style='grid-column-start: 3;'><ul class='xr-sections'><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-80eaa5a8-3b4c-4e3a-b5cc-de132398c100' class='xr-section-summary-in' type='checkbox'  ><label for='section-80eaa5a8-3b4c-4e3a-b5cc-de132398c100' class='xr-section-summary' >Attributes: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>sea ice model output for CICE</dd><dt><span>contents :</span></dt><dd>Diagnostic and Prognostic Variables</dd><dt><span>source :</span></dt><dd>Los Alamos Sea Ice Model (CICE) Version 5</dd><dt><span>time_period_freq :</span></dt><dd>month_1</dd><dt><span>comment3 :</span></dt><dd>seconds elapsed into model date:      0</dd><dt><span>conventions :</span></dt><dd>CF-1.0</dd><dt><span>io_flavor :</span></dt><dd>io_pio</dd><dt><span>intake_esm_attrs:realm :</span></dt><dd>seaIce</dd><dt><span>intake_esm_attrs:variable :</span></dt><dd>time_bounds,TLON,TLAT,ULON,ULAT,NCAT,tmask,blkmask,tarea,uarea,dxt,dyt,dxu,dyu,HTN,HTE,ANGLE,ANGLET,hi_m,hs_m,Tsfc_m,aice_m,uvel_m,vvel_m,uatm_m,vatm_m,sice_m,fswdn_m,fswup_m,flwdn_m,snow_ai_m,rain_ai_m,frzmlt_m,fswfac_m,fswabs_ai_m,albsni_m,alvdr_ai_m,alidr_ai_m,alvdf_ai_m,alidf_ai_m,albice_m,albsno_m,flat_ai_m,fsens_ai_m,flwup_ai_m,evap_ai_m,Tair_m,congel_m,frazil_m,snoice_m,meltt_m,melts_m,meltb_m,meltl_m,fresh_ai_m,fsalt_m,fsalt_ai_m,fhocn_ai_m,fswthru_ai_m,strairx_m,strairy_m,strtltx_m,strtlty_m,strcorx_m,strcory_m,strocnx_m,strocny_m,strintx_m,strinty_m,strength_m,divu_m,shear_m,dvidtt_m,dvidtd_m,daidtt_m,daidtd_m,trsig_m,ice_present_m,fcondtop_ai_m,fmeltt_ai_m,opening_m,aicen_m,vicen_m,fsurfn_ai_m,fcondtopn_ai_m,fmelttn_ai_m,flatn_ai_m</dd><dt><span>intake_esm_attrs:frequency :</span></dt><dd>1mon</dd><dt><span>intake_esm_attrs:variable_long_name :</span></dt><dd>boundaries for time-averaging interval,T grid center longitude,T grid center latitude,U grid center longitude,U grid center latitude,category maximum thickness,ocean grid mask,ice grid block mask,area of T grid cells,area of U grid cells,T cell width through middle,T cell height through middle,U cell width through middle,U cell height through middle,T cell width on North side,T cell width on East side,angle grid makes with latitude line on U grid,angle grid makes with latitude line on T grid,grid cell mean ice thickness,grid cell mean snow thickness,snow/ice surface temperature,ice area  (aggregate),ice velocity (x),ice velocity (y),atm velocity (x),atm velocity (y),bulk ice salinity,down solar flux,upward solar flux,down longwave flux,snowfall rate,rainfall rate,freeze/melt potential,shortwave scaling factor,snow/ice/ocn absorbed solar flux,snow/ice broad band albedo,visible direct albedo,near IR direct albedo,visible diffuse albedo,near IR diffuse albedo,bare ice albedo,snow albedo,latent heat flux,sensible heat flux,upward longwave flux,evaporative water flux,air temperature,congelation ice growth,frazil ice growth,snow-ice formation,top ice melt,top snow melt,basal ice melt,lateral ice melt,freshwtr flx ice to ocn,salt flux ice to ocn (cpl),salt flux ice to ocean,heat flux ice to ocean,SW flux thru ice to ocean,atm/ice stress (x),atm/ice stress (y),sea sfc tilt stress (x),sea sfc tilt stress (y),coriolis stress (x),coriolis stress (y),ocean/ice stress (x),ocean/ice stress (y),internal ice stress (x),internal ice stress (y),compressive ice strength,strain rate (divergence),strain rate (shear),volume tendency thermo,volume tendency dynamics,area tendency thermo,area tendency dynamics,internal stress tensor trace,fraction of time-avg interval that ice is present,top surface conductive heat flux,net surface heat flux causing melt,lead area opening rate,ice area, categories,ice volume, categories,net surface heat flux, categories,top sfc conductive heat flux, cat,net sfc heat flux causing melt, cat,latent heat flux, category</dd><dt><span>intake_esm_attrs:variable_standard_name :</span></dt><dd>,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,</dd><dt><span>intake_esm_attrs:variable_cell_methods :</span></dt><dd>,,,,,,,,,,,,,,,,,,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean</dd><dt><span>intake_esm_attrs:variable_units :</span></dt><dd>days since 1980-01-01 00:00:00,degrees_east,degrees_north,degrees_east,degrees_north,m,,,m^2,m^2,m,m,m,m,m,m,radians,radians,m,m,C,1,m/s,m/s,m/s,m/s,ppt,W/m^2,W/m^2,W/m^2,cm/day,cm/day,W/m^2,1,W/m^2,%,%,%,%,%,%,%,W/m^2,W/m^2,W/m^2,cm/day,C,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,kg/m^2/s,kg/m^2/s,W/m^2,W/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m,%/day,%/day,cm/day,cm/day,%/day,%/day,N/m^2,1,W/m^2,W/m^2,%/day,1,m,W/m^2,W/m^2,W/m^2,W/m^2</dd><dt><span>intake_esm_attrs:file_id :</span></dt><dd>iceh_XXXX_XX</dd><dt><span>intake_esm_attrs:_data_format_ :</span></dt><dd>netcdf</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>iceh_XXXX_XX.1mon</dd></dl></div></li></ul></div></div></ul></div></div><div style='display: inline-grid;'><div style='grid-column-start: 1;border-right: 0.2em solid;border-color: var(--xr-border-color);height: 100%;width: 0px;'></div><div style='grid-column-start: 2;grid-row-start: 1;height: 1em;width: 20px;border-bottom: 0.2em solid;border-color: var(--xr-border-color);'></div><div style='grid-column-start: 3;'><ul class='xr-sections'><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
        "<defs>\n",
        "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
        "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
@@ -1951,7 +2086,7 @@
        "  fill: currentColor;\n",
        "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DatasetView&gt;\n",
-       "Dimensions:   (time: 523, yt_ocean: 1080, xt_ocean: 1440)\n",
+       "Dimensions:   (time: 523, nc: 5, yt_ocean: 1080, xt_ocean: 1440)\n",
        "Coordinates:\n",
        "  * time      (time) datetime64[ns] 1980-01-01 1980-02-01 ... 2023-07-01\n",
        "  * xt_ocean  (xt_ocean) float64 -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n",
@@ -1959,9 +2094,11 @@
        "    geolat_t  (yt_ocean, xt_ocean) float32 ...\n",
        "    geolon_t  (yt_ocean, xt_ocean) float32 ...\n",
        "    area_t    (yt_ocean, xt_ocean) float32 ...\n",
+       "Dimensions without coordinates: nc\n",
        "Data variables:\n",
+       "    vicen_m   (time, nc, yt_ocean, xt_ocean) float32 dask.array&lt;chunksize=(1, 1, 1080, 1440), meta=np.ndarray&gt;\n",
        "    aice_m    (time, yt_ocean, xt_ocean) float32 dask.array&lt;chunksize=(1, 1080, 1440), meta=np.ndarray&gt;\n",
-       "Attributes: (12/18)\n",
+       "Attributes: (12/17)\n",
        "    title:                                    sea ice model output for CICE\n",
        "    contents:                                 Diagnostic and Prognostic Varia...\n",
        "    source:                                   Los Alamos Sea Ice Model (CICE)...\n",
@@ -1974,11 +2111,144 @@
        "    intake_esm_attrs:variable_units:          days since 1980-01-01 00:00:00,...\n",
        "    intake_esm_attrs:file_id:                 iceh_XXXX_XX\n",
        "    intake_esm_attrs:_data_format_:           netcdf\n",
-       "    intake_esm_dataset_key:                   iceh_XXXX_XX.1mon</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>025deg_jra55</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-ae921da2-7c86-4756-9819-4c4a156e3a7a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ae921da2-7c86-4756-9819-4c4a156e3a7a' class='xr-section-summary'  title='Expand/collapse section'>Groups: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%'></div></div></li><li class='xr-section-item'><input id='section-ae90a198-3748-4584-8de6-cdeb552b981d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ae90a198-3748-4584-8de6-cdeb552b981d' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 523</li><li><span class='xr-has-index'>yt_ocean</span>: 1080</li><li><span class='xr-has-index'>xt_ocean</span>: 1440</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-dd2e9227-c81b-44a8-9fb6-2f389d57951c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dd2e9227-c81b-44a8-9fb6-2f389d57951c' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1980-01-01 ... 2023-07-01</div><input id='attrs-cde922fc-cc71-49e4-9bcf-1557c1ca6ca4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cde922fc-cc71-49e4-9bcf-1557c1ca6ca4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e83841f4-7ebe-443a-861a-4397ec5c8163' class='xr-var-data-in' type='checkbox'><label for='data-e83841f4-7ebe-443a-861a-4397ec5c8163' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1980-01-01T00:00:00.000000000&#x27;, &#x27;1980-02-01T00:00:00.000000000&#x27;,\n",
+       "    intake_esm_dataset_key:                   iceh_XXXX_XX.1mon</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>025deg_jra55</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d5f8c214-a72f-41c7-ba0b-2610925436b1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d5f8c214-a72f-41c7-ba0b-2610925436b1' class='xr-section-summary'  title='Expand/collapse section'>Groups: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%'></div></div></li><li class='xr-section-item'><input id='section-e1c05323-9eeb-47a9-a118-5f6326f86b11' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e1c05323-9eeb-47a9-a118-5f6326f86b11' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 523</li><li><span>nc</span>: 5</li><li><span class='xr-has-index'>yt_ocean</span>: 1080</li><li><span class='xr-has-index'>xt_ocean</span>: 1440</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-73c73a26-017e-4054-9128-163603669514' class='xr-section-summary-in' type='checkbox'  checked><label for='section-73c73a26-017e-4054-9128-163603669514' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1980-01-01 ... 2023-07-01</div><input id='attrs-8bcf798e-1386-4ee8-93f0-30bff1c28817' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8bcf798e-1386-4ee8-93f0-30bff1c28817' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a093387e-bdbc-4586-be2e-d3730e41a711' class='xr-var-data-in' type='checkbox'><label for='data-a093387e-bdbc-4586-be2e-d3730e41a711' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1980-01-01T00:00:00.000000000&#x27;, &#x27;1980-02-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;1980-03-01T00:00:00.000000000&#x27;, ..., &#x27;2023-05-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;2023-06-01T00:00:00.000000000&#x27;, &#x27;2023-07-01T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.9 -279.6 ... 79.62 79.88</div><input id='attrs-d43e0727-82e7-4a3f-83aa-f21a6037629c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d43e0727-82e7-4a3f-83aa-f21a6037629c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a70c3b03-2cb0-4c0e-9226-512237336e4c' class='xr-var-data-in' type='checkbox'><label for='data-a70c3b03-2cb0-4c0e-9226-512237336e4c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-279.875, -279.625, -279.375, ...,   79.375,   79.625,   79.875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-81.08 -80.97 ... 89.84 89.95</div><input id='attrs-7c69eb63-1eed-4423-bfa2-663b008723d8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c69eb63-1eed-4423-bfa2-663b008723d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7046c210-1066-457b-bf5e-5eae36f21551' class='xr-var-data-in' type='checkbox'><label for='data-7046c210-1066-457b-bf5e-5eae36f21551' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-81.077001, -80.971402, -80.865804, ...,  89.736085,  89.841684,\n",
-       "        89.947282])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolat_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bb60ad10-db16-4660-865b-21e782c89f10' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb60ad10-db16-4660-865b-21e782c89f10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1671b555-ec5b-4d1e-b432-8c1b3f360b3f' class='xr-var-data-in' type='checkbox'><label for='data-1671b555-ec5b-4d1e-b432-8c1b3f360b3f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>valid_range :</span></dt><dd>[-91.  91.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[1555200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolon_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dd88d5a0-d2e8-4b53-9ec8-69bf71354261' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd88d5a0-d2e8-4b53-9ec8-69bf71354261' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ac54e00-03c7-467e-9f0c-94b4ecc3079b' class='xr-var-data-in' type='checkbox'><label for='data-6ac54e00-03c7-467e-9f0c-94b4ecc3079b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>valid_range :</span></dt><dd>[-281.  361.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[1555200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>area_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f5725fa9-9bfa-4953-9d6b-bcf958077a03' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f5725fa9-9bfa-4953-9d6b-bcf958077a03' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-590e189f-7746-44ea-b58c-164ad97fa0a5' class='xr-var-data-in' type='checkbox'><label for='data-590e189f-7746-44ea-b58c-164ad97fa0a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer cell area</dd><dt><span>units :</span></dt><dd>m^2</dd><dt><span>valid_range :</span></dt><dd>[0.e+00 1.e+15]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[1555200 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4591087b-2088-4995-a825-5152dfad9b09' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4591087b-2088-4995-a825-5152dfad9b09' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>aice_m</span></div><div class='xr-var-dims'>(time, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1440), meta=np.ndarray&gt;</div><input id='attrs-6587f948-bba0-4f4e-a4f4-9461ee019c54' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6587f948-bba0-4f4e-a4f4-9461ee019c54' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7be31ec0-fe5a-43e7-a341-49f4f78f7cb0' class='xr-var-data-in' type='checkbox'><label for='data-7be31ec0-fe5a-43e7-a341-49f4f78f7cb0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>ice area  (aggregate)</dd><dt><span>coordinates :</span></dt><dd>TLON TLAT time</dd><dt><span>cell_measures :</span></dt><dd>area: tarea</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_rep :</span></dt><dd>averaged</dd></dl></div><div class='xr-var-data'><table>\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.9 -279.6 ... 79.62 79.88</div><input id='attrs-1a3bed4a-280d-434e-9ca1-15b13133dc27' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a3bed4a-280d-434e-9ca1-15b13133dc27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-037db274-8a44-43ee-809e-e1c50e3658e8' class='xr-var-data-in' type='checkbox'><label for='data-037db274-8a44-43ee-809e-e1c50e3658e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-279.875, -279.625, -279.375, ...,   79.375,   79.625,   79.875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-81.08 -80.97 ... 89.84 89.95</div><input id='attrs-6ab152a0-2cae-4cde-911c-72ac2850fd43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ab152a0-2cae-4cde-911c-72ac2850fd43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fae198b-06a8-4e03-9b83-1610dcf17cc6' class='xr-var-data-in' type='checkbox'><label for='data-1fae198b-06a8-4e03-9b83-1610dcf17cc6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-81.077001, -80.971402, -80.865804, ...,  89.736085,  89.841684,\n",
+       "        89.947282])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolat_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b1bdb9fd-d43e-4a4d-8f9b-26bb6d5e9f54' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b1bdb9fd-d43e-4a4d-8f9b-26bb6d5e9f54' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22f6cd85-71b2-4aec-bceb-a40ff9469532' class='xr-var-data-in' type='checkbox'><label for='data-22f6cd85-71b2-4aec-bceb-a40ff9469532' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>valid_range :</span></dt><dd>[-91.  91.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[1555200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolon_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-084511f1-50d5-4934-97fd-5e18feaaef64' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-084511f1-50d5-4934-97fd-5e18feaaef64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b2175f6-4f6a-4f14-b53f-4b7274a8e889' class='xr-var-data-in' type='checkbox'><label for='data-1b2175f6-4f6a-4f14-b53f-4b7274a8e889' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>valid_range :</span></dt><dd>[-281.  361.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[1555200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>area_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0a92144e-91bf-4433-940e-0bf3e9004cd0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a92144e-91bf-4433-940e-0bf3e9004cd0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b6d6ed1-083f-4fe4-a748-04aad9a9b480' class='xr-var-data-in' type='checkbox'><label for='data-3b6d6ed1-083f-4fe4-a748-04aad9a9b480' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer cell area</dd><dt><span>units :</span></dt><dd>m^2</dd><dt><span>valid_range :</span></dt><dd>[0.e+00 1.e+15]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[1555200 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d698a12d-71fd-4b1e-85ee-bdd64fb477d4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d698a12d-71fd-4b1e-85ee-bdd64fb477d4' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>vicen_m</span></div><div class='xr-var-dims'>(time, nc, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 1080, 1440), meta=np.ndarray&gt;</div><input id='attrs-a3b1aacb-46c1-4d09-8269-e4304192239e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3b1aacb-46c1-4d09-8269-e4304192239e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b73cb84-f4f2-42b8-aa00-eb246888144e' class='xr-var-data-in' type='checkbox'><label for='data-9b73cb84-f4f2-42b8-aa00-eb246888144e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>ice volume, categories</dd><dt><span>coordinates :</span></dt><dd>TLON TLAT NCAT  time</dd><dt><span>cell_measures :</span></dt><dd>area: tarea</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_rep :</span></dt><dd>averaged</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 15.15 GiB </td>\n",
+       "                        <td> 5.93 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (523, 5, 1080, 1440) </td>\n",
+       "                        <td> (1, 1, 1080, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2615 chunks in 1047 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"412\" height=\"154\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"44\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"44\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"1\" y1=\"0\" x2=\"1\" y2=\"25\" />\n",
+       "  <line x1=\"2\" y1=\"0\" x2=\"2\" y2=\"25\" />\n",
+       "  <line x1=\"4\" y1=\"0\" x2=\"4\" y2=\"25\" />\n",
+       "  <line x1=\"5\" y1=\"0\" x2=\"5\" y2=\"25\" />\n",
+       "  <line x1=\"6\" y1=\"0\" x2=\"6\" y2=\"25\" />\n",
+       "  <line x1=\"8\" y1=\"0\" x2=\"8\" y2=\"25\" />\n",
+       "  <line x1=\"9\" y1=\"0\" x2=\"9\" y2=\"25\" />\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"25\" />\n",
+       "  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n",
+       "  <line x1=\"13\" y1=\"0\" x2=\"13\" y2=\"25\" />\n",
+       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n",
+       "  <line x1=\"16\" y1=\"0\" x2=\"16\" y2=\"25\" />\n",
+       "  <line x1=\"17\" y1=\"0\" x2=\"17\" y2=\"25\" />\n",
+       "  <line x1=\"19\" y1=\"0\" x2=\"19\" y2=\"25\" />\n",
+       "  <line x1=\"20\" y1=\"0\" x2=\"20\" y2=\"25\" />\n",
+       "  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n",
+       "  <line x1=\"23\" y1=\"0\" x2=\"23\" y2=\"25\" />\n",
+       "  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"25\" />\n",
+       "  <line x1=\"26\" y1=\"0\" x2=\"26\" y2=\"25\" />\n",
+       "  <line x1=\"27\" y1=\"0\" x2=\"27\" y2=\"25\" />\n",
+       "  <line x1=\"28\" y1=\"0\" x2=\"28\" y2=\"25\" />\n",
+       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n",
+       "  <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"25\" />\n",
+       "  <line x1=\"33\" y1=\"0\" x2=\"33\" y2=\"25\" />\n",
+       "  <line x1=\"34\" y1=\"0\" x2=\"34\" y2=\"25\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"35\" y2=\"25\" />\n",
+       "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
+       "  <line x1=\"38\" y1=\"0\" x2=\"38\" y2=\"25\" />\n",
+       "  <line x1=\"39\" y1=\"0\" x2=\"39\" y2=\"25\" />\n",
+       "  <line x1=\"41\" y1=\"0\" x2=\"41\" y2=\"25\" />\n",
+       "  <line x1=\"42\" y1=\"0\" x2=\"42\" y2=\"25\" />\n",
+       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 44.14651480641994,0.0 44.14651480641994,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"22.073257\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >523</text>\n",
+       "  <text x=\"64.146515\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,64.146515,12.706308)\">1</text>\n",
+       "\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"114\" y1=\"0\" x2=\"128\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"114\" y1=\"90\" x2=\"128\" y2=\"104\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"114\" y1=\"0\" x2=\"114\" y2=\"90\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"116\" y1=\"2\" x2=\"116\" y2=\"92\" />\n",
+       "  <line x1=\"119\" y1=\"5\" x2=\"119\" y2=\"95\" />\n",
+       "  <line x1=\"122\" y1=\"8\" x2=\"122\" y2=\"98\" />\n",
+       "  <line x1=\"125\" y1=\"11\" x2=\"125\" y2=\"101\" />\n",
+       "  <line x1=\"128\" y1=\"14\" x2=\"128\" y2=\"104\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"114.0,0.0 128.9485979497544,14.948597949754403 128.9485979497544,104.9485979497544 114.0,90.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"114\" y1=\"0\" x2=\"234\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"116\" y1=\"2\" x2=\"236\" y2=\"2\" />\n",
+       "  <line x1=\"119\" y1=\"5\" x2=\"239\" y2=\"5\" />\n",
+       "  <line x1=\"122\" y1=\"8\" x2=\"242\" y2=\"8\" />\n",
+       "  <line x1=\"125\" y1=\"11\" x2=\"245\" y2=\"11\" />\n",
+       "  <line x1=\"128\" y1=\"14\" x2=\"248\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"114\" y1=\"0\" x2=\"128\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"234\" y1=\"0\" x2=\"248\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"114.0,0.0 234.0,0.0 248.9485979497544,14.948597949754403 128.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"128\" y1=\"14\" x2=\"248\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"128\" y1=\"104\" x2=\"248\" y2=\"104\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"128\" y1=\"14\" x2=\"128\" y2=\"104\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"248\" y1=\"14\" x2=\"248\" y2=\"104\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"128.9485979497544,14.948597949754403 248.9485979497544,14.948597949754403 248.9485979497544,104.9485979497544 128.9485979497544,104.9485979497544\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"188.948598\" y=\"124.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"268.948598\" y=\"59.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,268.948598,59.948598)\">1080</text>\n",
+       "  <text x=\"111.474299\" y=\"117.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,111.474299,117.474299)\">5</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aice_m</span></div><div class='xr-var-dims'>(time, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1440), meta=np.ndarray&gt;</div><input id='attrs-3fd9c0bf-bab7-4e3b-851c-46da930c553c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3fd9c0bf-bab7-4e3b-851c-46da930c553c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa165e1f-6c47-400e-aac6-c690eca1156b' class='xr-var-data-in' type='checkbox'><label for='data-fa165e1f-6c47-400e-aac6-c690eca1156b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>ice area  (aggregate)</dd><dt><span>coordinates :</span></dt><dd>TLON TLAT time</dd><dt><span>cell_measures :</span></dt><dd>area: tarea</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_rep :</span></dt><dd>averaged</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2092,7 +2362,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-b2e4b938-3ddc-4248-b7a5-1b1647f542be' class='xr-section-summary-in' type='checkbox'  ><label for='section-b2e4b938-3ddc-4248-b7a5-1b1647f542be' class='xr-section-summary' >Attributes: <span>(18)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>sea ice model output for CICE</dd><dt><span>contents :</span></dt><dd>Diagnostic and Prognostic Variables</dd><dt><span>source :</span></dt><dd>Los Alamos Sea Ice Model (CICE) Version 5</dd><dt><span>time_period_freq :</span></dt><dd>month_1</dd><dt><span>comment3 :</span></dt><dd>seconds elapsed into model date:      0</dd><dt><span>conventions :</span></dt><dd>CF-1.0</dd><dt><span>io_flavor :</span></dt><dd>io_pio</dd><dt><span>intake_esm_vars :</span></dt><dd>[&#x27;aice_m&#x27;]</dd><dt><span>intake_esm_attrs:realm :</span></dt><dd>seaIce</dd><dt><span>intake_esm_attrs:variable :</span></dt><dd>time_bounds,TLON,TLAT,ULON,ULAT,NCAT,tmask,blkmask,tarea,uarea,dxt,dyt,dxu,dyu,HTN,HTE,ANGLE,ANGLET,hi_m,hs_m,Tsfc_m,aice_m,uvel_m,vvel_m,sice_m,fswdn_m,fswup_m,flwdn_m,snow_ai_m,rain_ai_m,frzmlt_m,fswfac_m,fswabs_ai_m,albsni_m,albice_m,albsno_m,flat_ai_m,fsens_ai_m,flwup_ai_m,evap_ai_m,Tair_m,congel_m,frazil_m,snoice_m,meltt_m,melts_m,meltb_m,meltl_m,fresh_ai_m,fsalt_m,fsalt_ai_m,fhocn_ai_m,fswthru_ai_m,strairx_m,strairy_m,strtltx_m,strtlty_m,strcorx_m,strcory_m,strocnx_m,strocny_m,strintx_m,strinty_m,strength_m,divu_m,shear_m,dvidtt_m,dvidtd_m,daidtt_m,daidtd_m,trsig_m,ice_present_m,fcondtop_ai_m,fmeltt_ai_m,opening_m,aicen_m,vicen_m,fsurfn_ai_m,fcondtopn_ai_m,fmelttn_ai_m,flatn_ai_m</dd><dt><span>intake_esm_attrs:frequency :</span></dt><dd>1mon</dd><dt><span>intake_esm_attrs:variable_long_name :</span></dt><dd>boundaries for time-averaging interval,T grid center longitude,T grid center latitude,U grid center longitude,U grid center latitude,category maximum thickness,ocean grid mask,ice grid block mask,area of T grid cells,area of U grid cells,T cell width through middle,T cell height through middle,U cell width through middle,U cell height through middle,T cell width on North side,T cell width on East side,angle grid makes with latitude line on U grid,angle grid makes with latitude line on T grid,grid cell mean ice thickness,grid cell mean snow thickness,snow/ice surface temperature,ice area  (aggregate),ice velocity (x),ice velocity (y),bulk ice salinity,down solar flux,upward solar flux,down longwave flux,snowfall rate,rainfall rate,freeze/melt potential,shortwave scaling factor,snow/ice/ocn absorbed solar flux,snow/ice broad band albedo,bare ice albedo,snow albedo,latent heat flux,sensible heat flux,upward longwave flux,evaporative water flux,air temperature,congelation ice growth,frazil ice growth,snow-ice formation,top ice melt,top snow melt,basal ice melt,lateral ice melt,freshwtr flx ice to ocn,salt flux ice to ocn (cpl),salt flux ice to ocean,heat flux ice to ocean,SW flux thru ice to ocean,atm/ice stress (x),atm/ice stress (y),sea sfc tilt stress (x),sea sfc tilt stress (y),coriolis stress (x),coriolis stress (y),ocean/ice stress (x),ocean/ice stress (y),internal ice stress (x),internal ice stress (y),compressive ice strength,strain rate (divergence),strain rate (shear),volume tendency thermo,volume tendency dynamics,area tendency thermo,area tendency dynamics,internal stress tensor trace,fraction of time-avg interval that ice is present,top surface conductive heat flux,net surface heat flux causing melt,lead area opening rate,ice area, categories,ice volume, categories,net surface heat flux, categories,top sfc conductive heat flux, cat,net sfc heat flux causing melt, cat,latent heat flux, category</dd><dt><span>intake_esm_attrs:variable_standard_name :</span></dt><dd>,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,</dd><dt><span>intake_esm_attrs:variable_cell_methods :</span></dt><dd>,,,,,,,,,,,,,,,,,,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean</dd><dt><span>intake_esm_attrs:variable_units :</span></dt><dd>days since 1980-01-01 00:00:00,degrees_east,degrees_north,degrees_east,degrees_north,m,,,m^2,m^2,m,m,m,m,m,m,radians,radians,m,m,C,1,m/s,m/s,ppt,W/m^2,W/m^2,W/m^2,cm/day,cm/day,W/m^2,1,W/m^2,%,%,%,W/m^2,W/m^2,W/m^2,cm/day,C,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,kg/m^2/s,kg/m^2/s,W/m^2,W/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m,%/day,%/day,cm/day,cm/day,%/day,%/day,N/m^2,1,W/m^2,W/m^2,%/day,1,m,W/m^2,W/m^2,W/m^2,W/m^2</dd><dt><span>intake_esm_attrs:file_id :</span></dt><dd>iceh_XXXX_XX</dd><dt><span>intake_esm_attrs:_data_format_ :</span></dt><dd>netcdf</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>iceh_XXXX_XX.1mon</dd></dl></div></li></ul></div></div></ul></div></div><div style='display: inline-grid;'><div style='grid-column-start: 1;border-right: 0.2em solid;border-color: var(--xr-border-color);height: 100%;width: 0px;'></div><div style='grid-column-start: 2;grid-row-start: 1;height: 1em;width: 20px;border-bottom: 0.2em solid;border-color: var(--xr-border-color);'></div><div style='grid-column-start: 3;'><ul class='xr-sections'><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-b1909cda-baa0-4d56-9f60-2a299b5ebabd' class='xr-section-summary-in' type='checkbox'  ><label for='section-b1909cda-baa0-4d56-9f60-2a299b5ebabd' class='xr-section-summary' >Attributes: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>sea ice model output for CICE</dd><dt><span>contents :</span></dt><dd>Diagnostic and Prognostic Variables</dd><dt><span>source :</span></dt><dd>Los Alamos Sea Ice Model (CICE) Version 5</dd><dt><span>time_period_freq :</span></dt><dd>month_1</dd><dt><span>comment3 :</span></dt><dd>seconds elapsed into model date:      0</dd><dt><span>conventions :</span></dt><dd>CF-1.0</dd><dt><span>io_flavor :</span></dt><dd>io_pio</dd><dt><span>intake_esm_attrs:realm :</span></dt><dd>seaIce</dd><dt><span>intake_esm_attrs:variable :</span></dt><dd>time_bounds,TLON,TLAT,ULON,ULAT,NCAT,tmask,blkmask,tarea,uarea,dxt,dyt,dxu,dyu,HTN,HTE,ANGLE,ANGLET,hi_m,hs_m,Tsfc_m,aice_m,uvel_m,vvel_m,sice_m,fswdn_m,fswup_m,flwdn_m,snow_ai_m,rain_ai_m,frzmlt_m,fswfac_m,fswabs_ai_m,albsni_m,albice_m,albsno_m,flat_ai_m,fsens_ai_m,flwup_ai_m,evap_ai_m,Tair_m,congel_m,frazil_m,snoice_m,meltt_m,melts_m,meltb_m,meltl_m,fresh_ai_m,fsalt_m,fsalt_ai_m,fhocn_ai_m,fswthru_ai_m,strairx_m,strairy_m,strtltx_m,strtlty_m,strcorx_m,strcory_m,strocnx_m,strocny_m,strintx_m,strinty_m,strength_m,divu_m,shear_m,dvidtt_m,dvidtd_m,daidtt_m,daidtd_m,trsig_m,ice_present_m,fcondtop_ai_m,fmeltt_ai_m,opening_m,aicen_m,vicen_m,fsurfn_ai_m,fcondtopn_ai_m,fmelttn_ai_m,flatn_ai_m</dd><dt><span>intake_esm_attrs:frequency :</span></dt><dd>1mon</dd><dt><span>intake_esm_attrs:variable_long_name :</span></dt><dd>boundaries for time-averaging interval,T grid center longitude,T grid center latitude,U grid center longitude,U grid center latitude,category maximum thickness,ocean grid mask,ice grid block mask,area of T grid cells,area of U grid cells,T cell width through middle,T cell height through middle,U cell width through middle,U cell height through middle,T cell width on North side,T cell width on East side,angle grid makes with latitude line on U grid,angle grid makes with latitude line on T grid,grid cell mean ice thickness,grid cell mean snow thickness,snow/ice surface temperature,ice area  (aggregate),ice velocity (x),ice velocity (y),bulk ice salinity,down solar flux,upward solar flux,down longwave flux,snowfall rate,rainfall rate,freeze/melt potential,shortwave scaling factor,snow/ice/ocn absorbed solar flux,snow/ice broad band albedo,bare ice albedo,snow albedo,latent heat flux,sensible heat flux,upward longwave flux,evaporative water flux,air temperature,congelation ice growth,frazil ice growth,snow-ice formation,top ice melt,top snow melt,basal ice melt,lateral ice melt,freshwtr flx ice to ocn,salt flux ice to ocn (cpl),salt flux ice to ocean,heat flux ice to ocean,SW flux thru ice to ocean,atm/ice stress (x),atm/ice stress (y),sea sfc tilt stress (x),sea sfc tilt stress (y),coriolis stress (x),coriolis stress (y),ocean/ice stress (x),ocean/ice stress (y),internal ice stress (x),internal ice stress (y),compressive ice strength,strain rate (divergence),strain rate (shear),volume tendency thermo,volume tendency dynamics,area tendency thermo,area tendency dynamics,internal stress tensor trace,fraction of time-avg interval that ice is present,top surface conductive heat flux,net surface heat flux causing melt,lead area opening rate,ice area, categories,ice volume, categories,net surface heat flux, categories,top sfc conductive heat flux, cat,net sfc heat flux causing melt, cat,latent heat flux, category</dd><dt><span>intake_esm_attrs:variable_standard_name :</span></dt><dd>,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,</dd><dt><span>intake_esm_attrs:variable_cell_methods :</span></dt><dd>,,,,,,,,,,,,,,,,,,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean</dd><dt><span>intake_esm_attrs:variable_units :</span></dt><dd>days since 1980-01-01 00:00:00,degrees_east,degrees_north,degrees_east,degrees_north,m,,,m^2,m^2,m,m,m,m,m,m,radians,radians,m,m,C,1,m/s,m/s,ppt,W/m^2,W/m^2,W/m^2,cm/day,cm/day,W/m^2,1,W/m^2,%,%,%,W/m^2,W/m^2,W/m^2,cm/day,C,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,kg/m^2/s,kg/m^2/s,W/m^2,W/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m,%/day,%/day,cm/day,cm/day,%/day,%/day,N/m^2,1,W/m^2,W/m^2,%/day,1,m,W/m^2,W/m^2,W/m^2,W/m^2</dd><dt><span>intake_esm_attrs:file_id :</span></dt><dd>iceh_XXXX_XX</dd><dt><span>intake_esm_attrs:_data_format_ :</span></dt><dd>netcdf</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>iceh_XXXX_XX.1mon</dd></dl></div></li></ul></div></div></ul></div></div><div style='display: inline-grid;'><div style='grid-column-start: 1;border-right: 0.2em solid;border-color: var(--xr-border-color);height: 100%;width: 0px;'></div><div style='grid-column-start: 2;grid-row-start: 1;height: 1em;width: 20px;border-bottom: 0.2em solid;border-color: var(--xr-border-color);'></div><div style='grid-column-start: 3;'><ul class='xr-sections'><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
        "<defs>\n",
        "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
        "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
@@ -2456,7 +2726,7 @@
        "  fill: currentColor;\n",
        "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DatasetView&gt;\n",
-       "Dimensions:   (time: 708, yt_ocean: 300, xt_ocean: 360)\n",
+       "Dimensions:   (time: 708, yt_ocean: 300, xt_ocean: 360, nc: 5)\n",
        "Coordinates:\n",
        "  * time      (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n",
        "  * xt_ocean  (xt_ocean) float64 -279.5 -278.5 -277.5 -276.5 ... 77.5 78.5 79.5\n",
@@ -2464,9 +2734,11 @@
        "    geolat_t  (yt_ocean, xt_ocean) float32 ...\n",
        "    geolon_t  (yt_ocean, xt_ocean) float32 ...\n",
        "    area_t    (yt_ocean, xt_ocean) float32 ...\n",
+       "Dimensions without coordinates: nc\n",
        "Data variables:\n",
        "    aice_m    (time, yt_ocean, xt_ocean) float32 dask.array&lt;chunksize=(1, 300, 360), meta=np.ndarray&gt;\n",
-       "Attributes: (12/18)\n",
+       "    vicen_m   (time, nc, yt_ocean, xt_ocean) float32 dask.array&lt;chunksize=(1, 1, 300, 360), meta=np.ndarray&gt;\n",
+       "Attributes: (12/17)\n",
        "    title:                                    sea ice model output for CICE\n",
        "    contents:                                 Diagnostic and Prognostic Varia...\n",
        "    source:                                   Los Alamos Sea Ice Model (CICE)...\n",
@@ -2479,11 +2751,11 @@
        "    intake_esm_attrs:variable_units:          days since 1960-01-01 00:00:00,...\n",
        "    intake_esm_attrs:file_id:                 iceh_XXXX_XX\n",
        "    intake_esm_attrs:_data_format_:           netcdf\n",
-       "    intake_esm_dataset_key:                   iceh_XXXX_XX.1mon</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>1deg_era5</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-3688a02c-8e18-49c8-8f1e-bdf840e0dd33' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3688a02c-8e18-49c8-8f1e-bdf840e0dd33' class='xr-section-summary'  title='Expand/collapse section'>Groups: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%'></div></div></li><li class='xr-section-item'><input id='section-abacfedd-01fc-4b83-adb3-f7e7b593c23d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-abacfedd-01fc-4b83-adb3-f7e7b593c23d' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 708</li><li><span class='xr-has-index'>yt_ocean</span>: 300</li><li><span class='xr-has-index'>xt_ocean</span>: 360</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-940b6587-27ec-4f1a-9b1b-881bcec73af1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-940b6587-27ec-4f1a-9b1b-881bcec73af1' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1960-01-01 ... 2018-12-01</div><input id='attrs-2a92f754-5b7a-4a9b-8886-571abfd7c6ae' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2a92f754-5b7a-4a9b-8886-571abfd7c6ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1ad5aef-ea10-4154-9d22-62923e58be03' class='xr-var-data-in' type='checkbox'><label for='data-b1ad5aef-ea10-4154-9d22-62923e58be03' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1960-01-01T00:00:00.000000000&#x27;, &#x27;1960-02-01T00:00:00.000000000&#x27;,\n",
+       "    intake_esm_dataset_key:                   iceh_XXXX_XX.1mon</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>1deg_era5</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-6b5c9964-3200-4dc7-962c-caf8e2de26ed' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6b5c9964-3200-4dc7-962c-caf8e2de26ed' class='xr-section-summary'  title='Expand/collapse section'>Groups: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%'></div></div></li><li class='xr-section-item'><input id='section-08ca3e99-2c31-4b1a-8365-df66a0320fd9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-08ca3e99-2c31-4b1a-8365-df66a0320fd9' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 708</li><li><span class='xr-has-index'>yt_ocean</span>: 300</li><li><span class='xr-has-index'>xt_ocean</span>: 360</li><li><span>nc</span>: 5</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8b3acf77-6c30-4081-a96a-a56b0b278061' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8b3acf77-6c30-4081-a96a-a56b0b278061' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1960-01-01 ... 2018-12-01</div><input id='attrs-0a4e3768-1171-47e3-bdf3-e7f077c4e2c9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0a4e3768-1171-47e3-bdf3-e7f077c4e2c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df87b420-ca76-4588-b3fc-cb0be262c6bc' class='xr-var-data-in' type='checkbox'><label for='data-df87b420-ca76-4588-b3fc-cb0be262c6bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1960-01-01T00:00:00.000000000&#x27;, &#x27;1960-02-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;1960-03-01T00:00:00.000000000&#x27;, ..., &#x27;2018-10-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;2018-11-01T00:00:00.000000000&#x27;, &#x27;2018-12-01T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.5 -278.5 -277.5 ... 78.5 79.5</div><input id='attrs-1c125723-5a56-4d63-8127-d79b8b5570a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c125723-5a56-4d63-8127-d79b8b5570a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2df6ed90-a657-4b69-a6eb-9a13fd769f7a' class='xr-var-data-in' type='checkbox'><label for='data-2df6ed90-a657-4b69-a6eb-9a13fd769f7a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-279.5, -278.5, -277.5, ...,   77.5,   78.5,   79.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-77.88 -77.63 ... 89.32 89.77</div><input id='attrs-29b322ab-8eff-46b7-bb9e-e48468eaa6f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29b322ab-8eff-46b7-bb9e-e48468eaa6f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-951766f7-6161-41a6-901f-5779503c3613' class='xr-var-data-in' type='checkbox'><label for='data-951766f7-6161-41a6-901f-5779503c3613' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-77.876623, -77.629713, -77.381707, ...,  88.872933,  89.324006,\n",
-       "        89.774476])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolat_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bcb7e46d-4b02-4cb5-ac83-dae0facafc63' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bcb7e46d-4b02-4cb5-ac83-dae0facafc63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-972904b7-6ba6-46fc-bdc4-5fde37664f85' class='xr-var-data-in' type='checkbox'><label for='data-972904b7-6ba6-46fc-bdc4-5fde37664f85' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>valid_range :</span></dt><dd>[-91.  91.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[108000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolon_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-32378b27-d293-4dfa-a406-f7e33b3e647c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32378b27-d293-4dfa-a406-f7e33b3e647c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d6dd471-dd8b-4867-b81e-5af3ec137fa0' class='xr-var-data-in' type='checkbox'><label for='data-1d6dd471-dd8b-4867-b81e-5af3ec137fa0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>valid_range :</span></dt><dd>[-281.  361.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[108000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>area_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1952082e-dc54-47ea-b1a2-96ad793a047b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1952082e-dc54-47ea-b1a2-96ad793a047b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77e9ef71-9ccc-4cc5-9ab7-108d31b7c958' class='xr-var-data-in' type='checkbox'><label for='data-77e9ef71-9ccc-4cc5-9ab7-108d31b7c958' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer cell area</dd><dt><span>units :</span></dt><dd>m^2</dd><dt><span>valid_range :</span></dt><dd>[0.e+00 1.e+15]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[108000 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-24a171e5-42ad-4f2c-b61a-8352540a89ab' class='xr-section-summary-in' type='checkbox'  checked><label for='section-24a171e5-42ad-4f2c-b61a-8352540a89ab' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>aice_m</span></div><div class='xr-var-dims'>(time, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 300, 360), meta=np.ndarray&gt;</div><input id='attrs-e03b53dc-0680-4e3c-b23a-2ec7c37cf9d9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e03b53dc-0680-4e3c-b23a-2ec7c37cf9d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d25663c4-a1d0-483a-b906-4529cba71f02' class='xr-var-data-in' type='checkbox'><label for='data-d25663c4-a1d0-483a-b906-4529cba71f02' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>ice area  (aggregate)</dd><dt><span>coordinates :</span></dt><dd>TLON TLAT time</dd><dt><span>cell_measures :</span></dt><dd>area: tarea</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_rep :</span></dt><dd>averaged</dd></dl></div><div class='xr-var-data'><table>\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.5 -278.5 -277.5 ... 78.5 79.5</div><input id='attrs-7f6c116a-5c65-4523-909c-91be0891b2ac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f6c116a-5c65-4523-909c-91be0891b2ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb53d3ee-a784-4e9b-aa6c-13b4d91fb059' class='xr-var-data-in' type='checkbox'><label for='data-bb53d3ee-a784-4e9b-aa6c-13b4d91fb059' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-279.5, -278.5, -277.5, ...,   77.5,   78.5,   79.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-77.88 -77.63 ... 89.32 89.77</div><input id='attrs-29abc89b-d884-4b8f-a45f-e29676eac490' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29abc89b-d884-4b8f-a45f-e29676eac490' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27b64804-7e36-437b-be11-a1c3d74168ce' class='xr-var-data-in' type='checkbox'><label for='data-27b64804-7e36-437b-be11-a1c3d74168ce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-77.876623, -77.629713, -77.381707, ...,  88.872933,  89.324006,\n",
+       "        89.774476])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolat_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3519a1ee-19a3-41d6-904f-f5e89a90a4fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3519a1ee-19a3-41d6-904f-f5e89a90a4fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1baf1ea9-9621-4549-9921-71b504a0458d' class='xr-var-data-in' type='checkbox'><label for='data-1baf1ea9-9621-4549-9921-71b504a0458d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>valid_range :</span></dt><dd>[-91.  91.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[108000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolon_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4b6cfd74-2721-4cd8-bfa6-8f99be73729e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b6cfd74-2721-4cd8-bfa6-8f99be73729e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5dbb18e-3898-4661-94b7-30b00395c05b' class='xr-var-data-in' type='checkbox'><label for='data-b5dbb18e-3898-4661-94b7-30b00395c05b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>valid_range :</span></dt><dd>[-281.  361.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[108000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>area_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b1e0b749-16e2-47d7-83c9-8129141c8d10' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b1e0b749-16e2-47d7-83c9-8129141c8d10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c98e136-763a-40e9-b55b-7fcae71ee0a4' class='xr-var-data-in' type='checkbox'><label for='data-7c98e136-763a-40e9-b55b-7fcae71ee0a4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer cell area</dd><dt><span>units :</span></dt><dd>m^2</dd><dt><span>valid_range :</span></dt><dd>[0.e+00 1.e+15]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[108000 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bceeac8c-c715-4847-b303-f9bc317ed2da' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bceeac8c-c715-4847-b303-f9bc317ed2da' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>aice_m</span></div><div class='xr-var-dims'>(time, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 300, 360), meta=np.ndarray&gt;</div><input id='attrs-25278f84-07fd-42b5-a2ca-458d8f62e5a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-25278f84-07fd-42b5-a2ca-458d8f62e5a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf9aa3f2-51d3-488a-a37d-4bf155eae5a1' class='xr-var-data-in' type='checkbox'><label for='data-bf9aa3f2-51d3-488a-a37d-4bf155eae5a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>ice area  (aggregate)</dd><dt><span>coordinates :</span></dt><dd>TLON TLAT time</dd><dt><span>cell_measures :</span></dt><dd>area: tarea</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_rep :</span></dt><dd>averaged</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2597,7 +2869,140 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-9e7803ff-bae2-4412-9101-650eb7f00715' class='xr-section-summary-in' type='checkbox'  ><label for='section-9e7803ff-bae2-4412-9101-650eb7f00715' class='xr-section-summary' >Attributes: <span>(18)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>sea ice model output for CICE</dd><dt><span>contents :</span></dt><dd>Diagnostic and Prognostic Variables</dd><dt><span>source :</span></dt><dd>Los Alamos Sea Ice Model (CICE) Version 5</dd><dt><span>time_period_freq :</span></dt><dd>month_1</dd><dt><span>comment3 :</span></dt><dd>seconds elapsed into model date:      0</dd><dt><span>conventions :</span></dt><dd>CF-1.0</dd><dt><span>io_flavor :</span></dt><dd>io_pio</dd><dt><span>intake_esm_vars :</span></dt><dd>[&#x27;aice_m&#x27;]</dd><dt><span>intake_esm_attrs:realm :</span></dt><dd>seaIce</dd><dt><span>intake_esm_attrs:variable :</span></dt><dd>time_bounds,TLON,TLAT,ULON,ULAT,NCAT,tmask,blkmask,tarea,uarea,dxt,dyt,dxu,dyu,HTN,HTE,ANGLE,ANGLET,hi_m,hs_m,Tsfc_m,aice_m,uvel_m,vvel_m,uatm_m,vatm_m,sice_m,fswdn_m,fswup_m,flwdn_m,snow_ai_m,rain_ai_m,frzmlt_m,fswfac_m,fswabs_ai_m,albsni_m,alvdr_ai_m,alidr_ai_m,alvdf_ai_m,alidf_ai_m,albice_m,albsno_m,flat_ai_m,fsens_ai_m,flwup_ai_m,evap_ai_m,Tair_m,congel_m,frazil_m,snoice_m,meltt_m,melts_m,meltb_m,meltl_m,fresh_ai_m,fsalt_m,fsalt_ai_m,fhocn_ai_m,fswthru_ai_m,strairx_m,strairy_m,strtltx_m,strtlty_m,strcorx_m,strcory_m,strocnx_m,strocny_m,strintx_m,strinty_m,strength_m,divu_m,shear_m,dvidtt_m,dvidtd_m,daidtt_m,daidtd_m,trsig_m,ice_present_m,fcondtop_ai_m,fmeltt_ai_m,opening_m,aicen_m,vicen_m,fsurfn_ai_m,fcondtopn_ai_m,fmelttn_ai_m,flatn_ai_m</dd><dt><span>intake_esm_attrs:frequency :</span></dt><dd>1mon</dd><dt><span>intake_esm_attrs:variable_long_name :</span></dt><dd>boundaries for time-averaging interval,T grid center longitude,T grid center latitude,U grid center longitude,U grid center latitude,category maximum thickness,ocean grid mask,ice grid block mask,area of T grid cells,area of U grid cells,T cell width through middle,T cell height through middle,U cell width through middle,U cell height through middle,T cell width on North side,T cell width on East side,angle grid makes with latitude line on U grid,angle grid makes with latitude line on T grid,grid cell mean ice thickness,grid cell mean snow thickness,snow/ice surface temperature,ice area  (aggregate),ice velocity (x),ice velocity (y),atm velocity (x),atm velocity (y),bulk ice salinity,down solar flux,upward solar flux,down longwave flux,snowfall rate,rainfall rate,freeze/melt potential,shortwave scaling factor,snow/ice/ocn absorbed solar flux,snow/ice broad band albedo,visible direct albedo,near IR direct albedo,visible diffuse albedo,near IR diffuse albedo,bare ice albedo,snow albedo,latent heat flux,sensible heat flux,upward longwave flux,evaporative water flux,air temperature,congelation ice growth,frazil ice growth,snow-ice formation,top ice melt,top snow melt,basal ice melt,lateral ice melt,freshwtr flx ice to ocn,salt flux ice to ocn (cpl),salt flux ice to ocean,heat flux ice to ocean,SW flux thru ice to ocean,atm/ice stress (x),atm/ice stress (y),sea sfc tilt stress (x),sea sfc tilt stress (y),coriolis stress (x),coriolis stress (y),ocean/ice stress (x),ocean/ice stress (y),internal ice stress (x),internal ice stress (y),compressive ice strength,strain rate (divergence),strain rate (shear),volume tendency thermo,volume tendency dynamics,area tendency thermo,area tendency dynamics,internal stress tensor trace,fraction of time-avg interval that ice is present,top surface conductive heat flux,net surface heat flux causing melt,lead area opening rate,ice area, categories,ice volume, categories,net surface heat flux, categories,top sfc conductive heat flux, cat,net sfc heat flux causing melt, cat,latent heat flux, category</dd><dt><span>intake_esm_attrs:variable_standard_name :</span></dt><dd>,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,</dd><dt><span>intake_esm_attrs:variable_cell_methods :</span></dt><dd>,,,,,,,,,,,,,,,,,,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean</dd><dt><span>intake_esm_attrs:variable_units :</span></dt><dd>days since 1960-01-01 00:00:00,degrees_east,degrees_north,degrees_east,degrees_north,m,,,m^2,m^2,m,m,m,m,m,m,radians,radians,m,m,C,1,m/s,m/s,m/s,m/s,ppt,W/m^2,W/m^2,W/m^2,cm/day,cm/day,W/m^2,1,W/m^2,%,%,%,%,%,%,%,W/m^2,W/m^2,W/m^2,cm/day,C,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,kg/m^2/s,kg/m^2/s,W/m^2,W/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m,%/day,%/day,cm/day,cm/day,%/day,%/day,N/m^2,1,W/m^2,W/m^2,%/day,1,m,W/m^2,W/m^2,W/m^2,W/m^2</dd><dt><span>intake_esm_attrs:file_id :</span></dt><dd>iceh_XXXX_XX</dd><dt><span>intake_esm_attrs:_data_format_ :</span></dt><dd>netcdf</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>iceh_XXXX_XX.1mon</dd></dl></div></li></ul></div></div></ul></div></div><div style='display: inline-grid;'><div style='grid-column-start: 1;border-right: 0.2em solid;border-color: var(--xr-border-color);height: 1.2em;width: 0px;'></div><div style='grid-column-start: 2;grid-row-start: 1;height: 1em;width: 20px;border-bottom: 0.2em solid;border-color: var(--xr-border-color);'></div><div style='grid-column-start: 3;'><ul class='xr-sections'><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vicen_m</span></div><div class='xr-var-dims'>(time, nc, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 300, 360), meta=np.ndarray&gt;</div><input id='attrs-6ccd90e1-8733-45a0-b0d3-018d275b1eff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ccd90e1-8733-45a0-b0d3-018d275b1eff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf7b1dcf-b0c5-4569-8eb4-e90487945a79' class='xr-var-data-in' type='checkbox'><label for='data-bf7b1dcf-b0c5-4569-8eb4-e90487945a79' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>ice volume, categories</dd><dt><span>coordinates :</span></dt><dd>TLON TLAT NCAT  time</dd><dt><span>cell_measures :</span></dt><dd>area: tarea</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_rep :</span></dt><dd>averaged</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.42 GiB </td>\n",
+       "                        <td> 421.88 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (708, 5, 300, 360) </td>\n",
+       "                        <td> (1, 1, 300, 360) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 3540 chunks in 1417 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"505\" height=\"115\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"3\" y1=\"0\" x2=\"3\" y2=\"25\" />\n",
+       "  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n",
+       "  <line x1=\"11\" y1=\"0\" x2=\"11\" y2=\"25\" />\n",
+       "  <line x1=\"14\" y1=\"0\" x2=\"14\" y2=\"25\" />\n",
+       "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
+       "  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n",
+       "  <line x1=\"26\" y1=\"0\" x2=\"26\" y2=\"25\" />\n",
+       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n",
+       "  <line x1=\"33\" y1=\"0\" x2=\"33\" y2=\"25\" />\n",
+       "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
+       "  <line x1=\"41\" y1=\"0\" x2=\"41\" y2=\"25\" />\n",
+       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n",
+       "  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"25\" />\n",
+       "  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n",
+       "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
+       "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
+       "  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n",
+       "  <line x1=\"71\" y1=\"0\" x2=\"71\" y2=\"25\" />\n",
+       "  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n",
+       "  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n",
+       "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
+       "  <line x1=\"86\" y1=\"0\" x2=\"86\" y2=\"25\" />\n",
+       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n",
+       "  <line x1=\"93\" y1=\"0\" x2=\"93\" y2=\"25\" />\n",
+       "  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n",
+       "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
+       "  <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"25\" />\n",
+       "  <line x1=\"108\" y1=\"0\" x2=\"108\" y2=\"25\" />\n",
+       "  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n",
+       "  <line x1=\"116\" y1=\"0\" x2=\"116\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >708</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"204\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"190\" y1=\"50\" x2=\"204\" y2=\"65\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"50\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"192\" y1=\"2\" x2=\"192\" y2=\"53\" />\n",
+       "  <line x1=\"195\" y1=\"5\" x2=\"195\" y2=\"56\" />\n",
+       "  <line x1=\"198\" y1=\"8\" x2=\"198\" y2=\"59\" />\n",
+       "  <line x1=\"201\" y1=\"11\" x2=\"201\" y2=\"62\" />\n",
+       "  <line x1=\"204\" y1=\"14\" x2=\"204\" y2=\"65\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"190.0,0.0 204.9485979497544,14.948597949754403 204.9485979497544,65.79605557687304 190.0,50.847457627118644\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"251\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"192\" y1=\"2\" x2=\"254\" y2=\"2\" />\n",
+       "  <line x1=\"195\" y1=\"5\" x2=\"256\" y2=\"5\" />\n",
+       "  <line x1=\"198\" y1=\"8\" x2=\"259\" y2=\"8\" />\n",
+       "  <line x1=\"201\" y1=\"11\" x2=\"262\" y2=\"11\" />\n",
+       "  <line x1=\"204\" y1=\"14\" x2=\"265\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"204\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"251\" y1=\"0\" x2=\"265\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"190.0,0.0 251.01694915254237,0.0 265.9655471022968,14.948597949754403 204.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"204\" y1=\"14\" x2=\"265\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"204\" y1=\"65\" x2=\"265\" y2=\"65\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"204\" y1=\"14\" x2=\"204\" y2=\"65\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"265\" y1=\"14\" x2=\"265\" y2=\"65\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"204.9485979497544,14.948597949754403 265.9655471022968,14.948597949754403 265.9655471022968,65.79605557687304 204.9485979497544,65.79605557687304\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"235.457073\" y=\"85.796056\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >360</text>\n",
+       "  <text x=\"285.965547\" y=\"40.372327\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,285.965547,40.372327)\">300</text>\n",
+       "  <text x=\"187.474299\" y=\"78.321757\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,187.474299,78.321757)\">5</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-e56869ae-5aed-46e2-8a71-874324d036df' class='xr-section-summary-in' type='checkbox'  ><label for='section-e56869ae-5aed-46e2-8a71-874324d036df' class='xr-section-summary' >Attributes: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>sea ice model output for CICE</dd><dt><span>contents :</span></dt><dd>Diagnostic and Prognostic Variables</dd><dt><span>source :</span></dt><dd>Los Alamos Sea Ice Model (CICE) Version 5</dd><dt><span>time_period_freq :</span></dt><dd>month_1</dd><dt><span>comment3 :</span></dt><dd>seconds elapsed into model date:      0</dd><dt><span>conventions :</span></dt><dd>CF-1.0</dd><dt><span>io_flavor :</span></dt><dd>io_pio</dd><dt><span>intake_esm_attrs:realm :</span></dt><dd>seaIce</dd><dt><span>intake_esm_attrs:variable :</span></dt><dd>time_bounds,TLON,TLAT,ULON,ULAT,NCAT,tmask,blkmask,tarea,uarea,dxt,dyt,dxu,dyu,HTN,HTE,ANGLE,ANGLET,hi_m,hs_m,Tsfc_m,aice_m,uvel_m,vvel_m,uatm_m,vatm_m,sice_m,fswdn_m,fswup_m,flwdn_m,snow_ai_m,rain_ai_m,frzmlt_m,fswfac_m,fswabs_ai_m,albsni_m,alvdr_ai_m,alidr_ai_m,alvdf_ai_m,alidf_ai_m,albice_m,albsno_m,flat_ai_m,fsens_ai_m,flwup_ai_m,evap_ai_m,Tair_m,congel_m,frazil_m,snoice_m,meltt_m,melts_m,meltb_m,meltl_m,fresh_ai_m,fsalt_m,fsalt_ai_m,fhocn_ai_m,fswthru_ai_m,strairx_m,strairy_m,strtltx_m,strtlty_m,strcorx_m,strcory_m,strocnx_m,strocny_m,strintx_m,strinty_m,strength_m,divu_m,shear_m,dvidtt_m,dvidtd_m,daidtt_m,daidtd_m,trsig_m,ice_present_m,fcondtop_ai_m,fmeltt_ai_m,opening_m,aicen_m,vicen_m,fsurfn_ai_m,fcondtopn_ai_m,fmelttn_ai_m,flatn_ai_m</dd><dt><span>intake_esm_attrs:frequency :</span></dt><dd>1mon</dd><dt><span>intake_esm_attrs:variable_long_name :</span></dt><dd>boundaries for time-averaging interval,T grid center longitude,T grid center latitude,U grid center longitude,U grid center latitude,category maximum thickness,ocean grid mask,ice grid block mask,area of T grid cells,area of U grid cells,T cell width through middle,T cell height through middle,U cell width through middle,U cell height through middle,T cell width on North side,T cell width on East side,angle grid makes with latitude line on U grid,angle grid makes with latitude line on T grid,grid cell mean ice thickness,grid cell mean snow thickness,snow/ice surface temperature,ice area  (aggregate),ice velocity (x),ice velocity (y),atm velocity (x),atm velocity (y),bulk ice salinity,down solar flux,upward solar flux,down longwave flux,snowfall rate,rainfall rate,freeze/melt potential,shortwave scaling factor,snow/ice/ocn absorbed solar flux,snow/ice broad band albedo,visible direct albedo,near IR direct albedo,visible diffuse albedo,near IR diffuse albedo,bare ice albedo,snow albedo,latent heat flux,sensible heat flux,upward longwave flux,evaporative water flux,air temperature,congelation ice growth,frazil ice growth,snow-ice formation,top ice melt,top snow melt,basal ice melt,lateral ice melt,freshwtr flx ice to ocn,salt flux ice to ocn (cpl),salt flux ice to ocean,heat flux ice to ocean,SW flux thru ice to ocean,atm/ice stress (x),atm/ice stress (y),sea sfc tilt stress (x),sea sfc tilt stress (y),coriolis stress (x),coriolis stress (y),ocean/ice stress (x),ocean/ice stress (y),internal ice stress (x),internal ice stress (y),compressive ice strength,strain rate (divergence),strain rate (shear),volume tendency thermo,volume tendency dynamics,area tendency thermo,area tendency dynamics,internal stress tensor trace,fraction of time-avg interval that ice is present,top surface conductive heat flux,net surface heat flux causing melt,lead area opening rate,ice area, categories,ice volume, categories,net surface heat flux, categories,top sfc conductive heat flux, cat,net sfc heat flux causing melt, cat,latent heat flux, category</dd><dt><span>intake_esm_attrs:variable_standard_name :</span></dt><dd>,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,</dd><dt><span>intake_esm_attrs:variable_cell_methods :</span></dt><dd>,,,,,,,,,,,,,,,,,,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean</dd><dt><span>intake_esm_attrs:variable_units :</span></dt><dd>days since 1960-01-01 00:00:00,degrees_east,degrees_north,degrees_east,degrees_north,m,,,m^2,m^2,m,m,m,m,m,m,radians,radians,m,m,C,1,m/s,m/s,m/s,m/s,ppt,W/m^2,W/m^2,W/m^2,cm/day,cm/day,W/m^2,1,W/m^2,%,%,%,%,%,%,%,W/m^2,W/m^2,W/m^2,cm/day,C,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,kg/m^2/s,kg/m^2/s,W/m^2,W/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m,%/day,%/day,cm/day,cm/day,%/day,%/day,N/m^2,1,W/m^2,W/m^2,%/day,1,m,W/m^2,W/m^2,W/m^2,W/m^2</dd><dt><span>intake_esm_attrs:file_id :</span></dt><dd>iceh_XXXX_XX</dd><dt><span>intake_esm_attrs:_data_format_ :</span></dt><dd>netcdf</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>iceh_XXXX_XX.1mon</dd></dl></div></li></ul></div></div></ul></div></div><div style='display: inline-grid;'><div style='grid-column-start: 1;border-right: 0.2em solid;border-color: var(--xr-border-color);height: 1.2em;width: 0px;'></div><div style='grid-column-start: 2;grid-row-start: 1;height: 1em;width: 20px;border-bottom: 0.2em solid;border-color: var(--xr-border-color);'></div><div style='grid-column-start: 3;'><ul class='xr-sections'><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
        "<defs>\n",
        "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
        "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
@@ -2961,7 +3366,7 @@
        "  fill: currentColor;\n",
        "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DatasetView&gt;\n",
-       "Dimensions:   (time: 720, yt_ocean: 300, xt_ocean: 360)\n",
+       "Dimensions:   (time: 720, nc: 5, yt_ocean: 300, xt_ocean: 360)\n",
        "Coordinates:\n",
        "  * time      (time) datetime64[ns] 1959-01-01 1959-02-01 ... 2019-12-01\n",
        "  * xt_ocean  (xt_ocean) float64 -279.5 -278.5 -277.5 -276.5 ... 77.5 78.5 79.5\n",
@@ -2969,9 +3374,11 @@
        "    geolat_t  (yt_ocean, xt_ocean) float32 ...\n",
        "    geolon_t  (yt_ocean, xt_ocean) float32 ...\n",
        "    area_t    (yt_ocean, xt_ocean) float32 ...\n",
+       "Dimensions without coordinates: nc\n",
        "Data variables:\n",
+       "    vicen_m   (time, nc, yt_ocean, xt_ocean) float32 dask.array&lt;chunksize=(1, 1, 300, 360), meta=np.ndarray&gt;\n",
        "    aice_m    (time, yt_ocean, xt_ocean) float32 dask.array&lt;chunksize=(1, 300, 360), meta=np.ndarray&gt;\n",
-       "Attributes: (12/17)\n",
+       "Attributes: (12/16)\n",
        "    title:                                    sea ice model output for CICE\n",
        "    contents:                                 Diagnostic and Prognostic Varia...\n",
        "    source:                                   Los Alamos Sea Ice Model (CICE)...\n",
@@ -2984,11 +3391,144 @@
        "    intake_esm_attrs:variable_cell_methods:   ,,,,,,,,,,,,,,,,,,time: mean,ti...\n",
        "    intake_esm_attrs:file_id:                 iceh_XXXX_XX\n",
        "    intake_esm_attrs:_data_format_:           netcdf\n",
-       "    intake_esm_dataset_key:                   iceh_XXXX_XX.1mon</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>1deg_jra55</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-6f20df52-1122-47c5-a213-7344174ad87c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6f20df52-1122-47c5-a213-7344174ad87c' class='xr-section-summary'  title='Expand/collapse section'>Groups: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%'></div></div></li><li class='xr-section-item'><input id='section-ae82c9f5-7fcb-4458-9969-cb6033d41ad0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ae82c9f5-7fcb-4458-9969-cb6033d41ad0' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 720</li><li><span class='xr-has-index'>yt_ocean</span>: 300</li><li><span class='xr-has-index'>xt_ocean</span>: 360</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e04e3869-cd3f-43b0-a71b-c9b840e08fe9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e04e3869-cd3f-43b0-a71b-c9b840e08fe9' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1959-01-01 ... 2019-12-01</div><input id='attrs-0b9c705b-c392-481d-8bcc-e318b6ba42a9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0b9c705b-c392-481d-8bcc-e318b6ba42a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9409bd6d-956d-479d-be64-ddf332c70757' class='xr-var-data-in' type='checkbox'><label for='data-9409bd6d-956d-479d-be64-ddf332c70757' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1959-01-01T00:00:00.000000000&#x27;, &#x27;1959-02-01T00:00:00.000000000&#x27;,\n",
+       "    intake_esm_dataset_key:                   iceh_XXXX_XX.1mon</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>1deg_jra55</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-e4a6fb48-bc12-4e02-8702-a4b65bbbe91d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e4a6fb48-bc12-4e02-8702-a4b65bbbe91d' class='xr-section-summary'  title='Expand/collapse section'>Groups: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%'></div></div></li><li class='xr-section-item'><input id='section-fc08f9a9-8cde-486e-8fa4-5f23c56f731d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fc08f9a9-8cde-486e-8fa4-5f23c56f731d' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 720</li><li><span>nc</span>: 5</li><li><span class='xr-has-index'>yt_ocean</span>: 300</li><li><span class='xr-has-index'>xt_ocean</span>: 360</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-14ab324e-98cf-47df-9646-bb160ed49d3f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-14ab324e-98cf-47df-9646-bb160ed49d3f' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1959-01-01 ... 2019-12-01</div><input id='attrs-85334c0d-95cf-4c30-996e-776289ae3581' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-85334c0d-95cf-4c30-996e-776289ae3581' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba6b97cd-5ad6-4f64-86f5-b9a369c17b2b' class='xr-var-data-in' type='checkbox'><label for='data-ba6b97cd-5ad6-4f64-86f5-b9a369c17b2b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1959-01-01T00:00:00.000000000&#x27;, &#x27;1959-02-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;1959-03-01T00:00:00.000000000&#x27;, ..., &#x27;2019-10-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;2019-11-01T00:00:00.000000000&#x27;, &#x27;2019-12-01T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.5 -278.5 -277.5 ... 78.5 79.5</div><input id='attrs-343e995c-986e-478e-bcd5-58807ccadba4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-343e995c-986e-478e-bcd5-58807ccadba4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b3e166b-a1ff-46d1-ad90-417e3498636a' class='xr-var-data-in' type='checkbox'><label for='data-1b3e166b-a1ff-46d1-ad90-417e3498636a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-279.5, -278.5, -277.5, ...,   77.5,   78.5,   79.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-77.88 -77.63 ... 89.32 89.77</div><input id='attrs-f588c2a1-a1b9-4869-9757-fc864e6c8380' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f588c2a1-a1b9-4869-9757-fc864e6c8380' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea35a730-11fe-4ab5-b948-db81f632e7e1' class='xr-var-data-in' type='checkbox'><label for='data-ea35a730-11fe-4ab5-b948-db81f632e7e1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-77.876623, -77.629713, -77.381707, ...,  88.872933,  89.324006,\n",
-       "        89.774476])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolat_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cb4edcc1-5666-4c5c-8f6d-7bda9349eee4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb4edcc1-5666-4c5c-8f6d-7bda9349eee4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6bd3f375-3f89-4712-9032-c437db49d511' class='xr-var-data-in' type='checkbox'><label for='data-6bd3f375-3f89-4712-9032-c437db49d511' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>valid_range :</span></dt><dd>[-91.  91.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[108000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolon_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7dbac691-c68e-4f1a-9fd1-b10fa2b0a2d8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7dbac691-c68e-4f1a-9fd1-b10fa2b0a2d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64f2171a-8383-4e3a-a29a-7d9f88efbb7a' class='xr-var-data-in' type='checkbox'><label for='data-64f2171a-8383-4e3a-a29a-7d9f88efbb7a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>valid_range :</span></dt><dd>[-281.  361.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[108000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>area_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f67583cb-08cb-4aff-9660-770c6e91aff2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f67583cb-08cb-4aff-9660-770c6e91aff2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8697a6bc-d397-4aa0-8c78-6c0dc46b22ff' class='xr-var-data-in' type='checkbox'><label for='data-8697a6bc-d397-4aa0-8c78-6c0dc46b22ff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer cell area</dd><dt><span>units :</span></dt><dd>m^2</dd><dt><span>valid_range :</span></dt><dd>[0.e+00 1.e+15]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[108000 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-480f586b-2bdf-4bb0-ac18-5b2133574be3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-480f586b-2bdf-4bb0-ac18-5b2133574be3' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>aice_m</span></div><div class='xr-var-dims'>(time, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 300, 360), meta=np.ndarray&gt;</div><input id='attrs-6966c779-07b0-45fd-8c50-62dff2402577' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6966c779-07b0-45fd-8c50-62dff2402577' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8bd7d2c-85c6-48f1-a415-e8bee683bcb5' class='xr-var-data-in' type='checkbox'><label for='data-e8bd7d2c-85c6-48f1-a415-e8bee683bcb5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>ice area  (aggregate)</dd><dt><span>coordinates :</span></dt><dd>TLON TLAT time</dd><dt><span>cell_measures :</span></dt><dd>area: tarea</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_rep :</span></dt><dd>averaged</dd></dl></div><div class='xr-var-data'><table>\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.5 -278.5 -277.5 ... 78.5 79.5</div><input id='attrs-a2a48d4d-d1a3-4304-9c83-90b1405613b4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a2a48d4d-d1a3-4304-9c83-90b1405613b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2bc5697-e218-4dad-8fa9-6fb4fffa76aa' class='xr-var-data-in' type='checkbox'><label for='data-d2bc5697-e218-4dad-8fa9-6fb4fffa76aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-279.5, -278.5, -277.5, ...,   77.5,   78.5,   79.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-77.88 -77.63 ... 89.32 89.77</div><input id='attrs-a22b0ee6-eb91-41f0-8748-03cf3e0f2f58' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a22b0ee6-eb91-41f0-8748-03cf3e0f2f58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fefeea2c-b83b-4b8a-8bdc-dcf2f3cc7b7d' class='xr-var-data-in' type='checkbox'><label for='data-fefeea2c-b83b-4b8a-8bdc-dcf2f3cc7b7d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-77.876623, -77.629713, -77.381707, ...,  88.872933,  89.324006,\n",
+       "        89.774476])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolat_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bea15ced-6c14-43e0-9cd9-0c2b354d7aa8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bea15ced-6c14-43e0-9cd9-0c2b354d7aa8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04d90a20-156c-4d5a-ba6a-e3be1d56e2ac' class='xr-var-data-in' type='checkbox'><label for='data-04d90a20-156c-4d5a-ba6a-e3be1d56e2ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>valid_range :</span></dt><dd>[-91.  91.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[108000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolon_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6c4c1b9c-a529-45be-9980-8907d3803c83' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c4c1b9c-a529-45be-9980-8907d3803c83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7955c13a-3124-4238-beef-76b219925616' class='xr-var-data-in' type='checkbox'><label for='data-7955c13a-3124-4238-beef-76b219925616' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>valid_range :</span></dt><dd>[-281.  361.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[108000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>area_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f9581d9b-5e9d-49a2-82c5-dc07725113c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f9581d9b-5e9d-49a2-82c5-dc07725113c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a148765c-7928-43d1-8230-75769a03b191' class='xr-var-data-in' type='checkbox'><label for='data-a148765c-7928-43d1-8230-75769a03b191' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer cell area</dd><dt><span>units :</span></dt><dd>m^2</dd><dt><span>valid_range :</span></dt><dd>[0.e+00 1.e+15]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[108000 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1952135c-da54-4c49-9a78-d7b435aaff51' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1952135c-da54-4c49-9a78-d7b435aaff51' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>vicen_m</span></div><div class='xr-var-dims'>(time, nc, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 300, 360), meta=np.ndarray&gt;</div><input id='attrs-de72b9f3-cb9a-452a-8e8c-c6add8359668' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de72b9f3-cb9a-452a-8e8c-c6add8359668' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93eabfe2-1063-41cc-acfa-bdb467c9e0dc' class='xr-var-data-in' type='checkbox'><label for='data-93eabfe2-1063-41cc-acfa-bdb467c9e0dc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>ice volume, categories</dd><dt><span>coordinates :</span></dt><dd>TLON TLAT NCAT  time</dd><dt><span>cell_measures :</span></dt><dd>area: tarea</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_rep :</span></dt><dd>averaged</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.45 GiB </td>\n",
+       "                        <td> 421.88 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (720, 5, 300, 360) </td>\n",
+       "                        <td> (1, 1, 300, 360) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 3600 chunks in 1441 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"504\" height=\"114\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"3\" y1=\"0\" x2=\"3\" y2=\"25\" />\n",
+       "  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n",
+       "  <line x1=\"11\" y1=\"0\" x2=\"11\" y2=\"25\" />\n",
+       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n",
+       "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
+       "  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n",
+       "  <line x1=\"26\" y1=\"0\" x2=\"26\" y2=\"25\" />\n",
+       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n",
+       "  <line x1=\"33\" y1=\"0\" x2=\"33\" y2=\"25\" />\n",
+       "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
+       "  <line x1=\"41\" y1=\"0\" x2=\"41\" y2=\"25\" />\n",
+       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n",
+       "  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"25\" />\n",
+       "  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n",
+       "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
+       "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
+       "  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n",
+       "  <line x1=\"71\" y1=\"0\" x2=\"71\" y2=\"25\" />\n",
+       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
+       "  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n",
+       "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
+       "  <line x1=\"86\" y1=\"0\" x2=\"86\" y2=\"25\" />\n",
+       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n",
+       "  <line x1=\"93\" y1=\"0\" x2=\"93\" y2=\"25\" />\n",
+       "  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n",
+       "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
+       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n",
+       "  <line x1=\"108\" y1=\"0\" x2=\"108\" y2=\"25\" />\n",
+       "  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n",
+       "  <line x1=\"116\" y1=\"0\" x2=\"116\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >720</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"204\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"190\" y1=\"50\" x2=\"204\" y2=\"64\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"50\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"192\" y1=\"2\" x2=\"192\" y2=\"52\" />\n",
+       "  <line x1=\"195\" y1=\"5\" x2=\"195\" y2=\"55\" />\n",
+       "  <line x1=\"198\" y1=\"8\" x2=\"198\" y2=\"58\" />\n",
+       "  <line x1=\"201\" y1=\"11\" x2=\"201\" y2=\"61\" />\n",
+       "  <line x1=\"204\" y1=\"14\" x2=\"204\" y2=\"64\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"190.0,0.0 204.9485979497544,14.948597949754403 204.9485979497544,64.9485979497544 190.0,50.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"250\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"192\" y1=\"2\" x2=\"252\" y2=\"2\" />\n",
+       "  <line x1=\"195\" y1=\"5\" x2=\"255\" y2=\"5\" />\n",
+       "  <line x1=\"198\" y1=\"8\" x2=\"258\" y2=\"8\" />\n",
+       "  <line x1=\"201\" y1=\"11\" x2=\"261\" y2=\"11\" />\n",
+       "  <line x1=\"204\" y1=\"14\" x2=\"264\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"204\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"250\" y1=\"0\" x2=\"264\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"190.0,0.0 250.0,0.0 264.9485979497544,14.948597949754403 204.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"204\" y1=\"14\" x2=\"264\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"204\" y1=\"64\" x2=\"264\" y2=\"64\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"204\" y1=\"14\" x2=\"204\" y2=\"64\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"264\" y1=\"14\" x2=\"264\" y2=\"64\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"204.9485979497544,14.948597949754403 264.9485979497544,14.948597949754403 264.9485979497544,64.9485979497544 204.9485979497544,64.9485979497544\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"234.948598\" y=\"84.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >360</text>\n",
+       "  <text x=\"284.948598\" y=\"39.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,284.948598,39.948598)\">300</text>\n",
+       "  <text x=\"187.474299\" y=\"77.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,187.474299,77.474299)\">5</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aice_m</span></div><div class='xr-var-dims'>(time, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 300, 360), meta=np.ndarray&gt;</div><input id='attrs-1c536d5f-39cf-452b-b8a0-9eaa2b82faf2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c536d5f-39cf-452b-b8a0-9eaa2b82faf2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2357fab5-6a4f-4832-a150-a0d1f45d8fde' class='xr-var-data-in' type='checkbox'><label for='data-2357fab5-6a4f-4832-a150-a0d1f45d8fde' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>ice area  (aggregate)</dd><dt><span>coordinates :</span></dt><dd>TLON TLAT time</dd><dt><span>cell_measures :</span></dt><dd>area: tarea</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_rep :</span></dt><dd>averaged</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3102,12 +3642,12 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-a11bea96-18af-4c1e-a43b-951265a463b0' class='xr-section-summary-in' type='checkbox'  ><label for='section-a11bea96-18af-4c1e-a43b-951265a463b0' class='xr-section-summary' >Attributes: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>sea ice model output for CICE</dd><dt><span>contents :</span></dt><dd>Diagnostic and Prognostic Variables</dd><dt><span>source :</span></dt><dd>Los Alamos Sea Ice Model (CICE) Version 5</dd><dt><span>time_period_freq :</span></dt><dd>month_1</dd><dt><span>comment3 :</span></dt><dd>seconds elapsed into model date:      0</dd><dt><span>conventions :</span></dt><dd>CF-1.0</dd><dt><span>io_flavor :</span></dt><dd>io_pio</dd><dt><span>intake_esm_vars :</span></dt><dd>[&#x27;aice_m&#x27;]</dd><dt><span>intake_esm_attrs:realm :</span></dt><dd>seaIce</dd><dt><span>intake_esm_attrs:variable :</span></dt><dd>time_bounds,TLON,TLAT,ULON,ULAT,NCAT,tmask,blkmask,tarea,uarea,dxt,dyt,dxu,dyu,HTN,HTE,ANGLE,ANGLET,hi_m,hs_m,Tsfc_m,aice_m,uvel_m,vvel_m,uatm_m,vatm_m,sice_m,fswdn_m,fswup_m,flwdn_m,snow_ai_m,rain_ai_m,frzmlt_m,fswfac_m,fswabs_ai_m,albsni_m,alvdr_ai_m,alidr_ai_m,alvdf_ai_m,alidf_ai_m,albice_m,albsno_m,flat_ai_m,fsens_ai_m,flwup_ai_m,evap_ai_m,Tair_m,congel_m,frazil_m,snoice_m,meltt_m,melts_m,meltb_m,meltl_m,fresh_ai_m,fsalt_m,fsalt_ai_m,fhocn_ai_m,fswthru_ai_m,strairx_m,strairy_m,strtltx_m,strtlty_m,strcorx_m,strcory_m,strocnx_m,strocny_m,strintx_m,strinty_m,strength_m,divu_m,shear_m,dvidtt_m,dvidtd_m,daidtt_m,daidtd_m,trsig_m,ice_present_m,fcondtop_ai_m,fmeltt_ai_m,opening_m,aicen_m,vicen_m,fsurfn_ai_m,fcondtopn_ai_m,fmelttn_ai_m,flatn_ai_m</dd><dt><span>intake_esm_attrs:frequency :</span></dt><dd>1mon</dd><dt><span>intake_esm_attrs:variable_long_name :</span></dt><dd>boundaries for time-averaging interval,T grid center longitude,T grid center latitude,U grid center longitude,U grid center latitude,category maximum thickness,ocean grid mask,ice grid block mask,area of T grid cells,area of U grid cells,T cell width through middle,T cell height through middle,U cell width through middle,U cell height through middle,T cell width on North side,T cell width on East side,angle grid makes with latitude line on U grid,angle grid makes with latitude line on T grid,grid cell mean ice thickness,grid cell mean snow thickness,snow/ice surface temperature,ice area  (aggregate),ice velocity (x),ice velocity (y),atm velocity (x),atm velocity (y),bulk ice salinity,down solar flux,upward solar flux,down longwave flux,snowfall rate,rainfall rate,freeze/melt potential,shortwave scaling factor,snow/ice/ocn absorbed solar flux,snow/ice broad band albedo,visible direct albedo,near IR direct albedo,visible diffuse albedo,near IR diffuse albedo,bare ice albedo,snow albedo,latent heat flux,sensible heat flux,upward longwave flux,evaporative water flux,air temperature,congelation ice growth,frazil ice growth,snow-ice formation,top ice melt,top snow melt,basal ice melt,lateral ice melt,freshwtr flx ice to ocn,salt flux ice to ocn (cpl),salt flux ice to ocean,heat flux ice to ocean,SW flux thru ice to ocean,atm/ice stress (x),atm/ice stress (y),sea sfc tilt stress (x),sea sfc tilt stress (y),coriolis stress (x),coriolis stress (y),ocean/ice stress (x),ocean/ice stress (y),internal ice stress (x),internal ice stress (y),compressive ice strength,strain rate (divergence),strain rate (shear),volume tendency thermo,volume tendency dynamics,area tendency thermo,area tendency dynamics,internal stress tensor trace,fraction of time-avg interval that ice is present,top surface conductive heat flux,net surface heat flux causing melt,lead area opening rate,ice area, categories,ice volume, categories,net surface heat flux, categories,top sfc conductive heat flux, cat,net sfc heat flux causing melt, cat,latent heat flux, category</dd><dt><span>intake_esm_attrs:variable_standard_name :</span></dt><dd>,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,</dd><dt><span>intake_esm_attrs:variable_cell_methods :</span></dt><dd>,,,,,,,,,,,,,,,,,,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean</dd><dt><span>intake_esm_attrs:file_id :</span></dt><dd>iceh_XXXX_XX</dd><dt><span>intake_esm_attrs:_data_format_ :</span></dt><dd>netcdf</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>iceh_XXXX_XX.1mon</dd></dl></div></li></ul></div></div></ul></div></div></div></div></li><li class='xr-section-item'><input id='section-2c783c48-2845-4011-a294-da49f9b9017d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2c783c48-2845-4011-a294-da49f9b9017d' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-bc5ca0f5-845a-40d8-8a22-dac724e8e674' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bc5ca0f5-845a-40d8-8a22-dac724e8e674' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-b0de26e2-2664-4f5c-89d4-f99cb830349f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b0de26e2-2664-4f5c-89d4-f99cb830349f' class='xr-section-summary'  title='Expand/collapse section'>Data variables: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-29946245-0465-4256-a088-d2300fe7d24a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-29946245-0465-4256-a088-d2300fe7d24a' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-b9a783fe-8cc3-466e-b8e2-4c712e265fec' class='xr-section-summary-in' type='checkbox'  ><label for='section-b9a783fe-8cc3-466e-b8e2-4c712e265fec' class='xr-section-summary' >Attributes: <span>(16)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>sea ice model output for CICE</dd><dt><span>contents :</span></dt><dd>Diagnostic and Prognostic Variables</dd><dt><span>source :</span></dt><dd>Los Alamos Sea Ice Model (CICE) Version 5</dd><dt><span>time_period_freq :</span></dt><dd>month_1</dd><dt><span>comment3 :</span></dt><dd>seconds elapsed into model date:      0</dd><dt><span>conventions :</span></dt><dd>CF-1.0</dd><dt><span>io_flavor :</span></dt><dd>io_pio</dd><dt><span>intake_esm_attrs:realm :</span></dt><dd>seaIce</dd><dt><span>intake_esm_attrs:variable :</span></dt><dd>time_bounds,TLON,TLAT,ULON,ULAT,NCAT,tmask,blkmask,tarea,uarea,dxt,dyt,dxu,dyu,HTN,HTE,ANGLE,ANGLET,hi_m,hs_m,Tsfc_m,aice_m,uvel_m,vvel_m,uatm_m,vatm_m,sice_m,fswdn_m,fswup_m,flwdn_m,snow_ai_m,rain_ai_m,frzmlt_m,fswfac_m,fswabs_ai_m,albsni_m,alvdr_ai_m,alidr_ai_m,alvdf_ai_m,alidf_ai_m,albice_m,albsno_m,flat_ai_m,fsens_ai_m,flwup_ai_m,evap_ai_m,Tair_m,congel_m,frazil_m,snoice_m,meltt_m,melts_m,meltb_m,meltl_m,fresh_ai_m,fsalt_m,fsalt_ai_m,fhocn_ai_m,fswthru_ai_m,strairx_m,strairy_m,strtltx_m,strtlty_m,strcorx_m,strcory_m,strocnx_m,strocny_m,strintx_m,strinty_m,strength_m,divu_m,shear_m,dvidtt_m,dvidtd_m,daidtt_m,daidtd_m,trsig_m,ice_present_m,fcondtop_ai_m,fmeltt_ai_m,opening_m,aicen_m,vicen_m,fsurfn_ai_m,fcondtopn_ai_m,fmelttn_ai_m,flatn_ai_m</dd><dt><span>intake_esm_attrs:frequency :</span></dt><dd>1mon</dd><dt><span>intake_esm_attrs:variable_long_name :</span></dt><dd>boundaries for time-averaging interval,T grid center longitude,T grid center latitude,U grid center longitude,U grid center latitude,category maximum thickness,ocean grid mask,ice grid block mask,area of T grid cells,area of U grid cells,T cell width through middle,T cell height through middle,U cell width through middle,U cell height through middle,T cell width on North side,T cell width on East side,angle grid makes with latitude line on U grid,angle grid makes with latitude line on T grid,grid cell mean ice thickness,grid cell mean snow thickness,snow/ice surface temperature,ice area  (aggregate),ice velocity (x),ice velocity (y),atm velocity (x),atm velocity (y),bulk ice salinity,down solar flux,upward solar flux,down longwave flux,snowfall rate,rainfall rate,freeze/melt potential,shortwave scaling factor,snow/ice/ocn absorbed solar flux,snow/ice broad band albedo,visible direct albedo,near IR direct albedo,visible diffuse albedo,near IR diffuse albedo,bare ice albedo,snow albedo,latent heat flux,sensible heat flux,upward longwave flux,evaporative water flux,air temperature,congelation ice growth,frazil ice growth,snow-ice formation,top ice melt,top snow melt,basal ice melt,lateral ice melt,freshwtr flx ice to ocn,salt flux ice to ocn (cpl),salt flux ice to ocean,heat flux ice to ocean,SW flux thru ice to ocean,atm/ice stress (x),atm/ice stress (y),sea sfc tilt stress (x),sea sfc tilt stress (y),coriolis stress (x),coriolis stress (y),ocean/ice stress (x),ocean/ice stress (y),internal ice stress (x),internal ice stress (y),compressive ice strength,strain rate (divergence),strain rate (shear),volume tendency thermo,volume tendency dynamics,area tendency thermo,area tendency dynamics,internal stress tensor trace,fraction of time-avg interval that ice is present,top surface conductive heat flux,net surface heat flux causing melt,lead area opening rate,ice area, categories,ice volume, categories,net surface heat flux, categories,top sfc conductive heat flux, cat,net sfc heat flux causing melt, cat,latent heat flux, category</dd><dt><span>intake_esm_attrs:variable_standard_name :</span></dt><dd>,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,</dd><dt><span>intake_esm_attrs:variable_cell_methods :</span></dt><dd>,,,,,,,,,,,,,,,,,,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean</dd><dt><span>intake_esm_attrs:file_id :</span></dt><dd>iceh_XXXX_XX</dd><dt><span>intake_esm_attrs:_data_format_ :</span></dt><dd>netcdf</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>iceh_XXXX_XX.1mon</dd></dl></div></li></ul></div></div></ul></div></div></div></div></li><li class='xr-section-item'><input id='section-013871b1-f447-463c-9125-1588be95778b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-013871b1-f447-463c-9125-1588be95778b' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8c530983-9879-4e70-9279-2ed98140a992' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8c530983-9879-4e70-9279-2ed98140a992' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-a3c3aa06-686a-4217-80f2-8dd929a99d23' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a3c3aa06-686a-4217-80f2-8dd929a99d23' class='xr-section-summary'  title='Expand/collapse section'>Data variables: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-1d9c80fa-d98d-48ae-9d1d-1a8297aac482' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1d9c80fa-d98d-48ae-9d1d-1a8297aac482' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "DataTree('None', parent=None)\n",
        "├── DataTree('025deg_era5')\n",
-       "│       Dimensions:   (time: 523, yt_ocean: 1080, xt_ocean: 1440)\n",
+       "│       Dimensions:   (time: 523, nc: 5, yt_ocean: 1080, xt_ocean: 1440)\n",
        "│       Coordinates:\n",
        "│         * time      (time) datetime64[ns] 1980-01-01 1980-02-01 ... 2023-07-01\n",
        "│         * xt_ocean  (xt_ocean) float64 -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n",
@@ -3115,9 +3655,11 @@
        "│           geolat_t  (yt_ocean, xt_ocean) float32 ...\n",
        "│           geolon_t  (yt_ocean, xt_ocean) float32 ...\n",
        "│           area_t    (yt_ocean, xt_ocean) float32 ...\n",
+       "│       Dimensions without coordinates: nc\n",
        "│       Data variables:\n",
+       "│           vicen_m   (time, nc, yt_ocean, xt_ocean) float32 dask.array<chunksize=(1, 1, 1080, 1440), meta=np.ndarray>\n",
        "│           aice_m    (time, yt_ocean, xt_ocean) float32 dask.array<chunksize=(1, 1080, 1440), meta=np.ndarray>\n",
-       "│       Attributes: (12/18)\n",
+       "│       Attributes: (12/17)\n",
        "│           title:                                    sea ice model output for CICE\n",
        "│           contents:                                 Diagnostic and Prognostic Varia...\n",
        "│           source:                                   Los Alamos Sea Ice Model (CICE)...\n",
@@ -3132,7 +3674,7 @@
        "│           intake_esm_attrs:_data_format_:           netcdf\n",
        "│           intake_esm_dataset_key:                   iceh_XXXX_XX.1mon\n",
        "├── DataTree('025deg_jra55')\n",
-       "│       Dimensions:   (time: 523, yt_ocean: 1080, xt_ocean: 1440)\n",
+       "│       Dimensions:   (time: 523, nc: 5, yt_ocean: 1080, xt_ocean: 1440)\n",
        "│       Coordinates:\n",
        "│         * time      (time) datetime64[ns] 1980-01-01 1980-02-01 ... 2023-07-01\n",
        "│         * xt_ocean  (xt_ocean) float64 -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n",
@@ -3140,9 +3682,11 @@
        "│           geolat_t  (yt_ocean, xt_ocean) float32 ...\n",
        "│           geolon_t  (yt_ocean, xt_ocean) float32 ...\n",
        "│           area_t    (yt_ocean, xt_ocean) float32 ...\n",
+       "│       Dimensions without coordinates: nc\n",
        "│       Data variables:\n",
+       "│           vicen_m   (time, nc, yt_ocean, xt_ocean) float32 dask.array<chunksize=(1, 1, 1080, 1440), meta=np.ndarray>\n",
        "│           aice_m    (time, yt_ocean, xt_ocean) float32 dask.array<chunksize=(1, 1080, 1440), meta=np.ndarray>\n",
-       "│       Attributes: (12/18)\n",
+       "│       Attributes: (12/17)\n",
        "│           title:                                    sea ice model output for CICE\n",
        "│           contents:                                 Diagnostic and Prognostic Varia...\n",
        "│           source:                                   Los Alamos Sea Ice Model (CICE)...\n",
@@ -3157,7 +3701,7 @@
        "│           intake_esm_attrs:_data_format_:           netcdf\n",
        "│           intake_esm_dataset_key:                   iceh_XXXX_XX.1mon\n",
        "├── DataTree('1deg_era5')\n",
-       "│       Dimensions:   (time: 708, yt_ocean: 300, xt_ocean: 360)\n",
+       "│       Dimensions:   (time: 708, yt_ocean: 300, xt_ocean: 360, nc: 5)\n",
        "│       Coordinates:\n",
        "│         * time      (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n",
        "│         * xt_ocean  (xt_ocean) float64 -279.5 -278.5 -277.5 -276.5 ... 77.5 78.5 79.5\n",
@@ -3165,9 +3709,11 @@
        "│           geolat_t  (yt_ocean, xt_ocean) float32 ...\n",
        "│           geolon_t  (yt_ocean, xt_ocean) float32 ...\n",
        "│           area_t    (yt_ocean, xt_ocean) float32 ...\n",
+       "│       Dimensions without coordinates: nc\n",
        "│       Data variables:\n",
        "│           aice_m    (time, yt_ocean, xt_ocean) float32 dask.array<chunksize=(1, 300, 360), meta=np.ndarray>\n",
-       "│       Attributes: (12/18)\n",
+       "│           vicen_m   (time, nc, yt_ocean, xt_ocean) float32 dask.array<chunksize=(1, 1, 300, 360), meta=np.ndarray>\n",
+       "│       Attributes: (12/17)\n",
        "│           title:                                    sea ice model output for CICE\n",
        "│           contents:                                 Diagnostic and Prognostic Varia...\n",
        "│           source:                                   Los Alamos Sea Ice Model (CICE)...\n",
@@ -3182,7 +3728,7 @@
        "│           intake_esm_attrs:_data_format_:           netcdf\n",
        "│           intake_esm_dataset_key:                   iceh_XXXX_XX.1mon\n",
        "└── DataTree('1deg_jra55')\n",
-       "        Dimensions:   (time: 720, yt_ocean: 300, xt_ocean: 360)\n",
+       "        Dimensions:   (time: 720, nc: 5, yt_ocean: 300, xt_ocean: 360)\n",
        "        Coordinates:\n",
        "          * time      (time) datetime64[ns] 1959-01-01 1959-02-01 ... 2019-12-01\n",
        "          * xt_ocean  (xt_ocean) float64 -279.5 -278.5 -277.5 -276.5 ... 77.5 78.5 79.5\n",
@@ -3190,9 +3736,11 @@
        "            geolat_t  (yt_ocean, xt_ocean) float32 ...\n",
        "            geolon_t  (yt_ocean, xt_ocean) float32 ...\n",
        "            area_t    (yt_ocean, xt_ocean) float32 ...\n",
+       "        Dimensions without coordinates: nc\n",
        "        Data variables:\n",
+       "            vicen_m   (time, nc, yt_ocean, xt_ocean) float32 dask.array<chunksize=(1, 1, 300, 360), meta=np.ndarray>\n",
        "            aice_m    (time, yt_ocean, xt_ocean) float32 dask.array<chunksize=(1, 300, 360), meta=np.ndarray>\n",
-       "        Attributes: (12/17)\n",
+       "        Attributes: (12/16)\n",
        "            title:                                    sea ice model output for CICE\n",
        "            contents:                                 Diagnostic and Prognostic Varia...\n",
        "            source:                                   Los Alamos Sea Ice Model (CICE)...\n",
@@ -3208,7 +3756,7 @@
        "            intake_esm_dataset_key:                   iceh_XXXX_XX.1mon"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3224,16 +3772,23 @@
     "# Load the observational dataset"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The CDR dataset has the area of each grid cell provided as a seperate file, which we need to load"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-11T00:17:58.735821Z",
-     "iopub.status.busy": "2023-12-11T00:17:58.732387Z",
-     "iopub.status.idle": "2023-12-11T00:17:58.816501Z",
-     "shell.execute_reply": "2023-12-11T00:17:58.800999Z",
-     "shell.execute_reply.started": "2023-12-11T00:17:58.735791Z"
+     "iopub.execute_input": "2024-01-08T03:07:42.567016Z",
+     "iopub.status.busy": "2024-01-08T03:07:42.566104Z",
+     "iopub.status.idle": "2024-01-08T03:07:42.578107Z",
+     "shell.execute_reply": "2024-01-08T03:07:42.577103Z",
+     "shell.execute_reply.started": "2024-01-08T03:07:42.566966Z"
     }
    },
    "outputs": [],
@@ -3241,11 +3796,10 @@
     "def open_cdr_dataset(path, area_file):\n",
     "    ds=xr.open_dataset(path).rename(\n",
     "        {'cdr_seaice_conc_monthly':'cdr_seaice_conc','xgrid':'x','ygrid':'y'}\n",
-    "    )#.swap_dims({'tdim':'time','x':'xgrid','y':'ygrid'})\n",
+    "    )\n",
     "\n",
     "    #we also need the area of each gridcell\n",
     "    datFile=open(area_file, 'rb')\n",
-    "    #pss25area_v3.dat: 316 columns x 332 rows\n",
     "    areasDmNd=np.fromfile(datFile, dtype=np.int32).reshape(ds.cdr_seaice_conc.isel(time=0).shape)\n",
     "    #Divide by 1000 to get km2\n",
     "    areasKmNd_sh=areasDmNd/1000\n",
@@ -3269,14 +3823,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-11T00:17:58.855242Z",
-     "iopub.status.busy": "2023-12-11T00:17:58.848852Z",
-     "iopub.status.idle": "2023-12-11T00:17:59.399533Z",
-     "shell.execute_reply": "2023-12-11T00:17:59.397040Z",
-     "shell.execute_reply.started": "2023-12-11T00:17:58.855209Z"
+     "iopub.execute_input": "2024-01-08T03:07:42.580659Z",
+     "iopub.status.busy": "2024-01-08T03:07:42.579694Z",
+     "iopub.status.idle": "2024-01-08T03:07:42.979631Z",
+     "shell.execute_reply": "2024-01-08T03:07:42.975865Z",
+     "shell.execute_reply.started": "2024-01-08T03:07:42.580613Z"
     }
    },
    "outputs": [
@@ -3293,16 +3847,23 @@
     "!wget --ftp-user=anonymous -nc ftp://sidads.colorado.edu/DATASETS/seaice/polar-stereo/tools/pss25area_v3.dat ftp://sidads.colorado.edu/DATASETS/seaice/polar-stereo/tools/psn25area_v3.dat "
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We are interested in the Antarctic, but the lines for the Arctic are below and commented out."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-11T00:17:59.406056Z",
-     "iopub.status.busy": "2023-12-11T00:17:59.403692Z",
-     "iopub.status.idle": "2023-12-11T00:21:40.273734Z",
-     "shell.execute_reply": "2023-12-11T00:21:40.272403Z",
-     "shell.execute_reply.started": "2023-12-11T00:17:59.405995Z"
+     "iopub.execute_input": "2024-01-08T03:07:42.984925Z",
+     "iopub.status.busy": "2024-01-08T03:07:42.984410Z",
+     "iopub.status.idle": "2024-01-08T03:11:33.123415Z",
+     "shell.execute_reply": "2024-01-08T03:11:33.119171Z",
+     "shell.execute_reply.started": "2024-01-08T03:07:42.984872Z"
     }
    },
    "outputs": [],
@@ -3327,7 +3888,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The result is a datatree, with seperate datasets for each hemisphere"
+    "The result is a datatree, with a datasets for the relevant hemisphere"
    ]
   },
   {
@@ -3346,14 +3907,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-11T00:21:40.278975Z",
-     "iopub.status.busy": "2023-12-11T00:21:40.278691Z",
-     "iopub.status.idle": "2023-12-11T00:21:40.285323Z",
-     "shell.execute_reply": "2023-12-11T00:21:40.284189Z",
-     "shell.execute_reply.started": "2023-12-11T00:21:40.278941Z"
+     "iopub.execute_input": "2024-01-08T03:11:33.130093Z",
+     "iopub.status.busy": "2024-01-08T03:11:33.129781Z",
+     "iopub.status.idle": "2024-01-08T03:11:33.139086Z",
+     "shell.execute_reply": "2024-01-08T03:11:33.137856Z",
+     "shell.execute_reply.started": "2024-01-08T03:11:33.130063Z"
     }
    },
    "outputs": [],
@@ -3371,14 +3932,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-11T00:21:40.287957Z",
-     "iopub.status.busy": "2023-12-11T00:21:40.287015Z",
-     "iopub.status.idle": "2023-12-11T00:21:40.295736Z",
-     "shell.execute_reply": "2023-12-11T00:21:40.294636Z",
-     "shell.execute_reply.started": "2023-12-11T00:21:40.287909Z"
+     "iopub.execute_input": "2024-01-08T03:11:33.140491Z",
+     "iopub.status.busy": "2024-01-08T03:11:33.140191Z",
+     "iopub.status.idle": "2024-01-08T03:11:33.170903Z",
+     "shell.execute_reply": "2024-01-08T03:11:33.169851Z",
+     "shell.execute_reply.started": "2024-01-08T03:11:33.140463Z"
     }
    },
    "outputs": [],
@@ -3397,14 +3958,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-11T00:21:40.304187Z",
-     "iopub.status.busy": "2023-12-11T00:21:40.303259Z",
-     "iopub.status.idle": "2023-12-11T00:21:41.072155Z",
-     "shell.execute_reply": "2023-12-11T00:21:41.070727Z",
-     "shell.execute_reply.started": "2023-12-11T00:21:40.304142Z"
+     "iopub.execute_input": "2024-01-08T03:11:33.173512Z",
+     "iopub.status.busy": "2024-01-08T03:11:33.172548Z",
+     "iopub.status.idle": "2024-01-08T03:11:34.344519Z",
+     "shell.execute_reply": "2024-01-08T03:11:34.343336Z",
+     "shell.execute_reply.started": "2024-01-08T03:11:33.173465Z"
     }
    },
    "outputs": [],
@@ -3414,19 +3975,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-11T00:21:41.074041Z",
-     "iopub.status.busy": "2023-12-11T00:21:41.073652Z",
-     "iopub.status.idle": "2023-12-11T00:21:41.078981Z",
-     "shell.execute_reply": "2023-12-11T00:21:41.077810Z",
-     "shell.execute_reply.started": "2023-12-11T00:21:41.074003Z"
+     "iopub.execute_input": "2024-01-08T03:11:34.346588Z",
+     "iopub.status.busy": "2024-01-08T03:11:34.346132Z",
+     "iopub.status.idle": "2024-01-08T03:11:34.351620Z",
+     "shell.execute_reply": "2024-01-08T03:11:34.350648Z",
+     "shell.execute_reply.started": "2024-01-08T03:11:34.346543Z"
     }
    },
    "outputs": [],
    "source": [
-    "#Theres another gap which should be nan\n",
+    "#Theres another gap which should be nan in the arctic only\n",
     "#obs_area_dt['cdr_nh'].to_dataset().loc[{'time':'1984-07'}]=np.nan"
    ]
   },
@@ -3439,14 +4000,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 20,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-11T00:21:41.081869Z",
-     "iopub.status.busy": "2023-12-11T00:21:41.080837Z",
-     "iopub.status.idle": "2023-12-11T00:21:41.095718Z",
-     "shell.execute_reply": "2023-12-11T00:21:41.094577Z",
-     "shell.execute_reply.started": "2023-12-11T00:21:41.081822Z"
+     "iopub.execute_input": "2024-01-08T03:11:34.354276Z",
+     "iopub.status.busy": "2024-01-08T03:11:34.353280Z",
+     "iopub.status.idle": "2024-01-08T03:11:34.364314Z",
+     "shell.execute_reply": "2024-01-08T03:11:34.363352Z",
+     "shell.execute_reply.started": "2024-01-08T03:11:34.354233Z"
     }
    },
    "outputs": [],
@@ -3461,14 +4022,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 21,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-11T00:21:41.098195Z",
-     "iopub.status.busy": "2023-12-11T00:21:41.097372Z",
-     "iopub.status.idle": "2023-12-11T00:23:10.571063Z",
-     "shell.execute_reply": "2023-12-11T00:23:10.563689Z",
-     "shell.execute_reply.started": "2023-12-11T00:21:41.098148Z"
+     "iopub.execute_input": "2024-01-08T03:11:34.366722Z",
+     "iopub.status.busy": "2024-01-08T03:11:34.365953Z",
+     "iopub.status.idle": "2024-01-08T03:13:03.847639Z",
+     "shell.execute_reply": "2024-01-08T03:13:03.839539Z",
+     "shell.execute_reply.started": "2024-01-08T03:11:34.366680Z"
     }
    },
    "outputs": [],
@@ -3487,19 +4048,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We are going to compare the trends in the minima and maxima over time, and the climatology"
+    "We are going to compare the trends in the minima and maxima over time, and the monthly climatology"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 22,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-11T00:23:10.580762Z",
-     "iopub.status.busy": "2023-12-11T00:23:10.579515Z",
-     "iopub.status.idle": "2023-12-11T00:23:10.900272Z",
-     "shell.execute_reply": "2023-12-11T00:23:10.899225Z",
-     "shell.execute_reply.started": "2023-12-11T00:23:10.580710Z"
+     "iopub.execute_input": "2024-01-08T03:13:03.868090Z",
+     "iopub.status.busy": "2024-01-08T03:13:03.867053Z",
+     "iopub.status.idle": "2024-01-08T03:13:04.303771Z",
+     "shell.execute_reply": "2024-01-08T03:13:04.295758Z",
+     "shell.execute_reply.started": "2024-01-08T03:13:03.868059Z"
     }
    },
    "outputs": [],
@@ -3519,14 +4080,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 23,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-11T00:23:10.903018Z",
-     "iopub.status.busy": "2023-12-11T00:23:10.902778Z",
-     "iopub.status.idle": "2023-12-11T00:23:12.077225Z",
-     "shell.execute_reply": "2023-12-11T00:23:12.076476Z",
-     "shell.execute_reply.started": "2023-12-11T00:23:10.902995Z"
+     "iopub.execute_input": "2024-01-08T03:13:04.312066Z",
+     "iopub.status.busy": "2024-01-08T03:13:04.311677Z",
+     "iopub.status.idle": "2024-01-08T03:13:05.746056Z",
+     "shell.execute_reply": "2024-01-08T03:13:05.741386Z",
+     "shell.execute_reply.started": "2024-01-08T03:13:04.312031Z"
     }
    },
    "outputs": [
@@ -3563,14 +4124,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 24,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-11T00:23:12.078306Z",
-     "iopub.status.busy": "2023-12-11T00:23:12.078021Z",
-     "iopub.status.idle": "2023-12-11T00:23:12.335569Z",
-     "shell.execute_reply": "2023-12-11T00:23:12.334731Z",
-     "shell.execute_reply.started": "2023-12-11T00:23:12.078289Z"
+     "iopub.execute_input": "2024-01-08T03:13:05.755763Z",
+     "iopub.status.busy": "2024-01-08T03:13:05.752902Z",
+     "iopub.status.idle": "2024-01-08T03:13:06.370307Z",
+     "shell.execute_reply": "2024-01-08T03:13:06.369079Z",
+     "shell.execute_reply.started": "2024-01-08T03:13:05.755738Z"
     }
    },
    "outputs": [
@@ -3615,24 +4176,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 25,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-11T00:23:12.336398Z",
-     "iopub.status.busy": "2023-12-11T00:23:12.336227Z",
-     "iopub.status.idle": "2023-12-11T00:23:12.599445Z",
-     "shell.execute_reply": "2023-12-11T00:23:12.598342Z",
-     "shell.execute_reply.started": "2023-12-11T00:23:12.336382Z"
+     "iopub.execute_input": "2024-01-08T03:13:06.376238Z",
+     "iopub.status.busy": "2024-01-08T03:13:06.375993Z",
+     "iopub.status.idle": "2024-01-08T03:13:06.679097Z",
+     "shell.execute_reply": "2024-01-08T03:13:06.678346Z",
+     "shell.execute_reply.started": "2024-01-08T03:13:06.376218Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x148c31cb0970>"
+       "<matplotlib.legend.Legend at 0x152abb3f94b0>"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -3690,19 +4251,19 @@
     "\n",
     "As that data are on different grids, we need to regrid to compare the datasets\n",
     "\n",
-    "Lets simplify a little to only look at 0.25"
+    "Lets simplify a little to only look at 0.25 degree results"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 26,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-11T00:23:12.600429Z",
-     "iopub.status.busy": "2023-12-11T00:23:12.600253Z",
-     "iopub.status.idle": "2023-12-11T00:23:12.604007Z",
-     "shell.execute_reply": "2023-12-11T00:23:12.603332Z",
-     "shell.execute_reply.started": "2023-12-11T00:23:12.600412Z"
+     "iopub.execute_input": "2024-01-08T03:13:06.679954Z",
+     "iopub.status.busy": "2024-01-08T03:13:06.679743Z",
+     "iopub.status.idle": "2024-01-08T03:13:06.683861Z",
+     "shell.execute_reply": "2024-01-08T03:13:06.683164Z",
+     "shell.execute_reply.started": "2024-01-08T03:13:06.679936Z"
     }
    },
    "outputs": [],
@@ -3712,14 +4273,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 27,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-11T00:23:12.604871Z",
-     "iopub.status.busy": "2023-12-11T00:23:12.604709Z",
-     "iopub.status.idle": "2023-12-11T00:23:12.648801Z",
-     "shell.execute_reply": "2023-12-11T00:23:12.647929Z",
-     "shell.execute_reply.started": "2023-12-11T00:23:12.604856Z"
+     "iopub.execute_input": "2024-01-08T03:13:06.684666Z",
+     "iopub.status.busy": "2024-01-08T03:13:06.684506Z",
+     "iopub.status.idle": "2024-01-08T03:13:06.728035Z",
+     "shell.execute_reply": "2024-01-08T03:13:06.727210Z",
+     "shell.execute_reply.started": "2024-01-08T03:13:06.684650Z"
     }
    },
    "outputs": [
@@ -4097,18 +4658,18 @@
        "  * x                (x) float32 -3.938e+06 -3.912e+06 ... 3.912e+06 3.938e+06\n",
        "    area             (y, x) float64 444.1 445.0 445.9 ... 462.1 461.1 460.1\n",
        "Data variables:\n",
-       "    cdr_seaice_conc  (time, y, x) float32 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>datatree.DataTree</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-7873b895-b588-4892-9594-dec217400676' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7873b895-b588-4892-9594-dec217400676' class='xr-section-summary'  title='Expand/collapse section'>Groups: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%'></div></div></li><li class='xr-section-item'><input id='section-0efcfb20-8d6c-4b2b-9c5e-b00f4a9d290a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0efcfb20-8d6c-4b2b-9c5e-b00f4a9d290a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 536</li><li><span class='xr-has-index'>y</span>: 332</li><li><span class='xr-has-index'>x</span>: 316</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-804e0b9f-b280-4916-8164-a933bb299416' class='xr-section-summary-in' type='checkbox'  checked><label for='section-804e0b9f-b280-4916-8164-a933bb299416' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1978-11-01 ... 2023-06-01</div><input id='attrs-436166c7-ceeb-4251-b350-d0aba408c3eb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-436166c7-ceeb-4251-b350-d0aba408c3eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67532312-88f9-4f14-b456-0bd02818b143' class='xr-var-data-in' type='checkbox'><label for='data-67532312-88f9-4f14-b456-0bd02818b143' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>1024</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Time</dd><dt><span>actual_range :</span></dt><dd>[2.7872640e+08 1.6855776e+09]</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>ioos_category :</span></dt><dd>Time</dd><dt><span>long_name :</span></dt><dd>ANSI date</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>01-JAN-1970 00:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1978-11-01T00:00:00.000000000&#x27;, &#x27;1978-12-01T00:00:00.000000000&#x27;,\n",
+       "    cdr_seaice_conc  (time, y, x) float32 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>datatree.DataTree</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-911b49c6-caa6-4e37-9a54-020dd5239f0e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-911b49c6-caa6-4e37-9a54-020dd5239f0e' class='xr-section-summary'  title='Expand/collapse section'>Groups: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%'></div></div></li><li class='xr-section-item'><input id='section-9fef5a7f-a96a-4722-b06b-aecf6e614aa8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9fef5a7f-a96a-4722-b06b-aecf6e614aa8' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 536</li><li><span class='xr-has-index'>y</span>: 332</li><li><span class='xr-has-index'>x</span>: 316</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-15d46b2d-7a72-4732-989c-032cf73654a0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-15d46b2d-7a72-4732-989c-032cf73654a0' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1978-11-01 ... 2023-06-01</div><input id='attrs-190f070b-7a80-49ee-9ad0-c06ffab3dd58' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-190f070b-7a80-49ee-9ad0-c06ffab3dd58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9055ae4-33de-4ed0-8776-93520b8f84fe' class='xr-var-data-in' type='checkbox'><label for='data-d9055ae4-33de-4ed0-8776-93520b8f84fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>1024</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Time</dd><dt><span>actual_range :</span></dt><dd>[2.7872640e+08 1.6855776e+09]</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>ioos_category :</span></dt><dd>Time</dd><dt><span>long_name :</span></dt><dd>ANSI date</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>01-JAN-1970 00:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1978-11-01T00:00:00.000000000&#x27;, &#x27;1978-12-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;1979-01-01T00:00:00.000000000&#x27;, ..., &#x27;2023-04-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;2023-05-01T00:00:00.000000000&#x27;, &#x27;2023-06-01T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>4.338e+06 4.312e+06 ... -3.938e+06</div><input id='attrs-4cf4085e-2eed-447d-a233-cbd646815a85' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4cf4085e-2eed-447d-a233-cbd646815a85' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65e02f95-6b4e-4609-82f5-661542f198f1' class='xr-var-data-in' type='checkbox'><label for='data-65e02f95-6b4e-4609-82f5-661542f198f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>332</dd><dt><span>actual_range :</span></dt><dd>[-3937500.  4337500.]</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>ioos_category :</span></dt><dd>Location</dd><dt><span>long_name :</span></dt><dd>projection_grid_y_centers</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>meters</dd><dt><span>valid_range :</span></dt><dd>[-3950000.  4350000.]</dd></dl></div><div class='xr-var-data'><pre>array([ 4337500.,  4312500.,  4287500., ..., -3887500., -3912500., -3937500.],\n",
-       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-3.938e+06 -3.912e+06 ... 3.938e+06</div><input id='attrs-b3158920-20fc-4cf1-9eb9-653ceb0faa0c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3158920-20fc-4cf1-9eb9-653ceb0faa0c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-801180d9-1773-41e9-9d3f-3ffdf75a3e6f' class='xr-var-data-in' type='checkbox'><label for='data-801180d9-1773-41e9-9d3f-3ffdf75a3e6f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>316</dd><dt><span>actual_range :</span></dt><dd>[-3937500.  3937500.]</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>ioos_category :</span></dt><dd>Location</dd><dt><span>long_name :</span></dt><dd>projection_grid_x_centers</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>meters</dd><dt><span>valid_range :</span></dt><dd>[-3950000.  3950000.]</dd></dl></div><div class='xr-var-data'><pre>array([-3937500., -3912500., -3887500., ...,  3887500.,  3912500.,  3937500.],\n",
-       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>area</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>444.1 445.0 445.9 ... 461.1 460.1</div><input id='attrs-baf2a960-67c3-46b0-a23c-0a732cc505b3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-baf2a960-67c3-46b0-a23c-0a732cc505b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1be9e16a-7309-4fac-ae9a-4960051bb623' class='xr-var-data-in' type='checkbox'><label for='data-1be9e16a-7309-4fac-ae9a-4960051bb623' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[444.053, 444.983, 445.91 , ..., 445.91 , 444.983, 444.053],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>4.338e+06 4.312e+06 ... -3.938e+06</div><input id='attrs-19c892d1-a2d6-44c3-949c-b397604e29f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-19c892d1-a2d6-44c3-949c-b397604e29f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0bce4eb7-7fed-407b-a8ce-d713e983323d' class='xr-var-data-in' type='checkbox'><label for='data-0bce4eb7-7fed-407b-a8ce-d713e983323d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>332</dd><dt><span>actual_range :</span></dt><dd>[-3937500.  4337500.]</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>ioos_category :</span></dt><dd>Location</dd><dt><span>long_name :</span></dt><dd>projection_grid_y_centers</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>meters</dd><dt><span>valid_range :</span></dt><dd>[-3950000.  4350000.]</dd></dl></div><div class='xr-var-data'><pre>array([ 4337500.,  4312500.,  4287500., ..., -3887500., -3912500., -3937500.],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-3.938e+06 -3.912e+06 ... 3.938e+06</div><input id='attrs-4330239b-7ceb-4d08-8940-cd6df42fef48' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4330239b-7ceb-4d08-8940-cd6df42fef48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-caf6394e-123b-43ec-b139-26fe8249ee4e' class='xr-var-data-in' type='checkbox'><label for='data-caf6394e-123b-43ec-b139-26fe8249ee4e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>316</dd><dt><span>actual_range :</span></dt><dd>[-3937500.  3937500.]</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>ioos_category :</span></dt><dd>Location</dd><dt><span>long_name :</span></dt><dd>projection_grid_x_centers</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>meters</dd><dt><span>valid_range :</span></dt><dd>[-3950000.  3950000.]</dd></dl></div><div class='xr-var-data'><pre>array([-3937500., -3912500., -3887500., ...,  3887500.,  3912500.,  3937500.],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>area</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>444.1 445.0 445.9 ... 461.1 460.1</div><input id='attrs-5a9f4884-39c2-46d4-9910-cea4ecd16670' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5a9f4884-39c2-46d4-9910-cea4ecd16670' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-acc70716-0b6a-4e80-bcca-7706c2c5af87' class='xr-var-data-in' type='checkbox'><label for='data-acc70716-0b6a-4e80-bcca-7706c2c5af87' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[444.053, 444.983, 445.91 , ..., 445.91 , 444.983, 444.053],\n",
        "       [445.078, 446.011, 446.942, ..., 446.942, 446.011, 445.078],\n",
        "       [446.101, 447.037, 447.971, ..., 447.971, 447.037, 446.101],\n",
        "       ...,\n",
        "       [462.098, 463.085, 464.069, ..., 464.069, 463.085, 462.098],\n",
        "       [461.12 , 462.104, 463.085, ..., 463.085, 462.104, 461.12 ],\n",
-       "       [460.139, 461.12 , 462.098, ..., 462.098, 461.12 , 460.139]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8baa8b77-e6a8-4b78-aeb4-e9c8be1ae7ce' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8baa8b77-e6a8-4b78-aeb4-e9c8be1ae7ce' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>cdr_seaice_conc</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-e80de25c-353e-4cae-b91a-3b561260e4fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e80de25c-353e-4cae-b91a-3b561260e4fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36f31937-28d5-46f7-9cdf-f8dc7c66aa54' class='xr-var-data-in' type='checkbox'><label for='data-36f31937-28d5-46f7-9cdf-f8dc7c66aa54' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>ancillary_variables :</span></dt><dd>stdev_of_cdr_seaice_conc_monthly qa_of_cdr_seaice_conc_monthly</dd><dt><span>colorBarMaximum :</span></dt><dd>1.0</dd><dt><span>colorBarMinimum :</span></dt><dd>0.0</dd><dt><span>colorBarPalette :</span></dt><dd>KT_ice</dd><dt><span>datum :</span></dt><dd>+ellps=urn:ogc:def:crs:EPSG::4326</dd><dt><span>flag_meanings :</span></dt><dd>pole_hole lakes coastal land_mask missing_data</dd><dt><span>flag_values :</span></dt><dd>[-5 -4 -3 -2 -1]</dd><dt><span>ioos_category :</span></dt><dd>Ice Distribution</dd><dt><span>long_name :</span></dt><dd>NOAA/NSIDC Climate Data Record of Passive Microwave Monthly Southern Hemisphere Sea Ice Concentration</dd><dt><span>references :</span></dt><dd>https://nsidc.org/data/g02202/versions/4/</dd><dt><span>standard_name :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_range :</span></dt><dd>[0. 1.]</dd></dl></div><div class='xr-var-data'><pre>array([[[0., 0., 0., ..., 0., 0., 0.],\n",
+       "       [460.139, 461.12 , 462.098, ..., 462.098, 461.12 , 460.139]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-91f0907a-b947-4325-9796-08b046f8d7b2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-91f0907a-b947-4325-9796-08b046f8d7b2' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>cdr_seaice_conc</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-85dfd0b0-8e51-441a-aeb8-47d442a5f7f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-85dfd0b0-8e51-441a-aeb8-47d442a5f7f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ffdabc4-b91e-4d5d-933f-dc2d3564f04d' class='xr-var-data-in' type='checkbox'><label for='data-5ffdabc4-b91e-4d5d-933f-dc2d3564f04d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>ancillary_variables :</span></dt><dd>stdev_of_cdr_seaice_conc_monthly qa_of_cdr_seaice_conc_monthly</dd><dt><span>colorBarMaximum :</span></dt><dd>1.0</dd><dt><span>colorBarMinimum :</span></dt><dd>0.0</dd><dt><span>colorBarPalette :</span></dt><dd>KT_ice</dd><dt><span>datum :</span></dt><dd>+ellps=urn:ogc:def:crs:EPSG::4326</dd><dt><span>flag_meanings :</span></dt><dd>pole_hole lakes coastal land_mask missing_data</dd><dt><span>flag_values :</span></dt><dd>[-5 -4 -3 -2 -1]</dd><dt><span>ioos_category :</span></dt><dd>Ice Distribution</dd><dt><span>long_name :</span></dt><dd>NOAA/NSIDC Climate Data Record of Passive Microwave Monthly Southern Hemisphere Sea Ice Concentration</dd><dt><span>references :</span></dt><dd>https://nsidc.org/data/g02202/versions/4/</dd><dt><span>standard_name :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_range :</span></dt><dd>[0. 1.]</dd></dl></div><div class='xr-var-data'><pre>array([[[0., 0., 0., ..., 0., 0., 0.],\n",
        "        [0., 0., 0., ..., 0., 0., 0.],\n",
        "        [0., 0., 0., ..., 0., 0., 0.],\n",
        "        ...,\n",
@@ -4148,7 +4709,7 @@
        "        ...,\n",
        "        [0., 0., 0., ..., 0., 0., 0.],\n",
        "        [0., 0., 0., ..., 0., 0., 0.],\n",
-       "        [0., 0., 0., ..., 0., 0., 0.]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-066660ba-6824-4983-a243-2c92f4b065e0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-066660ba-6824-4983-a243-2c92f4b065e0' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "        [0., 0., 0., ..., 0., 0., 0.]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-59338509-6ed7-4f37-b409-ed58717c7c76' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-59338509-6ed7-4f37-b409-ed58717c7c76' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "DataTree('cdr_sh', parent=\"None\")\n",
@@ -4162,7 +4723,7 @@
        "        cdr_seaice_conc  (time, y, x) float32 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4188,14 +4749,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 28,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-11T00:23:12.650117Z",
-     "iopub.status.busy": "2023-12-11T00:23:12.649768Z",
-     "iopub.status.idle": "2023-12-11T00:23:13.020585Z",
-     "shell.execute_reply": "2023-12-11T00:23:13.019084Z",
-     "shell.execute_reply.started": "2023-12-11T00:23:12.650091Z"
+     "iopub.execute_input": "2024-01-08T03:13:06.729255Z",
+     "iopub.status.busy": "2024-01-08T03:13:06.728998Z",
+     "iopub.status.idle": "2024-01-08T03:13:07.044700Z",
+     "shell.execute_reply": "2024-01-08T03:13:07.043099Z",
+     "shell.execute_reply.started": "2024-01-08T03:13:06.729231Z"
     }
    },
    "outputs": [
@@ -4214,14 +4775,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 29,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-11T00:23:13.024733Z",
-     "iopub.status.busy": "2023-12-11T00:23:13.024079Z",
-     "iopub.status.idle": "2023-12-11T00:23:13.099519Z",
-     "shell.execute_reply": "2023-12-11T00:23:13.098740Z",
-     "shell.execute_reply.started": "2023-12-11T00:23:13.024680Z"
+     "iopub.execute_input": "2024-01-08T03:13:07.049020Z",
+     "iopub.status.busy": "2024-01-08T03:13:07.048105Z",
+     "iopub.status.idle": "2024-01-08T03:13:07.129262Z",
+     "shell.execute_reply": "2024-01-08T03:13:07.128402Z",
+     "shell.execute_reply.started": "2024-01-08T03:13:07.048961Z"
     },
     "scrolled": true
    },
@@ -4239,14 +4800,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 30,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-11T00:23:13.100733Z",
-     "iopub.status.busy": "2023-12-11T00:23:13.100561Z",
-     "iopub.status.idle": "2023-12-11T00:23:31.520172Z",
-     "shell.execute_reply": "2023-12-11T00:23:31.518607Z",
-     "shell.execute_reply.started": "2023-12-11T00:23:13.100717Z"
+     "iopub.execute_input": "2024-01-08T03:13:07.130295Z",
+     "iopub.status.busy": "2024-01-08T03:13:07.130085Z",
+     "iopub.status.idle": "2024-01-08T03:13:31.730021Z",
+     "shell.execute_reply": "2024-01-08T03:13:31.724427Z",
+     "shell.execute_reply.started": "2024-01-08T03:13:07.130276Z"
     }
    },
    "outputs": [],
@@ -4262,14 +4823,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 31,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-11T00:23:31.527651Z",
-     "iopub.status.busy": "2023-12-11T00:23:31.527177Z",
-     "iopub.status.idle": "2023-12-11T00:24:37.575311Z",
-     "shell.execute_reply": "2023-12-11T00:24:37.571354Z",
-     "shell.execute_reply.started": "2023-12-11T00:23:31.527623Z"
+     "iopub.execute_input": "2024-01-08T03:13:31.738144Z",
+     "iopub.status.busy": "2024-01-08T03:13:31.737851Z",
+     "iopub.status.idle": "2024-01-08T03:14:27.330591Z",
+     "shell.execute_reply": "2024-01-08T03:14:27.323910Z",
+     "shell.execute_reply.started": "2024-01-08T03:13:31.738116Z"
     }
    },
    "outputs": [],
@@ -4279,7 +4840,7 @@
     "\n",
     "for iG in groups:\n",
     "    aice_sh_3976_ds[iG]=regridder_ACCESSOM2_025deg_sh(\n",
-    "        si_dt[iG].ds.aice_m\n",
+    "        si_dt[iG].ds.aice_m.copy()\n",
     "    ).load()\n",
     "    aice_sh_diff_ds[iG]=aice_sh_3976_ds[iG]-cdr_dt['cdr_sh'].ds.cdr_seaice_conc.load()"
    ]
@@ -4293,14 +4854,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 32,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-11T00:24:48.229582Z",
-     "iopub.status.busy": "2023-12-11T00:24:48.228372Z",
-     "iopub.status.idle": "2023-12-11T00:24:50.017347Z",
-     "shell.execute_reply": "2023-12-11T00:24:50.016509Z",
-     "shell.execute_reply.started": "2023-12-11T00:24:48.229528Z"
+     "iopub.execute_input": "2024-01-08T03:14:27.339589Z",
+     "iopub.status.busy": "2024-01-08T03:14:27.339223Z",
+     "iopub.status.idle": "2024-01-08T03:14:28.459646Z",
+     "shell.execute_reply": "2024-01-08T03:14:28.458472Z",
+     "shell.execute_reply.started": "2024-01-08T03:14:27.339556Z"
     }
    },
    "outputs": [
@@ -4372,16 +4933,208 @@
     "We see that OM2 under-represents sea ice in Summer, particularly in the Weddell Sea. In Winter, trends are less clear, although ERA5 forced sea ice concentration is too high at the northern boundary."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Sea Ice Volume"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can calculate volume in much the same way as area, except _vicen_ is volume (per unit area) in each of 5 thickness classes, so we can sum this for the volume irrespective of thickness. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-08T03:14:28.462372Z",
+     "iopub.status.busy": "2024-01-08T03:14:28.461324Z",
+     "iopub.status.idle": "2024-01-08T03:14:28.469906Z",
+     "shell.execute_reply": "2024-01-08T03:14:28.468766Z",
+     "shell.execute_reply.started": "2024-01-08T03:14:28.462322Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "@map_over_subtree\n",
+    "def sea_ice_vol_model_sh(ds):\n",
+    "    vice_m=ds.vicen_m.sum('nc').sel(yt_ocean=slice(-90,0))   \n",
+    "    \n",
+    "    siv_total_da=(vice_m*ds.area_t).where(~np.isnan(ds.area_t)).cf.sum(['longitude','latitude'])\n",
+    "    \n",
+    "    return siv_total_da.to_dataset(name='si_vol').load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-08T03:14:28.473906Z",
+     "iopub.status.busy": "2024-01-08T03:14:28.471365Z",
+     "iopub.status.idle": "2024-01-08T03:17:12.546394Z",
+     "shell.execute_reply": "2024-01-08T03:17:12.542999Z",
+     "shell.execute_reply.started": "2024-01-08T03:14:28.473860Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "si_vol_dt=sea_ice_vol_model_sh(si_dt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-08T03:17:12.563611Z",
+     "iopub.status.busy": "2024-01-08T03:17:12.563340Z",
+     "iopub.status.idle": "2024-01-08T03:17:13.001535Z",
+     "shell.execute_reply": "2024-01-08T03:17:13.000419Z",
+     "shell.execute_reply.started": "2024-01-08T03:17:12.563590Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "@map_over_subtree\n",
+    "def min_and_max(ds):\n",
+    "    def min_and_max_year(da):\n",
+    "        result = xr.Dataset()\n",
+    "        result['min']=da.min()\n",
+    "        result['max']=da.max()\n",
+    "        return result\n",
+    "    annual_min_max_ds=ds.si_vol.groupby('time.year').apply(min_and_max_year)\n",
+    "    return annual_min_max_ds\n",
+    "    \n",
+    "model_min_max_dt=min_and_max(si_vol_dt)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 36,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-11T00:24:56.014716Z",
-     "iopub.status.busy": "2023-12-11T00:24:56.013726Z",
-     "iopub.status.idle": "2023-12-11T00:24:59.394830Z",
-     "shell.execute_reply": "2023-12-11T00:24:59.393645Z",
-     "shell.execute_reply.started": "2023-12-11T00:24:56.014661Z"
+     "iopub.execute_input": "2024-01-08T03:17:13.003817Z",
+     "iopub.status.busy": "2024-01-08T03:17:13.002983Z",
+     "iopub.status.idle": "2024-01-08T03:17:13.330980Z",
+     "shell.execute_reply": "2024-01-08T03:17:13.330257Z",
+     "shell.execute_reply.started": "2024-01-08T03:17:13.003783Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for iGroup in model_min_max_dt.groups[1:]:\n",
+    "    ds=model_min_max_dt[iGroup].ds\n",
+    "    ds['min'].plot(label=iGroup[1:])\n",
+    "\n",
+    "plt.title('Trends in Sea-Ice Minimum Volume')\n",
+    "plt.ylabel('Sea-Ice Volume (m3)')\n",
+    "\n",
+    "_ = plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that all models have sea ice area which is too low in summer. Model runs forced by JRA have more variability than those forced by ERA5 and are slightly closer to the measured sea ice area from observations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-08T03:17:13.332067Z",
+     "iopub.status.busy": "2024-01-08T03:17:13.331869Z",
+     "iopub.status.idle": "2024-01-08T03:17:13.574359Z",
+     "shell.execute_reply": "2024-01-08T03:17:13.573654Z",
+     "shell.execute_reply.started": "2024-01-08T03:17:13.332049Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for iGroup in model_min_max_dt.groups[1:]:\n",
+    "    ds=model_min_max_dt[iGroup].ds\n",
+    "    ds['max'].plot(label=iGroup[1:])\n",
+    "\n",
+    "plt.title('Trends in Sea-Ice Maximum Volume')\n",
+    "plt.ylabel('Sea-Ice Volume (m3)')\n",
+    "\n",
+    "_ = plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-08T03:17:13.575361Z",
+     "iopub.status.busy": "2024-01-08T03:17:13.575174Z",
+     "iopub.status.idle": "2024-01-08T03:17:13.822577Z",
+     "shell.execute_reply": "2024-01-08T03:17:13.821751Z",
+     "shell.execute_reply.started": "2024-01-08T03:17:13.575344Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for iGroup in si_vol_dt.groups[1:]:\n",
+    "    si_vol_dt[iGroup].ds.si_vol.groupby('time.month').mean('time').plot(label=iGroup[1:])\n",
+    "\n",
+    "plt.title('Climatology of Sea-Ice Volume')\n",
+    "plt.ylabel('Sea-Ice Volume (m3)')\n",
+    "\n",
+    "_ = plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-08T03:17:13.823808Z",
+     "iopub.status.busy": "2024-01-08T03:17:13.823569Z",
+     "iopub.status.idle": "2024-01-08T03:17:16.960918Z",
+     "shell.execute_reply": "2024-01-08T03:17:16.959964Z",
+     "shell.execute_reply.started": "2024-01-08T03:17:13.823786Z"
     }
    },
    "outputs": [
@@ -4394,16 +5147,6 @@
       "0.3.0\n",
       "0.3.0\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2023-12-11 11:24:59,223 - distributed.nanny - WARNING - Worker process still alive after 3.199998474121094 seconds, killing\n",
-      "2023-12-11 11:24:59,227 - distributed.nanny - WARNING - Worker process still alive after 3.199998474121094 seconds, killing\n",
-      "2023-12-11 11:24:59,228 - distributed.nanny - WARNING - Worker process still alive after 3.1999992370605472 seconds, killing\n",
-      "2023-12-11 11:24:59,229 - distributed.nanny - WARNING - Worker process still alive after 3.1999992370605472 seconds, killing\n"
-     ]
     }
    ],
    "source": [

From 113e0ae56fae65df214be5115da2ce9c654f6810 Mon Sep 17 00:00:00 2001
From: anton-climate <anton.steketee@anu.edu.au>
Date: Wed, 10 Jan 2024 11:42:56 +1100
Subject: [PATCH 4/4] Review comments and tidyup

---
 .../SeaIce_Obs_Model_Compare.ipynb            | 3376 ++++++++---------
 1 file changed, 1638 insertions(+), 1738 deletions(-)

diff --git a/DocumentedExamples/SeaIce_Obs_Model_Compare.ipynb b/DocumentedExamples/SeaIce_Obs_Model_Compare.ipynb
index 4993c161..b1fe1542 100644
--- a/DocumentedExamples/SeaIce_Obs_Model_Compare.ipynb
+++ b/DocumentedExamples/SeaIce_Obs_Model_Compare.ipynb
@@ -24,7 +24,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "These are the ACCESS-OM2 runs we are going to use, we can compare results from prototype runs forced with ERA5 against normal runs using JRA55do, as [described on the ACCESS_HIVE](https://forum.access-hive.org.au/t/era-5-forced-access-om2-simulations/1103/5). To compare against the observational datasets, we use IAF (Inter-Annual Forcing)."
+    "These are the ACCESS-OM2 runs we are going to use, we can compare results from prototype runs forced with ERA5 against normal runs using JRA55do, as [described on the ACCESS_HIVE](https://forum.access-hive.org.au/t/era-5-forced-access-om2-simulations/1103/5). To compare against the observational datasets, we use IAF (Inter-Annual Forcing). _N.B._ The JRA55do runs used here a slightly different to the typical (e.g. _025deg_jra55_iaf_omip2_cycle6_) in the model version used and the timeframes evaluated."
    ]
   },
   {
@@ -32,20 +32,20 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-08T03:04:29.465533Z",
-     "iopub.status.busy": "2024-01-08T03:04:29.465106Z",
-     "iopub.status.idle": "2024-01-08T03:04:29.479853Z",
-     "shell.execute_reply": "2024-01-08T03:04:29.478557Z",
-     "shell.execute_reply.started": "2024-01-08T03:04:29.465479Z"
+     "iopub.execute_input": "2024-01-10T00:30:27.593974Z",
+     "iopub.status.busy": "2024-01-10T00:30:27.593168Z",
+     "iopub.status.idle": "2024-01-10T00:30:27.607009Z",
+     "shell.execute_reply": "2024-01-10T00:30:27.605954Z",
+     "shell.execute_reply.started": "2024-01-10T00:30:27.593924Z"
     }
    },
    "outputs": [],
    "source": [
-    "RUNS={\n",
-    "    '025deg_era5':['025deg_era5_iaf'], #(our name: run name(s))\n",
-    "    '025deg_jra55': ['025deg_jra55_iaf_era5comparison'],\n",
-    "    '1deg_era5':['1deg_era5_iaf'],\n",
-    "    '1deg_jra55':['1deg_jra55_iaf_era5comparison']\n",
+    "RUNS = {\n",
+    "    \"025deg_era5\": [\"025deg_era5_iaf\"],  # (our name: run name(s))\n",
+    "    \"025deg_jra55\": [\"025deg_jra55_iaf_era5comparison\"],\n",
+    "    \"1deg_era5\": [\"1deg_era5_iaf\"],\n",
+    "    \"1deg_jra55\": [\"1deg_jra55_iaf_era5comparison\"],\n",
     "}"
    ]
   },
@@ -61,17 +61,17 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-08T03:04:29.482723Z",
-     "iopub.status.busy": "2024-01-08T03:04:29.482042Z",
-     "iopub.status.idle": "2024-01-08T03:04:29.492234Z",
-     "shell.execute_reply": "2024-01-08T03:04:29.491211Z",
-     "shell.execute_reply.started": "2024-01-08T03:04:29.482678Z"
+     "iopub.execute_input": "2024-01-10T00:30:27.609801Z",
+     "iopub.status.busy": "2024-01-10T00:30:27.609154Z",
+     "iopub.status.idle": "2024-01-10T00:30:27.643996Z",
+     "shell.execute_reply": "2024-01-10T00:30:27.643064Z",
+     "shell.execute_reply.started": "2024-01-10T00:30:27.609759Z"
     }
    },
    "outputs": [],
    "source": [
-    "VARS=['aice_m', 'vicen_m'] # ice area fraction or sea ice concentration\n",
-    "VARS_2D=['area_t','geolat_t','geolon_t']"
+    "VARS = [\"aice_m\", \"hi_m\" ]  # ice area fraction or sea ice concentration, ice thickness averaged by grid cell area\n",
+    "VARS_2D = [\"area_t\", \"geolat_t\", \"geolon_t\"]"
    ]
   },
   {
@@ -85,7 +85,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Sea Ice concentration is measured through passice microwave remote sensing. We are going to used the NSIDC CDR Dataset (descibed at [nsidc.org](https://nsidc.org/data/g02202/versions/4))"
+    "Sea Ice concentration is measured through passive microwave remote sensing. We are going to use the NSIDC CDR Dataset (described at [nsidc.org](https://nsidc.org/data/g02202/versions/4))"
    ]
   },
   {
@@ -93,27 +93,25 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-08T03:04:29.493997Z",
-     "iopub.status.busy": "2024-01-08T03:04:29.493577Z",
-     "iopub.status.idle": "2024-01-08T03:04:29.501149Z",
-     "shell.execute_reply": "2024-01-08T03:04:29.500096Z",
-     "shell.execute_reply.started": "2024-01-08T03:04:29.493953Z"
+     "iopub.execute_input": "2024-01-10T00:30:27.645673Z",
+     "iopub.status.busy": "2024-01-10T00:30:27.645259Z",
+     "iopub.status.idle": "2024-01-10T00:30:27.653890Z",
+     "shell.execute_reply": "2024-01-10T00:30:27.652913Z",
+     "shell.execute_reply.started": "2024-01-10T00:30:27.645627Z"
     }
    },
    "outputs": [],
    "source": [
-    "ST='1979' #start date\n",
-    "ET='2022' #end date\n",
-    "OBS_TIME_SLICE=slice(ST,ET)\n",
-    "sh_obs_url='https://polarwatch.noaa.gov/erddap/griddap/nsidcG02202v4shmday'\n",
-    "nh_obs_url='https://polarwatch.noaa.gov/erddap/griddap/nsidcG02202v4nhmday'"
+    "OBS_TIME_SLICE = slice(\"1979\", \"2022\")\n",
+    "sh_obs_url = \"https://polarwatch.noaa.gov/erddap/griddap/nsidcG02202v4shmday\"\n",
+    "nh_obs_url = \"https://polarwatch.noaa.gov/erddap/griddap/nsidcG02202v4nhmday\""
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Load packages:"
+    "**Load depenencies:**"
    ]
   },
   {
@@ -121,17 +119,17 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-08T03:04:29.503170Z",
-     "iopub.status.busy": "2024-01-08T03:04:29.502560Z",
-     "iopub.status.idle": "2024-01-08T03:04:46.875562Z",
-     "shell.execute_reply": "2024-01-08T03:04:46.871803Z",
-     "shell.execute_reply.started": "2024-01-08T03:04:29.503125Z"
+     "iopub.execute_input": "2024-01-10T00:30:27.656834Z",
+     "iopub.status.busy": "2024-01-10T00:30:27.656408Z",
+     "iopub.status.idle": "2024-01-10T00:30:52.074336Z",
+     "shell.execute_reply": "2024-01-10T00:30:52.069367Z",
+     "shell.execute_reply.started": "2024-01-10T00:30:27.656789Z"
     }
    },
    "outputs": [],
    "source": [
     "from intake import cat\n",
-    "from datatree import DataTree,map_over_subtree\n",
+    "from datatree import DataTree, map_over_subtree\n",
     "\n",
     "from dask.distributed import Client\n",
     "\n",
@@ -141,7 +139,7 @@
     "import cf_xarray as cfxr\n",
     "import xesmf\n",
     "\n",
-    "#plotting\n",
+    "# plotting\n",
     "import matplotlib.pyplot as plt\n",
     "import cartopy.crs as ccrs\n",
     "import cmocean.cm as cmo\n",
@@ -152,7 +150,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Start a dask client"
+    "A standard way to calculate climatologies. (We start in 1991 as earlier decades are influenced by model spin-up for 0.25deg runs which only start in 1980.)"
    ]
   },
   {
@@ -160,16 +158,27 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-08T03:04:46.882392Z",
-     "iopub.status.busy": "2024-01-08T03:04:46.881957Z",
-     "iopub.status.idle": "2024-01-08T03:04:50.450062Z",
-     "shell.execute_reply": "2024-01-08T03:04:50.448558Z",
-     "shell.execute_reply.started": "2024-01-08T03:04:46.882373Z"
+     "iopub.execute_input": "2024-01-10T00:30:52.085307Z",
+     "iopub.status.busy": "2024-01-10T00:30:52.084882Z",
+     "iopub.status.idle": "2024-01-10T00:30:52.089501Z",
+     "shell.execute_reply": "2024-01-10T00:30:52.088835Z",
+     "shell.execute_reply.started": "2024-01-10T00:30:52.085287Z"
     }
    },
    "outputs": [],
    "source": [
-    "client = Client()"
+    "CLIMAT_TIME_SLICE = slice(\"1991\", \"2020\")\n",
+    "\n",
+    "\n",
+    "def climatology(ds):\n",
+    "    return ds.sel(time=CLIMAT_TIME_SLICE).groupby(\"time.month\").mean(\"time\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Start a dask client"
    ]
   },
   {
@@ -177,11 +186,28 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-08T03:04:50.453447Z",
-     "iopub.status.busy": "2024-01-08T03:04:50.452942Z",
-     "iopub.status.idle": "2024-01-08T03:04:50.544152Z",
-     "shell.execute_reply": "2024-01-08T03:04:50.542926Z",
-     "shell.execute_reply.started": "2024-01-08T03:04:50.453399Z"
+     "iopub.execute_input": "2024-01-10T00:30:52.090280Z",
+     "iopub.status.busy": "2024-01-10T00:30:52.090128Z",
+     "iopub.status.idle": "2024-01-10T00:30:57.155880Z",
+     "shell.execute_reply": "2024-01-10T00:30:57.153914Z",
+     "shell.execute_reply.started": "2024-01-10T00:30:52.090264Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "client = Client(threads_per_worker=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-10T00:30:57.163403Z",
+     "iopub.status.busy": "2024-01-10T00:30:57.163074Z",
+     "iopub.status.idle": "2024-01-10T00:30:57.307451Z",
+     "shell.execute_reply": "2024-01-10T00:30:57.306735Z",
+     "shell.execute_reply.started": "2024-01-10T00:30:57.163367Z"
     }
    },
    "outputs": [
@@ -192,7 +218,7 @@
        "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
        "    <div style=\"margin-left: 48px;\">\n",
        "        <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
-       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-adfcdc04-add2-11ee-975c-00000183fe80</p>\n",
+       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-82739af4-af4f-11ee-a8ab-00000087fe80</p>\n",
        "        <table style=\"width: 100%; text-align: left;\">\n",
        "\n",
        "        <tr>\n",
@@ -227,14 +253,14 @@
        "    </div>\n",
        "    <div style=\"margin-left: 48px;\">\n",
        "        <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">LocalCluster</h3>\n",
-       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">5d0a3821</p>\n",
+       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">368ce898</p>\n",
        "        <table style=\"width: 100%; text-align: left;\">\n",
        "            <tr>\n",
        "                <td style=\"text-align: left;\">\n",
        "                    <strong>Dashboard:</strong> <a href=\"/proxy/8787/status\" target=\"_blank\">/proxy/8787/status</a>\n",
        "                </td>\n",
        "                <td style=\"text-align: left;\">\n",
-       "                    <strong>Workers:</strong> 4\n",
+       "                    <strong>Workers:</strong> 12\n",
        "                </td>\n",
        "            </tr>\n",
        "            <tr>\n",
@@ -264,14 +290,14 @@
        "        <div style=\"width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;\"> </div>\n",
        "        <div style=\"margin-left: 48px;\">\n",
        "            <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n",
-       "            <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-ddd7101b-1410-4637-9589-cba912a601a9</p>\n",
+       "            <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-5e767eb4-9bb1-4909-9836-f71918916db3</p>\n",
        "            <table style=\"width: 100%; text-align: left;\">\n",
        "                <tr>\n",
        "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Comm:</strong> tcp://127.0.0.1:40921\n",
+       "                        <strong>Comm:</strong> tcp://127.0.0.1:36437\n",
        "                    </td>\n",
        "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Workers:</strong> 4\n",
+       "                        <strong>Workers:</strong> 12\n",
        "                    </td>\n",
        "                </tr>\n",
        "                <tr>\n",
@@ -310,29 +336,29 @@
        "                <table style=\"width: 100%; text-align: left;\">\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:34723\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:34807\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 3\n",
+       "                            <strong>Total threads: </strong> 1\n",
        "                        </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"/proxy/42735/status\" target=\"_blank\">/proxy/42735/status</a>\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/45181/status\" target=\"_blank\">/proxy/45181/status</a>\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 11.50 GiB\n",
+       "                            <strong>Memory: </strong> 3.83 GiB\n",
        "                        </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:37735\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:35097\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\"></td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> /jobfs/105674656.gadi-pbs/dask-scratch-space/worker-fd3ezkbc\n",
+       "                            <strong>Local directory: </strong> /jobfs/105828095.gadi-pbs/dask-scratch-space/worker-hss2qp4d\n",
        "                        </td>\n",
        "                    </tr>\n",
        "\n",
@@ -355,29 +381,29 @@
        "                <table style=\"width: 100%; text-align: left;\">\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:45459\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:35411\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 3\n",
+       "                            <strong>Total threads: </strong> 1\n",
        "                        </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"/proxy/39269/status\" target=\"_blank\">/proxy/39269/status</a>\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/44133/status\" target=\"_blank\">/proxy/44133/status</a>\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 11.50 GiB\n",
+       "                            <strong>Memory: </strong> 3.83 GiB\n",
        "                        </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:39831\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:35507\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\"></td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> /jobfs/105674656.gadi-pbs/dask-scratch-space/worker-s9itsvld\n",
+       "                            <strong>Local directory: </strong> /jobfs/105828095.gadi-pbs/dask-scratch-space/worker-jfi7ibr0\n",
        "                        </td>\n",
        "                    </tr>\n",
        "\n",
@@ -400,29 +426,29 @@
        "                <table style=\"width: 100%; text-align: left;\">\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:41387\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:45509\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 3\n",
+       "                            <strong>Total threads: </strong> 1\n",
        "                        </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"/proxy/41653/status\" target=\"_blank\">/proxy/41653/status</a>\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/44183/status\" target=\"_blank\">/proxy/44183/status</a>\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 11.50 GiB\n",
+       "                            <strong>Memory: </strong> 3.83 GiB\n",
        "                        </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:33555\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:32881\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\"></td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> /jobfs/105674656.gadi-pbs/dask-scratch-space/worker-idm357v8\n",
+       "                            <strong>Local directory: </strong> /jobfs/105828095.gadi-pbs/dask-scratch-space/worker-wteqer_y\n",
        "                        </td>\n",
        "                    </tr>\n",
        "\n",
@@ -445,29 +471,29 @@
        "                <table style=\"width: 100%; text-align: left;\">\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:45141\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:45305\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 3\n",
+       "                            <strong>Total threads: </strong> 1\n",
        "                        </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"/proxy/39915/status\" target=\"_blank\">/proxy/39915/status</a>\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/43281/status\" target=\"_blank\">/proxy/43281/status</a>\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 11.50 GiB\n",
+       "                            <strong>Memory: </strong> 3.83 GiB\n",
        "                        </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:36921\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:42787\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\"></td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> /jobfs/105674656.gadi-pbs/dask-scratch-space/worker-wcjlbs40\n",
+       "                            <strong>Local directory: </strong> /jobfs/105828095.gadi-pbs/dask-scratch-space/worker-qmj97pzg\n",
        "                        </td>\n",
        "                    </tr>\n",
        "\n",
@@ -480,394 +506,783 @@
        "            </div>\n",
        "        </div>\n",
        "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 4</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:32899\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/35169/status\" target=\"_blank\">/proxy/35169/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 3.83 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:37179\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /jobfs/105828095.gadi-pbs/dask-scratch-space/worker-9_dlevac\n",
+       "                        </td>\n",
+       "                    </tr>\n",
        "\n",
-       "    </details>\n",
-       "</div>\n",
+       "                    \n",
        "\n",
-       "        </details>\n",
-       "    </div>\n",
-       "</div>\n",
+       "                    \n",
+       "\n",
+       "                </table>\n",
        "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
        "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 5</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:46269\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/43181/status\" target=\"_blank\">/proxy/43181/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 3.83 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:46061\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /jobfs/105828095.gadi-pbs/dask-scratch-space/worker-pz5meqx9\n",
+       "                        </td>\n",
+       "                    </tr>\n",
        "\n",
-       "    </div>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "<Client: 'tcp://127.0.0.1:40921' processes=4 threads=12, memory=46.00 GiB>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "client"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Open the _ACCESS-NRI Intake Catalog_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-01-08T03:04:50.552154Z",
-     "iopub.status.busy": "2024-01-08T03:04:50.551797Z",
-     "iopub.status.idle": "2024-01-08T03:04:51.237424Z",
-     "shell.execute_reply": "2024-01-08T03:04:51.231153Z",
-     "shell.execute_reply.started": "2024-01-08T03:04:50.552122Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "catalog = cat.access_nri"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Load the ACCESS-OM2 results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For CICE data in OM2, we need to do some wrangling to make it easier to deal with. This is described in more detail in [DocumentedExamples/SeaIce_Plot_Example](https://cosima-recipes.readthedocs.io/en/latest/DocumentedExamples/SeaIce_Plot_Example.html). Its included in this function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-01-08T03:04:51.246049Z",
-     "iopub.status.busy": "2024-01-08T03:04:51.245738Z",
-     "iopub.status.idle": "2024-01-08T03:04:51.260159Z",
-     "shell.execute_reply": "2024-01-08T03:04:51.259333Z",
-     "shell.execute_reply.started": "2024-01-08T03:04:51.246028Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def open_by_experiment(exp_name, vars):\n",
-    "    \"\"\" Concatenate any datasets provided for this experiment into one ds, and add area and geo coordinates \"\"\"\n",
-    "\n",
-    "    import xarray as xr\n",
-    "    import cf_xarray as cfxr\n",
-    "    \n",
-    "    open_by_name = lambda name : catalog[name].search(\n",
-    "        variable=vars\n",
-    "    ).to_dask(\n",
-    "        xarray_open_kwargs={\"chunks\":{'time':'auto','ni':-1,'nj':-1},\"decode_coords\":False},\n",
-    "        xarray_combine_by_coords_kwargs={'compat':'override','data_vars':'minimal', 'coords':'minimal'}\n",
-    "    )\n",
-    "\n",
-    "    #get the data for each run of this config \n",
-    "    cice_ds=xr.concat([\n",
-    "        open_by_name(iName) for iName in RUNS[exp_name]\n",
-    "    ], dim='time'\n",
-    "    )\n",
-    "    \n",
-    "    #We also want the area/lat/lon fields, but these are not time dependent.\n",
-    "    area_ds=xr.merge([\n",
-    "        xr.open_dataset(\n",
-    "            catalog[RUNS[exp_name][0]].search(variable=iVar).df.path[0] \n",
-    "            # path of the first file with the area field, the geolon field and the geolat field \n",
-    "        ).drop('time')\n",
-    "    for iVar in VARS_2D])\n",
-    "\n",
-    "    #Label the lats and lons\n",
-    "    cice_ds.coords['ni'] = area_ds['xt_ocean'].values\n",
-    "    cice_ds.coords['nj'] = area_ds['yt_ocean'].values\n",
-    "    \n",
-    "    #Copy attributes for cf compliance\n",
-    "    cice_ds.ni.attrs=area_ds.xt_ocean.attrs\n",
-    "    cice_ds.nj.attrs=area_ds.yt_ocean.attrs\n",
-    "    \n",
-    "    cice_ds = cice_ds.rename(({'ni':'xt_ocean', 'nj':'yt_ocean'}))\n",
-    "    \n",
-    "    # Add the geolon, geolat, and area as extra co-ordinates fields from area_t\n",
-    "    \n",
-    "    cice_ds=cice_ds.assign_coords({\n",
-    "        'geolat_t':area_ds.geolat_t,\n",
-    "        'geolon_t':area_ds.geolon_t,\n",
-    "        'area_t':area_ds.area_t\n",
-    "    })\n",
-    "\n",
-    "    #cice timestamps are also misleading:\n",
-    "    cice_ds['time'] = cice_ds.time.to_pandas() - timedelta(days = 1)\n",
-    "       \n",
-    "    return cice_ds"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Because the dimensions are different for different experiments, they would not fit in a Dataset, a DataTree is required. The DataTree has a group for each experiment, which contains a xarray dataset with the data for that experiment. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-01-08T03:04:51.262147Z",
-     "iopub.status.busy": "2024-01-08T03:04:51.261856Z",
-     "iopub.status.idle": "2024-01-08T03:07:42.249461Z",
-     "shell.execute_reply": "2024-01-08T03:07:42.247261Z",
-     "shell.execute_reply.started": "2024-01-08T03:04:51.262119Z"
-    },
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.3.0\n",
-      "CPU times: user 51.2 s, sys: 8.23 s, total: 59.4 s\n",
-      "Wall time: 2min 50s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "\n",
-    "si_name_ds_pairs = [(iRun, open_by_experiment(iRun, VARS)) for iRun in RUNS.keys()]\n",
-    "\n",
-    "si_dt = DataTree.from_dict(\n",
-    "    dict(si_name_ds_pairs)\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-01-08T03:07:42.264050Z",
-     "iopub.status.busy": "2024-01-08T03:07:42.262435Z",
-     "iopub.status.idle": "2024-01-08T03:07:42.280237Z",
-     "shell.execute_reply": "2024-01-08T03:07:42.278906Z",
-     "shell.execute_reply.started": "2024-01-08T03:07:42.263994Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "@map_over_subtree\n",
-    "def match_timestamps_to_NSIDC(ds):\n",
-    "\n",
-    "    cice_ds = ds.copy()\n",
-    "\n",
-    "    # we are going to use the same timestamps as NSIDC\n",
-    "    cice_ds['time'] = [np.datetime64(str(i)[0:7]+'-01T00:00:00.000000000') for i in cice_ds.time.values]\n",
-    "\n",
-    "    return cice_ds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-01-08T03:07:42.282362Z",
-     "iopub.status.busy": "2024-01-08T03:07:42.281852Z",
-     "iopub.status.idle": "2024-01-08T03:07:42.335507Z",
-     "shell.execute_reply": "2024-01-08T03:07:42.334432Z",
-     "shell.execute_reply.started": "2024-01-08T03:07:42.282315Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "si_dt = match_timestamps_to_NSIDC(si_dt)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The result is a datatree, with a dataset for each experiment and timestamps which align with the observational timestamps"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-01-08T03:07:42.336544Z",
-     "iopub.status.busy": "2024-01-08T03:07:42.336304Z",
-     "iopub.status.idle": "2024-01-08T03:07:42.564314Z",
-     "shell.execute_reply": "2024-01-08T03:07:42.562888Z",
-     "shell.execute_reply.started": "2024-01-08T03:07:42.336522Z"
-    },
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
-       "<defs>\n",
-       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
-       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
-       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
-       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
-       "</symbol>\n",
-       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
-       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
-       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-       "</symbol>\n",
-       "</defs>\n",
-       "</svg>\n",
-       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
-       " *\n",
-       " */\n",
+       "                    \n",
        "\n",
-       ":root {\n",
-       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
-       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
-       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
-       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
-       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
-       "  --xr-background-color: var(--jp-layout-color0, white);\n",
-       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
-       "}\n",
+       "                    \n",
        "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
-       "body.vscode-dark {\n",
-       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
-       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
-       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
-       "  --xr-disabled-color: #515151;\n",
-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
-       "}\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 6</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:37447\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/39947/status\" target=\"_blank\">/proxy/39947/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 3.83 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:44547\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /jobfs/105828095.gadi-pbs/dask-scratch-space/worker-9xxdg58l\n",
+       "                        </td>\n",
+       "                    </tr>\n",
        "\n",
-       ".xr-wrap {\n",
-       "  display: block !important;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
-       "}\n",
+       "                    \n",
        "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
+       "                    \n",
        "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 7</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:40031\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/35517/status\" target=\"_blank\">/proxy/35517/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 3.83 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:43751\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /jobfs/105828095.gadi-pbs/dask-scratch-space/worker-1zozkmca\n",
+       "                        </td>\n",
+       "                    </tr>\n",
        "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
+       "                    \n",
        "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
+       "                    \n",
        "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 8</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:45655\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/36103/status\" target=\"_blank\">/proxy/36103/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 3.83 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:40749\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /jobfs/105828095.gadi-pbs/dask-scratch-space/worker-9jire7j6\n",
+       "                        </td>\n",
+       "                    </tr>\n",
        "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
+       "                    \n",
        "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
+       "                    \n",
        "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 9</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:35597\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/33815/status\" target=\"_blank\">/proxy/33815/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 3.83 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:41195\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /jobfs/105828095.gadi-pbs/dask-scratch-space/worker-u_kzqqfh\n",
+       "                        </td>\n",
+       "                    </tr>\n",
        "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
+       "                    \n",
        "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
+       "                    \n",
        "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 10</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:43801\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/41293/status\" target=\"_blank\">/proxy/41293/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 3.83 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:45225\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /jobfs/105828095.gadi-pbs/dask-scratch-space/worker-qa5n7tm3\n",
+       "                        </td>\n",
+       "                    </tr>\n",
        "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
+       "                    \n",
        "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
+       "                    \n",
        "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 11</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:45909\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/34123/status\" target=\"_blank\">/proxy/34123/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 3.83 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:43379\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /jobfs/105828095.gadi-pbs/dask-scratch-space/worker-nxj030bj\n",
+       "                        </td>\n",
+       "                    </tr>\n",
        "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
+       "                    \n",
        "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
+       "                    \n",
        "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
        "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
+       "    </details>\n",
+       "</div>\n",
        "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
+       "        </details>\n",
+       "    </div>\n",
+       "</div>\n",
+       "            </details>\n",
+       "        \n",
        "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
+       "    </div>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "<Client: 'tcp://127.0.0.1:36437' processes=12 threads=12, memory=46.00 GiB>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "client"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Open the _ACCESS-NRI Intake Catalog_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-10T00:30:57.308535Z",
+     "iopub.status.busy": "2024-01-10T00:30:57.308322Z",
+     "iopub.status.idle": "2024-01-10T00:30:58.196567Z",
+     "shell.execute_reply": "2024-01-10T00:30:58.193875Z",
+     "shell.execute_reply.started": "2024-01-10T00:30:57.308513Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "catalog = cat.access_nri"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Load the ACCESS-OM2 results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For CICE data in OM2, we need to do some wrangling to make it easier to deal with. This is described in more detail in [DocumentedExamples/SeaIce_Plot_Example](https://cosima-recipes.readthedocs.io/en/latest/DocumentedExamples/SeaIce_Plot_Example.html). Its included in this function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-10T00:30:58.202198Z",
+     "iopub.status.busy": "2024-01-10T00:30:58.201707Z",
+     "iopub.status.idle": "2024-01-10T00:30:58.213914Z",
+     "shell.execute_reply": "2024-01-10T00:30:58.212432Z",
+     "shell.execute_reply.started": "2024-01-10T00:30:58.202148Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def open_by_name(name, vars):\n",
+    "    \"\"\"Return a dataset for the requested name and vars\"\"\"\n",
+    "    return (\n",
+    "        catalog[name]\n",
+    "        .search(variable=vars)\n",
+    "        .to_dask(\n",
+    "            xarray_open_kwargs={\n",
+    "                \"chunks\": {\"time\": \"auto\", \"ni\": -1, \"nj\": -1},\n",
+    "                \"decode_coords\": False,\n",
+    "            },\n",
+    "            xarray_combine_by_coords_kwargs={\n",
+    "                \"compat\": \"override\",\n",
+    "                \"data_vars\": \"minimal\",\n",
+    "                \"coords\": \"minimal\",\n",
+    "            },\n",
+    "        )\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-10T00:30:58.216064Z",
+     "iopub.status.busy": "2024-01-10T00:30:58.215616Z",
+     "iopub.status.idle": "2024-01-10T00:30:58.228338Z",
+     "shell.execute_reply": "2024-01-10T00:30:58.227214Z",
+     "shell.execute_reply.started": "2024-01-10T00:30:58.216018Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def open_by_experiment(exp_name, vars):\n",
+    "    \"\"\"Concatenate any datasets provided for this experiment into one ds, and add area and geo coordinates\"\"\"\n",
+    "\n",
+    "    # get the data for each run of this config\n",
+    "    cice_ds = xr.concat(\n",
+    "        [open_by_name(iName, vars) for iName in RUNS[exp_name]], dim=\"time\"\n",
+    "    )\n",
+    "\n",
+    "    # We also want the area/lat/lon fields, but these are not time dependent.\n",
+    "    area_ds = xr.merge(\n",
+    "        [\n",
+    "            xr.open_dataset(\n",
+    "                catalog[RUNS[exp_name][0]]\n",
+    "                .search(variable=iVar)\n",
+    "                .df.path[0]\n",
+    "                # path of the first file with the area field, the geolon field and the geolat field\n",
+    "            ).drop_vars(\"time\")\n",
+    "            for iVar in VARS_2D\n",
+    "        ]\n",
+    "    )\n",
+    "\n",
+    "    # Label the lats and lons\n",
+    "    cice_ds.coords[\"ni\"] = area_ds[\"xt_ocean\"].values\n",
+    "    cice_ds.coords[\"nj\"] = area_ds[\"yt_ocean\"].values\n",
+    "\n",
+    "    # Copy attributes for cf compliance\n",
+    "    cice_ds.ni.attrs = area_ds.xt_ocean.attrs\n",
+    "    cice_ds.nj.attrs = area_ds.yt_ocean.attrs\n",
+    "\n",
+    "    cice_ds = cice_ds.rename(({\"ni\": \"xt_ocean\", \"nj\": \"yt_ocean\"}))\n",
+    "\n",
+    "    # Add the geolon, geolat, and area as extra co-ordinates fields from area_t\n",
+    "\n",
+    "    cice_ds = cice_ds.assign_coords(\n",
+    "        {\n",
+    "            \"geolat_t\": area_ds.geolat_t,\n",
+    "            \"geolon_t\": area_ds.geolon_t,\n",
+    "            \"area_t\": area_ds.area_t,\n",
+    "        }\n",
+    "    )\n",
+    "\n",
+    "    # cice timestamps are also misleading:\n",
+    "    cice_ds[\"time\"] = cice_ds.time.to_pandas() - timedelta(minutes=1)\n",
+    "\n",
+    "    return cice_ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because the dimensions are different for different experiments, they would not fit in a Dataset, a DataTree is required. The DataTree has a group for each experiment, which contains a xarray dataset with the data for that experiment. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-10T00:30:58.230394Z",
+     "iopub.status.busy": "2024-01-10T00:30:58.229780Z",
+     "iopub.status.idle": "2024-01-10T00:32:26.013192Z",
+     "shell.execute_reply": "2024-01-10T00:32:26.012306Z",
+     "shell.execute_reply.started": "2024-01-10T00:30:58.230355Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.3.0\n",
+      "CPU times: user 35.8 s, sys: 3.29 s, total: 39 s\n",
+      "Wall time: 1min 27s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "si_name_ds_pairs = [(iRun, open_by_experiment(iRun, VARS)) for iRun in RUNS.keys()]\n",
+    "\n",
+    "si_dt = DataTree.from_dict(dict(si_name_ds_pairs))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-10T00:32:26.017756Z",
+     "iopub.status.busy": "2024-01-10T00:32:26.016944Z",
+     "iopub.status.idle": "2024-01-10T00:32:26.022740Z",
+     "shell.execute_reply": "2024-01-10T00:32:26.022108Z",
+     "shell.execute_reply.started": "2024-01-10T00:32:26.017721Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "@map_over_subtree\n",
+    "def match_timestamps_to_CDR(ds):\n",
+    "    cice_ds = ds.copy()\n",
+    "\n",
+    "    # we are going to use the same timestamps as NSIDC\n",
+    "    cice_ds[\"time\"] = [\n",
+    "        np.datetime64(str(i)[0:7] + \"-01T00:00:00.000000000\")\n",
+    "        for i in cice_ds.time.values\n",
+    "    ]\n",
+    "\n",
+    "    return cice_ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-10T00:32:26.023941Z",
+     "iopub.status.busy": "2024-01-10T00:32:26.023596Z",
+     "iopub.status.idle": "2024-01-10T00:32:26.045437Z",
+     "shell.execute_reply": "2024-01-10T00:32:26.044896Z",
+     "shell.execute_reply.started": "2024-01-10T00:32:26.023919Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "si_dt = match_timestamps_to_CDR(si_dt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a datatree, with a dataset for each experiment and timestamps which align with the observational timestamps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-10T00:32:26.048719Z",
+     "iopub.status.busy": "2024-01-10T00:32:26.048313Z",
+     "iopub.status.idle": "2024-01-10T00:32:26.148388Z",
+     "shell.execute_reply": "2024-01-10T00:32:26.147609Z",
+     "shell.execute_reply.started": "2024-01-10T00:32:26.048695Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
        "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
        "  grid-column: 1 / -1;\n",
        "  margin-bottom: 5px;\n",
        "}\n",
@@ -1082,7 +1497,7 @@
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DatasetView&gt;\n",
        "Dimensions:  ()\n",
        "Data variables:\n",
-       "    *empty*</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>datatree.DataTree</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b74acdf8-99c8-4184-a01a-f8422bcd0621' class='xr-section-summary-in' type='checkbox'  ><label for='section-b74acdf8-99c8-4184-a01a-f8422bcd0621' class='xr-section-summary' >Groups: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%'><div style='display: inline-grid;'><div style='grid-column-start: 1;border-right: 0.2em solid;border-color: var(--xr-border-color);height: 100%;width: 0px;'></div><div style='grid-column-start: 2;grid-row-start: 1;height: 1em;width: 20px;border-bottom: 0.2em solid;border-color: var(--xr-border-color);'></div><div style='grid-column-start: 3;'><ul class='xr-sections'><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "    *empty*</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>datatree.DataTree</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-44ae9ceb-e0af-4842-80ab-e0f3eb41cc56' class='xr-section-summary-in' type='checkbox'  ><label for='section-44ae9ceb-e0af-4842-80ab-e0f3eb41cc56' class='xr-section-summary' >Groups: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%'><div style='display: inline-grid;'><div style='grid-column-start: 1;border-right: 0.2em solid;border-color: var(--xr-border-color);height: 100%;width: 0px;'></div><div style='grid-column-start: 2;grid-row-start: 1;height: 1em;width: 20px;border-bottom: 0.2em solid;border-color: var(--xr-border-color);'></div><div style='grid-column-start: 3;'><ul class='xr-sections'><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
        "<defs>\n",
        "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
        "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
@@ -1446,7 +1861,7 @@
        "  fill: currentColor;\n",
        "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DatasetView&gt;\n",
-       "Dimensions:   (time: 523, nc: 5, yt_ocean: 1080, xt_ocean: 1440)\n",
+       "Dimensions:   (time: 523, yt_ocean: 1080, xt_ocean: 1440)\n",
        "Coordinates:\n",
        "  * time      (time) datetime64[ns] 1980-01-01 1980-02-01 ... 2023-07-01\n",
        "  * xt_ocean  (xt_ocean) float64 -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n",
@@ -1454,9 +1869,8 @@
        "    geolat_t  (yt_ocean, xt_ocean) float32 ...\n",
        "    geolon_t  (yt_ocean, xt_ocean) float32 ...\n",
        "    area_t    (yt_ocean, xt_ocean) float32 ...\n",
-       "Dimensions without coordinates: nc\n",
        "Data variables:\n",
-       "    vicen_m   (time, nc, yt_ocean, xt_ocean) float32 dask.array&lt;chunksize=(1, 1, 1080, 1440), meta=np.ndarray&gt;\n",
+       "    hi_m      (time, yt_ocean, xt_ocean) float32 dask.array&lt;chunksize=(1, 1080, 1440), meta=np.ndarray&gt;\n",
        "    aice_m    (time, yt_ocean, xt_ocean) float32 dask.array&lt;chunksize=(1, 1080, 1440), meta=np.ndarray&gt;\n",
        "Attributes: (12/17)\n",
        "    title:                                    sea ice model output for CICE\n",
@@ -1471,11 +1885,11 @@
        "    intake_esm_attrs:variable_units:          days since 1980-01-01 00:00:00,...\n",
        "    intake_esm_attrs:file_id:                 iceh_XXXX_XX\n",
        "    intake_esm_attrs:_data_format_:           netcdf\n",
-       "    intake_esm_dataset_key:                   iceh_XXXX_XX.1mon</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>025deg_era5</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f5df2624-2970-4983-80b4-9ba073787a45' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f5df2624-2970-4983-80b4-9ba073787a45' class='xr-section-summary'  title='Expand/collapse section'>Groups: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%'></div></div></li><li class='xr-section-item'><input id='section-3f3ca772-5e73-43c2-bc7d-a5aca4054aae' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3f3ca772-5e73-43c2-bc7d-a5aca4054aae' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 523</li><li><span>nc</span>: 5</li><li><span class='xr-has-index'>yt_ocean</span>: 1080</li><li><span class='xr-has-index'>xt_ocean</span>: 1440</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-cb257768-9600-4fd8-a05c-84b45ef486f9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cb257768-9600-4fd8-a05c-84b45ef486f9' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1980-01-01 ... 2023-07-01</div><input id='attrs-c3824c80-c497-4eb3-872a-d8c701cd78c3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c3824c80-c497-4eb3-872a-d8c701cd78c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25d5f14e-21e4-42a0-a206-f3368b4195f5' class='xr-var-data-in' type='checkbox'><label for='data-25d5f14e-21e4-42a0-a206-f3368b4195f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1980-01-01T00:00:00.000000000&#x27;, &#x27;1980-02-01T00:00:00.000000000&#x27;,\n",
+       "    intake_esm_dataset_key:                   iceh_XXXX_XX.1mon</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>025deg_era5</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c58e863b-f7c9-4161-800a-86f8ebbfd47f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c58e863b-f7c9-4161-800a-86f8ebbfd47f' class='xr-section-summary'  title='Expand/collapse section'>Groups: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%'></div></div></li><li class='xr-section-item'><input id='section-0672e4ee-ccd9-4806-bd99-9f8968e44e92' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0672e4ee-ccd9-4806-bd99-9f8968e44e92' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 523</li><li><span class='xr-has-index'>yt_ocean</span>: 1080</li><li><span class='xr-has-index'>xt_ocean</span>: 1440</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-702dfe13-b959-4f1f-9b13-eded04ea88dd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-702dfe13-b959-4f1f-9b13-eded04ea88dd' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1980-01-01 ... 2023-07-01</div><input id='attrs-42a8197e-dc82-4ec4-889a-6319b8ce1bbe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-42a8197e-dc82-4ec4-889a-6319b8ce1bbe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-550184ed-b600-4539-aec6-5e8281e5613f' class='xr-var-data-in' type='checkbox'><label for='data-550184ed-b600-4539-aec6-5e8281e5613f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1980-01-01T00:00:00.000000000&#x27;, &#x27;1980-02-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;1980-03-01T00:00:00.000000000&#x27;, ..., &#x27;2023-05-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;2023-06-01T00:00:00.000000000&#x27;, &#x27;2023-07-01T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.9 -279.6 ... 79.62 79.88</div><input id='attrs-f9050e59-a636-4f68-8e79-5fee8864e594' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f9050e59-a636-4f68-8e79-5fee8864e594' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c0d0dcb-3825-47b1-86ab-780447416f99' class='xr-var-data-in' type='checkbox'><label for='data-6c0d0dcb-3825-47b1-86ab-780447416f99' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-279.875, -279.625, -279.375, ...,   79.375,   79.625,   79.875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-81.08 -80.97 ... 89.84 89.95</div><input id='attrs-a4f0a0db-599d-433f-98c9-aec2d363a168' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a4f0a0db-599d-433f-98c9-aec2d363a168' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9a8b87c-93a6-4b71-9993-1a424d20f221' class='xr-var-data-in' type='checkbox'><label for='data-b9a8b87c-93a6-4b71-9993-1a424d20f221' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-81.077001, -80.971402, -80.865804, ...,  89.736085,  89.841684,\n",
-       "        89.947282])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolat_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-29a6bb3b-2301-4a0b-b8ef-660ce81b2a43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29a6bb3b-2301-4a0b-b8ef-660ce81b2a43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac28bcd4-2af8-4d51-be5a-0235558223ed' class='xr-var-data-in' type='checkbox'><label for='data-ac28bcd4-2af8-4d51-be5a-0235558223ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>valid_range :</span></dt><dd>[-91.  91.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[1555200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolon_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-18a0385f-85f0-48aa-af54-2fde03690905' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-18a0385f-85f0-48aa-af54-2fde03690905' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2bbab1e-c086-4d24-b386-cec084309ef9' class='xr-var-data-in' type='checkbox'><label for='data-f2bbab1e-c086-4d24-b386-cec084309ef9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>valid_range :</span></dt><dd>[-281.  361.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[1555200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>area_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ab1ba62b-bcc6-4d19-bed8-47aa942a6e80' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab1ba62b-bcc6-4d19-bed8-47aa942a6e80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-968cffcb-09b3-46d2-8e00-7b8e1690fcd3' class='xr-var-data-in' type='checkbox'><label for='data-968cffcb-09b3-46d2-8e00-7b8e1690fcd3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer cell area</dd><dt><span>units :</span></dt><dd>m^2</dd><dt><span>valid_range :</span></dt><dd>[0.e+00 1.e+15]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[1555200 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cc150ed8-6cdc-4d59-9f9e-1ef201a25587' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cc150ed8-6cdc-4d59-9f9e-1ef201a25587' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>vicen_m</span></div><div class='xr-var-dims'>(time, nc, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 1080, 1440), meta=np.ndarray&gt;</div><input id='attrs-ce9c7996-5bcd-445f-a680-a26a5f32c8e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce9c7996-5bcd-445f-a680-a26a5f32c8e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8cb83f3-f28e-43db-9e50-699ac52d180b' class='xr-var-data-in' type='checkbox'><label for='data-e8cb83f3-f28e-43db-9e50-699ac52d180b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>ice volume, categories</dd><dt><span>coordinates :</span></dt><dd>TLON TLAT NCAT  time</dd><dt><span>cell_measures :</span></dt><dd>area: tarea</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_rep :</span></dt><dd>averaged</dd></dl></div><div class='xr-var-data'><table>\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.9 -279.6 ... 79.62 79.88</div><input id='attrs-8f76d605-a026-4544-950e-8b50bbfdd608' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8f76d605-a026-4544-950e-8b50bbfdd608' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90110470-693b-41e6-8430-b40c70217dd3' class='xr-var-data-in' type='checkbox'><label for='data-90110470-693b-41e6-8430-b40c70217dd3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-279.875, -279.625, -279.375, ...,   79.375,   79.625,   79.875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-81.08 -80.97 ... 89.84 89.95</div><input id='attrs-f7042393-7e0b-452e-84bf-ee9620994906' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f7042393-7e0b-452e-84bf-ee9620994906' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d4e98f7-33f7-4dc4-8748-3ab95227293d' class='xr-var-data-in' type='checkbox'><label for='data-0d4e98f7-33f7-4dc4-8748-3ab95227293d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-81.077001, -80.971402, -80.865804, ...,  89.736085,  89.841684,\n",
+       "        89.947282])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolat_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5cc3b755-2575-4c1b-b71a-ebd52991f3a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5cc3b755-2575-4c1b-b71a-ebd52991f3a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a31a2c42-47ea-4f6b-a3e6-904d2773efa3' class='xr-var-data-in' type='checkbox'><label for='data-a31a2c42-47ea-4f6b-a3e6-904d2773efa3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>valid_range :</span></dt><dd>[-91.  91.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[1555200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolon_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6460a79a-6f00-4b52-9f93-1c92f9fbd490' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6460a79a-6f00-4b52-9f93-1c92f9fbd490' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3bb45f08-2bcd-4800-b388-6b1005d223da' class='xr-var-data-in' type='checkbox'><label for='data-3bb45f08-2bcd-4800-b388-6b1005d223da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>valid_range :</span></dt><dd>[-281.  361.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[1555200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>area_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6fa075c1-feee-456a-b9b5-eaa5b1844f98' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6fa075c1-feee-456a-b9b5-eaa5b1844f98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ad73acb-064b-4c43-8786-aeccfca9496c' class='xr-var-data-in' type='checkbox'><label for='data-6ad73acb-064b-4c43-8786-aeccfca9496c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer cell area</dd><dt><span>units :</span></dt><dd>m^2</dd><dt><span>valid_range :</span></dt><dd>[0.e+00 1.e+15]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[1555200 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a9e378e1-21a5-478b-88f7-75f803df7794' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a9e378e1-21a5-478b-88f7-75f803df7794' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>hi_m</span></div><div class='xr-var-dims'>(time, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1440), meta=np.ndarray&gt;</div><input id='attrs-b6549945-ca7d-47e9-a05b-febe742bad9e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b6549945-ca7d-47e9-a05b-febe742bad9e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1ba7c65-3c5f-493e-98eb-c7bd407b2d03' class='xr-var-data-in' type='checkbox'><label for='data-e1ba7c65-3c5f-493e-98eb-c7bd407b2d03' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>grid cell mean ice thickness</dd><dt><span>coordinates :</span></dt><dd>TLON TLAT time</dd><dt><span>cell_measures :</span></dt><dd>area: tarea</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_rep :</span></dt><dd>averaged</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1490,18 +1904,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 15.15 GiB </td>\n",
+       "                        <td> 3.03 GiB </td>\n",
        "                        <td> 5.93 MiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (523, 5, 1080, 1440) </td>\n",
-       "                        <td> (1, 1, 1080, 1440) </td>\n",
+       "                        <td> (523, 1080, 1440) </td>\n",
+       "                        <td> (1, 1080, 1440) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 2615 chunks in 1047 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 523 chunks in 1047 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -1511,104 +1925,85 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"412\" height=\"154\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"44\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"44\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"1\" y1=\"0\" x2=\"1\" y2=\"25\" />\n",
-       "  <line x1=\"2\" y1=\"0\" x2=\"2\" y2=\"25\" />\n",
-       "  <line x1=\"4\" y1=\"0\" x2=\"4\" y2=\"25\" />\n",
-       "  <line x1=\"5\" y1=\"0\" x2=\"5\" y2=\"25\" />\n",
-       "  <line x1=\"6\" y1=\"0\" x2=\"6\" y2=\"25\" />\n",
-       "  <line x1=\"8\" y1=\"0\" x2=\"8\" y2=\"25\" />\n",
-       "  <line x1=\"9\" y1=\"0\" x2=\"9\" y2=\"25\" />\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"25\" />\n",
-       "  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n",
-       "  <line x1=\"13\" y1=\"0\" x2=\"13\" y2=\"25\" />\n",
-       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n",
-       "  <line x1=\"16\" y1=\"0\" x2=\"16\" y2=\"25\" />\n",
-       "  <line x1=\"17\" y1=\"0\" x2=\"17\" y2=\"25\" />\n",
-       "  <line x1=\"19\" y1=\"0\" x2=\"19\" y2=\"25\" />\n",
-       "  <line x1=\"20\" y1=\"0\" x2=\"20\" y2=\"25\" />\n",
-       "  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n",
-       "  <line x1=\"23\" y1=\"0\" x2=\"23\" y2=\"25\" />\n",
-       "  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"25\" />\n",
-       "  <line x1=\"26\" y1=\"0\" x2=\"26\" y2=\"25\" />\n",
-       "  <line x1=\"27\" y1=\"0\" x2=\"27\" y2=\"25\" />\n",
-       "  <line x1=\"28\" y1=\"0\" x2=\"28\" y2=\"25\" />\n",
-       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n",
-       "  <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"25\" />\n",
-       "  <line x1=\"33\" y1=\"0\" x2=\"33\" y2=\"25\" />\n",
-       "  <line x1=\"34\" y1=\"0\" x2=\"34\" y2=\"25\" />\n",
-       "  <line x1=\"35\" y1=\"0\" x2=\"35\" y2=\"25\" />\n",
-       "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
-       "  <line x1=\"38\" y1=\"0\" x2=\"38\" y2=\"25\" />\n",
-       "  <line x1=\"39\" y1=\"0\" x2=\"39\" y2=\"25\" />\n",
-       "  <line x1=\"41\" y1=\"0\" x2=\"41\" y2=\"25\" />\n",
-       "  <line x1=\"42\" y1=\"0\" x2=\"42\" y2=\"25\" />\n",
-       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 44.14651480641994,0.0 44.14651480641994,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"22.073257\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >523</text>\n",
-       "  <text x=\"64.146515\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,64.146515,12.706308)\">1</text>\n",
-       "\n",
+       "        <svg width=\"205\" height=\"165\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"114\" y1=\"0\" x2=\"128\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"114\" y1=\"90\" x2=\"128\" y2=\"104\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"90\" x2=\"35\" y2=\"115\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"114\" y1=\"0\" x2=\"114\" y2=\"90\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"116\" y1=\"2\" x2=\"116\" y2=\"92\" />\n",
-       "  <line x1=\"119\" y1=\"5\" x2=\"119\" y2=\"95\" />\n",
-       "  <line x1=\"122\" y1=\"8\" x2=\"122\" y2=\"98\" />\n",
-       "  <line x1=\"125\" y1=\"11\" x2=\"125\" y2=\"101\" />\n",
-       "  <line x1=\"128\" y1=\"14\" x2=\"128\" y2=\"104\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"90\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"91\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"92\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"94\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"95\" />\n",
+       "  <line x1=\"16\" y1=\"6\" x2=\"16\" y2=\"96\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"98\" />\n",
+       "  <line x1=\"19\" y1=\"9\" x2=\"19\" y2=\"99\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"100\" />\n",
+       "  <line x1=\"22\" y1=\"12\" x2=\"22\" y2=\"102\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"103\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"104\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"106\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"107\" />\n",
+       "  <line x1=\"29\" y1=\"19\" x2=\"29\" y2=\"109\" />\n",
+       "  <line x1=\"30\" y1=\"20\" x2=\"30\" y2=\"110\" />\n",
+       "  <line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"111\" />\n",
+       "  <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"113\" />\n",
+       "  <line x1=\"34\" y1=\"24\" x2=\"34\" y2=\"114\" />\n",
+       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"115\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"114.0,0.0 128.9485979497544,14.948597949754403 128.9485979497544,104.9485979497544 114.0,90.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"10.0,0.0 35.96853812142349,25.968538121423492 35.96853812142349,115.96853812142349 10.0,90.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"114\" y1=\"0\" x2=\"234\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"116\" y1=\"2\" x2=\"236\" y2=\"2\" />\n",
-       "  <line x1=\"119\" y1=\"5\" x2=\"239\" y2=\"5\" />\n",
-       "  <line x1=\"122\" y1=\"8\" x2=\"242\" y2=\"8\" />\n",
-       "  <line x1=\"125\" y1=\"11\" x2=\"245\" y2=\"11\" />\n",
-       "  <line x1=\"128\" y1=\"14\" x2=\"248\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"16\" y1=\"6\" x2=\"136\" y2=\"6\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"19\" y1=\"9\" x2=\"139\" y2=\"9\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"22\" y1=\"12\" x2=\"142\" y2=\"12\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" />\n",
+       "  <line x1=\"29\" y1=\"19\" x2=\"149\" y2=\"19\" />\n",
+       "  <line x1=\"30\" y1=\"20\" x2=\"150\" y2=\"20\" />\n",
+       "  <line x1=\"31\" y1=\"21\" x2=\"151\" y2=\"21\" />\n",
+       "  <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" />\n",
+       "  <line x1=\"34\" y1=\"24\" x2=\"154\" y2=\"24\" />\n",
+       "  <line x1=\"35\" y1=\"25\" x2=\"155\" y2=\"25\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"114\" y1=\"0\" x2=\"128\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"234\" y1=\"0\" x2=\"248\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"130\" y1=\"0\" x2=\"155\" y2=\"25\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"114.0,0.0 234.0,0.0 248.9485979497544,14.948597949754403 128.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"10.0,0.0 130.0,0.0 155.96853812142348,25.968538121423492 35.96853812142349,25.968538121423492\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"128\" y1=\"14\" x2=\"248\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"128\" y1=\"104\" x2=\"248\" y2=\"104\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"25\" x2=\"155\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"115\" x2=\"155\" y2=\"115\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"128\" y1=\"14\" x2=\"128\" y2=\"104\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"248\" y1=\"14\" x2=\"248\" y2=\"104\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"115\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"155\" y1=\"25\" x2=\"155\" y2=\"115\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"128.9485979497544,14.948597949754403 248.9485979497544,14.948597949754403 248.9485979497544,104.9485979497544 128.9485979497544,104.9485979497544\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"35.96853812142349,25.968538121423492 155.96853812142348,25.968538121423492 155.96853812142348,115.96853812142349 35.96853812142349,115.96853812142349\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"188.948598\" y=\"124.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
-       "  <text x=\"268.948598\" y=\"59.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,268.948598,59.948598)\">1080</text>\n",
-       "  <text x=\"111.474299\" y=\"117.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,111.474299,117.474299)\">5</text>\n",
+       "  <text x=\"95.968538\" y=\"135.968538\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"175.968538\" y=\"70.968538\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,175.968538,70.968538)\">1080</text>\n",
+       "  <text x=\"12.984269\" y=\"122.984269\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,12.984269,122.984269)\">523</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aice_m</span></div><div class='xr-var-dims'>(time, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1440), meta=np.ndarray&gt;</div><input id='attrs-84a7cc15-685c-40be-ba5e-6330af40f32d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84a7cc15-685c-40be-ba5e-6330af40f32d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1d0dd2a-bc32-40af-abd6-561705a4f2db' class='xr-var-data-in' type='checkbox'><label for='data-d1d0dd2a-bc32-40af-abd6-561705a4f2db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>ice area  (aggregate)</dd><dt><span>coordinates :</span></dt><dd>TLON TLAT time</dd><dt><span>cell_measures :</span></dt><dd>area: tarea</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_rep :</span></dt><dd>averaged</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aice_m</span></div><div class='xr-var-dims'>(time, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1440), meta=np.ndarray&gt;</div><input id='attrs-31dd2548-c6b8-417d-8463-104c57ceaa69' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-31dd2548-c6b8-417d-8463-104c57ceaa69' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ec18c74-c7a6-4279-aa33-eb43dfd76a24' class='xr-var-data-in' type='checkbox'><label for='data-0ec18c74-c7a6-4279-aa33-eb43dfd76a24' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>ice area  (aggregate)</dd><dt><span>coordinates :</span></dt><dd>TLON TLAT time</dd><dt><span>cell_measures :</span></dt><dd>area: tarea</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_rep :</span></dt><dd>averaged</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1722,7 +2117,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-80eaa5a8-3b4c-4e3a-b5cc-de132398c100' class='xr-section-summary-in' type='checkbox'  ><label for='section-80eaa5a8-3b4c-4e3a-b5cc-de132398c100' class='xr-section-summary' >Attributes: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>sea ice model output for CICE</dd><dt><span>contents :</span></dt><dd>Diagnostic and Prognostic Variables</dd><dt><span>source :</span></dt><dd>Los Alamos Sea Ice Model (CICE) Version 5</dd><dt><span>time_period_freq :</span></dt><dd>month_1</dd><dt><span>comment3 :</span></dt><dd>seconds elapsed into model date:      0</dd><dt><span>conventions :</span></dt><dd>CF-1.0</dd><dt><span>io_flavor :</span></dt><dd>io_pio</dd><dt><span>intake_esm_attrs:realm :</span></dt><dd>seaIce</dd><dt><span>intake_esm_attrs:variable :</span></dt><dd>time_bounds,TLON,TLAT,ULON,ULAT,NCAT,tmask,blkmask,tarea,uarea,dxt,dyt,dxu,dyu,HTN,HTE,ANGLE,ANGLET,hi_m,hs_m,Tsfc_m,aice_m,uvel_m,vvel_m,uatm_m,vatm_m,sice_m,fswdn_m,fswup_m,flwdn_m,snow_ai_m,rain_ai_m,frzmlt_m,fswfac_m,fswabs_ai_m,albsni_m,alvdr_ai_m,alidr_ai_m,alvdf_ai_m,alidf_ai_m,albice_m,albsno_m,flat_ai_m,fsens_ai_m,flwup_ai_m,evap_ai_m,Tair_m,congel_m,frazil_m,snoice_m,meltt_m,melts_m,meltb_m,meltl_m,fresh_ai_m,fsalt_m,fsalt_ai_m,fhocn_ai_m,fswthru_ai_m,strairx_m,strairy_m,strtltx_m,strtlty_m,strcorx_m,strcory_m,strocnx_m,strocny_m,strintx_m,strinty_m,strength_m,divu_m,shear_m,dvidtt_m,dvidtd_m,daidtt_m,daidtd_m,trsig_m,ice_present_m,fcondtop_ai_m,fmeltt_ai_m,opening_m,aicen_m,vicen_m,fsurfn_ai_m,fcondtopn_ai_m,fmelttn_ai_m,flatn_ai_m</dd><dt><span>intake_esm_attrs:frequency :</span></dt><dd>1mon</dd><dt><span>intake_esm_attrs:variable_long_name :</span></dt><dd>boundaries for time-averaging interval,T grid center longitude,T grid center latitude,U grid center longitude,U grid center latitude,category maximum thickness,ocean grid mask,ice grid block mask,area of T grid cells,area of U grid cells,T cell width through middle,T cell height through middle,U cell width through middle,U cell height through middle,T cell width on North side,T cell width on East side,angle grid makes with latitude line on U grid,angle grid makes with latitude line on T grid,grid cell mean ice thickness,grid cell mean snow thickness,snow/ice surface temperature,ice area  (aggregate),ice velocity (x),ice velocity (y),atm velocity (x),atm velocity (y),bulk ice salinity,down solar flux,upward solar flux,down longwave flux,snowfall rate,rainfall rate,freeze/melt potential,shortwave scaling factor,snow/ice/ocn absorbed solar flux,snow/ice broad band albedo,visible direct albedo,near IR direct albedo,visible diffuse albedo,near IR diffuse albedo,bare ice albedo,snow albedo,latent heat flux,sensible heat flux,upward longwave flux,evaporative water flux,air temperature,congelation ice growth,frazil ice growth,snow-ice formation,top ice melt,top snow melt,basal ice melt,lateral ice melt,freshwtr flx ice to ocn,salt flux ice to ocn (cpl),salt flux ice to ocean,heat flux ice to ocean,SW flux thru ice to ocean,atm/ice stress (x),atm/ice stress (y),sea sfc tilt stress (x),sea sfc tilt stress (y),coriolis stress (x),coriolis stress (y),ocean/ice stress (x),ocean/ice stress (y),internal ice stress (x),internal ice stress (y),compressive ice strength,strain rate (divergence),strain rate (shear),volume tendency thermo,volume tendency dynamics,area tendency thermo,area tendency dynamics,internal stress tensor trace,fraction of time-avg interval that ice is present,top surface conductive heat flux,net surface heat flux causing melt,lead area opening rate,ice area, categories,ice volume, categories,net surface heat flux, categories,top sfc conductive heat flux, cat,net sfc heat flux causing melt, cat,latent heat flux, category</dd><dt><span>intake_esm_attrs:variable_standard_name :</span></dt><dd>,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,</dd><dt><span>intake_esm_attrs:variable_cell_methods :</span></dt><dd>,,,,,,,,,,,,,,,,,,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean</dd><dt><span>intake_esm_attrs:variable_units :</span></dt><dd>days since 1980-01-01 00:00:00,degrees_east,degrees_north,degrees_east,degrees_north,m,,,m^2,m^2,m,m,m,m,m,m,radians,radians,m,m,C,1,m/s,m/s,m/s,m/s,ppt,W/m^2,W/m^2,W/m^2,cm/day,cm/day,W/m^2,1,W/m^2,%,%,%,%,%,%,%,W/m^2,W/m^2,W/m^2,cm/day,C,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,kg/m^2/s,kg/m^2/s,W/m^2,W/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m,%/day,%/day,cm/day,cm/day,%/day,%/day,N/m^2,1,W/m^2,W/m^2,%/day,1,m,W/m^2,W/m^2,W/m^2,W/m^2</dd><dt><span>intake_esm_attrs:file_id :</span></dt><dd>iceh_XXXX_XX</dd><dt><span>intake_esm_attrs:_data_format_ :</span></dt><dd>netcdf</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>iceh_XXXX_XX.1mon</dd></dl></div></li></ul></div></div></ul></div></div><div style='display: inline-grid;'><div style='grid-column-start: 1;border-right: 0.2em solid;border-color: var(--xr-border-color);height: 100%;width: 0px;'></div><div style='grid-column-start: 2;grid-row-start: 1;height: 1em;width: 20px;border-bottom: 0.2em solid;border-color: var(--xr-border-color);'></div><div style='grid-column-start: 3;'><ul class='xr-sections'><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-deac2b06-f79d-4cca-a7b4-b635bdc14809' class='xr-section-summary-in' type='checkbox'  ><label for='section-deac2b06-f79d-4cca-a7b4-b635bdc14809' class='xr-section-summary' >Attributes: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>sea ice model output for CICE</dd><dt><span>contents :</span></dt><dd>Diagnostic and Prognostic Variables</dd><dt><span>source :</span></dt><dd>Los Alamos Sea Ice Model (CICE) Version 5</dd><dt><span>time_period_freq :</span></dt><dd>month_1</dd><dt><span>comment3 :</span></dt><dd>seconds elapsed into model date:      0</dd><dt><span>conventions :</span></dt><dd>CF-1.0</dd><dt><span>io_flavor :</span></dt><dd>io_pio</dd><dt><span>intake_esm_attrs:realm :</span></dt><dd>seaIce</dd><dt><span>intake_esm_attrs:variable :</span></dt><dd>time_bounds,TLON,TLAT,ULON,ULAT,NCAT,tmask,blkmask,tarea,uarea,dxt,dyt,dxu,dyu,HTN,HTE,ANGLE,ANGLET,hi_m,hs_m,Tsfc_m,aice_m,uvel_m,vvel_m,uatm_m,vatm_m,sice_m,fswdn_m,fswup_m,flwdn_m,snow_ai_m,rain_ai_m,frzmlt_m,fswfac_m,fswabs_ai_m,albsni_m,alvdr_ai_m,alidr_ai_m,alvdf_ai_m,alidf_ai_m,albice_m,albsno_m,flat_ai_m,fsens_ai_m,flwup_ai_m,evap_ai_m,Tair_m,congel_m,frazil_m,snoice_m,meltt_m,melts_m,meltb_m,meltl_m,fresh_ai_m,fsalt_m,fsalt_ai_m,fhocn_ai_m,fswthru_ai_m,strairx_m,strairy_m,strtltx_m,strtlty_m,strcorx_m,strcory_m,strocnx_m,strocny_m,strintx_m,strinty_m,strength_m,divu_m,shear_m,dvidtt_m,dvidtd_m,daidtt_m,daidtd_m,trsig_m,ice_present_m,fcondtop_ai_m,fmeltt_ai_m,opening_m,aicen_m,vicen_m,fsurfn_ai_m,fcondtopn_ai_m,fmelttn_ai_m,flatn_ai_m</dd><dt><span>intake_esm_attrs:frequency :</span></dt><dd>1mon</dd><dt><span>intake_esm_attrs:variable_long_name :</span></dt><dd>boundaries for time-averaging interval,T grid center longitude,T grid center latitude,U grid center longitude,U grid center latitude,category maximum thickness,ocean grid mask,ice grid block mask,area of T grid cells,area of U grid cells,T cell width through middle,T cell height through middle,U cell width through middle,U cell height through middle,T cell width on North side,T cell width on East side,angle grid makes with latitude line on U grid,angle grid makes with latitude line on T grid,grid cell mean ice thickness,grid cell mean snow thickness,snow/ice surface temperature,ice area  (aggregate),ice velocity (x),ice velocity (y),atm velocity (x),atm velocity (y),bulk ice salinity,down solar flux,upward solar flux,down longwave flux,snowfall rate,rainfall rate,freeze/melt potential,shortwave scaling factor,snow/ice/ocn absorbed solar flux,snow/ice broad band albedo,visible direct albedo,near IR direct albedo,visible diffuse albedo,near IR diffuse albedo,bare ice albedo,snow albedo,latent heat flux,sensible heat flux,upward longwave flux,evaporative water flux,air temperature,congelation ice growth,frazil ice growth,snow-ice formation,top ice melt,top snow melt,basal ice melt,lateral ice melt,freshwtr flx ice to ocn,salt flux ice to ocn (cpl),salt flux ice to ocean,heat flux ice to ocean,SW flux thru ice to ocean,atm/ice stress (x),atm/ice stress (y),sea sfc tilt stress (x),sea sfc tilt stress (y),coriolis stress (x),coriolis stress (y),ocean/ice stress (x),ocean/ice stress (y),internal ice stress (x),internal ice stress (y),compressive ice strength,strain rate (divergence),strain rate (shear),volume tendency thermo,volume tendency dynamics,area tendency thermo,area tendency dynamics,internal stress tensor trace,fraction of time-avg interval that ice is present,top surface conductive heat flux,net surface heat flux causing melt,lead area opening rate,ice area, categories,ice volume, categories,net surface heat flux, categories,top sfc conductive heat flux, cat,net sfc heat flux causing melt, cat,latent heat flux, category</dd><dt><span>intake_esm_attrs:variable_standard_name :</span></dt><dd>,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,</dd><dt><span>intake_esm_attrs:variable_cell_methods :</span></dt><dd>,,,,,,,,,,,,,,,,,,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean</dd><dt><span>intake_esm_attrs:variable_units :</span></dt><dd>days since 1980-01-01 00:00:00,degrees_east,degrees_north,degrees_east,degrees_north,m,,,m^2,m^2,m,m,m,m,m,m,radians,radians,m,m,C,1,m/s,m/s,m/s,m/s,ppt,W/m^2,W/m^2,W/m^2,cm/day,cm/day,W/m^2,1,W/m^2,%,%,%,%,%,%,%,W/m^2,W/m^2,W/m^2,cm/day,C,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,kg/m^2/s,kg/m^2/s,W/m^2,W/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m,%/day,%/day,cm/day,cm/day,%/day,%/day,N/m^2,1,W/m^2,W/m^2,%/day,1,m,W/m^2,W/m^2,W/m^2,W/m^2</dd><dt><span>intake_esm_attrs:file_id :</span></dt><dd>iceh_XXXX_XX</dd><dt><span>intake_esm_attrs:_data_format_ :</span></dt><dd>netcdf</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>iceh_XXXX_XX.1mon</dd></dl></div></li></ul></div></div></ul></div></div><div style='display: inline-grid;'><div style='grid-column-start: 1;border-right: 0.2em solid;border-color: var(--xr-border-color);height: 100%;width: 0px;'></div><div style='grid-column-start: 2;grid-row-start: 1;height: 1em;width: 20px;border-bottom: 0.2em solid;border-color: var(--xr-border-color);'></div><div style='grid-column-start: 3;'><ul class='xr-sections'><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
        "<defs>\n",
        "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
        "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
@@ -2086,7 +2481,7 @@
        "  fill: currentColor;\n",
        "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DatasetView&gt;\n",
-       "Dimensions:   (time: 523, nc: 5, yt_ocean: 1080, xt_ocean: 1440)\n",
+       "Dimensions:   (time: 523, yt_ocean: 1080, xt_ocean: 1440)\n",
        "Coordinates:\n",
        "  * time      (time) datetime64[ns] 1980-01-01 1980-02-01 ... 2023-07-01\n",
        "  * xt_ocean  (xt_ocean) float64 -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n",
@@ -2094,9 +2489,8 @@
        "    geolat_t  (yt_ocean, xt_ocean) float32 ...\n",
        "    geolon_t  (yt_ocean, xt_ocean) float32 ...\n",
        "    area_t    (yt_ocean, xt_ocean) float32 ...\n",
-       "Dimensions without coordinates: nc\n",
        "Data variables:\n",
-       "    vicen_m   (time, nc, yt_ocean, xt_ocean) float32 dask.array&lt;chunksize=(1, 1, 1080, 1440), meta=np.ndarray&gt;\n",
+       "    hi_m      (time, yt_ocean, xt_ocean) float32 dask.array&lt;chunksize=(1, 1080, 1440), meta=np.ndarray&gt;\n",
        "    aice_m    (time, yt_ocean, xt_ocean) float32 dask.array&lt;chunksize=(1, 1080, 1440), meta=np.ndarray&gt;\n",
        "Attributes: (12/17)\n",
        "    title:                                    sea ice model output for CICE\n",
@@ -2111,11 +2505,11 @@
        "    intake_esm_attrs:variable_units:          days since 1980-01-01 00:00:00,...\n",
        "    intake_esm_attrs:file_id:                 iceh_XXXX_XX\n",
        "    intake_esm_attrs:_data_format_:           netcdf\n",
-       "    intake_esm_dataset_key:                   iceh_XXXX_XX.1mon</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>025deg_jra55</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d5f8c214-a72f-41c7-ba0b-2610925436b1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d5f8c214-a72f-41c7-ba0b-2610925436b1' class='xr-section-summary'  title='Expand/collapse section'>Groups: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%'></div></div></li><li class='xr-section-item'><input id='section-e1c05323-9eeb-47a9-a118-5f6326f86b11' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e1c05323-9eeb-47a9-a118-5f6326f86b11' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 523</li><li><span>nc</span>: 5</li><li><span class='xr-has-index'>yt_ocean</span>: 1080</li><li><span class='xr-has-index'>xt_ocean</span>: 1440</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-73c73a26-017e-4054-9128-163603669514' class='xr-section-summary-in' type='checkbox'  checked><label for='section-73c73a26-017e-4054-9128-163603669514' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1980-01-01 ... 2023-07-01</div><input id='attrs-8bcf798e-1386-4ee8-93f0-30bff1c28817' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8bcf798e-1386-4ee8-93f0-30bff1c28817' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a093387e-bdbc-4586-be2e-d3730e41a711' class='xr-var-data-in' type='checkbox'><label for='data-a093387e-bdbc-4586-be2e-d3730e41a711' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1980-01-01T00:00:00.000000000&#x27;, &#x27;1980-02-01T00:00:00.000000000&#x27;,\n",
+       "    intake_esm_dataset_key:                   iceh_XXXX_XX.1mon</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>025deg_jra55</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-1147cb51-5875-47af-90ea-e2e124e061e7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1147cb51-5875-47af-90ea-e2e124e061e7' class='xr-section-summary'  title='Expand/collapse section'>Groups: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%'></div></div></li><li class='xr-section-item'><input id='section-197d54df-9842-4bea-99b8-15d69191195d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-197d54df-9842-4bea-99b8-15d69191195d' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 523</li><li><span class='xr-has-index'>yt_ocean</span>: 1080</li><li><span class='xr-has-index'>xt_ocean</span>: 1440</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9383611a-8ff6-45f1-9f7b-f74aa80dec6b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9383611a-8ff6-45f1-9f7b-f74aa80dec6b' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1980-01-01 ... 2023-07-01</div><input id='attrs-5de9d21d-f786-43d9-bc51-d0c73733aef8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5de9d21d-f786-43d9-bc51-d0c73733aef8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2df908b9-e5e6-4bfa-ad3f-f84719bcf49b' class='xr-var-data-in' type='checkbox'><label for='data-2df908b9-e5e6-4bfa-ad3f-f84719bcf49b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1980-01-01T00:00:00.000000000&#x27;, &#x27;1980-02-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;1980-03-01T00:00:00.000000000&#x27;, ..., &#x27;2023-05-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;2023-06-01T00:00:00.000000000&#x27;, &#x27;2023-07-01T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.9 -279.6 ... 79.62 79.88</div><input id='attrs-1a3bed4a-280d-434e-9ca1-15b13133dc27' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a3bed4a-280d-434e-9ca1-15b13133dc27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-037db274-8a44-43ee-809e-e1c50e3658e8' class='xr-var-data-in' type='checkbox'><label for='data-037db274-8a44-43ee-809e-e1c50e3658e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-279.875, -279.625, -279.375, ...,   79.375,   79.625,   79.875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-81.08 -80.97 ... 89.84 89.95</div><input id='attrs-6ab152a0-2cae-4cde-911c-72ac2850fd43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ab152a0-2cae-4cde-911c-72ac2850fd43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fae198b-06a8-4e03-9b83-1610dcf17cc6' class='xr-var-data-in' type='checkbox'><label for='data-1fae198b-06a8-4e03-9b83-1610dcf17cc6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-81.077001, -80.971402, -80.865804, ...,  89.736085,  89.841684,\n",
-       "        89.947282])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolat_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b1bdb9fd-d43e-4a4d-8f9b-26bb6d5e9f54' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b1bdb9fd-d43e-4a4d-8f9b-26bb6d5e9f54' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22f6cd85-71b2-4aec-bceb-a40ff9469532' class='xr-var-data-in' type='checkbox'><label for='data-22f6cd85-71b2-4aec-bceb-a40ff9469532' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>valid_range :</span></dt><dd>[-91.  91.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[1555200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolon_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-084511f1-50d5-4934-97fd-5e18feaaef64' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-084511f1-50d5-4934-97fd-5e18feaaef64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b2175f6-4f6a-4f14-b53f-4b7274a8e889' class='xr-var-data-in' type='checkbox'><label for='data-1b2175f6-4f6a-4f14-b53f-4b7274a8e889' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>valid_range :</span></dt><dd>[-281.  361.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[1555200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>area_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0a92144e-91bf-4433-940e-0bf3e9004cd0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a92144e-91bf-4433-940e-0bf3e9004cd0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b6d6ed1-083f-4fe4-a748-04aad9a9b480' class='xr-var-data-in' type='checkbox'><label for='data-3b6d6ed1-083f-4fe4-a748-04aad9a9b480' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer cell area</dd><dt><span>units :</span></dt><dd>m^2</dd><dt><span>valid_range :</span></dt><dd>[0.e+00 1.e+15]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[1555200 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d698a12d-71fd-4b1e-85ee-bdd64fb477d4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d698a12d-71fd-4b1e-85ee-bdd64fb477d4' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>vicen_m</span></div><div class='xr-var-dims'>(time, nc, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 1080, 1440), meta=np.ndarray&gt;</div><input id='attrs-a3b1aacb-46c1-4d09-8269-e4304192239e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3b1aacb-46c1-4d09-8269-e4304192239e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b73cb84-f4f2-42b8-aa00-eb246888144e' class='xr-var-data-in' type='checkbox'><label for='data-9b73cb84-f4f2-42b8-aa00-eb246888144e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>ice volume, categories</dd><dt><span>coordinates :</span></dt><dd>TLON TLAT NCAT  time</dd><dt><span>cell_measures :</span></dt><dd>area: tarea</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_rep :</span></dt><dd>averaged</dd></dl></div><div class='xr-var-data'><table>\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.9 -279.6 ... 79.62 79.88</div><input id='attrs-4e322b92-93cb-4510-bd01-21f1d878d51b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e322b92-93cb-4510-bd01-21f1d878d51b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26acc235-436a-4e30-b2a0-0bc2be00e63a' class='xr-var-data-in' type='checkbox'><label for='data-26acc235-436a-4e30-b2a0-0bc2be00e63a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-279.875, -279.625, -279.375, ...,   79.375,   79.625,   79.875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-81.08 -80.97 ... 89.84 89.95</div><input id='attrs-826b3c8d-9981-49fa-b57c-23798f256790' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-826b3c8d-9981-49fa-b57c-23798f256790' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96dd438a-72bb-41d5-b1e0-245631b85c54' class='xr-var-data-in' type='checkbox'><label for='data-96dd438a-72bb-41d5-b1e0-245631b85c54' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-81.077001, -80.971402, -80.865804, ...,  89.736085,  89.841684,\n",
+       "        89.947282])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolat_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4f78d979-be30-4ce5-8a11-0226a1e85128' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f78d979-be30-4ce5-8a11-0226a1e85128' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6326ddeb-30b4-4c4b-a0b8-b0ab50818f0b' class='xr-var-data-in' type='checkbox'><label for='data-6326ddeb-30b4-4c4b-a0b8-b0ab50818f0b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>valid_range :</span></dt><dd>[-91.  91.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[1555200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolon_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7d459222-1d0a-4f36-a36a-100ab8f42bad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7d459222-1d0a-4f36-a36a-100ab8f42bad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75078e43-51c2-49b6-aeea-92a9a620f851' class='xr-var-data-in' type='checkbox'><label for='data-75078e43-51c2-49b6-aeea-92a9a620f851' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>valid_range :</span></dt><dd>[-281.  361.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[1555200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>area_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cd3ca491-d7d5-4609-9ace-96c61c11b22b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd3ca491-d7d5-4609-9ace-96c61c11b22b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94e18cab-fbbe-4bb9-a52e-69fd102a63f4' class='xr-var-data-in' type='checkbox'><label for='data-94e18cab-fbbe-4bb9-a52e-69fd102a63f4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer cell area</dd><dt><span>units :</span></dt><dd>m^2</dd><dt><span>valid_range :</span></dt><dd>[0.e+00 1.e+15]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[1555200 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4d541c75-2c9e-402d-a3f5-0b98a3bef1ff' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4d541c75-2c9e-402d-a3f5-0b98a3bef1ff' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>hi_m</span></div><div class='xr-var-dims'>(time, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1440), meta=np.ndarray&gt;</div><input id='attrs-45b49599-af6d-4222-9a97-a0978be55f10' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45b49599-af6d-4222-9a97-a0978be55f10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d3ba939-ac5b-4dd5-a833-7590485c5d5c' class='xr-var-data-in' type='checkbox'><label for='data-4d3ba939-ac5b-4dd5-a833-7590485c5d5c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>grid cell mean ice thickness</dd><dt><span>coordinates :</span></dt><dd>TLON TLAT time</dd><dt><span>cell_measures :</span></dt><dd>area: tarea</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_rep :</span></dt><dd>averaged</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2130,18 +2524,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 15.15 GiB </td>\n",
+       "                        <td> 3.03 GiB </td>\n",
        "                        <td> 5.93 MiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (523, 5, 1080, 1440) </td>\n",
-       "                        <td> (1, 1, 1080, 1440) </td>\n",
+       "                        <td> (523, 1080, 1440) </td>\n",
+       "                        <td> (1, 1080, 1440) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 2615 chunks in 1047 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 523 chunks in 1047 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -2151,104 +2545,85 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"412\" height=\"154\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "        <svg width=\"205\" height=\"165\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"44\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"44\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"90\" x2=\"35\" y2=\"115\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"1\" y1=\"0\" x2=\"1\" y2=\"25\" />\n",
-       "  <line x1=\"2\" y1=\"0\" x2=\"2\" y2=\"25\" />\n",
-       "  <line x1=\"4\" y1=\"0\" x2=\"4\" y2=\"25\" />\n",
-       "  <line x1=\"5\" y1=\"0\" x2=\"5\" y2=\"25\" />\n",
-       "  <line x1=\"6\" y1=\"0\" x2=\"6\" y2=\"25\" />\n",
-       "  <line x1=\"8\" y1=\"0\" x2=\"8\" y2=\"25\" />\n",
-       "  <line x1=\"9\" y1=\"0\" x2=\"9\" y2=\"25\" />\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"25\" />\n",
-       "  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n",
-       "  <line x1=\"13\" y1=\"0\" x2=\"13\" y2=\"25\" />\n",
-       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n",
-       "  <line x1=\"16\" y1=\"0\" x2=\"16\" y2=\"25\" />\n",
-       "  <line x1=\"17\" y1=\"0\" x2=\"17\" y2=\"25\" />\n",
-       "  <line x1=\"19\" y1=\"0\" x2=\"19\" y2=\"25\" />\n",
-       "  <line x1=\"20\" y1=\"0\" x2=\"20\" y2=\"25\" />\n",
-       "  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n",
-       "  <line x1=\"23\" y1=\"0\" x2=\"23\" y2=\"25\" />\n",
-       "  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"25\" />\n",
-       "  <line x1=\"26\" y1=\"0\" x2=\"26\" y2=\"25\" />\n",
-       "  <line x1=\"27\" y1=\"0\" x2=\"27\" y2=\"25\" />\n",
-       "  <line x1=\"28\" y1=\"0\" x2=\"28\" y2=\"25\" />\n",
-       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n",
-       "  <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"25\" />\n",
-       "  <line x1=\"33\" y1=\"0\" x2=\"33\" y2=\"25\" />\n",
-       "  <line x1=\"34\" y1=\"0\" x2=\"34\" y2=\"25\" />\n",
-       "  <line x1=\"35\" y1=\"0\" x2=\"35\" y2=\"25\" />\n",
-       "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
-       "  <line x1=\"38\" y1=\"0\" x2=\"38\" y2=\"25\" />\n",
-       "  <line x1=\"39\" y1=\"0\" x2=\"39\" y2=\"25\" />\n",
-       "  <line x1=\"41\" y1=\"0\" x2=\"41\" y2=\"25\" />\n",
-       "  <line x1=\"42\" y1=\"0\" x2=\"42\" y2=\"25\" />\n",
-       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"90\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"91\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"92\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"94\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"95\" />\n",
+       "  <line x1=\"16\" y1=\"6\" x2=\"16\" y2=\"96\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"98\" />\n",
+       "  <line x1=\"19\" y1=\"9\" x2=\"19\" y2=\"99\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"100\" />\n",
+       "  <line x1=\"22\" y1=\"12\" x2=\"22\" y2=\"102\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"103\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"104\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"106\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"107\" />\n",
+       "  <line x1=\"29\" y1=\"19\" x2=\"29\" y2=\"109\" />\n",
+       "  <line x1=\"30\" y1=\"20\" x2=\"30\" y2=\"110\" />\n",
+       "  <line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"111\" />\n",
+       "  <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"113\" />\n",
+       "  <line x1=\"34\" y1=\"24\" x2=\"34\" y2=\"114\" />\n",
+       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"115\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 44.14651480641994,0.0 44.14651480641994,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"22.073257\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >523</text>\n",
-       "  <text x=\"64.146515\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,64.146515,12.706308)\">1</text>\n",
-       "\n",
+       "  <polygon points=\"10.0,0.0 35.96853812142349,25.968538121423492 35.96853812142349,115.96853812142349 10.0,90.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"114\" y1=\"0\" x2=\"128\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"114\" y1=\"90\" x2=\"128\" y2=\"104\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"16\" y1=\"6\" x2=\"136\" y2=\"6\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"19\" y1=\"9\" x2=\"139\" y2=\"9\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"22\" y1=\"12\" x2=\"142\" y2=\"12\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" />\n",
+       "  <line x1=\"29\" y1=\"19\" x2=\"149\" y2=\"19\" />\n",
+       "  <line x1=\"30\" y1=\"20\" x2=\"150\" y2=\"20\" />\n",
+       "  <line x1=\"31\" y1=\"21\" x2=\"151\" y2=\"21\" />\n",
+       "  <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" />\n",
+       "  <line x1=\"34\" y1=\"24\" x2=\"154\" y2=\"24\" />\n",
+       "  <line x1=\"35\" y1=\"25\" x2=\"155\" y2=\"25\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"114\" y1=\"0\" x2=\"114\" y2=\"90\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"116\" y1=\"2\" x2=\"116\" y2=\"92\" />\n",
-       "  <line x1=\"119\" y1=\"5\" x2=\"119\" y2=\"95\" />\n",
-       "  <line x1=\"122\" y1=\"8\" x2=\"122\" y2=\"98\" />\n",
-       "  <line x1=\"125\" y1=\"11\" x2=\"125\" y2=\"101\" />\n",
-       "  <line x1=\"128\" y1=\"14\" x2=\"128\" y2=\"104\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"130\" y1=\"0\" x2=\"155\" y2=\"25\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"114.0,0.0 128.9485979497544,14.948597949754403 128.9485979497544,104.9485979497544 114.0,90.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"10.0,0.0 130.0,0.0 155.96853812142348,25.968538121423492 35.96853812142349,25.968538121423492\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"114\" y1=\"0\" x2=\"234\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"116\" y1=\"2\" x2=\"236\" y2=\"2\" />\n",
-       "  <line x1=\"119\" y1=\"5\" x2=\"239\" y2=\"5\" />\n",
-       "  <line x1=\"122\" y1=\"8\" x2=\"242\" y2=\"8\" />\n",
-       "  <line x1=\"125\" y1=\"11\" x2=\"245\" y2=\"11\" />\n",
-       "  <line x1=\"128\" y1=\"14\" x2=\"248\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"114\" y1=\"0\" x2=\"128\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"234\" y1=\"0\" x2=\"248\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"114.0,0.0 234.0,0.0 248.9485979497544,14.948597949754403 128.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"128\" y1=\"14\" x2=\"248\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"128\" y1=\"104\" x2=\"248\" y2=\"104\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"25\" x2=\"155\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"115\" x2=\"155\" y2=\"115\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"128\" y1=\"14\" x2=\"128\" y2=\"104\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"248\" y1=\"14\" x2=\"248\" y2=\"104\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"115\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"155\" y1=\"25\" x2=\"155\" y2=\"115\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"128.9485979497544,14.948597949754403 248.9485979497544,14.948597949754403 248.9485979497544,104.9485979497544 128.9485979497544,104.9485979497544\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"35.96853812142349,25.968538121423492 155.96853812142348,25.968538121423492 155.96853812142348,115.96853812142349 35.96853812142349,115.96853812142349\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"188.948598\" y=\"124.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
-       "  <text x=\"268.948598\" y=\"59.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,268.948598,59.948598)\">1080</text>\n",
-       "  <text x=\"111.474299\" y=\"117.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,111.474299,117.474299)\">5</text>\n",
+       "  <text x=\"95.968538\" y=\"135.968538\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"175.968538\" y=\"70.968538\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,175.968538,70.968538)\">1080</text>\n",
+       "  <text x=\"12.984269\" y=\"122.984269\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,12.984269,122.984269)\">523</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aice_m</span></div><div class='xr-var-dims'>(time, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1440), meta=np.ndarray&gt;</div><input id='attrs-3fd9c0bf-bab7-4e3b-851c-46da930c553c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3fd9c0bf-bab7-4e3b-851c-46da930c553c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa165e1f-6c47-400e-aac6-c690eca1156b' class='xr-var-data-in' type='checkbox'><label for='data-fa165e1f-6c47-400e-aac6-c690eca1156b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>ice area  (aggregate)</dd><dt><span>coordinates :</span></dt><dd>TLON TLAT time</dd><dt><span>cell_measures :</span></dt><dd>area: tarea</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_rep :</span></dt><dd>averaged</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aice_m</span></div><div class='xr-var-dims'>(time, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1440), meta=np.ndarray&gt;</div><input id='attrs-110fdffb-6013-4d9c-bda3-673c3d2bbd9e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-110fdffb-6013-4d9c-bda3-673c3d2bbd9e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e347dcaa-2075-4561-bb81-a6dcdc9537e9' class='xr-var-data-in' type='checkbox'><label for='data-e347dcaa-2075-4561-bb81-a6dcdc9537e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>ice area  (aggregate)</dd><dt><span>coordinates :</span></dt><dd>TLON TLAT time</dd><dt><span>cell_measures :</span></dt><dd>area: tarea</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_rep :</span></dt><dd>averaged</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2362,7 +2737,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-b1909cda-baa0-4d56-9f60-2a299b5ebabd' class='xr-section-summary-in' type='checkbox'  ><label for='section-b1909cda-baa0-4d56-9f60-2a299b5ebabd' class='xr-section-summary' >Attributes: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>sea ice model output for CICE</dd><dt><span>contents :</span></dt><dd>Diagnostic and Prognostic Variables</dd><dt><span>source :</span></dt><dd>Los Alamos Sea Ice Model (CICE) Version 5</dd><dt><span>time_period_freq :</span></dt><dd>month_1</dd><dt><span>comment3 :</span></dt><dd>seconds elapsed into model date:      0</dd><dt><span>conventions :</span></dt><dd>CF-1.0</dd><dt><span>io_flavor :</span></dt><dd>io_pio</dd><dt><span>intake_esm_attrs:realm :</span></dt><dd>seaIce</dd><dt><span>intake_esm_attrs:variable :</span></dt><dd>time_bounds,TLON,TLAT,ULON,ULAT,NCAT,tmask,blkmask,tarea,uarea,dxt,dyt,dxu,dyu,HTN,HTE,ANGLE,ANGLET,hi_m,hs_m,Tsfc_m,aice_m,uvel_m,vvel_m,sice_m,fswdn_m,fswup_m,flwdn_m,snow_ai_m,rain_ai_m,frzmlt_m,fswfac_m,fswabs_ai_m,albsni_m,albice_m,albsno_m,flat_ai_m,fsens_ai_m,flwup_ai_m,evap_ai_m,Tair_m,congel_m,frazil_m,snoice_m,meltt_m,melts_m,meltb_m,meltl_m,fresh_ai_m,fsalt_m,fsalt_ai_m,fhocn_ai_m,fswthru_ai_m,strairx_m,strairy_m,strtltx_m,strtlty_m,strcorx_m,strcory_m,strocnx_m,strocny_m,strintx_m,strinty_m,strength_m,divu_m,shear_m,dvidtt_m,dvidtd_m,daidtt_m,daidtd_m,trsig_m,ice_present_m,fcondtop_ai_m,fmeltt_ai_m,opening_m,aicen_m,vicen_m,fsurfn_ai_m,fcondtopn_ai_m,fmelttn_ai_m,flatn_ai_m</dd><dt><span>intake_esm_attrs:frequency :</span></dt><dd>1mon</dd><dt><span>intake_esm_attrs:variable_long_name :</span></dt><dd>boundaries for time-averaging interval,T grid center longitude,T grid center latitude,U grid center longitude,U grid center latitude,category maximum thickness,ocean grid mask,ice grid block mask,area of T grid cells,area of U grid cells,T cell width through middle,T cell height through middle,U cell width through middle,U cell height through middle,T cell width on North side,T cell width on East side,angle grid makes with latitude line on U grid,angle grid makes with latitude line on T grid,grid cell mean ice thickness,grid cell mean snow thickness,snow/ice surface temperature,ice area  (aggregate),ice velocity (x),ice velocity (y),bulk ice salinity,down solar flux,upward solar flux,down longwave flux,snowfall rate,rainfall rate,freeze/melt potential,shortwave scaling factor,snow/ice/ocn absorbed solar flux,snow/ice broad band albedo,bare ice albedo,snow albedo,latent heat flux,sensible heat flux,upward longwave flux,evaporative water flux,air temperature,congelation ice growth,frazil ice growth,snow-ice formation,top ice melt,top snow melt,basal ice melt,lateral ice melt,freshwtr flx ice to ocn,salt flux ice to ocn (cpl),salt flux ice to ocean,heat flux ice to ocean,SW flux thru ice to ocean,atm/ice stress (x),atm/ice stress (y),sea sfc tilt stress (x),sea sfc tilt stress (y),coriolis stress (x),coriolis stress (y),ocean/ice stress (x),ocean/ice stress (y),internal ice stress (x),internal ice stress (y),compressive ice strength,strain rate (divergence),strain rate (shear),volume tendency thermo,volume tendency dynamics,area tendency thermo,area tendency dynamics,internal stress tensor trace,fraction of time-avg interval that ice is present,top surface conductive heat flux,net surface heat flux causing melt,lead area opening rate,ice area, categories,ice volume, categories,net surface heat flux, categories,top sfc conductive heat flux, cat,net sfc heat flux causing melt, cat,latent heat flux, category</dd><dt><span>intake_esm_attrs:variable_standard_name :</span></dt><dd>,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,</dd><dt><span>intake_esm_attrs:variable_cell_methods :</span></dt><dd>,,,,,,,,,,,,,,,,,,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean</dd><dt><span>intake_esm_attrs:variable_units :</span></dt><dd>days since 1980-01-01 00:00:00,degrees_east,degrees_north,degrees_east,degrees_north,m,,,m^2,m^2,m,m,m,m,m,m,radians,radians,m,m,C,1,m/s,m/s,ppt,W/m^2,W/m^2,W/m^2,cm/day,cm/day,W/m^2,1,W/m^2,%,%,%,W/m^2,W/m^2,W/m^2,cm/day,C,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,kg/m^2/s,kg/m^2/s,W/m^2,W/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m,%/day,%/day,cm/day,cm/day,%/day,%/day,N/m^2,1,W/m^2,W/m^2,%/day,1,m,W/m^2,W/m^2,W/m^2,W/m^2</dd><dt><span>intake_esm_attrs:file_id :</span></dt><dd>iceh_XXXX_XX</dd><dt><span>intake_esm_attrs:_data_format_ :</span></dt><dd>netcdf</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>iceh_XXXX_XX.1mon</dd></dl></div></li></ul></div></div></ul></div></div><div style='display: inline-grid;'><div style='grid-column-start: 1;border-right: 0.2em solid;border-color: var(--xr-border-color);height: 100%;width: 0px;'></div><div style='grid-column-start: 2;grid-row-start: 1;height: 1em;width: 20px;border-bottom: 0.2em solid;border-color: var(--xr-border-color);'></div><div style='grid-column-start: 3;'><ul class='xr-sections'><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-0a9044a9-942f-476e-b938-4ecb3f5a3f54' class='xr-section-summary-in' type='checkbox'  ><label for='section-0a9044a9-942f-476e-b938-4ecb3f5a3f54' class='xr-section-summary' >Attributes: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>sea ice model output for CICE</dd><dt><span>contents :</span></dt><dd>Diagnostic and Prognostic Variables</dd><dt><span>source :</span></dt><dd>Los Alamos Sea Ice Model (CICE) Version 5</dd><dt><span>time_period_freq :</span></dt><dd>month_1</dd><dt><span>comment3 :</span></dt><dd>seconds elapsed into model date:      0</dd><dt><span>conventions :</span></dt><dd>CF-1.0</dd><dt><span>io_flavor :</span></dt><dd>io_pio</dd><dt><span>intake_esm_attrs:realm :</span></dt><dd>seaIce</dd><dt><span>intake_esm_attrs:variable :</span></dt><dd>time_bounds,TLON,TLAT,ULON,ULAT,NCAT,tmask,blkmask,tarea,uarea,dxt,dyt,dxu,dyu,HTN,HTE,ANGLE,ANGLET,hi_m,hs_m,Tsfc_m,aice_m,uvel_m,vvel_m,sice_m,fswdn_m,fswup_m,flwdn_m,snow_ai_m,rain_ai_m,frzmlt_m,fswfac_m,fswabs_ai_m,albsni_m,albice_m,albsno_m,flat_ai_m,fsens_ai_m,flwup_ai_m,evap_ai_m,Tair_m,congel_m,frazil_m,snoice_m,meltt_m,melts_m,meltb_m,meltl_m,fresh_ai_m,fsalt_m,fsalt_ai_m,fhocn_ai_m,fswthru_ai_m,strairx_m,strairy_m,strtltx_m,strtlty_m,strcorx_m,strcory_m,strocnx_m,strocny_m,strintx_m,strinty_m,strength_m,divu_m,shear_m,dvidtt_m,dvidtd_m,daidtt_m,daidtd_m,trsig_m,ice_present_m,fcondtop_ai_m,fmeltt_ai_m,opening_m,aicen_m,vicen_m,fsurfn_ai_m,fcondtopn_ai_m,fmelttn_ai_m,flatn_ai_m</dd><dt><span>intake_esm_attrs:frequency :</span></dt><dd>1mon</dd><dt><span>intake_esm_attrs:variable_long_name :</span></dt><dd>boundaries for time-averaging interval,T grid center longitude,T grid center latitude,U grid center longitude,U grid center latitude,category maximum thickness,ocean grid mask,ice grid block mask,area of T grid cells,area of U grid cells,T cell width through middle,T cell height through middle,U cell width through middle,U cell height through middle,T cell width on North side,T cell width on East side,angle grid makes with latitude line on U grid,angle grid makes with latitude line on T grid,grid cell mean ice thickness,grid cell mean snow thickness,snow/ice surface temperature,ice area  (aggregate),ice velocity (x),ice velocity (y),bulk ice salinity,down solar flux,upward solar flux,down longwave flux,snowfall rate,rainfall rate,freeze/melt potential,shortwave scaling factor,snow/ice/ocn absorbed solar flux,snow/ice broad band albedo,bare ice albedo,snow albedo,latent heat flux,sensible heat flux,upward longwave flux,evaporative water flux,air temperature,congelation ice growth,frazil ice growth,snow-ice formation,top ice melt,top snow melt,basal ice melt,lateral ice melt,freshwtr flx ice to ocn,salt flux ice to ocn (cpl),salt flux ice to ocean,heat flux ice to ocean,SW flux thru ice to ocean,atm/ice stress (x),atm/ice stress (y),sea sfc tilt stress (x),sea sfc tilt stress (y),coriolis stress (x),coriolis stress (y),ocean/ice stress (x),ocean/ice stress (y),internal ice stress (x),internal ice stress (y),compressive ice strength,strain rate (divergence),strain rate (shear),volume tendency thermo,volume tendency dynamics,area tendency thermo,area tendency dynamics,internal stress tensor trace,fraction of time-avg interval that ice is present,top surface conductive heat flux,net surface heat flux causing melt,lead area opening rate,ice area, categories,ice volume, categories,net surface heat flux, categories,top sfc conductive heat flux, cat,net sfc heat flux causing melt, cat,latent heat flux, category</dd><dt><span>intake_esm_attrs:variable_standard_name :</span></dt><dd>,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,</dd><dt><span>intake_esm_attrs:variable_cell_methods :</span></dt><dd>,,,,,,,,,,,,,,,,,,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean</dd><dt><span>intake_esm_attrs:variable_units :</span></dt><dd>days since 1980-01-01 00:00:00,degrees_east,degrees_north,degrees_east,degrees_north,m,,,m^2,m^2,m,m,m,m,m,m,radians,radians,m,m,C,1,m/s,m/s,ppt,W/m^2,W/m^2,W/m^2,cm/day,cm/day,W/m^2,1,W/m^2,%,%,%,W/m^2,W/m^2,W/m^2,cm/day,C,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,kg/m^2/s,kg/m^2/s,W/m^2,W/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m,%/day,%/day,cm/day,cm/day,%/day,%/day,N/m^2,1,W/m^2,W/m^2,%/day,1,m,W/m^2,W/m^2,W/m^2,W/m^2</dd><dt><span>intake_esm_attrs:file_id :</span></dt><dd>iceh_XXXX_XX</dd><dt><span>intake_esm_attrs:_data_format_ :</span></dt><dd>netcdf</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>iceh_XXXX_XX.1mon</dd></dl></div></li></ul></div></div></ul></div></div><div style='display: inline-grid;'><div style='grid-column-start: 1;border-right: 0.2em solid;border-color: var(--xr-border-color);height: 100%;width: 0px;'></div><div style='grid-column-start: 2;grid-row-start: 1;height: 1em;width: 20px;border-bottom: 0.2em solid;border-color: var(--xr-border-color);'></div><div style='grid-column-start: 3;'><ul class='xr-sections'><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
        "<defs>\n",
        "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
        "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
@@ -2726,7 +3101,7 @@
        "  fill: currentColor;\n",
        "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DatasetView&gt;\n",
-       "Dimensions:   (time: 708, yt_ocean: 300, xt_ocean: 360, nc: 5)\n",
+       "Dimensions:   (time: 708, yt_ocean: 300, xt_ocean: 360)\n",
        "Coordinates:\n",
        "  * time      (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n",
        "  * xt_ocean  (xt_ocean) float64 -279.5 -278.5 -277.5 -276.5 ... 77.5 78.5 79.5\n",
@@ -2734,10 +3109,9 @@
        "    geolat_t  (yt_ocean, xt_ocean) float32 ...\n",
        "    geolon_t  (yt_ocean, xt_ocean) float32 ...\n",
        "    area_t    (yt_ocean, xt_ocean) float32 ...\n",
-       "Dimensions without coordinates: nc\n",
        "Data variables:\n",
+       "    hi_m      (time, yt_ocean, xt_ocean) float32 dask.array&lt;chunksize=(1, 300, 360), meta=np.ndarray&gt;\n",
        "    aice_m    (time, yt_ocean, xt_ocean) float32 dask.array&lt;chunksize=(1, 300, 360), meta=np.ndarray&gt;\n",
-       "    vicen_m   (time, nc, yt_ocean, xt_ocean) float32 dask.array&lt;chunksize=(1, 1, 300, 360), meta=np.ndarray&gt;\n",
        "Attributes: (12/17)\n",
        "    title:                                    sea ice model output for CICE\n",
        "    contents:                                 Diagnostic and Prognostic Varia...\n",
@@ -2751,11 +3125,11 @@
        "    intake_esm_attrs:variable_units:          days since 1960-01-01 00:00:00,...\n",
        "    intake_esm_attrs:file_id:                 iceh_XXXX_XX\n",
        "    intake_esm_attrs:_data_format_:           netcdf\n",
-       "    intake_esm_dataset_key:                   iceh_XXXX_XX.1mon</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>1deg_era5</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-6b5c9964-3200-4dc7-962c-caf8e2de26ed' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6b5c9964-3200-4dc7-962c-caf8e2de26ed' class='xr-section-summary'  title='Expand/collapse section'>Groups: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%'></div></div></li><li class='xr-section-item'><input id='section-08ca3e99-2c31-4b1a-8365-df66a0320fd9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-08ca3e99-2c31-4b1a-8365-df66a0320fd9' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 708</li><li><span class='xr-has-index'>yt_ocean</span>: 300</li><li><span class='xr-has-index'>xt_ocean</span>: 360</li><li><span>nc</span>: 5</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8b3acf77-6c30-4081-a96a-a56b0b278061' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8b3acf77-6c30-4081-a96a-a56b0b278061' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1960-01-01 ... 2018-12-01</div><input id='attrs-0a4e3768-1171-47e3-bdf3-e7f077c4e2c9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0a4e3768-1171-47e3-bdf3-e7f077c4e2c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df87b420-ca76-4588-b3fc-cb0be262c6bc' class='xr-var-data-in' type='checkbox'><label for='data-df87b420-ca76-4588-b3fc-cb0be262c6bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1960-01-01T00:00:00.000000000&#x27;, &#x27;1960-02-01T00:00:00.000000000&#x27;,\n",
+       "    intake_esm_dataset_key:                   iceh_XXXX_XX.1mon</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>1deg_era5</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d286b0ec-8847-4f87-98ea-09f9f234bdbd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d286b0ec-8847-4f87-98ea-09f9f234bdbd' class='xr-section-summary'  title='Expand/collapse section'>Groups: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%'></div></div></li><li class='xr-section-item'><input id='section-e61cb10a-ce04-4cdb-98d7-664d8c3ff2c8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e61cb10a-ce04-4cdb-98d7-664d8c3ff2c8' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 708</li><li><span class='xr-has-index'>yt_ocean</span>: 300</li><li><span class='xr-has-index'>xt_ocean</span>: 360</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-30255179-b9bc-416c-82a8-b261879a5c73' class='xr-section-summary-in' type='checkbox'  checked><label for='section-30255179-b9bc-416c-82a8-b261879a5c73' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1960-01-01 ... 2018-12-01</div><input id='attrs-1011364e-d612-4684-85c6-60237aef6f08' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1011364e-d612-4684-85c6-60237aef6f08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-864e5d2a-c64f-45cb-b18a-2037220f9ccf' class='xr-var-data-in' type='checkbox'><label for='data-864e5d2a-c64f-45cb-b18a-2037220f9ccf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1960-01-01T00:00:00.000000000&#x27;, &#x27;1960-02-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;1960-03-01T00:00:00.000000000&#x27;, ..., &#x27;2018-10-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;2018-11-01T00:00:00.000000000&#x27;, &#x27;2018-12-01T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.5 -278.5 -277.5 ... 78.5 79.5</div><input id='attrs-7f6c116a-5c65-4523-909c-91be0891b2ac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f6c116a-5c65-4523-909c-91be0891b2ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb53d3ee-a784-4e9b-aa6c-13b4d91fb059' class='xr-var-data-in' type='checkbox'><label for='data-bb53d3ee-a784-4e9b-aa6c-13b4d91fb059' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-279.5, -278.5, -277.5, ...,   77.5,   78.5,   79.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-77.88 -77.63 ... 89.32 89.77</div><input id='attrs-29abc89b-d884-4b8f-a45f-e29676eac490' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29abc89b-d884-4b8f-a45f-e29676eac490' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27b64804-7e36-437b-be11-a1c3d74168ce' class='xr-var-data-in' type='checkbox'><label for='data-27b64804-7e36-437b-be11-a1c3d74168ce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-77.876623, -77.629713, -77.381707, ...,  88.872933,  89.324006,\n",
-       "        89.774476])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolat_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3519a1ee-19a3-41d6-904f-f5e89a90a4fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3519a1ee-19a3-41d6-904f-f5e89a90a4fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1baf1ea9-9621-4549-9921-71b504a0458d' class='xr-var-data-in' type='checkbox'><label for='data-1baf1ea9-9621-4549-9921-71b504a0458d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>valid_range :</span></dt><dd>[-91.  91.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[108000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolon_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4b6cfd74-2721-4cd8-bfa6-8f99be73729e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b6cfd74-2721-4cd8-bfa6-8f99be73729e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5dbb18e-3898-4661-94b7-30b00395c05b' class='xr-var-data-in' type='checkbox'><label for='data-b5dbb18e-3898-4661-94b7-30b00395c05b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>valid_range :</span></dt><dd>[-281.  361.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[108000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>area_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b1e0b749-16e2-47d7-83c9-8129141c8d10' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b1e0b749-16e2-47d7-83c9-8129141c8d10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c98e136-763a-40e9-b55b-7fcae71ee0a4' class='xr-var-data-in' type='checkbox'><label for='data-7c98e136-763a-40e9-b55b-7fcae71ee0a4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer cell area</dd><dt><span>units :</span></dt><dd>m^2</dd><dt><span>valid_range :</span></dt><dd>[0.e+00 1.e+15]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[108000 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bceeac8c-c715-4847-b303-f9bc317ed2da' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bceeac8c-c715-4847-b303-f9bc317ed2da' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>aice_m</span></div><div class='xr-var-dims'>(time, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 300, 360), meta=np.ndarray&gt;</div><input id='attrs-25278f84-07fd-42b5-a2ca-458d8f62e5a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-25278f84-07fd-42b5-a2ca-458d8f62e5a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf9aa3f2-51d3-488a-a37d-4bf155eae5a1' class='xr-var-data-in' type='checkbox'><label for='data-bf9aa3f2-51d3-488a-a37d-4bf155eae5a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>ice area  (aggregate)</dd><dt><span>coordinates :</span></dt><dd>TLON TLAT time</dd><dt><span>cell_measures :</span></dt><dd>area: tarea</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_rep :</span></dt><dd>averaged</dd></dl></div><div class='xr-var-data'><table>\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.5 -278.5 -277.5 ... 78.5 79.5</div><input id='attrs-fd5caa05-283f-42ef-b0eb-3a78b7c4fc93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fd5caa05-283f-42ef-b0eb-3a78b7c4fc93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6644ab1-a204-4ec5-b9d2-cea00c1523af' class='xr-var-data-in' type='checkbox'><label for='data-e6644ab1-a204-4ec5-b9d2-cea00c1523af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-279.5, -278.5, -277.5, ...,   77.5,   78.5,   79.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-77.88 -77.63 ... 89.32 89.77</div><input id='attrs-e87499dd-7abe-407e-b5bd-0d5c0f20c2a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e87499dd-7abe-407e-b5bd-0d5c0f20c2a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f937a594-a465-4efc-9871-a1ce8871ee46' class='xr-var-data-in' type='checkbox'><label for='data-f937a594-a465-4efc-9871-a1ce8871ee46' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-77.876623, -77.629713, -77.381707, ...,  88.872933,  89.324006,\n",
+       "        89.774476])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolat_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c885138d-68bc-487a-9a82-77828f8ebe98' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c885138d-68bc-487a-9a82-77828f8ebe98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1a85352-24b8-4c7a-a88c-20490ba961b9' class='xr-var-data-in' type='checkbox'><label for='data-b1a85352-24b8-4c7a-a88c-20490ba961b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>valid_range :</span></dt><dd>[-91.  91.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[108000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolon_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0863b321-4764-4a78-ac31-8258bc5d4e4a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0863b321-4764-4a78-ac31-8258bc5d4e4a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-952684d1-a146-43e1-af71-d4116341cbeb' class='xr-var-data-in' type='checkbox'><label for='data-952684d1-a146-43e1-af71-d4116341cbeb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>valid_range :</span></dt><dd>[-281.  361.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[108000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>area_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5f1bd179-ea5b-49c1-af65-5a05b0384252' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f1bd179-ea5b-49c1-af65-5a05b0384252' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-267e6744-9499-4745-958a-d6492c1b6440' class='xr-var-data-in' type='checkbox'><label for='data-267e6744-9499-4745-958a-d6492c1b6440' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer cell area</dd><dt><span>units :</span></dt><dd>m^2</dd><dt><span>valid_range :</span></dt><dd>[0.e+00 1.e+15]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[108000 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0768449d-a2cf-4dd0-afd8-f39c5a1e9bd0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0768449d-a2cf-4dd0-afd8-f39c5a1e9bd0' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>hi_m</span></div><div class='xr-var-dims'>(time, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 300, 360), meta=np.ndarray&gt;</div><input id='attrs-05cbe625-11d1-471d-9181-48ed75d8fcae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-05cbe625-11d1-471d-9181-48ed75d8fcae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2f54616-2986-4de0-ae50-bb8ac6d342af' class='xr-var-data-in' type='checkbox'><label for='data-a2f54616-2986-4de0-ae50-bb8ac6d342af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>grid cell mean ice thickness</dd><dt><span>coordinates :</span></dt><dd>TLON TLAT time</dd><dt><span>cell_measures :</span></dt><dd>area: tarea</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_rep :</span></dt><dd>averaged</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2869,7 +3243,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vicen_m</span></div><div class='xr-var-dims'>(time, nc, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 300, 360), meta=np.ndarray&gt;</div><input id='attrs-6ccd90e1-8733-45a0-b0d3-018d275b1eff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ccd90e1-8733-45a0-b0d3-018d275b1eff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf7b1dcf-b0c5-4569-8eb4-e90487945a79' class='xr-var-data-in' type='checkbox'><label for='data-bf7b1dcf-b0c5-4569-8eb4-e90487945a79' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>ice volume, categories</dd><dt><span>coordinates :</span></dt><dd>TLON TLAT NCAT  time</dd><dt><span>cell_measures :</span></dt><dd>area: tarea</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_rep :</span></dt><dd>averaged</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aice_m</span></div><div class='xr-var-dims'>(time, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 300, 360), meta=np.ndarray&gt;</div><input id='attrs-7466a17e-87df-499a-80da-ff58aec115e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7466a17e-87df-499a-80da-ff58aec115e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-453efa92-caf7-49ad-8cec-3ce8594db6d4' class='xr-var-data-in' type='checkbox'><label for='data-453efa92-caf7-49ad-8cec-3ce8594db6d4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>ice area  (aggregate)</dd><dt><span>coordinates :</span></dt><dd>TLON TLAT time</dd><dt><span>cell_measures :</span></dt><dd>area: tarea</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_rep :</span></dt><dd>averaged</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2884,18 +3258,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.42 GiB </td>\n",
+       "                        <td> 291.69 MiB </td>\n",
        "                        <td> 421.88 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (708, 5, 300, 360) </td>\n",
-       "                        <td> (1, 1, 300, 360) </td>\n",
+       "                        <td> (708, 300, 360) </td>\n",
+       "                        <td> (1, 300, 360) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 3540 chunks in 1417 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 708 chunks in 1417 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -2905,104 +3279,85 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"505\" height=\"115\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"3\" y1=\"0\" x2=\"3\" y2=\"25\" />\n",
-       "  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n",
-       "  <line x1=\"11\" y1=\"0\" x2=\"11\" y2=\"25\" />\n",
-       "  <line x1=\"14\" y1=\"0\" x2=\"14\" y2=\"25\" />\n",
-       "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
-       "  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n",
-       "  <line x1=\"26\" y1=\"0\" x2=\"26\" y2=\"25\" />\n",
-       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n",
-       "  <line x1=\"33\" y1=\"0\" x2=\"33\" y2=\"25\" />\n",
-       "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
-       "  <line x1=\"41\" y1=\"0\" x2=\"41\" y2=\"25\" />\n",
-       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n",
-       "  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"25\" />\n",
-       "  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n",
-       "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
-       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
-       "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
-       "  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n",
-       "  <line x1=\"71\" y1=\"0\" x2=\"71\" y2=\"25\" />\n",
-       "  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n",
-       "  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n",
-       "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
-       "  <line x1=\"86\" y1=\"0\" x2=\"86\" y2=\"25\" />\n",
-       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n",
-       "  <line x1=\"93\" y1=\"0\" x2=\"93\" y2=\"25\" />\n",
-       "  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n",
-       "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
-       "  <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"25\" />\n",
-       "  <line x1=\"108\" y1=\"0\" x2=\"108\" y2=\"25\" />\n",
-       "  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n",
-       "  <line x1=\"116\" y1=\"0\" x2=\"116\" y2=\"25\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >708</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "\n",
+       "        <svg width=\"191\" height=\"171\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"190\" y1=\"0\" x2=\"204\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"190\" y1=\"50\" x2=\"204\" y2=\"65\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"50\" x2=\"80\" y2=\"121\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"50\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"192\" y1=\"2\" x2=\"192\" y2=\"53\" />\n",
-       "  <line x1=\"195\" y1=\"5\" x2=\"195\" y2=\"56\" />\n",
-       "  <line x1=\"198\" y1=\"8\" x2=\"198\" y2=\"59\" />\n",
-       "  <line x1=\"201\" y1=\"11\" x2=\"201\" y2=\"62\" />\n",
-       "  <line x1=\"204\" y1=\"14\" x2=\"204\" y2=\"65\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"50\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"54\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"58\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"61\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"65\" />\n",
+       "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"69\" />\n",
+       "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"73\" />\n",
+       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"76\" />\n",
+       "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"80\" />\n",
+       "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"84\" />\n",
+       "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"87\" />\n",
+       "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"91\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"95\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"99\" />\n",
+       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"102\" />\n",
+       "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"106\" />\n",
+       "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"110\" />\n",
+       "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"113\" />\n",
+       "  <line x1=\"76\" y1=\"66\" x2=\"76\" y2=\"117\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"121\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"190.0,0.0 204.9485979497544,14.948597949754403 204.9485979497544,65.79605557687304 190.0,50.847457627118644\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,121.4356929212363 10.0,50.847457627118644\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"190\" y1=\"0\" x2=\"251\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"192\" y1=\"2\" x2=\"254\" y2=\"2\" />\n",
-       "  <line x1=\"195\" y1=\"5\" x2=\"256\" y2=\"5\" />\n",
-       "  <line x1=\"198\" y1=\"8\" x2=\"259\" y2=\"8\" />\n",
-       "  <line x1=\"201\" y1=\"11\" x2=\"262\" y2=\"11\" />\n",
-       "  <line x1=\"204\" y1=\"14\" x2=\"265\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"71\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"13\" y1=\"3\" x2=\"74\" y2=\"3\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"78\" y2=\"7\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"82\" y2=\"11\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"85\" y2=\"14\" />\n",
+       "  <line x1=\"28\" y1=\"18\" x2=\"89\" y2=\"18\" />\n",
+       "  <line x1=\"32\" y1=\"22\" x2=\"93\" y2=\"22\" />\n",
+       "  <line x1=\"35\" y1=\"25\" x2=\"96\" y2=\"25\" />\n",
+       "  <line x1=\"39\" y1=\"29\" x2=\"100\" y2=\"29\" />\n",
+       "  <line x1=\"43\" y1=\"33\" x2=\"104\" y2=\"33\" />\n",
+       "  <line x1=\"47\" y1=\"37\" x2=\"108\" y2=\"37\" />\n",
+       "  <line x1=\"50\" y1=\"40\" x2=\"111\" y2=\"40\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"115\" y2=\"44\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"119\" y2=\"48\" />\n",
+       "  <line x1=\"61\" y1=\"51\" x2=\"122\" y2=\"51\" />\n",
+       "  <line x1=\"65\" y1=\"55\" x2=\"126\" y2=\"55\" />\n",
+       "  <line x1=\"69\" y1=\"59\" x2=\"130\" y2=\"59\" />\n",
+       "  <line x1=\"73\" y1=\"63\" x2=\"134\" y2=\"63\" />\n",
+       "  <line x1=\"76\" y1=\"66\" x2=\"137\" y2=\"66\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"141\" y2=\"70\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"190\" y1=\"0\" x2=\"204\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"251\" y1=\"0\" x2=\"265\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"71\" y1=\"0\" x2=\"141\" y2=\"70\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"190.0,0.0 251.01694915254237,0.0 265.9655471022968,14.948597949754403 204.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"10.0,0.0 71.01694915254237,0.0 141.60518444666002,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"204\" y1=\"14\" x2=\"265\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"204\" y1=\"65\" x2=\"265\" y2=\"65\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"141\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"121\" x2=\"141\" y2=\"121\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"204\" y1=\"14\" x2=\"204\" y2=\"65\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"265\" y1=\"14\" x2=\"265\" y2=\"65\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"121\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"141\" y1=\"70\" x2=\"141\" y2=\"121\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"204.9485979497544,14.948597949754403 265.9655471022968,14.948597949754403 265.9655471022968,65.79605557687304 204.9485979497544,65.79605557687304\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 141.60518444666002,70.58823529411765 141.60518444666002,121.4356929212363 80.58823529411765,121.4356929212363\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"235.457073\" y=\"85.796056\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >360</text>\n",
-       "  <text x=\"285.965547\" y=\"40.372327\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,285.965547,40.372327)\">300</text>\n",
-       "  <text x=\"187.474299\" y=\"78.321757\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,187.474299,78.321757)\">5</text>\n",
+       "  <text x=\"111.096710\" y=\"141.435693\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >360</text>\n",
+       "  <text x=\"161.605184\" y=\"96.011964\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,161.605184,96.011964)\">300</text>\n",
+       "  <text x=\"35.294118\" y=\"106.141575\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,106.141575)\">708</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-e56869ae-5aed-46e2-8a71-874324d036df' class='xr-section-summary-in' type='checkbox'  ><label for='section-e56869ae-5aed-46e2-8a71-874324d036df' class='xr-section-summary' >Attributes: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>sea ice model output for CICE</dd><dt><span>contents :</span></dt><dd>Diagnostic and Prognostic Variables</dd><dt><span>source :</span></dt><dd>Los Alamos Sea Ice Model (CICE) Version 5</dd><dt><span>time_period_freq :</span></dt><dd>month_1</dd><dt><span>comment3 :</span></dt><dd>seconds elapsed into model date:      0</dd><dt><span>conventions :</span></dt><dd>CF-1.0</dd><dt><span>io_flavor :</span></dt><dd>io_pio</dd><dt><span>intake_esm_attrs:realm :</span></dt><dd>seaIce</dd><dt><span>intake_esm_attrs:variable :</span></dt><dd>time_bounds,TLON,TLAT,ULON,ULAT,NCAT,tmask,blkmask,tarea,uarea,dxt,dyt,dxu,dyu,HTN,HTE,ANGLE,ANGLET,hi_m,hs_m,Tsfc_m,aice_m,uvel_m,vvel_m,uatm_m,vatm_m,sice_m,fswdn_m,fswup_m,flwdn_m,snow_ai_m,rain_ai_m,frzmlt_m,fswfac_m,fswabs_ai_m,albsni_m,alvdr_ai_m,alidr_ai_m,alvdf_ai_m,alidf_ai_m,albice_m,albsno_m,flat_ai_m,fsens_ai_m,flwup_ai_m,evap_ai_m,Tair_m,congel_m,frazil_m,snoice_m,meltt_m,melts_m,meltb_m,meltl_m,fresh_ai_m,fsalt_m,fsalt_ai_m,fhocn_ai_m,fswthru_ai_m,strairx_m,strairy_m,strtltx_m,strtlty_m,strcorx_m,strcory_m,strocnx_m,strocny_m,strintx_m,strinty_m,strength_m,divu_m,shear_m,dvidtt_m,dvidtd_m,daidtt_m,daidtd_m,trsig_m,ice_present_m,fcondtop_ai_m,fmeltt_ai_m,opening_m,aicen_m,vicen_m,fsurfn_ai_m,fcondtopn_ai_m,fmelttn_ai_m,flatn_ai_m</dd><dt><span>intake_esm_attrs:frequency :</span></dt><dd>1mon</dd><dt><span>intake_esm_attrs:variable_long_name :</span></dt><dd>boundaries for time-averaging interval,T grid center longitude,T grid center latitude,U grid center longitude,U grid center latitude,category maximum thickness,ocean grid mask,ice grid block mask,area of T grid cells,area of U grid cells,T cell width through middle,T cell height through middle,U cell width through middle,U cell height through middle,T cell width on North side,T cell width on East side,angle grid makes with latitude line on U grid,angle grid makes with latitude line on T grid,grid cell mean ice thickness,grid cell mean snow thickness,snow/ice surface temperature,ice area  (aggregate),ice velocity (x),ice velocity (y),atm velocity (x),atm velocity (y),bulk ice salinity,down solar flux,upward solar flux,down longwave flux,snowfall rate,rainfall rate,freeze/melt potential,shortwave scaling factor,snow/ice/ocn absorbed solar flux,snow/ice broad band albedo,visible direct albedo,near IR direct albedo,visible diffuse albedo,near IR diffuse albedo,bare ice albedo,snow albedo,latent heat flux,sensible heat flux,upward longwave flux,evaporative water flux,air temperature,congelation ice growth,frazil ice growth,snow-ice formation,top ice melt,top snow melt,basal ice melt,lateral ice melt,freshwtr flx ice to ocn,salt flux ice to ocn (cpl),salt flux ice to ocean,heat flux ice to ocean,SW flux thru ice to ocean,atm/ice stress (x),atm/ice stress (y),sea sfc tilt stress (x),sea sfc tilt stress (y),coriolis stress (x),coriolis stress (y),ocean/ice stress (x),ocean/ice stress (y),internal ice stress (x),internal ice stress (y),compressive ice strength,strain rate (divergence),strain rate (shear),volume tendency thermo,volume tendency dynamics,area tendency thermo,area tendency dynamics,internal stress tensor trace,fraction of time-avg interval that ice is present,top surface conductive heat flux,net surface heat flux causing melt,lead area opening rate,ice area, categories,ice volume, categories,net surface heat flux, categories,top sfc conductive heat flux, cat,net sfc heat flux causing melt, cat,latent heat flux, category</dd><dt><span>intake_esm_attrs:variable_standard_name :</span></dt><dd>,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,</dd><dt><span>intake_esm_attrs:variable_cell_methods :</span></dt><dd>,,,,,,,,,,,,,,,,,,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean</dd><dt><span>intake_esm_attrs:variable_units :</span></dt><dd>days since 1960-01-01 00:00:00,degrees_east,degrees_north,degrees_east,degrees_north,m,,,m^2,m^2,m,m,m,m,m,m,radians,radians,m,m,C,1,m/s,m/s,m/s,m/s,ppt,W/m^2,W/m^2,W/m^2,cm/day,cm/day,W/m^2,1,W/m^2,%,%,%,%,%,%,%,W/m^2,W/m^2,W/m^2,cm/day,C,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,kg/m^2/s,kg/m^2/s,W/m^2,W/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m,%/day,%/day,cm/day,cm/day,%/day,%/day,N/m^2,1,W/m^2,W/m^2,%/day,1,m,W/m^2,W/m^2,W/m^2,W/m^2</dd><dt><span>intake_esm_attrs:file_id :</span></dt><dd>iceh_XXXX_XX</dd><dt><span>intake_esm_attrs:_data_format_ :</span></dt><dd>netcdf</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>iceh_XXXX_XX.1mon</dd></dl></div></li></ul></div></div></ul></div></div><div style='display: inline-grid;'><div style='grid-column-start: 1;border-right: 0.2em solid;border-color: var(--xr-border-color);height: 1.2em;width: 0px;'></div><div style='grid-column-start: 2;grid-row-start: 1;height: 1em;width: 20px;border-bottom: 0.2em solid;border-color: var(--xr-border-color);'></div><div style='grid-column-start: 3;'><ul class='xr-sections'><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-07e8573c-ce3e-493c-bcc7-1b11fad55bc7' class='xr-section-summary-in' type='checkbox'  ><label for='section-07e8573c-ce3e-493c-bcc7-1b11fad55bc7' class='xr-section-summary' >Attributes: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>sea ice model output for CICE</dd><dt><span>contents :</span></dt><dd>Diagnostic and Prognostic Variables</dd><dt><span>source :</span></dt><dd>Los Alamos Sea Ice Model (CICE) Version 5</dd><dt><span>time_period_freq :</span></dt><dd>month_1</dd><dt><span>comment3 :</span></dt><dd>seconds elapsed into model date:      0</dd><dt><span>conventions :</span></dt><dd>CF-1.0</dd><dt><span>io_flavor :</span></dt><dd>io_pio</dd><dt><span>intake_esm_attrs:realm :</span></dt><dd>seaIce</dd><dt><span>intake_esm_attrs:variable :</span></dt><dd>time_bounds,TLON,TLAT,ULON,ULAT,NCAT,tmask,blkmask,tarea,uarea,dxt,dyt,dxu,dyu,HTN,HTE,ANGLE,ANGLET,hi_m,hs_m,Tsfc_m,aice_m,uvel_m,vvel_m,uatm_m,vatm_m,sice_m,fswdn_m,fswup_m,flwdn_m,snow_ai_m,rain_ai_m,frzmlt_m,fswfac_m,fswabs_ai_m,albsni_m,alvdr_ai_m,alidr_ai_m,alvdf_ai_m,alidf_ai_m,albice_m,albsno_m,flat_ai_m,fsens_ai_m,flwup_ai_m,evap_ai_m,Tair_m,congel_m,frazil_m,snoice_m,meltt_m,melts_m,meltb_m,meltl_m,fresh_ai_m,fsalt_m,fsalt_ai_m,fhocn_ai_m,fswthru_ai_m,strairx_m,strairy_m,strtltx_m,strtlty_m,strcorx_m,strcory_m,strocnx_m,strocny_m,strintx_m,strinty_m,strength_m,divu_m,shear_m,dvidtt_m,dvidtd_m,daidtt_m,daidtd_m,trsig_m,ice_present_m,fcondtop_ai_m,fmeltt_ai_m,opening_m,aicen_m,vicen_m,fsurfn_ai_m,fcondtopn_ai_m,fmelttn_ai_m,flatn_ai_m</dd><dt><span>intake_esm_attrs:frequency :</span></dt><dd>1mon</dd><dt><span>intake_esm_attrs:variable_long_name :</span></dt><dd>boundaries for time-averaging interval,T grid center longitude,T grid center latitude,U grid center longitude,U grid center latitude,category maximum thickness,ocean grid mask,ice grid block mask,area of T grid cells,area of U grid cells,T cell width through middle,T cell height through middle,U cell width through middle,U cell height through middle,T cell width on North side,T cell width on East side,angle grid makes with latitude line on U grid,angle grid makes with latitude line on T grid,grid cell mean ice thickness,grid cell mean snow thickness,snow/ice surface temperature,ice area  (aggregate),ice velocity (x),ice velocity (y),atm velocity (x),atm velocity (y),bulk ice salinity,down solar flux,upward solar flux,down longwave flux,snowfall rate,rainfall rate,freeze/melt potential,shortwave scaling factor,snow/ice/ocn absorbed solar flux,snow/ice broad band albedo,visible direct albedo,near IR direct albedo,visible diffuse albedo,near IR diffuse albedo,bare ice albedo,snow albedo,latent heat flux,sensible heat flux,upward longwave flux,evaporative water flux,air temperature,congelation ice growth,frazil ice growth,snow-ice formation,top ice melt,top snow melt,basal ice melt,lateral ice melt,freshwtr flx ice to ocn,salt flux ice to ocn (cpl),salt flux ice to ocean,heat flux ice to ocean,SW flux thru ice to ocean,atm/ice stress (x),atm/ice stress (y),sea sfc tilt stress (x),sea sfc tilt stress (y),coriolis stress (x),coriolis stress (y),ocean/ice stress (x),ocean/ice stress (y),internal ice stress (x),internal ice stress (y),compressive ice strength,strain rate (divergence),strain rate (shear),volume tendency thermo,volume tendency dynamics,area tendency thermo,area tendency dynamics,internal stress tensor trace,fraction of time-avg interval that ice is present,top surface conductive heat flux,net surface heat flux causing melt,lead area opening rate,ice area, categories,ice volume, categories,net surface heat flux, categories,top sfc conductive heat flux, cat,net sfc heat flux causing melt, cat,latent heat flux, category</dd><dt><span>intake_esm_attrs:variable_standard_name :</span></dt><dd>,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,</dd><dt><span>intake_esm_attrs:variable_cell_methods :</span></dt><dd>,,,,,,,,,,,,,,,,,,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean</dd><dt><span>intake_esm_attrs:variable_units :</span></dt><dd>days since 1960-01-01 00:00:00,degrees_east,degrees_north,degrees_east,degrees_north,m,,,m^2,m^2,m,m,m,m,m,m,radians,radians,m,m,C,1,m/s,m/s,m/s,m/s,ppt,W/m^2,W/m^2,W/m^2,cm/day,cm/day,W/m^2,1,W/m^2,%,%,%,%,%,%,%,W/m^2,W/m^2,W/m^2,cm/day,C,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,cm/day,kg/m^2/s,kg/m^2/s,W/m^2,W/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m,%/day,%/day,cm/day,cm/day,%/day,%/day,N/m^2,1,W/m^2,W/m^2,%/day,1,m,W/m^2,W/m^2,W/m^2,W/m^2</dd><dt><span>intake_esm_attrs:file_id :</span></dt><dd>iceh_XXXX_XX</dd><dt><span>intake_esm_attrs:_data_format_ :</span></dt><dd>netcdf</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>iceh_XXXX_XX.1mon</dd></dl></div></li></ul></div></div></ul></div></div><div style='display: inline-grid;'><div style='grid-column-start: 1;border-right: 0.2em solid;border-color: var(--xr-border-color);height: 1.2em;width: 0px;'></div><div style='grid-column-start: 2;grid-row-start: 1;height: 1em;width: 20px;border-bottom: 0.2em solid;border-color: var(--xr-border-color);'></div><div style='grid-column-start: 3;'><ul class='xr-sections'><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
        "<defs>\n",
        "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
        "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
@@ -3366,7 +3721,7 @@
        "  fill: currentColor;\n",
        "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DatasetView&gt;\n",
-       "Dimensions:   (time: 720, nc: 5, yt_ocean: 300, xt_ocean: 360)\n",
+       "Dimensions:   (time: 720, yt_ocean: 300, xt_ocean: 360)\n",
        "Coordinates:\n",
        "  * time      (time) datetime64[ns] 1959-01-01 1959-02-01 ... 2019-12-01\n",
        "  * xt_ocean  (xt_ocean) float64 -279.5 -278.5 -277.5 -276.5 ... 77.5 78.5 79.5\n",
@@ -3374,9 +3729,8 @@
        "    geolat_t  (yt_ocean, xt_ocean) float32 ...\n",
        "    geolon_t  (yt_ocean, xt_ocean) float32 ...\n",
        "    area_t    (yt_ocean, xt_ocean) float32 ...\n",
-       "Dimensions without coordinates: nc\n",
        "Data variables:\n",
-       "    vicen_m   (time, nc, yt_ocean, xt_ocean) float32 dask.array&lt;chunksize=(1, 1, 300, 360), meta=np.ndarray&gt;\n",
+       "    hi_m      (time, yt_ocean, xt_ocean) float32 dask.array&lt;chunksize=(1, 300, 360), meta=np.ndarray&gt;\n",
        "    aice_m    (time, yt_ocean, xt_ocean) float32 dask.array&lt;chunksize=(1, 300, 360), meta=np.ndarray&gt;\n",
        "Attributes: (12/16)\n",
        "    title:                                    sea ice model output for CICE\n",
@@ -3391,11 +3745,11 @@
        "    intake_esm_attrs:variable_cell_methods:   ,,,,,,,,,,,,,,,,,,time: mean,ti...\n",
        "    intake_esm_attrs:file_id:                 iceh_XXXX_XX\n",
        "    intake_esm_attrs:_data_format_:           netcdf\n",
-       "    intake_esm_dataset_key:                   iceh_XXXX_XX.1mon</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>1deg_jra55</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-e4a6fb48-bc12-4e02-8702-a4b65bbbe91d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e4a6fb48-bc12-4e02-8702-a4b65bbbe91d' class='xr-section-summary'  title='Expand/collapse section'>Groups: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%'></div></div></li><li class='xr-section-item'><input id='section-fc08f9a9-8cde-486e-8fa4-5f23c56f731d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fc08f9a9-8cde-486e-8fa4-5f23c56f731d' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 720</li><li><span>nc</span>: 5</li><li><span class='xr-has-index'>yt_ocean</span>: 300</li><li><span class='xr-has-index'>xt_ocean</span>: 360</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-14ab324e-98cf-47df-9646-bb160ed49d3f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-14ab324e-98cf-47df-9646-bb160ed49d3f' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1959-01-01 ... 2019-12-01</div><input id='attrs-85334c0d-95cf-4c30-996e-776289ae3581' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-85334c0d-95cf-4c30-996e-776289ae3581' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba6b97cd-5ad6-4f64-86f5-b9a369c17b2b' class='xr-var-data-in' type='checkbox'><label for='data-ba6b97cd-5ad6-4f64-86f5-b9a369c17b2b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1959-01-01T00:00:00.000000000&#x27;, &#x27;1959-02-01T00:00:00.000000000&#x27;,\n",
+       "    intake_esm_dataset_key:                   iceh_XXXX_XX.1mon</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>1deg_jra55</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-4c957a4c-5ea1-467f-a75b-8bfdeb2b3c56' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4c957a4c-5ea1-467f-a75b-8bfdeb2b3c56' class='xr-section-summary'  title='Expand/collapse section'>Groups: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%'></div></div></li><li class='xr-section-item'><input id='section-e7eda760-e6cc-4190-95a1-cea53dea1fec' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e7eda760-e6cc-4190-95a1-cea53dea1fec' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 720</li><li><span class='xr-has-index'>yt_ocean</span>: 300</li><li><span class='xr-has-index'>xt_ocean</span>: 360</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-4a6cd42c-25ed-4aad-b0dc-cc8eafa3ac11' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4a6cd42c-25ed-4aad-b0dc-cc8eafa3ac11' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1959-01-01 ... 2019-12-01</div><input id='attrs-d09a985d-810a-43a2-a27f-0bf320f460f6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d09a985d-810a-43a2-a27f-0bf320f460f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa02a721-368f-4a60-a0bd-351f10dba14b' class='xr-var-data-in' type='checkbox'><label for='data-fa02a721-368f-4a60-a0bd-351f10dba14b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1959-01-01T00:00:00.000000000&#x27;, &#x27;1959-02-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;1959-03-01T00:00:00.000000000&#x27;, ..., &#x27;2019-10-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;2019-11-01T00:00:00.000000000&#x27;, &#x27;2019-12-01T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.5 -278.5 -277.5 ... 78.5 79.5</div><input id='attrs-a2a48d4d-d1a3-4304-9c83-90b1405613b4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a2a48d4d-d1a3-4304-9c83-90b1405613b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2bc5697-e218-4dad-8fa9-6fb4fffa76aa' class='xr-var-data-in' type='checkbox'><label for='data-d2bc5697-e218-4dad-8fa9-6fb4fffa76aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-279.5, -278.5, -277.5, ...,   77.5,   78.5,   79.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-77.88 -77.63 ... 89.32 89.77</div><input id='attrs-a22b0ee6-eb91-41f0-8748-03cf3e0f2f58' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a22b0ee6-eb91-41f0-8748-03cf3e0f2f58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fefeea2c-b83b-4b8a-8bdc-dcf2f3cc7b7d' class='xr-var-data-in' type='checkbox'><label for='data-fefeea2c-b83b-4b8a-8bdc-dcf2f3cc7b7d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-77.876623, -77.629713, -77.381707, ...,  88.872933,  89.324006,\n",
-       "        89.774476])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolat_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bea15ced-6c14-43e0-9cd9-0c2b354d7aa8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bea15ced-6c14-43e0-9cd9-0c2b354d7aa8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04d90a20-156c-4d5a-ba6a-e3be1d56e2ac' class='xr-var-data-in' type='checkbox'><label for='data-04d90a20-156c-4d5a-ba6a-e3be1d56e2ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>valid_range :</span></dt><dd>[-91.  91.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[108000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolon_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6c4c1b9c-a529-45be-9980-8907d3803c83' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c4c1b9c-a529-45be-9980-8907d3803c83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7955c13a-3124-4238-beef-76b219925616' class='xr-var-data-in' type='checkbox'><label for='data-7955c13a-3124-4238-beef-76b219925616' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>valid_range :</span></dt><dd>[-281.  361.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[108000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>area_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f9581d9b-5e9d-49a2-82c5-dc07725113c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f9581d9b-5e9d-49a2-82c5-dc07725113c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a148765c-7928-43d1-8230-75769a03b191' class='xr-var-data-in' type='checkbox'><label for='data-a148765c-7928-43d1-8230-75769a03b191' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer cell area</dd><dt><span>units :</span></dt><dd>m^2</dd><dt><span>valid_range :</span></dt><dd>[0.e+00 1.e+15]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[108000 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1952135c-da54-4c49-9a78-d7b435aaff51' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1952135c-da54-4c49-9a78-d7b435aaff51' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>vicen_m</span></div><div class='xr-var-dims'>(time, nc, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 300, 360), meta=np.ndarray&gt;</div><input id='attrs-de72b9f3-cb9a-452a-8e8c-c6add8359668' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de72b9f3-cb9a-452a-8e8c-c6add8359668' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93eabfe2-1063-41cc-acfa-bdb467c9e0dc' class='xr-var-data-in' type='checkbox'><label for='data-93eabfe2-1063-41cc-acfa-bdb467c9e0dc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>ice volume, categories</dd><dt><span>coordinates :</span></dt><dd>TLON TLAT NCAT  time</dd><dt><span>cell_measures :</span></dt><dd>area: tarea</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_rep :</span></dt><dd>averaged</dd></dl></div><div class='xr-var-data'><table>\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.5 -278.5 -277.5 ... 78.5 79.5</div><input id='attrs-76dba647-f2f5-4625-9074-343d6e5a76a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-76dba647-f2f5-4625-9074-343d6e5a76a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71e84956-6361-4ac7-8c3e-40bcc9857995' class='xr-var-data-in' type='checkbox'><label for='data-71e84956-6361-4ac7-8c3e-40bcc9857995' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-279.5, -278.5, -277.5, ...,   77.5,   78.5,   79.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-77.88 -77.63 ... 89.32 89.77</div><input id='attrs-fb9f1fb5-971c-4027-a1b8-2ad13a3c22f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fb9f1fb5-971c-4027-a1b8-2ad13a3c22f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b019ff99-cb07-4a8b-b7f3-b89fcee247bb' class='xr-var-data-in' type='checkbox'><label for='data-b019ff99-cb07-4a8b-b7f3-b89fcee247bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tcell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-77.876623, -77.629713, -77.381707, ...,  88.872933,  89.324006,\n",
+       "        89.774476])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolat_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-feb8fb8a-246a-487b-a1a4-c7e204a207cb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-feb8fb8a-246a-487b-a1a4-c7e204a207cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b744a648-2217-477c-8ddf-935499ac88a0' class='xr-var-data-in' type='checkbox'><label for='data-b744a648-2217-477c-8ddf-935499ac88a0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>valid_range :</span></dt><dd>[-91.  91.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[108000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolon_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7963912d-7ba6-41dc-8ffe-52b109a6b696' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7963912d-7ba6-41dc-8ffe-52b109a6b696' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5022deb1-c50b-4b5c-82a7-cfef1deec0f2' class='xr-var-data-in' type='checkbox'><label for='data-5022deb1-c50b-4b5c-82a7-cfef1deec0f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>valid_range :</span></dt><dd>[-281.  361.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[108000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>area_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d34e97de-1597-47ed-827c-5a511e3d1f53' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d34e97de-1597-47ed-827c-5a511e3d1f53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-daed0eda-7374-471c-8f03-d60bcd23a286' class='xr-var-data-in' type='checkbox'><label for='data-daed0eda-7374-471c-8f03-d60bcd23a286' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>tracer cell area</dd><dt><span>units :</span></dt><dd>m^2</dd><dt><span>valid_range :</span></dt><dd>[0.e+00 1.e+15]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>[108000 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8d182bd6-c0f0-4170-a110-2f969e446a9d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8d182bd6-c0f0-4170-a110-2f969e446a9d' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>hi_m</span></div><div class='xr-var-dims'>(time, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 300, 360), meta=np.ndarray&gt;</div><input id='attrs-9cc4ab3a-a4af-4814-9a5c-3dde81ffed27' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9cc4ab3a-a4af-4814-9a5c-3dde81ffed27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-552548db-e2ce-4921-a97b-2cca1ff3aac3' class='xr-var-data-in' type='checkbox'><label for='data-552548db-e2ce-4921-a97b-2cca1ff3aac3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>grid cell mean ice thickness</dd><dt><span>coordinates :</span></dt><dd>TLON TLAT time</dd><dt><span>cell_measures :</span></dt><dd>area: tarea</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_rep :</span></dt><dd>averaged</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3410,18 +3764,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.45 GiB </td>\n",
+       "                        <td> 296.63 MiB </td>\n",
        "                        <td> 421.88 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (720, 5, 300, 360) </td>\n",
-       "                        <td> (1, 1, 300, 360) </td>\n",
+       "                        <td> (720, 300, 360) </td>\n",
+       "                        <td> (1, 300, 360) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 3600 chunks in 1441 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 720 chunks in 1441 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -3431,104 +3785,85 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"504\" height=\"114\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"3\" y1=\"0\" x2=\"3\" y2=\"25\" />\n",
-       "  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n",
-       "  <line x1=\"11\" y1=\"0\" x2=\"11\" y2=\"25\" />\n",
-       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n",
-       "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
-       "  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n",
-       "  <line x1=\"26\" y1=\"0\" x2=\"26\" y2=\"25\" />\n",
-       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n",
-       "  <line x1=\"33\" y1=\"0\" x2=\"33\" y2=\"25\" />\n",
-       "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
-       "  <line x1=\"41\" y1=\"0\" x2=\"41\" y2=\"25\" />\n",
-       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n",
-       "  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"25\" />\n",
-       "  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n",
-       "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
-       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
-       "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
-       "  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n",
-       "  <line x1=\"71\" y1=\"0\" x2=\"71\" y2=\"25\" />\n",
-       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
-       "  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n",
-       "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
-       "  <line x1=\"86\" y1=\"0\" x2=\"86\" y2=\"25\" />\n",
-       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n",
-       "  <line x1=\"93\" y1=\"0\" x2=\"93\" y2=\"25\" />\n",
-       "  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n",
-       "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
-       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n",
-       "  <line x1=\"108\" y1=\"0\" x2=\"108\" y2=\"25\" />\n",
-       "  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n",
-       "  <line x1=\"116\" y1=\"0\" x2=\"116\" y2=\"25\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >720</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "\n",
+       "        <svg width=\"190\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"190\" y1=\"0\" x2=\"204\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"190\" y1=\"50\" x2=\"204\" y2=\"64\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"50\" x2=\"80\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"50\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"192\" y1=\"2\" x2=\"192\" y2=\"52\" />\n",
-       "  <line x1=\"195\" y1=\"5\" x2=\"195\" y2=\"55\" />\n",
-       "  <line x1=\"198\" y1=\"8\" x2=\"198\" y2=\"58\" />\n",
-       "  <line x1=\"201\" y1=\"11\" x2=\"201\" y2=\"61\" />\n",
-       "  <line x1=\"204\" y1=\"14\" x2=\"204\" y2=\"64\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"50\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"53\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"57\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"61\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"64\" />\n",
+       "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"68\" />\n",
+       "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"72\" />\n",
+       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"75\" />\n",
+       "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"79\" />\n",
+       "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"83\" />\n",
+       "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"87\" />\n",
+       "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"90\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"94\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"98\" />\n",
+       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"101\" />\n",
+       "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"105\" />\n",
+       "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"109\" />\n",
+       "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"113\" />\n",
+       "  <line x1=\"76\" y1=\"66\" x2=\"76\" y2=\"116\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"190.0,0.0 204.9485979497544,14.948597949754403 204.9485979497544,64.9485979497544 190.0,50.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,120.58823529411765 10.0,50.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"190\" y1=\"0\" x2=\"250\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"192\" y1=\"2\" x2=\"252\" y2=\"2\" />\n",
-       "  <line x1=\"195\" y1=\"5\" x2=\"255\" y2=\"5\" />\n",
-       "  <line x1=\"198\" y1=\"8\" x2=\"258\" y2=\"8\" />\n",
-       "  <line x1=\"201\" y1=\"11\" x2=\"261\" y2=\"11\" />\n",
-       "  <line x1=\"204\" y1=\"14\" x2=\"264\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"70\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"13\" y1=\"3\" x2=\"73\" y2=\"3\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"77\" y2=\"7\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"81\" y2=\"11\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"84\" y2=\"14\" />\n",
+       "  <line x1=\"28\" y1=\"18\" x2=\"88\" y2=\"18\" />\n",
+       "  <line x1=\"32\" y1=\"22\" x2=\"92\" y2=\"22\" />\n",
+       "  <line x1=\"35\" y1=\"25\" x2=\"95\" y2=\"25\" />\n",
+       "  <line x1=\"39\" y1=\"29\" x2=\"99\" y2=\"29\" />\n",
+       "  <line x1=\"43\" y1=\"33\" x2=\"103\" y2=\"33\" />\n",
+       "  <line x1=\"47\" y1=\"37\" x2=\"107\" y2=\"37\" />\n",
+       "  <line x1=\"50\" y1=\"40\" x2=\"110\" y2=\"40\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"114\" y2=\"44\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"118\" y2=\"48\" />\n",
+       "  <line x1=\"61\" y1=\"51\" x2=\"121\" y2=\"51\" />\n",
+       "  <line x1=\"65\" y1=\"55\" x2=\"125\" y2=\"55\" />\n",
+       "  <line x1=\"69\" y1=\"59\" x2=\"129\" y2=\"59\" />\n",
+       "  <line x1=\"73\" y1=\"63\" x2=\"133\" y2=\"63\" />\n",
+       "  <line x1=\"76\" y1=\"66\" x2=\"136\" y2=\"66\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"140\" y2=\"70\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"190\" y1=\"0\" x2=\"204\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"250\" y1=\"0\" x2=\"264\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"70\" y1=\"0\" x2=\"140\" y2=\"70\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"190.0,0.0 250.0,0.0 264.9485979497544,14.948597949754403 204.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"10.0,0.0 70.0,0.0 140.58823529411765,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"204\" y1=\"14\" x2=\"264\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"204\" y1=\"64\" x2=\"264\" y2=\"64\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"140\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"120\" x2=\"140\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"204\" y1=\"14\" x2=\"204\" y2=\"64\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"264\" y1=\"14\" x2=\"264\" y2=\"64\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"140\" y1=\"70\" x2=\"140\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"204.9485979497544,14.948597949754403 264.9485979497544,14.948597949754403 264.9485979497544,64.9485979497544 204.9485979497544,64.9485979497544\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 140.58823529411765,70.58823529411765 140.58823529411765,120.58823529411765 80.58823529411765,120.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"234.948598\" y=\"84.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >360</text>\n",
-       "  <text x=\"284.948598\" y=\"39.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,284.948598,39.948598)\">300</text>\n",
-       "  <text x=\"187.474299\" y=\"77.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,187.474299,77.474299)\">5</text>\n",
+       "  <text x=\"110.588235\" y=\"140.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >360</text>\n",
+       "  <text x=\"160.588235\" y=\"95.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,160.588235,95.588235)\">300</text>\n",
+       "  <text x=\"35.294118\" y=\"105.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,105.294118)\">720</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aice_m</span></div><div class='xr-var-dims'>(time, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 300, 360), meta=np.ndarray&gt;</div><input id='attrs-1c536d5f-39cf-452b-b8a0-9eaa2b82faf2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c536d5f-39cf-452b-b8a0-9eaa2b82faf2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2357fab5-6a4f-4832-a150-a0d1f45d8fde' class='xr-var-data-in' type='checkbox'><label for='data-2357fab5-6a4f-4832-a150-a0d1f45d8fde' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>ice area  (aggregate)</dd><dt><span>coordinates :</span></dt><dd>TLON TLAT time</dd><dt><span>cell_measures :</span></dt><dd>area: tarea</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_rep :</span></dt><dd>averaged</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aice_m</span></div><div class='xr-var-dims'>(time, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 300, 360), meta=np.ndarray&gt;</div><input id='attrs-34dc1af1-3cf4-4be4-bc48-c4fffe52183e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34dc1af1-3cf4-4be4-bc48-c4fffe52183e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bff8a484-cb62-4337-a300-532e1f856bd4' class='xr-var-data-in' type='checkbox'><label for='data-bff8a484-cb62-4337-a300-532e1f856bd4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>ice area  (aggregate)</dd><dt><span>coordinates :</span></dt><dd>TLON TLAT time</dd><dt><span>cell_measures :</span></dt><dd>area: tarea</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_rep :</span></dt><dd>averaged</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3642,12 +3977,12 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-b9a783fe-8cc3-466e-b8e2-4c712e265fec' class='xr-section-summary-in' type='checkbox'  ><label for='section-b9a783fe-8cc3-466e-b8e2-4c712e265fec' class='xr-section-summary' >Attributes: <span>(16)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>sea ice model output for CICE</dd><dt><span>contents :</span></dt><dd>Diagnostic and Prognostic Variables</dd><dt><span>source :</span></dt><dd>Los Alamos Sea Ice Model (CICE) Version 5</dd><dt><span>time_period_freq :</span></dt><dd>month_1</dd><dt><span>comment3 :</span></dt><dd>seconds elapsed into model date:      0</dd><dt><span>conventions :</span></dt><dd>CF-1.0</dd><dt><span>io_flavor :</span></dt><dd>io_pio</dd><dt><span>intake_esm_attrs:realm :</span></dt><dd>seaIce</dd><dt><span>intake_esm_attrs:variable :</span></dt><dd>time_bounds,TLON,TLAT,ULON,ULAT,NCAT,tmask,blkmask,tarea,uarea,dxt,dyt,dxu,dyu,HTN,HTE,ANGLE,ANGLET,hi_m,hs_m,Tsfc_m,aice_m,uvel_m,vvel_m,uatm_m,vatm_m,sice_m,fswdn_m,fswup_m,flwdn_m,snow_ai_m,rain_ai_m,frzmlt_m,fswfac_m,fswabs_ai_m,albsni_m,alvdr_ai_m,alidr_ai_m,alvdf_ai_m,alidf_ai_m,albice_m,albsno_m,flat_ai_m,fsens_ai_m,flwup_ai_m,evap_ai_m,Tair_m,congel_m,frazil_m,snoice_m,meltt_m,melts_m,meltb_m,meltl_m,fresh_ai_m,fsalt_m,fsalt_ai_m,fhocn_ai_m,fswthru_ai_m,strairx_m,strairy_m,strtltx_m,strtlty_m,strcorx_m,strcory_m,strocnx_m,strocny_m,strintx_m,strinty_m,strength_m,divu_m,shear_m,dvidtt_m,dvidtd_m,daidtt_m,daidtd_m,trsig_m,ice_present_m,fcondtop_ai_m,fmeltt_ai_m,opening_m,aicen_m,vicen_m,fsurfn_ai_m,fcondtopn_ai_m,fmelttn_ai_m,flatn_ai_m</dd><dt><span>intake_esm_attrs:frequency :</span></dt><dd>1mon</dd><dt><span>intake_esm_attrs:variable_long_name :</span></dt><dd>boundaries for time-averaging interval,T grid center longitude,T grid center latitude,U grid center longitude,U grid center latitude,category maximum thickness,ocean grid mask,ice grid block mask,area of T grid cells,area of U grid cells,T cell width through middle,T cell height through middle,U cell width through middle,U cell height through middle,T cell width on North side,T cell width on East side,angle grid makes with latitude line on U grid,angle grid makes with latitude line on T grid,grid cell mean ice thickness,grid cell mean snow thickness,snow/ice surface temperature,ice area  (aggregate),ice velocity (x),ice velocity (y),atm velocity (x),atm velocity (y),bulk ice salinity,down solar flux,upward solar flux,down longwave flux,snowfall rate,rainfall rate,freeze/melt potential,shortwave scaling factor,snow/ice/ocn absorbed solar flux,snow/ice broad band albedo,visible direct albedo,near IR direct albedo,visible diffuse albedo,near IR diffuse albedo,bare ice albedo,snow albedo,latent heat flux,sensible heat flux,upward longwave flux,evaporative water flux,air temperature,congelation ice growth,frazil ice growth,snow-ice formation,top ice melt,top snow melt,basal ice melt,lateral ice melt,freshwtr flx ice to ocn,salt flux ice to ocn (cpl),salt flux ice to ocean,heat flux ice to ocean,SW flux thru ice to ocean,atm/ice stress (x),atm/ice stress (y),sea sfc tilt stress (x),sea sfc tilt stress (y),coriolis stress (x),coriolis stress (y),ocean/ice stress (x),ocean/ice stress (y),internal ice stress (x),internal ice stress (y),compressive ice strength,strain rate (divergence),strain rate (shear),volume tendency thermo,volume tendency dynamics,area tendency thermo,area tendency dynamics,internal stress tensor trace,fraction of time-avg interval that ice is present,top surface conductive heat flux,net surface heat flux causing melt,lead area opening rate,ice area, categories,ice volume, categories,net surface heat flux, categories,top sfc conductive heat flux, cat,net sfc heat flux causing melt, cat,latent heat flux, category</dd><dt><span>intake_esm_attrs:variable_standard_name :</span></dt><dd>,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,</dd><dt><span>intake_esm_attrs:variable_cell_methods :</span></dt><dd>,,,,,,,,,,,,,,,,,,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean</dd><dt><span>intake_esm_attrs:file_id :</span></dt><dd>iceh_XXXX_XX</dd><dt><span>intake_esm_attrs:_data_format_ :</span></dt><dd>netcdf</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>iceh_XXXX_XX.1mon</dd></dl></div></li></ul></div></div></ul></div></div></div></div></li><li class='xr-section-item'><input id='section-013871b1-f447-463c-9125-1588be95778b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-013871b1-f447-463c-9125-1588be95778b' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8c530983-9879-4e70-9279-2ed98140a992' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8c530983-9879-4e70-9279-2ed98140a992' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-a3c3aa06-686a-4217-80f2-8dd929a99d23' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a3c3aa06-686a-4217-80f2-8dd929a99d23' class='xr-section-summary'  title='Expand/collapse section'>Data variables: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-1d9c80fa-d98d-48ae-9d1d-1a8297aac482' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1d9c80fa-d98d-48ae-9d1d-1a8297aac482' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-f3693e22-1753-412d-9be2-0e158c55a5ad' class='xr-section-summary-in' type='checkbox'  ><label for='section-f3693e22-1753-412d-9be2-0e158c55a5ad' class='xr-section-summary' >Attributes: <span>(16)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>sea ice model output for CICE</dd><dt><span>contents :</span></dt><dd>Diagnostic and Prognostic Variables</dd><dt><span>source :</span></dt><dd>Los Alamos Sea Ice Model (CICE) Version 5</dd><dt><span>time_period_freq :</span></dt><dd>month_1</dd><dt><span>comment3 :</span></dt><dd>seconds elapsed into model date:      0</dd><dt><span>conventions :</span></dt><dd>CF-1.0</dd><dt><span>io_flavor :</span></dt><dd>io_pio</dd><dt><span>intake_esm_attrs:realm :</span></dt><dd>seaIce</dd><dt><span>intake_esm_attrs:variable :</span></dt><dd>time_bounds,TLON,TLAT,ULON,ULAT,NCAT,tmask,blkmask,tarea,uarea,dxt,dyt,dxu,dyu,HTN,HTE,ANGLE,ANGLET,hi_m,hs_m,Tsfc_m,aice_m,uvel_m,vvel_m,uatm_m,vatm_m,sice_m,fswdn_m,fswup_m,flwdn_m,snow_ai_m,rain_ai_m,frzmlt_m,fswfac_m,fswabs_ai_m,albsni_m,alvdr_ai_m,alidr_ai_m,alvdf_ai_m,alidf_ai_m,albice_m,albsno_m,flat_ai_m,fsens_ai_m,flwup_ai_m,evap_ai_m,Tair_m,congel_m,frazil_m,snoice_m,meltt_m,melts_m,meltb_m,meltl_m,fresh_ai_m,fsalt_m,fsalt_ai_m,fhocn_ai_m,fswthru_ai_m,strairx_m,strairy_m,strtltx_m,strtlty_m,strcorx_m,strcory_m,strocnx_m,strocny_m,strintx_m,strinty_m,strength_m,divu_m,shear_m,dvidtt_m,dvidtd_m,daidtt_m,daidtd_m,trsig_m,ice_present_m,fcondtop_ai_m,fmeltt_ai_m,opening_m,aicen_m,vicen_m,fsurfn_ai_m,fcondtopn_ai_m,fmelttn_ai_m,flatn_ai_m</dd><dt><span>intake_esm_attrs:frequency :</span></dt><dd>1mon</dd><dt><span>intake_esm_attrs:variable_long_name :</span></dt><dd>boundaries for time-averaging interval,T grid center longitude,T grid center latitude,U grid center longitude,U grid center latitude,category maximum thickness,ocean grid mask,ice grid block mask,area of T grid cells,area of U grid cells,T cell width through middle,T cell height through middle,U cell width through middle,U cell height through middle,T cell width on North side,T cell width on East side,angle grid makes with latitude line on U grid,angle grid makes with latitude line on T grid,grid cell mean ice thickness,grid cell mean snow thickness,snow/ice surface temperature,ice area  (aggregate),ice velocity (x),ice velocity (y),atm velocity (x),atm velocity (y),bulk ice salinity,down solar flux,upward solar flux,down longwave flux,snowfall rate,rainfall rate,freeze/melt potential,shortwave scaling factor,snow/ice/ocn absorbed solar flux,snow/ice broad band albedo,visible direct albedo,near IR direct albedo,visible diffuse albedo,near IR diffuse albedo,bare ice albedo,snow albedo,latent heat flux,sensible heat flux,upward longwave flux,evaporative water flux,air temperature,congelation ice growth,frazil ice growth,snow-ice formation,top ice melt,top snow melt,basal ice melt,lateral ice melt,freshwtr flx ice to ocn,salt flux ice to ocn (cpl),salt flux ice to ocean,heat flux ice to ocean,SW flux thru ice to ocean,atm/ice stress (x),atm/ice stress (y),sea sfc tilt stress (x),sea sfc tilt stress (y),coriolis stress (x),coriolis stress (y),ocean/ice stress (x),ocean/ice stress (y),internal ice stress (x),internal ice stress (y),compressive ice strength,strain rate (divergence),strain rate (shear),volume tendency thermo,volume tendency dynamics,area tendency thermo,area tendency dynamics,internal stress tensor trace,fraction of time-avg interval that ice is present,top surface conductive heat flux,net surface heat flux causing melt,lead area opening rate,ice area, categories,ice volume, categories,net surface heat flux, categories,top sfc conductive heat flux, cat,net sfc heat flux causing melt, cat,latent heat flux, category</dd><dt><span>intake_esm_attrs:variable_standard_name :</span></dt><dd>,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,</dd><dt><span>intake_esm_attrs:variable_cell_methods :</span></dt><dd>,,,,,,,,,,,,,,,,,,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean</dd><dt><span>intake_esm_attrs:file_id :</span></dt><dd>iceh_XXXX_XX</dd><dt><span>intake_esm_attrs:_data_format_ :</span></dt><dd>netcdf</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>iceh_XXXX_XX.1mon</dd></dl></div></li></ul></div></div></ul></div></div></div></div></li><li class='xr-section-item'><input id='section-d3ebec50-9db3-4812-a07e-caf938e66e21' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d3ebec50-9db3-4812-a07e-caf938e66e21' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e9bd055b-0634-4607-8494-ff294c0d2f19' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e9bd055b-0634-4607-8494-ff294c0d2f19' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-cbffb5d8-9e0c-4659-afcf-e06adb80d306' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cbffb5d8-9e0c-4659-afcf-e06adb80d306' class='xr-section-summary'  title='Expand/collapse section'>Data variables: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-d80e6b77-d4a7-4557-8925-0efee490687a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d80e6b77-d4a7-4557-8925-0efee490687a' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "DataTree('None', parent=None)\n",
        "├── DataTree('025deg_era5')\n",
-       "│       Dimensions:   (time: 523, nc: 5, yt_ocean: 1080, xt_ocean: 1440)\n",
+       "│       Dimensions:   (time: 523, yt_ocean: 1080, xt_ocean: 1440)\n",
        "│       Coordinates:\n",
        "│         * time      (time) datetime64[ns] 1980-01-01 1980-02-01 ... 2023-07-01\n",
        "│         * xt_ocean  (xt_ocean) float64 -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n",
@@ -3655,9 +3990,8 @@
        "│           geolat_t  (yt_ocean, xt_ocean) float32 ...\n",
        "│           geolon_t  (yt_ocean, xt_ocean) float32 ...\n",
        "│           area_t    (yt_ocean, xt_ocean) float32 ...\n",
-       "│       Dimensions without coordinates: nc\n",
        "│       Data variables:\n",
-       "│           vicen_m   (time, nc, yt_ocean, xt_ocean) float32 dask.array<chunksize=(1, 1, 1080, 1440), meta=np.ndarray>\n",
+       "│           hi_m      (time, yt_ocean, xt_ocean) float32 dask.array<chunksize=(1, 1080, 1440), meta=np.ndarray>\n",
        "│           aice_m    (time, yt_ocean, xt_ocean) float32 dask.array<chunksize=(1, 1080, 1440), meta=np.ndarray>\n",
        "│       Attributes: (12/17)\n",
        "│           title:                                    sea ice model output for CICE\n",
@@ -3674,7 +4008,7 @@
        "│           intake_esm_attrs:_data_format_:           netcdf\n",
        "│           intake_esm_dataset_key:                   iceh_XXXX_XX.1mon\n",
        "├── DataTree('025deg_jra55')\n",
-       "│       Dimensions:   (time: 523, nc: 5, yt_ocean: 1080, xt_ocean: 1440)\n",
+       "│       Dimensions:   (time: 523, yt_ocean: 1080, xt_ocean: 1440)\n",
        "│       Coordinates:\n",
        "│         * time      (time) datetime64[ns] 1980-01-01 1980-02-01 ... 2023-07-01\n",
        "│         * xt_ocean  (xt_ocean) float64 -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n",
@@ -3682,9 +4016,8 @@
        "│           geolat_t  (yt_ocean, xt_ocean) float32 ...\n",
        "│           geolon_t  (yt_ocean, xt_ocean) float32 ...\n",
        "│           area_t    (yt_ocean, xt_ocean) float32 ...\n",
-       "│       Dimensions without coordinates: nc\n",
        "│       Data variables:\n",
-       "│           vicen_m   (time, nc, yt_ocean, xt_ocean) float32 dask.array<chunksize=(1, 1, 1080, 1440), meta=np.ndarray>\n",
+       "│           hi_m      (time, yt_ocean, xt_ocean) float32 dask.array<chunksize=(1, 1080, 1440), meta=np.ndarray>\n",
        "│           aice_m    (time, yt_ocean, xt_ocean) float32 dask.array<chunksize=(1, 1080, 1440), meta=np.ndarray>\n",
        "│       Attributes: (12/17)\n",
        "│           title:                                    sea ice model output for CICE\n",
@@ -3701,7 +4034,7 @@
        "│           intake_esm_attrs:_data_format_:           netcdf\n",
        "│           intake_esm_dataset_key:                   iceh_XXXX_XX.1mon\n",
        "├── DataTree('1deg_era5')\n",
-       "│       Dimensions:   (time: 708, yt_ocean: 300, xt_ocean: 360, nc: 5)\n",
+       "│       Dimensions:   (time: 708, yt_ocean: 300, xt_ocean: 360)\n",
        "│       Coordinates:\n",
        "│         * time      (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n",
        "│         * xt_ocean  (xt_ocean) float64 -279.5 -278.5 -277.5 -276.5 ... 77.5 78.5 79.5\n",
@@ -3709,10 +4042,9 @@
        "│           geolat_t  (yt_ocean, xt_ocean) float32 ...\n",
        "│           geolon_t  (yt_ocean, xt_ocean) float32 ...\n",
        "│           area_t    (yt_ocean, xt_ocean) float32 ...\n",
-       "│       Dimensions without coordinates: nc\n",
        "│       Data variables:\n",
+       "│           hi_m      (time, yt_ocean, xt_ocean) float32 dask.array<chunksize=(1, 300, 360), meta=np.ndarray>\n",
        "│           aice_m    (time, yt_ocean, xt_ocean) float32 dask.array<chunksize=(1, 300, 360), meta=np.ndarray>\n",
-       "│           vicen_m   (time, nc, yt_ocean, xt_ocean) float32 dask.array<chunksize=(1, 1, 300, 360), meta=np.ndarray>\n",
        "│       Attributes: (12/17)\n",
        "│           title:                                    sea ice model output for CICE\n",
        "│           contents:                                 Diagnostic and Prognostic Varia...\n",
@@ -3728,7 +4060,7 @@
        "│           intake_esm_attrs:_data_format_:           netcdf\n",
        "│           intake_esm_dataset_key:                   iceh_XXXX_XX.1mon\n",
        "└── DataTree('1deg_jra55')\n",
-       "        Dimensions:   (time: 720, nc: 5, yt_ocean: 300, xt_ocean: 360)\n",
+       "        Dimensions:   (time: 720, yt_ocean: 300, xt_ocean: 360)\n",
        "        Coordinates:\n",
        "          * time      (time) datetime64[ns] 1959-01-01 1959-02-01 ... 2019-12-01\n",
        "          * xt_ocean  (xt_ocean) float64 -279.5 -278.5 -277.5 -276.5 ... 77.5 78.5 79.5\n",
@@ -3736,9 +4068,8 @@
        "            geolat_t  (yt_ocean, xt_ocean) float32 ...\n",
        "            geolon_t  (yt_ocean, xt_ocean) float32 ...\n",
        "            area_t    (yt_ocean, xt_ocean) float32 ...\n",
-       "        Dimensions without coordinates: nc\n",
        "        Data variables:\n",
-       "            vicen_m   (time, nc, yt_ocean, xt_ocean) float32 dask.array<chunksize=(1, 1, 300, 360), meta=np.ndarray>\n",
+       "            hi_m      (time, yt_ocean, xt_ocean) float32 dask.array<chunksize=(1, 300, 360), meta=np.ndarray>\n",
        "            aice_m    (time, yt_ocean, xt_ocean) float32 dask.array<chunksize=(1, 300, 360), meta=np.ndarray>\n",
        "        Attributes: (12/16)\n",
        "            title:                                    sea ice model output for CICE\n",
@@ -3756,7 +4087,7 @@
        "            intake_esm_dataset_key:                   iceh_XXXX_XX.1mon"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3781,35 +4112,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-08T03:07:42.567016Z",
-     "iopub.status.busy": "2024-01-08T03:07:42.566104Z",
-     "iopub.status.idle": "2024-01-08T03:07:42.578107Z",
-     "shell.execute_reply": "2024-01-08T03:07:42.577103Z",
-     "shell.execute_reply.started": "2024-01-08T03:07:42.566966Z"
+     "iopub.execute_input": "2024-01-10T00:32:26.149690Z",
+     "iopub.status.busy": "2024-01-10T00:32:26.149168Z",
+     "iopub.status.idle": "2024-01-10T00:32:26.158605Z",
+     "shell.execute_reply": "2024-01-10T00:32:26.157507Z",
+     "shell.execute_reply.started": "2024-01-10T00:32:26.149665Z"
     }
    },
    "outputs": [],
    "source": [
     "def open_cdr_dataset(path, area_file):\n",
-    "    ds=xr.open_dataset(path).rename(\n",
-    "        {'cdr_seaice_conc_monthly':'cdr_seaice_conc','xgrid':'x','ygrid':'y'}\n",
+    "    ds = xr.open_dataset(path).rename(\n",
+    "        {'cdr_seaice_conc_monthly': 'cdr_conc', 'xgrid':'x','ygrid':'y'}\n",
     "    )\n",
     "\n",
-    "    #we also need the area of each gridcell\n",
-    "    datFile=open(area_file, 'rb')\n",
-    "    areasDmNd=np.fromfile(datFile, dtype=np.int32).reshape(ds.cdr_seaice_conc.isel(time=0).shape)\n",
-    "    #Divide by 1000 to get km2\n",
-    "    areasKmNd_sh=areasDmNd/1000\n",
-    "\n",
-    "    ds['area']=xr.DataArray(areasKmNd_sh, dims=['y','x'])\n",
-    "    ds=ds.set_coords('area')\n",
+    "    # # we also need the area of each gridcell\n",
+    "    areasNd = np.fromfile(area_file, dtype=np.int32).reshape(\n",
+    "        ds.cdr_conc.isel(time=0).shape\n",
+    "    )\n",
+    "    # # Divide by 1000 to get km2 (https://web.archive.org/web/20170817210544/http://nsidc.org/data/polar-stereo/tools_geo_pixel.html#pixel_area)\n",
+    "    areasKmNd_sh = areasNd / 1000\n",
+    "        \n",
+    "    ds[\"area\"] = xr.DataArray(areasKmNd_sh, dims=[\"y\", \"x\"])\n",
+    "    ds = ds.set_coords(\"area\")\n",
     "\n",
-    "    ds=ds.cdr_seaice_conc\n",
-    "    \n",
-    "    ds=ds.where(ds<=1) #convert error codes to Nan\n",
+    "    ds = ds.cdr_conc\n",
+    "    ds = ds.where(ds<=1)  # convert error codes to Nan\n",
     "\n",
     "    return ds"
    ]
@@ -3823,14 +4154,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-08T03:07:42.580659Z",
-     "iopub.status.busy": "2024-01-08T03:07:42.579694Z",
-     "iopub.status.idle": "2024-01-08T03:07:42.979631Z",
-     "shell.execute_reply": "2024-01-08T03:07:42.975865Z",
-     "shell.execute_reply.started": "2024-01-08T03:07:42.580613Z"
+     "iopub.execute_input": "2024-01-10T00:32:26.160686Z",
+     "iopub.status.busy": "2024-01-10T00:32:26.160486Z",
+     "iopub.status.idle": "2024-01-10T00:32:26.381794Z",
+     "shell.execute_reply": "2024-01-10T00:32:26.380484Z",
+     "shell.execute_reply.started": "2024-01-10T00:32:26.160666Z"
     }
    },
    "outputs": [
@@ -3844,7 +4175,7 @@
     }
    ],
    "source": [
-    "!wget --ftp-user=anonymous -nc ftp://sidads.colorado.edu/DATASETS/seaice/polar-stereo/tools/pss25area_v3.dat ftp://sidads.colorado.edu/DATASETS/seaice/polar-stereo/tools/psn25area_v3.dat "
+    "!wget --ftp-user=anonymous -nc ftp://sidads.colorado.edu/DATASETS/seaice/polar-stereo/tools/pss25area_v3.dat ftp://sidads.colorado.edu/DATASETS/seaice/polar-stereo/tools/psn25area_v3.dat"
    ]
   },
   {
@@ -3856,32 +4187,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-08T03:07:42.984925Z",
-     "iopub.status.busy": "2024-01-08T03:07:42.984410Z",
-     "iopub.status.idle": "2024-01-08T03:11:33.123415Z",
-     "shell.execute_reply": "2024-01-08T03:11:33.119171Z",
-     "shell.execute_reply.started": "2024-01-08T03:07:42.984872Z"
+     "iopub.execute_input": "2024-01-10T00:32:26.384299Z",
+     "iopub.status.busy": "2024-01-10T00:32:26.383687Z",
+     "iopub.status.idle": "2024-01-10T00:36:09.375334Z",
+     "shell.execute_reply": "2024-01-10T00:36:09.373918Z",
+     "shell.execute_reply.started": "2024-01-10T00:32:26.384243Z"
     }
    },
    "outputs": [],
    "source": [
-    "sh_cdr_xr = open_cdr_dataset(\n",
-    "    sh_obs_url,\n",
-    "    'pss25area_v3.dat'\n",
-    ")\n",
+    "sh_cdr_xr = open_cdr_dataset(sh_obs_url, \"pss25area_v3.dat\")\n",
     "\n",
     "# nh_cdr_xr = open_cdr_dataset(\n",
     "#     nh_obs_url,\n",
     "#     'psn25area_v3.dat'\n",
     "# )\n",
     "\n",
-    "cdr_dt = DataTree.from_dict({\n",
-    "    'cdr_sh':sh_cdr_xr,\n",
-    "    # 'cdr_nh':nh_cdr_xr\n",
-    "})"
+    "cdr_dt = DataTree.from_dict(\n",
+    "    {\n",
+    "        \"cdr_sh\": sh_cdr_xr,\n",
+    "        # 'cdr_nh':nh_cdr_xr\n",
+    "    }\n",
+    ")"
    ]
   },
   {
@@ -3907,20 +4237,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-08T03:11:33.130093Z",
-     "iopub.status.busy": "2024-01-08T03:11:33.129781Z",
-     "iopub.status.idle": "2024-01-08T03:11:33.139086Z",
-     "shell.execute_reply": "2024-01-08T03:11:33.137856Z",
-     "shell.execute_reply.started": "2024-01-08T03:11:33.130063Z"
+     "iopub.execute_input": "2024-01-10T00:36:09.376605Z",
+     "iopub.status.busy": "2024-01-10T00:36:09.376421Z",
+     "iopub.status.idle": "2024-01-10T00:36:09.383465Z",
+     "shell.execute_reply": "2024-01-10T00:36:09.382797Z",
+     "shell.execute_reply.started": "2024-01-10T00:36:09.376586Z"
     }
    },
    "outputs": [],
    "source": [
-    "def sea_ice_area(sic,area, range=[0.15,1]):\n",
-    "    return (sic*area).where((sic>=range[0])*(sic<=range[1])).cf.sum(['X','Y'])"
+    "def sea_ice_area(sic, area, range=[0.15, 1]):\n",
+    "    return (sic * area).where((sic >= range[0]) * (sic <= range[1])).cf.sum([\"X\", \"Y\"])"
    ]
   },
   {
@@ -3932,92 +4262,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-08T03:11:33.140491Z",
-     "iopub.status.busy": "2024-01-08T03:11:33.140191Z",
-     "iopub.status.idle": "2024-01-08T03:11:33.170903Z",
-     "shell.execute_reply": "2024-01-08T03:11:33.169851Z",
-     "shell.execute_reply.started": "2024-01-08T03:11:33.140463Z"
+     "iopub.execute_input": "2024-01-10T00:36:09.384313Z",
+     "iopub.status.busy": "2024-01-10T00:36:09.384158Z",
+     "iopub.status.idle": "2024-01-10T00:36:09.392936Z",
+     "shell.execute_reply": "2024-01-10T00:36:09.392245Z",
+     "shell.execute_reply.started": "2024-01-10T00:36:09.384297Z"
     }
    },
    "outputs": [],
    "source": [
     "@map_over_subtree\n",
     "def sea_ice_area_obs(ds):\n",
-    "    sic=ds.cdr_seaice_conc\n",
-    "    result=sea_ice_area(sic,sic.area).to_dataset(name='cdr_area')\n",
+    "    sic = ds.cdr_conc\n",
+    "    result = sea_ice_area(sic, sic.area).to_dataset(name=\"cdr_area\")\n",
     "\n",
-    "    #Theres a couple of data gaps which should be nan\n",
-    "    result.loc[{'time':'1988-01-01'}]=np.nan\n",
-    "    result.loc[{'time':'1987-12'}]=np.nan\n",
+    "    # Theres a couple of data gaps which should be nan\n",
+    "    result.loc[{\"time\": \"1988-01-01\"}] = np.nan\n",
+    "    result.loc[{\"time\": \"1987-12\"}] = np.nan\n",
     "\n",
     "    return result.sel(time=OBS_TIME_SLICE)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-01-08T03:11:33.173512Z",
-     "iopub.status.busy": "2024-01-08T03:11:33.172548Z",
-     "iopub.status.idle": "2024-01-08T03:11:34.344519Z",
-     "shell.execute_reply": "2024-01-08T03:11:34.343336Z",
-     "shell.execute_reply.started": "2024-01-08T03:11:33.173465Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "obs_area_dt=sea_ice_area_obs(cdr_dt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-01-08T03:11:34.346588Z",
-     "iopub.status.busy": "2024-01-08T03:11:34.346132Z",
-     "iopub.status.idle": "2024-01-08T03:11:34.351620Z",
-     "shell.execute_reply": "2024-01-08T03:11:34.350648Z",
-     "shell.execute_reply.started": "2024-01-08T03:11:34.346543Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "#Theres another gap which should be nan in the arctic only\n",
-    "#obs_area_dt['cdr_nh'].to_dataset().loc[{'time':'1984-07'}]=np.nan"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Calculate for model data, limit to southern hemisphere / Antarctica"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 20,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-08T03:11:34.354276Z",
-     "iopub.status.busy": "2024-01-08T03:11:34.353280Z",
-     "iopub.status.idle": "2024-01-08T03:11:34.364314Z",
-     "shell.execute_reply": "2024-01-08T03:11:34.363352Z",
-     "shell.execute_reply.started": "2024-01-08T03:11:34.354233Z"
+     "iopub.execute_input": "2024-01-10T00:36:09.393783Z",
+     "iopub.status.busy": "2024-01-10T00:36:09.393588Z",
+     "iopub.status.idle": "2024-01-10T00:36:10.104788Z",
+     "shell.execute_reply": "2024-01-10T00:36:10.103611Z",
+     "shell.execute_reply.started": "2024-01-10T00:36:09.393763Z"
     }
    },
    "outputs": [],
    "source": [
-    "@map_over_subtree\n",
-    "def sea_ice_area_model_sh(ds):\n",
-    "    sic=ds.aice_m.cf.sel(Y=slice(-90,0))   \n",
-    "    area_km2=ds.area_t/1e6\n",
-    "\n",
-    "    return sea_ice_area(sic,area_km2).to_dataset(name='si_area').load()"
+    "obs_area_dt = sea_ice_area_obs(cdr_dt)"
    ]
   },
   {
@@ -4025,711 +4308,301 @@
    "execution_count": 21,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-08T03:11:34.366722Z",
-     "iopub.status.busy": "2024-01-08T03:11:34.365953Z",
-     "iopub.status.idle": "2024-01-08T03:13:03.847639Z",
-     "shell.execute_reply": "2024-01-08T03:13:03.839539Z",
-     "shell.execute_reply.started": "2024-01-08T03:11:34.366680Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "model_area_dt=sea_ice_area_model_sh(si_dt)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Sea Ice Area Trends"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We are going to compare the trends in the minima and maxima over time, and the monthly climatology"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-01-08T03:13:03.868090Z",
-     "iopub.status.busy": "2024-01-08T03:13:03.867053Z",
-     "iopub.status.idle": "2024-01-08T03:13:04.303771Z",
-     "shell.execute_reply": "2024-01-08T03:13:04.295758Z",
-     "shell.execute_reply.started": "2024-01-08T03:13:03.868059Z"
+     "iopub.execute_input": "2024-01-10T00:36:10.107455Z",
+     "iopub.status.busy": "2024-01-10T00:36:10.106412Z",
+     "iopub.status.idle": "2024-01-10T00:36:10.111561Z",
+     "shell.execute_reply": "2024-01-10T00:36:10.110480Z",
+     "shell.execute_reply.started": "2024-01-10T00:36:10.107404Z"
     }
    },
    "outputs": [],
    "source": [
-    "@map_over_subtree\n",
-    "def min_and_max(ds):\n",
-    "    def min_and_max_year(da):\n",
-    "        result = xr.Dataset()\n",
-    "        result['min']=da.min()\n",
-    "        result['max']=da.max()\n",
-    "        return result\n",
-    "    annual_min_max_ds=ds.si_area.groupby('time.year').apply(min_and_max_year)\n",
-    "    return annual_min_max_ds\n",
-    "    \n",
-    "model_min_max_dt=min_and_max(model_area_dt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-01-08T03:13:04.312066Z",
-     "iopub.status.busy": "2024-01-08T03:13:04.311677Z",
-     "iopub.status.idle": "2024-01-08T03:13:05.746056Z",
-     "shell.execute_reply": "2024-01-08T03:13:05.741386Z",
-     "shell.execute_reply.started": "2024-01-08T03:13:04.312031Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for iGroup in model_min_max_dt.groups[1:]:\n",
-    "    ds=model_min_max_dt[iGroup].ds\n",
-    "    ds['min'].plot(label=iGroup[1:])\n",
-    "\n",
-    "obs_area_dt['cdr_sh'].ds.cdr_area.groupby('time.year').min().plot(label='CDR')\n",
-    "\n",
-    "plt.title('Trends in Sea-Ice Minima')\n",
-    "plt.ylabel('Sea-Ice Area (km2)')\n",
-    "\n",
-    "_ = plt.legend()"
+    "# Theres another gap which should be nan in the arctic only\n",
+    "# obs_area_dt['cdr_nh'].to_dataset().loc[{'time':'1984-07'}]=np.nan"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We see that all models have sea ice area which is too low in summer. Model runs forced by JRA have more variability than those forced by ERA5 and are slightly closer to the measured sea ice area from observations."
+    "Calculate for model data, limit to southern hemisphere / Antarctica"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-01-08T03:13:05.755763Z",
-     "iopub.status.busy": "2024-01-08T03:13:05.752902Z",
-     "iopub.status.idle": "2024-01-08T03:13:06.370307Z",
-     "shell.execute_reply": "2024-01-08T03:13:06.369079Z",
-     "shell.execute_reply.started": "2024-01-08T03:13:05.755738Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for iGroup in model_min_max_dt.groups[1:]:\n",
-    "    ds=model_min_max_dt[iGroup].ds\n",
-    "    ds['max'].plot(label=iGroup[1:])\n",
-    "\n",
-    "obs_area_dt['cdr_sh'].ds.cdr_area.groupby('time.year').max().plot(label='CDR')\n",
-    "\n",
-    "plt.title('Trends in Sea-Ice Maxima')\n",
-    "plt.ylabel('Sea-Ice Area (km2)')\n",
-    "\n",
-    "_ = plt.legend()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-12-08T03:00:12.062280Z",
-     "iopub.status.busy": "2023-12-08T03:00:12.060174Z",
-     "iopub.status.idle": "2023-12-08T03:00:12.075164Z",
-     "shell.execute_reply": "2023-12-08T03:00:12.073582Z",
-     "shell.execute_reply.started": "2023-12-08T03:00:12.062204Z"
-    }
-   },
-   "source": [
-    "We see that the closest runs are those forced by ERA5, and the runs forced by JRA55 are under representing Winter sea ice."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-01-08T03:13:06.376238Z",
-     "iopub.status.busy": "2024-01-08T03:13:06.375993Z",
-     "iopub.status.idle": "2024-01-08T03:13:06.679097Z",
-     "shell.execute_reply": "2024-01-08T03:13:06.678346Z",
-     "shell.execute_reply.started": "2024-01-08T03:13:06.376218Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x152abb3f94b0>"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for iGroup in model_area_dt.groups[1:]:\n",
-    "    model_area_dt[iGroup].ds.si_area.groupby('time.month').mean('time').plot(label=iGroup[1:])\n",
-    "\n",
-    "obs_area_dt['cdr_sh'].ds.cdr_area.groupby('time.month').mean('time').plot(label='CDR')\n",
-    "\n",
-    "plt.title('Climatology of Sea-Ice Area')\n",
-    "plt.ylabel('Sea-Ice Area (km2)')\n",
-    "\n",
-    "plt.legend()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-12-08T03:01:35.360367Z",
-     "iopub.status.busy": "2023-12-08T03:01:35.359044Z",
-     "iopub.status.idle": "2023-12-08T03:01:35.370817Z",
-     "shell.execute_reply": "2023-12-08T03:01:35.369364Z",
-     "shell.execute_reply.started": "2023-12-08T03:01:35.360297Z"
-    }
-   },
-   "source": [
-    "We see all model runs have too low sea ice in summer, and grow faster than observations in autumn and earlier that the observed maximum."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Sea Ice Area differences"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To examine the differences between the model results and observations, we calculate difference in each grid cell between observations and each experiment\n",
-    "\n",
-    "As that data are on different grids, we need to regrid to compare the datasets\n",
-    "\n",
-    "Lets simplify a little to only look at 0.25 degree results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-01-08T03:13:06.679954Z",
-     "iopub.status.busy": "2024-01-08T03:13:06.679743Z",
-     "iopub.status.idle": "2024-01-08T03:13:06.683861Z",
-     "shell.execute_reply": "2024-01-08T03:13:06.683164Z",
-     "shell.execute_reply.started": "2024-01-08T03:13:06.679936Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "groups=('/025deg_era5', '/025deg_jra55')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-01-08T03:13:06.684666Z",
-     "iopub.status.busy": "2024-01-08T03:13:06.684506Z",
-     "iopub.status.idle": "2024-01-08T03:13:06.728035Z",
-     "shell.execute_reply": "2024-01-08T03:13:06.727210Z",
-     "shell.execute_reply.started": "2024-01-08T03:13:06.684650Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
-       "<defs>\n",
-       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
-       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
-       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
-       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
-       "</symbol>\n",
-       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
-       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
-       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-       "</symbol>\n",
-       "</defs>\n",
-       "</svg>\n",
-       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
-       " *\n",
-       " */\n",
-       "\n",
-       ":root {\n",
-       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
-       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
-       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
-       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
-       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
-       "  --xr-background-color: var(--jp-layout-color0, white);\n",
-       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
-       "}\n",
-       "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
-       "body.vscode-dark {\n",
-       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
-       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
-       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
-       "  --xr-disabled-color: #515151;\n",
-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
-       "}\n",
-       "\n",
-       ".xr-wrap {\n",
-       "  display: block !important;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
-       "}\n",
-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DatasetView&gt;\n",
-       "Dimensions:          (time: 536, y: 332, x: 316)\n",
-       "Coordinates:\n",
-       "  * time             (time) datetime64[ns] 1978-11-01 1978-12-01 ... 2023-06-01\n",
-       "  * y                (y) float32 4.338e+06 4.312e+06 ... -3.912e+06 -3.938e+06\n",
-       "  * x                (x) float32 -3.938e+06 -3.912e+06 ... 3.912e+06 3.938e+06\n",
-       "    area             (y, x) float64 444.1 445.0 445.9 ... 462.1 461.1 460.1\n",
-       "Data variables:\n",
-       "    cdr_seaice_conc  (time, y, x) float32 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>datatree.DataTree</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-911b49c6-caa6-4e37-9a54-020dd5239f0e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-911b49c6-caa6-4e37-9a54-020dd5239f0e' class='xr-section-summary'  title='Expand/collapse section'>Groups: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%'></div></div></li><li class='xr-section-item'><input id='section-9fef5a7f-a96a-4722-b06b-aecf6e614aa8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9fef5a7f-a96a-4722-b06b-aecf6e614aa8' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 536</li><li><span class='xr-has-index'>y</span>: 332</li><li><span class='xr-has-index'>x</span>: 316</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-15d46b2d-7a72-4732-989c-032cf73654a0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-15d46b2d-7a72-4732-989c-032cf73654a0' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1978-11-01 ... 2023-06-01</div><input id='attrs-190f070b-7a80-49ee-9ad0-c06ffab3dd58' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-190f070b-7a80-49ee-9ad0-c06ffab3dd58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9055ae4-33de-4ed0-8776-93520b8f84fe' class='xr-var-data-in' type='checkbox'><label for='data-d9055ae4-33de-4ed0-8776-93520b8f84fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>1024</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Time</dd><dt><span>actual_range :</span></dt><dd>[2.7872640e+08 1.6855776e+09]</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>ioos_category :</span></dt><dd>Time</dd><dt><span>long_name :</span></dt><dd>ANSI date</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>01-JAN-1970 00:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1978-11-01T00:00:00.000000000&#x27;, &#x27;1978-12-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1979-01-01T00:00:00.000000000&#x27;, ..., &#x27;2023-04-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2023-05-01T00:00:00.000000000&#x27;, &#x27;2023-06-01T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>4.338e+06 4.312e+06 ... -3.938e+06</div><input id='attrs-19c892d1-a2d6-44c3-949c-b397604e29f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-19c892d1-a2d6-44c3-949c-b397604e29f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0bce4eb7-7fed-407b-a8ce-d713e983323d' class='xr-var-data-in' type='checkbox'><label for='data-0bce4eb7-7fed-407b-a8ce-d713e983323d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>332</dd><dt><span>actual_range :</span></dt><dd>[-3937500.  4337500.]</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>ioos_category :</span></dt><dd>Location</dd><dt><span>long_name :</span></dt><dd>projection_grid_y_centers</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>meters</dd><dt><span>valid_range :</span></dt><dd>[-3950000.  4350000.]</dd></dl></div><div class='xr-var-data'><pre>array([ 4337500.,  4312500.,  4287500., ..., -3887500., -3912500., -3937500.],\n",
-       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-3.938e+06 -3.912e+06 ... 3.938e+06</div><input id='attrs-4330239b-7ceb-4d08-8940-cd6df42fef48' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4330239b-7ceb-4d08-8940-cd6df42fef48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-caf6394e-123b-43ec-b139-26fe8249ee4e' class='xr-var-data-in' type='checkbox'><label for='data-caf6394e-123b-43ec-b139-26fe8249ee4e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>316</dd><dt><span>actual_range :</span></dt><dd>[-3937500.  3937500.]</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>ioos_category :</span></dt><dd>Location</dd><dt><span>long_name :</span></dt><dd>projection_grid_x_centers</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>meters</dd><dt><span>valid_range :</span></dt><dd>[-3950000.  3950000.]</dd></dl></div><div class='xr-var-data'><pre>array([-3937500., -3912500., -3887500., ...,  3887500.,  3912500.,  3937500.],\n",
-       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>area</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>444.1 445.0 445.9 ... 461.1 460.1</div><input id='attrs-5a9f4884-39c2-46d4-9910-cea4ecd16670' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5a9f4884-39c2-46d4-9910-cea4ecd16670' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-acc70716-0b6a-4e80-bcca-7706c2c5af87' class='xr-var-data-in' type='checkbox'><label for='data-acc70716-0b6a-4e80-bcca-7706c2c5af87' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[444.053, 444.983, 445.91 , ..., 445.91 , 444.983, 444.053],\n",
-       "       [445.078, 446.011, 446.942, ..., 446.942, 446.011, 445.078],\n",
-       "       [446.101, 447.037, 447.971, ..., 447.971, 447.037, 446.101],\n",
-       "       ...,\n",
-       "       [462.098, 463.085, 464.069, ..., 464.069, 463.085, 462.098],\n",
-       "       [461.12 , 462.104, 463.085, ..., 463.085, 462.104, 461.12 ],\n",
-       "       [460.139, 461.12 , 462.098, ..., 462.098, 461.12 , 460.139]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-91f0907a-b947-4325-9796-08b046f8d7b2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-91f0907a-b947-4325-9796-08b046f8d7b2' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>cdr_seaice_conc</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-85dfd0b0-8e51-441a-aeb8-47d442a5f7f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-85dfd0b0-8e51-441a-aeb8-47d442a5f7f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ffdabc4-b91e-4d5d-933f-dc2d3564f04d' class='xr-var-data-in' type='checkbox'><label for='data-5ffdabc4-b91e-4d5d-933f-dc2d3564f04d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>ancillary_variables :</span></dt><dd>stdev_of_cdr_seaice_conc_monthly qa_of_cdr_seaice_conc_monthly</dd><dt><span>colorBarMaximum :</span></dt><dd>1.0</dd><dt><span>colorBarMinimum :</span></dt><dd>0.0</dd><dt><span>colorBarPalette :</span></dt><dd>KT_ice</dd><dt><span>datum :</span></dt><dd>+ellps=urn:ogc:def:crs:EPSG::4326</dd><dt><span>flag_meanings :</span></dt><dd>pole_hole lakes coastal land_mask missing_data</dd><dt><span>flag_values :</span></dt><dd>[-5 -4 -3 -2 -1]</dd><dt><span>ioos_category :</span></dt><dd>Ice Distribution</dd><dt><span>long_name :</span></dt><dd>NOAA/NSIDC Climate Data Record of Passive Microwave Monthly Southern Hemisphere Sea Ice Concentration</dd><dt><span>references :</span></dt><dd>https://nsidc.org/data/g02202/versions/4/</dd><dt><span>standard_name :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_range :</span></dt><dd>[0. 1.]</dd></dl></div><div class='xr-var-data'><pre>array([[[0., 0., 0., ..., 0., 0., 0.],\n",
-       "        [0., 0., 0., ..., 0., 0., 0.],\n",
-       "        [0., 0., 0., ..., 0., 0., 0.],\n",
-       "        ...,\n",
-       "        [0., 0., 0., ..., 0., 0., 0.],\n",
-       "        [0., 0., 0., ..., 0., 0., 0.],\n",
-       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
-       "\n",
-       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
-       "        [0., 0., 0., ..., 0., 0., 0.],\n",
-       "        [0., 0., 0., ..., 0., 0., 0.],\n",
-       "        ...,\n",
-       "        [0., 0., 0., ..., 0., 0., 0.],\n",
-       "        [0., 0., 0., ..., 0., 0., 0.],\n",
-       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
-       "\n",
-       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
-       "        [0., 0., 0., ..., 0., 0., 0.],\n",
-       "        [0., 0., 0., ..., 0., 0., 0.],\n",
-       "        ...,\n",
-       "...\n",
-       "        ...,\n",
-       "        [0., 0., 0., ..., 0., 0., 0.],\n",
-       "        [0., 0., 0., ..., 0., 0., 0.],\n",
-       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
-       "\n",
-       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
-       "        [0., 0., 0., ..., 0., 0., 0.],\n",
-       "        [0., 0., 0., ..., 0., 0., 0.],\n",
-       "        ...,\n",
-       "        [0., 0., 0., ..., 0., 0., 0.],\n",
-       "        [0., 0., 0., ..., 0., 0., 0.],\n",
-       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
-       "\n",
-       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
-       "        [0., 0., 0., ..., 0., 0., 0.],\n",
-       "        [0., 0., 0., ..., 0., 0., 0.],\n",
-       "        ...,\n",
-       "        [0., 0., 0., ..., 0., 0., 0.],\n",
-       "        [0., 0., 0., ..., 0., 0., 0.],\n",
-       "        [0., 0., 0., ..., 0., 0., 0.]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-59338509-6ed7-4f37-b409-ed58717c7c76' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-59338509-6ed7-4f37-b409-ed58717c7c76' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-10T00:36:10.113820Z",
+     "iopub.status.busy": "2024-01-10T00:36:10.113067Z",
+     "iopub.status.idle": "2024-01-10T00:36:10.124678Z",
+     "shell.execute_reply": "2024-01-10T00:36:10.123655Z",
+     "shell.execute_reply.started": "2024-01-10T00:36:10.113771Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "@map_over_subtree\n",
+    "def sea_ice_area_model_sh(ds):\n",
+    "    sic = ds.aice_m.cf.sel(Y=slice(-90, 0))\n",
+    "    area_km2 = ds.area_t / 1e6\n",
+    "\n",
+    "    return sea_ice_area(sic, area_km2).to_dataset(name=\"si_area\").load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-10T00:36:10.126961Z",
+     "iopub.status.busy": "2024-01-10T00:36:10.126119Z",
+     "iopub.status.idle": "2024-01-10T00:36:28.663179Z",
+     "shell.execute_reply": "2024-01-10T00:36:28.662062Z",
+     "shell.execute_reply.started": "2024-01-10T00:36:10.126913Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "model_area_dt = sea_ice_area_model_sh(si_dt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Sea Ice Area Trends"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We are going to compare the trends in the minima and maxima over time, and the monthly climatology"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-10T00:36:28.664719Z",
+     "iopub.status.busy": "2024-01-10T00:36:28.664457Z",
+     "iopub.status.idle": "2024-01-10T00:36:29.160899Z",
+     "shell.execute_reply": "2024-01-10T00:36:29.159803Z",
+     "shell.execute_reply.started": "2024-01-10T00:36:28.664691Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "@map_over_subtree\n",
+    "def min_and_max(ds):\n",
+    "    def min_and_max_year(da):\n",
+    "        result = xr.Dataset()\n",
+    "        result[\"min\"] = da.min()\n",
+    "        result[\"max\"] = da.max()\n",
+    "        return result\n",
+    "\n",
+    "    annual_min_max_ds = ds.si_area.groupby(\"time.year\").apply(min_and_max_year)\n",
+    "    return annual_min_max_ds\n",
+    "\n",
+    "\n",
+    "model_min_max_dt = min_and_max(model_area_dt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-10T00:36:29.162016Z",
+     "iopub.status.busy": "2024-01-10T00:36:29.161836Z",
+     "iopub.status.idle": "2024-01-10T00:36:29.789709Z",
+     "shell.execute_reply": "2024-01-10T00:36:29.789011Z",
+     "shell.execute_reply.started": "2024-01-10T00:36:29.161998Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for iGroup in model_min_max_dt.groups[1:]:\n",
+    "    ds = model_min_max_dt[iGroup].ds\n",
+    "    ds[\"min\"].plot(label=iGroup[1:])\n",
+    "\n",
+    "obs_area_dt[\"cdr_sh\"].ds.cdr_area.groupby(\"time.year\").min().plot(label=\"CDR\")\n",
+    "\n",
+    "plt.title(\"Trends in Sea-Ice Minima\")\n",
+    "plt.ylabel(\"Sea-Ice Area ($km^2$)\")\n",
+    "\n",
+    "_ = plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that all models have sea ice area which is too low in summer. Model runs forced by JRA have more variability than those forced by ERA5 and are slightly closer to the measured sea ice area from observations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-10T00:36:29.790626Z",
+     "iopub.status.busy": "2024-01-10T00:36:29.790454Z",
+     "iopub.status.idle": "2024-01-10T00:36:30.054204Z",
+     "shell.execute_reply": "2024-01-10T00:36:30.053076Z",
+     "shell.execute_reply.started": "2024-01-10T00:36:29.790609Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for iGroup in model_min_max_dt.groups[1:]:\n",
+    "    ds = model_min_max_dt[iGroup].ds.sel(\n",
+    "        year=slice(1950, 2022)\n",
+    "    )  # drop 2023 because its incomplete\n",
+    "    ds[\"max\"].plot(label=iGroup[1:])\n",
+    "\n",
+    "obs_area_dt[\"cdr_sh\"].ds.cdr_area.groupby(\"time.year\").max().plot(label=\"CDR\")\n",
+    "\n",
+    "plt.title(\"Trends in Sea-Ice Maxima\")\n",
+    "plt.ylabel(\"Sea-Ice Area ($km^2$)\")\n",
+    "\n",
+    "_ = plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-12-08T03:00:12.062280Z",
+     "iopub.status.busy": "2023-12-08T03:00:12.060174Z",
+     "iopub.status.idle": "2023-12-08T03:00:12.075164Z",
+     "shell.execute_reply": "2023-12-08T03:00:12.073582Z",
+     "shell.execute_reply.started": "2023-12-08T03:00:12.062204Z"
+    }
+   },
+   "source": [
+    "We see that the closest runs are those forced by ERA5, and the runs forced by JRA55 are under representing Winter sea ice."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-10T00:36:30.055174Z",
+     "iopub.status.busy": "2024-01-10T00:36:30.054994Z",
+     "iopub.status.idle": "2024-01-10T00:36:30.313014Z",
+     "shell.execute_reply": "2024-01-10T00:36:30.312239Z",
+     "shell.execute_reply.started": "2024-01-10T00:36:30.055156Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
       "text/plain": [
-       "DataTree('cdr_sh', parent=\"None\")\n",
-       "    Dimensions:          (time: 536, y: 332, x: 316)\n",
-       "    Coordinates:\n",
-       "      * time             (time) datetime64[ns] 1978-11-01 1978-12-01 ... 2023-06-01\n",
-       "      * y                (y) float32 4.338e+06 4.312e+06 ... -3.912e+06 -3.938e+06\n",
-       "      * x                (x) float32 -3.938e+06 -3.912e+06 ... 3.912e+06 3.938e+06\n",
-       "        area             (y, x) float64 444.1 445.0 445.9 ... 462.1 461.1 460.1\n",
-       "    Data variables:\n",
-       "        cdr_seaice_conc  (time, y, x) float32 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0"
+       "<matplotlib.legend.Legend at 0x14e242887850>"
       ]
      },
      "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "cdr_dt['cdr_sh']"
+    "for iGroup in model_area_dt.groups[1:]:\n",
+    "    climatology(model_area_dt[iGroup].ds.si_area).plot(label=iGroup[1:])\n",
+    "\n",
+    "climatology(obs_area_dt[\"cdr_sh\"].ds.cdr_area).plot(label=\"CDR\")\n",
+    "\n",
+    "plt.title(\"Climatology of Sea-Ice Area\")\n",
+    "plt.ylabel(\"Sea-Ice Area ($km^2$)\")\n",
+    "\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-12-08T03:01:35.360367Z",
+     "iopub.status.busy": "2023-12-08T03:01:35.359044Z",
+     "iopub.status.idle": "2023-12-08T03:01:35.370817Z",
+     "shell.execute_reply": "2023-12-08T03:01:35.369364Z",
+     "shell.execute_reply.started": "2023-12-08T03:01:35.360297Z"
+    }
+   },
+   "source": [
+    "We see all model runs have too low sea ice in summer, and grow faster than observations in autumn and earlier that the observed maximum."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Sea Ice Concentration Anomalies"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To examine the differences between the model results and observations, we calculate difference in each grid cell between observations and each experiment\n",
+    "\n",
+    "As that data are on different grids, we need to regrid to compare the datasets\n",
+    "\n",
+    "Lets simplify a little to only look at 0.25 degree results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-10T00:36:30.314109Z",
+     "iopub.status.busy": "2024-01-10T00:36:30.313904Z",
+     "iopub.status.idle": "2024-01-10T00:36:30.317836Z",
+     "shell.execute_reply": "2024-01-10T00:36:30.317099Z",
+     "shell.execute_reply.started": "2024-01-10T00:36:30.314091Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "groups = (\"/025deg_era5\", \"/025deg_jra55\")"
    ]
   },
   {
@@ -4749,14 +4622,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-08T03:13:06.729255Z",
-     "iopub.status.busy": "2024-01-08T03:13:06.728998Z",
-     "iopub.status.idle": "2024-01-08T03:13:07.044700Z",
-     "shell.execute_reply": "2024-01-08T03:13:07.043099Z",
-     "shell.execute_reply.started": "2024-01-08T03:13:06.729231Z"
+     "iopub.execute_input": "2024-01-10T00:36:30.318764Z",
+     "iopub.status.busy": "2024-01-10T00:36:30.318605Z",
+     "iopub.status.idle": "2024-01-10T00:36:30.543796Z",
+     "shell.execute_reply": "2024-01-10T00:36:30.542462Z",
+     "shell.execute_reply.started": "2024-01-10T00:36:30.318747Z"
     }
    },
    "outputs": [
@@ -4775,20 +4648,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-08T03:13:07.049020Z",
-     "iopub.status.busy": "2024-01-08T03:13:07.048105Z",
-     "iopub.status.idle": "2024-01-08T03:13:07.129262Z",
-     "shell.execute_reply": "2024-01-08T03:13:07.128402Z",
-     "shell.execute_reply.started": "2024-01-08T03:13:07.048961Z"
+     "iopub.execute_input": "2024-01-10T00:36:30.546873Z",
+     "iopub.status.busy": "2024-01-10T00:36:30.545869Z",
+     "iopub.status.idle": "2024-01-10T00:36:30.589958Z",
+     "shell.execute_reply": "2024-01-10T00:36:30.589119Z",
+     "shell.execute_reply.started": "2024-01-10T00:36:30.546807Z"
     },
     "scrolled": true
    },
    "outputs": [],
    "source": [
-    "cdr_sps_ds=xr.open_dataset('G02202-cdr-ancillary-sh.nc')"
+    "cdr_sps_ds = xr.open_dataset(\"G02202-cdr-ancillary-sh.nc\")"
    ]
   },
   {
@@ -4800,49 +4673,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-08T03:13:07.130295Z",
-     "iopub.status.busy": "2024-01-08T03:13:07.130085Z",
-     "iopub.status.idle": "2024-01-08T03:13:31.730021Z",
-     "shell.execute_reply": "2024-01-08T03:13:31.724427Z",
-     "shell.execute_reply.started": "2024-01-08T03:13:07.130276Z"
+     "iopub.execute_input": "2024-01-10T00:36:30.591352Z",
+     "iopub.status.busy": "2024-01-10T00:36:30.590996Z",
+     "iopub.status.idle": "2024-01-10T00:36:48.903339Z",
+     "shell.execute_reply": "2024-01-10T00:36:48.902099Z",
+     "shell.execute_reply.started": "2024-01-10T00:36:30.591324Z"
     }
    },
    "outputs": [],
    "source": [
     "regridder_ACCESSOM2_025deg_sh = xesmf.Regridder(\n",
-    "    si_dt['025deg_era5'].ds.isel(time=0).drop(['xt_ocean','yt_ocean']), \n",
-    "    cdr_sps_ds, \n",
-    "    'bilinear', \n",
+    "    si_dt[\"025deg_era5\"].ds.isel(time=0).drop_vars([\"xt_ocean\", \"yt_ocean\"]),\n",
+    "    cdr_sps_ds,\n",
+    "    \"bilinear\",\n",
     "    periodic=True,\n",
-    "    unmapped_to_nan=True\n",
+    "    unmapped_to_nan=True,\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-08T03:13:31.738144Z",
-     "iopub.status.busy": "2024-01-08T03:13:31.737851Z",
-     "iopub.status.idle": "2024-01-08T03:14:27.330591Z",
-     "shell.execute_reply": "2024-01-08T03:14:27.323910Z",
-     "shell.execute_reply.started": "2024-01-08T03:13:31.738116Z"
+     "iopub.execute_input": "2024-01-10T00:36:48.904752Z",
+     "iopub.status.busy": "2024-01-10T00:36:48.904562Z",
+     "iopub.status.idle": "2024-01-10T00:36:53.582017Z",
+     "shell.execute_reply": "2024-01-10T00:36:53.581231Z",
+     "shell.execute_reply.started": "2024-01-10T00:36:48.904731Z"
     }
    },
    "outputs": [],
    "source": [
-    "aice_sh_3976_ds=xr.Dataset()\n",
-    "aice_sh_diff_ds=xr.Dataset()\n",
+    "aice_sh_3976_ds = xr.Dataset()\n",
+    "aice_sh_anom_ds = xr.Dataset()\n",
     "\n",
     "for iG in groups:\n",
-    "    aice_sh_3976_ds[iG]=regridder_ACCESSOM2_025deg_sh(\n",
+    "    aice_sh_3976_ds[iG] = regridder_ACCESSOM2_025deg_sh(\n",
     "        si_dt[iG].ds.aice_m.copy()\n",
-    "    ).load()\n",
-    "    aice_sh_diff_ds[iG]=aice_sh_3976_ds[iG]-cdr_dt['cdr_sh'].ds.cdr_seaice_conc.load()"
+    "    )\n",
+    "    aice_sh_anom_ds[iG] = aice_sh_3976_ds[iG] - cdr_dt[\"cdr_sh\"].ds.cdr_conc"
    ]
   },
   {
@@ -4854,22 +4727,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-08T03:14:27.339589Z",
-     "iopub.status.busy": "2024-01-08T03:14:27.339223Z",
-     "iopub.status.idle": "2024-01-08T03:14:28.459646Z",
-     "shell.execute_reply": "2024-01-08T03:14:28.458472Z",
-     "shell.execute_reply.started": "2024-01-08T03:14:27.339556Z"
+     "iopub.execute_input": "2024-01-10T00:36:53.583063Z",
+     "iopub.status.busy": "2024-01-10T00:36:53.582880Z",
+     "iopub.status.idle": "2024-01-10T00:37:06.983358Z",
+     "shell.execute_reply": "2024-01-10T00:37:06.982495Z",
+     "shell.execute_reply.started": "2024-01-10T00:36:53.583045Z"
     }
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.10/lib/python3.10/site-packages/distributed/client.py:3162: UserWarning: Sending large graph of size 217.46 MiB.\n",
+      "This may cause some slowdown.\n",
+      "Consider scattering data ahead of time and using futures.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.10/lib/python3.10/site-packages/distributed/client.py:3162: UserWarning: Sending large graph of size 217.46 MiB.\n",
+      "This may cause some slowdown.\n",
+      "Consider scattering data ahead of time and using futures.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.10/lib/python3.10/site-packages/distributed/client.py:3162: UserWarning: Sending large graph of size 217.46 MiB.\n",
+      "This may cause some slowdown.\n",
+      "Consider scattering data ahead of time and using futures.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.10/lib/python3.10/site-packages/distributed/client.py:3162: UserWarning: Sending large graph of size 217.46 MiB.\n",
+      "This may cause some slowdown.\n",
+      "Consider scattering data ahead of time and using futures.\n",
+      "  warnings.warn(\n"
+     ]
+    },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 900x800 with 5 Axes>"
+       "<Figure size 900x600 with 5 Axes>"
       ]
      },
      "metadata": {},
@@ -4877,53 +4772,50 @@
     }
    ],
    "source": [
-    "months=[2,9] #february, september\n",
-    "month_names=['Feb','Sep']\n",
-    "n_months=len(months)\n",
+    "months = [2, 9]  # february, september\n",
+    "month_names = [\"Feb\", \"Sep\"]\n",
+    "n_months = len(months)\n",
     "\n",
-    "plt.figure(figsize=(9,n_months*4))\n",
-    "j=0\n",
+    "plt.figure(figsize=(9, n_months * 3))\n",
     "\n",
-    "cdr=cdr_dt['cdr_sh'].ds.cdr_seaice_conc\n",
+    "cdr = climatology(cdr_dt[\"cdr_sh\"].ds.cdr_conc)\n",
     "\n",
-    "for iMonth in np.arange(0,n_months):\n",
+    "for j, iGroup in enumerate(aice_sh_anom_ds.data_vars):\n",
+    "    anoms = climatology(aice_sh_anom_ds[iGroup])\n",
+    "    aice = climatology(aice_sh_3976_ds[iGroup])\n",
     "\n",
-    "    i=1\n",
+    "    for i, iMonth in enumerate(months):\n",
+    "        plt.subplot(\n",
+    "            n_months,\n",
+    "            3,\n",
+    "            j + 1 + i * 3,\n",
+    "            projection=ccrs.SouthPolarStereo(true_scale_latitude=-70),\n",
+    "        )\n",
     "\n",
-    "    for iGroup in aice_sh_diff_ds.data_vars:\n",
-    "        ds=aice_sh_diff_ds[iGroup]\n",
-    "        \n",
-    "        ds=ds.sel(time=ds.time.dt.month.isin(months[iMonth])).mean('time')\n",
-    "        \n",
-    "        ax = plt.subplot(n_months,3,i+j*3,projection=ccrs.SouthPolarStereo(true_scale_latitude=-70))\n",
-    "            \n",
+    "        # Filled contours with concentration anomalies in this month\n",
+    "        ds = anoms.sel(month=iMonth).compute()\n",
     "        plt.contourf(\n",
-    "            ds.x, ds.y, ds,\n",
-    "            levels=np.arange(-0.9,0.91,.2), cmap=cmo.balance_r\n",
+    "            ds.x, ds.y, ds, levels=np.arange(-0.85, 0.86, 0.1), cmap=cmo.balance_r\n",
     "        )\n",
     "\n",
-    "        cs_cdr=cdr.sel(time=cdr.time.dt.month.isin(months[iMonth])).mean('time').plot.contour(levels=[0.15])\n",
-    "        \n",
-    "        da=aice_sh_3976_ds[iGroup]\n",
-    "        cs_mod=da.sel(time=da.time.dt.month.isin(months[iMonth])).mean('time').plot.contour(levels=[0.15], colors=['black'])\n",
-    "\n",
-    "        plt.title(month_names[iMonth]+'_'+iGroup[1:])\n",
-    "        i+=1\n",
-    "\n",
-    "    j+=1\n",
+    "        # Lines at 15% concentration (approx ice edge)\n",
+    "        cs_cdr = cdr.sel(month=iMonth).plot.contour(levels=[0.15])\n",
+    "        cs_mod = aice.sel(month=iMonth).plot.contour(levels=[0.15], colors=[\"black\"])\n",
     "\n",
+    "        plt.title(month_names[i] + \"_\" + iGroup[1:])\n",
     "\n",
+    "# Messy legend creation\n",
     "color_cdr = cs_cdr.collections[0].get_edgecolor()\n",
     "line_cdr = mlines.Line2D([], [], color=color_cdr, label=\"Observed Extent\")\n",
     "\n",
     "color_mod = cs_mod.collections[0].get_edgecolor()\n",
     "line_mod = mlines.Line2D([], [], color=color_mod, label=\"Modelled Extent\")\n",
     "\n",
-    "# cax = plt.subplot(3,12,34)\n",
-    "plt.legend(handles=[line_cdr,line_mod], loc='center left', bbox_to_anchor=(1.2,0.5))\n",
+    "plt.legend(handles=[line_cdr, line_mod], loc=\"center left\", bbox_to_anchor=(1.2, 0.5))\n",
     "\n",
-    "cax = plt.axes([0.7,0.6,0.05,0.2])\n",
-    "_ = plt.colorbar(cax=cax, label='Difference in \\nSea Ice Concentration')\n"
+    "# And colorbar\n",
+    "cax = plt.axes([0.7, 0.6, 0.05, 0.2])\n",
+    "_ = plt.colorbar(cax=cax, label=\"Difference in \\nSea Ice Concentration\")"
    ]
   },
   {
@@ -4949,54 +4841,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-08T03:14:28.462372Z",
-     "iopub.status.busy": "2024-01-08T03:14:28.461324Z",
-     "iopub.status.idle": "2024-01-08T03:14:28.469906Z",
-     "shell.execute_reply": "2024-01-08T03:14:28.468766Z",
-     "shell.execute_reply.started": "2024-01-08T03:14:28.462322Z"
+     "iopub.execute_input": "2024-01-10T00:37:06.986017Z",
+     "iopub.status.busy": "2024-01-10T00:37:06.985356Z",
+     "iopub.status.idle": "2024-01-10T00:37:06.993083Z",
+     "shell.execute_reply": "2024-01-10T00:37:06.992428Z",
+     "shell.execute_reply.started": "2024-01-10T00:37:06.985956Z"
     }
    },
    "outputs": [],
    "source": [
     "@map_over_subtree\n",
     "def sea_ice_vol_model_sh(ds):\n",
-    "    vice_m=ds.vicen_m.sum('nc').sel(yt_ocean=slice(-90,0))   \n",
-    "    \n",
-    "    siv_total_da=(vice_m*ds.area_t).where(~np.isnan(ds.area_t)).cf.sum(['longitude','latitude'])\n",
-    "    \n",
-    "    return siv_total_da.to_dataset(name='si_vol').load()"
+    "    vice_m = ds.hi_m.sel(yt_ocean=slice(-90, 0))\n",
+    "    siv_total_da = (\n",
+    "        (vice_m * ds.area_t)\n",
+    "        .where(~np.isnan(ds.area_t))\n",
+    "        .cf.sum([\"longitude\", \"latitude\"])\n",
+    "    )\n",
+    "\n",
+    "    return siv_total_da.to_dataset(name=\"si_vol\").load()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-08T03:14:28.473906Z",
-     "iopub.status.busy": "2024-01-08T03:14:28.471365Z",
-     "iopub.status.idle": "2024-01-08T03:17:12.546394Z",
-     "shell.execute_reply": "2024-01-08T03:17:12.542999Z",
-     "shell.execute_reply.started": "2024-01-08T03:14:28.473860Z"
+     "iopub.execute_input": "2024-01-10T00:37:06.993944Z",
+     "iopub.status.busy": "2024-01-10T00:37:06.993784Z",
+     "iopub.status.idle": "2024-01-10T00:37:26.063070Z",
+     "shell.execute_reply": "2024-01-10T00:37:26.061164Z",
+     "shell.execute_reply.started": "2024-01-10T00:37:06.993927Z"
     }
    },
    "outputs": [],
    "source": [
-    "si_vol_dt=sea_ice_vol_model_sh(si_dt)"
+    "si_vol_dt = sea_ice_vol_model_sh(si_dt)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-08T03:17:12.563611Z",
-     "iopub.status.busy": "2024-01-08T03:17:12.563340Z",
-     "iopub.status.idle": "2024-01-08T03:17:13.001535Z",
-     "shell.execute_reply": "2024-01-08T03:17:13.000419Z",
-     "shell.execute_reply.started": "2024-01-08T03:17:12.563590Z"
+     "iopub.execute_input": "2024-01-10T00:37:26.064667Z",
+     "iopub.status.busy": "2024-01-10T00:37:26.064135Z",
+     "iopub.status.idle": "2024-01-10T00:37:26.484796Z",
+     "shell.execute_reply": "2024-01-10T00:37:26.484040Z",
+     "shell.execute_reply.started": "2024-01-10T00:37:26.064646Z"
     }
    },
    "outputs": [],
@@ -5005,31 +4900,33 @@
     "def min_and_max(ds):\n",
     "    def min_and_max_year(da):\n",
     "        result = xr.Dataset()\n",
-    "        result['min']=da.min()\n",
-    "        result['max']=da.max()\n",
+    "        result[\"min\"] = da.min()\n",
+    "        result[\"max\"] = da.max()\n",
     "        return result\n",
-    "    annual_min_max_ds=ds.si_vol.groupby('time.year').apply(min_and_max_year)\n",
+    "\n",
+    "    annual_min_max_ds = ds.si_vol.groupby(\"time.year\").apply(min_and_max_year)\n",
     "    return annual_min_max_ds\n",
-    "    \n",
-    "model_min_max_dt=min_and_max(si_vol_dt)"
+    "\n",
+    "\n",
+    "model_min_max_dt = min_and_max(si_vol_dt)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-08T03:17:13.003817Z",
-     "iopub.status.busy": "2024-01-08T03:17:13.002983Z",
-     "iopub.status.idle": "2024-01-08T03:17:13.330980Z",
-     "shell.execute_reply": "2024-01-08T03:17:13.330257Z",
-     "shell.execute_reply.started": "2024-01-08T03:17:13.003783Z"
+     "iopub.execute_input": "2024-01-10T00:37:26.485810Z",
+     "iopub.status.busy": "2024-01-10T00:37:26.485635Z",
+     "iopub.status.idle": "2024-01-10T00:37:26.736134Z",
+     "shell.execute_reply": "2024-01-10T00:37:26.735163Z",
+     "shell.execute_reply.started": "2024-01-10T00:37:26.485793Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -5040,38 +4937,31 @@
    ],
    "source": [
     "for iGroup in model_min_max_dt.groups[1:]:\n",
-    "    ds=model_min_max_dt[iGroup].ds\n",
-    "    ds['min'].plot(label=iGroup[1:])\n",
+    "    ds = model_min_max_dt[iGroup].ds\n",
+    "    ds[\"min\"].plot(label=iGroup[1:])\n",
     "\n",
-    "plt.title('Trends in Sea-Ice Minimum Volume')\n",
-    "plt.ylabel('Sea-Ice Volume (m3)')\n",
+    "plt.title(\"Trends in Sea-Ice Minimum Volume\")\n",
+    "plt.ylabel(\"Sea-Ice Volume ($m^3$)\")\n",
     "\n",
     "_ = plt.legend()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We see that all models have sea ice area which is too low in summer. Model runs forced by JRA have more variability than those forced by ERA5 and are slightly closer to the measured sea ice area from observations."
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 38,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-08T03:17:13.332067Z",
-     "iopub.status.busy": "2024-01-08T03:17:13.331869Z",
-     "iopub.status.idle": "2024-01-08T03:17:13.574359Z",
-     "shell.execute_reply": "2024-01-08T03:17:13.573654Z",
-     "shell.execute_reply.started": "2024-01-08T03:17:13.332049Z"
+     "iopub.execute_input": "2024-01-10T00:37:26.737147Z",
+     "iopub.status.busy": "2024-01-10T00:37:26.736971Z",
+     "iopub.status.idle": "2024-01-10T00:37:26.960435Z",
+     "shell.execute_reply": "2024-01-10T00:37:26.959685Z",
+     "shell.execute_reply.started": "2024-01-10T00:37:26.737129Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjcAAAHFCAYAAAAOmtghAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy88F64QAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOydd3gUZdeH79303juk0DuEjkgRVIoiYgEECwL28lmwYAEVXrFhf60IWACxAC8gCkjvJXQIAUJCIATSSG9b5vvjyW6yyW6ymw4893XttbMzz8w8s9nsnD3nd85RKYqiIJFIJBKJRHKNoG7sCUgkEolEIpHUJdK4kUgkEolEck0hjRuJRCKRSCTXFNK4kUgkEolEck0hjRuJRCKRSCTXFNK4kUgkEolEck0hjRuJRCKRSCTXFNK4kUgkEolEck0hjRuJRCKRSCTXFNK4kTQ6KpXKqsfmzZsbdZ5vvfUWKpWqTo4VGRnJpEmT6uRYBvbs2cOYMWMIDw/HycmJoKAg+vXrx4svvlin57GWxMREVCoVH330UYOed9KkSahUKjw8PMjLy6u0/dy5c6jValQqFW+99Va9zcNw/QsXLqy3czQkn332GSqVin/++cfimO+//x6VSsWyZcusPm59/C9IJPaNPQGJZNeuXSavZ82axaZNm9i4caPJ+g4dOjTktOqV5cuX4+npWWfH++uvv7jjjjsYPHgwH3zwASEhIaSkpLB//35+/fVX5s6dW2fnuhpwcHBAq9WydOlSpkyZYrJtwYIFeHh4kJOTU69zCAkJYdeuXbRs2bJez9NQ3H///bzyyivMnz+f4cOHmx2zYMECAgICGDVqVAPPTiIxRRo3kkanb9++Jq8DAgJQq9WV1lekoKAAV1fX+pxavREdHV2nx/vggw+Iiopi7dq12NuX/VuPHz+eDz74oE7PdTXg6OjIqFGjmD9/volxoygKCxcuZNy4cXz//ff1OgcnJ6dqP8NXE35+fowePZoVK1aQkZGBn5+fyfaTJ0+ya9cuXnzxRRwcHBpplhKJQIalJFcFgwcPplOnTmzdupUbbrgBV1dXJk+eDEBOTg7Tpk0jKioKR0dHwsLCeO6558jPzzc5hkql4umnn+bnn3+mffv2uLq60rVrV1avXl3pfH/99RfdunXDycmJqKgoi6GV33//nT59+uDl5YWrqystWrQwzqsqKrriN2/ejEqlYsmSJbz++uuEhobi6enJzTffTFxcXLXHy8jIwN/f38SwMaBWV/43X7p0Kf369cPNzQ13d3eGDRvGwYMHTcbs37+f8ePHExkZiYuLC5GRkdx3332cO3eu2vlURXJyMo8++ijNmzfH0dGR0NBQ7rnnHi5fvmwcY+3ftComT57Mzp07Td6/f//9l3PnzvHwww9XGp+WlsaTTz5Jhw4dcHd3JzAwkCFDhrBt2zaTce+99x5qtZpVq1aZrJ80aRKurq4cPXoUMB+WMoQ2jxw5wr333ouXlxe+vr688MILaLVa4uLiGD58OB4eHkRGRlYyTBcuXIhKpSIxMdFkveHzUz50a/if2bVrFzfccIPxb7hgwQJAfMa7d++Oq6srnTt3rjLcZGDKlCmUlJSwePHiStsMxzV8/jMzM3nyyScJCwvD0dGRFi1a8Prrr1NcXFzlORr6Gk+fPs2ECRMIDAzEycmJ9u3b89///rfa90LSxFEkkibGQw89pLi5uZmsGzRokOLr66s0b95c+eKLL5RNmzYpW7ZsUfLz85Vu3bop/v7+yscff6z8+++/ymeffaZ4eXkpQ4YMUfR6vfEYgBIZGan07t1b+e2335Q1a9YogwcPVuzt7ZX4+HjjuH///Vexs7NTbrzxRmXZsmXK77//rvTq1UsJDw9Xyv/L7Ny5U1GpVMr48eOVNWvWKBs3blQWLFigPPDAA9VeY0REhPLQQw8ZX2/atMk4v4kTJyp//fWXsmTJEiU8PFxp3bq1otVqqzze1KlTFUB55plnlN27dyslJSUWx/7nP/9RVCqVMnnyZGX16tXKsmXLlH79+ilubm7K8ePHjeN+//13ZcaMGcry5cuVLVu2KL/++qsyaNAgJSAgQElLS6v2GhMSEhRA+fDDD43rLly4oISEhJj8vZYuXapMnjxZiY2NVRRFselvag7D50ev1ysRERHKyy+/bNw2btw4ZeDAgUpaWpoCKDNnzjRuO3nypPLEE08ov/76q7J582Zl9erVypQpUxS1Wq1s2rTJOE6v1ysjR45UfHx8lMTEREVRFGX+/PkKoMybN6/S9S9YsMC4bubMmQqgtG3bVpk1a5ayfv165eWXX1YA5emnn1batWunfP7558r69euVhx9+WAGUP//807j/ggULFEBJSEgwuWbD56f8PAcNGqT4+fkpbdu2VX744Qdl7dq1yu23364Ayttvv6107txZWbJkibJmzRqlb9++ipOTk5KcnFzle6vT6ZSIiAilW7duJuu1Wq0SEhKi9O3bV1EURSksLFS6dOmiuLm5KR999JGybt065c0331Ts7e2VkSNHmuxb8X+hIa/x+PHjipeXl9K5c2flp59+UtatW6e8+OKLilqtVt56660q3wtJ00YaN5ImhyXjBlA2bNhgsn7OnDmKWq1W9u3bZ7L+jz/+UABlzZo1xnWAEhQUpOTk5BjXXbp0SVGr1cqcOXOM6/r06aOEhoYqhYWFxnU5OTmKr6+viXHz0UcfKYCSlZVl8zVaMm4qfvH/9ttvCqDs2rWryuOlp6crN954owIogOLg4KDccMMNypw5c5Tc3FzjuKSkJMXe3l555plnTPbPzc1VgoODlbFjx1o8h1arVfLy8hQ3Nzfls88+q/YazRk3kydPVhwcHJQTJ05Y3M+Wv6k5yn9+Zs6cqQQHBysajUbJyMhQnJyclIULF5o1bsxdr0ajUYYOHaqMGTPGZFt6errSrFkzpXfv3sqBAwcUV1dX5f777zd7/eaMm7lz55qM7datmwIoy5YtM67TaDRKQECActdddxnX2XrjB5T9+/cb12VkZCh2dnaKi4uLyU3+0KFDCqB8/vnnFt+Pitdw4MAB47pVq1YpgPL9998riqIo33zzjQIov/32m8m+77//vgIo69atM66rrXFTm2scNmyY0qxZMyU7O9vkXE8//bTi7OysZGZmVvt+SJom13VYauvWrYwaNYrQ0FBUKhUrVqywaf+ioiImTZpE586dsbe3584776w0Zvv27fTv3x8/Pz9cXFxo164dn3zySd1cwHWGj48PQ4YMMVm3evVqOnXqRLdu3dBqtcbHsGHDzGZY3XTTTXh4eBhfBwUFERgYaAy15Ofns2/fPu666y6cnZ2N4zw8PCqJJHv16gXA2LFj+e2330hOTq71Nd5xxx0mr7t06QJQbSjIz8+Pbdu2sW/fPt577z1Gjx7NqVOnmD59Op07dyY9PR2AtWvXotVqefDBB03eL2dnZwYNGmTyfuXl5fHKK6/QqlUr7O3tsbe3x93dnfz8fGJjY43jyh9Hq9WiKIrFef7999/cdNNNtG/f3uIYW/+mVfHwww9z+fJl/v77bxYtWoSjoyP33nuvxfHffPMN3bt3x9nZGXt7exwcHNiwYYPJ9YJ4v5cuXcqBAwe44YYbCA8P55tvvrF6XrfffrvJ6/bt26NSqRgxYoRxnb29Pa1atapVGDAkJIQePXoYX/v6+hIYGEi3bt0IDQ01OT9U/zkD8Z6q1Wrmz59vXLdgwQLc3NwYN24cABs3bsTNzY177rnHZF9DKHbDhg01vqaK1PQai4qK2LBhA2PGjMHV1dXkszZy5EiKiorYvXt3nc1T0rBc18ZNfn4+Xbt25csvv6zR/jqdDhcXF5599lluvvlms2Pc3Nx4+umn2bp1K7Gxsbzxxhu88cYbfPfdd7WZ+nVJSEhIpXWXL1/myJEjODg4mDw8PDxQFMV4UzdQUQQJQvhZWFgIwJUrV9Dr9QQHB1caV3HdwIEDWbFihdFYaNasGZ06dWLJkiU1vsaK83NycgIwzq86evbsySuvvMLvv//OxYsXef7550lMTDRqNwy6ll69elV6z5YuXWryfk2YMIEvv/ySqVOnsnbtWvbu3cu+ffsICAgwmU/F4/z4448W55eWlkazZs2qvAZb/6ZVERERwdChQ5k/fz7z589n/PjxFkXoH3/8MU888QR9+vThzz//ZPfu3ezbt4/hw4ebff/79OlDx44dKSoq4oknnsDNzc3qefn6+pq8dnR0xNXV1cSgNqwvKiqy+rjVncdwTHPnB6w6l+E9Xbx4McXFxaSnp7N69Wruvfde4w+HjIwMgoODK5VOCAwMxN7enoyMjJpeUiVqeo0ZGRlotVq++OKLSp+1kSNHAtj0WZM0La7rbKkRI0aY/FKqSElJCW+88QaLFi0iKyuLTp068f777zN48GBAGC5ff/01ADt27CArK6vSMaKjo00yYyIjI1m2bBnbtm3j0UcfrdPrudYxV2PG398fFxcXk1+RFbfbgo+PDyqVikuXLlXaZm7d6NGjGT16NMXFxezevZs5c+YwYcIEIiMj6devn03nrmscHByYOXMmn3zyCceOHQPK3o8//viDiIgIi/tmZ2ezevVqZs6cyauvvmpcX1xcTGZmpsnYffv2mbyOioqyeNyAgAAuXLhQ5bzr+m86efJk7r//fvR6vfH/1Ry//PILgwcPrjQmNzfX7PiZM2dy9OhRevTowYwZM7j99ttp0aKFTXOzFYPxU1GU29A34SlTprB+/Xr+97//cfHiRUpKSkyy0vz8/NizZw+Kopj836ampqLVaqv8GzbUNfr4+GBnZ8cDDzzAU089ZXZMVZ9lSdPmujZuquPhhx8mMTGRX3/9ldDQUJYvX87w4cM5evQorVu3rtExDx48yM6dO5k9e3Ydz/b65Pbbb+fdd9/Fz8+vTr6I3Nzc6N27N8uWLePDDz80ftHm5uZWyo4pj5OTE4MGDcLb25u1a9dy8ODBBjVuUlJSzHq2DOEUg3t+2LBh2NvbEx8fz913323xeCqVCkVRjJ4jA/PmzUOn05ms69mzp9XzHDFiBD///DNxcXG0bdvW7Ji6/puOGTOGMWPG4OXlVWVqtkqlqnS9R44cYdeuXTRv3txk/fr165kzZw5vvPEGzz33HN26dWPcuHHs2LHD6CGoDyIjI43zKv/+rVy5st7OaY4777wTPz8/5s+fT0pKCm3atOHGG280bh86dCi//fYbK1asYMyYMcb1P/30k3G7JRrqGl1dXbnppps4ePAgXbp0qde/m6ThkcaNBeLj41myZAkXLlww3himTZvGP//8w4IFC3j33XdtOl6zZs1IS0tDq9Xy1ltvMXXq1PqY9nXHc889x59//snAgQN5/vnn6dKlC3q9nqSkJNatW8eLL75Inz59bDrmrFmzGD58OLfccgsvvvgiOp2O999/Hzc3NxOvxYwZM7hw4QJDhw6lWbNmZGVl8dlnn+Hg4MCgQYPq+lKrZNiwYTRr1oxRo0bRrl079Ho9hw4dYu7cubi7u/N///d/gLhxvPPOO7z++uucPXuW4cOH4+Pjw+XLl9m7dy9ubm68/fbbeHp6MnDgQD788EP8/f2JjIxky5Yt/PDDD3h7e9d4nu+88w5///03AwcO5LXXXqNz585kZWXxzz//8MILL9CuXbs6/5s6Ozvzxx9/VDvu9ttvZ9asWcycOZNBgwYRFxfHO++8Q1RUFFqt1jguJSWF+++/n0GDBjFz5kzUajVLly5l4MCBvPzyy3z66ac1eWusolevXrRt25Zp06ah1Wrx8fFh+fLlbN++vd7OaQ4nJycmTpzIF198gaIovPfeeybbH3zwQf773//y0EMPkZiYSOfOndm+fTvvvvsuI0eOtBjGh4a9xs8++4wbb7yRAQMG8MQTTxAZGUlubi5nzpxh1apVlQqJSq4epHFjgQMHDqAoCm3atDFZX1xcbFa3UR3btm0jLy+P3bt38+qrr9KqVSvuu+++uprudYubmxvbtm3jvffe47vvviMhIQEXFxfCw8O5+eabjb8CbeGWW25hxYoVvPHGG4wbN47g4GCefPJJCgsLefvtt43j+vTpw/79+3nllVdIS0vD29ubnj17snHjRjp27FiHV1k9b7zxBv/73//45JNPSElJobi4mJCQEG6++WamT59uIuCdPn06HTp04LPPPmPJkiUUFxcTHBxMr169ePzxx43jFi9ezP/93//x8ssvo9Vq6d+/P+vXr+e2226r8TzDwsLYu3cvM2fO5L333iMjI4OAgABuvPFGo0aiPv6m1vD6669TUFDADz/8wAcffECHDh345ptvWL58uVHErNPpuO+++1CpVCxevNhYQ6hv3768++67vPTSSwwePNhsckFdYGdnx6pVq3j66ad5/PHHcXJyYvz48Xz55Ze1+rvUhClTpvD5559jZ2fHgw8+aLLN2dmZTZs28frrr/Phhx+SlpZGWFgY06ZNY+bMmVUetyGvsUOHDhw4cIBZs2bxxhtvkJqaire3N61btzbqbiRXJyqlqtSG6wiVSsXy5cuNX0pLly5l4sSJHD9+HDs7O5Ox7u7ulcSlkyZNIisry6qMq9mzZxtd8xKJRCKRSOoW6bmxQHR0NDqdjtTUVAYMGFCnx1YUpdoqnRKJRCKRSGrGdW3c5OXlcebMGePrhIQEDh06hK+vL23atGHixIk8+OCDzJ07l+joaNLT09m4cSOdO3c2uixPnDhBSUkJmZmZ5ObmcujQIQC6desGwH//+1/Cw8Np164dIOrefPTRRzzzzDMNeq0SiUQikVwvXNdhqc2bN3PTTTdVWv/QQw+xcOFCNBoNs2fP5qeffiI5ORk/Pz/69evH22+/TefOnQEh0DRX+Mrwtn7xxRd8++23JCQkYG9vT8uWLXnkkUd47LHHzPb8kUgkEolEUjuua+NGIpFIJBLJtYd0HUgkEolEIrmmkMaNRCKRSCSSa4rrTlCs1+u5ePEiHh4eZsv5SyQSiUQiaXooikJubi6hoaHValavO+Pm4sWLlUqpSyQSiUQiuTo4f/58tQ14rzvjxtC19vz583h6ejbybCQSiUQikVhDTk4OzZs3N97Hq+K6M24MoShPT09p3EgkEolEcpVhjaRECoolEolEIpFcU0jjRiKRSCQSyTWFNG4kEolEIpFcU1x3mhtr0el0aDSaxp6G5CrE0dFRttaQSCSSRkQaNxVQFIVLly6RlZXV2FORXKWo1WqioqJwdHRs7KlIJBLJdYk0bipgMGwCAwNxdXWVhf4kNmEoEpmSkkJ4eLj8/EgkEkkjII2bcuh0OqNh4+fn19jTkVylBAQEcPHiRbRaLQ4ODo09HYlEIrnukMKAchg0Nq6uro08E8nVjCEcpdPpGnkmEolEcn0ijRszyFCCpDbIz49EIpE0LtK4kUgkEolEck0hjRtJrUlMTESlUnHo0KHGnopEIpFIJNK4udb46quviIqKwtnZmR49erBt2zZA6IleeeUVOnfujJubG6GhoTz44INcvHjRZP/BgwejUqlMHuPHj2+MS2k05HsgkUgkVzfSuLmGWLp0Kc899xyvv/46Bw8eZMCAAYwYMYKkpCQKCgo4cOAAb775JgcOHGDZsmWcOnWKO+64o9JxHnnkEVJSUoyPb7/9thGupm7R6XTo9Xqrx1+L74FEImlAdLIIbGMijZtriI8//pgpU6YwdepU2rdvz6effkrz5s35+uuv8fLyYv369YwdO5a2bdvSt29fvvjiC2JiYkhKSjI5jqurK8HBwcaHl5eXyfa9e/cSHR2Ns7MzPXv25ODBg5XmcuLECUaOHIm7uztBQUE88MADpKenG7fn5uYyceJE3NzcCAkJ4ZNPPmHw4ME899xzVl1rSUkJL7/8MmFhYbi5udGnTx82b95s3L5w4UK8vb1ZvXo1HTp0wMnJiXPnzrFv3z5uueUW/P398fLyYtCgQRw4cKDS8at7DyQSicQi++fDLH84ta6xZ3LdIo2balAUhYISbaM8FEWxep4lJSXExMRw6623mqy/9dZb2blzp9l9srOzUalUeHt7m6xftGgR/v7+dOzYkWnTppGbm2vclp+fz+23307btm2JiYnhrbfeYtq0aSb7p6SkMGjQILp168b+/fv5559/uHz5MmPHjjWOeeGFF9ixYwcrV65k/fr1bNu2zayRYYmHH36YHTt28Ouvv3LkyBHuvfdehg8fzunTp41jCgoKmDNnDvPmzeP48eMEBgaSm5vLQw89xLZt29i9ezetW7dm5MiRJtdY3XsgkUgkFlEU2PWVWD65qnHnch0ji/hVQ6FGR4cZaxvl3CfeGYaro3V/ovT0dHQ6HUFBQSbrg4KCuHTpUqXxRUVFvPrqq0yYMAFPT0/j+okTJxIVFUVwcDDHjh1j+vTpHD58mPXr1wPipq/T6Zg/fz6urq507NiRCxcu8MQTTxiP8fXXX9O9e3feffdd47r58+fTvHlzTp06RUhICD/++COLFy9m6NChACxYsIDQ0FCrrjU+Pp4lS5Zw4cIF4z7Tpk3jn3/+YcGCBcbzajQavvrqK7p27Wrcd8iQISbH+vbbb/Hx8WHLli3cfvvtVr0HEolEYpG0k5BR+iMr9WTjzuU6Rho31xgVa6woilJpnUajYfz48ej1er766iuTbY888ohxuVOnTrRu3ZqePXty4MABunfvTmxsLF27djUpdNivXz+TY8TExLBp0ybc3d0rzS8+Pp7CwkI0Gg29e/c2rvfy8qJt27ZWXeOBAwdQFIU2bdqYrC8uLjapLO3o6EiXLl1MxqSmpjJjxgw2btzI5cuX0el0FBQUmITmqnsPJBKJxCInVpYtp50UnhxZ+6rBkcZNNbg42HHinWGNdm5r8ff3x87OrpKXJjU11cSbo9FoGDt2LAkJCWzcuNHEa2OO7t274+DgwOnTp+nevbtVoTK9Xs+oUaN4//33K20LCQkxho7MGWLWoNfrsbOzIyYmBjs70/eovEHl4uJS6RyTJk0iLS2NTz/9lIiICJycnOjXrx8lJSUWz1fxPZBIJBKLnPhf2XJxDuQkg1ezxpvPdUqjam7mzJlDr1698PDwIDAwkDvvvJO4uLhq99uyZQs9evTA2dmZFi1a8M0339TbHFUqFa6O9o3ysKXSraOjIz169KgUOlm/fj033HADUGbYnD59mn///deq/lnHjx9Ho9EQEhICQIcOHTh8+DCFhYXGMbt37zbZp3v37hw/fpzIyEhatWpl8nBzc6Nly5Y4ODiwd+9e4z45OTkmepmqiI6ORqfTkZqaWun4wcHBVe67bds2nn32WUaOHEnHjh1xcnIyETpb8x5IJBKJWTLiIfU4qO3BM0ysS41t3DldpzSqcbNlyxaeeuopdu/ezfr169Fqtdx6663k5+db3CchIYGRI0cyYMAADh48yGuvvcazzz7Ln3/+2YAzb5q88MILzJs3j/nz5xMbG8vzzz9PUlISjz/+OFqtlnvuuYf9+/cbdTOXLl3i0qVLRq9FfHw877zzDvv37ycxMZE1a9Zw7733Eh0dTf/+/QGYMGECarWaKVOmcOLECdasWcNHH31kMo+nnnqKzMxM7rvvPvbu3cvZs2dZt24dkydPRqfT4eHhwUMPPcRLL73Epk2bOH78OJMnT0atVltl0LVp04aJEyfy4IMPsmzZMhISEti3bx/vv/8+a9asqXLfVq1a8fPPPxMbG8uePXuYOHEiLi4uxu3WvAcSiURiFoPXJmogNOsllqVx0zgoTYjU1FQFULZs2WJxzMsvv6y0a9fOZN1jjz2m9O3b16pzZGdnK4CSnZ1daVthYaFy4sQJpbCw0LaJNyH++9//KhEREYqjo6PSvXt343uZkJCgAGYfmzZtUhRFUZKSkpSBAwcqvr6+iqOjo9KyZUvl2WefVTIyMkzOsWvXLqVr166Ko6Oj0q1bN+XPP/9UAOXgwYPGMadOnVLGjBmjeHt7Ky4uLkq7du2U5557TtHr9YqiKEpOTo4yYcIExdXVVQkODlY+/vhjpXfv3sqrr75q1XWWlJQoM2bMUCIjIxUHBwclODhYGTNmjHLkyBFFURRlwYIFipeXV6X9Dhw4oPTs2VNxcnJSWrdurfz+++9KRESE8sknn9j0HlTFtfA5kkgkNeCbgYoy01NR9s1XlE3vieVljzf2rK4Zqrp/V0SlKDbkG9czZ86coXXr1hw9epROnTqZHTNw4ECio6P57LPPjOuWL1/O2LFjKSgowMHBocpz5OTk4OXlRXZ2diW9SVFREQkJCcYKv5KGIz8/n7CwMObOncuUKVMaezq1Qn6OJJLrkCvn4LMuoFLDi6cgaRf89gCERsOjmxt7dtcEVd2/K9JkBMWKovDCCy9w4403WjRsAC5dumQ23Vmr1ZKenl5JF1FcXExxcbHxdU5OTt1OXFIjDh48yMmTJ+nduzfZ2dm88847AIwePbqRZyaRSCQ1ILY0SyqiP7gHQGB78Tr1JOj1oJZl5RqSJvNuP/300xw5coQlS5ZUO9ZSlo05vcacOXPw8vIyPpo3b143E5bUmo8++oiuXbty8803k5+fz7Zt2/D392fbtm24u7tbfEgkEkmTw5AC3qH0B5pPFNg5gbYQshIbbVrXK03Cc/PMM8+wcuVKtm7dSrNmVafMBQcHm013tre3N5v9M336dF544QXj65ycHGngNAGio6OJiYkxu61nz56yw7hEIrl6yE6GC3sBFbQTxUCxswf/NnD5qPDe+LZo1ClebzSqcaMoCs888wzLly9n8+bNREVFVbtPv379WLXKtKT1unXr6Nmzp1m9jZOTE05OTnU2Z0n94+LiQqtWrRp7GhKJRGIdJ1eL5+Z9wLOcNCKwfalxcwLajWycuV2nNGpY6qmnnuKXX35h8eLFeHh4GFOTy9dQmT59Og8++KDx9eOPP865c+d44YUXiI2NZf78+fzwww+V+htJJBKJRNIgGFLAO1TQDAa2E89psg1DQ9Ooxs3XX39NdnY2gwcPJiQkxPhYunSpcUxKSopJafyoqCjWrFnD5s2b6datG7NmzeLzzz/n7rvvboxLkEgkEsn1TF4qnCttTtx+lOm2wA7iWda6aXAaPSxVHQsXLqy0btCgQTZ1kJZIJBKJpF6IXQUoENYDvCvoOQ0ZU+mnQKcVOhxJg9BksqUkEolEIrnqMKSAt7+j8javcHBwBV0JZJ5t2Hld50jjRiKRSCSSmlCQCQnbxHIHM8aNWg0BBt2NDE01JNK4kdSaxMREVCpVg6ZvDx48mOeee67BzieRSCSVOPkXKDoI7mw51dtYzE8aNw2JNG6uMb766itj2f8ePXqwbZv4VaHRaHjllVfo3Lkzbm5uhIaG8uCDD3Lx4kWT/QcPHoxKpTJ5jB8/vjEupUqWLVvGrFmzan2czZs3V7pelUrFyZNl2Q0LFy40O6aoqKjW55dIJFcxlrKkyiONm0ZBqpuuIZYuXcpzzz3HV199Rf/+/fn2228ZMWIEJ06cwMvLiwMHDvDmm2/StWtXrly5wnPPPccdd9zB/v37TY7zyCOPGNshACZds5sKvr6+VW4vKSnB0dHR6uPFxcWZ9CoJCAgw2e7p6UlcXJzJOtk3SiK5jinMgrObxXL7KoybAGncNAbSc3MN8fHHHzNlyhSmTp1K+/bt+fTTT2nevDlff/01Xl5erF+/nrFjx9K2bVv69u3LF198QUxMjEmqPYCrqyvBwcHGh5eXl8n2vXv3Eh0djbOzMz179uTgwYOV5nLixAlGjhyJu7s7QUFBPPDAA6Snpxu35+bmMnHiRNzc3AgJCeGTTz6xKdRUcWxkZCSzZ89m0qRJeHl58cgjjwDwyiuv0KZNG1xdXWnRogVvvvkmGo2m0vECAwNNrtnOzs5ku0qlMtkeHBxs1TwlEsk1yql/QK8RxktAG8vjDJ6bjDOgLbY8TlKnSOOmOhQFSvIb52FDw/aSkhJiYmK49dZbTdbfeuut7Ny50+w+2dnZqFQqvL29TdYvWrQIf39/OnbsyLRp08jNzTVuy8/P5/bbb6dt27bExMTw1ltvVSqgmJKSwqBBg+jWrRv79+/nn3/+4fLly4wdO9Y45oUXXmDHjh2sXLmS9evXs23btlqn93/44Yd06tSJmJgY3nzzTQA8PDxYuHAhJ06c4LPPPuP777/nk08+qbRvdHQ0ISEhDB06lE2bNlXanpeXR0REBM2aNeP22283a9BJJJLriDMbxHP726se5xkKTl5Cm5Nxpv7nJQFkWKp6NAXwbmjjnPu1i+DoZtXQ9PR0dDqd2Y7pFXtxARQVFfHqq68yYcIEk3DMxIkTiYqKIjg4mGPHjjF9+nQOHz7M+vXrAWH46HQ65s+fj6urKx07duTChQs88cQTxmN8/fXXdO/enXfffde4bv78+TRv3pxTp04REhLCjz/+yOLFixk6dCgACxYsIDS0du/zkCFDKhlab7zxhnE5MjKSF198kaVLl/Lyyy8DEBISwnfffUePHj0oLi7m559/ZujQoWzevJmBAwcC0K5dOxYuXEjnzp3Jycnhs88+o3///hw+fJjWrVvXas4SieQqJTNePAd3qbSpoETLxpOp3NohGEd7tahUfH6PCE0FdWzgiV6fSOPmGsNcx/SK6zQaDePHj0ev1/PVV1+ZbDOEcwA6depE69at6dmzJwcOHKB79+7ExsbStWtXXF1djeP69etncoyYmBg2bdpktoN3fHw8hYWFaDQaevfubVzv5eVF27Ztbb/gcvTs2bPSuj/++INPP/2UM2fOkJeXh1arNTHm2rZta3Lefv36cf78eT766COjcdO3b1/69u1rHNO/f3+6d+/OF198weeff16rOUskkquUzATx7Fu5J+J7f5/kp13nmDmqAw/3jxKhKYNxI2kQpHFTHQ6uwoPSWOe2En9/f+zs7Mx2TC/vzdFoNIwdO5aEhAQ2btxocqM3R/fu3XFwcOD06dN0797dqqrSer2eUaNG8f7771faFhISwunTpwHzhlhtcHMz9XLt3r2b8ePH8/bbbzNs2DC8vLz49ddfmTt3bpXH6du3L7/88ovF7Wq1ml69ehmvQyKRXGcUZUNhplj2iTTZpCgK645fBiDuUmlIX4qKGxxp3FSHSmV1aKgxcXR0pEePHqxfv54xY8YY169fv57Ro4WS32DYnD59mk2bNuHn51ftcY8fP45GoyEkRHS67dChAz///DOFhYXGLKrdu3eb7NO9e3f+/PNPIiMjsbev/BFr2bIlDg4O7N27l+bNRbnynJwcTp8+zaBBg2r2Bphhx44dRERE8PrrrxvXnTt3rtr9Dh48aLxecyiKwqFDh+jcuXOdzFMikVxlGLw2bgHg5GGy6dTlPC7liDIRyVmlTaANomJZyK/BkILia4gXXniBefPmMX/+fGJjY3n++edJSkri8ccfR6vVcs8997B//36jbsbQhb2kpAQQIaN33nmH/fv3k5iYyJo1a7j33nuJjo6mf//+AEyYMAG1Ws2UKVM4ceIEa9as4aOPPjKZx1NPPUVmZib33Xcfe/fu5ezZs6xbt47Jkyej0+nw8PDgoYce4qWXXmLTpk0cP36cyZMno1arK3lzakOrVq1ISkri119/JT4+ns8//5zly5ebjPn0009ZsWIFp0+f5vjx40yfPp0///yTp59+2jjm7bffZu3atZw9e5ZDhw4xZcoUDh06xOOPP15nc5VIJFcRV0qNG5/KIaktp1KNy5WMm8wEKCmo79lJkJ6ba4px48aRkZHBO++8Q0pKCp06dWLNmjVERESQmJjIypWiB0q3bt1M9tu0aRODBw/G0dGRDRs28Nlnn5GXl0fz5s257bbbmDlzpjE12t3dnVWrVvH4448THR1Nhw4deP/99026soeGhrJjxw5eeeUVhg0bRnFxMREREQwfPhy1WtjTH3/8MY8//ji33347np6evPzyy5w/f75Oa8eMHj2a559/nqeffpri4mJuu+023nzzTd566y3jmJKSEqZNm0ZycjIuLi507NiRv/76i5EjRxrHZGVl8eijj3Lp0iW8vLyIjo5m69atJpohiURyHVGF3mZzXJpx+WJWodA9ugWAqx8UZIgmmqHdGmii1y8qpbZCh6uMnJwcvLy8yM7OrqQ3KSoqIiEhwVjhV9Jw5OfnExYWxty5c5kyZUq14/v168fQoUOZPXt2A8zONuTnSCK5xln5DBz4CQa9CjdNN67OL9bS7Z11aHRlt9WYN27Gz90JFt4Oidvgzm+g232NMeurnqru3xWRYSlJo3Dw4EGWLFlCfHw8Bw4cYOLEiQBGfZAliouL2b9/P8ePH6djR5lSKZFIGgELnptd8RlodArNfV0I9HACyoWmDA00U0801Cyva2RYStJofPTRR8TFxRnF0Nu2bcPf359t27YxYsQIi/up1WpGjRrFPffc04CzlUgkklKuJIrnCpqbLadESGpQmwCOX8whNbeYi1mFdGnmXU5UfBJJ/SONG0mjEB0dTUxMjNltPXv2rLLDeKtWreppVhKJRFIN2mLIviCWy3luFEVhc6mYeFCbQK4UaDiYlMWFKxVExTIdvEGQxo2kyeHi4iINGIlE0jTJSgIUcHATqeClJGYUcD6zEAc7Ff1a+rE/UdTBqRSWyj4PRTngXLVmRFI7pOZGIpFIJBJrKa+3KVe6YnOc8Nr0ivTF3cmeUG9RB+yiwbhx9QX30oa7aXENNt3rFWncSCQSiURiLcYaN5Emq8vrbQDCSo0bo+cGZDG/BkQaNxKJRCKRWIuZTKkijY7dZzMAGNRWGDdlnpuisn0DO4hnqbupd6RxI5FIJBKJtZipTrw3IZMijZ4gTyfaBol2DGE+wrjJzC+hoEQrBgYa0sGlcVPfSONGIpFIJBJrMeO5KR+SMrSQ8XJxwMNJ5OwYvTfSc9NgSOPmOiYyMpJPP/20sachkUgkVwd6vdkaN2XGTaDJ8NCKupuAtuI57xIUZNbrVK93pHFzjbB161ZGjRpFaGgoKpWKFStWNPaUGpS33noLlUpl8ggODm7saUkkkmuJ3BTQFYPaHryaA3DhSgFnUvOwU6u4sbW/yXBDaMqYMeXkAV7hYlkW86tXpHFzjZCfn0/Xrl358ssvG3sqdYpGo7F6bMeOHUlJSTE+jh49Wo8zk0gk1x0GvY1Xc7ATISeD1ya6uTdeLg4mw0O9RW+55CvlM6ZkG4aGQBo31wgjRoxg9uzZ3HXXXWa3p6amMmrUKFxcXIiKimLRokWVxmRnZ/Poo48SGBiIp6cnQ4YM4fDhwyZjZs+eTWBgIB4eHkydOpVXX321UpfxqliwYAHt27fH2dmZdu3a8dVXXxm3JSYmolKp+O233xg8eDDOzs788ssvZGRkcN9999GsWTNcXV3p3LkzS5YsqXRse3t7goODjY+AgIBKYyQSiaTGmNPbxJmmgJcnzNsVqJAObijml3aqfuYoAWSF4mpRFIVCbWH1A+sBF3sXozittkyaNInz58+zceNGHB0defbZZ0lNTTVuVxSF2267DV9fX9asWYOXlxfffvstQ4cO5dSpU/j6+rJo0SL+85//8NVXX9G/f39+/fVX5s6dS1RUVBVnLuP7779n5syZfPnll0RHR3Pw4EEeeeQR3NzceOihh4zjXnnlFebOncuCBQtwcnKiqKiIHj168Morr+Dp6clff/3FAw88QIsWLejTp49xv9OnTxMaGoqTkxN9+vTh3XffpUWLFnXy/kkkEknFTKkSrZ6d8aYp4OUxem7KGze+LUyPJakXpHFTDYXaQvos7lP9wHpgz4Q9uDq41vo4p06d4u+//2b37t1GY+CHH36gffv2xjGbNm3i6NGjpKam4uQkutl+9NFHrFixgj/++INHH32UL774gilTpvDwww8DMGPGDNatW0deXp5V85g1axZz5841epeioqI4ceIE3377rYlx89xzz1XyQE2bNs24/Mwzz/DPP//w+++/G6+nT58+/PTTT7Rp04bLly8ze/ZsbrjhBo4fP46fn5+tb5lEIpFUpoLn5kDSFfKKtfi5OdIp1KvS8GalmhuTsJTB65MpjZv6RBo31wGxsbHY29vTs2dP47p27drh7e1tfB0TE0NeXl4lQ6CwsJD4+HgA4uLiePLJJ0229+7dm40bN1Y7h7S0NM6fP8+UKVN45JFHjOu1Wi1eXqZfCuXnCaDT6XjvvfdYunQpycnJFBcXU1xcjJubm3FM+S7inTt3pl+/frRs2ZIff/yRF154odr5SSQSSbVU8NwY9DYD2wSgVlf2shuypS7lFKHTK9ipVWVZVlnnQK8DtV39z/s6RBo31eBi78KeCXsa7dx1gaIoAFWGuPR6PSEhIWzevLnStvJGUMVjGI5dHXq9HhChqfKhJAA7O9N/7vJGC8DcuXP55JNP+PTTT+ncuTNubm4899xzlJSUWDyfm5sbnTt35vTp01bNTyKRXMOUFEB+aqWWCTZTwXOzuQq9DUCghzP2ahVavcLlnCJh7Hg1A7UD6Eog5yJ4N6/dnCRmkcZNNahUqjoJDTUm7du3R6vVsn//fnr37g0IL0xWVpZxTPfu3bl06RL29vZERkaaPU7btm3Zu3cvDzzwgHHd/v37rZpDUFAQYWFhnD17lokTJ9o0/23btjF69Gjuv/9+QBhKp0+fNgmrVaS4uJjY2FgGDBhg07kkEsk1yPJH4eRfMHktNO9ds2MUXoGiLLHsE8nlnCJiU3JQqWBAhRRwA3ZqFcFezly4UsjFrEJh3KjtwDscMuOFJ0gaN/WCzJa6RsjLy+PQoUMcOnQIgISEBA4dOkRSUhJt27Zl+PDhPPLII+zZs4eYmBimTp2Ki0uZZ+jmm2+mX79+3Hnnnaxdu5bExER27tzJG2+8YTRgnnnmGX744Qd+/PFHTp8+zezZszly5IjVoue33nqLOXPm8Nlnn3Hq1CmOHj3KggUL+Pjjj6vcr1WrVqxfv56dO3cSGxvLY489xqVLl0zGTJs2jS1btpCQkMCePXu45557yMnJMdHySCSS65D8DDi5BhQ9HP615scxeG3cg8DRja2lIanOYV74uTtZ3M1sA02DB0nqbuoNadxcI+zfv5/o6Giio6MBeOGFF4iOjmbGjBmASMFu3rw5gwYN4q677jKmfBtQqVSsWbOGgQMHMnnyZNq0acP48eNJTEwkKCgIgIkTJzJ9+nSmTZtG9+7dSUhIYNKkSTg7O1s1x6lTpzJv3jwWLlxI586dGTRoEAsXLqw22+rNN9+ke/fuDBs2jMGDBxMcHMydd95pMubChQvcd999tG3blrvuugtHR0d2795NRESEtW+hRCKxlewLkJ3c2LOomlN/g6ITyydXiyrDNaGC3saQJTWwddUlJwyF/EwzpqJMjympc2RY6hph8ODBVepfgoODWb16tcm68uElAA8PDz7//HM+//xzi8d58803efPNN42vb7nlFlq1amX1PCdMmMCECRPMbouMjDR7Db6+vtVWXP7111r8IpNIJLZTkg/f3Cj0LKM+g273NfaMzBO7qmw57zJc2AvhfW0/TgW9zbHkbAC6R3hXuZvRc1M+Y8qnCWVMpZ8Bz1BwvLrlFxWRnhuJ1RQUFPDxxx9z/PhxTp48ycyZM/n3339l6EciuR45v1foUHTFsOJx+PtV0FlfUbxBKM6F+NJszpBu4rm8sWMLRs9NJIUlOuLTRAmMjmZSwMtjMG4umvXcJNZsLnXFuZ3wZU/44+HGnUc9II0bidUYQlcDBgygR48erFq1ij///JObb74ZAHd3d4uPbdu2NfLsJRJJnXJuh3j2bCae93wNP4+B/PTGm1NFTq8TWUm+LWHAi2Jd7EqwMsvThMxE8ewTRdzlXPQK+Ls7EuhhWW8DZppnlh4DaPyw1MFFgAKn/oHLxxt3LnWMDEtJrMbFxYV///3X4naDmNkcYWFh9TAjiUTSaCSWGjeDXgZXP1j+GCRug+8Gw/hFENK1UacHlHlp2o+CVjeDgytkJcGlI7bP70pZWOr4RRGSah/iWW1CRVi5Qn6KoojxBkFxUbboDu7qa9tc6gJtCZws58Xa/TWMvnZ6E0rPjaTOaNWqlcVH+cwsiURylaMphOTSMhCRN0L722HqBuEhyT4PPwyDI7838hyL4NQ6sdz+DqEpaSW8zDaHpjRFoiYNgE8Uxy/mANWHpABCvcR3X36JjpxCrVjp6AruwWK5sbw3CVuEcWVfmhBy5Lem5XWrJdK4kUgkEoltXNgvwj3uwWW9kgLbwSMbodUtoC2EZVNhwzuNN8ezm0CTD55hECqySGl/h3g+sdK2Y2WdAxRwdAc3f06UGjcdQj2r3dXF0Q4/N0cALmQVlG1o7DYMx1eI5+j7xfujK4aYBY0zl3pAGjcSiUQisQ2D3iayP5QPy7h4w4SlZfqWbXPh0jHbjr30Afi8uxAr1waDd6bd7aAuvdW1uVVUB06Pg7Q464+VWZYGrlPg5CWD56Z64wbKdDcXs4rKVjam7kanEWnxAB3HQJ8nxPLeeSJcdQ0gjRuJRCKR2EbidvEc0b/yNrUdDJ1R5iU5vMT64146KgS/mfGi8F5N0WkgrnT/9qPK1jt7QYvBYtmW0JRRbxNJQnoeRRo9Lg52RPq5Vb1fKWXp4OY8N4nWz6OuOLtFVFt2C4TwfsLAcQ+CvEtwYkXDz6cekMaNRCKRSKxHWwwX9onlyBstj+tWWs/qyG+g01p37IOLypZP/lWz+YHwLBVeEULn8H6m2wzGji3GTTnPjUFv0z7EQzTCtAKj5ya7iXhuji8Xzx3uEMaovSP0Km1ovPurmmWTNTGkcSORSCQS60k+ANoicAsA/zaWx7W6GVz9RcPK+A3VH1dbDEeWlr2O3ygKBNaE2NKQS9uRYFchKbjtSFCpIeWQyJyyhnKZUidsEBMbKJ8xZaSxNDflQ1Id7ixb3/NhsHOCiwdFDaOrHGncXMdERkby6aefNtj53nrrLbp169Zg55NIJPXAOUNI6gZTvU1F7Byg871i2ZrQVNzfUJgJHiHg1VyIks9utn1+en3ZzdsQGiuPewCE3yCWrfXemPHcWCMmNhDmLTKSzNa6yb0oss8aivIhqYgbyta7+UOXsWJ591cNN596Qho31whbt25l1KhRhIaGolKpqm1X0BhMmzaNDRus+AVnBSqVqtLjm2++MW5PTEw0O+aff/6pk/NLJNcthvo2EVWEpAwYWjKcXFO9QPjgL+K5633CuwIQV4PQVHIM5KaAowe0GGR+jC2hKb2uNFsKFJ9ITqTYJiYGCPMWrQ1MjBtXX3AqPcaVc1Yfq9acqBCSKk/fUmFx7CrIOt9wc6oHpHFzjZCfn0/Xrl358sumW4TJ3d0dPz8/i9tLSmxT6S9YsICUlBTjw1wbiH///ddkzJAhQ2yet+Q650oi/DrxmqvgWiN0mrKQRaQZMXFFgrtAYEeRZmzQeZgjO7ksdBV9P7S7TSzH/SOMC1uILU3zbjMM7C1UD25/u3hO2g25l6s+Xs5FkfautueSyp/M/BLs1CraBHlYPSVDWCott5giTen1lC/m11C6G52mTMtUPiRlIKgjRA0UjUb3fd8wc6onpHFzjTBixAhmz57NXXfdZXZ7amoqo0aNwsXFhaioKBYtWlRpTHZ2trFbuKenJ0OGDOHw4cMmY2bPnk1gYCAeHh5MnTqVV1991epQU8Ww1KRJk7jzzjuZM2cOoaGhtGkj4ve//PILPXv2xMPDg+DgYCZMmEBqamql43l7exMcHGx8mCsU6OfnZzLG0dHRqrlKJEZ2fyPCHNs/aeyZND4XD4naMS4+ENC++vEqVZn35lAVoanDS0DRi3CRX0sRLnH2goJ02/QfimJaldgSXs0grAegVO8dMvR/8g7nxCWhAWoV4I6zg53lfSrg4+qAs4O43V4qLypuaN1NwhbhQasYkipP3yfFc8xC0Rz1KkUaN9WgKAr6goJGeVTV5dtWJk2aRGJiIhs3buSPP/7gq6++MjEYFEXhtttu49KlS6xZs4aYmBi6d+/O0KFDyczMBGDRokX85z//4f333ycmJobw8HC+/vrrWs1rw4YNxMbGsn79emPX8pKSEmbNmsXhw4dZsWIFCQkJTJo0qdK+Tz/9NP7+/vTq1YtvvvkGvV5facwdd9xBYGAg/fv3548//qjVXCXXKaknxPPlE407j+rQ62Hjf2qXZVQd58qlgKutvH10HgsqO9GNO/1M5e2KUhaSir5fPNs5QOthYtmW0NTl48ILYudUVo3YEtaGpq7UTm8DIowe1hR6TBm8Z+1HVQ5JGWg9TMyrKNu2NP4mhuwtVQ1KYSFx3Xs0yrnbHohB5Vr7NvSnTp3i77//Zvfu3fTp0weAH374gfbty355bdq0iaNHj5KamoqTk3DlfvTRR6xYsYI//viDRx99lC+++IIpU6bw8MOig+yMGTNYt24deXl5NZ6bm5sb8+bNM/GoTJ482bjcokULPv/8c3r37k1eXh7u7u4AzJo1i6FDh+Li4sKGDRt48cUXSU9P54033gBECOzjjz+mf//+qNVqVq5cybhx4/jxxx+5//77azxfyXWIwbhJPyUKnNk3Ue9fwmbY+gGggrE/CU1FXWPU21gRkjLgEQSthoomloeXwNA3Tbef2ylu7o7u0GF02fp2t8HR34SxdsusqsXLBgyGSquh4ORe9dh2o+DftyBhq/BmuPiYH5dZlill6Clli97GQKi3C/Fp+abGTUN6bsqHpDreaXmcWg19Hod/XhFeyx6TrTdkmxBX34wlNhMbG4u9vT09e/Y0rmvXrh3e3t7G1zExMeTl5eHn52fSzTshIYH4+HgA4uLi6N27t8mxK762lc6dO1cKFR08eJDRo0cTERGBh4cHgwcPBiApqSxt84033qBfv35069aNF198kXfeeYcPP/zQuN3f35/nn3+e3r1707NnT9555x2efPJJPvjgg1rNV3KdkZcG+WliWa+BjNONO5+quBBTuqDAn1OF0VCX6LRCowLW6W3K07U0NHVkqfAwledQaYi84xhTg6TVULBzhMyz1lcTtiYkZcC/FQR2AL1WaHssUc5zYxATdwix3bhpZi4dvCE9N8aQVED1xmn0RCF2zjgtUvKvQqTnphpULi60PRBT/cB6OnddYAhvVdW9Vq/XExISwubNmyttK28EVTxGbUNnbm6mFT7z8/O59dZbufXWW/nll18ICAggKSmJYcOGVSk47tu3Lzk5OVy+fJmgoCCLY+bNm1er+UquM9JiTV9fPiFEl02RiwfEs4uvSKleMh4mr4VAK7Qx1nDpCJTkgpMXBHWybd+2I4WGJvu86BxuyGIqzi0LlUQ/YLqPkwdEDYIz60VoKrBd1efIiIfU4yIE1ma4dfNqP0p45mJXlWmDKlLqVcl3b875TGGY2BqWgrIGmhfNeW6unBPCaUuhorrA0EuqqpCUAScP8ffY/V8hLG5dTYivCSI9N9WgUqlQu7o2yqMqY8QW2rdvj1arZf/+/cZ1cXFxZGVlGV93796dS5cuYW9vX6mjt7+/PwBt27Zl715TcV/5Y9YFJ0+eJD09nffee48BAwbQrl07s2Liihw8eBBnZ2cTQ8zcmJCQkDqcreSaJ7WicWNjn6SGJLnUuLlnPjTvIzQTv9wN2Rfq5viGflIR/Wy/CTs4Q8fSZIfyOo7jy0FTAH6tobkZL3C70pRwa3REBq9N1ACRZm0NBg9P/AYothBeL/WqnNEEAKKVgrer7aFJYyG/8saNZ5jodaXXQE6yzce0moq9pKyh093i+eLB+plTPdOoxk1NarMsWrSIrl274urqSkhICA8//DAZGRn1P9kmTl5eHocOHeLQoUMAJCQkcOjQIZKSkmjbti3Dhw/nkUceYc+ePcTExDB16lST7KKbb76Zfv36ceedd7J27VoSExPZuXMnb7zxhtGAeeaZZ/jhhx/48ccfOX36NLNnz+bIkSN1ZoQBhIeH4+joyBdffMHZs2dZuXIls2bNMhmzatUqvv/+e44dO0Z8fDzz5s3j9ddf59FHHzXqhX788UcWL15MbGwscXFxfPTRR3z++ec888wzdTZXyXWAQW/jKgz8JpsOnnNR9AVS2QnD5r5fwb+tuGH+cnftm1BCzfQ25TG0YzixssyQMAqJJ5rX1Bjq3STHQE6K5WNrisqOZU1IykBQJxEa0hbB4rGQetJ0e0GmMBKBg3neQM28NlC+eWY540ZtBz4RYrk+dTe2hKQM+LcSz3mXoSin/uZWTzSqcWNrbZbt27fz4IMPMmXKFI4fP87vv//Ovn37mDp1aj3PtOmzf/9+oqOjiY6OBuCFF14gOjqaGTNmAKImTPPmzRk0aBB33XWXMeXbgEqlYs2aNQwcOJDJkyfTpk0bxo8fT2JiojHMM3HiRKZPn860adPo3r27MYvJ2dm5zq4jICCAhQsX8vvvv9OhQwfee+89PvroI5MxDg4OfPXVV/Tr148uXbrw2Wef8c477zB37lyTcbNnz6Znz5706tWLX3/9lfnz5/P888/X2Vwl1wEGz43h125qE82YMnhtAtuDo6vwXNz/p6j2m3YSltxXuyq4eh0klWp4bNXbGGjWC3xbilTy2FWQdgrO7xEGWVcLISGPYAgr1Qqe+tvysTe8LfQhbgFlHiJrUKnglnfA3kV4pr7pD/++Xdb2waCFcQ/m6GUNUDMxMZQ1z7yYVYReXy6cXxe6m6Ic+Gc6bH5faGQqGiO2hKQMOHuJlHEQjUyvMhpVczNixAhGjBhh9fjdu3cTGRnJs88+C0BUVBSPPfaYFIkCgwcPrlL/EhwcbEy1NvDAA6Yxbg8PDz7//HM+//xzi8d58803efPNsmyHW265hVatWlk1x+LiYmO2E8DChQvNjrvvvvu47z7TL7vy1zZ8+HCGD686pv7QQw+ZLeonkViNopQZN53vEdqDnOSqM2saC4PeJjS6bJ13c2HgzB8BSbuEyHjsTzXTdVw+LjwYjh4Q3LVmczTUvNk4Gw4vLtMztb5FGDGWaDcSkveL0FTPyZW3n9lQ1i5g9FfWh6QMdLgDQrrC3y/DqX9g+8dw7A8Y8SGUlHqYymVK1URMDBDs5YxaBSU6Pel5xQR6OhuPDdTOc3N4SYWWCSph6DbrJcJ95npJWYNfK9EbLCPe9LN1FXBVaW5uuOEGLly4wJo1a1AUhcuXL/PHH39w2223WdynuLiYnJwck4ekZhQUFPDxxx9z/PhxTp48ycyZM/n333+rNSIURSE+Pp4NGzbQsWMTFWNKJBXJSYbiHFDbQ2h38AoX65tivRuD5yasu+n6oI4wfpHIOjq5GtbPqNnxDXqb8D6VG1HaQpfx4jlhG8T8KJajqynN0K60mnDCViFALk9+BqwoLTrXayq0ubVm8/KJEKG8cYvAs5loqLlkHKwTpSV03pGcSRWGTscw6xtmlsfBTk2QZxU9pmrjuUkvzeLzawXe4YAivIwHfoT/PVXaId3f9pCiX0vxnGGmPlET56ozbhYtWsS4ceNwdHQkODgYb29vvvjiC4v7zJkzBy8vL+OjefPmDTjjawtD6GrAgAH06NGDVatW8eeff3LzzUJJXz6FvPzDw8ODNm3a4OjoyGuvvdbIVyGRWInBa+PXWtS2CeogXjc13Y2ilIk+Q7tX3h41AMaU9l3bN09037aVxHLF+2qDd3OIHAAoonmjq39ZsT5L+LcR4SxdCZz5t2y9osCqZ4XWyL+NqIVTG1Qq0ZbhqT1ww7MiXJYrdD7pDqFo9QpeLg6EetU8DB9qrpBfXXhuMs+K5xueheeOwounYNwv4nV4P3D2hv7/Z7thajRuZFiqXjlx4gTPPvssM2bMYNiwYaSkpPDSSy/x+OOP88MPP5jdZ/r06bzwwgvG1zk5OdLAqSEuLi78+++/FrcbxMzmCAsLM9seQSJpshiMGEMqdVBHEbZIbWLGTeZZYSjYOVlOU+94F/z9iqjZkxxjufS+OfT6spo5kVY0y6yObhNEOjhA1/HVF0VUqURoaucXogGnQf904CfhjVI7wN3zhNaoLnByh1tnibmtfgGS93PcUYTiOoZ61iqBIszbhZhzV0xFxUbPTaIw2GpyfIPXx7eFePYIEvoaW8TV5vArlRxchZ6bq8q4mTNnDv379+ell14CoEuXLri5uTFgwABmz55tNs3XycnJmEEjqV+s1d5IJFcFBs9NYAfT56bmuTF4bYI7i7YF5lCphEFz4n8i68kW4ybtpKib4+BaN7qL9nfA36+KkF91ISkDbW8Txs3ptSKtOSsJ/nlVbBv6ptDM1DVBHWHKWigpYMvfZ4FzNRYTGwgzW8ivNFuqOEdkZ7lZbi5sFp1WvB9Q5gWqK4zGTXzNDa9G4qoKSxUUFKCuUAbazk6I4+qyD5NEIpEYM6MM4ShD4brU2MpVdhsTS3qbikSUel0M/aGsxaC3ad7bsvFkC07uMGkVPLTS+gKDzXuLEFZRNpzdIsTRmgIR4upXz+UdHF1r3FOqImVhqXLNMx1cwCNULNdEd5N9XlRZtnMqO05d4RMFqKA4G/LT6/bY9UyjGjdV1WYBEVJ68MEHjeNHjRrFsmXL+Prrrzl79iw7duzg2WefpXfv3oSG1t0fVRpKktogPz/XAHpdWcl/ww3Yr6UQ5pbkQda5xptbRYyZUtUYN4YU7vN7hffDWox6mzoISRkI6QpRA60fr7aDtqUZkssfE9fs7C20RPXc90ivV4gtbbvQMbRmYmIDzcxpbqB2uhuD3sYnsu7fCwdnoZOCqy401ajGTXW1WVJSUkz6CU2aNImPP/6YL7/8kk6dOnHvvffStm1bli1bVifzcXAQv0oKCgrq5HiS6xNDmwiDV1FyFZKZALpiUf/EO1Kss3OAgLZiuanUu9FpIeWwWK7OcxPQXqSwawqsrzqrKGWem5rWt6kr2pZmxRaUehBGfQpezer9tOcyC8gv0eFkr6aFv1v1O1SB2UJ+ULuMqYp6m7rmKtXdNKrmprraLObqoDzzzDP1VmXWzs4Ob29vY7l/1zpsgSC5PtDr9aSlpeHq6oq9/VUlaZOUx2C8BLYz/TUc2BEuHRW6m3aWS1A0GOlxwlhx9BBZXVWhVotsp5OrhTfGXLuDilw+JkTI9i4Q1qNu5lxTWgwW89AWQtcJ1rcRqCUnSkNS7YI9sLernT8g1FtkWmUXasgr1uLuVPod4RspnmvkuSnrWl4v+LYUhQGvskJ+8tu3AsHBopiUNf2MJBJzqNVqwsPDry3D+PCvkH4Khrx5VYkKa0xFMbEBQzZSUxEVG/Q2od2sC0kYjJtzO2DAC9WPN/R0ajkE7Bs5McPRFYbPEWG1Ee832GmNxftqqbcB8HB2wNPZnpwiLRezCmkT5CE21MZzkyk9N+aQxk0FVCoVISEhBAYGotHYEJeWSEpxdHSsJHy/qtFpYdVz4hdz61shvG9jz6j+MXpuKghem1qtm+QY8WxtFpMhtJS0W/xdq6t7Yqhs2xS8VAA9HxaPBuREikFMXDu9jYEwH1dyUnJIvlLOuKkTzU09eW7KZ0xdRUjjxgJ2dnZSMyGRgCiTry3VCFw8dJ0YNwbPTUXjpjRjKjNe9GpyaOTaTRetzJQyENQJnLxE9sulw1WHmq6cEyE4lRraVN3u5FrGkClV2zRwA2HezsSm5HDBXK2bvEuir5W1NXv0elEfB+ovLFW+kJ9eX+8C7rri6pilRCJpPAyhD4CUQ402jQZDW1zmgq8YlnIPAlc/UPSi/ktjoikq8yBVlyllQG0HEf3EsqHLtyXi1ojn8Btsr71yjZCaW0RabrGoIxjsUSfHNDTQvHClXOKKq69oVAllxoo15F0SPzxUdqVtF+oB73BRKFFXDDkX6ucc9YA0biQSSdVcLGfcXDzUaNNoMNJPgaITqcYeFQqDqlRNp5jf5WOivomrn203NkMLhXPVGDexTSwkVQ2b4lJ5d00scZdyqx9sJQYxcQt/N1wdrQ90lCQlcendd9FmZFTaFlWacZWQlm+6oSa6G0MYy7t53dQgMofarswrdBXpbqRxI5FIqqa85yY9DkryLY+9FigvJjYnnjaEphq7gWZyufo2toi8DS0Uzu0S9XzMkZ8BSaUtF64C4+bExRwe+zmG77aeZdinW3lo/l52nEmvdc2pmuptUt6cwZWffubKokWVtrUMdAfgTFqe6Yaa6G7qW29j4CrU3UjjRiKRWEZTVCautXcR4ZhLxxp3TvWNJTGxAaOouJHfB1v1NgaCu4jU8eJsy9dw6h/xtw7uXNYeoIlSUKLl6SUHKNHqCfN2Qa2CLafSmDhvD7d/sZ3/HUpGo7O9orSiKGyOSwOgQ4j1epuiuFMU7NkDQHH82UrbWwYI4yYpo8B0XjXx3NR3jRsDV2EDTWncSCQSy5QPfbQYJNZd67obS2JiA4Z08MYu5JdsZWXiitjZl4nCLeluDCng7W6v2dwakLdWHudsWj5Bnk6seuZGNk0bzEP9InBxsOP4xRz+79dDDPpgEwt2JKDTW+/J+SPmAnsTMnGyV3Nb58p9Cy1x5ZdfjMsliYmVtod4OePqaIdWr3Auo5zupjaem/oSExu4CtPBpXEjkUgsU/4GGtJNLF/ruhuj56aD+e0B7QGVKG6XV0U9rNxL8OcjEL+pzqdIca7QBoHtnhsoSwk3p7spKRBF26DJh6RWHr7Ib/svoFLBJ+O64evmSISfG2+P7sTOV4fw4i1t8Hd35GJ2EW+vOsEn609ZddzU3CJm/yWM3OdvaUO4n3XZS9orV8hetcr4uuTcOZQKfchUKpXRe3MmtVxoqjaam3r33EjjRiKRXEuUD32EdhPL17Lnpji3rMNyqecmPi2PB+fv5dD5LLHe0bXsZlKVqHjdm3D0N1hyH5zfV7fzvHgIUMCzGbgH2r6/sYnmjspNQOM3iAwc7/AyfVETJCmjgNeWHQXg6ZtacUNLf5PtPm6OPDO0NdtfGcKrI9oB8OWmM/x74nK1x35r5XGyCzV0CvNk6o3We0Wy//wTpagIp7Ztwd4epagI7eXK52sZIETF8eV1NwbvS1aSqEFUHYpSZtw0lOYm6xxoS+r3XHWENG4kEollzHlu0k6KX/fXIoZmme7BIj0XWLgjka2n0nj/73Kp39UV87t8Ao7+Lpa1hbBkXN3qFYxGp5XF+yoS2g0c3KDwiqhjVB5jSGpUk61GrdHpeebXg+QVa+kZ4cP/DbXcesLZwY7HB7XkoX5CO/T8b4dITLcsiv/n2CXWHL2EnVrF+3d3sbrlgqLVkrloMQC+Dz2EY3PRcLIkobInplWpqNjEuPEIFZ299VrrUq4LrwjdFIimmfWJexA4ugsdli2p6o2ING4kEol5KoY+PEPEl5yiF8XdrGH7p7BgJOSn19s06xSDsVJOb3M0WdxAdidkcDmnSKw0eDQs6W42/QdQoM0IUT24IAN+uQvy0upmnjXV2xiwcyjrLVVed6PTQtzfYrkJh6Q+WhfH4fNZeDrb89l90VYZIK/f1oHu4d7kFml5/JcYCksqZ4plF2h4839CZP34oBY2dQHP3bARbUoKdr6+eN42EsfISACKzehuDGGp+PJhKbW6TLxtje7GoLfxCLG+6F9NUanKvJVXSY8padxIJBLzmAt9GLw31oSmtMWw9UMR+tj7Xf3Msa6p0FNKo9Mb04EVBVYdvmiy3Wy2UXKMaFugUsMtb8OE38A7QvziXTy2blLpa5opVR6j7mZ72bqknVCUJQTkzfvU/Ni1YF9iJvO2nWVnfDp5xZXDM1tPpfHtFnFj/+CersaieNXhaK/mq4k98Hd35OSlXF5ffrRSqvi7a2JJyy2mRYAbzwypphFpBa78/DMA3uPGonZywjFKhIrMiYrLPDf5pnOwRXfTUHobA1eZ7kYaNxKJxDzmQh8G3Y01ouKkXVBS+st0/4KrI1Zv8MSUhp1OXc6lRFumSVlpMG4MGVNpcZX1ERtni+cu4yGgrTAM718GLr7iPf1jsnWaCkvkp5fpggzGZk0w6m52CssNykJSbUZU33eqHkjLLeah+XuZ/VcsE77fQ5e31jLsk6288scRFu9JYm9CJi/8dgiA+/uGM7xTsE3HD/Zy5vP7olGrYNnBZH7Zk2TctuNMOkv3nwfg/bu74OxgffudothYCvbvB3t7fMaPB8AxUnhhzBk34X6u2KlV5BVrSc0tLttgLJZnhXekoWrcGJDGjUQiuSYwF/qwxXNzen3Zcn4qnPhfXc2s/qiQBn70gghJtQv2wE6t4siFbBLS88UNxcEVtEVlNxmAxO0i00jtAINfKVvv3wru+xXsnUUNmTUvlhkUtnLxoHj2awUu3jU7Bgivj72zyPpKPyXmY9TbNE5I6r+bzlBQoiPQw4lQL2f0CsRdzmXp/vO8tvwoY7/dRXpeCe2CPXjjNgvZbNVwQ0t/XhkuBMbvrDrOgaQrFJbomF4qTn6gbwS9In1tOmbmzyL92/PWW3EICgIwhqVKEhIrjXeytyPcV4SSTDKmDOHOC/urP6mxxk1DGzcyLCWRSK5mDDfR8qEPg+fGGlGxwbgJ6Sqe935bp9Orc/LThREGECBufkdK9TaD2wbSv5XIxll56KLQRxh0OamlOh1FgQ2zxHL3ByuLPMP7wN3zABXELIRtc2s2z9rqbQzYO0GzXmI5cTukHIbs88Joa3lT7Y5dAy5cKWBxqSfl47Hd2Dl9KHtfG8p3D/TgycEtuaGlH+5O9ni7OvDFfdE2eVYq8ujAFgzvGIxGp/DUogPMXHmMpMwCQr2ceXl4W5uOpc3MJGe1aFXh88D9xvUG40aTnIy+pLLX0qi7KS8qNlSPTo6pPnzZUDVuDBgL+UnPjUQiuVrJzxBpn2Aa+vAIAbdAISquqkLvlXOiVYNKDXf/IDwZF/aZtnJoahi8Nj6R4ChSdY+VGjedw7wY3TUUgP8dThY6CUNoyiBCPvMvnN8tvCEDXzJ/jvajYMQHYnnjLDj8q+3zrAu9jYHIcinhBq9Nq6GN0u38s39PU6LT06+FH/1biUadgZ7O3NoxmJeHt2PxI305PPNW9r9+M62DatfEUqVS8eG9XWjh70ZKdhG/7RfZSf8Z0xkPZ9t6NGX99jtKSQnOnTrh0q2bcb19QABqNzfQ69GcP19pv5aBpengJrVuIoXGTa+B83urPnFDa24M58lNgeK8qsc2AaRxI5FIKmPw2vi2NA19qFTW6W7OlHptmvcB/9bQcYx4vW9e7ed25Df4ZkDdN66sICYu1uqILRUTd2nmxa0dg3CyV3M2LZ/jF3MgsJxxo9fDhnfE696PiMwyS/R5FPr/n1he+YxtNXAUpe48N1DWRDNxhxBBQ6NUJT6TmsefB4SB8dLwtqgspKDbqVVWp2ZXh4ezA9880AOXUg/Qnd1CuamdbTWDFI2GK0uWAOD7wP0m81apVGWhqSoypkx6TKlUVRdYNFCcW+ZlbCjNjauvEJqDaSi2iVLjT4lGo+H8+fPExcWRmZlZl3OSSCSNTVXeAWt0N6f/Fc+tbhbPvR8Vz0f/qH1a+O6v4dIRWPV/NdetmKNCT6lTl/LQ6BS8XR1o5uOCh7MDQ9uLm9/KwxdNPTexK8WcHD2g//PVn2voW8KI0JXA0omQc9G6OWafFzc1lZ3o+1RbmvUEO0fIuySuX2UHrW+t/XFt5OP1cegVuKVDEN3DfRrsvG2CPJj3UE8e6BvB23fYXrAw999/0V6+jJ2/Px4jRlTaXqa7qaLWTWqF8JPBm5a4HYsYas24+NZOd2UrV5Go2CbjJi8vj2+//ZbBgwfj5eVFZGQkHTp0ICAggIiICB555BH27avjSpwSiaThqco7UJ3nRlMECVvEsuFG2aynqPeiK4YDP9V8Xpqisho7F/aVFcqrCyp4bo4kZwEiJGX4RX5H1zBApITrA0oFrVnnYMPbYrnfU+DmV/251GoY8404V95l+HUiaAqr3ic/A357UCyHdqub2iYOLhDWs+x1ZH9j8cKG4uiFbNYcvYRKBdNutU3vUhf0b+XPrDs74eVqWzgKIPMnkf7tM24cakfHSturrHXjL4ybSzlFpinvBuPmwn7LurbMBhYTG7iKRMVWGzeffPIJkZGRfP/99wwZMoRly5Zx6NAh4uLi2LVrFzNnzkSr1XLLLbcwfPhwTp8+XZ/zlkgk9YWiWOe5STtp/oactBM0BaLKr8G7oFKVeW/2z695KvTlY0KPYGD9jLqJ/ytKpUyp8nobA4PbBuDhZE9KdhH7UhEaJBBuehcfYdxYi5MHjF8s9rt4oGpPVPYFWDBchAtdfOG2ymLki1mFTFqwl++22njjMYRBoFFCUh+uE1Wh7+wWRtvg2mlpGpLCo8coPHgQHBzwHjfW7Jiqat14uTrg7+4EVNTdRIlqxXqNMODNYRQTN5DexsBVJCq22rjZuXMnmzZtYv/+/cyYMYPhw4fTuXNnWrVqRe/evZk8eTILFizg8uXL3HHHHWzZsqU+5y2RSOqLnIvCm6Cyg+Aulbd7hoJbACg6uGRGVGzIkmp9s2n5/o53iZh99nk49XfN5mZIkW0xWIgvc1Ng+yc1O1Z5cpJFKXu1PfiJ4m1HStPAuzQrM26cHeyMtVX+d/iiaXPN/s+Bs6dt5/WNgnt/FO/1kaWw8/PKY9JOwQ/DRLq2ZxhMXiu8YOU4n1nA2G93sTkujQ/XxpVVUraGiPLGTcOmgO8+m8HWU2nYq1U8d7NtRfMam6xlfwLgOXw4DoHmtTplmptzZre3MoiKK+luqglNGdLAG0pvY8D3GjRufv/9dzp3rj7G6+TkxJNPPsnUqVNrNTGJRNJIGLw2ge3Nhz5Uqqp1N0bjpoJ2w8EZuj8klmtasTi51LiJ6A+3/kcs7/yi9v1uDF4bv9Zg70iRRkfcpVwAOjfzNhk6upsITa05moLOYNy4B5V5pmylxSAY/p5YXj/TtD5Q8gHhscm5IOY2eS0EtDHZPTE9n3Hf7uLCFeFF0+gUftlt/mZqloj+0HYk9HkCvJrV7BpqgKIofLhWeG3G925OhJ9bg527LiiOE61J3AcNsjjGUMhPl56OLje30naz3cHBNIvNHI3mublGNTcSieQ6wJApVcE7YIIl3U3mWcg4LTwgLQZX3q/nZJEenrAVUk9W3l4dyTHiOayH8DK0GCx0POvesP1Y5anQU+rkpVy0egU/N0dCvZxNhvZr6Ye/uxNZBRr2eY8Qht7tn9ZOA9P7EVEbBwX+mALpp8V79OMo0ZcqNBom/wPezU12O5Oax9hvd3Exu4iWAW68fYcQOS/ak0SRpnLvJLPYO8J9S2DEezWffw3YFJdKzLkrODuobW510BQwhJocoyItjrFzd8c+IMBkfHnMNtCEcrqbfeZDv5mlx2pozY3BmCrKgoKmnUhks3Gzfv16Zs6cycaNGwHYunUrI0aMYMiQISxYsKDOJyiRSBqYZCvqqFjy3BiypJr3BWczTQe9m5eFPmz13hRklv1iDesuPEjD5oiQTuwqOFuLULihWWSpQWdoltmpnJjYgJ1axe1dhNZmSYIrPLYF2o2s+blBXMvIueJ9K86Gn8fAL/eI9hVRA+GhVeDmbzrlS7mM/24XqbnFtA3y4NdH+zGxTzhh3i5k5peIYoNNFL1e4cO1wvPx0A2RBHk6V7NH00KXnY2uNEvYMSKyyrHWpIPHp1XImPJtIfRcupLKuhttsQjtQsOHpRxdRR0eaPLeG5uMm19++YWRI0eyevVqRo8ezcKFCxk9ejTNmjWjRYsWPP744/zxxx/1NVeJRFLfKIptnpvUWNNflob6Nq1vsbyvIXxz+FcoyrZ+bgajy6+VEOGC6AHVa4pY/md6zYTK6WdE8T2VGjrfC8DRC1mAqd6mPKO7iYJ+645fpqCkFn2iymPvCON+FjeP7PPCI9XudpjwuxAfl+NYcjbjvxOtCDqEeLLk0b4EeDhhb6fmoRtEKGT+joRKjSGbCquPphCbkoOHkz2PD2zZ2NOxmZJzIuxnHxCAnXvV4bSq2jC0LPXcJKbno9GV9TAz1d1UCE1lJQEKOLiVNbRtSK4SUbFNxs3cuXOZO3cuMTExrFixgieffJIZM2bw/fffM2/ePObMmcOnn35aT1OVSCT1TuZZ4XK2cywrUmcOzzBw9ReiYkNIR1MoQilQtXETOQAC2oMmHw4ttn5uBr1NWA/T9YOnC2Mn9TjE1MB7fGiReG451Fh8zyAmLp8pVZ5uzb0J93WlUKPj39hU289pCfdAuG+xeH/6PCHExg6mXo3D57OY8P1urhRo6NrMiyWP9MXXrSwNeVzPcFwc7Dh5KZddZzPqbm51xOWcIt7/W4QkHx3YAh+3yinUTR1jSKrUcKmKMs9N5Vo3IZ7OuDraodUrJGVWSPs2FlisICou33bBQrHDeuVaNG5Onz7NqFGjABg6dCharZahQ4cat992222cPFmDOLpEImkaGLw2wZ2FJ8ESJpWKS/dJ3CEaSXqGmWYRmdu39yNiee/3orqvNRgypcrXZQFRl+Wm18Xypv/YpgXQ68paIERPBKCwRMfpUoFnlwpiYgMqlYo7StsxrDyUbP35rCGkKzy1W2hgKnTmjruUy/3z9pBTpKVHhA+/TO1TqT6Ll6sD9/QQoYP52xPrdm61JC23mPu+301yViHhvq48fGMDh1XqiDK9TfXzN4wxV+tGrVbRIsBMGwYQPwKgVHdTLvutsWrcGLhKRMU2GTcODg6UlGsA5uTkhLu7u/G1o6MjhYXVFKKSSCRNF1tK+1fU3ZxeJ55b3Vz9L8ou48DJEzLjIXFr9edSFFMxcUV6PCw8TYVXYLMNwtj4TZB7UXh+2grdzImUHHR6BX93J4I8nSzuaghNbTmVRlZB5caIdU1esZYnFsWQW6yld6QvP03ubbEP0qT+kQBsOHmZcxnVNGBsIDLyipk4bzdn0/IJ9XJm0dQ+uDvZV79jE6RmnptzZsOEZtswgPCQuAeL8KTBawllnpuG1tsY53V1FPKzybhp1aqViWcmOTmZqHKWa3x8PM2aNVwqoUQiqWNsacpo9NwcFs9nLKSAm8PJvazf1ImV1Y+/kgCFmSJcFmymTL6dPQyfI5b3zYPLJ6o/JsChX8Rz53tFl2zKivd1aVZZTFye1kEetA/xRKNTWHP0knXnqyGKovDKn0c4m5ZPiJczX9/fHbcqDIOWAe4MbhuAosDCnYn1OjdryCoo4f4f9nLqch5Bnk4sebQvzX3roMJyI1Fsi3HTLAzs7FAKCtCmVg5htjKIiiu2YSjfZ6p8aMpQ46ah08ANGIybzLPWe10bAZuMm9deew0fn7K+H56enib//Pv372fsWPOVGiUSSRNHp4WUUkPFFs9NWqzQ3WSeFd2/W1iu+2FCexHiJm5N9V+SF0q9NsFdjEZIJVoMEsdUdLDu9er7ThVklnXCjr7fuLo6vU15DN6bFXUdmqrAT7vO8deRFOzVKr6c0B0/d8seJQMP9xc/PH/ff4HcIk01o+uP7EIND/ywl9iUHPzdnVj8SN+rrqZNeRRFMRbls8a4UTk64tBM1EaqSlRcKR0czBfzK6+5aQy8w0WpB02BKKLZRLHJuBkzZgwDBw60uP3VV19l1qxZtZ6URCJpBNJPiS8sR3fRybs6vJqJisN6LWz/VKyL6Fcps8ciUQNFo8nclDKPkSWqCkmV55ZZwrsTv9G0GJ45jv0pUm2DOgudSylHS3tKWcqUKs/obqGoVLA3IZPzFQWhdcTBpCvM/kt4oqaPbE+PCOsaSw5s7U+rQHfyirX8vv9CvcytOnKLNExasJejydn4ujmy+JE+xjDM1Yo2NQ2loADs7IRXxgqcIi23YTCmg6fmVQ5bRZSvd1MkNGJXSgs0Npbnxs5BVAeHJq27kUX8JBKJwGBghHQDtV3148tXKj5WWgLClo7S9k5lWVUnV1c91qA5aNaz6nG+UdDncbG89jXQVeGxOFgakioVEgMUlGiN1WKt8dyEeLlwQ0vRKHP5wbr33lzJL+GpRQfQ6BRGdApmcqmWxhpUKhUPl45fuDMRnb5u08JLtHpWHb7IvG1nWXbgAltOpXEsOZtL2UWUaPXkF2uZvHAfB5Oy8HZ14JcpfWgTdPX0jrKEwUBxaBaGykyzTHNUVesm0t8VtQpyi7Wk5RabbvRvDW6BQqifHCN6jOk1wkPqaZ1hVS9cBW0YaqTmysjIYMaMGWzatInU1FT0FVzKmZlNu3KhRCIxg1FM3M36fUK7QfwGUEq/A1pVkQJujna3wfFlELsabn7L/BhtCaQcEcvVeW4ABk4TKeYZp0WTzj6PVR5z6ZgQQqsdoHNZKP3ExRz0CgR5OhFoZWG5u6KbseNMBssPJvPMkFZV6nRsQa9XeP63Q1zMLiLK340P7uli87Hvim7GB//EkZRZwMaTqdzSIajW88or1vLr3iR+2J5ASrblHlZO9mqKtXo8nO35ZUofOoTa2HeriWKLmNiAoYpxSULldHAnezvCfV1JzCjgTFqe6efOUO/m+DLRiqF5b7HeJ9K6HyD1hV8rOL22SYuKa2Tc3H///cTHxzNlyhSCgoLq7J9ZIpE0EjoNnN8jlq0RExsweG4AvMIhoK1t5219qwgjZZyGtDjz+18+JjJGXHysc8U7e8GQ12H187B5DnQZW1b0z4Chtk3b4eDmZ1xdprfxtvoShncK5o0Vx0hIz+fg+Sy6h1sXNqqOrzafYXNcGk72ar6a2N1iZlRVuDjacV/vcL7ZEs/87Qm1Mm7ScotZuDOBn3edI6dIFC4M8HCibws/sgpKSM8rISOvmIz8EnR6hWKtHk9ne36c3JtOVnjBrhYMxo1TFcZNgaaAXRd3MaDZABztHKv03IBow5CYUUB8ah43tDStRE1kf2HcJG4rq1JdR3qbEq2eBTsSSM0tRqvTo9EraHV6tDqFEp0eBRjXszkD2wSY7ngV1LqpkXGzfft2tm/fTteuXasfLJFImjaJO+CvF4UwWGUHzXpbv295L0/rW2wvKubsCVGDRKbVydXmjZvyehtrjx/9oKihk3oCtnxQlkkFwhN0ZKlY7na/yW5Hy2VKWYubkz0jOgWz7GAyyw5cqBPjZseZdD5eL9oTzL6zE+1Dau71eLBfBN9vO8uusxnEpuTYfKykjAK+2RrPHzEXKNEKD10LfzceHdiCO6PDcHYw9SDo9Qo5RRrS80oI9XbG1fHqTPe2RHWeG0VReG7Tc+xK2cWrvV9lYvuJxlo3JRcuoGg0qBxMDdWWAe78G5tauQ0DlNW7Ob+3rH5UHeltvtx0hs83nK5yzKlLuax/oUKSgOH8tW1YW4/USHPTrl07Wc9GIrnayUuFZY/BwpHCsHH1g7u+q9ScsUq8motKxVB1VeKqMPSairWgu7FWTFweO3sYVto1fO93ohGlgdNrRTNK9yBRk6ccR0rbLlijtynPXd1FCYxVh1Mo1lrZsNICsSk5/N+vB9ErMLZnM+7tacPfwwyh3i4M7xQMwPztlcMiVZGSXchtX2xj8Z4kSrR6ujX35pv7e/DvC4MY3zu8kmEDojCdt6sjrQLdrznDBqo3bn6L+41dKbsAOJEhhOD2gYGoXFxAp6PkQmVxd1mPKTMZU/5twC1A6G6OrxDr6qDGTXxaHt9sFmGlcT2b88yQVjx/cxteGtaW10a248VbRPf5xIz8ynotQ9+4kqZRQ8kcNTJuvvrqK15//XW2bNlCRkYGOTk5Jg+JRNKE0etgz3fwRU848iugEkXwnt4Pne+x7VgqFdzxBQx6BVoPq9l82o4Uc7h4ALLNiHItVSaujpZDxJz0Wlj3Ztn6g6Uhqa7jTSoA5xVrOZsuvqxtDaP0a+lHkKcT2YUaNp2sWTsGvV5h3razjP5yB+l5JbQP8eSd0WZq+tSAyaVp4csPJhsF09Ywd90pcou0tAv24LfH+rH8yRsY3ikYtVp40BS9HqUJ1zqpaxStlpLzommlOeMmKSeJuTFzja/P5YjMJpVKZVWPKbN/G5WqrBVDXmk9pVp6bhRF4fXlRynR6bmpbQDv3d2ZF29ty//d3JqnbmrFowNb8tRNrXC0V6PRKSRfqeDMcHARz9qm6+SokXHj7e1NdnY2Q4YMITAwEB8fH3x8fPD29japgyORSJoYFw/B9zfB3y+J7tMh3WDqBhj1qWhjUBPajYSbXgN1DZMvPYLKhJJxa0y3FWYJPQ7YpgUycOtsUZPj1N9wdjPkXi6rpFwhJHU8ORtFgVAvZwI8qq8jUx47tYo7o0X2yrIDtmdNXc4p4qEFe5n9VywlOj03tw/ilym9zXpGakKPCB+GtgtEq1f4z1/WFTiMTcnhzwPCy/De3V3oHeVroq9UtFoSxtxF/IgRFB4/XifzbOpokpNBq0Xl7Ix9kKl+SafX8caONyjUFhLmLj4L53PPG7c7RoqGpubTwUXdn5TsIvKKzTRiNdS7MVBLzc2yA8nsPpuJs4Oad0Z3MqubVatVRJXWIzqbXsHosi8VPWssC8obmxp9G02cOBFHR0cWL17Mhg0b2LhxIxs3bmTTpk1s3LixrucokUjqgpJ8+Gm0KNTn7AW3zYVHNkIzG8I99UW728VzxZRwQ3q6T2SZmNIWAtpAz9Ku4Wtfh8OLRZG/Zr3EtnIY9DadbdDblOeuaBGa2hSXSma+9e0Y1h6/xPBPt7LtdDrODmr+M6YT3z/Yw6pCfbbw+m3tcbBTsSkujc1x1XuX5vx9EkWB27qE0K25d6XtxWfOUBwXh+ZcEucmTCR71ao6nW9TxBiSiohAVcGY/+nETxxMPYibgxtfDPkCgMyiTHJLcgFwMuhuzBg33q6O+LuLtPKEqnQ3ILrXe4fX+Bqu5JfwnzWxAPzf0DZVVoqO8hfGTUJ6hTkZjBtt0zVuahQQPXbsGAcPHqRtWxszIyQSSeNxer3o+O0VLowa94Bqd2kw2t0G698UlVgLr5RlN12ogd6mIoNfFQLiy8fKtDfdJlYaZktlYnO0DfagU5gnx5JzWH3kIg/2i6xyfEGJllmrY1myNwmAjqGefDY+mlaB9VPkrkWAOw/1i2Te9gRmrT5B/1b+ONiZ/3277XQaW0+l4WCn4uVh5r/nC4+Upufb2aEUF3PxpZcpOn6CwGkvorK/9rQ2YFlvc+bKGb44KAyal3u9TGuf1vg5+5FRlEFSThId/TtWmzHVMsCd9LxMzqTlVjawA9oKbVtBOng2q1Slu0Sr56+jF+kT5Ueot0uV1zDn71gy80toG+TB1AFVe4AiS42bxIrGjaFTvaITmZZ2tmfy1Tc18tz07NmT8+fPVz9QIpE0HU6sEM+dxjQtwwZEamlgB6GPObWubL1RTGyj3qY8rr7CwAGRUm7vAp3uqjSszHPjXeNTGbw3f1YTmjqXkc/tn29nyd4kVCp4bFALlj/Zv94MGwPPDG2Nr5sj8Wn5/LL7nNkxer3CnDWih+D9fSMstkooOnoUAL/JD+P3mKgllLlwIUmPPIL2ypV6mH3jU1xap6a8caPRa3ht+2to9BoGNhvImFaiZ1qEpwhDGXQ3ZZob86JuYxuGij2mwLTPlJmQ1O8x53l+6WGGfbqVf45Z7nO2NyGT30qrVb97VyeLxq2BFv6GsFRFz005A0rTNHU3NTJunnnmGf7v//6PhQsXEhMTw5EjR0weEomkiVFSAKfWiuUOdzbqVCxiyJo6WRreUBTrKxNXR6+pZQ3/2o8qy/YoJadIY3S919RzA3BHt1Ds1CoOn88yn/kCXLhSwITv93A2PZ9gT2cWTenD9BHtcbSv/4LxXi4OvHirCMd9+u9prpgJn604lMyJlBw8nO15dojlNhyFh8V3vUu3bgQ+/xxhn32GytWVgl27SbznXorKNVm+VjDXU+r7I98TmxmLl5MXb/V7y6hfCfcUoaNzuab7aNPS0OVVNmCqzJiCsv/bivobYPvpdAByi7Q8/ksMb608Xilrr0Sr57XlwiC9r3dzekRUr7GLCrAUlnICSnU6TTQ0VaP/pnHjxhEbG8vkyZPp1asX3bp1Izo62vgskUiaGGfWi75R3uEQavv/aFFsLHlbt9bDxMph0N2c2SB+DWafh/w0IQgO7ly7Y9s5wN3zRCfym16rtNnQCbyZjwu+btaV1DeHv7sTg0oLni034725lF3EhO/3kJxVSIsAN1Y+058bWtVAS1QLxvcKp12wB9mFGj7595TJtiKNjo/WxgHw5OBW+Fh4L/T5+RSfEQXcnDuLv43nsFuJ/HUJDuHhaJKTSRx/Hzl//12PV9LwlIWlhFfmePpxvjvyHQBv9HmDANcyj6jBc5OUI8KOdp6e2PmJgpEl5xIrHbtVVRlTILyNzx6EG583Wa0oCnsSRFcAQ5HGhTsTuefrXZzLKDNKvt92ljOpefi5OfLK8HZWXa9Bc5OcVUiRppyxpFI1ed1NjYybhISESo+zZ88anyWVKdAUGD/kEkmDc+J/4rnDaNsL7QHnn3qK8489TnF8PZZbD+kq6uZoCiB+U1kKeFCnstTT2hAaDfcuNOvWP1pLvU157uouMmWWH0xGX64+SFpuMRPm7SYps4BwX1cWT+1LoId1LR7qEju1ihmjRDG4RXuSOHU517ht4c5ELmYXEerlbOxLZY6iEydAr8c+JASHwEDjeuc2bYj6/TfcbrwRpaiI5JdeRnPJcpjkakJfUIC29FocIyMp1hXz2vbX0Ck6hkUOY3jUcJPx4R7Cc1P+e78q3Y0hYyoxIx+tzkJ6vW+LSvqW06l5ZOaX4Oyg5r8TujN/Uk+8XR04mpzN7Z9v568jKZzLyDcW63vj9vZ4u1pnwPu5OeLhZI+iQFLFxrAG3U8TzZiqkXETERFR5UNiSmpBKn0W92H0itHo9LUr8CWR2IymEOL+Ecsdxti8uzYzE+3FFFAUCg5U0727NqhU5UJTq2tWvK+G7EsUv3zNZQXZys3tg/Bwtic5q9D4izozv4T75+3hbFo+Yd4uLH6kD8FeDW/YGLihpT/DOgah0yvMWn0CRVG4kl/CfzcJb8yLt7atMg298IgIb7h0ruxRs/Pyovm33+DStStoteSsaXzvTc4//5D84jR0tajDVpJU6oHx8sLex4cFxxZwNvssfs5+vNHnjUrjjZqb3DJtkzEd3Eytm1AvF1wc7NDoFM5XrCtTBXvOZgDQPdwHR3s1Q9oFsebZAfSM8CG3WMtTiw8w9ttdFGv19G/lx53drG+4qVKpLIemmnitG6uNm127dll90Pz8fI5fJ3UPrMHP2Q87lR1aRUtGUUZjT0dyvXHmX9DkC69IDWrFGMIPAEWlN7V6wxCaivu7rNdVbfU21aDV6dlzVhghlfr61ABnBztu7xICwLIDF8gu1PDAD3uIu5xLoIcTi6b2oZmP5fTbhuL1kR1wtFOz7XQ6G0+m8sXGM+QWaWkf4smY6KpvgIZMKZcu5sOFKjs7vO4cDUDOX3/V7cRtRNHpuPSf/5Dz119cWbykxsepmCm17cI2AJ7t/izezt6Vxjf3EJWls4uzyS4WnsGq0sHVahUtSg0JWwot7i41oPtElfVIC/V2YcmjfXlysOgBdTmnGEd7NbPv7GxzL8hq08Gvds/Ngw8+yC233MJvv/1GXp75N/7EiRO89tprtGrVigP1+QvvKsNObWeMxV7KvzZctJKriFqGpErKhaIKjx2rq1mZJ7wfuPhCYSZc2CfW1bPn5khyNrnFWrxcHOqsc7WhHcPfxy7x4Py9HL+Yg7+7I4sf6WtMr21swv1cmXyjuNnO+N9xft6dCMBrI9sZKxBbovCoMG6cO3exOMZj+HCwt6fo+HGKz9rW9qEuKTx4EF2aENxmLVuGoijV7GGe8sZNia6E2ExRK6ZXUC+z410dXAl0ESG7ShlTVaSDQxWi4gooimI0zPu0MBUIO9ipeXl4O36c3JvocG9m39nJaKjYgtG4qVh/51rx3Jw4cYLRo0czY8YMfHx86NixI7fccgujRo3ixhtvxN/fnx49enDu3DnWr1/PAw88UJ/zvuoIdhW9XaRxI2lQNEXlQlJ31ugQxWfKjJviU6fQ29BXrjghgZx166ofaMDOHtqOKHvt5AV+ljN26oJd8cKb2reFL3bV3NStpWeED819Xcgr1nL4fBberg78MrVPvad628pTN7XE392J5KxCNDqFgW0CGNC66jIB2rQ0EaZUq3Hp1NHiOHsfH9xu6Ac0rvcmZ23Z50+TlETBvn01Oo4hlOQYFUlcZhwavQZvJ2+aeTSzuI8xY8pMOrg5I6ttsAcAMeesS6WPT8snPU94ZSyFVAe1CWD5k/0ZW8MeZZY9N6WaG21xjY5b31ht3Dg4OPD0009z8uRJ9uzZw6OPPkqnTp0ICwtj8ODBfPvttyQnJ7No0SI6daqbfijXEsFu0riRNALxG6AkFzzDauwBMRER63QUxVqf4pv8/AskP/t/5G7ebP0JDbobgLDomrd1sJIdZ8Sv+v4WspZKEhNJevRRm1oMqFQqxpTWvPFwtufnyX1oF1w3XqG6xMPZgZeGidRwlQqmj6g+i6awtL6NU8uWqN2q9gR43S7CjDl//VVjj0ltUPR6ckuNa4cIYWhk/7msRscq77k5ki48V538zbcuMGDMmMoVeh2H8HBQq9Hn56NLT680/qa2wtOz9VQa+ebaMFRgT4IwzKObe9dZq46KRFVX66aJ1rmpURnJ7t270717Dfq8XMcYjZsCadxIGpDyIakaGgmGsJR9YCDa1FSKjh7BtXv16eTa9HSKS2udZK/4Hx6DB1t3wpZDwMFVZE3Vc0iqSKNjf+mvZEt6m4yFC8nfug07d3fCPv7Y6mM/OrAFer3CyM4hdRbuqg/u6dGci1lFhHo70z6k+nka9DbOFvQ25XEfMhSVkxMliYkUHT9RpaenPig8fBjt5cuo3dwImTWLpAcfImftWoLeeB07Dw+bjmU0bqKiOJq6GYAu/pbDclCmuzFkTKkdHXEIC0Nz/jwliYnYB5h6ydqHeBDh58q5jAI2xaVye5fQKo9fFpLyq3JcbTCEUdPziskt0uDhXJqt5XANpoJLbMdg3FzOv9zIM5FcN2iLhTAXahyS0mVno01LAyC93xAACo9ap7sp2L/fuJy3aRM6C1q9Sji4QJdxoodO25G2TdhGDpy7QolWT6CHkzEVtyKGYnVFJ2JtOra7kz3ThrVt0oYNiNTw529pw7he1vUrKjJmSlV9Ywewc3fDfchNQOOEpnJLQ1LuQ4bg2qsXji1bohQV2ZzBpb1yBV22EAU7hodzLF38D3QOqNrAq1jrBspCU8VmdDcqlYrhncS9oqpKw2Cob1MaUo2yXJBPURQyf1mELiuryuNZwtPZAf/SPmeJ6eXSwZu450YaNw2EUXMjPTeShiJ+IxTngEeoaBRZA4rjRd2qDFdvPkwTv3QNYtLqKNhbpm1QiovJXbfe+hOP/BCmna73TKkd8WUhKXPhBX1BAcVxoqhdyblzZivLXk8oer1RVG4pU6oiXreJMGPOmjUoegv1W+oBRVHIWSeqcnsOuxWVSoX33XcDkPXnnzYdy+C1sQ8JIVddYtTQdPav+j0oX6XYEJYz6m4siKxHdBKZdptOppoWzqvAuYwCkQVlpyY63MfsGEVRSPvkUy7Pns25yZNRSqxv6FqesjYM5X6gXCuam/pg69atjBo1itDQUFQqFStWrKh2n+LiYl5//XUiIiJwcnKiZcuWzJ8/v/4nW0uC3ETlSKm5kTQYx1eI5w531DgkVRwv0sAT3QM55S1c7JpzSVb9CjR4bpw7ilBEzmobukbbOdSsC7iN7CwVE/drad6tX3jsGBhuyIpCcdy111LAFkoSz6HPyUHl7IxTa+uE3m4DB6L28EB7+bKJN6++KTp2DO3FFFSurrjdKFoWeN0xSmRwHTlC0alT1RyhjLK2CxEcTReeqwjPCLycqi76aAhL5ZbkklWcBYBTi9J0cAsFb7s28yLEy5n8Eh3bTlfW5RjYXVrfpmtzL1wcK+ttFEUh7dPPyPhOVFD2HnMXKseaVd82Kyq+VrKl6oP8/Hy6du3Kl19+afU+Y8eOZcOGDfzwww/ExcWxZMkS2rWzrpR0Y2IIS6UXpqPVVy8Uk0hqRR2EpABKSjOlkjyCyHN0JdNbGOmFx6oW12qvXKG49OYRPHMGAPm7dqO5nFrjudQ1uUUaYyfwGywZN4cPm7y2NTTVkChaLamffkrmTz/X+Bd6dRQZUsA7dEDlYF0naLWjIx633gJAzuqGC03lrhVeG4/Bg1A7C32Ivb8/7oMHAbYJi8uLiY+mCeOmk3/1iTMu9i4EuYr/GWPGVAtRe6bYgnGjUqkY1lHcL/4+lmLx2HvM1LcxoCgKaZ9/Tsa33wIQ9Npr+D5wf7XztUSkOePmWqlzY4mioppf2IgRI5g9ezZ33VW5Q685/vnnH7Zs2cKaNWu4+eabiYyMpHfv3txwww01nkND4evsi73aHr2iJ60grbGnI7nWObsZirPBPRia96nxYQyZUuc9xBf0UU9R3K2omtBUYYyoLuzYsiUuXbrgEh0NikLOmjU1nktdszchE51eIcLP1WJRPYNxY+ftDZS2HWii5KxdS8Y333L53Xc5O+Yu8nfvqfNzVFWZuCoMWVO5a9fWm+FVHkVRjCngHrcOM9lmCE1lr1xp9VwMxo1TuUyp6kJSBipmTDm1EsaN5sIF9BbunyNKdTf/nrhMibZyKE/UtxGem4r1bQDSv/iSjK+/ASBo+qv4Pli70iwGz03ite650ev1zJo1i7CwMNzd3Y39pN58801++OGHOp1geVauXEnPnj354IMPCAsLo02bNkybNo3CKupuFBcXk5OTY/JoDNQqtdGCl7obSb1TByEpKDNukjyCcLBTcdJTpDdXJyo21BJx7SU0M56jSlOCV9kQmrLA4j1JDP5wk/HLvabsOCP2t5QlpSiK0bjxulv8ACuKbbqeG2OTSpWKkvh4kiZNIvnFaXXqLTOkgVuTKVUe1969sQvwR5edTd6OHVWOrYsmrcWxsWjOn0fl7Iz7wAEm29wHDMA+IADdlStWlygwGDcOERFGMXF1mVIGKta6sfP1xc7LCxSFkgTzupuekb74uzuSU6Q1hp/Kc+FKIRezi7BXq+gRYaq3SfviS9K/+gqAwFdfwfehh6yaZ1UYKiefTc8vS+m/FjU3s2fPZuHChXzwwQc4lovhde7cmXnz5tXZ5Cpy9uxZtm/fzrFjx1i+fDmffvopf/zxB0899ZTFfebMmYOXl5fx0bx5zQoZ1QWy1o2kQdCWQFyp+78WISldXj7aFOEWzwoIY3inEOJ8xBd14dEjVdYtyTcaN0LI7DlihNA6nDhRq+abOr3Cl+tjybtwkcd+iTH9JWkjO0vFxJZCUtqLF0VlW3t7vO++BxCtKPQN4HmwFV1eHvlbRTuA8B8X4jNhAqjV5Pz1F2dHjiRj4UIUjaZW59CXlFBcaty5dLHuxm5AZWcnPgNUHZrK27adxLHjOP/oYxSVCrlrQs4/IiTlPnAgaldTr5zK3h6vO+8ErBMWK3o9JeeEYZIZ4ExWcRYOagfa+ra1ai4RHqYZUyqVCseWpaGpePOhKTu1iluNoanK94tdpQZPl2ZeuDqWVXRJ+/K/pP/3vwAEvvwyfpMmWTXH6gj3dUWlgtwiLRn5pZ//azFb6qeffuK7775j4sSJ2NmVCZm6dOnCyZP1J7jT6/WoVCoWLVpE7969GTlyJB9//DELFy606L2ZPn062dnZxsf58+frbX7VIY0bSYOQsAWKssEtEML71vgwJQniizfTyYOO7ZozoJU/8d5h6FRqdGnpaC+bL2ugy8mhuLTQn8G4sffxwb1U1JldC+/N7rMZ3LZrGT+vnU3XU3uZ/OM+sgtsv2mn5xVz8pLohl2d3sa5XTscoyLFr22tluJTp2s8//oib+NGlJISHKOicO3Vi+AZbxL5+284d+2CPj+f1PfeJ+Hue6rVSlVF8cmTKBoNdj4+ODSzXJXXEsbQ1MaN6AsKKm3P37WLC08/bTTCctf/W6N5ipCUqMrtMexW83O5SzSQzd+2HY2Fz7EB7eXLKEVFYG/PMUfhBWvv2x5HO+vEuRU9NwBOLVsAUHLWsqFvCE2tP3EJnd70h4S5+jZpX31Feql+NfCll/Cb/LBV87MGZwc7wryFMWPU3VyLdW6Sk5Np1apVpfV6vR5NLX8dVEVISAhhYWF4eZUp1Nu3b4+iKFy4cMHsPk5OTnh6epo8GgtDOvjlAlnrRlKPmISkal61tNgoJg6kX0s/bmjlR4mdA4me4nNsKOZWkYKYGFAUHCMicAgMNK43hqZW17xa7fIDF7jxojjv48dXkXIxg6cWH0Cjsy3F2NByoV2wB36lNTwqYjBuXLp2RaVS4dyxAwBFJ5peU2BD3RbPESOMKe0uHTsSuWQJwbPewc7bm+JTp7jw1FM1Tsc26G2cu9jefBHAuXNnHMLDUQoLyd24yWRb/p69nH/iSZTiYuxDRCp03saNNZpn8alTaM4loXJ0xH3QYLNjnKKicOnZA/R6spevqPJ4RjFx8+YcvSI0V9XVtylPec2NMR28RdWeG4C+LfzwcnEgPa/E2LXegKG+TZ/S+jZ527aT/vkXAAROexG/KZOtnp+1VOoxZRQUX0Oem44dO7Jt27ZK63///Xeio6uvXFpT+vfvz8WLF00ad546dQq1Wk2zGvySaGik50ZiNYoCq56Dg7/Ytl/mWYgt9YzUIiQFUHhaeCiSPIK4oaU/zXxcifBzNYamio6a7xBesE+k+7r2Nq2t4zFkCGpXVzQXLlB48JDt8ynRcWDPcQILswDwLsxhYvxmtp9J5+1Vx20ymAwp4FV1AS84JObo0q0rAE7t2wNNT3dTXsfiOWK4yTaVWo3PvffS4u81qN3d0V6+bPHvVh0GEbk1xfvMoVKp8LxNFGUsX9CvYP9+zj/+OEpREW6DBhK5ZDGoVBSdOIHmku3flYYsKbcBA7Bzt9wewvuu0po31TTTrGmmlIFmHs1QoSJfk09GkfjcWeO5cbBTc0sHodMsX9DvwpUCLlwpxE6tomekMG4KDggBv+fIkfhNnWr13GzBaNxkGDw3BkHxNeS5mTlzJk8//TTvv/8+er2eZcuW8cgjj/Duu+8yY8YMq4+Tl5fHoUOHOFT6JZKQkMChQ4dIShKxyenTp/Pggw8ax0+YMAE/Pz8efvhhTpw4wdatW3nppZeYPHkyLi4uNbmUBsUoKJbGjaQ6YldCzAL431Owba4wdqoj/TQsuE1kSQV2gIjaZRGmHxeah0y/UNoEiYaPN7T0N9a7sSQqLqigtzGgdnHB4xaREpy9aqXN81kfe5mWF0V6ubrUA3tX/BaCCzL4ZXcSP+5MtPpYO43F+8yHpPQlJRSXpn27dBXGjXMHg+emaWVM5f67ATQanFq3slh7xt7Hx1jrxaY+X+UwVGq2tnifOQwF/fK2b0eXlUXBgYOcf/QxlMJC3Pr3p9nnn+MQHIxLt25irjXw3hiypDwthKQMeA4fJoztapppGgv4RTQ3dgK3VkwM4GTnZPxha9DdGD03iedQtJZLgwzvWFatWF8amjKEpDqFeeHuJPQ2mvMicuHcob3V87IVi56ba8m4GTVqFEuXLmXNmjWoVCpmzJhBbGwsq1at4pbSLy9r2L9/P9HR0UZvzwsvvEB0dLTRQEpJSTEaOgDu7u6sX7+erKwsevbsycSJExk1ahSff/55TS6jwZGem+uTQ6mHbP+bt78D+j8nlje8A3+/DHrL1UpJPQkLRkLuRQhoBw+sqFVICsp6Snm1b2MMQ/Rv5UecjzBuio4dqxTi0OXlG2/+FY0bAM9RowDI/fsfmwWuKw4m0yVdzMln/Hhc+/ZFpdHwQfoWAN5ZfYLNcdVnBl24UsC5jALs1Cp6WyhbX3zihNCX+PriUJqE4NxeGDfFcaeqvCE1NDn/lOpLRoyocpyhvkve5i02n0OXnW28yTvbmAZeHqdWrXBq1w40GlI//ZTzjzyCvqAA1359afbfL1E7iRChx1DR6iOvQviqOorPnBGfWwcH3G+6qcqxaldXoyepqpo3hjYJmQHOxk7ghuJ81mLQ3RgbaIaGoHJxAY2Gkip0oDe29sfN0Y5LOUUcvpAFYLblQsn50uM2t66FRk2oVMjvWq1zM2zYMLZs2UJeXh4FBQVs376dW2+t2lKuyODBg1EUpdJj4cKFACxcuJDNFX5ltGvXjvXr11NQUMD58+eZO3fuVeG1gTLjJqMogxJd08u4kNQ9h9MO88DfD/DUBssZfWZRqeCWt2H4e+L13u/gj8nm0y4vHYOFt0F+KgR1gkl/QWldmpqiLyrCJV1ow6J6lt3M+rXwI8kjiCI7B/R5ecYbnoHCgwdAp8OhWTMcQkJIzS0iprQxJYBb3z7Y+fujy8oib/t2q+eTkVfMllNpdE4XGgXX3r0Jmj4d1GoCDuzkGd8c9Ao8s/ggpy/nVnksQ0iqSzOvsiaAFaiotwFRnVbt6opSVGQxhbeh0V65Qv6uXQB4Dq/GuBk4EFQqkSadYrk4nDkMLRccwsOx9zFf6t9aDAZF1q9L0efn49q7N82/+spYaA9ELyiA/D17rO9Jhqj1A+B+ww1WNcY01LzJWbvW2DuqIobPeLyn0JZU1wncHJUyptRqHKMixfEtFPMDIeQd0t40NGUs3leuvo0mSRhIjs3rT55RPiyl1yvlBMXXkOamPHl5eU2ijszVgLeTN0524peJFBVfH6w8I8Ivp66cMmmeVx16vcKh81n84z4G7v4B1A5wYgX8crfIhDJw8RD8eDsUpENIV3hoVZ20Lcg9HY8KhRwHV3p2a2lc7+fuRNswH854l9a7qSAqNuptevZEp1e4f94e7v56J2uOipupyt4ez5GlKcGrVls9n9VHUvDLzSCo8ArY2eEa3Q3ntm3wHnsvAHfuWEqfcG9yi7VM/nEf6XmWa2/sPFN1CjiYGjcGVGq18DrQdHQ3uevXg1aLU7t2xrL+lrD39TWGe/K22Oa9Meh0bC3eZw6vkWXNUF169qD511+hrvAD1TEqCseICNBoyN9edV2c8hgaZXoMH17NSIFz167GZpoJ944lf89ek+1KSQmaC8kAHHIWXkFbQlIGzGZMWSEqhrKsqb+PXSIlu5BzGQWoVRj1Nrq8PHRXxA8Ih/D689yEebvgYKeiRKvnYnZhuVTwa8hzk5CQwG233YabmxteXl74+Pjg4+ODt7c3PrW06q9lVCqVDE1dR2j0GtadW2d8vS25sgjfErvOZnDnf3fwxopj6DveDff/CY4ekLhNhJ9yUuDCfvjxDii8AmE94cGV4Gq5O7AtnN4rjJYUn2BaBbqbbOvf0s+ouymqoLspr7dZe/wSpy6LX91vrTxOTpEIQ3kZQlMbN1rdiHLZwWS6ZIiQlEunTqjdxK/IgGefRe3hQUlcHHM9kgj3deV8ZiHjvt3FpezKX7qKohg9N/2rEBMXHio1brp1NVlfprtpIsZNaUjKs5qQlAH3wYMByNu02abz1IXexoBDWBj+Tz+N1+g7aP7Nt8a/ZXlUKpXRe5O7cYNVxy0+myBaftjb4zGk6pBU+fOEzJ6FfVAQmqQkkh56iJSZbxm9RSUXkkGnQ+Xqyl6NENjbkilloGKVYignKq6m7tPgtgE4O6hJyixg4Y5EADqGeuFZ6nXUlIa17Hx8sHN3t3SYWmNvpybcV9QMSkwvKKe5uYaK+E2cOJErV64wf/58NmzYwMaNG9m4cSObNm1iYw3T964XjN3BpXFzzbP74m5jszyA7cnWh2F6Rfri4WRPel4xB89nQYtB8PBfonbN5WPwwy3w051CPBzeDx5YDi7edTb3lCPi5q1tFlnJBd+/tT+nfAyi4rLMG31hYVnH6F49+e8m0XRTrYLU3GLmrhUCZedOnXCMjEQpKiL33+o7hZ9Ny+Pw+Sy6ZBhCUmVaHntfX/yfehKAgq+/ZOHYDoR4OROfls+93+7kfKZpPZX4tDxSc4txtFfTPcL8DzFNaiqaixdBpcK5k+mNzNmQMdUERMXajAxjiwWDN6w63G8aDED+7t3oq6jsXh5FUcoqE9cwU6oiAU8/Rej771eZzWTU3WzZapXGKXed+CHh1q+fqElkJa7R0bRYvQrvceMAyFq6lLO3jyJvy5YyMXF4M86VGibWtl0oT3nPTaV08CrCUgCujvYMahMAwPwdIhzap7zepjQk5dAABWqj/IXxlJCed22GpY4cOcKCBQsYN24cgwcPZtCgQSYPiWUM3cFlWOra5+8EUXukd3BvAPZd2keRlZkFjvZqBrcTNWLWnyj9rIR0hSnrwLcFZJ+HklyIHAAT/wDnuq3fVFLqKvduVzn7pnekL/G+pengsbHG/jyFhw6BRoN9cDA7C5w4fjEHV0c7vrivOwA/7T7H4fNZIiXY2I6h+tDUikMXAeiZnQhUFir7TpiAY1QUusxM3H77id8e60eEn/Dg3PvNLs6klmk2DF6bnhE+ODuYF1wXlYbanFq3rnTzNda6iY2tca2euiJ33TrQ64WxaOWNzal1axxCQ1GKi8nftduqfbQpKejSRaXm+szGqYhLt27YeXujz86m4MCBKscqWi3Z//sfUH2WlDnsPDwIefstwhcuxCE8HO2lS5x/7HEuz5kDQF6wMJbCPcKr7QRujubuzVGr1BRqC0kvFGHRsnTws9V+loaXhqY0OjGufPE+zQWD3qb+jZvybRiuSUFxr169GrXS79WMTAdvulzMu8g9K+/hx+M/1vpYRdoiNiQJd/oz0c8Q5BpEsa6YfZcsp5xWxFDjYv2Jcp8V3yiYvA7a3gZd74MJv4FT3bqiC0t0eFwW/9/lxcQG3JzsCWnfkhwHV9BoKIoT6dnl+0n9d5NwtU/sE85tXUIYEx2GosBry4+i1emN1Wrzd+1Ck2o5w0lRFFYcTMa/IAufrDSws8Ole3eTMSpHR4JefQWAzJ9/Jignld8f60frQHcu5RQx7ttdHL8odEo7zhhSwKsISZnR2xhwatkSlYMD+txcNBYKhzYUZYX7rNOXQGm4xxCasjIl3Fi8r00bE9FvfaOyt8e99MdydVlTWcuXU5KQgJ2XFx7DhlU5tirc+vahxf9W4DtpEqjVxpBPip/wXtYkJAXgYOdAiJsoTmjsDh4eDnZ26PPzLVb7NjCknejtBiLXoHekGc9NeP0bN5F+5TKmrsXGmfPmzeP999/nxx9/JCYmhiNHjpg8JJYxaG4u50vPTVNjXeI64q7E8dH+j1h22nJqqDVsvbCVAm0BoW6hdA3oyoBmonmfLaGpwW0DcLBTEZ+Wz9m0chkj7gFw32IY8w04mu9mXRtizqQSmieMgObRHcyOuaFVQLnQlPifL9grjJvLUR3Yf+4KjnZqpg4Qv05fv609ns72HL+Yw4+7zuEYESGMB72+Su/NgaQrJGUW0DNbuOOdO3QwqytwHzQItwEDQKPh8gcfEujpzNLH+tEpzJOM/BLu+243Mecy2V1aI6RfVWJiC3obAJWDA05t2gCNq7vRpKZSsF+Itz2tFM8aMKRI523ebJX3ySAat7VZZl1QprvZaHGu+oICY3Ve/yefsCpLqirULi4EvfoKkUsW49RaVOI/FCx0JTUJSRmoqLtROToKAweq7bfm5eJgNMjbB3vi5VqW5acpTQN3bNYQYalyxo3Bc6PXgq7plEYwUCPjJi0tjfj4eB5++GF69epFt27diI6ONj5LLGMUFMvO4E2Ok1fK+qK9s+sddiRbn6VREUNIanjUcFQqFTeGiQJqtoiKPZ0d6FvqfjaGphqAo3uPYqfoKXFywSE42OyY/q3K6W6OHEVfXGy8Cf5UIOZ8b89mBHmKL0B/dydeHSFCGh+viyMlu9DYaTvrjz8s3riWHxSZKsO0IjRlrnaOgaBXXwE7O/I2biR/92583RxZ/Ehfekb4kFOkZfx3u8ku1ODuZE+XMPOhBUWrLdMNmfHcQFmhtMbU3eSuXQeKgkvXrjiEhdm0r2vvXqhcXdGmplp1DYYwXU0rE9cG9xv7o3JwQJOUZFF4m/nTT2jT0nAIC8P7vvvq7NwuXbsStXw5LTduYE2A+BzWJFPKQLhH5YwpR6OouGrdDcADfYVxNCba9O9dUlrAz7EBPDeGsNSFK4WUqMr11mqChfxqZNxMnjyZ6Ohodu3axdmzZ0lISDB5llhGZks1XeIyheC1pVdLdIqOFza/YFxnC7kluWy9sBWAkVEi7bVvSF/s1faczz1v8uVWHWWhqYYzblIOG8TEERbreXRr7k2iv/iyzTl4mMLDh1FKSlB8/ViRpsZOreKxgS1N9hnfqzk9InzIL9Hx1srjeI68DZWrKyUJCRTGxFQ6R4lWz+ojIoW8dWllYtdePS3O26llS3zGjwcg/etvAGEg/jSlNze28i/TK0T5Ym9n/quv+NQplMJC1O7uOLZoYXaMMWMqtvGMm5y/hfHsYUNIyoDayQn3/qJ6dXWhKUWjobDUAHLp2vDGjdrNDdd+ovlrxX5UANrMTDK+nwdAwPPPo3a0rpmltajs7bnsobO5E7g5jIX8cspnTAnPUHWeG4Ch7YM49vYwpg4oS/lXNBohfqd+08ANBHo44epoh06vcD63XAHPa8W4OXfuHO+//z59+vQhMjKSiIgIk4fEMgbjJqs4i8ImGqu8HinWFZNQGvr4cuiX9A7uTYG2gCc3PGmzIboxaSMl+hKivKJo4/P/7d13eFRl9sDx752WmUklPYHQe+/FlSIIgoKroK4rKgr2LlZcFayoq6hYfiqCrOsqiAXFgp2OKCV0IYHQ0iG9T7m/PyYzJJAyCZPMJDmf58mzZubeuW/uhsyZ9z3vOY4lDH+9P4MiBwGw/oT7szcXlhfw2nYsu8baLZ5SWGrFfsRxH4K6d632OINOQ2D5m5167Iir6u3B6C6gKFzaL5a2YZWXzDQahecu741Oo/DD3nR+O1bg2uWTs+Kzs66x5kAGOUUWumhL0KYmg0aDedCgGscfdtMs0Oko2rLFlTtjNuh4f8ZgJpQHipP6xFR7vivfpm9fFE3Vfx5P75iqfVmq5K+/KE1IwJaX57EEZEtaGsXlCbZ1XZJycndL+KkPlqIWFaENC8PQoeY6Og0lsHxpquCXs7eEn3z7/7AXFmLs1cvtHWN15ewnVZdO4FVxLksdza+iO7gbwQ1AgJ+u0gcOS2qqY6u6wYAuIqLeY3OXoigVlqaKobxumy82z6xXcDN27Fh2lv8REHUTqA/ErHP80Ze8G9+RmJOITbXRyq8VrQNa8+oFr9IpuBMZRRnc8csd5JfVXPW2IueS1KQOkyr9IXIuTdUl7yY2xETv1kGoKvy6v/bWAufqzyNZtMlz/F6G9qw+uAHo378T6aYQFFUlZ8UKAH7SxwJwx5hOVZ7TPTqIWeWfPOd+vRfT3y8HyivEnlEAdGW8YylgutGR/2Ps3h1tUM27wvQxMa5k5VPvv+963KjX8u51g9jwyAVMG1j9Mk5N+TZOft26gUaD7eTJGpOhcz7/nKTLLufwlEs5OHQYBwYN5tDESRy94UZSHnmEjNdeq1djSGe7BdOgQdUuG9YmYNQowNFCo7qfoezIEU6++SYAUQ8/hKI9t3Ye9eXMESretQtrZubp8R09SvayZQBEPvQgJ0tOsSF5Q53+rbpj98m6N8usinNZ6njeceyqY9bD3e3g1XG2btDHxVUbjHta+4p5N67t4M1k5mbKlCncf//9zJs3j88//5yvv/660peoXqVCfpJ34zOcy09dQx19lIIMQbx94duEm8JJyE5g9prZWOy190LKKsni91THFlvnkpSTM6n4z7Q/6zRrN76H4/flx0ZYmtp8+BRt8x3XMXSqOkBxOq9TOAnleTf2fMcbyu6wjlzUK4ouUdUndd47rgutQ0wk5xTzToYJvy6dUUtKyP3mdGJxbrGFn8uDuaHVbAGvTthNswBHQ8mKbxqKotCmlbnG0vk17ZRy0phMGMqrAVeXs2IvLCTjtdcc1zU7PsyoRUWUHTlC0e+/k/vV15x6512OXj/D7XozTs4lqfrO2gDoIiIw9nXMvBWuW3fW86rdTuoTT6KWleH/t78RdOml9b7WudJHRWHs3RtUtVJl5YxXXwOrFf9RI7EN7Ml131/H7T/fzshlI7nuu+v4v/j/Iz4jHqv93JJdd5105BzVd6eUU+vA1mgVLSW2EjKLHEGas6q0LSsLa3Z2TadXybmbqzG2gTt1DK9iO3hzCW5uu+02Tpw4wdNPP82VV17JZZdd5vq6/PLLPT3GZkd2TPkeZ3DTvVV312OxAbG8Ne4tTDoTv6f+zlObnqp1aeHHIz9iU230DOvpmoZ26hjckRj/GMrsZfXaEr4hMZPishqaZ3rAHwkZtClwBBV+nTvXeGzPmCCORZ5eqsg1+HMsMIo7L6j5PLNBxzOX9QLg3fVJvB/seJONf+c/3PPJDp5atZcnv9pDmdVO16gA/PaWLy8NG+rWz+DXuTMB48aBqnJq8WK3zgFHnyZXc8i+NeeXOPNuSqtpw3Bq6VJsmSfRx8XR7ffNdNu+jU6rv6ft0qXEvvQiEQ/MdlXFzXj1VbfHWHo4iZKdu0BRCKxHPZeKnI0086tYmsr5/HOK/vwTxWQi+ql5de6l5GkB5RWHnXk3xTt3OqozKwoRs2fzzOZnSC5IRq/RY1NtxGfG8/bOt7nu++sYtWwU9/92P6uTVtf5uhabhb9OOTYanEsyMYBeoyc2wDGz6dwxpTGb0cU6lklr6jFVncYs4OfkXJY64uO1buoV3Njt9mq/bLaG/ePbHEitG9/zV5bjD9iZCYM9w3ry8uiX0Spavjr0FW/seKPGAMe5JHXmrA1QeddUHfJuesQE0qaViRKLnfUJmbWfUE95JRYyDyaht9vAz4g+NrbG4zUaBWOf01P1u8M6MrJrBH3bhNR6rbHdo1y7P1ZF9sOi0RKTcYy9a//gg41H+Kq8cN+VHUyOJpWKUmu+TUXO2Zvcr1dhqaWGiJOzf5Khfftam0M6O4RXNXNjPXmSU4uXABA5+34UgwGN2YyhfXv8hw8j+NJLCb/5ZmKefQaA7P9+5NrWXRN7SQnJDzwAgP/556OPjHTr56pOYHneTeGmTdhLT+dzWTIyyHjp3wBE3HsPhjYN14zRXc68m8JNm7AXF5Px75cBCL7sMn7Q/cXqI6vRKlo+mPgBP0z7gXkj5jGh3QSCDEHkW/L5+djPPLTuIde/T3cdyD5Amb2sXp3Aq1Jzjyn38m4q8sbMTaXt4D5c66ZxFulaAEt6OimPPMKJu++u9VhZlvItqqpyMNuxG6eq3RCj2ozisWGPAbBo9yIWbFtQZYCTWpDK9oztKChc1L7qQmIjWzuWptYnr3c7wVRRlEbZNfVnUhZx5fk2xk4d3VrD73DeIOw4PtXvDu/IXbXM2lT0zGW9+eNf41j+4ETs548B4F8c5PYxnbhyUBv+ObQtlyqO8fh161bnkvrmwYPBYiFrqXtFGV35NjUsSTnV1GMq8623UIuKMPbpU2MDx4CRIx3b4VWVlH/9q8blKVVVSZv3FKX796MNDSXm6adqHWNt/Hr0QBcVhVpcTNEfpxtGpj/zLPb8fIx9+hB63XXnfB1P8OvWzVFZuaSE9OfnU7R1K4qfH6Uzp/L8lucBuLP/nfSL6EdsQCzTuk7jlTGvsO4f6/j44o+5pOMlACzevbhOid0V8208MXt1ZndwqJhUXI+ZG2fOTSNsA3dyBjdpeSXYXAnFvjdzo6vPSU8//XSNzz/55JP1GkxTpuh05H71NSgK9rKyGrckynZw35JckEyBpQC9Rk+H4Kp3hFzV7SrKbGW8+OeLLN27lGJrMY8NewyNcjoAWH3EMe09MGqg6//jMw2LGYZeoye5IJkjeUeqvd6ZxveM4oONR/jlrwxsdhWtxvPLBJsPnSKuwL18G6cRfdrxR1gHumUfo2TgMIZW6HnjjshAI5GBRgpnXsuxdb/Qeus6xix4Bk15nkrqU45iihX7Sbkr7OabKNq6lZzlywm/7dZagyNXvk0NycROxh6O5UtLcjK2nBy0ISGAY9ko51NHcnXkQw/W+oYY9eijFG7chOXoMTJfe42oOXOqPC5n2TJyV64EjYbWC15BH1P9ji93OasV5yxfTsFvvxEwciR5P/7o6DSu0xHzzNNeSyI+k7ORZvZHH7mS14Ovnc59B/5NsbWYIdFDmNl75lnnaTVa+kT0YU7QHH499isHsg/we+rvjIgd4dZ1ncvH57ok5VTVzE19k4pVVcVyrLyAXyPO3ISYDbQy68kuslCi6vGH5pNz8+WXX1b6+vTTT3nxxRd55ZVXWLlypYeH2DRoQ0Mdf5BVtday7NI807c48206h3RGr9FXe9y1Pa9l3oh5KCgsP7CcJzY+USlZsaYlKSez3sygKMfySl12TQ1tH0qwSU9WYRnbjtY98bA2eSUWfjuQQdvymRs/N4Ob9mFm/nPxndxy4cNcM/Vv9f50ax46FH1cHPaCAvJ+ON1J3Vn12N1k4or8R43Cr2tX7EVFZH/ySY3Hqna7qwjhmTM3J/JP8Nj6x0jMTnQ9pg0KcuU5lPx1uvhj5qsLwGYj4IIL8B9ae46QNjDQNQuT9eF/Kaqi3k/Rjh2kPe/ocRT5wGz8hw+v9XXd5Wykmb9mDba8PNKfeRaAsFmzMHbvXv2JXlCx07c2OJgVw6zsO7WPYL9gnj//ebSa6gOxYL9gLu/syAddunepW9c7mH2Qn4/+DMCYuDH1HndF59Id/Ey27GzsRUWgKOgbeenQOXtTpJb/vWwuwc2OHTsqfe3Zs4fU1FTGjRvH/fff7+kxNgmKoriKKJUdO1bjsZJQ7FuclYndKdA1res05o+cj1bR8vWhr3lk3SNYbBaScpPYn7UfnaJjfLvxNb5GffJudFoNY12NND0bFP9++BSTXlvPocxC2hc6k4ndC24UReGNW0bx0p0XMaZb/XNAFI2GkGnTAEfFYnB0vXb+wTcPrr54X01jC7v5ZsARONS07FOWlIQ9Px/FaHS1V3BasG0Bqw6v4qU/X6r0uKvezV5H3k3R9u3k//QzaDREPjDb7XEGjBp1ennqsccqjdN68iTJ994HFguBEyYQOvPs2Ylz4T98OIrRiDUllRN334M1MxND+/aE33G7R6/jCebBg9GUt1YouGYSi5I+BuCp856qdqa0out6XodG0bApZZNbxTkXbl+IisqEdhPoEeaZhqHOZanj+RW2g5d/kLCkpDiCFTc5Z210UVFo/Pw8Mj53ObuDF1jLF3+aS52bqgQFBfH000/zxBNPeOolmxxnnxCLm8FNviWfQkthg49L1My1UyrUvU+ql3S8hFfGvIJeo+fHoz9y35r7WJm4EoDhscNpZaw5GdW5JXxr+laKLO7/MauYd+OJgnClVhvPf7effy76neScYtq1MtKxfIuqu8tSAJ0iAs4psHEKvvxy0Gop3raN0kOHKPrTkWTr17VrrQm+1QmaNBF969bYsrLI+aL6fmHOSr2m3r1RdKdX61MLUl0NUH9P/Z2UghTXc642DOUdwp1JuCHTptW60+xMUY884tg9dfQYma+9DjhaQSTPfgBrRgaGjh2Jef55j+9a0hiNrpmgoi1bAIh++qlGf7N0h2IwEPP8c/jPup5HItYAcFXXqxjXdpxb57cJbOP64FFbc9zt6dtZe2ItWkXL3QNqz6N0V0xADDpFR6mt1PXhVteqFdry3+/SpCS3X8uZb+ONhG9nG4YcZ3DTXGZuqpOTk0Nubq4nX7JJcfb2cG7Pq45ZbybQ4PgEIktT3ucMbrq1cr+0+ri243hj7Bv4af1Yd2IdS/Y4dsfUtCTl1CGoA60DWmOxW/gj7Y9aj3ca1TUCg1bDkVNFJGYU1H5CDfan5vH3Nzfy3rrDqKqjNcJXV3RCKS1F0evPWsP/aN9HzNs0z61aP/Wlj4p0dYHO+exzV5JrfZaknBSdjtCZNwKQteQDVGvlmifFe/ZybOZM1+4b8xlLPssOLHN9wlZR+SrxK9dzp5OK95H/008Ux8ejmEyE33VXncepDQoi5hlHLmPWhx9StG0bGa8soOiPP9CYzbR58w20Af51fl13OIvkAYRceaVby2neEnjhhfx7YApplpN0Cu7Eg0MerNP5N/Zy/C58n/R9tX97VVXlte2vAXB5l8tpH9z+XIZciU6jo3Wgo4hkxUrFrh5Tdci7OZ1M3PBtF87k7A6eXVoeQvhgcFOvhOKFCxdW+l5VVVJTU/nvf//LxHMoLNXUuZaljtc8cwOO7eD5ZfmkF6bTKcT9T8nCs3JLc0kpdHwa7xpac0XeM/2t9d/4vwv/j7t+uYsiaxF+Wj/Gth1b63nOLeHLDyxnQ/IGt9fzA/x0nNc5jDUHMvlxX3qNhfKqY7OrLFp/mFd+PIDFphLmb+CFaX0Z3zPKVSDN0KFDpdmLUlspC7YtwGK3MLbtWEa1GVXn67or5IorKPj1V3JXrnQl6Z5LcAMQMnUqJ996G0tyMnnfryZ4ymTKjhwh4/XXyf++vPaJXk+rq68m7OabXOeVWEv4POFzAC5qfxE/HPmBlYkrubXfrWgUjWtZqiwpyRUchd14A/qo+s1iBYwaRfDUqeR+8QUn7r4HW5ajg3nM/Pn4VdPnyhMCLhiD5kUzmuBgIh+qW7Dgaaqq8saON1ifvB6D1oCf1g8/rR9GrRGD1kCRtYg1J9Zg0Bh4cdSLmHSmOr1+r/BeDI4azNb0rfxv//94YPADZx2z7sQ6dmTswE/rx219b/PUj+bSNrAtR/OOcizvGMNjHMG0X8dOFG/dVqft4JbyD9GN0TDzTDEhjvo2edbyPKfmslvq1TOKTmk0GiIiIpgxYwZzqsn2bwlcy1JHaw9uov2jScxJlO3gXubcAt46oDVBhppL+1dlSPQQFk1YxGMbHmNCuwn46937dD2y9UhXcKOqqtvLDeN7RrHmQCY/7UuvtVjemdLzSrjnkx1sSXK8aV7YI4oXpvUhPMCxBFGa6PjD6vwU6bT35F7XjM36E+sbNLgJGDUSXWQk1owMbOUVW2tqlukOjclE6HXXkvn6Qk699x5F27c5ellZraAoBE2ZTMQ9Z9dz+S7pO3JLc2kd0Jqnz3uaTcmbSClM4ffU3zkv9jx0ERHoIiKwZmZiOX4cbWgooTNnndNYox59hMKNG7GW1+YJu2kWQedYrK82+shIOn73LRqjsdb2Fg3tq0NfsWj3olqPmz14dr2bWN7Y+0a2pm9lxcEV3NL3FtcsOoDNbnPN2kzvMZ0o/6h6XaMm7YLasT55vauXHdRvO7hr5qZN4wc3ZoMjqCmyOxOKfS/npl7BTVId1gVbEmdwU5acjGqz1biNUraD+4b6LEmdqW9EX765/JvaD6xgSPQQ15bwpNwkOoa498n8wh5R/OvLPcQfzyEjr4TIIKNb561PyOS+ZfFEHD/IpYUZTLhoCBdNaofO/3TJAuenRmenYqf4zHjXf9c1GKsrRacjeOrlnHrnXQAMnTuhCws759dtdc01nFr0PqUJCZQmJADgP2okkbNnV7krSFVVPt7vSFi9utvVmPVmLu54McsPLOfLhC85L9bRVduvZw+sax15SuF33XnOS0faoCBinn2G47ffgf/w4UTcd5/b5x7OOcyH+z5kUNQgpnSaUqfr1rdHlScdzjnsqllzXc/rGBw1mFJbqePLWkqJrYQyWxnhpnAu63xZva9zfuvz6RTciUO5h/js4Gfc2PtG13PfJX1HYk4igYbAKreWe4Kzma7zgxXUbzu4q4CfF2ZuTHrHe1uBXQ8KzWfmRlRNFxWFotc72tCnpmFoU32DPtkO7huqq0zc0Mx6M0Oih7ApZRPrk9e7HdxEBRnpFxfCzuM5/Lw/g2uG1bzebrOrLPwlgYW/JhBUUsCLG9/FYLPAtk9JfB40QUH4deiAoWNHCrc4emKduVNqR8YO13+fKDjB0byjHs1DOFPItGmu4OZcl6SctMHBhM6cyck338TYry+RDzxQY27JtvRtHMg+gFFr5PIuji3EU7tMZfmB5fxy7BdySnIIMYZg6tWLwrXrMLRrR6srr/TIWANGjqTLurVog4IqLQ9WJ7c0l//b+X8s+2sZNtXGysSVdG3VtdF/p89FibWEB9Y+QLG1mOExw3lw8IOVakh5kkbRMKPXDJ7c9CQf7f+Ia3tci16rx2Kz8Fb8WwDM7D2TYD/3i0bWhXPjwl9Zf7k+KLhmbo4eRbVYUPTVl6QAR7Vqa3nD08ZsveBkKp+5KbDpHFFEU565mTp1qtsv+kUNuxKaM0WrRd+mDWVJSViOH6s5uJGZG59QU2XihnZ+6/Ndwc2MXjPcPm9Czyh2Hs9hZXwyF/aIrHb2JjO/lPuW72Bj4ikAHlITMNgsjppM/v5YTpzAnpdH8c6drgJ2ULmnlKqq7MxwPBdqDCWrJIsNyRsaNLgxxMURMHo0BWvXElgh2fVchd95ByHTpqKLjq515unjvxyzNpM7TXa9yfUM60n30O78lfUX3yZ9y/Qe02n1z39iSUsn9Nrptb4h1YUutPZiiBa7hRUHVvD2zrfJLXVs5AgzhnGq5BRPb36a/1783wYLEDztxT9fJDEnkTBjGPNHzm/wcV/S8RIW7lhIRlEG3x/5nks7XcqnBz8luSCZCFME03tMb7BrdwrphE7RkVeWR1phmmMHVUwMGrMZe1ERZceO1VpnyjlrowkIcOWmNSbnzE2Jq85NaQ1He4fbv0HBwcFuf7VkrqWpWvJupAWD91lsFhJzHIXZ3N0G7knOpY2dGTux2d3vyTahfEv4H0lZDH3+Fy56dR3PfLOP3w5kUFTm2A205fApLlm4no2JpzDptSy4og/D9zvq6kTOvp/OP/1It/gddPjqK1q/9irh99xN0OTJhN9xe6Vt4EfzjpJdmo1BY+C6no5S/HUpPlhfsS//m3affEzAKM/l9yiKgj4mptbAJrUglV+P/QrANd2vqfSccznki4QvUFUVXUQEsc8/59o51Vg2Jm/kiq+vYP4f88ktzaVzSGfeG/8eyycvx1/vz66Tu/j0wKeNOqb6Wp20ms8OfoaCwvyR8wk3hTf4NQ1agyuAWbp3KYWWQt7b9R4At/W7rc6JynW9docQR2Vy58yxoigYypPG3UkqLjvuKBSrbxvnlaamruCG8mVtH6xz4/bMzQcffNCQ42g23N0xVXHmpiFzGET1DucexmK3EKgPJNa/5iaRDaFDcAdMOhPF1mKO5h11e2mqS1QgT07uyZc7ktmTksuB9HwOpOezeEMSeq1Cn9bBxB/Pwa5Cl8gA3p4+kNjDezh29BiagACCLnZsV9f4+WHs1hVjt+p3iTmXpHqH9+aCuAt4ffvr/Jn2J8XW4gZ9A9AGBmIeMKDBXr8myw8sx6baGBo9lC6tulR6bnLHySzYuoCD2QfZl7WPXmG9GnVsRZYiHln/CGuOrwEgxC+Eu/rfxbSu09BpHH/O7xlwD/P/mM/r219nbNuxRJrPvQZRQzmed5x5m+cBcFOfm9xui+AJV3a9kvd2vUdCdgL3/XYfWSVZtAtq51qGbEjdW3UnITuBv7L/4oK2jtlJv04dKdmzx63t4JbjzrYLjb8NHBxFRQ1azengxge3gp/T3F9mZiYbNmxg48aNZGY2XLfipsTdQn7OzuDF1mLyyvIafFzibM4lqa6hXb0SXGoUjSuReX/W2Q0YazLz/A6suvt8tj0+njevGcDVQ+JoHWLCYlPZfswR2Ewd0Jqv7vobXaICyV62HIDgS6e4+ja5w5lM3C+yHx2DOxLjH0OZvczVc6e5KbGW8FmCo0LyNT2uOev5YL9gV9G4LxO+bNSxAby+/XXWHF+DTtFxXc/r+Hbqt/yj+z9cgQ3AP7r9gz7hfSiwFPDCHy80+hjdVWYr48F1D1JoKWRg5EDu6H9Ho14/2C+YaV0cVbF/T3Xkm9014K4aW7B4inMZvGKlZFdSsRs7ppy11Axx3uvYbtRrKFF9d+amXsFNYWEhM2fOJCYmhlGjRjFy5EhiY2OZNWsWRXUoH90cuVvIz6gzEuIXAkB6kbRh8AbnlLA3lqScnNfef6puwY1TqL+ByX1jeWFaXzY8cgG/PTiG+VP7sPTGIbxyVT/MBh3WzEzyf3FU2Q35xz/q9PrxGfEADIgYgKIop7ua16F1RFPyfdL35JbmEusfy5g2Y6o8xvnJ/rvD31HSiJ9Yd2bu5JO/HD2y3hz3Jg8PebjK8gVajZa5I+aiVbT8dPQn1h5f22hjrItXt73q6g314qgXKwVojeXanteiVRxLLD1CezChXcNuu3dy/ruvGNzUpcdU2YnybeBemrkBMBt0lNIMcm4qmj17NmvXrmXVqlXk5OSQk5PDV199xdq1a3nggbOLIrUkp5eljtdaIl+Sir3LE9vAz1XPMEeuhjPQOheKotAh3J9/Dm3LmG6RrtmonC++BKsVU79+GLu5/7PmluZyONfxKbJ/ZH/gdF8s55bw2qiqSqnN9/7wVUVVVf63/38AXN396mobMQ6LGUbrgNbkW/L56ehP9bqWzW5j3Yl1ZJe41wTVYrMwb9M8VFQu7XQpf2v9txqP7xbajet7Xg/Ac1ueq1Obj8bw27Hf+Gj/RwA8+7dn3eoN1RBaB7Tmiq5XoNfoeWjIQ42WgO38m3Oi4AT5ZflAhZmbpCRUu73G871ZwM/JZNBS6lqWaiYzN59//jmLFy9m0qRJBAUFERQUxMUXX8yiRYv4rLzpXUtlaN0aNBrUoiJsJ0/WeKxsB/ceVVU5kF0e3Hhxy6xr5iZrv0f6RZ1JtdvJWbECqP+sTfug9q5+WcNihqHX6F1bwmvzwNoHGL18NFvTttZt4F5Qcfv31C7V7w7VKBr+3vnvAHyZWPelKVVVmbd5Hnf+cif//PafZBbVvqS/eM9iEnMSCTWG8tDgh9y6zm39bqN1QGtSC1NdW5x9wcHsg/xrw78AuLbHtR7ruF1fjw17jA1Xb2BItGfKDrgjxBjiCuicy+OGtnGg16MWF2NNTa32XNVmw3KiPKHYC9vAnYx6bYWE4maSc1NUVERU1NmVGyMjI1v8spRiMLgKYjkrSFbHWf1SgpvGl16UTk5pDjpF59X2F51DOqPTOLaFphZW/wetvgo3bsJy4gSaoCCCJtWtNYoz38Y5awOO+jwDowYCsD655qWp7enb+enoTxRaCrl/zf0cz6v534O3Obd/X9LxklprnFzW6TIUFP5M+7POP9er2151NVpNLkjmtp9vqzHv7nDOYddOnkeGPEKIMcSt65j1Zv41zBFEfLT/o3ovfXpSckEyt/10G/mWfAZEDmD2IPe7pzcUjaLBrHc/D81Turc6Xe8GHAUsDe0cM/81FfOzZmSgWiyg03m1+KLZoD2dc9NcZm5GjBjB3LlzKSk5Ha0VFxfz1FNPMWJE42W7+yp9u7ptB5ecm8bn/LTUPrg9flrvdUDWa/V0DnHUlWmIN5/s5csACP7739GY6ra7yblTakBk5V1Lzryb2raEvx3/NgA6RUdOaQ53/XqXawre16QVpp3e/l1FIvGZYgJiXFv56zJ788GeD/hgr2Pn6d0D7ibcFM7B7IPc8+s9Vebv2FU7T21+CovdwsjWI5nUYZLb1wJHB/qJ7Se6XqcuJQc8Laski9t+uo3M4kw6h3TmjbFvoNc2fPKur6oqqdjPlVRcfd6NM59T3zrWrSKPDcWk1za/nJvXXnuNTZs20aZNG8aNG8eFF15IXFwcmzZt4vXXX/f0GJsc5/Y8Sx22g4vG5QvJxE4Vl6Y8yZKeQcFvawBo9Y+r6nau3cKek3sA6B/Rv9JzzuBma9pWiqv5xPZn2p9sSduCTqNj6aSlRJojOZx7mAfXPojVbq3ynPpQVRWLzUJBWQGnik+RUpDi1jLPmf6777/YVBtDooe4yuPXxplY/NWhr9wKGr5M+JIF2xYAMHvQbG7pewvvXPgOAfoAtqVv45F1j5x1bz47+BnbM7Zj0pl4YvgT9drV98jQRwjUB7L31F5XQnJjK7IUcefPd3Ik7wgx/jG8c+E7DVYBuKmoWKnYya+Lo/RA4aZN1Z7n2gbuhZ5SFTWrZanXXnuNrKws+vTpQ0JCAvPnz6d///707duXF154gYSEBHr1aty6D77I4O7MjeTceI0vBTc9Qh3dpT2RVFxRzuefgc2GadCgSlWH3fHXqb8otZUS7Bd8VjXiDsEdiPWPrXFLuHPWZlqXafSL6McbY9/ApDOxKWUTL/35Ur1+HoDMokye2vwUo5aNYshHQ+j3YT8GfjSQEZ+MYMynY7jo84sYu2Is3x7+1u3XTClIcb3p39jrxlqOPu2CuAsI8QshoyiD1UdW15gz9cuxX1z1XG7sdaOrn1G30G4sHLsQg8bAr8d/5dnfn3W9TnphuisYunfgvcQExLg9torCTeHcN+g+AN7Y8Uaj/72x2Czcv+Z+9pzaQ4hfCO+Mf6dBGlI2Nc6Zm8ScRFdj2uApk0FRKFy3ntLExCrPq1jAz5ua1bLUU089RWxsLFdddRXr16/npptu4pVXXmHBggXcdNNNmOo47d1cOZO83M25SS9Kb5BkUlE9V40bNz+lN6QeYY7gxpPLUqrN5uh8Td1nbeD0klT/iP5n7SBRFMW1a6qqLeF/pP7B1vSt6DV6bupzE+DYFTb//PkAfPLXJ3WeQSiyFPF/O/+PS768hM8OfkZ2aTYlthJUKv+7cW4nfunPl9yuH/Xmjjex2C0MjR7q+rncYdAamNxxMgCPrn+Uq765io/3f+xqheD0Z9qfPLz2Yeyqncs6X8b9g+6v9PyQ6CG8NOolNIqGzxM+540db6CqKs9teY5CSyF9w/tydber3R5XVa7oegUDIgdQZC1yNadsDHbVzr82/otNKZsw6Uy8Ne4tOga7V6yyuWsd0JoAfQAWu8XVIdzQvj2BFzrqKJ1aUnXhXG8X8HMy6bWUOJelbGXgxSXPqtQpuElLS2Px4sVkZWUxadIk2rVrx9y5czly5EgDDa9pMrRrB7hfyK/UVkp2qXtbQsW5K7QUcizP8f+NLzQX7NaqGwoKGcUZnCo+5ZHXLFi/HmtqKtrgYAIvuqjO51eVTFxRdVvCVVV17cy5ousVlbb4jms3jnsH3gvAi3+8yKbk6qfenWx2G18kfMHkLyfzdvzbFFuL6Rvel/fGv8fqaav57arf2PTPTWy7dhu7rt/FH9f8QYfgDmSVZLlmj2pyIOsA3xx2dHSfPWh2nZd97ux/J5M7TsagMfBX1l/M/2M+Yz8dyyPrHmFL6hb2ndrH3b/eTZm9jAviLmDuiLlVXmNcu3E8MfwJABbtXsSDax/kt+O/oVN0zDtvXrXb0t2lUTQ8OfxJdBodvx3/jV+O/nJOr+cOVVX595//5vuk79EpOl4d8yp9I/o2+HWbCo2icX24qph3EzrT0Y08d9UqLOkZZ53nyrnxYgE/OGMrOPhcleI6BTd+fn5Mnz6dn3/+mUOHDnHjjTfy4Ycf0rlzZy688EKWLVtGaanvJRY1NkMbxy+dLScHW171nx4NWgNhxjDAMQUtGkdCdgIqKpHmSEKNtTcobGhmvZl2QY6A2FNLUznOisSXX47Gr24J06qquraBn5lv41RxS/iRvCOux39P/Z3tGdsxaAyuWZuKZvWexaWdLsWm2nhg7QMczql6V4iqqo7+SauuYO6muWQWZ9I6oDUvj36Zjy7+iBGxI2gd0JpwUziBhkAMWoOjd5RWz5yhcwDHDFHFN42qvLrtVVRUJrafSK/wui+pBxgCmD9yPr9e9SuPDn2Urq26UmYv47uk77jpx5u4+purKbQUMjhqMP8e/e8aC9Vd0fUK7up/FwA/Hv0RgJl9Zp7VAqK+Orfq7Fp2e/6P5ykoK/DI656p2FrMtvRtvPDHC65aNs+c/0yttXlaIueHq4r/7s0DBmAaOBAsFrI/+uisc5xNM53V8L3FZKiQcwM+l1Rc74pF7du356mnniIpKYnVq1cTFRXFrFmziI1t/B49vkbj74823NH8rbZKxZJU3Ph8oXjfmZx5N55IKrakplKwbh0AIVfVfUkquSCZzOJMdIqu2jd8s97MoKhBwOldUxVnba7qdlWVPY0URWHuiLkMjBxIgaWAmT/MZPp305n69VQmfT6JMcvHMOx/w+j/3/7c9vNtJOYkEmQI4qHBD/H1ZV9zUfuLap1dGRE7gvHtxmNX7Ty/5flql3w3p2xmY8pGdBod9wy4x+37U5Vgv2Cm95jOZ1M+Y9kly7iq61UE6ANQUeke2p2FYxe6tSvvlr638M/u/wQc9YVu6XvLOY2rqtdvG9iWjKIMFu5YeM6vp6oqR/OOsurQKp79/VmuWnUVIz4ewQ2rb3BtrX9o8EOu5TtRmatScXblIDxslmP2JnvZMmwFha7HbXl52HIdy57OD9HeYtJrsaPBqpQH7D7WgsEj+8g0Gg2KoqCqKvZaKiu2FIa2bSk+eRLLsaOYelf/iTDaP5q9p/ZKd/BG9Fe27yQTO3UP6873R773SN5NzorPwG7HPHQofh071Pl855JUj7AeNTbHPL/1+fye+jsbkjdwXc/r2JSyiZ2ZO/HT+jGz98xqzzNoDbx6watc8+01JBckc6qk6qU4vUbPNd2v4ea+N9d5Z81Dgx9iQ/IGtmds57uk77ik4yWVnrerdl7d9irg6MUUF+SZ5ExFUegV3ote4b14cMiDbE/fTv/I/vjr/d0+/9GhjzImbgy9wnp5vEyBUWfkiRFPcPOPN7Psr2VM7ji53ktF6YXp3PnLnWe9MQNEmCLoF9GPce3GSWBTg4rbwSs2UA644AIM7dtTduQIOZ+tIOyGG4DTH5a1YWFo/N37nWooZoNjqdSi+KFTrT63LFXv4Obo0aMsXbqUpUuXcvz4cUaNGsWiRYuYNm2aJ8fXZBni4ijevl1mbnyQa+bGB/JtnKraFloXttxcCjdvpmDDBvK/Xw1ASD0SieF0ZeLq8m2cRrYeyctbX2Zr2laKLEWuWZt/dPsHEeaIGs8NNYayYsoKNqdsRqfRYdKZzvoK8guq95t7TEAMN/e5mYU7FvLK1lcYEzemUoDxfdL37M/aj7/e3+OzI04mnaleSzEaReOqodMQhscMZ0rHKaw6vIqnNj/FssnL6twsMq0wjZk/zOR4/nEMGgM9w3rSN6IvfSP60i+iH1HmKK80o21qOod0RqtoySnNIb0o3fV+oGg0hN54I2lz55L1nw8JnT4dRa/HcsLZMNO7O6XAsRUcoEwxYKKwac/clJSU8Pnnn7NkyRLWrl1LTEwMM2bMYObMmXTsKBnwFbkK+dWSVCzbwRuXzW4jITsB8M1lqWP5xygoKyDAEIBqtWIvLgZFAZTy/1FA41hNLtm/n8INGyncsIHi3buhwqypX48eBI4fX6+xVNwpVRPnlvCUwhQWbFvA7pO7MWqNrm3OtQk0BDKhfcM1KpzRawYrE1dyLP8Y7+x8hwcGO/reldnKeGPHGwDM7D3TJ/KuGtuDQx5kffJ6DmYf5KN9H7n9/xk4li1n/TCL5IJkWge0ZslFS4gNkHSE+vDT+tEhuAOJOYkcyDpQKQE/+LK/k7lwIdbUVPJW/0DwlMmnk4m9vA0cHMtSAGWu/lK+lXNTp+AmOjqakpISJk+ezKpVq7jooovQaBqn0VhT4yrkV1twIzM3jepo/lFKbCWYdCbiAr3/B8KplbEV0f7RpBWmcSD7AP0MHUmaOq3GHjNnMnTqRMD5f8P//PMxDx2KxmCo/aQzFJQVuIK/MysTn0lRFEa2GcnyA8tZfsCRwPzP7v8k3BRe5+s2BIPWwCNDH+HOX+7ko30fcXnny+kY0pHlB5aTXJBMhCmCa3tc6+1hekWoMZQHBj/AExuf4O34txnfbjxtAmvP4Tief5xZP8witTCVtoFtWXzRYq81vWwuuod2JzEnkb+y/mJ03GjX4xo/P0KvnU7m6ws5tWQJQZMv8ZkCfnB6WcpXm2fWKTJ58sknOXHiBJ999hmTJk2SwKYGBjdnbirWuhENb9+pfYCjvs25bq/1NFel4lP7ObXo/VoDG01QEIEXXUT0M0/T+bdf6fTtN0TNmUPAyJF13iHltCtzFyoqrQNa17q0BFSqC2PSmbih9w31um5DGdVmFGPixmBVrcz/Yz75ZfmuPk139L/DKz2FfMXfO/2dodFDKbGV8OyWZ2uttXU07yg3rr6R1MJU2ge1Z8lFSySw8YDqkooBQq6+GsVkonT/foo2b/aZAn4AxvLgxlerFNdp5mb2bO83OWsqnIX8rBkZ2EtK0BiNVR7nXJZKL0rHrtrPKpgmPOurxK8AGBw12MsjOVuP0B6sOb6GI4e2k/0/R5+jNv/3Nv7nnQeqWulLVUFjMqJoPRug1Vbf5kxDo4ei1+ix2C1c0/0an1zieXjIw2xK3sTvqb9z+8+3k1OaQ8fgjlzW+TJvD82rFEXhieFPMO3raWxM3siKgyuY3HFylQFfUm4SN/1wExnFGXQM7sj7E953K/gVtatqO7iTrlUrQqZNI/ujjzi1eIlrJcDb28ABzOXLUiWqs79UE565Ee7ThoSgCQwETtclqEqEOQK9Ro/Vbq1UL0R43qGcQ/ye+jsaRcNV3eqXbNuQnJ/g2nz+O2ppKabBgwgYMwaNnx8aoxGNyYTGbHaUGgjw93hgAxWaZUbUvCTlZNabuanPTQyNHlqnvI3GFBcYx6w+swDYmbkTgPsG3ldjzZmWon1we27uezMAz/z+DMM/Hs6kzydx9y938/r21/nm8DdsTN7IzB9mklGcQeeQziy+aLEENh7kzP07nn+8ytpDoTfMAI2Gwo0bsaSkAKD38jZwcNS5gYrBTRPOuRHuUxQFQ9u2lOzdS9mxY66GaGfSaXQMjhrM5tTNrD+xXkqTN6CP9zvqboyNG+uTCZA9QnsQla0y6A9HterI++5r1B0nVruVXZm7APdnbsCxvOPrZvaeydeHvia5IJmBkQMZEzfG20PyGTN7zyS1MJW1x9dyquQUJwpOcKLgBGtOrKl0XNdWXVk0YZFPzs41Za2MrYgyR5FelM7B7IMMjBpY6XlDmzYEXjTBtQtSMZnQRXg/uHQmFBc5g5umvFtK1I2+bVx5cFPzdvDRcaPZnLqZNcfXMKPXjMYZXAuTW5rLqsOrALimxzVeHk3Vov2jmb5Jh85uQx3WH/Pgxl06S8xJpMhaRIA+gM4hdWu06euMOiMvjHyB93e/X682C82ZQWvgqfOeAiCrJItDOYdIyE7gUM4hEnMSScxJpGNwR94Y+wYhxhDvDraZ6h7anfSidP7K+uus4AYgbOYsV3BjaNPGJ35/nTM3RXa9Yw2oudS5ASgrKyMpKYlOnTqh00mcdCZDW0dJ/bJjR2s8bnSb0bzwxwvsyNhBbmlunQuWidqtTFxJsbWYrq26ejzfRlVVbCdPog0NPaelotKEBIbtdkztHv7HcHp6aoBuci5J9Y3o63PJ1p7QP7I/b45709vD8GmhxlBCo0MZEj3E20NpUbqFdmPtibWuhr5nMvXpjXnoUIr++MOVz+ltzpmbQh+dualXzk1RURGzZs3CbDbTq1cvjpUnOd1zzz288MILHh1gU2Yoz2i31DJz0yawDZ1DOmNTbWxM3tgYQ/MK1WKh9HAS+b/+yqn33yflX//iyD+v4dAlkymOj2+w69rsNlcX6uk9pp/zpx57SQlFW7dy6v33OX7nXSScP5KEkaM4NOliCn/fUu/XzVy4EEWFzd0V4kPzz2mM9eGqb1OHJSkhxLlzp4hn5EMP4delCyHTpp71XKGlkF2Zu7CrjdchwDlzU+rKuWkGMzdz5sxh586drFmzhokTJ7oev/DCC5k7dy6PPvqoxwbYlDkz2mvbDg6O2ZvEnETWnFjDxR0vbuihNaqCjRtJnz+fsiNHwWqt8piMV1+j3X+WNsj1155YS3JBMsF+wVzcofp7q5aVkfXf/2I9leUoiKfaUe1q+X+rqJYySv46QMn+/VX+HJZjxzh2ww2EXHkFkQ89hDYoyO0xFu/aRcHPv6BqFD4dqSHMAz2m6qLEWsK2tG1A7cX7hBCe5UwqTshOwGq3VpnsburTm46rvq7y/PXJ63lo7UMMjBzIfyb9p0HH6mTUnVnnphkENytXrmT58uUMHz680qfgnj17cujQIY8NrqnTlwc3lpQUVIsFRV99ifMxcWNYvGcxG5I3YLFb6lwO3ZdlLf0PZYmO3wvFZMLQoT1+HTpi6NABfUw0qU/OpWjLFkr278fYo8c5X8+Smkra088QfustmPr3dyUSX9HlCoy6qrfkA+R+8y0Z/37ZrWtoI8Ix9x+AacAATP37Y2jXlsw33yTnk2XkrPiMgjVriXryCYLcrBKc+dprAOgmjSM5fA2nsg5is9sabXno5a0vk1GcQZgxTGZuhGhkbQLbYNaZKbIWcST3CJ1b1S3nbe3xtQD17hNWHxqNglGvoURtBnVunDIzM4mMPLvjb2FhoU8kOvkKXUQEip8famkpltTUGmsT9AnvQyu/VmSXZhOfEd+s1rxL9jtmIeLeexf/kSPP+h0p3LiRvO++J+vD/xI7//lzvl728uUU/PYbqs1K6YsPsSVtC1pFyz+6/aPG84p3bAfANHgQ5v79QdE4Wh1oFJTy/za0b4+pf3/0rWPP+jli5s4l+JJLSH38CcqOHCH57nvImzCB6Ccer3F3Q+HvWyjctBn0etrd/zCmtVsothZzJO8InUI6nfP9qM1PR39yVRh+/vzna2yWKYTwPI2ioVtoN3Zk7OCv7L/qFNxY7VbWnVgHOFYAGpPZoKOkpBlUKHYaMmQI3377ret75x/5RYsWMWLECLdfZ926dUyZMoXYWMcbxcqVK90+d+PGjeh0Ovr37+/2OY1N0WhceTdlR2temtJqtIxsMxI4HYU3B5aMDGwnT4JGg3nw4CqD39Drrwcg75tvsJ48ec7XLCufPSzZtZuP9/8PgLFtxxITEFPjecW7djvGM2MGkQ8+SOQDs4m8/z4i772XiHvuJuKuOwmefAmGNq2rDeLNgwfT4auVhN12K+h05P/4I4cumczJ//s/Sv7666wqsKqqumZtWl15JX5t4lxT1PsbYWkqpSCFuZvmAo4twee1briGjUKI6jn/3Tsb+7orPiOevLI8ggxBjT7ratJrfbZCcb2Cm/nz5/Ovf/2L22+/HavVyuuvv8748eNZunQpzz33nNuvU1hYSL9+/XjzzbrtYMjNzeX6669n3LhxdR16o9OX95gqO+5e3g04ckSai9LyWRtDhw5ozFWXujf174+pXz9Ui4XsT5ad+zUPHQbAlpPDH1sd27+n95he4zn2wkJKExw9lUx9+53T9TV+fkTedx8dPluBsXdv7Hl5ZL6+kKTLLidx9BhSn3iC/J9/xlZQSMGaNRTHx6MYjY6AiArJhafq1yHcXVa7lUfWPUJ+WT59w/ty14C7GvR6QojquZNUXBXn+8WoNqMavTClUa+hFN9MKK5XcHPeeeexceNGioqK6NSpEz/++CNRUVFs3ryZQYMGuf06kyZN4tlnn2Xq1LOzv2ty6623cs0119RplshbnEtRllpmbgDOiz0PnUbHkbwjHMk90sAjaxzOJanacmlCZzhmb7KXLcNeWv9Kl2pZWaUE7nbHS+ge2p2BkWfXjqioeO9esNvRRUejjzp7ybU+jN27037ZJ8Q8/zwBF1yAYjJhzcggZ8VnnLjrbg6OGEHKI47k+9DrrkVfvtTbI8xxrxp65ubt+LeJz4wnQB/Ai6NebFZ5XkI0Na4eU1kHau3zVdGa42sAKjXdbCxmg+70zI2PBTf1DvP69OnDf/7TOFnZFX3wwQccOnSIjz76iGeffbbW40tLSymt8GaZl5fXkMM7i7PBWVkNLRicAgwBDI4azO+pv7P2xFraB7dv4NE1vJJ97gU3gePHo4uOxpqWRt4331a53dEdZceOVdrJ1CVZZXz3a2rNBSvZ7ViSMvXpU6/rVkfR6QiZejkhUy/HXlpK0Z9bKVi7loK1a7EcO4bdYkETEEDYrFmuc3qEng5uVFVtkDy2LalbeH/3+wDMPW+uWx2hhRANp1NIJ7SKluzSbDKKMlxNlWtyJPcIR/KOoFN0/C32b40wyspMem2FhOJmkHPz3Xff8cMPP5z1+A8//MD3339/zoOqTkJCAo8++ij/+9//3C4aOH/+fIKDg11fcY1cAMndQn5OzrLwzWVpyjVz07Pm4EbR6wm91rF0lPXhh3X65FKRc0nKqUeq1q2t9cU7HW0HTP0abreBxs+PgPP/RvS/HqPzjz/QafX3RM+bS9sPlqANCXEd1zmkMzqNjvyyfFIKUzw+jlPFp3h0/aOoqEzrMo2J7SfWfpIQokEZdUY6BHcAqu4QXhXn+8Sg6EEEGgIbbGzVMRq0PjtzU6/g5tFHH8Vms531uKqqDVbjxmazcc011/DUU0/RtWtXt8+bM2cOubm5rq/jbsygeJKrkN/xE6j22gssjWozCoDt6dvJK2vcWSZPs+Xnu5qGurPFO+TKK1FMJkoPHKBoyx/1umbZYUcy8bHOjhozcRl29JbaA6Xi8pkbY5/G20ppaN+eVldffdZskV6rd7U/qGveTUpBCg+ve5gbVt/Ai3+8yKpDqziUcwib3fHv1a7aeXzj45wsPkmn4E48MvQRz/wwQohz5uwQvv+Ue0vSziWpC+IuaKAR1cys1/pszk29lqUSEhLo2fPs4vDdu3cnMTHxnAdVlfz8fLZu3cqOHTu46y5H4qPdbkdVVXQ6HT/++CNjx4496zw/Pz/8/PwaZEzu0MfEgFaLWlqKNTMTfVTNU41xgXF0Cu7EodxDbEzeyKQOkxpppJ7nnLXRxcZUmpmojjY4mJDLLyP740/I+s9/8B8+rM7XdM7crG9TwMVp0KrATsm+fZgHVp9zY8nIwJqaChoNpt696nzNhtA9tDt/Zf3Fvqx9jGtXe+K8XbXz6YFPeXXbqxRZiwDYlr7N9bxJZ6J7aHeCDcFsSN6An9aPl0a/JNu+hfAh/SP68+3hb/nm8Dfc1OemGutc5ZbmuqqKN/YWcCeTQUtuc9otFRwczOHDh896PDExEX9//3MeVFWCgoLYvXs38fHxrq/bbruNbt26ER8fz7BhdX8jbAyKXo++dWsAyo66tzTlTAxzRuVNVakrmdj9Lkmtrr0OgII1ayg7cqTu1yzfBp4cBjmdHYFkcfzOGs8p2eVYkvLr1AlNA/3+1pUz78adnRPH8o4x64dZPLflOYqsRQyMHMjT5z3NtT2uZWDkQEw6E8XWYnZk7HB1en54yMN0beX+DKgQouFN6TSFQEMgR/KO8Nvx32o8dkPyBmyqjc4hnb2WM2cyaCu0X/CtnJt6zdxceuml3HfffXz55Zd06uQoMpaYmMgDDzzApZde6vbrFBQUVJrpSUpKIj4+ntDQUNq2bcucOXNITk7mww8/RKPR0Lt370rnR0ZGYjQaz3rc1xji4rAcO+ZYohk6tNbjR7cZzZI9S9iQvKHaUtxNgbvJxBX5deyA/+hRFK5dR9Z/PyL6icfdPle12ylLSgIgOUxBZ+oB8ekU76w5uHHWtzE2YL5NXTl3TK0/sZ7p307nb63/xnmx59EnvI/r05zNbuN/+//HGzveoMRWgkln4r6B93F196vRKKc/t9jsNo7mHWXvqb3sz9pPlDmKK7te6ZWfSwhRPX+9P1d3u5pFuxexePdixrUdV+2GAtcuKS/N2kAzrHPz73//G39/f7p3706HDh3o0KEDPXr0ICwsjJdfdq98PcDWrVsZMGAAAwYMAGD27NkMGDCAJ598EoDU1FRXU86mzNCuvNaNG9vBAfpF9CPYL5i8sjziM+I9Pp6C9RvIePlljxTMq0nJvn1A7cnEZwqbMQOAnC+/xFaH3W2WlBTUkhKsWkhvBSGDHIFk7cFNeTJxX98JbnqH9WZw1GBUVHad3MX/7fw/rvv+OkYtH8UDax5g+V/LuX719fx7678psZUwLHoYX1z6Bdf0uKZSYAOOApEdQzoypdMUHh7yMDN6zZBK4kL4qOk9puOn9WPPqT38mfZnlcdY7BZXk2XnJhRvMFXKuWkGMzfBwcFs2rSJn376iZ07d2Iymejbty+jRo2q0+uMGTOmxl0xS5curfH8efPmMW/evDpd0xvqUsgPyqsVtx7JN4e/Ye2JtQyOHuyxsVizski+917sRUXkfPElMc8+S+BYzyej2UtKKC1fuqxrvyjziBH4delMaUIiOSs+I2zWTLfOc1YmTglVsGsUOgy7kJOal7GmpWFJT68y30m1209vA/eh4Eav1fPBxA9IK0xjc8pmNiRvYHPqZvLK8vjx6I/8ePRHwPFJ78HBDzKtyzQJWIRoBsJMYVzW+TKWH1jOkj1LGBpz9mz/9vTt5FvyCTWG0ifcs+Ur6sJkqLAV3Fr/+mQNoV4zN+BouTBhwgQeeugh7rrrrjoHNi2Jc+bGnUJ+Tg2Vd3Pq3XexFxWBomDLyuLEHXeQOnee4zEPKk1IAJsNbUgIuujoOp2rKAqtylsyZP3vI9RqOomfdc3yZOLkMAjxCyG8VRv8ujl2H1Q3e1N2+DD2wkIUkwm/znVrVtcYov2jubzL5bwy5hXW/WMd/530X27rdxsDIwdycYeLWfn3lVzR9QoJbIRoRmb0moFG0bAxZWOVeXfO94WRrUc2WnPdqlRalrKWQD1LeDSEOs3cLFy40K3j7rnnnnoNprkylNfWKTt2zO2ibH+L/Rs6xVGt+GjeUdoFtTvncVhSUsj++BMA2rz1FkVbt5K1ZAk5y5dTtGULsf/+N6Y+nslfcuXb9OxRrzfe4ClTyFzwKtaUVPJ//oWgiRfVek7pIUf+1okw6NKqC4qiYOrXl9L9+yneuZOgCRPOOseVb9OrJ4qbtZO8RafR0T+yP/0j+3Nn/zu9PRwhRAOJC4zjonYX8f2R71myZwkvjXrJ9Zyqqq76Nt5ckgIwV6xzA44AR+8bOzDr9Nf81VdfrfUYRVEkuDmDvm1bFJMJe0EBed9+R/DkS2o9J9AQyKCoQWxJ28La42u5vtf15zyOzDffQrVYMA8bRsAFYwgcewEBo0aS8sijlB05wpF//pOIu+4k7OabUbTn9mmgZL8z38b9nVIVaYxGQqZN5dT7i8n/1b3gpqx85uZEuOKqE2Pq24+cZcurnbkp3rXTdZwQQviKG3vfyPdHvueHIz9wz4B7XDuiDuce5nj+cfQaPefFerfRrclQIecGHFWKfSS4qdOyVFJSUq1fVW0Rb+k0fn6E33IzABkvvYStoNCt85xLU56oVlx66BC55V3XI++/zzWb4j98OB2/WkngpIlgtZL52uscvX4Gtpycc7qes8aNXx3zbSoyl/cOK966rZYjHZ9mnDk+yeEKXVp1AcDU3xG0lOzZi2qxnHWeLyYTCyFEj7AenBd7HnbVzn/2nm515FySGhozFLO+6mbEjcWo12JFh80ZSvhQ3k29c26cTpw4gd2NyrstXejMmejbtsWakcHJt99265wxbcYAjuSxgrKCc7p+5muvg91OwLhxmPr3r/ScNiSE1gsWEPPCfDT+/hRv20byAw+iVlGF2h2qzUbpgYNA3WrcnMnUrz9oNFhSUrCkptZ4rO3kSex5edgVSA2FLiGO4MbQvj2aoCDUkhJKDh6sdI69pMQ1TlNf7yXlCSFEVWb2dmymWJm4kqySLOD0h13n+4M3mQ2OGf5Sygvl+tCOqXMObnr27MmRehRba2k0fn5E/+sxwNE7qdSNSs5xQXGEGcOwqlaO5rtXALAqxbt3k//TT6AoRN53b5XHKIpCyGWX0e6j/6IYjRRu3OgIiOqhLCkJtaQExWzG0L7+uULaAH/XTquibdtrPNaZTJwRDBbd6WUpRaNxzco4i/U5lezb50h6jghHFxNT73EKIURDGBo9lF5hvSixlfDx/o/JKsliZ6ZjKd2b9W2cTHpncON7tW7OObipb4PDlihg9GgCxo4Fq5W0Z59z697F+DvedNMK0+p93czyXKngSy/Fr0uXGo819uhBTHm39VOLFpG3enWdr+dqltmtG4rm3H7FTIMcbROKt9e8NOVKJg5XiPWPJcAQcPo1+jmWps6sVOxakurTV3YbCSF8jqIozOozC4BP/vqEH478gF21061VN2ICvP+BzFge3JT4YK2bcw5uRN1EPTYHxc+Pot9/J9+NDurR/o5t1PUNbgo3b6Zw02bQ6wm/+263zgmefAmhN94IQMpj/zprOac29alMXB3zIEeNn6Ja8m6cycTJ4bjybZycnb7PTCoukXwbIYSPGxs3lnZB7cgry+PVbY4Pqs58TG9zLkudDm6aUc7NY489RmhoqCfG0iIY2rQhrDy5OP3Fl7AX1pxc7Axu0gvT63wtVVXJWOD4x9DqH//A0Ka12+dGPjAb84jhqEVFnLjrbmy5uW6f65q5qWNl4qqYy2duShMSahyDM5n4RNjpJSknY3nX7bIjR7BmZ7seL97pDG4k30YI4Zu0Gi039LoBgOLymRFvdQE/k8kZ3DgL+Vma0czNnDlzCHGj47M4Leymm9DHxWFNT+fk//1fjceey8xN/k8/UbJ7N4rZTPhtt9bpXEWno/WCBehjY7EcO0byQw+5lWCsqqpHdko56cLDMbRrB6pK0Y4d1R5X5mqYqZw1c6Nr1QpD+/YArmrE1lOnsCQng6K4gh8hhPBFUzpNIdwUDkC4KZyeYfXfqOFJZr2jmkyxq3lmM8i5OXHiBG+//TaPPvoos2fPrvQlaqbx8yPqsTkAnFr6H9esQ1Wi/B0tA9KK6hbcqFYrma87ii6GzrgeXXh4ncepa9WKNm++4UgwXreezDfeqPUca0oK9txc0Olqze9xl2nwIACKq0kqtuXlYc3MBBzVic8MbqBC3k35bI0z38bQsSPawECPjFMIIRqCn9bPtXNqYvuJZ/WP8xajwTEOX5y5qVdJ1l9++YVLL72UDh06cODAAXr37s2RI0dQVZWBAwd6eozNUuAFFxAwZgwFa9aQ/uyzxC1eXGVSa7S5fjM3uV99TdmhQ2iDgwmb6V5vpqoYe/Yk5plnSHnoIU698y7Gnj2rrPTr5Jq16dwZjcFQ7XF1YR44iNzPv6BoW9V5N6XlszanAsFi0tMhqMNZx5j69yP3q69ceTeuflIyayOEaAKu7XEt/SL60S20m7eH4mLQatAoVGie2cRnbubMmcMDDzzAnj17MBqNfP755xw/fpzRo0dz5ZVXenqMzVbUvx5DMRgo3LSZ/B9+rPIY57JURlEGNrv7dWdOfbAEgLBbbjnnmYngKZMJLe/UnfroHEoPJ1V7rCeTiZ3M5TM3Jbt3Yy89O2GtzFm8L0yhfXB79Fr9WccYy5OGi3ftQrXbT+fb9JNkYiGE71MUhb4RffHT+nl7KC6KomA26Cr3l/IR9Qpu9u/fz4zyNzudTkdxcTEBAQE8/fTTvPjiix4dYHNmiIsj7KabAEh/4YUqk4sjTBFoFS021UZmcaZbr2vLz6cs0TGbETz1co+MNfKhBzEPHYq9qIjUxx9HraZwoyuZ2IPBjb5tW7Th4agWi2vGpaKKDTPPTCZ2MnbtimI0Ys/LoywpieLy1zH2keBGCCHqy1ixeWZTr3Pj7+9Pafkn6NjYWA6VLwsAnDx50jMjayHCbrkZfevWWNPSOPnOu2c9r9VoiTRHAu4vTZXsdfR10sfGomvVyiPjVHQ6Yl+Yj8Zspnj7dlcDzrOu7cGdUq5rKwrmQY7Zm6qK+TmTiU+En51M7HoNvR5j714A5H69CnteHorBgLFbV4+NUwghWhqTQXM656ap17kZPnw4GzduBOCSSy7hgQce4LnnnmPmzJkMHz7cowNs7jRGY4Xk4qVVLvm4dky5mVRcsncPAMbenunw7aSPjSXiwQcAyFiwwLHbqAJrdjbWNMcY/bp7LriB01vCi7ZtPeu50orBTUj1SczOpOKc5csBRz6Roj97CUsIIYR7zHrd6Zybpj5zs2DBAoYNGwbAvHnzGD9+PMuXL6ddu3YsXrzYowNsCQLGjsV/9CiwWEh/7uzKxc6kYndr3ZTs3QuAsVcvzw4UaHX11ZgGD0ItKiL1ybmVxlqyzzFjZGjXDm2Av0evayqfuSneEV9pS7q9pMQVZCWHQedWVS9LwengxtkUVPJthBDi3BgN2uaTc9OxY0f6lidoms1m3n77bXbt2sUXX3xBu3b17yXUUimKQvRjj6Ho9RRu3OjoA1VBXWvdFO8pD256ez64UTQaYp55xpEIvXEjuSu/cj1X6twp5cElKSdjt25ozGbs+fmUJiS4Hi9LSgJVJd8IliATrQOqL1Ro6te/8mtKZWIhhDgnZr32dG+pph7cAOTk5PD+++8zZ84csrIc3Uq3b99O8hlLFcI9hnbtCL3J0UMkff4L2IuKXM+5at24EdzYcnOxHDsGOJZdGoJfhw6E330X4EiEdtaYcc7cnEsn8OooOh2mAQMAKm0JL63UdqFrjfUf9FGR6KKjXd9L2wUhhDg3JoO2Qp2bJh7c7Nq1i65du/Liiy/y8ssvk1M+zf/ll18yZ84cT46vRQm/5Rb0sbFYU1M5+e57rsfrMnPjDDD0bdp4LJm4KmE33oixVy/submkPfNs+bU9v1OqIrOrmF/F4MbRMDM5TKlxScrJuTSlbdUKfZs2DTBKIYRoOUx6bfNpnDl79mxuuOEGEhISMBqNrscnTZrEunXrPDa4lkZjMhE551EAspYsoezIEaBuCcUNmW9TkaLTEfPcs6DTkf/jj+R8uZKyo0cd126AZSkA08DyHVNbt7lyfZwNM2tLJnYyD3TM/pj695dO4EIIcY5MhorLUk28ceaff/7Jrbee3auodevWpKXVr3u1cAi88EL8zz8f1WIh7bnnUVXVlVB8svgkZbayGs9vyHybMxm7dyfsZkednrQnnwRVRRcZiS4srEGuZ+rbB/R6rBkZriTi0sPlO6WqabtwppCrrybi/vuJevSRBhmjEEK0JCa9lhJnbykfar9Qr+DGaDSSl5d31uMHDhwgIiLinAfVkimKQtS/HgO9nsL16yn45RdCjaEYNI7IOL2o5h1TzpkbUwPP3DiF3347hk6dUC0WoOGWpMAxs2UqzyMq3rYN1Wql7Ihjtig5/Oxu4FW+hp8f4bfe4mjGKYQQ4pyYmtNuqb///e88/fTTWMrf0BRF4dixYzz66KNMmzbNowNsifw6dCDsxhsBSH9+PmpJiVt5N7acHCzHjwMNvyzlpDEYiHn2GShf4mmInVIVOZtoFm3dRtmx42C1UqIHNSKUMFPDzBgJIYSomqlSheImPnPz8ssvk5mZSWRkJMXFxYwePZrOnTsTGBjIc8895+kxtkjht92KLiYGS0oKJ997z63gxpVMHBeHNji4UcYJYB4wgIh77kYTFFRjU02PXMtZqXj79grJxNA5VCoNCyFEY3Pk3Phe48x6dQUPCgpiw4YN/Prrr2zfvh273c7AgQO58MILPT2+FktjNhP16KMk33svWe8vpt2L4/mDmpelGjPf5kzht99O2G23NXiSrnM7eNmhQxRvdeyaSg6rvu2CEEKIhmOuuBW8qQc3TmPHjmXs2LGeGos4Q+CE8fj17EHpvv302lvIishaZm4aOd/mTI2x+0jXqhWGzp0oSzxE7leOAoLJ4Qr9JbgRQohG1ywaZ27ZsoXvv/++0mMffvghHTp0IDIykltuucXVUFOcO0VRCLpoIgBx2x27g9wJbjzdU8rXmAcNBk63UThRQzdwIYQQDadSzk1TrXMzb948du3a5fp+9+7dzJo1iwsvvJBHH32UVatWMX/+fI8PsiULHD8egIBdh/EvVqsNbqzZ2VhOnAAarjKxr3AW83M64eZOKSGEEJ5lrphz01RnbuLj4xk3bpzr+2XLljFs2DAWLVrE7NmzWbhwIZ9++qnHB9mS+XXsgF+Xzig2OwMT1WoL+bmSidu2RRsU1JhDbHTmgQNd/23VOKoxm/VmL45ICCFaJpNeS2nFnJszGj97S52Cm+zsbKKiolzfr127lokTJ7q+HzJkCMfLtyILz3HO3gw7qJJbmkuRpeisY0rKk4lNXkgmbmz61q3RxcQAkBoKHcMl30YIIbyhUldwVKil0GxjqVNwExUVRVJSEgBlZWVs376dESNGuJ7Pz89Hr9d7doTCFdz0P6ziV1b17E1jtV3wFc7Zm+Qw99ouCCGE8DxzpeAGn6l1U6fgZuLEiTz66KOsX7+eOXPmYDabGTlypOv5Xbt20alTJ48PsqXz694dfVwcBqsjwKkq76Zkzx4AjL2adzKxU8hVV1EYoGN9L9kGLoQQ3mLSa7GgxaaW75b1ke3gdQpunn32WbRaLaNHj2bRokUsWrQIg+F0xLZkyRImNHARt5ZIUZTTS1MHVNILK9e6sWZnY0lJAcDYq3knEzuZhw7hrtn+/NlNIzM3QgjhJSaDFlAqNM/0jeCmTnVuIiIiWL9+Pbm5uQQEBKDVais9v2LFCgICAjw6QOEQOP5CspYsYWCiyp85Jyo958y3MbRrhzYw0BvDa3QphSkUWgrRaXS0C5Y+UUII4Q0mvSMOKEGPmVKf2TFVr/YLwcHBZwU2AKGhoZVmcoTnmPr1o7SVP+YyYOuuSs+1tHwbgMRsR+uFDsEd0Gskz0sIIbzhdHDjW7Vu6hXciManaDQUndcHgLAtCZWeK9lbnm/TzIv3VZSQ47gHsiQlhBDeo9NqMGg1p1swNOWZG+Ed+gscydsddmWiWq2ux4tb4MxNSoEjx6htUFsvj0QIIVo2o15ToXmmzNyIOgobMYo8E/gX2Sn8808ArFlZWFNSgZaTTAyQWuj4maPN0V4eiRBCtGyOzuDOZSnfaMEkwU0TEhPcmq1dHNvtsn74Djidb2No3x5tC0rmdm6Hj/GP8fJIhBCiZTMbdBWaZ8rMjagjk87E3t6O3VCFP/+Care3mGaZZ3IGN9H+MnMjhBDeZNRrT+fc+MhWcAlumpisPm0oMgAnsyneuZNiV/G+lpNvk1+WT4GlAJDgRgghvK1y80yZuRH1EBEUy/bOjqWp/J9+pmSvo2FmS+gp5eSctQkyBEnDTCGE8DKTvkILBsm5EfUR5R/Flm6O4Cb3q6+wpqaCouDXo+UkE0u+jRBC+I7Ky1IycyPqIdo/mviOCla9BtupUwAYOnRAG+Dv5ZE1HtdOKVmSEkIIr3M0z3QuS0nOjaiHaP9oSg0KSd2DXY+1pHwbkGRiIYTwJSZ9xa3gMnMj6sFZ1+XP7qfbX7SkfBuA9CJH41AJboQQwvtMFWduJOdG1EdMgCPPZE3bQtA5+p62tJkb57KU5NwIIYT3mQwVcm58ZLdUnbqCC++LNEeioJBjsBD4yH3oT2RgGjjQ28NqVLIsJYQQvsOk15KLb9W5keCmidFr9ISbwsksziR30nB6hbesWRu7apfdUkII4UPMBi3pUqFYnCvnjIXzTb4lySrJwmK3oKAQYY7w9nCEEKLFM+orFPHzkZkbCW6aIFdwU9TyghtnQBdhikCv0Xt5NEIIIUyV6txIQrGopyhzFNAyZ25c+TYBkm8jhBC+wFHnRpalXNatW8eUKVOIjY1FURRWrlxZ4/FffPEF48ePJyIigqCgIEaMGMEPP/zQOIP1IS15WcpVwM8swY0QQvgCY8XgRpaloLCwkH79+vHmm2+6dfy6desYP3483333Hdu2beOCCy5gypQp7Nixo4FH6ltacnAjycRCCOFbTHotpapvNc706m6pSZMmMWnSJLePf+211yp9//zzz/PVV1+xatUqBgwY4OHR+S7nG3tLzLmR1gtCCOFbKi1L+UjOTZPeCm6328nPzyc0NLTaY0pLSyktPX2z8/LyGmNoDcr5xp5ZlInNbkOr0dZyRvORXuioTiwzN0II4RsqdwX3jZmbJp1Q/Morr1BYWMhVV11V7THz588nODjY9RUXF9eII2wYYcYwdIoOm2ojszjT28NpVDJzI4QQvsUkjTM955NPPmHevHksX76cyMjIao+bM2cOubm5rq/jx4834igbhlajJdLs+JlbUt6NxWbhZPFJQIIbIYTwFY6cG8fMjWotBlX18oia6LLU8uXLmTVrFitWrODCCy+s8Vg/Pz/8/PwaaWSNJ9o/mpTClBYV3GQUZ6CiYtAYCDVWvxQphBCi8VScuVFUO9itoPVuHbImN3PzySefcMMNN/Dxxx9zySWXeHs4XhPl3/Jq3aQWnF6SUhTFy6MRQggBYNRpKXXm3IBP7Jjy6sxNQUEBiYmJru+TkpKIj48nNDSUtm3bMmfOHJKTk/nwww8BR2Bz/fXX8/rrrzN8+HDS0hxv7CaTieDgYK/8DN7SEqsUO39WWZISQgjfodEoKHo/7KqCRlHLa90EeXdM3rz41q1bGTBggGsb9+zZsxkwYABPPvkkAKmpqRw7dsx1/LvvvovVauXOO+8kJibG9XXvvfd6Zfze5Cxi15JmbqQbuBBC+CaTXne6v1RLn7kZM2YMag2JR0uXLq30/Zo1axp2QE2Iq9aNBDdCCCG8zGzQUVqsx0SZT1QpbnI5N8KhJVYplm3gQgjhm4x6jU+1YJDgpolyvsGfKjlFma3My6NpHNJ6QQghfJPZoDvdGdwHat1IcNNEhfiFYNQaATie3/Rr97hDmmYKIYRv8rUqxRLcNFGKojAwaiAAnx38zMujaXiFlkLyy/IBWZYSQghfYzRoKyQUy8yNOAfX97wegM8TPie3NNfLo2lYziWpQH0gAYYAL49GCCFEReZKMzcS3IhzcF7seXQO6UyxtbjZz964dkoFyKyNEEL4GpNBS6laPnMjwY04F4qicEOvGwD43/7/YbFZvDugBuQKbiTfRgghfI6x4syND9S5keCmibu4w8VEmiLJLM7k26RvvT2cBuNMJpadUkII4XvMFXNuZOZGnCu9Vs81Pa4B4D97/1NjUcSmTAr4CSGE7zLptae3gktwIzzhym5XYtaZScxJZGPKRm8Pp0FIcCOEEL7L0Rlc6twIDwoyBDGt6zQAlu5d6t3BNBBpmimEEL5L6tyIBnFtj2vRKlq2pG5h/6n93h6OR6mqKtWJhRDCh5mkzo1oCLEBsUxoPwFofrM32aXZlNpKUVCIMkd5ezhCCCHOYDZUzLmRmRvhQc5t4T8c+YHUglTvDsaDnDulwkxh6LV6L49GCCHEmSptBbeWencwSHDTrPQM68mw6GHYVBsf7f/I28PxGFmSEkII32aSOjeiIc3oNQNw9JvKK8vz8mg8Q3ZKCSGEb5M6N6JBnd/6fDoFd6LIWsTnBz/39nA8QoIbIYTwbUa9llLVd2ZudN4egPAsRVGY0WsGT256kqV7l5Jbmku4KZxwUzhhpjDXfwfoA1AUxdvDdYsz50ZaLwghhG8yG7SUuGZuvJ9zI8FNM3RJx0t4c8ebZBRnsHjP4iqPiTJH8dHFHzWJ2RBXzk2A5NwIIYQvqlTEzwd2S0lw0wwZtAbeHf8ua06s4WTxSdfXqeJTnCw+SYGlgPSidFYcXMHdA+729nBrJU0zhRDCt1Vsv6BaSvD2uoAEN81U51ad6dyqc5XPfZ/0PQ+ve5iVCSu5vd/t6DS++2tgtVvJLM4EZOZGCCF8VcUifqql2OvBjSQUt0Dj2o4jxC+EjOIMNia734vqVPEp1p9Yj121N+DoKsssysSu2tFpdIQaQxvtukIIIdxn0GooU6RxpvAig9bApZ0uBeCzhM/cOkdVVe777T7u+OUOXtn6SkMOr5KKycQaRX5dhRDCFymKAnqz4xtpvyC8ZVoXR6PN9SfWk1GUUevxm1M3E58ZD8CH+z7k0wOfNuTwXGQbuBBCNBE6IwCKTYIb4SUdQzoyIHIANtXGV4lf1Xisqqq8u/NdANoGtgXg+S3Psyl5U4OP0zlzI9WJhRDCt2kMfgAodivYrN4di1evLrxqapepAHyR8EWNeTRb07eyPWM7eo2eJRctYUrHKdhUGw+sfYDE7MQGHaPM3AghRNOgdS5Lgde3g0tw04JNaDeBAH0AJwpO8Gfan9Ue987OdwBHMBTlH8W88+YxMHIgBZYC7vzlTk4Wn2ywMUpwI4QQTYPGYDr9jZcL+Ulw04KZ9WYu7nAxQLWtGranb+ePtD/QaXTM6j0LcCQkv37B67QNbEtKYQr3/nYvJQ2UHZ9WJMGNEEI0BUaDjlK1vEqxl1swSHDTwk3r6kgs/vnYz+SU5Jz1/Lu7HLk2f+/090p1ZkKMIbw57k2CDEHsytzFExufaJAt4q7dUhLcCCGETzMbdBVaMHg3qViCmxauZ1hPeoT2wGK3sOrwqkrP7crcxaaUTWgVLbP6zDrr3A7BHXjtgtfQKTpWH1nNW/FveXRsRZYicktzAUkoFkIIX2fSaynFN5pnSnAjXNvCv0j4AlVVXY87Z20md5xMXGBclecOiR7CkyOeBOC9Xe/xR+ofHhuXc0nKX+9PoCHQY68rhBDC80wGLSWqbzTPlOBGcHHHizFqjSTmJLIzcycAe0/tZd2JdWgUDTf3vbnG8y/vcjmTO04GYO2JtR4bl6thpszaCCGEzzPpfad5pgQ3gkBDIBPaTwAcszcA7+18D4BJHSbRLqhdra8xInYE4FjK8pT0wnQAovyjPPaaQgghGkalzuBerlIswY0ATi9NrT6ymu3p2/n1+K8oKNzS5xa3zu8b3heAfaf2YbFZPDKmlMIUQGZuhBCiKXDk3DiXpWTmRviAAZED6BDcgWJrMff+di8AE9pPoGNIR7fObxfUjmC/YMrsZRzIPuCRMe1I3wFA55Cqu5sLIYTwHY6cG+eylOTcCB+gKIpr9ianNAeAW/q6N2vjPN85e+PM2zkX+WX5bEvfBsDI1iPP+fWEEEI0rEo5N7JbSviKKZ2moNPoABjXdhxdW3Wt0/l9IzwX3GxO2YxVtdI+qD1tg9qe8+sJIYRoWCZDha3gUudG+IpQYyj/7P5PQvxCuLP/nXU+v19EP8AzScXrTqwDYFSbUef8WkIIIRpepZwbmbkRvuThIQ+z/ur1dGnVpc7n9g7vjYJCckHyOfWbsqt21ievByS4EUKIpsJcKedGZm5EMxFoCKRTSCfg3GZv9p7cS1ZJFv56fwZGDvTU8IQQQjSgynVuJLgRzYgz7+Zcgpt1yY4lqfNiz0Ov1XtkXEIIIRqW0aA93VtK6tyI5sSVd3PyHIIbybcRQogmp/KylOTciGbEuR18z8k9WO3WOp+fWZTJvlP7ADi/9fkeHZsQQoiGUzmhWGZuRDPSMaQjAfoAiq3FJOYk1vl8ZyJx77DehJvCPT08IYQQDaRizo0qOTeiOdEoGvqE9wHql3cjS1JCCNE0VewtZS+TZSnRzNS3mF+ZrYxNKZsAGBUnwY0QQjQlJv3pnBu71LkRzU19d0xtTd9KsbWYcFM4PUJ7NMTQhBBCNBCdVoNd4weAKjM3orlxJhUfyTtCTkmO2+etP+HItxnZeiQaRX41hRCiqbHryoMbybkRzU2IMYT2Qe0B2H1yt1vnqKrK2hNrAcm3EUKIpkrVGR3/K8tSojlyLU25We/mSN4RjucfR6fRMSJ2REMOTQghRANR9CbH/8rMjWiOnEtTOzPcSyp27pIaHDUYf71/g41LCCFEAyqfuZHgRjRL/SIdlYp3n9yNXbXXerwz30aWpIQQounK84vm3rI72D3waa+Ow6vBzbp165gyZQqxsbEoisLKlStrPWft2rUMGjQIo9FIx44deeeddxp+oKLOOod0xqQzUWApICk3qcZjC8oK2Ja+DZDgRgghmjRjCF/Zz+d4xBivDsOrwU1hYSH9+vXjzTffdOv4pKQkLr74YkaOHMmOHTt47LHHuOeee/j8888beKSirnQaHb3CegG1bwnflLIJq2qlfVB72gW1a4zhCSGEaABGvRaAYovNq+PQefPikyZNYtKkSW4f/84779C2bVtee+01AHr06MHWrVt5+eWXmTZtWgONUtRX34i+bE3fys7MnVze5fJqj3Pm24xsM7KxhiaEEKIBmA2O4KaozLvBTZPKudm8eTMTJkyo9NhFF13E1q1bsVgsXhqVqI6zQ3hNlYrtqt3VT0qWpIQQomkzlc/clHh55qZJBTdpaWlERUVVeiwqKgqr1crJkyerPKe0tJS8vLxKX6JxOLeDH8o5REFZQZXH7D25l6ySLPz1/gyKHNSYwxNCCOFhJtfMjdWr42hSwQ2AoiiVvldVtcrHnebPn09wcLDrKy4ursHHKBzCTeG0DmiNisqeU3vOej6/LJ93d70LwHmx56HX6ht7iEIIITzIGdwUl9W+S7YhNangJjo6mrS0tEqPZWRkoNPpCAsLq/KcOXPmkJub6/o6fvx4YwxVlHM10Tyj3s2OjB1cuepK1p5Yi0bRcEXXK7wxPCGEEB5kkoTiuhsxYgSrVq2q9NiPP/7I4MGD0eur/tTv5+eHn59fYwxPVKFfRD++T/reVanYarfy7q53eW/Xe9hVO60DWvPCyBfoH9nfuwMVQghxzlzBTUteliooKCA+Pp74+HjAsdU7Pj6eY8eOAY5Zl+uvv951/G233cbRo0eZPXs2+/fvZ8mSJSxevJgHH3zQG8MXbnBWKt6VuYvjeceZsXoG7+x8B7tqZ0rHKXw25TMJbIQQoplwLUu15JmbrVu3csEFF7i+nz17NgAzZsxg6dKlpKamugIdgA4dOvDdd99x//3389ZbbxEbG8vChQtlG7gP6x7aHYPGQE5pDpd/fTmltlIC9YE8PvxxLu54sbeHJ4QQwoNOL0t5N+fGq8HNmDFjXAnBVVm6dOlZj40ePZrt27c34KiEJ+m1enqG9SQ+M55SWykDIwcyf+R8YgNivT00IYQQHmY2+MayVJPKuRFN06WdL+VQ7iFu6HUDs3rPQqvRentIQgghGoBRlqVES3Fl1yu5ossV1W7XF0II0Tw4l6WkQrFoESSwEUKI5s+5LFUiwY0QQgghmgOTXoteq6DVevcDrSxLCSGEEMIjOkcGkPCc93fCysyNEEIIITzCV1IQJLgRQgghRLMiwY0QQgghmhUJboQQQgjRrEhwI4QQQohmRYIbIYQQQjQrEtwIIYQQolmR4EYIIYQQzYoEN0IIIYRoViS4EUIIIUSzIsGNEEIIIZoVCW6EEEII0axIcCOEEEKIZkWCGyGEEEI0KzpvD6CxqaoKQF5enpdHIoQQQgh3Od+3ne/jNWlxwU1+fj4AcXFxXh6JEEIIIeoqPz+f4ODgGo9RVHdCoGbEbreTkpJCYGAgiqI02nXz8vKIi4vj+PHjBAUFNdp1fZ3cl+rJvama3Jfqyb2pmtyX6jWle6OqKvn5+cTGxqLR1JxV0+JmbjQaDW3atPHa9YOCgnz+F8gb5L5UT+5N1eS+VE/uTdXkvlSvqdyb2mZsnCShWAghhBDNigQ3QgghhGhWJLhpJH5+fsydOxc/Pz9vD8WnyH2pntybqsl9qZ7cm6rJfalec703LS6hWAghhBDNm8zcCCGEEKJZkeBGCCGEEM2KBDdCCCGEaFYkuBFCCCFEsyLBTR2sW7eOKVOmEBsbi6IorFy5stLz6enp3HDDDcTGxmI2m5k4cSIJCQlnvc7mzZsZO3Ys/v7+hISEMGbMGIqLi13PZ2dnc9111xEcHExwcDDXXXcdOTk5DfzT1d+53pcjR46gKEqVXytWrHAd19TuC3jmdyYtLY3rrruO6Oho/P39GThwIJ999lmlY5ravfHEfTl06BCXX345ERERBAUFcdVVV5Genl7pmKZ2X+bPn8+QIUMIDAwkMjKSyy67jAMHDlQ6RlVV5s2bR2xsLCaTiTFjxrB3795Kx5SWlnL33XcTHh6Ov78/l156KSdOnKh0TEu9N++99x5jxowhKCgIRVGq/Jmb0r3xxH3Jysri7rvvplu3bpjNZtq2bcs999xDbm5upddpSvdFgps6KCwspF+/frz55ptnPaeqKpdddhmHDx/mq6++YseOHbRr144LL7yQwsJC13GbN29m4sSJTJgwgT/++IM///yTu+66q1Ip6WuuuYb4+HhWr17N6tWriY+P57rrrmuUn7E+zvW+xMXFkZqaWunrqaeewt/fn0mTJrleq6ndF/DM78x1113HgQMH+Prrr9m9ezdTp07lH//4Bzt27HAd09Tuzbnel8LCQiZMmICiKPz6669s3LiRsrIypkyZgt1ud71WU7sva9eu5c477+T333/np59+wmq1MmHChEq/Dy+99BILFizgzTff5M8//yQ6Oprx48e7+uYB3HfffXz55ZcsW7aMDRs2UFBQwOTJk7HZbK5jWuq9KSoqYuLEiTz22GPVXqsp3RtP3JeUlBRSUlJ4+eWX2b17N0uXLmX16tXMmjWr0rWa0n1BFfUCqF9++aXr+wMHDqiAumfPHtdjVqtVDQ0NVRctWuR6bNiwYerjjz9e7evu27dPBdTff//d9djmzZtVQP3rr788+0M0gPrelzP1799fnTlzpuv7pn5fVLX+98bf31/98MMPK71WaGio+v7776uq2vTvTX3uyw8//KBqNBo1NzfXdUxWVpYKqD/99JOqqk3/vqiqqmZkZKiAunbtWlVVVdVut6vR0dHqCy+84DqmpKREDQ4OVt955x1VVVU1JydH1ev16rJly1zHJCcnqxqNRl29erWqqi333lT022+/qYCanZ1d6fGmfm/O9b44ffrpp6rBYFAtFouqqk3vvsjMjYeUlpYCYDQaXY9ptVoMBgMbNmwAICMjgy1bthAZGcl5551HVFQUo0ePdj0Pjpmd4OBghg0b5nps+PDhBAcHs2nTpkb6aTzHnftypm3bthEfH1/pU0Nzuy/g/r05//zzWb58OVlZWdjtdpYtW0ZpaSljxowBmt+9cee+lJaWoihKpcJjRqMRjUbjOqY53BfnskBoaCgASUlJpKWlMWHCBNcxfn5+jB492vUzbdu2DYvFUumY2NhYevfu7Tqmpd4bdzT1e+Op+5Kbm0tQUBA6naMFZVO7LxLceEj37t1p164dc+bMITs7m7KyMl544QXS0tJITU0F4PDhwwDMmzePm2++mdWrVzNw4EDGjRvnyidIS0sjMjLyrNePjIwkLS2t8X4gD3Hnvpxp8eLF9OjRg/POO8/1WHO7L+D+vVm+fDlWq5WwsDD8/Py49dZb+fLLL+nUqRPQ/O6NO/dl+PDh+Pv788gjj1BUVERhYSEPPfQQdrvddUxTvy+qqjJ79mzOP/98evfuDeAad1RUVKVjo6KiXM+lpaVhMBho1apVjce0xHvjjqZ8bzx1X06dOsUzzzzDrbfe6nqsqd0XCW48RK/X8/nnn3Pw4EFCQ0Mxm82sWbOGSZMmodVqAVy5ALfeeis33ngjAwYM4NVXX6Vbt24sWbLE9VqKopz1+qqqVvm4r3PnvlRUXFzMxx9/fNZaLzSv+wLu35vHH3+c7Oxsfv75Z7Zu3crs2bO58sor2b17t+uY5nRv3LkvERERrFixglWrVhEQEEBwcDC5ubkMHDiw0r1ryvflrrvuYteuXXzyySdnPXfm+N35mc48Ru5N9ZrqvfHEfcnLy+OSSy6hZ8+ezJ07t8bXqOl1vE3n7QE0J4MGDSI+Pp7c3FzKysqIiIhg2LBhDB48GICYmBgAevbsWem8Hj16cOzYMQCio6PP2vEBkJmZeVbk3VTUdl8q+uyzzygqKuL666+v9HhzvC9Q+705dOgQb775Jnv27KFXr14A9OvXj/Xr1/PWW2/xzjvvNMt7487vzIQJEzh0kfsH8AAAB3pJREFU6BAnT55Ep9MREhJCdHQ0HTp0AJr278zdd9/N119/zbp162jTpo3r8ejoaMDxKdr59wQcS97Onyk6OpqysjKys7Mrzd5kZGS4ZkNb6r1xR1O9N564L/n5+UycOJGAgAC+/PJL9Hp9pddpSvdFZm4aQHBwMBERESQkJLB161b+/ve/A9C+fXtiY2PP2qZ38OBB2rVrB8CIESPIzc3ljz/+cD2/ZcsWcnNzKy3TNEXV3ZeKFi9ezKWXXkpERESlx5vzfYHq701RURFApd104MhBcc4ENud7487vTHh4OCEhIfz6669kZGRw6aWXAk3zvqiqyl133cUXX3zBr7/+6grUnDp06EB0dDQ//fST67GysjLWrl3r+pkGDRqEXq+vdExqaip79uxxHdNS7407mtq98dR9ycvLY8KECRgMBr7++utKOW/Q9O6L7Jaqg/z8fHXHjh3qjh07VEBdsGCBumPHDvXo0aOqqjqyy3/77Tf10KFD6sqVK9V27dqpU6dOrfQar776qhoUFKSuWLFCTUhIUB9//HHVaDSqiYmJrmMmTpyo9u3bV928ebO6efNmtU+fPurkyZMb9WetC0/cF1VV1YSEBFVRFPX777+v8jpN7b6o6rnfm7KyMrVz587qyJEj1S1btqiJiYnqyy+/rCqKon777beu45ravfHE78ySJUvUzZs3q4mJiep///tfNTQ0VJ09e3alY5rafbn99tvV4OBgdc2aNWpqaqrrq6ioyHXMCy+8oAYHB6tffPGFunv3bvWf//ynGhMTo+bl5bmOue2229Q2bdqoP//8s7p9+3Z17Nixar9+/VSr1eo6pqXem9TUVHXHjh3qokWLVEBdt26dumPHDvXUqVOuY5rSvfHEfcnLy1OHDRum9unTR01MTKz0Ok31d0aCmzpwbh0882vGjBmqqqrq66+/rrZp00bV6/Vq27Zt1ccff1wtLS0963Xmz5+vtmnTRjWbzeqIESPU9evXV3r+1KlT6vTp09XAwEA1MDBQnT59+lnbFX2Jp+7LnDlz1DZt2qg2m63K6zS1+6Kqnrk3Bw8eVKdOnapGRkaqZrNZ7du371lbw5vavfHEfXnkkUfUqKgoVa/Xq126dFFfeeUV1W63Vzqmqd2Xqu4JoH7wwQeuY+x2uzp37lw1Ojpa9fPzU0eNGqXu3r270usUFxerd911lxoaGqqaTCZ18uTJ6rFjxyod01Lvzdy5c2t9naZ0bzxxX6r79wioSUlJruOa0n1RVFVVPTkTJIQQQgjhTZJzI4QQQohmRYIbIYQQQjQrEtwIIYQQolmR4EYIIYQQzYoEN0IIIYRoViS4EUIIIUSzIsGNEEIIIZoVCW6EEEII0axIcCOEEEKIZkWCGyGEAGw2m6sZqRCiaZPgRgjhcz788EPCwsIoLS2t9Pi0adO4/vrrAVi1ahWDBg3CaDTSsWNHnnrqKaxWq+vYBQsW0KdPH/z9/YmLi+OOO+6goKDA9fzSpUsJCQnhm2++oWfPnvj5+XH06NHG+QGFEA1KghshhM+58sorsdlsfP31167HTp48yTfffMONN97IDz/8wLXXXss999zDvn37ePfdd1m6dCnPPfec63iNRsPChQvZs2cP//nPf/j11195+OGHK12nqKiI+fPn8/7777N3714iIyMb7WcUQjQcaZwphPBJd9xxB0eOHOG7774D4PXXX2fhwoUkJiYyevRoJk2axJw5c1zHf/TRRzz88MOkpKRU+XorVqzg9ttv5+TJk4Bj5ubGG28kPj6efv36NfwPJIRoNBLcCCF80o4dOxgyZAhHjx6ldevW9O/fn2nTpvHEE0/g7++P3W5Hq9W6jrfZbJSUlFBYWIjZbOa3337j+eefZ9++feTl5WG1WikpKaGgoAB/f3+WLl3KrbfeSklJCYqiePEnFUJ4ms7bAxBCiKoMGDCAfv368eGHH3LRRRexe/duVq1aBYDdbuepp55i6tSpZ51nNBo5evQoF198MbfddhvPPPMMoaGhbNiwgVmzZmGxWFzHmkwmCWyEaIYkuBFC+KybbrqJV199leTkZC688ELi4uIAGDhwIAcOHKBz585Vnrd161asViuvvPIKGo0jtfDTTz9ttHELIbxLghshhM+aPn06Dz74IIsWLeLDDz90Pf7kk08yefJk4uLiuPLKK9FoNOzatYvdu3fz7LPP0qlTJ6xWK2+88QZTpkxh48aNvPPOO178SYQQjUl2SwkhfFZQUBDTpk0jICCAyy67zPX4RRddxDfffMNPP/3EkCFDGD58OAsWLKBdu3YA9O/fnwULFvDiiy/Su3dv/ve//zF//nwv/RRCiMYmCcVCCJ82fvx4evTowcKFC709FCFEEyHBjRDCJ2VlZfHjjz8yffp09u3bR7du3bw9JCFEEyE5N0IInzRw4ECys7N58cUXJbARQtSJzNwIIYQQolmRhGIhhBBCNCsS3AghhBCiWZHgRgghhBDNigQ3QgghhGhWJLgRQgghRLMiwY0QQgghmhUJboQQQgjRrEhwI4QQQohmRYIbIYQQQjQr/w9eeVF9Ni+4fgAAAABJRU5ErkJggg==",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -5082,31 +4972,33 @@
    ],
    "source": [
     "for iGroup in model_min_max_dt.groups[1:]:\n",
-    "    ds=model_min_max_dt[iGroup].ds\n",
-    "    ds['max'].plot(label=iGroup[1:])\n",
+    "    ds = model_min_max_dt[iGroup].ds.sel(\n",
+    "        year=slice(1950, 2022)\n",
+    "    )  # drop the last year because its incomplete\n",
+    "    ds[\"max\"].plot(label=iGroup[1:])\n",
     "\n",
-    "plt.title('Trends in Sea-Ice Maximum Volume')\n",
-    "plt.ylabel('Sea-Ice Volume (m3)')\n",
+    "plt.title(\"Trends in Sea-Ice Maximum Volume\")\n",
+    "plt.ylabel(\"Sea-Ice Volume ($m^3$)\")\n",
     "\n",
     "_ = plt.legend()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 39,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-08T03:17:13.575361Z",
-     "iopub.status.busy": "2024-01-08T03:17:13.575174Z",
-     "iopub.status.idle": "2024-01-08T03:17:13.822577Z",
-     "shell.execute_reply": "2024-01-08T03:17:13.821751Z",
-     "shell.execute_reply.started": "2024-01-08T03:17:13.575344Z"
+     "iopub.execute_input": "2024-01-10T00:37:26.961380Z",
+     "iopub.status.busy": "2024-01-10T00:37:26.961206Z",
+     "iopub.status.idle": "2024-01-10T00:37:27.209376Z",
+     "shell.execute_reply": "2024-01-10T00:37:27.208491Z",
+     "shell.execute_reply.started": "2024-01-10T00:37:26.961363Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -5117,24 +5009,24 @@
    ],
    "source": [
     "for iGroup in si_vol_dt.groups[1:]:\n",
-    "    si_vol_dt[iGroup].ds.si_vol.groupby('time.month').mean('time').plot(label=iGroup[1:])\n",
+    "    climatology(si_vol_dt[iGroup].ds.si_vol).plot(label=iGroup[1:])\n",
     "\n",
-    "plt.title('Climatology of Sea-Ice Volume')\n",
-    "plt.ylabel('Sea-Ice Volume (m3)')\n",
+    "plt.title(\"Climatology of Sea-Ice Volume\")\n",
+    "plt.ylabel(\"Sea-Ice Volume ($m^3$)\")\n",
     "\n",
     "_ = plt.legend()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 40,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-08T03:17:13.823808Z",
-     "iopub.status.busy": "2024-01-08T03:17:13.823569Z",
-     "iopub.status.idle": "2024-01-08T03:17:16.960918Z",
-     "shell.execute_reply": "2024-01-08T03:17:16.959964Z",
-     "shell.execute_reply.started": "2024-01-08T03:17:13.823786Z"
+     "iopub.execute_input": "2024-01-10T00:37:27.210292Z",
+     "iopub.status.busy": "2024-01-10T00:37:27.210127Z",
+     "iopub.status.idle": "2024-01-10T00:37:29.394884Z",
+     "shell.execute_reply": "2024-01-10T00:37:29.393978Z",
+     "shell.execute_reply.started": "2024-01-10T00:37:27.210275Z"
     }
    },
    "outputs": [
@@ -5142,6 +5034,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "0.3.0\n",
+      "0.3.0\n",
+      "0.3.0\n",
+      "0.3.0\n",
+      "0.3.0\n",
+      "0.3.0\n",
+      "0.3.0\n",
+      "0.3.0\n",
       "0.3.0\n",
       "0.3.0\n",
       "0.3.0\n",