Regrid_NSIDC_SIC.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interpolate NSIDC SIC onto 1deg grid\n",
    "Regrid NSIDC sea ice concentration onto an ACCESS-OM2 T grid  using xESMF\n",
    "\n",
    "see https://cosima-recipes.readthedocs.io/en/latest/documented_examples/Regridding.html\n",
    "and https://xesmf.readthedocs.io\n",
    "\n",
    "Slack discussion: https://arccss.slack.com/archives/C08KM5KS6/p1619069437037300\n",
    "\n",
    "See emails with Will Hobbs, 29-30 April 2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import cosima_cookbook as cc\n",
    "from dask.distributed import Client\n",
    "from glob import glob\n",
    "import os,sys\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "from tqdm import tqdm_notebook\n",
    "import pandas as pd\n",
    "import xesmf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"border: 2px solid white;\">\n",
       "<tr>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Client</h3>\n",
       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
       "  <li><b>Scheduler: </b>tcp://127.0.0.1:40971</li>\n",
       "  <li><b>Dashboard: </b><a href='/proxy/35703/status' target='_blank'>/proxy/35703/status</a></li>\n",
       "</ul>\n",
       "</td>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
       "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
       "  <li><b>Workers: </b>47</li>\n",
       "  <li><b>Cores: </b>47</li>\n",
       "  <li><b>Memory: </b>204.01 GB</li>\n",
       "</ul>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Client: 'tcp://127.0.0.1:40971' processes=47 threads=47, memory=204.01 GB>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import climtas.nci\n",
    "climtas.nci.GadiClient()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sic_obs(path, pattern, variable='goddard_merged_seaice_conc_monthly'): # seaice_conc_monthly_cdr'): # \n",
    "    '''\n",
    "    Return a dataarray from the monthly nc files in obsFileList.\n",
    "    '''\n",
    "    dataarrays = []\n",
    "    files = glob(os.path.join(path, pattern))\n",
    "    for f in tqdm_notebook(files, leave=False, desc='loading files'):\n",
    "        dataarrays.append(xr.open_dataset(f, decode_times=False)[variable])\n",
    "    dataarray = xr.concat(dataarrays, dim='time', coords='all')\n",
    "    if 'time' in dataarray.coords:\n",
    "        time_units = dataarray.time.units\n",
    "        decoded_time = xr.conventions.times.decode_cf_datetime(dataarray.time, time_units)\n",
    "        dataarray.coords['time'] = ('time', decoded_time,\n",
    "                                    {'long_name': 'time', 'decoded_using': time_units })\n",
    "\n",
    "    # replace values outside valid range with nan\n",
    "    dataarray = dataarray.where(xr.ufuncs.logical_and(dataarray>=0, dataarray<=1), np.nan)\n",
    "    \n",
    "    # use the same coord names as access-om2 t grid\n",
    "    dataarray = dataarray.rename(({'xgrid': 'xt_ocean', 'ygrid': 'yt_ocean'}))\n",
    "    \n",
    "    # sort by time\n",
    "    dataarray = dataarray.sortby('time')\n",
    "\n",
    "    return dataarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "loading files:   0%|          | 0/493 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "loading files:   0%|          | 0/493 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ObsDir = '/g/data3/hh5/tmp/cosima/observations/NOAA/G02202_V3'  # from http://nsidc.org/data/G02202\n",
    "\n",
    "obs_NH = get_sic_obs(ObsDir, 'north/monthly/*.nc')\n",
    "obs_SH = get_sic_obs(ObsDir, 'south/monthly/*.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "def G02202_obs_regridder(grid_in, grid_out, method='bilinear', **kwargs):\n",
    "    '''Return a function that takes one dataarray argument on grid_in and returns that dataarray regridded onto grid_out.\n",
    "    \n",
    "    grid_in -- path to a NSIDC G02202 NetCDF grid file\n",
    "    grid_out -- path to a MOM grid file (tracer points are used)\n",
    "    '''\n",
    "    weightfn = '_'.join(['regrid', 'weights', os.path.splitext(os.path.basename(grid_in))[0],\n",
    "                         'to', os.path.splitext(os.path.basename(grid_out))[0], method])+'.nc'\n",
    "    ds_in = xr.open_dataset(grid_in)\\\n",
    "             .rename({'xgrid': 'x', 'ygrid': 'y',\n",
    "                     'longitude': 'lon', 'latitude': 'lat'})\n",
    "    try: # for /g/data/ik11/grids/ocean_grid_10.nc and /g/data/ik11/grids/ocean_grid_025.nc\n",
    "        ds_out = xr.open_dataset(grid_out).rename({'xt_ocean': 'x', 'yt_ocean': 'y', 'geolon_t': 'lon', 'geolat_t': 'lat'})\n",
    "    except ValueError: # for /g/data/ik11/grids/ocean_grid_01.nc\n",
    "# BUG: should be fixed as in ice_maps.ipynb\n",
    "        ds_out = xr.open_dataset(grid_out).rename({'grid_x_T': 'x', 'grid_y_T': 'y', 'geolon_t': 'lon', 'geolat_t': 'lat'})         \n",
    "\n",
    "    # make regridder\n",
    "    rg = xesmf.Regridder(ds_in, ds_out, method=method, reuse_weights=os.path.exists(weightfn), filename=weightfn, **kwargs)\n",
    "\n",
    "    def outf(da): # define regridding function to output\n",
    "        outds = rg(da)\n",
    "        outds.coords['x'] = ds_out['x']\n",
    "        outds.coords['y'] = ds_out['y']\n",
    "        return outds.rename({'x': 'xt_ocean', 'y': 'yt_ocean', 'lon': 'longitude', 'lat': 'latitude'})\n",
    "\n",
    "    return outf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "obsfile_NH = ObsDir+'/north/monthly/seaice_conc_monthly_nh_f08_198708_v03r01.nc'\n",
    "regrid_NHobs_to_1 = G02202_obs_regridder(obsfile_NH, '/g/data/ik11/grids/ocean_grid_10.nc')\n",
    "# regrid_NHobs_to_025 = G02202_obs_regridder(obsfile_NH, '/g/data/ik11/grids/ocean_grid_025.nc')\n",
    "# regrid_NHobs_to_01 = G02202_obs_regridder(obsfile_NH, '/g/data/ik11/grids/ocean_grid_01.nc')\n",
    "\n",
    "obsfile_SH = ObsDir+'/south/monthly/seaice_conc_monthly_sh_f08_198708_v03r01.nc'\n",
    "regrid_SHobs_to_1 = G02202_obs_regridder(obsfile_SH, '/g/data/ik11/grids/ocean_grid_10.nc')\n",
    "# regrid_SHobs_to_025 = G02202_obs_regridder(obsfile_SH, '/g/data/ik11/grids/ocean_grid_025.nc')\n",
    "# regrid_SHobs_to_01 = G02202_obs_regridder(obsfile_SH, '/g/data/ik11/grids/ocean_grid_01.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "regridded_NH = regrid_NHobs_to_1(obs_NH)\n",
    "regridded_SH = regrid_SHobs_to_1(obs_SH)\n",
    "regridded = regridded_NH.where(regridded_NH.yt_ocean > 0, other=regridded_SH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x14ba1ea47070>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "regridded.isel(time=10).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 G02202_V3_SIC_ACCESSOM2_1deg_1978_11.nc\n",
      "1 G02202_V3_SIC_ACCESSOM2_1deg_1978_12.nc\n",
      "2 G02202_V3_SIC_ACCESSOM2_1deg_1979_01.nc\n",
      "3 G02202_V3_SIC_ACCESSOM2_1deg_1979_02.nc\n",
      "4 G02202_V3_SIC_ACCESSOM2_1deg_1979_03.nc\n",
      "5 G02202_V3_SIC_ACCESSOM2_1deg_1979_04.nc\n",
      "6 G02202_V3_SIC_ACCESSOM2_1deg_1979_05.nc\n",
      "7 G02202_V3_SIC_ACCESSOM2_1deg_1979_06.nc\n",
      "8 G02202_V3_SIC_ACCESSOM2_1deg_1979_07.nc\n",
      "9 G02202_V3_SIC_ACCESSOM2_1deg_1979_08.nc\n",
      "10 G02202_V3_SIC_ACCESSOM2_1deg_1979_09.nc\n",
      "11 G02202_V3_SIC_ACCESSOM2_1deg_1979_10.nc\n",
      "12 G02202_V3_SIC_ACCESSOM2_1deg_1979_11.nc\n",
      "13 G02202_V3_SIC_ACCESSOM2_1deg_1979_12.nc\n",
      "14 G02202_V3_SIC_ACCESSOM2_1deg_1980_01.nc\n",
      "15 G02202_V3_SIC_ACCESSOM2_1deg_1980_02.nc\n",
      "16 G02202_V3_SIC_ACCESSOM2_1deg_1980_03.nc\n",
      "17 G02202_V3_SIC_ACCESSOM2_1deg_1980_04.nc\n",
      "18 G02202_V3_SIC_ACCESSOM2_1deg_1980_05.nc\n",
      "19 G02202_V3_SIC_ACCESSOM2_1deg_1980_06.nc\n",
      "20 G02202_V3_SIC_ACCESSOM2_1deg_1980_07.nc\n",
      "21 G02202_V3_SIC_ACCESSOM2_1deg_1980_08.nc\n",
      "22 G02202_V3_SIC_ACCESSOM2_1deg_1980_09.nc\n",
      "23 G02202_V3_SIC_ACCESSOM2_1deg_1980_10.nc\n",
      "24 G02202_V3_SIC_ACCESSOM2_1deg_1980_11.nc\n",
      "25 G02202_V3_SIC_ACCESSOM2_1deg_1980_12.nc\n",
      "26 G02202_V3_SIC_ACCESSOM2_1deg_1981_01.nc\n",
      "27 G02202_V3_SIC_ACCESSOM2_1deg_1981_02.nc\n",
      "28 G02202_V3_SIC_ACCESSOM2_1deg_1981_03.nc\n",
      "29 G02202_V3_SIC_ACCESSOM2_1deg_1981_04.nc\n",
      "30 G02202_V3_SIC_ACCESSOM2_1deg_1981_05.nc\n",
      "31 G02202_V3_SIC_ACCESSOM2_1deg_1981_06.nc\n",
      "32 G02202_V3_SIC_ACCESSOM2_1deg_1981_07.nc\n",
      "33 G02202_V3_SIC_ACCESSOM2_1deg_1981_08.nc\n",
      "34 G02202_V3_SIC_ACCESSOM2_1deg_1981_09.nc\n",
      "35 G02202_V3_SIC_ACCESSOM2_1deg_1981_10.nc\n",
      "36 G02202_V3_SIC_ACCESSOM2_1deg_1981_11.nc\n",
      "37 G02202_V3_SIC_ACCESSOM2_1deg_1981_12.nc\n",
      "38 G02202_V3_SIC_ACCESSOM2_1deg_1982_01.nc\n",
      "39 G02202_V3_SIC_ACCESSOM2_1deg_1982_02.nc\n",
      "40 G02202_V3_SIC_ACCESSOM2_1deg_1982_03.nc\n",
      "41 G02202_V3_SIC_ACCESSOM2_1deg_1982_04.nc\n",
      "42 G02202_V3_SIC_ACCESSOM2_1deg_1982_05.nc\n",
      "43 G02202_V3_SIC_ACCESSOM2_1deg_1982_06.nc\n",
      "44 G02202_V3_SIC_ACCESSOM2_1deg_1982_07.nc\n",
      "45 G02202_V3_SIC_ACCESSOM2_1deg_1982_08.nc\n",
      "46 G02202_V3_SIC_ACCESSOM2_1deg_1982_09.nc\n",
      "47 G02202_V3_SIC_ACCESSOM2_1deg_1982_10.nc\n",
      "48 G02202_V3_SIC_ACCESSOM2_1deg_1982_11.nc\n",
      "49 G02202_V3_SIC_ACCESSOM2_1deg_1982_12.nc\n",
      "50 G02202_V3_SIC_ACCESSOM2_1deg_1983_01.nc\n",
      "51 G02202_V3_SIC_ACCESSOM2_1deg_1983_02.nc\n",
      "52 G02202_V3_SIC_ACCESSOM2_1deg_1983_03.nc\n",
      "53 G02202_V3_SIC_ACCESSOM2_1deg_1983_04.nc\n",
      "54 G02202_V3_SIC_ACCESSOM2_1deg_1983_05.nc\n",
      "55 G02202_V3_SIC_ACCESSOM2_1deg_1983_06.nc\n",
      "56 G02202_V3_SIC_ACCESSOM2_1deg_1983_07.nc\n",
      "57 G02202_V3_SIC_ACCESSOM2_1deg_1983_08.nc\n",
      "58 G02202_V3_SIC_ACCESSOM2_1deg_1983_09.nc\n",
      "59 G02202_V3_SIC_ACCESSOM2_1deg_1983_10.nc\n",
      "60 G02202_V3_SIC_ACCESSOM2_1deg_1983_11.nc\n",
      "61 G02202_V3_SIC_ACCESSOM2_1deg_1983_12.nc\n",
      "62 G02202_V3_SIC_ACCESSOM2_1deg_1984_01.nc\n",
      "63 G02202_V3_SIC_ACCESSOM2_1deg_1984_02.nc\n",
      "64 G02202_V3_SIC_ACCESSOM2_1deg_1984_03.nc\n",
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      "336 G02202_V3_SIC_ACCESSOM2_1deg_2006_12.nc\n",
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      "350 G02202_V3_SIC_ACCESSOM2_1deg_2008_02.nc\n",
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      "354 G02202_V3_SIC_ACCESSOM2_1deg_2008_06.nc\n",
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      "357 G02202_V3_SIC_ACCESSOM2_1deg_2008_09.nc\n",
      "358 G02202_V3_SIC_ACCESSOM2_1deg_2008_10.nc\n",
      "359 G02202_V3_SIC_ACCESSOM2_1deg_2008_11.nc\n",
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      "362 G02202_V3_SIC_ACCESSOM2_1deg_2009_02.nc\n",
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      "370 G02202_V3_SIC_ACCESSOM2_1deg_2009_10.nc\n",
      "371 G02202_V3_SIC_ACCESSOM2_1deg_2009_11.nc\n",
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      "373 G02202_V3_SIC_ACCESSOM2_1deg_2010_01.nc\n",
      "374 G02202_V3_SIC_ACCESSOM2_1deg_2010_02.nc\n",
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      "383 G02202_V3_SIC_ACCESSOM2_1deg_2010_11.nc\n",
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      "396 G02202_V3_SIC_ACCESSOM2_1deg_2011_12.nc\n",
      "397 G02202_V3_SIC_ACCESSOM2_1deg_2012_01.nc\n",
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      "400 G02202_V3_SIC_ACCESSOM2_1deg_2012_04.nc\n",
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      "404 G02202_V3_SIC_ACCESSOM2_1deg_2012_08.nc\n",
      "405 G02202_V3_SIC_ACCESSOM2_1deg_2012_09.nc\n",
      "406 G02202_V3_SIC_ACCESSOM2_1deg_2012_10.nc\n",
      "407 G02202_V3_SIC_ACCESSOM2_1deg_2012_11.nc\n",
      "408 G02202_V3_SIC_ACCESSOM2_1deg_2012_12.nc\n",
      "409 G02202_V3_SIC_ACCESSOM2_1deg_2013_01.nc\n",
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      "411 G02202_V3_SIC_ACCESSOM2_1deg_2013_03.nc\n",
      "412 G02202_V3_SIC_ACCESSOM2_1deg_2013_04.nc\n",
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      "416 G02202_V3_SIC_ACCESSOM2_1deg_2013_08.nc\n",
      "417 G02202_V3_SIC_ACCESSOM2_1deg_2013_09.nc\n",
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      "419 G02202_V3_SIC_ACCESSOM2_1deg_2013_11.nc\n",
      "420 G02202_V3_SIC_ACCESSOM2_1deg_2013_12.nc\n",
      "421 G02202_V3_SIC_ACCESSOM2_1deg_2014_01.nc\n",
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      "424 G02202_V3_SIC_ACCESSOM2_1deg_2014_04.nc\n",
      "425 G02202_V3_SIC_ACCESSOM2_1deg_2014_05.nc\n",
      "426 G02202_V3_SIC_ACCESSOM2_1deg_2014_06.nc\n",
      "427 G02202_V3_SIC_ACCESSOM2_1deg_2014_07.nc\n",
      "428 G02202_V3_SIC_ACCESSOM2_1deg_2014_08.nc\n",
      "429 G02202_V3_SIC_ACCESSOM2_1deg_2014_09.nc\n",
      "430 G02202_V3_SIC_ACCESSOM2_1deg_2014_10.nc\n",
      "431 G02202_V3_SIC_ACCESSOM2_1deg_2014_11.nc\n",
      "432 G02202_V3_SIC_ACCESSOM2_1deg_2014_12.nc\n",
      "433 G02202_V3_SIC_ACCESSOM2_1deg_2015_01.nc\n",
      "434 G02202_V3_SIC_ACCESSOM2_1deg_2015_02.nc\n",
      "435 G02202_V3_SIC_ACCESSOM2_1deg_2015_03.nc\n",
      "436 G02202_V3_SIC_ACCESSOM2_1deg_2015_04.nc\n",
      "437 G02202_V3_SIC_ACCESSOM2_1deg_2015_05.nc\n",
      "438 G02202_V3_SIC_ACCESSOM2_1deg_2015_06.nc\n",
      "439 G02202_V3_SIC_ACCESSOM2_1deg_2015_07.nc\n",
      "440 G02202_V3_SIC_ACCESSOM2_1deg_2015_08.nc\n",
      "441 G02202_V3_SIC_ACCESSOM2_1deg_2015_09.nc\n",
      "442 G02202_V3_SIC_ACCESSOM2_1deg_2015_10.nc\n",
      "443 G02202_V3_SIC_ACCESSOM2_1deg_2015_11.nc\n",
      "444 G02202_V3_SIC_ACCESSOM2_1deg_2015_12.nc\n",
      "445 G02202_V3_SIC_ACCESSOM2_1deg_2016_01.nc\n",
      "446 G02202_V3_SIC_ACCESSOM2_1deg_2016_02.nc\n",
      "447 G02202_V3_SIC_ACCESSOM2_1deg_2016_03.nc\n",
      "448 G02202_V3_SIC_ACCESSOM2_1deg_2016_04.nc\n",
      "449 G02202_V3_SIC_ACCESSOM2_1deg_2016_05.nc\n",
      "450 G02202_V3_SIC_ACCESSOM2_1deg_2016_06.nc\n",
      "451 G02202_V3_SIC_ACCESSOM2_1deg_2016_07.nc\n",
      "452 G02202_V3_SIC_ACCESSOM2_1deg_2016_08.nc\n",
      "453 G02202_V3_SIC_ACCESSOM2_1deg_2016_09.nc\n",
      "454 G02202_V3_SIC_ACCESSOM2_1deg_2016_10.nc\n",
      "455 G02202_V3_SIC_ACCESSOM2_1deg_2016_11.nc\n",
      "456 G02202_V3_SIC_ACCESSOM2_1deg_2016_12.nc\n",
      "457 G02202_V3_SIC_ACCESSOM2_1deg_2017_01.nc\n",
      "458 G02202_V3_SIC_ACCESSOM2_1deg_2017_02.nc\n",
      "459 G02202_V3_SIC_ACCESSOM2_1deg_2017_03.nc\n",
      "460 G02202_V3_SIC_ACCESSOM2_1deg_2017_04.nc\n",
      "461 G02202_V3_SIC_ACCESSOM2_1deg_2017_05.nc\n",
      "462 G02202_V3_SIC_ACCESSOM2_1deg_2017_06.nc\n",
      "463 G02202_V3_SIC_ACCESSOM2_1deg_2017_07.nc\n",
      "464 G02202_V3_SIC_ACCESSOM2_1deg_2017_08.nc\n",
      "465 G02202_V3_SIC_ACCESSOM2_1deg_2017_09.nc\n",
      "466 G02202_V3_SIC_ACCESSOM2_1deg_2017_10.nc\n",
      "467 G02202_V3_SIC_ACCESSOM2_1deg_2017_11.nc\n",
      "468 G02202_V3_SIC_ACCESSOM2_1deg_2017_12.nc\n",
      "469 G02202_V3_SIC_ACCESSOM2_1deg_2018_01.nc\n",
      "470 G02202_V3_SIC_ACCESSOM2_1deg_2018_02.nc\n",
      "471 G02202_V3_SIC_ACCESSOM2_1deg_2018_03.nc\n",
      "472 G02202_V3_SIC_ACCESSOM2_1deg_2018_04.nc\n",
      "473 G02202_V3_SIC_ACCESSOM2_1deg_2018_05.nc\n",
      "474 G02202_V3_SIC_ACCESSOM2_1deg_2018_06.nc\n",
      "475 G02202_V3_SIC_ACCESSOM2_1deg_2018_07.nc\n",
      "476 G02202_V3_SIC_ACCESSOM2_1deg_2018_08.nc\n",
      "477 G02202_V3_SIC_ACCESSOM2_1deg_2018_09.nc\n",
      "478 G02202_V3_SIC_ACCESSOM2_1deg_2018_10.nc\n",
      "479 G02202_V3_SIC_ACCESSOM2_1deg_2018_11.nc\n",
      "480 G02202_V3_SIC_ACCESSOM2_1deg_2018_12.nc\n",
      "481 G02202_V3_SIC_ACCESSOM2_1deg_2019_01.nc\n",
      "482 G02202_V3_SIC_ACCESSOM2_1deg_2019_02.nc\n",
      "483 G02202_V3_SIC_ACCESSOM2_1deg_2019_03.nc\n",
      "484 G02202_V3_SIC_ACCESSOM2_1deg_2019_04.nc\n",
      "485 G02202_V3_SIC_ACCESSOM2_1deg_2019_05.nc\n",
      "486 G02202_V3_SIC_ACCESSOM2_1deg_2019_06.nc\n",
      "487 G02202_V3_SIC_ACCESSOM2_1deg_2019_07.nc\n",
      "488 G02202_V3_SIC_ACCESSOM2_1deg_2019_08.nc\n",
      "489 G02202_V3_SIC_ACCESSOM2_1deg_2019_09.nc\n",
      "490 G02202_V3_SIC_ACCESSOM2_1deg_2019_10.nc\n",
      "491 G02202_V3_SIC_ACCESSOM2_1deg_2019_11.nc\n",
      "492 G02202_V3_SIC_ACCESSOM2_1deg_2019_12.nc\n"
     ]
    }
   ],
   "source": [
    "for i, t in enumerate(regridded.time):\n",
    "    fname = '_'.join(['G02202_V3_SIC_ACCESSOM2_1deg'] + str(t.data).split('-')[0:2])+'.nc'\n",
    "    print(i, fname)\n",
    "    regridded.isel(time=[i]).to_netcdf(path=fname, unlimited_dims='time')  # BUG: no time axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:analysis3]",
   "language": "python",
   "name": "conda-env-analysis3-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
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 },
 "nbformat": 4,
 "nbformat_minor": 4
}