GMTplot_midc.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1fffd8dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import cartopy.crs as ccrs\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os \n",
    "%matplotlib inline\n",
    "from datetime import timedelta\n",
    "#### TODO: check selection time periods before averaging taht is consistent across models and vars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "19873b5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "ddir= '/dodrio/scratch/projects/2022_200/project_output/bclimate/sdeherto/postprocessing/'\n",
    "#read in different files\n",
    "def open_ds(var,case,esm='cesm',model='cam',ens='001',stream='h0'):\n",
    "    if esm=='cesm':\n",
    "        if case[:4]=='hist':\n",
    "            comp='BHIST'\n",
    "            tseriesdir = ddir + 'b.e213.'+comp+'_BPRP.f09_g17.esm-hist-i308.'+ens+'/' \n",
    "        else:\n",
    "            comp='BSSP126'\n",
    "            tseriesdir = ddir + 'b.e213.'+comp+'_BPRP.f09_g17.esm-'+case+'-i308.'+ens+'/' \n",
    "\n",
    "        # define filename\n",
    "        fn = var+'_b.e213.'+comp+'_BPRP.f09_g17.esm-'+case+'-i308.'+ ens + '.' + model + '.' + stream + '.nc'\n",
    "    else:\n",
    "        ens='i'+ens[-1]\n",
    "\n",
    "        if case=='hist':\n",
    "            case='histctl'\n",
    "            tspan='1980-2014'\n",
    "        else:\n",
    "            tspan='2015-2099'\n",
    "        tseriesdir = ddir + esm+'/'+case+'/'\n",
    "        # define filename\n",
    "        if stream=='h0':\n",
    "            fn = var+'_monthly_'+esm+'_'+case+'_'+ens+'_no-dynveg_'+tspan+ '.nc'\n",
    "        elif stream=='h2':\n",
    "            fn = var+'_3hr_'+esm+'_'+case+'_'+ens+'_no-dynveg_'+tspan+ '.nc'\n",
    "        elif stream=='h1':\n",
    "            fn = var+'_'+esm+'_'+case+'_'+ens+'_no-dynveg_'+tspan+ '.nc'\n",
    "\n",
    "    # check if variable timeseries exists and open variable as data array\n",
    "    if not os.path.isfile(tseriesdir + fn):\n",
    "        print(fn + ' does not exists in ')\n",
    "        print(tseriesdir)\n",
    "        return\n",
    "    else: \n",
    "        \n",
    "        if esm=='cesm':\n",
    "            ds = xr.open_dataset(tseriesdir+fn)\n",
    "        else:\n",
    "           # ds = xr.open_dataset(savedir+fn)\n",
    "            #print(ds.time.attrs['units'])\n",
    "            ds = xr.open_dataset(tseriesdir+fn)\n",
    "            ds['time']=pd.to_datetime(ds['time'], format='%Y%m%d') \n",
    "        return ds\n",
    "\n",
    "def open_da(var, case,esm='cesm', model='cam',ens='001',stream='h0'):\n",
    "    ds = open_ds(var, case, esm,model,ens,stream)\n",
    "    if var=='2t':\n",
    "        var='var167'\n",
    "    da = ds[var]\n",
    "    return da\n",
    "\n",
    "\n",
    "\n",
    "ds_TREFHT_hist_1=open_da('TREFHT','hist','cesm','cam','001')\n",
    "ds_TREFHT_hist_2=open_da('TREFHT','hist','cesm','cam','002')\n",
    "ds_TREFHT_hist_3=open_da('TREFHT','hist','cesm','cam','003')\n",
    "\n",
    "ds_TREFHT_ctl_1=open_da('TREFHT','futctl','cesm','cam','001')\n",
    "ds_TREFHT_ctl_2=open_da('TREFHT','futctl','cesm','cam','002')\n",
    "ds_TREFHT_ctl_3=open_da('TREFHT','futctl','cesm','cam','003')\n",
    "\n",
    "ds_TREFHT_sust_1=open_da('TREFHT','futsust','cesm','cam','001')\n",
    "ds_TREFHT_sust_2=open_da('TREFHT','futsust','cesm','cam','002')\n",
    "ds_TREFHT_sust_3=open_da('TREFHT','futsust','cesm','cam','003')\n",
    "\n",
    "ds_TREFHT_ineq_1=open_da('TREFHT','futineq','cesm','cam','001')\n",
    "ds_TREFHT_ineq_2=open_da('TREFHT','futineq','cesm','cam','002')\n",
    "ds_TREFHT_ineq_3=open_da('TREFHT','futineq','cesm','cam','003')\n",
    "\n",
    "\n",
    "ds_TREFHT_mpiesm_hist_1=open_da('tas','hist','mpiesm','cam','001')\n",
    "ds_TREFHT_mpiesm_hist_2=open_da('tas','hist','mpiesm','cam','002')\n",
    "ds_TREFHT_mpiesm_hist_3=open_da('tas','hist','mpiesm','cam','003')\n",
    "\n",
    "ds_TREFHT_mpiesm_ctl_1=open_da('tas','futctl','mpiesm','cam','001')\n",
    "ds_TREFHT_mpiesm_ctl_2=open_da('tas','futctl','mpiesm','cam','002')\n",
    "ds_TREFHT_mpiesm_ctl_3=open_da('tas','futctl','mpiesm','cam','003')\n",
    "\n",
    "ds_TREFHT_mpiesm_sust_1=open_da('tas','futsust','mpiesm','cam','001')\n",
    "ds_TREFHT_mpiesm_sust_2=open_da('tas','futsust','mpiesm','cam','002')\n",
    "ds_TREFHT_mpiesm_sust_3=open_da('tas','futsust','mpiesm','cam','003')\n",
    "\n",
    "ds_TREFHT_mpiesm_ineq_1=open_da('tas','futineq','mpiesm','cam','001')\n",
    "ds_TREFHT_mpiesm_ineq_2=open_da('tas','futineq','mpiesm','cam','002')\n",
    "ds_TREFHT_mpiesm_ineq_3=open_da('tas','futineq','mpiesm','cam','003')\n",
    "\n",
    "ds_TREFHT_ecearth_hist_1=open_da('tas','hist','ecearth','cam','001')\n",
    "ds_TREFHT_ecearth_hist_2=open_da('tas','hist','ecearth','cam','002')\n",
    "ds_TREFHT_ecearth_hist_3=open_da('tas','hist','ecearth','cam','003')\n",
    "\n",
    "ds_TREFHT_ecearth_ctl_1=open_da('tas','futctl','ecearth','cam','001')\n",
    "ds_TREFHT_ecearth_ctl_2=open_da('tas','futctl','ecearth','cam','002')\n",
    "ds_TREFHT_ecearth_ctl_3=open_da('tas','futctl','ecearth','cam','003')\n",
    "\n",
    "ds_TREFHT_ecearth_sust_1=open_da('tas','futsust','ecearth','cam','001')\n",
    "ds_TREFHT_ecearth_sust_2=open_da('tas','futsust','ecearth','cam','002')\n",
    "ds_TREFHT_ecearth_sust_3=open_da('tas','futsust','ecearth','cam','003')\n",
    "\n",
    "ds_TREFHT_ecearth_ineq_1=open_da('tas','futineq','ecearth','cam','001')\n",
    "ds_TREFHT_ecearth_ineq_2=open_da('tas','futineq','ecearth','cam','002')\n",
    "ds_TREFHT_ecearth_ineq_3=open_da('tas','futineq','ecearth','cam','003')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3471416a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CESM\n",
      "futctl-presctl\n",
      "1.0825913\n",
      "0.54460794\n",
      "(1.0501109841721965, 1.1150716063124226)\n",
      "1.8242789235048913e-176\n",
      "sust-futctl\n",
      "0.121675394\n",
      "0.38069963\n",
      "(0.09897054258693659, 0.1443802463639835)\n",
      "1.1192057594042001e-22\n",
      "ineq-futctl\n",
      "0.23021735\n",
      "0.3812199\n",
      "(0.2074814724673104, 0.2529532325516868)\n",
      "2.0966039636655324e-69\n",
      "sust-ineq\n",
      "-0.10854196\n",
      "0.35909072\n",
      "(-0.1299580582173171, -0.08712585785075998)\n",
      "1.2149037904445706e-22\n",
      "EC-EARTH\n",
      "futctl-presctl\n",
      "1.2030952\n",
      "0.5757659\n",
      "(1.168756631333475, 1.2374337640217496)\n",
      "3.1267855006135732e-177\n",
      "sust-futctl\n",
      "-0.049071956\n",
      "0.36665198\n",
      "(-0.07093900906257758, -0.027204903789232873)\n",
      "1.07825228823115e-05\n",
      "ineq-futctl\n",
      "0.05170989\n",
      "0.36900255\n",
      "(0.029702650266257367, 0.0737171304649438)\n",
      "2.5482960712228947e-06\n",
      "sust-ineq\n",
      "-0.10078184\n",
      "0.37736544\n",
      "(-0.1232878442502909, -0.07827584188214012)\n",
      "1.7580571166658953e-16\n",
      "MPI-ESM\n",
      "futctl-presctl\n",
      "0.5704117\n",
      "0.43543306\n",
      "(0.5444425394352719, 0.5963808248225406)\n",
      "1.9420746107694703e-158\n",
      "sust-futctl\n",
      "0.049819637\n",
      "0.32995448\n",
      "(0.0301412166013865, 0.06949805757854896)\n",
      "4.0848601709386584e-07\n",
      "ineq-futctl\n",
      "0.14518286\n",
      "0.3540712\n",
      "(0.12406612563420694, 0.1662996001214846)\n",
      "1.811696963691474e-35\n",
      "sust-ineq\n",
      "-0.09536322\n",
      "0.3554276\n",
      "(-0.11656085432586014, -0.07416558979931534)\n",
      "2.3671582108639387e-17\n"
     ]
    }
   ],
   "source": [
    "##compute statistical significance of differences\n",
    "#1) pool all ensemble members\n",
    "#2) take spatial mean\n",
    "#3) compute differences, stdev and significance\n",
    "\n",
    "from scipy import stats\n",
    "ds_TREFHT_hist_mean=xr.concat([ds_TREFHT_hist_1.mean(['lat','lon'])[-30*12-1:-1],ds_TREFHT_hist_2.mean(['lat','lon'])[-30*12-1:-1],ds_TREFHT_hist_3.mean(['lat','lon'])[-30*12-1:-1]],'time')\n",
    "ds_TREFHT_ineq_mean=xr.concat([ds_TREFHT_ineq_1.mean(['lat','lon'])[-65*12-1:-35*12-1],ds_TREFHT_ineq_2.mean(['lat','lon'])[-65*12-1:-35*12-1],ds_TREFHT_ineq_3.mean(['lat','lon'])[-65*12-1:-35*12-1]],'time')\n",
    "ds_TREFHT_sust_mean=xr.concat([ds_TREFHT_sust_1.mean(['lat','lon'])[-65*12-1:-35*12-1],ds_TREFHT_sust_2.mean(['lat','lon'])[-65*12-1:-35*12-1],ds_TREFHT_sust_3.mean(['lat','lon'])[-65*12-1:-35*12-1]],'time')\n",
    "ds_TREFHT_ctl_mean=xr.concat([ds_TREFHT_ctl_1.mean(['lat','lon'])[-65*12-1:-35*12-1],ds_TREFHT_ctl_2.mean(['lat','lon'])[-65*12-1:-35*12-1],ds_TREFHT_ctl_3.mean(['lat','lon'])[-65*12-1:-35*12-1]],'time')\n",
    "\n",
    "diff_ctl=ds_TREFHT_ctl_mean.values-ds_TREFHT_hist_mean.values\n",
    "diff_sust=ds_TREFHT_sust_mean.values-ds_TREFHT_ctl_mean.values\n",
    "diff_ineq=ds_TREFHT_ineq_mean.values-ds_TREFHT_ctl_mean.values\n",
    "diff_lcc=ds_TREFHT_sust_mean.values-ds_TREFHT_ineq_mean.values\n",
    "\n",
    "diff_list=[diff_ctl,diff_sust,diff_ineq,diff_lcc]\n",
    "case_list=['futctl-presctl','sust-futctl','ineq-futctl','sust-ineq']\n",
    "print('CESM')\n",
    "for i in range(len(diff_list)):\n",
    "    print(case_list[i])\n",
    "    print(np.mean(diff_list[i]))\n",
    "    print(np.std(diff_list[i]))\n",
    "    n = len(diff_list[i])\n",
    "    std_err = np.std(diff_list[i]) / np.sqrt(n)\n",
    "    ci = stats.norm.interval(0.95, loc=np.mean(diff_list[i]), scale=std_err)    \n",
    "    print(ci)\n",
    "    jos,pvalue= stats.wilcoxon(diff_list[i])\n",
    "    print(pvalue)\n",
    "\n",
    "\n",
    "ds_TREFHT_ecearth_hist_mean=xr.concat([ds_TREFHT_ecearth_hist_1.mean(['lat','lon'])[-30*12:],ds_TREFHT_ecearth_hist_2.mean(['lat','lon'])[-30*12:],ds_TREFHT_ecearth_hist_3.mean(['lat','lon'])[-30*12:]],'time')\n",
    "ds_TREFHT_ecearth_ineq_mean=xr.concat([ds_TREFHT_ecearth_ineq_1.mean(['lat','lon'])[-65*12:-35*12],ds_TREFHT_ecearth_ineq_2.mean(['lat','lon'])[-65*12:-35*12],ds_TREFHT_ecearth_ineq_3.mean(['lat','lon'])[-65*12:-35*12]],'time')\n",
    "ds_TREFHT_ecearth_sust_mean=xr.concat([ds_TREFHT_ecearth_sust_1.mean(['lat','lon'])[-65*12:-35*12],ds_TREFHT_ecearth_sust_2.mean(['lat','lon'])[-65*12:-35*12],ds_TREFHT_ecearth_sust_3.mean(['lat','lon'])[-65*12:-35*12]],'time')\n",
    "ds_TREFHT_ecearth_ctl_mean=xr.concat([ds_TREFHT_ecearth_ctl_1.mean(['lat','lon'])[-65*12:-35*12],ds_TREFHT_ecearth_ctl_2.mean(['lat','lon'])[-65*12:-35*12],ds_TREFHT_ecearth_ctl_3.mean(['lat','lon'])[-65*12:-35*12]],'time')\n",
    "\n",
    "diff_ctl=ds_TREFHT_ecearth_ctl_mean.values-ds_TREFHT_ecearth_hist_mean.values\n",
    "diff_sust=ds_TREFHT_ecearth_sust_mean.values-ds_TREFHT_ecearth_ctl_mean.values\n",
    "diff_ineq=ds_TREFHT_ecearth_ineq_mean.values-ds_TREFHT_ecearth_ctl_mean.values\n",
    "diff_lcc=ds_TREFHT_ecearth_sust_mean.values-ds_TREFHT_ecearth_ineq_mean.values\n",
    "\n",
    "diff_list=[diff_ctl,diff_sust,diff_ineq,diff_lcc]\n",
    "case_list=['futctl-presctl','sust-futctl','ineq-futctl','sust-ineq']\n",
    "print('EC-EARTH')\n",
    "for i in range(len(diff_list)):\n",
    "    print(case_list[i])\n",
    "    print(np.mean(diff_list[i]))\n",
    "    print(np.std(diff_list[i]))\n",
    "    n = len(diff_list[i])\n",
    "    std_err = np.std(diff_list[i]) / np.sqrt(n)\n",
    "    ci = stats.norm.interval(0.95, loc=np.mean(diff_list[i]), scale=std_err)    \n",
    "    print(ci)\n",
    "    jos,pvalue= stats.wilcoxon(diff_list[i])\n",
    "    print(pvalue)\n",
    "    \n",
    "\n",
    "ds_TREFHT_mpiesm_hist_mean=xr.concat([ds_TREFHT_mpiesm_hist_1.mean(['lat','lon'])[-30*12:],ds_TREFHT_mpiesm_hist_2.mean(['lat','lon'])[-30*12:],ds_TREFHT_mpiesm_hist_3.mean(['lat','lon'])[-30*12:]],'time')\n",
    "ds_TREFHT_mpiesm_ineq_mean=xr.concat([ds_TREFHT_mpiesm_ineq_1.mean(['lat','lon'])[-65*12:-35*12],ds_TREFHT_mpiesm_ineq_2.mean(['lat','lon'])[-65*12:-35*12],ds_TREFHT_mpiesm_ineq_3.mean(['lat','lon'])[-65*12:-35*12]],'time')\n",
    "ds_TREFHT_mpiesm_sust_mean=xr.concat([ds_TREFHT_mpiesm_sust_1.mean(['lat','lon'])[-65*12:-35*12],ds_TREFHT_mpiesm_sust_2.mean(['lat','lon'])[-65*12:-35*12],ds_TREFHT_mpiesm_sust_3.mean(['lat','lon'])[-65*12:-35*12]],'time')\n",
    "ds_TREFHT_mpiesm_ctl_mean=xr.concat([ds_TREFHT_mpiesm_ctl_1.mean(['lat','lon'])[-65*12:-35*12],ds_TREFHT_mpiesm_ctl_2.mean(['lat','lon'])[-65*12:-35*12],ds_TREFHT_mpiesm_ctl_3.mean(['lat','lon'])[-65*12:-35*12]],'time')\n",
    "\n",
    "diff_ctl=ds_TREFHT_mpiesm_ctl_mean.values-ds_TREFHT_mpiesm_hist_mean.values\n",
    "diff_sust=ds_TREFHT_mpiesm_sust_mean.values-ds_TREFHT_mpiesm_ctl_mean.values\n",
    "diff_ineq=ds_TREFHT_mpiesm_ineq_mean.values-ds_TREFHT_mpiesm_ctl_mean.values\n",
    "diff_lcc=ds_TREFHT_mpiesm_sust_mean.values-ds_TREFHT_mpiesm_ineq_mean.values\n",
    "\n",
    "diff_list=[diff_ctl,diff_sust,diff_ineq,diff_lcc]\n",
    "case_list=['futctl-presctl','sust-futctl','ineq-futctl','sust-ineq']\n",
    "print('MPI-ESM')\n",
    "for i in range(len(diff_list)):\n",
    "    print(case_list[i])\n",
    "    print(np.mean(diff_list[i]))\n",
    "    print(np.std(diff_list[i]))\n",
    "    n = len(diff_list[i])\n",
    "    std_err = np.std(diff_list[i]) / np.sqrt(n)\n",
    "    ci = stats.norm.interval(0.95, loc=np.mean(diff_list[i]), scale=std_err)    \n",
    "    print(ci)\n",
    "    jos,pvalue= stats.wilcoxon(diff_list[i])\n",
    "    print(pvalue)"
   ]
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       ".xr-wrap {\n",
       "  display: block;\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-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",
       "  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",
       "  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-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-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",
       "  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.DataArray &#x27;tas&#x27; (time: 360)&gt;\n",
       "array([279.13208, 278.63065, 279.04898, 279.87622, 281.5337 , 282.68427,\n",
       "       283.6505 , 283.30618, 282.4308 , 281.151  , 279.84113, 279.53442,\n",
       "       279.3335 , 278.5105 , 279.18314, 280.1665 , 282.02457, 282.99707,\n",
       "       283.45126, 283.41537, 282.73645, 281.06592, 279.93198, 279.31927,\n",
       "       279.12787, 278.95047, 279.55746, 280.15042, 281.5426 , 282.79175,\n",
       "       283.49347, 283.046  , 282.2527 , 280.73267, 279.66544, 278.82767,\n",
       "       278.69373, 278.2277 , 278.599  , 279.77814, 281.1432 , 282.9118 ,\n",
       "       283.90237, 283.17194, 282.1124 , 281.12045, 279.90408, 278.8373 ,\n",
       "       278.6391 , 278.20282, 278.60294, 279.8982 , 281.28598, 282.8876 ,\n",
       "       283.31888, 283.4689 , 282.58328, 280.69934, 279.69946, 279.2471 ,\n",
       "       279.24762, 278.72974, 279.09122, 279.7734 , 281.6122 , 283.07117,\n",
       "       283.91876, 283.74857, 282.6729 , 281.1124 , 280.254  , 279.32007,\n",
       "       279.3397 , 279.1049 , 279.23358, 280.5334 , 281.8882 , 283.34506,\n",
       "       283.76337, 283.85007, 282.50046, 281.2603 , 280.16132, 279.4678 ,\n",
       "       278.72565, 278.70273, 278.60315, 280.3143 , 281.29578, 283.04767,\n",
       "       283.51718, 283.56158, 282.6093 , 281.10275, 280.0131 , 279.13998,\n",
       "       278.9017 , 278.70792, 278.43497, 279.65762, 281.52292, 283.00314,\n",
       "       283.84518, 283.3943 , 282.6383 , 280.96936, 279.8021 , 279.17453,\n",
       "       279.0646 , 279.11096, 279.04773, 279.97897, 281.58057, 283.49722,\n",
       "       283.74945, 283.87888, 282.75238, 281.50702, 280.1674 , 279.2737 ,\n",
       "...\n",
       "       283.94464, 283.62622, 282.64517, 280.92657, 279.75198, 279.43832,\n",
       "       279.61713, 278.764  , 279.1398 , 280.28595, 281.67154, 282.99173,\n",
       "       283.79294, 283.58115, 282.79034, 281.45206, 280.11908, 279.57562,\n",
       "       278.82803, 278.63477, 279.14005, 279.93448, 281.21106, 283.0544 ,\n",
       "       283.53094, 283.25342, 282.24103, 281.2096 , 280.14984, 279.39792,\n",
       "       278.98618, 279.0219 , 278.86563, 279.6062 , 281.23523, 282.9765 ,\n",
       "       283.88242, 283.5864 , 282.36288, 281.1013 , 280.02866, 279.37378,\n",
       "       278.90018, 278.83917, 279.0288 , 280.53452, 281.48676, 282.7476 ,\n",
       "       283.47903, 283.3738 , 282.53427, 281.52405, 279.923  , 279.45428,\n",
       "       279.36023, 279.0737 , 278.6527 , 279.91714, 281.20053, 282.7366 ,\n",
       "       283.48517, 283.4045 , 282.39264, 281.09772, 279.5098 , 278.88293,\n",
       "       278.84308, 278.77335, 278.99503, 279.92523, 281.23044, 282.87595,\n",
       "       283.42972, 283.38998, 282.45914, 281.41107, 280.23047, 279.68347,\n",
       "       279.42816, 279.0865 , 279.1005 , 279.9884 , 281.3154 , 283.2195 ,\n",
       "       283.8401 , 283.55853, 282.32373, 281.07138, 280.01077, 279.22888,\n",
       "       278.5474 , 278.48956, 279.05038, 279.95435, 281.47604, 282.68115,\n",
       "       283.58743, 283.34906, 282.57684, 281.0569 , 279.87906, 279.1576 ,\n",
       "       279.17703, 278.91653, 278.92307, 280.18878, 281.74652, 283.30432,\n",
       "       283.6421 , 283.22385, 282.3808 , 280.67163, 279.7584 , 279.32373],\n",
       "      dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2035-01-16T10:30:00 ... 2064-12-16T10:30:00\n",
       "    height   float64 2.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tas'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 360</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-17a908b9-b130-437d-9373-0f1d07a32cc1' class='xr-array-in' type='checkbox' checked><label for='section-17a908b9-b130-437d-9373-0f1d07a32cc1' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>279.1 278.6 279.0 279.9 281.5 282.7 ... 283.2 282.4 280.7 279.8 279.3</span></div><div class='xr-array-data'><pre>array([279.13208, 278.63065, 279.04898, 279.87622, 281.5337 , 282.68427,\n",
       "       283.6505 , 283.30618, 282.4308 , 281.151  , 279.84113, 279.53442,\n",
       "       279.3335 , 278.5105 , 279.18314, 280.1665 , 282.02457, 282.99707,\n",
       "       283.45126, 283.41537, 282.73645, 281.06592, 279.93198, 279.31927,\n",
       "       279.12787, 278.95047, 279.55746, 280.15042, 281.5426 , 282.79175,\n",
       "       283.49347, 283.046  , 282.2527 , 280.73267, 279.66544, 278.82767,\n",
       "       278.69373, 278.2277 , 278.599  , 279.77814, 281.1432 , 282.9118 ,\n",
       "       283.90237, 283.17194, 282.1124 , 281.12045, 279.90408, 278.8373 ,\n",
       "       278.6391 , 278.20282, 278.60294, 279.8982 , 281.28598, 282.8876 ,\n",
       "       283.31888, 283.4689 , 282.58328, 280.69934, 279.69946, 279.2471 ,\n",
       "       279.24762, 278.72974, 279.09122, 279.7734 , 281.6122 , 283.07117,\n",
       "       283.91876, 283.74857, 282.6729 , 281.1124 , 280.254  , 279.32007,\n",
       "       279.3397 , 279.1049 , 279.23358, 280.5334 , 281.8882 , 283.34506,\n",
       "       283.76337, 283.85007, 282.50046, 281.2603 , 280.16132, 279.4678 ,\n",
       "       278.72565, 278.70273, 278.60315, 280.3143 , 281.29578, 283.04767,\n",
       "       283.51718, 283.56158, 282.6093 , 281.10275, 280.0131 , 279.13998,\n",
       "       278.9017 , 278.70792, 278.43497, 279.65762, 281.52292, 283.00314,\n",
       "       283.84518, 283.3943 , 282.6383 , 280.96936, 279.8021 , 279.17453,\n",
       "       279.0646 , 279.11096, 279.04773, 279.97897, 281.58057, 283.49722,\n",
       "       283.74945, 283.87888, 282.75238, 281.50702, 280.1674 , 279.2737 ,\n",
       "...\n",
       "       283.94464, 283.62622, 282.64517, 280.92657, 279.75198, 279.43832,\n",
       "       279.61713, 278.764  , 279.1398 , 280.28595, 281.67154, 282.99173,\n",
       "       283.79294, 283.58115, 282.79034, 281.45206, 280.11908, 279.57562,\n",
       "       278.82803, 278.63477, 279.14005, 279.93448, 281.21106, 283.0544 ,\n",
       "       283.53094, 283.25342, 282.24103, 281.2096 , 280.14984, 279.39792,\n",
       "       278.98618, 279.0219 , 278.86563, 279.6062 , 281.23523, 282.9765 ,\n",
       "       283.88242, 283.5864 , 282.36288, 281.1013 , 280.02866, 279.37378,\n",
       "       278.90018, 278.83917, 279.0288 , 280.53452, 281.48676, 282.7476 ,\n",
       "       283.47903, 283.3738 , 282.53427, 281.52405, 279.923  , 279.45428,\n",
       "       279.36023, 279.0737 , 278.6527 , 279.91714, 281.20053, 282.7366 ,\n",
       "       283.48517, 283.4045 , 282.39264, 281.09772, 279.5098 , 278.88293,\n",
       "       278.84308, 278.77335, 278.99503, 279.92523, 281.23044, 282.87595,\n",
       "       283.42972, 283.38998, 282.45914, 281.41107, 280.23047, 279.68347,\n",
       "       279.42816, 279.0865 , 279.1005 , 279.9884 , 281.3154 , 283.2195 ,\n",
       "       283.8401 , 283.55853, 282.32373, 281.07138, 280.01077, 279.22888,\n",
       "       278.5474 , 278.48956, 279.05038, 279.95435, 281.47604, 282.68115,\n",
       "       283.58743, 283.34906, 282.57684, 281.0569 , 279.87906, 279.1576 ,\n",
       "       279.17703, 278.91653, 278.92307, 280.18878, 281.74652, 283.30432,\n",
       "       283.6421 , 283.22385, 282.3808 , 280.67163, 279.7584 , 279.32373],\n",
       "      dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-e11ec1f9-69eb-4653-915c-52485bda7814' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e11ec1f9-69eb-4653-915c-52485bda7814' class='xr-section-summary' >Coordinates: <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 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'>2035-01-16T10:30:00 ... 2064-12-...</div><input id='attrs-34368c81-2206-43d8-8381-96f9381b38ba' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-34368c81-2206-43d8-8381-96f9381b38ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6c0f125-6070-4b35-aed3-49e7f25510a1' class='xr-var-data-in' type='checkbox'><label for='data-e6c0f125-6070-4b35-aed3-49e7f25510a1' 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;2035-01-16T10:30:00.000000000&#x27;, &#x27;2035-02-14T22:30:00.000000000&#x27;,\n",
       "       &#x27;2035-03-16T10:30:00.000000000&#x27;, ..., &#x27;2064-10-16T10:30:00.000000000&#x27;,\n",
       "       &#x27;2064-11-15T22:30:00.000000000&#x27;, &#x27;2064-12-16T10:30:00.000000000&#x27;],\n",
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       "    height   float64 ..."
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   "source": [
    "ds_TREFHT_ecearth_ineq_3.mean(['lat','lon'])[-65*12:-35*12]\n"
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  {
   "cell_type": "code",
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       "    height   float64 2.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tas'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 30</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-c1937101-49e1-4049-84d3-4018864f645d' class='xr-array-in' type='checkbox' checked><label for='section-c1937101-49e1-4049-84d3-4018864f645d' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>281.0 281.0 280.7 280.7 281.0 280.7 ... 280.8 280.8 280.9 280.9 280.7</span></div><div class='xr-array-data'><pre>array([281.01242, 281.0107 , 280.7295 , 280.74957, 280.97025, 280.72083,\n",
       "       280.6569 , 280.71838, 280.62817, 280.835  , 280.9795 , 280.71164,\n",
       "       280.9001 , 280.89865, 280.93198, 280.7035 , 280.59518, 280.73672,\n",
       "       280.5998 , 280.67157, 280.40903, 280.2796 , 280.37192, 280.44772,\n",
       "       280.55667, 280.80463, 280.84317, 280.85477, 280.90054, 280.6758 ],\n",
       "      dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-192b4d7c-0887-4602-b443-731198e1a577' class='xr-section-summary-in' type='checkbox'  checked><label for='section-192b4d7c-0887-4602-b443-731198e1a577' class='xr-section-summary' >Coordinates: <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 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'>2070-01-01 ... 2099-01-01</div><input id='attrs-77b30fb5-d1db-48d3-b0a0-5320c1a2ff84' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-77b30fb5-d1db-48d3-b0a0-5320c1a2ff84' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81e90add-3cc9-4773-944c-778156598030' class='xr-var-data-in' type='checkbox'><label for='data-81e90add-3cc9-4773-944c-778156598030' 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;2070-01-01T00:00:00.000000000&#x27;, &#x27;2071-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2072-01-01T00:00:00.000000000&#x27;, &#x27;2073-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2074-01-01T00:00:00.000000000&#x27;, &#x27;2075-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2076-01-01T00:00:00.000000000&#x27;, &#x27;2077-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2078-01-01T00:00:00.000000000&#x27;, &#x27;2079-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2080-01-01T00:00:00.000000000&#x27;, &#x27;2081-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2082-01-01T00:00:00.000000000&#x27;, &#x27;2083-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2084-01-01T00:00:00.000000000&#x27;, &#x27;2085-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2086-01-01T00:00:00.000000000&#x27;, &#x27;2087-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2088-01-01T00:00:00.000000000&#x27;, &#x27;2089-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2090-01-01T00:00:00.000000000&#x27;, &#x27;2091-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2092-01-01T00:00:00.000000000&#x27;, &#x27;2093-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2094-01-01T00:00:00.000000000&#x27;, &#x27;2095-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2096-01-01T00:00:00.000000000&#x27;, &#x27;2097-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2098-01-01T00:00:00.000000000&#x27;, &#x27;2099-01-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d58b3e54-2a21-46f9-994b-5189d063b25f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d58b3e54-2a21-46f9-994b-5189d063b25f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7c89a5c-f42b-49bc-b568-8b2dd7aa2f29' class='xr-var-data-in' type='checkbox'><label for='data-b7c89a5c-f42b-49bc-b568-8b2dd7aa2f29' 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>standard_name :</span></dt><dd>height</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>array(2.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b82e1ba8-5d5a-437a-aa25-b6de6d771800' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b82e1ba8-5d5a-437a-aa25-b6de6d771800' 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>"
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       "array([281.01242, 281.0107 , 280.7295 , 280.74957, 280.97025, 280.72083,\n",
       "       280.6569 , 280.71838, 280.62817, 280.835  , 280.9795 , 280.71164,\n",
       "       280.9001 , 280.89865, 280.93198, 280.7035 , 280.59518, 280.73672,\n",
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       "    height   float64 ..."
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "ds_TREFHT_ecearth_ctl_1.mean(['lat','lon']).resample(time='AS').mean('time')[-30:]"
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  },
  {
   "cell_type": "code",
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       "array([279.32626, 279.2097 , 279.19077, 279.09177, 279.36365, 279.33914,\n",
       "       279.21103, 279.20816, 279.29318, 279.25607, 279.36945, 279.3576 ,\n",
       "       279.35858, 279.47528, 279.2214 , 279.1276 , 279.2312 , 279.36963,\n",
       "       279.43497, 279.0896 ], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2040-01-01 2041-01-01 ... 2059-01-01</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tas'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 20</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-9a04f685-d814-42a2-a6dc-be006c63b56d' class='xr-array-in' type='checkbox' checked><label for='section-9a04f685-d814-42a2-a6dc-be006c63b56d' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>279.3 279.2 279.2 279.1 279.4 279.3 ... 279.1 279.2 279.4 279.4 279.1</span></div><div class='xr-array-data'><pre>array([279.32626, 279.2097 , 279.19077, 279.09177, 279.36365, 279.33914,\n",
       "       279.21103, 279.20816, 279.29318, 279.25607, 279.36945, 279.3576 ,\n",
       "       279.35858, 279.47528, 279.2214 , 279.1276 , 279.2312 , 279.36963,\n",
       "       279.43497, 279.0896 ], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-3dd50db8-79c2-4346-9e67-37636288b7dc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3dd50db8-79c2-4346-9e67-37636288b7dc' class='xr-section-summary' >Coordinates: <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 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'>2040-01-01 ... 2059-01-01</div><input id='attrs-f4459d09-bb3e-43b5-ab72-19820b05c9b6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f4459d09-bb3e-43b5-ab72-19820b05c9b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9635d6e7-46ca-4b20-b4ce-70a382e74688' class='xr-var-data-in' type='checkbox'><label for='data-9635d6e7-46ca-4b20-b4ce-70a382e74688' 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;2040-01-01T00:00:00.000000000&#x27;, &#x27;2041-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2042-01-01T00:00:00.000000000&#x27;, &#x27;2043-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2044-01-01T00:00:00.000000000&#x27;, &#x27;2045-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2046-01-01T00:00:00.000000000&#x27;, &#x27;2047-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2048-01-01T00:00:00.000000000&#x27;, &#x27;2049-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2050-01-01T00:00:00.000000000&#x27;, &#x27;2051-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2052-01-01T00:00:00.000000000&#x27;, &#x27;2053-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2054-01-01T00:00:00.000000000&#x27;, &#x27;2055-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2056-01-01T00:00:00.000000000&#x27;, &#x27;2057-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2058-01-01T00:00:00.000000000&#x27;, &#x27;2059-01-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-99f379fd-9580-453e-b28a-857525bf324d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-99f379fd-9580-453e-b28a-857525bf324d' 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": [
       "<xarray.DataArray 'tas' (time: 20)>\n",
       "array([279.32626, 279.2097 , 279.19077, 279.09177, 279.36365, 279.33914,\n",
       "       279.21103, 279.20816, 279.29318, 279.25607, 279.36945, 279.3576 ,\n",
       "       279.35858, 279.47528, 279.2214 , 279.1276 , 279.2312 , 279.36963,\n",
       "       279.43497, 279.0896 ], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2040-01-01 2041-01-01 ... 2059-01-01"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_TREFHT_mpiesm_ctl_1.mean(['lat','lon']).resample(time='AS').mean('time')[-60:-40]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "083c3ed0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#compute spatial means (global and/or regional)\n",
    "\n",
    "ds_TREFHT_ecearth_hist_1_mean=ds_TREFHT_ecearth_hist_1.mean(['lat','lon']).resample(time='AS').mean('time')[-30:]\n",
    "ds_TREFHT_ecearth_hist_2_mean=ds_TREFHT_ecearth_hist_2.mean(['lat','lon']).resample(time='AS').mean('time')[-30:]\n",
    "ds_TREFHT_ecearth_hist_3_mean=ds_TREFHT_ecearth_hist_3.mean(['lat','lon']).resample(time='AS').mean('time')[-30:]\n",
    "\n",
    "ds_TREFHT_ecearth_hist_mean=xr.concat([ds_TREFHT_ecearth_hist_1_mean,ds_TREFHT_ecearth_hist_2_mean,ds_TREFHT_ecearth_hist_3_mean],'time')\n",
    "\n",
    "ds_TREFHT_ecearth_ctl_1_mean=ds_TREFHT_ecearth_ctl_1.mean(['lat','lon']).resample(time='AS').mean('time')[-65:-35]\n",
    "ds_TREFHT_ecearth_ctl_2_mean=ds_TREFHT_ecearth_ctl_2.mean(['lat','lon']).resample(time='AS').mean('time')[-65:-35]\n",
    "ds_TREFHT_ecearth_ctl_3_mean=ds_TREFHT_ecearth_ctl_3.mean(['lat','lon']).resample(time='AS').mean('time')[-65:-35]\n",
    "ds_TREFHT_ecearth_ctl_mean=xr.concat([ds_TREFHT_ecearth_ctl_1_mean,ds_TREFHT_ecearth_ctl_2_mean,ds_TREFHT_ecearth_ctl_3_mean],'time')\n",
    "\n",
    "ds_TREFHT_ecearth_sust_1_mean=ds_TREFHT_ecearth_sust_1.mean(['lat','lon']).resample(time='AS').mean('time')[-65:-35]\n",
    "ds_TREFHT_ecearth_sust_2_mean=ds_TREFHT_ecearth_sust_2.mean(['lat','lon']).resample(time='AS').mean('time')[-65:-35]\n",
    "ds_TREFHT_ecearth_sust_3_mean=ds_TREFHT_ecearth_sust_3.mean(['lat','lon']).resample(time='AS').mean('time')[-65:-35]\n",
    "ds_TREFHT_ecearth_sust_mean=xr.concat([ds_TREFHT_ecearth_sust_1_mean,ds_TREFHT_ecearth_sust_2_mean,ds_TREFHT_ecearth_sust_3_mean],'time')\n",
    "\n",
    "ds_TREFHT_ecearth_ineq_1_mean=ds_TREFHT_ecearth_ineq_1.mean(['lat','lon']).resample(time='AS').mean('time')[-65:-35]\n",
    "ds_TREFHT_ecearth_ineq_2_mean=ds_TREFHT_ecearth_ineq_2.mean(['lat','lon']).resample(time='AS').mean('time')[-65:-35]\n",
    "ds_TREFHT_ecearth_ineq_3_mean=ds_TREFHT_ecearth_ineq_3.mean(['lat','lon']).resample(time='AS').mean('time')[-65:-35]\n",
    "ds_TREFHT_ecearth_ineq_mean=xr.concat([ds_TREFHT_ecearth_ineq_1_mean,ds_TREFHT_ecearth_ineq_2_mean,ds_TREFHT_ecearth_ineq_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_mpiesm_hist_1_mean=ds_TREFHT_mpiesm_hist_1.mean(['lat','lon']).resample(time='AS').mean('time')[-30:]\n",
    "ds_TREFHT_mpiesm_hist_2_mean=ds_TREFHT_mpiesm_hist_2.mean(['lat','lon']).resample(time='AS').mean('time')[-30:]\n",
    "ds_TREFHT_mpiesm_hist_3_mean=ds_TREFHT_mpiesm_hist_3.mean(['lat','lon']).resample(time='AS').mean('time')[-30:]\n",
    "\n",
    "ds_TREFHT_mpiesm_hist_mean=xr.concat([ds_TREFHT_mpiesm_hist_1_mean,ds_TREFHT_mpiesm_hist_2_mean,ds_TREFHT_mpiesm_hist_3_mean],'time')\n",
    "\n",
    "ds_TREFHT_mpiesm_ctl_1_mean=ds_TREFHT_mpiesm_ctl_1.mean(['lat','lon']).resample(time='AS').mean('time')[-65:-35]\n",
    "ds_TREFHT_mpiesm_ctl_2_mean=ds_TREFHT_mpiesm_ctl_2.mean(['lat','lon']).resample(time='AS').mean('time')[-65:-35]\n",
    "ds_TREFHT_mpiesm_ctl_3_mean=ds_TREFHT_mpiesm_ctl_3.mean(['lat','lon']).resample(time='AS').mean('time')[-65:-35]\n",
    "ds_TREFHT_mpiesm_ctl_mean=xr.concat([ds_TREFHT_mpiesm_ctl_1_mean,ds_TREFHT_mpiesm_ctl_2_mean,ds_TREFHT_mpiesm_ctl_3_mean],'time')\n",
    "\n",
    "ds_TREFHT_mpiesm_sust_1_mean=ds_TREFHT_mpiesm_sust_1.mean(['lat','lon']).resample(time='AS').mean('time')[-65:-35]\n",
    "ds_TREFHT_mpiesm_sust_2_mean=ds_TREFHT_mpiesm_sust_2.mean(['lat','lon']).resample(time='AS').mean('time')[-65:-35]\n",
    "ds_TREFHT_mpiesm_sust_3_mean=ds_TREFHT_mpiesm_sust_3.mean(['lat','lon']).resample(time='AS').mean('time')[-65:-35]\n",
    "ds_TREFHT_mpiesm_sust_mean=xr.concat([ds_TREFHT_mpiesm_sust_1_mean,ds_TREFHT_mpiesm_sust_2_mean,ds_TREFHT_mpiesm_sust_3_mean],'time')\n",
    "\n",
    "ds_TREFHT_mpiesm_ineq_1_mean=ds_TREFHT_mpiesm_ineq_1.mean(['lat','lon']).resample(time='AS').mean('time')[-65:-35]\n",
    "ds_TREFHT_mpiesm_ineq_2_mean=ds_TREFHT_mpiesm_ineq_2.mean(['lat','lon']).resample(time='AS').mean('time')[-65:-35]\n",
    "ds_TREFHT_mpiesm_ineq_3_mean=ds_TREFHT_mpiesm_ineq_3.mean(['lat','lon']).resample(time='AS').mean('time')[-65:-35]\n",
    "ds_TREFHT_mpiesm_ineq_mean=xr.concat([ds_TREFHT_mpiesm_ineq_1_mean,ds_TREFHT_mpiesm_ineq_2_mean,ds_TREFHT_mpiesm_ineq_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_hist_1_mean=ds_TREFHT_hist_1.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_hist_2_mean=ds_TREFHT_hist_2.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_hist_3_mean=ds_TREFHT_hist_3.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_hist_mean=xr.concat([ds_TREFHT_hist_1_mean,ds_TREFHT_hist_2_mean,ds_TREFHT_hist_3_mean],'time')\n",
    "\n",
    "ds_TREFHT_ctl_1_mean=ds_TREFHT_ctl_1.mean(['lat','lon']).resample(time='AS').mean('time')[-66:-36]\n",
    "ds_TREFHT_ctl_2_mean=ds_TREFHT_ctl_2.mean(['lat','lon']).resample(time='AS').mean('time')[-66:-36]\n",
    "ds_TREFHT_ctl_3_mean=ds_TREFHT_ctl_3.mean(['lat','lon']).resample(time='AS').mean('time')[-66:-36]\n",
    "ds_TREFHT_ctl_mean=xr.concat([ds_TREFHT_ctl_1_mean,ds_TREFHT_ctl_2_mean,ds_TREFHT_ctl_3_mean],'time')\n",
    "\n",
    "ds_TREFHT_sust_1_mean=ds_TREFHT_sust_1.mean(['lat','lon']).resample(time='AS').mean('time')[-66:-36]\n",
    "ds_TREFHT_sust_2_mean=ds_TREFHT_sust_2.mean(['lat','lon']).resample(time='AS').mean('time')[-66:-36]\n",
    "ds_TREFHT_sust_3_mean=ds_TREFHT_sust_3.mean(['lat','lon']).resample(time='AS').mean('time')[-66:-36]\n",
    "ds_TREFHT_sust_mean=xr.concat([ds_TREFHT_sust_1_mean,ds_TREFHT_sust_2_mean,ds_TREFHT_sust_3_mean],'time')\n",
    "\n",
    "ds_TREFHT_ineq_1_mean=ds_TREFHT_ineq_1.mean(['lat','lon']).resample(time='AS').mean('time')[-66:-36]\n",
    "ds_TREFHT_ineq_2_mean=ds_TREFHT_ineq_2.mean(['lat','lon']).resample(time='AS').mean('time')[-66:-36]\n",
    "ds_TREFHT_ineq_3_mean=ds_TREFHT_ineq_3.mean(['lat','lon']).resample(time='AS').mean('time')[-66:-36]\n",
    "ds_TREFHT_ineq_mean=xr.concat([ds_TREFHT_ineq_1_mean,ds_TREFHT_ineq_2_mean,ds_TREFHT_ineq_3_mean],'time')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b714c57e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#compute spatial means (global and/or regional)\n",
    "mask_dir='/dodrio/scratch/projects/2022_200/project_output/bclimate/sdeherto/'\n",
    "mask_cesm=xr.open_dataset(mask_dir+'landmask_ecearth_no_ice.nc')\n",
    "da_mask=mask_cesm['landmask']\n",
    "#    da_mask['lat']=da_delta.lat\n",
    "#    da_mask['lon']=da_delta.lon\n",
    "ds_TREFHT_ecearth_land_hist_1_mean=ds_TREFHT_ecearth_hist_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_hist_2_mean=ds_TREFHT_ecearth_hist_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_hist_3_mean=ds_TREFHT_ecearth_hist_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_hist_mean=xr.concat([ds_TREFHT_ecearth_land_hist_1_mean,ds_TREFHT_ecearth_land_hist_2_mean,ds_TREFHT_ecearth_land_hist_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_ecearth_land_ctl_1_mean=ds_TREFHT_ecearth_ctl_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_ctl_2_mean=ds_TREFHT_ecearth_ctl_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_ctl_3_mean=ds_TREFHT_ecearth_ctl_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_ctl_mean=xr.concat([ds_TREFHT_ecearth_land_ctl_1_mean,ds_TREFHT_ecearth_land_ctl_2_mean,ds_TREFHT_ecearth_land_ctl_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_ecearth_land_sust_1_mean=ds_TREFHT_ecearth_sust_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_sust_2_mean=ds_TREFHT_ecearth_sust_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_sust_3_mean=ds_TREFHT_ecearth_sust_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_sust_mean=xr.concat([ds_TREFHT_ecearth_land_sust_1_mean,ds_TREFHT_ecearth_land_sust_2_mean,ds_TREFHT_ecearth_land_sust_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_ecearth_land_ineq_1_mean=ds_TREFHT_ecearth_ineq_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_ineq_2_mean=ds_TREFHT_ecearth_ineq_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_ineq_3_mean=ds_TREFHT_ecearth_ineq_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_ineq_mean=xr.concat([ds_TREFHT_ecearth_land_ineq_1_mean,ds_TREFHT_ecearth_land_ineq_2_mean,ds_TREFHT_ecearth_land_ineq_3_mean],'time')\n",
    "\n",
    "mask_cesm=xr.open_dataset(mask_dir+'landmask_mpiesm_no_ice.nc')\n",
    "da_mask=mask_cesm['landmask']\n",
    "da_mask['lat']=ds_TREFHT_mpiesm_hist_1.lat\n",
    "da_mask['lon']=ds_TREFHT_mpiesm_hist_1.lon\n",
    "\n",
    "ds_TREFHT_mpiesm_land_hist_1_mean=ds_TREFHT_mpiesm_hist_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_hist_2_mean=ds_TREFHT_mpiesm_hist_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_hist_3_mean=ds_TREFHT_mpiesm_hist_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_hist_mean=xr.concat([ds_TREFHT_mpiesm_land_hist_1_mean,ds_TREFHT_mpiesm_land_hist_2_mean,ds_TREFHT_mpiesm_land_hist_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_mpiesm_land_ctl_1_mean=ds_TREFHT_mpiesm_ctl_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_ctl_2_mean=ds_TREFHT_mpiesm_ctl_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_ctl_3_mean=ds_TREFHT_mpiesm_ctl_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_ctl_mean=xr.concat([ds_TREFHT_mpiesm_land_ctl_1_mean,ds_TREFHT_mpiesm_land_ctl_2_mean,ds_TREFHT_mpiesm_land_ctl_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_mpiesm_land_sust_1_mean=ds_TREFHT_mpiesm_sust_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_sust_2_mean=ds_TREFHT_mpiesm_sust_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_sust_3_mean=ds_TREFHT_mpiesm_sust_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_sust_mean=xr.concat([ds_TREFHT_mpiesm_land_sust_1_mean,ds_TREFHT_mpiesm_land_sust_2_mean,ds_TREFHT_mpiesm_land_sust_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_mpiesm_land_ineq_1_mean=ds_TREFHT_mpiesm_ineq_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_ineq_2_mean=ds_TREFHT_mpiesm_ineq_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_ineq_3_mean=ds_TREFHT_mpiesm_ineq_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_ineq_mean=xr.concat([ds_TREFHT_mpiesm_land_ineq_1_mean,ds_TREFHT_mpiesm_land_ineq_2_mean,ds_TREFHT_mpiesm_land_ineq_3_mean],'time')\n",
    "\n",
    "mask_cesm=xr.open_dataset(mask_dir+'landmask_cesm_no_ice.nc')\n",
    "da_mask=mask_cesm['landmask']\n",
    "\n",
    "ds_TREFHT_land_hist_1_mean=ds_TREFHT_hist_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_hist_2_mean=ds_TREFHT_hist_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_hist_3_mean=ds_TREFHT_hist_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_hist_mean=xr.concat([ds_TREFHT_land_hist_1_mean,ds_TREFHT_land_hist_2_mean,ds_TREFHT_land_hist_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_land_ctl_1_mean=ds_TREFHT_ctl_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_ctl_2_mean=ds_TREFHT_ctl_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_ctl_3_mean=ds_TREFHT_ctl_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_ctl_mean=xr.concat([ds_TREFHT_land_ctl_1_mean,ds_TREFHT_land_ctl_2_mean,ds_TREFHT_land_ctl_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_land_sust_1_mean=ds_TREFHT_sust_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_sust_2_mean=ds_TREFHT_sust_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_sust_3_mean=ds_TREFHT_sust_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_sust_mean=xr.concat([ds_TREFHT_land_sust_1_mean,ds_TREFHT_land_sust_2_mean,ds_TREFHT_land_sust_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_land_ineq_1_mean=ds_TREFHT_ineq_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_ineq_2_mean=ds_TREFHT_ineq_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_ineq_3_mean=ds_TREFHT_ineq_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_ineq_mean=xr.concat([ds_TREFHT_land_ineq_1_mean,ds_TREFHT_land_ineq_2_mean,ds_TREFHT_land_ineq_3_mean],'time')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d1e1f8e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4da91956",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c1d96d80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(1.5, 0, 'futctl-histctl'),\n",
       " Text(3.5, 0, 'inequality-histctl'),\n",
       " Text(5.5, 0, 'equality-histctl')]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#make bar plots (3 bars ctl-hist, ineq-ctl and sust-ctl with variability shown through min/max or boxplot?)\n",
    "data=[ds_TREFHT_ctl_1_mean.values-ds_TREFHT_hist_1_mean.values,ds_TREFHT_ineq_1_mean.values-ds_TREFHT_ctl_1_mean.values,ds_TREFHT_sust_1_mean.values-ds_TREFHT_ctl_1_mean.values]\n",
    "data1=[ds_TREFHT_ctl_1_mean.values-ds_TREFHT_hist_1_mean.values,ds_TREFHT_ctl_2_mean.values-ds_TREFHT_hist_2_mean.values,ds_TREFHT_ctl_3_mean.values-ds_TREFHT_hist_3_mean.values]\n",
    "data2=[ds_TREFHT_ineq_1_mean.values-ds_TREFHT_ctl_1_mean.values,ds_TREFHT_ineq_2_mean.values-ds_TREFHT_ctl_2_mean.values,ds_TREFHT_ineq_3_mean.values-ds_TREFHT_ctl_3_mean.values]\n",
    "data3=[ds_TREFHT_sust_1_mean.values-ds_TREFHT_ctl_1_mean.values,ds_TREFHT_sust_2_mean.values-ds_TREFHT_ctl_2_mean.values,ds_TREFHT_sust_3_mean.values-ds_TREFHT_ctl_3_mean.values]\n",
    "data4=[ds_TREFHT_ineq_1_mean.values-ds_TREFHT_hist_1_mean.values,ds_TREFHT_ineq_2_mean.values-ds_TREFHT_hist_2_mean.values,ds_TREFHT_ineq_3_mean.values-ds_TREFHT_hist_3_mean.values]\n",
    "data5=[ds_TREFHT_sust_1_mean.values-ds_TREFHT_hist_1_mean.values,ds_TREFHT_sust_2_mean.values-ds_TREFHT_hist_2_mean.values,ds_TREFHT_sust_3_mean.values-ds_TREFHT_hist_3_mean.values]\n",
    "\n",
    "\n",
    "fig1, ax = plt.subplots()\n",
    "\n",
    "\n",
    "# first boxplot pair\n",
    "bp = ax.boxplot(data1, positions = [1, 1.5,2], widths = 0.4)\n",
    "\n",
    "# second boxplot pair\n",
    "bp = ax.boxplot(data4, positions = [3, 3.5,4], widths = 0.4)\n",
    "\n",
    "# thrid boxplot pair\n",
    "bp = ax.boxplot(data5, positions = [5, 5.5,6], widths = 0.4)\n",
    "\n",
    "ax.set_xticks([1.5, 3.5, 5.5])\n",
    "\n",
    "ax.set_xticklabels(['futctl-histctl', 'inequality-histctl', 'equality-histctl'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0c42b6fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'GMT change to preindustrial')"
      ]
     },
     "execution_count": 10,
     "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": [
    "fig1, ax = plt.subplots()\n",
    "ax2 = ax.twinx()\n",
    "ax.set_ylim(-0.2,2.2)\n",
    "ax2.set_ylim(0.58,2.98)\n",
    "ax.set_xlim(0.5,6.5)\n",
    "\n",
    "data2=[ds_TREFHT_ineq_1_mean.values-ds_TREFHT_ctl_1_mean.values+1,ds_TREFHT_ineq_2_mean.values-ds_TREFHT_ctl_2_mean.values+1,ds_TREFHT_ineq_3_mean.values-ds_TREFHT_ctl_3_mean.values+1]\n",
    "data3=[ds_TREFHT_sust_1_mean.values-ds_TREFHT_ctl_1_mean.values+1,ds_TREFHT_sust_2_mean.values-ds_TREFHT_ctl_2_mean.values+1,ds_TREFHT_sust_3_mean.values-ds_TREFHT_ctl_3_mean.values+1]\n",
    "\n",
    "# first boxplot pair\n",
    "bp = ax.boxplot(data1, positions = [1, 1.5,2], widths = 0.4)\n",
    "\n",
    "# second boxplot pair\n",
    "bp = ax.boxplot(data2, positions = [3, 3.5,4], widths = 0.4)\n",
    "\n",
    "# thrid boxplot pair\n",
    "bp = ax.boxplot(data3, positions = [5, 5.5,6], widths = 0.4)\n",
    "\n",
    "ax.set_xticks([1.5, 3.5, 5.5])\n",
    "\n",
    "ax.set_xticklabels(['SSP1-1.9', 'inequality', 'sustainability'])\n",
    "ax.hlines(y=0,xmin=0,xmax=10,alpha=0.1)\n",
    "ax.hlines(y=1,xmin=0,xmax=10,alpha=0.1,color='r')\n",
    "ax2.hlines(y=1.5,xmin=0,xmax=10,alpha=0.15,color='black')\n",
    "ax2.hlines(y=2,xmin=0,xmax=10,alpha=0.15,color='black')\n",
    "\n",
    "ax.set_ylabel('GMT change to presctl')\n",
    "ax2.set_ylabel('GMT change to preindustrial')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2ce0a0eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.238372802734375\n"
     ]
    }
   ],
   "source": [
    "print(np.median(data2[:])-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bd08e148",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'GMT change to futctl')"
      ]
     },
     "execution_count": 12,
     "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": [
    "fig1, ax = plt.subplots()\n",
    "ax2 = ax.twinx()\n",
    "ax.set_ylim(-0.6,3)\n",
    "ax2.set_ylim(-0.85,2.75)\n",
    "ax.set_xlim(0.5,6.5)\n",
    "\n",
    "data1=[ds_TREFHT_mpiesm_ctl_1_mean.values-ds_TREFHT_mpiesm_hist_1_mean.values,ds_TREFHT_mpiesm_ctl_2_mean.values-ds_TREFHT_mpiesm_hist_2_mean.values,ds_TREFHT_mpiesm_ctl_3_mean.values-ds_TREFHT_mpiesm_hist_3_mean.values]\n",
    "data2=[ds_TREFHT_mpiesm_ineq_1_mean.values-ds_TREFHT_mpiesm_ctl_1_mean.values+0.25,ds_TREFHT_mpiesm_ineq_2_mean.values-ds_TREFHT_mpiesm_ctl_2_mean.values+0.25,ds_TREFHT_mpiesm_ineq_3_mean.values-ds_TREFHT_mpiesm_ctl_3_mean.values+0.25]\n",
    "data3=[ds_TREFHT_mpiesm_sust_1_mean.values-ds_TREFHT_mpiesm_ctl_1_mean.values+0.25,ds_TREFHT_mpiesm_sust_2_mean.values-ds_TREFHT_mpiesm_ctl_2_mean.values+0.25,ds_TREFHT_mpiesm_sust_3_mean.values-ds_TREFHT_mpiesm_ctl_3_mean.values+0.25]\n",
    "\n",
    "\n",
    "# first boxplot pair\n",
    "bp = ax.boxplot(data1, positions = [1, 1.5,2], widths = 0.4)\n",
    "\n",
    "# second boxplot pair\n",
    "bp = ax.boxplot(data2, positions = [3, 3.5,4], widths = 0.4)\n",
    "\n",
    "# thrid boxplot pair\n",
    "bp = ax.boxplot(data3, positions = [5, 5.5,6], widths = 0.4)\n",
    "\n",
    "ax.set_xticks([1.5, 3.5, 5.5])\n",
    "\n",
    "ax.set_xticklabels(['futctl-histctl', 'inequality-futctl', 'equality-futctl'])\n",
    "ax.hlines(y=0,xmin=0,xmax=10,alpha=0.1)\n",
    "ax2.hlines(y=0,xmin=0,xmax=10,alpha=0.1,color='r')\n",
    "ax.set_ylabel('GMT change to histctl')\n",
    "ax2.set_ylabel('GMT change to futctl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "76b3bfbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'GMT change to futctl')"
      ]
     },
     "execution_count": 13,
     "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": [
    "fig1, ax = plt.subplots()\n",
    "ax2 = ax.twinx()\n",
    "ax.set_ylim(-0.6,3)\n",
    "ax2.set_ylim(-1.8,1.8)\n",
    "ax.set_xlim(0.5,6.5)\n",
    "\n",
    "data1=[ds_TREFHT_ecearth_ctl_1_mean.values-ds_TREFHT_ecearth_hist_1_mean.values,ds_TREFHT_ecearth_ctl_2_mean.values-ds_TREFHT_ecearth_hist_2_mean.values,ds_TREFHT_ecearth_ctl_3_mean.values-ds_TREFHT_ecearth_hist_3_mean.values]\n",
    "data2=[ds_TREFHT_ecearth_ineq_1_mean.values-ds_TREFHT_ecearth_ctl_1_mean.values+1.2,ds_TREFHT_ecearth_ineq_2_mean.values-ds_TREFHT_ecearth_ctl_2_mean.values+1.2,ds_TREFHT_ecearth_ineq_3_mean.values-ds_TREFHT_ecearth_ctl_3_mean.values+1.2]\n",
    "data3=[ds_TREFHT_ecearth_sust_1_mean.values-ds_TREFHT_ecearth_ctl_1_mean.values+1.2,ds_TREFHT_ecearth_sust_2_mean.values-ds_TREFHT_ecearth_ctl_2_mean.values+1.2,ds_TREFHT_ecearth_sust_3_mean.values-ds_TREFHT_ecearth_ctl_3_mean.values+1.2]\n",
    "\n",
    "\n",
    "# first boxplot pair\n",
    "bp = ax.boxplot(data1, positions = [1, 1.5,2], widths = 0.4)\n",
    "\n",
    "# second boxplot pair\n",
    "bp = ax.boxplot(data2, positions = [3, 3.5,4], widths = 0.4)\n",
    "\n",
    "# thrid boxplot pair\n",
    "bp = ax.boxplot(data3, positions = [5, 5.5,6], widths = 0.4)\n",
    "\n",
    "ax.set_xticks([1.5, 3.5, 5.5])\n",
    "\n",
    "ax.set_xticklabels(['futctl-histctl', 'inequality-futctl', 'equality-futctl'])\n",
    "ax.hlines(y=0,xmin=0,xmax=10,alpha=0.1)\n",
    "ax2.hlines(y=0,xmin=0,xmax=10,alpha=0.1,color='r')\n",
    "ax.set_ylabel('GMT change to histctl')\n",
    "ax2.set_ylabel('GMT change to futctl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2944075e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'GMT change to preindustrial (°C)')"
      ]
     },
     "execution_count": 14,
     "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": [
    "##make boxplot per esm and case use color coding for cases\n",
    "\n",
    "fig1, ax = plt.subplots()\n",
    "ax2 = ax.twinx()\n",
    "ax.set_ylim(-0.5,2.5)\n",
    "ax2.set_ylim(0.22,3.28)\n",
    "ax.set_xlim(0.5,6.5)\n",
    "\n",
    "\n",
    "list_futctl_histctl=[ds_TREFHT_ctl_mean.values-ds_TREFHT_hist_mean.values,ds_TREFHT_mpiesm_ctl_mean.values-ds_TREFHT_mpiesm_hist_mean.values,ds_TREFHT_ecearth_ctl_mean.values-ds_TREFHT_ecearth_hist_mean.values]\n",
    "list_futineq_futctl=[ds_TREFHT_ineq_mean.values-ds_TREFHT_hist_mean.values,ds_TREFHT_mpiesm_ineq_mean.values-ds_TREFHT_mpiesm_hist_mean.values,ds_TREFHT_ecearth_ineq_mean.values-ds_TREFHT_ecearth_hist_mean.values]\n",
    "list_futsust_futctl=[ds_TREFHT_sust_mean.values-ds_TREFHT_hist_mean.values,ds_TREFHT_mpiesm_sust_mean.values-ds_TREFHT_mpiesm_hist_mean.values,ds_TREFHT_ecearth_sust_mean.values-ds_TREFHT_ecearth_hist_mean.values]\n",
    "\n",
    "data1=[list_futctl_histctl[0],list_futctl_histctl[0]+list_futineq_futctl[0],list_futctl_histctl[0]+list_futsust_futctl[0]]\n",
    "data2=[list_futctl_histctl[1],list_futctl_histctl[1]+list_futineq_futctl[1],list_futctl_histctl[1]+list_futsust_futctl[1]]\n",
    "data3=[list_futctl_histctl[2],list_futctl_histctl[2]+list_futineq_futctl[2],list_futctl_histctl[2]+list_futsust_futctl[2]]\n",
    "\n",
    "\n",
    "def box_plot(data, edge_color, fill_color,pos0):\n",
    "    bp = ax.boxplot(data, positions = [pos0,pos0+2,pos0+4], widths = 0.4,patch_artist=True)\n",
    "    \n",
    "    for element in ['boxes', 'whiskers', 'fliers', 'means', 'medians', 'caps']:\n",
    "        plt.setp(bp[element], color=edge_color)\n",
    "\n",
    "    for patch in bp['boxes']:\n",
    "        patch.set(facecolor=fill_color)       \n",
    "        \n",
    "    return bp\n",
    "\n",
    "# first boxplot pair\n",
    "bp1=box_plot(list_futctl_histctl, edge_color='black', fill_color='pink',pos0=1)\n",
    "\n",
    "# second boxplot pair\n",
    "bp2=box_plot(list_futineq_futctl, edge_color='black', fill_color='lightblue',pos0=1.5)\n",
    "\n",
    "# thrid boxplot pair\n",
    "bp3=box_plot(list_futsust_futctl, edge_color='black', fill_color='lightgreen',pos0=2)\n",
    "\n",
    "ax.set_xticks([1.5, 3.5, 5.5])\n",
    "\n",
    "ax.set_xticklabels(['CESM', 'MPI-ESM', 'EC-EARTH'])\n",
    "ax.hlines(y=0,xmin=0,xmax=10,alpha=0.1)\n",
    "#ax2.hlines(y=0,xmin=0,xmax=10,alpha=0.1,color='r')\n",
    "ax.set_ylabel('GMT change to presCTL (°C)')\n",
    "ax.legend([bp1[\"boxes\"][0], bp2[\"boxes\"][0],bp3[\"boxes\"][0]], ['futCTL-presCTL', 'futineq-presCTL','futsust-presCTL'])\n",
    "\n",
    "#ax2.set_ylabel('GMT change to futctl')\n",
    "ax2.hlines(y=1.5,xmin=0,xmax=10,alpha=0.15,color='black')\n",
    "ax2.hlines(y=2,xmin=0,xmax=10,alpha=0.15,color='black')\n",
    "\n",
    "ax2.set_ylabel('GMT change to preindustrial (°C)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4d34fcce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cesm 1.0823632,mpiesm 0.57041425,ecearth 1.2030978\n"
     ]
    }
   ],
   "source": [
    "print('cesm '+str(list_futctl_histctl[0].mean())+',mpiesm '+str(list_futctl_histctl[1].mean())+',ecearth '+str(list_futctl_histctl[2].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f845beee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cesm 1.3125818,mpiesm 0.7155928,ecearth 1.254801\n"
     ]
    }
   ],
   "source": [
    "print('cesm '+str(list_futineq_futctl[0].mean())+',mpiesm '+str(list_futineq_futctl[1].mean())+',ecearth '+str(list_futineq_futctl[2].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "77040017",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cesm 1.2040429,mpiesm 0.6202335,ecearth 1.1540215\n"
     ]
    }
   ],
   "source": [
    "print('cesm '+str(list_futsust_futctl[0].mean())+',mpiesm '+str(list_futsust_futctl[1].mean())+',ecearth '+str(list_futsust_futctl[2].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "73afe4ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'GMT change to preindustrial')"
      ]
     },
     "execution_count": 18,
     "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": [
    "##make boxplot per esm and case use color coding for cases\n",
    "\n",
    "fig1, ax = plt.subplots()\n",
    "ax2 = ax.twinx()\n",
    "ax.set_ylim(-1,3)\n",
    "ax2.set_ylim(-0.28,3.78)\n",
    "ax.set_xlim(0.5,6.5)\n",
    "\n",
    "\n",
    "list_futctl_histctl=[ds_TREFHT_land_ctl_mean.values-ds_TREFHT_land_hist_mean.values,ds_TREFHT_mpiesm_land_ctl_mean.values-ds_TREFHT_mpiesm_land_hist_mean.values,ds_TREFHT_ecearth_land_ctl_mean.values-ds_TREFHT_ecearth_land_hist_mean.values]\n",
    "list_futineq_futctl=[ds_TREFHT_land_ineq_mean.values-ds_TREFHT_land_hist_mean.values,ds_TREFHT_mpiesm_land_ineq_mean.values-ds_TREFHT_mpiesm_land_hist_mean.values,ds_TREFHT_ecearth_land_ineq_mean.values-ds_TREFHT_ecearth_land_hist_mean.values]\n",
    "list_futsust_futctl=[ds_TREFHT_land_sust_mean.values-ds_TREFHT_land_hist_mean.values,ds_TREFHT_mpiesm_land_sust_mean.values-ds_TREFHT_mpiesm_land_hist_mean.values,ds_TREFHT_ecearth_land_sust_mean.values-ds_TREFHT_ecearth_land_hist_mean.values]\n",
    "\n",
    "data1=[list_futctl_histctl[0],list_futctl_histctl[0]+list_futineq_futctl[0],list_futctl_histctl[0]+list_futsust_futctl[0]]\n",
    "data2=[list_futctl_histctl[1],list_futctl_histctl[1]+list_futineq_futctl[1],list_futctl_histctl[1]+list_futsust_futctl[1]]\n",
    "data3=[list_futctl_histctl[2],list_futctl_histctl[2]+list_futineq_futctl[2],list_futctl_histctl[2]+list_futsust_futctl[2]]\n",
    "\n",
    "\n",
    "def box_plot(data, edge_color, fill_color,pos0):\n",
    "    bp = ax.boxplot(data, positions = [pos0,pos0+2,pos0+4], widths = 0.4,patch_artist=True)\n",
    "    \n",
    "    for element in ['boxes', 'whiskers', 'fliers', 'means', 'medians', 'caps']:\n",
    "        plt.setp(bp[element], color=edge_color)\n",
    "\n",
    "    for patch in bp['boxes']:\n",
    "        patch.set(facecolor=fill_color)       \n",
    "        \n",
    "    return bp\n",
    "\n",
    "# first boxplot pair\n",
    "bp1=box_plot(list_futctl_histctl, edge_color='black', fill_color='pink',pos0=1)\n",
    "\n",
    "# second boxplot pair\n",
    "bp2=box_plot(list_futineq_futctl, edge_color='black', fill_color='lightblue',pos0=1.5)\n",
    "\n",
    "# thrid boxplot pair\n",
    "bp3=box_plot(list_futsust_futctl, edge_color='black', fill_color='lightgreen',pos0=2)\n",
    "\n",
    "ax.set_xticks([1.5, 3.5, 5.5])\n",
    "\n",
    "ax.set_xticklabels(['CESM', 'MPI-ESM', 'EC-EARTH'])\n",
    "ax.hlines(y=0,xmin=0,xmax=10,alpha=0.1)\n",
    "#ax2.hlines(y=0,xmin=0,xmax=10,alpha=0.1,color='r')\n",
    "ax.set_ylabel('GMT change to histctl')\n",
    "ax.legend([bp1[\"boxes\"][0], bp2[\"boxes\"][0],bp3[\"boxes\"][0]], ['futctl-histctl', 'futineq-histctl','futsust-histctl'])\n",
    "\n",
    "#ax2.set_ylabel('GMT change to futland_ctl')\n",
    "ax2.hlines(y=1.5,xmin=0,xmax=10,alpha=0.15,color='black')\n",
    "ax2.hlines(y=2,xmin=0,xmax=10,alpha=0.15,color='black')\n",
    "\n",
    "ax2.set_ylabel('GMT change to preindustrial')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e3cb6523",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cesm 1.584035,mpiesm 0.36705932,ecearth 1.130245\n"
     ]
    }
   ],
   "source": [
    "print('cesm '+str(list_futctl_histctl[0].mean())+',mpiesm '+str(list_futctl_histctl[1].mean())+',ecearth '+str(list_futctl_histctl[2].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "52bcd2b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cesm 1.845182,mpiesm 0.561872,ecearth 1.4087412\n"
     ]
    }
   ],
   "source": [
    "print('cesm '+str(list_futineq_futctl[0].mean())+',mpiesm '+str(list_futineq_futctl[1].mean())+',ecearth '+str(list_futineq_futctl[2].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bc037114",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cesm 1.4800557,mpiesm 0.45736796,ecearth 1.1713209\n"
     ]
    }
   ],
   "source": [
    "print('cesm '+str(list_futsust_futctl[0].mean())+',mpiesm '+str(list_futsust_futctl[1].mean())+',ecearth '+str(list_futsust_futctl[2].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1ece2d2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "ds_TREFHT_hist_1=open_da('wbgt','hist','cesm','cam','001')\n",
    "ds_TREFHT_hist_2=open_da('wbgt','hist','cesm','cam','002')\n",
    "ds_TREFHT_hist_3=open_da('wbgt','hist','cesm','cam','003')\n",
    "\n",
    "ds_TREFHT_ctl_1=open_da('wbgt','futctl','cesm','cam','001')\n",
    "ds_TREFHT_ctl_2=open_da('wbgt','futctl','cesm','cam','002')\n",
    "ds_TREFHT_ctl_3=open_da('wbgt','futctl','cesm','cam','003')\n",
    "\n",
    "ds_TREFHT_sust_1=open_da('wbgt','futsust','cesm','cam','001')\n",
    "ds_TREFHT_sust_2=open_da('wbgt','futsust','cesm','cam','002')\n",
    "ds_TREFHT_sust_3=open_da('wbgt','futsust','cesm','cam','003')\n",
    "\n",
    "ds_TREFHT_ineq_1=open_da('wbgt','futineq','cesm','cam','001')\n",
    "ds_TREFHT_ineq_2=open_da('wbgt','futineq','cesm','cam','002')\n",
    "ds_TREFHT_ineq_3=open_da('wbgt','futineq','cesm','cam','003')\n",
    "\n",
    "\n",
    "ds_TREFHT_mpiesm_hist_1=open_da('wbgt','hist','mpiesm','cam','001')\n",
    "ds_TREFHT_mpiesm_hist_2=open_da('wbgt','hist','mpiesm','cam','002')\n",
    "ds_TREFHT_mpiesm_hist_3=open_da('wbgt','hist','mpiesm','cam','003')\n",
    "\n",
    "ds_TREFHT_mpiesm_ctl_1=open_da('wbgt','futctl','mpiesm','cam','001')\n",
    "ds_TREFHT_mpiesm_ctl_2=open_da('wbgt','futctl','mpiesm','cam','002')\n",
    "ds_TREFHT_mpiesm_ctl_3=open_da('wbgt','futctl','mpiesm','cam','003')\n",
    "\n",
    "ds_TREFHT_mpiesm_sust_1=open_da('wbgt','futsust','mpiesm','cam','001')\n",
    "ds_TREFHT_mpiesm_sust_2=open_da('wbgt','futsust','mpiesm','cam','002')\n",
    "ds_TREFHT_mpiesm_sust_3=open_da('wbgt','futsust','mpiesm','cam','003')\n",
    "\n",
    "ds_TREFHT_mpiesm_ineq_1=open_da('wbgt','futineq','mpiesm','cam','001')\n",
    "ds_TREFHT_mpiesm_ineq_2=open_da('wbgt','futineq','mpiesm','cam','002')\n",
    "ds_TREFHT_mpiesm_ineq_3=open_da('wbgt','futineq','mpiesm','cam','003')\n",
    "\n",
    "ds_TREFHT_ecearth_hist_1=open_da('wbgt','hist','ecearth','cam','001')\n",
    "ds_TREFHT_ecearth_hist_2=open_da('wbgt','hist','ecearth','cam','002')\n",
    "ds_TREFHT_ecearth_hist_3=open_da('wbgt','hist','ecearth','cam','003')\n",
    "\n",
    "ds_TREFHT_ecearth_ctl_1=open_da('wbgt','futctl','ecearth','cam','001')\n",
    "ds_TREFHT_ecearth_ctl_2=open_da('wbgt','futctl','ecearth','cam','002')\n",
    "ds_TREFHT_ecearth_ctl_3=open_da('wbgt','futctl','ecearth','cam','003')\n",
    "\n",
    "ds_TREFHT_ecearth_sust_1=open_da('wbgt','futsust','ecearth','cam','001')\n",
    "ds_TREFHT_ecearth_sust_2=open_da('wbgt','futsust','ecearth','cam','002')\n",
    "ds_TREFHT_ecearth_sust_3=open_da('wbgt','futsust','ecearth','cam','003')\n",
    "\n",
    "ds_TREFHT_ecearth_ineq_1=open_da('wbgt','futineq','ecearth','cam','001')\n",
    "ds_TREFHT_ecearth_ineq_2=open_da('wbgt','futineq','ecearth','cam','002')\n",
    "ds_TREFHT_ecearth_ineq_3=open_da('wbgt','futineq','ecearth','cam','003')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "85527c44",
   "metadata": {},
   "outputs": [],
   "source": [
    "#compute spatial means (global and/or regional)\n",
    "\n",
    "ds_TREFHT_ecearth_hist_1_mean=ds_TREFHT_ecearth_hist_1.mean(['lat','lon']).resample(time='AS').mean('time')[-31:]\n",
    "ds_TREFHT_ecearth_hist_2_mean=ds_TREFHT_ecearth_hist_2.mean(['lat','lon']).resample(time='AS').mean('time')[-31:]\n",
    "ds_TREFHT_ecearth_hist_3_mean=ds_TREFHT_ecearth_hist_3.mean(['lat','lon']).resample(time='AS').mean('time')[-31:]\n",
    "\n",
    "ds_TREFHT_ecearth_hist_mean=xr.concat([ds_TREFHT_ecearth_hist_1_mean,ds_TREFHT_ecearth_hist_2_mean,ds_TREFHT_ecearth_hist_3_mean],'time')\n",
    "\n",
    "ds_TREFHT_ecearth_ctl_1_mean=ds_TREFHT_ecearth_ctl_1.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_ctl_2_mean=ds_TREFHT_ecearth_ctl_2.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_ctl_3_mean=ds_TREFHT_ecearth_ctl_3.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_ctl_mean=xr.concat([ds_TREFHT_ecearth_ctl_1_mean,ds_TREFHT_ecearth_ctl_2_mean,ds_TREFHT_ecearth_ctl_3_mean],'time')\n",
    "\n",
    "ds_TREFHT_ecearth_sust_1_mean=ds_TREFHT_ecearth_sust_1.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_sust_2_mean=ds_TREFHT_ecearth_sust_2.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_sust_3_mean=ds_TREFHT_ecearth_sust_3.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_sust_mean=xr.concat([ds_TREFHT_ecearth_sust_1_mean,ds_TREFHT_ecearth_sust_2_mean,ds_TREFHT_ecearth_sust_3_mean],'time')\n",
    "\n",
    "ds_TREFHT_ecearth_ineq_1_mean=ds_TREFHT_ecearth_ineq_1.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_ineq_2_mean=ds_TREFHT_ecearth_ineq_2.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_ineq_3_mean=ds_TREFHT_ecearth_ineq_3.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_ineq_mean=xr.concat([ds_TREFHT_ecearth_ineq_1_mean,ds_TREFHT_ecearth_ineq_2_mean,ds_TREFHT_ecearth_ineq_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_mpiesm_hist_1_mean=ds_TREFHT_mpiesm_hist_1.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_hist_2_mean=ds_TREFHT_mpiesm_hist_2.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_hist_3_mean=ds_TREFHT_mpiesm_hist_3.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "\n",
    "ds_TREFHT_mpiesm_hist_mean=xr.concat([ds_TREFHT_mpiesm_hist_1_mean,ds_TREFHT_mpiesm_hist_2_mean,ds_TREFHT_mpiesm_hist_3_mean],'time')\n",
    "\n",
    "ds_TREFHT_mpiesm_ctl_1_mean=ds_TREFHT_mpiesm_ctl_1.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_ctl_2_mean=ds_TREFHT_mpiesm_ctl_2.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_ctl_3_mean=ds_TREFHT_mpiesm_ctl_3.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_ctl_mean=xr.concat([ds_TREFHT_mpiesm_ctl_1_mean,ds_TREFHT_mpiesm_ctl_2_mean,ds_TREFHT_mpiesm_ctl_3_mean],'time')\n",
    "\n",
    "ds_TREFHT_mpiesm_sust_1_mean=ds_TREFHT_mpiesm_sust_1.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_sust_2_mean=ds_TREFHT_mpiesm_sust_2.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_sust_3_mean=ds_TREFHT_mpiesm_sust_3.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_sust_mean=xr.concat([ds_TREFHT_mpiesm_sust_1_mean,ds_TREFHT_mpiesm_sust_2_mean,ds_TREFHT_mpiesm_sust_3_mean],'time')\n",
    "\n",
    "ds_TREFHT_mpiesm_ineq_1_mean=ds_TREFHT_mpiesm_ineq_1.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_ineq_2_mean=ds_TREFHT_mpiesm_ineq_2.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_ineq_3_mean=ds_TREFHT_mpiesm_ineq_3.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_ineq_mean=xr.concat([ds_TREFHT_mpiesm_ineq_1_mean,ds_TREFHT_mpiesm_ineq_2_mean,ds_TREFHT_mpiesm_ineq_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_hist_1_mean=ds_TREFHT_hist_1.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_hist_2_mean=ds_TREFHT_hist_2.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_hist_3_mean=ds_TREFHT_hist_3.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_hist_mean=xr.concat([ds_TREFHT_hist_1_mean,ds_TREFHT_hist_2_mean,ds_TREFHT_hist_3_mean],'time')\n",
    "\n",
    "ds_TREFHT_ctl_1_mean=ds_TREFHT_ctl_1.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ctl_2_mean=ds_TREFHT_ctl_2.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ctl_3_mean=ds_TREFHT_ctl_3.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ctl_mean=xr.concat([ds_TREFHT_ctl_1_mean,ds_TREFHT_ctl_2_mean,ds_TREFHT_ctl_3_mean],'time')\n",
    "\n",
    "ds_TREFHT_sust_1_mean=ds_TREFHT_sust_1.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_sust_2_mean=ds_TREFHT_sust_2.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_sust_3_mean=ds_TREFHT_sust_3.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_sust_mean=xr.concat([ds_TREFHT_sust_1_mean,ds_TREFHT_sust_2_mean,ds_TREFHT_sust_3_mean],'time')\n",
    "\n",
    "ds_TREFHT_ineq_1_mean=ds_TREFHT_ineq_1.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ineq_2_mean=ds_TREFHT_ineq_2.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ineq_3_mean=ds_TREFHT_ineq_3.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ineq_mean=xr.concat([ds_TREFHT_ineq_1_mean,ds_TREFHT_ineq_2_mean,ds_TREFHT_ineq_3_mean],'time')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2484d1a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'GMT change to preindustrial')"
      ]
     },
     "execution_count": 24,
     "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": [
    "##make boxplot per esm and case use color coding for cases\n",
    "\n",
    "fig1, ax = plt.subplots()\n",
    "ax2 = ax.twinx()\n",
    "ax.set_ylim(-1,3)\n",
    "ax2.set_ylim(-0.28,3.78)\n",
    "ax.set_xlim(0.5,6.5)\n",
    "\n",
    "\n",
    "list_futctl_histctl=[ds_TREFHT_ctl_mean.values-ds_TREFHT_hist_mean.values,ds_TREFHT_mpiesm_ctl_mean.values-ds_TREFHT_mpiesm_hist_mean.values,ds_TREFHT_ecearth_ctl_mean.values-ds_TREFHT_ecearth_hist_mean.values]\n",
    "list_futineq_futctl=[ds_TREFHT_ineq_mean.values-ds_TREFHT_hist_mean.values,ds_TREFHT_mpiesm_ineq_mean.values-ds_TREFHT_mpiesm_hist_mean.values,ds_TREFHT_ecearth_ineq_mean.values-ds_TREFHT_ecearth_hist_mean.values]\n",
    "list_futsust_futctl=[ds_TREFHT_sust_mean.values-ds_TREFHT_hist_mean.values,ds_TREFHT_mpiesm_sust_mean.values-ds_TREFHT_mpiesm_hist_mean.values,ds_TREFHT_ecearth_sust_mean.values-ds_TREFHT_ecearth_hist_mean.values]\n",
    "\n",
    "data1=[list_futctl_histctl[0],list_futctl_histctl[0]+list_futineq_futctl[0],list_futctl_histctl[0]+list_futsust_futctl[0]]\n",
    "data2=[list_futctl_histctl[1],list_futctl_histctl[1]+list_futineq_futctl[1],list_futctl_histctl[1]+list_futsust_futctl[1]]\n",
    "data3=[list_futctl_histctl[2],list_futctl_histctl[2]+list_futineq_futctl[2],list_futctl_histctl[2]+list_futsust_futctl[2]]\n",
    "\n",
    "\n",
    "def box_plot(data, edge_color, fill_color,pos0):\n",
    "    bp = ax.boxplot(data, positions = [pos0,pos0+2,pos0+4], widths = 0.4,patch_artist=True)\n",
    "    \n",
    "    for element in ['boxes', 'whiskers', 'fliers', 'means', 'medians', 'caps']:\n",
    "        plt.setp(bp[element], color=edge_color)\n",
    "\n",
    "    for patch in bp['boxes']:\n",
    "        patch.set(facecolor=fill_color)       \n",
    "        \n",
    "    return bp\n",
    "\n",
    "# first boxplot pair\n",
    "bp1=box_plot(list_futctl_histctl, edge_color='black', fill_color='pink',pos0=1)\n",
    "\n",
    "# second boxplot pair\n",
    "bp2=box_plot(list_futineq_futctl, edge_color='black', fill_color='lightblue',pos0=1.5)\n",
    "\n",
    "# thrid boxplot pair\n",
    "bp3=box_plot(list_futsust_futctl, edge_color='black', fill_color='lightgreen',pos0=2)\n",
    "\n",
    "ax.set_xticks([1.5, 3.5, 5.5])\n",
    "\n",
    "ax.set_xticklabels(['CESM', 'MPI-ESM', 'EC-EARTH'])\n",
    "ax.hlines(y=0,xmin=0,xmax=10,alpha=0.1)\n",
    "#ax2.hlines(y=0,xmin=0,xmax=10,alpha=0.1,color='r')\n",
    "ax.set_ylabel('Global mean WBGT change to histctl')\n",
    "ax.legend([bp1[\"boxes\"][0], bp2[\"boxes\"][0],bp3[\"boxes\"][0]], ['futctl-histctl', 'futineq-histctl','futsust-histctl'])\n",
    "\n",
    "#ax2.set_ylabel('GMT change to futctl')\n",
    "ax2.hlines(y=1.5,xmin=0,xmax=10,alpha=0.15,color='black')\n",
    "ax2.hlines(y=2,xmin=0,xmax=10,alpha=0.15,color='black')\n",
    "\n",
    "ax2.set_ylabel('GMT change to preindustrial')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ed95d0b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cesm 1.1216241457159608,mpiesm 0.3604595238730503,ecearth 0.9842903163544912\n"
     ]
    }
   ],
   "source": [
    "print('cesm '+str(list_futctl_histctl[0].mean())+',mpiesm '+str(list_futctl_histctl[1].mean())+',ecearth '+str(list_futctl_histctl[2].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "db173e61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cesm 1.4125281374896215,mpiesm 0.5125076952808261,ecearth 1.2844391481957524\n"
     ]
    }
   ],
   "source": [
    "print('cesm '+str(list_futineq_futctl[0].mean())+',mpiesm '+str(list_futineq_futctl[1].mean())+',ecearth '+str(list_futineq_futctl[2].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f4c021bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cesm 1.0733757145189662,mpiesm 0.4380604513860293,ecearth 1.0494218535279862\n"
     ]
    }
   ],
   "source": [
    "print('cesm '+str(list_futsust_futctl[0].mean())+',mpiesm '+str(list_futsust_futctl[1].mean())+',ecearth '+str(list_futsust_futctl[2].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6e46dca2",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "ds_TREFHT_hist_1=open_da('wbgt_monmax','hist',esm='cesm',model='clm2',ens='001',stream='h1')\n",
    "ds_TREFHT_hist_2=open_da('wbgt_monmax','hist','cesm','clm2','002',stream='h1')\n",
    "ds_TREFHT_hist_3=open_da('wbgt_monmax','hist','cesm','clm2','003',stream='h1')\n",
    "\n",
    "ds_TREFHT_ctl_1=open_da('wbgt_monmax','futctl','cesm','clm2','001',stream='h1')\n",
    "ds_TREFHT_ctl_2=open_da('wbgt_monmax','futctl','cesm','clm2','002',stream='h1')\n",
    "ds_TREFHT_ctl_3=open_da('wbgt_monmax','futctl','cesm','clm2','003',stream='h1')\n",
    "\n",
    "ds_TREFHT_sust_1=open_da('wbgt_monmax','futsust','cesm','clm2','001',stream='h1')\n",
    "ds_TREFHT_sust_2=open_da('wbgt_monmax','futsust','cesm','clm2','002',stream='h1')\n",
    "ds_TREFHT_sust_3=open_da('wbgt_monmax','futsust','cesm','clm2','003',stream='h1')\n",
    "\n",
    "ds_TREFHT_ineq_1=open_da('wbgt_monmax','futineq','cesm','clm2','001',stream='h1')\n",
    "ds_TREFHT_ineq_2=open_da('wbgt_monmax','futineq','cesm','clm2','002',stream='h1')\n",
    "ds_TREFHT_ineq_3=open_da('wbgt_monmax','futineq','cesm','clm2','003',stream='h1')\n",
    "\n",
    "\n",
    "ds_TREFHT_mpiesm_hist_1=open_da('wbgt_monmax','hist','mpiesm','clm2','001',stream='h1')\n",
    "ds_TREFHT_mpiesm_hist_2=open_da('wbgt_monmax','hist','mpiesm','clm2','002',stream='h1')\n",
    "ds_TREFHT_mpiesm_hist_3=open_da('wbgt_monmax','hist','mpiesm','clm2','003',stream='h1')\n",
    "\n",
    "ds_TREFHT_mpiesm_ctl_1=open_da('wbgt_monmax','futctl','mpiesm','clm2','001',stream='h1')\n",
    "ds_TREFHT_mpiesm_ctl_2=open_da('wbgt_monmax','futctl','mpiesm','clm2','002',stream='h1')\n",
    "ds_TREFHT_mpiesm_ctl_3=open_da('wbgt_monmax','futctl','mpiesm','clm2','003',stream='h1')\n",
    "\n",
    "ds_TREFHT_mpiesm_sust_1=open_da('wbgt_monmax','futsust','mpiesm','clm2','001',stream='h1')\n",
    "ds_TREFHT_mpiesm_sust_2=open_da('wbgt_monmax','futsust','mpiesm','clm2','002',stream='h1')\n",
    "ds_TREFHT_mpiesm_sust_3=open_da('wbgt_monmax','futsust','mpiesm','clm2','003',stream='h1')\n",
    "\n",
    "ds_TREFHT_mpiesm_ineq_1=open_da('wbgt_monmax','futineq','mpiesm','clm2','001',stream='h1')\n",
    "ds_TREFHT_mpiesm_ineq_2=open_da('wbgt_monmax','futineq','mpiesm','clm2','002',stream='h1')\n",
    "ds_TREFHT_mpiesm_ineq_3=open_da('wbgt_monmax','futineq','mpiesm','clm2','003',stream='h1')\n",
    "\n",
    "ds_TREFHT_ecearth_hist_1=open_da('wbgt_monmax','hist','ecearth','clm2','001',stream='h1')\n",
    "ds_TREFHT_ecearth_hist_2=open_da('wbgt_monmax','hist','ecearth','clm2','002',stream='h1')\n",
    "ds_TREFHT_ecearth_hist_3=open_da('wbgt_monmax','hist','ecearth','clm2','003',stream='h1')\n",
    "\n",
    "ds_TREFHT_ecearth_ctl_1=open_da('wbgt_monmax','futctl','ecearth','clm2','001',stream='h1')\n",
    "ds_TREFHT_ecearth_ctl_2=open_da('wbgt_monmax','futctl','ecearth','clm2','002',stream='h1')\n",
    "ds_TREFHT_ecearth_ctl_3=open_da('wbgt_monmax','futctl','ecearth','clm2','003',stream='h1')\n",
    "\n",
    "ds_TREFHT_ecearth_sust_1=open_da('wbgt_monmax','futsust','ecearth','clm2','001',stream='h1')\n",
    "ds_TREFHT_ecearth_sust_2=open_da('wbgt_monmax','futsust','ecearth','clm2','002',stream='h1')\n",
    "ds_TREFHT_ecearth_sust_3=open_da('wbgt_monmax','futsust','ecearth','clm2','003',stream='h1')\n",
    "\n",
    "ds_TREFHT_ecearth_ineq_1=open_da('wbgt_monmax','futineq','ecearth','clm2','001',stream='h1')\n",
    "ds_TREFHT_ecearth_ineq_2=open_da('wbgt_monmax','futineq','ecearth','clm2','002',stream='h1')\n",
    "ds_TREFHT_ecearth_ineq_3=open_da('wbgt_monmax','futineq','ecearth','clm2','003',stream='h1')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e362a2f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#compute spatial means (global and/or regional)\n",
    "mask_dir='/dodrio/scratch/projects/2022_200/project_output/bclimate/sdeherto/wam2layer/scripts/'\n",
    "mask_cesm=xr.open_dataset(mask_dir+'landmask_ecearth.nc')\n",
    "da_mask=mask_cesm['landmask']\n",
    "da_mask=da_mask.mean('time')\n",
    "#    da_mask['lat']=da_delta.lat\n",
    "#    da_mask['lon']=da_delta.lon\n",
    "ds_TREFHT_ecearth_land_hist_1_mean=ds_TREFHT_ecearth_hist_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:]\n",
    "ds_TREFHT_ecearth_land_hist_2_mean=ds_TREFHT_ecearth_hist_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:]\n",
    "ds_TREFHT_ecearth_land_hist_3_mean=ds_TREFHT_ecearth_hist_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:]\n",
    "ds_TREFHT_ecearth_land_hist_mean=xr.concat([ds_TREFHT_ecearth_land_hist_1_mean,ds_TREFHT_ecearth_land_hist_2_mean,ds_TREFHT_ecearth_land_hist_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_ecearth_land_ctl_1_mean=ds_TREFHT_ecearth_ctl_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_ctl_2_mean=ds_TREFHT_ecearth_ctl_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_ctl_3_mean=ds_TREFHT_ecearth_ctl_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_ctl_mean=xr.concat([ds_TREFHT_ecearth_land_ctl_1_mean,ds_TREFHT_ecearth_land_ctl_2_mean,ds_TREFHT_ecearth_land_ctl_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_ecearth_land_sust_1_mean=ds_TREFHT_ecearth_sust_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_sust_2_mean=ds_TREFHT_ecearth_sust_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_sust_3_mean=ds_TREFHT_ecearth_sust_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_sust_mean=xr.concat([ds_TREFHT_ecearth_land_sust_1_mean,ds_TREFHT_ecearth_land_sust_2_mean,ds_TREFHT_ecearth_land_sust_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_ecearth_land_ineq_1_mean=ds_TREFHT_ecearth_ineq_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_ineq_2_mean=ds_TREFHT_ecearth_ineq_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_ineq_3_mean=ds_TREFHT_ecearth_ineq_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_ineq_mean=xr.concat([ds_TREFHT_ecearth_land_ineq_1_mean,ds_TREFHT_ecearth_land_ineq_2_mean,ds_TREFHT_ecearth_land_ineq_3_mean],'time')\n",
    "\n",
    "mask_dir='/dodrio/scratch/projects/2022_200/project_output/bclimate/sdeherto/wam2layer/scripts/'\n",
    "mask_cesm=xr.open_dataset(mask_dir+'landmask_mpiesm.nc')\n",
    "da_mask=mask_cesm['landmask']\n",
    "da_mask['lat']=ds_TREFHT_mpiesm_hist_1.lat\n",
    "da_mask['lon']=ds_TREFHT_mpiesm_hist_1.lon\n",
    "\n",
    "ds_TREFHT_mpiesm_land_hist_1_mean=ds_TREFHT_mpiesm_hist_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_hist_2_mean=ds_TREFHT_mpiesm_hist_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_hist_3_mean=ds_TREFHT_mpiesm_hist_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_hist_mean=xr.concat([ds_TREFHT_mpiesm_land_hist_1_mean,ds_TREFHT_mpiesm_land_hist_2_mean,ds_TREFHT_mpiesm_land_hist_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_mpiesm_land_ctl_1_mean=ds_TREFHT_mpiesm_ctl_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_ctl_2_mean=ds_TREFHT_mpiesm_ctl_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_ctl_3_mean=ds_TREFHT_mpiesm_ctl_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_ctl_mean=xr.concat([ds_TREFHT_mpiesm_land_ctl_1_mean,ds_TREFHT_mpiesm_land_ctl_2_mean,ds_TREFHT_mpiesm_land_ctl_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_mpiesm_land_sust_1_mean=ds_TREFHT_mpiesm_sust_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_sust_2_mean=ds_TREFHT_mpiesm_sust_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_sust_3_mean=ds_TREFHT_mpiesm_sust_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_sust_mean=xr.concat([ds_TREFHT_mpiesm_land_sust_1_mean,ds_TREFHT_mpiesm_land_sust_2_mean,ds_TREFHT_mpiesm_land_sust_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_mpiesm_land_ineq_1_mean=ds_TREFHT_mpiesm_ineq_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_ineq_2_mean=ds_TREFHT_mpiesm_ineq_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_ineq_3_mean=ds_TREFHT_mpiesm_ineq_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_ineq_mean=xr.concat([ds_TREFHT_mpiesm_land_ineq_1_mean,ds_TREFHT_mpiesm_land_ineq_2_mean,ds_TREFHT_mpiesm_land_ineq_3_mean],'time')\n",
    "\n",
    "mask_dir='/dodrio/scratch/projects/2022_200/project_output/bclimate/sdeherto/wam2layer/scripts/'\n",
    "mask_cesm=xr.open_dataset(mask_dir+'landmask_cesm.nc')\n",
    "da_mask=mask_cesm['landmask']\n",
    "\n",
    "ds_TREFHT_land_hist_1_mean=ds_TREFHT_hist_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_hist_2_mean=ds_TREFHT_hist_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_hist_3_mean=ds_TREFHT_hist_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_hist_mean=xr.concat([ds_TREFHT_land_hist_1_mean,ds_TREFHT_land_hist_2_mean,ds_TREFHT_land_hist_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_land_ctl_1_mean=ds_TREFHT_ctl_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_ctl_2_mean=ds_TREFHT_ctl_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_ctl_3_mean=ds_TREFHT_ctl_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_ctl_mean=xr.concat([ds_TREFHT_land_ctl_1_mean,ds_TREFHT_land_ctl_2_mean,ds_TREFHT_land_ctl_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_land_sust_1_mean=ds_TREFHT_sust_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_sust_2_mean=ds_TREFHT_sust_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_sust_3_mean=ds_TREFHT_sust_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_sust_mean=xr.concat([ds_TREFHT_land_sust_1_mean,ds_TREFHT_land_sust_2_mean,ds_TREFHT_land_sust_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_land_ineq_1_mean=ds_TREFHT_ineq_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_ineq_2_mean=ds_TREFHT_ineq_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_ineq_3_mean=ds_TREFHT_ineq_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_ineq_mean=xr.concat([ds_TREFHT_land_ineq_1_mean,ds_TREFHT_land_ineq_2_mean,ds_TREFHT_land_ineq_3_mean],'time')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a84b9cb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'WBGTmonmax change to preindustrial')"
      ]
     },
     "execution_count": 30,
     "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": [
    "##make boxplot per esm and case use color coding for cases\n",
    "\n",
    "fig1, ax = plt.subplots()\n",
    "ax2 = ax.twinx()\n",
    "ax.set_ylim(-1,3)\n",
    "ax2.set_ylim(-0.28,3.78)\n",
    "ax.set_xlim(0.5,6.5)\n",
    "\n",
    "\n",
    "list_futctl_histctl=[ds_TREFHT_land_ctl_mean.values-ds_TREFHT_land_hist_mean.values,ds_TREFHT_mpiesm_land_ctl_mean.values-ds_TREFHT_mpiesm_land_hist_mean.values,ds_TREFHT_ecearth_land_ctl_mean.values-ds_TREFHT_ecearth_land_hist_mean.values]\n",
    "list_futineq_futctl=[ds_TREFHT_land_ineq_mean.values-ds_TREFHT_land_hist_mean.values,ds_TREFHT_mpiesm_land_ineq_mean.values-ds_TREFHT_mpiesm_land_hist_mean.values,ds_TREFHT_ecearth_land_ineq_mean.values-ds_TREFHT_ecearth_land_hist_mean.values]\n",
    "list_futsust_futctl=[ds_TREFHT_land_sust_mean.values-ds_TREFHT_land_hist_mean.values,ds_TREFHT_mpiesm_land_sust_mean.values-ds_TREFHT_mpiesm_land_hist_mean.values,ds_TREFHT_ecearth_land_sust_mean.values-ds_TREFHT_ecearth_land_hist_mean.values]\n",
    "\n",
    "data1=[list_futctl_histctl[0],list_futctl_histctl[0]+list_futineq_futctl[0],list_futctl_histctl[0]+list_futsust_futctl[0]]\n",
    "data2=[list_futctl_histctl[1],list_futctl_histctl[1]+list_futineq_futctl[1],list_futctl_histctl[1]+list_futsust_futctl[1]]\n",
    "data3=[list_futctl_histctl[2],list_futctl_histctl[2]+list_futineq_futctl[2],list_futctl_histctl[2]+list_futsust_futctl[2]]\n",
    "\n",
    "\n",
    "def box_plot(data, edge_color, fill_color,pos0):\n",
    "    bp = ax.boxplot(data, positions = [pos0,pos0+2,pos0+4], widths = 0.4,patch_artist=True)\n",
    "    \n",
    "    for element in ['boxes', 'whiskers', 'fliers', 'means', 'medians', 'caps']:\n",
    "        plt.setp(bp[element], color=edge_color)\n",
    "\n",
    "    for patch in bp['boxes']:\n",
    "        patch.set(facecolor=fill_color)       \n",
    "        \n",
    "    return bp\n",
    "\n",
    "# first boxplot pair\n",
    "bp1=box_plot(list_futctl_histctl, edge_color='black', fill_color='pink',pos0=1)\n",
    "\n",
    "# second boxplot pair\n",
    "bp2=box_plot(list_futineq_futctl, edge_color='black', fill_color='lightblue',pos0=1.5)\n",
    "\n",
    "# thrid boxplot pair\n",
    "bp3=box_plot(list_futsust_futctl, edge_color='black', fill_color='lightgreen',pos0=2)\n",
    "\n",
    "ax.set_xticks([1.5, 3.5, 5.5])\n",
    "\n",
    "ax.set_xticklabels(['CESM', 'MPI-ESM', 'EC-EARTH'])\n",
    "ax.hlines(y=0,xmin=0,xmax=10,alpha=0.1)\n",
    "#ax2.hlines(y=0,xmin=0,xmax=10,alpha=0.1,color='r')\n",
    "ax.set_ylabel('WBGTmonmax change to histctl')\n",
    "ax.legend([bp1[\"boxes\"][0], bp2[\"boxes\"][0],bp3[\"boxes\"][0]], ['futctl-histctl', 'futineq-histctl','futsust-histctl'])\n",
    "\n",
    "#ax2.set_ylabel('GMT change to futland_ctl')\n",
    "ax2.hlines(y=1.5,xmin=0,xmax=10,alpha=0.15,color='black')\n",
    "ax2.hlines(y=2,xmin=0,xmax=10,alpha=0.15,color='black')\n",
    "\n",
    "ax2.set_ylabel('WBGTmonmax change to preindustrial')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ad0da2f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cesm 1.1099837238692039,mpiesm 0.3146596703072349,ecearth 0.8702498750516061\n"
     ]
    }
   ],
   "source": [
    "print('cesm '+str(list_futctl_histctl[0].mean())+',mpiesm '+str(list_futctl_histctl[1].mean())+',ecearth '+str(list_futctl_histctl[2].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "be80209b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cesm 1.3272003672912283,mpiesm 0.438719768617716,ecearth 0.9700679124038761\n"
     ]
    }
   ],
   "source": [
    "print('cesm '+str(list_futineq_futctl[0].mean())+',mpiesm '+str(list_futineq_futctl[1].mean())+',ecearth '+str(list_futineq_futctl[2].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d712b339",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cesm 1.1224539807531617,mpiesm 0.3869800439882214,ecearth 0.9081704654235939\n"
     ]
    }
   ],
   "source": [
    "print('cesm '+str(list_futsust_futctl[0].mean())+',mpiesm '+str(list_futsust_futctl[1].mean())+',ecearth '+str(list_futsust_futctl[2].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d07cdf7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f678ba7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b88d1f5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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