loader.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load packages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import xarray as xr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lib.utils import latest_version, fix_360_longitudes\n",
    "from lib.interpolater import Interpolator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load UGT catalogue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load catalogue\n",
    "df = pd.read_csv(\"./catalogue/inventory_UDG.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>activity</th>\n",
       "      <th>description</th>\n",
       "      <th>domain</th>\n",
       "      <th>ensemble</th>\n",
       "      <th>experiment</th>\n",
       "      <th>from</th>\n",
       "      <th>grid_label</th>\n",
       "      <th>institution</th>\n",
       "      <th>model</th>\n",
       "      <th>product</th>\n",
       "      <th>project</th>\n",
       "      <th>rcm</th>\n",
       "      <th>rcm_version</th>\n",
       "      <th>smg_activity</th>\n",
       "      <th>smg_kind</th>\n",
       "      <th>table</th>\n",
       "      <th>variable</th>\n",
       "      <th>version</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https://data.meteo.unican.es/tds5/dodsC/Spain0...</td>\n",
       "      <td>UC-Spain02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>https://data.meteo.unican.es/tds5/dodsC/Spain0...</td>\n",
       "      <td>UC-Spain02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://data.meteo.unican.es/tds5/dodsC/Spain0...</td>\n",
       "      <td>UC-Spain02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://data.meteo.unican.es/tds5/dodsC/best/B...</td>\n",
       "      <td>BEST</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://data.meteo.unican.es/tds5/dodsC/best/B...</td>\n",
       "      <td>BESTmon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18953</th>\n",
       "      <td>https://data.meteo.unican.es/thredds/dodsC/eth...</td>\n",
       "      <td>CMIP5</td>\n",
       "      <td>CMIP5 NcMLs for ATLAS AR6 from ETHZ snapshot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>r1i1p1</td>\n",
       "      <td>rcp85</td>\n",
       "      <td>ESGF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bcc-csm1-1</td>\n",
       "      <td>output</td>\n",
       "      <td>CMIP5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ATLAS</td>\n",
       "      <td>project</td>\n",
       "      <td>day</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18954</th>\n",
       "      <td>https://data.meteo.unican.es/thredds/dodsC/eth...</td>\n",
       "      <td>CMIP5</td>\n",
       "      <td>CMIP5 NcMLs for ATLAS AR6 from ETHZ snapshot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>r1i1p1</td>\n",
       "      <td>rcp85</td>\n",
       "      <td>ESGF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>inmcm4</td>\n",
       "      <td>output</td>\n",
       "      <td>CMIP5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ATLAS</td>\n",
       "      <td>project</td>\n",
       "      <td>day</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18955</th>\n",
       "      <td>https://data.meteo.unican.es/thredds/dodsC/mir...</td>\n",
       "      <td>ERA5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CDS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>data</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18956</th>\n",
       "      <td>https://data.meteo.unican.es/thredds/dodsC/mir...</td>\n",
       "      <td>ERA5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CDS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>data</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18957</th>\n",
       "      <td>https://data.meteo.unican.es/thredds/dodsC/mir...</td>\n",
       "      <td>W5E5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18958 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                location    activity  \\\n",
       "0      https://data.meteo.unican.es/tds5/dodsC/Spain0...  UC-Spain02   \n",
       "1      https://data.meteo.unican.es/tds5/dodsC/Spain0...  UC-Spain02   \n",
       "2      https://data.meteo.unican.es/tds5/dodsC/Spain0...  UC-Spain02   \n",
       "3      https://data.meteo.unican.es/tds5/dodsC/best/B...        BEST   \n",
       "4      https://data.meteo.unican.es/tds5/dodsC/best/B...     BESTmon   \n",
       "...                                                  ...         ...   \n",
       "18953  https://data.meteo.unican.es/thredds/dodsC/eth...       CMIP5   \n",
       "18954  https://data.meteo.unican.es/thredds/dodsC/eth...       CMIP5   \n",
       "18955  https://data.meteo.unican.es/thredds/dodsC/mir...        ERA5   \n",
       "18956  https://data.meteo.unican.es/thredds/dodsC/mir...        ERA5   \n",
       "18957  https://data.meteo.unican.es/thredds/dodsC/mir...        W5E5   \n",
       "\n",
       "                                        description domain ensemble  \\\n",
       "0                                               NaN    NaN      NaN   \n",
       "1                                               NaN    NaN      NaN   \n",
       "2                                               NaN    NaN      NaN   \n",
       "3                                               NaN    NaN      NaN   \n",
       "4                                               NaN    NaN      NaN   \n",
       "...                                             ...    ...      ...   \n",
       "18953  CMIP5 NcMLs for ATLAS AR6 from ETHZ snapshot    NaN   r1i1p1   \n",
       "18954  CMIP5 NcMLs for ATLAS AR6 from ETHZ snapshot    NaN   r1i1p1   \n",
       "18955                                           NaN    NaN      NaN   \n",
       "18956                                           NaN    NaN      NaN   \n",
       "18957                                           NaN    NaN      NaN   \n",
       "\n",
       "      experiment  from grid_label institution       model product project  \\\n",
       "0            NaN   NaN        NaN         NaN         NaN     NaN     NaN   \n",
       "1            NaN   NaN        NaN         NaN         NaN     NaN     NaN   \n",
       "2            NaN   NaN        NaN         NaN         NaN     NaN     NaN   \n",
       "3            NaN   NaN        NaN         NaN         NaN     NaN     NaN   \n",
       "4            NaN   NaN        NaN         NaN         NaN     NaN     NaN   \n",
       "...          ...   ...        ...         ...         ...     ...     ...   \n",
       "18953      rcp85  ESGF        NaN         NaN  bcc-csm1-1  output   CMIP5   \n",
       "18954      rcp85  ESGF        NaN         NaN      inmcm4  output   CMIP5   \n",
       "18955        NaN   CDS        NaN         NaN         NaN     NaN     NaN   \n",
       "18956        NaN   CDS        NaN         NaN         NaN     NaN     NaN   \n",
       "18957        NaN   NaN        NaN         NaN         NaN     NaN     NaN   \n",
       "\n",
       "       rcm rcm_version smg_activity smg_kind table variable version  \n",
       "0      NaN         NaN          NaN      NaN   NaN      NaN     NaN  \n",
       "1      NaN         NaN          NaN      NaN   NaN      NaN     NaN  \n",
       "2      NaN         NaN          NaN      NaN   NaN      NaN     NaN  \n",
       "3      NaN         NaN          NaN      NaN   NaN      NaN     NaN  \n",
       "4      NaN         NaN          NaN      NaN   NaN      NaN     NaN  \n",
       "...    ...         ...          ...      ...   ...      ...     ...  \n",
       "18953  NaN         NaN        ATLAS  project   day      NaN     NaN  \n",
       "18954  NaN         NaN        ATLAS  project   day      NaN     NaN  \n",
       "18955  NaN         NaN          NaN     data   NaN      NaN     NaN  \n",
       "18956  NaN         NaN          NaN     data   NaN      NaN     NaN  \n",
       "18957  NaN         NaN          NaN      NaN   NaN      NaN     NaN  \n",
       "\n",
       "[18958 rows x 19 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract CMIP6 models with daily resolution\n",
    "df_CMIP6 = df[(df[\"activity\"] == \"CMIP6\") &\n",
    "              (df[\"table\"] == \"day\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>activity</th>\n",
       "      <th>description</th>\n",
       "      <th>domain</th>\n",
       "      <th>ensemble</th>\n",
       "      <th>experiment</th>\n",
       "      <th>from</th>\n",
       "      <th>grid_label</th>\n",
       "      <th>institution</th>\n",
       "      <th>model</th>\n",
       "      <th>product</th>\n",
       "      <th>project</th>\n",
       "      <th>rcm</th>\n",
       "      <th>rcm_version</th>\n",
       "      <th>smg_activity</th>\n",
       "      <th>smg_kind</th>\n",
       "      <th>table</th>\n",
       "      <th>variable</th>\n",
       "      <th>version</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>https://data.meteo.unican.es/thredds/dodsC/CMI...</td>\n",
       "      <td>CMIP6</td>\n",
       "      <td>CMIP6 NcMLs for ATLAS AR6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>historical</td>\n",
       "      <td>ESGF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AS-RCEC</td>\n",
       "      <td>TaiESM1</td>\n",
       "      <td>CMIP</td>\n",
       "      <td>CMIP6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ATLAS</td>\n",
       "      <td>project</td>\n",
       "      <td>day</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>https://data.meteo.unican.es/thredds/dodsC/CMI...</td>\n",
       "      <td>CMIP6</td>\n",
       "      <td>CMIP6 NcMLs for ATLAS AR6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>historical</td>\n",
       "      <td>ESGF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AWI</td>\n",
       "      <td>AWI-CM-1-1-MR</td>\n",
       "      <td>CMIP</td>\n",
       "      <td>CMIP6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ATLAS</td>\n",
       "      <td>project</td>\n",
       "      <td>day</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>https://data.meteo.unican.es/thredds/dodsC/CMI...</td>\n",
       "      <td>CMIP6</td>\n",
       "      <td>CMIP6 NcMLs for ATLAS AR6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>historical</td>\n",
       "      <td>ESGF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BCC</td>\n",
       "      <td>BCC-CSM2-MR</td>\n",
       "      <td>CMIP</td>\n",
       "      <td>CMIP6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ATLAS</td>\n",
       "      <td>project</td>\n",
       "      <td>day</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>https://data.meteo.unican.es/thredds/dodsC/CMI...</td>\n",
       "      <td>CMIP6</td>\n",
       "      <td>CMIP6 NcMLs for ATLAS AR6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>r2i1p1f1</td>\n",
       "      <td>historical</td>\n",
       "      <td>ESGF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CAMS</td>\n",
       "      <td>CAMS-CSM1-0</td>\n",
       "      <td>CMIP</td>\n",
       "      <td>CMIP6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ATLAS</td>\n",
       "      <td>project</td>\n",
       "      <td>day</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>https://data.meteo.unican.es/thredds/dodsC/CMI...</td>\n",
       "      <td>CMIP6</td>\n",
       "      <td>CMIP6 NcMLs for ATLAS AR6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>historical</td>\n",
       "      <td>ESGF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CAS</td>\n",
       "      <td>FGOALS-g3</td>\n",
       "      <td>CMIP</td>\n",
       "      <td>CMIP6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ATLAS</td>\n",
       "      <td>project</td>\n",
       "      <td>day</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18568</th>\n",
       "      <td>https://data.meteo.unican.es/thredds/dodsC/esg...</td>\n",
       "      <td>CMIP6</td>\n",
       "      <td>CMIP6 ESGF replica temporal aggregation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>ssp585</td>\n",
       "      <td>ESGF</td>\n",
       "      <td>gn</td>\n",
       "      <td>NUIST</td>\n",
       "      <td>NESM3</td>\n",
       "      <td>ScenarioMIP</td>\n",
       "      <td>CMIP6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>replica</td>\n",
       "      <td>data</td>\n",
       "      <td>day</td>\n",
       "      <td>pr</td>\n",
       "      <td>v20190811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18569</th>\n",
       "      <td>https://data.meteo.unican.es/thredds/dodsC/esg...</td>\n",
       "      <td>CMIP6</td>\n",
       "      <td>CMIP6 ESGF replica temporal aggregation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>ssp585</td>\n",
       "      <td>ESGF</td>\n",
       "      <td>gn</td>\n",
       "      <td>NUIST</td>\n",
       "      <td>NESM3</td>\n",
       "      <td>ScenarioMIP</td>\n",
       "      <td>CMIP6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>replica</td>\n",
       "      <td>data</td>\n",
       "      <td>day</td>\n",
       "      <td>psl</td>\n",
       "      <td>v20190811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18570</th>\n",
       "      <td>https://data.meteo.unican.es/thredds/dodsC/esg...</td>\n",
       "      <td>CMIP6</td>\n",
       "      <td>CMIP6 ESGF replica temporal aggregation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>ssp585</td>\n",
       "      <td>ESGF</td>\n",
       "      <td>gn</td>\n",
       "      <td>NUIST</td>\n",
       "      <td>NESM3</td>\n",
       "      <td>ScenarioMIP</td>\n",
       "      <td>CMIP6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>replica</td>\n",
       "      <td>data</td>\n",
       "      <td>day</td>\n",
       "      <td>tas</td>\n",
       "      <td>v20190811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18571</th>\n",
       "      <td>https://data.meteo.unican.es/thredds/dodsC/esg...</td>\n",
       "      <td>CMIP6</td>\n",
       "      <td>CMIP6 ESGF replica temporal aggregation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>ssp585</td>\n",
       "      <td>ESGF</td>\n",
       "      <td>gn</td>\n",
       "      <td>NUIST</td>\n",
       "      <td>NESM3</td>\n",
       "      <td>ScenarioMIP</td>\n",
       "      <td>CMIP6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>replica</td>\n",
       "      <td>data</td>\n",
       "      <td>day</td>\n",
       "      <td>tasmax</td>\n",
       "      <td>v20190811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18572</th>\n",
       "      <td>https://data.meteo.unican.es/thredds/dodsC/esg...</td>\n",
       "      <td>CMIP6</td>\n",
       "      <td>CMIP6 ESGF replica temporal aggregation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>ssp585</td>\n",
       "      <td>ESGF</td>\n",
       "      <td>gn</td>\n",
       "      <td>NUIST</td>\n",
       "      <td>NESM3</td>\n",
       "      <td>ScenarioMIP</td>\n",
       "      <td>CMIP6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>replica</td>\n",
       "      <td>data</td>\n",
       "      <td>day</td>\n",
       "      <td>tasmin</td>\n",
       "      <td>v20190811</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2245 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                location activity  \\\n",
       "23     https://data.meteo.unican.es/thredds/dodsC/CMI...    CMIP6   \n",
       "30     https://data.meteo.unican.es/thredds/dodsC/CMI...    CMIP6   \n",
       "37     https://data.meteo.unican.es/thredds/dodsC/CMI...    CMIP6   \n",
       "46     https://data.meteo.unican.es/thredds/dodsC/CMI...    CMIP6   \n",
       "61     https://data.meteo.unican.es/thredds/dodsC/CMI...    CMIP6   \n",
       "...                                                  ...      ...   \n",
       "18568  https://data.meteo.unican.es/thredds/dodsC/esg...    CMIP6   \n",
       "18569  https://data.meteo.unican.es/thredds/dodsC/esg...    CMIP6   \n",
       "18570  https://data.meteo.unican.es/thredds/dodsC/esg...    CMIP6   \n",
       "18571  https://data.meteo.unican.es/thredds/dodsC/esg...    CMIP6   \n",
       "18572  https://data.meteo.unican.es/thredds/dodsC/esg...    CMIP6   \n",
       "\n",
       "                                   description domain  ensemble  experiment  \\\n",
       "23                   CMIP6 NcMLs for ATLAS AR6    NaN  r1i1p1f1  historical   \n",
       "30                   CMIP6 NcMLs for ATLAS AR6    NaN  r1i1p1f1  historical   \n",
       "37                   CMIP6 NcMLs for ATLAS AR6    NaN  r1i1p1f1  historical   \n",
       "46                   CMIP6 NcMLs for ATLAS AR6    NaN  r2i1p1f1  historical   \n",
       "61                   CMIP6 NcMLs for ATLAS AR6    NaN  r1i1p1f1  historical   \n",
       "...                                        ...    ...       ...         ...   \n",
       "18568  CMIP6 ESGF replica temporal aggregation    NaN  r1i1p1f1      ssp585   \n",
       "18569  CMIP6 ESGF replica temporal aggregation    NaN  r1i1p1f1      ssp585   \n",
       "18570  CMIP6 ESGF replica temporal aggregation    NaN  r1i1p1f1      ssp585   \n",
       "18571  CMIP6 ESGF replica temporal aggregation    NaN  r1i1p1f1      ssp585   \n",
       "18572  CMIP6 ESGF replica temporal aggregation    NaN  r1i1p1f1      ssp585   \n",
       "\n",
       "       from grid_label institution          model      product project  rcm  \\\n",
       "23     ESGF        NaN     AS-RCEC        TaiESM1         CMIP   CMIP6  NaN   \n",
       "30     ESGF        NaN         AWI  AWI-CM-1-1-MR         CMIP   CMIP6  NaN   \n",
       "37     ESGF        NaN         BCC    BCC-CSM2-MR         CMIP   CMIP6  NaN   \n",
       "46     ESGF        NaN        CAMS    CAMS-CSM1-0         CMIP   CMIP6  NaN   \n",
       "61     ESGF        NaN         CAS      FGOALS-g3         CMIP   CMIP6  NaN   \n",
       "...     ...        ...         ...            ...          ...     ...  ...   \n",
       "18568  ESGF         gn       NUIST          NESM3  ScenarioMIP   CMIP6  NaN   \n",
       "18569  ESGF         gn       NUIST          NESM3  ScenarioMIP   CMIP6  NaN   \n",
       "18570  ESGF         gn       NUIST          NESM3  ScenarioMIP   CMIP6  NaN   \n",
       "18571  ESGF         gn       NUIST          NESM3  ScenarioMIP   CMIP6  NaN   \n",
       "18572  ESGF         gn       NUIST          NESM3  ScenarioMIP   CMIP6  NaN   \n",
       "\n",
       "      rcm_version smg_activity smg_kind table variable    version  \n",
       "23            NaN        ATLAS  project   day      NaN        NaN  \n",
       "30            NaN        ATLAS  project   day      NaN        NaN  \n",
       "37            NaN        ATLAS  project   day      NaN        NaN  \n",
       "46            NaN        ATLAS  project   day      NaN        NaN  \n",
       "61            NaN        ATLAS  project   day      NaN        NaN  \n",
       "...           ...          ...      ...   ...      ...        ...  \n",
       "18568         NaN      replica     data   day       pr  v20190811  \n",
       "18569         NaN      replica     data   day      psl  v20190811  \n",
       "18570         NaN      replica     data   day      tas  v20190811  \n",
       "18571         NaN      replica     data   day   tasmax  v20190811  \n",
       "18572         NaN      replica     data   day   tasmin  v20190811  \n",
       "\n",
       "[2245 rows x 19 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_CMIP6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ACCESS-CM2', 'ACCESS-ESM1-5', 'AWI-CM-1-1-MR', 'BCC-CSM2-MR',\n",
       "       'CAMS-CSM1-0', 'CESM2', 'CESM2-WACCM', 'CMCC-CM2-SR5',\n",
       "       'CNRM-CM6-1', 'CNRM-CM6-1-HR', 'CNRM-ESM2-1', 'CanESM5',\n",
       "       'EC-Earth3', 'EC-Earth3-AerChem', 'EC-Earth3-Veg',\n",
       "       'EC-Earth3-Veg-LR', 'FGOALS-g3', 'GFDL-CM4', 'GFDL-ESM4',\n",
       "       'HadGEM3-GC31-LL', 'IITM-ESM', 'INM-CM4-8', 'INM-CM5-0',\n",
       "       'IPSL-CM6A-LR', 'KACE-1-0-G', 'KIOST-ESM', 'MIROC-ES2L', 'MIROC6',\n",
       "       'MPI-ESM-1-2-HAM', 'MPI-ESM1-2-HR', 'MPI-ESM1-2-LR', 'MRI-ESM2-0',\n",
       "       'NESM3', 'NorESM2-LM', 'NorESM2-MM', 'TaiESM1', 'UKESM1-0-LL'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(df_CMIP6[\"model\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# extract \"CanESM5\" model for the sceneario \"ssp245\" the variable \"tas\" and the last \"version\"\n",
    "df_CMIP6_subset = df_CMIP6[(df_CMIP6[\"model\"]==\"CESM2\") & \n",
    "                           (df_CMIP6[\"experiment\"]==\"historical\") & \n",
    "                           (df_CMIP6[\"variable\"]==\"tas\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>activity</th>\n",
       "      <th>description</th>\n",
       "      <th>domain</th>\n",
       "      <th>ensemble</th>\n",
       "      <th>experiment</th>\n",
       "      <th>from</th>\n",
       "      <th>grid_label</th>\n",
       "      <th>institution</th>\n",
       "      <th>model</th>\n",
       "      <th>product</th>\n",
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       "      <th>rcm</th>\n",
       "      <th>rcm_version</th>\n",
       "      <th>smg_activity</th>\n",
       "      <th>smg_kind</th>\n",
       "      <th>table</th>\n",
       "      <th>variable</th>\n",
       "      <th>version</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4166</th>\n",
       "      <td>https://data.meteo.unican.es/thredds/dodsC/esg...</td>\n",
       "      <td>CMIP6</td>\n",
       "      <td>CMIP6 ESGF replica ensemble aggregation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>historical</td>\n",
       "      <td>ESGF</td>\n",
       "      <td>gn</td>\n",
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       "      <td>CESM2</td>\n",
       "      <td>CMIP</td>\n",
       "      <td>CMIP6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>replica</td>\n",
       "      <td>data</td>\n",
       "      <td>day</td>\n",
       "      <td>tas</td>\n",
       "      <td>v20190308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11616</th>\n",
       "      <td>https://data.meteo.unican.es/thredds/dodsC/esg...</td>\n",
       "      <td>CMIP6</td>\n",
       "      <td>CMIP6 ESGF replica temporal aggregation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>historical</td>\n",
       "      <td>ESGF</td>\n",
       "      <td>gn</td>\n",
       "      <td>NCAR</td>\n",
       "      <td>CESM2</td>\n",
       "      <td>CMIP</td>\n",
       "      <td>CMIP6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>replica</td>\n",
       "      <td>data</td>\n",
       "      <td>day</td>\n",
       "      <td>tas</td>\n",
       "      <td>v20190308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11638</th>\n",
       "      <td>https://data.meteo.unican.es/thredds/dodsC/esg...</td>\n",
       "      <td>CMIP6</td>\n",
       "      <td>CMIP6 ESGF replica temporal aggregation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>r4i1p1f1</td>\n",
       "      <td>historical</td>\n",
       "      <td>ESGF</td>\n",
       "      <td>gn</td>\n",
       "      <td>NCAR</td>\n",
       "      <td>CESM2</td>\n",
       "      <td>CMIP</td>\n",
       "      <td>CMIP6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>replica</td>\n",
       "      <td>data</td>\n",
       "      <td>day</td>\n",
       "      <td>tas</td>\n",
       "      <td>v20190308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                location activity  \\\n",
       "4166   https://data.meteo.unican.es/thredds/dodsC/esg...    CMIP6   \n",
       "11616  https://data.meteo.unican.es/thredds/dodsC/esg...    CMIP6   \n",
       "11638  https://data.meteo.unican.es/thredds/dodsC/esg...    CMIP6   \n",
       "\n",
       "                                   description domain  ensemble  experiment  \\\n",
       "4166   CMIP6 ESGF replica ensemble aggregation    NaN       NaN  historical   \n",
       "11616  CMIP6 ESGF replica temporal aggregation    NaN  r1i1p1f1  historical   \n",
       "11638  CMIP6 ESGF replica temporal aggregation    NaN  r4i1p1f1  historical   \n",
       "\n",
       "       from grid_label institution  model product project  rcm rcm_version  \\\n",
       "4166   ESGF         gn        NCAR  CESM2    CMIP   CMIP6  NaN         NaN   \n",
       "11616  ESGF         gn        NCAR  CESM2    CMIP   CMIP6  NaN         NaN   \n",
       "11638  ESGF         gn        NCAR  CESM2    CMIP   CMIP6  NaN         NaN   \n",
       "\n",
       "      smg_activity smg_kind table variable    version  \n",
       "4166       replica     data   day      tas  v20190308  \n",
       "11616      replica     data   day      tas  v20190308  \n",
       "11638      replica     data   day      tas  v20190308  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract simulation (last_version)\n",
    "df_CMIP6_simulation = df_CMIP6_subset[\n",
    "                        df_CMIP6_subset[\"version\"]==latest_version(df_CMIP6_subset)] \n",
    "df_CMIP6_simulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Open all member with xarray (open_dataset)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/oceano/gmeteo/users/diezsj/miniconda3/envs/cmip6-downscaling/lib/python3.9/site-packages/xarray/conventions.py:523: SerializationWarning: variable 'tas' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  new_vars[k] = decode_cf_variable(\n"
     ]
    }
   ],
   "source": [
    "dataset = xr.open_dataset(df_CMIP6_simulation.loc[4166]['location'], \n",
    "                          chunks={\"time\":100})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (lat: 192, lon: 288, nbnd: 2, time: 60226, ensemble: 2)\n",
       "Coordinates:\n",
       "  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
       "  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
       "  * time       (time) object 1850-01-01 00:00:00 ... 2015-01-01 00:00:00\n",
       "  * ensemble   (ensemble) |S64 b&#x27;r1i1p1f1&#x27; b&#x27;r4i1p1f1&#x27;\n",
       "Dimensions without coordinates: nbnd\n",
       "Data variables:\n",
       "    lat_bnds   (lat, nbnd) float32 dask.array&lt;chunksize=(192, 2), meta=np.ndarray&gt;\n",
       "    lon_bnds   (lon, nbnd) float32 dask.array&lt;chunksize=(288, 2), meta=np.ndarray&gt;\n",
       "    time_bnds  (time, nbnd) object dask.array&lt;chunksize=(100, 2), meta=np.ndarray&gt;\n",
       "    tas        (ensemble, time, lat, lon) float32 dask.array&lt;chunksize=(2, 100, 192, 288), meta=np.ndarray&gt;\n",
       "Attributes: (12/47)\n",
       "    Conventions:            CF-1.7 CMIP-6.2\n",
       "    activity_id:            CMIP\n",
       "    case_id:                15\n",
       "    cesm_casename:          b.e21.BHIST.f09_g17.CMIP6-historical.001\n",
       "    contact:                cesm_cmip6@ucar.edu\n",
       "    creation_date:          2019-01-16T21:32:05Z\n",
       "    ...                     ...\n",
       "    branch_time_in_parent:  219000.0\n",
       "    branch_time_in_child:   674885.0\n",
       "    branch_method:          standard\n",
       "    further_info_url:       https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2.h...\n",
       "    size:                   2147483647\n",
       "    size_human:             14.1 GiB</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-dc40cbba-d9a7-4d19-95ba-3911e2925221' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dc40cbba-d9a7-4d19-95ba-3911e2925221' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li><li><span>nbnd</span>: 2</li><li><span class='xr-has-index'>time</span>: 60226</li><li><span class='xr-has-index'>ensemble</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2ab9a13a-2dd0-4332-ba1f-1c6b473a9bd8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2ab9a13a-2dd0-4332-ba1f-1c6b473a9bd8' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-2bddec57-1c76-4903-ad75-898b71d99ae0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2bddec57-1c76-4903-ad75-898b71d99ae0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4dfb7e8-30f2-4e8c-88f2-db1c620fa7c5' class='xr-var-data-in' type='checkbox'><label for='data-c4dfb7e8-30f2-4e8c-88f2-db1c620fa7c5' 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>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>title :</span></dt><dd>Latitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
       "       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
       "       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
       "       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
       "       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n",
       "       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n",
       "       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n",
       "       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n",
       "       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n",
       "       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n",
       "       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n",
       "       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n",
       "       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n",
       "       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n",
       "       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,\n",
       "        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,\n",
       "         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,\n",
       "         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,\n",
       "        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,\n",
       "        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,\n",
       "        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,\n",
       "        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,\n",
       "        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,\n",
       "        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,\n",
       "        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,\n",
       "        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,\n",
       "        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,\n",
       "        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,\n",
       "        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,\n",
       "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
       "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-6d855122-3aea-4b65-91be-8e91d19e040e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6d855122-3aea-4b65-91be-8e91d19e040e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0744330-ecfc-49f7-aece-202cf2dd52c2' class='xr-var-data-in' type='checkbox'><label for='data-b0744330-ecfc-49f7-aece-202cf2dd52c2' 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>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>title :</span></dt><dd>Longitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>360.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li><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'>object</div><div class='xr-var-preview xr-preview'>1850-01-01 00:00:00 ... 2015-01-...</div><input id='attrs-a511191f-a531-4773-b1d7-9732a2b3d7dc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a511191f-a531-4773-b1d7-9732a2b3d7dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92f6e214-bedf-4570-9905-9e7dcf1878e7' class='xr-var-data-in' type='checkbox'><label for='data-92f6e214-bedf-4570-9905-9e7dcf1878e7' 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>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>title :</span></dt><dd>time</dd><dt><span>type :</span></dt><dd>double</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 1, 2, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 1, 3, 0, 0, 0, 0, has_year_zero=True), ...,\n",
       "       cftime.DatetimeNoLeap(2014, 12, 30, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 31, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2015, 1, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ensemble</span></div><div class='xr-var-dims'>(ensemble)</div><div class='xr-var-dtype'>|S64</div><div class='xr-var-preview xr-preview'>b&#x27;r1i1p1f1&#x27; b&#x27;r4i1p1f1&#x27;</div><input id='attrs-40d89f99-3602-4c2c-b043-c648760121d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40d89f99-3602-4c2c-b043-c648760121d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-feecc505-7e7a-4618-80e3-031cf73f1062' class='xr-var-data-in' type='checkbox'><label for='data-feecc505-7e7a-4618-80e3-031cf73f1062' 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>realization</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Ensemble</dd></dl></div><div class='xr-var-data'><pre>array([b&#x27;r1i1p1f1&#x27;, b&#x27;r4i1p1f1&#x27;], dtype=&#x27;|S64&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f0b246d7-5af6-4a80-b474-9af2f69bf20e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f0b246d7-5af6-4a80-b474-9af2f69bf20e' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(lat, nbnd)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(192, 2), meta=np.ndarray&gt;</div><input id='attrs-91563dea-6772-469e-b112-b8f1e8c41fca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-91563dea-6772-469e-b112-b8f1e8c41fca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cbe8147b-63c1-4579-a0c3-5944f9ae02c2' class='xr-var-data-in' type='checkbox'><label for='data-cbe8147b-63c1-4579-a0c3-5944f9ae02c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 1.50 kiB </td>\n",
       "                        <td> 1.50 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (192, 2) </td>\n",
       "                        <td> (192, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 2 Tasks </td>\n",
       "                        <td> 1 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, nbnd)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(288, 2), meta=np.ndarray&gt;</div><input id='attrs-a783f368-d69e-47f2-af3f-eaeed5ce050c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a783f368-d69e-47f2-af3f-eaeed5ce050c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa0ee830-74f4-4658-91c2-42fa931448fe' class='xr-var-data-in' type='checkbox'><label for='data-aa0ee830-74f4-4658-91c2-42fa931448fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 2.25 kiB </td>\n",
       "                        <td> 2.25 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (288, 2) </td>\n",
       "                        <td> (288, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 2 Tasks </td>\n",
       "                        <td> 1 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, nbnd)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 2), meta=np.ndarray&gt;</div><input id='attrs-a622e9e9-fce6-4111-8035-cf304ce1fcaa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a622e9e9-fce6-4111-8035-cf304ce1fcaa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a671560b-23e0-48d1-80f5-a051c54543ad' class='xr-var-data-in' type='checkbox'><label for='data-a671560b-23e0-48d1-80f5-a051c54543ad' 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'><table>\n",
       "    <tr>\n",
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       "            <table>\n",
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       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 0.92 MiB </td>\n",
       "                        <td> 1.56 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (60226, 2) </td>\n",
       "                        <td> (100, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 604 Tasks </td>\n",
       "                        <td> 603 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> object </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tas</span></div><div class='xr-var-dims'>(ensemble, time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 192, 288), meta=np.ndarray&gt;</div><input id='attrs-dfa83c62-0307-4ef5-815e-26f51e0d56af' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dfa83c62-0307-4ef5-815e-26f51e0d56af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64c880b9-88fd-4b8b-83c2-14535b23a8d8' class='xr-var-data-in' type='checkbox'><label for='data-64c880b9-88fd-4b8b-83c2-14535b23a8d8' 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>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>comment :</span></dt><dd>near-surface (usually, 2 meter) air temperature</dd><dt><span>description :</span></dt><dd>near-surface (usually, 2 meter) air temperature</dd><dt><span>frequency :</span></dt><dd>day</dd><dt><span>id :</span></dt><dd>tas</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>mipTable :</span></dt><dd>day</dd><dt><span>out_name :</span></dt><dd>tas</dd><dt><span>prov :</span></dt><dd>day ((isd.003))</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>time :</span></dt><dd>time</dd><dt><span>time_label :</span></dt><dd>time-mean</dd><dt><span>time_title :</span></dt><dd>Temporal mean</dd><dt><span>title :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>type :</span></dt><dd>real</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>variable_id :</span></dt><dd>tas</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 24.81 GiB </td>\n",
       "                        <td> 42.19 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2, 60226, 192, 288) </td>\n",
       "                        <td> (2, 100, 192, 288) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 604 Tasks </td>\n",
       "                        <td> 603 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-8adfa945-371a-43b6-a051-b43629c6434b' class='xr-section-summary-in' type='checkbox'  ><label for='section-8adfa945-371a-43b6-a051-b43629c6434b' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-65d700e4-2c78-4aca-97d9-d1f3f2f128b4' class='xr-index-data-in' type='checkbox'/><label for='index-65d700e4-2c78-4aca-97d9-d1f3f2f128b4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([             -90.0, -89.05759162303664,  -88.1151832460733,\n",
       "              -87.17277486910994,  -86.2303664921466, -85.28795811518324,\n",
       "               -84.3455497382199, -83.40314136125654, -82.46073298429319,\n",
       "              -81.51832460732984,\n",
       "              ...\n",
       "               81.51832460732984,   82.4607329842932,  83.40314136125653,\n",
       "               84.34554973821989,  85.28795811518324,   86.2303664921466,\n",
       "               87.17277486910996,  88.11518324607329,  89.05759162303664,\n",
       "                            90.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c4ec904d-82a9-4422-9e49-09e1eae22d4c' class='xr-index-data-in' type='checkbox'/><label for='index-c4ec904d-82a9-4422-9e49-09e1eae22d4c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([   0.0,   1.25,    2.5,   3.75,    5.0,   6.25,    7.5,   8.75,\n",
       "                10.0,  11.25,\n",
       "              ...\n",
       "               347.5, 348.75,  350.0, 351.25,  352.5, 353.75,  355.0, 356.25,\n",
       "               357.5, 358.75],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=288))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e097a411-215e-4b41-9d10-27e9445fb6eb' class='xr-index-data-in' type='checkbox'/><label for='index-e097a411-215e-4b41-9d10-27e9445fb6eb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([1850-01-01 00:00:00, 1850-01-02 00:00:00, 1850-01-03 00:00:00,\n",
       "             1850-01-04 00:00:00, 1850-01-05 00:00:00, 1850-01-06 00:00:00,\n",
       "             1850-01-07 00:00:00, 1850-01-08 00:00:00, 1850-01-09 00:00:00,\n",
       "             1850-01-10 00:00:00,\n",
       "             ...\n",
       "             2014-12-23 00:00:00, 2014-12-24 00:00:00, 2014-12-25 00:00:00,\n",
       "             2014-12-26 00:00:00, 2014-12-27 00:00:00, 2014-12-28 00:00:00,\n",
       "             2014-12-29 00:00:00, 2014-12-30 00:00:00, 2014-12-31 00:00:00,\n",
       "             2015-01-01 00:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=60226, calendar=&#x27;noleap&#x27;, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>ensemble</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5c1bd6f8-24ce-4a30-98c6-0710d9248b4d' class='xr-index-data-in' type='checkbox'/><label for='index-5c1bd6f8-24ce-4a30-98c6-0710d9248b4d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([b&#x27;r1i1p1f1&#x27;, b&#x27;r4i1p1f1&#x27;], dtype=&#x27;object&#x27;, name=&#x27;ensemble&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0fa6e47b-0407-42dd-80be-efde22f1a468' class='xr-section-summary-in' type='checkbox'  ><label for='section-0fa6e47b-0407-42dd-80be-efde22f1a468' class='xr-section-summary' >Attributes: <span>(47)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>CMIP</dd><dt><span>case_id :</span></dt><dd>15</dd><dt><span>cesm_casename :</span></dt><dd>b.e21.BHIST.f09_g17.CMIP6-historical.001</dd><dt><span>contact :</span></dt><dd>cesm_cmip6@ucar.edu</dd><dt><span>creation_date :</span></dt><dd>2019-01-16T21:32:05Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.29</dd><dt><span>experiment :</span></dt><dd>all-forcing simulation of the recent past</dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>day</dd><dt><span>grid :</span></dt><dd>native 0.9x1.25 finite volume grid (192x288 latxlon)</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>National Center for Atmospheric Research, Climate and Global Dynamics Laboratory, 1850 Table Mesa Drive, Boulder, CO 80305, USA</dd><dt><span>institution_id :</span></dt><dd>NCAR</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by &lt;The National Center for Atmospheric Research&gt; is licensed under a Creative Commons Attribution-[]ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file)[]. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>model_doi_url :</span></dt><dd>https://doi.org/10.5065/D67H1H0V</dd><dt><span>nominal_resolution :</span></dt><dd>100 km</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>piControl</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>CESM2</dd><dt><span>parent_time_units :</span></dt><dd>days since 0001-01-01 00:00:00</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>1</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>source :</span></dt><dd>CESM2 (2017): atmosphere: CAM6 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); ocean: POP2 (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m); sea_ice: CICE5.1 (same grid as ocean); land: CLM5 0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); aerosol: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); atmoschem: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); landIce: CISM2.1; ocnBgchem: MARBL (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m)</dd><dt><span>source_id :</span></dt><dd>CESM2</dd><dt><span>source_type :</span></dt><dd>AOGCM BGC</dd><dt><span>table_id :</span></dt><dd>day</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/ee4dcacc-1783-41de-96d0-88dcba6bd1e2</dd><dt><span>variable_id :</span></dt><dd>tas</dd><dt><span>variant_info :</span></dt><dd>CMIP6 20th century experiments (1850-2014) with CAM6, interactive land (CLM5), coupled ocean (POP2) with biogeochemistry (MARBL), interactive sea ice (CICE5.1), and non-evolving land ice (CISM2.1)\r\n",
       "</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>branch_time_in_parent :</span></dt><dd>219000.0</dd><dt><span>branch_time_in_child :</span></dt><dd>674885.0</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2.historical.none.r1i1p1f1</dd><dt><span>size :</span></dt><dd>2147483647</dd><dt><span>size_human :</span></dt><dd>14.1 GiB</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (lat: 192, lon: 288, nbnd: 2, time: 60226, ensemble: 2)\n",
       "Coordinates:\n",
       "  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
       "  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
       "  * time       (time) object 1850-01-01 00:00:00 ... 2015-01-01 00:00:00\n",
       "  * ensemble   (ensemble) |S64 b'r1i1p1f1' b'r4i1p1f1'\n",
       "Dimensions without coordinates: nbnd\n",
       "Data variables:\n",
       "    lat_bnds   (lat, nbnd) float32 dask.array<chunksize=(192, 2), meta=np.ndarray>\n",
       "    lon_bnds   (lon, nbnd) float32 dask.array<chunksize=(288, 2), meta=np.ndarray>\n",
       "    time_bnds  (time, nbnd) object dask.array<chunksize=(100, 2), meta=np.ndarray>\n",
       "    tas        (ensemble, time, lat, lon) float32 dask.array<chunksize=(2, 100, 192, 288), meta=np.ndarray>\n",
       "Attributes: (12/47)\n",
       "    Conventions:            CF-1.7 CMIP-6.2\n",
       "    activity_id:            CMIP\n",
       "    case_id:                15\n",
       "    cesm_casename:          b.e21.BHIST.f09_g17.CMIP6-historical.001\n",
       "    contact:                cesm_cmip6@ucar.edu\n",
       "    creation_date:          2019-01-16T21:32:05Z\n",
       "    ...                     ...\n",
       "    branch_time_in_parent:  219000.0\n",
       "    branch_time_in_child:   674885.0\n",
       "    branch_method:          standard\n",
       "    further_info_url:       https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2.h...\n",
       "    size:                   2147483647\n",
       "    size_human:             14.1 GiB"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Open all member with xarray (open_mfdataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def extract_ensemble(ds):\n",
    "    if 'variant_label' in ds.attrs.keys():\n",
    "        return ds.assign_coords(ensemble=ds.attrs['variant_label'])\n",
    "    raise ValueError(\"variant_label missing: {0}\".format(self.search_time_attr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = xr.open_mfdataset(list(df_CMIP6_simulation['location'].iloc[1:]), \n",
    "                            preprocess=extract_ensemble,\n",
    "                            concat_dim = 'ensemble',\n",
    "                            combine = 'nested',\n",
    "                            chunks={\"time\":100})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/oceano/gmeteo/users/diezsj/miniconda3/envs/cmip6-downscaling/lib/python3.9/site-packages/xarray/core/indexing.py:1374: PerformanceWarning: Slicing is producing a large chunk. To accept the large\n",
      "chunk and silence this warning, set the option\n",
      "    >>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):\n",
      "    ...     array[indexer]\n",
      "\n",
      "To avoid creating the large chunks, set the option\n",
      "    >>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):\n",
      "    ...     array[indexer]\n",
      "  return self.array[key]\n"
     ]
    }
   ],
   "source": [
    "# fix 360 longitudes\n",
    "dataset = fix_360_longitudes(dataset, 'CMIP6', 'lon')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# monthly aggregation\n",
    "dataset_mon = dataset.resample(time=\"M\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f7e358e4a60>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_mon['tas'].isel(time=0, ensemble=0).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select area\n",
    "dataset_mon_subset = dataset_mon.sel(lat=slice(32, 65), \n",
    "                                     lon=slice(-15, 30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f7e3533a5e0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_mon_subset['tas'].isel(time=0, ensemble=0).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpolate data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Interpolate data\n",
    "int_attr = {'interpolation_method' : 'conservative_normed', \n",
    "            'lats' : np.arange(32, 65, 0.5),\n",
    "            'lons' : np.arange(-15, 30, 0.5),\n",
    "            'var_name' : 'tas'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# \n",
    "INTER = Interpolator(int_attr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_inter = INTER(dataset_mon_subset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
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       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:   (lon: 90, lat: 66, time: 1981, ensemble: 2, bnds: 2)\n",
       "Coordinates:\n",
       "  * lon       (lon) float64 -15.0 -14.5 -14.0 -13.5 ... 28.0 28.5 29.0 29.5\n",
       "  * lat       (lat) float64 32.0 32.5 33.0 33.5 34.0 ... 63.0 63.5 64.0 64.5\n",
       "  * time      (time) object 1850-01-31 00:00:00 ... 2015-01-31 00:00:00\n",
       "  * ensemble  (ensemble) &lt;U8 &#x27;r1i1p1f1&#x27; &#x27;r4i1p1f1&#x27;\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    tas       (time, ensemble, lat, lon) float32 dask.array&lt;chunksize=(1, 1, 66, 90), meta=np.ndarray&gt;\n",
       "    lon_bnds  (lon, bnds) float64 -15.25 -14.75 -14.75 ... 29.25 29.25 29.75\n",
       "    lat_bnds  (lat, bnds) float64 31.75 32.25 32.25 32.75 ... 64.25 64.25 64.75\n",
       "    crs       int64 0\n",
       "Attributes: (12/47)\n",
       "    Conventions:            CF-1.7 CMIP-6.2\n",
       "    activity_id:            CMIP\n",
       "    case_id:                15\n",
       "    cesm_casename:          b.e21.BHIST.f09_g17.CMIP6-historical.001\n",
       "    contact:                cesm_cmip6@ucar.edu\n",
       "    creation_date:          2019-01-16T21:32:05Z\n",
       "    ...                     ...\n",
       "    branch_time_in_parent:  219000.0\n",
       "    branch_time_in_child:   674885.0\n",
       "    branch_method:          standard\n",
       "    further_info_url:       https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2.h...\n",
       "    size:                   2147483647\n",
       "    size_human:             7.1 GiB</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-0b09ea25-e1aa-49f0-b958-9ed4efee59f5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0b09ea25-e1aa-49f0-b958-9ed4efee59f5' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lon</span>: 90</li><li><span class='xr-has-index'>lat</span>: 66</li><li><span class='xr-has-index'>time</span>: 1981</li><li><span class='xr-has-index'>ensemble</span>: 2</li><li><span>bnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9ea3ba8e-3371-4b83-a992-ed6760188b66' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9ea3ba8e-3371-4b83-a992-ed6760188b66' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-15.0 -14.5 -14.0 ... 29.0 29.5</div><input id='attrs-9f01bc87-ec0e-4a9a-8e83-1fc7b188bcca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f01bc87-ec0e-4a9a-8e83-1fc7b188bcca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-031a6ad1-30e9-41c5-9d69-c447023f4b8c' class='xr-var-data-in' type='checkbox'><label for='data-031a6ad1-30e9-41c5-9d69-c447023f4b8c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd></dl></div><div class='xr-var-data'><pre>array([-15. , -14.5, -14. , -13.5, -13. , -12.5, -12. , -11.5, -11. , -10.5,\n",
       "       -10. ,  -9.5,  -9. ,  -8.5,  -8. ,  -7.5,  -7. ,  -6.5,  -6. ,  -5.5,\n",
       "        -5. ,  -4.5,  -4. ,  -3.5,  -3. ,  -2.5,  -2. ,  -1.5,  -1. ,  -0.5,\n",
       "         0. ,   0.5,   1. ,   1.5,   2. ,   2.5,   3. ,   3.5,   4. ,   4.5,\n",
       "         5. ,   5.5,   6. ,   6.5,   7. ,   7.5,   8. ,   8.5,   9. ,   9.5,\n",
       "        10. ,  10.5,  11. ,  11.5,  12. ,  12.5,  13. ,  13.5,  14. ,  14.5,\n",
       "        15. ,  15.5,  16. ,  16.5,  17. ,  17.5,  18. ,  18.5,  19. ,  19.5,\n",
       "        20. ,  20.5,  21. ,  21.5,  22. ,  22.5,  23. ,  23.5,  24. ,  24.5,\n",
       "        25. ,  25.5,  26. ,  26.5,  27. ,  27.5,  28. ,  28.5,  29. ,  29.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>32.0 32.5 33.0 ... 63.5 64.0 64.5</div><input id='attrs-ca09c726-64c6-4ed5-9c64-3a0edac3ef2f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca09c726-64c6-4ed5-9c64-3a0edac3ef2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab3ae10d-4fb7-47bc-93b8-e37014e5b314' class='xr-var-data-in' type='checkbox'><label for='data-ab3ae10d-4fb7-47bc-93b8-e37014e5b314' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd></dl></div><div class='xr-var-data'><pre>array([32. , 32.5, 33. , 33.5, 34. , 34.5, 35. , 35.5, 36. , 36.5, 37. , 37.5,\n",
       "       38. , 38.5, 39. , 39.5, 40. , 40.5, 41. , 41.5, 42. , 42.5, 43. , 43.5,\n",
       "       44. , 44.5, 45. , 45.5, 46. , 46.5, 47. , 47.5, 48. , 48.5, 49. , 49.5,\n",
       "       50. , 50.5, 51. , 51.5, 52. , 52.5, 53. , 53.5, 54. , 54.5, 55. , 55.5,\n",
       "       56. , 56.5, 57. , 57.5, 58. , 58.5, 59. , 59.5, 60. , 60.5, 61. , 61.5,\n",
       "       62. , 62.5, 63. , 63.5, 64. , 64.5])</pre></div></li><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'>object</div><div class='xr-var-preview xr-preview'>1850-01-31 00:00:00 ... 2015-01-...</div><input id='attrs-6020f01d-f376-412a-8608-d37ae9680133' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6020f01d-f376-412a-8608-d37ae9680133' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23fb5333-dd0c-4f81-8325-a9c3d8c0e717' class='xr-var-data-in' type='checkbox'><label for='data-23fb5333-dd0c-4f81-8325-a9c3d8c0e717' 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([cftime.DatetimeNoLeap(1850, 1, 31, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 2, 28, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 3, 31, 0, 0, 0, 0, has_year_zero=True), ...,\n",
       "       cftime.DatetimeNoLeap(2014, 11, 30, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 31, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2015, 1, 31, 0, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ensemble</span></div><div class='xr-var-dims'>(ensemble)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;r1i1p1f1&#x27; &#x27;r4i1p1f1&#x27;</div><input id='attrs-fcfbd18e-3cc8-43a4-8cce-92d2e6c96537' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fcfbd18e-3cc8-43a4-8cce-92d2e6c96537' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33750a80-b222-41f6-8961-13d4e6d47477' class='xr-var-data-in' type='checkbox'><label for='data-33750a80-b222-41f6-8961-13d4e6d47477' 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;r1i1p1f1&#x27;, &#x27;r4i1p1f1&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-66169b67-6a3a-4512-a2e7-c97e80fcf75c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-66169b67-6a3a-4512-a2e7-c97e80fcf75c' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>tas</span></div><div class='xr-var-dims'>(time, ensemble, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 66, 90), meta=np.ndarray&gt;</div><input id='attrs-27a72a9c-e7f9-4f2a-8411-1e52e5f85f0a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-27a72a9c-e7f9-4f2a-8411-1e52e5f85f0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-115970ca-871c-4f7c-942a-866d007ef994' class='xr-var-data-in' type='checkbox'><label for='data-115970ca-871c-4f7c-942a-866d007ef994' 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>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>comment :</span></dt><dd>near-surface (usually, 2 meter) air temperature</dd><dt><span>description :</span></dt><dd>near-surface (usually, 2 meter) air temperature</dd><dt><span>frequency :</span></dt><dd>day</dd><dt><span>id :</span></dt><dd>tas</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>mipTable :</span></dt><dd>day</dd><dt><span>out_name :</span></dt><dd>tas</dd><dt><span>prov :</span></dt><dd>day ((isd.003))</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>time :</span></dt><dd>time</dd><dt><span>time_label :</span></dt><dd>time-mean</dd><dt><span>time_title :</span></dt><dd>Temporal mean</dd><dt><span>title :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>type :</span></dt><dd>real</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>variable_id :</span></dt><dd>tas</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 89.78 MiB </td>\n",
       "                        <td> 23.20 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1981, 2, 66, 90) </td>\n",
       "                        <td> (1, 1, 66, 90) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 39099 Tasks </td>\n",
       "                        <td> 3962 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-15.25 -14.75 ... 29.25 29.75</div><input id='attrs-3abe7d98-ab59-48f3-9f41-daa79f3b306f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3abe7d98-ab59-48f3-9f41-daa79f3b306f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de2cc180-236d-4aa6-89f3-eb9ef84bb527' class='xr-var-data-in' type='checkbox'><label for='data-de2cc180-236d-4aa6-89f3-eb9ef84bb527' 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([[-15.25, -14.75],\n",
       "       [-14.75, -14.25],\n",
       "       [-14.25, -13.75],\n",
       "       [-13.75, -13.25],\n",
       "       [-13.25, -12.75],\n",
       "       [-12.75, -12.25],\n",
       "       [-12.25, -11.75],\n",
       "       [-11.75, -11.25],\n",
       "       [-11.25, -10.75],\n",
       "       [-10.75, -10.25],\n",
       "       [-10.25,  -9.75],\n",
       "       [ -9.75,  -9.25],\n",
       "       [ -9.25,  -8.75],\n",
       "       [ -8.75,  -8.25],\n",
       "       [ -8.25,  -7.75],\n",
       "       [ -7.75,  -7.25],\n",
       "       [ -7.25,  -6.75],\n",
       "       [ -6.75,  -6.25],\n",
       "       [ -6.25,  -5.75],\n",
       "       [ -5.75,  -5.25],\n",
       "...\n",
       "       [ 19.75,  20.25],\n",
       "       [ 20.25,  20.75],\n",
       "       [ 20.75,  21.25],\n",
       "       [ 21.25,  21.75],\n",
       "       [ 21.75,  22.25],\n",
       "       [ 22.25,  22.75],\n",
       "       [ 22.75,  23.25],\n",
       "       [ 23.25,  23.75],\n",
       "       [ 23.75,  24.25],\n",
       "       [ 24.25,  24.75],\n",
       "       [ 24.75,  25.25],\n",
       "       [ 25.25,  25.75],\n",
       "       [ 25.75,  26.25],\n",
       "       [ 26.25,  26.75],\n",
       "       [ 26.75,  27.25],\n",
       "       [ 27.25,  27.75],\n",
       "       [ 27.75,  28.25],\n",
       "       [ 28.25,  28.75],\n",
       "       [ 28.75,  29.25],\n",
       "       [ 29.25,  29.75]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>31.75 32.25 32.25 ... 64.25 64.75</div><input id='attrs-4adfe1cc-3d7d-49c6-8599-43a27ebe05df' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4adfe1cc-3d7d-49c6-8599-43a27ebe05df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-693a7e3c-1a2f-4b8f-8fd8-d983a1faeb30' class='xr-var-data-in' type='checkbox'><label for='data-693a7e3c-1a2f-4b8f-8fd8-d983a1faeb30' 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([[31.75, 32.25],\n",
       "       [32.25, 32.75],\n",
       "       [32.75, 33.25],\n",
       "       [33.25, 33.75],\n",
       "       [33.75, 34.25],\n",
       "       [34.25, 34.75],\n",
       "       [34.75, 35.25],\n",
       "       [35.25, 35.75],\n",
       "       [35.75, 36.25],\n",
       "       [36.25, 36.75],\n",
       "       [36.75, 37.25],\n",
       "       [37.25, 37.75],\n",
       "       [37.75, 38.25],\n",
       "       [38.25, 38.75],\n",
       "       [38.75, 39.25],\n",
       "       [39.25, 39.75],\n",
       "       [39.75, 40.25],\n",
       "       [40.25, 40.75],\n",
       "       [40.75, 41.25],\n",
       "       [41.25, 41.75],\n",
       "...\n",
       "       [54.75, 55.25],\n",
       "       [55.25, 55.75],\n",
       "       [55.75, 56.25],\n",
       "       [56.25, 56.75],\n",
       "       [56.75, 57.25],\n",
       "       [57.25, 57.75],\n",
       "       [57.75, 58.25],\n",
       "       [58.25, 58.75],\n",
       "       [58.75, 59.25],\n",
       "       [59.25, 59.75],\n",
       "       [59.75, 60.25],\n",
       "       [60.25, 60.75],\n",
       "       [60.75, 61.25],\n",
       "       [61.25, 61.75],\n",
       "       [61.75, 62.25],\n",
       "       [62.25, 62.75],\n",
       "       [62.75, 63.25],\n",
       "       [63.25, 63.75],\n",
       "       [63.75, 64.25],\n",
       "       [64.25, 64.75]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>crs</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-328d93a1-31c2-4b5b-aab0-d29372f4ffbf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-328d93a1-31c2-4b5b-aab0-d29372f4ffbf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0304295f-e292-4e8a-91a5-e923106831b6' class='xr-var-data-in' type='checkbox'><label for='data-0304295f-e292-4e8a-91a5-e923106831b6' 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>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b6743635-bb95-4252-9697-4af789ea0db5' class='xr-section-summary-in' type='checkbox'  ><label for='section-b6743635-bb95-4252-9697-4af789ea0db5' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9f37672d-3b84-493a-aa13-163a864f7366' class='xr-index-data-in' type='checkbox'/><label for='index-9f37672d-3b84-493a-aa13-163a864f7366' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([-15.0, -14.5, -14.0, -13.5, -13.0, -12.5, -12.0, -11.5, -11.0,\n",
       "              -10.5, -10.0,  -9.5,  -9.0,  -8.5,  -8.0,  -7.5,  -7.0,  -6.5,\n",
       "               -6.0,  -5.5,  -5.0,  -4.5,  -4.0,  -3.5,  -3.0,  -2.5,  -2.0,\n",
       "               -1.5,  -1.0,  -0.5,   0.0,   0.5,   1.0,   1.5,   2.0,   2.5,\n",
       "                3.0,   3.5,   4.0,   4.5,   5.0,   5.5,   6.0,   6.5,   7.0,\n",
       "                7.5,   8.0,   8.5,   9.0,   9.5,  10.0,  10.5,  11.0,  11.5,\n",
       "               12.0,  12.5,  13.0,  13.5,  14.0,  14.5,  15.0,  15.5,  16.0,\n",
       "               16.5,  17.0,  17.5,  18.0,  18.5,  19.0,  19.5,  20.0,  20.5,\n",
       "               21.0,  21.5,  22.0,  22.5,  23.0,  23.5,  24.0,  24.5,  25.0,\n",
       "               25.5,  26.0,  26.5,  27.0,  27.5,  28.0,  28.5,  29.0,  29.5],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7f54d4a9-4a46-47e8-9594-361b4e3cb033' class='xr-index-data-in' type='checkbox'/><label for='index-7f54d4a9-4a46-47e8-9594-361b4e3cb033' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([32.0, 32.5, 33.0, 33.5, 34.0, 34.5, 35.0, 35.5, 36.0, 36.5, 37.0,\n",
       "              37.5, 38.0, 38.5, 39.0, 39.5, 40.0, 40.5, 41.0, 41.5, 42.0, 42.5,\n",
       "              43.0, 43.5, 44.0, 44.5, 45.0, 45.5, 46.0, 46.5, 47.0, 47.5, 48.0,\n",
       "              48.5, 49.0, 49.5, 50.0, 50.5, 51.0, 51.5, 52.0, 52.5, 53.0, 53.5,\n",
       "              54.0, 54.5, 55.0, 55.5, 56.0, 56.5, 57.0, 57.5, 58.0, 58.5, 59.0,\n",
       "              59.5, 60.0, 60.5, 61.0, 61.5, 62.0, 62.5, 63.0, 63.5, 64.0,\n",
       "              64.5],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c218244f-6b61-4066-bf8b-03d8dc7a10f6' class='xr-index-data-in' type='checkbox'/><label for='index-c218244f-6b61-4066-bf8b-03d8dc7a10f6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([1850-01-31 00:00:00, 1850-02-28 00:00:00, 1850-03-31 00:00:00,\n",
       "             1850-04-30 00:00:00, 1850-05-31 00:00:00, 1850-06-30 00:00:00,\n",
       "             1850-07-31 00:00:00, 1850-08-31 00:00:00, 1850-09-30 00:00:00,\n",
       "             1850-10-31 00:00:00,\n",
       "             ...\n",
       "             2014-04-30 00:00:00, 2014-05-31 00:00:00, 2014-06-30 00:00:00,\n",
       "             2014-07-31 00:00:00, 2014-08-31 00:00:00, 2014-09-30 00:00:00,\n",
       "             2014-10-31 00:00:00, 2014-11-30 00:00:00, 2014-12-31 00:00:00,\n",
       "             2015-01-31 00:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=1981, calendar=&#x27;noleap&#x27;, freq=&#x27;M&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>ensemble</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-823f944a-11da-424a-9a77-f53d5cd0bd8b' class='xr-index-data-in' type='checkbox'/><label for='index-823f944a-11da-424a-9a77-f53d5cd0bd8b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;r1i1p1f1&#x27;, &#x27;r4i1p1f1&#x27;], dtype=&#x27;object&#x27;, name=&#x27;ensemble&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-30d5c2be-1bb4-4ba0-a93a-9ed4555e937f' class='xr-section-summary-in' type='checkbox'  ><label for='section-30d5c2be-1bb4-4ba0-a93a-9ed4555e937f' class='xr-section-summary' >Attributes: <span>(47)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>CMIP</dd><dt><span>case_id :</span></dt><dd>15</dd><dt><span>cesm_casename :</span></dt><dd>b.e21.BHIST.f09_g17.CMIP6-historical.001</dd><dt><span>contact :</span></dt><dd>cesm_cmip6@ucar.edu</dd><dt><span>creation_date :</span></dt><dd>2019-01-16T21:32:05Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.29</dd><dt><span>experiment :</span></dt><dd>all-forcing simulation of the recent past</dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>day</dd><dt><span>grid :</span></dt><dd>native 0.9x1.25 finite volume grid (192x288 latxlon)</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>National Center for Atmospheric Research, Climate and Global Dynamics Laboratory, 1850 Table Mesa Drive, Boulder, CO 80305, USA</dd><dt><span>institution_id :</span></dt><dd>NCAR</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by &lt;The National Center for Atmospheric Research&gt; is licensed under a Creative Commons Attribution-[]ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file)[]. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>model_doi_url :</span></dt><dd>https://doi.org/10.5065/D67H1H0V</dd><dt><span>nominal_resolution :</span></dt><dd>100 km</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>piControl</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>CESM2</dd><dt><span>parent_time_units :</span></dt><dd>days since 0001-01-01 00:00:00</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>1</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>source :</span></dt><dd>CESM2 (2017): atmosphere: CAM6 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); ocean: POP2 (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m); sea_ice: CICE5.1 (same grid as ocean); land: CLM5 0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); aerosol: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); atmoschem: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); landIce: CISM2.1; ocnBgchem: MARBL (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m)</dd><dt><span>source_id :</span></dt><dd>CESM2</dd><dt><span>source_type :</span></dt><dd>AOGCM BGC</dd><dt><span>table_id :</span></dt><dd>day</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/ee4dcacc-1783-41de-96d0-88dcba6bd1e2</dd><dt><span>variable_id :</span></dt><dd>tas</dd><dt><span>variant_info :</span></dt><dd>CMIP6 20th century experiments (1850-2014) with CAM6, interactive land (CLM5), coupled ocean (POP2) with biogeochemistry (MARBL), interactive sea ice (CICE5.1), and non-evolving land ice (CISM2.1)\r\n",
       "</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>branch_time_in_parent :</span></dt><dd>219000.0</dd><dt><span>branch_time_in_child :</span></dt><dd>674885.0</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2.historical.none.r1i1p1f1</dd><dt><span>size :</span></dt><dd>2147483647</dd><dt><span>size_human :</span></dt><dd>7.1 GiB</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:   (lon: 90, lat: 66, time: 1981, ensemble: 2, bnds: 2)\n",
       "Coordinates:\n",
       "  * lon       (lon) float64 -15.0 -14.5 -14.0 -13.5 ... 28.0 28.5 29.0 29.5\n",
       "  * lat       (lat) float64 32.0 32.5 33.0 33.5 34.0 ... 63.0 63.5 64.0 64.5\n",
       "  * time      (time) object 1850-01-31 00:00:00 ... 2015-01-31 00:00:00\n",
       "  * ensemble  (ensemble) <U8 'r1i1p1f1' 'r4i1p1f1'\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    tas       (time, ensemble, lat, lon) float32 dask.array<chunksize=(1, 1, 66, 90), meta=np.ndarray>\n",
       "    lon_bnds  (lon, bnds) float64 -15.25 -14.75 -14.75 ... 29.25 29.25 29.75\n",
       "    lat_bnds  (lat, bnds) float64 31.75 32.25 32.25 32.75 ... 64.25 64.25 64.75\n",
       "    crs       int64 0\n",
       "Attributes: (12/47)\n",
       "    Conventions:            CF-1.7 CMIP-6.2\n",
       "    activity_id:            CMIP\n",
       "    case_id:                15\n",
       "    cesm_casename:          b.e21.BHIST.f09_g17.CMIP6-historical.001\n",
       "    contact:                cesm_cmip6@ucar.edu\n",
       "    creation_date:          2019-01-16T21:32:05Z\n",
       "    ...                     ...\n",
       "    branch_time_in_parent:  219000.0\n",
       "    branch_time_in_child:   674885.0\n",
       "    branch_method:          standard\n",
       "    further_info_url:       https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2.h...\n",
       "    size:                   2147483647\n",
       "    size_human:             7.1 GiB"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_inter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f7e274870a0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_inter['tas'].isel(time=0, ensemble=0).plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}