trial_code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "48d5d1b3-ff2e-4bdb-9988-804c54d514d0",
   "metadata": {},
   "source": [
    "# Setting\n",
    "### Finding data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "522ec956-70d9-468f-b403-f878cccf8514",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from dask.diagnostics import ProgressBar\n",
    "import intake\n",
    "col = intake.open_esm_datastore(\"https://storage.googleapis.com/cmip6/pangeo-cmip6.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8cb520a3-bdd7-4eb6-b3d1-24e6e3bc36e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>activity_id</th>\n",
       "      <th>institution_id</th>\n",
       "      <th>source_id</th>\n",
       "      <th>experiment_id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>table_id</th>\n",
       "      <th>variable_id</th>\n",
       "      <th>grid_label</th>\n",
       "      <th>zstore</th>\n",
       "      <th>dcpp_init_year</th>\n",
       "      <th>version</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HighResMIP</td>\n",
       "      <td>CMCC</td>\n",
       "      <td>CMCC-CM2-HR4</td>\n",
       "      <td>highresSST-present</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>hfls</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20170706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HighResMIP</td>\n",
       "      <td>CMCC</td>\n",
       "      <td>CMCC-CM2-HR4</td>\n",
       "      <td>highresSST-present</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>hus</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20170706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HighResMIP</td>\n",
       "      <td>CMCC</td>\n",
       "      <td>CMCC-CM2-HR4</td>\n",
       "      <td>highresSST-present</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>zg</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20170706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HighResMIP</td>\n",
       "      <td>CMCC</td>\n",
       "      <td>CMCC-CM2-HR4</td>\n",
       "      <td>highresSST-present</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>prw</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20170706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HighResMIP</td>\n",
       "      <td>CMCC</td>\n",
       "      <td>CMCC-CM2-HR4</td>\n",
       "      <td>highresSST-present</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>ps</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20170706</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511420</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>CMCC</td>\n",
       "      <td>CMCC-CM2-HR4</td>\n",
       "      <td>abrupt-4xCO2</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>sfcWind</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/CMCC/CMCC-CM2-HR4/abrupt...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20210624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511421</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>CMCC</td>\n",
       "      <td>CMCC-CM2-HR4</td>\n",
       "      <td>abrupt-4xCO2</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>uas</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/CMCC/CMCC-CM2-HR4/abrupt...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20210624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511422</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>CMCC</td>\n",
       "      <td>CMCC-CM2-HR4</td>\n",
       "      <td>abrupt-4xCO2</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>day</td>\n",
       "      <td>rlut</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/CMCC/CMCC-CM2-HR4/abrupt...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20210624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511423</th>\n",
       "      <td>ScenarioMIP</td>\n",
       "      <td>AS-RCEC</td>\n",
       "      <td>TaiESM1</td>\n",
       "      <td>ssp585</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>day</td>\n",
       "      <td>tasmin</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/ScenarioMIP/AS-RCEC/TaiESM1/s...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20210721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511424</th>\n",
       "      <td>ScenarioMIP</td>\n",
       "      <td>AS-RCEC</td>\n",
       "      <td>TaiESM1</td>\n",
       "      <td>ssp585</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>day</td>\n",
       "      <td>tasmax</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/ScenarioMIP/AS-RCEC/TaiESM1/s...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20210721</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>511425 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        activity_id institution_id     source_id       experiment_id  \\\n",
       "0        HighResMIP           CMCC  CMCC-CM2-HR4  highresSST-present   \n",
       "1        HighResMIP           CMCC  CMCC-CM2-HR4  highresSST-present   \n",
       "2        HighResMIP           CMCC  CMCC-CM2-HR4  highresSST-present   \n",
       "3        HighResMIP           CMCC  CMCC-CM2-HR4  highresSST-present   \n",
       "4        HighResMIP           CMCC  CMCC-CM2-HR4  highresSST-present   \n",
       "...             ...            ...           ...                 ...   \n",
       "511420         CMIP           CMCC  CMCC-CM2-HR4        abrupt-4xCO2   \n",
       "511421         CMIP           CMCC  CMCC-CM2-HR4        abrupt-4xCO2   \n",
       "511422         CMIP           CMCC  CMCC-CM2-HR4        abrupt-4xCO2   \n",
       "511423  ScenarioMIP        AS-RCEC       TaiESM1              ssp585   \n",
       "511424  ScenarioMIP        AS-RCEC       TaiESM1              ssp585   \n",
       "\n",
       "       member_id table_id variable_id grid_label  \\\n",
       "0       r1i1p1f1     Amon        hfls         gn   \n",
       "1       r1i1p1f1     Amon         hus         gn   \n",
       "2       r1i1p1f1     Amon          zg         gn   \n",
       "3       r1i1p1f1     Amon         prw         gn   \n",
       "4       r1i1p1f1     Amon          ps         gn   \n",
       "...          ...      ...         ...        ...   \n",
       "511420  r1i1p1f1     Amon     sfcWind         gn   \n",
       "511421  r1i1p1f1     Amon         uas         gn   \n",
       "511422  r1i1p1f1      day        rlut         gn   \n",
       "511423  r1i1p1f1      day      tasmin         gn   \n",
       "511424  r1i1p1f1      day      tasmax         gn   \n",
       "\n",
       "                                                   zstore  dcpp_init_year  \\\n",
       "0       gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...             NaN   \n",
       "1       gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...             NaN   \n",
       "2       gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...             NaN   \n",
       "3       gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...             NaN   \n",
       "4       gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...             NaN   \n",
       "...                                                   ...             ...   \n",
       "511420  gs://cmip6/CMIP6/CMIP/CMCC/CMCC-CM2-HR4/abrupt...             NaN   \n",
       "511421  gs://cmip6/CMIP6/CMIP/CMCC/CMCC-CM2-HR4/abrupt...             NaN   \n",
       "511422  gs://cmip6/CMIP6/CMIP/CMCC/CMCC-CM2-HR4/abrupt...             NaN   \n",
       "511423  gs://cmip6/CMIP6/ScenarioMIP/AS-RCEC/TaiESM1/s...             NaN   \n",
       "511424  gs://cmip6/CMIP6/ScenarioMIP/AS-RCEC/TaiESM1/s...             NaN   \n",
       "\n",
       "         version  \n",
       "0       20170706  \n",
       "1       20170706  \n",
       "2       20170706  \n",
       "3       20170706  \n",
       "4       20170706  \n",
       "...          ...  \n",
       "511420  20210624  \n",
       "511421  20210624  \n",
       "511422  20210624  \n",
       "511423  20210721  \n",
       "511424  20210721  \n",
       "\n",
       "[511425 rows x 11 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col.df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4a2727b2-da2d-48ec-b3fb-698ca086d274",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ACCESS-CM2', 'ACCESS-ESM1-5', 'AWI-CM-1-1-MR', 'AWI-ESM-1-1-LR',\n",
       "       'BCC-CSM2-HR', 'BCC-CSM2-MR', 'BCC-ESM1', 'CAMS-CSM1-0',\n",
       "       'CAS-ESM2-0', 'CESM1-1-CAM5-CMIP5', 'CESM1-WACCM-SC', 'CESM2',\n",
       "       'CESM2-FV2', 'CESM2-WACCM', 'CESM2-WACCM-FV2', 'CIESM',\n",
       "       'CMCC-CM2-HR4', 'CMCC-CM2-SR5', 'CMCC-CM2-VHR4', 'CMCC-ESM2',\n",
       "       'CNRM-CM6-1', 'CNRM-CM6-1-HR', 'CNRM-ESM2-1', 'CanESM5',\n",
       "       'CanESM5-CanOE', 'E3SM-1-0', 'E3SM-1-1', 'E3SM-1-1-ECA',\n",
       "       'EC-Earth3', 'EC-Earth3-AerChem', 'EC-Earth3-CC', 'EC-Earth3-LR',\n",
       "       'EC-Earth3-Veg', 'EC-Earth3-Veg-LR', 'EC-Earth3P', 'EC-Earth3P-HR',\n",
       "       'EC-Earth3P-VHR', 'ECMWF-IFS-HR', 'ECMWF-IFS-LR', 'FGOALS-f3-H',\n",
       "       'FGOALS-f3-L', 'FGOALS-g3', 'FIO-ESM-2-0', 'GFDL-AM4', 'GFDL-CM4',\n",
       "       'GFDL-CM4C192', 'GFDL-ESM2M', 'GFDL-ESM4', 'GFDL-OM4p5B',\n",
       "       'GISS-E2-1-G', 'GISS-E2-1-G-CC', 'GISS-E2-1-H', 'GISS-E2-2-G',\n",
       "       'HadGEM3-GC31-HM', 'HadGEM3-GC31-LL', 'HadGEM3-GC31-LM',\n",
       "       'HadGEM3-GC31-MM', 'IITM-ESM', 'INM-CM4-8', 'INM-CM5-0',\n",
       "       'INM-CM5-H', 'IPSL-CM5A2-INCA', 'IPSL-CM6A-ATM-HR', 'IPSL-CM6A-LR',\n",
       "       'IPSL-CM6A-LR-INCA', 'KACE-1-0-G', 'KIOST-ESM', 'MCM-UA-1-0',\n",
       "       'MIROC-ES2H', 'MIROC-ES2L', 'MIROC6', 'MPI-ESM-1-2-HAM',\n",
       "       'MPI-ESM1-2-HR', 'MPI-ESM1-2-LR', 'MPI-ESM1-2-XR', 'MRI-AGCM3-2-H',\n",
       "       'MRI-AGCM3-2-S', 'MRI-ESM2-0', 'NESM3', 'NorCPM1', 'NorESM1-F',\n",
       "       'NorESM2-LM', 'NorESM2-MM', 'SAM0-UNICON', 'TaiESM1',\n",
       "       'UKESM1-0-LL', 'ssp585'], dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(col.df['source_id'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e3b8e34-5253-42c6-a9d4-a9a2b01119e4",
   "metadata": {},
   "source": [
    "### Choosing datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "b07099d2-b649-4744-afe6-f330199bf180",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ACCESS-ESM1-5',\n",
       " 'CESM2',\n",
       " 'CESM2-WACCM',\n",
       " 'CanESM5',\n",
       " 'GFDL-ESM4',\n",
       " 'GISS-E2-1-G',\n",
       " 'IPSL-CM6A-LR',\n",
       " 'MPI-ESM1-2-HR',\n",
       " 'MPI-ESM1-2-LR',\n",
       " 'NorESM2-LM',\n",
       " 'NorESM2-MM'}"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# choose datasets by experiment_id & variable_id\n",
    "# e.g. when you want the datasets with experiment_id == 'ssp585' & 'historical' and variable_id == 'no3os' & 'tos'\n",
    "\n",
    "# all source_id with experiment_id == 'ssp585' or 'historical'\n",
    "elist1 = col.df[col.df.experiment_id == 'ssp585'].source_id.unique()\n",
    "elist2 = col.df[col.df.experiment_id == 'historical'].source_id.unique()\n",
    "\n",
    "#elist = set(elist1).intersection(elist2)\n",
    "\n",
    "# all source_id with variable_id == 'no3os' or 'tos'\n",
    "vlist1 = col.df[col.df.variable_id == 'no3os'].source_id.unique()\n",
    "vlist2 = col.df[col.df.variable_id == 'tos'].source_id.unique()\n",
    "\n",
    "vlist = set(vlist1).intersection(vlist2)\n",
    "\n",
    "mlist = col.df[col.df.member_id == 'r1i1p1f1'].source_id.unique()\n",
    "\n",
    "model_list = set(elist).intersection(vlist).intersection(mlist)\n",
    "model_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8825ad9-1c82-499a-bb2f-176dc9b9d5db",
   "metadata": {},
   "source": [
    "### Creating a subcollection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79c70b24-6d05-4ed7-b502-4e3fb05313e3",
   "metadata": {},
   "source": [
    "\n",
    "As an example, choose some models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "64b87f17-1c5b-4ac0-bb58-941f1c6f7ff9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
      "\t'activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.zstore.dcpp_init_year.version'\n"
     ]
    },
    {
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   ],
   "source": [
    "# adapted from from Julius's notebook\n",
    "# create a subcollection with certain search criteria\n",
    "\n",
    "models = ['CanESM5','NorESM2-LM']\n",
    "cat = col.search(\n",
    "    variable_id=['tos', 'no3os'], # SST & surface dissolved nitrate concentration\n",
    "    source_id=models,\n",
    "    grid_label='gn',\n",
    "    table_id='Omon',\n",
    "    experiment_id=['historical', 'ssp585'],\n",
    "    member_id = ['r1i1p1f1','r2i1p1f1']#, 'r2i1p2f1', 'r3i1p2f1'] # load only two members for demo purposes\n",
    ")\n",
    "\n",
    "# load all datasets into a python dictionary\n",
    "ddict = cat.to_dataset_dict(\n",
    "    zarr_kwargs={'consolidated':True, 'use_cftime':True}, # recommended for faster reading and better time handling\n",
    "    storage_options={'token': 'anon'}, # needed to access the public CMIP6 data on google\n",
    "    aggregate=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ffe4be7-4e84-4371-9d0f-502cb9a0f21e",
   "metadata": {},
   "source": [
    "Looing into the subselected dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dd64355a-839d-4b22-9c07-9fc815203387",
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:             (i: 360, j: 385, time: 1980, bnds: 2, vertices: 4)\n",
       "Coordinates:\n",
       "  * i                   (i) int32 1 2 3 4 5 6 7 ... 354 355 356 357 358 359 360\n",
       "  * j                   (j) int32 1 2 3 4 5 6 7 ... 379 380 381 382 383 384 385\n",
       "    latitude            (j, i) float64 dask.array&lt;chunksize=(385, 360), meta=np.ndarray&gt;\n",
       "    longitude           (j, i) float64 dask.array&lt;chunksize=(385, 360), meta=np.ndarray&gt;\n",
       "  * time                (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:0...\n",
       "    time_bnds           (time, bnds) object dask.array&lt;chunksize=(1980, 2), meta=np.ndarray&gt;\n",
       "Dimensions without coordinates: bnds, vertices\n",
       "Data variables:\n",
       "    no3os               (time, j, i) float32 dask.array&lt;chunksize=(173, 385, 360), meta=np.ndarray&gt;\n",
       "    vertices_latitude   (j, i, vertices) float64 dask.array&lt;chunksize=(385, 360, 4), meta=np.ndarray&gt;\n",
       "    vertices_longitude  (j, i, vertices) float64 dask.array&lt;chunksize=(385, 360, 4), meta=np.ndarray&gt;\n",
       "Attributes: (12/54)\n",
       "    Conventions:               CF-1.7 CMIP-6.2\n",
       "    activity_id:               CMIP\n",
       "    branch_method:             Hybrid-restart from year 1600-01-01 of piControl\n",
       "    branch_time:               0.0\n",
       "    branch_time_in_child:      0.0\n",
       "    branch_time_in_parent:     430335.0\n",
       "    ...                        ...\n",
       "    variable_id:               no3os\n",
       "    variant_label:             r1i1p1f1\n",
       "    netcdf_tracking_ids:       hdl:21.14100/b550fc86-72a2-4bc3-a8a7-1e2286301...\n",
       "    version_id:                v20191108\n",
       "    intake_esm_varname:        None\n",
       "    intake_esm_dataset_key:    CMIP.NCC.NorESM2-LM.historical.r1i1p1f1.Omon.n...</pre><div class='xr-wrap' hidden><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-66722a86-bbe1-4d3c-acbe-f594376f83fc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-66722a86-bbe1-4d3c-acbe-f594376f83fc' 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'>i</span>: 360</li><li><span class='xr-has-index'>j</span>: 385</li><li><span class='xr-has-index'>time</span>: 1980</li><li><span>bnds</span>: 2</li><li><span>vertices</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f76256a5-625b-4a33-ace6-0c2551222c71' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f76256a5-625b-4a33-ace6-0c2551222c71' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>i</span></div><div class='xr-var-dims'>(i)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 ... 356 357 358 359 360</div><input id='attrs-2b0686e6-1286-4127-9780-c4f7241ee575' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2b0686e6-1286-4127-9780-c4f7241ee575' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-deb51b64-d47e-4291-9e3c-456508292d04' class='xr-var-data-in' type='checkbox'><label for='data-deb51b64-d47e-4291-9e3c-456508292d04' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>cell index along first dimension</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([  1,   2,   3, ..., 358, 359, 360], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>j</span></div><div class='xr-var-dims'>(j)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 ... 381 382 383 384 385</div><input id='attrs-55773b72-3495-4307-8f64-8799dd244658' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-55773b72-3495-4307-8f64-8799dd244658' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55785a5c-d0ba-48fc-a702-84f7b0ac206a' class='xr-var-data-in' type='checkbox'><label for='data-55785a5c-d0ba-48fc-a702-84f7b0ac206a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>cell index along second dimension</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([  1,   2,   3, ..., 383, 384, 385], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(j, i)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(385, 360), meta=np.ndarray&gt;</div><input id='attrs-93e36b0d-e4e8-4a37-a49c-607c41eb61db' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-93e36b0d-e4e8-4a37-a49c-607c41eb61db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2b700b8-1696-484a-8b28-9984ca57aa5c' class='xr-var-data-in' type='checkbox'><label for='data-e2b700b8-1696-484a-8b28-9984ca57aa5c' 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>bounds :</span></dt><dd>vertices_latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
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       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(j, i)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(385, 360), meta=np.ndarray&gt;</div><input id='attrs-5f2a3081-4f74-4f24-8511-4f5326f002cf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f2a3081-4f74-4f24-8511-4f5326f002cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8179686-343e-46c3-8622-cf44f99b92a3' class='xr-var-data-in' type='checkbox'><label for='data-a8179686-343e-46c3-8622-cf44f99b92a3' 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>bounds :</span></dt><dd>vertices_longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
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       "  <thead>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></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'>1850-01-16 12:00:00 ... 2014-12-...</div><input id='attrs-1ff5e7fa-8d9e-45cd-aade-08662b0514ea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ff5e7fa-8d9e-45cd-aade-08662b0514ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c87a113f-791f-4653-9486-9ee8fa4e7b53' class='xr-var-data-in' type='checkbox'><label for='data-c87a113f-791f-4653-9486-9ee8fa4e7b53' 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>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       ...,\n",
       "       cftime.DatetimeNoLeap(2014, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 16, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1980, 2), meta=np.ndarray&gt;</div><input id='attrs-4888c631-9b85-41f7-8c3e-4b10a6588352' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4888c631-9b85-41f7-8c3e-4b10a6588352' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34598233-9207-4dfa-8b73-78c95e709c15' class='xr-var-data-in' type='checkbox'><label for='data-34598233-9207-4dfa-8b73-78c95e709c15' 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",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-68ebd880-5298-44d6-b389-44dccf81e744' class='xr-section-summary-in' type='checkbox'  checked><label for='section-68ebd880-5298-44d6-b389-44dccf81e744' class='xr-section-summary' >Data variables: <span>(3)</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>no3os</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(173, 385, 360), meta=np.ndarray&gt;</div><input id='attrs-a3438639-b2b0-4876-a3c0-2efe62519700' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3438639-b2b0-4876-a3c0-2efe62519700' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-925fb090-05ea-44a6-8cf0-e8c981df99f3' class='xr-var-data-in' type='checkbox'><label for='data-925fb090-05ea-44a6-8cf0-e8c981df99f3' 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: areacello</dd><dt><span>cell_methods :</span></dt><dd>area: mean where sea time: mean</dd><dt><span>comment :</span></dt><dd>Mole concentration means moles (amount of substance) per unit volume and is used in the construction mole_concentration_of_X_in_Y, where X is a material constituent of Y.</dd><dt><span>history :</span></dt><dd>2019-11-16T03:56:36Z altered by CMOR: Converted type from &#x27;d&#x27; to &#x27;f&#x27;.</dd><dt><span>long_name :</span></dt><dd>Surface Dissolved Nitrate Concentration</dd><dt><span>original_name :</span></dt><dd>srfno3</dd><dt><span>standard_name :</span></dt><dd>mole_concentration_of_nitrate_in_sea_water</dd><dt><span>units :</span></dt><dd>mol m-3</dd></dl></div><div class='xr-var-data'><table>\n",
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       "    <tr><th> Shape </th><td> (1980, 385, 360) </td> <td> (173, 385, 360) </td></tr>\n",
       "    <tr><th> Count </th><td> 13 Tasks </td><td> 12 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vertices_latitude</span></div><div class='xr-var-dims'>(j, i, vertices)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(385, 360, 4), meta=np.ndarray&gt;</div><input id='attrs-1718c1d5-bfda-4ddb-97d1-b3cd5eac59ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1718c1d5-bfda-4ddb-97d1-b3cd5eac59ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-094e0c33-869f-4805-ba37-8fd01fbb78d5' class='xr-var-data-in' type='checkbox'><label for='data-094e0c33-869f-4805-ba37-8fd01fbb78d5' 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",
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       "aerosol: OsloAero\n",
       "atmos: CAM-OSLO (2 degree resolution; 144 x 96; 32 levels; top level 3 mb)\n",
       "atmosChem: OsloChemSimp\n",
       "land: CLM\n",
       "landIce: CISM\n",
       "ocean: MICOM (1 degree resolution; 360 x 384; 70 levels; top grid cell minimum 0-2.5 m [native model uses hybrid density and generic upper-layer coordinate interpolated to z-level for contributed data])\n",
       "ocnBgchem: HAMOCC\n",
       "seaIce: CICE</dd><dt><span>source_id :</span></dt><dd>NorESM2-LM</dd><dt><span>source_type :</span></dt><dd>AOGCM AER BGC</dd><dt><span>status :</span></dt><dd>2020-04-26;created; by gcs.cmip6.ldeo@gmail.com</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>Omon</dd><dt><span>table_info :</span></dt><dd>Creation Date:(24 July 2019) MD5:0bb394a356ef9d214d027f1aca45853e</dd><dt><span>title :</span></dt><dd>NorESM2-LM output prepared for CMIP6</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/b550fc86-72a2-4bc3-a8a7-1e22863018c6\n",
       "hdl:21.14100/52936cab-ac1b-4dee-bbfa-5a79b9dda939\n",
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       "hdl:21.14100/63d653e2-1f84-4519-bb86-98091f1a0213\n",
       "hdl:21.14100/4c30c8a6-08c8-4ed5-86b9-02d60826c5f1\n",
       "hdl:21.14100/5d8b18af-141c-4216-8a13-0e336b522825\n",
       "hdl:21.14100/8a5aec4b-09e7-4d04-bb3d-dd2d325fa132\n",
       "hdl:21.14100/4847a48d-71f6-4b67-8771-17d7f25016c1\n",
       "hdl:21.14100/42413b90-a35e-41c6-9d36-fdbbc7b927d3\n",
       "hdl:21.14100/2cc979f3-b51a-4a0d-bc13-7f125fb6daac\n",
       "hdl:21.14100/7f5a550b-1b5d-47c8-83a9-d537ae4cb0ca\n",
       "hdl:21.14100/6fddd118-1699-4abe-bda6-f02a29e2332d\n",
       "hdl:21.14100/128999d5-3d6e-49e6-a738-ef461d59f1b7\n",
       "hdl:21.14100/57b4127b-2a9d-4fbd-b0b6-5aa586602f14\n",
       "hdl:21.14100/59bf22fd-11f0-4bb9-9fbd-01fb3686e65e</dd><dt><span>variable_id :</span></dt><dd>no3os</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>netcdf_tracking_ids :</span></dt><dd>hdl:21.14100/b550fc86-72a2-4bc3-a8a7-1e22863018c6\n",
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       "hdl:21.14100/128999d5-3d6e-49e6-a738-ef461d59f1b7\n",
       "hdl:21.14100/57b4127b-2a9d-4fbd-b0b6-5aa586602f14\n",
       "hdl:21.14100/59bf22fd-11f0-4bb9-9fbd-01fb3686e65e</dd><dt><span>version_id :</span></dt><dd>v20191108</dd><dt><span>intake_esm_varname :</span></dt><dd>None</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>CMIP.NCC.NorESM2-LM.historical.r1i1p1f1.Omon.no3os.gn.gs://cmip6/CMIP6/CMIP/NCC/NorESM2-LM/historical/r1i1p1f1/Omon/no3os/gn/v20191108/.nan.20191108</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:             (i: 360, j: 385, time: 1980, bnds: 2, vertices: 4)\n",
       "Coordinates:\n",
       "  * i                   (i) int32 1 2 3 4 5 6 7 ... 354 355 356 357 358 359 360\n",
       "  * j                   (j) int32 1 2 3 4 5 6 7 ... 379 380 381 382 383 384 385\n",
       "    latitude            (j, i) float64 dask.array<chunksize=(385, 360), meta=np.ndarray>\n",
       "    longitude           (j, i) float64 dask.array<chunksize=(385, 360), meta=np.ndarray>\n",
       "  * time                (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:0...\n",
       "    time_bnds           (time, bnds) object dask.array<chunksize=(1980, 2), meta=np.ndarray>\n",
       "Dimensions without coordinates: bnds, vertices\n",
       "Data variables:\n",
       "    no3os               (time, j, i) float32 dask.array<chunksize=(173, 385, 360), meta=np.ndarray>\n",
       "    vertices_latitude   (j, i, vertices) float64 dask.array<chunksize=(385, 360, 4), meta=np.ndarray>\n",
       "    vertices_longitude  (j, i, vertices) float64 dask.array<chunksize=(385, 360, 4), meta=np.ndarray>\n",
       "Attributes: (12/54)\n",
       "    Conventions:               CF-1.7 CMIP-6.2\n",
       "    activity_id:               CMIP\n",
       "    branch_method:             Hybrid-restart from year 1600-01-01 of piControl\n",
       "    branch_time:               0.0\n",
       "    branch_time_in_child:      0.0\n",
       "    branch_time_in_parent:     430335.0\n",
       "    ...                        ...\n",
       "    variable_id:               no3os\n",
       "    variant_label:             r1i1p1f1\n",
       "    netcdf_tracking_ids:       hdl:21.14100/b550fc86-72a2-4bc3-a8a7-1e2286301...\n",
       "    version_id:                v20191108\n",
       "    intake_esm_varname:        None\n",
       "    intake_esm_dataset_key:    CMIP.NCC.NorESM2-LM.historical.r1i1p1f1.Omon.n..."
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first dataset on ddict list\n",
    "ds_a = ddict['CMIP.NCC.NorESM2-LM.historical.r1i1p1f1.Omon.no3os.gn.gs://cmip6/CMIP6/CMIP/NCC/NorESM2-LM/historical/r1i1p1f1/Omon/no3os/gn/v20191108/.nan.20191108']\n",
    "ds_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "53bbdc31-0070-42a2-bf6f-8da8feb5ce1d",
   "metadata": {},
   "outputs": [
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       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:             (i: 360, j: 291, time: 1980, bnds: 2, vertices: 4)\n",
       "Coordinates:\n",
       "  * i                   (i) int32 0 1 2 3 4 5 6 ... 353 354 355 356 357 358 359\n",
       "  * j                   (j) int32 0 1 2 3 4 5 6 ... 284 285 286 287 288 289 290\n",
       "    latitude            (j, i) float64 dask.array&lt;chunksize=(291, 360), meta=np.ndarray&gt;\n",
       "    longitude           (j, i) float64 dask.array&lt;chunksize=(291, 360), meta=np.ndarray&gt;\n",
       "  * time                (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:0...\n",
       "    time_bnds           (time, bnds) object dask.array&lt;chunksize=(1980, 2), meta=np.ndarray&gt;\n",
       "Dimensions without coordinates: bnds, vertices\n",
       "Data variables:\n",
       "    no3os               (time, j, i) float32 dask.array&lt;chunksize=(210, 291, 360), meta=np.ndarray&gt;\n",
       "    vertices_latitude   (j, i, vertices) float64 dask.array&lt;chunksize=(291, 360, 4), meta=np.ndarray&gt;\n",
       "    vertices_longitude  (j, i, vertices) float64 dask.array&lt;chunksize=(291, 360, 4), meta=np.ndarray&gt;\n",
       "Attributes: (12/58)\n",
       "    CCCma_model_hash:            3dedf95315d603326fde4f5340dc0519d80d10c0\n",
       "    CCCma_parent_runid:          rc3-pictrl\n",
       "    CCCma_pycmor_hash:           33c30511acc319a98240633965a04ca99c26427e\n",
       "    CCCma_runid:                 rc3.1-his01\n",
       "    Conventions:                 CF-1.7 CMIP-6.2\n",
       "    YMDH_branch_time_in_child:   1850:01:01:00\n",
       "    ...                          ...\n",
       "    variant_label:               r1i1p1f1\n",
       "    version:                     v20190429\n",
       "    netcdf_tracking_ids:         hdl:21.14100/1e2686dc-0343-474e-88a6-8ee95aa...\n",
       "    version_id:                  v20190429\n",
       "    intake_esm_varname:          None\n",
       "    intake_esm_dataset_key:      CMIP.CCCma.CanESM5.historical.r1i1p1f1.Omon....</pre><div class='xr-wrap' hidden><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-88f836cc-d910-4435-b4c9-ceadbad32f6e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-88f836cc-d910-4435-b4c9-ceadbad32f6e' 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'>i</span>: 360</li><li><span class='xr-has-index'>j</span>: 291</li><li><span class='xr-has-index'>time</span>: 1980</li><li><span>bnds</span>: 2</li><li><span>vertices</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-21737c10-06d4-42e0-b194-101344544510' class='xr-section-summary-in' type='checkbox'  checked><label for='section-21737c10-06d4-42e0-b194-101344544510' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>i</span></div><div class='xr-var-dims'>(i)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 355 356 357 358 359</div><input id='attrs-174e2c4f-f10b-4820-b724-0cde329ae5ac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-174e2c4f-f10b-4820-b724-0cde329ae5ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-405739cd-1ab2-46b5-a373-9e456638f5ab' class='xr-var-data-in' type='checkbox'><label for='data-405739cd-1ab2-46b5-a373-9e456638f5ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>cell index along first dimension</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 357, 358, 359], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>j</span></div><div class='xr-var-dims'>(j)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 286 287 288 289 290</div><input id='attrs-ee4971cf-90fb-4da3-a58c-047e28d8d405' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ee4971cf-90fb-4da3-a58c-047e28d8d405' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c17cfdff-5bc0-4417-baaa-e9e9d4fdfad2' class='xr-var-data-in' type='checkbox'><label for='data-c17cfdff-5bc0-4417-baaa-e9e9d4fdfad2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>cell index along second dimension</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 288, 289, 290], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(j, i)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(291, 360), meta=np.ndarray&gt;</div><input id='attrs-8000175a-1c38-4ffc-8416-9636569ecb75' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8000175a-1c38-4ffc-8416-9636569ecb75' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9700ce86-8b86-4a8b-99e4-26edb8fd4e0f' class='xr-var-data-in' type='checkbox'><label for='data-9700ce86-8b86-4a8b-99e4-26edb8fd4e0f' 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>bounds :</span></dt><dd>vertices_latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><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><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 818.44 kiB </td> <td> 818.44 kiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (291, 360) </td> <td> (291, 360) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"170\" height=\"147\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "\n",
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       "\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>longitude</span></div><div class='xr-var-dims'>(j, i)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(291, 360), meta=np.ndarray&gt;</div><input id='attrs-422815b6-b071-47e1-b55d-23b9b4c4bc6d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-422815b6-b071-47e1-b55d-23b9b4c4bc6d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3cbbf71a-c1f1-408d-84a1-e977296d1bad' class='xr-var-data-in' type='checkbox'><label for='data-3cbbf71a-c1f1-408d-84a1-e977296d1bad' 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>bounds :</span></dt><dd>vertices_longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><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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       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 818.44 kiB </td> <td> 818.44 kiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (291, 360) </td> <td> (291, 360) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</table>\n",
       "</td>\n",
       "<td>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></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'>1850-01-16 12:00:00 ... 2014-12-...</div><input id='attrs-543fd5f8-30ae-4d95-a22f-db3fefce9f86' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-543fd5f8-30ae-4d95-a22f-db3fefce9f86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f3e8bd3-07ab-4ad4-a841-a79a0246b448' class='xr-var-data-in' type='checkbox'><label for='data-9f3e8bd3-07ab-4ad4-a841-a79a0246b448' 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>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       ...,\n",
       "       cftime.DatetimeNoLeap(2014, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 16, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1980, 2), meta=np.ndarray&gt;</div><input id='attrs-82ad2238-db20-4a3b-b78a-19bbf4eb4352' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-82ad2238-db20-4a3b-b78a-19bbf4eb4352' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e05def4c-46f7-45d6-8ca6-a3c6fc6ad8c6' class='xr-var-data-in' type='checkbox'><label for='data-e05def4c-46f7-45d6-8ca6-a3c6fc6ad8c6' 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",
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       "    <tr><th> Shape </th><td> (1980, 2) </td> <td> (1980, 2) </td></tr>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-e38051a0-91a5-4724-9c3e-801f7d475e18' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e38051a0-91a5-4724-9c3e-801f7d475e18' class='xr-section-summary' >Data variables: <span>(3)</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>no3os</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(210, 291, 360), meta=np.ndarray&gt;</div><input id='attrs-82e6b618-957e-4ea9-8c99-0b42b191bd0b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-82e6b618-957e-4ea9-8c99-0b42b191bd0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b51db6c4-058f-40f7-824f-419edfb2dce8' class='xr-var-data-in' type='checkbox'><label for='data-b51db6c4-058f-40f7-824f-419edfb2dce8' 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: areacello</dd><dt><span>cell_methods :</span></dt><dd>area: mean where sea time: mean</dd><dt><span>comment :</span></dt><dd>Mole concentration means moles (amount of substance) per unit volume and is used in the construction mole_concentration_of_X_in_Y, where X is a material constituent of Y.</dd><dt><span>history :</span></dt><dd>deptht_l1_mltby1em3</dd><dt><span>long_name :</span></dt><dd>Surface Dissolved Nitrate Concentration</dd><dt><span>original_name :</span></dt><dd>NO3</dd><dt><span>standard_name :</span></dt><dd>mole_concentration_of_nitrate_in_sea_water</dd><dt><span>units :</span></dt><dd>mol m-3</dd></dl></div><div class='xr-var-data'><table>\n",
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       "    <tr><th> Shape </th><td> (1980, 291, 360) </td> <td> (210, 291, 360) </td></tr>\n",
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       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vertices_longitude</span></div><div class='xr-var-dims'>(j, i, vertices)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(291, 360, 4), meta=np.ndarray&gt;</div><input id='attrs-49294a80-b9fc-4058-b50b-fb320a9b820d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49294a80-b9fc-4058-b50b-fb320a9b820d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5824e62-3861-4ddf-8c03-d259364db742' class='xr-var-data-in' type='checkbox'><label for='data-d5824e62-3861-4ddf-8c03-d259364db742' 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",
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       "  <text x=\"28.529412\" y=\"168.529412\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,28.529412,168.529412)\">291</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-881e258d-c005-47d4-b2cf-74fef8ec07e2' class='xr-section-summary-in' type='checkbox'  ><label for='section-881e258d-c005-47d4-b2cf-74fef8ec07e2' class='xr-section-summary' >Attributes: <span>(58)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CCCma_model_hash :</span></dt><dd>3dedf95315d603326fde4f5340dc0519d80d10c0</dd><dt><span>CCCma_parent_runid :</span></dt><dd>rc3-pictrl</dd><dt><span>CCCma_pycmor_hash :</span></dt><dd>33c30511acc319a98240633965a04ca99c26427e</dd><dt><span>CCCma_runid :</span></dt><dd>rc3.1-his01</dd><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>YMDH_branch_time_in_child :</span></dt><dd>1850:01:01:00</dd><dt><span>YMDH_branch_time_in_parent :</span></dt><dd>5201:01:01:00</dd><dt><span>activity_id :</span></dt><dd>CMIP</dd><dt><span>branch_method :</span></dt><dd>Spin-up documentation</dd><dt><span>branch_time_in_child :</span></dt><dd>0.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>1223115.0</dd><dt><span>cmor_version :</span></dt><dd>3.4.0</dd><dt><span>contact :</span></dt><dd>ec.cccma.info-info.ccmac.ec@canada.ca</dd><dt><span>creation_date :</span></dt><dd>2019-05-01T03:05:05Z</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>areacello</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.CCCma.CanESM5.historical.none.r1i1p1f1</dd><dt><span>grid :</span></dt><dd>ORCA1 tripolar grid, 1 deg with refinement to 1/3 deg within 20 degrees of the equator; 361 x 290 longitude/latitude; 45 vertical levels; top grid cell 0-6.19 m</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>history :</span></dt><dd>2019-05-01T03:05:05Z ;rewrote data to be consistent with CMIP for variable no3os found in table Omon.;\n",
       "Output from $runid</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, BC V8P 5C2, Canada</dd><dt><span>institution_id :</span></dt><dd>CCCma</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by The Government of Canada (Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada) 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) and at https:///pcmdi.llnl.gov/. 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>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>CanESM5</dd><dt><span>parent_time_units :</span></dt><dd>days since 1850-01-01 0:0:0.0</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>ocnBgchem</dd><dt><span>references :</span></dt><dd>Geophysical Model Development Special issue on CanESM5 (https://www.geosci-model-dev.net/special_issues.html)</dd><dt><span>source :</span></dt><dd>CanESM5 (2019): \n",
       "aerosol: interactive\n",
       "atmos: CanAM5 (T63L49 native atmosphere, T63 Linear Gaussian Grid; 128 x 64 longitude/latitude; 49 levels; top level 1 hPa)\n",
       "atmosChem: specified oxidants for aerosols\n",
       "land: CLASS3.6/CTEM1.2\n",
       "landIce: specified ice sheets\n",
       "ocean: NEMO3.4.1 (ORCA1 tripolar grid, 1 deg with refinement to 1/3 deg within 20 degrees of the equator; 361 x 290 longitude/latitude; 45 vertical levels; top grid cell 0-6.19 m)\n",
       "ocnBgchem: Canadian Model of Ocean Carbon (CMOC); NPZD ecosystem with OMIP prescribed carbonate chemistry\n",
       "seaIce: LIM2</dd><dt><span>source_id :</span></dt><dd>CanESM5</dd><dt><span>source_type :</span></dt><dd>AOGCM</dd><dt><span>status :</span></dt><dd>2020-04-26;created; by gcs.cmip6.ldeo@gmail.com</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>Omon</dd><dt><span>table_info :</span></dt><dd>Creation Date:(20 February 2019) MD5:374fbe5a2bcca535c40f7f23da271e49</dd><dt><span>title :</span></dt><dd>CanESM5 output prepared for CMIP6</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/1e2686dc-0343-474e-88a6-8ee95aa54fa3</dd><dt><span>variable_id :</span></dt><dd>no3os</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>version :</span></dt><dd>v20190429</dd><dt><span>netcdf_tracking_ids :</span></dt><dd>hdl:21.14100/1e2686dc-0343-474e-88a6-8ee95aa54fa3</dd><dt><span>version_id :</span></dt><dd>v20190429</dd><dt><span>intake_esm_varname :</span></dt><dd>None</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>CMIP.CCCma.CanESM5.historical.r1i1p1f1.Omon.no3os.gn.gs://cmip6/CMIP6/CMIP/CCCma/CanESM5/historical/r1i1p1f1/Omon/no3os/gn/v20190429/.nan.20190429</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:             (i: 360, j: 291, time: 1980, bnds: 2, vertices: 4)\n",
       "Coordinates:\n",
       "  * i                   (i) int32 0 1 2 3 4 5 6 ... 353 354 355 356 357 358 359\n",
       "  * j                   (j) int32 0 1 2 3 4 5 6 ... 284 285 286 287 288 289 290\n",
       "    latitude            (j, i) float64 dask.array<chunksize=(291, 360), meta=np.ndarray>\n",
       "    longitude           (j, i) float64 dask.array<chunksize=(291, 360), meta=np.ndarray>\n",
       "  * time                (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:0...\n",
       "    time_bnds           (time, bnds) object dask.array<chunksize=(1980, 2), meta=np.ndarray>\n",
       "Dimensions without coordinates: bnds, vertices\n",
       "Data variables:\n",
       "    no3os               (time, j, i) float32 dask.array<chunksize=(210, 291, 360), meta=np.ndarray>\n",
       "    vertices_latitude   (j, i, vertices) float64 dask.array<chunksize=(291, 360, 4), meta=np.ndarray>\n",
       "    vertices_longitude  (j, i, vertices) float64 dask.array<chunksize=(291, 360, 4), meta=np.ndarray>\n",
       "Attributes: (12/58)\n",
       "    CCCma_model_hash:            3dedf95315d603326fde4f5340dc0519d80d10c0\n",
       "    CCCma_parent_runid:          rc3-pictrl\n",
       "    CCCma_pycmor_hash:           33c30511acc319a98240633965a04ca99c26427e\n",
       "    CCCma_runid:                 rc3.1-his01\n",
       "    Conventions:                 CF-1.7 CMIP-6.2\n",
       "    YMDH_branch_time_in_child:   1850:01:01:00\n",
       "    ...                          ...\n",
       "    variant_label:               r1i1p1f1\n",
       "    version:                     v20190429\n",
       "    netcdf_tracking_ids:         hdl:21.14100/1e2686dc-0343-474e-88a6-8ee95aa...\n",
       "    version_id:                  v20190429\n",
       "    intake_esm_varname:          None\n",
       "    intake_esm_dataset_key:      CMIP.CCCma.CanESM5.historical.r1i1p1f1.Omon...."
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# second dataset on ddict list\n",
    "ds_b = ddict['CMIP.CCCma.CanESM5.historical.r1i1p1f1.Omon.no3os.gn.gs://cmip6/CMIP6/CMIP/CCCma/CanESM5/historical/r1i1p1f1/Omon/no3os/gn/v20190429/.nan.20190429']\n",
    "ds_b"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c93fff62-6edd-4368-a632-63804dd095f3",
   "metadata": {},
   "source": [
    "Checking the dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b7da89be-cba7-4f21-9df6-dbf2ba6fb54f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frozen({'i': 360, 'j': 385, 'time': 1980, 'bnds': 2, 'vertices': 4})\n",
      "Frozen({'i': 360, 'j': 291, 'time': 1980, 'bnds': 2, 'vertices': 4})\n"
     ]
    }
   ],
   "source": [
    "print(ds_a.dims)\n",
    "print(ds_b.dims)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "46e55e3a-efcf-41e8-94ea-aaef0b14dca3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frozen({'time': 1980, 'y': 385, 'x': 360, 'bnds': 2, 'vertex': 4})\n",
      "Frozen({'time': 1980, 'y': 291, 'x': 360, 'bnds': 2, 'vertex': 4})\n"
     ]
    }
   ],
   "source": [
    "# copied from Julius's notebook\n",
    "from cmip6_preprocessing.preprocessing import combined_preprocessing\n",
    "print(combined_preprocessing(ds_a).dims)\n",
    "print(combined_preprocessing(ds_b).dims)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68e71a7f-ca11-4767-b8e6-656c179d6782",
   "metadata": {},
   "source": [
    "Note that CanESM5 and NorESM2 have different number of points in the y-direction. The ranges are given below:\n",
    "1. NorESM2 has longitude $\\in$(-80, 62)\n",
    "2. CanESM5 has longitude $\\in$(-78, 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6545ae1a-228c-4783-aa8b-f5ef312f66ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
      "\t'activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.zstore.dcpp_init_year.version'\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
       "            }\n",
       "        </style>\n",
       "      <progress value='14' class='' max='14' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [14/14 00:02<00:00]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# copied from Julius's notebook\n",
    "# load all datasets into a python dictionary\n",
    "ddict = cat.to_dataset_dict(\n",
    "    zarr_kwargs={'consolidated':True, 'use_cftime':True}, # recommended for faster reading and better time handling\n",
    "    storage_options={'token': 'anon'}, # needed to access the public CMIP6 data on google\n",
    "    aggregate=False,\n",
    "    preprocess=combined_preprocessing, # this applies the preprocessing to all datasets\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "6678647a-4de2-4c28-ac30-8524e78fd81f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 29 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# copied from Julius's notebook\n",
    "# quick example of how to loop over several models\n",
    "from cmip6_preprocessing.utils import cmip6_dataset_id\n",
    "fig, axarr = plt.subplots(ncols = 3, nrows = 5, figsize = (16,16))\n",
    "for ax, (name, ds) in zip(axarr.flat, ddict.items()):\n",
    "    # select the first time step\n",
    "    ds = ds.isel(time=0)\n",
    "    # select the datavariable\n",
    "    da = ds[ds.variable_id]\n",
    "    # plot\n",
    "    \n",
    "    da.plot(ax=ax)\n",
    "    ax.set_title('.'.join(cmip6_dataset_id(ds).split('.')[2:6]))\n",
    "    \n",
    "fig.subplots_adjust(hspace=0.5, wspace=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5bae444-2950-4bd9-95fb-ba6e5515b345",
   "metadata": {},
   "source": [
    "### Grouping the dataset (by source_id??)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "b1133df1-c5e2-4d22-8ecb-210f95035409",
   "metadata": {},
   "outputs": [],
   "source": [
    "from cmip6_preprocessing.postprocessing import merge_variables, concat_members, concat_experiments\n",
    "ddict_combined_a = merge_variables(ddict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "e38ad920-3242-4f42-88b8-49c0382e6566",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'CanESM5.gn.historical.Omon.r1i1p1f1'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_27/4195990957.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mddict_combined_a\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mddict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m: 'CanESM5.gn.historical.Omon.r1i1p1f1'"
     ]
    }
   ],
   "source": [
    "for key in ddict_combined_a:\n",
    "    ddict[str(key)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "7794d3b0-00ce-482b-8655-be8e657ec305",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'CanESM5.gn.historical.Omon.r1i1p1f1'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_27/646781810.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mddict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'CanESM5.gn.historical.Omon.r1i1p1f1'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m: 'CanESM5.gn.historical.Omon.r1i1p1f1'"
     ]
    }
   ],
   "source": [
    "ddict['CanESM5.gn.historical.Omon.r1i1p1f1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "6ff0ac8d-0281-4a0f-98bd-a532dc29943c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CanESM5.gn.historical.Omon.r1i1p1f1': <xarray.Dataset>\n",
       " Dimensions:        (time: 1980, y: 291, x: 360, bnds: 2, vertex: 4)\n",
       " Coordinates:\n",
       "   * x              (x) int32 0 1 2 3 4 5 6 7 ... 352 353 354 355 356 357 358 359\n",
       "   * y              (y) int32 0 1 2 3 4 5 6 7 ... 283 284 285 286 287 288 289 290\n",
       "     lat            (y, x) float64 dask.array<chunksize=(291, 360), meta=np.ndarray>\n",
       "     lon            (y, x) float64 dask.array<chunksize=(291, 360), meta=np.ndarray>\n",
       "   * time           (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "     time_bounds    (time, bnds) object dask.array<chunksize=(1980, 2), meta=np.ndarray>\n",
       "     lat_verticies  (y, x, vertex) float64 dask.array<chunksize=(291, 360, 4), meta=np.ndarray>\n",
       "     lon_verticies  (y, x, vertex) float64 dask.array<chunksize=(291, 360, 4), meta=np.ndarray>\n",
       "     lon_bounds     (bnds, y, x) float64 dask.array<chunksize=(1, 291, 360), meta=np.ndarray>\n",
       "     lat_bounds     (bnds, y, x) float64 dask.array<chunksize=(1, 291, 360), meta=np.ndarray>\n",
       " Dimensions without coordinates: bnds, vertex\n",
       " Data variables:\n",
       "     no3os          (time, y, x) float32 dask.array<chunksize=(210, 291, 360), meta=np.ndarray>\n",
       "     tos            (time, y, x) float32 dask.array<chunksize=(215, 291, 360), meta=np.ndarray>\n",
       " Attributes: (12/51)\n",
       "     CCCma_model_hash:            3dedf95315d603326fde4f5340dc0519d80d10c0\n",
       "     CCCma_parent_runid:          rc3-pictrl\n",
       "     CCCma_pycmor_hash:           33c30511acc319a98240633965a04ca99c26427e\n",
       "     CCCma_runid:                 rc3.1-his01\n",
       "     Conventions:                 CF-1.7 CMIP-6.2\n",
       "     YMDH_branch_time_in_child:   1850:01:01:00\n",
       "     ...                          ...\n",
       "     title:                       CanESM5 output prepared for CMIP6\n",
       "     variant_label:               r1i1p1f1\n",
       "     version:                     v20190429\n",
       "     version_id:                  v20190429\n",
       "     intake_esm_varname:          None\n",
       "     original_key:                CMIP.CCCma.CanESM5.historical.r1i1p1f1.Omon....,\n",
       " 'CanESM5.gn.ssp585.Omon.r1i1p1f1': <xarray.Dataset>\n",
       " Dimensions:        (time: 3432, y: 291, x: 360, bnds: 2, vertex: 4)\n",
       " Coordinates:\n",
       "   * x              (x) int32 0 1 2 3 4 5 6 7 ... 352 353 354 355 356 357 358 359\n",
       "   * y              (y) int32 0 1 2 3 4 5 6 7 ... 283 284 285 286 287 288 289 290\n",
       "     lat            (y, x) float64 dask.array<chunksize=(291, 360), meta=np.ndarray>\n",
       "     lon            (y, x) float64 dask.array<chunksize=(291, 360), meta=np.ndarray>\n",
       "   * time           (time) object 2015-01-16 12:00:00 ... 2300-12-16 12:00:00\n",
       "     time_bounds    (time, bnds) object dask.array<chunksize=(3432, 2), meta=np.ndarray>\n",
       "     lat_verticies  (y, x, vertex) float64 dask.array<chunksize=(291, 360, 4), meta=np.ndarray>\n",
       "     lon_verticies  (y, x, vertex) float64 dask.array<chunksize=(291, 360, 4), meta=np.ndarray>\n",
       "     lon_bounds     (bnds, y, x) float64 dask.array<chunksize=(1, 291, 360), meta=np.ndarray>\n",
       "     lat_bounds     (bnds, y, x) float64 dask.array<chunksize=(1, 291, 360), meta=np.ndarray>\n",
       " Dimensions without coordinates: bnds, vertex\n",
       " Data variables:\n",
       "     tos            (time, y, x) float32 dask.array<chunksize=(214, 291, 360), meta=np.ndarray>\n",
       "     no3os          (time, y, x) float32 dask.array<chunksize=(210, 291, 360), meta=np.ndarray>\n",
       " Attributes: (12/51)\n",
       "     CCCma_model_hash:            1f91f92cb6d607391f44831504025d32fc44faa1\n",
       "     CCCma_parent_runid:          rc3.1-his01\n",
       "     CCCma_pycmor_hash:           33c30511acc319a98240633965a04ca99c26427e\n",
       "     CCCma_runid:                 rc3.1-s8501\n",
       "     Conventions:                 CF-1.7 CMIP-6.2\n",
       "     YMDH_branch_time_in_child:   2015:01:01:00\n",
       "     ...                          ...\n",
       "     title:                       CanESM5 output prepared for CMIP6\n",
       "     variant_label:               r1i1p1f1\n",
       "     version:                     v20190429\n",
       "     version_id:                  v20190429\n",
       "     intake_esm_varname:          None\n",
       "     original_key:                ScenarioMIP.CCCma.CanESM5.ssp585.r1i1p1f1.Om...,\n",
       " 'CanESM5.gn.historical.Omon.r2i1p1f1': <xarray.Dataset>\n",
       " Dimensions:        (time: 1980, y: 291, x: 360, bnds: 2, vertex: 4)\n",
       " Coordinates:\n",
       "   * x              (x) int32 0 1 2 3 4 5 6 7 ... 352 353 354 355 356 357 358 359\n",
       "   * y              (y) int32 0 1 2 3 4 5 6 7 ... 283 284 285 286 287 288 289 290\n",
       "     lat            (y, x) float64 dask.array<chunksize=(291, 360), meta=np.ndarray>\n",
       "     lon            (y, x) float64 dask.array<chunksize=(291, 360), meta=np.ndarray>\n",
       "   * time           (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "     time_bounds    (time, bnds) object dask.array<chunksize=(1980, 2), meta=np.ndarray>\n",
       "     lat_verticies  (y, x, vertex) float64 dask.array<chunksize=(291, 360, 4), meta=np.ndarray>\n",
       "     lon_verticies  (y, x, vertex) float64 dask.array<chunksize=(291, 360, 4), meta=np.ndarray>\n",
       "     lon_bounds     (bnds, y, x) float64 dask.array<chunksize=(1, 291, 360), meta=np.ndarray>\n",
       "     lat_bounds     (bnds, y, x) float64 dask.array<chunksize=(1, 291, 360), meta=np.ndarray>\n",
       " Dimensions without coordinates: bnds, vertex\n",
       " Data variables:\n",
       "     tos            (time, y, x) float32 dask.array<chunksize=(215, 291, 360), meta=np.ndarray>\n",
       "     no3os          (time, y, x) float32 dask.array<chunksize=(210, 291, 360), meta=np.ndarray>\n",
       " Attributes: (12/51)\n",
       "     CCCma_model_hash:            3dedf95315d603326fde4f5340dc0519d80d10c0\n",
       "     CCCma_parent_runid:          rc3-pictrl\n",
       "     CCCma_pycmor_hash:           33c30511acc319a98240633965a04ca99c26427e\n",
       "     CCCma_runid:                 rc3.1-his02\n",
       "     Conventions:                 CF-1.7 CMIP-6.2\n",
       "     YMDH_branch_time_in_child:   1850:01:01:00\n",
       "     ...                          ...\n",
       "     title:                       CanESM5 output prepared for CMIP6\n",
       "     variant_label:               r2i1p1f1\n",
       "     version:                     v20190429\n",
       "     version_id:                  v20190429\n",
       "     intake_esm_varname:          None\n",
       "     original_key:                CMIP.CCCma.CanESM5.historical.r2i1p1f1.Omon....,\n",
       " 'NorESM2-LM.gn.historical.Omon.r2i1p1f1': <xarray.Dataset>\n",
       " Dimensions:        (time: 1980, y: 385, x: 360, bnds: 2, vertex: 4)\n",
       " Coordinates:\n",
       "   * x              (x) int32 1 2 3 4 5 6 7 8 ... 353 354 355 356 357 358 359 360\n",
       "   * y              (y) int32 1 2 3 4 5 6 7 8 ... 378 379 380 381 382 383 384 385\n",
       "     lat            (y, x) float64 dask.array<chunksize=(385, 360), meta=np.ndarray>\n",
       "     lon            (y, x) float64 dask.array<chunksize=(385, 360), meta=np.ndarray>\n",
       "   * time           (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "     time_bounds    (time, bnds) object dask.array<chunksize=(1980, 2), meta=np.ndarray>\n",
       "     lat_verticies  (y, x, vertex) float64 dask.array<chunksize=(385, 360, 4), meta=np.ndarray>\n",
       "     lon_verticies  (y, x, vertex) float64 dask.array<chunksize=(385, 360, 4), meta=np.ndarray>\n",
       "     lon_bounds     (bnds, y, x) float64 dask.array<chunksize=(1, 385, 360), meta=np.ndarray>\n",
       "     lat_bounds     (bnds, y, x) float64 dask.array<chunksize=(1, 385, 360), meta=np.ndarray>\n",
       " Dimensions without coordinates: bnds, vertex\n",
       " Data variables:\n",
       "     tos            (time, y, x) float32 dask.array<chunksize=(185, 385, 360), meta=np.ndarray>\n",
       "     no3os          (time, y, x) float32 dask.array<chunksize=(173, 385, 360), meta=np.ndarray>\n",
       " Attributes: (12/46)\n",
       "     Conventions:               CF-1.7 CMIP-6.2\n",
       "     activity_id:               CMIP\n",
       "     branch_method:             Hybrid-restart from year 1631-01-01 of piControl\n",
       "     branch_time:               0.0\n",
       "     branch_time_in_child:      0.0\n",
       "     branch_time_in_parent:     441650.0\n",
       "     ...                        ...\n",
       "     table_id:                  Omon\n",
       "     table_info:                Creation Date:(24 July 2019) MD5:0bb394a356ef9...\n",
       "     title:                     NorESM2-LM output prepared for CMIP6\n",
       "     variant_label:             r2i1p1f1\n",
       "     intake_esm_varname:        None\n",
       "     original_key:              CMIP.NCC.NorESM2-LM.historical.r2i1p1f1.Omon.n...,\n",
       " 'NorESM2-LM.gn.historical.Omon.r1i1p1f1': <xarray.Dataset>\n",
       " Dimensions:        (time: 1980, y: 385, x: 360, bnds: 2, vertex: 4)\n",
       " Coordinates:\n",
       "   * x              (x) int32 1 2 3 4 5 6 7 8 ... 353 354 355 356 357 358 359 360\n",
       "   * y              (y) int32 1 2 3 4 5 6 7 8 ... 378 379 380 381 382 383 384 385\n",
       "     lat            (y, x) float64 dask.array<chunksize=(385, 360), meta=np.ndarray>\n",
       "     lon            (y, x) float64 dask.array<chunksize=(385, 360), meta=np.ndarray>\n",
       "   * time           (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "     time_bounds    (time, bnds) object dask.array<chunksize=(1980, 2), meta=np.ndarray>\n",
       "     lat_verticies  (y, x, vertex) float64 dask.array<chunksize=(385, 360, 4), meta=np.ndarray>\n",
       "     lon_verticies  (y, x, vertex) float64 dask.array<chunksize=(385, 360, 4), meta=np.ndarray>\n",
       "     lon_bounds     (bnds, y, x) float64 dask.array<chunksize=(1, 385, 360), meta=np.ndarray>\n",
       "     lat_bounds     (bnds, y, x) float64 dask.array<chunksize=(1, 385, 360), meta=np.ndarray>\n",
       " Dimensions without coordinates: bnds, vertex\n",
       " Data variables:\n",
       "     no3os          (time, y, x) float32 dask.array<chunksize=(173, 385, 360), meta=np.ndarray>\n",
       "     tos            (time, y, x) float32 dask.array<chunksize=(185, 385, 360), meta=np.ndarray>\n",
       " Attributes: (12/44)\n",
       "     Conventions:               CF-1.7 CMIP-6.2\n",
       "     activity_id:               CMIP\n",
       "     branch_method:             Hybrid-restart from year 1600-01-01 of piControl\n",
       "     branch_time:               0.0\n",
       "     branch_time_in_child:      0.0\n",
       "     branch_time_in_parent:     430335.0\n",
       "     ...                        ...\n",
       "     sub_experiment_id:         none\n",
       "     table_id:                  Omon\n",
       "     title:                     NorESM2-LM output prepared for CMIP6\n",
       "     variant_label:             r1i1p1f1\n",
       "     intake_esm_varname:        None\n",
       "     original_key:              CMIP.NCC.NorESM2-LM.historical.r1i1p1f1.Omon.t...,\n",
       " 'CanESM5.gn.ssp585.Omon.r2i1p1f1': <xarray.Dataset>\n",
       " Dimensions:        (time: 1032, y: 291, x: 360, bnds: 2, vertex: 4)\n",
       " Coordinates:\n",
       "   * x              (x) int32 0 1 2 3 4 5 6 7 ... 352 353 354 355 356 357 358 359\n",
       "   * y              (y) int32 0 1 2 3 4 5 6 7 ... 283 284 285 286 287 288 289 290\n",
       "     lat            (y, x) float64 dask.array<chunksize=(291, 360), meta=np.ndarray>\n",
       "     lon            (y, x) float64 dask.array<chunksize=(291, 360), meta=np.ndarray>\n",
       "   * time           (time) object 2015-01-16 12:00:00 ... 2100-12-16 12:00:00\n",
       "     time_bounds    (time, bnds) object dask.array<chunksize=(1032, 2), meta=np.ndarray>\n",
       "     lat_verticies  (y, x, vertex) float64 dask.array<chunksize=(291, 360, 4), meta=np.ndarray>\n",
       "     lon_verticies  (y, x, vertex) float64 dask.array<chunksize=(291, 360, 4), meta=np.ndarray>\n",
       "     lon_bounds     (bnds, y, x) float64 dask.array<chunksize=(1, 291, 360), meta=np.ndarray>\n",
       "     lat_bounds     (bnds, y, x) float64 dask.array<chunksize=(1, 291, 360), meta=np.ndarray>\n",
       " Dimensions without coordinates: bnds, vertex\n",
       " Data variables:\n",
       "     tos            (time, y, x) float32 dask.array<chunksize=(214, 291, 360), meta=np.ndarray>\n",
       "     no3os          (time, y, x) float32 dask.array<chunksize=(210, 291, 360), meta=np.ndarray>\n",
       " Attributes: (12/51)\n",
       "     CCCma_model_hash:            1f91f92cb6d607391f44831504025d32fc44faa1\n",
       "     CCCma_parent_runid:          rc3.1-his02\n",
       "     CCCma_pycmor_hash:           33c30511acc319a98240633965a04ca99c26427e\n",
       "     CCCma_runid:                 rc3.1-s8502\n",
       "     Conventions:                 CF-1.7 CMIP-6.2\n",
       "     YMDH_branch_time_in_child:   2015:01:01:00\n",
       "     ...                          ...\n",
       "     title:                       CanESM5 output prepared for CMIP6\n",
       "     variant_label:               r2i1p1f1\n",
       "     version:                     v20190429\n",
       "     version_id:                  v20190429\n",
       "     intake_esm_varname:          None\n",
       "     original_key:                ScenarioMIP.CCCma.CanESM5.ssp585.r2i1p1f1.Om...,\n",
       " 'NorESM2-LM.gn.ssp585.Omon.r1i1p1f1': <xarray.Dataset>\n",
       " Dimensions:        (time: 1032, y: 385, x: 360, bnds: 2, vertex: 4)\n",
       " Coordinates:\n",
       "   * x              (x) int32 1 2 3 4 5 6 7 8 ... 353 354 355 356 357 358 359 360\n",
       "   * y              (y) int32 1 2 3 4 5 6 7 8 ... 378 379 380 381 382 383 384 385\n",
       "     lat            (y, x) float64 dask.array<chunksize=(385, 360), meta=np.ndarray>\n",
       "     lon            (y, x) float64 dask.array<chunksize=(385, 360), meta=np.ndarray>\n",
       "   * time           (time) object 2015-01-16 12:00:00 ... 2100-12-16 12:00:00\n",
       "     time_bounds    (time, bnds) object dask.array<chunksize=(1032, 2), meta=np.ndarray>\n",
       "     lat_verticies  (y, x, vertex) float64 dask.array<chunksize=(385, 360, 4), meta=np.ndarray>\n",
       "     lon_verticies  (y, x, vertex) float64 dask.array<chunksize=(385, 360, 4), meta=np.ndarray>\n",
       "     lon_bounds     (bnds, y, x) float64 dask.array<chunksize=(1, 385, 360), meta=np.ndarray>\n",
       "     lat_bounds     (bnds, y, x) float64 dask.array<chunksize=(1, 385, 360), meta=np.ndarray>\n",
       " Dimensions without coordinates: bnds, vertex\n",
       " Data variables:\n",
       "     no3os          (time, y, x) float32 dask.array<chunksize=(167, 385, 360), meta=np.ndarray>\n",
       "     tos            (time, y, x) float32 dask.array<chunksize=(178, 385, 360), meta=np.ndarray>\n",
       " Attributes: (12/47)\n",
       "     Conventions:               CF-1.7 CMIP-6.2\n",
       "     activity_id:               ScenarioMIP\n",
       "     branch_method:             Hybrid-restart from year 2015-01-01 of historical\n",
       "     branch_time:               0.0\n",
       "     branch_time_in_child:      0.0\n",
       "     branch_time_in_parent:     60225.0\n",
       "     ...                        ...\n",
       "     table_info:                Creation Date:(24 July 2019) MD5:0bb394a356ef9...\n",
       "     title:                     NorESM2-LM output prepared for CMIP6\n",
       "     variant_label:             r1i1p1f1\n",
       "     version_id:                v20191108\n",
       "     intake_esm_varname:        None\n",
       "     original_key:              ScenarioMIP.NCC.NorESM2-LM.ssp585.r1i1p1f1.Omo...}"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddict_combined_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d3dbf3f4-57af-46b9-9162-a8c51113ae70",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/lib/python3.9/site-packages/cmip6_preprocessing/postprocessing.py:113: UserWarning: Match attributes ['variable_id'] not found in any of the datasets.         This can happen when several combination functions are used.\n",
      "  warnings.warn(\n",
      "/srv/conda/lib/python3.9/site-packages/cmip6_preprocessing/postprocessing.py:113: UserWarning: Match attributes ['experiment_id', 'variable_id'] not found in any of the datasets.         This can happen when several combination functions are used.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# modified from the original ohw_21_cmip6_pp_tutorial notebook due to experiment_id & variable_id causing errors\n",
    "def maybe_remove_attrs(ds, attr):\n",
    "    if attr in ds.attrs.keys():\n",
    "        del ds.attrs[attr]\n",
    "    return ds\n",
    "# remove the `variable_id` attr manually\n",
    "ddict_combined_aa = {k:maybe_remove_attrs(ds, 'variable_id') for k,ds in ddict_combined_a.items()}\n",
    "ddict_combined_b = concat_experiments(ddict_combined_aa)\n",
    "# same thing for experiment_id\n",
    "ddict_combined_bb = {k:maybe_remove_attrs(ds, 'experiment_id') for k,ds in ddict_combined_b.items()}\n",
    "ddict_combined = concat_members(ddict_combined_bb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a2abb1b-f00a-439d-b397-9249af228f2e",
   "metadata": {},
   "source": [
    "Dictionary keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "f7e535ca-f842-4c29-a513-1704c91fea20",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ddict_combined' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_27/4204088109.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mddict_combined\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'ddict_combined' is not defined"
     ]
    }
   ],
   "source": [
    "ddict_combined.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "186f5a97-b2b2-461f-9dbe-2dea2ff9b1b3",
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:        (member_id: 2, time: 1980, y: 90, x: 144, bnds: 2, vertex: 4)\n",
       "Coordinates:\n",
       "  * y              (y) float64 -89.0 -87.0 -85.0 -83.0 ... 83.0 85.0 87.0 89.0\n",
       "    lat_bounds     (y, bnds, x) float64 -90.0 -90.0 -90.0 ... 90.0 90.0 90.0\n",
       "  * x              (x) float64 1.25 3.75 6.25 8.75 ... 351.2 353.8 356.2 358.8\n",
       "    lon_bounds     (x, bnds, y) float64 360.0 360.0 360.0 ... 360.0 360.0 360.0\n",
       "  * time           (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "    time_bounds    (time, bnds) object 1850-01-01 00:00:00 ... 2015-01-01 00:...\n",
       "    lon            (x, y) float64 1.25 1.25 1.25 1.25 ... 358.8 358.8 358.8\n",
       "    lat            (x, y) float64 -89.0 -87.0 -85.0 -83.0 ... 85.0 87.0 89.0\n",
       "    lon_verticies  (vertex, x, y) float64 360.0 360.0 360.0 ... 360.0 360.0\n",
       "    lat_verticies  (vertex, x, y) float64 -90.0 -88.0 -86.0 ... 84.0 86.0 88.0\n",
       "Dimensions without coordinates: member_id, bnds, vertex\n",
       "Data variables:\n",
       "    tos            (member_id, time, y, x) float32 dask.array&lt;chunksize=(1, 963, 90, 144), meta=np.ndarray&gt;\n",
       "Attributes: (12/41)\n",
       "    Conventions:            CF-1.7 CMIP-6.2\n",
       "    activity_id:            CMIP\n",
       "    branch_method:          standard\n",
       "    branch_time_in_child:   0.0\n",
       "    cmor_version:           3.3.2\n",
       "    contact:                Kenneth Lo (cdkkl@giss.nasa.gov)\n",
       "    ...                     ...\n",
       "    table_id:               Omon\n",
       "    table_info:             Creation Date:(21 March 2018) MD5:652eaa766045a77...\n",
       "    title:                  GISS-E2-1-G output prepared for CMIP6\n",
       "    status:                 2019-11-10;created;by nhn2@columbia.edu\n",
       "    intake_esm_varname:     None\n",
       "    original_key:           GISS-E2-1-G.gn.Omon.r3i1p1f1</pre><div class='xr-wrap' hidden><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-eef1d0d7-8ca8-47e3-b05a-9cd5e16a8dd7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-eef1d0d7-8ca8-47e3-b05a-9cd5e16a8dd7' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>member_id</span>: 2</li><li><span class='xr-has-index'>time</span>: 1980</li><li><span class='xr-has-index'>y</span>: 90</li><li><span class='xr-has-index'>x</span>: 144</li><li><span>bnds</span>: 2</li><li><span>vertex</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e6432bfd-7197-4c0b-9718-5acf19800b16' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e6432bfd-7197-4c0b-9718-5acf19800b16' class='xr-section-summary' >Coordinates: <span>(10)</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'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.0 -87.0 -85.0 ... 87.0 89.0</div><input id='attrs-34715faf-bb48-4562-8f22-213163e0c39e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34715faf-bb48-4562-8f22-213163e0c39e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf2b7ab8-10e9-4e47-ae82-3a10ed12b7af' class='xr-var-data-in' type='checkbox'><label for='data-bf2b7ab8-10e9-4e47-ae82-3a10ed12b7af' 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>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd></dl></div><div class='xr-var-data'><pre>array([-89., -87., -85., -83., -81., -79., -77., -75., -73., -71., -69., -67.,\n",
       "       -65., -63., -61., -59., -57., -55., -53., -51., -49., -47., -45., -43.,\n",
       "       -41., -39., -37., -35., -33., -31., -29., -27., -25., -23., -21., -19.,\n",
       "       -17., -15., -13., -11.,  -9.,  -7.,  -5.,  -3.,  -1.,   1.,   3.,   5.,\n",
       "         7.,   9.,  11.,  13.,  15.,  17.,  19.,  21.,  23.,  25.,  27.,  29.,\n",
       "        31.,  33.,  35.,  37.,  39.,  41.,  43.,  45.,  47.,  49.,  51.,  53.,\n",
       "        55.,  57.,  59.,  61.,  63.,  65.,  67.,  69.,  71.,  73.,  75.,  77.,\n",
       "        79.,  81.,  83.,  85.,  87.,  89.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bounds</span></div><div class='xr-var-dims'>(y, bnds, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -90.0 -90.0 ... 90.0 90.0</div><input id='attrs-6bec614d-d023-4fde-8a12-22668ff79b1b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6bec614d-d023-4fde-8a12-22668ff79b1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6db9d189-75bc-4e56-b2b4-b204e369bde0' class='xr-var-data-in' type='checkbox'><label for='data-6db9d189-75bc-4e56-b2b4-b204e369bde0' 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([[[-90., -90., -90., ..., -90., -90., -90.],\n",
       "        [-88., -88., -88., ..., -88., -88., -88.]],\n",
       "\n",
       "       [[-88., -88., -88., ..., -88., -88., -88.],\n",
       "        [-86., -86., -86., ..., -86., -86., -86.]],\n",
       "\n",
       "       [[-86., -86., -86., ..., -86., -86., -86.],\n",
       "        [-84., -84., -84., ..., -84., -84., -84.]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[ 84.,  84.,  84., ...,  84.,  84.,  84.],\n",
       "        [ 86.,  86.,  86., ...,  86.,  86.,  86.]],\n",
       "\n",
       "       [[ 86.,  86.,  86., ...,  86.,  86.,  86.],\n",
       "        [ 88.,  88.,  88., ...,  88.,  88.,  88.]],\n",
       "\n",
       "       [[ 88.,  88.,  88., ...,  88.,  88.,  88.],\n",
       "        [ 90.,  90.,  90., ...,  90.,  90.,  90.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.25 3.75 6.25 ... 356.2 358.8</div><input id='attrs-18c28590-f799-4aa0-9d54-8948b55325a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-18c28590-f799-4aa0-9d54-8948b55325a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70e474a2-c6fe-4ba1-87be-c8146642efec' class='xr-var-data-in' type='checkbox'><label for='data-70e474a2-c6fe-4ba1-87be-c8146642efec' 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>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd></dl></div><div class='xr-var-data'><pre>array([  1.25,   3.75,   6.25,   8.75,  11.25,  13.75,  16.25,  18.75,  21.25,\n",
       "        23.75,  26.25,  28.75,  31.25,  33.75,  36.25,  38.75,  41.25,  43.75,\n",
       "        46.25,  48.75,  51.25,  53.75,  56.25,  58.75,  61.25,  63.75,  66.25,\n",
       "        68.75,  71.25,  73.75,  76.25,  78.75,  81.25,  83.75,  86.25,  88.75,\n",
       "        91.25,  93.75,  96.25,  98.75, 101.25, 103.75, 106.25, 108.75, 111.25,\n",
       "       113.75, 116.25, 118.75, 121.25, 123.75, 126.25, 128.75, 131.25, 133.75,\n",
       "       136.25, 138.75, 141.25, 143.75, 146.25, 148.75, 151.25, 153.75, 156.25,\n",
       "       158.75, 161.25, 163.75, 166.25, 168.75, 171.25, 173.75, 176.25, 178.75,\n",
       "       181.25, 183.75, 186.25, 188.75, 191.25, 193.75, 196.25, 198.75, 201.25,\n",
       "       203.75, 206.25, 208.75, 211.25, 213.75, 216.25, 218.75, 221.25, 223.75,\n",
       "       226.25, 228.75, 231.25, 233.75, 236.25, 238.75, 241.25, 243.75, 246.25,\n",
       "       248.75, 251.25, 253.75, 256.25, 258.75, 261.25, 263.75, 266.25, 268.75,\n",
       "       271.25, 273.75, 276.25, 278.75, 281.25, 283.75, 286.25, 288.75, 291.25,\n",
       "       293.75, 296.25, 298.75, 301.25, 303.75, 306.25, 308.75, 311.25, 313.75,\n",
       "       316.25, 318.75, 321.25, 323.75, 326.25, 328.75, 331.25, 333.75, 336.25,\n",
       "       338.75, 341.25, 343.75, 346.25, 348.75, 351.25, 353.75, 356.25, 358.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bounds</span></div><div class='xr-var-dims'>(x, bnds, y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>360.0 360.0 360.0 ... 360.0 360.0</div><input id='attrs-4c5d245b-c48b-4489-b22a-b6f4369b4f92' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4c5d245b-c48b-4489-b22a-b6f4369b4f92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91f4e4cd-1115-4756-a1f6-d439bc11f8b6' class='xr-var-data-in' type='checkbox'><label for='data-91f4e4cd-1115-4756-a1f6-d439bc11f8b6' 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([[[360. , 360. , 360. , ..., 360. , 360. , 360. ],\n",
       "        [  2.5,   2.5,   2.5, ...,   2.5,   2.5,   2.5]],\n",
       "\n",
       "       [[  2.5,   2.5,   2.5, ...,   2.5,   2.5,   2.5],\n",
       "        [  5. ,   5. ,   5. , ...,   5. ,   5. ,   5. ]],\n",
       "\n",
       "       [[  5. ,   5. ,   5. , ...,   5. ,   5. ,   5. ],\n",
       "        [  7.5,   7.5,   7.5, ...,   7.5,   7.5,   7.5]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[352.5, 352.5, 352.5, ..., 352.5, 352.5, 352.5],\n",
       "        [355. , 355. , 355. , ..., 355. , 355. , 355. ]],\n",
       "\n",
       "       [[355. , 355. , 355. , ..., 355. , 355. , 355. ],\n",
       "        [357.5, 357.5, 357.5, ..., 357.5, 357.5, 357.5]],\n",
       "\n",
       "       [[357.5, 357.5, 357.5, ..., 357.5, 357.5, 357.5],\n",
       "        [360. , 360. , 360. , ..., 360. , 360. , 360. ]]])</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'>1850-01-16 12:00:00 ... 2014-12-...</div><input id='attrs-64ba354a-d83b-4555-8217-51d8a1fcce96' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64ba354a-d83b-4555-8217-51d8a1fcce96' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c17ae91-d985-4337-bb9e-bf4a566548fc' class='xr-var-data-in' type='checkbox'><label for='data-9c17ae91-d985-4337-bb9e-bf4a566548fc' 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>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       ...,\n",
       "       cftime.DatetimeNoLeap(2014, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 16, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bounds</span></div><div class='xr-var-dims'>(time, bnds)</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-1d67bef5-1745-4779-be3a-436559ac06bd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1d67bef5-1745-4779-be3a-436559ac06bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36cdb7ee-52a3-4b1e-888c-637c41612264' class='xr-var-data-in' type='checkbox'><label for='data-36cdb7ee-52a3-4b1e-888c-637c41612264' 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, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(1850, 2, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(1850, 2, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(1850, 3, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(1850, 3, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(1850, 4, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       ...,\n",
       "       [cftime.DatetimeNoLeap(2014, 10, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(2014, 11, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(2014, 11, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(2014, 12, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(2014, 12, 1, 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>lon</span></div><div class='xr-var-dims'>(x, y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.25 1.25 1.25 ... 358.8 358.8</div><input id='attrs-9c798de9-6a8f-437d-87df-525de883d972' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9c798de9-6a8f-437d-87df-525de883d972' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97d85a04-2942-4956-9ecc-bae1b9495ef8' class='xr-var-data-in' type='checkbox'><label for='data-97d85a04-2942-4956-9ecc-bae1b9495ef8' 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([[  1.25,   1.25,   1.25, ...,   1.25,   1.25,   1.25],\n",
       "       [  3.75,   3.75,   3.75, ...,   3.75,   3.75,   3.75],\n",
       "       [  6.25,   6.25,   6.25, ...,   6.25,   6.25,   6.25],\n",
       "       ...,\n",
       "       [353.75, 353.75, 353.75, ..., 353.75, 353.75, 353.75],\n",
       "       [356.25, 356.25, 356.25, ..., 356.25, 356.25, 356.25],\n",
       "       [358.75, 358.75, 358.75, ..., 358.75, 358.75, 358.75]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(x, y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.0 -87.0 -85.0 ... 87.0 89.0</div><input id='attrs-3fc02619-3c9a-4285-bd99-a91eb967ed5c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3fc02619-3c9a-4285-bd99-a91eb967ed5c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8662cac0-fb2a-42db-949b-703ece1bdc61' class='xr-var-data-in' type='checkbox'><label for='data-8662cac0-fb2a-42db-949b-703ece1bdc61' 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([[-89., -87., -85., ...,  85.,  87.,  89.],\n",
       "       [-89., -87., -85., ...,  85.,  87.,  89.],\n",
       "       [-89., -87., -85., ...,  85.,  87.,  89.],\n",
       "       ...,\n",
       "       [-89., -87., -85., ...,  85.,  87.,  89.],\n",
       "       [-89., -87., -85., ...,  85.,  87.,  89.],\n",
       "       [-89., -87., -85., ...,  85.,  87.,  89.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_verticies</span></div><div class='xr-var-dims'>(vertex, x, y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>360.0 360.0 360.0 ... 360.0 360.0</div><input id='attrs-b3e0ae3e-c0d8-4d7e-b8d1-4e56be579469' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b3e0ae3e-c0d8-4d7e-b8d1-4e56be579469' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0680fc7d-137a-4432-a9c4-89d455ee76ed' class='xr-var-data-in' type='checkbox'><label for='data-0680fc7d-137a-4432-a9c4-89d455ee76ed' 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([[[360. , 360. , 360. , ..., 360. , 360. , 360. ],\n",
       "        [  2.5,   2.5,   2.5, ...,   2.5,   2.5,   2.5],\n",
       "        [  5. ,   5. ,   5. , ...,   5. ,   5. ,   5. ],\n",
       "        ...,\n",
       "        [352.5, 352.5, 352.5, ..., 352.5, 352.5, 352.5],\n",
       "        [355. , 355. , 355. , ..., 355. , 355. , 355. ],\n",
       "        [357.5, 357.5, 357.5, ..., 357.5, 357.5, 357.5]],\n",
       "\n",
       "       [[360. , 360. , 360. , ..., 360. , 360. , 360. ],\n",
       "        [  2.5,   2.5,   2.5, ...,   2.5,   2.5,   2.5],\n",
       "        [  5. ,   5. ,   5. , ...,   5. ,   5. ,   5. ],\n",
       "        ...,\n",
       "        [352.5, 352.5, 352.5, ..., 352.5, 352.5, 352.5],\n",
       "        [355. , 355. , 355. , ..., 355. , 355. , 355. ],\n",
       "        [357.5, 357.5, 357.5, ..., 357.5, 357.5, 357.5]],\n",
       "\n",
       "       [[  2.5,   2.5,   2.5, ...,   2.5,   2.5,   2.5],\n",
       "        [  5. ,   5. ,   5. , ...,   5. ,   5. ,   5. ],\n",
       "        [  7.5,   7.5,   7.5, ...,   7.5,   7.5,   7.5],\n",
       "        ...,\n",
       "        [355. , 355. , 355. , ..., 355. , 355. , 355. ],\n",
       "        [357.5, 357.5, 357.5, ..., 357.5, 357.5, 357.5],\n",
       "        [360. , 360. , 360. , ..., 360. , 360. , 360. ]],\n",
       "\n",
       "       [[  2.5,   2.5,   2.5, ...,   2.5,   2.5,   2.5],\n",
       "        [  5. ,   5. ,   5. , ...,   5. ,   5. ,   5. ],\n",
       "        [  7.5,   7.5,   7.5, ...,   7.5,   7.5,   7.5],\n",
       "        ...,\n",
       "        [355. , 355. , 355. , ..., 355. , 355. , 355. ],\n",
       "        [357.5, 357.5, 357.5, ..., 357.5, 357.5, 357.5],\n",
       "        [360. , 360. , 360. , ..., 360. , 360. , 360. ]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_verticies</span></div><div class='xr-var-dims'>(vertex, x, y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -88.0 -86.0 ... 86.0 88.0</div><input id='attrs-7bde4962-af09-427f-8687-d93535ef7f43' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7bde4962-af09-427f-8687-d93535ef7f43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c59bf904-332d-496d-aefe-79e3ec7298ba' class='xr-var-data-in' type='checkbox'><label for='data-c59bf904-332d-496d-aefe-79e3ec7298ba' 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([[[-90., -88., -86., ...,  84.,  86.,  88.],\n",
       "        [-90., -88., -86., ...,  84.,  86.,  88.],\n",
       "        [-90., -88., -86., ...,  84.,  86.,  88.],\n",
       "        ...,\n",
       "        [-90., -88., -86., ...,  84.,  86.,  88.],\n",
       "        [-90., -88., -86., ...,  84.,  86.,  88.],\n",
       "        [-90., -88., -86., ...,  84.,  86.,  88.]],\n",
       "\n",
       "       [[-88., -86., -84., ...,  86.,  88.,  90.],\n",
       "        [-88., -86., -84., ...,  86.,  88.,  90.],\n",
       "        [-88., -86., -84., ...,  86.,  88.,  90.],\n",
       "        ...,\n",
       "        [-88., -86., -84., ...,  86.,  88.,  90.],\n",
       "        [-88., -86., -84., ...,  86.,  88.,  90.],\n",
       "        [-88., -86., -84., ...,  86.,  88.,  90.]],\n",
       "\n",
       "       [[-88., -86., -84., ...,  86.,  88.,  90.],\n",
       "        [-88., -86., -84., ...,  86.,  88.,  90.],\n",
       "        [-88., -86., -84., ...,  86.,  88.,  90.],\n",
       "        ...,\n",
       "        [-88., -86., -84., ...,  86.,  88.,  90.],\n",
       "        [-88., -86., -84., ...,  86.,  88.,  90.],\n",
       "        [-88., -86., -84., ...,  86.,  88.,  90.]],\n",
       "\n",
       "       [[-90., -88., -86., ...,  84.,  86.,  88.],\n",
       "        [-90., -88., -86., ...,  84.,  86.,  88.],\n",
       "        [-90., -88., -86., ...,  84.,  86.,  88.],\n",
       "        ...,\n",
       "        [-90., -88., -86., ...,  84.,  86.,  88.],\n",
       "        [-90., -88., -86., ...,  84.,  86.,  88.],\n",
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       "atmosChem: Varies with physics-version (p==1 Non-interactive, p&gt;1 GPUCCINI)\n",
       "land: GISS LSM\n",
       "landIce: none\n",
       "ocean: GISS Ocean (1.25x1 degree; 288 x 180 longitude/latitude; 32 levels; top grid cell 0-10 m)\n",
       "ocnBgchem: none\n",
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       "    contact:                Kenneth Lo (cdkkl@giss.nasa.gov)\n",
       "    ...                     ...\n",
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       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:        (member_id: 4, time: 3012, y: 291, x: 360, bnds: 2, vertex: 4)\n",
       "Coordinates:\n",
       "  * x              (x) int32 0 1 2 3 4 5 6 7 ... 352 353 354 355 356 357 358 359\n",
       "  * y              (y) int32 0 1 2 3 4 5 6 7 ... 283 284 285 286 287 288 289 290\n",
       "    lat            (y, x) float64 -78.39 -78.39 -78.39 ... 50.51 50.23 50.01\n",
       "    lon            (y, x) float64 73.5 74.5 75.5 76.5 ... 72.95 72.96 72.99\n",
       "  * time           (time) object 1850-01-16 12:00:00 ... 2100-12-16 12:00:00\n",
       "    time_bounds    (time, bnds) object 1850-01-01 00:00:00 ... 2101-01-01 00:...\n",
       "    lat_verticies  (y, x, vertex) float64 -78.49 -78.29 -78.29 ... 50.0 49.98\n",
       "    lon_verticies  (y, x, vertex) float64 73.0 73.0 74.0 74.0 ... 73.0 73.0 73.0\n",
       "    lon_bounds     (bnds, y, x) float64 73.0 74.0 75.0 76.0 ... 72.95 72.97 73.0\n",
       "    lat_bounds     (bnds, y, x) float64 -78.49 -14.06 -13.92 ... 50.24 50.05\n",
       "Dimensions without coordinates: member_id, bnds, vertex\n",
       "Data variables:\n",
       "    tos            (member_id, time, y, x) float32 dask.array&lt;chunksize=(1, 215, 291, 360), meta=np.ndarray&gt;\n",
       "    no3os          (member_id, time, y, x) float32 dask.array&lt;chunksize=(1, 210, 291, 360), meta=np.ndarray&gt;\n",
       "Attributes: (12/35)\n",
       "    CCCma_pycmor_hash:     33c30511acc319a98240633965a04ca99c26427e\n",
       "    Conventions:           CF-1.7 CMIP-6.2\n",
       "    branch_method:         Spin-up documentation\n",
       "    cmor_version:          3.4.0\n",
       "    contact:               ec.cccma.info-info.ccmac.ec@canada.ca\n",
       "    data_specs_version:    01.00.29\n",
       "    ...                    ...\n",
       "    table_info:            Creation Date:(20 February 2019) MD5:374fbe5a2bcca...\n",
       "    title:                 CanESM5 output prepared for CMIP6\n",
       "    version:               v20190429\n",
       "    version_id:            v20190429\n",
       "    intake_esm_varname:    None\n",
       "    CCCma_model_hash:      Unknown</pre><div class='xr-wrap' hidden><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-664586ed-c7aa-405e-b85a-8e718af2a91c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-664586ed-c7aa-405e-b85a-8e718af2a91c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>member_id</span>: 4</li><li><span class='xr-has-index'>time</span>: 3012</li><li><span class='xr-has-index'>y</span>: 291</li><li><span class='xr-has-index'>x</span>: 360</li><li><span>bnds</span>: 2</li><li><span>vertex</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-90517fa1-3c71-428c-8ab7-7d4f39322985' class='xr-section-summary-in' type='checkbox'  checked><label for='section-90517fa1-3c71-428c-8ab7-7d4f39322985' class='xr-section-summary' >Coordinates: <span>(10)</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'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 355 356 357 358 359</div><input id='attrs-6f61b056-2f5f-48ed-809a-417103199c10' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f61b056-2f5f-48ed-809a-417103199c10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b241c67-cff8-4a61-be84-dfb05ed047a3' class='xr-var-data-in' type='checkbox'><label for='data-0b241c67-cff8-4a61-be84-dfb05ed047a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>cell index along first dimension</dd><dt><span>units :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 357, 358, 359], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 286 287 288 289 290</div><input id='attrs-18a0d5d4-b5ff-4b5c-8431-49021627242b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-18a0d5d4-b5ff-4b5c-8431-49021627242b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4cc15110-093e-4596-83e1-eb33628ce8c7' class='xr-var-data-in' type='checkbox'><label for='data-4cc15110-093e-4596-83e1-eb33628ce8c7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>cell index along second dimension</dd><dt><span>units :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 288, 289, 290], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-78.39 -78.39 ... 50.23 50.01</div><input id='attrs-e85c64cc-1c9c-482b-8ac7-2b5130326965' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e85c64cc-1c9c-482b-8ac7-2b5130326965' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df65d199-e7d5-4825-840e-07a40cc915bc' class='xr-var-data-in' type='checkbox'><label for='data-df65d199-e7d5-4825-840e-07a40cc915bc' 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>bounds :</span></dt><dd>vertices_latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd></dl></div><div class='xr-var-data'><pre>array([[-78.39350128, -78.39350128, -78.39350128, ..., -78.39350128,\n",
       "        -78.39350128, -78.39350128],\n",
       "       [-78.19058228, -78.19058228, -78.19058228, ..., -78.19058228,\n",
       "        -78.19058228, -78.19058228],\n",
       "       [-77.98416901, -77.98416901, -77.98416901, ..., -77.98416901,\n",
       "        -77.98416901, -77.98416901],\n",
       "       ...,\n",
       "       [ 49.99324036,  50.2142067 ,  50.49226379, ...,  50.49226379,\n",
       "         50.2142067 ,  49.99324036],\n",
       "       [ 50.00502014,  50.22440338,  50.50234604, ...,  50.50234604,\n",
       "         50.22440338,  50.00502014],\n",
       "       [ 50.01094055,  50.22936249,  50.50726318, ...,  50.50726318,\n",
       "         50.22936249,  50.01094055]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>73.5 74.5 75.5 ... 72.96 72.99</div><input id='attrs-ea43ad5e-7fc0-4cd8-90e8-001673af10c0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea43ad5e-7fc0-4cd8-90e8-001673af10c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7a7962f-2aeb-4ef1-97d1-f35b2296b793' class='xr-var-data-in' type='checkbox'><label for='data-e7a7962f-2aeb-4ef1-97d1-f35b2296b793' 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>bounds :</span></dt><dd>vertices_longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd></dl></div><div class='xr-var-data'><pre>array([[73.5       , 74.5       , 75.5       , ..., 70.5       ,\n",
       "        71.5       , 72.5       ],\n",
       "       [73.5       , 74.5       , 75.5       , ..., 70.5       ,\n",
       "        71.5       , 72.5       ],\n",
       "       [73.5       , 74.5       , 75.5       , ..., 70.5       ,\n",
       "        71.5       , 72.5       ],\n",
       "       ...,\n",
       "       [73.07927704, 73.19734192, 73.28417206, ..., 72.71582794,\n",
       "        72.80265808, 72.92072296],\n",
       "       [73.04235077, 73.1153183 , 73.16770172, ..., 72.83229828,\n",
       "        72.8846817 , 72.95764923],\n",
       "       [73.010849  , 73.03703308, 73.05471802, ..., 72.94528198,\n",
       "        72.96296692, 72.989151  ]])</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'>1850-01-16 12:00:00 ... 2100-12-...</div><input id='attrs-30da9ee8-9b4e-4dd7-8d93-8eda3590eef5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-30da9ee8-9b4e-4dd7-8d93-8eda3590eef5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7727dc12-82f8-4964-a643-e8b06ea6a853' class='xr-var-data-in' type='checkbox'><label for='data-7727dc12-82f8-4964-a643-e8b06ea6a853' 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>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       ...,\n",
       "       cftime.DatetimeNoLeap(2100, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2100, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2100, 12, 16, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bounds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1850-01-01 00:00:00 ... 2101-01-...</div><input id='attrs-64d841c8-cad4-4508-91b3-4e5a454462f1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-64d841c8-cad4-4508-91b3-4e5a454462f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1449b4b9-6c57-4dff-a44e-e168ae78d9be' class='xr-var-data-in' type='checkbox'><label for='data-1449b4b9-6c57-4dff-a44e-e168ae78d9be' 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, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(1850, 2, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(1850, 2, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(1850, 3, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(1850, 3, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(1850, 4, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       ...,\n",
       "       [cftime.DatetimeNoLeap(2100, 10, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(2100, 11, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(2100, 11, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(2100, 12, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(2100, 12, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(2101, 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>lat_verticies</span></div><div class='xr-var-dims'>(y, x, vertex)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-78.49 -78.29 -78.29 ... 50.0 49.98</div><input id='attrs-0590dc18-079e-429d-bce1-5891544d32ac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0590dc18-079e-429d-bce1-5891544d32ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-098a629d-e991-4325-a1cd-363fc30f33c4' class='xr-var-data-in' type='checkbox'><label for='data-098a629d-e991-4325-a1cd-363fc30f33c4' 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_N</dd></dl></div><div class='xr-var-data'><pre>array([[[-78.49452209, -78.29248047, -78.29248047, -78.49452209],\n",
       "        [-78.49452209, -78.49452209, -78.29248047,  50.36460495],\n",
       "        [-78.49452209, -78.49452209, -78.29248047,  50.6574707 ],\n",
       "        ...,\n",
       "        [-78.49452209, -78.49452209, -78.29248047,  50.36460495],\n",
       "        [-78.49452209, -78.49452209, -78.29248047,  50.10834122],\n",
       "        [-78.49452209, -78.49452209, -78.29248047,  50.        ]],\n",
       "\n",
       "       [[-78.29248047, -78.08782196, -78.08782196, -78.29248047],\n",
       "        [-78.29248047, -78.08782196, -78.08782196, -78.29248047],\n",
       "        [-78.29248047, -78.08782196, -78.08782196, -78.29248047],\n",
       "        ...,\n",
       "        [-78.29248047, -78.08782196, -78.08782196, -78.29248047],\n",
       "        [-78.29248047, -78.08782196, -78.08782196, -78.29248047],\n",
       "        [-78.29248047, -78.08782196, -78.08782196, -78.29248047]],\n",
       "\n",
       "       [[-78.08782196, -77.87963104, -77.87963104, -78.08782196],\n",
       "        [-78.08782196, -77.87963104, -77.87963104, -78.08782196],\n",
       "        [-78.08782196, -77.87963104, -77.87963104, -78.08782196],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [ 50.63483429,  50.64756393,  50.35468674,  50.3419342 ],\n",
       "        [ 50.3419342 ,  50.35468674,  50.09809875,  50.08496094],\n",
       "        [ 50.08496094,  50.09809875,  49.96048355,  49.94006729]],\n",
       "\n",
       "       [[ 50.09809875,  50.10586166,  50.10586166,  50.09809875],\n",
       "        [ 50.09809875,  50.10586166,  50.3621788 ,  50.35468674],\n",
       "        [ 50.35468674,  50.3621788 ,  50.65504074,  50.64756393],\n",
       "        ...,\n",
       "        [ 50.64756393,  50.65504074,  50.3621788 ,  50.35468674],\n",
       "        [ 50.35468674,  50.3621788 ,  50.10586166,  50.09809875],\n",
       "        [ 50.09809875,  50.10586166,  49.98045349,  49.96048355]],\n",
       "\n",
       "       [[ 50.10586166,  50.10834122,  50.10834122,  50.10586166],\n",
       "        [ 50.10586166,  50.10834122,  50.36460495,  50.3621788 ],\n",
       "        [ 50.3621788 ,  50.36460495,  50.6574707 ,  50.65504074],\n",
       "        ...,\n",
       "        [ 50.65504074,  50.6574707 ,  50.36460495,  50.3621788 ],\n",
       "        [ 50.3621788 ,  50.36460495,  50.10834122,  50.10586166],\n",
       "        [ 50.10586166,  50.10834122,  50.        ,  49.98045349]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_verticies</span></div><div class='xr-var-dims'>(y, x, vertex)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>73.0 73.0 74.0 ... 73.0 73.0 73.0</div><input id='attrs-c6d0a026-22a0-48bc-8200-a5df28ba51ca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c6d0a026-22a0-48bc-8200-a5df28ba51ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3c1d4e8-4cfa-48a3-b740-dc84f3ab925e' class='xr-var-data-in' type='checkbox'><label for='data-a3c1d4e8-4cfa-48a3-b740-dc84f3ab925e' 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_E</dd></dl></div><div class='xr-var-data'><pre>array([[[73.        , 73.        , 74.        , 74.        ],\n",
       "        [74.        , 74.        , 75.        , 73.        ],\n",
       "        [75.        , 75.        , 76.        , 73.        ],\n",
       "        ...,\n",
       "        [70.        , 70.        , 71.        , 73.        ],\n",
       "        [71.        , 71.        , 72.        , 73.        ],\n",
       "        [72.        , 72.        , 73.        , 73.        ]],\n",
       "\n",
       "       [[73.        , 73.        , 74.        , 74.        ],\n",
       "        [74.        , 74.        , 75.        , 75.        ],\n",
       "        [75.        , 75.        , 76.        , 76.        ],\n",
       "        ...,\n",
       "        [70.        , 70.        , 71.        , 71.        ],\n",
       "        [71.        , 71.        , 72.        , 72.        ],\n",
       "        [72.        , 72.        , 73.        , 73.        ]],\n",
       "\n",
       "       [[73.        , 73.        , 74.        , 74.        ],\n",
       "        [74.        , 74.        , 75.        , 75.        ],\n",
       "        [75.        , 75.        , 76.        , 76.        ],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [72.61153412, 72.74427032, 72.80727386, 72.70604706],\n",
       "        [72.70604706, 72.80727386, 72.8866272 , 72.82427216],\n",
       "        [72.82427216, 72.8866272 , 73.        , 73.        ]],\n",
       "\n",
       "       [[73.09062195, 73.03097534, 73.0537262 , 73.1133728 ],\n",
       "        [73.1133728 , 73.0537262 , 73.09414673, 73.19272614],\n",
       "        [73.19272614, 73.09414673, 73.12578583, 73.25572968],\n",
       "        ...,\n",
       "        [72.74427032, 72.87421417, 72.90585327, 72.80727386],\n",
       "        [72.80727386, 72.90585327, 72.9462738 , 72.8866272 ],\n",
       "        [72.8866272 , 72.9462738 , 73.        , 73.        ]],\n",
       "\n",
       "       [[73.07542419, 73.021698  , 73.        , 73.0537262 ],\n",
       "        [73.0537262 , 73.        , 73.        , 73.09414673],\n",
       "        [73.09414673, 73.        , 73.        , 73.12578583],\n",
       "        ...,\n",
       "        [72.87421417, 73.        , 73.        , 72.90585327],\n",
       "        [72.90585327, 73.        , 73.        , 72.9462738 ],\n",
       "        [72.9462738 , 73.        , 73.        , 73.        ]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bounds</span></div><div class='xr-var-dims'>(bnds, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>73.0 74.0 75.0 ... 72.95 72.97 73.0</div><input id='attrs-474d6f28-28e5-48d7-8639-69cbee9fbe3a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-474d6f28-28e5-48d7-8639-69cbee9fbe3a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7ae8d8e-5498-4d12-b3ac-23d7c8bad823' class='xr-var-data-in' type='checkbox'><label for='data-b7ae8d8e-5498-4d12-b3ac-23d7c8bad823' 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([[[73.        , 74.        , 75.        , ..., 70.        ,\n",
       "         71.        , 72.        ],\n",
       "        [73.        , 74.        , 75.        , ..., 70.        ,\n",
       "         71.        , 72.        ],\n",
       "        [73.        , 74.        , 75.        , ..., 70.        ,\n",
       "         71.        , 72.        ],\n",
       "        ...,\n",
       "        [73.0763588 , 73.14455032, 73.24333954, ..., 72.67790222,\n",
       "         72.75666046, 72.85544968],\n",
       "        [73.06079865, 73.0835495 , 73.14343643, ..., 72.80924225,\n",
       "         72.85656357, 72.9164505 ],\n",
       "        [73.0485611 , 73.0268631 , 73.04707336, ..., 72.93710709,\n",
       "         72.95292664, 72.9731369 ]],\n",
       "\n",
       "       [[74.        , 74.        , 74.5       , ..., 72.        ,\n",
       "         72.5       , 73.        ],\n",
       "        [74.        , 75.        , 76.        , ..., 71.        ,\n",
       "         72.        , 73.        ],\n",
       "        [74.        , 75.        , 76.        , ..., 71.        ,\n",
       "         72.        , 73.        ],\n",
       "        ...,\n",
       "        [73.14455032, 73.24333954, 73.32209778, ..., 72.75666046,\n",
       "         72.85544968, 73.        ],\n",
       "        [73.0835495 , 73.14343643, 73.19075775, ..., 72.85656357,\n",
       "         72.9164505 , 73.        ],\n",
       "        [73.0268631 , 73.04707336, 73.06289291, ..., 72.95292664,\n",
       "         72.9731369 , 73.        ]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bounds</span></div><div class='xr-var-dims'>(bnds, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-78.49 -14.06 ... 50.24 50.05</div><input id='attrs-890d6169-b820-4a84-a6f1-4d0ceba329a7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-890d6169-b820-4a84-a6f1-4d0ceba329a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c81f1564-faa2-4f50-bb42-af9c26a0cbbb' class='xr-var-data-in' type='checkbox'><label for='data-c81f1564-faa2-4f50-bb42-af9c26a0cbbb' 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([[[-78.49452209, -14.06495857, -13.9185257 , ..., -14.06495857,\n",
       "         -14.19309044, -14.24726105],\n",
       "        [-78.29248047, -78.29248047, -78.29248047, ..., -78.29248047,\n",
       "         -78.29248047, -78.29248047],\n",
       "        [-78.08782196, -78.08782196, -78.08782196, ..., -78.08782196,\n",
       "         -78.08782196, -78.08782196],\n",
       "        ...,\n",
       "        [ 50.08496094,  50.21344757,  50.48838425, ...,  50.48838425,\n",
       "          50.21344757,  50.01251411],\n",
       "        [ 50.09809875,  50.22639275,  50.50112534, ...,  50.50112534,\n",
       "          50.22639275,  50.02929115],\n",
       "        [ 50.10586166,  50.23402023,  50.50860977, ...,  50.50860977,\n",
       "          50.23402023,  50.04315758]],\n",
       "\n",
       "       [[-78.29248047, -78.39350128, -78.39350128, ..., -78.39350128,\n",
       "         -78.39350128, -78.39350128],\n",
       "        [-78.08782196, -78.08782196, -78.08782196, ..., -78.08782196,\n",
       "         -78.08782196, -78.08782196],\n",
       "        [-77.87963104, -77.87963104, -77.87963104, ..., -77.87963104,\n",
       "         -77.87963104, -77.87963104],\n",
       "        ...,\n",
       "        [ 50.09809875,  50.22639275,  50.50112534, ...,  50.50112534,\n",
       "          50.22639275,  50.02929115],\n",
       "        [ 50.10586166,  50.23402023,  50.50860977, ...,  50.50860977,\n",
       "          50.23402023,  50.04315758],\n",
       "        [ 50.10834122,  50.23647308,  50.51103783, ...,  50.51103783,\n",
       "          50.23647308,  50.05417061]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-346f990e-55da-4253-912a-e748eb52bb40' class='xr-section-summary-in' type='checkbox'  checked><label for='section-346f990e-55da-4253-912a-e748eb52bb40' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>tos</span></div><div class='xr-var-dims'>(member_id, time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 215, 291, 360), meta=np.ndarray&gt;</div><input id='attrs-03a3f3b3-86a3-496d-9f44-6c81ad39312e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03a3f3b3-86a3-496d-9f44-6c81ad39312e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b228b55-8920-482c-b9da-02ac8e7cea5a' class='xr-var-data-in' type='checkbox'><label for='data-3b228b55-8920-482c-b9da-02ac8e7cea5a' 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: areacello</dd><dt><span>cell_methods :</span></dt><dd>area: mean where sea time: mean</dd><dt><span>comment :</span></dt><dd>Temperature of upper boundary of the liquid ocean, including temperatures below sea-ice and floating ice shelves.</dd><dt><span>long_name :</span></dt><dd>Sea Surface Temperature</dd><dt><span>original_name :</span></dt><dd>sosstsst</dd><dt><span>standard_name :</span></dt><dd>sea_surface_temperature</dd><dt><span>units :</span></dt><dd>°C</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 4.70 GiB </td> <td> 85.92 MiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (4, 3012, 291, 360) </td> <td> (1, 215, 291, 360) </td></tr>\n",
       "    <tr><th> Count </th><td> 274 Tasks </td><td> 60 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "aerosol: interactive\n",
       "atmos: CanAM5 (T63L49 native atmosphere, T63 Linear Gaussian Grid; 128 x 64 longitude/latitude; 49 levels; top level 1 hPa)\n",
       "atmosChem: specified oxidants for aerosols\n",
       "land: CLASS3.6/CTEM1.2\n",
       "landIce: specified ice sheets\n",
       "ocean: NEMO3.4.1 (ORCA1 tripolar grid, 1 deg with refinement to 1/3 deg within 20 degrees of the equator; 361 x 290 longitude/latitude; 45 vertical levels; top grid cell 0-6.19 m)\n",
       "ocnBgchem: Canadian Model of Ocean Carbon (CMOC); NPZD ecosystem with OMIP prescribed carbonate chemistry\n",
       "seaIce: LIM2</dd><dt><span>source_id :</span></dt><dd>CanESM5</dd><dt><span>source_type :</span></dt><dd>AOGCM</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>Omon</dd><dt><span>table_info :</span></dt><dd>Creation Date:(20 February 2019) MD5:374fbe5a2bcca535c40f7f23da271e49</dd><dt><span>title :</span></dt><dd>CanESM5 output prepared for CMIP6</dd><dt><span>version :</span></dt><dd>v20190429</dd><dt><span>version_id :</span></dt><dd>v20190429</dd><dt><span>intake_esm_varname :</span></dt><dd>None</dd><dt><span>CCCma_model_hash :</span></dt><dd>Unknown</dd></dl></div></li></ul></div></div>"
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       "Attributes: (12/35)\n",
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     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddict_combined['CanESM5.gn.Omon']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd38dc7a-7845-4444-a2ba-881831e6be95",
   "metadata": {},
   "source": [
    "It seems that \"CanESM5.gn.Omon\" is the only dataset with tos & no3os data although we chose (at least aimed so) those datasets with tos & no3os data. This caused some errors when plotting a map in the next section."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9498cab-ac80-4f76-a2d0-561d7015d647",
   "metadata": {},
   "source": [
    "# Masking the Arabian Sea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e49b11df-f1c2-4314-9d8a-18399772d1a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import regionmask\n",
    "from cmip6_preprocessing.regionmask import merged_mask\n",
    "# load ocean basin data\n",
    "basins = regionmask.defined_regions.natural_earth.ocean_basins_50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ea9dd2de-2794-408b-a3d5-c14fcb85760d",
   "metadata": {},
   "outputs": [
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       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:        (member_id: 2, y: 576, x: 720, vertex: 4, bnds: 2)\n",
       "Coordinates:\n",
       "  * x              (x) float64 -299.8 -299.2 -298.8 -298.2 ... 58.75 59.25 59.75\n",
       "  * y              (y) float64 -77.91 -77.72 -77.54 -77.36 ... 89.47 89.68 89.89\n",
       "    lat            (y, x) float32 -77.91 -77.91 -77.91 ... 65.39 65.18 64.97\n",
       "    lat_verticies  (y, x, vertex) float32 -78.0 -77.82 -77.82 ... 64.87 64.87\n",
       "    lon            (y, x) float32 60.25 60.75 61.25 61.75 ... 59.99 59.99 60.0\n",
       "    lon_verticies  (y, x, vertex) float32 60.0 60.0 60.5 60.5 ... 60.0 60.0 60.0\n",
       "    time           object 1850-01-16 12:00:00\n",
       "    time_bounds    (bnds) object 1850-01-01 00:00:00 1850-02-01 00:00:00\n",
       "    lon_bounds     (bnds, y, x) float32 60.0 60.5 61.0 61.5 ... 59.99 60.0 60.0\n",
       "    lat_bounds     (bnds, y, x) float32 -78.0 -78.0 -78.0 ... 65.39 65.18 64.97\n",
       "Dimensions without coordinates: member_id, vertex, bnds\n",
       "Data variables:\n",
       "    tos            (member_id, y, x) float32 dask.array&lt;chunksize=(1, 576, 720), meta=np.ndarray&gt;\n",
       "Attributes: (12/40)\n",
       "    Conventions:           CF-1.7 CMIP-6.0 UGRID-1.0\n",
       "    activity_id:           CMIP\n",
       "    branch_method:         standard\n",
       "    branch_time_in_child:  0.0\n",
       "    comment:               &lt;null ref&gt;\n",
       "    contact:               gfdl.climate.model.info@noaa.gov\n",
       "    ...                    ...\n",
       "    table_id:              Omon\n",
       "    title:                 NOAA GFDL GFDL-ESM4 model output prepared for CMIP...\n",
       "    variant_info:          N/A\n",
       "    version_id:            v20180701\n",
       "    intake_esm_varname:    None\n",
       "    original_key:          GFDL-ESM4.gn.Omon.r3i1p1f1</pre><div class='xr-wrap' hidden><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-235c53da-622a-4faa-82ce-acd0f87845de' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-235c53da-622a-4faa-82ce-acd0f87845de' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>member_id</span>: 2</li><li><span class='xr-has-index'>y</span>: 576</li><li><span class='xr-has-index'>x</span>: 720</li><li><span>vertex</span>: 4</li><li><span>bnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ec220b3a-a756-47d5-8353-d5f460abc2be' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ec220b3a-a756-47d5-8353-d5f460abc2be' class='xr-section-summary' >Coordinates: <span>(10)</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'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-299.8 -299.2 ... 59.25 59.75</div><input id='attrs-1ac2a2d4-30d6-46bd-9c4f-3b0780c5ae76' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ac2a2d4-30d6-46bd-9c4f-3b0780c5ae76' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e8440be-c697-479f-a321-cc819b001223' class='xr-var-data-in' type='checkbox'><label for='data-0e8440be-c697-479f-a321-cc819b001223' 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>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><pre>array([-299.75, -299.25, -298.75, ...,   58.75,   59.25,   59.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-77.91 -77.72 ... 89.68 89.89</div><input id='attrs-efc38322-8912-4d24-bb12-4ec8519cf0e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-efc38322-8912-4d24-bb12-4ec8519cf0e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed1b79d3-7208-4732-bc26-f21a551f85d9' class='xr-var-data-in' type='checkbox'><label for='data-ed1b79d3-7208-4732-bc26-f21a551f85d9' 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>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><pre>array([-77.907938, -77.723813, -77.539688, ...,  89.472   ,  89.6832  ,\n",
       "        89.8944  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-77.91 -77.91 ... 65.18 64.97</div><input id='attrs-15ed925f-0466-4136-8643-5d97efee2778' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-15ed925f-0466-4136-8643-5d97efee2778' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05a20f46-861c-42dc-bb6c-1f6667ebc494' class='xr-var-data-in' type='checkbox'><label for='data-05a20f46-861c-42dc-bb6c-1f6667ebc494' 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>bounds :</span></dt><dd>lat_bnds</dd><dt><span>cell_methods :</span></dt><dd>y: point x: point</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd></dl></div><div class='xr-var-data'><pre>array([[-77.907936, -77.907936, -77.907936, ..., -77.907936, -77.907936,\n",
       "        -77.907936],\n",
       "       [-77.723816, -77.723816, -77.723816, ..., -77.723816, -77.723816,\n",
       "        -77.723816],\n",
       "       [-77.53969 , -77.53969 , -77.53969 , ..., -77.53969 , -77.53969 ,\n",
       "        -77.53969 ],\n",
       "       ...,\n",
       "       [ 64.973076,  65.183586,  65.39247 , ...,  65.39247 ,  65.183586,\n",
       "         64.973076],\n",
       "       [ 64.97313 ,  65.18376 ,  65.39275 , ...,  65.39275 ,  65.18376 ,\n",
       "         64.97313 ],\n",
       "       [ 64.97316 ,  65.183846,  65.3929  , ...,  65.3929  ,  65.183846,\n",
       "         64.97316 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_verticies</span></div><div class='xr-var-dims'>(y, x, vertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-78.0 -77.82 -77.82 ... 64.87 64.87</div><input id='attrs-f33ee1da-6380-410f-9bbe-ef331a43fffc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f33ee1da-6380-410f-9bbe-ef331a43fffc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7b525c9-2318-4920-b379-f30b235a9835' class='xr-var-data-in' type='checkbox'><label for='data-e7b525c9-2318-4920-b379-f30b235a9835' 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>long_name :</span></dt><dd>latitude bounds</dd><dt><span>units :</span></dt><dd>degrees_N</dd></dl></div><div class='xr-var-data'><pre>array([[[-78.      , -77.81587 , -77.81587 , -78.      ],\n",
       "        [-78.      , -77.81587 , -77.81587 , -78.      ],\n",
       "        [-78.      , -77.81587 , -77.81587 , -78.      ],\n",
       "        ...,\n",
       "        [-78.      , -77.81587 , -77.81587 , -78.      ],\n",
       "        [-78.      , -77.81587 , -77.81587 , -78.      ],\n",
       "        [-78.      , -77.81587 , -77.81587 , -78.      ]],\n",
       "\n",
       "       [[-77.81587 , -77.63175 , -77.63175 , -77.81587 ],\n",
       "        [-77.81587 , -77.63175 , -77.63175 , -77.81587 ],\n",
       "        [-77.81587 , -77.63175 , -77.63175 , -77.81587 ],\n",
       "        ...,\n",
       "        [-77.81587 , -77.63175 , -77.63175 , -77.81587 ],\n",
       "        [-77.81587 , -77.63175 , -77.63175 , -77.81587 ],\n",
       "        [-77.81587 , -77.63175 , -77.63175 , -77.81587 ]],\n",
       "\n",
       "       [[-77.63175 , -77.447624, -77.447624, -77.63175 ],\n",
       "        [-77.63175 , -77.447624, -77.447624, -77.63175 ],\n",
       "        [-77.63175 , -77.447624, -77.447624, -77.63175 ],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [ 65.49607 ,  65.4965  ,  65.28836 ,  65.28808 ],\n",
       "        [ 65.28808 ,  65.28836 ,  65.078606,  65.07846 ],\n",
       "        [ 65.07846 ,  65.078606,  64.867195,  64.867195]],\n",
       "\n",
       "       [[ 64.867195,  64.867195,  65.07869 ,  65.078606],\n",
       "        [ 65.078606,  65.07869 ,  65.288536,  65.28836 ],\n",
       "        [ 65.28836 ,  65.288536,  65.49675 ,  65.4965  ],\n",
       "        ...,\n",
       "        [ 65.4965  ,  65.49675 ,  65.288536,  65.28836 ],\n",
       "        [ 65.28836 ,  65.288536,  65.07869 ,  65.078606],\n",
       "        [ 65.078606,  65.07869 ,  64.867195,  64.867195]],\n",
       "\n",
       "       [[ 64.867195,  64.867195,  65.07872 ,  65.07869 ],\n",
       "        [ 65.07869 ,  65.07872 ,  65.28859 ,  65.288536],\n",
       "        [ 65.288536,  65.28859 ,  65.49684 ,  65.49675 ],\n",
       "        ...,\n",
       "        [ 65.49675 ,  65.49684 ,  65.28859 ,  65.288536],\n",
       "        [ 65.288536,  65.28859 ,  65.07872 ,  65.07869 ],\n",
       "        [ 65.07869 ,  65.07872 ,  64.867195,  64.867195]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>60.25 60.75 61.25 ... 59.99 60.0</div><input id='attrs-8cd0e24f-8b68-4d3b-a3d0-49e0339ba541' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8cd0e24f-8b68-4d3b-a3d0-49e0339ba541' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3904552d-8454-4a4e-aec8-ff761cb46f0f' class='xr-var-data-in' type='checkbox'><label for='data-3904552d-8454-4a4e-aec8-ff761cb46f0f' 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>bounds :</span></dt><dd>lon_bnds</dd><dt><span>cell_methods :</span></dt><dd>y: point x: point</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd></dl></div><div class='xr-var-data'><pre>array([[60.25    , 60.75    , 61.25    , ..., 58.75    , 59.25    ,\n",
       "        59.75    ],\n",
       "       [60.25    , 60.75    , 61.25    , ..., 58.75    , 59.25    ,\n",
       "        59.75    ],\n",
       "       [60.25    , 60.75    , 61.25    , ..., 58.75    , 59.25    ,\n",
       "        59.75    ],\n",
       "       ...,\n",
       "       [60.010345, 60.031006, 60.051666, ..., 59.948338, 59.969006,\n",
       "        59.98967 ],\n",
       "       [60.006195, 60.018616, 60.031006, ..., 59.968994, 59.9814  ,\n",
       "        59.9938  ],\n",
       "       [60.002075, 60.006195, 60.010345, ..., 59.989662, 59.993797,\n",
       "        59.997932]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_verticies</span></div><div class='xr-var-dims'>(y, x, vertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>60.0 60.0 60.5 ... 60.0 60.0 60.0</div><input id='attrs-6f666043-9c99-4a5b-a3c5-5d06ed82cd65' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f666043-9c99-4a5b-a3c5-5d06ed82cd65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9ceeeb3-6dc6-4ca8-9414-987addde50bd' class='xr-var-data-in' type='checkbox'><label for='data-c9ceeeb3-6dc6-4ca8-9414-987addde50bd' 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>long_name :</span></dt><dd>longitude bounds</dd><dt><span>units :</span></dt><dd>degrees_E</dd></dl></div><div class='xr-var-data'><pre>array([[[60.      , 60.      , 60.5     , 60.5     ],\n",
       "        [60.5     , 60.5     , 61.      , 61.      ],\n",
       "        [61.      , 61.      , 61.5     , 61.5     ],\n",
       "        ...,\n",
       "        [58.5     , 58.5     , 59.      , 59.      ],\n",
       "        [59.      , 59.      , 59.5     , 59.5     ],\n",
       "        [59.5     , 59.5     , 60.      , 60.      ]],\n",
       "\n",
       "       [[60.      , 60.      , 60.5     , 60.5     ],\n",
       "        [60.5     , 60.5     , 61.      , 61.      ],\n",
       "        [61.      , 61.      , 61.5     , 61.5     ],\n",
       "        ...,\n",
       "        [58.5     , 58.5     , 59.      , 59.      ],\n",
       "        [59.      , 59.      , 59.5     , 59.5     ],\n",
       "        [59.5     , 59.5     , 60.      , 60.      ]],\n",
       "\n",
       "       [[60.      , 60.      , 60.5     , 60.5     ],\n",
       "        [60.5     , 60.5     , 61.      , 61.      ],\n",
       "        [61.      , 61.      , 61.5     , 61.5     ],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [59.925613, 59.950394, 59.966934, 59.950417],\n",
       "        [59.950417, 59.966934, 59.983467, 59.97521 ],\n",
       "        [59.97521 , 59.983467, 60.      , 60.      ]],\n",
       "\n",
       "       [[60.      , 60.      , 60.00827 , 60.01654 ],\n",
       "        [60.01654 , 60.00827 , 60.01654 , 60.03308 ],\n",
       "        [60.03308 , 60.01654 , 60.02481 , 60.04962 ],\n",
       "        ...,\n",
       "        [59.950394, 59.975193, 59.983463, 59.966934],\n",
       "        [59.966934, 59.983463, 59.991734, 59.983467],\n",
       "        [59.983467, 59.991734, 60.      , 60.      ]],\n",
       "\n",
       "       [[60.      , 60.      , 60.      , 60.00827 ],\n",
       "        [60.00827 , 60.      , 60.      , 60.01654 ],\n",
       "        [60.01654 , 60.      , 60.      , 60.02481 ],\n",
       "        ...,\n",
       "        [59.975193, 60.      , 60.      , 59.983463],\n",
       "        [59.983463, 60.      , 60.      , 59.991734],\n",
       "        [59.991734, 60.      , 60.      , 60.      ]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1850-01-16 12:00:00</div><input id='attrs-890cc44d-ab4e-4a9d-a7e2-d07854ed86cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-890cc44d-ab4e-4a9d-a7e2-d07854ed86cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a102ffa2-a49f-4b33-b9e8-54255dad40e4' class='xr-var-data-in' type='checkbox'><label for='data-a102ffa2-a49f-4b33-b9e8-54255dad40e4' 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>calendar_type :</span></dt><dd>noleap</dd><dt><span>description :</span></dt><dd>Temporal mean</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array(cftime.DatetimeNoLeap(1850, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bounds</span></div><div class='xr-var-dims'>(bnds)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1850-01-01 00:00:00 1850-02-01 0...</div><input id='attrs-0cbd364c-9c8b-443e-babd-ac066f7479c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0cbd364c-9c8b-443e-babd-ac066f7479c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2f024bb-5c7c-453d-b7e9-af3323f3d5c2' class='xr-var-data-in' type='checkbox'><label for='data-c2f024bb-5c7c-453d-b7e9-af3323f3d5c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time axis boundaries</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, 2, 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>lon_bounds</span></div><div class='xr-var-dims'>(bnds, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>60.0 60.5 61.0 ... 59.99 60.0 60.0</div><input id='attrs-d1e8ac24-2919-46d6-b4e1-d0733d7b699a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d1e8ac24-2919-46d6-b4e1-d0733d7b699a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fcf99e77-fb34-40ff-a2cc-3b812caf86a6' class='xr-var-data-in' type='checkbox'><label for='data-fcf99e77-fb34-40ff-a2cc-3b812caf86a6' 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([[[60.      , 60.5     , 61.      , ..., 58.5     , 59.      ,\n",
       "         59.5     ],\n",
       "        [60.      , 60.5     , 61.      , ..., 58.5     , 59.      ,\n",
       "         59.5     ],\n",
       "        [60.      , 60.5     , 61.      , ..., 58.5     , 59.      ,\n",
       "         59.5     ],\n",
       "        ...,\n",
       "        [60.      , 60.02066 , 60.041336, ..., 59.938004, 59.958675,\n",
       "         59.97934 ],\n",
       "        [60.      , 60.012405, 60.02481 , ..., 59.96279 , 59.975197,\n",
       "         59.987602],\n",
       "        [60.      , 60.004135, 60.00827 , ..., 59.987595, 59.99173 ,\n",
       "         59.995865]],\n",
       "\n",
       "       [[60.5     , 61.      , 61.5     , ..., 59.      , 59.5     ,\n",
       "         60.      ],\n",
       "        [60.5     , 61.      , 61.5     , ..., 59.      , 59.5     ,\n",
       "         60.      ],\n",
       "        [60.5     , 61.      , 61.5     , ..., 59.      , 59.5     ,\n",
       "         60.      ],\n",
       "        ...,\n",
       "        [60.02066 , 60.041336, 60.061996, ..., 59.958675, 59.97934 ,\n",
       "         60.      ],\n",
       "        [60.012405, 60.02481 , 60.037216, ..., 59.975197, 59.987602,\n",
       "         60.      ],\n",
       "        [60.004135, 60.00827 , 60.012405, ..., 59.99173 , 59.995865,\n",
       "         60.      ]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bounds</span></div><div class='xr-var-dims'>(bnds, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-78.0 -78.0 -78.0 ... 65.18 64.97</div><input id='attrs-272a4dd6-0807-41e9-b84a-14a0babf83c6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-272a4dd6-0807-41e9-b84a-14a0babf83c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-839874de-872b-4dc1-95c8-6cda04efdf41' class='xr-var-data-in' type='checkbox'><label for='data-839874de-872b-4dc1-95c8-6cda04efdf41' 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([[[-78.      , -78.      , -78.      , ..., -78.      ,\n",
       "         -78.      , -78.      ],\n",
       "        [-77.81587 , -77.81587 , -77.81587 , ..., -77.81587 ,\n",
       "         -77.81587 , -77.81587 ],\n",
       "        [-77.63175 , -77.63175 , -77.63175 , ..., -77.63175 ,\n",
       "         -77.63175 , -77.63175 ],\n",
       "        ...,\n",
       "        [ 64.972824,  65.18327 ,  65.392075, ...,  65.392075,\n",
       "          65.18327 ,  64.972824],\n",
       "        [ 64.9729  ,  65.18349 ,  65.392426, ...,  65.392426,\n",
       "          65.18349 ,  64.9729  ],\n",
       "        [ 64.972946,  65.18361 ,  65.39264 , ...,  65.39264 ,\n",
       "          65.18361 ,  64.972946]],\n",
       "\n",
       "       [[-77.81587 , -77.81587 , -77.81587 , ..., -77.81587 ,\n",
       "         -77.81587 , -77.81587 ],\n",
       "        [-77.63175 , -77.63175 , -77.63175 , ..., -77.63175 ,\n",
       "         -77.63175 , -77.63175 ],\n",
       "        [-77.447624, -77.447624, -77.447624, ..., -77.447624,\n",
       "         -77.447624, -77.447624],\n",
       "        ...,\n",
       "        [ 64.9729  ,  65.18349 ,  65.392426, ...,  65.392426,\n",
       "          65.18349 ,  64.9729  ],\n",
       "        [ 64.972946,  65.18361 ,  65.39264 , ...,  65.39264 ,\n",
       "          65.18361 ,  64.972946],\n",
       "        [ 64.97296 ,  65.183655,  65.392715, ...,  65.392715,\n",
       "          65.183655,  64.97296 ]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5a400d52-1fcb-4d4d-82b5-1961f1c91d11' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5a400d52-1fcb-4d4d-82b5-1961f1c91d11' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>tos</span></div><div class='xr-var-dims'>(member_id, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 576, 720), meta=np.ndarray&gt;</div><input id='attrs-a900b44c-7c5b-407e-a86e-f44a2dcada8c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a900b44c-7c5b-407e-a86e-f44a2dcada8c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04d9bcc4-8e03-4d31-a2c8-61a65c10cc39' class='xr-var-data-in' type='checkbox'><label for='data-04d9bcc4-8e03-4d31-a2c8-61a65c10cc39' 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: areacello</dd><dt><span>cell_methods :</span></dt><dd>area: mean where sea time: mean</dd><dt><span>long_name :</span></dt><dd>Sea Surface Temperature</dd><dt><span>original_name :</span></dt><dd>tos</dd><dt><span>standard_name :</span></dt><dd>sea_surface_temperature</dd><dt><span>units :</span></dt><dd>°C</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 3.16 MiB </td> <td> 1.58 MiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (2, 576, 720) </td> <td> (1, 576, 720) </td></tr>\n",
       "    <tr><th> Count </th><td> 190 Tasks </td><td> 2 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-61ebd787-7085-4e8f-8d2a-13f103a2dff5' class='xr-section-summary-in' type='checkbox'  ><label for='section-61ebd787-7085-4e8f-8d2a-13f103a2dff5' class='xr-section-summary' >Attributes: <span>(40)</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-6.0 UGRID-1.0</dd><dt><span>activity_id :</span></dt><dd>CMIP</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>branch_time_in_child :</span></dt><dd>0.0</dd><dt><span>comment :</span></dt><dd>&lt;null ref&gt;</dd><dt><span>contact :</span></dt><dd>gfdl.climate.model.info@noaa.gov</dd><dt><span>data_specs_version :</span></dt><dd>01.00.27</dd><dt><span>experiment :</span></dt><dd>all-forcing simulation of the recent past</dd><dt><span>external_variables :</span></dt><dd>areacello</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>grid :</span></dt><dd>ocean data on native grid tripolar - nominal 0.5 deg latitude/longitude</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 Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory, Princeton, NJ 08540, USA</dd><dt><span>institution_id :</span></dt><dd>NOAA-GFDL</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by NOAA-GFDL 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>nominal_resolution :</span></dt><dd>50 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>GFDL-ESM4</dd><dt><span>parent_time_units :</span></dt><dd>days since 0001-1-1</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>realm :</span></dt><dd>ocean</dd><dt><span>references :</span></dt><dd>see further_info_url attribute</dd><dt><span>source_id :</span></dt><dd>GFDL-ESM4</dd><dt><span>source_type :</span></dt><dd>AOGCM AER CHEM BGC</dd><dt><span>status :</span></dt><dd>2020-10-03;created; by gcs.cmip6.ldeo@gmail.com</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>Omon</dd><dt><span>title :</span></dt><dd>NOAA GFDL GFDL-ESM4 model output prepared for CMIP6 all-forcing simulation of the recent past</dd><dt><span>variant_info :</span></dt><dd>N/A</dd><dt><span>version_id :</span></dt><dd>v20180701</dd><dt><span>intake_esm_varname :</span></dt><dd>None</dd><dt><span>original_key :</span></dt><dd>GFDL-ESM4.gn.Omon.r3i1p1f1</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:        (member_id: 2, y: 576, x: 720, vertex: 4, bnds: 2)\n",
       "Coordinates:\n",
       "  * x              (x) float64 -299.8 -299.2 -298.8 -298.2 ... 58.75 59.25 59.75\n",
       "  * y              (y) float64 -77.91 -77.72 -77.54 -77.36 ... 89.47 89.68 89.89\n",
       "    lat            (y, x) float32 -77.91 -77.91 -77.91 ... 65.39 65.18 64.97\n",
       "    lat_verticies  (y, x, vertex) float32 -78.0 -77.82 -77.82 ... 64.87 64.87\n",
       "    lon            (y, x) float32 60.25 60.75 61.25 61.75 ... 59.99 59.99 60.0\n",
       "    lon_verticies  (y, x, vertex) float32 60.0 60.0 60.5 60.5 ... 60.0 60.0 60.0\n",
       "    time           object 1850-01-16 12:00:00\n",
       "    time_bounds    (bnds) object 1850-01-01 00:00:00 1850-02-01 00:00:00\n",
       "    lon_bounds     (bnds, y, x) float32 60.0 60.5 61.0 61.5 ... 59.99 60.0 60.0\n",
       "    lat_bounds     (bnds, y, x) float32 -78.0 -78.0 -78.0 ... 65.39 65.18 64.97\n",
       "Dimensions without coordinates: member_id, vertex, bnds\n",
       "Data variables:\n",
       "    tos            (member_id, y, x) float32 dask.array<chunksize=(1, 576, 720), meta=np.ndarray>\n",
       "Attributes: (12/40)\n",
       "    Conventions:           CF-1.7 CMIP-6.0 UGRID-1.0\n",
       "    activity_id:           CMIP\n",
       "    branch_method:         standard\n",
       "    branch_time_in_child:  0.0\n",
       "    comment:               <null ref>\n",
       "    contact:               gfdl.climate.model.info@noaa.gov\n",
       "    ...                    ...\n",
       "    table_id:              Omon\n",
       "    title:                 NOAA GFDL GFDL-ESM4 model output prepared for CMIP...\n",
       "    variant_info:          N/A\n",
       "    version_id:            v20180701\n",
       "    intake_esm_varname:    None\n",
       "    original_key:          GFDL-ESM4.gn.Omon.r3i1p1f1"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f91a6fd5-5d45-40e6-b2eb-eb5c7694686f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/lib/python3.9/site-packages/dask/array/numpy_compat.py:41: RuntimeWarning: invalid value encountered in true_divide\n",
      "  x = np.divide(x1, x2, out)\n",
      "/srv/conda/lib/python3.9/site-packages/dask/array/numpy_compat.py:41: RuntimeWarning: invalid value encountered in true_divide\n",
      "  x = np.divide(x1, x2, out)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axarr = plt.subplots(ncols=2, nrows=1, figsize=[14,4])\n",
    "for ax, (name, ds) in zip(axarr.flat, ddict_combined.items()):\n",
    "    # show just the first time step\n",
    "    ds = ds.isel(time=0)\n",
    "    \n",
    "    # average all members\n",
    "    if 'member_id' in ds.dims:\n",
    "        ds = ds.mean('member_id')\n",
    "    ds.tos.plot(ax=ax, robust=True)\n",
    "    ax.set_title(name)\n",
    "fig.subplots_adjust(hspace=0.5, wspace=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b0a8e4d7-4357-4ea5-89a5-e8723d2b9ccf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/lib/python3.9/site-packages/dask/array/numpy_compat.py:41: RuntimeWarning: invalid value encountered in true_divide\n",
      "  x = np.divide(x1, x2, out)\n",
      "/srv/conda/lib/python3.9/site-packages/dask/array/numpy_compat.py:41: RuntimeWarning: invalid value encountered in true_divide\n",
      "  x = np.divide(x1, x2, out)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axarr = plt.subplots(ncols=2, nrows=1, figsize=[14,4])\n",
    "for ax, (name, ds) in zip(axarr.flat, ddict_combined.items()):\n",
    "    # show just the first time step\n",
    "    ds = ds.isel(time=0)\n",
    "\n",
    "    # average all members\n",
    "    if 'member_id' in ds.dims:\n",
    "        ds = ds.mean('member_id')\n",
    "        \n",
    "    # mask out the indian ocean\n",
    "    mask = merged_mask(basins, ds)\n",
    "    \n",
    "    ds_masked = ds.where(mask == 10) # Does anyone know which number the Arabian sea? (thought it's #53 was it isn't quite working..., maybe #10?)\n",
    "    \n",
    "    ds_masked.tos.plot(ax=ax, robust=True)\n",
    "    ax.set_title(name)\n",
    "fig.subplots_adjust(hspace=0.5, wspace=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87db585f-640a-41cd-b7ec-cda24d1528dd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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