{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../images/cmip6-cookbook-thumbnail.png\" width=250 alt=\"CMIP6 image\"></img>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Google Cloud CMIP6 Public Data: Basic Python Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview\n",
    "\n",
    "This notebooks shows how to query the Google Cloud CMIP6 catalog and load the data using Python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prerequisites\n",
    "\n",
    "| Concepts | Importance | Notes |\n",
    "| --- | --- | --- |\n",
    "| [Intro to Xarray](https://foundations.projectpythia.org/core/xarray/xarray-intro.html) | Necessary | |\n",
    "| [Understanding of NetCDF](https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html) | Helpful | Familiarity with metadata structure |\n",
    "\n",
    "- **Time to learn**: 10 minutes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-20T21:33:08.125945Z",
     "iopub.status.busy": "2023-11-20T21:33:08.125721Z",
     "iopub.status.idle": "2023-11-20T21:33:09.109328Z",
     "shell.execute_reply": "2023-11-20T21:33:09.108359Z"
    }
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import zarr\n",
    "import fsspec\n",
    "import nc_time_axis\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = 12, 6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Browse Catalog"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data catatalog is stored as a CSV file. Here we read it with Pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-20T21:33:09.113529Z",
     "iopub.status.busy": "2023-11-20T21:33:09.113042Z",
     "iopub.status.idle": "2023-11-20T21:33:11.388982Z",
     "shell.execute_reply": "2023-11-20T21:33:11.388281Z"
    }
   },
   "outputs": [
    {
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       "      <th>activity_id</th>\n",
       "      <th>institution_id</th>\n",
       "      <th>source_id</th>\n",
       "      <th>experiment_id</th>\n",
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       "      <th>table_id</th>\n",
       "      <th>variable_id</th>\n",
       "      <th>grid_label</th>\n",
       "      <th>zstore</th>\n",
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       "      <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>ps</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HighResMIP</td>\n",
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       "      <td>rsds</td>\n",
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       "      <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>rlus</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",
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       "      <td>rlds</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>psl</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  activity_id institution_id     source_id       experiment_id member_id  \\\n",
       "0  HighResMIP           CMCC  CMCC-CM2-HR4  highresSST-present  r1i1p1f1   \n",
       "1  HighResMIP           CMCC  CMCC-CM2-HR4  highresSST-present  r1i1p1f1   \n",
       "2  HighResMIP           CMCC  CMCC-CM2-HR4  highresSST-present  r1i1p1f1   \n",
       "3  HighResMIP           CMCC  CMCC-CM2-HR4  highresSST-present  r1i1p1f1   \n",
       "4  HighResMIP           CMCC  CMCC-CM2-HR4  highresSST-present  r1i1p1f1   \n",
       "\n",
       "  table_id variable_id grid_label  \\\n",
       "0     Amon          ps         gn   \n",
       "1     Amon        rsds         gn   \n",
       "2     Amon        rlus         gn   \n",
       "3     Amon        rlds         gn   \n",
       "4     Amon         psl         gn   \n",
       "\n",
       "                                              zstore  dcpp_init_year   version  \n",
       "0  gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...             NaN  20170706  \n",
       "1  gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...             NaN  20170706  \n",
       "2  gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...             NaN  20170706  \n",
       "3  gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...             NaN  20170706  \n",
       "4  gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...             NaN  20170706  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('https://storage.googleapis.com/cmip6/cmip6-zarr-consolidated-stores.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The columns of the dataframe correspond to the [CMI6 controlled vocabulary](https://docs.google.com/document/d/1yUx6jr9EdedCOLd--CPdTfGDwEwzPpCF6p1jRmqx-0Q/edit).\n",
    "\n",
    "Here we filter the data to find monthly surface air temperature for historical experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-20T21:33:11.416186Z",
     "iopub.status.busy": "2023-11-20T21:33:11.415909Z",
     "iopub.status.idle": "2023-11-20T21:33:11.470951Z",
     "shell.execute_reply": "2023-11-20T21:33:11.470118Z"
    },
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
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       "      <th>973</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NOAA-GFDL</td>\n",
       "      <td>GFDL-ESM4</td>\n",
       "      <td>historical</td>\n",
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       "    <tr>\n",
       "      <th>1766</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NOAA-GFDL</td>\n",
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       "      <td>tas</td>\n",
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       "      <td>gs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/histo...</td>\n",
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       "      <th>22298</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>IPSL</td>\n",
       "      <td>IPSL-CM6A-LR</td>\n",
       "      <td>historical</td>\n",
       "      <td>r2i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gr</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20180803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <th>522952</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>MRI</td>\n",
       "      <td>MRI-ESM2-0</td>\n",
       "      <td>historical</td>\n",
       "      <td>r7i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/MRI/MRI-ESM2-0/historica...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20210813</td>\n",
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       "    <tr>\n",
       "      <th>523274</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>MRI</td>\n",
       "      <td>MRI-ESM2-0</td>\n",
       "      <td>historical</td>\n",
       "      <td>r6i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/MRI/MRI-ESM2-0/historica...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20210907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523712</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>CMCC</td>\n",
       "      <td>CMCC-CM2-SR5</td>\n",
       "      <td>historical</td>\n",
       "      <td>r3i1p2f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/CMCC/CMCC-CM2-SR5/histor...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20211108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523721</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>CMCC</td>\n",
       "      <td>CMCC-CM2-SR5</td>\n",
       "      <td>historical</td>\n",
       "      <td>r2i1p2f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/CMCC/CMCC-CM2-SR5/histor...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20211109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523769</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>EC-Earth-Consortium</td>\n",
       "      <td>EC-Earth3-Veg</td>\n",
       "      <td>historical</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gr</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-E...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20211207</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "<p>635 rows × 11 columns</p>\n",
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      ],
      "text/plain": [
       "       activity_id       institution_id      source_id experiment_id  \\\n",
       "973           CMIP            NOAA-GFDL      GFDL-ESM4    historical   \n",
       "1766          CMIP            NOAA-GFDL      GFDL-ESM4    historical   \n",
       "8074          CMIP            NOAA-GFDL       GFDL-CM4    historical   \n",
       "22185         CMIP                 IPSL   IPSL-CM6A-LR    historical   \n",
       "22298         CMIP                 IPSL   IPSL-CM6A-LR    historical   \n",
       "...            ...                  ...            ...           ...   \n",
       "522952        CMIP                  MRI     MRI-ESM2-0    historical   \n",
       "523274        CMIP                  MRI     MRI-ESM2-0    historical   \n",
       "523712        CMIP                 CMCC   CMCC-CM2-SR5    historical   \n",
       "523721        CMIP                 CMCC   CMCC-CM2-SR5    historical   \n",
       "523769        CMIP  EC-Earth-Consortium  EC-Earth3-Veg    historical   \n",
       "\n",
       "       member_id table_id variable_id grid_label  \\\n",
       "973     r3i1p1f1     Amon         tas        gr1   \n",
       "1766    r2i1p1f1     Amon         tas        gr1   \n",
       "8074    r1i1p1f1     Amon         tas        gr1   \n",
       "22185   r8i1p1f1     Amon         tas         gr   \n",
       "22298   r2i1p1f1     Amon         tas         gr   \n",
       "...          ...      ...         ...        ...   \n",
       "522952  r7i1p1f1     Amon         tas         gn   \n",
       "523274  r6i1p1f1     Amon         tas         gn   \n",
       "523712  r3i1p2f1     Amon         tas         gn   \n",
       "523721  r2i1p2f1     Amon         tas         gn   \n",
       "523769  r1i1p1f1     Amon         tas         gr   \n",
       "\n",
       "                                                   zstore  dcpp_init_year  \\\n",
       "973     gs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-ESM4/hist...             NaN   \n",
       "1766    gs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-ESM4/hist...             NaN   \n",
       "8074    gs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/histo...             NaN   \n",
       "22185   gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...             NaN   \n",
       "22298   gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...             NaN   \n",
       "...                                                   ...             ...   \n",
       "522952  gs://cmip6/CMIP6/CMIP/MRI/MRI-ESM2-0/historica...             NaN   \n",
       "523274  gs://cmip6/CMIP6/CMIP/MRI/MRI-ESM2-0/historica...             NaN   \n",
       "523712  gs://cmip6/CMIP6/CMIP/CMCC/CMCC-CM2-SR5/histor...             NaN   \n",
       "523721  gs://cmip6/CMIP6/CMIP/CMCC/CMCC-CM2-SR5/histor...             NaN   \n",
       "523769  gs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-E...             NaN   \n",
       "\n",
       "         version  \n",
       "973     20180701  \n",
       "1766    20180701  \n",
       "8074    20180701  \n",
       "22185   20180803  \n",
       "22298   20180803  \n",
       "...          ...  \n",
       "522952  20210813  \n",
       "523274  20210907  \n",
       "523712  20211108  \n",
       "523721  20211109  \n",
       "523769  20211207  \n",
       "\n",
       "[635 rows x 11 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ta = df.query(\"activity_id=='CMIP' & table_id == 'Amon' & variable_id == 'tas' & experiment_id == 'historical'\")\n",
    "df_ta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we do further filtering to find just the models from NCAR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-20T21:33:11.474107Z",
     "iopub.status.busy": "2023-11-20T21:33:11.473306Z",
     "iopub.status.idle": "2023-11-20T21:33:11.486156Z",
     "shell.execute_reply": "2023-11-20T21:33:11.485490Z"
    }
   },
   "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>56049</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NCAR</td>\n",
       "      <td>CESM2-WACCM</td>\n",
       "      <td>historical</td>\n",
       "      <td>r2i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/histori...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20190227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56143</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NCAR</td>\n",
       "      <td>CESM2-WACCM</td>\n",
       "      <td>historical</td>\n",
       "      <td>r3i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/histori...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20190227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56326</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NCAR</td>\n",
       "      <td>CESM2-WACCM</td>\n",
       "      <td>historical</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/histori...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20190227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59875</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NCAR</td>\n",
       "      <td>CESM2</td>\n",
       "      <td>historical</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r1...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20190308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61655</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NCAR</td>\n",
       "      <td>CESM2</td>\n",
       "      <td>historical</td>\n",
       "      <td>r4i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r4...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20190308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61862</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NCAR</td>\n",
       "      <td>CESM2</td>\n",
       "      <td>historical</td>\n",
       "      <td>r5i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r5...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20190308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62691</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NCAR</td>\n",
       "      <td>CESM2</td>\n",
       "      <td>historical</td>\n",
       "      <td>r2i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r2...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20190308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63131</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NCAR</td>\n",
       "      <td>CESM2</td>\n",
       "      <td>historical</td>\n",
       "      <td>r3i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r3...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20190308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63266</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NCAR</td>\n",
       "      <td>CESM2</td>\n",
       "      <td>historical</td>\n",
       "      <td>r6i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r6...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20190308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64615</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NCAR</td>\n",
       "      <td>CESM2</td>\n",
       "      <td>historical</td>\n",
       "      <td>r8i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r8...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20190311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64914</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NCAR</td>\n",
       "      <td>CESM2</td>\n",
       "      <td>historical</td>\n",
       "      <td>r7i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r7...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20190311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64983</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NCAR</td>\n",
       "      <td>CESM2</td>\n",
       "      <td>historical</td>\n",
       "      <td>r9i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r9...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20190311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66341</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NCAR</td>\n",
       "      <td>CESM2</td>\n",
       "      <td>historical</td>\n",
       "      <td>r10i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r1...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20190313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200772</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NCAR</td>\n",
       "      <td>CESM2</td>\n",
       "      <td>historical</td>\n",
       "      <td>r11i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r1...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20190514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385224</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NCAR</td>\n",
       "      <td>CESM2-FV2</td>\n",
       "      <td>historical</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/NCAR/CESM2-FV2/historica...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20191120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386297</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NCAR</td>\n",
       "      <td>CESM2-WACCM-FV2</td>\n",
       "      <td>historical</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM-FV2/his...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20191120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420771</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NCAR</td>\n",
       "      <td>CESM2-WACCM-FV2</td>\n",
       "      <td>historical</td>\n",
       "      <td>r3i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM-FV2/his...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20200226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421251</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NCAR</td>\n",
       "      <td>CESM2-WACCM-FV2</td>\n",
       "      <td>historical</td>\n",
       "      <td>r2i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM-FV2/his...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20200226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422013</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NCAR</td>\n",
       "      <td>CESM2-FV2</td>\n",
       "      <td>historical</td>\n",
       "      <td>r3i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/NCAR/CESM2-FV2/historica...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20200226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422459</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NCAR</td>\n",
       "      <td>CESM2-FV2</td>\n",
       "      <td>historical</td>\n",
       "      <td>r2i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/NCAR/CESM2-FV2/historica...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20200226</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       activity_id institution_id        source_id experiment_id  member_id  \\\n",
       "56049         CMIP           NCAR      CESM2-WACCM    historical   r2i1p1f1   \n",
       "56143         CMIP           NCAR      CESM2-WACCM    historical   r3i1p1f1   \n",
       "56326         CMIP           NCAR      CESM2-WACCM    historical   r1i1p1f1   \n",
       "59875         CMIP           NCAR            CESM2    historical   r1i1p1f1   \n",
       "61655         CMIP           NCAR            CESM2    historical   r4i1p1f1   \n",
       "61862         CMIP           NCAR            CESM2    historical   r5i1p1f1   \n",
       "62691         CMIP           NCAR            CESM2    historical   r2i1p1f1   \n",
       "63131         CMIP           NCAR            CESM2    historical   r3i1p1f1   \n",
       "63266         CMIP           NCAR            CESM2    historical   r6i1p1f1   \n",
       "64615         CMIP           NCAR            CESM2    historical   r8i1p1f1   \n",
       "64914         CMIP           NCAR            CESM2    historical   r7i1p1f1   \n",
       "64983         CMIP           NCAR            CESM2    historical   r9i1p1f1   \n",
       "66341         CMIP           NCAR            CESM2    historical  r10i1p1f1   \n",
       "200772        CMIP           NCAR            CESM2    historical  r11i1p1f1   \n",
       "385224        CMIP           NCAR        CESM2-FV2    historical   r1i1p1f1   \n",
       "386297        CMIP           NCAR  CESM2-WACCM-FV2    historical   r1i1p1f1   \n",
       "420771        CMIP           NCAR  CESM2-WACCM-FV2    historical   r3i1p1f1   \n",
       "421251        CMIP           NCAR  CESM2-WACCM-FV2    historical   r2i1p1f1   \n",
       "422013        CMIP           NCAR        CESM2-FV2    historical   r3i1p1f1   \n",
       "422459        CMIP           NCAR        CESM2-FV2    historical   r2i1p1f1   \n",
       "\n",
       "       table_id variable_id grid_label  \\\n",
       "56049      Amon         tas         gn   \n",
       "56143      Amon         tas         gn   \n",
       "56326      Amon         tas         gn   \n",
       "59875      Amon         tas         gn   \n",
       "61655      Amon         tas         gn   \n",
       "61862      Amon         tas         gn   \n",
       "62691      Amon         tas         gn   \n",
       "63131      Amon         tas         gn   \n",
       "63266      Amon         tas         gn   \n",
       "64615      Amon         tas         gn   \n",
       "64914      Amon         tas         gn   \n",
       "64983      Amon         tas         gn   \n",
       "66341      Amon         tas         gn   \n",
       "200772     Amon         tas         gn   \n",
       "385224     Amon         tas         gn   \n",
       "386297     Amon         tas         gn   \n",
       "420771     Amon         tas         gn   \n",
       "421251     Amon         tas         gn   \n",
       "422013     Amon         tas         gn   \n",
       "422459     Amon         tas         gn   \n",
       "\n",
       "                                                   zstore  dcpp_init_year  \\\n",
       "56049   gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/histori...             NaN   \n",
       "56143   gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/histori...             NaN   \n",
       "56326   gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/histori...             NaN   \n",
       "59875   gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r1...             NaN   \n",
       "61655   gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r4...             NaN   \n",
       "61862   gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r5...             NaN   \n",
       "62691   gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r2...             NaN   \n",
       "63131   gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r3...             NaN   \n",
       "63266   gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r6...             NaN   \n",
       "64615   gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r8...             NaN   \n",
       "64914   gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r7...             NaN   \n",
       "64983   gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r9...             NaN   \n",
       "66341   gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r1...             NaN   \n",
       "200772  gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r1...             NaN   \n",
       "385224  gs://cmip6/CMIP6/CMIP/NCAR/CESM2-FV2/historica...             NaN   \n",
       "386297  gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM-FV2/his...             NaN   \n",
       "420771  gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM-FV2/his...             NaN   \n",
       "421251  gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM-FV2/his...             NaN   \n",
       "422013  gs://cmip6/CMIP6/CMIP/NCAR/CESM2-FV2/historica...             NaN   \n",
       "422459  gs://cmip6/CMIP6/CMIP/NCAR/CESM2-FV2/historica...             NaN   \n",
       "\n",
       "         version  \n",
       "56049   20190227  \n",
       "56143   20190227  \n",
       "56326   20190227  \n",
       "59875   20190308  \n",
       "61655   20190308  \n",
       "61862   20190308  \n",
       "62691   20190308  \n",
       "63131   20190308  \n",
       "63266   20190308  \n",
       "64615   20190311  \n",
       "64914   20190311  \n",
       "64983   20190311  \n",
       "66341   20190313  \n",
       "200772  20190514  \n",
       "385224  20191120  \n",
       "386297  20191120  \n",
       "420771  20200226  \n",
       "421251  20200226  \n",
       "422013  20200226  \n",
       "422459  20200226  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ta_ncar = df_ta.query('institution_id == \"NCAR\"')\n",
    "df_ta_ncar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will load a single store using `fsspec`, `zarr`, and `xarray`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-20T21:33:11.488488Z",
     "iopub.status.busy": "2023-11-20T21:33:11.488303Z",
     "iopub.status.idle": "2023-11-20T21:33:22.565989Z",
     "shell.execute_reply": "2023-11-20T21:33:22.565312Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gs://cmip6/CMIP6/CMIP/NCAR/CESM2-FV2/historical/r2i1p1f1/Amon/tas/gn/v20200226/\n"
     ]
    },
    {
     "data": {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (lat: 96, nbnd: 2, lon: 144, time: 1980)\n",
       "Coordinates:\n",
       "  * lat        (lat) float64 -90.0 -88.11 -86.21 -84.32 ... 86.21 88.11 90.0\n",
       "    lat_bnds   (lat, nbnd) float64 dask.array&lt;chunksize=(96, 2), meta=np.ndarray&gt;\n",
       "  * lon        (lon) float64 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5\n",
       "    lon_bnds   (lon, nbnd) float64 dask.array&lt;chunksize=(144, 2), meta=np.ndarray&gt;\n",
       "  * time       (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00\n",
       "    time_bnds  (time, nbnd) object dask.array&lt;chunksize=(1980, 2), meta=np.ndarray&gt;\n",
       "Dimensions without coordinates: nbnd\n",
       "Data variables:\n",
       "    tas        (time, lat, lon) float32 dask.array&lt;chunksize=(990, 96, 144), meta=np.ndarray&gt;\n",
       "Attributes: (12/48)\n",
       "    Conventions:                     CF-1.7 CMIP-6.2\n",
       "    DODS_EXTRA.Unlimited_Dimension:  time\n",
       "    activity_id:                     CMIP\n",
       "    branch_method:                   standard\n",
       "    branch_time_in_child:            674885.0\n",
       "    branch_time_in_parent:           10950.0\n",
       "    ...                              ...\n",
       "    tracking_id:                     hdl:21.14100/99cdfde8-5b6d-452b-9b78-62a...\n",
       "    variable_id:                     tas\n",
       "    variant_info:                    CMIP6 CESM2-FV2 historical experiment (1...\n",
       "    variant_label:                   r2i1p1f1\n",
       "    netcdf_tracking_ids:             hdl:21.14100/99cdfde8-5b6d-452b-9b78-62a...\n",
       "    version_id:                      v20200226</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-7810d806-09ca-4e75-b004-36f772b702e9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7810d806-09ca-4e75-b004-36f772b702e9' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 96</li><li><span>nbnd</span>: 2</li><li><span class='xr-has-index'>lon</span>: 144</li><li><span class='xr-has-index'>time</span>: 1980</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7847a220-4b5e-4c16-b034-3de4774dcc50' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7847a220-4b5e-4c16-b034-3de4774dcc50' 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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -88.11 -86.21 ... 88.11 90.0</div><input id='attrs-e3c7c9f9-4b02-4bfd-8d31-383f16c9f2c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e3c7c9f9-4b02-4bfd-8d31-383f16c9f2c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9cf37e67-cc9d-4619-bb96-9ddb58bbf35d' class='xr-var-data-in' type='checkbox'><label for='data-9cf37e67-cc9d-4619-bb96-9ddb58bbf35d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>96</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>title :</span></dt><dd>Latitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -88.105263, -86.210526, -84.315789, -82.421053, -80.526316,\n",
       "       -78.631579, -76.736842, -74.842105, -72.947368, -71.052632, -69.157895,\n",
       "       -67.263158, -65.368421, -63.473684, -61.578947, -59.684211, -57.789474,\n",
       "       -55.894737, -54.      , -52.105263, -50.210526, -48.315789, -46.421053,\n",
       "       -44.526316, -42.631579, -40.736842, -38.842105, -36.947368, -35.052632,\n",
       "       -33.157895, -31.263158, -29.368421, -27.473684, -25.578947, -23.684211,\n",
       "       -21.789474, -19.894737, -18.      , -16.105263, -14.210526, -12.315789,\n",
       "       -10.421053,  -8.526316,  -6.631579,  -4.736842,  -2.842105,  -0.947368,\n",
       "         0.947368,   2.842105,   4.736842,   6.631579,   8.526316,  10.421053,\n",
       "        12.315789,  14.210526,  16.105263,  18.      ,  19.894737,  21.789474,\n",
       "        23.684211,  25.578947,  27.473684,  29.368421,  31.263158,  33.157895,\n",
       "        35.052632,  36.947368,  38.842105,  40.736842,  42.631579,  44.526316,\n",
       "        46.421053,  48.315789,  50.210526,  52.105263,  54.      ,  55.894737,\n",
       "        57.789474,  59.684211,  61.578947,  63.473684,  65.368421,  67.263158,\n",
       "        69.157895,  71.052632,  72.947368,  74.842105,  76.736842,  78.631579,\n",
       "        80.526316,  82.421053,  84.315789,  86.210526,  88.105263,  90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(lat, nbnd)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96, 2), meta=np.ndarray&gt;</div><input id='attrs-3fe97512-e94b-45cf-ac9b-b0b98e3b291e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3fe97512-e94b-45cf-ac9b-b0b98e3b291e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-246108cc-db6f-42d6-8691-42d93f214d0f' class='xr-var-data-in' type='checkbox'><label for='data-246108cc-db6f-42d6-8691-42d93f214d0f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[96, 2]</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 1.50 kiB </td>\n",
       "                        <td> 1.50 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (96, 2) </td>\n",
       "                        <td> (96, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 2.5 5.0 ... 352.5 355.0 357.5</div><input id='attrs-ea2e04e2-7a60-417f-b34d-9eb310c0786f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea2e04e2-7a60-417f-b34d-9eb310c0786f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-604a6c4e-1c24-482c-9064-eecb687dc868' class='xr-var-data-in' type='checkbox'><label for='data-604a6c4e-1c24-482c-9064-eecb687dc868' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>144</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>title :</span></dt><dd>Longitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>360.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([  0. ,   2.5,   5. ,   7.5,  10. ,  12.5,  15. ,  17.5,  20. ,  22.5,\n",
       "        25. ,  27.5,  30. ,  32.5,  35. ,  37.5,  40. ,  42.5,  45. ,  47.5,\n",
       "        50. ,  52.5,  55. ,  57.5,  60. ,  62.5,  65. ,  67.5,  70. ,  72.5,\n",
       "        75. ,  77.5,  80. ,  82.5,  85. ,  87.5,  90. ,  92.5,  95. ,  97.5,\n",
       "       100. , 102.5, 105. , 107.5, 110. , 112.5, 115. , 117.5, 120. , 122.5,\n",
       "       125. , 127.5, 130. , 132.5, 135. , 137.5, 140. , 142.5, 145. , 147.5,\n",
       "       150. , 152.5, 155. , 157.5, 160. , 162.5, 165. , 167.5, 170. , 172.5,\n",
       "       175. , 177.5, 180. , 182.5, 185. , 187.5, 190. , 192.5, 195. , 197.5,\n",
       "       200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n",
       "       225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n",
       "       250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n",
       "       275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n",
       "       300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n",
       "       325. , 327.5, 330. , 332.5, 335. , 337.5, 340. , 342.5, 345. , 347.5,\n",
       "       350. , 352.5, 355. , 357.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, nbnd)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(144, 2), meta=np.ndarray&gt;</div><input id='attrs-f03fc0ba-1ba9-44a5-b770-7afa44642b32' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f03fc0ba-1ba9-44a5-b770-7afa44642b32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfe89521-8acf-443d-9bdd-ff64d817cc35' class='xr-var-data-in' type='checkbox'><label for='data-dfe89521-8acf-443d-9bdd-ff64d817cc35' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[144, 2]</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 2.25 kiB </td>\n",
       "                        <td> 2.25 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (144, 2) </td>\n",
       "                        <td> (144, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "        </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'>1850-01-15 12:00:00 ... 2014-12-...</div><input id='attrs-b18a21c1-6665-4513-aec2-6f006c128cf9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b18a21c1-6665-4513-aec2-6f006c128cf9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-598794bb-c7d8-4780-9015-703e8de08e7f' class='xr-var-data-in' type='checkbox'><label for='data-598794bb-c7d8-4780-9015-703e8de08e7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>512</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>title :</span></dt><dd>time</dd><dt><span>type :</span></dt><dd>double</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 2, 14, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 3, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       ...,\n",
       "       cftime.DatetimeNoLeap(2014, 10, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 11, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 15, 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, nbnd)</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-adc3dcab-0ff2-414a-9335-203b4d905376' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-adc3dcab-0ff2-414a-9335-203b4d905376' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5967d253-e6f6-4f00-8775-f71a21d8721f' class='xr-var-data-in' type='checkbox'><label for='data-5967d253-e6f6-4f00-8775-f71a21d8721f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[1, 2]</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 30.94 kiB </td>\n",
       "                        <td> 30.94 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1980, 2) </td>\n",
       "                        <td> (1980, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> object numpy.ndarray </td>\n",
       "                    </tr>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-8bb2d998-878b-46a0-ab78-4914f8f5350e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8bb2d998-878b-46a0-ab78-4914f8f5350e' 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>tas</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(990, 96, 144), meta=np.ndarray&gt;</div><input id='attrs-08f39467-8adc-43b7-b42b-293e0415b38d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08f39467-8adc-43b7-b42b-293e0415b38d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc5c7685-2849-4fd6-832a-0a1ed8cf9eca' class='xr-var-data-in' type='checkbox'><label for='data-cc5c7685-2849-4fd6-832a-0a1ed8cf9eca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[1, 96, 144]</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>comment :</span></dt><dd>TREFHT</dd><dt><span>description :</span></dt><dd>near-surface (usually, 2 meter) air temperature</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>id :</span></dt><dd>tas</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>mipTable :</span></dt><dd>Amon</dd><dt><span>out_name :</span></dt><dd>tas</dd><dt><span>prov :</span></dt><dd>Amon ((isd.003))</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>time :</span></dt><dd>time</dd><dt><span>time_label :</span></dt><dd>time-mean</dd><dt><span>time_title :</span></dt><dd>Temporal mean</dd><dt><span>title :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>type :</span></dt><dd>real</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>variable_id :</span></dt><dd>tas</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                <thead>\n",
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       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 104.41 MiB </td>\n",
       "                        <td> 52.21 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1980, 96, 144) </td>\n",
       "                        <td> (990, 96, 144) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 2 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-ffc218c7-e11d-454d-9506-4f7c036b9e7d' class='xr-section-summary-in' type='checkbox'  ><label for='section-ffc218c7-e11d-454d-9506-4f7c036b9e7d' class='xr-section-summary' >Indexes: <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-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-77311794-e0bc-413f-9140-6da34c1cce18' class='xr-index-data-in' type='checkbox'/><label for='index-77311794-e0bc-413f-9140-6da34c1cce18' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([              -90.0,  -88.10526315789474,  -86.21052631578948,\n",
       "         -84.3157894736842,  -82.42105263157895,  -80.52631578947368,\n",
       "        -78.63157894736842,  -76.73684210526316,  -74.84210526315789,\n",
       "        -72.94736842105263,  -71.05263157894737,  -69.15789473684211,\n",
       "        -67.26315789473685,  -65.36842105263158, -63.473684210526315,\n",
       "       -61.578947368421055, -59.684210526315795,  -57.78947368421053,\n",
       "        -55.89473684210527,               -54.0,  -52.10526315789474,\n",
       "        -50.21052631578947,  -48.31578947368421,  -46.42105263157895,\n",
       "       -44.526315789473685, -42.631578947368425,  -40.73684210526316,\n",
       "         -38.8421052631579,  -36.94736842105264,  -35.05263157894737,\n",
       "        -33.15789473684211, -31.263157894736842, -29.368421052631582,\n",
       "       -27.473684210526322, -25.578947368421055, -23.684210526315795,\n",
       "       -21.789473684210535,  -19.89473684210526,               -18.0,\n",
       "        -16.10526315789474,  -14.21052631578948,  -12.31578947368422,\n",
       "       -10.421052631578945,  -8.526315789473685,  -6.631578947368425,\n",
       "        -4.736842105263165, -2.8421052631579045, -0.9473684210526301,\n",
       "        0.9473684210526301,  2.8421052631578902,    4.73684210526315,\n",
       "         6.631578947368411,   8.526315789473685,  10.421052631578945,\n",
       "        12.315789473684205,  14.210526315789465,  16.105263157894726,\n",
       "                      18.0,   19.89473684210526,   21.78947368421052,\n",
       "         23.68421052631578,   25.57894736842104,  27.473684210526315,\n",
       "        29.368421052631575,  31.263157894736835,  33.157894736842096,\n",
       "        35.052631578947356,   36.94736842105263,   38.84210526315789,\n",
       "         40.73684210526315,   42.63157894736841,   44.52631578947367,\n",
       "         46.42105263157893,   48.31578947368419,   50.21052631578948,\n",
       "         52.10526315789474,                54.0,   55.89473684210526,\n",
       "         57.78947368421052,   59.68421052631578,   61.57894736842104,\n",
       "          63.4736842105263,   65.36842105263156,   67.26315789473682,\n",
       "         69.15789473684211,   71.05263157894737,   72.94736842105263,\n",
       "         74.84210526315789,   76.73684210526315,   78.63157894736841,\n",
       "         80.52631578947367,   82.42105263157893,   84.31578947368419,\n",
       "         86.21052631578945,   88.10526315789474,                90.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-65d1bcb2-e71c-470c-8703-feb5b050caa8' class='xr-index-data-in' type='checkbox'/><label for='index-65d1bcb2-e71c-470c-8703-feb5b050caa8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0.0,   2.5,   5.0,   7.5,  10.0,  12.5,  15.0,  17.5,  20.0,  22.5,\n",
       "       ...\n",
       "       335.0, 337.5, 340.0, 342.5, 345.0, 347.5, 350.0, 352.5, 355.0, 357.5],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=144))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-85d64594-59eb-4e4c-9a82-d4ff3319f2ca' class='xr-index-data-in' type='checkbox'/><label for='index-85d64594-59eb-4e4c-9a82-d4ff3319f2ca' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([1850-01-15 12:00:00, 1850-02-14 00:00:00, 1850-03-15 12:00:00,\n",
       "             1850-04-15 00:00:00, 1850-05-15 12:00:00, 1850-06-15 00:00:00,\n",
       "             1850-07-15 12:00:00, 1850-08-15 12:00:00, 1850-09-15 00:00:00,\n",
       "             1850-10-15 12:00:00,\n",
       "             ...\n",
       "             2014-03-15 12:00:00, 2014-04-15 00:00:00, 2014-05-15 12:00:00,\n",
       "             2014-06-15 00:00:00, 2014-07-15 12:00:00, 2014-08-15 12:00:00,\n",
       "             2014-09-15 00:00:00, 2014-10-15 12:00:00, 2014-11-15 00:00:00,\n",
       "             2014-12-15 12:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=1980, calendar=&#x27;noleap&#x27;, freq=&#x27;None&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1c89ba72-6a63-428a-a77c-3f179a933200' class='xr-section-summary-in' type='checkbox'  ><label for='section-1c89ba72-6a63-428a-a77c-3f179a933200' class='xr-section-summary' >Attributes: <span>(48)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>DODS_EXTRA.Unlimited_Dimension :</span></dt><dd>time</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>674885.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>10950.0</dd><dt><span>case_id :</span></dt><dd>1560</dd><dt><span>cesm_casename :</span></dt><dd>b.e21.BHIST.f19_g17.CMIP6-historical-2deg.002</dd><dt><span>contact :</span></dt><dd>cesm_cmip6@ucar.edu</dd><dt><span>creation_date :</span></dt><dd>2019-11-05T02:05:46Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.31</dd><dt><span>experiment :</span></dt><dd>Simulation of recent past (1850 to 2014). Impose changing conditions (consistent with observations). Should be initialised from a point early enough in the pre-industrial control run to ensure that the end of all the perturbed runs branching from the end of this historical run end before the end of the control. Only one ensemble member is requested but modelling groups are strongly encouraged to submit at least three ensemble members of their CMIP historical simulation. </dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2-FV2.historical.none.r2i1p1f1</dd><dt><span>grid :</span></dt><dd>native 1.9x2.5 finite volume grid (96x144 latxlon)</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>National Center for Atmospheric Research, Climate and Global Dynamics Laboratory, 1850 Table Mesa Drive, Boulder, CO 80305, USA</dd><dt><span>institution_id :</span></dt><dd>NCAR</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by &lt;The National Center for Atmospheric Research&gt; is licensed under a Creative Commons Attribution-[]ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file)[]. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>model_doi_url :</span></dt><dd>https://doi.org/10.5065/D67H1H0V</dd><dt><span>nominal_resolution :</span></dt><dd>250 km</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>piControl</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>CESM2-FV2</dd><dt><span>parent_time_units :</span></dt><dd>days since 0001-01-01 00:00:00</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>2</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>source :</span></dt><dd>CESM2 (2017): atmosphere: CAM6 (1.9x2.5 finite volume grid; 144 x 96 longitude/latitude; 32 levels; top level 2.25 mb); ocean: POP2 (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m); sea_ice: CICE5.1 (same grid as ocean); land: CLM5 1.9x2.5 finite volume grid; 144 x 96 longitude/latitude; 32 levels; top level 2.25 mb); aerosol: MAM4 (1.9x2.5 finite volume grid; 144 x 96 longitude/latitude; 32 levels; top level 2.25 mb); atmoschem: MAM4 (1.9x2.5 finite volume grid; 144 x 96 longitude/latitude; 32 levels; top level 2.25 mb); landIce: CISM2.1; ocnBgchem: MARBL (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m)</dd><dt><span>source_id :</span></dt><dd>CESM2-FV2</dd><dt><span>source_type :</span></dt><dd>AOGCM BGC AER</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>Amon</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/99cdfde8-5b6d-452b-9b78-62a5048ff496</dd><dt><span>variable_id :</span></dt><dd>tas</dd><dt><span>variant_info :</span></dt><dd>CMIP6 CESM2-FV2 historical experiment (1850-2014) with CAM6-FV2 and interactive land CLM5 at 1.9 degree latitude x 2.5 degree longitude, coupled ocean (POP2) with biogeochemistry (MARBL), interactive sea ice (CICE5.1), and non-evolving land ice (CISM2.1).</dd><dt><span>variant_label :</span></dt><dd>r2i1p1f1</dd><dt><span>netcdf_tracking_ids :</span></dt><dd>hdl:21.14100/99cdfde8-5b6d-452b-9b78-62a5048ff496</dd><dt><span>version_id :</span></dt><dd>v20200226</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (lat: 96, nbnd: 2, lon: 144, time: 1980)\n",
       "Coordinates:\n",
       "  * lat        (lat) float64 -90.0 -88.11 -86.21 -84.32 ... 86.21 88.11 90.0\n",
       "    lat_bnds   (lat, nbnd) float64 dask.array<chunksize=(96, 2), meta=np.ndarray>\n",
       "  * lon        (lon) float64 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5\n",
       "    lon_bnds   (lon, nbnd) float64 dask.array<chunksize=(144, 2), meta=np.ndarray>\n",
       "  * time       (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00\n",
       "    time_bnds  (time, nbnd) object dask.array<chunksize=(1980, 2), meta=np.ndarray>\n",
       "Dimensions without coordinates: nbnd\n",
       "Data variables:\n",
       "    tas        (time, lat, lon) float32 dask.array<chunksize=(990, 96, 144), meta=np.ndarray>\n",
       "Attributes: (12/48)\n",
       "    Conventions:                     CF-1.7 CMIP-6.2\n",
       "    DODS_EXTRA.Unlimited_Dimension:  time\n",
       "    activity_id:                     CMIP\n",
       "    branch_method:                   standard\n",
       "    branch_time_in_child:            674885.0\n",
       "    branch_time_in_parent:           10950.0\n",
       "    ...                              ...\n",
       "    tracking_id:                     hdl:21.14100/99cdfde8-5b6d-452b-9b78-62a...\n",
       "    variable_id:                     tas\n",
       "    variant_info:                    CMIP6 CESM2-FV2 historical experiment (1...\n",
       "    variant_label:                   r2i1p1f1\n",
       "    netcdf_tracking_ids:             hdl:21.14100/99cdfde8-5b6d-452b-9b78-62a...\n",
       "    version_id:                      v20200226"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the path to a specific zarr store (the first one from the dataframe above)\n",
    "zstore = df_ta_ncar.zstore.values[-1]\n",
    "print(zstore)\n",
    "\n",
    "# create a mutable-mapping-style interface to the store\n",
    "mapper = fsspec.get_mapper(zstore)\n",
    "\n",
    "# open it using xarray and zarr\n",
    "ds = xr.open_zarr(mapper, consolidated=True)\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot a map from a specific date:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-20T21:33:22.569723Z",
     "iopub.status.busy": "2023-11-20T21:33:22.569092Z",
     "iopub.status.idle": "2023-11-20T21:33:23.538172Z",
     "shell.execute_reply": "2023-11-20T21:33:23.537454Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f1ee8b571c0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.tas.sel(time='1950-01').squeeze().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a timeseries of global-average surface air temperature. For this we need the area weighting factor for each gridpoint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-20T21:33:23.540962Z",
     "iopub.status.busy": "2023-11-20T21:33:23.540711Z",
     "iopub.status.idle": "2023-11-20T21:33:23.621140Z",
     "shell.execute_reply": "2023-11-20T21:33:23.620474Z"
    }
   },
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (lat: 192, lon: 288, nbnd: 2)\n",
       "Coordinates:\n",
       "  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
       "    lat_bnds   (lat, nbnd) float32 dask.array&lt;chunksize=(192, 2), meta=np.ndarray&gt;\n",
       "  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
       "    lon_bnds   (lon, nbnd) float32 dask.array&lt;chunksize=(288, 2), meta=np.ndarray&gt;\n",
       "Dimensions without coordinates: nbnd\n",
       "Data variables:\n",
       "    areacella  (lat, lon) float32 dask.array&lt;chunksize=(192, 288), meta=np.ndarray&gt;\n",
       "Attributes: (12/47)\n",
       "    Conventions:            CF-1.7 CMIP-6.2\n",
       "    activity_id:            CMIP\n",
       "    branch_method:          no parent\n",
       "    branch_time_in_child:   711385.0\n",
       "    branch_time_in_parent:  0.0\n",
       "    case_id:                38\n",
       "    ...                     ...\n",
       "    variable_id:            areacella\n",
       "    variant_info:           f.e21.FHIST_BGC.f09_f09_mg17.CMIP6-AMIP.001 \r",
       "\\n\r",
       "\\...\n",
       "    variant_label:          r1i1p1f1\n",
       "    status:                 2019-11-04;created;by nhn2@columbia.edu\n",
       "    netcdf_tracking_ids:    hdl:21.14100/23fa9dc3-4f8f-4943-b99b-58eb804c06f0\n",
       "    version_id:             v20190218</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-94663362-0986-40db-a0c3-319cdbb46553' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-94663362-0986-40db-a0c3-319cdbb46553' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li><li><span>nbnd</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ca7de2a2-fc3b-4364-9b8f-3f6b4c7012d6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ca7de2a2-fc3b-4364-9b8f-3f6b4c7012d6' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-893ef3ce-1d6a-4b90-abc6-b407b6649842' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-893ef3ce-1d6a-4b90-abc6-b407b6649842' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50892b2e-9435-4597-89a2-8d44e6f5142b' class='xr-var-data-in' type='checkbox'><label for='data-50892b2e-9435-4597-89a2-8d44e6f5142b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>title :</span></dt><dd>Latitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
       "       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
       "       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
       "       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
       "       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n",
       "       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n",
       "       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n",
       "       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n",
       "       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n",
       "       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n",
       "       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n",
       "       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n",
       "       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n",
       "       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n",
       "       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,\n",
       "        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,\n",
       "         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,\n",
       "         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,\n",
       "        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,\n",
       "        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,\n",
       "        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,\n",
       "        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,\n",
       "        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,\n",
       "        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,\n",
       "        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,\n",
       "        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,\n",
       "        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,\n",
       "        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,\n",
       "        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,\n",
       "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
       "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(lat, nbnd)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(192, 2), meta=np.ndarray&gt;</div><input id='attrs-6c426ad1-4031-4ad3-a871-efbd486b9eb6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c426ad1-4031-4ad3-a871-efbd486b9eb6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83f32be6-9c41-4c1f-b50c-30cd459db993' class='xr-var-data-in' type='checkbox'><label for='data-83f32be6-9c41-4c1f-b50c-30cd459db993' 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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       "                        <td> 1.50 kiB </td>\n",
       "                        <td> 1.50 kiB </td>\n",
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       "                        <td> (192, 2) </td>\n",
       "                        <td> (192, 2) </td>\n",
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       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "  <text x=\"45.609134\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.609134,60.000000)\">192</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-11080d79-5c27-4413-be75-7cff379c941d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-11080d79-5c27-4413-be75-7cff379c941d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33d684fa-6be4-497f-96da-2313b4232f17' class='xr-var-data-in' type='checkbox'><label for='data-33d684fa-6be4-497f-96da-2313b4232f17' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>title :</span></dt><dd>Longitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>360.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, nbnd)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(288, 2), meta=np.ndarray&gt;</div><input id='attrs-9a210cfb-7de8-4050-b025-a23fe384284f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9a210cfb-7de8-4050-b025-a23fe384284f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2dbf660-64d5-430a-bc77-490faec1b9bb' class='xr-var-data-in' type='checkbox'><label for='data-c2dbf660-64d5-430a-bc77-490faec1b9bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
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       "                        <td> </td>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 2.25 kiB </td>\n",
       "                        <td> 2.25 kiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (288, 2) </td>\n",
       "                        <td> (288, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-998446d5-f062-4be3-8214-a8139aabe51b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-998446d5-f062-4be3-8214-a8139aabe51b' 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>areacella</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(192, 288), meta=np.ndarray&gt;</div><input id='attrs-f68807f5-c0bc-4ce8-8247-b4c7455e3619' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f68807f5-c0bc-4ce8-8247-b4c7455e3619' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5aed1f02-b86b-4fec-a420-59ab65137ce6' class='xr-var-data-in' type='checkbox'><label for='data-5aed1f02-b86b-4fec-a420-59ab65137ce6' 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_methods :</span></dt><dd>area: sum</dd><dt><span>comment :</span></dt><dd>Cell areas for any grid used to report atmospheric variables and any other variable using that grid (e.g., soil moisture content). These cell areas should be defined to enable exact calculation of global integrals (e.g., of vertical fluxes of energy at the surface and top of the atmosphere).</dd><dt><span>description :</span></dt><dd>Cell areas for any grid used to report atmospheric variables and any other variable using that grid (e.g., soil moisture content). These cell areas should be defined to enable exact calculation of global integrals (e.g., of vertical fluxes of energy at the surface and top of the atmosphere).</dd><dt><span>frequency :</span></dt><dd>fx</dd><dt><span>id :</span></dt><dd>areacella</dd><dt><span>long_name :</span></dt><dd>Grid-Cell Area for Atmospheric Grid Variables</dd><dt><span>mipTable :</span></dt><dd>fx</dd><dt><span>out_name :</span></dt><dd>areacella</dd><dt><span>prov :</span></dt><dd>fx ((isd.003))</dd><dt><span>realm :</span></dt><dd>atmos land</dd><dt><span>standard_name :</span></dt><dd>cell_area</dd><dt><span>time_label :</span></dt><dd>None</dd><dt><span>time_title :</span></dt><dd>No temporal dimensions ... fixed field</dd><dt><span>title :</span></dt><dd>Grid-Cell Area for Atmospheric Grid Variables</dd><dt><span>type :</span></dt><dd>real</dd><dt><span>units :</span></dt><dd>m2</dd><dt><span>variable_id :</span></dt><dd>areacella</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 216.00 kiB </td>\n",
       "                        <td> 216.00 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (192, 288) </td>\n",
       "                        <td> (192, 288) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"130\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"80\" x2=\"120\" y2=\"80\" style=\"stroke-width:2\" />\n",
       "\n",
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       "\n",
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       "\n",
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       "  <text x=\"60.000000\" y=\"100.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >288</text>\n",
       "  <text x=\"140.000000\" y=\"40.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,40.000000)\">192</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-9fda008a-1a44-495d-8bc4-087d3658aff5' class='xr-section-summary-in' type='checkbox'  ><label for='section-9fda008a-1a44-495d-8bc4-087d3658aff5' class='xr-section-summary' >Indexes: <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-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-82b130a3-7e15-4e41-8fea-79f6dbfa3d3e' class='xr-index-data-in' type='checkbox'/><label for='index-82b130a3-7e15-4e41-8fea-79f6dbfa3d3e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([             -90.0, -89.05759162303664,  -88.1151832460733,\n",
       "       -87.17277486910994,  -86.2303664921466, -85.28795811518324,\n",
       "        -84.3455497382199, -83.40314136125654, -82.46073298429319,\n",
       "       -81.51832460732984,\n",
       "       ...\n",
       "        81.51832460732984,   82.4607329842932,  83.40314136125653,\n",
       "        84.34554973821989,  85.28795811518324,   86.2303664921466,\n",
       "        87.17277486910996,  88.11518324607329,  89.05759162303664,\n",
       "                     90.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6dca2ab0-47e8-433a-9b3a-1427f5e0ee80' class='xr-index-data-in' type='checkbox'/><label for='index-6dca2ab0-47e8-433a-9b3a-1427f5e0ee80' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   1.25,    2.5,   3.75,    5.0,   6.25,    7.5,   8.75,   10.0,\n",
       "        11.25,\n",
       "       ...\n",
       "        347.5, 348.75,  350.0, 351.25,  352.5, 353.75,  355.0, 356.25,  357.5,\n",
       "       358.75],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=288))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-84203193-863b-4223-bb2e-fb7082aab830' class='xr-section-summary-in' type='checkbox'  ><label for='section-84203193-863b-4223-bb2e-fb7082aab830' class='xr-section-summary' >Attributes: <span>(47)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>CMIP</dd><dt><span>branch_method :</span></dt><dd>no parent</dd><dt><span>branch_time_in_child :</span></dt><dd>711385.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>0.0</dd><dt><span>case_id :</span></dt><dd>38</dd><dt><span>cesm_casename :</span></dt><dd>f.e21.FHIST_BGC.f09_f09_mg17.CMIP6-AMIP.001</dd><dt><span>contact :</span></dt><dd>cesm_cmip6@ucar.edu</dd><dt><span>creation_date :</span></dt><dd>2019-01-20T15:47:37Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.29</dd><dt><span>experiment :</span></dt><dd>AMIP</dd><dt><span>experiment_id :</span></dt><dd>amip</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>fx</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2.amip.none.r1i1p1f1</dd><dt><span>grid :</span></dt><dd>native 0.9x1.25 finite volume grid (192x288 latxlon)</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>National Center for Atmospheric Research, Climate and Global Dynamics Laboratory, 1850 Table Mesa Drive, Boulder, CO 80305, USA</dd><dt><span>institution_id :</span></dt><dd>NCAR</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by &lt;The National Center for Atmospheric Research&gt; is licensed under a Creative Commons Attribution-[]ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file)[]. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>model_doi_url :</span></dt><dd>https://doi.org/10.5065/D67H1H0V</dd><dt><span>nominal_resolution :</span></dt><dd>100 km</dd><dt><span>parent_activity_id :</span></dt><dd>no parent</dd><dt><span>parent_experiment_id :</span></dt><dd>no parent</dd><dt><span>parent_mip_era :</span></dt><dd>no parent</dd><dt><span>parent_source_id :</span></dt><dd>no parent</dd><dt><span>parent_time_units :</span></dt><dd>no parent</dd><dt><span>parent_variant_label :</span></dt><dd>no parent</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>1</dd><dt><span>realm :</span></dt><dd>atmos land</dd><dt><span>source :</span></dt><dd>CESM2 (2017): atmosphere: CAM6 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); ocean: POP2 (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m); sea_ice: CICE5.1 (same grid as ocean); land: CLM5 0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); aerosol: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); atmoschem: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); landIce: CISM2.1; ocnBgchem: MARBL (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m)</dd><dt><span>source_id :</span></dt><dd>CESM2</dd><dt><span>source_type :</span></dt><dd>AGCM BGC AER</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>fx</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/23fa9dc3-4f8f-4943-b99b-58eb804c06f0</dd><dt><span>variable_id :</span></dt><dd>areacella</dd><dt><span>variant_info :</span></dt><dd>f.e21.FHIST_BGC.f09_f09_mg17.CMIP6-AMIP.001 \r\n",
       "\r\n",
       "CMIP6 CESM2 AMIP hindcast (1950-2014) with interactive land (CLM5), data ocean, prescribed sea ice, and non-evolving land ice (CISM2.1)</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>status :</span></dt><dd>2019-11-04;created;by nhn2@columbia.edu</dd><dt><span>netcdf_tracking_ids :</span></dt><dd>hdl:21.14100/23fa9dc3-4f8f-4943-b99b-58eb804c06f0</dd><dt><span>version_id :</span></dt><dd>v20190218</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (lat: 192, lon: 288, nbnd: 2)\n",
       "Coordinates:\n",
       "  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
       "    lat_bnds   (lat, nbnd) float32 dask.array<chunksize=(192, 2), meta=np.ndarray>\n",
       "  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
       "    lon_bnds   (lon, nbnd) float32 dask.array<chunksize=(288, 2), meta=np.ndarray>\n",
       "Dimensions without coordinates: nbnd\n",
       "Data variables:\n",
       "    areacella  (lat, lon) float32 dask.array<chunksize=(192, 288), meta=np.ndarray>\n",
       "Attributes: (12/47)\n",
       "    Conventions:            CF-1.7 CMIP-6.2\n",
       "    activity_id:            CMIP\n",
       "    branch_method:          no parent\n",
       "    branch_time_in_child:   711385.0\n",
       "    branch_time_in_parent:  0.0\n",
       "    case_id:                38\n",
       "    ...                     ...\n",
       "    variable_id:            areacella\n",
       "    variant_info:           f.e21.FHIST_BGC.f09_f09_mg17.CMIP6-AMIP.001 \n",
       "\\n\n",
       "\\...\n",
       "    variant_label:          r1i1p1f1\n",
       "    status:                 2019-11-04;created;by nhn2@columbia.edu\n",
       "    netcdf_tracking_ids:    hdl:21.14100/23fa9dc3-4f8f-4943-b99b-58eb804c06f0\n",
       "    version_id:             v20190218"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_area = df.query(\"variable_id == 'areacella' & source_id == 'CESM2'\")\n",
    "ds_area = xr.open_zarr(fsspec.get_mapper(df_area.zstore.values[0]), consolidated=True)\n",
    "ds_area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-20T21:33:23.623570Z",
     "iopub.status.busy": "2023-11-20T21:33:23.623357Z",
     "iopub.status.idle": "2023-11-20T21:33:23.662096Z",
     "shell.execute_reply": "2023-11-20T21:33:23.661514Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  display: inline-block;\n",
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       "\n",
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       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
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       "  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",
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       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (time: 1980)&gt;\n",
       "dask.array&lt;truediv, shape=(1980,), dtype=float32, chunksize=(990,), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * time     (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1980</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-552a09a3-39c5-444a-a338-c9d192478458' class='xr-array-in' type='checkbox' checked><label for='section-552a09a3-39c5-444a-a338-c9d192478458' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(990,), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 7.73 kiB </td>\n",
       "                        <td> 3.87 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1980,) </td>\n",
       "                        <td> (990,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 2 chunks in 21 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1980</text>\n",
       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-2f93f61b-2d95-4af3-a3e5-52224f0b401c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2f93f61b-2d95-4af3-a3e5-52224f0b401c' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1850-01-15 12:00:00 ... 2014-12-...</div><input id='attrs-089d5e36-c7b9-4de0-9cf3-beeb88719c66' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-089d5e36-c7b9-4de0-9cf3-beeb88719c66' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13fe6aaa-2cd2-4a89-9b28-7431c6890138' class='xr-var-data-in' type='checkbox'><label for='data-13fe6aaa-2cd2-4a89-9b28-7431c6890138' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>512</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>title :</span></dt><dd>time</dd><dt><span>type :</span></dt><dd>double</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 2, 14, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 3, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       ...,\n",
       "       cftime.DatetimeNoLeap(2014, 10, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 11, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 15, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-170aa67b-a0ab-4181-bd08-98195bc69b48' class='xr-section-summary-in' type='checkbox'  ><label for='section-170aa67b-a0ab-4181-bd08-98195bc69b48' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-502d3873-b49e-4edc-9ca0-13a0f09c523e' class='xr-index-data-in' type='checkbox'/><label for='index-502d3873-b49e-4edc-9ca0-13a0f09c523e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([1850-01-15 12:00:00, 1850-02-14 00:00:00, 1850-03-15 12:00:00,\n",
       "             1850-04-15 00:00:00, 1850-05-15 12:00:00, 1850-06-15 00:00:00,\n",
       "             1850-07-15 12:00:00, 1850-08-15 12:00:00, 1850-09-15 00:00:00,\n",
       "             1850-10-15 12:00:00,\n",
       "             ...\n",
       "             2014-03-15 12:00:00, 2014-04-15 00:00:00, 2014-05-15 12:00:00,\n",
       "             2014-06-15 00:00:00, 2014-07-15 12:00:00, 2014-08-15 12:00:00,\n",
       "             2014-09-15 00:00:00, 2014-10-15 12:00:00, 2014-11-15 00:00:00,\n",
       "             2014-12-15 12:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=1980, calendar=&#x27;noleap&#x27;, freq=&#x27;None&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-149de345-e07c-4a7b-b648-29a837420317' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-149de345-e07c-4a7b-b648-29a837420317' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (time: 1980)>\n",
       "dask.array<truediv, shape=(1980,), dtype=float32, chunksize=(990,), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time     (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_area = ds_area.areacella.sum(dim=['lon', 'lat'])\n",
    "ta_timeseries = (ds.tas * ds_area.areacella).sum(dim=['lon', 'lat']) / total_area\n",
    "ta_timeseries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default the data are loaded lazily, as Dask arrays. Here we trigger computation explicitly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-20T21:33:23.664431Z",
     "iopub.status.busy": "2023-11-20T21:33:23.664242Z",
     "iopub.status.idle": "2023-11-20T21:33:25.220759Z",
     "shell.execute_reply": "2023-11-20T21:33:25.220190Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 369 ms, sys: 187 ms, total: 556 ms\n",
      "Wall time: 1.54 s\n"
     ]
    },
    {
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
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       "\n",
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (time: 1980)&gt;\n",
       "array([0.00407822, 0.00405088, 0.0039282 , ..., 0.00411686, 0.00414686,\n",
       "       0.00418852], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1980</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-dd8673b9-a96e-413a-99ac-20f55afe6900' class='xr-array-in' type='checkbox' checked><label for='section-dd8673b9-a96e-413a-99ac-20f55afe6900' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.004078 0.004051 0.003928 0.003958 ... 0.004117 0.004147 0.004189</span></div><div class='xr-array-data'><pre>array([0.00407822, 0.00405088, 0.0039282 , ..., 0.00411686, 0.00414686,\n",
       "       0.00418852], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-79a9abf5-fc17-4a7e-afbf-65ae5fc3986a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-79a9abf5-fc17-4a7e-afbf-65ae5fc3986a' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1850-01-15 12:00:00 ... 2014-12-...</div><input id='attrs-158087f5-b2d4-43a7-8abe-5bfdce748dd3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-158087f5-b2d4-43a7-8abe-5bfdce748dd3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5bbf13f3-6c27-48f2-9a3d-9c72804c750a' class='xr-var-data-in' type='checkbox'><label for='data-5bbf13f3-6c27-48f2-9a3d-9c72804c750a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>512</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>title :</span></dt><dd>time</dd><dt><span>type :</span></dt><dd>double</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 2, 14, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 3, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       ...,\n",
       "       cftime.DatetimeNoLeap(2014, 10, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 11, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 15, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5ec9ee92-e107-43cb-b80b-7bce0d1f1c5c' class='xr-section-summary-in' type='checkbox'  ><label for='section-5ec9ee92-e107-43cb-b80b-7bce0d1f1c5c' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5b45a823-90e8-47c7-a253-f07ef3bba870' class='xr-index-data-in' type='checkbox'/><label for='index-5b45a823-90e8-47c7-a253-f07ef3bba870' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([1850-01-15 12:00:00, 1850-02-14 00:00:00, 1850-03-15 12:00:00,\n",
       "             1850-04-15 00:00:00, 1850-05-15 12:00:00, 1850-06-15 00:00:00,\n",
       "             1850-07-15 12:00:00, 1850-08-15 12:00:00, 1850-09-15 00:00:00,\n",
       "             1850-10-15 12:00:00,\n",
       "             ...\n",
       "             2014-03-15 12:00:00, 2014-04-15 00:00:00, 2014-05-15 12:00:00,\n",
       "             2014-06-15 00:00:00, 2014-07-15 12:00:00, 2014-08-15 12:00:00,\n",
       "             2014-09-15 00:00:00, 2014-10-15 12:00:00, 2014-11-15 00:00:00,\n",
       "             2014-12-15 12:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=1980, calendar=&#x27;noleap&#x27;, freq=&#x27;None&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7db831cf-aa43-4b36-9032-364b3cc5eb97' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7db831cf-aa43-4b36-9032-364b3cc5eb97' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
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       "array([0.00407822, 0.00405088, 0.0039282 , ..., 0.00411686, 0.00414686,\n",
       "       0.00418852], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time ta_timeseries.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-20T21:33:25.222929Z",
     "iopub.status.busy": "2023-11-20T21:33:25.222731Z",
     "iopub.status.idle": "2023-11-20T21:33:25.499005Z",
     "shell.execute_reply": "2023-11-20T21:33:25.498275Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Global Mean Surface Air Temperature')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ta_timeseries.plot(label='monthly')\n",
    "ta_timeseries.rolling(time=12).mean().plot(label='12 month rolling mean')\n",
    "plt.legend()\n",
    "plt.title('Global Mean Surface Air Temperature')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "In this notebook, we opened a CESM2 dataset with `fsspec` and `zarr`. We also used a cell area dataset to calculate and plot global average surface air temperature. \n",
    "\n",
    "### What's next?\n",
    "We will open a dataset with ESGF and OPenDAP."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Resources and references\n",
    "- [Original notebook in the Pangeo Gallery](http://gallery.pangeo.io/repos/pangeo-gallery/cmip6/basic_search_and_load.html) by Henri Drake and [Ryan Abernathey](https://ocean-transport.github.io/)"
   ]
  }
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