{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using masks and computing weighted average"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example is based from xarray example http://xarray.pydata.org/en/stable/examples/area_weighted_temperature.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import python packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "xr.set_options(display_style='html')\n",
    "import intake\n",
    "import cftime\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<p><strong>pangeo-cmip6 catalog with 7632 dataset(s) from 517667 asset(s)</strong>:</p> <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>unique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>activity_id</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>institution_id</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source_id</th>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>experiment_id</th>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member_id</th>\n",
       "      <td>657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>table_id</th>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variable_id</th>\n",
       "      <td>709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grid_label</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zstore</th>\n",
       "      <td>517667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dcpp_init_year</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>version</th>\n",
       "      <td>715</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cat_url = \"https://storage.googleapis.com/cmip6/pangeo-cmip6.json\"\n",
    "col = intake.open_esm_datastore(cat_url)\n",
    "col"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Search data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>activity_id</th>\n",
       "      <th>institution_id</th>\n",
       "      <th>source_id</th>\n",
       "      <th>experiment_id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>table_id</th>\n",
       "      <th>variable_id</th>\n",
       "      <th>grid_label</th>\n",
       "      <th>zstore</th>\n",
       "      <th>dcpp_init_year</th>\n",
       "      <th>version</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NCC</td>\n",
       "      <td>NorESM2-LM</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/NCC/NorESM2-LM/historica...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20190815</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  activity_id institution_id   source_id experiment_id member_id table_id  \\\n",
       "0        CMIP            NCC  NorESM2-LM    historical  r1i1p1f1     Amon   \n",
       "\n",
       "  variable_id grid_label                                             zstore  \\\n",
       "0         tas         gn  gs://cmip6/CMIP6/CMIP/NCC/NorESM2-LM/historica...   \n",
       "\n",
       "   dcpp_init_year   version  \n",
       "0             NaN  20190815  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat = col.search(source_id=['NorESM2-LM'], experiment_id=['historical'], table_id=['Amon'], variable_id=['tas'], member_id=['r1i1p1f1'])\n",
    "cat.df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create dictionary from the list of datasets we found\n",
    "- This step may take several minutes so be patient!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
      "\t'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
       "            }\n",
       "        </style>\n",
       "      <progress value='1' class='' max='1' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [1/1 00:00<00:00]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dset_dict = cat.to_dataset_dict(zarr_kwargs={'use_cftime':True})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CMIP.NCC.NorESM2-LM.historical.Amon.gn']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(dset_dict.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (bnds: 2, lat: 96, lon: 144, member_id: 1, time: 1980)\n",
       "Coordinates:\n",
       "    height     float64 ...\n",
       "  * lat        (lat) float64 -90.0 -88.11 -86.21 -84.32 ... 86.21 88.11 90.0\n",
       "    lat_bnds   (lat, bnds) 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, bnds) float64 dask.array&lt;chunksize=(144, 2), meta=np.ndarray&gt;\n",
       "  * time       (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "    time_bnds  (time, bnds) object dask.array&lt;chunksize=(1980, 2), meta=np.ndarray&gt;\n",
       "  * member_id  (member_id) &lt;U8 &#x27;r1i1p1f1&#x27;\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    tas        (member_id, time, lat, lon) float32 dask.array&lt;chunksize=(1, 990, 96, 144), meta=np.ndarray&gt;\n",
       "Attributes: (12/52)\n",
       "    Conventions:               CF-1.7 CMIP-6.2\n",
       "    activity_id:               CMIP\n",
       "    branch_method:             Hybrid-restart from year 1600-01-01 of piControl\n",
       "    branch_time:               0.0\n",
       "    branch_time_in_child:      0.0\n",
       "    branch_time_in_parent:     430335.0\n",
       "    ...                        ...\n",
       "    title:                     NorESM2-LM output prepared for CMIP6\n",
       "    tracking_id:               hdl:21.14100/2486cf87-033c-4848-ab3e-e828c3b7c...\n",
       "    variable_id:               tas\n",
       "    variant_label:             r1i1p1f1\n",
       "    intake_esm_varname:        [&#x27;tas&#x27;]\n",
       "    intake_esm_dataset_key:    CMIP.NCC.NorESM2-LM.historical.Amon.gn</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-7220d76c-4ac9-4b94-a27a-9dff192bd8c8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7220d76c-4ac9-4b94-a27a-9dff192bd8c8' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lat</span>: 96</li><li><span class='xr-has-index'>lon</span>: 144</li><li><span class='xr-has-index'>member_id</span>: 1</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-5c4909da-3573-4b39-a725-010f02ea4a8a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5c4909da-3573-4b39-a725-010f02ea4a8a' class='xr-section-summary' >Coordinates: <span>(8)</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>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a0fc7ac3-45c3-4d45-bd19-0827f2f0ef38' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a0fc7ac3-45c3-4d45-bd19-0827f2f0ef38' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbd72ee7-cf12-40de-bba7-03b2dbbe15f3' class='xr-var-data-in' type='checkbox'><label for='data-dbd72ee7-cf12-40de-bba7-03b2dbbe15f3' 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>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array(2.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -88.11 -86.21 ... 88.11 90.0</div><input id='attrs-8653a11f-9019-45e5-92b8-605c670fbc43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8653a11f-9019-45e5-92b8-605c670fbc43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-466ef707-ab2e-480a-9a80-2bb3629ba2d6' class='xr-var-data-in' type='checkbox'><label for='data-466ef707-ab2e-480a-9a80-2bb3629ba2d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</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, bnds)</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-34694861-e56e-452f-ac78-109d23886068' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-34694861-e56e-452f-ac78-109d23886068' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcc1d60e-ec4d-4b06-bdd5-ec7bbb87e1a3' class='xr-var-data-in' type='checkbox'><label for='data-bcc1d60e-ec4d-4b06-bdd5-ec7bbb87e1a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 1.54 kB </td> <td> 1.54 kB </td></tr>\n",
       "    <tr><th> Shape </th><td> (96, 2) </td> <td> (96, 2) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"79\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"29\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"29\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"29\" y1=\"0\" x2=\"29\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 29.2612335534925,0.0 29.2612335534925,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"14.630617\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
       "  <text x=\"49.261234\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,49.261234,60.000000)\">96</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 2.5 5.0 ... 352.5 355.0 357.5</div><input id='attrs-01c722a1-7c85-46f1-b970-5129d3aa3bf2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01c722a1-7c85-46f1-b970-5129d3aa3bf2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4bf4bff7-edef-4b11-ac1a-75054e438d04' class='xr-var-data-in' type='checkbox'><label for='data-4bf4bff7-edef-4b11-ac1a-75054e438d04' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</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, bnds)</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-88f1f71b-a45d-4efa-983f-66a6bffc283d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-88f1f71b-a45d-4efa-983f-66a6bffc283d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52404616-6ff1-496c-9324-26e9fa27f34f' class='xr-var-data-in' type='checkbox'><label for='data-52404616-6ff1-496c-9324-26e9fa27f34f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 2.30 kB </td> <td> 2.30 kB </td></tr>\n",
       "    <tr><th> Shape </th><td> (144, 2) </td> <td> (144, 2) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"77\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"27\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"27\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"27\" y1=\"0\" x2=\"27\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 27.054035963169408,0.0 27.054035963169408,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"13.527018\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
       "  <text x=\"47.054036\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,47.054036,60.000000)\">144</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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1850-01-16 12:00:00 ... 2014-12-...</div><input id='attrs-7ec21bbf-380b-47a9-a554-895bb03a68f0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7ec21bbf-380b-47a9-a554-895bb03a68f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b33e9c9-20b1-415f-9474-c333f9d595f7' class='xr-var-data-in' type='checkbox'><label for='data-4b33e9c9-20b1-415f-9474-c333f9d595f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1850, 2, 15, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1850, 3, 16, 12, 0, 0, 0), ...,\n",
       "       cftime.DatetimeNoLeap(2014, 10, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2014, 11, 16, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 16, 12, 0, 0, 0)], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1980, 2), meta=np.ndarray&gt;</div><input id='attrs-435a9d8c-a6ed-46e7-871a-3e3b9d2d71ab' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-435a9d8c-a6ed-46e7-871a-3e3b9d2d71ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-548caa46-14c2-4611-aee2-b013e6c8073b' class='xr-var-data-in' type='checkbox'><label for='data-548caa46-14c2-4611-aee2-b013e6c8073b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 31.68 kB </td> <td> 31.68 kB </td></tr>\n",
       "    <tr><th> Shape </th><td> (1980, 2) </td> <td> (1980, 2) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> object </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
       "  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">1980</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'>member_id</span></div><div class='xr-var-dims'>(member_id)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;r1i1p1f1&#x27;</div><input id='attrs-dde602e1-0b11-426f-a595-9729405b9d45' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dde602e1-0b11-426f-a595-9729405b9d45' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c97846ae-6a22-4c7b-b7bd-21abb5aa0e34' class='xr-var-data-in' type='checkbox'><label for='data-c97846ae-6a22-4c7b-b7bd-21abb5aa0e34' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;r1i1p1f1&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9d5b162c-f1c1-41e1-8987-89b59386b482' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9d5b162c-f1c1-41e1-8987-89b59386b482' 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'>(member_id, time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 990, 96, 144), meta=np.ndarray&gt;</div><input id='attrs-aa3d9763-5bf1-48e0-9e13-a02a086b11ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa3d9763-5bf1-48e0-9e13-a02a086b11ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83621c0e-1869-4476-84d5-91554606c33e' class='xr-var-data-in' type='checkbox'><label for='data-83621c0e-1869-4476-84d5-91554606c33e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>comment :</span></dt><dd>near-surface (usually, 2 meter) air temperature</dd><dt><span>history :</span></dt><dd>2019-08-15T12:42:20Z altered by CMOR: Treated scalar dimension: &#x27;height&#x27;. 2019-08-15T12:42:21Z altered by CMOR: Converted type from &#x27;d&#x27; to &#x27;f&#x27;.</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>original_name :</span></dt><dd>TREFHT</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 109.49 MB </td> <td> 54.74 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (1, 1980, 96, 144) </td> <td> (1, 990, 96, 144) </td></tr>\n",
       "    <tr><th> Count </th><td> 5 Tasks </td><td> 2 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"347\" height=\"154\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"25\" x2=\"25\" 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=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"12.706308\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
       "  <text x=\"45.412617\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,45.412617,12.706308)\">1</text>\n",
       "\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"95\" y1=\"0\" x2=\"165\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"95\" y1=\"34\" x2=\"165\" y2=\"104\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"95\" y1=\"0\" x2=\"95\" y2=\"34\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"130\" y1=\"35\" x2=\"130\" y2=\"69\" />\n",
       "  <line x1=\"165\" y1=\"70\" x2=\"165\" y2=\"104\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"95.0,0.0 165.58823529411765,70.58823529411765 165.58823529411765,104.9007638365262 95.0,34.31252854240854\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"95\" y1=\"0\" x2=\"131\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"130\" y1=\"35\" x2=\"167\" y2=\"35\" />\n",
       "  <line x1=\"165\" y1=\"70\" x2=\"202\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"95\" y1=\"0\" x2=\"165\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"131\" y1=\"0\" x2=\"202\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"95.0,0.0 131.7664046331794,0.0 202.35463992729706,70.58823529411765 165.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"165\" y1=\"70\" x2=\"202\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"165\" y1=\"104\" x2=\"202\" y2=\"104\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"165\" y1=\"70\" x2=\"165\" y2=\"104\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"202\" y1=\"70\" x2=\"202\" y2=\"104\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"165.58823529411765,70.58823529411765 202.35463992729706,70.58823529411765 202.35463992729706,104.9007638365262 165.58823529411765,104.9007638365262\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"183.971438\" y=\"124.900764\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >144</text>\n",
       "  <text x=\"222.354640\" y=\"87.744500\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,222.354640,87.744500)\">96</text>\n",
       "  <text x=\"120.294118\" y=\"89.606646\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,120.294118,89.606646)\">1980</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-88a1f1e2-7b69-4291-8abf-e750e289b7f3' class='xr-section-summary-in' type='checkbox'  ><label for='section-88a1f1e2-7b69-4291-8abf-e750e289b7f3' class='xr-section-summary' >Attributes: <span>(52)</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>Hybrid-restart from year 1600-01-01 of piControl</dd><dt><span>branch_time :</span></dt><dd>0.0</dd><dt><span>branch_time_in_child :</span></dt><dd>0.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>430335.0</dd><dt><span>cmor_version :</span></dt><dd>3.5.0</dd><dt><span>contact :</span></dt><dd>Please send any requests or bug reports to noresm-ncc@met.no.</dd><dt><span>creation_date :</span></dt><dd>2019-08-15T12:42:21Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.31</dd><dt><span>experiment :</span></dt><dd>all-forcing simulation of the recent past</dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.NCC.NorESM2-LM.historical.none.r1i1p1f1</dd><dt><span>grid :</span></dt><dd>finite-volume grid with 1.9x2.5 degree lat/lon resolution</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>history :</span></dt><dd>2019-08-15T12:42:21Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>NorESM Climate modeling Consortium consisting of CICERO (Center for International Climate and Environmental Research, Oslo 0349), MET-Norway (Norwegian Meteorological Institute, Oslo 0313), NERSC (Nansen Environmental and Remote Sensing Center, Bergen 5006), NILU (Norwegian Institute for Air Research, Kjeller 2027), UiB (University of Bergen, Bergen 5007), UiO (University of Oslo, Oslo 0313) and UNI (Uni Research, Bergen 5008), Norway. Mailing address: NCC, c/o MET-Norway, Henrik Mohns plass 1, Oslo 0313, Norway</dd><dt><span>institution_id :</span></dt><dd>NCC</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by NCC is licensed under a Creative Commons Attribution ShareAlike 4.0 International License (https://creativecommons.org/licenses). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file) and at https:///pcmdi.llnl.gov/. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>model_id :</span></dt><dd>NorESM2-LM</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>NorESM2-LM</dd><dt><span>parent_sub_experiment_id :</span></dt><dd>none</dd><dt><span>parent_time_units :</span></dt><dd>days since 0421-01-01</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>1</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>run_variant :</span></dt><dd>N/A</dd><dt><span>source :</span></dt><dd>NorESM2-LM (2017): \n",
       "aerosol: OsloAero\n",
       "atmos: CAM-OSLO (2 degree resolution; 144 x 96; 32 levels; top level 3 mb)\n",
       "atmosChem: OsloChemSimp\n",
       "land: CLM\n",
       "landIce: CISM\n",
       "ocean: MICOM (1 degree resolution; 360 x 384; 70 levels; top grid cell minimum 0-2.5 m [native model uses hybrid density and generic upper-layer coordinate interpolated to z-level for contributed data])\n",
       "ocnBgchem: HAMOCC\n",
       "seaIce: CICE</dd><dt><span>source_id :</span></dt><dd>NorESM2-LM</dd><dt><span>source_type :</span></dt><dd>AOGCM</dd><dt><span>status :</span></dt><dd>2020-04-30;created; by gcs.cmip6.ldeo@gmail.com</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>Amon</dd><dt><span>table_info :</span></dt><dd>Creation Date:(24 July 2019) MD5:08e314340b9dd440c36c1b8e484514f4</dd><dt><span>title :</span></dt><dd>NorESM2-LM output prepared for CMIP6</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/2486cf87-033c-4848-ab3e-e828c3b7c759\n",
       "hdl:21.14100/8d2ec216-30d3-4f90-8664-7cf75e29780b\n",
       "hdl:21.14100/9f798305-d0cf-4eac-89b0-b13dc25574ca\n",
       "hdl:21.14100/e0da9277-4784-446b-b3b5-ba33bb18cc3a\n",
       "hdl:21.14100/ca48487c-955a-4b15-9582-5ce55ce39c73\n",
       "hdl:21.14100/fc7602aa-fd09-49d8-94c6-e619622c211b\n",
       "hdl:21.14100/cd51a9c2-0a94-45b2-8d9b-59bcb3467214\n",
       "hdl:21.14100/9b7ad334-1217-4229-98e8-b5a9287c0e5e\n",
       "hdl:21.14100/043ca527-40c7-451f-990b-ccc94720f612\n",
       "hdl:21.14100/a9613498-ea0d-4be4-97e8-77661872798d\n",
       "hdl:21.14100/52699832-d86c-491b-a0a4-2f27d9ee4d69\n",
       "hdl:21.14100/358c4a18-33b2-4c44-bd30-f6125a4df63c\n",
       "hdl:21.14100/77254eaf-90ca-44e7-9a3c-763a840e188f\n",
       "hdl:21.14100/62aed49c-6ca5-4027-80fe-4ddd99af575a\n",
       "hdl:21.14100/83ffe172-769b-42b1-b979-b92d768f60fd\n",
       "hdl:21.14100/91a8c4e2-d33b-4b87-a978-0247d8112ef3\n",
       "hdl:21.14100/e8fb384f-d5a7-41e1-bf12-b8de3e2ffb80</dd><dt><span>variable_id :</span></dt><dd>tas</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>intake_esm_varname :</span></dt><dd>[&#x27;tas&#x27;]</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>CMIP.NCC.NorESM2-LM.historical.Amon.gn</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (bnds: 2, lat: 96, lon: 144, member_id: 1, time: 1980)\n",
       "Coordinates:\n",
       "    height     float64 ...\n",
       "  * lat        (lat) float64 -90.0 -88.11 -86.21 -84.32 ... 86.21 88.11 90.0\n",
       "    lat_bnds   (lat, bnds) 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, bnds) float64 dask.array<chunksize=(144, 2), meta=np.ndarray>\n",
       "  * time       (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "    time_bnds  (time, bnds) object dask.array<chunksize=(1980, 2), meta=np.ndarray>\n",
       "  * member_id  (member_id) <U8 'r1i1p1f1'\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    tas        (member_id, time, lat, lon) float32 dask.array<chunksize=(1, 990, 96, 144), meta=np.ndarray>\n",
       "Attributes: (12/52)\n",
       "    Conventions:               CF-1.7 CMIP-6.2\n",
       "    activity_id:               CMIP\n",
       "    branch_method:             Hybrid-restart from year 1600-01-01 of piControl\n",
       "    branch_time:               0.0\n",
       "    branch_time_in_child:      0.0\n",
       "    branch_time_in_parent:     430335.0\n",
       "    ...                        ...\n",
       "    title:                     NorESM2-LM output prepared for CMIP6\n",
       "    tracking_id:               hdl:21.14100/2486cf87-033c-4848-ab3e-e828c3b7c...\n",
       "    variable_id:               tas\n",
       "    variant_label:             r1i1p1f1\n",
       "    intake_esm_varname:        ['tas']\n",
       "    intake_esm_dataset_key:    CMIP.NCC.NorESM2-LM.historical.Amon.gn"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dset = dset_dict[list(dset_dict.keys())[0]]\n",
    "dset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the first timestep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7f91101352b0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "projection = ccrs.Mercator(central_longitude=-10)\n",
    "\n",
    "f, ax = plt.subplots(subplot_kw=dict(projection=projection))\n",
    "\n",
    "dset['tas'].isel(time=0).plot(transform=ccrs.PlateCarree(), cbar_kwargs=dict(shrink=0.7), cmap='coolwarm')\n",
    "ax.coastlines()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute weighted mean\n",
    "\n",
    "1. Creating weights: for a rectangular grid the cosine of the latitude is proportional to the grid cell area.\n",
    "2. Compute weighted mean values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def computeWeightedMean(ds):\n",
    "    # Compute weights based on the xarray you pass\n",
    "    weights = np.cos(np.deg2rad(ds.lat))\n",
    "    weights.name = \"weights\"\n",
    "    # Compute weighted mean\n",
    "    air_weighted = ds.weighted(weights)\n",
    "    weighted_mean = air_weighted.mean((\"lon\", \"lat\"))\n",
    "    return weighted_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute weighted average over the entire globe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "weighted_mean = computeWeightedMean(dset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison with unweighted mean\n",
    "- We select a time range\n",
    "- Note how the weighted mean temperature is higher than the unweighted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f91113efd60>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weighted_mean['tas'].sel(time=slice('2000-01-01', '2010-01-01')).plot(label=\"weighted\")\n",
    "dset['tas'].sel(time=slice('2000-01-01', '2010-01-01')).mean((\"lon\", \"lat\")).plot(label=\"unweighted\")\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute Weigted arctic average\n",
    "Let's try to also take only the data above 60$^\\circ$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "weighted_mean = computeWeightedMean(dset.where(dset['lat']>60.))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f9110c6a040>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weighted_mean['tas'].sel(time=slice('2000-01-01', '2010-01-01')).plot(label=\"weighted\")\n",
    "dset['tas'].where(dset['lat']>60.).sel(time=slice('2000-01-01', '2010-01-01')).mean((\"lon\", \"lat\")).plot(label=\"unweighted\")\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "jupytext": {
   "formats": "ipynb,md"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}