xarray_07_statistical_resample_roll_climatology_CMIP6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Statistical Operations, Resampling and Climatologies Using Xarray\n",
    "\n",
    "In this notebook:\n",
    "\n",
    "* Setup and Load Data \n",
    "* Common Operations \n",
    "* Resampling and Rolling Mean\n",
    "* Groupby and Climatology\n",
    "* Plot - Extras\n",
    "\n",
    "This example uses Coupled Model Intercomparison Project (CMIP6) collections. For more information, please visit [data catalogue]( https://geonetwork.nci.org.au/geonetwork/srv/eng/catalog.search#/metadata/f6600_2266_8675_3563) and [terms of use]( https://pcmdi.llnl.gov/CMIP6/TermsOfUse/TermsOfUse6-1.html).\n",
    "\n",
    "---------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Authors: NCI Virtual Research Environment Team\n",
    "- Keywords: CMIP6, xarray, resample, rolling mean, groupby, climatology, cartopy\n",
    "- Create Date: 2020-Apr\n",
    "- Lineage/Reference: This example was modified from the original example developed by Dr Aidan Heerdegen from ARC COE Clex group\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load the required modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import netCDF4 as nc\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Opening Multiple Files at Once\n",
    "\n",
    "xarray's `open_mfdataset` allows multiple files to be opened simultaneously and concatenated as one file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tas_Amon_GFDL-CM4_historical_r1i1p1f1_gr1_185001-194912.nc\n",
      "tas_Amon_GFDL-CM4_historical_r1i1p1f1_gr1_195001-201412.nc\n"
     ]
    }
   ],
   "source": [
    "!ls /g/data/oi10/replicas/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/historical/r1i1p1f1/Amon/tas/gr1/v20180701\n",
    "path = '/g/data/oi10/replicas/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/historical/r1i1p1f1/Amon/tas/gr1/v20180701/*'\n",
    "f_hist = xr.open_mfdataset(path, combine='by_coords')\n",
    "tas = f_hist.tas - 273.15"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Common Operations\n",
    "\n",
    "Xarray supports many of the statistical operations commonly performed on climate data arrays. Examples include:\n",
    "\n",
    "    Mean\n",
    "    Standard Deviation\n",
    "    Minimum\n",
    "    Maximum\n",
    "\n",
    "The operations are done with numpy-like syntax that many users are familiar with: `.mean()`, `.stddef()`, etc. For example, calculating a simple mean would be done via:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
       "<title>Show/Hide data repr</title>\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",
       "<title>Show/Hide attributes</title>\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",
       ".xr-wrap {\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\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, 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><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tas'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-fd2660ff-d3a4-4865-bc0f-24b7298572f4' class='xr-array-in' type='checkbox' ><label for='section-fd2660ff-d3a4-4865-bc0f-24b7298572f4' 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=(), meta=np.ndarray&gt;</span></div><pre class='xr-array-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 4 B </td> <td> 4 B </td></tr>\n",
       "    <tr><th> Shape </th><td> () </td> <td> () </td></tr>\n",
       "    <tr><th> Count </th><td> 11 Tasks </td><td> 1 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",
       "\n",
       "</td>\n",
       "</tr>\n",
       "</table></pre></div></li><li class='xr-section-item'><input id='section-d1df7078-fbd0-4b37-be43-9e89f1bef422' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d1df7078-fbd0-4b37-be43-9e89f1bef422' 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>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-799df20e-2ca2-4a0e-9cfd-8ec7628ea6e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-799df20e-2ca2-4a0e-9cfd-8ec7628ea6e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc9eb18d-801d-4e16-8f2b-cbd9e95f39db' class='xr-var-data-in' type='checkbox'><label for='data-bc9eb18d-801d-4e16-8f2b-cbd9e95f39db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>description :</span></dt><dd>~2 m standard surface air temperature and surface humidity  height</dd></dl></div><pre class='xr-var-data'>array(2.)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-25d1cde8-d75e-4d78-9288-ecb3bb821971' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-25d1cde8-d75e-4d78-9288-ecb3bb821971' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'tas' ()>\n",
       "dask.array<mean_agg-aggregate, shape=(), dtype=float32, chunksize=(), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "    height   float64 2.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tas.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above mean( ) happened very quickly because of lazy loading, which we talked about before. The resultant array still remains an xarray DataArray."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Xarray Operations and Missing Values\n",
    "\n",
    "In general when using numpy, a user would need to mask out the missing or nan values of an array. As long as the nan or missing values are defined in the data array, the operations in general will be performed excluding those values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
       "<title>Show/Hide data repr</title>\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",
       "<title>Show/Hide attributes</title>\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",
       ".xr-wrap {\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\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, 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><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-860d08fa-eb94-4800-b42f-4b1f9ba0dc9a' class='xr-array-in' type='checkbox' ><label for='section-860d08fa-eb94-4800-b42f-4b1f9ba0dc9a' 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>2.0</span></div><pre class='xr-array-data'>array(2.)</pre></div></li><li class='xr-section-item'><input id='section-6000c094-4d75-4b09-bfc0-f7a1da946b3e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6000c094-4d75-4b09-bfc0-f7a1da946b3e' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-eee2a1e5-62bf-4d85-b22a-757a926175f6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-eee2a1e5-62bf-4d85-b22a-757a926175f6' 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 ()>\n",
       "array(2.)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "nan_test=xr.DataArray([1,3,np.nan])\n",
    "nan_test.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Xarray Operations on Subsets\n",
    "\n",
    "As with data subsetting, one of the advantages of xarray is that it permits operations to be performed via a specified list of dimension labels.\n",
    "\n",
    "For example, to calculate the mean in time would be:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
       "<title>Show/Hide data repr</title>\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",
       "<title>Show/Hide attributes</title>\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",
       ".xr-wrap {\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\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, 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><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tas'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 180</li><li><span class='xr-has-index'>lon</span>: 288</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-4694db57-b01e-4d7f-82a5-cb50d1e52b14' class='xr-array-in' type='checkbox' ><label for='section-4694db57-b01e-4d7f-82a5-cb50d1e52b14' 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=(180, 288), meta=np.ndarray&gt;</span></div><pre class='xr-array-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> 207.36 kB </td> <td> 207.36 kB </td></tr>\n",
       "    <tr><th> Shape </th><td> (180, 288) </td> <td> (180, 288) </td></tr>\n",
       "    <tr><th> Count </th><td> 11 Tasks </td><td> 1 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=\"170\" height=\"125\" 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=\"75\" x2=\"120\" y2=\"75\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"75\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"75\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.000000,0.000000 120.000000,0.000000 120.000000,75.000000 0.000000,75.000000\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"95.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >288</text>\n",
       "  <text x=\"140.000000\" y=\"37.500000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,37.500000)\">180</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></pre></div></li><li class='xr-section-item'><input id='section-b1dc244a-a577-48b9-bc12-a7f64f95b70a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b1dc244a-a577-48b9-bc12-a7f64f95b70a' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-d6295952-ff23-47b5-b913-d983c9c28b68' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6295952-ff23-47b5-b913-d983c9c28b68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82af0ecf-847f-46cf-9999-58443db9fd91' class='xr-var-data-in' type='checkbox'><label for='data-82af0ecf-847f-46cf-9999-58443db9fd91' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>description :</span></dt><dd>~2 m standard surface air temperature and surface humidity  height</dd></dl></div><pre class='xr-var-data'>array(2.)</pre></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'>0.625 1.875 3.125 ... 358.1 359.4</div><input id='attrs-de317f81-c4ae-4b93-baef-477c70b662e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de317f81-c4ae-4b93-baef-477c70b662e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3f4d2c4-eb44-479a-8783-e41f58ad7b18' class='xr-var-data-in' type='checkbox'><label for='data-d3f4d2c4-eb44-479a-8783-e41f58ad7b18' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</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>cell_methods :</span></dt><dd>time: point</dd></dl></div><pre class='xr-var-data'>array([  0.625,   1.875,   3.125, ..., 356.875, 358.125, 359.375])</pre></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'>-89.5 -88.5 -87.5 ... 88.5 89.5</div><input id='attrs-56b80e59-d041-4997-a260-156a2428226d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-56b80e59-d041-4997-a260-156a2428226d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00b0e9ad-bc96-46fe-8706-4b6ff579a1d1' class='xr-var-data-in' type='checkbox'><label for='data-00b0e9ad-bc96-46fe-8706-4b6ff579a1d1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</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>cell_methods :</span></dt><dd>time: point</dd></dl></div><pre class='xr-var-data'>array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
       "       -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,\n",
       "       -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,\n",
       "       -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,\n",
       "       -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,\n",
       "       -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,\n",
       "       -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,\n",
       "       -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,\n",
       "        -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,\n",
       "         0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
       "        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,\n",
       "        20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,\n",
       "        30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,\n",
       "        40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,\n",
       "        50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,\n",
       "        60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,\n",
       "        70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,\n",
       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-34b48a6a-c3ae-4eee-b18e-d77174069777' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-34b48a6a-c3ae-4eee-b18e-d77174069777' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'tas' (lat: 180, lon: 288)>\n",
       "dask.array<mean_agg-aggregate, shape=(180, 288), dtype=float32, chunksize=(180, 288), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "    height   float64 2.0\n",
       "  * lon      (lon) float64 0.625 1.875 3.125 4.375 ... 355.6 356.9 358.1 359.4\n",
       "  * lat      (lat) float64 -89.5 -88.5 -87.5 -86.5 -85.5 ... 86.5 87.5 88.5 89.5"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tas.mean(dim = 'time')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To calculate the mean over lat and lon you will need to take into account the size of the grid cells and weight the mean by the area of those grid cells.\n",
    "\n",
    "In CMIP6 the cell areas are included under the CMIP_table parameter 'fx' for atmosphere and land parameters and 'Ofx' for ocean. In this case we will use the area of atmospheric cells areacella."
   ]
  },
  {
   "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",
       "<title>Show/Hide data repr</title>\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",
       "<title>Show/Hide attributes</title>\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",
       ".xr-wrap {\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\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, 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><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'areacella'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 180</li><li><span class='xr-has-index'>lon</span>: 288</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-3d6378a7-32d9-4f4d-bf3a-99139e022304' class='xr-array-in' type='checkbox' ><label for='section-3d6378a7-32d9-4f4d-bf3a-99139e022304' 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>134870290.0 134870290.0 134870290.0 ... 134870290.0 134870290.0</span></div><pre class='xr-array-data'>array([[1.348703e+08, 1.348703e+08, 1.348703e+08, ..., 1.348703e+08,\n",
       "        1.348703e+08, 1.348703e+08],\n",
       "       [4.045698e+08, 4.045698e+08, 4.045698e+08, ..., 4.045698e+08,\n",
       "        4.045698e+08, 4.045698e+08],\n",
       "       [6.741460e+08, 6.741460e+08, 6.741460e+08, ..., 6.741460e+08,\n",
       "        6.741460e+08, 6.741460e+08],\n",
       "       ...,\n",
       "       [6.741460e+08, 6.741460e+08, 6.741460e+08, ..., 6.741460e+08,\n",
       "        6.741460e+08, 6.741460e+08],\n",
       "       [4.045698e+08, 4.045698e+08, 4.045698e+08, ..., 4.045698e+08,\n",
       "        4.045698e+08, 4.045698e+08],\n",
       "       [1.348703e+08, 1.348703e+08, 1.348703e+08, ..., 1.348703e+08,\n",
       "        1.348703e+08, 1.348703e+08]], dtype=float32)</pre></div></li><li class='xr-section-item'><input id='section-5f02e63c-aba4-445a-9ba3-7fccc88ff6dc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5f02e63c-aba4-445a-9ba3-7fccc88ff6dc' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.5 -88.5 -87.5 ... 88.5 89.5</div><input id='attrs-87b38e63-f7d2-4be7-a90c-a36e224389e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-87b38e63-f7d2-4be7-a90c-a36e224389e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fffcab48-901d-4a30-932b-478ddc527b00' class='xr-var-data-in' type='checkbox'><label for='data-fffcab48-901d-4a30-932b-478ddc527b00' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><pre class='xr-var-data'>array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
       "       -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,\n",
       "       -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,\n",
       "       -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,\n",
       "       -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,\n",
       "       -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,\n",
       "       -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,\n",
       "       -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,\n",
       "        -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,\n",
       "         0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
       "        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,\n",
       "        20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,\n",
       "        30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,\n",
       "        40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,\n",
       "        50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,\n",
       "        60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,\n",
       "        70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,\n",
       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])</pre></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'>0.625 1.875 3.125 ... 358.1 359.4</div><input id='attrs-c83bf475-36ff-4607-8919-65b0d729cea2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c83bf475-36ff-4607-8919-65b0d729cea2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb78d466-f41d-4877-bb26-8975e02a8d61' class='xr-var-data-in' type='checkbox'><label for='data-fb78d466-f41d-4877-bb26-8975e02a8d61' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><pre class='xr-var-data'>array([  0.625,   1.875,   3.125, ..., 356.875, 358.125, 359.375])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-3271ca8c-dfc8-4813-8357-2d258c18bf8c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3271ca8c-dfc8-4813-8357-2d258c18bf8c' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Grid-Cell Area for Atmospheric Variables</dd><dt><span>units :</span></dt><dd>m2</dd><dt><span>standard_name :</span></dt><dd>cell_area</dd><dt><span>cell_methods :</span></dt><dd>area: sum</dd><dt><span>interp_method :</span></dt><dd>conserve_order1</dd><dt><span>original_name :</span></dt><dd>areacella</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'areacella' (lat: 180, lon: 288)>\n",
       "array([[1.348703e+08, 1.348703e+08, 1.348703e+08, ..., 1.348703e+08,\n",
       "        1.348703e+08, 1.348703e+08],\n",
       "       [4.045698e+08, 4.045698e+08, 4.045698e+08, ..., 4.045698e+08,\n",
       "        4.045698e+08, 4.045698e+08],\n",
       "       [6.741460e+08, 6.741460e+08, 6.741460e+08, ..., 6.741460e+08,\n",
       "        6.741460e+08, 6.741460e+08],\n",
       "       ...,\n",
       "       [6.741460e+08, 6.741460e+08, 6.741460e+08, ..., 6.741460e+08,\n",
       "        6.741460e+08, 6.741460e+08],\n",
       "       [4.045698e+08, 4.045698e+08, 4.045698e+08, ..., 4.045698e+08,\n",
       "        4.045698e+08, 4.045698e+08],\n",
       "       [1.348703e+08, 1.348703e+08, 1.348703e+08, ..., 1.348703e+08,\n",
       "        1.348703e+08, 1.348703e+08]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 -89.5 -88.5 -87.5 -86.5 -85.5 ... 86.5 87.5 88.5 89.5\n",
       "  * lon      (lon) float64 0.625 1.875 3.125 4.375 ... 355.6 356.9 358.1 359.4\n",
       "Attributes:\n",
       "    long_name:      Grid-Cell Area for Atmospheric Variables\n",
       "    units:          m2\n",
       "    standard_name:  cell_area\n",
       "    cell_methods:   area: sum\n",
       "    interp_method:  conserve_order1\n",
       "    original_name:  areacella"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_area = xr.open_dataset('/g/data/oi10/replicas/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/historical/\\\n",
    "r1i1p1f1/fx/areacella/gr1/v20180701/areacella_fx_GFDL-CM4_historical_r1i1p1f1_gr1.nc')\n",
    "area = file_area.areacella\n",
    "area"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now calculate the area weighted mean using the above variable and xarray operations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1466450cafd0>]"
      ]
     },
     "execution_count": 7,
     "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": [
    "tas_area = tas*area\n",
    "tas_wmean = tas_area.sum(['lat','lon']) / (area.sum())\n",
    "tas_anom = tas_wmean - tas_wmean.mean()\n",
    "tas_anom.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "\n",
    "Find the minimum temperature in a box spanning the coordinates\n",
    "\n",
    "    lon: 80 - 120\n",
    "    lat: 10N - 20N\n",
    "\n",
    "<a href=\"#ans1\" data-toggle=\"collapse\">Answer</a>\n",
    "<div class=\"collapse\" id=\"ans1\">\n",
    "<pre><code>\n",
    "tas.sel(lat=slice(10,20),lon=slice(80,120)).min()\n",
    "</code></pre>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resampling and Rolling Mean\n",
    "\n",
    "The previous time-series plot was a bit hectic. We probably want to resample in time or take a rolling mean to remove the smaller scales of the data and see the longer term trend.\n",
    "\n",
    "xarray very easily supports both resampling and rolling means with the `.rolling` and the `.resample` operations. \n",
    "\n",
    "Starting with a rolling mean we'll use a 1 year window (time=12 as is monthly data)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1465dcaf9e50>]"
      ]
     },
     "execution_count": 8,
     "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": [
    "tas_roll = tas_anom.rolling(time=12,center=True).mean()\n",
    "tas_anom.plot()\n",
    "tas_roll.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Resampling can work in a similar way to the rolling mean. Resampling works with time data (with datetime64 variable in dimensions) and allows you to change the time frequency of your data. If you have a monthly dataset and you want that as a yearly dataset (or a 6hrly dataset that you want daily, etc) you can use the resample command while taking the mean over the resampling period.\n",
    "\n",
    "In the example below we have defined the new resampling period to be annual 'A' and have taken the mean over the monthly data in each year to produce a yearly average time series. Compare it to the rolling mean case above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1466480589d0>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tas_res=tas_anom.resample(time='A').mean(dim='time')\n",
    "tas_roll.plot()\n",
    "tas_res.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "\n",
    "Try rolling or resampling at other frequencies. Look at smaller subsets to see the difference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Climatology\n",
    "\n",
    "With `.groupby()` you can easily create a climatology and calculate anomalies. In this example, working with monthly data, a new dimension will be created that is the months of the year. \n",
    "\n",
    "Let's create a climatology from pre-1900 data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "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",
       "<title>Show/Hide data repr</title>\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",
       "<title>Show/Hide attributes</title>\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",
       ".xr-wrap {\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\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, 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><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tas'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>month</span>: 12</li><li><span class='xr-has-index'>lat</span>: 180</li><li><span class='xr-has-index'>lon</span>: 288</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-356544ca-0c8a-4a03-b5a0-d2f0dff5d6f3' class='xr-array-in' type='checkbox' ><label for='section-356544ca-0c8a-4a03-b5a0-d2f0dff5d6f3' 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=(1, 180, 288), meta=np.ndarray&gt;</span></div><pre class='xr-array-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> 2.49 MB </td> <td> 207.36 kB </td></tr>\n",
       "    <tr><th> Shape </th><td> (12, 180, 288) </td> <td> (1, 180, 288) </td></tr>\n",
       "    <tr><th> Count </th><td> 57 Tasks </td><td> 12 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=\"199\" height=\"144\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"29\" y2=\"19\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"75\" x2=\"29\" y2=\"94\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"75\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"76\" />\n",
       "  <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"78\" />\n",
       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"79\" />\n",
       "  <line x1=\"16\" y1=\"6\" x2=\"16\" y2=\"81\" />\n",
       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"83\" />\n",
       "  <line x1=\"19\" y1=\"9\" x2=\"19\" y2=\"84\" />\n",
       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"86\" />\n",
       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"88\" />\n",
       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"89\" />\n",
       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"91\" />\n",
       "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"93\" />\n",
       "  <line x1=\"29\" y1=\"19\" x2=\"29\" y2=\"94\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.000000,0.000000 29.637470,19.637470 29.637470,94.637470 10.000000,75.000000\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
       "  <line x1=\"13\" y1=\"3\" x2=\"133\" y2=\"3\" />\n",
       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
       "  <line x1=\"16\" y1=\"6\" x2=\"136\" y2=\"6\" />\n",
       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
       "  <line x1=\"19\" y1=\"9\" x2=\"139\" y2=\"9\" />\n",
       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
       "  <line x1=\"28\" y1=\"18\" x2=\"148\" y2=\"18\" />\n",
       "  <line x1=\"29\" y1=\"19\" x2=\"149\" y2=\"19\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"29\" y2=\"19\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"130\" y1=\"0\" x2=\"149\" y2=\"19\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.000000,0.000000 130.000000,0.000000 149.637470,19.637470 29.637470,19.637470\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"29\" y1=\"19\" x2=\"149\" y2=\"19\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"29\" y1=\"94\" x2=\"149\" y2=\"94\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"29\" y1=\"19\" x2=\"29\" y2=\"94\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"149\" y1=\"19\" x2=\"149\" y2=\"94\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"29.637470,19.637470 149.637470,19.637470 149.637470,94.637470 29.637470,94.637470\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"89.637470\" y=\"114.637470\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >288</text>\n",
       "  <text x=\"169.637470\" y=\"57.137470\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,169.637470,57.137470)\">180</text>\n",
       "  <text x=\"9.818735\" y=\"104.818735\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,9.818735,104.818735)\">12</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></pre></div></li><li class='xr-section-item'><input id='section-de6631c9-0a8c-4357-9980-357a5ce42099' class='xr-section-summary-in' type='checkbox'  checked><label for='section-de6631c9-0a8c-4357-9980-357a5ce42099' 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>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-29a82e17-49c1-41b1-9a78-53715485b3e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29a82e17-49c1-41b1-9a78-53715485b3e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96cefeb1-b4e8-4c63-bbc6-785c381dec6d' class='xr-var-data-in' type='checkbox'><label for='data-96cefeb1-b4e8-4c63-bbc6-785c381dec6d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>description :</span></dt><dd>~2 m standard surface air temperature and surface humidity  height</dd></dl></div><pre class='xr-var-data'>array(2.)</pre></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'>0.625 1.875 3.125 ... 358.1 359.4</div><input id='attrs-b6eda38f-bd2a-42d9-861d-b8d7bf26268d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b6eda38f-bd2a-42d9-861d-b8d7bf26268d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef3b8e97-469b-4015-8d91-ecd7f4cb8da9' class='xr-var-data-in' type='checkbox'><label for='data-ef3b8e97-469b-4015-8d91-ecd7f4cb8da9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</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>cell_methods :</span></dt><dd>time: point</dd></dl></div><pre class='xr-var-data'>array([  0.625,   1.875,   3.125, ..., 356.875, 358.125, 359.375])</pre></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'>-89.5 -88.5 -87.5 ... 88.5 89.5</div><input id='attrs-a00d77df-d85f-4d0b-8f16-8e169d236b8d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a00d77df-d85f-4d0b-8f16-8e169d236b8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-588075c9-652a-4855-9f13-7e3fb7085fd9' class='xr-var-data-in' type='checkbox'><label for='data-588075c9-652a-4855-9f13-7e3fb7085fd9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</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>cell_methods :</span></dt><dd>time: point</dd></dl></div><pre class='xr-var-data'>array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
       "       -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,\n",
       "       -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,\n",
       "       -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,\n",
       "       -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,\n",
       "       -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,\n",
       "       -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,\n",
       "       -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,\n",
       "        -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,\n",
       "         0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
       "        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,\n",
       "        20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,\n",
       "        30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,\n",
       "        40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,\n",
       "        50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,\n",
       "        60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,\n",
       "        70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,\n",
       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>month</span></div><div class='xr-var-dims'>(month)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-1a95560f-296a-4010-bedf-4294b38af5e6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1a95560f-296a-4010-bedf-4294b38af5e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-234160d6-1f5a-4410-a3f6-53184f52a35c' class='xr-var-data-in' type='checkbox'><label for='data-234160d6-1f5a-4410-a3f6-53184f52a35c' 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><pre class='xr-var-data'>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-2a0b4d08-0621-4660-92d6-c147c51a9da1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2a0b4d08-0621-4660-92d6-c147c51a9da1' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'tas' (month: 12, lat: 180, lon: 288)>\n",
       "dask.array<stack, shape=(12, 180, 288), dtype=float32, chunksize=(1, 180, 288), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "    height   float64 2.0\n",
       "  * lon      (lon) float64 0.625 1.875 3.125 4.375 ... 355.6 356.9 358.1 359.4\n",
       "  * lat      (lat) float64 -89.5 -88.5 -87.5 -86.5 -85.5 ... 86.5 87.5 88.5 89.5\n",
       "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "climatology = tas.sel(time=slice('1800','1900')).groupby('time.month').mean(dim='time')\n",
    "climatology"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of grouping by month, group by 'season'. Plot one of the seasons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x1465dc846850>"
      ]
     },
     "execution_count": 11,
     "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": [
    "climatology_seas = tas.sel(time=slice('1800','1900')).groupby('time.season').mean(dim='time')\n",
    "climatology_seas.sel(season='DJF').plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot using Cartopy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subplots\n",
    "\n",
    "In the seasonal climatology case we have created a climatology with 4 periods in time relating to seasons. It is very simple to plot all the seasons in a single plot simply by specifying which variable defines the `row` and how many columns to produce (we have also defined the colourbar limits using `vmin` and `vmax`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.plot.facetgrid.FacetGrid at 0x1464086ada10>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x432 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "climatology_seas.plot(row='season',col_wrap=2,vmin=-10,vmax=40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can integrate projections with the xarray datasets using the cartopy command.\n",
    "\n",
    "Notes: you need to install or load Cartopy to be able to run this part of the exercise.\n",
    "\n",
    "**Please note the following cell won't work within Pangeo environment on Gadi as the computes nodes are restricted to external access. However, you can run this piece of code on your local computer, VDI, and Gadi login node.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cartopy.crs as ccrs\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(6, 4))\n",
    "ax = plt.axes(projection=ccrs.Orthographic(central_longitude=140))\n",
    "ax.coastlines()\n",
    "\n",
    "climatology_seas.sel(season='DJF').plot(transform=ccrs.PlateCarree(), vmin=-2, vmax=30,\n",
    "            cbar_kwargs={'shrink': 0.8})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "\n",
    "Make your own plot using your favorite projection: https://scitools.org.uk/cartopy/docs/latest/crs/projections.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary\n",
    "\n",
    "This example shows some common operations in xarray.\n",
    "\n",
    "## References\n",
    "https://github.com/coecms-training/introduction_to_xarray\n",
    "\n",
    "http://xarray.pydata.org/en/stable/"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}