diff --git a/docs/demos/24-07-11-scipy-2024/scipy-2024.ipynb b/docs/demos/24-07-11-scipy-2024/scipy-2024.ipynb
index 5ad0d018..cf083368 100644
--- a/docs/demos/24-07-11-scipy-2024/scipy-2024.ipynb
+++ b/docs/demos/24-07-11-scipy-2024/scipy-2024.ipynb
@@ -76,7 +76,7 @@
     "jupyter nbconvert —to html <PATH-TO-NOTEBOOK>\n",
     "\n",
     "# Then Print HTML to PDF (Browser)\n",
-    "```"
+    "```\n"
    ]
   },
   {
@@ -117,12 +117,11 @@
     "- NumFocus fiscally sponsored project since August 2018\n",
     "- Based on NumPy, heavily inspired by Pandas\n",
     "\n",
-    "\n",
     "<div style=\"text-align: center;\">\n",
     "  <img src=\"../../_static/NumFocus-logo.png\" alt=\"NumFocus logo\" align=\\\"center\\\" style=\"display: inline-block; width:200px;\">\n",
     "  <img src=\"../../_static/numpy-logo.svg\" alt=\"NumPy logo\" align=\\\"center\\\" style=\"display: inline-block; width:200px;\">\n",
     "  <img src=\"../../_static/pandas-logo.svg\" alt=\"Pandas logo\" align=\\\"center\\\" style=\"display: inline-block; width:200px;\">\n",
-    "</div>"
+    "</div>\n"
    ]
   },
   {
@@ -147,7 +146,7 @@
     "<div style=\"text-align: center;\">\n",
     "  <img src=\"../../_static/dask-logo.svg\" alt=\"NumFocus logo\" align=\\\"center\\\" style=\"display: inline-block; width:200px;\">\n",
     "  <img src=\"../../_static/matplotlib-logo.svg\" alt=\"NumPy logo\" align=\\\"center\\\" style=\"display: inline-block; width:200px;\">\n",
-    "</div>"
+    "</div>\n"
    ]
   },
   {
@@ -204,8 +203,7 @@
     "    <img src=\"../../_static/thumbnails/spatial-avg.png\" alt=\"Spatial average chart\" style=\"display: inline-block; width:200px\">\n",
     "    <img src=\"../../_static/thumbnails/temporal-average.png\" alt=\"Temporal average chart\" style=\"display: inline-block; margin-right:50px; width:200px;\">\n",
     "    <img src=\"../../_static/thumbnails/regridding-vertical.png\" alt=\"Vertical regridding chart\" style=\"display: inline-block; margin-right:50px; width:175px;\">\n",
-    "</div>\n",
-    "\n"
+    "</div>\n"
    ]
   },
   {
@@ -275,14 +273,12 @@
     "\" alt=\"Anaconda logo\" align=\\\"center\\\" style=\"display: inline-block;     margin-right:50px; width:500px;\">\n",
     "</div>\n",
     "\n",
-    "- `temporal` \n",
-    "  - `.average()`, `.group_average()`, `.climatology()`, `.depatures()` \n",
-    "- `spatial` \n",
+    "- `temporal`\n",
+    "  - `.average()`, `.group_average()`, `.climatology()`, `.depatures()`\n",
+    "- `spatial`\n",
     "  - `.average()`, `.get_weights()`\n",
-    "- `bounds` \n",
-    "  - `.get_bounds()`, `.add_bounds()`, `.add_missing_bounds()`\n",
-    "\n",
-    "\n"
+    "- `bounds`\n",
+    "  - `.get_bounds()`, `.add_bounds()`, `.add_missing_bounds()`\n"
    ]
   },
   {
@@ -294,6 +290,7 @@
    },
    "source": [
     "xCDAT also provides general utilities in the form of functions\n",
+    "\n",
     "- `open_dataset()`, `open_mfdataset()`\n",
     "- `center_times()`, `decode_time()`\n",
     "- `swap_lon_axis()`\n",
@@ -301,8 +298,8 @@
     "- `create_grid()`\n",
     "- `get_dim_coords()`\n",
     "- `get_dim_keys()`\n",
-    " \n",
-    " Visit the API Reference page for a complete list: https://xcdat.readthedocs.io/en/latest/api.html"
+    "\n",
+    "Visit the API Reference page for a complete list: https://xcdat.readthedocs.io/en/latest/api.html\n"
    ]
   },
   {
@@ -313,26 +310,30 @@
     }
    },
    "source": [
-    "### Technical Demo: End-to-end analysis workflow with xCDAT\n",
+    "## End-to-End Analysis and Visualization of E3SM Data using nco and xCDAT\n",
+    "\n",
+    "### Overview\n",
     "\n",
-    "Sample problem: Calculate annual averages for global climatological anomalies\n",
+    "This exercise will walkthrough using regridding E3SM data to a\n",
+    "rectilinear grid using `ncremap`, then performing analysis and visualization using xCDAT.\n",
     "\n",
-    "1. I/O – load in dataset (`xc.open_dataset()`)\n",
-    "2. Calculate monthly departures (`ds.temporal.departures()`)\n",
-    "3. Compute global averages (`ds.spatial.average()`)\n",
-    "4. Calculate annual averages (`ds.temporal.average()`)\n",
-    "5. Plot the anomaly map using `matplotlib`"
+    "### Sections\n",
+    "\n",
+    "1. Prerequisite: Set up the E3SM Unified Environment v1.10.0 Python Kernel\n",
+    "2. Setup Code\n",
+    "3. Use NCO to regrid E3SM data to a rectilinear grid\n",
+    "4. I/O\n",
+    "5. Regridding\n",
+    "6. Spatial Averaging\n",
+    "7. Temporal Computations\n",
+    "8. General Dataset Utilities\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
+   "metadata": {},
    "source": [
-    "### 1. Import xCDAT and open the dataset\n"
+    "### Setup Code\n"
    ]
   },
   {
@@ -399,2678 +400,749 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "cell_style": "center",
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
+    "import glob\n",
+    "\n",
+    "import numpy as np\n",
+    "from xarray.coding.calendar_ops import _datetime_to_decimal_year as dt2decimal\n",
     "import xcdat as xc\n",
+    "import cartopy.crs as ccrs\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Use NCO to regrid E3SM data to a rectilinear grid\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Now call ncremap to regrid the file to a 0.5 x 0.5 degree grid\n",
+    "\n",
+    "Typically a user would call this command directly from the shell or write a batch script to run ncremap. Here we use the bash decorator in Jupyter to run `ncremap` on a directory of files (using a wildcard to filter for `.h0` files, which include monthly output we’d like to analyze). We will then move the remapped files to a `remapped/` directory.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "# create output directory\n",
+    "mkdir -p remapped\n",
+    "# source e3sm-unified environment\n",
+    "source /global/common/software/e3sm/anaconda_envs/load_latest_e3sm_unified_pm-cpu.sh\n",
+    "# do regridding\n",
+    "# format: ncremap -m REMAPFILE.nc -t 1 -v VAR_OF_INTEREST /PATH/TO/DATA/*nc\n",
+    "# Subsetting files for \"h0\", which is the monthly history field.\n",
+    "ncremap -m /global/cfs/cdirs/e3sm/diagnostics/maps/map_ne30pg2_to_cmip6_180x360_aave.20200201.nc -t 1 -v TREFHT /global/cfs/cdirs/e3sm/www/Tutorials/2024/simulations/extendedOutput.v3.LR.historical_0101/archive/atm/hist/extendedOutput.v3.LR.historical_0101.eam.h0.*nc >/dev/null 2>&1\n",
+    "# move output to remapped directory\n",
+    "mv extendedOutput.v3.LR.historical_0101.eam.h0.*nc remapped/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### I/O\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Now let's load in the regridded data and use xcdat to perform additional calculations on the 0.5 x 0.5 degree grid\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use `xc.open_mfdataset()` to open all the remapped netcdf files in a single `xr.Dataset` object. With `xcdat`, you can specify the directory `remapped` and xcdat will read in all netcdf files as one `xr.dataset` object. You could also use a wildcard with xarray (`ds = xr.open_mfdataset('remapped/*.nc’)`). `open_mfdataset` is essentially the same operation in both Xarray and xCDAT, but `xcdat` will add missing bounds and handles some additional time axes.\n",
     "\n",
-    "# 1. Open the dataset.\n",
-    "filepath = \"https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r10i1p1f1/Amon/tas/gn/v20200605/tas_Amon_ACCESS-ESM1-5_historical_r10i1p1f1_gn_185001-201412.nc\"\n",
+    "We will be analyzing a few years of temperature (`TREFHT`) data from E3SM v3.\n",
+    "\n",
+    "- Documentation: https://xcdat.readthedocs.io/en/stable/generated/xcdat.open_mfdataset.html\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = xc.open_mfdataset(\"remapped\")\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### ...but checkout the time coordinate:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds.time.values[0:3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The monthly time coordinates begin in 2/2000, even though our first file is for 1/2000. This is because E3SM saves out monthly history at midnight at the end of the month. xCDAT can handle this by centering the time coordinates between the monthly time bounds (using `center_times=True`):\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = xc.open_mfdataset(\"remapped\", center_times=True)\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Regridding with xCDAT\n",
     "\n",
-    "ds = xc.open_dataset(\n",
-    "    filepath, add_bounds=[\"X\", \"Y\", \"T\"], decode_times=True, center_times=True, chunks={\"time\": \"auto\"}\n",
+    "We often want to regrid a dataset to a new grid to facilitate data analysis or comparisons with other datasets. The current dataset is at 0.5 x 0.5<sup>o</sup> resolution, so we'll start be remapping it to a 4 x 4<sup>o</sup> grid.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### First, we specify the target grid\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create target axes\n",
+    "nlat = xc.create_axis(\n",
+    "    \"lat\", np.arange(-88, 90, 4), attrs={\"units\": \"degrees_north\", \"axis\": \"Y\"}\n",
     ")\n",
+    "nlon = xc.create_axis(\n",
+    "    \"lon\", np.arange(2, 360, 4), attrs={\"units\": \"degrees_east\", \"axis\": \"X\"}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create the target grid using the target axes and bounds.\n",
     "\n",
-    "# Unit adjustment from Kelvin to Celcius.\n",
-    "ds[\"tas\"] = ds.tas - 273.15"
+    "- Documentation: https://xcdat.readthedocs.io/en/latest/generated/xcdat.create_grid.html#xcdat.create_grid\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "cell_style": "center",
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
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-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tas&#x27; (time: 1980, lat: 145, lon: 192)&gt; Size: 220MB\n",
-       "dask.array&lt;sub, shape=(1980, 145, 192), dtype=float32, chunksize=(1205, 145, 192), chunktype=numpy.ndarray&gt;\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float64 1kB -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0\n",
-       "  * lon      (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
-       "    height   float64 8B ...\n",
-       "  * time     (time) object 16kB 1850-01-16 12:00:00 ... 2014-12-16 12:00:00</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tas'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1980</li><li><span class='xr-has-index'>lat</span>: 145</li><li><span class='xr-has-index'>lon</span>: 192</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-40ef319f-2f85-475f-ba32-7545c345f591' class='xr-array-in' type='checkbox' checked><label for='section-40ef319f-2f85-475f-ba32-7545c345f591' 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=(1205, 145, 192), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 210.28 MiB </td>\n",
-       "                        <td> 127.97 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1980, 145, 192) </td>\n",
-       "                        <td> (1205, 145, 192) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 2 chunks in 3 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"169\" height=\"157\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"10\" y1=\"36\" x2=\"80\" y2=\"107\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"36\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"52\" y1=\"42\" x2=\"52\" y2=\"79\" />\n",
-       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"107\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,107.39560902339261 10.0,36.80737372927496\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"48\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"52\" y1=\"42\" x2=\"91\" y2=\"42\" />\n",
-       "  <line x1=\"80\" y1=\"70\" x2=\"119\" y2=\"70\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"48\" y1=\"0\" x2=\"119\" y2=\"70\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"10.0,0.0 48.42563224869931,0.0 119.01386754281697,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"80\" y1=\"70\" x2=\"119\" y2=\"70\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"80\" y1=\"107\" x2=\"119\" y2=\"107\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"107\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"119\" y1=\"70\" x2=\"119\" y2=\"107\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"80.58823529411765,70.58823529411765 119.01386754281697,70.58823529411765 119.01386754281697,107.39560902339261 80.58823529411765,107.39560902339261\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"99.801051\" y=\"127.395609\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >192</text>\n",
-       "  <text x=\"139.013868\" y=\"88.991922\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,139.013868,88.991922)\">145</text>\n",
-       "  <text x=\"35.294118\" y=\"92.101491\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,92.101491)\">1980</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></div></li><li class='xr-section-item'><input id='section-e002f5d1-376d-4b65-a6ac-030eb3b46d8c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e002f5d1-376d-4b65-a6ac-030eb3b46d8c' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -88.75 -87.5 ... 88.75 90.0</div><input id='attrs-55d2c5b3-7434-4f07-abf8-4c99c5773c64' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-55d2c5b3-7434-4f07-abf8-4c99c5773c64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9df6b12c-8ddc-48a5-9d83-3eaca21ceadf' class='xr-var-data-in' type='checkbox'><label for='data-9df6b12c-8ddc-48a5-9d83-3eaca21ceadf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-90.  , -88.75, -87.5 , -86.25, -85.  , -83.75, -82.5 , -81.25, -80.  ,\n",
-       "       -78.75, -77.5 , -76.25, -75.  , -73.75, -72.5 , -71.25, -70.  , -68.75,\n",
-       "       -67.5 , -66.25, -65.  , -63.75, -62.5 , -61.25, -60.  , -58.75, -57.5 ,\n",
-       "       -56.25, -55.  , -53.75, -52.5 , -51.25, -50.  , -48.75, -47.5 , -46.25,\n",
-       "       -45.  , -43.75, -42.5 , -41.25, -40.  , -38.75, -37.5 , -36.25, -35.  ,\n",
-       "       -33.75, -32.5 , -31.25, -30.  , -28.75, -27.5 , -26.25, -25.  , -23.75,\n",
-       "       -22.5 , -21.25, -20.  , -18.75, -17.5 , -16.25, -15.  , -13.75, -12.5 ,\n",
-       "       -11.25, -10.  ,  -8.75,  -7.5 ,  -6.25,  -5.  ,  -3.75,  -2.5 ,  -1.25,\n",
-       "         0.  ,   1.25,   2.5 ,   3.75,   5.  ,   6.25,   7.5 ,   8.75,  10.  ,\n",
-       "        11.25,  12.5 ,  13.75,  15.  ,  16.25,  17.5 ,  18.75,  20.  ,  21.25,\n",
-       "        22.5 ,  23.75,  25.  ,  26.25,  27.5 ,  28.75,  30.  ,  31.25,  32.5 ,\n",
-       "        33.75,  35.  ,  36.25,  37.5 ,  38.75,  40.  ,  41.25,  42.5 ,  43.75,\n",
-       "        45.  ,  46.25,  47.5 ,  48.75,  50.  ,  51.25,  52.5 ,  53.75,  55.  ,\n",
-       "        56.25,  57.5 ,  58.75,  60.  ,  61.25,  62.5 ,  63.75,  65.  ,  66.25,\n",
-       "        67.5 ,  68.75,  70.  ,  71.25,  72.5 ,  73.75,  75.  ,  76.25,  77.5 ,\n",
-       "        78.75,  80.  ,  81.25,  82.5 ,  83.75,  85.  ,  86.25,  87.5 ,  88.75,\n",
-       "        90.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.875 3.75 ... 356.2 358.1</div><input id='attrs-34375a99-e24c-4248-adaf-2b92fdd660ea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34375a99-e24c-4248-adaf-2b92fdd660ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3c76540-c93d-4daa-b322-bc23a909d062' class='xr-var-data-in' type='checkbox'><label for='data-a3c76540-c93d-4daa-b322-bc23a909d062' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  0.   ,   1.875,   3.75 ,   5.625,   7.5  ,   9.375,  11.25 ,  13.125,\n",
-       "        15.   ,  16.875,  18.75 ,  20.625,  22.5  ,  24.375,  26.25 ,  28.125,\n",
-       "        30.   ,  31.875,  33.75 ,  35.625,  37.5  ,  39.375,  41.25 ,  43.125,\n",
-       "        45.   ,  46.875,  48.75 ,  50.625,  52.5  ,  54.375,  56.25 ,  58.125,\n",
-       "        60.   ,  61.875,  63.75 ,  65.625,  67.5  ,  69.375,  71.25 ,  73.125,\n",
-       "        75.   ,  76.875,  78.75 ,  80.625,  82.5  ,  84.375,  86.25 ,  88.125,\n",
-       "        90.   ,  91.875,  93.75 ,  95.625,  97.5  ,  99.375, 101.25 , 103.125,\n",
-       "       105.   , 106.875, 108.75 , 110.625, 112.5  , 114.375, 116.25 , 118.125,\n",
-       "       120.   , 121.875, 123.75 , 125.625, 127.5  , 129.375, 131.25 , 133.125,\n",
-       "       135.   , 136.875, 138.75 , 140.625, 142.5  , 144.375, 146.25 , 148.125,\n",
-       "       150.   , 151.875, 153.75 , 155.625, 157.5  , 159.375, 161.25 , 163.125,\n",
-       "       165.   , 166.875, 168.75 , 170.625, 172.5  , 174.375, 176.25 , 178.125,\n",
-       "       180.   , 181.875, 183.75 , 185.625, 187.5  , 189.375, 191.25 , 193.125,\n",
-       "       195.   , 196.875, 198.75 , 200.625, 202.5  , 204.375, 206.25 , 208.125,\n",
-       "       210.   , 211.875, 213.75 , 215.625, 217.5  , 219.375, 221.25 , 223.125,\n",
-       "       225.   , 226.875, 228.75 , 230.625, 232.5  , 234.375, 236.25 , 238.125,\n",
-       "       240.   , 241.875, 243.75 , 245.625, 247.5  , 249.375, 251.25 , 253.125,\n",
-       "       255.   , 256.875, 258.75 , 260.625, 262.5  , 264.375, 266.25 , 268.125,\n",
-       "       270.   , 271.875, 273.75 , 275.625, 277.5  , 279.375, 281.25 , 283.125,\n",
-       "       285.   , 286.875, 288.75 , 290.625, 292.5  , 294.375, 296.25 , 298.125,\n",
-       "       300.   , 301.875, 303.75 , 305.625, 307.5  , 309.375, 311.25 , 313.125,\n",
-       "       315.   , 316.875, 318.75 , 320.625, 322.5  , 324.375, 326.25 , 328.125,\n",
-       "       330.   , 331.875, 333.75 , 335.625, 337.5  , 339.375, 341.25 , 343.125,\n",
-       "       345.   , 346.875, 348.75 , 350.625, 352.5  , 354.375, 356.25 , 358.125])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-11e78861-b98a-403a-882c-0bbc862d91a9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-11e78861-b98a-403a-882c-0bbc862d91a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a08bcee-74c8-48eb-a1e6-437afbc15867' class='xr-var-data-in' type='checkbox'><label for='data-2a08bcee-74c8-48eb-a1e6-437afbc15867' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1850-01-16 12:00:00 ... 2014-12-...</div><input id='attrs-cd95c974-ddba-4d38-ba66-ca3d43c673d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd95c974-ddba-4d38-ba66-ca3d43c673d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9937b721-6bf1-48c1-be07-1784d491b0c3' class='xr-var-data-in' type='checkbox'><label for='data-9937b721-6bf1-48c1-be07-1784d491b0c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeProlepticGregorian(1850, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1850, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1850, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       ...,\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 12, 16, 12, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7f60fcec-97f1-4587-a3ff-59630a400073' class='xr-section-summary-in' type='checkbox'  ><label for='section-7f60fcec-97f1-4587-a3ff-59630a400073' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0c52dd92-c197-4476-89e3-615334179cd1' class='xr-index-data-in' type='checkbox'/><label for='index-0c52dd92-c197-4476-89e3-615334179cd1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -90.0, -88.75,  -87.5, -86.25,  -85.0, -83.75,  -82.5, -81.25,  -80.0,\n",
-       "       -78.75,\n",
-       "       ...\n",
-       "        78.75,   80.0,  81.25,   82.5,  83.75,   85.0,  86.25,   87.5,  88.75,\n",
-       "         90.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=145))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5e0638f2-2126-49d4-bb74-59184bff90d8' class='xr-index-data-in' type='checkbox'/><label for='index-5e0638f2-2126-49d4-bb74-59184bff90d8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([    0.0,   1.875,    3.75,   5.625,     7.5,   9.375,   11.25,  13.125,\n",
-       "          15.0,  16.875,\n",
-       "       ...\n",
-       "        341.25, 343.125,   345.0, 346.875,  348.75, 350.625,   352.5, 354.375,\n",
-       "        356.25, 358.125],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-761e21a4-7006-4f51-bcfc-d9dab7b8592c' class='xr-index-data-in' type='checkbox'/><label for='index-761e21a4-7006-4f51-bcfc-d9dab7b8592c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([1850-01-16 12:00:00, 1850-02-15 00:00:00, 1850-03-16 12:00:00,\n",
-       "             1850-04-16 00:00:00, 1850-05-16 12:00:00, 1850-06-16 00:00:00,\n",
-       "             1850-07-16 12:00:00, 1850-08-16 12:00:00, 1850-09-16 00:00:00,\n",
-       "             1850-10-16 12:00:00,\n",
-       "             ...\n",
-       "             2014-03-16 12:00:00, 2014-04-16 00:00:00, 2014-05-16 12:00:00,\n",
-       "             2014-06-16 00:00:00, 2014-07-16 12:00:00, 2014-08-16 12:00:00,\n",
-       "             2014-09-16 00:00:00, 2014-10-16 12:00:00, 2014-11-16 00:00:00,\n",
-       "             2014-12-16 12:00:00],\n",
-       "            dtype=&#x27;object&#x27;,\n",
-       "            length=1980,\n",
-       "            calendar=&#x27;proleptic_gregorian&#x27;,\n",
-       "            freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e71e25a3-a663-4769-9df7-dbaf114a088e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e71e25a3-a663-4769-9df7-dbaf114a088e' 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' (time: 1980, lat: 145, lon: 192)> Size: 220MB\n",
-       "dask.array<sub, shape=(1980, 145, 192), dtype=float32, chunksize=(1205, 145, 192), chunktype=numpy.ndarray>\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float64 1kB -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0\n",
-       "  * lon      (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
-       "    height   float64 8B ...\n",
-       "  * time     (time) object 16kB 1850-01-16 12:00:00 ... 2014-12-16 12:00:00"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds.tas"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "### 2. Calculate monthly departures\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/miniconda3/envs/xcdat_scipy_2024/lib/python3.11/site-packages/xarray/core/indexing.py:1617: PerformanceWarning: Slicing with an out-of-order index is generating 165 times more chunks\n",
-      "  return self.array[key]\n"
-     ]
-    }
-   ],
-   "source": [
-    "ds_anom = ds.temporal.departures(\"tas\", freq=\"month\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "source": [
-    "(Disregard the performance warning. Normally we don't need to chunk and parallelize computation on a dataset this small)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
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-     "slide_type": "subslide"
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-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tas&#x27; (time: 1980, lat: 145, lon: 192)&gt; Size: 441MB\n",
-       "dask.array&lt;sub, shape=(1980, 145, 192), dtype=float64, chunksize=(1, 145, 192), chunktype=numpy.ndarray&gt;\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float64 1kB -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0\n",
-       "  * lon      (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
-       "    height   float64 8B ...\n",
-       "  * time     (time) object 16kB 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
-       "Attributes:\n",
-       "    operation:  temporal_avg\n",
-       "    mode:       departures\n",
-       "    freq:       month\n",
-       "    weighted:   True</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tas'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1980</li><li><span class='xr-has-index'>lat</span>: 145</li><li><span class='xr-has-index'>lon</span>: 192</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-c557edd5-c8a7-450c-8736-50a229c1ac23' class='xr-array-in' type='checkbox' checked><label for='section-c557edd5-c8a7-450c-8736-50a229c1ac23' 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, 145, 192), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 420.56 MiB </td>\n",
-       "                        <td> 217.50 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1980, 145, 192) </td>\n",
-       "                        <td> (1, 145, 192) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1980 chunks in 138 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"169\" height=\"157\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
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-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"99.801051\" y=\"127.395609\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >192</text>\n",
-       "  <text x=\"139.013868\" y=\"88.991922\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,139.013868,88.991922)\">145</text>\n",
-       "  <text x=\"35.294118\" y=\"92.101491\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,92.101491)\">1980</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></div></li><li class='xr-section-item'><input id='section-d1027d76-9af7-44d4-b39f-58fa8b959d24' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d1027d76-9af7-44d4-b39f-58fa8b959d24' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -88.75 -87.5 ... 88.75 90.0</div><input id='attrs-4e518124-d1af-4dfa-8f1a-b4f7d3aad7e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e518124-d1af-4dfa-8f1a-b4f7d3aad7e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a69a5f7-16ed-4636-afbb-a1cf6acdd18d' class='xr-var-data-in' type='checkbox'><label for='data-0a69a5f7-16ed-4636-afbb-a1cf6acdd18d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-90.  , -88.75, -87.5 , -86.25, -85.  , -83.75, -82.5 , -81.25, -80.  ,\n",
-       "       -78.75, -77.5 , -76.25, -75.  , -73.75, -72.5 , -71.25, -70.  , -68.75,\n",
-       "       -67.5 , -66.25, -65.  , -63.75, -62.5 , -61.25, -60.  , -58.75, -57.5 ,\n",
-       "       -56.25, -55.  , -53.75, -52.5 , -51.25, -50.  , -48.75, -47.5 , -46.25,\n",
-       "       -45.  , -43.75, -42.5 , -41.25, -40.  , -38.75, -37.5 , -36.25, -35.  ,\n",
-       "       -33.75, -32.5 , -31.25, -30.  , -28.75, -27.5 , -26.25, -25.  , -23.75,\n",
-       "       -22.5 , -21.25, -20.  , -18.75, -17.5 , -16.25, -15.  , -13.75, -12.5 ,\n",
-       "       -11.25, -10.  ,  -8.75,  -7.5 ,  -6.25,  -5.  ,  -3.75,  -2.5 ,  -1.25,\n",
-       "         0.  ,   1.25,   2.5 ,   3.75,   5.  ,   6.25,   7.5 ,   8.75,  10.  ,\n",
-       "        11.25,  12.5 ,  13.75,  15.  ,  16.25,  17.5 ,  18.75,  20.  ,  21.25,\n",
-       "        22.5 ,  23.75,  25.  ,  26.25,  27.5 ,  28.75,  30.  ,  31.25,  32.5 ,\n",
-       "        33.75,  35.  ,  36.25,  37.5 ,  38.75,  40.  ,  41.25,  42.5 ,  43.75,\n",
-       "        45.  ,  46.25,  47.5 ,  48.75,  50.  ,  51.25,  52.5 ,  53.75,  55.  ,\n",
-       "        56.25,  57.5 ,  58.75,  60.  ,  61.25,  62.5 ,  63.75,  65.  ,  66.25,\n",
-       "        67.5 ,  68.75,  70.  ,  71.25,  72.5 ,  73.75,  75.  ,  76.25,  77.5 ,\n",
-       "        78.75,  80.  ,  81.25,  82.5 ,  83.75,  85.  ,  86.25,  87.5 ,  88.75,\n",
-       "        90.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.875 3.75 ... 356.2 358.1</div><input id='attrs-4656803e-cbe6-45e9-8280-ee891d3dfbb1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4656803e-cbe6-45e9-8280-ee891d3dfbb1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-209f7215-714b-46f1-b5d2-d845f91bc9c5' class='xr-var-data-in' type='checkbox'><label for='data-209f7215-714b-46f1-b5d2-d845f91bc9c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  0.   ,   1.875,   3.75 ,   5.625,   7.5  ,   9.375,  11.25 ,  13.125,\n",
-       "        15.   ,  16.875,  18.75 ,  20.625,  22.5  ,  24.375,  26.25 ,  28.125,\n",
-       "        30.   ,  31.875,  33.75 ,  35.625,  37.5  ,  39.375,  41.25 ,  43.125,\n",
-       "        45.   ,  46.875,  48.75 ,  50.625,  52.5  ,  54.375,  56.25 ,  58.125,\n",
-       "        60.   ,  61.875,  63.75 ,  65.625,  67.5  ,  69.375,  71.25 ,  73.125,\n",
-       "        75.   ,  76.875,  78.75 ,  80.625,  82.5  ,  84.375,  86.25 ,  88.125,\n",
-       "        90.   ,  91.875,  93.75 ,  95.625,  97.5  ,  99.375, 101.25 , 103.125,\n",
-       "       105.   , 106.875, 108.75 , 110.625, 112.5  , 114.375, 116.25 , 118.125,\n",
-       "       120.   , 121.875, 123.75 , 125.625, 127.5  , 129.375, 131.25 , 133.125,\n",
-       "       135.   , 136.875, 138.75 , 140.625, 142.5  , 144.375, 146.25 , 148.125,\n",
-       "       150.   , 151.875, 153.75 , 155.625, 157.5  , 159.375, 161.25 , 163.125,\n",
-       "       165.   , 166.875, 168.75 , 170.625, 172.5  , 174.375, 176.25 , 178.125,\n",
-       "       180.   , 181.875, 183.75 , 185.625, 187.5  , 189.375, 191.25 , 193.125,\n",
-       "       195.   , 196.875, 198.75 , 200.625, 202.5  , 204.375, 206.25 , 208.125,\n",
-       "       210.   , 211.875, 213.75 , 215.625, 217.5  , 219.375, 221.25 , 223.125,\n",
-       "       225.   , 226.875, 228.75 , 230.625, 232.5  , 234.375, 236.25 , 238.125,\n",
-       "       240.   , 241.875, 243.75 , 245.625, 247.5  , 249.375, 251.25 , 253.125,\n",
-       "       255.   , 256.875, 258.75 , 260.625, 262.5  , 264.375, 266.25 , 268.125,\n",
-       "       270.   , 271.875, 273.75 , 275.625, 277.5  , 279.375, 281.25 , 283.125,\n",
-       "       285.   , 286.875, 288.75 , 290.625, 292.5  , 294.375, 296.25 , 298.125,\n",
-       "       300.   , 301.875, 303.75 , 305.625, 307.5  , 309.375, 311.25 , 313.125,\n",
-       "       315.   , 316.875, 318.75 , 320.625, 322.5  , 324.375, 326.25 , 328.125,\n",
-       "       330.   , 331.875, 333.75 , 335.625, 337.5  , 339.375, 341.25 , 343.125,\n",
-       "       345.   , 346.875, 348.75 , 350.625, 352.5  , 354.375, 356.25 , 358.125])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-48559b77-bda7-4733-9f01-0a77764fe5f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48559b77-bda7-4733-9f01-0a77764fe5f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e0065c4-7d1f-4a3c-8c51-4e3bbc21065c' class='xr-var-data-in' type='checkbox'><label for='data-8e0065c4-7d1f-4a3c-8c51-4e3bbc21065c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1850-01-16 12:00:00 ... 2014-12-...</div><input id='attrs-5425f725-d6a3-4b4d-b55e-6410ea994d64' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5425f725-d6a3-4b4d-b55e-6410ea994d64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06ffb916-0934-4262-bc0b-76cd7d5e0b34' class='xr-var-data-in' type='checkbox'><label for='data-06ffb916-0934-4262-bc0b-76cd7d5e0b34' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeProlepticGregorian(1850, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1850, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1850, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       ...,\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 12, 16, 12, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c8e3e33c-8b66-47fe-8658-5e691948b3d0' class='xr-section-summary-in' type='checkbox'  ><label for='section-c8e3e33c-8b66-47fe-8658-5e691948b3d0' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-23fe6ba4-ee28-4686-8767-ff6aac83815c' class='xr-index-data-in' type='checkbox'/><label for='index-23fe6ba4-ee28-4686-8767-ff6aac83815c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -90.0, -88.75,  -87.5, -86.25,  -85.0, -83.75,  -82.5, -81.25,  -80.0,\n",
-       "       -78.75,\n",
-       "       ...\n",
-       "        78.75,   80.0,  81.25,   82.5,  83.75,   85.0,  86.25,   87.5,  88.75,\n",
-       "         90.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=145))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e74f038f-68d9-49da-a5ff-951eb87b501d' class='xr-index-data-in' type='checkbox'/><label for='index-e74f038f-68d9-49da-a5ff-951eb87b501d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([    0.0,   1.875,    3.75,   5.625,     7.5,   9.375,   11.25,  13.125,\n",
-       "          15.0,  16.875,\n",
-       "       ...\n",
-       "        341.25, 343.125,   345.0, 346.875,  348.75, 350.625,   352.5, 354.375,\n",
-       "        356.25, 358.125],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a6b15213-ba6b-4301-9b9d-128080bd1021' class='xr-index-data-in' type='checkbox'/><label for='index-a6b15213-ba6b-4301-9b9d-128080bd1021' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([1850-01-16 12:00:00, 1850-02-15 00:00:00, 1850-03-16 12:00:00,\n",
-       "             1850-04-16 00:00:00, 1850-05-16 12:00:00, 1850-06-16 00:00:00,\n",
-       "             1850-07-16 12:00:00, 1850-08-16 12:00:00, 1850-09-16 00:00:00,\n",
-       "             1850-10-16 12:00:00,\n",
-       "             ...\n",
-       "             2014-03-16 12:00:00, 2014-04-16 00:00:00, 2014-05-16 12:00:00,\n",
-       "             2014-06-16 00:00:00, 2014-07-16 12:00:00, 2014-08-16 12:00:00,\n",
-       "             2014-09-16 00:00:00, 2014-10-16 12:00:00, 2014-11-16 00:00:00,\n",
-       "             2014-12-16 12:00:00],\n",
-       "            dtype=&#x27;object&#x27;,\n",
-       "            length=1980,\n",
-       "            calendar=&#x27;proleptic_gregorian&#x27;,\n",
-       "            freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-71f1a688-66e7-421d-9919-571c2bf20301' class='xr-section-summary-in' type='checkbox'  checked><label for='section-71f1a688-66e7-421d-9919-571c2bf20301' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>operation :</span></dt><dd>temporal_avg</dd><dt><span>mode :</span></dt><dd>departures</dd><dt><span>freq :</span></dt><dd>month</dd><dt><span>weighted :</span></dt><dd>True</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'tas' (time: 1980, lat: 145, lon: 192)> Size: 441MB\n",
-       "dask.array<sub, shape=(1980, 145, 192), dtype=float64, chunksize=(1, 145, 192), chunktype=numpy.ndarray>\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float64 1kB -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0\n",
-       "  * lon      (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
-       "    height   float64 8B ...\n",
-       "  * time     (time) object 16kB 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
-       "Attributes:\n",
-       "    operation:  temporal_avg\n",
-       "    mode:       departures\n",
-       "    freq:       month\n",
-       "    weighted:   True"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds_anom[\"tas\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "### 3. Calculate global average\n",
-    "\n",
-    "Related accessor: `ds.spatial`\n",
-    "\n",
-    "In this example, we **calculate the global spatial average** of `tas` and **plot the first 100 time steps**.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
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-   "outputs": [
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-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tas&#x27; (time: 1980)&gt; Size: 16kB\n",
-       "dask.array&lt;truediv, shape=(1980,), dtype=float64, chunksize=(1,), chunktype=numpy.ndarray&gt;\n",
-       "Coordinates:\n",
-       "    height   float64 8B ...\n",
-       "  * time     (time) object 16kB 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
-       "Attributes:\n",
-       "    operation:  temporal_avg\n",
-       "    mode:       departures\n",
-       "    freq:       month\n",
-       "    weighted:   True</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tas'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1980</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-14a740e4-3e73-405c-9b4b-e00f0444fb03' class='xr-array-in' type='checkbox' checked><label for='section-14a740e4-3e73-405c-9b4b-e00f0444fb03' 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,), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 15.47 kiB </td>\n",
-       "                        <td> 8 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1980,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1980 chunks in 165 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"6\" y1=\"0\" x2=\"6\" y2=\"25\" />\n",
-       "  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n",
-       "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
-       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" />\n",
-       "  <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"25\" />\n",
-       "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
-       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n",
-       "  <line x1=\"50\" y1=\"0\" x2=\"50\" y2=\"25\" />\n",
-       "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
-       "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
-       "  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n",
-       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
-       "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
-       "  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n",
-       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
-       "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
-       "  <line x1=\"107\" y1=\"0\" x2=\"107\" y2=\"25\" />\n",
-       "  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1980</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></div></li><li class='xr-section-item'><input id='section-a13cf045-eb66-4d3a-a627-f55f59ba52b6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a13cf045-eb66-4d3a-a627-f55f59ba52b6' 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>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-beed275b-7407-4a22-ac3c-803bbc1e5307' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-beed275b-7407-4a22-ac3c-803bbc1e5307' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1733f715-5b35-487c-b164-28d1dbda86b9' class='xr-var-data-in' type='checkbox'><label for='data-1733f715-5b35-487c-b164-28d1dbda86b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1850-01-16 12:00:00 ... 2014-12-...</div><input id='attrs-381b61e8-db77-4990-99e7-09fd1a353d1a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-381b61e8-db77-4990-99e7-09fd1a353d1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-901dd45b-b9f1-49f8-9513-a6ad7e4863e8' class='xr-var-data-in' type='checkbox'><label for='data-901dd45b-b9f1-49f8-9513-a6ad7e4863e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeProlepticGregorian(1850, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1850, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1850, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       ...,\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 12, 16, 12, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-41528292-5660-4381-bb80-c329baa33037' class='xr-section-summary-in' type='checkbox'  ><label for='section-41528292-5660-4381-bb80-c329baa33037' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b34dedb2-ac92-4a68-9760-73b80b97f206' class='xr-index-data-in' type='checkbox'/><label for='index-b34dedb2-ac92-4a68-9760-73b80b97f206' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([1850-01-16 12:00:00, 1850-02-15 00:00:00, 1850-03-16 12:00:00,\n",
-       "             1850-04-16 00:00:00, 1850-05-16 12:00:00, 1850-06-16 00:00:00,\n",
-       "             1850-07-16 12:00:00, 1850-08-16 12:00:00, 1850-09-16 00:00:00,\n",
-       "             1850-10-16 12:00:00,\n",
-       "             ...\n",
-       "             2014-03-16 12:00:00, 2014-04-16 00:00:00, 2014-05-16 12:00:00,\n",
-       "             2014-06-16 00:00:00, 2014-07-16 12:00:00, 2014-08-16 12:00:00,\n",
-       "             2014-09-16 00:00:00, 2014-10-16 12:00:00, 2014-11-16 00:00:00,\n",
-       "             2014-12-16 12:00:00],\n",
-       "            dtype=&#x27;object&#x27;,\n",
-       "            length=1980,\n",
-       "            calendar=&#x27;proleptic_gregorian&#x27;,\n",
-       "            freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c6c54c40-6252-451a-9307-f24948e33044' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c6c54c40-6252-451a-9307-f24948e33044' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>operation :</span></dt><dd>temporal_avg</dd><dt><span>mode :</span></dt><dd>departures</dd><dt><span>freq :</span></dt><dd>month</dd><dt><span>weighted :</span></dt><dd>True</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'tas' (time: 1980)> Size: 16kB\n",
-       "dask.array<truediv, shape=(1980,), dtype=float64, chunksize=(1,), chunktype=numpy.ndarray>\n",
-       "Coordinates:\n",
-       "    height   float64 8B ...\n",
-       "  * time     (time) object 16kB 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
-       "Attributes:\n",
-       "    operation:  temporal_avg\n",
-       "    mode:       departures\n",
-       "    freq:       month\n",
-       "    weighted:   True"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds_anom_glb = ds_anom.spatial.average(\"tas\")\n",
-    "ds_anom_glb[\"tas\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x147097bd0>]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ds_anom_glb.tas.isel().plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "### 4. Calculate annual averages\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "ds_anom_glb_ann = ds_anom_glb.temporal.group_average(\"tas\", freq=\"year\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "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",
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-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
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-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tas&#x27; (time: 165)&gt; Size: 1kB\n",
-       "dask.array&lt;truediv, shape=(165,), dtype=float64, chunksize=(1,), chunktype=numpy.ndarray&gt;\n",
-       "Coordinates:\n",
-       "    height   float64 8B ...\n",
-       "  * time     (time) object 1kB 1850-01-01 00:00:00 ... 2014-01-01 00:00:00\n",
-       "Attributes:\n",
-       "    operation:  temporal_avg\n",
-       "    mode:       group_average\n",
-       "    freq:       year\n",
-       "    weighted:   True</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tas'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 165</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-61ba38d2-f789-4f66-8c79-7e7e206f4c31' class='xr-array-in' type='checkbox' checked><label for='section-61ba38d2-f789-4f66-8c79-7e7e206f4c31' 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,), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.29 kiB </td>\n",
-       "                        <td> 8 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (165,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 165 chunks in 2154 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></div></li><li class='xr-section-item'><input id='section-27c57f1e-8001-4c4c-9e82-a5ad2432c10f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-27c57f1e-8001-4c4c-9e82-a5ad2432c10f' 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>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6f922ba3-34f2-4ee6-bcf3-426c02f4d5a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f922ba3-34f2-4ee6-bcf3-426c02f4d5a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a11fa2ac-9941-406f-b23d-ed5d39118e54' class='xr-var-data-in' type='checkbox'><label for='data-a11fa2ac-9941-406f-b23d-ed5d39118e54' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1850-01-01 00:00:00 ... 2014-01-...</div><input id='attrs-0e5eabf8-bfc5-4409-a3e0-1f359aa60171' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e5eabf8-bfc5-4409-a3e0-1f359aa60171' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa5e43a6-11b8-471c-a976-cdc9013521e2' class='xr-var-data-in' type='checkbox'><label for='data-aa5e43a6-11b8-471c-a976-cdc9013521e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeProlepticGregorian(1850, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1851, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1852, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1853, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1854, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1855, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1856, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1857, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1858, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1859, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1860, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1861, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1862, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1863, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1864, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1865, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1866, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1867, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1868, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1869, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1870, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1871, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1872, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1873, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1874, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1875, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1876, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1877, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1878, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1879, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1880, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1881, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1882, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1883, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1884, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1885, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1886, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1887, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1888, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1889, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1890, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1891, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1892, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1893, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1894, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1895, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1896, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1897, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1898, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1899, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1900, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1901, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1902, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1903, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1904, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1905, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1906, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1907, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1908, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1909, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1910, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1911, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1912, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1913, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1914, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1915, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1916, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1917, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1918, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1919, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1920, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1921, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1922, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1923, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1924, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1925, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1926, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1927, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1928, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1929, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1930, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1931, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1932, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1933, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1934, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1935, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1936, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1937, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1938, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1939, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1940, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1941, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1942, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1943, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1944, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1945, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1946, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1947, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1948, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1949, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1950, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1951, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1952, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1953, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1954, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1955, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1956, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1957, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1958, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1959, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1960, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1961, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1962, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1963, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1964, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1965, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1966, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1967, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1968, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1969, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1970, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1971, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1972, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1973, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1974, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1975, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1976, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1977, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1978, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1979, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1980, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1981, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1982, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1983, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1984, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1985, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1986, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1987, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1988, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1989, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1990, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1991, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1992, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1993, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1994, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1995, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1996, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1997, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1998, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1999, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2000, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2001, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2002, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2003, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2004, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2005, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2006, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2007, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2008, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2009, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2010, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2011, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2012, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2013, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 1, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d7ecf88c-084f-4e17-b753-28ccd7885bfa' class='xr-section-summary-in' type='checkbox'  ><label for='section-d7ecf88c-084f-4e17-b753-28ccd7885bfa' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-08e8837b-4786-48c3-a2f6-6e9faee96d71' class='xr-index-data-in' type='checkbox'/><label for='index-08e8837b-4786-48c3-a2f6-6e9faee96d71' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([1850-01-01 00:00:00, 1851-01-01 00:00:00, 1852-01-01 00:00:00,\n",
-       "             1853-01-01 00:00:00, 1854-01-01 00:00:00, 1855-01-01 00:00:00,\n",
-       "             1856-01-01 00:00:00, 1857-01-01 00:00:00, 1858-01-01 00:00:00,\n",
-       "             1859-01-01 00:00:00,\n",
-       "             ...\n",
-       "             2005-01-01 00:00:00, 2006-01-01 00:00:00, 2007-01-01 00:00:00,\n",
-       "             2008-01-01 00:00:00, 2009-01-01 00:00:00, 2010-01-01 00:00:00,\n",
-       "             2011-01-01 00:00:00, 2012-01-01 00:00:00, 2013-01-01 00:00:00,\n",
-       "             2014-01-01 00:00:00],\n",
-       "            dtype=&#x27;object&#x27;,\n",
-       "            length=165,\n",
-       "            calendar=&#x27;proleptic_gregorian&#x27;,\n",
-       "            freq=&#x27;YS-JAN&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cda4a1c2-ebb2-4bc7-b354-33ba6ce603d4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cda4a1c2-ebb2-4bc7-b354-33ba6ce603d4' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>operation :</span></dt><dd>temporal_avg</dd><dt><span>mode :</span></dt><dd>group_average</dd><dt><span>freq :</span></dt><dd>year</dd><dt><span>weighted :</span></dt><dd>True</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'tas' (time: 165)> Size: 1kB\n",
-       "dask.array<truediv, shape=(165,), dtype=float64, chunksize=(1,), chunktype=numpy.ndarray>\n",
-       "Coordinates:\n",
-       "    height   float64 8B ...\n",
-       "  * time     (time) object 1kB 1850-01-01 00:00:00 ... 2014-01-01 00:00:00\n",
-       "Attributes:\n",
-       "    operation:  temporal_avg\n",
-       "    mode:       group_average\n",
-       "    freq:       year\n",
-       "    weighted:   True"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ngrid = xc.create_grid(x=nlon, y=nlat)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Call the xESMF regridder\n",
+    "\n",
+    "Here we're using bilinear regridding, but other methods may be appropriate (e.g., you may want to use \"conservative_normed\" for fields that should be conserved globally).\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Regrid \"TREFHT\" with the `ngrid` created above using `xesmf` and `bilinear`.\n",
+    "\n",
+    "- API Documentation: https://xcdat.readthedocs.io/en/stable/generated/xarray.Dataset.regridder.horizontal.html\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds_xesmf = ds.regridder.horizontal(\"TREFHT\", ngrid, tool=\"xesmf\", method=\"bilinear\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Compare the results (for the first timestep)\n",
+    "\n",
+    "Now we just plot the results for comparison.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "map_proj = ccrs.Robinson()\n",
+    "\n",
+    "# plot original data (first time step)\n",
+    "plt.figure(figsize=(12, 5))\n",
+    "plt.subplot(1, 2, 1, projection=map_proj)\n",
+    "p = ds.TREFHT[0].plot(\n",
+    "    transform=ccrs.PlateCarree(),  # the data's projection\n",
+    "    subplot_kws={\"projection\": map_proj},\n",
+    "    cbar_kwargs={\"orientation\": \"horizontal\"},\n",
+    "    cmap=plt.cm.RdBu_r,\n",
+    ")\n",
+    "ax = plt.gca()\n",
+    "ax.coastlines()\n",
+    "plt.title(\"Original\")\n",
+    "\n",
+    "# plot the remapped data (first time step)\n",
+    "plt.subplot(1, 2, 2, projection=map_proj)\n",
+    "p = ds_xesmf.TREFHT[0].plot(\n",
+    "    transform=ccrs.PlateCarree(),  # the data's projection\n",
+    "    subplot_kws={\"projection\": map_proj},\n",
+    "    cbar_kwargs={\"orientation\": \"horizontal\"},\n",
+    "    cmap=plt.cm.RdBu_r,\n",
+    ")\n",
+    "ax = plt.gca()\n",
+    "ax.coastlines()\n",
+    "plt.title(\"xESMF 4$^{\\circ}$ x 4$^{\\circ}$\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Vertical Regridding\n",
+    "\n",
+    "xcdat can also regrid in the vertical. Here we'll grab some 3D temperature data and regrid it in the vertical. First, we need to remap some 3-dimensional data to a rectilinear grid (like we did for the surface air temperature data, `TREFHT`).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "# source e3sm-unified environment\n",
+    "source /global/common/software/e3sm/anaconda_envs/load_latest_e3sm_unified_pm-cpu.sh\n",
+    "# remap (we are only remapping one file and specifying the output location)\n",
+    "ncremap -m /global/cfs/cdirs/e3sm/diagnostics/maps/map_ne30pg2_to_cmip6_180x360_aave.20200201.nc -t 1 -v T /global/cfs/cdirs/e3sm/www/Tutorials/2024/simulations/extendedOutput.v3.LR.historical_0101/archive/atm/hist/extendedOutput.v3.LR.historical_0101.eam.h0.2000-01.nc T_extendedOutput.v3.LR.historical_0101.eam.h0.2000-01.nc >/dev/null 2>&1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's load the data:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "ds_anom_glb_ann[\"tas\"]"
+    "# specify file we just regridded\n",
+    "fn = \"T_extendedOutput.v3.LR.historical_0101.eam.h0.2000-01.nc\"\n",
+    "\n",
+    "# load regridded data\n",
+    "ds3d = xc.open_dataset(fn)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
+   "metadata": {},
    "source": [
-    "### 5. Plot the anomaly map\n"
+    "Next, we will do the vertical remapping...\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "import cartopy.crs as ccrs\n",
-    "from cartopy.util import add_cyclic_point\n",
-    "import matplotlib.colors as mcolors\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "def mycolormap():\n",
-    "    \"\"\"Combine two colormap to generate a new colormap for blue-white(middle)-yellow-red\n",
+    "# first construct the 3D pressure field\n",
+    "pressure = ds3d[\"hyam\"] * 1000.0 + ds3d[\"hybm\"] * ds3d[\"PS\"]\n",
+    "\n",
+    "# next, construct the target pressure axis\n",
+    "target_plevs = [\n",
+    "    100000,\n",
+    "    92500,\n",
+    "    85000,\n",
+    "    75000,\n",
+    "    70000,\n",
+    "    60000,\n",
+    "    50000,\n",
+    "    40000,\n",
+    "    30000,\n",
+    "    25000,\n",
+    "    20000,\n",
+    "    15000,\n",
+    "    10000,\n",
+    "    7000,\n",
+    "    5000,\n",
+    "    3000,\n",
+    "    1000,\n",
+    "    500,\n",
+    "    300,\n",
+    "    100,\n",
+    "]\n",
+    "nplev = xc.create_grid(z=xc.create_axis(\"lev\", target_plevs))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Regrid the `\"T\"` variable using `nplev` as the output grid, `\"log\"` method, and `pressure` as the target data.\n",
     "\n",
-    "    Returns\n",
-    "    -------\n",
-    "    matplotlib colormap\n",
-    "    \"\"\"\n",
-    "    # Adjust colormap\n",
+    "- Example Documentation: https://xcdat.readthedocs.io/en/stable/examples/regridding-vertical.html#4:-Remap-cloud-fraction-from-model-hybrid-coordinate-to-pressure-levels\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dsvr = ds3d.regridder.vertical(\"T\", nplev, method=\"log\", target_data=pressure)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, we plot the result:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot result\n",
+    "dsvr_zonal = dsvr.spatial.average(\"T\", axis=[\"X\"]).squeeze()\n",
+    "dsvr_zonal.T.plot(cmap=plt.cm.RdBu_r)\n",
+    "plt.gca().invert_yaxis()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Spatial Averaging with xCDAT\n",
     "\n",
-    "    # sample the colormaps that you want to use. Use 128 from each so we get 256\n",
-    "    # colors in total\n",
-    "    colors1 = plt.cm.Blues_r(np.linspace(0.0, 1, 127))\n",
-    "    colors2 = plt.cm.YlOrBr(np.linspace(0, 1, 127))\n",
+    "Area-weighted spatial averaging is a common technique to reduce dimensionality in geospatial datasets. xCDAT can perform this calculation over full domains or regions of interest.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculate the spatial average of \"TREFHT\" and store the results in a Python variable.\n",
     "\n",
-    "    # add white in the middle\n",
-    "    colors1 = np.append(colors1, [[0, 0, 0, 0]], axis=0)\n",
-    "    colors2 = np.vstack((np.array([0, 0, 0, 0]), colors2))\n",
+    "- API Documentation: https://xcdat.readthedocs.io/en/stable/generated/xarray.Dataset.spatial.average.html\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds_global = ds.spatial.average(\"TREFHT\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Now let's plot the results.\n",
     "\n",
-    "    # combine them and build a new colormap\n",
-    "    colors = np.vstack((colors1, colors2))\n",
-    "    mymap = mcolors.LinearSegmentedColormap.from_list(\"my_colormap\", colors)\n",
+    "Note that the spatial averager returns a dataset object so we still need to specify \"TREFHT\" to plot the dataarray.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dtime = dt2decimal(ds_global.time)  # decimal time\n",
+    "plt.plot(dtime, ds_global[\"TREFHT\"].values)\n",
+    "plt.xlabel(\"Year\")\n",
+    "plt.ylabel(\"Global Mean Temperature [K]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Above, we did not specify any constraints. So xCDAT calculated the domain (global) average. Users can also specify their own bounds.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculate the the average surface temperature (`\"TREFHT\"`) in the Niño 3.4 region.\n",
     "\n",
-    "    return mymap\n",
+    "- API Documentation: https://xcdat.readthedocs.io/en/stable/generated/xarray.Dataset.spatial.average.html\n",
+    "- Hint: Pass latitude bounds of (-5, 5) and longitude bounds of (190, 240) and keep the weights.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds_nino34 = ds_xesmf.spatial.average(\n",
+    "    \"TREFHT\", lat_bounds=(-5, 5), lon_bounds=(190, 240), keep_weights=True\n",
+    ").load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this case, we specified `keep_weights=True`. The weights provide full spatial weighting for grid cells entirely within the Niño 3.4 region. If a grid cell is partially in the Niño 3.4 region, it received partial weight (note we use the 4 x 4 degree grid in this example to show the partial weights and to speed up plotting). Note that you can also supply your own weights (but you can't automatically subset with `lat_bounds` and `lon_bounds` if you supply your own weights).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# show the nino 3.4 time series\n",
+    "plt.figure(figsize=(10, 2))\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.plot(dtime, ds_nino34[\"TREFHT\"].values)\n",
+    "plt.xlabel(\"Year\")\n",
+    "plt.ylabel(\"Surface Temperature [K]\")\n",
+    "plt.title(\"Niño 3.4 time series\")\n",
+    "\n",
+    "# show the weights\n",
+    "map_proj = ccrs.PlateCarree(central_longitude=180)\n",
+    "ax = plt.subplot(1, 2, 2, projection=map_proj)\n",
+    "plt.pcolor(\n",
+    "    ds_nino34.lon,\n",
+    "    ds_nino34.lat,\n",
+    "    ds_nino34.lat_lon_wts.T,\n",
+    "    transform=ccrs.PlateCarree(),\n",
+    "    cmap=plt.cm.GnBu,\n",
+    ")\n",
+    "ax.set_extent([120, 300, -30, 30], crs=ccrs.PlateCarree())\n",
+    "ax.coastlines()\n",
+    "plt.colorbar(orientation=\"horizontal\")\n",
+    "plt.title(\"Nino 3.4 Weights\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Temporal Computations with xCDAT\n",
     "\n",
-    "def plot_colormap():\n",
-    "    plt.figure(figsize=(6.5, 8))\n",
-    "    font = {\"size\": 11}\n",
+    "In the examples below, we will performing temporal computations on the `xarray.Dataset` object using xCDAT.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Annual cycle\n",
     "\n",
-    "    plt.rc(\"font\", **font)\n",
+    "In the global mean time series above, there are large seasonal swings in global temperature. Here we compute the seasonal mean climatology.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculate the seasonal mean climatology for the `\"TREFHT\"` variable.\n",
     "\n",
-    "    # Anomaly map\n",
-    "    plt.subplot(2, 1, 1, projection=ccrs.Mollweide())\n",
-    "    ts_anom, lon = add_cyclic_point(ds_anom.tas.isel(time=8), coord=ds_anom.lon)\n",
-    "    plt.contourf(\n",
-    "        lon,\n",
-    "        ds_anom.lat,\n",
-    "        ts_anom,\n",
-    "        levels=np.arange(-4, 4.01, 0.25),\n",
+    "- API Documentation: https://xcdat.readthedocs.io/en/stable/generated/xarray.Dataset.temporal.climatology.html\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute the climatology\n",
+    "ds_clim = ds.temporal.climatology(\"TREFHT\", freq=\"season\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Now we plot the season means\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "map_proj = ccrs.Robinson()\n",
+    "titles = [\"DJF\", \"MAM\", \"JJA\", \"SON\"]\n",
+    "plt.figure(figsize=(12, 10))\n",
+    "for i in range(4):\n",
+    "    plt.subplot(2, 2, i + 1, projection=map_proj)\n",
+    "    p = ds_clim.TREFHT[i].plot(\n",
     "        transform=ccrs.PlateCarree(),\n",
-    "        cmap=mycolormap(),\n",
-    "        extend=\"both\",\n",
-    "    )\n",
-    "    plt.colorbar(\n",
-    "        label=\"[K]\", orientation=\"horizontal\", ticks=np.arange(-4.0, 4.01, 1), shrink=0.9\n",
+    "        subplot_kws={\"projection\": map_proj},\n",
+    "        cbar_kwargs={\"orientation\": \"horizontal\"},\n",
+    "        cmap=plt.cm.RdBu_r,\n",
+    "        vmin=220,\n",
+    "        vmax=310,\n",
     "    )\n",
     "    ax = plt.gca()\n",
     "    ax.coastlines()\n",
-    "    plt.title(\"\")\n",
-    "\n",
+    "    plt.title(titles[i])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Departures\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It can also be useful to show the departures from the climatological average.\n",
     "\n",
-    "    # Anomaly map\n",
-    "    plt.subplot(2, 1, 2)\n",
-    "    ds_anom_glb.tas.plot(color=\"gray\")\n",
-    "    ds_anom_glb_ann.tas.plot(color=\"k\")\n",
-    "    # update spines\n",
-    "    ax = plt.gca()\n",
-    "    # Move left and bottom spines outward by 10 points\n",
-    "    ax.spines.left.set_position((\"outward\", 10))\n",
-    "    ax.spines.bottom.set_position((\"outward\", 10))\n",
-    "    # Hide the right and top spines\n",
-    "    ax.spines.right.set_visible(False)\n",
-    "    ax.spines.top.set_visible(False)\n",
-    "    # Only show ticks on the left and bottom spines\n",
-    "    ax.yaxis.set_ticks_position(\"left\")\n",
-    "    ax.xaxis.set_ticks_position(\"bottom\")\n",
-    "    ax.set_ylabel(\"Surface Temperature Anomalies [K]\")\n",
+    "Calculate the seasonal mean climatology for the `\"TREFHT\"` variable. In this case, `xcdat` will operate on the global mean time series we calculated above. Note that you can set the climatological reference period (e.g., with `reference_period=(\"1950-01-01\", \"1999-12-31\")` for historical era departures).\n",
     "\n",
-    "    # plot labels\n",
-    "    f = plt.gcf()\n",
-    "    f.text(0.2, 1.025, \"A. Surface Temperature Anomalies (September 1850)\", fontsize=12)\n",
-    "    f.text(0.2, 0.475, \"B. Global Mean Surface Temperature Anomalies\", fontsize=12)"
+    "- API Documentation: https://xcdat.readthedocs.io/en/stable/generated/xarray.Dataset.temporal.departures.html\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 650x800 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "plot_colormap()"
+    "ds_global_anomaly = ds_global.temporal.departures(\n",
+    "    \"TREFHT\", freq=\"month\", reference_period=(\"2000-01-01\", \"2009-12-31\")\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
+   "metadata": {},
+   "source": [
+    "#### Now let's plot the departures from the climatological average.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "TODO: Fix the above plot"
+    "plt.plot(dtime, ds_global_anomaly.TREFHT.values)\n",
+    "plt.xlabel(\"Year\")\n",
+    "plt.ylabel(\"Global Mean Surface Temperature Anomaly [K]\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
+   "metadata": {},
    "source": [
-    "### For context, here's how xCDAT's code (left) compares to pure Xarray code (right)\n",
+    "### Group averages\n",
     "\n",
-    "Including imports, xCDAT takes 8 lines of code while pure Xarray takes 31.\n",
+    "`xcdat` also allows you to calculate group averages (e.g., annual or seasonal mean from monthly data or monthly mean from daily data).\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculate the annual mean from anomaly time series.\n",
     "\n",
-    "If you wanted to implement the same functionalities yourself in pure Xarray, you would need to consider these factors:\n",
-    "- String references for time components (e.g., `\"time.month\"` -> `\"time.day\"`)\n",
-    "- Change referenced data variable (e.g., `ds.tas` -> `ds.ts`, or `ds[\"tas\"]` -> `ds[\"ts\"]`)\n",
-    "- Renaming variable\n"
+    "- API Documentation: https://xcdat.readthedocs.io/en/stable/generated/xarray.Dataset.temporal.group_averages.html\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "cell_style": "split",
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "import xcdat as xc\n",
-    "\n",
-    "# 1. Open the dataset.\n",
-    "filepath = \"https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r10i1p1f1/Amon/tas/gn/v20200605/tas_Amon_ACCESS-ESM1-5_historical_r10i1p1f1_gn_185001-201412.nc\"\n",
-    "\n",
-    "ds = xc.open_dataset(\n",
-    "    filepath, add_bounds=[\"X\", \"Y\", \"T\"], decode_times=True, center_times=True\n",
+    "# compute annual mean from anomaly time series\n",
+    "ds_global_anomaly_annual = ds_global_anomaly.temporal.group_average(\n",
+    "    \"TREFHT\", freq=\"year\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Now let's plot the results.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot data\n",
+    "dtime_annual = dt2decimal(ds_global_anomaly_annual.time) + 0.5\n",
+    "plt.plot(\n",
+    "    dtime, ds_global_anomaly.TREFHT.values, label=\"Monthly departure\", color=\"gray\"\n",
     ")\n",
+    "plt.plot(\n",
+    "    dtime_annual,\n",
+    "    ds_global_anomaly_annual.TREFHT.values,\n",
+    "    color=\"k\",\n",
+    "    linestyle=\"\",\n",
+    "    marker=\"_\",\n",
+    "    label=\"Annual Mean\",\n",
+    ")\n",
+    "plt.legend(frameon=False)\n",
+    "plt.xlabel(\"Year\")\n",
+    "plt.ylabel(\"Global Mean Surface Temperature [K]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### General Dataset Utilities\n",
     "\n",
-    "# Unit adjustment from Kelvin to Celcius.\n",
-    "ds[\"tas\"] = ds.tas - 273.15\n",
-    "\n",
-    "# 2. Calculate monthly departures.\n",
-    "ds_anom = ds.temporal.departures(\"tas\", freq=\"month\")\n",
-    "\n",
-    "# 3. Compute global average.\n",
-    "ds_anom_glb = ds_anom.spatial.average(\"tas\")\n",
+    "xCDAT includes various utilities for data manipulation, including\n",
+    "reorientation of the longitude axis, centering of time coordinates using time bounds, and adding and getting bounds.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Reorient the longitude axis\n",
     "\n",
-    "# 4. Calculate annual averages\n",
-    "ds_anom_glb_ann = ds_anom_glb.temporal.group_average(\"tas\", freq=\"year\")"
+    "Longitude can be represented from 0 to 360 E or as 180 W to 180 E. xcdat allows you to convert between these axes systems.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "cell_style": "split",
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
-    "import xarray as xr\n",
-    "\n",
-    "# 1. Open the dataset.\n",
-    "filepath = \"https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r10i1p1f1/Amon/tas/gn/v20200605/tas_Amon_ACCESS-ESM1-5_historical_r10i1p1f1_gn_185001-201412.nc\"\n",
-    "\n",
-    "ds = xr.open_dataset(filepath)\n",
-    "\n",
-    "# 2. Calculate monthly departures.\n",
-    "tas_mon = ds.tas.groupby(\"time.month\")\n",
-    "tas_mon_clim = tas_mon.mean(dim=\"time\")\n",
-    "tas_anom = tas_mon - tas_mon_clim\n",
+    "ds.lon"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use `xc.swap_lon_axis` to swap the longitude axis from (0, 360) to (-180, 180) and view\n",
+    "the new longitude axis.\n",
     "\n",
-    "# 3. Compute global average.\n",
-    "coslat = np.cos(np.deg2rad(ds.lat))\n",
-    "tas_anom_wgt = tas_anom.weighted(coslat)\n",
-    "tas_anom_global = tas_anom_wgt.mean(dim=\"lat\").mean(dim=\"lon\")\n",
+    "- Documentation: https://xcdat.readthedocs.io/en/stable/generated/xcdat.swap_lon_axis.html\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds2 = xc.swap_lon_axis(ds, to=(-180, 180))\n",
     "\n",
-    "# 4. Calculate annual averages.\n",
-    "# ncar.github.io/esds/posts/2021/yearly-averages-xarray/\n",
-    "mon_len = tas_anom_global.time.dt.days_in_month\n",
-    "mon_len_by_year = mon_len.groupby(\"time.year\")\n",
-    "wgts = mon_len_by_year / mon_len_by_year.sum()\n",
+    "ds2.lon"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Add missing bounds\n",
     "\n",
-    "temp_sum = tas_anom_global * wgts\n",
-    "temp_sum = temp_sum.resample(time=\"YS\").sum(dim=\"time\")\n",
-    "denom_sum = (wgts).resample(time=\"YS\").sum(dim=\"time\")\n",
+    "Bounds are critical to many `xcdat` operations. For example, they are used in determining the weights in spatial or temporal averages and in regridding operations. `add_missing_bounds()` will attempt to produce bounds if they do not exist in the original dataset.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We are dropping the existing bounds to demonstrate adding bounds.\n",
+    "ds4 = ds.drop_vars(\"time_bnds\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    ds4.bounds.get_bounds(\"T\")\n",
+    "except KeyError as e:\n",
+    "    print(e)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Add the missing time bounds using `.bounds.add_missing_bounds()`.\n",
     "\n",
-    "tas_anom_global_ann = temp_sum / denom_sum"
+    "- Documentation: https://xcdat.readthedocs.io/en/stable/generated/xarray.Dataset.bounds.add_missing_bounds.html\n",
+    "- Hint: Use the `axes` arg and pass a list containing a single string, `\"T\"` for time.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds5 = ds4.bounds.add_missing_bounds(axes=[\"T\"])\n",
+    "ds5"
    ]
   },
   {
@@ -3699,7 +1771,7 @@
     }
    },
    "source": [
-    "Now let's run the xCDAT `spatial.average()` method and view the output"
+    "Now let's run the xCDAT `spatial.average()` method and view the output\n"
    ]
   },
   {
@@ -4201,7 +2273,7 @@
     }
    },
    "source": [
-    "Notice how the `tas` array is still a Dask Array. "
+    "Notice how the `tas` array is still a Dask Array.\n"
    ]
   },
   {
@@ -4216,8 +2288,6 @@
     "\n",
     "Visit these pages for more guidance (e.g., when to parallelize):\n",
     "\n",
-    " # TODO: Add link to xCDAT Dask guidance\n",
-    "\n",
     "- Ongoing xCDAT Dask Investigation: https://github.com/xCDAT/xcdat/discussions/376\n",
     "  - Performance metrics, best practices, and possibly a guide\n",
     "- Parallel computing with Dask: https://docs.xarray.dev/en/stable/user-guide/dask.html\n",