docs/examples/introduction-to-xcdat.ipynb

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    "<div style=\"text-align: center\">\n",
    "    <img src=\"../_static/xcdat-logo.png\" alt=\"xCDAT logo\" style=\"display: inline-block; width:600px;\">\n",
    "</div>\n",
    "\n",
    "# A Gentle Introduction to xCDAT (Xarray Climate Data Analysis Tools)\n",
    "\n",
    "<h3 style=\"text-align: left;\">\n",
    "    \"A Python package for simple and robust climate data analysis.\"</h2>\n",
    "<h3 style=\"text-align: left; font-style:italic\">Core Developers: Tom Vo, Stephen Po-Chedley, Jason Boutte, Jill Zhang, Jiwoo Lee</h3>\n",
    "\n",
    "<p style=\"text-align: left;\">With thanks to Peter Gleckler, Paul Durack, Karl Taylor, and Chris Golaz</p>\n",
    "\n",
    "Updated: 03/18/25 [v0.8.0]\n",
    "\n",
    "---\n",
    "\n",
    "_This work is performed under the auspices of the U. S. DOE by Lawrence Livermore National Laboratory under contract No. DE-AC52-07NA27344._\n"
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    "## Presentation Overview\n",
    "\n",
    "Intended audience: Some or no familiarity with `xarray` and/or `xcdat`\n",
    "\n",
    "1. **Driving force** behind xCDAT\n",
    "2. **Goals and milestones** of CDAT's successor\n",
    "3. Introducing xCDAT\n",
    "4. Understanding the **basics of Xarray**\n",
    "5. How **xCDAT extends Xarray** for climate data analysis\n",
    "6. Technical design philosophy and APIs\n",
    "7. **Demo** of capabilities\n",
    "8. How to **get involved**\n",
    "\n",
    "The data used in this example can be found in the [xcdat-data repository](https://github.com/xCDAT/xcdat-data).\n"
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    "### Notebook Kernel Setup\n",
    "\n",
    "Users can [install their own instance of xcdat](../getting-started-guide/installation.rst) and follow these examples using their own environment (e.g., with VS Code, Jupyter, Spyder, iPython) or [enable xcdat with existing JupyterHub instances](../getting-started-guide/getting-started-hpc-jupyter.rst).\n",
    "\n",
    "First, create the conda environment:\n",
    "\n",
    "```bash\n",
    "conda create -n xcdat_notebook -c conda-forge xcdat xesmf matplotlib ipython ipykernel cartopy nc-time-axis gsw-xarray jupyter pooch\n",
    "```\n",
    "\n",
    "Then install the kernel from the `xcdat_notebook` environment using `ipykernel` and name the kernel with the display name (e.g., `xcdat_notebook`):\n",
    "\n",
    "```bash\n",
    "python -m ipykernel install --user --name xcdat_notebook --display-name xcdat_notebook\n",
    "```\n",
    "\n",
    "Then to select the kernel `xcdat_notebook` in Jupyter to use this kernel.\n"
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    "## The Driving Force Behind xCDAT\n",
    "\n",
    "- The **CDAT (Community Data Analysis Tools)** library has provided a suite of robust and comprehensive open-source climate data analysis and visualization packages for over **20 years**\n",
    "- A driving **need** for a **modern successor**\n",
    "  - Focus on a maintainable and extensible library\n",
    "  - Serve the needs of the climate community in the long-term\n",
    "\n",
    " <div style=\"text-align: center\">\n",
    "    <img src=\"../_static/cdat-logo.png\" alt=\"CMIP6 logo\" style=\"display: inline-block; margin-right:50px; width:400px\">\n",
    "    <img src=\"../_static/cmip-logo.jpg\" alt=\"CMIP6 logo\" style=\"display: inline-block; width:400px\">\n",
    "    \n",
    "</div>\n"
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    "## Goals and Milestones for CDAT's Successor\n",
    "\n",
    "1. **Offer similar core capabilities**\n",
    "\n",
    "   1. _For example geospatial averaging, temporal averaging, and regridding_\n",
    "\n",
    "2. Use **modern technologies** in the library’s stack\n",
    "\n",
    "   1. Support parallelism and lazy operations\n",
    "\n",
    "3. Be **maintainable**, **extensible**, and **easy-to-use**\n",
    "\n",
    "   1. Python Enhancement Proposals (PEPs)\n",
    "   2. Automate DevOps processes (unit testing, code coverage)\n",
    "   3. Actively maintain documentation\n",
    "\n",
    "4. **Cultivate an open-source community** that can sustain the project\n",
    "\n",
    "   1. Encourage GitHub contributions\n",
    "   2. Community engagement efforts (e.g., Pangeo, ESGF)\n"
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    "## Introducing xCDAT\n",
    "\n",
    "- xCDAT is an **extension of xarray** for **climate data analysis** on **structured grids**\n",
    "- **Goal** of providing features and utilities for **simple and robust analysis of climate data**\n",
    "- Jointly developed by scientists and developers from:\n",
    "  - **E3SM Project** (Energy Exascale Earth System Model Project)\n",
    "  - **PCMDI** (Program for Climate Model Diagnosis and Intercomparison)\n",
    "  - **SEATS Project** (Simplifying ESM Analysis Through Standards Project)\n",
    "  - Users around the world **via** **GitHub**\n",
    "\n",
    "<div style=\"text-align: center; margin-top:10px\">\n",
    "<img src=\"../_static/e3sm-logo.jpg\" alt=\"E3SM logo\" align=\\\"center\\\" style=\"display: inline-block;     margin-right:100px; width:300px;\">\n",
    "<img src=\"../_static/pcmdi-logo.png\" alt=\"PCMDI logo\" style=\"display: inline-block; margin-right:100px; width:300px;\">\n",
    "<img src=\"../_static/seats-logo.png\" alt=\"SEATS logo\" style=\"display: inline-block; width:300px;\">\n",
    "\n",
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    "## Before We Dive Deeper, Let's Talk About Xarray\n",
    "\n",
    "- Xarray is an evolution of an internal tool developed at The Climate Corporation\n",
    "- Released as open source in May 2014\n",
    "- **NumFocus** fiscally sponsored project since August 2018\n",
    "\n",
    "<div style=\"text-align: center\">\n",
    "    <img src=\"../_static/xarray-logo.png\" alt=\"xarray logo\" style=\"display: inline-block; margin-right: 50px; width:400px;\">\n",
    "    <img src=\"../_static/NumFocus-logo.png\" alt=\"NumFOCUS logo\" style=\"display: inline-block; width:400px\">\n",
    "</div>\n"
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    "## Key Features and Capabilities in Xarray\n",
    "\n",
    "- **“N-D labeled arrays and datasets in Python”**\n",
    "  - Built upon and extends NumPy and pandas\n",
    "- **Interoperable with scientific Python ecosystem** including **NumPy, Dask, Pandas, and Matplotlib**\n",
    "- Supports file I/O, indexing and selecting, interpolating, grouping, aggregating, parallelism (Dask), plotting (matplotlib wrapper)\n",
    "  - Supported formats include: netCDF, Iris, OPeNDAP, Zarr, and GRIB\n",
    "\n",
    "<!-- <div style=\"text-align: center\">\n",
    "<img src=\"../../_static/numpy-logo.svg\" alt=\"NumPy logo\" style=\"display: inline-block; width:300px; margin-right: 50px\">\n",
    "<img src=\"../../_static/pandas-logo.svg\" alt=\"Pandas logo\" style=\"display: inline-block; width:300px; margin-right: 50px\">\n",
    "    <img src=\"../../_static/dask-logo.svg\" alt=\"Dask logo\" style=\"display: inline-block; width:300px\">\n",
    "</div> -->\n",
    "\n",
    "Source: <cite>https://xarray.dev/#features</cite>\n"
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    "## Why use Xarray?\n",
    "\n",
    "> _\"Xarray introduces **labels** in the form of **dimensions, coordinates and attributes** on **top of raw NumPy-like\n",
    "> multidimensional arrays**, which allows for a **more intuitive, more concise, and less error-prone developer\n",
    "> experience**.\"_\n",
    ">\n",
    "> &mdash; <cite> https://xarray.pydata.org/en/v2022.10.0/getting-started-guide/why-xarray.html</cite>\n"
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    "- **Apply operations over dimensions by name**\n",
    "  - `x.sum('time')`\n",
    "- **Select values by label** (or logical location) instead of integer location\n",
    "  - `x.loc['2014-01-01']` or `x.sel(time='2014-01-01')`\n",
    "- **Mathematical operations vectorize across multiple dimensions** (array broadcasting) based on **dimension names**, not shape\n",
    "  - `x - y`\n",
    "- Easily use the **split-apply-combine paradigm** with **groupby**\n",
    "  - `x.groupby('time.dayofyear').mean()`.\n",
    "- **Database-like alignment** based on coordinate labels that **smoothly handles missing values**\n",
    "  - `x, y = xr.align(x, y, join='outer')`\n",
    "- Keep track of **arbitrary metadata in** the form of a **Python dictionary**\n",
    "  - `x.attrs`\n",
    "\n",
    "Source: <cite>https://docs.xarray.dev/en/v2022.10.0/getting-started-guide/why-xarray.html#what-labels-enable</cite>\n"
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    "## The Xarray Data Models\n",
    "\n",
    "> \"Xarray data models are **borrowed from netCDF file format**, which provides xarray with a **natural and portable\n",
    "> serialization format**.\"\n",
    ">\n",
    "> &mdash; <cite>https://docs.xarray.dev/en/v2022.10.0/getting-started-guide/why-xarray.html</cite>\n"
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    "1. **`xarray.Dataset`**\n",
    "   - A **dictionary-like container** of **DataArray objects** with **aligned dimensions**\n",
    "     - **DataArray** objects are **classified** as **\"coordinate variables\"** or **\"data variables\"**\n",
    "     - All **data variables** have a **shared union of coordinates**\n",
    "   - Serves a similar purpose to a `pandas.DataFrame`\n",
    "\n",
    "---\n",
    "\n",
    "2. **`xarray.DataArray`**\n",
    "   - A class that attaches **dimension names**, **coordinates**, and **attributes** to **multi-dimensional arrays** (aka \"labeled arrays\")\n",
    "   - An N-D generalization of a `pandas.Series`\n"
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    "<div style=\"text-align: center\">\n",
    "    <img src=\"../_static/dataset-diagram.webp\" alt=\"xarray logo\" style=\"display: inline-block; width:1000px\">\n",
    "</div>\n"
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    "### Exploring the Xarray Data Models\n",
    "\n",
    "The data used in this example can be found in the [xcdat-data repository](https://github.com/pydata/xcdat-data).\n"
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    "import xcdat as xc\n",
    "\n",
    "ds = xc.tutorial.open_dataset(\"tas_amon_access\")"
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    "### The `Dataset` Model\n"
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       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: \"(\";\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: \")\";\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: \",\";\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-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.Dataset&gt; Size: 7MB\n",
       "Dimensions:    (time: 60, bnds: 2, lat: 145, lon: 192)\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 ... 354.4 356.2 358.1\n",
       "    height     float64 8B 2.0\n",
       "  * time       (time) object 480B 1870-01-16 12:00:00 ... 1874-12-16 12:00:00\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    time_bnds  (time, bnds) object 960B ...\n",
       "    lat_bnds   (lat, bnds) float64 2kB ...\n",
       "    lon_bnds   (lon, bnds) float64 3kB ...\n",
       "    tas        (time, lat, lon) float32 7MB ...\n",
       "Attributes: (12/48)\n",
       "    Conventions:                     CF-1.7 CMIP-6.2\n",
       "    activity_id:                     CMIP\n",
       "    branch_method:                   standard\n",
       "    branch_time_in_child:            0.0\n",
       "    branch_time_in_parent:           87658.0\n",
       "    creation_date:                   2020-06-05T04:06:11Z\n",
       "    ...                              ...\n",
       "    variant_label:                   r10i1p1f1\n",
       "    version:                         v20200605\n",
       "    license:                         CMIP6 model data produced by CSIRO is li...\n",
       "    cmor_version:                    3.4.0\n",
       "    tracking_id:                     hdl:21.14100/af78ae5e-f3a6-4e99-8cfe-5f2...\n",
       "    DODS_EXTRA.Unlimited_Dimension:  time</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-52ec865a-36c1-47c1-837a-bbc80fd92668' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-52ec865a-36c1-47c1-837a-bbc80fd92668' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 60</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lat</span>: 145</li><li><span class='xr-has-index'>lon</span>: 192</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-207e0292-6849-4f24-aa41-da2015a97fa7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-207e0292-6849-4f24-aa41-da2015a97fa7' 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-93c065de-4895-4fb8-8528-10c7e5d19e20' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-93c065de-4895-4fb8-8528-10c7e5d19e20' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-785bb195-7c0a-4dcd-a972-ee29be049241' class='xr-var-data-in' type='checkbox'><label for='data-785bb195-7c0a-4dcd-a972-ee29be049241' 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-4ba4eba1-b40e-4760-bf06-a282dfbd9a8e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ba4eba1-b40e-4760-bf06-a282dfbd9a8e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6631ed33-291b-4c93-9b40-81fc23d5593c' class='xr-var-data-in' type='checkbox'><label for='data-6631ed33-291b-4c93-9b40-81fc23d5593c' 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'>2.0</div><input id='attrs-118ecbf9-5c2e-4b86-a7a4-822ba4447890' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-118ecbf9-5c2e-4b86-a7a4-822ba4447890' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8883869-5895-4207-93ca-20a008fbe7f5' class='xr-var-data-in' type='checkbox'><label for='data-b8883869-5895-4207-93ca-20a008fbe7f5' 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>array(2.)</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'>1870-01-16 12:00:00 ... 1874-12-...</div><input id='attrs-20080b49-bf17-4275-b78d-5a615f9c2210' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20080b49-bf17-4275-b78d-5a615f9c2210' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af98c5e7-c157-45ef-998e-39f6ddf5bff6' class='xr-var-data-in' type='checkbox'><label for='data-af98c5e7-c157-45ef-998e-39f6ddf5bff6' 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(1870, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 2, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 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-98acbcc2-b8ba-4bab-bf97-f27e2830f80a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-98acbcc2-b8ba-4bab-bf97-f27e2830f80a' class='xr-section-summary' >Data variables: <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>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3ef75b34-3f2b-4242-ba3b-d2d4de1f9426' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ef75b34-3f2b-4242-ba3b-d2d4de1f9426' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f0a8d79-66a1-4044-9b3a-c5895aecd168' class='xr-var-data-in' type='checkbox'><label for='data-2f0a8d79-66a1-4044-9b3a-c5895aecd168' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[1 2]</dd></dl></div><div class='xr-var-data'><pre>[120 values with dtype=object]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-71be58eb-755a-49aa-b7ae-38bd49c05aa9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71be58eb-755a-49aa-b7ae-38bd49c05aa9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6cbb109e-a14a-404d-ac2e-9077db005ed8' class='xr-var-data-in' type='checkbox'><label for='data-6cbb109e-a14a-404d-ac2e-9077db005ed8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[145   2]</dd></dl></div><div class='xr-var-data'><pre>[290 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e0bffd25-65eb-42cd-8baa-f7cc8ea6fbf1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e0bffd25-65eb-42cd-8baa-f7cc8ea6fbf1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5df7b580-0fde-41b2-8f13-a6fb18bc3fd5' class='xr-var-data-in' type='checkbox'><label for='data-5df7b580-0fde-41b2-8f13-a6fb18bc3fd5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[192   2]</dd></dl></div><div class='xr-var-data'><pre>[384 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tas</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1bdbd3cc-0168-4acb-83d8-189e0039e666' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1bdbd3cc-0168-4acb-83d8-189e0039e666' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-216d636c-4181-4204-9ecc-4ef12e137c6c' class='xr-var-data-in' type='checkbox'><label for='data-216d636c-4181-4204-9ecc-4ef12e137c6c' 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>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>comment :</span></dt><dd>near-surface (usually, 2 meter) air temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>history :</span></dt><dd>2020-06-05T04:06:10Z altered by CMOR: Treated scalar dimension: &#x27;height&#x27;. 2020-06-05T04:06:10Z altered by CMOR: replaced missing value flag (-1.07374e+09) with standard missing value (1e+20).</dd><dt><span>_ChunkSizes :</span></dt><dd>[  1 145 192]</dd></dl></div><div class='xr-var-data'><pre>[1670400 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9f91fbb8-cbad-4fd4-8dc5-823e413c4e38' class='xr-section-summary-in' type='checkbox'  ><label for='section-9f91fbb8-cbad-4fd4-8dc5-823e413c4e38' 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><input type='checkbox' disabled/><label></label><input id='index-54953df6-c197-4f66-a8be-f0d72b68e7bc' class='xr-index-data-in' type='checkbox'/><label for='index-54953df6-c197-4f66-a8be-f0d72b68e7bc' 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><input type='checkbox' disabled/><label></label><input id='index-1ed70409-2e05-4ef9-954c-21a518ce337a' class='xr-index-data-in' type='checkbox'/><label for='index-1ed70409-2e05-4ef9-954c-21a518ce337a' 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><input type='checkbox' disabled/><label></label><input id='index-d8b1d812-ffde-4c26-a9c0-76a0d7ab8873' class='xr-index-data-in' type='checkbox'/><label for='index-d8b1d812-ffde-4c26-a9c0-76a0d7ab8873' 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([1870-01-16 12:00:00, 1870-02-15 00:00:00, 1870-03-16 12:00:00,\n",
       "             1870-04-16 00:00:00, 1870-05-16 12:00:00, 1870-06-16 00:00:00,\n",
       "             1870-07-16 12:00:00, 1870-08-16 12:00:00, 1870-09-16 00:00:00,\n",
       "             1870-10-16 12:00:00, 1870-11-16 00:00:00, 1870-12-16 12:00:00,\n",
       "             1871-01-16 12:00:00, 1871-02-15 00:00:00, 1871-03-16 12:00:00,\n",
       "             1871-04-16 00:00:00, 1871-05-16 12:00:00, 1871-06-16 00:00:00,\n",
       "             1871-07-16 12:00:00, 1871-08-16 12:00:00, 1871-09-16 00:00:00,\n",
       "             1871-10-16 12:00:00, 1871-11-16 00:00:00, 1871-12-16 12:00:00,\n",
       "             1872-01-16 12:00:00, 1872-02-15 12:00:00, 1872-03-16 12:00:00,\n",
       "             1872-04-16 00:00:00, 1872-05-16 12:00:00, 1872-06-16 00:00:00,\n",
       "             1872-07-16 12:00:00, 1872-08-16 12:00:00, 1872-09-16 00:00:00,\n",
       "             1872-10-16 12:00:00, 1872-11-16 00:00:00, 1872-12-16 12:00:00,\n",
       "             1873-01-16 12:00:00, 1873-02-15 00:00:00, 1873-03-16 12:00:00,\n",
       "             1873-04-16 00:00:00, 1873-05-16 12:00:00, 1873-06-16 00:00:00,\n",
       "             1873-07-16 12:00:00, 1873-08-16 12:00:00, 1873-09-16 00:00:00,\n",
       "             1873-10-16 12:00:00, 1873-11-16 00:00:00, 1873-12-16 12:00:00,\n",
       "             1874-01-16 12:00:00, 1874-02-15 00:00:00, 1874-03-16 12:00:00,\n",
       "             1874-04-16 00:00:00, 1874-05-16 12:00:00, 1874-06-16 00:00:00,\n",
       "             1874-07-16 12:00:00, 1874-08-16 12:00:00, 1874-09-16 00:00:00,\n",
       "             1874-10-16 12:00:00, 1874-11-16 00:00:00, 1874-12-16 12:00:00],\n",
       "            dtype=&#x27;object&#x27;,\n",
       "            length=60,\n",
       "            calendar=&#x27;proleptic_gregorian&#x27;,\n",
       "            freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c0549074-061f-4a1d-80a0-35aa8f336439' class='xr-section-summary-in' type='checkbox'  ><label for='section-c0549074-061f-4a1d-80a0-35aa8f336439' class='xr-section-summary' >Attributes: <span>(48)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>CMIP</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>branch_time_in_child :</span></dt><dd>0.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>87658.0</dd><dt><span>creation_date :</span></dt><dd>2020-06-05T04:06:11Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.30</dd><dt><span>experiment :</span></dt><dd>all-forcing simulation of the recent past</dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.CSIRO.ACCESS-ESM1-5.historical.none.r10i1p1f1</dd><dt><span>grid :</span></dt><dd>native atmosphere N96 grid (145x192 latxlon)</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>history :</span></dt><dd>2020-06-05T04:06:11Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia</dd><dt><span>institution_id :</span></dt><dd>CSIRO</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>nominal_resolution :</span></dt><dd>250 km</dd><dt><span>notes :</span></dt><dd>Exp: ESM-historical; Local ID: HI-14; Variable: tas ([&#x27;fld_s03i236&#x27;])</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>piControl</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>ACCESS-ESM1-5</dd><dt><span>parent_time_units :</span></dt><dd>days since 0101-1-1</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>10</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>run_variant :</span></dt><dd>forcing: GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = CO2, N2O, CH4, CFC11, CFC12, CFC113, HCFC22, HFC125, HFC134a)</dd><dt><span>source :</span></dt><dd>ACCESS-ESM1.5 (2019): \n",
       "aerosol: CLASSIC (v1.0)\n",
       "atmos: HadGAM2 (r1.1, N96; 192 x 145 longitude/latitude; 38 levels; top level 39255 m)\n",
       "atmosChem: none\n",
       "land: CABLE2.4\n",
       "landIce: none\n",
       "ocean: ACCESS-OM2 (MOM5, tripolar primarily 1deg; 360 x 300 longitude/latitude; 50 levels; top grid cell 0-10 m)\n",
       "ocnBgchem: WOMBAT (same grid as ocean)\n",
       "seaIce: CICE4.1 (same grid as ocean)</dd><dt><span>source_id :</span></dt><dd>ACCESS-ESM1-5</dd><dt><span>source_type :</span></dt><dd>AOGCM</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>Amon</dd><dt><span>table_info :</span></dt><dd>Creation Date:(30 April 2019) MD5:5bd755e94c2173cb3050a0f3480f60c4</dd><dt><span>title :</span></dt><dd>ACCESS-ESM1-5 output prepared for CMIP6</dd><dt><span>variable_id :</span></dt><dd>tas</dd><dt><span>variant_label :</span></dt><dd>r10i1p1f1</dd><dt><span>version :</span></dt><dd>v20200605</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by CSIRO is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment.  Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file). The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>cmor_version :</span></dt><dd>3.4.0</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/af78ae5e-f3a6-4e99-8cfe-5f29eb467cd1</dd><dt><span>DODS_EXTRA.Unlimited_Dimension :</span></dt><dd>time</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 7MB\n",
       "Dimensions:    (time: 60, bnds: 2, lat: 145, lon: 192)\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 ... 354.4 356.2 358.1\n",
       "    height     float64 8B 2.0\n",
       "  * time       (time) object 480B 1870-01-16 12:00:00 ... 1874-12-16 12:00:00\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    time_bnds  (time, bnds) object 960B ...\n",
       "    lat_bnds   (lat, bnds) float64 2kB ...\n",
       "    lon_bnds   (lon, bnds) float64 3kB ...\n",
       "    tas        (time, lat, lon) float32 7MB ...\n",
       "Attributes: (12/48)\n",
       "    Conventions:                     CF-1.7 CMIP-6.2\n",
       "    activity_id:                     CMIP\n",
       "    branch_method:                   standard\n",
       "    branch_time_in_child:            0.0\n",
       "    branch_time_in_parent:           87658.0\n",
       "    creation_date:                   2020-06-05T04:06:11Z\n",
       "    ...                              ...\n",
       "    variant_label:                   r10i1p1f1\n",
       "    version:                         v20200605\n",
       "    license:                         CMIP6 model data produced by CSIRO is li...\n",
       "    cmor_version:                    3.4.0\n",
       "    tracking_id:                     hdl:21.14100/af78ae5e-f3a6-4e99-8cfe-5f2...\n",
       "    DODS_EXTRA.Unlimited_Dimension:  time"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "**A dictionary-like container of labeled arrays (DataArray objects) with aligned dimensions.**\n",
    "\n",
    "Key properties:\n",
    "\n",
    "- `dims`: a dictionary mapping from dimension names to the fixed length of each dimension (e.g., {'x': 6, 'y': 6, 'time': 8})\n",
    "- `coords`: another dict-like container of DataArrays intended to label points used in data_vars (e.g., arrays of numbers, datetime objects or strings)\n",
    "- `data_vars`: a dict-like container of DataArrays corresponding to variables\n",
    "- `attrs`: dict to hold arbitrary metadata\n",
    "\n",
    "Source: <cite>https://docs.xarray.dev/en/stable/user-guide/data-structures.html#dataset</cite>\n"
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    "### The `DataArray` Model\n"
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    "**A class that attaches dimension names, coordinates, and attributes to multi-dimensional arrays (aka \"labeled arrays\")**\n",
    "\n",
    "Key properties:\n",
    "\n",
    "- `values`: a numpy.ndarray holding the array’s values\n",
    "- `dims`: dimension names for each axis (e.g., ('x', 'y', 'z'))\n",
    "- `coords`: a dict-like container of arrays (coordinates) that label each point (e.g., 1-dimensional arrays of numbers, datetime objects or strings)\n",
    "- `attrs`: dict to hold arbitrary metadata (attributes)\n",
    "\n",
    "Source: <cite>https://docs.xarray.dev/en/stable/user-guide/data-structures.html#dataarray</cite>\n"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tas&#x27; (time: 60, lat: 145, lon: 192)&gt; Size: 7MB\n",
       "[1670400 values with dtype=float32]\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 2.0\n",
       "  * time     (time) object 480B 1870-01-16 12:00:00 ... 1874-12-16 12:00:00\n",
       "Attributes:\n",
       "    standard_name:  air_temperature\n",
       "    long_name:      Near-Surface Air Temperature\n",
       "    comment:        near-surface (usually, 2 meter) air temperature\n",
       "    units:          K\n",
       "    cell_methods:   area: time: mean\n",
       "    cell_measures:  area: areacella\n",
       "    history:        2020-06-05T04:06:10Z altered by CMOR: Treated scalar dime...\n",
       "    _ChunkSizes:    [  1 145 192]</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>: 60</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-6c0a867a-30eb-4e0f-a970-6ae24cb29c79' class='xr-array-in' type='checkbox' checked><label for='section-6c0a867a-30eb-4e0f-a970-6ae24cb29c79' 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>...</span></div><div class='xr-array-data'><pre>[1670400 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-af868521-7122-4332-a08b-4288b499d9b9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-af868521-7122-4332-a08b-4288b499d9b9' 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-13b19f12-c9f3-4e91-b7e8-fec5c696f381' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-13b19f12-c9f3-4e91-b7e8-fec5c696f381' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2a89863-c122-4c32-ab77-6fc1e0a5acc8' class='xr-var-data-in' type='checkbox'><label for='data-e2a89863-c122-4c32-ab77-6fc1e0a5acc8' 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-1a38273e-7570-429f-bb1c-1740070b7495' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a38273e-7570-429f-bb1c-1740070b7495' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-496e5e7f-77cd-46ef-9409-fb5538aba9f8' class='xr-var-data-in' type='checkbox'><label for='data-496e5e7f-77cd-46ef-9409-fb5538aba9f8' 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'>2.0</div><input id='attrs-c7e22d51-2a0d-436c-80d2-210bd46ae491' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c7e22d51-2a0d-436c-80d2-210bd46ae491' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0018d36-bfe3-4201-a6e5-420be5deeb55' class='xr-var-data-in' type='checkbox'><label for='data-f0018d36-bfe3-4201-a6e5-420be5deeb55' 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>array(2.)</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'>1870-01-16 12:00:00 ... 1874-12-...</div><input id='attrs-03b3b6f2-e9d2-45d0-a3ed-bd6f3ce336f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03b3b6f2-e9d2-45d0-a3ed-bd6f3ce336f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2242770b-2b2b-4cc8-937c-a889f6fecafa' class='xr-var-data-in' type='checkbox'><label for='data-2242770b-2b2b-4cc8-937c-a889f6fecafa' 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(1870, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 2, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 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-d3762040-1544-4774-9a51-37b7f8255cc4' class='xr-section-summary-in' type='checkbox'  ><label for='section-d3762040-1544-4774-9a51-37b7f8255cc4' 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><input type='checkbox' disabled/><label></label><input id='index-bfdc455e-8299-48dd-be52-a6a3ef27ebef' class='xr-index-data-in' type='checkbox'/><label for='index-bfdc455e-8299-48dd-be52-a6a3ef27ebef' 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><input type='checkbox' disabled/><label></label><input id='index-c111c9a5-203d-40c5-87e6-54eed93b2b92' class='xr-index-data-in' type='checkbox'/><label for='index-c111c9a5-203d-40c5-87e6-54eed93b2b92' 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><input type='checkbox' disabled/><label></label><input id='index-426a43fe-9602-4552-be6d-2a9e11a7e692' class='xr-index-data-in' type='checkbox'/><label for='index-426a43fe-9602-4552-be6d-2a9e11a7e692' 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([1870-01-16 12:00:00, 1870-02-15 00:00:00, 1870-03-16 12:00:00,\n",
       "             1870-04-16 00:00:00, 1870-05-16 12:00:00, 1870-06-16 00:00:00,\n",
       "             1870-07-16 12:00:00, 1870-08-16 12:00:00, 1870-09-16 00:00:00,\n",
       "             1870-10-16 12:00:00, 1870-11-16 00:00:00, 1870-12-16 12:00:00,\n",
       "             1871-01-16 12:00:00, 1871-02-15 00:00:00, 1871-03-16 12:00:00,\n",
       "             1871-04-16 00:00:00, 1871-05-16 12:00:00, 1871-06-16 00:00:00,\n",
       "             1871-07-16 12:00:00, 1871-08-16 12:00:00, 1871-09-16 00:00:00,\n",
       "             1871-10-16 12:00:00, 1871-11-16 00:00:00, 1871-12-16 12:00:00,\n",
       "             1872-01-16 12:00:00, 1872-02-15 12:00:00, 1872-03-16 12:00:00,\n",
       "             1872-04-16 00:00:00, 1872-05-16 12:00:00, 1872-06-16 00:00:00,\n",
       "             1872-07-16 12:00:00, 1872-08-16 12:00:00, 1872-09-16 00:00:00,\n",
       "             1872-10-16 12:00:00, 1872-11-16 00:00:00, 1872-12-16 12:00:00,\n",
       "             1873-01-16 12:00:00, 1873-02-15 00:00:00, 1873-03-16 12:00:00,\n",
       "             1873-04-16 00:00:00, 1873-05-16 12:00:00, 1873-06-16 00:00:00,\n",
       "             1873-07-16 12:00:00, 1873-08-16 12:00:00, 1873-09-16 00:00:00,\n",
       "             1873-10-16 12:00:00, 1873-11-16 00:00:00, 1873-12-16 12:00:00,\n",
       "             1874-01-16 12:00:00, 1874-02-15 00:00:00, 1874-03-16 12:00:00,\n",
       "             1874-04-16 00:00:00, 1874-05-16 12:00:00, 1874-06-16 00:00:00,\n",
       "             1874-07-16 12:00:00, 1874-08-16 12:00:00, 1874-09-16 00:00:00,\n",
       "             1874-10-16 12:00:00, 1874-11-16 00:00:00, 1874-12-16 12:00:00],\n",
       "            dtype=&#x27;object&#x27;,\n",
       "            length=60,\n",
       "            calendar=&#x27;proleptic_gregorian&#x27;,\n",
       "            freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1d6d0863-da16-4714-b77b-6c7d116f32dc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1d6d0863-da16-4714-b77b-6c7d116f32dc' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>comment :</span></dt><dd>near-surface (usually, 2 meter) air temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>history :</span></dt><dd>2020-06-05T04:06:10Z altered by CMOR: Treated scalar dimension: &#x27;height&#x27;. 2020-06-05T04:06:10Z altered by CMOR: replaced missing value flag (-1.07374e+09) with standard missing value (1e+20).</dd><dt><span>_ChunkSizes :</span></dt><dd>[  1 145 192]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'tas' (time: 60, lat: 145, lon: 192)> Size: 7MB\n",
       "[1670400 values with dtype=float32]\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 2.0\n",
       "  * time     (time) object 480B 1870-01-16 12:00:00 ... 1874-12-16 12:00:00\n",
       "Attributes:\n",
       "    standard_name:  air_temperature\n",
       "    long_name:      Near-Surface Air Temperature\n",
       "    comment:        near-surface (usually, 2 meter) air temperature\n",
       "    units:          K\n",
       "    cell_methods:   area: time: mean\n",
       "    cell_measures:  area: areacella\n",
       "    history:        2020-06-05T04:06:10Z altered by CMOR: Treated scalar dime...\n",
       "    _ChunkSizes:    [  1 145 192]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.tas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Resources for Learning Xarray\n",
    "\n",
    "- Here are some highly recommended resources:\n",
    "  - [Xarray Tutorial](https://tutorial.xarray.dev/intro.html)\n",
    "  - [\"Xarray in 45 minutes\"](https://tutorial.xarray.dev/overview/xarray-in-45-min.html#)\n",
    "  - [Xarray Documentation](https://docs.xarray.dev/en/stable/index.html)\n",
    "  - [Xarray API Reference](https://docs.xarray.dev/en/stable/api.html)\n"
   ]
  },
  {
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    "## xCDAT Extends Xarray for Climate Data Analysis\n",
    "\n",
    "- **Some key xCDAT features** are inspired by or ported **from the core CDAT library**\n",
    "  - _e.g., spatial averaging, temporal averaging, regrid2 for horizontal regridding_\n",
    "- Other features **leverage powerful libraries** in the **xarray ecosystem**\n",
    "\n",
    "  - **xESMF** for horizontal regridding\n",
    "  - **xgcm** for vertical interpolation\n",
    "  - **CF-xarray** for CF convention metadata interpretation\n",
    "\n",
    "- xCDAT strives to **support datasets** **CF compliant** and **common non-CF compliant metadata** (_time units in “months since …” or “years since …”_)\n",
    "- Inherent support for **lazy operations** and **parallelism** through **xarray + dask**\n",
    "\n",
    "<div style=\"text-align: center; margin-top:10px\">\n",
    "    <img src=\"../_static/esmf-logo.png\" alt=\"esmf logo\" style=\"display: inline-block; margin-right:50px; width:400px;\">\n",
    "    <img src=\"../_static/CF-xarray.png\" alt=\"cf-xarray logo\" style=\"display: inline-block; margin-right:50px; width:400px;\">\n",
    "     <img src=\"../_static/dask-logo.png\" alt=\"dask logo\" style=\"display: inline-block; width:400px;\">\n",
    "\n",
    "</div>\n"
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    "## The Technical Design Philosophy\n",
    "\n",
    "- **Streamline the user experience** of developing code to analyze climate data\n",
    "- **Reduce the complexity and overhead** for implementing certain features with xarray (_e.g., temporal averaging, spatial averaging_)\n",
    "- Encourage **reusable functionalities** through a **single library**\n"
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    "## Leveraging the APIs\n",
    "\n",
    "xCDAT provides public APIs in two ways:\n",
    "\n",
    "1. Top-level APIs functions\n",
    "   - e.g., `xcdat.open_dataset()`, `xcdat.center_times()`\n",
    "   - Usually for opening datasets and performing dataset level operations\n",
    "2. Accessor classes\n",
    "   - xcdat provides `Dataset` accessors, which are **implicit namespaces for custom functionality**.\n",
    "   - Accessor **namespaces** clearly identifies **separation from built-in xarray methods**.\n",
    "   - Operate on variables within the `xr.Dataset`\n",
    "   - e.g., `ds.spatial`, `ds.temporal`, `ds.regridder`\n",
    "\n",
    "<div style=\"text-align: center; margin-top: 10px\">\n",
    "    <figure>\n",
    "    <img src=\"../_static/accessor_api.svg\" alt=\"xcdat accessor\" style=\"display: inline-block; width:75%\">\n",
    "        <figcaption style=\"font-style: italic\">xcdat spatial functionality is exposed by chaining the <span style=\"background-color: #e4e6e8\">.spatial</span> accessor attribute to the <span style=\"background-color: #e4e6e8\">xr.Dataset</span> object.</figcaption>\n",
    "    </figure>\n",
    "</div>\n",
    "\n",
    "Source: <cite>https://xcdat.readthedocs.io/en/latest/api.html</cite>\n"
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    "## Key Features in xCDAT\n",
    "\n",
    "<style type=\"text/css\">\n",
    "table td, table th {\n",
    "    border: 1px solid lightgray;\n",
    "}\n",
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    "\n",
    "<table>\n",
    "    <tr>\n",
    "        <td>Feature</td>\n",
    "        <td>API</td>\n",
    "        <td>Description</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>Extend `xr.open_dataset()` and `xr.open_mfdataset()`</td>\n",
    "        <td>\n",
    "            <ul>\n",
    "                <li>`open_dataset()`</li>\n",
    "                <li>`open_mfdataset()`</li>\n",
    "            </ul>\n",
    "        </td>\n",
    "        <td>\n",
    "            <ul>\n",
    "                <li>Bounds generation</li>\n",
    "                <li>Time decoding (CF and select non-CF time units)</li>\n",
    "                <li>Centering of time coordinates</li>\n",
    "                <li>Conversion of longitudinal axis orientation</li>\n",
    "            </ul>\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>Temporal averaging</td>\n",
    "        <td>\n",
    "            <ul>\n",
    "                <li>`ds.temporal.average()`</li>\n",
    "                <li>`ds.temporal.group_average()`</li>\n",
    "                <li>`ds.temporal.climatology()`</li>\n",
    "                <li>`ds.temporal.departures()`</li>\n",
    "            </ul>\n",
    "        <td>\n",
    "            <ul>\n",
    "                <li>Single snapshot and group average</li>\n",
    "                <li>Climatology and departure</li>\n",
    "                <li>Weighted or unweighted</li>\n",
    "                <li>Optional seasonal configuration< (e.g., custom seasons)</li>\n",
    "            </ul>\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>Geospatial averaging</td>\n",
    "        <td>`ds.spatial.average()`</td>\n",
    "        <td>\n",
    "            <ul>\n",
    "                <li>Rectilinear grids</li>\n",
    "                <li>Weighted</li>\n",
    "                <li>Optional specification of region domain</li>\n",
    "            </ul>\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>Horizontal regridding</td>\n",
    "        <td>`ds.regridder.horizontal()`</td>\n",
    "         <td>\n",
    "            <ul>\n",
    "                <li>Rectilinear and curvilinear grids</li>\n",
    "                <li>Extends xESMF horizontal regridding</li>\n",
    "                <li>Python implementation of regrid2</li>\n",
    "            </ul>\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>Vertical regridding</td>\n",
    "        <td>`ds.regridder.vertical()`</td>\n",
    "        <td>\n",
    "            <ul>\n",
    "                <li>Transform vertical coordinates</li>\n",
    "                <li>Extends xgcm vertical interpolation</li>\n",
    "                <li>Linear, logarithmic, and conservative interpolation</li>\n",
    "                <li>Decode parametric vertical coordinates if required</li>\n",
    "            </ul>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>\n"
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    "## A Demo of xCDAT Capabilities\n",
    "\n",
    "- Prerequisites\n",
    "  - Installing `xcdat`\n",
    "  - Import `xcdat`\n",
    "  - Open a dataset and apply postprocessing operations\n",
    "- Scenario 1 - Calculate the spatial averages over the tropical region\n",
    "- Scenario 2 - Calculate the annual anomalies\n",
    "- Scenario 3 - Horizontal regridding (bilinear, gaussian grid)\n"
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    "### Installing `xcdat`\n",
    "\n",
    "xCDAT is available on Anaconda under the `conda-forge` channel (https://anaconda.org/conda-forge/xcdat)\n",
    "\n",
    "Two ways to install `xcdat`:\n",
    "\n",
    "1. Create a conda environment from scratch (`conda create`)\n",
    "\n",
    "   ```bash\n",
    "   conda create -n <ENV_NAME> -c conda-forge xcdat\n",
    "   conda activate <ENV_NAME>\n",
    "   ```\n",
    "\n",
    "2. Install `xcdat` in an existing conda environment (`conda install`)\n",
    "\n",
    "   ```bash\n",
    "   conda activate <ENV_NAME>\n",
    "   conda install -c conda-forge xcdat\n",
    "   ```\n",
    "\n",
    "3. If you're working on HPC, we have a guide for that too!\n",
    "\n",
    "- https://xcdat.readthedocs.io/en/latest/getting-started-guide/getting-started-hpc-jupyter.html\n",
    "\n",
    "_Source_: <cite>https://xcdat.readthedocs.io/en/latest/getting-started.html</cite>\n"
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    "### Opening a dataset\n",
    "\n",
    "Example dataset: `tas_Amon_ACCESS-ESM1-5_historical_r10i1p1f1_gn_185001-201412_subset.nc` (same as before)\n"
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    "# This gives access to all xcdat public top-level APIs and accessor classes.\n",
    "import xcdat as xc\n",
    "\n",
    "# We import these packages specifically for plotting. It is not required to use xcdat.\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "ds = xc.tutorial.open_dataset(\"tas_amon_access\")\n",
    "\n",
    "# Unit adjustment from Kelvin to Celcius.\n",
    "ds[\"tas\"] = ds.tas - 273.15"
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       ".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.Dataset&gt; Size: 7MB\n",
       "Dimensions:    (time: 60, bnds: 2, lat: 145, lon: 192)\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 ... 354.4 356.2 358.1\n",
       "    height     float64 8B 2.0\n",
       "  * time       (time) object 480B 1870-01-16 12:00:00 ... 1874-12-16 12:00:00\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    time_bnds  (time, bnds) object 960B ...\n",
       "    lat_bnds   (lat, bnds) float64 2kB ...\n",
       "    lon_bnds   (lon, bnds) float64 3kB ...\n",
       "    tas        (time, lat, lon) float32 7MB -29.36 -29.36 ... -31.07 -31.07\n",
       "Attributes: (12/48)\n",
       "    Conventions:                     CF-1.7 CMIP-6.2\n",
       "    activity_id:                     CMIP\n",
       "    branch_method:                   standard\n",
       "    branch_time_in_child:            0.0\n",
       "    branch_time_in_parent:           87658.0\n",
       "    creation_date:                   2020-06-05T04:06:11Z\n",
       "    ...                              ...\n",
       "    variant_label:                   r10i1p1f1\n",
       "    version:                         v20200605\n",
       "    license:                         CMIP6 model data produced by CSIRO is li...\n",
       "    cmor_version:                    3.4.0\n",
       "    tracking_id:                     hdl:21.14100/af78ae5e-f3a6-4e99-8cfe-5f2...\n",
       "    DODS_EXTRA.Unlimited_Dimension:  time</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-cd852f60-edb2-4c73-924d-cde775142e60' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cd852f60-edb2-4c73-924d-cde775142e60' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 60</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lat</span>: 145</li><li><span class='xr-has-index'>lon</span>: 192</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d021879b-3556-488f-9020-957aa6c02045' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d021879b-3556-488f-9020-957aa6c02045' 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-f12713c7-53c2-4bb9-b7dc-6e302d68237b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f12713c7-53c2-4bb9-b7dc-6e302d68237b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6bcf56b9-6164-4c30-86bc-b286997adc30' class='xr-var-data-in' type='checkbox'><label for='data-6bcf56b9-6164-4c30-86bc-b286997adc30' 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-5236b4d7-278b-4616-9b1f-57810ec46c5c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5236b4d7-278b-4616-9b1f-57810ec46c5c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21b2bfd5-e8cb-4205-945e-9c483041aa0b' class='xr-var-data-in' type='checkbox'><label for='data-21b2bfd5-e8cb-4205-945e-9c483041aa0b' 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'>2.0</div><input id='attrs-99427470-ab6b-41c5-a6ea-8d82fa3cd6dc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99427470-ab6b-41c5-a6ea-8d82fa3cd6dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c68ad9c4-9428-42ef-ac41-cc87593d500e' class='xr-var-data-in' type='checkbox'><label for='data-c68ad9c4-9428-42ef-ac41-cc87593d500e' 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>array(2.)</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'>1870-01-16 12:00:00 ... 1874-12-...</div><input id='attrs-045028ef-1c23-40fd-9dd6-e4bc0753e935' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-045028ef-1c23-40fd-9dd6-e4bc0753e935' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f8632de-7152-4703-a4e1-2f7b02690108' class='xr-var-data-in' type='checkbox'><label for='data-8f8632de-7152-4703-a4e1-2f7b02690108' 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(1870, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 2, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 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-00c0dda5-d743-4607-9824-6ae1fae00eaf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-00c0dda5-d743-4607-9824-6ae1fae00eaf' class='xr-section-summary' >Data variables: <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>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ea6bfe85-1533-475e-b5a7-e7ec0537d503' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea6bfe85-1533-475e-b5a7-e7ec0537d503' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a8ccd96-0c0e-48c5-aed2-17d9ffbbad9d' class='xr-var-data-in' type='checkbox'><label for='data-0a8ccd96-0c0e-48c5-aed2-17d9ffbbad9d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[1 2]</dd></dl></div><div class='xr-var-data'><pre>[120 values with dtype=object]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-357e0d7a-2952-445a-938e-9acf2c73a241' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-357e0d7a-2952-445a-938e-9acf2c73a241' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9c4d019-485a-4c17-b90c-f754a80f3b65' class='xr-var-data-in' type='checkbox'><label for='data-f9c4d019-485a-4c17-b90c-f754a80f3b65' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[145   2]</dd></dl></div><div class='xr-var-data'><pre>[290 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b80dd411-4e41-4f20-af18-5ffcf58771d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b80dd411-4e41-4f20-af18-5ffcf58771d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f1f539d-4401-4d28-a282-5904224a1e04' class='xr-var-data-in' type='checkbox'><label for='data-8f1f539d-4401-4d28-a282-5904224a1e04' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[192   2]</dd></dl></div><div class='xr-var-data'><pre>[384 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tas</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-29.36 -29.36 ... -31.07 -31.07</div><input id='attrs-45da45bc-a4d3-4cc8-9432-b51d892b35d5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-45da45bc-a4d3-4cc8-9432-b51d892b35d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-905d9828-2f7a-4afa-a7fb-d2b6b491e89e' class='xr-var-data-in' type='checkbox'><label for='data-905d9828-2f7a-4afa-a7fb-d2b6b491e89e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-29.355972 , -29.355972 , -29.355972 , ..., -29.355972 ,\n",
       "         -29.355972 , -29.355972 ],\n",
       "        [-28.003357 , -28.017807 , -28.036758 , ..., -27.963135 ,\n",
       "         -27.975769 , -27.98819  ],\n",
       "        [-27.37091  , -27.45134  , -27.535522 , ..., -27.139175 ,\n",
       "         -27.210953 , -27.292435 ],\n",
       "        ...,\n",
       "        [-36.016327 , -36.020416 , -36.030487 , ..., -36.001053 ,\n",
       "         -35.978714 , -35.989914 ],\n",
       "        [-35.292633 , -35.31819  , -35.345062 , ..., -35.26677  ,\n",
       "         -35.279694 , -35.281097 ],\n",
       "        [-34.954376 , -34.954376 , -34.954376 , ..., -34.954376 ,\n",
       "         -34.954376 , -34.954376 ]],\n",
       "\n",
       "       [[-38.76712  , -38.76712  , -38.76712  , ..., -38.76712  ,\n",
       "         -38.76712  , -38.76712  ],\n",
       "        [-37.042587 , -37.086456 , -37.132782 , ..., -36.91037  ,\n",
       "         -36.95671  , -36.99539  ],\n",
       "        [-36.136673 , -36.267227 , -36.397842 , ..., -35.73355  ,\n",
       "         -35.865616 , -36.001617 ],\n",
       "...\n",
       "        [-23.614487 , -23.538742 , -23.457184 , ..., -23.859207 ,\n",
       "         -23.767532 , -23.697678 ],\n",
       "        [-24.92923  , -24.90483  , -24.878616 , ..., -25.023895 ,\n",
       "         -25.009842 , -24.970001 ],\n",
       "        [-26.15857  , -26.15857  , -26.15857  , ..., -26.15857  ,\n",
       "         -26.15857  , -26.15857  ]],\n",
       "\n",
       "       [[-29.958633 , -29.958633 , -29.958633 , ..., -29.958633 ,\n",
       "         -29.958633 , -29.958633 ],\n",
       "        [-28.73851  , -28.776047 , -28.814606 , ..., -28.628693 ,\n",
       "         -28.662338 , -28.6996   ],\n",
       "        [-28.04181  , -28.164856 , -28.287155 , ..., -27.6837   ,\n",
       "         -27.798935 , -27.91861  ],\n",
       "        ...,\n",
       "        [-29.491394 , -29.39035  , -29.287476 , ..., -29.817932 ,\n",
       "         -29.700745 , -29.59967  ],\n",
       "        [-30.09056  , -30.031967 , -29.971268 , ..., -30.301758 ,\n",
       "         -30.243591 , -30.158798 ],\n",
       "        [-31.06688  , -31.06688  , -31.06688  , ..., -31.06688  ,\n",
       "         -31.06688  , -31.06688  ]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-67589430-bfc8-4988-b54a-41a901219910' class='xr-section-summary-in' type='checkbox'  ><label for='section-67589430-bfc8-4988-b54a-41a901219910' 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><input type='checkbox' disabled/><label></label><input id='index-af81cd31-fb98-4bad-82d4-b1d87664c544' class='xr-index-data-in' type='checkbox'/><label for='index-af81cd31-fb98-4bad-82d4-b1d87664c544' 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><input type='checkbox' disabled/><label></label><input id='index-0abe263b-0a0a-44a1-8830-1c196285f2d4' class='xr-index-data-in' type='checkbox'/><label for='index-0abe263b-0a0a-44a1-8830-1c196285f2d4' 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><input type='checkbox' disabled/><label></label><input id='index-0b13e371-5d49-484a-aa3b-ffeee1467687' class='xr-index-data-in' type='checkbox'/><label for='index-0b13e371-5d49-484a-aa3b-ffeee1467687' 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([1870-01-16 12:00:00, 1870-02-15 00:00:00, 1870-03-16 12:00:00,\n",
       "             1870-04-16 00:00:00, 1870-05-16 12:00:00, 1870-06-16 00:00:00,\n",
       "             1870-07-16 12:00:00, 1870-08-16 12:00:00, 1870-09-16 00:00:00,\n",
       "             1870-10-16 12:00:00, 1870-11-16 00:00:00, 1870-12-16 12:00:00,\n",
       "             1871-01-16 12:00:00, 1871-02-15 00:00:00, 1871-03-16 12:00:00,\n",
       "             1871-04-16 00:00:00, 1871-05-16 12:00:00, 1871-06-16 00:00:00,\n",
       "             1871-07-16 12:00:00, 1871-08-16 12:00:00, 1871-09-16 00:00:00,\n",
       "             1871-10-16 12:00:00, 1871-11-16 00:00:00, 1871-12-16 12:00:00,\n",
       "             1872-01-16 12:00:00, 1872-02-15 12:00:00, 1872-03-16 12:00:00,\n",
       "             1872-04-16 00:00:00, 1872-05-16 12:00:00, 1872-06-16 00:00:00,\n",
       "             1872-07-16 12:00:00, 1872-08-16 12:00:00, 1872-09-16 00:00:00,\n",
       "             1872-10-16 12:00:00, 1872-11-16 00:00:00, 1872-12-16 12:00:00,\n",
       "             1873-01-16 12:00:00, 1873-02-15 00:00:00, 1873-03-16 12:00:00,\n",
       "             1873-04-16 00:00:00, 1873-05-16 12:00:00, 1873-06-16 00:00:00,\n",
       "             1873-07-16 12:00:00, 1873-08-16 12:00:00, 1873-09-16 00:00:00,\n",
       "             1873-10-16 12:00:00, 1873-11-16 00:00:00, 1873-12-16 12:00:00,\n",
       "             1874-01-16 12:00:00, 1874-02-15 00:00:00, 1874-03-16 12:00:00,\n",
       "             1874-04-16 00:00:00, 1874-05-16 12:00:00, 1874-06-16 00:00:00,\n",
       "             1874-07-16 12:00:00, 1874-08-16 12:00:00, 1874-09-16 00:00:00,\n",
       "             1874-10-16 12:00:00, 1874-11-16 00:00:00, 1874-12-16 12:00:00],\n",
       "            dtype=&#x27;object&#x27;,\n",
       "            length=60,\n",
       "            calendar=&#x27;proleptic_gregorian&#x27;,\n",
       "            freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cadcf75e-bb98-4da6-a939-3333499d88bf' class='xr-section-summary-in' type='checkbox'  ><label for='section-cadcf75e-bb98-4da6-a939-3333499d88bf' class='xr-section-summary' >Attributes: <span>(48)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>CMIP</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>branch_time_in_child :</span></dt><dd>0.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>87658.0</dd><dt><span>creation_date :</span></dt><dd>2020-06-05T04:06:11Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.30</dd><dt><span>experiment :</span></dt><dd>all-forcing simulation of the recent past</dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.CSIRO.ACCESS-ESM1-5.historical.none.r10i1p1f1</dd><dt><span>grid :</span></dt><dd>native atmosphere N96 grid (145x192 latxlon)</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>history :</span></dt><dd>2020-06-05T04:06:11Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia</dd><dt><span>institution_id :</span></dt><dd>CSIRO</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>nominal_resolution :</span></dt><dd>250 km</dd><dt><span>notes :</span></dt><dd>Exp: ESM-historical; Local ID: HI-14; Variable: tas ([&#x27;fld_s03i236&#x27;])</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>piControl</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>ACCESS-ESM1-5</dd><dt><span>parent_time_units :</span></dt><dd>days since 0101-1-1</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>10</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>run_variant :</span></dt><dd>forcing: GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = CO2, N2O, CH4, CFC11, CFC12, CFC113, HCFC22, HFC125, HFC134a)</dd><dt><span>source :</span></dt><dd>ACCESS-ESM1.5 (2019): \n",
       "aerosol: CLASSIC (v1.0)\n",
       "atmos: HadGAM2 (r1.1, N96; 192 x 145 longitude/latitude; 38 levels; top level 39255 m)\n",
       "atmosChem: none\n",
       "land: CABLE2.4\n",
       "landIce: none\n",
       "ocean: ACCESS-OM2 (MOM5, tripolar primarily 1deg; 360 x 300 longitude/latitude; 50 levels; top grid cell 0-10 m)\n",
       "ocnBgchem: WOMBAT (same grid as ocean)\n",
       "seaIce: CICE4.1 (same grid as ocean)</dd><dt><span>source_id :</span></dt><dd>ACCESS-ESM1-5</dd><dt><span>source_type :</span></dt><dd>AOGCM</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>Amon</dd><dt><span>table_info :</span></dt><dd>Creation Date:(30 April 2019) MD5:5bd755e94c2173cb3050a0f3480f60c4</dd><dt><span>title :</span></dt><dd>ACCESS-ESM1-5 output prepared for CMIP6</dd><dt><span>variable_id :</span></dt><dd>tas</dd><dt><span>variant_label :</span></dt><dd>r10i1p1f1</dd><dt><span>version :</span></dt><dd>v20200605</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by CSIRO is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment.  Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file). The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>cmor_version :</span></dt><dd>3.4.0</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/af78ae5e-f3a6-4e99-8cfe-5f29eb467cd1</dd><dt><span>DODS_EXTRA.Unlimited_Dimension :</span></dt><dd>time</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 7MB\n",
       "Dimensions:    (time: 60, bnds: 2, lat: 145, lon: 192)\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 ... 354.4 356.2 358.1\n",
       "    height     float64 8B 2.0\n",
       "  * time       (time) object 480B 1870-01-16 12:00:00 ... 1874-12-16 12:00:00\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    time_bnds  (time, bnds) object 960B ...\n",
       "    lat_bnds   (lat, bnds) float64 2kB ...\n",
       "    lon_bnds   (lon, bnds) float64 3kB ...\n",
       "    tas        (time, lat, lon) float32 7MB -29.36 -29.36 ... -31.07 -31.07\n",
       "Attributes: (12/48)\n",
       "    Conventions:                     CF-1.7 CMIP-6.2\n",
       "    activity_id:                     CMIP\n",
       "    branch_method:                   standard\n",
       "    branch_time_in_child:            0.0\n",
       "    branch_time_in_parent:           87658.0\n",
       "    creation_date:                   2020-06-05T04:06:11Z\n",
       "    ...                              ...\n",
       "    variant_label:                   r10i1p1f1\n",
       "    version:                         v20200605\n",
       "    license:                         CMIP6 model data produced by CSIRO is li...\n",
       "    cmor_version:                    3.4.0\n",
       "    tracking_id:                     hdl:21.14100/af78ae5e-f3a6-4e99-8cfe-5f2...\n",
       "    DODS_EXTRA.Unlimited_Dimension:  time"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "center",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Scenario 1: Spatial Averaging\n",
    "\n",
    "Related accessor: `ds.spatial`\n",
    "\n",
    "In this example, we **calculate the spatial average** of `tas` over the tropical region and **plot the first 100 time steps**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "cell_style": "split",
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       ".xr-header > div,\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       ".xr-array-in:checked ~ .xr-array-preview {\n",
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       "\n",
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       "  display: inline-block;\n",
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       "\n",
       ".xr-dim-list:before {\n",
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       "\n",
       ".xr-dim-list:after {\n",
       "  content: \")\";\n",
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       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: \",\";\n",
       "  padding-right: 5px;\n",
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       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
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       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
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       "\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",
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       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
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       "\n",
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       "\n",
       ".xr-var-preview {\n",
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       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
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       "  padding-right: 10px;\n",
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       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
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       "}\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: 60)&gt; Size: 480B\n",
       "array([25.00067275, 25.30644298, 25.7617891 , 26.10626959, 26.01841282,\n",
       "       25.5047623 , 25.28394064, 25.37295231, 25.72527095, 25.87376074,\n",
       "       25.67985682, 25.22942709, 24.95699101, 25.48591822, 26.02279319,\n",
       "       26.29556201, 26.18619426, 25.791096  , 25.50230606, 25.52068064,\n",
       "       25.68326088, 25.78873469, 25.63775696, 25.15228934, 25.08228764,\n",
       "       25.30220953, 25.69973578, 26.0328945 , 25.9973611 , 25.51339722,\n",
       "       25.39540446, 25.45184812, 25.70660688, 25.75817938, 25.54315004,\n",
       "       25.13706986, 24.96105631, 25.311973  , 25.7646741 , 26.01738372,\n",
       "       26.01281503, 25.69728948, 25.3639977 , 25.5225464 , 25.77108789,\n",
       "       25.84422784, 25.70096048, 25.11534178, 25.0415591 , 25.34789389,\n",
       "       25.72121083, 25.91918215, 25.83924512, 25.55205376, 25.13493311,\n",
       "       25.22239226, 25.43492285, 25.43073544, 25.26357054, 24.80680714])\n",
       "Coordinates:\n",
       "    height   float64 8B 2.0\n",
       "  * time     (time) object 480B 1870-01-16 12:00:00 ... 1874-12-16 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>: 60</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-72674066-f16c-4215-b0ce-b5ffd62ce4d4' class='xr-array-in' type='checkbox' checked><label for='section-72674066-f16c-4215-b0ce-b5ffd62ce4d4' 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>25.0 25.31 25.76 26.11 26.02 25.5 ... 25.22 25.43 25.43 25.26 24.81</span></div><div class='xr-array-data'><pre>array([25.00067275, 25.30644298, 25.7617891 , 26.10626959, 26.01841282,\n",
       "       25.5047623 , 25.28394064, 25.37295231, 25.72527095, 25.87376074,\n",
       "       25.67985682, 25.22942709, 24.95699101, 25.48591822, 26.02279319,\n",
       "       26.29556201, 26.18619426, 25.791096  , 25.50230606, 25.52068064,\n",
       "       25.68326088, 25.78873469, 25.63775696, 25.15228934, 25.08228764,\n",
       "       25.30220953, 25.69973578, 26.0328945 , 25.9973611 , 25.51339722,\n",
       "       25.39540446, 25.45184812, 25.70660688, 25.75817938, 25.54315004,\n",
       "       25.13706986, 24.96105631, 25.311973  , 25.7646741 , 26.01738372,\n",
       "       26.01281503, 25.69728948, 25.3639977 , 25.5225464 , 25.77108789,\n",
       "       25.84422784, 25.70096048, 25.11534178, 25.0415591 , 25.34789389,\n",
       "       25.72121083, 25.91918215, 25.83924512, 25.55205376, 25.13493311,\n",
       "       25.22239226, 25.43492285, 25.43073544, 25.26357054, 24.80680714])</pre></div></div></li><li class='xr-section-item'><input id='section-0ed8325c-2c41-40fa-8378-7a1de64bf994' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0ed8325c-2c41-40fa-8378-7a1de64bf994' 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'>2.0</div><input id='attrs-3b81aa81-5007-4e9e-9aca-c1ac4d18da14' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b81aa81-5007-4e9e-9aca-c1ac4d18da14' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d79664fa-05ae-4a82-b670-2337083cdaeb' class='xr-var-data-in' type='checkbox'><label for='data-d79664fa-05ae-4a82-b670-2337083cdaeb' 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>array(2.)</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'>1870-01-16 12:00:00 ... 1874-12-...</div><input id='attrs-ac3cad03-0d7f-45bc-a5bd-0bf69f713db5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac3cad03-0d7f-45bc-a5bd-0bf69f713db5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-acfd31ab-c1b6-4fa9-9b2a-a4ddce3a0eb6' class='xr-var-data-in' type='checkbox'><label for='data-acfd31ab-c1b6-4fa9-9b2a-a4ddce3a0eb6' 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(1870, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 2, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 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-d492a87f-f5a4-40bf-a7ff-c866e8122c33' class='xr-section-summary-in' type='checkbox'  ><label for='section-d492a87f-f5a4-40bf-a7ff-c866e8122c33' 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><input type='checkbox' disabled/><label></label><input id='index-218ecceb-7b76-4dd4-b521-1feeda3b5051' class='xr-index-data-in' type='checkbox'/><label for='index-218ecceb-7b76-4dd4-b521-1feeda3b5051' 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([1870-01-16 12:00:00, 1870-02-15 00:00:00, 1870-03-16 12:00:00,\n",
       "             1870-04-16 00:00:00, 1870-05-16 12:00:00, 1870-06-16 00:00:00,\n",
       "             1870-07-16 12:00:00, 1870-08-16 12:00:00, 1870-09-16 00:00:00,\n",
       "             1870-10-16 12:00:00, 1870-11-16 00:00:00, 1870-12-16 12:00:00,\n",
       "             1871-01-16 12:00:00, 1871-02-15 00:00:00, 1871-03-16 12:00:00,\n",
       "             1871-04-16 00:00:00, 1871-05-16 12:00:00, 1871-06-16 00:00:00,\n",
       "             1871-07-16 12:00:00, 1871-08-16 12:00:00, 1871-09-16 00:00:00,\n",
       "             1871-10-16 12:00:00, 1871-11-16 00:00:00, 1871-12-16 12:00:00,\n",
       "             1872-01-16 12:00:00, 1872-02-15 12:00:00, 1872-03-16 12:00:00,\n",
       "             1872-04-16 00:00:00, 1872-05-16 12:00:00, 1872-06-16 00:00:00,\n",
       "             1872-07-16 12:00:00, 1872-08-16 12:00:00, 1872-09-16 00:00:00,\n",
       "             1872-10-16 12:00:00, 1872-11-16 00:00:00, 1872-12-16 12:00:00,\n",
       "             1873-01-16 12:00:00, 1873-02-15 00:00:00, 1873-03-16 12:00:00,\n",
       "             1873-04-16 00:00:00, 1873-05-16 12:00:00, 1873-06-16 00:00:00,\n",
       "             1873-07-16 12:00:00, 1873-08-16 12:00:00, 1873-09-16 00:00:00,\n",
       "             1873-10-16 12:00:00, 1873-11-16 00:00:00, 1873-12-16 12:00:00,\n",
       "             1874-01-16 12:00:00, 1874-02-15 00:00:00, 1874-03-16 12:00:00,\n",
       "             1874-04-16 00:00:00, 1874-05-16 12:00:00, 1874-06-16 00:00:00,\n",
       "             1874-07-16 12:00:00, 1874-08-16 12:00:00, 1874-09-16 00:00:00,\n",
       "             1874-10-16 12:00:00, 1874-11-16 00:00:00, 1874-12-16 12:00:00],\n",
       "            dtype=&#x27;object&#x27;,\n",
       "            length=60,\n",
       "            calendar=&#x27;proleptic_gregorian&#x27;,\n",
       "            freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-31d8d865-b768-4bbe-9e32-9f1a4686bcd1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-31d8d865-b768-4bbe-9e32-9f1a4686bcd1' 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: 60)> Size: 480B\n",
       "array([25.00067275, 25.30644298, 25.7617891 , 26.10626959, 26.01841282,\n",
       "       25.5047623 , 25.28394064, 25.37295231, 25.72527095, 25.87376074,\n",
       "       25.67985682, 25.22942709, 24.95699101, 25.48591822, 26.02279319,\n",
       "       26.29556201, 26.18619426, 25.791096  , 25.50230606, 25.52068064,\n",
       "       25.68326088, 25.78873469, 25.63775696, 25.15228934, 25.08228764,\n",
       "       25.30220953, 25.69973578, 26.0328945 , 25.9973611 , 25.51339722,\n",
       "       25.39540446, 25.45184812, 25.70660688, 25.75817938, 25.54315004,\n",
       "       25.13706986, 24.96105631, 25.311973  , 25.7646741 , 26.01738372,\n",
       "       26.01281503, 25.69728948, 25.3639977 , 25.5225464 , 25.77108789,\n",
       "       25.84422784, 25.70096048, 25.11534178, 25.0415591 , 25.34789389,\n",
       "       25.72121083, 25.91918215, 25.83924512, 25.55205376, 25.13493311,\n",
       "       25.22239226, 25.43492285, 25.43073544, 25.26357054, 24.80680714])\n",
       "Coordinates:\n",
       "    height   float64 8B 2.0\n",
       "  * time     (time) object 480B 1870-01-16 12:00:00 ... 1874-12-16 12:00:00"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_trop_avg = ds.spatial.average(\"tas\", axis=[\"X\", \"Y\"], lat_bounds=(-25, 25))\n",
    "ds_trop_avg.tas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "cell_style": "split",
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x16185ed50>]"
      ]
     },
     "execution_count": 8,
     "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_trop_avg.tas.isel(time=slice(1, 100)).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Scenario 2: Calculate temporal average\n",
    "\n",
    "Related accessor: `ds.temporal`\n",
    "\n",
    "In this example, we calculate the **temporal average of `tas` as a single snapshot** (time dimension is collapsed).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "cell_style": "split",
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "\n",
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       "\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",
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       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tas&#x27; (lat: 145, lon: 192)&gt; Size: 223kB\n",
       "array([[-48.33789281, -48.33789281, -48.33789281, ..., -48.33789281,\n",
       "        -48.33789281, -48.33789281],\n",
       "       [-45.11841101, -45.15598013, -45.19300779, ..., -45.00752255,\n",
       "        -45.04406075, -45.08005955],\n",
       "       [-44.16135662, -44.27207786, -44.3812548 , ..., -43.8142947 ,\n",
       "        -43.93083878, -44.04664427],\n",
       "       ...,\n",
       "       [-18.87074617, -18.84301489, -18.81438219, ..., -18.96065605,\n",
       "        -18.92790196, -18.89991471],\n",
       "       [-18.98835924, -18.97666923, -18.96525417, ..., -19.02792192,\n",
       "        -19.01732752, -19.00373892],\n",
       "       [-19.36861239, -19.36861239, -19.36861239, ..., -19.36861239,\n",
       "        -19.36861239, -19.36861239]])\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 2.0\n",
       "Attributes:\n",
       "    operation:  temporal_avg\n",
       "    mode:       average\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'>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-a0c58293-93eb-4d20-b737-66ba0edf71d2' class='xr-array-in' type='checkbox' checked><label for='section-a0c58293-93eb-4d20-b737-66ba0edf71d2' 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>-48.34 -48.34 -48.34 -48.34 -48.34 ... -19.37 -19.37 -19.37 -19.37</span></div><div class='xr-array-data'><pre>array([[-48.33789281, -48.33789281, -48.33789281, ..., -48.33789281,\n",
       "        -48.33789281, -48.33789281],\n",
       "       [-45.11841101, -45.15598013, -45.19300779, ..., -45.00752255,\n",
       "        -45.04406075, -45.08005955],\n",
       "       [-44.16135662, -44.27207786, -44.3812548 , ..., -43.8142947 ,\n",
       "        -43.93083878, -44.04664427],\n",
       "       ...,\n",
       "       [-18.87074617, -18.84301489, -18.81438219, ..., -18.96065605,\n",
       "        -18.92790196, -18.89991471],\n",
       "       [-18.98835924, -18.97666923, -18.96525417, ..., -19.02792192,\n",
       "        -19.01732752, -19.00373892],\n",
       "       [-19.36861239, -19.36861239, -19.36861239, ..., -19.36861239,\n",
       "        -19.36861239, -19.36861239]])</pre></div></div></li><li class='xr-section-item'><input id='section-45b4b39e-ae10-4e94-957c-394f2916ff70' class='xr-section-summary-in' type='checkbox'  checked><label for='section-45b4b39e-ae10-4e94-957c-394f2916ff70' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span 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-3bdeff22-41d6-4188-b305-13fdb668a5d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3bdeff22-41d6-4188-b305-13fdb668a5d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9aee7274-92c6-4a88-aa6b-e5bd31b98abb' class='xr-var-data-in' type='checkbox'><label for='data-9aee7274-92c6-4a88-aa6b-e5bd31b98abb' 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-d74dc076-37b8-424f-8fee-c74aa2129340' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d74dc076-37b8-424f-8fee-c74aa2129340' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88d5e603-4599-4668-91bc-c7776e7e4837' class='xr-var-data-in' type='checkbox'><label for='data-88d5e603-4599-4668-91bc-c7776e7e4837' 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'>2.0</div><input id='attrs-51295a41-7266-4bf0-8a6e-722b3aa60f90' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-51295a41-7266-4bf0-8a6e-722b3aa60f90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-958cb31a-f7dc-4cbd-ae29-66b23161ecd2' class='xr-var-data-in' type='checkbox'><label for='data-958cb31a-f7dc-4cbd-ae29-66b23161ecd2' 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>array(2.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-77d66c7e-05fd-4d93-9583-f72702ab6d69' class='xr-section-summary-in' type='checkbox'  ><label for='section-77d66c7e-05fd-4d93-9583-f72702ab6d69' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-b87eafdd-8d32-4e5a-93c3-8cc2e03063c8' class='xr-index-data-in' type='checkbox'/><label for='index-b87eafdd-8d32-4e5a-93c3-8cc2e03063c8' 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><input type='checkbox' disabled/><label></label><input id='index-e76bd9c7-9ed7-4298-b666-5620eedecb7d' class='xr-index-data-in' type='checkbox'/><label for='index-e76bd9c7-9ed7-4298-b666-5620eedecb7d' 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></ul></div></li><li class='xr-section-item'><input id='section-e3425116-4e5c-473c-ba32-fedaa8ce2caf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e3425116-4e5c-473c-ba32-fedaa8ce2caf' 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>average</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' (lat: 145, lon: 192)> Size: 223kB\n",
       "array([[-48.33789281, -48.33789281, -48.33789281, ..., -48.33789281,\n",
       "        -48.33789281, -48.33789281],\n",
       "       [-45.11841101, -45.15598013, -45.19300779, ..., -45.00752255,\n",
       "        -45.04406075, -45.08005955],\n",
       "       [-44.16135662, -44.27207786, -44.3812548 , ..., -43.8142947 ,\n",
       "        -43.93083878, -44.04664427],\n",
       "       ...,\n",
       "       [-18.87074617, -18.84301489, -18.81438219, ..., -18.96065605,\n",
       "        -18.92790196, -18.89991471],\n",
       "       [-18.98835924, -18.97666923, -18.96525417, ..., -19.02792192,\n",
       "        -19.01732752, -19.00373892],\n",
       "       [-19.36861239, -19.36861239, -19.36861239, ..., -19.36861239,\n",
       "        -19.36861239, -19.36861239]])\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 2.0\n",
       "Attributes:\n",
       "    operation:  temporal_avg\n",
       "    mode:       average\n",
       "    freq:       month\n",
       "    weighted:   True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_avg = ds.temporal.average(\"tas\", weighted=True)\n",
    "ds_avg.tas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "cell_style": "split",
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x1617d7a10>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_avg.tas.plot(label=\"weighted\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Scenario 3: Horizontal Regridding\n",
    "\n",
    "Related accessor: `ds.regridder`\n",
    "\n",
    "In this example, we will generate a gaussian grid with 32 latitudes to regrid our input data to.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Create the output grid\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
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       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: \"(\";\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: \")\";\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: \",\";\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-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.Dataset&gt; Size: 2kB\n",
       "Dimensions:   (lon: 65, bnds: 2, lat: 32)\n",
       "Coordinates:\n",
       "  * lon       (lon) float64 520B 0.0 5.625 11.25 16.88 ... 348.8 354.4 360.0\n",
       "  * lat       (lat) float64 256B 85.76 80.27 74.74 ... -74.74 -80.27 -85.76\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    lon_bnds  (lon, bnds) float64 1kB -2.812 2.812 2.812 ... 357.2 357.2 362.8\n",
       "    lat_bnds  (lat, bnds) float64 512B 90.0 83.21 83.21 ... -83.21 -83.21 -90.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8dcb7f18-2462-441e-b237-782f107617a0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8dcb7f18-2462-441e-b237-782f107617a0' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lon</span>: 65</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lat</span>: 32</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-39434d90-b248-4926-b699-3e6866cd2a69' class='xr-section-summary-in' type='checkbox'  checked><label for='section-39434d90-b248-4926-b699-3e6866cd2a69' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>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 5.625 11.25 ... 354.4 360.0</div><input id='attrs-3fa6b631-32bf-4609-a795-5bfd6fe405b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3fa6b631-32bf-4609-a795-5bfd6fe405b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af70fa96-5a9f-4837-823b-ef4dde636ae3' class='xr-var-data-in' type='checkbox'><label for='data-af70fa96-5a9f-4837-823b-ef4dde636ae3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate :</span></dt><dd>longitude</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd></dl></div><div class='xr-var-data'><pre>array([  0.   ,   5.625,  11.25 ,  16.875,  22.5  ,  28.125,  33.75 ,  39.375,\n",
       "        45.   ,  50.625,  56.25 ,  61.875,  67.5  ,  73.125,  78.75 ,  84.375,\n",
       "        90.   ,  95.625, 101.25 , 106.875, 112.5  , 118.125, 123.75 , 129.375,\n",
       "       135.   , 140.625, 146.25 , 151.875, 157.5  , 163.125, 168.75 , 174.375,\n",
       "       180.   , 185.625, 191.25 , 196.875, 202.5  , 208.125, 213.75 , 219.375,\n",
       "       225.   , 230.625, 236.25 , 241.875, 247.5  , 253.125, 258.75 , 264.375,\n",
       "       270.   , 275.625, 281.25 , 286.875, 292.5  , 298.125, 303.75 , 309.375,\n",
       "       315.   , 320.625, 326.25 , 331.875, 337.5  , 343.125, 348.75 , 354.375,\n",
       "       360.   ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>85.76 80.27 74.74 ... -80.27 -85.76</div><input id='attrs-ce9f6fcf-bf71-4d4d-bdf1-161f2b81acfb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce9f6fcf-bf71-4d4d-bdf1-161f2b81acfb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-697c45b3-5889-4e65-bebe-55c3da798deb' class='xr-var-data-in' type='checkbox'><label for='data-697c45b3-5889-4e65-bebe-55c3da798deb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd></dl></div><div class='xr-var-data'><pre>array([ 85.760587,  80.268779,  74.74454 ,  69.212976,  63.678636,  58.142954,\n",
       "        52.606526,  47.069642,  41.532461,  35.995078,  30.457554,  24.919929,\n",
       "        19.382231,  13.844484,   8.306703,   2.768903,  -2.768903,  -8.306703,\n",
       "       -13.844484, -19.382231, -24.919929, -30.457554, -35.995078, -41.532461,\n",
       "       -47.069642, -52.606526, -58.142954, -63.678636, -69.212976, -74.74454 ,\n",
       "       -80.268779, -85.760587])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-079477ba-0ef1-4cd0-b12b-d010a4f41cd8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-079477ba-0ef1-4cd0-b12b-d010a4f41cd8' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-2.812 2.812 2.812 ... 357.2 362.8</div><input id='attrs-75bffb21-6e82-4eea-971b-3080a733f2c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75bffb21-6e82-4eea-971b-3080a733f2c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa0de565-1765-4c13-bb72-b334af599faa' class='xr-var-data-in' type='checkbox'><label for='data-fa0de565-1765-4c13-bb72-b334af599faa' 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>xcdat_bounds :</span></dt><dd>True</dd></dl></div><div class='xr-var-data'><pre>array([[ -2.8125,   2.8125],\n",
       "       [  2.8125,   8.4375],\n",
       "       [  8.4375,  14.0625],\n",
       "       [ 14.0625,  19.6875],\n",
       "       [ 19.6875,  25.3125],\n",
       "       [ 25.3125,  30.9375],\n",
       "       [ 30.9375,  36.5625],\n",
       "       [ 36.5625,  42.1875],\n",
       "       [ 42.1875,  47.8125],\n",
       "       [ 47.8125,  53.4375],\n",
       "       [ 53.4375,  59.0625],\n",
       "       [ 59.0625,  64.6875],\n",
       "       [ 64.6875,  70.3125],\n",
       "       [ 70.3125,  75.9375],\n",
       "       [ 75.9375,  81.5625],\n",
       "       [ 81.5625,  87.1875],\n",
       "       [ 87.1875,  92.8125],\n",
       "       [ 92.8125,  98.4375],\n",
       "       [ 98.4375, 104.0625],\n",
       "       [104.0625, 109.6875],\n",
       "...\n",
       "       [250.3125, 255.9375],\n",
       "       [255.9375, 261.5625],\n",
       "       [261.5625, 267.1875],\n",
       "       [267.1875, 272.8125],\n",
       "       [272.8125, 278.4375],\n",
       "       [278.4375, 284.0625],\n",
       "       [284.0625, 289.6875],\n",
       "       [289.6875, 295.3125],\n",
       "       [295.3125, 300.9375],\n",
       "       [300.9375, 306.5625],\n",
       "       [306.5625, 312.1875],\n",
       "       [312.1875, 317.8125],\n",
       "       [317.8125, 323.4375],\n",
       "       [323.4375, 329.0625],\n",
       "       [329.0625, 334.6875],\n",
       "       [334.6875, 340.3125],\n",
       "       [340.3125, 345.9375],\n",
       "       [345.9375, 351.5625],\n",
       "       [351.5625, 357.1875],\n",
       "       [357.1875, 362.8125]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>90.0 83.21 83.21 ... -83.21 -90.0</div><input id='attrs-734990be-04d1-4b74-af2d-8c820d7e141e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-734990be-04d1-4b74-af2d-8c820d7e141e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53a4da0b-65ee-4e1e-93f4-7f57a728ad5a' class='xr-var-data-in' type='checkbox'><label for='data-53a4da0b-65ee-4e1e-93f4-7f57a728ad5a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 9.00000000e+01,  8.32076896e+01],\n",
       "       [ 8.32076896e+01,  7.76092666e+01],\n",
       "       [ 7.76092666e+01,  7.20479751e+01],\n",
       "       [ 7.20479751e+01,  6.64972690e+01],\n",
       "       [ 6.64972690e+01,  6.09510468e+01],\n",
       "       [ 6.09510468e+01,  5.54071385e+01],\n",
       "       [ 5.54071385e+01,  4.98645761e+01],\n",
       "       [ 4.98645761e+01,  4.43228612e+01],\n",
       "       [ 4.43228612e+01,  3.87817101e+01],\n",
       "       [ 3.87817101e+01,  3.32409482e+01],\n",
       "       [ 3.32409482e+01,  2.77004616e+01],\n",
       "       [ 2.77004616e+01,  2.21601714e+01],\n",
       "       [ 2.21601714e+01,  1.66200203e+01],\n",
       "       [ 1.66200203e+01,  1.10799644e+01],\n",
       "       [ 1.10799644e+01,  5.53996787e+00],\n",
       "       [ 5.53996787e+00, -4.77083202e-15],\n",
       "       [-4.77083202e-15, -5.53996787e+00],\n",
       "       [-5.53996787e+00, -1.10799644e+01],\n",
       "       [-1.10799644e+01, -1.66200203e+01],\n",
       "       [-1.66200203e+01, -2.21601714e+01],\n",
       "       [-2.21601714e+01, -2.77004616e+01],\n",
       "       [-2.77004616e+01, -3.32409482e+01],\n",
       "       [-3.32409482e+01, -3.87817101e+01],\n",
       "       [-3.87817101e+01, -4.43228612e+01],\n",
       "       [-4.43228612e+01, -4.98645761e+01],\n",
       "       [-4.98645761e+01, -5.54071385e+01],\n",
       "       [-5.54071385e+01, -6.09510468e+01],\n",
       "       [-6.09510468e+01, -6.64972690e+01],\n",
       "       [-6.64972690e+01, -7.20479751e+01],\n",
       "       [-7.20479751e+01, -7.76092666e+01],\n",
       "       [-7.76092666e+01, -8.32076896e+01],\n",
       "       [-8.32076896e+01, -9.00000000e+01]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2857c487-88fc-4168-a4f5-ce05b87fce2a' class='xr-section-summary-in' type='checkbox'  ><label for='section-2857c487-88fc-4168-a4f5-ce05b87fce2a' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-b6a37301-f615-45f5-a274-8a37b59802b6' class='xr-index-data-in' type='checkbox'/><label for='index-b6a37301-f615-45f5-a274-8a37b59802b6' 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,   5.625,   11.25,  16.875,    22.5,  28.125,   33.75,  39.375,\n",
       "          45.0,  50.625,   56.25,  61.875,    67.5,  73.125,   78.75,  84.375,\n",
       "          90.0,  95.625,  101.25, 106.875,   112.5, 118.125,  123.75, 129.375,\n",
       "         135.0, 140.625,  146.25, 151.875,   157.5, 163.125,  168.75, 174.375,\n",
       "         180.0, 185.625,  191.25, 196.875,   202.5, 208.125,  213.75, 219.375,\n",
       "         225.0, 230.625,  236.25, 241.875,   247.5, 253.125,  258.75, 264.375,\n",
       "         270.0, 275.625,  281.25, 286.875,   292.5, 298.125,  303.75, 309.375,\n",
       "         315.0, 320.625,  326.25, 331.875,   337.5, 343.125,  348.75, 354.375,\n",
       "         360.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-121db1cd-e7ed-4d0f-afe5-4847007f3056' class='xr-index-data-in' type='checkbox'/><label for='index-121db1cd-e7ed-4d0f-afe5-4847007f3056' 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([   85.7605871204438,      80.26877907225,   74.74454036863577,\n",
       "         69.21297616937085,   63.67863556109686,   58.14295404920328,\n",
       "        52.606526034345265,   47.06964205968768,   41.53246124665608,\n",
       "         35.99507841127159,  30.457553961152087,   24.91992862994861,\n",
       "         19.38223134643438,   13.84448373438486,   8.306702856518806,\n",
       "         2.768903007736009,  -2.768903007736009,  -8.306702856518806,\n",
       "        -13.84448373438486,  -19.38223134643438,  -24.91992862994861,\n",
       "       -30.457553961152087,  -35.99507841127159,  -41.53246124665608,\n",
       "        -47.06964205968768, -52.606526034345265,  -58.14295404920328,\n",
       "        -63.67863556109686,  -69.21297616937085,  -74.74454036863577,\n",
       "           -80.26877907225,   -85.7605871204438],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1521132d-1b53-4214-982a-4a7f34dcaca2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1521132d-1b53-4214-982a-4a7f34dcaca2' 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.Dataset> Size: 2kB\n",
       "Dimensions:   (lon: 65, bnds: 2, lat: 32)\n",
       "Coordinates:\n",
       "  * lon       (lon) float64 520B 0.0 5.625 11.25 16.88 ... 348.8 354.4 360.0\n",
       "  * lat       (lat) float64 256B 85.76 80.27 74.74 ... -74.74 -80.27 -85.76\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    lon_bnds  (lon, bnds) float64 1kB -2.812 2.812 2.812 ... 357.2 357.2 362.8\n",
       "    lat_bnds  (lat, bnds) float64 512B 90.0 83.21 83.21 ... -83.21 -83.21 -90.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_grid = xc.create_gaussian_grid(32)\n",
    "output_grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Plot the Input vs. Output Grid\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, figsize=(16, 6))\n",
    "\n",
    "input_grid = ds.regridder.grid\n",
    "input_grid.plot.scatter(\n",
    "    x=\"lon\", y=\"lat\", s=5, ax=axes[0], add_colorbar=False, cmap=plt.cm.RdBu\n",
    ")\n",
    "axes[0].set_title(\"Input Grid\")\n",
    "\n",
    "output_grid.plot.scatter(\n",
    "    x=\"lon\", y=\"lat\", s=5, ax=axes[1], add_colorbar=False, cmap=plt.cm.RdBu\n",
    ")\n",
    "axes[1].set_title(\"Output Grid\")\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Regrid the data\n",
    "\n",
    "xCDAT offers horizontal regridding with `xESMF` (default) and a Python port of `regrid2`.\n",
    "We will be using `xESMF` to regrid.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# xesmf supports \"bilinear\", \"conservative\", \"nearest_s2d\", \"nearest_d2s\", and \"patch\"\n",
    "output = ds.regridder.horizontal(\"tas\", output_grid, tool=\"xesmf\", method=\"bilinear\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, figsize=(16, 4))\n",
    "\n",
    "ds.tas.isel(time=0).plot(ax=axes[0])\n",
    "axes[0].set_title(\"Input data\")\n",
    "\n",
    "output.tas.isel(time=0).plot(ax=axes[1])\n",
    "axes[1].set_title(\"Output data\")\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Parallelism with Dask\n",
    "\n",
    "<div style=\"text-align:center\">\n",
    "  <img src=\"../_static/dask-logo.svg\" alt=\"Dask logo\" style=\"display: inline-block; width:300px;\">\n",
    "</div>\n",
    "\n",
    "> Nearly all existing xarray methods have been extended to work automatically with Dask arrays for parallelism\n",
    ">\n",
    "> &mdash; <cite>https://docs.xarray.dev/en/stable/user-guide/dask.html#using-dask-with-xarray</cite>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "- Parallelized xarray methods include **indexing, computation, concatenating and grouped operations**\n",
    "- xCDAT APIs that build upon xarray methods inherently support Dask parallelism\n",
    "  - Dask arrays are loaded into memory only when absolutely required (e.g., decoding time, handling bounds)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "center",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### High-level Overview of Dask Mechanics\n",
    "\n",
    "- **Dask divides arrays** into many small pieces, called **\"chunks\"** (each presumed to be small enough to fit into memory)\n",
    "- Dask arrays **operations are lazy**\n",
    "  - Operations **queue** up a series of tasks mapped over blocks\n",
    "  - No computation is performed until values need to be computed (lazy)\n",
    "  - Data is loaded into memory and **computation** is performed in **streaming fashion**, **block-by-block**\n",
    "- Computation is controlled by multi-processing or thread pool\n",
    "\n",
    "Source: <cite>https://docs.xarray.dev/en/stable/user-guide/dask.html</cite>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### How do I activate Dask with Xarray/xCDAT?\n",
    "\n",
    "- The usual way to create a Dataset filled with Dask arrays is to load the data from a netCDF file or files\n",
    "- You can do this by supplying a `chunks` argument to `open_dataset()` or using the `open_mfdataset()` function\n",
    "  - By default, `open_mfdataset()` will chunk each netCDF file into a single Dask array\n",
    "  - Supply the `chunks` argument to control the size of the resulting Dask arrays\n",
    "  - Xarray maintains a Dask array until it is not possible (raises an exception instead of loading into memory)\n",
    "\n",
    "Source: <cite>https://docs.xarray.dev/en/stable/user-guide/dask.html#reading-and-writing-data</cite>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# Use .chunk() to activate Dask arrays\n",
    "# NOTE: `open_mfdataset()` automatically chunks by the number of files, which\n",
    "# might not be optimal.\n",
    "ds = xc.tutorial.open_dataset(\"tas_amon_access\", chunks={\"time\": \"auto\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
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       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=\"dark\"],\n",
       "html[data-theme=\"dark\"],\n",
       "body[data-theme=\"dark\"],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1f1f1f;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: inline-block;\n",
       "  opacity: 0;\n",
       "  height: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:focus + label {\n",
       "  border: 2px solid var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: \"►\";\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: \"▼\";\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: \"(\";\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: \")\";\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: \",\";\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-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.Dataset&gt; Size: 7MB\n",
       "Dimensions:    (time: 60, bnds: 2, lat: 145, lon: 192)\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 ... 354.4 356.2 358.1\n",
       "    height     float64 8B ...\n",
       "  * time       (time) object 480B 1870-01-16 12:00:00 ... 1874-12-16 12:00:00\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    time_bnds  (time, bnds) object 960B dask.array&lt;chunksize=(60, 2), meta=np.ndarray&gt;\n",
       "    lat_bnds   (lat, bnds) float64 2kB dask.array&lt;chunksize=(145, 2), meta=np.ndarray&gt;\n",
       "    lon_bnds   (lon, bnds) float64 3kB dask.array&lt;chunksize=(192, 2), meta=np.ndarray&gt;\n",
       "    tas        (time, lat, lon) float32 7MB dask.array&lt;chunksize=(60, 145, 192), meta=np.ndarray&gt;\n",
       "Attributes: (12/48)\n",
       "    Conventions:                     CF-1.7 CMIP-6.2\n",
       "    activity_id:                     CMIP\n",
       "    branch_method:                   standard\n",
       "    branch_time_in_child:            0.0\n",
       "    branch_time_in_parent:           87658.0\n",
       "    creation_date:                   2020-06-05T04:06:11Z\n",
       "    ...                              ...\n",
       "    variant_label:                   r10i1p1f1\n",
       "    version:                         v20200605\n",
       "    license:                         CMIP6 model data produced by CSIRO is li...\n",
       "    cmor_version:                    3.4.0\n",
       "    tracking_id:                     hdl:21.14100/af78ae5e-f3a6-4e99-8cfe-5f2...\n",
       "    DODS_EXTRA.Unlimited_Dimension:  time</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-abf1e400-2fcd-42d9-8d94-ecb055323949' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-abf1e400-2fcd-42d9-8d94-ecb055323949' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 60</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lat</span>: 145</li><li><span class='xr-has-index'>lon</span>: 192</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-857e0a60-6acc-46f0-bf2d-d72efd2fc986' class='xr-section-summary-in' type='checkbox'  checked><label for='section-857e0a60-6acc-46f0-bf2d-d72efd2fc986' 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-934ce22e-2170-4e3d-8d7e-c59a7f1dfed2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-934ce22e-2170-4e3d-8d7e-c59a7f1dfed2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a377af6-e9da-41ff-9ccf-f152d8439b1f' class='xr-var-data-in' type='checkbox'><label for='data-9a377af6-e9da-41ff-9ccf-f152d8439b1f' 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-b8937e45-1828-4b56-86d3-f6b3e6b57815' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b8937e45-1828-4b56-86d3-f6b3e6b57815' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1793f3d-6410-415b-bb9d-5c2740b05a2d' class='xr-var-data-in' type='checkbox'><label for='data-d1793f3d-6410-415b-bb9d-5c2740b05a2d' 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-a14b0966-f9e1-4679-b864-ea4dadb8bbb3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a14b0966-f9e1-4679-b864-ea4dadb8bbb3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec0b5775-eb70-4dbc-ae39-65820cb5b1b3' class='xr-var-data-in' type='checkbox'><label for='data-ec0b5775-eb70-4dbc-ae39-65820cb5b1b3' 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'>1870-01-16 12:00:00 ... 1874-12-...</div><input id='attrs-d335d9cb-b64f-462d-9211-7ab3367d31a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d335d9cb-b64f-462d-9211-7ab3367d31a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc5a9641-ffb8-4306-b1c4-ad2db51ec8cc' class='xr-var-data-in' type='checkbox'><label for='data-bc5a9641-ffb8-4306-b1c4-ad2db51ec8cc' 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(1870, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 2, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 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-7c697177-3608-4b29-a386-144722ac536b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7c697177-3608-4b29-a386-144722ac536b' class='xr-section-summary' >Data variables: <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>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(60, 2), meta=np.ndarray&gt;</div><input id='attrs-4f530088-01ae-488c-a782-92c7f1c0906c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f530088-01ae-488c-a782-92c7f1c0906c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36115890-26ce-4369-af8e-fa8104498cdb' class='xr-var-data-in' type='checkbox'><label for='data-36115890-26ce-4369-af8e-fa8104498cdb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[1 2]</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 0.94 kiB </td>\n",
       "                        <td> 0.94 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (60, 2) </td>\n",
       "                        <td> (60, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> object numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"82\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"32\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"32\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"32\" y1=\"0\" x2=\"32\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 32.02435249410096,0.0 32.02435249410096,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"16.012176\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
       "  <text x=\"52.024352\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,52.024352,60.000000)\">60</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(145, 2), meta=np.ndarray&gt;</div><input id='attrs-a2f901f2-0aee-405e-80a3-729208a9bbac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a2f901f2-0aee-405e-80a3-729208a9bbac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-193602c1-0c66-465b-bf66-eba6eeee4e6b' class='xr-var-data-in' type='checkbox'><label for='data-193602c1-0c66-465b-bf66-eba6eeee4e6b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[145   2]</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 2.27 kiB </td>\n",
       "                        <td> 2.27 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (145, 2) </td>\n",
       "                        <td> (145, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"77\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"27\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"27\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"27\" y1=\"0\" x2=\"27\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 27.018056951081515,0.0 27.018056951081515,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"13.509028\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
       "  <text x=\"47.018057\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,47.018057,60.000000)\">145</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(192, 2), meta=np.ndarray&gt;</div><input id='attrs-22944128-e91f-4678-9148-ecbb44d773d7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-22944128-e91f-4678-9148-ecbb44d773d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc39c0c6-ccfc-4a66-ac78-316579af5079' class='xr-var-data-in' type='checkbox'><label for='data-fc39c0c6-ccfc-4a66-ac78-316579af5079' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[192   2]</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 3.00 kiB </td>\n",
       "                        <td> 3.00 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (192, 2) </td>\n",
       "                        <td> (192, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
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       "  <polygon points=\"0.0,0.0 25.60913412115106,0.0 25.60913412115106,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"12.804567\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
       "  <text x=\"45.609134\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.609134,60.000000)\">192</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tas</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(60, 145, 192), meta=np.ndarray&gt;</div><input id='attrs-e4cd1098-840f-41b5-b4be-ebdad1214a68' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e4cd1098-840f-41b5-b4be-ebdad1214a68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c376c423-8a85-4712-9068-7dc34c9e0aec' class='xr-var-data-in' type='checkbox'><label for='data-c376c423-8a85-4712-9068-7dc34c9e0aec' 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>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>comment :</span></dt><dd>near-surface (usually, 2 meter) air temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>history :</span></dt><dd>2020-06-05T04:06:10Z altered by CMOR: Treated scalar dimension: &#x27;height&#x27;. 2020-06-05T04:06:10Z altered by CMOR: replaced missing value flag (-1.07374e+09) with standard missing value (1e+20).</dd><dt><span>_ChunkSizes :</span></dt><dd>[  1 145 192]</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-17d6b931-f5e7-4d35-8074-25b34371cd98' class='xr-section-summary-in' type='checkbox'  ><label for='section-17d6b931-f5e7-4d35-8074-25b34371cd98' 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><input type='checkbox' disabled/><label></label><input id='index-4b8163c9-ddf8-4a30-b4b4-e6ac666d2b2b' class='xr-index-data-in' type='checkbox'/><label for='index-4b8163c9-ddf8-4a30-b4b4-e6ac666d2b2b' 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><input type='checkbox' disabled/><label></label><input id='index-db60f936-b415-4d1e-adbf-fd70c229e642' class='xr-index-data-in' type='checkbox'/><label for='index-db60f936-b415-4d1e-adbf-fd70c229e642' 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><input type='checkbox' disabled/><label></label><input id='index-5b1b80ef-fb39-47a5-95fa-a66163950338' class='xr-index-data-in' type='checkbox'/><label for='index-5b1b80ef-fb39-47a5-95fa-a66163950338' 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([1870-01-16 12:00:00, 1870-02-15 00:00:00, 1870-03-16 12:00:00,\n",
       "             1870-04-16 00:00:00, 1870-05-16 12:00:00, 1870-06-16 00:00:00,\n",
       "             1870-07-16 12:00:00, 1870-08-16 12:00:00, 1870-09-16 00:00:00,\n",
       "             1870-10-16 12:00:00, 1870-11-16 00:00:00, 1870-12-16 12:00:00,\n",
       "             1871-01-16 12:00:00, 1871-02-15 00:00:00, 1871-03-16 12:00:00,\n",
       "             1871-04-16 00:00:00, 1871-05-16 12:00:00, 1871-06-16 00:00:00,\n",
       "             1871-07-16 12:00:00, 1871-08-16 12:00:00, 1871-09-16 00:00:00,\n",
       "             1871-10-16 12:00:00, 1871-11-16 00:00:00, 1871-12-16 12:00:00,\n",
       "             1872-01-16 12:00:00, 1872-02-15 12:00:00, 1872-03-16 12:00:00,\n",
       "             1872-04-16 00:00:00, 1872-05-16 12:00:00, 1872-06-16 00:00:00,\n",
       "             1872-07-16 12:00:00, 1872-08-16 12:00:00, 1872-09-16 00:00:00,\n",
       "             1872-10-16 12:00:00, 1872-11-16 00:00:00, 1872-12-16 12:00:00,\n",
       "             1873-01-16 12:00:00, 1873-02-15 00:00:00, 1873-03-16 12:00:00,\n",
       "             1873-04-16 00:00:00, 1873-05-16 12:00:00, 1873-06-16 00:00:00,\n",
       "             1873-07-16 12:00:00, 1873-08-16 12:00:00, 1873-09-16 00:00:00,\n",
       "             1873-10-16 12:00:00, 1873-11-16 00:00:00, 1873-12-16 12:00:00,\n",
       "             1874-01-16 12:00:00, 1874-02-15 00:00:00, 1874-03-16 12:00:00,\n",
       "             1874-04-16 00:00:00, 1874-05-16 12:00:00, 1874-06-16 00:00:00,\n",
       "             1874-07-16 12:00:00, 1874-08-16 12:00:00, 1874-09-16 00:00:00,\n",
       "             1874-10-16 12:00:00, 1874-11-16 00:00:00, 1874-12-16 12:00:00],\n",
       "            dtype=&#x27;object&#x27;,\n",
       "            length=60,\n",
       "            calendar=&#x27;proleptic_gregorian&#x27;,\n",
       "            freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9720bbe1-444a-4f52-8713-89908c6085d6' class='xr-section-summary-in' type='checkbox'  ><label for='section-9720bbe1-444a-4f52-8713-89908c6085d6' class='xr-section-summary' >Attributes: <span>(48)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>CMIP</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>branch_time_in_child :</span></dt><dd>0.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>87658.0</dd><dt><span>creation_date :</span></dt><dd>2020-06-05T04:06:11Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.30</dd><dt><span>experiment :</span></dt><dd>all-forcing simulation of the recent past</dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.CSIRO.ACCESS-ESM1-5.historical.none.r10i1p1f1</dd><dt><span>grid :</span></dt><dd>native atmosphere N96 grid (145x192 latxlon)</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>history :</span></dt><dd>2020-06-05T04:06:11Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia</dd><dt><span>institution_id :</span></dt><dd>CSIRO</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>nominal_resolution :</span></dt><dd>250 km</dd><dt><span>notes :</span></dt><dd>Exp: ESM-historical; Local ID: HI-14; Variable: tas ([&#x27;fld_s03i236&#x27;])</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>piControl</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>ACCESS-ESM1-5</dd><dt><span>parent_time_units :</span></dt><dd>days since 0101-1-1</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>10</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>run_variant :</span></dt><dd>forcing: GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = CO2, N2O, CH4, CFC11, CFC12, CFC113, HCFC22, HFC125, HFC134a)</dd><dt><span>source :</span></dt><dd>ACCESS-ESM1.5 (2019): \n",
       "aerosol: CLASSIC (v1.0)\n",
       "atmos: HadGAM2 (r1.1, N96; 192 x 145 longitude/latitude; 38 levels; top level 39255 m)\n",
       "atmosChem: none\n",
       "land: CABLE2.4\n",
       "landIce: none\n",
       "ocean: ACCESS-OM2 (MOM5, tripolar primarily 1deg; 360 x 300 longitude/latitude; 50 levels; top grid cell 0-10 m)\n",
       "ocnBgchem: WOMBAT (same grid as ocean)\n",
       "seaIce: CICE4.1 (same grid as ocean)</dd><dt><span>source_id :</span></dt><dd>ACCESS-ESM1-5</dd><dt><span>source_type :</span></dt><dd>AOGCM</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>Amon</dd><dt><span>table_info :</span></dt><dd>Creation Date:(30 April 2019) MD5:5bd755e94c2173cb3050a0f3480f60c4</dd><dt><span>title :</span></dt><dd>ACCESS-ESM1-5 output prepared for CMIP6</dd><dt><span>variable_id :</span></dt><dd>tas</dd><dt><span>variant_label :</span></dt><dd>r10i1p1f1</dd><dt><span>version :</span></dt><dd>v20200605</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by CSIRO is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment.  Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file). The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>cmor_version :</span></dt><dd>3.4.0</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/af78ae5e-f3a6-4e99-8cfe-5f29eb467cd1</dd><dt><span>DODS_EXTRA.Unlimited_Dimension :</span></dt><dd>time</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 7MB\n",
       "Dimensions:    (time: 60, bnds: 2, lat: 145, lon: 192)\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 ... 354.4 356.2 358.1\n",
       "    height     float64 8B ...\n",
       "  * time       (time) object 480B 1870-01-16 12:00:00 ... 1874-12-16 12:00:00\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    time_bnds  (time, bnds) object 960B dask.array<chunksize=(60, 2), meta=np.ndarray>\n",
       "    lat_bnds   (lat, bnds) float64 2kB dask.array<chunksize=(145, 2), meta=np.ndarray>\n",
       "    lon_bnds   (lon, bnds) float64 3kB dask.array<chunksize=(192, 2), meta=np.ndarray>\n",
       "    tas        (time, lat, lon) float32 7MB dask.array<chunksize=(60, 145, 192), meta=np.ndarray>\n",
       "Attributes: (12/48)\n",
       "    Conventions:                     CF-1.7 CMIP-6.2\n",
       "    activity_id:                     CMIP\n",
       "    branch_method:                   standard\n",
       "    branch_time_in_child:            0.0\n",
       "    branch_time_in_parent:           87658.0\n",
       "    creation_date:                   2020-06-05T04:06:11Z\n",
       "    ...                              ...\n",
       "    variant_label:                   r10i1p1f1\n",
       "    version:                         v20200605\n",
       "    license:                         CMIP6 model data produced by CSIRO is li...\n",
       "    cmor_version:                    3.4.0\n",
       "    tracking_id:                     hdl:21.14100/af78ae5e-f3a6-4e99-8cfe-5f2...\n",
       "    DODS_EXTRA.Unlimited_Dimension:  time"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Example of Parallelism in xCDAT's Spatial Averager\n",
    "\n",
    "This is a **demonstration** that **chunked Dataset objects work** with **xCDAT APIs**.\n",
    "\n",
    "- The generation of weights is serial\n",
    "- The weighted average operation _should_ be parallelized (uses xarray's `.weighted()` API)\n",
    "- We intend on doing performance metrics and giving guidance on when to chunk\n",
    "  - For now visit https://github.com/xCDAT/xcdat/discussions/376 for best practices\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Queue up the spatial average operation in the Dask task graph\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
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       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=\"dark\"],\n",
       "html[data-theme=\"dark\"],\n",
       "body[data-theme=\"dark\"],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1f1f1f;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: inline-block;\n",
       "  opacity: 0;\n",
       "  height: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:focus + label {\n",
       "  border: 2px solid var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: \"►\";\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: \"▼\";\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: \"(\";\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: \")\";\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: \",\";\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-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: 60)&gt; Size: 480B\n",
       "dask.array&lt;truediv, shape=(60,), dtype=float64, chunksize=(60,), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "    height   float64 8B ...\n",
       "  * time     (time) object 480B 1870-01-16 12:00:00 ... 1874-12-16 12:00:00\n",
       "Attributes:\n",
       "    standard_name:  air_temperature\n",
       "    long_name:      Near-Surface Air Temperature\n",
       "    comment:        near-surface (usually, 2 meter) air temperature\n",
       "    units:          K\n",
       "    cell_methods:   area: time: mean\n",
       "    cell_measures:  area: areacella\n",
       "    history:        2020-06-05T04:06:10Z altered by CMOR: Treated scalar dime...\n",
       "    _ChunkSizes:    [  1 145 192]</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>: 60</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-f3a55a2e-40c6-420b-a8ac-831032e5bb20' class='xr-array-in' type='checkbox' checked><label for='section-f3a55a2e-40c6-420b-a8ac-831032e5bb20' 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=(60,), 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> 480 B </td>\n",
       "                        <td> 480 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (60,) </td>\n",
       "                        <td> (60,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 29 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=\"78\" 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=\"28\" x2=\"120\" y2=\"28\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"28\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"28\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,28.02293761910704 0.0,28.02293761910704\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"48.022938\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >60</text>\n",
       "  <text x=\"140.000000\" y=\"14.011469\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,14.011469)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-0b26aed4-6f16-4434-a43e-8a704b43e382' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0b26aed4-6f16-4434-a43e-8a704b43e382' 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-1894e125-f874-46c2-95c3-b8afc8409845' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1894e125-f874-46c2-95c3-b8afc8409845' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56931125-0dd0-4bff-8c53-0e3da3c8449a' class='xr-var-data-in' type='checkbox'><label for='data-56931125-0dd0-4bff-8c53-0e3da3c8449a' 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'>1870-01-16 12:00:00 ... 1874-12-...</div><input id='attrs-0025fc09-0fc1-4ddf-aa01-1f2aea0de7cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0025fc09-0fc1-4ddf-aa01-1f2aea0de7cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08b58f2d-c2d5-4014-b2da-d6a5b80cefc2' class='xr-var-data-in' type='checkbox'><label for='data-08b58f2d-c2d5-4014-b2da-d6a5b80cefc2' 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(1870, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 2, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 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-bc05ad20-3304-4f55-b20a-a816bbd2ead1' class='xr-section-summary-in' type='checkbox'  ><label for='section-bc05ad20-3304-4f55-b20a-a816bbd2ead1' 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><input type='checkbox' disabled/><label></label><input id='index-af9c2f7a-52e6-453d-82cd-ed08e177e31d' class='xr-index-data-in' type='checkbox'/><label for='index-af9c2f7a-52e6-453d-82cd-ed08e177e31d' 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([1870-01-16 12:00:00, 1870-02-15 00:00:00, 1870-03-16 12:00:00,\n",
       "             1870-04-16 00:00:00, 1870-05-16 12:00:00, 1870-06-16 00:00:00,\n",
       "             1870-07-16 12:00:00, 1870-08-16 12:00:00, 1870-09-16 00:00:00,\n",
       "             1870-10-16 12:00:00, 1870-11-16 00:00:00, 1870-12-16 12:00:00,\n",
       "             1871-01-16 12:00:00, 1871-02-15 00:00:00, 1871-03-16 12:00:00,\n",
       "             1871-04-16 00:00:00, 1871-05-16 12:00:00, 1871-06-16 00:00:00,\n",
       "             1871-07-16 12:00:00, 1871-08-16 12:00:00, 1871-09-16 00:00:00,\n",
       "             1871-10-16 12:00:00, 1871-11-16 00:00:00, 1871-12-16 12:00:00,\n",
       "             1872-01-16 12:00:00, 1872-02-15 12:00:00, 1872-03-16 12:00:00,\n",
       "             1872-04-16 00:00:00, 1872-05-16 12:00:00, 1872-06-16 00:00:00,\n",
       "             1872-07-16 12:00:00, 1872-08-16 12:00:00, 1872-09-16 00:00:00,\n",
       "             1872-10-16 12:00:00, 1872-11-16 00:00:00, 1872-12-16 12:00:00,\n",
       "             1873-01-16 12:00:00, 1873-02-15 00:00:00, 1873-03-16 12:00:00,\n",
       "             1873-04-16 00:00:00, 1873-05-16 12:00:00, 1873-06-16 00:00:00,\n",
       "             1873-07-16 12:00:00, 1873-08-16 12:00:00, 1873-09-16 00:00:00,\n",
       "             1873-10-16 12:00:00, 1873-11-16 00:00:00, 1873-12-16 12:00:00,\n",
       "             1874-01-16 12:00:00, 1874-02-15 00:00:00, 1874-03-16 12:00:00,\n",
       "             1874-04-16 00:00:00, 1874-05-16 12:00:00, 1874-06-16 00:00:00,\n",
       "             1874-07-16 12:00:00, 1874-08-16 12:00:00, 1874-09-16 00:00:00,\n",
       "             1874-10-16 12:00:00, 1874-11-16 00:00:00, 1874-12-16 12:00:00],\n",
       "            dtype=&#x27;object&#x27;,\n",
       "            length=60,\n",
       "            calendar=&#x27;proleptic_gregorian&#x27;,\n",
       "            freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d0586303-bce2-4a9b-9628-ad963eae9d68' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d0586303-bce2-4a9b-9628-ad963eae9d68' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>comment :</span></dt><dd>near-surface (usually, 2 meter) air temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>history :</span></dt><dd>2020-06-05T04:06:10Z altered by CMOR: Treated scalar dimension: &#x27;height&#x27;. 2020-06-05T04:06:10Z altered by CMOR: replaced missing value flag (-1.07374e+09) with standard missing value (1e+20).</dd><dt><span>_ChunkSizes :</span></dt><dd>[  1 145 192]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'tas' (time: 60)> Size: 480B\n",
       "dask.array<truediv, shape=(60,), dtype=float64, chunksize=(60,), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "    height   float64 8B ...\n",
       "  * time     (time) object 480B 1870-01-16 12:00:00 ... 1874-12-16 12:00:00\n",
       "Attributes:\n",
       "    standard_name:  air_temperature\n",
       "    long_name:      Near-Surface Air Temperature\n",
       "    comment:        near-surface (usually, 2 meter) air temperature\n",
       "    units:          K\n",
       "    cell_methods:   area: time: mean\n",
       "    cell_measures:  area: areacella\n",
       "    history:        2020-06-05T04:06:10Z altered by CMOR: Treated scalar dime...\n",
       "    _ChunkSizes:    [  1 145 192]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tas_global = ds.spatial.average(\"tas\", axis=[\"X\", \"Y\"], weights=\"generate\")[\"tas\"]\n",
    "tas_global"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trigger the computation by calling `.compute` (or `.load()`)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "  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: 60)&gt; Size: 480B\n",
       "array([285.93786485, 286.13421262, 286.7490564 , 287.69901353,\n",
       "       288.66531877, 289.2849388 , 289.4554574 , 289.2113224 ,\n",
       "       288.71069397, 287.73554556, 286.8887745 , 286.06478887,\n",
       "       285.76087424, 285.94144997, 286.88658365, 287.91910113,\n",
       "       288.72826295, 289.4255935 , 289.60842754, 289.46180917,\n",
       "       288.83951416, 287.69437127, 286.6302508 , 285.92133502,\n",
       "       285.52870277, 286.04431938, 286.81330396, 287.74066554,\n",
       "       288.75756679, 289.20544572, 289.43636353, 289.2439505 ,\n",
       "       288.5621524 , 287.6875869 , 286.66253259, 285.87743643,\n",
       "       285.46136136, 285.99801414, 286.77392694, 287.7801178 ,\n",
       "       288.63981417, 289.33845699, 289.58879631, 289.33847706,\n",
       "       288.62508434, 287.66263098, 286.57925976, 285.81967067,\n",
       "       285.63337371, 285.96644419, 286.74119623, 287.56311925,\n",
       "       288.48967744, 289.27879854, 289.44709627, 289.32515088,\n",
       "       288.48535271, 287.45920005, 286.63793814, 285.68170571])\n",
       "Coordinates:\n",
       "    height   float64 8B 2.0\n",
       "  * time     (time) object 480B 1870-01-16 12:00:00 ... 1874-12-16 12:00:00\n",
       "Attributes:\n",
       "    standard_name:  air_temperature\n",
       "    long_name:      Near-Surface Air Temperature\n",
       "    comment:        near-surface (usually, 2 meter) air temperature\n",
       "    units:          K\n",
       "    cell_methods:   area: time: mean\n",
       "    cell_measures:  area: areacella\n",
       "    history:        2020-06-05T04:06:10Z altered by CMOR: Treated scalar dime...\n",
       "    _ChunkSizes:    [  1 145 192]</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>: 60</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-470de860-5fb7-422a-ac03-e60972f86f29' class='xr-array-in' type='checkbox' checked><label for='section-470de860-5fb7-422a-ac03-e60972f86f29' 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>285.9 286.1 286.7 287.7 288.7 289.3 ... 289.3 288.5 287.5 286.6 285.7</span></div><div class='xr-array-data'><pre>array([285.93786485, 286.13421262, 286.7490564 , 287.69901353,\n",
       "       288.66531877, 289.2849388 , 289.4554574 , 289.2113224 ,\n",
       "       288.71069397, 287.73554556, 286.8887745 , 286.06478887,\n",
       "       285.76087424, 285.94144997, 286.88658365, 287.91910113,\n",
       "       288.72826295, 289.4255935 , 289.60842754, 289.46180917,\n",
       "       288.83951416, 287.69437127, 286.6302508 , 285.92133502,\n",
       "       285.52870277, 286.04431938, 286.81330396, 287.74066554,\n",
       "       288.75756679, 289.20544572, 289.43636353, 289.2439505 ,\n",
       "       288.5621524 , 287.6875869 , 286.66253259, 285.87743643,\n",
       "       285.46136136, 285.99801414, 286.77392694, 287.7801178 ,\n",
       "       288.63981417, 289.33845699, 289.58879631, 289.33847706,\n",
       "       288.62508434, 287.66263098, 286.57925976, 285.81967067,\n",
       "       285.63337371, 285.96644419, 286.74119623, 287.56311925,\n",
       "       288.48967744, 289.27879854, 289.44709627, 289.32515088,\n",
       "       288.48535271, 287.45920005, 286.63793814, 285.68170571])</pre></div></div></li><li class='xr-section-item'><input id='section-825cf69e-c5c6-452b-b0c6-67cfe65983f5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-825cf69e-c5c6-452b-b0c6-67cfe65983f5' 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'>2.0</div><input id='attrs-2481b06b-85b8-4941-b60b-355f01aca4bf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2481b06b-85b8-4941-b60b-355f01aca4bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44660aa6-e3a7-4d27-b5f9-0e32a1c80fc5' class='xr-var-data-in' type='checkbox'><label for='data-44660aa6-e3a7-4d27-b5f9-0e32a1c80fc5' 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>array(2.)</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'>1870-01-16 12:00:00 ... 1874-12-...</div><input id='attrs-c93c28d8-4e9a-472c-8bef-02c1f5c39eb7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c93c28d8-4e9a-472c-8bef-02c1f5c39eb7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44a7e68d-1f4e-411d-a107-6f4f2ea3c580' class='xr-var-data-in' type='checkbox'><label for='data-44a7e68d-1f4e-411d-a107-6f4f2ea3c580' 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(1870, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1870, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1871, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 2, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1872, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1873, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeProlepticGregorian(1874, 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-22374731-8b4b-4623-b63c-73fb2f8d7eb6' class='xr-section-summary-in' type='checkbox'  ><label for='section-22374731-8b4b-4623-b63c-73fb2f8d7eb6' 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><input type='checkbox' disabled/><label></label><input id='index-2ac9067d-6679-4045-9a9f-d4ec18594720' class='xr-index-data-in' type='checkbox'/><label for='index-2ac9067d-6679-4045-9a9f-d4ec18594720' 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([1870-01-16 12:00:00, 1870-02-15 00:00:00, 1870-03-16 12:00:00,\n",
       "             1870-04-16 00:00:00, 1870-05-16 12:00:00, 1870-06-16 00:00:00,\n",
       "             1870-07-16 12:00:00, 1870-08-16 12:00:00, 1870-09-16 00:00:00,\n",
       "             1870-10-16 12:00:00, 1870-11-16 00:00:00, 1870-12-16 12:00:00,\n",
       "             1871-01-16 12:00:00, 1871-02-15 00:00:00, 1871-03-16 12:00:00,\n",
       "             1871-04-16 00:00:00, 1871-05-16 12:00:00, 1871-06-16 00:00:00,\n",
       "             1871-07-16 12:00:00, 1871-08-16 12:00:00, 1871-09-16 00:00:00,\n",
       "             1871-10-16 12:00:00, 1871-11-16 00:00:00, 1871-12-16 12:00:00,\n",
       "             1872-01-16 12:00:00, 1872-02-15 12:00:00, 1872-03-16 12:00:00,\n",
       "             1872-04-16 00:00:00, 1872-05-16 12:00:00, 1872-06-16 00:00:00,\n",
       "             1872-07-16 12:00:00, 1872-08-16 12:00:00, 1872-09-16 00:00:00,\n",
       "             1872-10-16 12:00:00, 1872-11-16 00:00:00, 1872-12-16 12:00:00,\n",
       "             1873-01-16 12:00:00, 1873-02-15 00:00:00, 1873-03-16 12:00:00,\n",
       "             1873-04-16 00:00:00, 1873-05-16 12:00:00, 1873-06-16 00:00:00,\n",
       "             1873-07-16 12:00:00, 1873-08-16 12:00:00, 1873-09-16 00:00:00,\n",
       "             1873-10-16 12:00:00, 1873-11-16 00:00:00, 1873-12-16 12:00:00,\n",
       "             1874-01-16 12:00:00, 1874-02-15 00:00:00, 1874-03-16 12:00:00,\n",
       "             1874-04-16 00:00:00, 1874-05-16 12:00:00, 1874-06-16 00:00:00,\n",
       "             1874-07-16 12:00:00, 1874-08-16 12:00:00, 1874-09-16 00:00:00,\n",
       "             1874-10-16 12:00:00, 1874-11-16 00:00:00, 1874-12-16 12:00:00],\n",
       "            dtype=&#x27;object&#x27;,\n",
       "            length=60,\n",
       "            calendar=&#x27;proleptic_gregorian&#x27;,\n",
       "            freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-31d0ab31-fffe-469c-a60a-2899f2b4ffbc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-31d0ab31-fffe-469c-a60a-2899f2b4ffbc' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>comment :</span></dt><dd>near-surface (usually, 2 meter) air temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>history :</span></dt><dd>2020-06-05T04:06:10Z altered by CMOR: Treated scalar dimension: &#x27;height&#x27;. 2020-06-05T04:06:10Z altered by CMOR: replaced missing value flag (-1.07374e+09) with standard missing value (1e+20).</dd><dt><span>_ChunkSizes :</span></dt><dd>[  1 145 192]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'tas' (time: 60)> Size: 480B\n",
       "array([285.93786485, 286.13421262, 286.7490564 , 287.69901353,\n",
       "       288.66531877, 289.2849388 , 289.4554574 , 289.2113224 ,\n",
       "       288.71069397, 287.73554556, 286.8887745 , 286.06478887,\n",
       "       285.76087424, 285.94144997, 286.88658365, 287.91910113,\n",
       "       288.72826295, 289.4255935 , 289.60842754, 289.46180917,\n",
       "       288.83951416, 287.69437127, 286.6302508 , 285.92133502,\n",
       "       285.52870277, 286.04431938, 286.81330396, 287.74066554,\n",
       "       288.75756679, 289.20544572, 289.43636353, 289.2439505 ,\n",
       "       288.5621524 , 287.6875869 , 286.66253259, 285.87743643,\n",
       "       285.46136136, 285.99801414, 286.77392694, 287.7801178 ,\n",
       "       288.63981417, 289.33845699, 289.58879631, 289.33847706,\n",
       "       288.62508434, 287.66263098, 286.57925976, 285.81967067,\n",
       "       285.63337371, 285.96644419, 286.74119623, 287.56311925,\n",
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       "       288.48535271, 287.45920005, 286.63793814, 285.68170571])\n",
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       "    height   float64 8B 2.0\n",
       "  * time     (time) object 480B 1870-01-16 12:00:00 ... 1874-12-16 12:00:00\n",
       "Attributes:\n",
       "    standard_name:  air_temperature\n",
       "    long_name:      Near-Surface Air Temperature\n",
       "    comment:        near-surface (usually, 2 meter) air temperature\n",
       "    units:          K\n",
       "    cell_methods:   area: time: mean\n",
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       "    history:        2020-06-05T04:06:10Z altered by CMOR: Treated scalar dime...\n",
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    "tas_global.compute()"
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    "### Further Dask Guidance\n",
    "\n",
    "Visit these pages for more guidance (e.g., when to parallelize):\n",
    "\n",
    "- Parallel computing with Dask (xCDAT): https://xcdat.readthedocs.io/en/latest/examples/parallel-computing-with-dask.html\n",
    "- Parallel computing with Dask (Xarray): https://docs.xarray.dev/en/stable/user-guide/dask.html\n",
    "- Xarray with Dask Arrays: https://examples.dask.org/xarray.html\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
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   "source": [
    "## Key Takeaways\n",
    "\n",
    "- A driving need for a **modern successor to CDAT**\n",
    "- Serves the climate community in the long-term\n",
    "- xCDAT is an **extension of xarray** for **climate data analysis** on **structured grids**\n",
    "- Goal of providing features and utilities for **simple and robust analysis of climate data**\n",
    "\n",
    "<div style=\"text-align:center\">\n",
    "  <img src=\"../_static/xcdat-logo.png\" alt=\"xcdat logo\" style=\"display: inline-block; width:600px;\">\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   "source": [
    "## Get Involved on GitHub!\n",
    "\n",
    "- **Code contributions** are welcome and appreciated\n",
    "  - GitHub Repository: https://github.com/xCDAT/xcdat\n",
    "  - Contributing Guide: https://xcdat.readthedocs.io/en/latest/contributing.html\n",
    "- **Submit and/or address tickets** for feature suggestions, bugs, and documentation updates\n",
    "  - GitHub Issues: https://github.com/xCDAT/xcdat/issues\n",
    "- **Participate in forum discussions** on version releases, architecture, feature suggestions, etc.\n",
    "  - GitHub Discussions: https://github.com/xCDAT/xcdat/discussions\n",
    "\n",
    "<div style=\"text-align:center\">\n",
    "  <img src=\"../_static/github-logo-icon.png\" alt=\"GitHub logo\" style=\"display: inline-block; max-width:200px;\">\n",
    "</div>\n"
   ]
  }
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