diff --git a/.buildinfo b/.buildinfo
index 613d84f..342cff0 100644
--- a/.buildinfo
+++ b/.buildinfo
@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 64af5a04313eafb9df65466b89719939
+config: 9c41254e7f24206330a1a64f1f60c3dd
 tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/README.html b/README.html
index d5f15d1..b404a18 100644
--- a/README.html
+++ b/README.html
@@ -567,7 +567,7 @@ <h3>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalin
         
           By Negin Sobhani, Brian Vanderwende, Deepak Cherian, and Ben Kirk.
         
-          Last updated on 4 October 2023.
+          Last updated on 9 October 2023.
         </p>
       </div>
     </div>
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diff --git a/_sources/notebooks/03-dask-xarray.ipynb b/_sources/notebooks/03-dask-xarray.ipynb
index 58d6c7a..4ff3232 100644
--- a/_sources/notebooks/03-dask-xarray.ipynb
+++ b/_sources/notebooks/03-dask-xarray.ipynb
@@ -67,7 +67,7 @@
     "* `Dataset` : holds multiple DataArrays that potentially share the same coordinates\n",
     "\n",
     "\n",
-    "##### Xarray DataArray\n",
+    "**Xarray DataArray**\n",
     "\n",
     "A `DataArray` has four essential attributes:\n",
     "* `data`: a `numpy.ndarray` holding the values.\n",
@@ -75,16 +75,9 @@
     "* `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`: a dictionary to hold arbitrary metadata (attributes).\n",
     "\n",
-    "##### Xarray DataSet\n",
+    "**Xarray DataSet**\n",
     "\n",
-    "A dataset is simply an object containing multiple Xarray DataArrays indexed by variable name.\n",
-    "\n",
-    "\n",
-    "Xarray integrates with Dask to support parallel computations and streaming datasets that **don’t fit into memory.** \n",
-    "\n",
-    "\n",
-    "\n",
-    "### Xarray can wrap many array types like Numpy and Dask.\n"
+    "A dataset is simply an object containing multiple Xarray DataArrays indexed by variable name."
    ]
   },
   {
@@ -92,29 +85,34 @@
    "id": "598055e0-3bac-491b-8b7f-d7313a306bc8",
    "metadata": {},
    "source": [
-    "Let's start with a random 2D NumPy array, for example this can be SST values of a domain with dimension of 300x450 grid:"
+    "### Xarray can wrap many array types like Numpy and Dask.\n",
+    "\n",
+    "Let's start with a random 2D NumPy array, for example this can be SST (sea-surface temperature) values of a domain with dimension of 300x450 grid:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "id": "0b6d9a1d-6520-4374-a178-ad91af454628",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "import numpy as np \n",
     "import dask.array as da\n",
     "import xarray as xr\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "xr.set_options(display_expand_data=False);\n"
+    "\n",
+    "xr.set_options(display_expand_data=False);"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "id": "c5185246-289d-4bc3-a355-4c5101bd6ddd",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# -- numpy array \n",
@@ -134,7 +132,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "744e09ca-7a23-428b-9032-1808610c19b0",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "sst_da = da.from_array( sst_np)\n",
@@ -148,14 +148,16 @@
    "source": [
     "This is great and fast! BUT\n",
     "* What if we want to attach coordinate values to this array?\n",
-    "* What if we want to add metadat (e.g. units) to this array?"
+    "* What if we want to add metadata (e.g. units) to this array?"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "id": "2006822d-c6db-4e67-995e-732d92ff10b6",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# similarly we can convert them to xarray datarray\n",
@@ -175,7 +177,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "81aad904-f133-4a28-b1fc-aecc3fab7a11",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# we can add dimension names to this:\n",
@@ -196,7 +200,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "d9525a4b-e99b-46e5-9154-f0badf205ff9",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# -- create some dummy values for lat and lon dimensions\n",
@@ -228,7 +234,7 @@
    "source": [
     "<div class=\"alert alert-success fade show\" markdown=\"1\">\n",
     "\n",
-    "<b>TakeAway</b> \n",
+    "<b>Take Away</b> \n",
     "\n",
     "Xarray DataArray provides a wrapper around arrays, and uses labeled dimensions and coordinates to support metadata-aware operations (e.g. `da.sum(dim=\"time\")` instead of `array.sum(axis=-1)`)\n",
     "\n",
@@ -258,7 +264,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "778c8357-fcca-4b06-bce6-9c7531c0181b",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from dask.distributed import LocalCluster, Client\n",
@@ -294,7 +302,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "a53169fd-46dd-41bd-94ca-aed13e0f6357",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "!./get_data.sh notebook3"
@@ -320,7 +330,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "d1b5337d-28c3-4f88-b9c3-ce3d92445c37",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "import os\n",
@@ -339,25 +351,40 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "920f5f93-db35-4ae8-b9bd-d9374efabe40",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "%%time\n",
     "ds = xr.open_mfdataset(\n",
     "    sorted(files),\n",
     "    # concatenate along this dimension\n",
-    "    concat_dim='ensemble_member',\n",
+    "    concat_dim=\"time\",\n",
     "    # concatenate files in the order provided\n",
     "    combine=\"nested\",\n",
     "    # parallelize the reading of individual files using dask\n",
     "    # This means the returned arrays will be dask arrays\n",
     "    parallel=True,\n",
-    "    # these are netCDF4 files, use the netcdf4-python package to read them\n",
-    "    engine=\"netcdf4\",\n",
+    "    # these are netCDF4 files, use the h5netcdf package to read them\n",
+    "    engine=\"h5netcdf\",\n",
+    "    # hold off on decoding time\n",
+    "    decode_cf=False,\n",
+    "    # specify that data should be automatically chunked\n",
+    "    chunks=\"auto\",\n",
     ")\n",
+    "ds = xr.decode_cf(ds)\n",
     "ds"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "2d757c05-8258-44ff-a232-1a278edc2e28",
+   "metadata": {},
+   "source": [
+    "For complex scenarios, you can access each file individually by utilizing the `open_dataset` function with the specified `chunk`s and then combine the outputs into a single dataset later."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "56497135-6ad3-4323-bb8a-4f073eaf8939",
@@ -380,79 +407,54 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "ac192d70-f708-4db9-822c-4f2848cb0e02",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "tref = ds.TREFHT\n",
     "tref"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "ea3f1dd4-ded8-4985-a121-2ffaf00c25c5",
-   "metadata": {},
-   "source": [
-    "* How many chunks do we have? \n",
-    "* What is the size of each chunk size?\n",
-    "\n",
-    "\n",
-    "Here we can see that we have a total of 15 chunks equal to the number of our netCDF files. In general `open_mfdataset` will chunk each netCDF file into a single Dask array. By providing the `chunks` argument, we can control the size of the resulting Dask arrays. \n",
-    "\n",
-    "\n",
-    "<div class=\"alert alert-block alert-warning\">\n",
-    "\n",
-    "<b>WARNING:</b> When `chunks` argument is not given to `open_mfdataset`, it will return dask arrays with chunk sizes equal to each netCDF file.  \n",
-    "    Re-chunking the dataset after creation with ds.chunk() will lead to an ineffective use of memory and is not recommended.\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ba0dd86f-cf4d-46b2-9aa9-448c3fecfd5a",
-   "metadata": {},
-   "source": [
-    "In the example below, we call `open_mfdataset` to open multiple netCDF files and using the `chunks` argument to control the size of the resulting Dask arrays. "
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ec46fc4f-dc56-4110-8215-24b230103110",
+   "id": "d511a271-fe12-4552-b59c-672605543cb2",
    "metadata": {},
    "outputs": [],
    "source": [
-    "#del ds\n",
-    "chunk_dict = {\"lat\": 96, \"lon\": 144}"
+    "tref.chunks"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a0bc29ce-0259-4f8b-9f45-f16872dcabba",
+   "cell_type": "markdown",
+   "id": "0fbf4db0-f4b8-4b48-8337-12ace3882861",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "%%time\n",
-    "ds = xr.open_mfdataset(\n",
-    "    sorted(files),\n",
-    "    concat_dim='time',\n",
-    "    combine=\"nested\",\n",
-    "    parallel=True,\n",
-    "    engine=\"netcdf4\",\n",
-    "    chunks=chunk_dict,\n",
-    ")\n",
-    "tref = ds.TREFHT\n",
-    "tref"
+    "* How many chunks do we have? \n",
+    "* What is the size of each chunk size?\n",
+    "\n",
+    "Here we can see that we have a total of 9 chunks - equal to the number of our netCDF files. In general `open_mfdataset` will return one chunk per netCDF file.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "\n",
+    "**WARNING:** The chunk structure within the file is important. When re-chunking the dataset after creation with `ds.chunk()` it is recommended to only use multiples of the on-file chunk shape.\n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "We can check what that shape is by looking at the encoding:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ec8c62fb-8399-4780-ab9b-445b6cdc4781",
-   "metadata": {},
+   "id": "c54779d9-92d5-4272-b300-94391b34bb52",
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "tref.chunks"
+    "tref.encoding"
    ]
   },
   {
@@ -462,7 +464,7 @@
    "source": [
     "<div class=\"alert alert-block alert-info\" markdown=\"1\">\n",
     "\n",
-    "<b>TIP:</b> The `chunks` parameter can significantly affect total performance when using Dask Arrays. `chunks` be small enough that each chunk fit in the memory, but large enough to avoid that the communication overhead. \n",
+    "**TIP:** The `chunks` parameter can significantly affect total performance when using Dask Arrays. `chunks` should be small enough that each chunk fit in the memory, but large enough to avoid that the communication overhead. \n",
     "\n",
     "</div>"
    ]
@@ -472,16 +474,14 @@
    "id": "c094dcba-fdc8-4457-bf49-5c6ddf9b28a3",
    "metadata": {},
    "source": [
-    "A good rule of thumb is to create arrays with a minimum chunksize of at least one million elements. Here we have 120*96*144 elements in each chunk (except for the last chunk).   \n",
-    "With large arrays (10+ GB), the cost of queueing up Dask operations can be noticeable, and you may need even larger chunksizes. See the [dask.array best practices](https://docs.dask.org/en/stable/array-best-practices.html#select-a-good-chunk-size)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2d757c05-8258-44ff-a232-1a278edc2e28",
-   "metadata": {},
-   "source": [
-    "For complex scenarios, you can access each file individually by utilizing the `open_dataset` function with the specified `chunk`s and then combine the outputs into a single dataset later."
+    "A good rule of thumb is to create arrays with a minimum chunksize of at least one million elements. Here we have 120x192x288 elements in each chunk (except for the last chunk).   \n",
+    "With large arrays (10+ GB), the cost of queuing up Dask operations can be noticeable, and you may need even larger chunksizes. \n",
+    "\n",
+    "**Additional Reading**\n",
+    "\n",
+    " - [dask.array best practices](https://docs.dask.org/en/stable/array-best-practices.html#select-a-good-chunk-size)\n",
+    " - [NCAR chunking tutorial](https://ncar.github.io/dask-tutorial/notebooks/06-dask-chunking.html)\n",
+    " - [Dask blog post on chunking](https://blog.dask.org/2021/11/02/choosing-dask-chunk-sizes)"
    ]
   },
   {
@@ -493,11 +493,11 @@
     "\n",
     "This means you can call the following Dask-related functions on Xarray Data Arrays and Datasets:\n",
     "\n",
-    "* `dask.visualize()`\n",
-    "* `dask.compute()`\n",
-    "* `dask.persist()`\n",
+    "* `.visualize()`\n",
+    "* `.compute()`\n",
+    "* `.persist()`\n",
     "\n",
-    "For more information abour Dask Arrays, please see [the first tutorial](https://ncar.github.io/dask-tutorial/notebooks/01-dask-array.html)."
+    "For more information about Dask Arrays, please see [Dask Array chapter](https://projectpythia.org/dask-cookbook/notebooks/01-dask-array.html)."
    ]
   },
   {
@@ -507,9 +507,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import dask\n",
-    "tref_mean = tref.mean(axis=-1)\n",
-    "dask.visualize(tref_mean)"
+    "tref_mean = tref.mean('time')\n",
+    "tref_mean.data.dask"
    ]
   },
   {
@@ -517,7 +516,7 @@
    "id": "f1628d55-7bda-42f4-b615-b86274571ec9",
    "metadata": {},
    "source": [
-    "If we check Dask Task Graph for `tref_mean`, we can see all the steps required for calculating it (from openning the netcdf file to calculating mean and aggreagting it). "
+    "If we check Dask Task Graph for `tref_mean`, we can see all the steps required for calculating it (from opening the netcdf file to calculating mean and aggreagting it). "
    ]
   },
   {
@@ -525,24 +524,23 @@
    "id": "889e9971-43eb-4837-8d13-e560de21f908",
    "metadata": {},
    "source": [
-    "Getting concrete values\n",
-    "At some point, you will want to actually do the calculations and recieve concreate values from Dask.\n",
+    "### Getting concrete values\n",
+    "At some point, you will want to actually do the calculations and receive concrete values from Dask.\n",
     "\n",
     "There are two ways to compute values on dask arrays.\n",
     "\n",
-    "1.`compute()` returns an xarray object just like a dask array.\n",
+    "  1. `compute()` returns a new xarray object with the data now represented as a numpy array.\n",
+    "  2. `load()` replaces the dask array in the xarray object with a numpy array. Equivalent to `ds = ds.compute()`.\n",
     "\n",
-    "2.`load()` replaces the dask array in the xarray object with a numpy array. equivalent to `ds = ds.compute()`.\n",
+    "`.load()` operates *inplace* and `.compute()` returns a new xarray object.\n",
     "\n",
-    "So the difference is that `.load()` operates inplace and `.compute()` returns a new xarray object.\n",
+    "#### Distributed non-blocking concrete values\n",
     "\n",
-    "Tip: There is another option available  third option : “persisting”. `.persist()` loads the values into distributed RAM.\n",
-    "The values are computed but remain distributed across workers. So `ds.air.persist()` still returns a dask array. This is useful if you will be repeatedly using a dataset for computation but it is too large to load into local memory. \n",
+    "There is another option available  third option : “persisting”. `.persist()` loads the values into distributed RAM. The values are computed but remain distributed across workers. So essentially `persist` turns a lazy Dask collection into a Dask collection where the results are either fully computed or actively computing in the background.\n",
     "\n",
-    "To summarize, `persist` turns a lazy Dask collection into a Dask collection with the same metadata, but now with the results fully computed or actively computing in the background.\n",
+    "So `ds.air.persist()` is still backed by a Dask array. This is useful if you will be repeatedly using a dataset for computation but it is too large to load into local memory. \n",
     "\n",
-    "\n",
-    "See the [Dask user guide](https://docs.dask.org/en/stable/generated/dask.dataframe.Series.persist.html) if you `.persist()`."
+    "Read more: [Dask user guide](https://docs.dask.org/en/stable/generated/dask.dataframe.Series.persist.html)\n"
    ]
   },
   {
@@ -590,6 +588,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "%%time\n",
+    "\n",
     "tref.data.compute()"
    ]
   },
@@ -647,7 +647,19 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "tref_boulder = tref.sel(lat=40.0150, lon=-105.2705, method='nearest').load()\n"
+    "tref_boulder = tref.sel(lat=40.0150, lon=-105.2705, method='nearest').load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dea9cfcd-ba5e-4c45-bfd1-c079c7a34e73",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-block alert-warning\" markdown=\"1\">\n",
+    "\n",
+    "**WARNING:** Remember as soon as you call `.load()` you are telling Dask to trigger computation.\n",
+    "\n",
+    "</div>"
    ]
   },
   {
@@ -667,7 +679,7 @@
    "id": "06872e46-54ec-4df1-a0cb-4e25653f5f7d",
    "metadata": {},
    "source": [
-    "We can either see the values of our DataArray using `.compute()` or `.data` or by plotting it:\n"
+    "We can either see the values of our DataArray in the text representation above or by plotting it:"
    ]
   },
   {
@@ -696,7 +708,7 @@
    "outputs": [],
    "source": [
     "# change the unit from Kelvin to degree Celsius \n",
-    "tref_c = tref- 273.15\n",
+    "tref_c = tref - 273.15\n",
     "tref_c"
    ]
   },
@@ -707,9 +719,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Until we explicitly call load() or compute(), Dask actually didn't do any real calculation\n",
-    "# We are doing the calculations below parallelly. However not much benefit from parallel computing since it's not a big problem\n",
-    "%time tref_c=tref_c.load()"
+    "%%time \n",
+    "\n",
+    "tref_c = tref_c.load()"
    ]
   },
   {
@@ -732,24 +744,6 @@
     "tos_anom"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "8b426230-905c-4146-a1ff-784f033f36e1",
-   "metadata": {},
-   "source": [
-    "Dask actually constructs a graph of the required computation."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "edc4ca9e-85c5-496e-b0bd-e342869e4737",
-   "metadata": {},
-   "source": [
-    "Call `.compute()` or `.load()` when you want your result as a xarray.DataArray to access underlying dataset:\n",
-    "* `.compute() ` works similarly here as Dask DataArray. It basically triggers the computations across different chunks of the DataArray. \n",
-    "* `.load()` replaces the dask array in the Xarray object with a numpy array. "
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -794,47 +788,6 @@
     "</div>"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "a4c0091e-09fe-42bb-9048-600fa8fa4785",
-   "metadata": {},
-   "source": [
-    "### Supplementary Material: Rechunking\n",
-    "* rechunking will be covered in-depth in the next tutorial. \n",
-    "\n",
-    "We can always rechunk an Xarray DataArray. For example, what is the chunk size of `tref`?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "abdc9e94-fc96-4a3c-aad4-aa14666542cb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tref"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c7e2003d-e168-4af6-950e-fc6e14187958",
-   "metadata": {},
-   "source": [
-    "Is this a good chunk size?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "809158b0-2584-4697-9bb7-74426c4976b1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chunk_dict = {\"time\":90, \"lat\": 192, \"lon\": 288}\n",
-    "tref = tref.chunk(chunk_dict)\n",
-    "tref"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "3da23b0c-d3f7-4bfb-aa80-19eeb640823e",
@@ -899,10 +852,10 @@
    "outputs": [],
    "source": [
     "import numpy as np\n",
-    "# return saturation vapor pressure\n",
-    "# using Clausius-Clapeyron equation\n",
+    "\n",
     "def sat_p(t):\n",
-    "    return 0.611*np.exp(17.67*(t-273.15)*((t-29.65)**(-1)))"
+    "    \"\"\"Calculate saturation vapor pressure using Clausius-Clapeyron equation\"\"\"\n",
+    "    return 0.611 * np.exp(17.67 * (t-273.15)*((t-29.65)**(-1)))"
    ]
   },
   {
@@ -912,16 +865,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "es=xr.apply_ufunc(sat_p,tref,dask=\"parallelized\",output_dtypes=[float])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2d5c95bc-ad42-4a34-af7b-73db8684e7df",
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "es = xr.apply_ufunc(sat_p, tref, dask=\"parallelized\", output_dtypes=[float])\n",
     "es"
    ]
   },
@@ -1045,7 +989,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.18"
+   "version": "3.10.12"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
diff --git a/_static/__pycache__/__init__.cpython-310.pyc b/_static/__pycache__/__init__.cpython-310.pyc
index ca39331..088b0e5 100644
Binary files a/_static/__pycache__/__init__.cpython-310.pyc and b/_static/__pycache__/__init__.cpython-310.pyc differ
diff --git a/genindex.html b/genindex.html
index 9e87d35..5200bf3 100644
--- a/genindex.html
+++ b/genindex.html
@@ -367,7 +367,7 @@ <h1 id="index">Index</h1>
         
           By Negin Sobhani, Brian Vanderwende, Deepak Cherian, and Ben Kirk.
         
-          Last updated on 4 October 2023.
+          Last updated on 9 October 2023.
         </p>
       </div>
     </div>
diff --git a/notebooks/00-dask-overview.html b/notebooks/00-dask-overview.html
index bc19b4d..6c0198e 100644
--- a/notebooks/00-dask-overview.html
+++ b/notebooks/00-dask-overview.html
@@ -712,7 +712,7 @@ <h2>Useful Resources<a class="headerlink" href="#useful-resources" title="Permal
         
           By Negin Sobhani, Brian Vanderwende, Deepak Cherian, and Ben Kirk.
         
-          Last updated on 4 October 2023.
+          Last updated on 9 October 2023.
         </p>
       </div>
     </div>
diff --git a/notebooks/01-dask-array.html b/notebooks/01-dask-array.html
index 331cfbc..a45741d 100644
--- a/notebooks/01-dask-array.html
+++ b/notebooks/01-dask-array.html
@@ -468,7 +468,7 @@ <h2>Setup: Start a Dask Client<a class="headerlink" href="#setup-start-a-dask-cl
     <div style="width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;"> </div>
     <div style="margin-left: 48px;">
         <h3 style="margin-bottom: 0px;">Client</h3>
-        <p style="color: #9D9D9D; margin-bottom: 0px;">Client-fccaa25a-62e2-11ee-8dfa-6045bd7f0ab1</p>
+        <p style="color: #9D9D9D; margin-bottom: 0px;">Client-dce1f761-66e3-11ee-8d96-000d3a01f98b</p>
         <table style="width: 100%; text-align: left;">
 
         <tr>
@@ -503,7 +503,7 @@ <h3 style="margin-bottom: 0px;">Client</h3>
     </div>
     <div style="margin-left: 48px;">
         <h3 style="margin-bottom: 0px; margin-top: 0px;">LocalCluster</h3>
-        <p style="color: #9D9D9D; margin-bottom: 0px;">e29f08ad</p>
+        <p style="color: #9D9D9D; margin-bottom: 0px;">a459aafa</p>
         <table style="width: 100%; text-align: left;">
             <tr>
                 <td style="text-align: left;">
@@ -540,11 +540,11 @@ <h3 style="display: inline;">Scheduler Info</h3>
         <div style="width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;"> </div>
         <div style="margin-left: 48px;">
             <h3 style="margin-bottom: 0px;">Scheduler</h3>
-            <p style="color: #9D9D9D; margin-bottom: 0px;">Scheduler-42065e18-0158-4503-a605-df7f74f1232d</p>
+            <p style="color: #9D9D9D; margin-bottom: 0px;">Scheduler-58667023-6a89-4fe5-b93e-65b16b064537</p>
             <table style="width: 100%; text-align: left;">
                 <tr>
                     <td style="text-align: left;">
-                        <strong>Comm:</strong> tcp://127.0.0.1:40625
+                        <strong>Comm:</strong> tcp://127.0.0.1:36369
                     </td>
                     <td style="text-align: left;">
                         <strong>Workers:</strong> 2
@@ -586,7 +586,7 @@ <h4 style="margin-bottom: 0px; display: inline;">Worker: 0</h4>
                 <table style="width: 100%; text-align: left;">
                     <tr>
                         <td style="text-align: left;">
-                            <strong>Comm: </strong> tcp://127.0.0.1:41231
+                            <strong>Comm: </strong> tcp://127.0.0.1:36769
                         </td>
                         <td style="text-align: left;">
                             <strong>Total threads: </strong> 1
@@ -594,7 +594,7 @@ <h4 style="margin-bottom: 0px; display: inline;">Worker: 0</h4>
                     </tr>
                     <tr>
                         <td style="text-align: left;">
-                            <strong>Dashboard: </strong> <a href="http://127.0.0.1:45765/status" target="_blank">http://127.0.0.1:45765/status</a>
+                            <strong>Dashboard: </strong> <a href="http://127.0.0.1:35859/status" target="_blank">http://127.0.0.1:35859/status</a>
                         </td>
                         <td style="text-align: left;">
                             <strong>Memory: </strong> 3.38 GiB
@@ -602,13 +602,13 @@ <h4 style="margin-bottom: 0px; display: inline;">Worker: 0</h4>
                     </tr>
                     <tr>
                         <td style="text-align: left;">
-                            <strong>Nanny: </strong> tcp://127.0.0.1:33555
+                            <strong>Nanny: </strong> tcp://127.0.0.1:45229
                         </td>
                         <td style="text-align: left;"></td>
                     </tr>
                     <tr>
                         <td colspan="2" style="text-align: left;">
-                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-v_aybmpi
+                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-otr8ix7h
                         </td>
                     </tr>
 
@@ -631,7 +631,7 @@ <h4 style="margin-bottom: 0px; display: inline;">Worker: 1</h4>
                 <table style="width: 100%; text-align: left;">
                     <tr>
                         <td style="text-align: left;">
-                            <strong>Comm: </strong> tcp://127.0.0.1:39051
+                            <strong>Comm: </strong> tcp://127.0.0.1:35821
                         </td>
                         <td style="text-align: left;">
                             <strong>Total threads: </strong> 1
@@ -639,7 +639,7 @@ <h4 style="margin-bottom: 0px; display: inline;">Worker: 1</h4>
                     </tr>
                     <tr>
                         <td style="text-align: left;">
-                            <strong>Dashboard: </strong> <a href="http://127.0.0.1:38467/status" target="_blank">http://127.0.0.1:38467/status</a>
+                            <strong>Dashboard: </strong> <a href="http://127.0.0.1:42923/status" target="_blank">http://127.0.0.1:42923/status</a>
                         </td>
                         <td style="text-align: left;">
                             <strong>Memory: </strong> 3.38 GiB
@@ -647,13 +647,13 @@ <h4 style="margin-bottom: 0px; display: inline;">Worker: 1</h4>
                     </tr>
                     <tr>
                         <td style="text-align: left;">
-                            <strong>Nanny: </strong> tcp://127.0.0.1:39037
+                            <strong>Nanny: </strong> tcp://127.0.0.1:33625
                         </td>
                         <td style="text-align: left;"></td>
                     </tr>
                     <tr>
                         <td colspan="2" style="text-align: left;">
-                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-qdhyzu5d
+                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-vxo5z8au
                         </td>
                     </tr>
 
@@ -951,8 +951,8 @@ <h2>Performance Comparison<a class="headerlink" href="#performance-comparison" t
 </div>
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-Wall time: 82.7 ms
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 77.9 ms, sys: 0 ns, total: 77.9 ms
+Wall time: 76.5 ms
 </pre></div>
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 <div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>1.0
@@ -969,8 +969,8 @@ <h2>Performance Comparison<a class="headerlink" href="#performance-comparison" t
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 1.25 ms, sys: 414 µs, total: 1.66 ms
-Wall time: 1.65 ms
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 1.46 ms, sys: 0 ns, total: 1.46 ms
+Wall time: 1.46 ms
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@@ -992,8 +992,8 @@ <h2>Performance Comparison<a class="headerlink" href="#performance-comparison" t
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 18.1 ms, sys: 4.75 ms, total: 22.8 ms
-Wall time: 193 ms
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 22.1 ms, sys: 1.2 ms, total: 23.3 ms
+Wall time: 192 ms
 </pre></div>
 </div>
 <div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>1.0
@@ -1244,8 +1244,8 @@ <h2>Larger Data<a class="headerlink" href="#larger-data" title="Permalink to thi
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 285 ms, sys: 29.7 ms, total: 314 ms
-Wall time: 2.28 s
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 244 ms, sys: 24.1 ms, total: 268 ms
+Wall time: 2.12 s
 </pre></div>
 </div>
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@@ -1445,8 +1445,8 @@ <h2>Supplementary Material: Rechunking Arrays<a class="headerlink" href="#supple
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-Wall time: 46.3 s
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 17 s, sys: 766 ms, total: 17.7 s
+Wall time: 41 s
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 </div>
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@@ -1573,8 +1573,8 @@ <h2>Supplementary Material: Rechunking Arrays<a class="headerlink" href="#supple
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+Wall time: 13.8 s
 </pre></div>
 </div>
 </div>
@@ -1710,7 +1710,7 @@ <h2>Resources and references<a class="headerlink" href="#resources-and-reference
         
           By Negin Sobhani, Brian Vanderwende, Deepak Cherian, and Ben Kirk.
         
-          Last updated on 4 October 2023.
+          Last updated on 9 October 2023.
         </p>
       </div>
     </div>
diff --git a/notebooks/02-dask-dataframe.html b/notebooks/02-dask-dataframe.html
index 5348d72..e3eddef 100644
--- a/notebooks/02-dask-dataframe.html
+++ b/notebooks/02-dask-dataframe.html
@@ -677,7 +677,9 @@ <h3>Download the data<a class="headerlink" href="#download-the-data" title="Perm
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>USC00027390.csv
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+</div>
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 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>USC00031596.csv
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 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>USC00035908.csv
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 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>USC00044259.csv
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 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>USC00050848.csv
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 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>USC00051564.csv
@@ -728,14 +732,16 @@ <h3>Download the data<a class="headerlink" href="#download-the-data" title="Perm
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>USC00053146.csv
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 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>USC00053951.csv
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 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>USC00054834.csv
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 </div>
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 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>USC00112193.csv
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 </div>
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 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>data_for_cesm/
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 </pre></div>
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-</pre></div>
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 </div>
 </div>
 <p>Remember, the above rule?</p>
@@ -1297,8 +1304,8 @@ <h2>Scaling up to a larger dataset<a class="headerlink" href="#scaling-up-to-a-l
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 <div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>idxmax    (USC00052281, 1983)
@@ -1376,7 +1383,7 @@ <h3>Create a “LocalCluster” Client with Dask<a class="headerlink" href="#cre
     <div style="width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;"> </div>
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         <h3 style="margin-bottom: 0px;">Client</h3>
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     </div>
     <div style="margin-left: 48px;">
         <h3 style="margin-bottom: 0px; margin-top: 0px;">LocalCluster</h3>
-        <p style="color: #9D9D9D; margin-bottom: 0px;">f774e9f0</p>
+        <p style="color: #9D9D9D; margin-bottom: 0px;">0665574a</p>
         <table style="width: 100%; text-align: left;">
             <tr>
                 <td style="text-align: left;">
@@ -1448,11 +1455,11 @@ <h3 style="display: inline;">Scheduler Info</h3>
         <div style="width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;"> </div>
         <div style="margin-left: 48px;">
             <h3 style="margin-bottom: 0px;">Scheduler</h3>
-            <p style="color: #9D9D9D; margin-bottom: 0px;">Scheduler-a12069f2-7148-475c-92ae-c2dc96b3925b</p>
+            <p style="color: #9D9D9D; margin-bottom: 0px;">Scheduler-48cfbe0a-cd5a-43dd-b138-9f9ac2bf8341</p>
             <table style="width: 100%; text-align: left;">
                 <tr>
                     <td style="text-align: left;">
-                        <strong>Comm:</strong> tcp://127.0.0.1:39085
+                        <strong>Comm:</strong> tcp://127.0.0.1:44783
                     </td>
                     <td style="text-align: left;">
                         <strong>Workers:</strong> 2
@@ -1494,7 +1501,7 @@ <h4 style="margin-bottom: 0px; display: inline;">Worker: 0</h4>
                 <table style="width: 100%; text-align: left;">
                     <tr>
                         <td style="text-align: left;">
-                            <strong>Comm: </strong> tcp://127.0.0.1:34243
+                            <strong>Comm: </strong> tcp://127.0.0.1:39191
                         </td>
                         <td style="text-align: left;">
                             <strong>Total threads: </strong> 1
@@ -1502,7 +1509,7 @@ <h4 style="margin-bottom: 0px; display: inline;">Worker: 0</h4>
                     </tr>
                     <tr>
                         <td style="text-align: left;">
-                            <strong>Dashboard: </strong> <a href="http://127.0.0.1:46181/status" target="_blank">http://127.0.0.1:46181/status</a>
+                            <strong>Dashboard: </strong> <a href="http://127.0.0.1:38755/status" target="_blank">http://127.0.0.1:38755/status</a>
                         </td>
                         <td style="text-align: left;">
                             <strong>Memory: </strong> 3.38 GiB
@@ -1510,13 +1517,13 @@ <h4 style="margin-bottom: 0px; display: inline;">Worker: 0</h4>
                     </tr>
                     <tr>
                         <td style="text-align: left;">
-                            <strong>Nanny: </strong> tcp://127.0.0.1:40143
+                            <strong>Nanny: </strong> tcp://127.0.0.1:35575
                         </td>
                         <td style="text-align: left;"></td>
                     </tr>
                     <tr>
                         <td colspan="2" style="text-align: left;">
-                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-e4erk9vd
+                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-oyh3hie_
                         </td>
                     </tr>
 
@@ -1539,7 +1546,7 @@ <h4 style="margin-bottom: 0px; display: inline;">Worker: 1</h4>
                 <table style="width: 100%; text-align: left;">
                     <tr>
                         <td style="text-align: left;">
-                            <strong>Comm: </strong> tcp://127.0.0.1:44457
+                            <strong>Comm: </strong> tcp://127.0.0.1:33963
                         </td>
                         <td style="text-align: left;">
                             <strong>Total threads: </strong> 1
@@ -1547,7 +1554,7 @@ <h4 style="margin-bottom: 0px; display: inline;">Worker: 1</h4>
                     </tr>
                     <tr>
                         <td style="text-align: left;">
-                            <strong>Dashboard: </strong> <a href="http://127.0.0.1:37977/status" target="_blank">http://127.0.0.1:37977/status</a>
+                            <strong>Dashboard: </strong> <a href="http://127.0.0.1:33735/status" target="_blank">http://127.0.0.1:33735/status</a>
                         </td>
                         <td style="text-align: left;">
                             <strong>Memory: </strong> 3.38 GiB
@@ -1555,13 +1562,13 @@ <h4 style="margin-bottom: 0px; display: inline;">Worker: 1</h4>
                     </tr>
                     <tr>
                         <td style="text-align: left;">
-                            <strong>Nanny: </strong> tcp://127.0.0.1:40169
+                            <strong>Nanny: </strong> tcp://127.0.0.1:41365
                         </td>
                         <td style="text-align: left;"></td>
                     </tr>
                     <tr>
                         <td colspan="2" style="text-align: left;">
-                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-f6svaw8t
+                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-cbh9mjb1
                         </td>
                     </tr>
 
@@ -1607,8 +1614,8 @@ <h3>Dask DataFrame <code class="docutils literal notranslate"><span class="pre">
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 266 ms, sys: 43.5 ms, total: 309 ms
-Wall time: 358 ms
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 248 ms, sys: 39.2 ms, total: 287 ms
+Wall time: 308 ms
 </pre></div>
 </div>
 <div class="output text_html"><div><strong>Dask DataFrame Structure:</strong></div>
@@ -2246,8 +2253,8 @@ <h4>Lazy Evaluation<a class="headerlink" href="#lazy-evaluation" title="Permalin
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>This site has the highest number of snow days in a year : 
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 540 ms, sys: 108 ms, total: 648 ms
-Wall time: 13.1 s
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 411 ms, sys: 72.8 ms, total: 484 ms
+Wall time: 11.2 s
 </pre></div>
 </div>
 <div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>idxmax    (USC00052281, 1983)
@@ -2755,7 +2762,7 @@ <h4 style="display: inline;">Layer7: dataframe-groupby-size-agg</h4>
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/e515b975794e39f7bf7fb7ef0c3ff199a5fcbb4261434a61d2e62b0402bc4ccf.png" src="../_images/e515b975794e39f7bf7fb7ef0c3ff199a5fcbb4261434a61d2e62b0402bc4ccf.png" />
+<img alt="../_images/6bc56857ba1b0421004b8bea51b80eea9e531bfe231a39c6ecc8368c04a43ff4.png" src="../_images/6bc56857ba1b0421004b8bea51b80eea9e531bfe231a39c6ecc8368c04a43ff4.png" />
 </div>
 </div>
 </div>
@@ -2785,8 +2792,8 @@ <h4>Share intermediate results<a class="headerlink" href="#share-intermediate-re
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 1.09 s, sys: 218 ms, total: 1.31 s
-Wall time: 25.5 s
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 906 ms, sys: 104 ms, total: 1.01 s
+Wall time: 22.1 s
 </pre></div>
 </div>
 </div>
@@ -2800,8 +2807,8 @@ <h4>Share intermediate results<a class="headerlink" href="#share-intermediate-re
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 785 ms, sys: 112 ms, total: 897 ms
-Wall time: 13.2 s
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 527 ms, sys: 65.4 ms, total: 593 ms
+Wall time: 11.3 s
 </pre></div>
 </div>
 </div>
@@ -3488,14 +3495,14 @@ <h4 style="display: inline;">Layer11: series-mean</h4>
                     
                         <tr>
                             <th style="text-align: left; width: 150px;"> depends on </th>
-                            <td style="text-align: left;">series-count-agg-fcd354190bfc78379462c584cbc9370e</td>
+                            <td style="text-align: left;">series-sum-agg-72c811251d18a4021cc9cd824a02a28f</td>
                         </tr>
                     
                 
                     
                         <tr>
                             <th style="text-align: left; width: 150px;"></th>
-                            <td style="text-align: left;">series-sum-agg-72c811251d18a4021cc9cd824a02a28f</td>
+                            <td style="text-align: left;">series-count-agg-fcd354190bfc78379462c584cbc9370e</td>
                         </tr>
                     
                 
@@ -3541,8 +3548,8 @@ <h3><code class="docutils literal notranslate"><span class="pre">.persist</span>
 </div>
 <div class="cell_output docutils container">
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-74.82074711099168 37.419103836866114
-CPU times: user 1.18 s, sys: 178 ms, total: 1.36 s
-Wall time: 25.7 s
+CPU times: user 1.02 s, sys: 133 ms, total: 1.16 s
+Wall time: 22.3 s
 </pre></div>
 </div>
 </div>
@@ -3566,8 +3573,8 @@ <h3><code class="docutils literal notranslate"><span class="pre">.persist</span>
 </div>
 <div class="cell_output docutils container">
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-74.82074711099168 37.419103836866114
-CPU times: user 781 ms, sys: 102 ms, total: 883 ms
-Wall time: 13.3 s
+CPU times: user 649 ms, sys: 56.5 ms, total: 705 ms
+Wall time: 11.7 s
 </pre></div>
 </div>
 </div>
@@ -3616,6 +3623,11 @@ <h3>Close your local Dask Cluster<a class="headerlink" href="#close-your-local-d
 </pre></div>
 </div>
 </div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2023-10-09 20:42:22,254 - distributed.worker.state_machine - WARNING - Async instruction for &lt;Task cancelled name=&quot;execute((&#39;getitem-5461fc912f94285c632e3db2b3f71aaa&#39;, 63))&quot; coro=&lt;Worker.execute() done, defined at /usr/share/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/distributed/worker_state_machine.py:3609&gt;&gt; ended with CancelledError
+</pre></div>
+</div>
+</div>
 </div>
 </div>
 </div>
@@ -3735,7 +3747,7 @@ <h2>Resources and references<a class="headerlink" href="#resources-and-reference
         
           By Negin Sobhani, Brian Vanderwende, Deepak Cherian, and Ben Kirk.
         
-          Last updated on 4 October 2023.
+          Last updated on 9 October 2023.
         </p>
       </div>
     </div>
diff --git a/notebooks/03-dask-xarray.html b/notebooks/03-dask-xarray.html
index 4d512c4..e260cd5 100644
--- a/notebooks/03-dask-xarray.html
+++ b/notebooks/03-dask-xarray.html
@@ -369,18 +369,6 @@
       <a class="reference internal nav-link" href="#xarray-data-structures">
        Xarray Data Structures
       </a>
-      <ul class="nav section-nav flex-column">
-       <li class="toc-h5 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#xarray-dataarray">
-         Xarray DataArray
-        </a>
-       </li>
-       <li class="toc-h5 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#xarray-dataset">
-         Xarray DataSet
-        </a>
-       </li>
-      </ul>
      </li>
     </ul>
    </li>
@@ -421,6 +409,18 @@
      Xarray data structures are Dask collections.
     </a>
    </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#getting-concrete-values">
+     Getting concrete values
+    </a>
+    <ul class="nav section-nav flex-column">
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#distributed-non-blocking-concrete-values">
+       Distributed non-blocking concrete values
+      </a>
+     </li>
+    </ul>
+   </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
@@ -443,11 +443,6 @@
      Calculations over all grids
     </a>
    </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#supplementary-material-rechunking">
-     Supplementary Material: Rechunking
-    </a>
-   </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#supplementary-material-advanced-workflows-and-automatic-parallelization-using-apply-ufunc">
      Supplementary Material: Advanced workflows and automatic parallelization using
@@ -562,8 +557,7 @@ <h4>Xarray Data Structures<a class="headerlink" href="#xarray-data-structures" t
 <li><p><code class="docutils literal notranslate"><span class="pre">DataArray</span></code> : holds a single multi-dimensional variable and its coordinates</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">Dataset</span></code> : holds multiple DataArrays that potentially share the same coordinates</p></li>
 </ul>
-<div class="section" id="xarray-dataarray">
-<h5>Xarray DataArray<a class="headerlink" href="#xarray-dataarray" title="Permalink to this headline"><i class="fas fa-link"></i></a></h5>
+<p><strong>Xarray DataArray</strong></p>
 <p>A <code class="docutils literal notranslate"><span class="pre">DataArray</span></code> has four essential attributes:</p>
 <ul class="simple">
 <li><p><code class="docutils literal notranslate"><span class="pre">data</span></code>: a <code class="docutils literal notranslate"><span class="pre">numpy.ndarray</span></code> holding the values.</p></li>
@@ -571,24 +565,19 @@ <h5>Xarray DataArray<a class="headerlink" href="#xarray-dataarray" title="Permal
 <li><p><code class="docutils literal notranslate"><span class="pre">coords</span></code>: a dict-like container of arrays (coordinates) that label each point (e.g., 1-dimensional arrays of numbers, datetime objects or strings).</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">attrs</span></code>: a dictionary to hold arbitrary metadata (attributes).</p></li>
 </ul>
-</div>
-<div class="section" id="xarray-dataset">
-<h5>Xarray DataSet<a class="headerlink" href="#xarray-dataset" title="Permalink to this headline"><i class="fas fa-link"></i></a></h5>
+<p><strong>Xarray DataSet</strong></p>
 <p>A dataset is simply an object containing multiple Xarray DataArrays indexed by variable name.</p>
-<p>Xarray integrates with Dask to support parallel computations and streaming datasets that <strong>don’t fit into memory.</strong></p>
-</div>
 </div>
 </div>
 <div class="section" id="xarray-can-wrap-many-array-types-like-numpy-and-dask">
 <h3>Xarray can wrap many array types like Numpy and Dask.<a class="headerlink" href="#xarray-can-wrap-many-array-types-like-numpy-and-dask" title="Permalink to this headline"><i class="fas fa-link"></i></a></h3>
-<p>Let’s start with a random 2D NumPy array, for example this can be SST values of a domain with dimension of 300x450 grid:</p>
+<p>Let’s start with a random 2D NumPy array, for example this can be SST (sea-surface temperature) values of a domain with dimension of 300x450 grid:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> 
 <span class="kn">import</span> <span class="nn">dask.array</span> <span class="k">as</span> <span class="nn">da</span>
 <span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
-<span class="kn">import</span> <span class="nn">warnings</span>
-<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s1">&#39;ignore&#39;</span><span class="p">)</span>
+
 <span class="n">xr</span><span class="o">.</span><span class="n">set_options</span><span class="p">(</span><span class="n">display_expand_data</span><span class="o">=</span><span class="kc">False</span><span class="p">);</span>
 </pre></div>
 </div>
@@ -677,7 +666,7 @@ <h3>Xarray can wrap many array types like Numpy and Dask.<a class="headerlink" h
 <p>This is great and fast! BUT</p>
 <ul class="simple">
 <li><p>What if we want to attach coordinate values to this array?</p></li>
-<li><p>What if we want to add metadat (e.g. units) to this array?</p></li>
+<li><p>What if we want to add metadata (e.g. units) to this array?</p></li>
 </ul>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1051,10 +1040,10 @@ <h3>Xarray can wrap many array types like Numpy and Dask.<a class="headerlink" h
   stroke: currentColor;
   fill: currentColor;
 }
-</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;array-1aa6c65806de99e481deb7bcf7bd2bf2&#x27; (dim_0: 300,
+</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;array-fc618fa9e0f9da5fad972d432fc39efc&#x27; (dim_0: 300,
                                                             dim_1: 450)&gt;
 dask.array&lt;chunksize=(300, 450), meta=np.ndarray&gt;
-Dimensions without coordinates: dim_0, dim_1</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'>'array-1aa6c65806de99e481deb7bcf7bd2bf2'</div><ul class='xr-dim-list'><li><span>dim_0</span>: 300</li><li><span>dim_1</span>: 450</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-f8843719-403b-4ff5-b99e-fa0442c3d195' class='xr-array-in' type='checkbox' ><label for='section-f8843719-403b-4ff5-b99e-fa0442c3d195' 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=(300, 450), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>
+Dimensions without coordinates: dim_0, dim_1</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'>'array-fc618fa9e0f9da5fad972d432fc39efc'</div><ul class='xr-dim-list'><li><span>dim_0</span>: 300</li><li><span>dim_1</span>: 450</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-bb0d40ac-ff5d-4a2f-a3a5-d28fd8b11eab' class='xr-array-in' type='checkbox' ><label for='section-bb0d40ac-ff5d-4a2f-a3a5-d28fd8b11eab' 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=(300, 450), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>
     <tr>
         <td>
             <table style="border-collapse: collapse;">
@@ -1109,7 +1098,7 @@ <h3>Xarray can wrap many array types like Numpy and Dask.<a class="headerlink" h
 </svg>
         </td>
     </tr>
-</table></div></div></li><li class='xr-section-item'><input id='section-2fce79ab-10b8-42d0-930f-014ee2816a58' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2fce79ab-10b8-42d0-930f-014ee2816a58' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-6672bdb2-4dc7-4cca-ac3a-8cfe20f40e6f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6672bdb2-4dc7-4cca-ac3a-8cfe20f40e6f' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-f4bfdd8f-e689-4231-96cb-4b2b930a86b9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f4bfdd8f-e689-4231-96cb-4b2b930a86b9' 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></div></div>
+</table></div></div></li><li class='xr-section-item'><input id='section-c7fccb84-4c13-487e-a2cd-a89202c65167' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c7fccb84-4c13-487e-a2cd-a89202c65167' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-b2c2cc74-3a96-48a8-9428-e88970a769cb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b2c2cc74-3a96-48a8-9428-e88970a769cb' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-934e4072-9aa2-427e-ad89-ece0a5867b04' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-934e4072-9aa2-427e-ad89-ece0a5867b04' 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></div></div>
 </div>
 <p>A simple DataArray without dimensions or coordinates isn’t much use.</p>
 <div class="cell docutils container">
@@ -1509,14 +1498,14 @@ <h3>Xarray can wrap many array types like Numpy and Dask.<a class="headerlink" h
   stroke: currentColor;
   fill: currentColor;
 }
-</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;array-1aa6c65806de99e481deb7bcf7bd2bf2&#x27; (lat: 300, lon: 450)&gt;
+</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;array-fc618fa9e0f9da5fad972d432fc39efc&#x27; (lat: 300, lon: 450)&gt;
 dask.array&lt;chunksize=(300, 450), meta=np.ndarray&gt;
 Coordinates:
-  * lat      (lat) float64 52.86 24.56 63.8 32.35 ... -42.29 28.98 -84.88 2.582
-  * lon      (lon) float64 159.8 -24.26 -2.123 168.4 ... -169.3 -29.64 145.7
+  * lat      (lat) float64 -24.24 26.75 -50.3 -18.59 ... 28.39 -88.84 -14.49
+  * lon      (lon) float64 -27.0 -144.4 10.26 42.14 ... -78.56 25.13 -74.08
 Attributes:
     description:  Sea Surface Temperature.
-    units:        degC</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'>'array-1aa6c65806de99e481deb7bcf7bd2bf2'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 300</li><li><span class='xr-has-index'>lon</span>: 450</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-703063dd-53d4-431c-be13-69c23644824d' class='xr-array-in' type='checkbox' ><label for='section-703063dd-53d4-431c-be13-69c23644824d' 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=(300, 450), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>
+    units:        degC</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'>'array-fc618fa9e0f9da5fad972d432fc39efc'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 300</li><li><span class='xr-has-index'>lon</span>: 450</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-8040410e-832a-4c40-bb3e-443c4530920e' class='xr-array-in' type='checkbox' ><label for='section-8040410e-832a-4c40-bb3e-443c4530920e' 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=(300, 450), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>
     <tr>
         <td>
             <table style="border-collapse: collapse;">
@@ -1571,31 +1560,31 @@ <h3>Xarray can wrap many array types like Numpy and Dask.<a class="headerlink" h
 </svg>
         </td>
     </tr>
-</table></div></div></li><li class='xr-section-item'><input id='section-8a5ec1ed-a66b-4959-bc10-edbe6d963013' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8a5ec1ed-a66b-4959-bc10-edbe6d963013' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>52.86 24.56 63.8 ... -84.88 2.582</div><input id='attrs-ff2a7e32-3183-4683-91c2-2096424f8680' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ff2a7e32-3183-4683-91c2-2096424f8680' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-251c2ec6-9ff8-4d9b-ab26-cc2cc1ccf51a' class='xr-var-data-in' type='checkbox'><label for='data-251c2ec6-9ff8-4d9b-ab26-cc2cc1ccf51a' 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([ 52.855829,  24.559202,  63.79908 , ...,  28.978729, -84.87872 ,
-         2.581973])</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'>159.8 -24.26 ... -29.64 145.7</div><input id='attrs-08e1849e-0228-496a-8522-7d02dd44e716' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-08e1849e-0228-496a-8522-7d02dd44e716' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43c11290-1b31-47b9-8db1-c44ee9f5c5db' class='xr-var-data-in' type='checkbox'><label for='data-43c11290-1b31-47b9-8db1-c44ee9f5c5db' 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([ 159.829659,  -24.260178,   -2.122797, ..., -169.315479,  -29.635402,
-        145.681913])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2561d856-6bbd-4691-b9cc-95c4649b7ffb' class='xr-section-summary-in' type='checkbox'  ><label for='section-2561d856-6bbd-4691-b9cc-95c4649b7ffb' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cbf9c366-466e-419e-893d-8254faf0ac5b' class='xr-index-data-in' type='checkbox'/><label for='index-cbf9c366-466e-419e-893d-8254faf0ac5b' 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([  52.85582855518078,  24.559202010634294,   63.79908000007191,
-         32.34843772172543, -25.291145112586364,   37.44456953584448,
-          42.6017511009226,  -79.15068226109904,   75.44566458878109,
-        -81.74374501709438,
+</table></div></div></li><li class='xr-section-item'><input id='section-4dd92ddc-7d46-4898-9657-ee771d4949ab' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4dd92ddc-7d46-4898-9657-ee771d4949ab' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-24.24 26.75 ... -88.84 -14.49</div><input id='attrs-cb902160-dd62-4a34-96b5-d7577ec83643' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cb902160-dd62-4a34-96b5-d7577ec83643' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99e2a826-1f6d-4138-90ab-5c99715c7bb6' class='xr-var-data-in' type='checkbox'><label for='data-99e2a826-1f6d-4138-90ab-5c99715c7bb6' 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([-24.241312,  26.749816, -50.302662, ...,  28.39402 , -88.840111,
+       -14.494982])</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'>-27.0 -144.4 10.26 ... 25.13 -74.08</div><input id='attrs-00abe310-b3da-4dae-a848-05929d8e73d0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-00abe310-b3da-4dae-a848-05929d8e73d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5e6c619-99db-42c2-bf8c-e1a76d97b881' class='xr-var-data-in' type='checkbox'><label for='data-e5e6c619-99db-42c2-bf8c-e1a76d97b881' 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([ -26.999656, -144.37775 ,   10.259233, ...,  -78.557882,   25.131206,
+        -74.077443])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d5f6d020-85da-4097-86be-d4f9e4a417ff' class='xr-section-summary-in' type='checkbox'  ><label for='section-d5f6d020-85da-4097-86be-d4f9e4a417ff' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f150cb8f-4224-4080-a97b-30edeb612bf0' class='xr-index-data-in' type='checkbox'/><label for='index-f150cb8f-4224-4080-a97b-30edeb612bf0' 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([-24.241311999909342,  26.749815538698428,  -50.30266205353354,
+       -18.593533041300546,  -88.67981428151185,  -35.45211893251931,
+        15.159073402716515,  -78.08774242593147,  -62.26617926434206,
+       -18.582049277109718,
        ...
-       -25.734355804607105,    30.2766390459023,  -63.49245869822251,
-        23.631537713516693, -2.0885671418733125,   64.56089420836275,
-        -42.29426426657898,   28.97872936312868,   -84.8787200051418,
-        2.5819733353780236],
-      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=300))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-52429956-fd82-40cc-8dc1-af8669a30056' class='xr-index-data-in' type='checkbox'/><label for='index-52429956-fd82-40cc-8dc1-af8669a30056' 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([ 159.82965923621725,  -24.26017798820928, -2.1227965252672902,
-        168.37168425949545,   52.27526747027534, -105.53693422376908,
-         83.57734594424255,  -27.16887300113794, -156.81497227992998,
-        146.57779792887567,
+        -26.34644516202662,   87.93422100168527,   64.94999253261636,
+         -39.1920093401121,  -41.15764160720399,   84.68562985405558,
+         68.83999758043734,  28.394019801718386,  -88.84011086793195,
+       -14.494982036832681],
+      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=300))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0cbc3083-93ee-433a-8007-68c41d5f1176' class='xr-index-data-in' type='checkbox'/><label for='index-0cbc3083-93ee-433a-8007-68c41d5f1176' 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([  -26.9996558628726, -144.37774967996486,   10.25923335178868,
+         42.13946728729471,  118.95905524122088, -25.536384874662843,
+        -60.59087821886253, -33.091755980792016,   92.53117290215363,
+          76.4451693688348,
        ...
-        -99.49259545287664, -116.30274057967767,  148.96333714821458,
-         62.67866994470765,  -140.5017625681893, -108.64458278475801,
-        -161.1237501334366, -169.31547860406357,   -29.6354022706268,
-        145.68191315839454],
-      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=450))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-40671cdd-ce9b-4c31-9c51-7e633ef7df9e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-40671cdd-ce9b-4c31-9c51-7e633ef7df9e' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Sea Surface Temperature.</dd><dt><span>units :</span></dt><dd>degC</dd></dl></div></li></ul></div></div></div></div>
+        -159.9575353946674,  -98.16052440219161, -110.73943644384296,
+         138.0533238882391,   85.57515418742628,   57.04633084136205,
+       -38.169858031432994,    -78.557881944298,  25.131206222625508,
+         -74.0774429840396],
+      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=450))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9216474f-9a1e-458c-a409-951c204aa8e6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9216474f-9a1e-458c-a409-951c204aa8e6' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Sea Surface Temperature.</dd><dt><span>units :</span></dt><dd>degC</dd></dl></div></li></ul></div></div></div></div>
 </div>
 <p>Xarray data structures are a very powerful tool that allows us to use metadata to express different analysis patterns (slicing, selecting, groupby, averaging, and many other things).</p>
 <div class="alert alert-success fade show" markdown="1">
-<p><b>TakeAway</b></p>
+<p><b>Take Away</b></p>
 <p>Xarray DataArray provides a wrapper around arrays, and uses labeled dimensions and coordinates to support metadata-aware operations (e.g. <code class="docutils literal notranslate"><span class="pre">da.sum(dim=&quot;time&quot;)</span></code> instead of <code class="docutils literal notranslate"><span class="pre">array.sum(axis=-1)</span></code>)</p>
 <p><strong>Xarray can wrap dask arrays instead of numpy arrays.</strong></p>
 <p>This capability turns Xarray into an extremely useful tool for Big Data earth science.</p>
@@ -1617,7 +1606,7 @@ <h3>Setup: Spinning up a cluster<a class="headerlink" href="#setup-spinning-up-a
     <div style="width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;"> </div>
     <div style="margin-left: 48px;">
         <h3 style="margin-bottom: 0px;">Client</h3>
-        <p style="color: #9D9D9D; margin-bottom: 0px;">Client-89f8e492-62e3-11ee-8f88-6045bd7f0ab1</p>
+        <p style="color: #9D9D9D; margin-bottom: 0px;">Client-5c9ba7e1-66e4-11ee-8f3e-000d3a01f98b</p>
         <table style="width: 100%; text-align: left;">
 
         <tr>
@@ -1652,7 +1641,7 @@ <h3 style="margin-bottom: 0px;">Client</h3>
     </div>
     <div style="margin-left: 48px;">
         <h3 style="margin-bottom: 0px; margin-top: 0px;">LocalCluster</h3>
-        <p style="color: #9D9D9D; margin-bottom: 0px;">af135246</p>
+        <p style="color: #9D9D9D; margin-bottom: 0px;">ccd29390</p>
         <table style="width: 100%; text-align: left;">
             <tr>
                 <td style="text-align: left;">
@@ -1689,11 +1678,11 @@ <h3 style="display: inline;">Scheduler Info</h3>
         <div style="width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;"> </div>
         <div style="margin-left: 48px;">
             <h3 style="margin-bottom: 0px;">Scheduler</h3>
-            <p style="color: #9D9D9D; margin-bottom: 0px;">Scheduler-e146c250-a94e-47f2-a826-8c37f253eedf</p>
+            <p style="color: #9D9D9D; margin-bottom: 0px;">Scheduler-f9389cf9-036f-46b2-97a7-7c2a3645478f</p>
             <table style="width: 100%; text-align: left;">
                 <tr>
                     <td style="text-align: left;">
-                        <strong>Comm:</strong> tcp://127.0.0.1:35751
+                        <strong>Comm:</strong> tcp://127.0.0.1:38813
                     </td>
                     <td style="text-align: left;">
                         <strong>Workers:</strong> 2
@@ -1735,7 +1724,7 @@ <h4 style="margin-bottom: 0px; display: inline;">Worker: 0</h4>
                 <table style="width: 100%; text-align: left;">
                     <tr>
                         <td style="text-align: left;">
-                            <strong>Comm: </strong> tcp://127.0.0.1:40941
+                            <strong>Comm: </strong> tcp://127.0.0.1:38837
                         </td>
                         <td style="text-align: left;">
                             <strong>Total threads: </strong> 1
@@ -1743,7 +1732,7 @@ <h4 style="margin-bottom: 0px; display: inline;">Worker: 0</h4>
                     </tr>
                     <tr>
                         <td style="text-align: left;">
-                            <strong>Dashboard: </strong> <a href="http://127.0.0.1:33465/status" target="_blank">http://127.0.0.1:33465/status</a>
+                            <strong>Dashboard: </strong> <a href="http://127.0.0.1:36163/status" target="_blank">http://127.0.0.1:36163/status</a>
                         </td>
                         <td style="text-align: left;">
                             <strong>Memory: </strong> 3.38 GiB
@@ -1751,13 +1740,13 @@ <h4 style="margin-bottom: 0px; display: inline;">Worker: 0</h4>
                     </tr>
                     <tr>
                         <td style="text-align: left;">
-                            <strong>Nanny: </strong> tcp://127.0.0.1:46035
+                            <strong>Nanny: </strong> tcp://127.0.0.1:41555
                         </td>
                         <td style="text-align: left;"></td>
                     </tr>
                     <tr>
                         <td colspan="2" style="text-align: left;">
-                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-qipoc8cg
+                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-_ztqq3w4
                         </td>
                     </tr>
 
@@ -1780,7 +1769,7 @@ <h4 style="margin-bottom: 0px; display: inline;">Worker: 1</h4>
                 <table style="width: 100%; text-align: left;">
                     <tr>
                         <td style="text-align: left;">
-                            <strong>Comm: </strong> tcp://127.0.0.1:34765
+                            <strong>Comm: </strong> tcp://127.0.0.1:46625
                         </td>
                         <td style="text-align: left;">
                             <strong>Total threads: </strong> 1
@@ -1788,7 +1777,7 @@ <h4 style="margin-bottom: 0px; display: inline;">Worker: 1</h4>
                     </tr>
                     <tr>
                         <td style="text-align: left;">
-                            <strong>Dashboard: </strong> <a href="http://127.0.0.1:35969/status" target="_blank">http://127.0.0.1:35969/status</a>
+                            <strong>Dashboard: </strong> <a href="http://127.0.0.1:44751/status" target="_blank">http://127.0.0.1:44751/status</a>
                         </td>
                         <td style="text-align: left;">
                             <strong>Memory: </strong> 3.38 GiB
@@ -1796,13 +1785,13 @@ <h4 style="margin-bottom: 0px; display: inline;">Worker: 1</h4>
                     </tr>
                     <tr>
                         <td style="text-align: left;">
-                            <strong>Nanny: </strong> tcp://127.0.0.1:36945
+                            <strong>Nanny: </strong> tcp://127.0.0.1:36725
                         </td>
                         <td style="text-align: left;"></td>
                     </tr>
                     <tr>
                         <td colspan="2" style="text-align: left;">
-                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-ghvgqmwj
+                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-77fcnobo
                         </td>
                     </tr>
 
@@ -1915,22 +1904,27 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
 <span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_mfdataset</span><span class="p">(</span>
     <span class="nb">sorted</span><span class="p">(</span><span class="n">files</span><span class="p">),</span>
     <span class="c1"># concatenate along this dimension</span>
-    <span class="n">concat_dim</span><span class="o">=</span><span class="s1">&#39;ensemble_member&#39;</span><span class="p">,</span>
+    <span class="n">concat_dim</span><span class="o">=</span><span class="s2">&quot;time&quot;</span><span class="p">,</span>
     <span class="c1"># concatenate files in the order provided</span>
     <span class="n">combine</span><span class="o">=</span><span class="s2">&quot;nested&quot;</span><span class="p">,</span>
     <span class="c1"># parallelize the reading of individual files using dask</span>
     <span class="c1"># This means the returned arrays will be dask arrays</span>
     <span class="n">parallel</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <span class="c1"># these are netCDF4 files, use the netcdf4-python package to read them</span>
-    <span class="n">engine</span><span class="o">=</span><span class="s2">&quot;netcdf4&quot;</span><span class="p">,</span>
+    <span class="c1"># these are netCDF4 files, use the h5netcdf package to read them</span>
+    <span class="n">engine</span><span class="o">=</span><span class="s2">&quot;h5netcdf&quot;</span><span class="p">,</span>
+    <span class="c1"># hold off on decoding time</span>
+    <span class="n">decode_cf</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="c1"># specify that data should be automatically chunked</span>
+    <span class="n">chunks</span><span class="o">=</span><span class="s2">&quot;auto&quot;</span><span class="p">,</span>
 <span class="p">)</span>
+<span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">decode_cf</span><span class="p">(</span><span class="n">ds</span><span class="p">)</span>
 <span class="n">ds</span>
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 1.45 s, sys: 118 ms, total: 1.57 s
-Wall time: 2.17 s
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 400 ms, sys: 41.3 ms, total: 441 ms
+Wall time: 1.84 s
 </pre></div>
 </div>
 <div class="output text_html"><div><svg style="position: absolute; width: 0; height: 0; overflow: hidden">
@@ -2297,8 +2291,8 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
   fill: currentColor;
 }
 </style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;
-Dimensions:       (lat: 192, zlon: 1, lon: 288, lev: 32, ilev: 33, time: 1032,
-                   ensemble_member: 9, nbnd: 2)
+Dimensions:       (lat: 192, zlon: 1, time: 1032, nbnd: 2, lon: 288, lev: 32,
+                   ilev: 33)
 Coordinates:
   * lat           (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
   * zlon          (zlon) float64 0.0
@@ -2306,21 +2300,21 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
   * lev           (lev) float64 3.643 7.595 14.36 24.61 ... 957.5 976.3 992.6
   * ilev          (ilev) float64 2.255 5.032 10.16 18.56 ... 967.5 985.1 1e+03
   * time          (time) object 2015-02-01 00:00:00 ... 2101-01-01 00:00:00
-Dimensions without coordinates: ensemble_member, nbnd
+Dimensions without coordinates: nbnd
 Data variables: (12/27)
-    zlon_bnds     (ensemble_member, zlon, nbnd) float64 dask.array&lt;chunksize=(1, 1, 2), meta=np.ndarray&gt;
-    gw            (ensemble_member, lat) float64 dask.array&lt;chunksize=(1, 192), meta=np.ndarray&gt;
-    hyam          (ensemble_member, lev) float64 dask.array&lt;chunksize=(1, 32), meta=np.ndarray&gt;
-    hybm          (ensemble_member, lev) float64 dask.array&lt;chunksize=(1, 32), meta=np.ndarray&gt;
-    P0            (ensemble_member) float64 1e+05 1e+05 1e+05 ... 1e+05 1e+05
-    hyai          (ensemble_member, ilev) float64 dask.array&lt;chunksize=(1, 33), meta=np.ndarray&gt;
+    zlon_bnds     (time, zlon, nbnd) float64 dask.array&lt;chunksize=(120, 1, 2), meta=np.ndarray&gt;
+    gw            (time, lat) float64 dask.array&lt;chunksize=(120, 192), meta=np.ndarray&gt;
+    hyam          (time, lev) float64 dask.array&lt;chunksize=(120, 32), meta=np.ndarray&gt;
+    hybm          (time, lev) float64 dask.array&lt;chunksize=(120, 32), meta=np.ndarray&gt;
+    P0            (time) float64 ...
+    hyai          (time, ilev) float64 dask.array&lt;chunksize=(120, 33), meta=np.ndarray&gt;
     ...            ...
-    n2ovmr        (ensemble_member, time) float64 dask.array&lt;chunksize=(1, 1032), meta=np.ndarray&gt;
-    f11vmr        (ensemble_member, time) float64 dask.array&lt;chunksize=(1, 1032), meta=np.ndarray&gt;
-    f12vmr        (ensemble_member, time) float64 dask.array&lt;chunksize=(1, 1032), meta=np.ndarray&gt;
-    sol_tsi       (ensemble_member, time) float64 dask.array&lt;chunksize=(1, 1032), meta=np.ndarray&gt;
-    nsteph        (ensemble_member, time) float64 dask.array&lt;chunksize=(1, 1032), meta=np.ndarray&gt;
-    TREFHT        (ensemble_member, time, lat, lon) float32 dask.array&lt;chunksize=(1, 1, 192, 288), meta=np.ndarray&gt;
+    n2ovmr        (time) float64 dask.array&lt;chunksize=(120,), meta=np.ndarray&gt;
+    f11vmr        (time) float64 dask.array&lt;chunksize=(120,), meta=np.ndarray&gt;
+    f12vmr        (time) float64 dask.array&lt;chunksize=(120,), meta=np.ndarray&gt;
+    sol_tsi       (time) float64 dask.array&lt;chunksize=(120,), meta=np.ndarray&gt;
+    nsteph        (time) int32 dask.array&lt;chunksize=(120,), meta=np.ndarray&gt;
+    TREFHT        (time, lat, lon) float32 dask.array&lt;chunksize=(120, 192, 288), meta=np.ndarray&gt;
 Attributes:
     Conventions:       CF-1.0
     source:            CAM
@@ -2330,7 +2324,7 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
     initial_file:      b.e21.BHISTsmbb.f09_g17.LE2-1301.013.cam.i.2015-01-01-...
     topography_file:   /mnt/lustre/share/CESM/cesm_input/atm/cam/topo/fv_0.9x...
     model_doi_url:     https://doi.org/10.5065/D67H1H0V
-    time_period_freq:  month_1</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-4a39ff99-73f3-4c62-ae65-1c27c62f475a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4a39ff99-73f3-4c62-ae65-1c27c62f475a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>zlon</span>: 1</li><li><span class='xr-has-index'>lon</span>: 288</li><li><span class='xr-has-index'>lev</span>: 32</li><li><span class='xr-has-index'>ilev</span>: 33</li><li><span class='xr-has-index'>time</span>: 1032</li><li><span>ensemble_member</span>: 9</li><li><span>nbnd</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-71da80b4-489b-4077-b7db-8fdab07caf27' class='xr-section-summary-in' type='checkbox'  checked><label for='section-71da80b4-489b-4077-b7db-8fdab07caf27' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-97471f7a-5b1a-4e3e-9388-7124933550b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-97471f7a-5b1a-4e3e-9388-7124933550b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ed9aae4-c309-4802-9c9f-30d1fb03b99b' class='xr-var-data-in' type='checkbox'><label for='data-3ed9aae4-c309-4802-9c9f-30d1fb03b99b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,
+    time_period_freq:  month_1</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-2a8aacaf-d0c6-421b-bc25-f3cf24484205' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2a8aacaf-d0c6-421b-bc25-f3cf24484205' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>zlon</span>: 1</li><li><span class='xr-has-index'>time</span>: 1032</li><li><span>nbnd</span>: 2</li><li><span class='xr-has-index'>lon</span>: 288</li><li><span class='xr-has-index'>lev</span>: 32</li><li><span class='xr-has-index'>ilev</span>: 33</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a1daad68-9081-42c9-a88d-fb306f296697' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a1daad68-9081-42c9-a88d-fb306f296697' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-f2f3d207-a0e7-4010-bf03-c08e776e2ac4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f2f3d207-a0e7-4010-bf03-c08e776e2ac4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9fd83397-ff4b-45b7-ad13-bdd6d37f173c' class='xr-var-data-in' type='checkbox'><label for='data-9fd83397-ff4b-45b7-ad13-bdd6d37f173c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,
        -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,
        -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,
        -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,
@@ -2361,24 +2355,24 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
         68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,
         73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,
         79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,
-        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>zlon</span></div><div class='xr-var-dims'>(zlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-1e423b17-1322-4522-9f12-0e8a76197d5f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1e423b17-1322-4522-9f12-0e8a76197d5f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0660c767-396f-42f6-a169-e1b0b896b069' class='xr-var-data-in' type='checkbox'><label for='data-0660c767-396f-42f6-a169-e1b0b896b069' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>bounds :</span></dt><dd>zlon_bnds</dd></dl></div><div class='xr-var-data'><pre>array([0.])</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 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-dddf2e66-00a0-44e6-bd84-017fa55ad0bf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dddf2e66-00a0-44e6-bd84-017fa55ad0bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03ecaa7c-b3b3-4163-acf8-3fd76d87d6a3' class='xr-var-data-in' type='checkbox'><label for='data-03ecaa7c-b3b3-4163-acf8-3fd76d87d6a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lev</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.643 7.595 14.36 ... 976.3 992.6</div><input id='attrs-2a62a0a0-ae43-4d80-a1f5-ffe5c37c7b32' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2a62a0a0-ae43-4d80-a1f5-ffe5c37c7b32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0ac88f9-2c0f-4226-82b4-793040ce2657' class='xr-var-data-in' type='checkbox'><label for='data-d0ac88f9-2c0f-4226-82b4-793040ce2657' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>hybrid level at midpoints (1000*(A+B))</dd><dt><span>units :</span></dt><dd>hPa</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>standard_name :</span></dt><dd>atmosphere_hybrid_sigma_pressure_coordinate</dd><dt><span>formula_terms :</span></dt><dd>a: hyam b: hybm p0: P0 ps: PS</dd></dl></div><div class='xr-var-data'><pre>array([  3.643466,   7.59482 ,  14.356632,  24.61222 ,  35.92325 ,  43.19375 ,
+        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>zlon</span></div><div class='xr-var-dims'>(zlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-cf68d8b8-a8ab-4ad4-84ec-37847263264d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf68d8b8-a8ab-4ad4-84ec-37847263264d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0aa15af3-7cab-496e-94c2-65dcc169add5' class='xr-var-data-in' type='checkbox'><label for='data-0aa15af3-7cab-496e-94c2-65dcc169add5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>bounds :</span></dt><dd>zlon_bnds</dd></dl></div><div class='xr-var-data'><pre>array([0.])</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 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-a299d226-090e-444e-9e7d-e9a2c5aa029e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a299d226-090e-444e-9e7d-e9a2c5aa029e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a68051f8-6b5b-4471-85aa-c9ce10977b39' class='xr-var-data-in' type='checkbox'><label for='data-a68051f8-6b5b-4471-85aa-c9ce10977b39' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lev</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.643 7.595 14.36 ... 976.3 992.6</div><input id='attrs-43480fb2-d30d-42ee-942b-d9ee7842d661' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-43480fb2-d30d-42ee-942b-d9ee7842d661' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1db8dbd8-58ac-470b-9b49-3e06e1a09625' class='xr-var-data-in' type='checkbox'><label for='data-1db8dbd8-58ac-470b-9b49-3e06e1a09625' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>hybrid level at midpoints (1000*(A+B))</dd><dt><span>units :</span></dt><dd>hPa</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>standard_name :</span></dt><dd>atmosphere_hybrid_sigma_pressure_coordinate</dd><dt><span>formula_terms :</span></dt><dd>a: hyam b: hybm p0: P0 ps: PS</dd></dl></div><div class='xr-var-data'><pre>array([  3.643466,   7.59482 ,  14.356632,  24.61222 ,  35.92325 ,  43.19375 ,
         51.677499,  61.520498,  73.750958,  87.82123 , 103.317127, 121.547241,
        142.994039, 168.22508 , 197.908087, 232.828619, 273.910817, 322.241902,
        379.100904, 445.992574, 524.687175, 609.778695, 691.38943 , 763.404481,
        820.858369, 859.534767, 887.020249, 912.644547, 936.198398, 957.48548 ,
-       976.325407, 992.556095])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ilev</span></div><div class='xr-var-dims'>(ilev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.255 5.032 10.16 ... 985.1 1e+03</div><input id='attrs-f9ba31d9-a3fc-460d-96a5-1320c29acd3a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f9ba31d9-a3fc-460d-96a5-1320c29acd3a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf77c59c-fea7-403e-9812-6f33c90c5174' class='xr-var-data-in' type='checkbox'><label for='data-cf77c59c-fea7-403e-9812-6f33c90c5174' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>hybrid level at interfaces (1000*(A+B))</dd><dt><span>units :</span></dt><dd>hPa</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>standard_name :</span></dt><dd>atmosphere_hybrid_sigma_pressure_coordinate</dd><dt><span>formula_terms :</span></dt><dd>a: hyai b: hybi p0: P0 ps: PS</dd></dl></div><div class='xr-var-data'><pre>array([   2.25524 ,    5.031692,   10.157947,   18.555317,   29.734676,
+       976.325407, 992.556095])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ilev</span></div><div class='xr-var-dims'>(ilev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.255 5.032 10.16 ... 985.1 1e+03</div><input id='attrs-4aa7f215-3040-4161-a4d3-d7b2f3489603' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4aa7f215-3040-4161-a4d3-d7b2f3489603' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2b36dfa-6406-4b2f-b9be-f9300fcb9ce2' class='xr-var-data-in' type='checkbox'><label for='data-e2b36dfa-6406-4b2f-b9be-f9300fcb9ce2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>hybrid level at interfaces (1000*(A+B))</dd><dt><span>units :</span></dt><dd>hPa</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>standard_name :</span></dt><dd>atmosphere_hybrid_sigma_pressure_coordinate</dd><dt><span>formula_terms :</span></dt><dd>a: hyai b: hybi p0: P0 ps: PS</dd></dl></div><div class='xr-var-data'><pre>array([   2.25524 ,    5.031692,   10.157947,   18.555317,   29.734676,
          39.273001,   47.114499,   56.240499,   66.800497,   80.701418,
          94.941042,  111.693211,  131.401271,  154.586807,  181.863353,
         213.952821,  251.704417,  296.117216,  348.366588,  409.835219,
         482.149929,  567.224421,  652.332969,  730.445892,  796.363071,
         845.353667,  873.715866,  900.324631,  924.964462,  947.432335,
-        967.538625,  985.11219 , 1000.      ])</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'>2015-02-01 00:00:00 ... 2101-01-...</div><input id='attrs-795e245c-5d8d-433c-b435-66a45369c920' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-795e245c-5d8d-433c-b435-66a45369c920' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-362446ba-803c-4e35-8318-e6d60b4b4e67' class='xr-var-data-in' type='checkbox'><label for='data-362446ba-803c-4e35-8318-e6d60b4b4e67' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2015, 2, 1, 0, 0, 0, 0, has_year_zero=True),
+        967.538625,  985.11219 , 1000.      ])</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'>2015-02-01 00:00:00 ... 2101-01-...</div><input id='attrs-50b97cb0-a74c-4e09-bd76-9b042af28489' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-50b97cb0-a74c-4e09-bd76-9b042af28489' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d47db0ec-8ced-45f7-9e2a-f670ae07eb23' class='xr-var-data-in' type='checkbox'><label for='data-d47db0ec-8ced-45f7-9e2a-f670ae07eb23' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2015, 2, 1, 0, 0, 0, 0, has_year_zero=True),
        cftime.DatetimeNoLeap(2015, 3, 1, 0, 0, 0, 0, has_year_zero=True),
        cftime.DatetimeNoLeap(2015, 4, 1, 0, 0, 0, 0, has_year_zero=True), ...,
        cftime.DatetimeNoLeap(2100, 11, 1, 0, 0, 0, 0, has_year_zero=True),
        cftime.DatetimeNoLeap(2100, 12, 1, 0, 0, 0, 0, has_year_zero=True),
        cftime.DatetimeNoLeap(2101, 1, 1, 0, 0, 0, 0, has_year_zero=True)],
-      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8f861c1e-fc53-41a5-bb5c-956c79c4eccf' class='xr-section-summary-in' type='checkbox'  ><label for='section-8f861c1e-fc53-41a5-bb5c-956c79c4eccf' class='xr-section-summary' >Data variables: <span>(27)</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>zlon_bnds</span></div><div class='xr-var-dims'>(ensemble_member, zlon, nbnd)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 2), meta=np.ndarray&gt;</div><input id='attrs-a7affc87-4fc2-41ae-8975-1e21bd76621e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7affc87-4fc2-41ae-8975-1e21bd76621e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9fe6386e-c571-4a7e-b6ac-196c4cacf9fc' class='xr-var-data-in' type='checkbox'><label for='data-9fe6386e-c571-4a7e-b6ac-196c4cacf9fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>zlon bounds</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>
+      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3026b6cc-f0b5-4f59-891e-4cc84f0c076f' class='xr-section-summary-in' type='checkbox'  ><label for='section-3026b6cc-f0b5-4f59-891e-4cc84f0c076f' class='xr-section-summary' >Data variables: <span>(27)</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>zlon_bnds</span></div><div class='xr-var-dims'>(time, zlon, nbnd)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(120, 1, 2), meta=np.ndarray&gt;</div><input id='attrs-193e257b-2d83-4792-8071-8ba612d4e79a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-193e257b-2d83-4792-8071-8ba612d4e79a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31eb9fb0-a227-47ff-be0c-6d55864956e4' class='xr-var-data-in' type='checkbox'><label for='data-31eb9fb0-a227-47ff-be0c-6d55864956e4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>zlon bounds</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>
     <tr>
         <td>
             <table style="border-collapse: collapse;">
@@ -2393,14 +2387,14 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
                     
                     <tr>
                         <th> Bytes </th>
-                        <td> 144 B </td>
-                        <td> 16 B </td>
+                        <td> 16.12 kiB </td>
+                        <td> 1.88 kiB </td>
                     </tr>
                     
                     <tr>
                         <th> Shape </th>
-                        <td> (9, 1, 2) </td>
-                        <td> (1, 1, 2) </td>
+                        <td> (1032, 1, 2) </td>
+                        <td> (120, 1, 2) </td>
                     </tr>
                     <tr>
                         <th> Dask graph </th>
@@ -2414,65 +2408,65 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
             </table>
         </td>
         <td>
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         </td>
     </tr>
-</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gw</span></div><div class='xr-var-dims'>(ensemble_member, lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 192), meta=np.ndarray&gt;</div><input id='attrs-3caa0ec3-2f0b-4135-872f-e12371bfa3dc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3caa0ec3-2f0b-4135-872f-e12371bfa3dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6a8db94-51d8-436e-b108-b21c9669d349' class='xr-var-data-in' type='checkbox'><label for='data-d6a8db94-51d8-436e-b108-b21c9669d349' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude weights</dd></dl></div><div class='xr-var-data'><table>
+</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gw</span></div><div class='xr-var-dims'>(time, lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(120, 192), meta=np.ndarray&gt;</div><input id='attrs-b80916e0-aa28-43f6-91a5-d185f5cd81f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b80916e0-aa28-43f6-91a5-d185f5cd81f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a6cca87-b8cb-468c-b762-ce5003f3a76e' class='xr-var-data-in' type='checkbox'><label for='data-2a6cca87-b8cb-468c-b762-ce5003f3a76e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude weights</dd></dl></div><div class='xr-var-data'><table>
     <tr>
         <td>
             <table style="border-collapse: collapse;">
@@ -2487,18 +2481,18 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
                     
                     <tr>
                         <th> Bytes </th>
-                        <td> 13.50 kiB </td>
-                        <td> 1.50 kiB </td>
+                        <td> 1.51 MiB </td>
+                        <td> 180.00 kiB </td>
                     </tr>
                     
                     <tr>
                         <th> Shape </th>
-                        <td> (9, 192) </td>
-                        <td> (1, 192) </td>
+                        <td> (1032, 192) </td>
+                        <td> (120, 192) </td>
                     </tr>
                     <tr>
                         <th> Dask graph </th>
-                        <td colspan="2"> 9 chunks in 28 graph layers </td>
+                        <td colspan="2"> 9 chunks in 29 graph layers </td>
                     </tr>
                     <tr>
                         <th> Data type </th>
@@ -2508,34 +2502,34 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
             </table>
         </td>
         <td>
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     </tr>
-</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hyam</span></div><div class='xr-var-dims'>(ensemble_member, lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 32), meta=np.ndarray&gt;</div><input id='attrs-187b3861-d643-4ffc-970f-f5d5bd50eab1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-187b3861-d643-4ffc-970f-f5d5bd50eab1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41b3595c-aa24-4338-a37b-5eba7a5eb90d' class='xr-var-data-in' type='checkbox'><label for='data-41b3595c-aa24-4338-a37b-5eba7a5eb90d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>hybrid A coefficient at layer midpoints</dd></dl></div><div class='xr-var-data'><table>
+</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hyam</span></div><div class='xr-var-dims'>(time, lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(120, 32), meta=np.ndarray&gt;</div><input id='attrs-a408d170-3fd8-4448-99cd-811abd5ae4b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a408d170-3fd8-4448-99cd-811abd5ae4b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6088fd35-c382-411d-b955-8951717523aa' class='xr-var-data-in' type='checkbox'><label for='data-6088fd35-c382-411d-b955-8951717523aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>hybrid A coefficient at layer midpoints</dd></dl></div><div class='xr-var-data'><table>
     <tr>
         <td>
             <table style="border-collapse: collapse;">
@@ -2550,14 +2544,14 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
                     
                     <tr>
                         <th> Bytes </th>
-                        <td> 2.25 kiB </td>
-                        <td> 256 B </td>
+                        <td> 258.00 kiB </td>
+                        <td> 30.00 kiB </td>
                     </tr>
                     
                     <tr>
                         <th> Shape </th>
-                        <td> (9, 32) </td>
-                        <td> (1, 32) </td>
+                        <td> (1032, 32) </td>
+                        <td> (120, 32) </td>
                     </tr>
                     <tr>
                         <th> Dask graph </th>
@@ -2571,34 +2565,34 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
             </table>
         </td>
         <td>
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         </td>
     </tr>
-</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hybm</span></div><div class='xr-var-dims'>(ensemble_member, lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 32), meta=np.ndarray&gt;</div><input id='attrs-e12453db-4dc0-4c7b-9961-7511f1fef97d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e12453db-4dc0-4c7b-9961-7511f1fef97d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-128ac994-ea9a-4051-8e92-149d5e9dc924' class='xr-var-data-in' type='checkbox'><label for='data-128ac994-ea9a-4051-8e92-149d5e9dc924' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>hybrid B coefficient at layer midpoints</dd></dl></div><div class='xr-var-data'><table>
+</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hybm</span></div><div class='xr-var-dims'>(time, lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(120, 32), meta=np.ndarray&gt;</div><input id='attrs-49f31d2c-f276-49a7-bb8b-a4da18817eff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49f31d2c-f276-49a7-bb8b-a4da18817eff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a49d98e-9ac2-4e94-93d9-36b9e477b7c9' class='xr-var-data-in' type='checkbox'><label for='data-2a49d98e-9ac2-4e94-93d9-36b9e477b7c9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>hybrid B coefficient at layer midpoints</dd></dl></div><div class='xr-var-data'><table>
     <tr>
         <td>
             <table style="border-collapse: collapse;">
@@ -2613,14 +2607,14 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
                     
                     <tr>
                         <th> Bytes </th>
-                        <td> 2.25 kiB </td>
-                        <td> 256 B </td>
+                        <td> 258.00 kiB </td>
+                        <td> 30.00 kiB </td>
                     </tr>
                     
                     <tr>
                         <th> Shape </th>
-                        <td> (9, 32) </td>
-                        <td> (1, 32) </td>
+                        <td> (1032, 32) </td>
+                        <td> (120, 32) </td>
                     </tr>
                     <tr>
                         <th> Dask graph </th>
@@ -2634,35 +2628,34 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
             </table>
         </td>
         <td>
-        <svg width="170" height="93" style="stroke:rgb(0,0,0);stroke-width:1" >
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         </td>
     </tr>
-</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>P0</span></div><div class='xr-var-dims'>(ensemble_member)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+05 1e+05 1e+05 ... 1e+05 1e+05</div><input id='attrs-c0cfaf6d-6d69-4626-896c-9948934780df' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0cfaf6d-6d69-4626-896c-9948934780df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53576501-736c-46d7-a087-9674895ae9a6' class='xr-var-data-in' type='checkbox'><label for='data-53576501-736c-46d7-a087-9674895ae9a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>reference pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><pre>array([100000., 100000., 100000., 100000., 100000., 100000., 100000.,
-       100000., 100000.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hyai</span></div><div class='xr-var-dims'>(ensemble_member, ilev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 33), meta=np.ndarray&gt;</div><input id='attrs-b688bafa-53b3-4f83-9597-005671a533fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b688bafa-53b3-4f83-9597-005671a533fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a3559ad-b247-4657-9cea-9eb31dd38671' class='xr-var-data-in' type='checkbox'><label for='data-5a3559ad-b247-4657-9cea-9eb31dd38671' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>hybrid A coefficient at layer interfaces</dd></dl></div><div class='xr-var-data'><table>
+</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>P0</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0d0f8409-5058-4dc4-9255-ee69d51334f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0d0f8409-5058-4dc4-9255-ee69d51334f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84d93202-da71-4849-8cfa-ff733c831ebb' class='xr-var-data-in' type='checkbox'><label for='data-84d93202-da71-4849-8cfa-ff733c831ebb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>reference pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><pre>[1032 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hyai</span></div><div class='xr-var-dims'>(time, ilev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(120, 33), meta=np.ndarray&gt;</div><input id='attrs-f1e17bf0-3fad-4b26-906c-4befb25841b0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f1e17bf0-3fad-4b26-906c-4befb25841b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52ee35d6-32a6-4e39-8124-86e8a6279172' class='xr-var-data-in' type='checkbox'><label for='data-52ee35d6-32a6-4e39-8124-86e8a6279172' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>hybrid A coefficient at layer interfaces</dd></dl></div><div class='xr-var-data'><table>
     <tr>
         <td>
             <table style="border-collapse: collapse;">
@@ -2677,14 +2670,14 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
                     
                     <tr>
                         <th> Bytes </th>
-                        <td> 2.32 kiB </td>
-                        <td> 264 B </td>
+                        <td> 266.06 kiB </td>
+                        <td> 30.94 kiB </td>
                     </tr>
                     
                     <tr>
                         <th> Shape </th>
-                        <td> (9, 33) </td>
-                        <td> (1, 33) </td>
+                        <td> (1032, 33) </td>
+                        <td> (120, 33) </td>
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                     <tr>
                         <th> Dask graph </th>
@@ -2698,34 +2691,34 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
             </table>
         </td>
         <td>
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     </tr>
-</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hybi</span></div><div class='xr-var-dims'>(ensemble_member, ilev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 33), meta=np.ndarray&gt;</div><input id='attrs-33f678ca-4691-4836-81f5-0811b26e62ee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-33f678ca-4691-4836-81f5-0811b26e62ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73d362ef-ab25-4da1-9da7-ab416c9e1056' class='xr-var-data-in' type='checkbox'><label for='data-73d362ef-ab25-4da1-9da7-ab416c9e1056' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>hybrid B coefficient at layer interfaces</dd></dl></div><div class='xr-var-data'><table>
+</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hybi</span></div><div class='xr-var-dims'>(time, ilev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(120, 33), meta=np.ndarray&gt;</div><input id='attrs-4a50dff4-c90f-470d-8993-6be0ac1250e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a50dff4-c90f-470d-8993-6be0ac1250e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23ca47d7-c665-4e68-b8c8-d84dfdaf7b14' class='xr-var-data-in' type='checkbox'><label for='data-23ca47d7-c665-4e68-b8c8-d84dfdaf7b14' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>hybrid B coefficient at layer interfaces</dd></dl></div><div class='xr-var-data'><table>
     <tr>
         <td>
             <table style="border-collapse: collapse;">
@@ -2740,14 +2733,14 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
                     
                     <tr>
                         <th> Bytes </th>
-                        <td> 2.32 kiB </td>
-                        <td> 264 B </td>
+                        <td> 266.06 kiB </td>
+                        <td> 30.94 kiB </td>
                     </tr>
                     
                     <tr>
                         <th> Shape </th>
-                        <td> (9, 33) </td>
-                        <td> (1, 33) </td>
+                        <td> (1032, 33) </td>
+                        <td> (120, 33) </td>
                     </tr>
                     <tr>
                         <th> Dask graph </th>
@@ -2761,35 +2754,34 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
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         </td>
         <td>
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         </td>
     </tr>
-</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ndbase</span></div><div class='xr-var-dims'>(ensemble_member)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 0</div><input id='attrs-b2193845-5732-4b74-9e0b-4a497c69f852' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b2193845-5732-4b74-9e0b-4a497c69f852' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f5cd5a5-4a83-4606-a190-cc94c06959b6' class='xr-var-data-in' type='checkbox'><label for='data-4f5cd5a5-4a83-4606-a190-cc94c06959b6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>base day</dd></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nsbase</span></div><div class='xr-var-dims'>(ensemble_member)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 0</div><input id='attrs-840a3852-6128-4fd6-89d4-fe895ef2b6a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-840a3852-6128-4fd6-89d4-fe895ef2b6a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbfb06b0-f39c-4d4f-a573-1d7ed062a99f' class='xr-var-data-in' type='checkbox'><label for='data-dbfb06b0-f39c-4d4f-a573-1d7ed062a99f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>seconds of base day</dd></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nbdate</span></div><div class='xr-var-dims'>(ensemble_member)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>20150101 20150101 ... 20150101</div><input id='attrs-9ed0f4b9-cb61-4dae-bb84-83e607be5e3a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9ed0f4b9-cb61-4dae-bb84-83e607be5e3a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76ef2596-83ce-406a-b104-843201f0e86f' class='xr-var-data-in' type='checkbox'><label for='data-76ef2596-83ce-406a-b104-843201f0e86f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>base date (YYYYMMDD)</dd></dl></div><div class='xr-var-data'><pre>array([20150101, 20150101, 20150101, 20150101, 20150101, 20150101,
-       20150101, 20150101, 20150101], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nbsec</span></div><div class='xr-var-dims'>(ensemble_member)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 0</div><input id='attrs-f8898623-032a-4e7c-8d70-ae686c1f13b0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f8898623-032a-4e7c-8d70-ae686c1f13b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c005ab41-07f8-41cb-ba70-c9946c9c50c4' class='xr-var-data-in' type='checkbox'><label for='data-c005ab41-07f8-41cb-ba70-c9946c9c50c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>seconds of base date</dd></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mdt</span></div><div class='xr-var-dims'>(ensemble_member)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1800 1800 1800 ... 1800 1800 1800</div><input id='attrs-f2f29e95-fa85-482b-86cd-d7981075c53c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f2f29e95-fa85-482b-86cd-d7981075c53c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60a70703-9793-4342-a61b-cd38e27ca397' class='xr-var-data-in' type='checkbox'><label for='data-60a70703-9793-4342-a61b-cd38e27ca397' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>timestep</dd><dt><span>units :</span></dt><dd>s</dd></dl></div><div class='xr-var-data'><pre>array([1800, 1800, 1800, 1800, 1800, 1800, 1800, 1800, 1800], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>date</span></div><div class='xr-var-dims'>(ensemble_member, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1032), meta=np.ndarray&gt;</div><input id='attrs-4ded5763-3112-416d-ae11-0a04dcfbc783' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ded5763-3112-416d-ae11-0a04dcfbc783' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78c5f2e3-dc28-43a7-857c-a00d250948ea' class='xr-var-data-in' type='checkbox'><label for='data-78c5f2e3-dc28-43a7-857c-a00d250948ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>current date (YYYYMMDD)</dd></dl></div><div class='xr-var-data'><table>
+</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ndbase</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7d48b295-ae92-4ca7-a6f4-4678aa1bbc16' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7d48b295-ae92-4ca7-a6f4-4678aa1bbc16' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e44365fd-ada0-4518-86b0-2a5a44bbe171' class='xr-var-data-in' type='checkbox'><label for='data-e44365fd-ada0-4518-86b0-2a5a44bbe171' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>base day</dd></dl></div><div class='xr-var-data'><pre>[1032 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nsbase</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-780c1598-f9a0-48b7-8121-c0010c5f12ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-780c1598-f9a0-48b7-8121-c0010c5f12ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84e2d7a9-62ec-4db1-aacb-217be3961eb3' class='xr-var-data-in' type='checkbox'><label for='data-84e2d7a9-62ec-4db1-aacb-217be3961eb3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>seconds of base day</dd></dl></div><div class='xr-var-data'><pre>[1032 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nbdate</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c58b2eee-0922-4b32-b83b-5fda2aaeebcd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c58b2eee-0922-4b32-b83b-5fda2aaeebcd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc10b931-fbdd-4be2-8596-f85ed2f39aa6' class='xr-var-data-in' type='checkbox'><label for='data-bc10b931-fbdd-4be2-8596-f85ed2f39aa6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>base date (YYYYMMDD)</dd></dl></div><div class='xr-var-data'><pre>[1032 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nbsec</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1cf0063e-a331-49fc-a303-18f0cf1d5b5d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1cf0063e-a331-49fc-a303-18f0cf1d5b5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0953994-f264-4303-857e-9df77a0dd9e4' class='xr-var-data-in' type='checkbox'><label for='data-a0953994-f264-4303-857e-9df77a0dd9e4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>seconds of base date</dd></dl></div><div class='xr-var-data'><pre>[1032 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mdt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-85207f34-64fd-4897-a7f1-6d1637c48584' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-85207f34-64fd-4897-a7f1-6d1637c48584' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79bbdae1-3ba2-4615-8080-4c6b53bc39ec' class='xr-var-data-in' type='checkbox'><label for='data-79bbdae1-3ba2-4615-8080-4c6b53bc39ec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>timestep</dd><dt><span>units :</span></dt><dd>s</dd></dl></div><div class='xr-var-data'><pre>[1032 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>date</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(120,), meta=np.ndarray&gt;</div><input id='attrs-353c197d-b89d-41c7-b0f4-294e181f481a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-353c197d-b89d-41c7-b0f4-294e181f481a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9d64246-5589-47a6-a27f-d91e94b2640b' class='xr-var-data-in' type='checkbox'><label for='data-d9d64246-5589-47a6-a27f-d91e94b2640b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>current date (YYYYMMDD)</dd></dl></div><div class='xr-var-data'><table>
     <tr>
         <td>
             <table style="border-collapse: collapse;">
@@ -2804,22 +2796,22 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
                     
                     <tr>
                         <th> Bytes </th>
-                        <td> 72.56 kiB </td>
-                        <td> 8.06 kiB </td>
+                        <td> 4.03 kiB </td>
+                        <td> 480 B </td>
                     </tr>
                     
                     <tr>
                         <th> Shape </th>
-                        <td> (9, 1032) </td>
-                        <td> (1, 1032) </td>
+                        <td> (1032,) </td>
+                        <td> (120,) </td>
                     </tr>
                     <tr>
                         <th> Dask graph </th>
-                        <td colspan="2"> 9 chunks in 82 graph layers </td>
+                        <td colspan="2"> 9 chunks in 19 graph layers </td>
                     </tr>
                     <tr>
                         <th> Data type </th>
-                        <td colspan="2"> float64 numpy.ndarray </td>
+                        <td colspan="2"> int32 numpy.ndarray </td>
                     </tr>
                 </tbody>
             </table>
@@ -2829,18 +2821,18 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
 
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+</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>datesec</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(120,), meta=np.ndarray&gt;</div><input id='attrs-43ac4562-9ce2-46a6-9c07-a3c17cf98418' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-43ac4562-9ce2-46a6-9c07-a3c17cf98418' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4298f25-c239-4460-9218-994bed0a47f4' class='xr-var-data-in' type='checkbox'><label for='data-b4298f25-c239-4460-9218-994bed0a47f4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>current seconds of current date</dd></dl></div><div class='xr-var-data'><table>
     <tr>
         <td>
             <table style="border-collapse: collapse;">
@@ -2867,22 +2859,22 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
                     
                     <tr>
                         <th> Bytes </th>
-                        <td> 72.56 kiB </td>
-                        <td> 8.06 kiB </td>
+                        <td> 4.03 kiB </td>
+                        <td> 480 B </td>
                     </tr>
                     
                     <tr>
                         <th> Shape </th>
-                        <td> (9, 1032) </td>
-                        <td> (1, 1032) </td>
+                        <td> (1032,) </td>
+                        <td> (120,) </td>
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                     <tr>
                         <th> Dask graph </th>
-                        <td colspan="2"> 9 chunks in 82 graph layers </td>
+                        <td colspan="2"> 9 chunks in 19 graph layers </td>
                     </tr>
                     <tr>
                         <th> Data type </th>
-                        <td colspan="2"> float64 numpy.ndarray </td>
+                        <td colspan="2"> int32 numpy.ndarray </td>
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     </tr>
-</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(ensemble_member, time, nbnd)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 2), meta=np.ndarray&gt;</div><input id='attrs-3fd761ce-fd3b-4f18-a2b8-1393dfa9aa12' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3fd761ce-fd3b-4f18-a2b8-1393dfa9aa12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a21cfd14-f292-4cad-8869-f89b143f5395' class='xr-var-data-in' type='checkbox'><label for='data-a21cfd14-f292-4cad-8869-f89b143f5395' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time interval endpoints</dd></dl></div><div class='xr-var-data'><table>
+</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, nbnd)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(120, 2), meta=np.ndarray&gt;</div><input id='attrs-451e0eac-2133-480b-b377-d1bb447f4914' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-451e0eac-2133-480b-b377-d1bb447f4914' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4c8ba60-4d5e-4f2e-9031-d284a1fa5053' class='xr-var-data-in' type='checkbox'><label for='data-a4c8ba60-4d5e-4f2e-9031-d284a1fa5053' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time interval endpoints</dd></dl></div><div class='xr-var-data'><table>
     <tr>
         <td>
             <table style="border-collapse: collapse;">
@@ -2930,18 +2922,18 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
                     
                     <tr>
                         <th> Bytes </th>
-                        <td> 145.12 kiB </td>
-                        <td> 32 B </td>
+                        <td> 16.12 kiB </td>
+                        <td> 1.88 kiB </td>
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                     <tr>
                         <th> Shape </th>
-                        <td> (9, 1032, 2) </td>
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+                        <td> (120, 2) </td>
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                     <tr>
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-                        <td colspan="2"> 9216 chunks in 93 graph layers </td>
+                        <td colspan="2"> 9 chunks in 20 graph layers </td>
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                     <tr>
                         <th> Data type </th>
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+</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>date_written</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>|S8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(120,), meta=np.ndarray&gt;</div><input id='attrs-7ab8e1f7-07d4-4dcb-b259-33c3a553e94f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7ab8e1f7-07d4-4dcb-b259-33c3a553e94f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8fe72b62-473e-4028-a7da-6c46fd991c23' class='xr-var-data-in' type='checkbox'><label for='data-8fe72b62-473e-4028-a7da-6c46fd991c23' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>
     <tr>
         <td>
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@@ -3060,22 +2985,22 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
                     
                     <tr>
                         <th> Bytes </th>
-                        <td> 72.56 kiB </td>
-                        <td> 16 B </td>
+                        <td> 8.06 kiB </td>
+                        <td> 0.94 kiB </td>
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                     <tr>
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-                        <td> (9, 1032) </td>
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+                        <td> (1032,) </td>
+                        <td> (120,) </td>
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                     <tr>
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-                        <td colspan="2"> 9216 chunks in 91 graph layers </td>
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                     <tr>
                         <th> Data type </th>
-                        <td colspan="2"> object numpy.ndarray </td>
+                        <td colspan="2"> |S8 numpy.ndarray </td>
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     </tr>
-</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_written</span></div><div class='xr-var-dims'>(ensemble_member, time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1), meta=np.ndarray&gt;</div><input id='attrs-8bc3c5db-3c2b-4592-bddc-517d5f292641' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8bc3c5db-3c2b-4592-bddc-517d5f292641' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-929faec3-52b2-4ea5-925f-e2ae3c1d7c5f' class='xr-var-data-in' type='checkbox'><label for='data-929faec3-52b2-4ea5-925f-e2ae3c1d7c5f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>
+</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_written</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>|S8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(120,), meta=np.ndarray&gt;</div><input id='attrs-1a4c369c-3a39-4ac7-85ae-8911423382c5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1a4c369c-3a39-4ac7-85ae-8911423382c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-666f995f-a438-434e-98ba-1bf82f1e9159' class='xr-var-data-in' type='checkbox'><label for='data-666f995f-a438-434e-98ba-1bf82f1e9159' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>
     <tr>
         <td>
             <table style="border-collapse: collapse;">
@@ -3141,22 +3048,22 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
                     
                     <tr>
                         <th> Bytes </th>
-                        <td> 72.56 kiB </td>
-                        <td> 16 B </td>
+                        <td> 8.06 kiB </td>
+                        <td> 0.94 kiB </td>
                     </tr>
                     
                     <tr>
                         <th> Shape </th>
-                        <td> (9, 1032) </td>
-                        <td> (1, 2) </td>
+                        <td> (1032,) </td>
+                        <td> (120,) </td>
                     </tr>
                     <tr>
                         <th> Dask graph </th>
-                        <td colspan="2"> 9216 chunks in 91 graph layers </td>
+                        <td colspan="2"> 9 chunks in 20 graph layers </td>
                     </tr>
                     <tr>
                         <th> Data type </th>
-                        <td colspan="2"> object numpy.ndarray </td>
+                        <td colspan="2"> |S8 numpy.ndarray </td>
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     </tr>
-</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ndcur</span></div><div class='xr-var-dims'>(ensemble_member, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1032), meta=np.ndarray&gt;</div><input id='attrs-d3c65848-e921-47db-b679-3f39d8247a0b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d3c65848-e921-47db-b679-3f39d8247a0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec2755b5-67f6-45e9-9a68-807451951102' class='xr-var-data-in' type='checkbox'><label for='data-ec2755b5-67f6-45e9-9a68-807451951102' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>current day (from base day)</dd></dl></div><div class='xr-var-data'><table>
+</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ndcur</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(120,), meta=np.ndarray&gt;</div><input id='attrs-3b09409b-99c0-4944-9b8a-47a89ab156bf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b09409b-99c0-4944-9b8a-47a89ab156bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6afab8ca-abfc-451d-b57b-cba14bd449f0' class='xr-var-data-in' type='checkbox'><label for='data-6afab8ca-abfc-451d-b57b-cba14bd449f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>current day (from base day)</dd></dl></div><div class='xr-var-data'><table>
     <tr>
         <td>
             <table style="border-collapse: collapse;">
@@ -3222,22 +3111,22 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
                     
                     <tr>
                         <th> Bytes </th>
-                        <td> 72.56 kiB </td>
-                        <td> 8.06 kiB </td>
+                        <td> 4.03 kiB </td>
+                        <td> 480 B </td>
                     </tr>
                     
                     <tr>
                         <th> Shape </th>
-                        <td> (9, 1032) </td>
-                        <td> (1, 1032) </td>
+                        <td> (1032,) </td>
+                        <td> (120,) </td>
                     </tr>
                     <tr>
                         <th> Dask graph </th>
-                        <td colspan="2"> 9 chunks in 82 graph layers </td>
+                        <td colspan="2"> 9 chunks in 19 graph layers </td>
                     </tr>
                     <tr>
                         <th> Data type </th>
-                        <td colspan="2"> float64 numpy.ndarray </td>
+                        <td colspan="2"> int32 numpy.ndarray </td>
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                 </tbody>
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@@ -3247,18 +3136,18 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
 
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         </td>
     </tr>
-</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nscur</span></div><div class='xr-var-dims'>(ensemble_member, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1032), meta=np.ndarray&gt;</div><input id='attrs-e2c02847-f23e-46ad-a217-eb37536d58b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2c02847-f23e-46ad-a217-eb37536d58b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25ba20d8-40c6-4cdb-ba02-8077246a4947' class='xr-var-data-in' type='checkbox'><label for='data-25ba20d8-40c6-4cdb-ba02-8077246a4947' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>current seconds of current day</dd></dl></div><div class='xr-var-data'><table>
+</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nscur</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(120,), meta=np.ndarray&gt;</div><input id='attrs-d21c94d4-a770-46a8-b5ca-fcde5d3447ca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d21c94d4-a770-46a8-b5ca-fcde5d3447ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1dc14563-e88c-4ed4-8765-6b2860c71464' class='xr-var-data-in' type='checkbox'><label for='data-1dc14563-e88c-4ed4-8765-6b2860c71464' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>current seconds of current day</dd></dl></div><div class='xr-var-data'><table>
     <tr>
         <td>
             <table style="border-collapse: collapse;">
@@ -3285,22 +3174,22 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
                     
                     <tr>
                         <th> Bytes </th>
-                        <td> 72.56 kiB </td>
-                        <td> 8.06 kiB </td>
+                        <td> 4.03 kiB </td>
+                        <td> 480 B </td>
                     </tr>
                     
                     <tr>
                         <th> Shape </th>
-                        <td> (9, 1032) </td>
-                        <td> (1, 1032) </td>
+                        <td> (1032,) </td>
+                        <td> (120,) </td>
                     </tr>
                     <tr>
                         <th> Dask graph </th>
-                        <td colspan="2"> 9 chunks in 82 graph layers </td>
+                        <td colspan="2"> 9 chunks in 19 graph layers </td>
                     </tr>
                     <tr>
                         <th> Data type </th>
-                        <td colspan="2"> float64 numpy.ndarray </td>
+                        <td colspan="2"> int32 numpy.ndarray </td>
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         </td>
     </tr>
-</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>co2vmr</span></div><div class='xr-var-dims'>(ensemble_member, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1032), meta=np.ndarray&gt;</div><input id='attrs-397d265c-28f1-4e84-817d-838889a525d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-397d265c-28f1-4e84-817d-838889a525d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a111558c-0229-49dc-8646-5a0f85bfc5ae' class='xr-var-data-in' type='checkbox'><label for='data-a111558c-0229-49dc-8646-5a0f85bfc5ae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>co2 volume mixing ratio</dd></dl></div><div class='xr-var-data'><table>
+</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>co2vmr</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(120,), meta=np.ndarray&gt;</div><input id='attrs-95eca009-2cab-4723-a2a6-72dfae4589e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95eca009-2cab-4723-a2a6-72dfae4589e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f2b89c6-cc39-4549-9ce6-e0a5bed268aa' class='xr-var-data-in' type='checkbox'><label for='data-6f2b89c6-cc39-4549-9ce6-e0a5bed268aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>co2 volume mixing ratio</dd></dl></div><div class='xr-var-data'><table>
     <tr>
         <td>
             <table style="border-collapse: collapse;">
@@ -3348,18 +3237,18 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
                     
                     <tr>
                         <th> Bytes </th>
-                        <td> 72.56 kiB </td>
                         <td> 8.06 kiB </td>
+                        <td> 0.94 kiB </td>
                     </tr>
                     
                     <tr>
                         <th> Shape </th>
-                        <td> (9, 1032) </td>
-                        <td> (1, 1032) </td>
+                        <td> (1032,) </td>
+                        <td> (120,) </td>
                     </tr>
                     <tr>
                         <th> Dask graph </th>
-                        <td colspan="2"> 9 chunks in 73 graph layers </td>
+                        <td colspan="2"> 9 chunks in 19 graph layers </td>
                     </tr>
                     <tr>
                         <th> Data type </th>
@@ -3373,18 +3262,18 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
 
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+  <text x="140.000000" y="12.706308" font-size="1.0rem" font-weight="100" text-anchor="middle" transform="rotate(0,140.000000,12.706308)">1</text>
 </svg>
         </td>
     </tr>
-</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ch4vmr</span></div><div class='xr-var-dims'>(ensemble_member, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1032), meta=np.ndarray&gt;</div><input id='attrs-e39050e4-3656-4541-8ad5-b68a958fc10c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e39050e4-3656-4541-8ad5-b68a958fc10c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bba97ac6-c018-4a7c-85a6-5eaa428d625d' class='xr-var-data-in' type='checkbox'><label for='data-bba97ac6-c018-4a7c-85a6-5eaa428d625d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>ch4 volume mixing ratio</dd></dl></div><div class='xr-var-data'><table>
+</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ch4vmr</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(120,), meta=np.ndarray&gt;</div><input id='attrs-bdf7ee67-a6f5-4809-a38a-cc0853e18056' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bdf7ee67-a6f5-4809-a38a-cc0853e18056' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfaed891-af77-43dc-997b-c4ebc7cc7926' class='xr-var-data-in' type='checkbox'><label for='data-dfaed891-af77-43dc-997b-c4ebc7cc7926' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>ch4 volume mixing ratio</dd></dl></div><div class='xr-var-data'><table>
     <tr>
         <td>
             <table style="border-collapse: collapse;">
@@ -3411,18 +3300,18 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
                     
                     <tr>
                         <th> Bytes </th>
-                        <td> 72.56 kiB </td>
                         <td> 8.06 kiB </td>
+                        <td> 0.94 kiB </td>
                     </tr>
                     
                     <tr>
                         <th> Shape </th>
-                        <td> (9, 1032) </td>
-                        <td> (1, 1032) </td>
+                        <td> (1032,) </td>
+                        <td> (120,) </td>
                     </tr>
                     <tr>
                         <th> Dask graph </th>
-                        <td colspan="2"> 9 chunks in 73 graph layers </td>
+                        <td colspan="2"> 9 chunks in 19 graph layers </td>
                     </tr>
                     <tr>
                         <th> Data type </th>
@@ -3436,18 +3325,18 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
 
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+  <text x="140.000000" y="12.706308" font-size="1.0rem" font-weight="100" text-anchor="middle" transform="rotate(0,140.000000,12.706308)">1</text>
 </svg>
         </td>
     </tr>
-</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n2ovmr</span></div><div class='xr-var-dims'>(ensemble_member, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1032), meta=np.ndarray&gt;</div><input id='attrs-1ff0bff0-1b76-4500-9bc7-475183d706e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ff0bff0-1b76-4500-9bc7-475183d706e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2efd51b7-2748-4cc9-bdbd-f0c9dde13a29' class='xr-var-data-in' type='checkbox'><label for='data-2efd51b7-2748-4cc9-bdbd-f0c9dde13a29' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>n2o volume mixing ratio</dd></dl></div><div class='xr-var-data'><table>
+</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n2ovmr</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(120,), meta=np.ndarray&gt;</div><input id='attrs-705b9808-2397-4cfc-af4e-25879301019f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-705b9808-2397-4cfc-af4e-25879301019f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4cd842ca-3446-4804-ab60-4efdbf1a18f4' class='xr-var-data-in' type='checkbox'><label for='data-4cd842ca-3446-4804-ab60-4efdbf1a18f4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>n2o volume mixing ratio</dd></dl></div><div class='xr-var-data'><table>
     <tr>
         <td>
             <table style="border-collapse: collapse;">
@@ -3474,18 +3363,18 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
                     
                     <tr>
                         <th> Bytes </th>
-                        <td> 72.56 kiB </td>
                         <td> 8.06 kiB </td>
+                        <td> 0.94 kiB </td>
                     </tr>
                     
                     <tr>
                         <th> Shape </th>
-                        <td> (9, 1032) </td>
-                        <td> (1, 1032) </td>
+                        <td> (1032,) </td>
+                        <td> (120,) </td>
                     </tr>
                     <tr>
                         <th> Dask graph </th>
-                        <td colspan="2"> 9 chunks in 73 graph layers </td>
+                        <td colspan="2"> 9 chunks in 19 graph layers </td>
                     </tr>
                     <tr>
                         <th> Data type </th>
@@ -3499,18 +3388,18 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
 
   <!-- Horizontal lines -->
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@@ -3518,11 +3407,11 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
 
   <!-- Text -->
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 </svg>
         </td>
     </tr>
-</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>f11vmr</span></div><div class='xr-var-dims'>(ensemble_member, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1032), meta=np.ndarray&gt;</div><input id='attrs-440217ad-38c6-4e54-b5ab-61b1db233364' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-440217ad-38c6-4e54-b5ab-61b1db233364' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e59c886-e4e0-44e2-b753-4085a4046d51' class='xr-var-data-in' type='checkbox'><label for='data-2e59c886-e4e0-44e2-b753-4085a4046d51' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>f11 volume mixing ratio</dd></dl></div><div class='xr-var-data'><table>
+</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>f11vmr</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(120,), meta=np.ndarray&gt;</div><input id='attrs-174c1c66-9600-4142-8a32-87e7abe7cf38' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-174c1c66-9600-4142-8a32-87e7abe7cf38' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b037915-1f27-496e-8d89-7010aa333b33' class='xr-var-data-in' type='checkbox'><label for='data-7b037915-1f27-496e-8d89-7010aa333b33' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>f11 volume mixing ratio</dd></dl></div><div class='xr-var-data'><table>
     <tr>
         <td>
             <table style="border-collapse: collapse;">
@@ -3537,18 +3426,18 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
                     
                     <tr>
                         <th> Bytes </th>
-                        <td> 72.56 kiB </td>
                         <td> 8.06 kiB </td>
+                        <td> 0.94 kiB </td>
                     </tr>
                     
                     <tr>
                         <th> Shape </th>
-                        <td> (9, 1032) </td>
-                        <td> (1, 1032) </td>
+                        <td> (1032,) </td>
+                        <td> (120,) </td>
                     </tr>
                     <tr>
                         <th> Dask graph </th>
-                        <td colspan="2"> 9 chunks in 73 graph layers </td>
+                        <td colspan="2"> 9 chunks in 19 graph layers </td>
                     </tr>
                     <tr>
                         <th> Data type </th>
@@ -3562,18 +3451,18 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
 
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   <!-- Vertical lines -->
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 </svg>
         </td>
     </tr>
-</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>f12vmr</span></div><div class='xr-var-dims'>(ensemble_member, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1032), meta=np.ndarray&gt;</div><input id='attrs-169e97ff-aa4f-4c77-8965-cab9db7a7129' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-169e97ff-aa4f-4c77-8965-cab9db7a7129' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d4aa0292-6d88-4f99-b8cb-80699e882cc9' class='xr-var-data-in' type='checkbox'><label for='data-d4aa0292-6d88-4f99-b8cb-80699e882cc9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>f12 volume mixing ratio</dd></dl></div><div class='xr-var-data'><table>
+</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>f12vmr</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(120,), meta=np.ndarray&gt;</div><input id='attrs-8d4588b0-8750-42e0-810a-68e9d08b4e48' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d4588b0-8750-42e0-810a-68e9d08b4e48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25cc01f0-150d-49a9-8901-a6d6bcab3c8a' class='xr-var-data-in' type='checkbox'><label for='data-25cc01f0-150d-49a9-8901-a6d6bcab3c8a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>f12 volume mixing ratio</dd></dl></div><div class='xr-var-data'><table>
     <tr>
         <td>
             <table style="border-collapse: collapse;">
@@ -3600,18 +3489,18 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
                     
                     <tr>
                         <th> Bytes </th>
-                        <td> 72.56 kiB </td>
                         <td> 8.06 kiB </td>
+                        <td> 0.94 kiB </td>
                     </tr>
                     
                     <tr>
                         <th> Shape </th>
-                        <td> (9, 1032) </td>
-                        <td> (1, 1032) </td>
+                        <td> (1032,) </td>
+                        <td> (120,) </td>
                     </tr>
                     <tr>
                         <th> Dask graph </th>
-                        <td colspan="2"> 9 chunks in 73 graph layers </td>
+                        <td colspan="2"> 9 chunks in 19 graph layers </td>
                     </tr>
                     <tr>
                         <th> Data type </th>
@@ -3625,18 +3514,18 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
 
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 </svg>
         </td>
     </tr>
-</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sol_tsi</span></div><div class='xr-var-dims'>(ensemble_member, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1032), meta=np.ndarray&gt;</div><input id='attrs-f159629c-ac01-4841-935e-96bf823342a1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f159629c-ac01-4841-935e-96bf823342a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82a1932b-e94a-413a-8a47-b5b4eb5db69f' class='xr-var-data-in' type='checkbox'><label for='data-82a1932b-e94a-413a-8a47-b5b4eb5db69f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>total solar irradiance</dd><dt><span>units :</span></dt><dd>W/m2</dd></dl></div><div class='xr-var-data'><table>
+</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sol_tsi</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(120,), meta=np.ndarray&gt;</div><input id='attrs-2d585dc7-aeb8-45ee-a8fa-692e775b5955' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d585dc7-aeb8-45ee-a8fa-692e775b5955' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88ab531d-a7c3-489d-917f-809729b61c62' class='xr-var-data-in' type='checkbox'><label for='data-88ab531d-a7c3-489d-917f-809729b61c62' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>total solar irradiance</dd><dt><span>units :</span></dt><dd>W/m2</dd></dl></div><div class='xr-var-data'><table>
     <tr>
         <td>
             <table style="border-collapse: collapse;">
@@ -3663,18 +3552,18 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
                     
                     <tr>
                         <th> Bytes </th>
-                        <td> 72.56 kiB </td>
                         <td> 8.06 kiB </td>
+                        <td> 0.94 kiB </td>
                     </tr>
                     
                     <tr>
                         <th> Shape </th>
-                        <td> (9, 1032) </td>
-                        <td> (1, 1032) </td>
+                        <td> (1032,) </td>
+                        <td> (120,) </td>
                     </tr>
                     <tr>
                         <th> Dask graph </th>
-                        <td colspan="2"> 9 chunks in 73 graph layers </td>
+                        <td colspan="2"> 9 chunks in 19 graph layers </td>
                     </tr>
                     <tr>
                         <th> Data type </th>
@@ -3688,18 +3577,18 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
 
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         </td>
     </tr>
-</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nsteph</span></div><div class='xr-var-dims'>(ensemble_member, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1032), meta=np.ndarray&gt;</div><input id='attrs-85730b4a-a0d2-46b0-87ff-51ec45c270c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-85730b4a-a0d2-46b0-87ff-51ec45c270c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a46da0f7-b039-4361-8863-4cee9303f00a' class='xr-var-data-in' type='checkbox'><label for='data-a46da0f7-b039-4361-8863-4cee9303f00a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>current timestep</dd></dl></div><div class='xr-var-data'><table>
+</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nsteph</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(120,), meta=np.ndarray&gt;</div><input id='attrs-99c59db1-496e-4dae-8692-c17db299cf08' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99c59db1-496e-4dae-8692-c17db299cf08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f19a8971-966a-454d-b2d8-32bd48efbe3f' class='xr-var-data-in' type='checkbox'><label for='data-f19a8971-966a-454d-b2d8-32bd48efbe3f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>current timestep</dd></dl></div><div class='xr-var-data'><table>
     <tr>
         <td>
             <table style="border-collapse: collapse;">
@@ -3726,22 +3615,22 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
                     
                     <tr>
                         <th> Bytes </th>
-                        <td> 72.56 kiB </td>
-                        <td> 8.06 kiB </td>
+                        <td> 4.03 kiB </td>
+                        <td> 480 B </td>
                     </tr>
                     
                     <tr>
                         <th> Shape </th>
-                        <td> (9, 1032) </td>
-                        <td> (1, 1032) </td>
+                        <td> (1032,) </td>
+                        <td> (120,) </td>
                     </tr>
                     <tr>
                         <th> Dask graph </th>
-                        <td colspan="2"> 9 chunks in 82 graph layers </td>
+                        <td colspan="2"> 9 chunks in 19 graph layers </td>
                     </tr>
                     <tr>
                         <th> Data type </th>
-                        <td colspan="2"> float64 numpy.ndarray </td>
+                        <td colspan="2"> int32 numpy.ndarray </td>
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@@ -3751,18 +3640,18 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
 
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         </td>
     </tr>
-</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TREFHT</span></div><div class='xr-var-dims'>(ensemble_member, time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 192, 288), meta=np.ndarray&gt;</div><input id='attrs-71470db6-249a-4636-954d-60388fb25bf6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71470db6-249a-4636-954d-60388fb25bf6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3009e6fb-d4a7-49b4-bf68-603b763e0cf8' class='xr-var-data-in' type='checkbox'><label for='data-3009e6fb-d4a7-49b4-bf68-603b763e0cf8' 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>K</dd><dt><span>long_name :</span></dt><dd>Reference height temperature</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><table>
+</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TREFHT</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=(120, 192, 288), meta=np.ndarray&gt;</div><input id='attrs-5ea92f42-593d-40a4-a8e4-b7d2b564dd64' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ea92f42-593d-40a4-a8e4-b7d2b564dd64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ad25abb-1cc2-442c-b575-80f96f0fba4d' class='xr-var-data-in' type='checkbox'><label for='data-9ad25abb-1cc2-442c-b575-80f96f0fba4d' 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>K</dd><dt><span>long_name :</span></dt><dd>Reference height temperature</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><table>
     <tr>
         <td>
             <table style="border-collapse: collapse;">
@@ -3789,18 +3678,18 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
                     
                     <tr>
                         <th> Bytes </th>
-                        <td> 1.91 GiB </td>
-                        <td> 432.00 kiB </td>
+                        <td> 217.69 MiB </td>
+                        <td> 25.31 MiB </td>
                     </tr>
                     
                     <tr>
                         <th> Shape </th>
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                     <tr>
                         <th> Dask graph </th>
-                        <td colspan="2"> 9216 chunks in 104 graph layers </td>
+                        <td colspan="2"> 9 chunks in 19 graph layers </td>
                     </tr>
                     <tr>
                         <th> Data type </th>
@@ -3810,135 +3699,65 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
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-</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-7f9159d8-2ab3-4d45-84eb-26914da6366c' class='xr-section-summary-in' type='checkbox'  ><label for='section-7f9159d8-2ab3-4d45-84eb-26914da6366c' class='xr-section-summary' >Indexes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-de1e4dab-3420-434f-aeb8-1586c7d008ea' class='xr-index-data-in' type='checkbox'/><label for='index-de1e4dab-3420-434f-aeb8-1586c7d008ea' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([             -90.0, -89.05759162303664,  -88.1151832460733,
+</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-576bb280-585a-452c-9993-6c13eb59523c' class='xr-section-summary-in' type='checkbox'  ><label for='section-576bb280-585a-452c-9993-6c13eb59523c' class='xr-section-summary' >Indexes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d6b3b03a-7b20-418f-90f1-bb2453601e8b' class='xr-index-data-in' type='checkbox'/><label for='index-d6b3b03a-7b20-418f-90f1-bb2453601e8b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([             -90.0, -89.05759162303664,  -88.1151832460733,
        -87.17277486910994,  -86.2303664921466, -85.28795811518324,
         -84.3455497382199, -83.40314136125654, -82.46073298429319,
        -81.51832460732984,
@@ -3947,12 +3766,12 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
          84.3455497382199,  85.28795811518324,   86.2303664921466,
         87.17277486910994,   88.1151832460733,  89.05759162303664,
                      90.0],
-      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>zlon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2cbbfa3a-a30b-4d4f-8600-a3d93851d0d4' class='xr-index-data-in' type='checkbox'/><label for='index-2cbbfa3a-a30b-4d4f-8600-a3d93851d0d4' 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], dtype=&#x27;float64&#x27;, name=&#x27;zlon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-20a28590-dd13-4276-8512-07e5498d1ca4' class='xr-index-data-in' type='checkbox'/><label for='index-20a28590-dd13-4276-8512-07e5498d1ca4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   1.25,    2.5,   3.75,    5.0,   6.25,    7.5,   8.75,   10.0,
+      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>zlon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5216a94b-6f58-47df-a96d-007fdb49f237' class='xr-index-data-in' type='checkbox'/><label for='index-5216a94b-6f58-47df-a96d-007fdb49f237' 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], dtype=&#x27;float64&#x27;, name=&#x27;zlon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-90b3494a-a38a-4f6e-8d30-077b4fead637' class='xr-index-data-in' type='checkbox'/><label for='index-90b3494a-a38a-4f6e-8d30-077b4fead637' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   1.25,    2.5,   3.75,    5.0,   6.25,    7.5,   8.75,   10.0,
         11.25,
        ...
         347.5, 348.75,  350.0, 351.25,  352.5, 353.75,  355.0, 356.25,  357.5,
        358.75],
-      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=288))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lev</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-199f63e0-0af4-4d20-87be-b08a19eb1ac2' class='xr-index-data-in' type='checkbox'/><label for='index-199f63e0-0af4-4d20-87be-b08a19eb1ac2' 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([  3.64346569404006,  7.594819646328688, 14.356632251292467,
+      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=288))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lev</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9bea6ed3-1dce-4765-8a48-aada445bf85f' class='xr-index-data-in' type='checkbox'/><label for='index-9bea6ed3-1dce-4765-8a48-aada445bf85f' 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([  3.64346569404006,  7.594819646328688, 14.356632251292467,
        24.612220004200935,  35.92325001955032,   43.1937500834465,
         51.67749896645546,  61.52049824595451,  73.75095784664154,
         87.82123029232025, 103.31712663173676, 121.54724076390266,
@@ -3963,7 +3782,7 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
         820.8583686500788,  859.5347665250301,  887.0202489197254,
          912.644546944648,  936.1983984708786,   957.485479535535,
          976.325407391414,   992.556095123291],
-      dtype=&#x27;float64&#x27;, name=&#x27;lev&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>ilev</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bd9113d1-15db-49b8-b771-bee874ffead6' class='xr-index-data-in' type='checkbox'/><label for='index-bd9113d1-15db-49b8-b771-bee874ffead6' 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([ 2.255239523947239,  5.031691864132881,  10.15794742852449,
+      dtype=&#x27;float64&#x27;, name=&#x27;lev&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>ilev</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b9c29da2-b4f5-47bb-9f57-bc457d7cac9b' class='xr-index-data-in' type='checkbox'/><label for='index-b9c29da2-b4f5-47bb-9f57-bc457d7cac9b' 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([ 2.255239523947239,  5.031691864132881,  10.15794742852449,
         18.55531707406044,  29.73467559512106,  39.27300125360489,
         47.11449891328812,   56.2404990196228,  66.80049747228622,
         80.70141822099686,  94.94104236364365, 111.69321089982986,
@@ -3974,7 +3793,7 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
         796.3630706071854,  845.3536666929722,  873.7158663570881,
         900.3246314823627,  924.9644624069333,  947.4323345348239,
         967.5386245362461,   985.112190246582,             1000.0],
-      dtype=&#x27;float64&#x27;, name=&#x27;ilev&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-69951364-5c3c-4291-b268-8eda07fcac6b' class='xr-index-data-in' type='checkbox'/><label for='index-69951364-5c3c-4291-b268-8eda07fcac6b' 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([2015-02-01 00:00:00, 2015-03-01 00:00:00, 2015-04-01 00:00:00,
+      dtype=&#x27;float64&#x27;, name=&#x27;ilev&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e228041c-9760-472a-a801-26ed4c054cbb' class='xr-index-data-in' type='checkbox'/><label for='index-e228041c-9760-472a-a801-26ed4c054cbb' 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([2015-02-01 00:00:00, 2015-03-01 00:00:00, 2015-04-01 00:00:00,
              2015-05-01 00:00:00, 2015-06-01 00:00:00, 2015-07-01 00:00:00,
              2015-08-01 00:00:00, 2015-09-01 00:00:00, 2015-10-01 00:00:00,
              2015-11-01 00:00:00,
@@ -3983,8 +3802,9 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
              2100-07-01 00:00:00, 2100-08-01 00:00:00, 2100-09-01 00:00:00,
              2100-10-01 00:00:00, 2100-11-01 00:00:00, 2100-12-01 00:00:00,
              2101-01-01 00:00:00],
-            dtype=&#x27;object&#x27;, length=1032, calendar=&#x27;noleap&#x27;, freq=&#x27;MS&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-99e03721-ca04-415e-93ae-66dc285c6796' class='xr-section-summary-in' type='checkbox'  checked><label for='section-99e03721-ca04-415e-93ae-66dc285c6796' class='xr-section-summary' >Attributes: <span>(9)</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-1.0</dd><dt><span>source :</span></dt><dd>CAM</dd><dt><span>case :</span></dt><dd>b.e21.BSSP370smbb.f09_g17.LE2-1301.013</dd><dt><span>logname :</span></dt><dd>sunseon</dd><dt><span>host :</span></dt><dd>mom1</dd><dt><span>initial_file :</span></dt><dd>b.e21.BHISTsmbb.f09_g17.LE2-1301.013.cam.i.2015-01-01-00000.nc</dd><dt><span>topography_file :</span></dt><dd>/mnt/lustre/share/CESM/cesm_input/atm/cam/topo/fv_0.9x1.25_nc3000_Nsw042_Nrs008_Co060_Fi001_ZR_sgh30_24km_GRNL_c170103.nc</dd><dt><span>model_doi_url :</span></dt><dd>https://doi.org/10.5065/D67H1H0V</dd><dt><span>time_period_freq :</span></dt><dd>month_1</dd></dl></div></li></ul></div></div></div></div>
+            dtype=&#x27;object&#x27;, length=1032, calendar=&#x27;noleap&#x27;, freq=&#x27;MS&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cd0a6498-1f3a-4ed4-8c1b-aa5fba446071' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cd0a6498-1f3a-4ed4-8c1b-aa5fba446071' class='xr-section-summary' >Attributes: <span>(9)</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-1.0</dd><dt><span>source :</span></dt><dd>CAM</dd><dt><span>case :</span></dt><dd>b.e21.BSSP370smbb.f09_g17.LE2-1301.013</dd><dt><span>logname :</span></dt><dd>sunseon</dd><dt><span>host :</span></dt><dd>mom1</dd><dt><span>initial_file :</span></dt><dd>b.e21.BHISTsmbb.f09_g17.LE2-1301.013.cam.i.2015-01-01-00000.nc</dd><dt><span>topography_file :</span></dt><dd>/mnt/lustre/share/CESM/cesm_input/atm/cam/topo/fv_0.9x1.25_nc3000_Nsw042_Nrs008_Co060_Fi001_ZR_sgh30_24km_GRNL_c170103.nc</dd><dt><span>model_doi_url :</span></dt><dd>https://doi.org/10.5065/D67H1H0V</dd><dt><span>time_period_freq :</span></dt><dd>month_1</dd></dl></div></li></ul></div></div></div></div>
 </div>
+<p>For complex scenarios, you can access each file individually by utilizing the <code class="docutils literal notranslate"><span class="pre">open_dataset</span></code> function with the specified <code class="docutils literal notranslate"><span class="pre">chunk</span></code>s and then combine the outputs into a single dataset later.</p>
 <p>Note that the “real” values are not displayed, since that would trigger actual computation.</p>
 <div class="alert alert-success" markdown="1">
 <p>Xarray automatically wraps Dask Arrays and Dask is lazy, meaning that operations are not computed until we explicitly request them, for example by calling <code class="docutils literal notranslate"><span class="pre">.compute()</span></code>.</p>
@@ -4362,17 +4182,16 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
   stroke: currentColor;
   fill: currentColor;
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-dask.array&lt;chunksize=(1, 1, 192, 288), meta=np.ndarray&gt;
+</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;TREFHT&#x27; (time: 1032, lat: 192, lon: 288)&gt;
+dask.array&lt;chunksize=(120, 192, 288), meta=np.ndarray&gt;
 Coordinates:
   * lat      (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 87.17 88.12 89.06 90.0
   * lon      (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
   * time     (time) object 2015-02-01 00:00:00 ... 2101-01-01 00:00:00
-Dimensions without coordinates: ensemble_member
 Attributes:
     units:         K
     long_name:     Reference height temperature
-    cell_methods:  time: mean</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'>'TREFHT'</div><ul class='xr-dim-list'><li><span>ensemble_member</span>: 9</li><li><span class='xr-has-index'>time</span>: 1032</li><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-62bcc212-592c-44e7-84c7-37d5eeeff674' class='xr-array-in' type='checkbox' ><label for='section-62bcc212-592c-44e7-84c7-37d5eeeff674' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 1, 192, 288), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>
+    cell_methods:  time: mean</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'>'TREFHT'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1032</li><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-2c2b2a72-f10d-48f7-b45a-d132d9525b51' class='xr-array-in' type='checkbox' ><label for='section-2c2b2a72-f10d-48f7-b45a-d132d9525b51' 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=(120, 192, 288), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>
     <tr>
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@@ -4387,18 +4206,18 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
                     
                     <tr>
                         <th> Bytes </th>
-                        <td> 1.91 GiB </td>
-                        <td> 432.00 kiB </td>
+                        <td> 217.69 MiB </td>
+                        <td> 25.31 MiB </td>
                     </tr>
                     
                     <tr>
                         <th> Shape </th>
-                        <td> (9, 1032, 192, 288) </td>
-                        <td> (1, 2, 192, 288) </td>
+                        <td> (1032, 192, 288) </td>
+                        <td> (120, 192, 288) </td>
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                     <tr>
                         <th> Dask graph </th>
-                        <td colspan="2"> 9216 chunks in 104 graph layers </td>
+                        <td colspan="2"> 9 chunks in 19 graph layers </td>
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                     <tr>
                         <th> Data type </th>
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     </tr>
-</table></div></div></li><li class='xr-section-item'><input id='section-da4a8e48-a2bb-4693-aa07-299f7c323c7d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-da4a8e48-a2bb-4693-aa07-299f7c323c7d' 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 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-b0152aae-3cab-4f9e-95d8-f84ab6ef347e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0152aae-3cab-4f9e-95d8-f84ab6ef347e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fadd1cd6-fd73-40d8-8de1-c2167d465565' class='xr-var-data-in' type='checkbox'><label for='data-fadd1cd6-fd73-40d8-8de1-c2167d465565' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,
+</table></div></div></li><li class='xr-section-item'><input id='section-d22c93ff-b3da-4749-becc-dac485074bdd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d22c93ff-b3da-4749-becc-dac485074bdd' 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 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-3b5bcbc6-7945-470e-b6cb-cd2504327373' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b5bcbc6-7945-470e-b6cb-cd2504327373' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19a6f9b9-028e-4630-88eb-ac130dba41f0' class='xr-var-data-in' type='checkbox'><label for='data-19a6f9b9-028e-4630-88eb-ac130dba41f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,
        -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,
        -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,
        -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,
@@ -4567,13 +4316,13 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
         68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,
         73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,
         79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,
-        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-2cb071cd-e50f-40c1-8495-ce244eb37c84' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2cb071cd-e50f-40c1-8495-ce244eb37c84' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61c54675-de1c-4fd3-8b9f-347618d07821' class='xr-var-data-in' type='checkbox'><label for='data-61c54675-de1c-4fd3-8b9f-347618d07821' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span 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'>2015-02-01 00:00:00 ... 2101-01-...</div><input id='attrs-d7af2d9a-89c3-4264-9128-606ecaa3e769' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d7af2d9a-89c3-4264-9128-606ecaa3e769' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c3354a5-48e4-41ab-b4ac-9e842952471c' class='xr-var-data-in' type='checkbox'><label for='data-3c3354a5-48e4-41ab-b4ac-9e842952471c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2015, 2, 1, 0, 0, 0, 0, has_year_zero=True),
+        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-0ccb72a3-7e88-4b41-94a1-870ac2733593' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ccb72a3-7e88-4b41-94a1-870ac2733593' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-990f642a-46e2-428f-af4b-c858332fb617' class='xr-var-data-in' type='checkbox'><label for='data-990f642a-46e2-428f-af4b-c858332fb617' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span 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'>2015-02-01 00:00:00 ... 2101-01-...</div><input id='attrs-3d0b6ae4-abaf-41af-8acc-aabe48efded9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3d0b6ae4-abaf-41af-8acc-aabe48efded9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-412861df-0035-43ed-8d5b-5f4845f9e413' class='xr-var-data-in' type='checkbox'><label for='data-412861df-0035-43ed-8d5b-5f4845f9e413' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2015, 2, 1, 0, 0, 0, 0, has_year_zero=True),
        cftime.DatetimeNoLeap(2015, 3, 1, 0, 0, 0, 0, has_year_zero=True),
        cftime.DatetimeNoLeap(2015, 4, 1, 0, 0, 0, 0, has_year_zero=True), ...,
        cftime.DatetimeNoLeap(2100, 11, 1, 0, 0, 0, 0, has_year_zero=True),
        cftime.DatetimeNoLeap(2100, 12, 1, 0, 0, 0, 0, has_year_zero=True),
        cftime.DatetimeNoLeap(2101, 1, 1, 0, 0, 0, 0, has_year_zero=True)],
-      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-66a9e233-28db-4b60-9258-0cb1d0cbd007' class='xr-section-summary-in' type='checkbox'  ><label for='section-66a9e233-28db-4b60-9258-0cb1d0cbd007' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c636947c-079e-45bf-b9cd-65520bae2174' class='xr-index-data-in' type='checkbox'/><label for='index-c636947c-079e-45bf-b9cd-65520bae2174' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([             -90.0, -89.05759162303664,  -88.1151832460733,
+      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-99c2635e-1544-4b9a-b137-3bc743e0d367' class='xr-section-summary-in' type='checkbox'  ><label for='section-99c2635e-1544-4b9a-b137-3bc743e0d367' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4bb66af5-37d5-4d70-ba18-96299057db8a' class='xr-index-data-in' type='checkbox'/><label for='index-4bb66af5-37d5-4d70-ba18-96299057db8a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([             -90.0, -89.05759162303664,  -88.1151832460733,
        -87.17277486910994,  -86.2303664921466, -85.28795811518324,
         -84.3455497382199, -83.40314136125654, -82.46073298429319,
        -81.51832460732984,
@@ -4582,12 +4331,12 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
          84.3455497382199,  85.28795811518324,   86.2303664921466,
         87.17277486910994,   88.1151832460733,  89.05759162303664,
                      90.0],
-      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-52a650ae-c0fd-4288-af4b-16e00c16e634' class='xr-index-data-in' type='checkbox'/><label for='index-52a650ae-c0fd-4288-af4b-16e00c16e634' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   1.25,    2.5,   3.75,    5.0,   6.25,    7.5,   8.75,   10.0,
+      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-794b2dce-9d7b-499b-9384-c157c9e5a0a9' class='xr-index-data-in' type='checkbox'/><label for='index-794b2dce-9d7b-499b-9384-c157c9e5a0a9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   1.25,    2.5,   3.75,    5.0,   6.25,    7.5,   8.75,   10.0,
         11.25,
        ...
         347.5, 348.75,  350.0, 351.25,  352.5, 353.75,  355.0, 356.25,  357.5,
        358.75],
-      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=288))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-38f67d5e-2c11-4e6c-8671-9d250cb53995' class='xr-index-data-in' type='checkbox'/><label for='index-38f67d5e-2c11-4e6c-8671-9d250cb53995' 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([2015-02-01 00:00:00, 2015-03-01 00:00:00, 2015-04-01 00:00:00,
+      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=288))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-41524004-67cd-4337-b4a3-7809a2ea3aa6' class='xr-index-data-in' type='checkbox'/><label for='index-41524004-67cd-4337-b4a3-7809a2ea3aa6' 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([2015-02-01 00:00:00, 2015-03-01 00:00:00, 2015-04-01 00:00:00,
              2015-05-01 00:00:00, 2015-06-01 00:00:00, 2015-07-01 00:00:00,
              2015-08-01 00:00:00, 2015-09-01 00:00:00, 2015-10-01 00:00:00,
              2015-11-01 00:00:00,
@@ -4596,2950 +4345,2160 @@ <h3>Constructing Xarray Datasets from files<a class="headerlink" href="#construc
              2100-07-01 00:00:00, 2100-08-01 00:00:00, 2100-09-01 00:00:00,
              2100-10-01 00:00:00, 2100-11-01 00:00:00, 2100-12-01 00:00:00,
              2101-01-01 00:00:00],
-            dtype=&#x27;object&#x27;, length=1032, calendar=&#x27;noleap&#x27;, freq=&#x27;MS&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d95d30ad-c4de-4b1e-9196-7d6835623353' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d95d30ad-c4de-4b1e-9196-7d6835623353' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Reference height temperature</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div></li></ul></div></div></div></div>
+            dtype=&#x27;object&#x27;, length=1032, calendar=&#x27;noleap&#x27;, freq=&#x27;MS&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1c504d09-6e8d-4db1-be47-a793468ee375' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1c504d09-6e8d-4db1-be47-a793468ee375' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Reference height temperature</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div></li></ul></div></div></div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">tref</span><span class="o">.</span><span class="n">chunks</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>((120, 120, 120, 120, 120, 120, 120, 120, 72), (192,), (288,))
+</pre></div>
+</div>
+</div>
 </div>
 <ul class="simple">
 <li><p>How many chunks do we have?</p></li>
 <li><p>What is the size of each chunk size?</p></li>
 </ul>
-<p>Here we can see that we have a total of 15 chunks equal to the number of our netCDF files. In general <code class="docutils literal notranslate"><span class="pre">open_mfdataset</span></code> will chunk each netCDF file into a single Dask array. By providing the <code class="docutils literal notranslate"><span class="pre">chunks</span></code> argument, we can control the size of the resulting Dask arrays.</p>
+<p>Here we can see that we have a total of 9 chunks - equal to the number of our netCDF files. In general <code class="docutils literal notranslate"><span class="pre">open_mfdataset</span></code> will return one chunk per netCDF file.</p>
 <div class="alert alert-block alert-warning">
-<p><b>WARNING:</b> When <code class="docutils literal notranslate"><span class="pre">chunks</span></code> argument is not given to <code class="docutils literal notranslate"><span class="pre">open_mfdataset</span></code>, it will return dask arrays with chunk sizes equal to each netCDF file.<br />
-Re-chunking the dataset after creation with ds.chunk() will lead to an ineffective use of memory and is not recommended.</p>
-</div><p>In the example below, we call <code class="docutils literal notranslate"><span class="pre">open_mfdataset</span></code> to open multiple netCDF files and using the <code class="docutils literal notranslate"><span class="pre">chunks</span></code> argument to control the size of the resulting Dask arrays.</p>
+<p><strong>WARNING:</strong> The chunk structure within the file is important. When re-chunking the dataset after creation with <code class="docutils literal notranslate"><span class="pre">ds.chunk()</span></code> it is recommended to only use multiples of the on-file chunk shape.</p>
+</div>
+<p>We can check what that shape is by looking at the encoding:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">#del ds</span>
-<span class="n">chunk_dict</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;lat&quot;</span><span class="p">:</span> <span class="mi">96</span><span class="p">,</span> <span class="s2">&quot;lon&quot;</span><span class="p">:</span> <span class="mi">144</span><span class="p">}</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">tref</span><span class="o">.</span><span class="n">encoding</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;chunksizes&#39;: (1, 192, 288),
+ &#39;fletcher32&#39;: False,
+ &#39;shuffle&#39;: True,
+ &#39;preferred_chunks&#39;: {&#39;time&#39;: 1, &#39;lat&#39;: 192, &#39;lon&#39;: 288},
+ &#39;zlib&#39;: True,
+ &#39;complevel&#39;: 1,
+ &#39;source&#39;: &#39;/home/runner/work/dask-cookbook/dask-cookbook/data/data_for_cesm/b.e21.BSSP370smbb.f09_g17.LE2-1301.013.cam.h0.TREFHT.201501-202412.nc&#39;,
+ &#39;original_shape&#39;: (120, 192, 288),
+ &#39;dtype&#39;: dtype(&#39;float32&#39;)}
 </pre></div>
 </div>
 </div>
 </div>
+<div class="alert alert-block alert-info" markdown="1">
+<p><strong>TIP:</strong> The <code class="docutils literal notranslate"><span class="pre">chunks</span></code> parameter can significantly affect total performance when using Dask Arrays. <code class="docutils literal notranslate"><span class="pre">chunks</span></code> should be small enough that each chunk fit in the memory, but large enough to avoid that the communication overhead.</p>
+</div><p>A good rule of thumb is to create arrays with a minimum chunksize of at least one million elements. Here we have 120x192x288 elements in each chunk (except for the last chunk).<br />
+With large arrays (10+ GB), the cost of queuing up Dask operations can be noticeable, and you may need even larger chunksizes.</p>
+<p><strong>Additional Reading</strong></p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://docs.dask.org/en/stable/array-best-practices.html#select-a-good-chunk-size">dask.array best practices</a></p></li>
+<li><p><a class="reference external" href="https://ncar.github.io/dask-tutorial/notebooks/06-dask-chunking.html">NCAR chunking tutorial</a></p></li>
+<li><p><a class="reference external" href="https://blog.dask.org/2021/11/02/choosing-dask-chunk-sizes">Dask blog post on chunking</a></p></li>
+</ul>
+</div>
+<div class="section" id="xarray-data-structures-are-dask-collections">
+<h3>Xarray data structures are Dask collections.<a class="headerlink" href="#xarray-data-structures-are-dask-collections" title="Permalink to this headline"><i class="fas fa-link"></i></a></h3>
+<p>This means you can call the following Dask-related functions on Xarray Data Arrays and Datasets:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">.visualize()</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">.compute()</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">.persist()</span></code></p></li>
+</ul>
+<p>For more information about Dask Arrays, please see <a class="reference external" href="https://projectpythia.org/dask-cookbook/notebooks/01-dask-array.html">Dask Array chapter</a>.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%%time</span>
-<span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_mfdataset</span><span class="p">(</span>
-    <span class="nb">sorted</span><span class="p">(</span><span class="n">files</span><span class="p">),</span>
-    <span class="n">concat_dim</span><span class="o">=</span><span class="s1">&#39;time&#39;</span><span class="p">,</span>
-    <span class="n">combine</span><span class="o">=</span><span class="s2">&quot;nested&quot;</span><span class="p">,</span>
-    <span class="n">parallel</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <span class="n">engine</span><span class="o">=</span><span class="s2">&quot;netcdf4&quot;</span><span class="p">,</span>
-    <span class="n">chunks</span><span class="o">=</span><span class="n">chunk_dict</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">tref</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">TREFHT</span>
-<span class="n">tref</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">tref_mean</span> <span class="o">=</span> <span class="n">tref</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="s1">&#39;time&#39;</span><span class="p">)</span>
+<span class="n">tref_mean</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">dask</span>
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 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/usr/share/miniconda3/envs/dask-cookbook/lib/python3.9/site-packages/xarray/core/dataset.py:270: UserWarning: The specified chunks separate the stored chunks along dimension &quot;lat&quot; starting at index 96. This could degrade performance. Instead, consider rechunking after loading.
-  warnings.warn(
-/usr/share/miniconda3/envs/dask-cookbook/lib/python3.9/site-packages/xarray/core/dataset.py:270: UserWarning: The specified chunks separate the stored chunks along dimension &quot;lat&quot; starting at index 96. This could degrade performance. Instead, consider rechunking after loading.
-  warnings.warn(
-/usr/share/miniconda3/envs/dask-cookbook/lib/python3.9/site-packages/xarray/core/dataset.py:270: UserWarning: The specified chunks separate the stored chunks along dimension &quot;lon&quot; starting at index 144. This could degrade performance. Instead, consider rechunking after loading.
-  warnings.warn(
-/usr/share/miniconda3/envs/dask-cookbook/lib/python3.9/site-packages/xarray/core/dataset.py:270: UserWarning: The specified chunks separate the stored chunks along dimension &quot;lon&quot; starting at index 144. This could degrade performance. Instead, consider rechunking after loading.
-  warnings.warn(
-/usr/share/miniconda3/envs/dask-cookbook/lib/python3.9/site-packages/xarray/core/dataset.py:270: UserWarning: The specified chunks separate the stored chunks along dimension &quot;lat&quot; starting at index 96. This could degrade performance. Instead, consider rechunking after loading.
-  warnings.warn(
-/usr/share/miniconda3/envs/dask-cookbook/lib/python3.9/site-packages/xarray/core/dataset.py:270: UserWarning: The specified chunks separate the stored chunks along dimension &quot;lat&quot; starting at index 96. This could degrade performance. Instead, consider rechunking after loading.
-  warnings.warn(
-/usr/share/miniconda3/envs/dask-cookbook/lib/python3.9/site-packages/xarray/core/dataset.py:270: UserWarning: The specified chunks separate the stored chunks along dimension &quot;lon&quot; starting at index 144. This could degrade performance. Instead, consider rechunking after loading.
-  warnings.warn(
-/usr/share/miniconda3/envs/dask-cookbook/lib/python3.9/site-packages/xarray/core/dataset.py:270: UserWarning: The specified chunks separate the stored chunks along dimension &quot;lon&quot; starting at index 144. This could degrade performance. Instead, consider rechunking after loading.
-  warnings.warn(
-/usr/share/miniconda3/envs/dask-cookbook/lib/python3.9/site-packages/xarray/core/dataset.py:270: UserWarning: The specified chunks separate the stored chunks along dimension &quot;lat&quot; starting at index 96. This could degrade performance. Instead, consider rechunking after loading.
-  warnings.warn(
-/usr/share/miniconda3/envs/dask-cookbook/lib/python3.9/site-packages/xarray/core/dataset.py:270: UserWarning: The specified chunks separate the stored chunks along dimension &quot;lat&quot; starting at index 96. This could degrade performance. Instead, consider rechunking after loading.
-  warnings.warn(
-/usr/share/miniconda3/envs/dask-cookbook/lib/python3.9/site-packages/xarray/core/dataset.py:270: UserWarning: The specified chunks separate the stored chunks along dimension &quot;lon&quot; starting at index 144. This could degrade performance. Instead, consider rechunking after loading.
-  warnings.warn(
-/usr/share/miniconda3/envs/dask-cookbook/lib/python3.9/site-packages/xarray/core/dataset.py:270: UserWarning: The specified chunks separate the stored chunks along dimension &quot;lon&quot; starting at index 144. This could degrade performance. Instead, consider rechunking after loading.
-  warnings.warn(
-/usr/share/miniconda3/envs/dask-cookbook/lib/python3.9/site-packages/xarray/core/dataset.py:270: UserWarning: The specified chunks separate the stored chunks along dimension &quot;lat&quot; starting at index 96. This could degrade performance. Instead, consider rechunking after loading.
-  warnings.warn(
-/usr/share/miniconda3/envs/dask-cookbook/lib/python3.9/site-packages/xarray/core/dataset.py:270: UserWarning: The specified chunks separate the stored chunks along dimension &quot;lat&quot; starting at index 96. This could degrade performance. Instead, consider rechunking after loading.
-  warnings.warn(
-/usr/share/miniconda3/envs/dask-cookbook/lib/python3.9/site-packages/xarray/core/dataset.py:270: UserWarning: The specified chunks separate the stored chunks along dimension &quot;lon&quot; starting at index 144. This could degrade performance. Instead, consider rechunking after loading.
-  warnings.warn(
-/usr/share/miniconda3/envs/dask-cookbook/lib/python3.9/site-packages/xarray/core/dataset.py:270: UserWarning: The specified chunks separate the stored chunks along dimension &quot;lon&quot; starting at index 144. This could degrade performance. Instead, consider rechunking after loading.
-  warnings.warn(
-/usr/share/miniconda3/envs/dask-cookbook/lib/python3.9/site-packages/xarray/core/dataset.py:270: UserWarning: The specified chunks separate the stored chunks along dimension &quot;lat&quot; starting at index 96. This could degrade performance. Instead, consider rechunking after loading.
-  warnings.warn(
-/usr/share/miniconda3/envs/dask-cookbook/lib/python3.9/site-packages/xarray/core/dataset.py:270: UserWarning: The specified chunks separate the stored chunks along dimension &quot;lon&quot; starting at index 144. This could degrade performance. Instead, consider rechunking after loading.
-  warnings.warn(
-</pre></div>
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-</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;TREFHT&#x27; (time: 1032, lat: 192, lon: 288)&gt;
-dask.array&lt;chunksize=(1, 96, 144), meta=np.ndarray&gt;
-Coordinates:
-  * lat      (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 87.17 88.12 89.06 90.0
-  * lon      (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
-  * time     (time) object 2015-02-01 00:00:00 ... 2101-01-01 00:00:00
-Attributes:
-    units:         K
-    long_name:     Reference height temperature
-    cell_methods:  time: mean</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'>'TREFHT'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1032</li><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b20f1146-6bd7-4f0c-9cae-247f4eacf705' class='xr-array-in' type='checkbox' ><label for='section-b20f1146-6bd7-4f0c-9cae-247f4eacf705' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 96, 144), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>
-    <tr>
-        <td>
-            <table style="border-collapse: collapse;">
-                <thead>
+        <table>
+        <tr>
+            <td>
+                <table>
+                
                     <tr>
-                        <td> </td>
-                        <th> Array </th>
-                        <th> Chunk </th>
+                        <th style="text-align: left; width: 150px;">layer_type</th>
+                        <td style="text-align: left;">Blockwise</td>
                     </tr>
-                </thead>
-                <tbody>
-                    
+                
                     <tr>
-                        <th> Bytes </th>
-                        <td> 217.69 MiB </td>
-                        <td> 54.00 kiB </td>
+                        <th style="text-align: left; width: 150px;">is_materialized</th>
+                        <td style="text-align: left;">False</td>
                     </tr>
-                    
+                
                     <tr>
-                        <th> Shape </th>
-                        <td> (1032, 192, 288) </td>
-                        <td> (1, 96, 144) </td>
+                        <th style="text-align: left; width: 150px;">number of outputs</th>
+                        <td style="text-align: left;">1</td>
                     </tr>
+                
                     <tr>
-                        <th> Dask graph </th>
-                        <td colspan="2"> 4128 chunks in 19 graph layers </td>
+                        <th style="text-align: left; width: 150px;">shape</th>
+                        <td style="text-align: left;">(120, 192, 288)</td>
                     </tr>
+                
                     <tr>
-                        <th> Data type </th>
-                        <td colspan="2"> float32 numpy.ndarray </td>
+                        <th style="text-align: left; width: 150px;">dtype</th>
+                        <td style="text-align: left;">float32</td>
                     </tr>
-                </tbody>
-            </table>
-        </td>
-        <td>
-        <svg width="173" height="162" style="stroke:rgb(0,0,0);stroke-width:1" >
+                
+                    <tr>
+                        <th style="text-align: left; width: 150px;">chunksize</th>
+                        <td style="text-align: left;">(120, 192, 288)</td>
+                    </tr>
+                
+                    <tr>
+                        <th style="text-align: left; width: 150px;">type</th>
+                        <td style="text-align: left;">dask.array.core.Array</td>
+                    </tr>
+                
+                    <tr>
+                        <th style="text-align: left; width: 150px;">chunk_type</th>
+                        <td style="text-align: left;">numpy.ndarray</td>
+                    </tr>
+                
+                
+                    
+                        <tr>
+                            <th style="text-align: left; width: 150px;"> depends on </th>
+                            <td style="text-align: left;">original-open_dataset-TREFHT-1bec5697677600ec285823b8894b0aef</td>
+                        </tr>
+                    
+                
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-</table></div></div></li><li class='xr-section-item'><input id='section-e0ceaf9a-1af8-4db2-bc5a-a46eb643319c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e0ceaf9a-1af8-4db2-bc5a-a46eb643319c' 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 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-f20dafc4-402c-4346-9c08-403f4dfcdb80' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f20dafc4-402c-4346-9c08-403f4dfcdb80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33fd9324-4f5e-438b-bf9c-e140697a1f90' class='xr-var-data-in' type='checkbox'><label for='data-33fd9324-4f5e-438b-bf9c-e140697a1f90' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,
-       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,
-       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,
-       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,
-       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,
-       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,
-       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,
-       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,
-       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,
-       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,
-       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,
-       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,
-       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,
-       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,
-       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,
-        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,
-         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,
-         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,
-        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,
-        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,
-        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,
-        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,
-        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,
-        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,
-        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,
-        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,
-        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,
-        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,
-        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,
-        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,
-        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,
-        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-1ad24b4e-b967-4e60-b500-916fa8710c43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ad24b4e-b967-4e60-b500-916fa8710c43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-adc8ec5b-22a5-404c-bf1b-7c9d553c592f' class='xr-var-data-in' type='checkbox'><label for='data-adc8ec5b-22a5-404c-bf1b-7c9d553c592f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span 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'>2015-02-01 00:00:00 ... 2101-01-...</div><input id='attrs-f30df842-355d-4ac5-ae59-b3c22f92f51f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f30df842-355d-4ac5-ae59-b3c22f92f51f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8f34a70-fdc6-4912-8a16-aa7fe5c5dfc5' class='xr-var-data-in' type='checkbox'><label for='data-c8f34a70-fdc6-4912-8a16-aa7fe5c5dfc5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2015, 2, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2015, 3, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2015, 4, 1, 0, 0, 0, 0, has_year_zero=True), ...,
-       cftime.DatetimeNoLeap(2100, 11, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2100, 12, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2101, 1, 1, 0, 0, 0, 0, has_year_zero=True)],
-      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f0bbffc8-5ba6-4f4d-97a8-5bc4149245b7' class='xr-section-summary-in' type='checkbox'  ><label for='section-f0bbffc8-5ba6-4f4d-97a8-5bc4149245b7' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cdf19a57-13be-4a29-ac79-23164fc80781' class='xr-index-data-in' type='checkbox'/><label for='index-cdf19a57-13be-4a29-ac79-23164fc80781' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([             -90.0, -89.05759162303664,  -88.1151832460733,
-       -87.17277486910994,  -86.2303664921466, -85.28795811518324,
-        -84.3455497382199, -83.40314136125654, -82.46073298429319,
-       -81.51832460732984,
-       ...
-        81.51832460732984,  82.46073298429319,  83.40314136125654,
-         84.3455497382199,  85.28795811518324,   86.2303664921466,
-        87.17277486910994,   88.1151832460733,  89.05759162303664,
-                     90.0],
-      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6747ac7d-985b-4023-9255-0ae654744d03' class='xr-index-data-in' type='checkbox'/><label for='index-6747ac7d-985b-4023-9255-0ae654744d03' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   1.25,    2.5,   3.75,    5.0,   6.25,    7.5,   8.75,   10.0,
-        11.25,
-       ...
-        347.5, 348.75,  350.0, 351.25,  352.5, 353.75,  355.0, 356.25,  357.5,
-       358.75],
-      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=288))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cea91e9e-6e6b-4781-9d3a-c995d8bdfe07' class='xr-index-data-in' type='checkbox'/><label for='index-cea91e9e-6e6b-4781-9d3a-c995d8bdfe07' 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([2015-02-01 00:00:00, 2015-03-01 00:00:00, 2015-04-01 00:00:00,
-             2015-05-01 00:00:00, 2015-06-01 00:00:00, 2015-07-01 00:00:00,
-             2015-08-01 00:00:00, 2015-09-01 00:00:00, 2015-10-01 00:00:00,
-             2015-11-01 00:00:00,
-             ...
-             2100-04-01 00:00:00, 2100-05-01 00:00:00, 2100-06-01 00:00:00,
-             2100-07-01 00:00:00, 2100-08-01 00:00:00, 2100-09-01 00:00:00,
-             2100-10-01 00:00:00, 2100-11-01 00:00:00, 2100-12-01 00:00:00,
-             2101-01-01 00:00:00],
-            dtype=&#x27;object&#x27;, length=1032, calendar=&#x27;noleap&#x27;, freq=&#x27;MS&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1b4bad17-b838-43f6-8489-6e5f8e9bca0a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1b4bad17-b838-43f6-8489-6e5f8e9bca0a' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Reference height temperature</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div></li></ul></div></div></div></div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">tref</span><span class="o">.</span><span class="n">chunks</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
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- (144, 144))
-</pre></div>
-</div>
-</div>
-</div>
-<div class="alert alert-block alert-info" markdown="1">
-<p><b>TIP:</b> The <code class="docutils literal notranslate"><span class="pre">chunks</span></code> parameter can significantly affect total performance when using Dask Arrays. <code class="docutils literal notranslate"><span class="pre">chunks</span></code> be small enough that each chunk fit in the memory, but large enough to avoid that the communication overhead.</p>
-</div><p>A good rule of thumb is to create arrays with a minimum chunksize of at least one million elements. Here we have 120<em>96</em>144 elements in each chunk (except for the last chunk).<br />
-With large arrays (10+ GB), the cost of queueing up Dask operations can be noticeable, and you may need even larger chunksizes. See the <a class="reference external" href="https://docs.dask.org/en/stable/array-best-practices.html#select-a-good-chunk-size">dask.array best practices</a></p>
-<p>For complex scenarios, you can access each file individually by utilizing the <code class="docutils literal notranslate"><span class="pre">open_dataset</span></code> function with the specified <code class="docutils literal notranslate"><span class="pre">chunk</span></code>s and then combine the outputs into a single dataset later.</p>
-</div>
-<div class="section" id="xarray-data-structures-are-dask-collections">
-<h3>Xarray data structures are Dask collections.<a class="headerlink" href="#xarray-data-structures-are-dask-collections" title="Permalink to this headline"><i class="fas fa-link"></i></a></h3>
-<p>This means you can call the following Dask-related functions on Xarray Data Arrays and Datasets:</p>
-<ul class="simple">
-<li><p><code class="docutils literal notranslate"><span class="pre">dask.visualize()</span></code></p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">dask.compute()</span></code></p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">dask.persist()</span></code></p></li>
-</ul>
-<p>For more information abour Dask Arrays, please see <a class="reference external" href="https://ncar.github.io/dask-tutorial/notebooks/01-dask-array.html">the first tutorial</a>.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">dask</span>
-<span class="n">tref_mean</span> <span class="o">=</span> <span class="n">tref</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">dask</span><span class="o">.</span><span class="n">visualize</span><span class="p">(</span><span class="n">tref_mean</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>dot: graph is too large for cairo-renderer bitmaps. Scaling by 0.0236245 to fit
-</pre></div>
-</div>
-<img alt="../_images/5e499b516de5d88f3435a47f0270c0bda00f80510897be80343246410564c957.png" src="../_images/5e499b516de5d88f3435a47f0270c0bda00f80510897be80343246410564c957.png" />
-</div>
-</div>
-<p>If we check Dask Task Graph for <code class="docutils literal notranslate"><span class="pre">tref_mean</span></code>, we can see all the steps required for calculating it (from openning the netcdf file to calculating mean and aggreagting it).</p>
-<p>Getting concrete values
-At some point, you will want to actually do the calculations and recieve concreate values from Dask.</p>
-<p>There are two ways to compute values on dask arrays.</p>
-<p>1.<code class="docutils literal notranslate"><span class="pre">compute()</span></code> returns an xarray object just like a dask array.</p>
-<p>2.<code class="docutils literal notranslate"><span class="pre">load()</span></code> replaces the dask array in the xarray object with a numpy array. equivalent to <code class="docutils literal notranslate"><span class="pre">ds</span> <span class="pre">=</span> <span class="pre">ds.compute()</span></code>.</p>
-<p>So the difference is that <code class="docutils literal notranslate"><span class="pre">.load()</span></code> operates inplace and <code class="docutils literal notranslate"><span class="pre">.compute()</span></code> returns a new xarray object.</p>
-<p>Tip: There is another option available  third option : “persisting”. <code class="docutils literal notranslate"><span class="pre">.persist()</span></code> loads the values into distributed RAM.
-The values are computed but remain distributed across workers. So <code class="docutils literal notranslate"><span class="pre">ds.air.persist()</span></code> still returns a dask array. This is useful if you will be repeatedly using a dataset for computation but it is too large to load into local memory.</p>
-<p>To summarize, <code class="docutils literal notranslate"><span class="pre">persist</span></code> turns a lazy Dask collection into a Dask collection with the same metadata, but now with the results fully computed or actively computing in the background.</p>
-<p>See the <a class="reference external" href="https://docs.dask.org/en/stable/generated/dask.dataframe.Series.persist.html">Dask user guide</a> if you <code class="docutils literal notranslate"><span class="pre">.persist()</span></code>.</p>
-</div>
-</div>
-<div class="section" id="how-to-access-underlying-data-in-an-xarray-object">
-<h2>How to access underlying data in an Xarray object?<a class="headerlink" href="#how-to-access-underlying-data-in-an-xarray-object" title="Permalink to this headline"><i class="fas fa-link"></i></a></h2>
-<p>There are two basic ways to extract values from an Xarray object:</p>
-<ol class="arabic simple">
-<li><p>Using <code class="docutils literal notranslate"><span class="pre">.data</span></code> will return a Dask array. For example:</p></li>
-</ol>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">tref</span><span class="o">.</span><span class="n">data</span>
-</pre></div>
-</div>
+            </td>
+        </tr>
+        </table>
+
+    </details>
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-<div class="cell_output docutils container">
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-        <td>
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+
+    <details style="margin-left: 32px;">
+        <summary style="margin-bottom: 10px; margin-top: 10px;">
+            <h4 style="display: inline;">Layer9: original</h4>
+        </summary>
+        <p style="color: var(--jp-ui-font-color2, #5D5851); margin: -0.25em 0px 0px 0px;">
+            original-open_dataset-TREFHT-b376d457af800028faa60fbb881274e7
+        </p>
+
+        <table>
+        <tr>
+            <td>
+                <table>
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-                        <td> </td>
-                        <th> Array </th>
-                        <th> Chunk </th>
+                        <th style="text-align: left; width: 150px;">layer_type</th>
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-                </thead>
-                <tbody>
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+                
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-                        <th> Bytes </th>
-                        <td> 217.69 MiB </td>
-                        <td> 54.00 kiB </td>
+                        <th style="text-align: left; width: 150px;">is_materialized</th>
+                        <td style="text-align: left;">True</td>
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-                    
+                
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-                        <th> Shape </th>
-                        <td> (1032, 192, 288) </td>
-                        <td> (1, 96, 144) </td>
+                        <th style="text-align: left; width: 150px;">number of outputs</th>
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+                
+                
+                </table>
+            </td>
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+                
+            </td>
+        </tr>
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+    </details>
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+            
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+        
+    </svg>
+
+    <details style="margin-left: 32px;">
+        <summary style="margin-bottom: 10px; margin-top: 10px;">
+            <h4 style="display: inline;">Layer10: open_dataset-TREFHT</h4>
+        </summary>
+        <p style="color: var(--jp-ui-font-color2, #5D5851); margin: -0.25em 0px 0px 0px;">
+            open_dataset-TREFHT-b376d457af800028faa60fbb881274e7
+        </p>
+
+        <table>
+        <tr>
+            <td>
+                <table>
+                
                     <tr>
-                        <th> Dask graph </th>
-                        <td colspan="2"> 4128 chunks in 19 graph layers </td>
+                        <th style="text-align: left; width: 150px;">layer_type</th>
+                        <td style="text-align: left;">Blockwise</td>
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+                
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-                        <th> Data type </th>
-                        <td colspan="2"> float32 numpy.ndarray </td>
+                        <th style="text-align: left; width: 150px;">is_materialized</th>
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-        <svg width="173" height="162" style="stroke:rgb(0,0,0);stroke-width:1" >
+                
+                    <tr>
+                        <th style="text-align: left; width: 150px;">number of outputs</th>
+                        <td style="text-align: left;">1</td>
+                    </tr>
+                
+                    <tr>
+                        <th style="text-align: left; width: 150px;">shape</th>
+                        <td style="text-align: left;">(120, 192, 288)</td>
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+                        <td style="text-align: left;">float32</td>
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+                        <th style="text-align: left; width: 150px;">chunksize</th>
+                        <td style="text-align: left;">(120, 192, 288)</td>
+                    </tr>
+                
+                    <tr>
+                        <th style="text-align: left; width: 150px;">type</th>
+                        <td style="text-align: left;">dask.array.core.Array</td>
+                    </tr>
+                
+                    <tr>
+                        <th style="text-align: left; width: 150px;">chunk_type</th>
+                        <td style="text-align: left;">numpy.ndarray</td>
+                    </tr>
+                
+                
+                    
+                        <tr>
+                            <th style="text-align: left; width: 150px;"> depends on </th>
+                            <td style="text-align: left;">original-open_dataset-TREFHT-b376d457af800028faa60fbb881274e7</td>
+                        </tr>
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-</pre></div>
+            </td>
+        </tr>
+        </table>
+
+    </details>
 </div>
+            
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+
+    <details style="margin-left: 32px;">
+        <summary style="margin-bottom: 10px; margin-top: 10px;">
+            <h4 style="display: inline;">Layer11: original</h4>
+        </summary>
+        <p style="color: var(--jp-ui-font-color2, #5D5851); margin: -0.25em 0px 0px 0px;">
+            original-open_dataset-TREFHT-490e30721b157769a0556664ad692090
+        </p>
+
+        <table>
+        <tr>
+            <td>
+                <table>
+                
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+                        <td style="text-align: left;">MaterializedLayer</td>
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+                
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+                        <td style="text-align: left;">True</td>
+                    </tr>
+                
+                    <tr>
+                        <th style="text-align: left; width: 150px;">number of outputs</th>
+                        <td style="text-align: left;">1</td>
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+                
+                
+                </table>
+            </td>
+            <td>
+                
+            </td>
+        </tr>
+        </table>
+
+    </details>
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-         247.26259]],
+    <details style="margin-left: 32px;">
+        <summary style="margin-bottom: 10px; margin-top: 10px;">
+            <h4 style="display: inline;">Layer12: open_dataset-TREFHT</h4>
+        </summary>
+        <p style="color: var(--jp-ui-font-color2, #5D5851); margin: -0.25em 0px 0px 0px;">
+            open_dataset-TREFHT-490e30721b157769a0556664ad692090
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+        <table>
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+            <td>
+                <table>
+                
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+                        <th style="text-align: left; width: 150px;">layer_type</th>
+                        <td style="text-align: left;">Blockwise</td>
+                    </tr>
+                
+                    <tr>
+                        <th style="text-align: left; width: 150px;">is_materialized</th>
+                        <td style="text-align: left;">False</td>
+                    </tr>
+                
+                    <tr>
+                        <th style="text-align: left; width: 150px;">number of outputs</th>
+                        <td style="text-align: left;">1</td>
+                    </tr>
+                
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-<div class="section" id="computation">
-<h2>Computation<a class="headerlink" href="#computation" title="Permalink to this headline"><i class="fas fa-link"></i></a></h2>
-<p>All built-in Xarray methods (<code class="docutils literal notranslate"><span class="pre">.mean</span></code>, <code class="docutils literal notranslate"><span class="pre">.max</span></code>, <code class="docutils literal notranslate"><span class="pre">.rolling</span></code>, <code class="docutils literal notranslate"><span class="pre">.groupby</span></code> etc.) support dask arrays.</p>
-<p>Now, let’s do some computations on this Xarray dataset.</p>
-<div class="section" id="single-point-calculations">
-<h3>Single Point Calculations<a class="headerlink" href="#single-point-calculations" title="Permalink to this headline"><i class="fas fa-link"></i></a></h3>
-<p>To start out, let’s do the calculations on a single point first. First, we extract the time series data at a grid point and save it to a variable. Here we select the closest point using <code class="docutils literal notranslate"><span class="pre">.sel</span></code> and load the data.</p>
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+    <svg width="24" height="24" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg" style="position: absolute;">
+        
+        <circle cx="16" cy="16" r="14" fill="#8F8F8F" style="stroke: var(--jp-ui-font-color2, #1D1D1D);" stroke-width="2"/>
+        
+    </svg>
 
-.xr-attrs dd {
-  grid-column: 2;
-  white-space: pre-wrap;
-  word-break: break-all;
-}
+    <details style="margin-left: 32px;">
+        <summary style="margin-bottom: 10px; margin-top: 10px;">
+            <h4 style="display: inline;">Layer19: concatenate</h4>
+        </summary>
+        <p style="color: var(--jp-ui-font-color2, #5D5851); margin: -0.25em 0px 0px 0px;">
+            concatenate-a6c5fea69b791b9738ccc6ae9cc7b754
+        </p>
 
-.xr-icon-database,
-.xr-icon-file-text2,
-.xr-no-icon {
-  display: inline-block;
-  vertical-align: middle;
-  width: 1em;
-  height: 1.5em !important;
-  stroke-width: 0;
-  stroke: currentColor;
-  fill: currentColor;
-}
-</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;TREFHT&#x27; (time: 87)&gt;
-291.8 290.3 290.3 290.1 290.7 291.2 ... 294.3 293.5 293.5 293.4 293.9 289.8
-Coordinates:
-    lat      float64 40.05
-    lon      float64 0.0
-  * time     (time) object 2015-01-01 00:00:00 ... 2101-01-01 00:00:00
-Attributes:
-    units:         K
-    long_name:     Reference height temperature
-    cell_methods:  time: mean</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'>'TREFHT'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 87</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-f35ac8ea-f4e0-40b1-b331-7b42bbb8c742' class='xr-array-in' type='checkbox' ><label for='section-f35ac8ea-f4e0-40b1-b331-7b42bbb8c742' 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>291.8 290.3 290.3 290.1 290.7 291.2 ... 293.5 293.5 293.4 293.9 289.8</span></div><div class='xr-array-data'><pre>array([291.76175, 290.29858, 290.3258 , 290.0671 , 290.73373, 291.21622,
-       291.2185 , 290.6878 , 290.36618, 291.24475, 291.10925, 290.66568,
-       291.23413, 290.79117, 291.17935, 291.0817 , 291.26492, 290.43915,
-       291.0101 , 290.19608, 290.921  , 291.27866, 291.41956, 291.19473,
-       290.73257, 291.3107 , 291.65024, 291.84177, 291.73816, 291.89676,
-       291.94705, 292.9821 , 291.53247, 291.66183, 290.74997, 291.86865,
-       290.99942, 291.76474, 291.97342, 291.66153, 291.37723, 291.7956 ,
-       292.2146 , 292.07352, 291.9124 , 291.9819 , 292.81973, 292.5683 ,
-       292.7518 , 293.21613, 292.87967, 292.64758, 291.84106, 293.1604 ,
-       292.63492, 292.23972, 291.64505, 291.90146, 292.02017, 292.77676,
-       292.519  , 293.4558 , 292.83826, 292.3904 , 293.33334, 293.31696,
-       292.63467, 292.94614, 292.9144 , 293.40817, 292.76138, 292.56082,
-       292.9273 , 292.94125, 293.18338, 293.6574 , 293.61575, 293.9967 ,
-       294.24548, 293.02676, 293.1884 , 294.33173, 293.5257 , 293.45236,
-       293.43143, 293.89886, 289.79712], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-80b6113e-8a1c-43f9-b66e-f1964869904b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-80b6113e-8a1c-43f9-b66e-f1964869904b' 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>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>40.05</div><input id='attrs-a7a3b5f2-92d6-4f32-9d83-798fc606389d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7a3b5f2-92d6-4f32-9d83-798fc606389d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60c3d84d-f583-4692-82a8-13495f98e7f1' class='xr-var-data-in' type='checkbox'><label for='data-60c3d84d-f583-4692-82a8-13495f98e7f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array(40.05235602)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-d6ba947c-611e-42ad-a8df-64da2bc7e359' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6ba947c-611e-42ad-a8df-64da2bc7e359' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f00e6e2-963f-4522-af1b-144ae0516781' class='xr-var-data-in' type='checkbox'><label for='data-3f00e6e2-963f-4522-af1b-144ae0516781' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array(0.)</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'>2015-01-01 00:00:00 ... 2101-01-...</div><input id='attrs-66039b9f-ab1a-4eda-abc0-916a183739f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66039b9f-ab1a-4eda-abc0-916a183739f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6cdf383-8d0f-4f10-939e-ff6074fdddbe' class='xr-var-data-in' type='checkbox'><label for='data-a6cdf383-8d0f-4f10-939e-ff6074fdddbe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2015, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2016, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2017, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2018, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2019, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2020, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2021, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2022, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2023, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2024, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2025, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2026, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2027, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2028, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2029, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2030, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2031, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2032, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2033, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2034, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2035, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2036, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2037, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2038, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2039, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2040, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2041, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2042, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2043, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2044, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2045, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2046, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2047, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2048, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2049, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2050, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2051, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2052, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2053, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2054, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2055, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2056, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2057, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2058, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2059, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2060, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2061, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2062, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2063, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2064, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2065, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2066, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2067, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2068, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2069, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2070, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2071, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2072, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2073, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2074, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2075, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2076, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2077, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2078, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2079, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2080, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2081, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2082, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2083, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2084, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2085, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2086, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2087, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2088, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2089, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2090, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2091, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2092, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2093, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2094, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2095, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2096, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2097, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2098, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2099, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2100, 1, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2101, 1, 1, 0, 0, 0, 0, has_year_zero=True)],
-      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-237fc399-fed0-4eee-beb7-20104d1a9a70' class='xr-section-summary-in' type='checkbox'  ><label for='section-237fc399-fed0-4eee-beb7-20104d1a9a70' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-16af876f-aa3c-493e-b02a-391d7d4dc199' class='xr-index-data-in' type='checkbox'/><label for='index-16af876f-aa3c-493e-b02a-391d7d4dc199' 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([2015-01-01 00:00:00, 2016-01-01 00:00:00, 2017-01-01 00:00:00,
-             2018-01-01 00:00:00, 2019-01-01 00:00:00, 2020-01-01 00:00:00,
-             2021-01-01 00:00:00, 2022-01-01 00:00:00, 2023-01-01 00:00:00,
-             2024-01-01 00:00:00, 2025-01-01 00:00:00, 2026-01-01 00:00:00,
-             2027-01-01 00:00:00, 2028-01-01 00:00:00, 2029-01-01 00:00:00,
-             2030-01-01 00:00:00, 2031-01-01 00:00:00, 2032-01-01 00:00:00,
-             2033-01-01 00:00:00, 2034-01-01 00:00:00, 2035-01-01 00:00:00,
-             2036-01-01 00:00:00, 2037-01-01 00:00:00, 2038-01-01 00:00:00,
-             2039-01-01 00:00:00, 2040-01-01 00:00:00, 2041-01-01 00:00:00,
-             2042-01-01 00:00:00, 2043-01-01 00:00:00, 2044-01-01 00:00:00,
-             2045-01-01 00:00:00, 2046-01-01 00:00:00, 2047-01-01 00:00:00,
-             2048-01-01 00:00:00, 2049-01-01 00:00:00, 2050-01-01 00:00:00,
-             2051-01-01 00:00:00, 2052-01-01 00:00:00, 2053-01-01 00:00:00,
-             2054-01-01 00:00:00, 2055-01-01 00:00:00, 2056-01-01 00:00:00,
-             2057-01-01 00:00:00, 2058-01-01 00:00:00, 2059-01-01 00:00:00,
-             2060-01-01 00:00:00, 2061-01-01 00:00:00, 2062-01-01 00:00:00,
-             2063-01-01 00:00:00, 2064-01-01 00:00:00, 2065-01-01 00:00:00,
-             2066-01-01 00:00:00, 2067-01-01 00:00:00, 2068-01-01 00:00:00,
-             2069-01-01 00:00:00, 2070-01-01 00:00:00, 2071-01-01 00:00:00,
-             2072-01-01 00:00:00, 2073-01-01 00:00:00, 2074-01-01 00:00:00,
-             2075-01-01 00:00:00, 2076-01-01 00:00:00, 2077-01-01 00:00:00,
-             2078-01-01 00:00:00, 2079-01-01 00:00:00, 2080-01-01 00:00:00,
-             2081-01-01 00:00:00, 2082-01-01 00:00:00, 2083-01-01 00:00:00,
-             2084-01-01 00:00:00, 2085-01-01 00:00:00, 2086-01-01 00:00:00,
-             2087-01-01 00:00:00, 2088-01-01 00:00:00, 2089-01-01 00:00:00,
-             2090-01-01 00:00:00, 2091-01-01 00:00:00, 2092-01-01 00:00:00,
-             2093-01-01 00:00:00, 2094-01-01 00:00:00, 2095-01-01 00:00:00,
-             2096-01-01 00:00:00, 2097-01-01 00:00:00, 2098-01-01 00:00:00,
-             2099-01-01 00:00:00, 2100-01-01 00:00:00, 2101-01-01 00:00:00],
-            dtype=&#x27;object&#x27;, length=87, calendar=&#x27;noleap&#x27;, freq=&#x27;AS-JAN&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a35dcf70-28c8-4d0c-a221-efa7a8bc30c5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a35dcf70-28c8-4d0c-a221-efa7a8bc30c5' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Reference height temperature</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div></li></ul></div></div></div></div>
-</div>
-<p>We can either see the values of our DataArray using <code class="docutils literal notranslate"><span class="pre">.compute()</span></code> or <code class="docutils literal notranslate"><span class="pre">.data</span></code> or by plotting it:</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">tb</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&lt;matplotlib.lines.Line2D at 0x7f622ccdc400&gt;]
-</pre></div>
-</div>
-<img alt="../_images/8bc4b912a3d759f27fd30727dd6bfca7f132caed4b92d2fe905854a33760d874.png" src="../_images/8bc4b912a3d759f27fd30727dd6bfca7f132caed4b92d2fe905854a33760d874.png" />
-</div>
-</div>
-</div>
-<div class="section" id="calculations-over-all-grids">
-<h3>Calculations over all grids<a class="headerlink" href="#calculations-over-all-grids" title="Permalink to this headline"><i class="fas fa-link"></i></a></h3>
-<div class="cell docutils container">
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-<span class="n">tref_c</span> <span class="o">=</span> <span class="n">tref</span><span class="o">-</span> <span class="mf">273.15</span>
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-</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;TREFHT&#x27; (time: 1032, lat: 192, lon: 288)&gt;
-dask.array&lt;chunksize=(1, 96, 144), meta=np.ndarray&gt;
-Coordinates:
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-  * time     (time) object 2015-02-01 00:00:00 ... 2101-01-01 00:00:00</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'>'TREFHT'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1032</li><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-0961d2d2-2438-492b-a149-494e6faff608' class='xr-array-in' type='checkbox' ><label for='section-0961d2d2-2438-492b-a149-494e6faff608' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 96, 144), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>
+    </details>
+</div>
+            
+        </div>
+    </div>
+</div></div></div>
+</div>
+<p>If we check Dask Task Graph for <code class="docutils literal notranslate"><span class="pre">tref_mean</span></code>, we can see all the steps required for calculating it (from opening the netcdf file to calculating mean and aggreagting it).</p>
+</div>
+<div class="section" id="getting-concrete-values">
+<h3>Getting concrete values<a class="headerlink" href="#getting-concrete-values" title="Permalink to this headline"><i class="fas fa-link"></i></a></h3>
+<p>At some point, you will want to actually do the calculations and receive concrete values from Dask.</p>
+<p>There are two ways to compute values on dask arrays.</p>
+<ol class="arabic simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">compute()</span></code> returns a new xarray object with the data now represented as a numpy array.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">load()</span></code> replaces the dask array in the xarray object with a numpy array. Equivalent to <code class="docutils literal notranslate"><span class="pre">ds</span> <span class="pre">=</span> <span class="pre">ds.compute()</span></code>.</p></li>
+</ol>
+<p><code class="docutils literal notranslate"><span class="pre">.load()</span></code> operates <em>inplace</em> and <code class="docutils literal notranslate"><span class="pre">.compute()</span></code> returns a new xarray object.</p>
+<div class="section" id="distributed-non-blocking-concrete-values">
+<h4>Distributed non-blocking concrete values<a class="headerlink" href="#distributed-non-blocking-concrete-values" title="Permalink to this headline"><i class="fas fa-link"></i></a></h4>
+<p>There is another option available  third option : “persisting”. <code class="docutils literal notranslate"><span class="pre">.persist()</span></code> loads the values into distributed RAM. The values are computed but remain distributed across workers. So essentially <code class="docutils literal notranslate"><span class="pre">persist</span></code> turns a lazy Dask collection into a Dask collection where the results are either fully computed or actively computing in the background.</p>
+<p>So <code class="docutils literal notranslate"><span class="pre">ds.air.persist()</span></code> is still backed by a Dask array. This is useful if you will be repeatedly using a dataset for computation but it is too large to load into local memory.</p>
+<p>Read more: <a class="reference external" href="https://docs.dask.org/en/stable/generated/dask.dataframe.Series.persist.html">Dask user guide</a></p>
+</div>
+</div>
+</div>
+<div class="section" id="how-to-access-underlying-data-in-an-xarray-object">
+<h2>How to access underlying data in an Xarray object?<a class="headerlink" href="#how-to-access-underlying-data-in-an-xarray-object" title="Permalink to this headline"><i class="fas fa-link"></i></a></h2>
+<p>There are two basic ways to extract values from an Xarray object:</p>
+<ol class="arabic simple">
+<li><p>Using <code class="docutils literal notranslate"><span class="pre">.data</span></code> will return a Dask array. For example:</p></li>
+</ol>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">tref</span><span class="o">.</span><span class="n">data</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
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+                        <td colspan="2"> 9 chunks in 19 graph layers </td>
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@@ -7657,104 +6592,241 @@ <h3>Calculations over all grids<a class="headerlink" href="#calculations-over-al
 </svg>
         </td>
     </tr>
-</table></div></div></li><li class='xr-section-item'><input id='section-18e556ff-a318-4330-8d82-3a7e8d618e33' class='xr-section-summary-in' type='checkbox'  checked><label for='section-18e556ff-a318-4330-8d82-3a7e8d618e33' 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 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-91766879-3f48-4486-ba55-6d078c1f3903' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-91766879-3f48-4486-ba55-6d078c1f3903' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa29a3d1-6691-433d-8802-9c2f94902c15' class='xr-var-data-in' type='checkbox'><label for='data-fa29a3d1-6691-433d-8802-9c2f94902c15' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,
-       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,
-       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,
-       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,
-       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,
-       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,
-       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,
-       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,
-       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,
-       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,
-       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,
-       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,
-       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,
-       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,
-       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,
-        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,
-         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,
-         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,
-        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,
-        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,
-        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,
-        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,
-        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,
-        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,
-        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,
-        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,
-        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,
-        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,
-        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,
-        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,
-        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,
-        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-b9ff9da1-04f3-4115-940f-87c6a0d1ad4a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b9ff9da1-04f3-4115-940f-87c6a0d1ad4a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6cbc6a7-0bba-4749-9db8-fcc7538ce5e7' class='xr-var-data-in' type='checkbox'><label for='data-f6cbc6a7-0bba-4749-9db8-fcc7538ce5e7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span 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'>2015-02-01 00:00:00 ... 2101-01-...</div><input id='attrs-78e0f868-82fe-48d6-99ce-5715fd87d4a5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78e0f868-82fe-48d6-99ce-5715fd87d4a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9c3e1b2-83db-4cdb-b1c5-6b3783bb2179' class='xr-var-data-in' type='checkbox'><label for='data-f9c3e1b2-83db-4cdb-b1c5-6b3783bb2179' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2015, 2, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2015, 3, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2015, 4, 1, 0, 0, 0, 0, has_year_zero=True), ...,
-       cftime.DatetimeNoLeap(2100, 11, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2100, 12, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2101, 1, 1, 0, 0, 0, 0, has_year_zero=True)],
-      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5b1fdae3-3f56-41d6-af2d-374d9a63b62a' class='xr-section-summary-in' type='checkbox'  ><label for='section-5b1fdae3-3f56-41d6-af2d-374d9a63b62a' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-20184fa9-52cd-4e77-9999-0e0b601bba2a' class='xr-index-data-in' type='checkbox'/><label for='index-20184fa9-52cd-4e77-9999-0e0b601bba2a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([             -90.0, -89.05759162303664,  -88.1151832460733,
-       -87.17277486910994,  -86.2303664921466, -85.28795811518324,
-        -84.3455497382199, -83.40314136125654, -82.46073298429319,
-       -81.51832460732984,
-       ...
-        81.51832460732984,  82.46073298429319,  83.40314136125654,
-         84.3455497382199,  85.28795811518324,   86.2303664921466,
-        87.17277486910994,   88.1151832460733,  89.05759162303664,
-                     90.0],
-      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-80ebf7dc-8bac-4eb1-a381-dfea1f74840f' class='xr-index-data-in' type='checkbox'/><label for='index-80ebf7dc-8bac-4eb1-a381-dfea1f74840f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   1.25,    2.5,   3.75,    5.0,   6.25,    7.5,   8.75,   10.0,
-        11.25,
-       ...
-        347.5, 348.75,  350.0, 351.25,  352.5, 353.75,  355.0, 356.25,  357.5,
-       358.75],
-      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=288))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6d51ecbc-8115-4761-8883-04163a9b7014' class='xr-index-data-in' type='checkbox'/><label for='index-6d51ecbc-8115-4761-8883-04163a9b7014' 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([2015-02-01 00:00:00, 2015-03-01 00:00:00, 2015-04-01 00:00:00,
-             2015-05-01 00:00:00, 2015-06-01 00:00:00, 2015-07-01 00:00:00,
-             2015-08-01 00:00:00, 2015-09-01 00:00:00, 2015-10-01 00:00:00,
-             2015-11-01 00:00:00,
-             ...
-             2100-04-01 00:00:00, 2100-05-01 00:00:00, 2100-06-01 00:00:00,
-             2100-07-01 00:00:00, 2100-08-01 00:00:00, 2100-09-01 00:00:00,
-             2100-10-01 00:00:00, 2100-11-01 00:00:00, 2100-12-01 00:00:00,
-             2101-01-01 00:00:00],
-            dtype=&#x27;object&#x27;, length=1032, calendar=&#x27;noleap&#x27;, freq=&#x27;MS&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-79dca306-9dd1-4f1b-8bb5-0a74c296bc88' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-79dca306-9dd1-4f1b-8bb5-0a74c296bc88' 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></div></div>
+</table></div></div>
 </div>
+<p><strong>This means that for Dask-backed Xarray object, we can access the values using <code class="docutils literal notranslate"><span class="pre">.compute</span></code></strong></p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Until we explicitly call load() or compute(), Dask actually didn&#39;t do any real calculation</span>
-<span class="c1"># We are doing the calculations below parallelly. However not much benefit from parallel computing since it&#39;s not a big problem</span>
-<span class="o">%</span><span class="k">time</span> tref_c=tref_c.load()
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%%time</span>
+
+<span class="n">tref</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">compute</span><span class="p">()</span>
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 7.3 s, sys: 627 ms, total: 7.93 s
-Wall time: 15.2 s
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 91.4 ms, sys: 231 ms, total: 323 ms
+Wall time: 1.13 s
+</pre></div>
+</div>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([[[248.39987, 248.39989, 248.39992, ..., 248.39989, 248.39989,
+         248.39989],
+        [248.95004, 248.9094 , 248.75017, ..., 248.98384, 248.97466,
+         248.9626 ],
+        [249.20784, 249.17082, 249.15718, ..., 249.42188, 249.37703,
+         249.31105],
+        ...,
+        [251.5821 , 251.6182 , 251.65166, ..., 251.48357, 251.51555,
+         251.54646],
+        [251.34143, 251.35114, 251.36166, ..., 251.30699, 251.32008,
+         251.3312 ],
+        [251.35237, 251.35286, 251.35332, ..., 251.35059, 251.35123,
+         251.35182]],
+
+       [[237.44759, 237.44759, 237.44759, ..., 237.44759, 237.44759,
+         237.44759],
+        [238.10292, 238.05934, 237.8917 , ..., 238.16042, 238.14326,
+         238.12119],
+        [238.86865, 238.82155, 238.79092, ..., 239.1196 , 239.04976,
+         238.96754],
+        ...,
+        [246.23404, 246.25865, 246.28221, ..., 246.15912, 246.18312,
+         246.20715],
+        [246.70511, 246.7112 , 246.71793, ..., 246.68782, 246.69476,
+         246.69987],
+        [247.26158, 247.26067, 247.25984, ..., 247.26494, 247.2637 ,
+         247.26259]],
+
+       [[227.2174 , 227.2174 , 227.2174 , ..., 227.2174 , 227.2174 ,
+         227.2174 ],
+        [227.27689, 227.23364, 227.07358, ..., 227.32939, 227.3166 ,
+         227.2957 ],
+        [227.58324, 227.54382, 227.52094, ..., 227.81204, 227.76472,
+         227.69241],
+        ...,
+        [247.57649, 247.60806, 247.63834, ..., 247.49849, 247.52321,
+         247.54758],
+        [247.34538, 247.35608, 247.36736, ..., 247.30998, 247.32211,
+         247.33403],
+        [247.2048 , 247.20529, 247.20573, ..., 247.20308, 247.20372,
+         247.20428]],
+
+       ...,
+
+       [[230.82976, 230.82976, 230.82976, ..., 230.82976, 230.82976,
+         230.82976],
+        [231.51309, 231.47168, 231.3188 , ..., 231.55086, 231.54416,
+         231.53171],
+        [232.11208, 232.0665 , 232.04721, ..., 232.34473, 232.29143,
+         232.22214],
+        ...,
+        [273.3896 , 273.38998, 273.3906 , ..., 273.3897 , 273.3895 ,
+         273.3894 ],
+        [273.46066, 273.46008, 273.45963, ..., 273.4635 , 273.4624 ,
+         273.46143],
+        [273.5544 , 273.55417, 273.55392, ..., 273.5554 , 273.55505,
+         273.55472]],
+
+       [[240.62163, 240.62163, 240.62163, ..., 240.62163, 240.62163,
+         240.62163],
+        [241.24452, 241.20761, 241.05177, ..., 241.27812, 241.26988,
+         241.2583 ],
+        [241.4827 , 241.44122, 241.42111, ..., 241.70222, 241.65529,
+         241.58728],
+        ...,
+        [271.83112, 271.82843, 271.82672, ..., 271.84128, 271.83743,
+         271.8343 ],
+        [272.06815, 272.06625, 272.06448, ..., 272.07593, 272.07327,
+         272.07053],
+        [272.28296, 272.2825 , 272.2821 , ..., 272.28467, 272.28403,
+         272.28348]],
+
+       [[250.27185, 250.2721 , 250.27223, ..., 250.27199, 250.27199,
+         250.27194],
+        [251.08855, 251.05937, 250.90967, ..., 251.1056 , 251.10072,
+         251.09595],
+        [251.64214, 251.61182, 251.6042 , ..., 251.83585, 251.79689,
+         251.7379 ],
+        ...,
+        [271.2762 , 271.2781 , 271.28018, ..., 271.2714 , 271.27295,
+         271.27457],
+        [271.2889 , 271.28967, 271.2904 , ..., 271.28693, 271.28738,
+         271.28815],
+        [271.26477, 271.26483, 271.2649 , ..., 271.26456, 271.26465,
+         271.2647 ]]], dtype=float32)
 </pre></div>
 </div>
 </div>
 </div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%%time</span>
-
-<span class="c1"># Compute monthly anomaly</span>
-
-<span class="c1"># -- 1. calculate monthly average</span>
-<span class="n">tref_grouped</span> <span class="o">=</span> <span class="n">tref</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;time.month&#39;</span><span class="p">)</span>
-<span class="n">tmean</span> <span class="o">=</span> <span class="n">tref_grouped</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="s1">&#39;time&#39;</span><span class="p">)</span>
-
-<span class="c1">#-- 2. calculate monthly anomaly</span>
-<span class="n">tos_anom</span> <span class="o">=</span> <span class="n">tref_grouped</span> <span class="o">-</span> <span class="n">tmean</span>
-<span class="n">tos_anom</span>
-</pre></div>
-</div>
-</div>
+<ol class="arabic simple" start="2">
+<li><p>We can also use <code class="docutils literal notranslate"><span class="pre">.values</span></code> to see the “real” values of Xarray object. Another option is using <code class="docutils literal notranslate"><span class="pre">.to_numpy</span></code>. Both of these option return the values of underlying Dask object in a numpy array.</p></li>
+</ol>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%%time</span>
+<span class="n">tref</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 325 ms, sys: 4.85 ms, total: 329 ms
-Wall time: 349 ms
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 113 ms, sys: 210 ms, total: 323 ms
+Wall time: 1.14 s
 </pre></div>
 </div>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([[[248.39987, 248.39989, 248.39992, ..., 248.39989, 248.39989,
+         248.39989],
+        [248.95004, 248.9094 , 248.75017, ..., 248.98384, 248.97466,
+         248.9626 ],
+        [249.20784, 249.17082, 249.15718, ..., 249.42188, 249.37703,
+         249.31105],
+        ...,
+        [251.5821 , 251.6182 , 251.65166, ..., 251.48357, 251.51555,
+         251.54646],
+        [251.34143, 251.35114, 251.36166, ..., 251.30699, 251.32008,
+         251.3312 ],
+        [251.35237, 251.35286, 251.35332, ..., 251.35059, 251.35123,
+         251.35182]],
+
+       [[237.44759, 237.44759, 237.44759, ..., 237.44759, 237.44759,
+         237.44759],
+        [238.10292, 238.05934, 237.8917 , ..., 238.16042, 238.14326,
+         238.12119],
+        [238.86865, 238.82155, 238.79092, ..., 239.1196 , 239.04976,
+         238.96754],
+        ...,
+        [246.23404, 246.25865, 246.28221, ..., 246.15912, 246.18312,
+         246.20715],
+        [246.70511, 246.7112 , 246.71793, ..., 246.68782, 246.69476,
+         246.69987],
+        [247.26158, 247.26067, 247.25984, ..., 247.26494, 247.2637 ,
+         247.26259]],
+
+       [[227.2174 , 227.2174 , 227.2174 , ..., 227.2174 , 227.2174 ,
+         227.2174 ],
+        [227.27689, 227.23364, 227.07358, ..., 227.32939, 227.3166 ,
+         227.2957 ],
+        [227.58324, 227.54382, 227.52094, ..., 227.81204, 227.76472,
+         227.69241],
+        ...,
+        [247.57649, 247.60806, 247.63834, ..., 247.49849, 247.52321,
+         247.54758],
+        [247.34538, 247.35608, 247.36736, ..., 247.30998, 247.32211,
+         247.33403],
+        [247.2048 , 247.20529, 247.20573, ..., 247.20308, 247.20372,
+         247.20428]],
+
+       ...,
+
+       [[230.82976, 230.82976, 230.82976, ..., 230.82976, 230.82976,
+         230.82976],
+        [231.51309, 231.47168, 231.3188 , ..., 231.55086, 231.54416,
+         231.53171],
+        [232.11208, 232.0665 , 232.04721, ..., 232.34473, 232.29143,
+         232.22214],
+        ...,
+        [273.3896 , 273.38998, 273.3906 , ..., 273.3897 , 273.3895 ,
+         273.3894 ],
+        [273.46066, 273.46008, 273.45963, ..., 273.4635 , 273.4624 ,
+         273.46143],
+        [273.5544 , 273.55417, 273.55392, ..., 273.5554 , 273.55505,
+         273.55472]],
+
+       [[240.62163, 240.62163, 240.62163, ..., 240.62163, 240.62163,
+         240.62163],
+        [241.24452, 241.20761, 241.05177, ..., 241.27812, 241.26988,
+         241.2583 ],
+        [241.4827 , 241.44122, 241.42111, ..., 241.70222, 241.65529,
+         241.58728],
+        ...,
+        [271.83112, 271.82843, 271.82672, ..., 271.84128, 271.83743,
+         271.8343 ],
+        [272.06815, 272.06625, 272.06448, ..., 272.07593, 272.07327,
+         272.07053],
+        [272.28296, 272.2825 , 272.2821 , ..., 272.28467, 272.28403,
+         272.28348]],
+
+       [[250.27185, 250.2721 , 250.27223, ..., 250.27199, 250.27199,
+         250.27194],
+        [251.08855, 251.05937, 250.90967, ..., 251.1056 , 251.10072,
+         251.09595],
+        [251.64214, 251.61182, 251.6042 , ..., 251.83585, 251.79689,
+         251.7379 ],
+        ...,
+        [271.2762 , 271.2781 , 271.28018, ..., 271.2714 , 271.27295,
+         271.27457],
+        [271.2889 , 271.28967, 271.2904 , ..., 271.28693, 271.28738,
+         271.28815],
+        [271.26477, 271.26483, 271.2649 , ..., 271.26456, 271.26465,
+         271.2647 ]]], dtype=float32)
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="section" id="computation">
+<h2>Computation<a class="headerlink" href="#computation" title="Permalink to this headline"><i class="fas fa-link"></i></a></h2>
+<p>All built-in Xarray methods (<code class="docutils literal notranslate"><span class="pre">.mean</span></code>, <code class="docutils literal notranslate"><span class="pre">.max</span></code>, <code class="docutils literal notranslate"><span class="pre">.rolling</span></code>, <code class="docutils literal notranslate"><span class="pre">.groupby</span></code> etc.) support dask arrays.</p>
+<p>Now, let’s do some computations on this Xarray dataset.</p>
+<div class="section" id="single-point-calculations">
+<h3>Single Point Calculations<a class="headerlink" href="#single-point-calculations" title="Permalink to this headline"><i class="fas fa-link"></i></a></h3>
+<p>To start out, let’s do the calculations on a single point first. First, we extract the time series data at a grid point and save it to a variable. Here we select the closest point using <code class="docutils literal notranslate"><span class="pre">.sel</span></code> and load the data.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">tref_boulder</span> <span class="o">=</span> <span class="n">tref</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">lat</span><span class="o">=</span><span class="mf">40.0150</span><span class="p">,</span> <span class="n">lon</span><span class="o">=-</span><span class="mf">105.2705</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;nearest&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">load</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="alert alert-block alert-warning" markdown="1">
+<p><strong>WARNING:</strong> Remember as soon as you call <code class="docutils literal notranslate"><span class="pre">.load()</span></code> you are telling Dask to trigger computation.</p>
+</div><div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># -- take annual average</span>
+<span class="n">tb</span> <span class="o">=</span> <span class="n">tref_boulder</span><span class="o">.</span><span class="n">resample</span><span class="p">(</span><span class="n">time</span><span class="o">=</span><span class="s1">&#39;AS&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+<span class="n">tb</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
 <div class="output text_html"><div><svg style="position: absolute; width: 0; height: 0; overflow: hidden">
 <defs>
 <symbol id="icon-database" viewBox="0 0 32 32">
@@ -8118,254 +7190,169 @@ <h3>Calculations over all grids<a class="headerlink" href="#calculations-over-al
   stroke: currentColor;
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-dask.array&lt;chunksize=(1, 96, 144), meta=np.ndarray&gt;
+</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;TREFHT&#x27; (time: 87)&gt;
+291.8 290.3 290.3 290.1 290.7 291.2 ... 294.3 293.5 293.5 293.4 293.9 289.8
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-  * lat      (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 87.17 88.12 89.06 90.0
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-  * time     (time) object 2015-02-01 00:00:00 ... 2101-01-01 00:00:00
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-        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,
-        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,
-        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-a0b6de58-8cd7-44c3-853c-ca0ef56ec48f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a0b6de58-8cd7-44c3-853c-ca0ef56ec48f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3ad3e97-44f5-40c9-8310-d63bfa82aba4' class='xr-var-data-in' type='checkbox'><label for='data-e3ad3e97-44f5-40c9-8310-d63bfa82aba4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span 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'>2015-02-01 00:00:00 ... 2101-01-...</div><input id='attrs-23a604f7-a4a0-48f5-bbc2-6de372e76482' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-23a604f7-a4a0-48f5-bbc2-6de372e76482' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-897ae9e7-3405-4cc1-81b5-e2cc890112ae' class='xr-var-data-in' type='checkbox'><label for='data-897ae9e7-3405-4cc1-81b5-e2cc890112ae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2015, 2, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2015, 3, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2015, 4, 1, 0, 0, 0, 0, has_year_zero=True), ...,
-       cftime.DatetimeNoLeap(2100, 11, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2100, 12, 1, 0, 0, 0, 0, has_year_zero=True),
-       cftime.DatetimeNoLeap(2101, 1, 1, 0, 0, 0, 0, has_year_zero=True)],
-      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>month</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2 3 4 5 6 7 8 ... 7 8 9 10 11 12 1</div><input id='attrs-3fa98bfb-89a8-4f9f-8ba2-bdb341a6d182' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3fa98bfb-89a8-4f9f-8ba2-bdb341a6d182' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-897ddeff-5951-4364-9723-4134571daa01' class='xr-var-data-in' type='checkbox'><label for='data-897ddeff-5951-4364-9723-4134571daa01' 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([ 2,  3,  4, ..., 11, 12,  1])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4e16606e-7644-45ec-b4f5-56b43b872249' class='xr-section-summary-in' type='checkbox'  ><label for='section-4e16606e-7644-45ec-b4f5-56b43b872249' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ec8cf117-f846-4337-9e2f-e98acc011352' class='xr-index-data-in' type='checkbox'/><label for='index-ec8cf117-f846-4337-9e2f-e98acc011352' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([             -90.0, -89.05759162303664,  -88.1151832460733,
-       -87.17277486910994,  -86.2303664921466, -85.28795811518324,
-        -84.3455497382199, -83.40314136125654, -82.46073298429319,
-       -81.51832460732984,
-       ...
-        81.51832460732984,  82.46073298429319,  83.40314136125654,
-         84.3455497382199,  85.28795811518324,   86.2303664921466,
-        87.17277486910994,   88.1151832460733,  89.05759162303664,
-                     90.0],
-      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b16fdac3-be78-4d53-ba72-034010ddc0dd' class='xr-index-data-in' type='checkbox'/><label for='index-b16fdac3-be78-4d53-ba72-034010ddc0dd' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   1.25,    2.5,   3.75,    5.0,   6.25,    7.5,   8.75,   10.0,
-        11.25,
-       ...
-        347.5, 348.75,  350.0, 351.25,  352.5, 353.75,  355.0, 356.25,  357.5,
-       358.75],
-      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=288))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-73d705ab-dea7-48c4-8417-b94108c5e948' class='xr-index-data-in' type='checkbox'/><label for='index-73d705ab-dea7-48c4-8417-b94108c5e948' 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([2015-02-01 00:00:00, 2015-03-01 00:00:00, 2015-04-01 00:00:00,
-             2015-05-01 00:00:00, 2015-06-01 00:00:00, 2015-07-01 00:00:00,
-             2015-08-01 00:00:00, 2015-09-01 00:00:00, 2015-10-01 00:00:00,
-             2015-11-01 00:00:00,
-             ...
-             2100-04-01 00:00:00, 2100-05-01 00:00:00, 2100-06-01 00:00:00,
-             2100-07-01 00:00:00, 2100-08-01 00:00:00, 2100-09-01 00:00:00,
-             2100-10-01 00:00:00, 2100-11-01 00:00:00, 2100-12-01 00:00:00,
-             2101-01-01 00:00:00],
-            dtype=&#x27;object&#x27;, length=1032, calendar=&#x27;noleap&#x27;, freq=&#x27;MS&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-12dab721-4166-4b53-ab6b-fbcbe8024e0c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-12dab721-4166-4b53-ab6b-fbcbe8024e0c' 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></div></div>
-</div>
-<p>Dask actually constructs a graph of the required computation.</p>
-<p>Call <code class="docutils literal notranslate"><span class="pre">.compute()</span></code> or <code class="docutils literal notranslate"><span class="pre">.load()</span></code> when you want your result as a xarray.DataArray to access underlying dataset:</p>
-<ul class="simple">
-<li><p><code class="docutils literal notranslate"><span class="pre">.compute()</span> </code> works similarly here as Dask DataArray. It basically triggers the computations across different chunks of the DataArray.</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">.load()</span></code> replaces the dask array in the Xarray object with a numpy array.</p></li>
-</ul>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%%time</span>
-<span class="n">computed_anom</span> <span class="o">=</span> <span class="n">tos_anom</span><span class="o">.</span><span class="n">load</span><span class="p">()</span>
-<span class="nb">type</span><span class="p">(</span><span class="n">computed_anom</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 16.5 s, sys: 962 ms, total: 17.5 s
-Wall time: 32.9 s
-</pre></div>
-</div>
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>xarray.core.dataarray.DataArray
-</pre></div>
-</div>
-</div>
+    lat      float64 40.05
+    lon      float64 0.0
+  * time     (time) object 2015-01-01 00:00:00 ... 2101-01-01 00:00:00
+Attributes:
+    units:         K
+    long_name:     Reference height temperature
+    cell_methods:  time: mean</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'>'TREFHT'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 87</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-8f398713-c7d8-4691-90b6-1319ad5699ff' class='xr-array-in' type='checkbox' ><label for='section-8f398713-c7d8-4691-90b6-1319ad5699ff' 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>291.8 290.3 290.3 290.1 290.7 291.2 ... 293.5 293.5 293.4 293.9 289.8</span></div><div class='xr-array-data'><pre>array([291.76175, 290.29858, 290.3258 , 290.0671 , 290.73373, 291.21622,
+       291.2185 , 290.6878 , 290.36618, 291.24475, 291.10925, 290.66568,
+       291.23413, 290.79117, 291.17935, 291.0817 , 291.26492, 290.43915,
+       291.0101 , 290.19608, 290.921  , 291.27866, 291.41956, 291.19473,
+       290.73257, 291.3107 , 291.65024, 291.84177, 291.73816, 291.89676,
+       291.94705, 292.9821 , 291.53247, 291.66183, 290.74997, 291.86865,
+       290.99942, 291.76474, 291.97342, 291.66153, 291.37723, 291.7956 ,
+       292.2146 , 292.07352, 291.9124 , 291.9819 , 292.81973, 292.5683 ,
+       292.7518 , 293.21613, 292.87967, 292.64758, 291.84106, 293.1604 ,
+       292.63492, 292.23972, 291.64505, 291.90146, 292.02017, 292.77676,
+       292.519  , 293.4558 , 292.83826, 292.3904 , 293.33334, 293.31696,
+       292.63467, 292.94614, 292.9144 , 293.40817, 292.76138, 292.56082,
+       292.9273 , 292.94125, 293.18338, 293.6574 , 293.61575, 293.9967 ,
+       294.24548, 293.02676, 293.1884 , 294.33173, 293.5257 , 293.45236,
+       293.43143, 293.89886, 289.79712], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-98020859-dc9f-41a2-b157-de33c3810f56' class='xr-section-summary-in' type='checkbox'  checked><label for='section-98020859-dc9f-41a2-b157-de33c3810f56' 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>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>40.05</div><input id='attrs-efbadb44-e2d3-402e-8ff0-bc7c4b371065' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-efbadb44-e2d3-402e-8ff0-bc7c4b371065' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b08881ec-85c0-490f-a08c-96b276d17c54' class='xr-var-data-in' type='checkbox'><label for='data-b08881ec-85c0-490f-a08c-96b276d17c54' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array(40.05235602)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-3fe34ee1-bfae-41eb-a804-96c2599a316b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3fe34ee1-bfae-41eb-a804-96c2599a316b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5da11f7e-4407-4a8e-8135-a5885a6743dc' class='xr-var-data-in' type='checkbox'><label for='data-5da11f7e-4407-4a8e-8135-a5885a6743dc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array(0.)</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'>2015-01-01 00:00:00 ... 2101-01-...</div><input id='attrs-1837d3bc-64f8-4c22-927c-8fdf4be31be1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1837d3bc-64f8-4c22-927c-8fdf4be31be1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b414e26d-d969-4f0e-ac1f-469e06fbf672' class='xr-var-data-in' type='checkbox'><label for='data-b414e26d-d969-4f0e-ac1f-469e06fbf672' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2015, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2016, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2017, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2018, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2019, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2020, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2021, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2022, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2023, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2024, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2025, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2026, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2027, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2028, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2029, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2030, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2031, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2032, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2033, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2034, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2035, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2036, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2037, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2038, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2039, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2040, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2041, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2042, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2043, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2044, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2045, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2046, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2047, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2048, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2049, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2050, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2051, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2052, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2053, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2054, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2055, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2056, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2057, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2058, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2059, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2060, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2061, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2062, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2063, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2064, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2065, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2066, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2067, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2068, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2069, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2070, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2071, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2072, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2073, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2074, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2075, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2076, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2077, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2078, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2079, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2080, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2081, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2082, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2083, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2084, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2085, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2086, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2087, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2088, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2089, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2090, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2091, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2092, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2093, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2094, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2095, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2096, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2097, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2098, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2099, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2100, 1, 1, 0, 0, 0, 0, has_year_zero=True),
+       cftime.DatetimeNoLeap(2101, 1, 1, 0, 0, 0, 0, has_year_zero=True)],
+      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-68886163-e8b9-417c-923f-7ea8dde7aeec' class='xr-section-summary-in' type='checkbox'  ><label for='section-68886163-e8b9-417c-923f-7ea8dde7aeec' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f3c78db0-c263-4779-8229-65874391f578' class='xr-index-data-in' type='checkbox'/><label for='index-f3c78db0-c263-4779-8229-65874391f578' 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([2015-01-01 00:00:00, 2016-01-01 00:00:00, 2017-01-01 00:00:00,
+             2018-01-01 00:00:00, 2019-01-01 00:00:00, 2020-01-01 00:00:00,
+             2021-01-01 00:00:00, 2022-01-01 00:00:00, 2023-01-01 00:00:00,
+             2024-01-01 00:00:00, 2025-01-01 00:00:00, 2026-01-01 00:00:00,
+             2027-01-01 00:00:00, 2028-01-01 00:00:00, 2029-01-01 00:00:00,
+             2030-01-01 00:00:00, 2031-01-01 00:00:00, 2032-01-01 00:00:00,
+             2033-01-01 00:00:00, 2034-01-01 00:00:00, 2035-01-01 00:00:00,
+             2036-01-01 00:00:00, 2037-01-01 00:00:00, 2038-01-01 00:00:00,
+             2039-01-01 00:00:00, 2040-01-01 00:00:00, 2041-01-01 00:00:00,
+             2042-01-01 00:00:00, 2043-01-01 00:00:00, 2044-01-01 00:00:00,
+             2045-01-01 00:00:00, 2046-01-01 00:00:00, 2047-01-01 00:00:00,
+             2048-01-01 00:00:00, 2049-01-01 00:00:00, 2050-01-01 00:00:00,
+             2051-01-01 00:00:00, 2052-01-01 00:00:00, 2053-01-01 00:00:00,
+             2054-01-01 00:00:00, 2055-01-01 00:00:00, 2056-01-01 00:00:00,
+             2057-01-01 00:00:00, 2058-01-01 00:00:00, 2059-01-01 00:00:00,
+             2060-01-01 00:00:00, 2061-01-01 00:00:00, 2062-01-01 00:00:00,
+             2063-01-01 00:00:00, 2064-01-01 00:00:00, 2065-01-01 00:00:00,
+             2066-01-01 00:00:00, 2067-01-01 00:00:00, 2068-01-01 00:00:00,
+             2069-01-01 00:00:00, 2070-01-01 00:00:00, 2071-01-01 00:00:00,
+             2072-01-01 00:00:00, 2073-01-01 00:00:00, 2074-01-01 00:00:00,
+             2075-01-01 00:00:00, 2076-01-01 00:00:00, 2077-01-01 00:00:00,
+             2078-01-01 00:00:00, 2079-01-01 00:00:00, 2080-01-01 00:00:00,
+             2081-01-01 00:00:00, 2082-01-01 00:00:00, 2083-01-01 00:00:00,
+             2084-01-01 00:00:00, 2085-01-01 00:00:00, 2086-01-01 00:00:00,
+             2087-01-01 00:00:00, 2088-01-01 00:00:00, 2089-01-01 00:00:00,
+             2090-01-01 00:00:00, 2091-01-01 00:00:00, 2092-01-01 00:00:00,
+             2093-01-01 00:00:00, 2094-01-01 00:00:00, 2095-01-01 00:00:00,
+             2096-01-01 00:00:00, 2097-01-01 00:00:00, 2098-01-01 00:00:00,
+             2099-01-01 00:00:00, 2100-01-01 00:00:00, 2101-01-01 00:00:00],
+            dtype=&#x27;object&#x27;, length=87, calendar=&#x27;noleap&#x27;, freq=&#x27;AS-JAN&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3282a916-f40d-4233-91ae-5c5b84848790' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3282a916-f40d-4233-91ae-5c5b84848790' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Reference height temperature</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div></li></ul></div></div></div></div>
 </div>
+<p>We can either see the values of our DataArray in the text representation above or by plotting it:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">tos_anom</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">lon</span><span class="o">=</span><span class="mi">310</span><span class="p">,</span> <span class="n">lat</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;nearest&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span> <span class="n">size</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">tb</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&lt;matplotlib.lines.Line2D at 0x7f621d5b7f10&gt;]
-</pre></div>
-</div>
-<img alt="../_images/2a0d88b34b27a5c1e18219304e58cb83ef0755002ed171ab49f455d75c199ff2.png" src="../_images/2a0d88b34b27a5c1e18219304e58cb83ef0755002ed171ab49f455d75c199ff2.png" />
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">tos_anom</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">time</span><span class="o">=</span><span class="s1">&#39;2030-01-01&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&lt;matplotlib.lines.Line2D at 0x7fd734178c70&gt;]
 </pre></div>
 </div>
+<img alt="../_images/8bc4b912a3d759f27fd30727dd6bfca7f132caed4b92d2fe905854a33760d874.png" src="../_images/8bc4b912a3d759f27fd30727dd6bfca7f132caed4b92d2fe905854a33760d874.png" />
 </div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.collections.QuadMesh at 0x7f622a33f190&gt;
-</pre></div>
-</div>
-<img alt="../_images/424ca44abe28ae5fd4b25e476757f05341b1585083efa86f867f0ea4507c7d7b.png" src="../_images/424ca44abe28ae5fd4b25e476757f05341b1585083efa86f867f0ea4507c7d7b.png" />
 </div>
 </div>
-<div class="alert alert-block alert-info" markdown="1">
-<p><b>TIP:</b> Using Xarray plotting functionality automatically triggers computations on the Dask Array, similar to <code class="docutils literal notranslate"><span class="pre">.compute()</span></code>.</p>
-</div></div>
-<div class="section" id="supplementary-material-rechunking">
-<h3>Supplementary Material: Rechunking<a class="headerlink" href="#supplementary-material-rechunking" title="Permalink to this headline"><i class="fas fa-link"></i></a></h3>
-<ul class="simple">
-<li><p>rechunking will be covered in-depth in the next tutorial.</p></li>
-</ul>
-<p>We can always rechunk an Xarray DataArray. For example, what is the chunk size of <code class="docutils literal notranslate"><span class="pre">tref</span></code>?</p>
+<div class="section" id="calculations-over-all-grids">
+<h3>Calculations over all grids<a class="headerlink" href="#calculations-over-all-grids" title="Permalink to this headline"><i class="fas fa-link"></i></a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">tref</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># change the unit from Kelvin to degree Celsius </span>
+<span class="n">tref_c</span> <span class="o">=</span> <span class="n">tref</span> <span class="o">-</span> <span class="mf">273.15</span>
+<span class="n">tref_c</span>
 </pre></div>
 </div>
 </div>
@@ -8734,15 +7721,11 @@ <h3>Supplementary Material: Rechunking<a class="headerlink" href="#supplementary
   fill: currentColor;
 }
 </style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;TREFHT&#x27; (time: 1032, lat: 192, lon: 288)&gt;
-dask.array&lt;chunksize=(1, 96, 144), meta=np.ndarray&gt;
+dask.array&lt;chunksize=(120, 192, 288), meta=np.ndarray&gt;
 Coordinates:
   * lat      (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 87.17 88.12 89.06 90.0
   * lon      (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
-  * time     (time) object 2015-02-01 00:00:00 ... 2101-01-01 00:00:00
-Attributes:
-    units:         K
-    long_name:     Reference height temperature
-    cell_methods:  time: mean</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'>'TREFHT'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1032</li><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-7e55d852-584d-4892-af6c-faaa504fbcef' class='xr-array-in' type='checkbox' ><label for='section-7e55d852-584d-4892-af6c-faaa504fbcef' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 96, 144), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>
+  * time     (time) object 2015-02-01 00:00:00 ... 2101-01-01 00:00:00</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'>'TREFHT'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1032</li><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-f6a51508-cacb-4181-938d-f7cebdfc819d' class='xr-array-in' type='checkbox' ><label for='section-f6a51508-cacb-4181-938d-f7cebdfc819d' 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=(120, 192, 288), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>
     <tr>
         <td>
             <table style="border-collapse: collapse;">
@@ -8758,17 +7741,17 @@ <h3>Supplementary Material: Rechunking<a class="headerlink" href="#supplementary
                     <tr>
                         <th> Bytes </th>
                         <td> 217.69 MiB </td>
-                        <td> 54.00 kiB </td>
+                        <td> 25.31 MiB </td>
                     </tr>
                     
                     <tr>
                         <th> Shape </th>
                         <td> (1032, 192, 288) </td>
-                        <td> (1, 96, 144) </td>
+                        <td> (120, 192, 288) </td>
                     </tr>
                     <tr>
                         <th> Dask graph </th>
-                        <td colspan="2"> 4128 chunks in 19 graph layers </td>
+                        <td colspan="2"> 9 chunks in 20 graph layers </td>
                     </tr>
                     <tr>
                         <th> Data type </th>
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 </svg>
         </td>
     </tr>
-</table></div></div></li><li class='xr-section-item'><input id='section-44ff8b03-b520-4440-966e-f2b6458cde26' class='xr-section-summary-in' type='checkbox'  checked><label for='section-44ff8b03-b520-4440-966e-f2b6458cde26' 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 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-039c05a1-de3d-4b30-aad8-3e17dc6b9507' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-039c05a1-de3d-4b30-aad8-3e17dc6b9507' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e206eb40-4722-4ad5-a706-95cf85cfdcf7' class='xr-var-data-in' type='checkbox'><label for='data-e206eb40-4722-4ad5-a706-95cf85cfdcf7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,
+</table></div></div></li><li class='xr-section-item'><input id='section-36d4206f-d378-4f74-9bb1-022abc8007bf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-36d4206f-d378-4f74-9bb1-022abc8007bf' 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 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-b6e347f8-0ea8-4a00-be7c-cd749032ab0c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b6e347f8-0ea8-4a00-be7c-cd749032ab0c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4dca562d-9b0f-4db9-a638-2d9a8c8bdd28' class='xr-var-data-in' type='checkbox'><label for='data-4dca562d-9b0f-4db9-a638-2d9a8c8bdd28' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,
        -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,
        -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,
        -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,
@@ -8891,13 +7850,13 @@ <h3>Supplementary Material: Rechunking<a class="headerlink" href="#supplementary
         68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,
         73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,
         79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,
-        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-d96561fa-0ed3-4a12-a6b7-7467d348bbc5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d96561fa-0ed3-4a12-a6b7-7467d348bbc5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1027b52d-539b-4a9c-a31b-3c33cd47ab28' class='xr-var-data-in' type='checkbox'><label for='data-1027b52d-539b-4a9c-a31b-3c33cd47ab28' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span 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'>2015-02-01 00:00:00 ... 2101-01-...</div><input id='attrs-5c27dc39-a38d-4188-b5fd-381655ed0473' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5c27dc39-a38d-4188-b5fd-381655ed0473' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7f7ef84-1e97-4c3d-9ef2-523a465a63cc' class='xr-var-data-in' type='checkbox'><label for='data-f7f7ef84-1e97-4c3d-9ef2-523a465a63cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2015, 2, 1, 0, 0, 0, 0, has_year_zero=True),
+        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-4dd13359-b053-4ddb-827b-97e5bb5adb6d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4dd13359-b053-4ddb-827b-97e5bb5adb6d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d9618dd-2968-4499-9ebd-81718341f548' class='xr-var-data-in' type='checkbox'><label for='data-1d9618dd-2968-4499-9ebd-81718341f548' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span 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'>2015-02-01 00:00:00 ... 2101-01-...</div><input id='attrs-f0afe531-d19e-4ca4-aaa0-aeafed7fb32a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f0afe531-d19e-4ca4-aaa0-aeafed7fb32a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-afe82298-4305-4dbb-80e4-32ba421e77ca' class='xr-var-data-in' type='checkbox'><label for='data-afe82298-4305-4dbb-80e4-32ba421e77ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2015, 2, 1, 0, 0, 0, 0, has_year_zero=True),
        cftime.DatetimeNoLeap(2015, 3, 1, 0, 0, 0, 0, has_year_zero=True),
        cftime.DatetimeNoLeap(2015, 4, 1, 0, 0, 0, 0, has_year_zero=True), ...,
        cftime.DatetimeNoLeap(2100, 11, 1, 0, 0, 0, 0, has_year_zero=True),
        cftime.DatetimeNoLeap(2100, 12, 1, 0, 0, 0, 0, has_year_zero=True),
        cftime.DatetimeNoLeap(2101, 1, 1, 0, 0, 0, 0, has_year_zero=True)],
-      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0c55cd05-8489-4952-baa8-fdb727508daa' class='xr-section-summary-in' type='checkbox'  ><label for='section-0c55cd05-8489-4952-baa8-fdb727508daa' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-04d0e94c-1275-47d8-8797-932b55783120' class='xr-index-data-in' type='checkbox'/><label for='index-04d0e94c-1275-47d8-8797-932b55783120' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([             -90.0, -89.05759162303664,  -88.1151832460733,
+      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7ae9a7ad-4659-4a64-8d9d-91c32c8d0967' class='xr-section-summary-in' type='checkbox'  ><label for='section-7ae9a7ad-4659-4a64-8d9d-91c32c8d0967' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f1abb380-69e0-4557-99a0-510e5d444aec' class='xr-index-data-in' type='checkbox'/><label for='index-f1abb380-69e0-4557-99a0-510e5d444aec' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([             -90.0, -89.05759162303664,  -88.1151832460733,
        -87.17277486910994,  -86.2303664921466, -85.28795811518324,
         -84.3455497382199, -83.40314136125654, -82.46073298429319,
        -81.51832460732984,
@@ -8906,12 +7865,12 @@ <h3>Supplementary Material: Rechunking<a class="headerlink" href="#supplementary
          84.3455497382199,  85.28795811518324,   86.2303664921466,
         87.17277486910994,   88.1151832460733,  89.05759162303664,
                      90.0],
-      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5b65deae-8ab0-4356-b68f-498520e14107' class='xr-index-data-in' type='checkbox'/><label for='index-5b65deae-8ab0-4356-b68f-498520e14107' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   1.25,    2.5,   3.75,    5.0,   6.25,    7.5,   8.75,   10.0,
+      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-abd7a670-3655-4d0e-8af8-8643a6d83b87' class='xr-index-data-in' type='checkbox'/><label for='index-abd7a670-3655-4d0e-8af8-8643a6d83b87' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   1.25,    2.5,   3.75,    5.0,   6.25,    7.5,   8.75,   10.0,
         11.25,
        ...
         347.5, 348.75,  350.0, 351.25,  352.5, 353.75,  355.0, 356.25,  357.5,
        358.75],
-      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=288))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cf1b0ee0-3a9f-40f5-8aaa-c851575f8523' class='xr-index-data-in' type='checkbox'/><label for='index-cf1b0ee0-3a9f-40f5-8aaa-c851575f8523' 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([2015-02-01 00:00:00, 2015-03-01 00:00:00, 2015-04-01 00:00:00,
+      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=288))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-095cd359-d738-480b-b5b0-c98f89023b01' class='xr-index-data-in' type='checkbox'/><label for='index-095cd359-d738-480b-b5b0-c98f89023b01' 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([2015-02-01 00:00:00, 2015-03-01 00:00:00, 2015-04-01 00:00:00,
              2015-05-01 00:00:00, 2015-06-01 00:00:00, 2015-07-01 00:00:00,
              2015-08-01 00:00:00, 2015-09-01 00:00:00, 2015-10-01 00:00:00,
              2015-11-01 00:00:00,
@@ -8920,18 +7879,48 @@ <h3>Supplementary Material: Rechunking<a class="headerlink" href="#supplementary
              2100-07-01 00:00:00, 2100-08-01 00:00:00, 2100-09-01 00:00:00,
              2100-10-01 00:00:00, 2100-11-01 00:00:00, 2100-12-01 00:00:00,
              2101-01-01 00:00:00],
-            dtype=&#x27;object&#x27;, length=1032, calendar=&#x27;noleap&#x27;, freq=&#x27;MS&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-36266cd3-300c-44eb-85e4-fb85c77b77b3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-36266cd3-300c-44eb-85e4-fb85c77b77b3' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Reference height temperature</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div></li></ul></div></div></div></div>
+            dtype=&#x27;object&#x27;, length=1032, calendar=&#x27;noleap&#x27;, freq=&#x27;MS&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e5d04957-727b-4c7e-a647-c09869c5a7f2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e5d04957-727b-4c7e-a647-c09869c5a7f2' 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></div></div>
 </div>
-<p>Is this a good chunk size?</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">chunk_dict</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;time&quot;</span><span class="p">:</span><span class="mi">90</span><span class="p">,</span> <span class="s2">&quot;lat&quot;</span><span class="p">:</span> <span class="mi">192</span><span class="p">,</span> <span class="s2">&quot;lon&quot;</span><span class="p">:</span> <span class="mi">288</span><span class="p">}</span>
-<span class="n">tref</span> <span class="o">=</span> <span class="n">tref</span><span class="o">.</span><span class="n">chunk</span><span class="p">(</span><span class="n">chunk_dict</span><span class="p">)</span>
-<span class="n">tref</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%%time</span> 
+
+<span class="n">tref_c</span> <span class="o">=</span> <span class="n">tref_c</span><span class="o">.</span><span class="n">load</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 124 ms, sys: 212 ms, total: 336 ms
+Wall time: 1.08 s
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%%time</span>
+
+<span class="c1"># Compute monthly anomaly</span>
+
+<span class="c1"># -- 1. calculate monthly average</span>
+<span class="n">tref_grouped</span> <span class="o">=</span> <span class="n">tref</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;time.month&#39;</span><span class="p">)</span>
+<span class="n">tmean</span> <span class="o">=</span> <span class="n">tref_grouped</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="s1">&#39;time&#39;</span><span class="p">)</span>
+
+<span class="c1">#-- 2. calculate monthly anomaly</span>
+<span class="n">tos_anom</span> <span class="o">=</span> <span class="n">tref_grouped</span> <span class="o">-</span> <span class="n">tmean</span>
+<span class="n">tos_anom</span>
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 51.5 ms, sys: 418 µs, total: 51.9 ms
+Wall time: 51.2 ms
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/usr/share/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/xarray/core/indexing.py:1446: PerformanceWarning: Slicing with an out-of-order index is generating 86 times more chunks
+  return self.array[key]
+</pre></div>
+</div>
 <div class="output text_html"><div><svg style="position: absolute; width: 0; height: 0; overflow: hidden">
 <defs>
 <symbol id="icon-database" viewBox="0 0 32 32">
@@ -9296,15 +8285,12 @@ <h3>Supplementary Material: Rechunking<a class="headerlink" href="#supplementary
   fill: currentColor;
 }
 </style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;TREFHT&#x27; (time: 1032, lat: 192, lon: 288)&gt;
-dask.array&lt;chunksize=(90, 192, 288), meta=np.ndarray&gt;
+dask.array&lt;chunksize=(1, 192, 288), meta=np.ndarray&gt;
 Coordinates:
   * lat      (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 87.17 88.12 89.06 90.0
   * lon      (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
   * time     (time) object 2015-02-01 00:00:00 ... 2101-01-01 00:00:00
-Attributes:
-    units:         K
-    long_name:     Reference height temperature
-    cell_methods:  time: mean</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'>'TREFHT'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1032</li><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-9682dd64-b3bb-484c-85fd-c7ea442f2ffe' class='xr-array-in' type='checkbox' ><label for='section-9682dd64-b3bb-484c-85fd-c7ea442f2ffe' 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=(90, 192, 288), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>
+    month    (time) int64 2 3 4 5 6 7 8 9 10 11 12 ... 3 4 5 6 7 8 9 10 11 12 1</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'>'TREFHT'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1032</li><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-37b7b1b2-cec4-4220-af85-aa6af1329158' class='xr-array-in' type='checkbox' ><label for='section-37b7b1b2-cec4-4220-af85-aa6af1329158' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 192, 288), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>
     <tr>
         <td>
             <table style="border-collapse: collapse;">
@@ -9320,17 +8306,17 @@ <h3>Supplementary Material: Rechunking<a class="headerlink" href="#supplementary
                     <tr>
                         <th> Bytes </th>
                         <td> 217.69 MiB </td>
-                        <td> 18.98 MiB </td>
+                        <td> 216.00 kiB </td>
                     </tr>
                     
                     <tr>
                         <th> Shape </th>
                         <td> (1032, 192, 288) </td>
-                        <td> (90, 192, 288) </td>
+                        <td> (1, 192, 288) </td>
                     </tr>
                     <tr>
                         <th> Dask graph </th>
-                        <td colspan="2"> 12 chunks in 20 graph layers </td>
+                        <td colspan="2"> 1032 chunks in 71 graph layers </td>
                     </tr>
                     <tr>
                         <th> Data type </th>
@@ -9348,35 +8334,49 @@ <h3>Supplementary Material: Rechunking<a class="headerlink" href="#supplementary
 
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@@ -9384,7 +8384,7 @@ <h3>Supplementary Material: Rechunking<a class="headerlink" href="#supplementary
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   <line x1="80" y1="70" x2="123" y2="70" style="stroke-width:2" />
@@ -9404,7 +8404,7 @@ <h3>Supplementary Material: Rechunking<a class="headerlink" href="#supplementary
 </svg>
         </td>
     </tr>
-</table></div></div></li><li class='xr-section-item'><input id='section-8f8c1af4-5445-4511-a89d-d417e58a5610' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8f8c1af4-5445-4511-a89d-d417e58a5610' 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 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-a9f9cd1d-9db5-4c85-8ab4-b0eef2b18523' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a9f9cd1d-9db5-4c85-8ab4-b0eef2b18523' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3bdc253-ee03-4ad4-9cc2-28457b54d360' class='xr-var-data-in' type='checkbox'><label for='data-d3bdc253-ee03-4ad4-9cc2-28457b54d360' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,
+</table></div></div></li><li class='xr-section-item'><input id='section-8d0b0a37-1212-43ca-8a76-e20dd154da1e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8d0b0a37-1212-43ca-8a76-e20dd154da1e' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-858059d2-5080-4a71-a5c8-dec2fabd1721' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-858059d2-5080-4a71-a5c8-dec2fabd1721' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8783ff01-305b-4047-bba5-d4bbbbe57371' class='xr-var-data-in' type='checkbox'><label for='data-8783ff01-305b-4047-bba5-d4bbbbe57371' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,
        -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,
        -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,
        -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,
@@ -9435,13 +8435,13 @@ <h3>Supplementary Material: Rechunking<a class="headerlink" href="#supplementary
         68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,
         73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,
         79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,
-        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-e250b0b7-f3eb-40e3-84c4-e7f8273c193d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e250b0b7-f3eb-40e3-84c4-e7f8273c193d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1dc944c-96ef-4600-87c0-ea38ef154185' class='xr-var-data-in' type='checkbox'><label for='data-b1dc944c-96ef-4600-87c0-ea38ef154185' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span 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'>2015-02-01 00:00:00 ... 2101-01-...</div><input id='attrs-d6488f0d-a66c-4b19-82f2-db1d624292b9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6488f0d-a66c-4b19-82f2-db1d624292b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49c4135e-7f03-42d7-a208-9d042f6584bc' class='xr-var-data-in' type='checkbox'><label for='data-49c4135e-7f03-42d7-a208-9d042f6584bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2015, 2, 1, 0, 0, 0, 0, has_year_zero=True),
+        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-037d7824-7c13-41d8-a23e-9859219a8821' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-037d7824-7c13-41d8-a23e-9859219a8821' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00b6853e-36e9-4a5c-8025-cb441ff92cc0' class='xr-var-data-in' type='checkbox'><label for='data-00b6853e-36e9-4a5c-8025-cb441ff92cc0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span 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'>2015-02-01 00:00:00 ... 2101-01-...</div><input id='attrs-03e9cdf9-f599-451c-b47f-e7bb9601dffd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03e9cdf9-f599-451c-b47f-e7bb9601dffd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d99fb9d1-f69d-4ac7-a63d-c25338c30a0e' class='xr-var-data-in' type='checkbox'><label for='data-d99fb9d1-f69d-4ac7-a63d-c25338c30a0e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2015, 2, 1, 0, 0, 0, 0, has_year_zero=True),
        cftime.DatetimeNoLeap(2015, 3, 1, 0, 0, 0, 0, has_year_zero=True),
        cftime.DatetimeNoLeap(2015, 4, 1, 0, 0, 0, 0, has_year_zero=True), ...,
        cftime.DatetimeNoLeap(2100, 11, 1, 0, 0, 0, 0, has_year_zero=True),
        cftime.DatetimeNoLeap(2100, 12, 1, 0, 0, 0, 0, has_year_zero=True),
        cftime.DatetimeNoLeap(2101, 1, 1, 0, 0, 0, 0, has_year_zero=True)],
-      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-80368dca-f6cd-4ff6-8732-e9f78342d633' class='xr-section-summary-in' type='checkbox'  ><label for='section-80368dca-f6cd-4ff6-8732-e9f78342d633' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a895f6e7-0beb-42c7-b1c0-635ca3171e26' class='xr-index-data-in' type='checkbox'/><label for='index-a895f6e7-0beb-42c7-b1c0-635ca3171e26' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([             -90.0, -89.05759162303664,  -88.1151832460733,
+      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>month</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2 3 4 5 6 7 8 ... 7 8 9 10 11 12 1</div><input id='attrs-9ab1d33c-fd74-40c6-b151-61b677d877aa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9ab1d33c-fd74-40c6-b151-61b677d877aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89543856-c1dd-4a75-b823-05f67f3a527a' class='xr-var-data-in' type='checkbox'><label for='data-89543856-c1dd-4a75-b823-05f67f3a527a' 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([ 2,  3,  4, ..., 11, 12,  1])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-134cf251-3760-4f1b-bdff-514010a27544' class='xr-section-summary-in' type='checkbox'  ><label for='section-134cf251-3760-4f1b-bdff-514010a27544' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-39d5558b-ec5c-4303-b4be-91161cb8895c' class='xr-index-data-in' type='checkbox'/><label for='index-39d5558b-ec5c-4303-b4be-91161cb8895c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([             -90.0, -89.05759162303664,  -88.1151832460733,
        -87.17277486910994,  -86.2303664921466, -85.28795811518324,
         -84.3455497382199, -83.40314136125654, -82.46073298429319,
        -81.51832460732984,
@@ -9450,12 +8450,12 @@ <h3>Supplementary Material: Rechunking<a class="headerlink" href="#supplementary
          84.3455497382199,  85.28795811518324,   86.2303664921466,
         87.17277486910994,   88.1151832460733,  89.05759162303664,
                      90.0],
-      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6caef154-70d0-46e6-982c-cdc9373ba05f' class='xr-index-data-in' type='checkbox'/><label for='index-6caef154-70d0-46e6-982c-cdc9373ba05f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   1.25,    2.5,   3.75,    5.0,   6.25,    7.5,   8.75,   10.0,
+      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-15c6c356-73c0-4f84-b68c-af1531c1e91c' class='xr-index-data-in' type='checkbox'/><label for='index-15c6c356-73c0-4f84-b68c-af1531c1e91c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   1.25,    2.5,   3.75,    5.0,   6.25,    7.5,   8.75,   10.0,
         11.25,
        ...
         347.5, 348.75,  350.0, 351.25,  352.5, 353.75,  355.0, 356.25,  357.5,
        358.75],
-      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=288))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c8498dcf-fab8-4154-9bc9-25515af8767e' class='xr-index-data-in' type='checkbox'/><label for='index-c8498dcf-fab8-4154-9bc9-25515af8767e' 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([2015-02-01 00:00:00, 2015-03-01 00:00:00, 2015-04-01 00:00:00,
+      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=288))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4e66db00-83b3-49b3-9c84-4754a9fdd640' class='xr-index-data-in' type='checkbox'/><label for='index-4e66db00-83b3-49b3-9c84-4754a9fdd640' 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([2015-02-01 00:00:00, 2015-03-01 00:00:00, 2015-04-01 00:00:00,
              2015-05-01 00:00:00, 2015-06-01 00:00:00, 2015-07-01 00:00:00,
              2015-08-01 00:00:00, 2015-09-01 00:00:00, 2015-10-01 00:00:00,
              2015-11-01 00:00:00,
@@ -9464,9 +8464,55 @@ <h3>Supplementary Material: Rechunking<a class="headerlink" href="#supplementary
              2100-07-01 00:00:00, 2100-08-01 00:00:00, 2100-09-01 00:00:00,
              2100-10-01 00:00:00, 2100-11-01 00:00:00, 2100-12-01 00:00:00,
              2101-01-01 00:00:00],
-            dtype=&#x27;object&#x27;, length=1032, calendar=&#x27;noleap&#x27;, freq=&#x27;MS&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1c7fac8e-245d-4352-a11d-09f235d6f8a7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1c7fac8e-245d-4352-a11d-09f235d6f8a7' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Reference height temperature</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div></li></ul></div></div></div></div>
+            dtype=&#x27;object&#x27;, length=1032, calendar=&#x27;noleap&#x27;, freq=&#x27;MS&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ef13de0d-9bbc-43f9-b523-6526f2d7599d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ef13de0d-9bbc-43f9-b523-6526f2d7599d' 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></div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%%time</span>
+<span class="n">computed_anom</span> <span class="o">=</span> <span class="n">tos_anom</span><span class="o">.</span><span class="n">load</span><span class="p">()</span>
+<span class="nb">type</span><span class="p">(</span><span class="n">computed_anom</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 2.5 s, sys: 296 ms, total: 2.79 s
+Wall time: 5.88 s
+</pre></div>
+</div>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>xarray.core.dataarray.DataArray
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">tos_anom</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">lon</span><span class="o">=</span><span class="mi">310</span><span class="p">,</span> <span class="n">lat</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;nearest&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span> <span class="n">size</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&lt;matplotlib.lines.Line2D at 0x7fd72020fd30&gt;]
+</pre></div>
+</div>
+<img alt="../_images/7399b91edaf79a827264bf510c3982b92ecd9683536efc4efd3bb51aaf10c3bc.png" src="../_images/7399b91edaf79a827264bf510c3982b92ecd9683536efc4efd3bb51aaf10c3bc.png" />
+</div>
 </div>
-<p>We can do more complex calculations too:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">tos_anom</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">time</span><span class="o">=</span><span class="s1">&#39;2030-01-01&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.collections.QuadMesh at 0x7fd6e8e0ce20&gt;
+</pre></div>
+</div>
+<img alt="../_images/bb55cc8c334bd8d58adacbb04b3b14d8198d830abb3c613abc2d4b31a85ab222.png" src="../_images/bb55cc8c334bd8d58adacbb04b3b14d8198d830abb3c613abc2d4b31a85ab222.png" />
+</div>
+</div>
+<div class="alert alert-block alert-info" markdown="1">
+<p><b>TIP:</b> Using Xarray plotting functionality automatically triggers computations on the Dask Array, similar to <code class="docutils literal notranslate"><span class="pre">.compute()</span></code>.</p>
+</div><p>We can do more complex calculations too:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">rolling_mean</span> <span class="o">=</span> <span class="n">tref</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="n">time</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
@@ -9839,7 +8885,7 @@ <h3>Supplementary Material: Rechunking<a class="headerlink" href="#supplementary
   fill: currentColor;
 }
 </style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;TREFHT&#x27; (time: 1032, lat: 192, lon: 288)&gt;
-dask.array&lt;chunksize=(94, 192, 288), meta=np.ndarray&gt;
+dask.array&lt;chunksize=(124, 192, 288), meta=np.ndarray&gt;
 Coordinates:
   * lat      (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 87.17 88.12 89.06 90.0
   * lon      (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
@@ -9847,7 +8893,7 @@ <h3>Supplementary Material: Rechunking<a class="headerlink" href="#supplementary
 Attributes:
     units:         K
     long_name:     Reference height temperature
-    cell_methods:  time: mean</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'>'TREFHT'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1032</li><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-577ae23d-889c-4b6b-92c5-669c132f351e' class='xr-array-in' type='checkbox' ><label for='section-577ae23d-889c-4b6b-92c5-669c132f351e' 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=(94, 192, 288), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>
+    cell_methods:  time: mean</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'>'TREFHT'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1032</li><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-48a6d997-7cec-4323-9274-2570c1157cf5' class='xr-array-in' type='checkbox' ><label for='section-48a6d997-7cec-4323-9274-2570c1157cf5' 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=(124, 192, 288), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>
     <tr>
         <td>
             <table style="border-collapse: collapse;">
@@ -9863,17 +8909,17 @@ <h3>Supplementary Material: Rechunking<a class="headerlink" href="#supplementary
                     <tr>
                         <th> Bytes </th>
                         <td> 217.69 MiB </td>
-                        <td> 19.83 MiB </td>
+                        <td> 26.16 MiB </td>
                     </tr>
                     
                     <tr>
                         <th> Shape </th>
                         <td> (1032, 192, 288) </td>
-                        <td> (94, 192, 288) </td>
+                        <td> (124, 192, 288) </td>
                     </tr>
                     <tr>
                         <th> Dask graph </th>
-                        <td colspan="2"> 12 chunks in 45 graph layers </td>
+                        <td colspan="2"> 9 chunks in 44 graph layers </td>
                     </tr>
                     <tr>
                         <th> Data type </th>
@@ -9891,17 +8937,14 @@ <h3>Supplementary Material: Rechunking<a class="headerlink" href="#supplementary
 
   <!-- Vertical lines -->
   <line x1="10" y1="0" x2="10" y2="41" style="stroke-width:2" />
-  <line x1="16" y1="6" x2="16" y2="48" />
-  <line x1="22" y1="12" x2="22" y2="54" />
-  <line x1="28" y1="18" x2="28" y2="60" />
+  <line x1="18" y1="8" x2="18" y2="50" />
+  <line x1="26" y1="16" x2="26" y2="58" />
   <line x1="34" y1="24" x2="34" y2="66" />
-  <line x1="41" y1="31" x2="41" y2="72" />
-  <line x1="47" y1="37" x2="47" y2="78" />
-  <line x1="53" y1="43" x2="53" y2="85" />
+  <line x1="43" y1="33" x2="43" y2="74" />
+  <line x1="51" y1="41" x2="51" y2="83" />
   <line x1="59" y1="49" x2="59" y2="91" />
-  <line x1="65" y1="55" x2="65" y2="97" />
-  <line x1="71" y1="61" x2="71" y2="103" />
-  <line x1="77" y1="67" x2="77" y2="109" />
+  <line x1="67" y1="57" x2="67" y2="99" />
+  <line x1="75" y1="65" x2="75" y2="107" />
   <line x1="80" y1="70" x2="80" y2="112" style="stroke-width:2" />
 
   <!-- Colored Rectangle -->
@@ -9909,17 +8952,14 @@ <h3>Supplementary Material: Rechunking<a class="headerlink" href="#supplementary
 
   <!-- Horizontal lines -->
   <line x1="10" y1="0" x2="53" y2="0" style="stroke-width:2" />
-  <line x1="16" y1="6" x2="59" y2="6" />
-  <line x1="22" y1="12" x2="65" y2="12" />
-  <line x1="28" y1="18" x2="72" y2="18" />
+  <line x1="18" y1="8" x2="61" y2="8" />
+  <line x1="26" y1="16" x2="69" y2="16" />
   <line x1="34" y1="24" x2="78" y2="24" />
-  <line x1="41" y1="31" x2="84" y2="31" />
-  <line x1="47" y1="37" x2="90" y2="37" />
-  <line x1="53" y1="43" x2="96" y2="43" />
+  <line x1="43" y1="33" x2="86" y2="33" />
+  <line x1="51" y1="41" x2="94" y2="41" />
   <line x1="59" y1="49" x2="102" y2="49" />
-  <line x1="65" y1="55" x2="108" y2="55" />
-  <line x1="71" y1="61" x2="115" y2="61" />
-  <line x1="77" y1="67" x2="121" y2="67" />
+  <line x1="67" y1="57" x2="111" y2="57" />
+  <line x1="75" y1="65" x2="119" y2="65" />
   <line x1="80" y1="70" x2="123" y2="70" style="stroke-width:2" />
 
   <!-- Vertical lines -->
@@ -9947,7 +8987,7 @@ <h3>Supplementary Material: Rechunking<a class="headerlink" href="#supplementary
 </svg>
         </td>
     </tr>
-</table></div></div></li><li class='xr-section-item'><input id='section-08c365f5-51de-4cec-a04e-c30ce004169e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-08c365f5-51de-4cec-a04e-c30ce004169e' 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 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-83471358-dd61-4c71-b6c9-7242f0d61d8b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83471358-dd61-4c71-b6c9-7242f0d61d8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cdf19e2e-6003-44bf-a2d4-fdd9c86da991' class='xr-var-data-in' type='checkbox'><label for='data-cdf19e2e-6003-44bf-a2d4-fdd9c86da991' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,
+</table></div></div></li><li class='xr-section-item'><input id='section-b3b10729-8ab3-4954-96aa-b93c79b9fc19' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b3b10729-8ab3-4954-96aa-b93c79b9fc19' 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 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-ea5d7fa7-0e8f-4398-8f56-68163a606c28' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea5d7fa7-0e8f-4398-8f56-68163a606c28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-544e2c30-ff67-4c5b-aa9b-be2ee06b17bd' class='xr-var-data-in' type='checkbox'><label for='data-544e2c30-ff67-4c5b-aa9b-be2ee06b17bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,
        -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,
        -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,
        -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,
@@ -9978,13 +9018,13 @@ <h3>Supplementary Material: Rechunking<a class="headerlink" href="#supplementary
         68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,
         73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,
         79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,
-        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-8e1a1bef-cd85-4981-b080-522239ad20e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8e1a1bef-cd85-4981-b080-522239ad20e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8be441b-c754-4c4b-8c7f-4bfc69d5c6a7' class='xr-var-data-in' type='checkbox'><label for='data-c8be441b-c754-4c4b-8c7f-4bfc69d5c6a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span 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'>2015-02-01 00:00:00 ... 2101-01-...</div><input id='attrs-fa9f732e-df13-44d7-a2cb-3c6c8828b911' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa9f732e-df13-44d7-a2cb-3c6c8828b911' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e1f58be-a224-418f-b7a2-3243dbfb7a8e' class='xr-var-data-in' type='checkbox'><label for='data-1e1f58be-a224-418f-b7a2-3243dbfb7a8e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2015, 2, 1, 0, 0, 0, 0, has_year_zero=True),
+        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-40969261-f76a-435a-9031-764a612bba9d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40969261-f76a-435a-9031-764a612bba9d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7775e326-2289-4cb5-8a74-a1a471ad9ecf' class='xr-var-data-in' type='checkbox'><label for='data-7775e326-2289-4cb5-8a74-a1a471ad9ecf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span 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'>2015-02-01 00:00:00 ... 2101-01-...</div><input id='attrs-3fcca60d-3e03-4d81-af56-fab539cd984d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3fcca60d-3e03-4d81-af56-fab539cd984d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a565829-bc6e-42a1-bbfb-d4c3c69bb731' class='xr-var-data-in' type='checkbox'><label for='data-7a565829-bc6e-42a1-bbfb-d4c3c69bb731' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2015, 2, 1, 0, 0, 0, 0, has_year_zero=True),
        cftime.DatetimeNoLeap(2015, 3, 1, 0, 0, 0, 0, has_year_zero=True),
        cftime.DatetimeNoLeap(2015, 4, 1, 0, 0, 0, 0, has_year_zero=True), ...,
        cftime.DatetimeNoLeap(2100, 11, 1, 0, 0, 0, 0, has_year_zero=True),
        cftime.DatetimeNoLeap(2100, 12, 1, 0, 0, 0, 0, has_year_zero=True),
        cftime.DatetimeNoLeap(2101, 1, 1, 0, 0, 0, 0, has_year_zero=True)],
-      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7a74d308-81ec-46df-9bef-c87cc8a7ca46' class='xr-section-summary-in' type='checkbox'  ><label for='section-7a74d308-81ec-46df-9bef-c87cc8a7ca46' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-92cd5518-41f9-42d2-ad47-cbb2997c9d24' class='xr-index-data-in' type='checkbox'/><label for='index-92cd5518-41f9-42d2-ad47-cbb2997c9d24' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([             -90.0, -89.05759162303664,  -88.1151832460733,
+      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-93e9a296-e253-4dc7-b9b4-cebe72322230' class='xr-section-summary-in' type='checkbox'  ><label for='section-93e9a296-e253-4dc7-b9b4-cebe72322230' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1fc236d8-43d6-4681-8101-524d480d49df' class='xr-index-data-in' type='checkbox'/><label for='index-1fc236d8-43d6-4681-8101-524d480d49df' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([             -90.0, -89.05759162303664,  -88.1151832460733,
        -87.17277486910994,  -86.2303664921466, -85.28795811518324,
         -84.3455497382199, -83.40314136125654, -82.46073298429319,
        -81.51832460732984,
@@ -9993,12 +9033,12 @@ <h3>Supplementary Material: Rechunking<a class="headerlink" href="#supplementary
          84.3455497382199,  85.28795811518324,   86.2303664921466,
         87.17277486910994,   88.1151832460733,  89.05759162303664,
                      90.0],
-      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-82dc3780-358e-4376-aa4e-f27d5c7e0d25' class='xr-index-data-in' type='checkbox'/><label for='index-82dc3780-358e-4376-aa4e-f27d5c7e0d25' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   1.25,    2.5,   3.75,    5.0,   6.25,    7.5,   8.75,   10.0,
+      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-80108e08-2733-498e-801d-f5bb987534fe' class='xr-index-data-in' type='checkbox'/><label for='index-80108e08-2733-498e-801d-f5bb987534fe' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   1.25,    2.5,   3.75,    5.0,   6.25,    7.5,   8.75,   10.0,
         11.25,
        ...
         347.5, 348.75,  350.0, 351.25,  352.5, 353.75,  355.0, 356.25,  357.5,
        358.75],
-      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=288))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bacfb5ba-49dc-4403-ba41-4bc2f0edfbfa' class='xr-index-data-in' type='checkbox'/><label for='index-bacfb5ba-49dc-4403-ba41-4bc2f0edfbfa' 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([2015-02-01 00:00:00, 2015-03-01 00:00:00, 2015-04-01 00:00:00,
+      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=288))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-98006a7d-c780-4d76-b26a-354343b43d5f' class='xr-index-data-in' type='checkbox'/><label for='index-98006a7d-c780-4d76-b26a-354343b43d5f' 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([2015-02-01 00:00:00, 2015-03-01 00:00:00, 2015-04-01 00:00:00,
              2015-05-01 00:00:00, 2015-06-01 00:00:00, 2015-07-01 00:00:00,
              2015-08-01 00:00:00, 2015-09-01 00:00:00, 2015-10-01 00:00:00,
              2015-11-01 00:00:00,
@@ -10007,7 +9047,7 @@ <h3>Supplementary Material: Rechunking<a class="headerlink" href="#supplementary
              2100-07-01 00:00:00, 2100-08-01 00:00:00, 2100-09-01 00:00:00,
              2100-10-01 00:00:00, 2100-11-01 00:00:00, 2100-12-01 00:00:00,
              2101-01-01 00:00:00],
-            dtype=&#x27;object&#x27;, length=1032, calendar=&#x27;noleap&#x27;, freq=&#x27;MS&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c47a1ea6-7652-4ba7-b9cf-ef2e40a9a99a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c47a1ea6-7652-4ba7-b9cf-ef2e40a9a99a' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Reference height temperature</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div></li></ul></div></div></div></div>
+            dtype=&#x27;object&#x27;, length=1032, calendar=&#x27;noleap&#x27;, freq=&#x27;MS&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-574b10fe-37cf-4522-b683-1b5f5b87e757' class='xr-section-summary-in' type='checkbox'  checked><label for='section-574b10fe-37cf-4522-b683-1b5f5b87e757' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Reference height temperature</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div></li></ul></div></div></div></div>
 </div>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -10381,7 +9421,7 @@ <h3>Supplementary Material: Rechunking<a class="headerlink" href="#supplementary
   fill: currentColor;
 }
 </style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;TREFHT&#x27; (time: 1032)&gt;
-dask.array&lt;chunksize=(94,), meta=np.ndarray&gt;
+dask.array&lt;chunksize=(124,), meta=np.ndarray&gt;
 Coordinates:
     lat      float64 -71.15
     lon      float64 1.25
@@ -10389,7 +9429,7 @@ <h3>Supplementary Material: Rechunking<a class="headerlink" href="#supplementary
 Attributes:
     units:         K
     long_name:     Reference height temperature
-    cell_methods:  time: mean</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'>'TREFHT'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1032</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-9d27a1d6-bc21-4784-a306-ef4e41c0f411' class='xr-array-in' type='checkbox' ><label for='section-9d27a1d6-bc21-4784-a306-ef4e41c0f411' 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=(94,), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>
+    cell_methods:  time: mean</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'>'TREFHT'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1032</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-efcd91ae-b8c3-4f40-ba0b-6e1fa8ea25b1' class='xr-array-in' type='checkbox' ><label for='section-efcd91ae-b8c3-4f40-ba0b-6e1fa8ea25b1' 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=(124,), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>
     <tr>
         <td>
             <table style="border-collapse: collapse;">
@@ -10405,17 +9445,17 @@ <h3>Supplementary Material: Rechunking<a class="headerlink" href="#supplementary
                     <tr>
                         <th> Bytes </th>
                         <td> 4.03 kiB </td>
-                        <td> 376 B </td>
+                        <td> 496 B </td>
                     </tr>
                     
                     <tr>
                         <th> Shape </th>
                         <td> (1032,) </td>
-                        <td> (94,) </td>
+                        <td> (124,) </td>
                     </tr>
                     <tr>
                         <th> Dask graph </th>
-                        <td colspan="2"> 12 chunks in 46 graph layers </td>
+                        <td colspan="2"> 9 chunks in 45 graph layers </td>
                     </tr>
                     <tr>
                         <th> Data type </th>
@@ -10433,17 +9473,14 @@ <h3>Supplementary Material: Rechunking<a class="headerlink" href="#supplementary
 
   <!-- Vertical lines -->
   <line x1="0" y1="0" x2="0" y2="25" style="stroke-width:2" />
-  <line x1="10" y1="0" x2="10" y2="25" />
-  <line x1="21" y1="0" x2="21" y2="25" />
-  <line x1="31" y1="0" x2="31" y2="25" />
+  <line x1="14" y1="0" x2="14" y2="25" />
+  <line x1="28" y1="0" x2="28" y2="25" />
   <line x1="42" y1="0" x2="42" y2="25" />
-  <line x1="52" y1="0" x2="52" y2="25" />
-  <line x1="63" y1="0" x2="63" y2="25" />
-  <line x1="73" y1="0" x2="73" y2="25" />
+  <line x1="56" y1="0" x2="56" y2="25" />
+  <line x1="70" y1="0" x2="70" y2="25" />
   <line x1="84" y1="0" x2="84" y2="25" />
-  <line x1="94" y1="0" x2="94" y2="25" />
-  <line x1="105" y1="0" x2="105" y2="25" />
-  <line x1="115" y1="0" x2="115" y2="25" />
+  <line x1="98" y1="0" x2="98" y2="25" />
+  <line x1="112" y1="0" x2="112" y2="25" />
   <line x1="120" y1="0" x2="120" y2="25" style="stroke-width:2" />
 
   <!-- Colored Rectangle -->
@@ -10455,13 +9492,13 @@ <h3>Supplementary Material: Rechunking<a class="headerlink" href="#supplementary
 </svg>
         </td>
     </tr>
-</table></div></div></li><li class='xr-section-item'><input id='section-74d4fa3d-ec6b-4fcd-b354-d3b9589e9e96' class='xr-section-summary-in' type='checkbox'  checked><label for='section-74d4fa3d-ec6b-4fcd-b354-d3b9589e9e96' 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>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-71.15</div><input id='attrs-580073af-d864-4b0c-9fa6-72ff63032af2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-580073af-d864-4b0c-9fa6-72ff63032af2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-98d0ea0e-bba1-4339-af97-38f407d35587' class='xr-var-data-in' type='checkbox'><label for='data-98d0ea0e-bba1-4339-af97-38f407d35587' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array(-71.15183246)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.25</div><input id='attrs-661fb6ee-3c38-4ee4-a013-645a2eef5f66' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-661fb6ee-3c38-4ee4-a013-645a2eef5f66' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b566c0be-cbc9-46ff-8a27-6288e4354da0' class='xr-var-data-in' type='checkbox'><label for='data-b566c0be-cbc9-46ff-8a27-6288e4354da0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array(1.25)</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'>2015-02-01 00:00:00 ... 2101-01-...</div><input id='attrs-4d9f530e-a33e-432b-ac7c-c2bc932f382d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4d9f530e-a33e-432b-ac7c-c2bc932f382d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d4af45cc-ac54-4955-aea3-66c65479dfe3' class='xr-var-data-in' type='checkbox'><label for='data-d4af45cc-ac54-4955-aea3-66c65479dfe3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2015, 2, 1, 0, 0, 0, 0, has_year_zero=True),
+</table></div></div></li><li class='xr-section-item'><input id='section-52de6a67-ae63-45fd-ba66-cb3664968b84' class='xr-section-summary-in' type='checkbox'  checked><label for='section-52de6a67-ae63-45fd-ba66-cb3664968b84' 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>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-71.15</div><input id='attrs-bae4ad4b-870d-4708-8ad1-deb965aa00b9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bae4ad4b-870d-4708-8ad1-deb965aa00b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-460f6dc5-3693-435b-a153-22588d773b72' class='xr-var-data-in' type='checkbox'><label for='data-460f6dc5-3693-435b-a153-22588d773b72' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array(-71.15183246)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.25</div><input id='attrs-ea28bb68-db53-4574-a4d9-0cffad4a1bce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea28bb68-db53-4574-a4d9-0cffad4a1bce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52442333-e371-4550-8b96-143090483f29' class='xr-var-data-in' type='checkbox'><label for='data-52442333-e371-4550-8b96-143090483f29' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array(1.25)</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'>2015-02-01 00:00:00 ... 2101-01-...</div><input id='attrs-a34af9bd-1fea-4900-b51a-30e77a621485' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a34af9bd-1fea-4900-b51a-30e77a621485' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a12fac0-8542-4a94-adcb-e0d424dd4d2b' class='xr-var-data-in' type='checkbox'><label for='data-3a12fac0-8542-4a94-adcb-e0d424dd4d2b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2015, 2, 1, 0, 0, 0, 0, has_year_zero=True),
        cftime.DatetimeNoLeap(2015, 3, 1, 0, 0, 0, 0, has_year_zero=True),
        cftime.DatetimeNoLeap(2015, 4, 1, 0, 0, 0, 0, has_year_zero=True), ...,
        cftime.DatetimeNoLeap(2100, 11, 1, 0, 0, 0, 0, has_year_zero=True),
        cftime.DatetimeNoLeap(2100, 12, 1, 0, 0, 0, 0, has_year_zero=True),
        cftime.DatetimeNoLeap(2101, 1, 1, 0, 0, 0, 0, has_year_zero=True)],
-      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f70806fa-e31e-4432-9d4c-c261de077fe4' class='xr-section-summary-in' type='checkbox'  ><label for='section-f70806fa-e31e-4432-9d4c-c261de077fe4' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fe2ba630-f84e-464e-84a5-1be7af57b05b' class='xr-index-data-in' type='checkbox'/><label for='index-fe2ba630-f84e-464e-84a5-1be7af57b05b' 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([2015-02-01 00:00:00, 2015-03-01 00:00:00, 2015-04-01 00:00:00,
+      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6a903b69-9fa8-4b49-8c3f-8ffcf11c3c0e' class='xr-section-summary-in' type='checkbox'  ><label for='section-6a903b69-9fa8-4b49-8c3f-8ffcf11c3c0e' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-596b32c8-c006-46c3-8d52-3c8e79936fc2' class='xr-index-data-in' type='checkbox'/><label for='index-596b32c8-c006-46c3-8d52-3c8e79936fc2' 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([2015-02-01 00:00:00, 2015-03-01 00:00:00, 2015-04-01 00:00:00,
              2015-05-01 00:00:00, 2015-06-01 00:00:00, 2015-07-01 00:00:00,
              2015-08-01 00:00:00, 2015-09-01 00:00:00, 2015-10-01 00:00:00,
              2015-11-01 00:00:00,
@@ -10470,7 +9507,7 @@ <h3>Supplementary Material: Rechunking<a class="headerlink" href="#supplementary
              2100-07-01 00:00:00, 2100-08-01 00:00:00, 2100-09-01 00:00:00,
              2100-10-01 00:00:00, 2100-11-01 00:00:00, 2100-12-01 00:00:00,
              2101-01-01 00:00:00],
-            dtype=&#x27;object&#x27;, length=1032, calendar=&#x27;noleap&#x27;, freq=&#x27;MS&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-53e93e12-d3f2-4c76-9e53-18d925b9f5f3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-53e93e12-d3f2-4c76-9e53-18d925b9f5f3' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Reference height temperature</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div></li></ul></div></div></div></div>
+            dtype=&#x27;object&#x27;, length=1032, calendar=&#x27;noleap&#x27;, freq=&#x27;MS&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7fa90848-de37-4a46-bd5f-23c4b9655661' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7fa90848-de37-4a46-bd5f-23c4b9655661' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Reference height temperature</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div></li></ul></div></div></div></div>
 </div>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -10852,7 +9889,7 @@ <h3>Supplementary Material: Rechunking<a class="headerlink" href="#supplementary
 Attributes:
     units:         K
     long_name:     Reference height temperature
-    cell_methods:  time: mean</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'>'TREFHT'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1032</li><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-9e0f38b9-8783-4a36-8c88-b5647bb78b0b' class='xr-array-in' type='checkbox' ><label for='section-9e0f38b9-8783-4a36-8c88-b5647bb78b0b' 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>nan nan nan nan nan nan nan ... 273.4 273.4 273.4 273.4 273.4 273.4</span></div><div class='xr-array-data'><pre>array([[[      nan,       nan,       nan, ...,       nan,       nan,
+    cell_methods:  time: mean</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'>'TREFHT'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1032</li><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-adef2406-aaf1-42c8-a649-2852aac1a4dc' class='xr-array-in' type='checkbox' ><label for='section-adef2406-aaf1-42c8-a649-2852aac1a4dc' 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>nan nan nan nan nan nan nan ... 273.4 273.4 273.4 273.4 273.4 273.4</span></div><div class='xr-array-data'><pre>array([[[      nan,       nan,       nan, ...,       nan,       nan,
                nan],
         [      nan,       nan,       nan, ...,       nan,       nan,
                nan],
@@ -10892,7 +9929,7 @@ <h3>Supplementary Material: Rechunking<a class="headerlink" href="#supplementary
         [273.29013, 273.28958, 273.28906, ..., 273.2929 , 273.29178,
          273.29086],
         [273.3635 , 273.36328, 273.36313, ..., 273.36423, 273.36398,
-         273.3637 ]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-c17e19c6-6408-4b1e-8e26-254f5798ee2e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c17e19c6-6408-4b1e-8e26-254f5798ee2e' 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 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-bd02a5af-319e-4599-bd95-950486088bb1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd02a5af-319e-4599-bd95-950486088bb1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b185f469-cb1d-421e-95e9-ac80e6733e17' class='xr-var-data-in' type='checkbox'><label for='data-b185f469-cb1d-421e-95e9-ac80e6733e17' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,
+         273.3637 ]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-37795ef9-8c84-41e0-82d2-ae3379775bf5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-37795ef9-8c84-41e0-82d2-ae3379775bf5' 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 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-da8ed527-e0b3-4e3d-86d0-4305d5371e36' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-da8ed527-e0b3-4e3d-86d0-4305d5371e36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a85e5cc-763d-44e9-9c55-256392253db5' class='xr-var-data-in' type='checkbox'><label for='data-2a85e5cc-763d-44e9-9c55-256392253db5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,
        -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,
        -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,
        -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,
@@ -10923,13 +9960,13 @@ <h3>Supplementary Material: Rechunking<a class="headerlink" href="#supplementary
         68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,
         73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,
         79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,
-        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-746c3538-1b4c-4479-b90d-4bda3fcd64f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-746c3538-1b4c-4479-b90d-4bda3fcd64f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38178f5d-7a98-47f6-87e7-14501f534a5e' class='xr-var-data-in' type='checkbox'><label for='data-38178f5d-7a98-47f6-87e7-14501f534a5e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span 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'>2015-02-01 00:00:00 ... 2101-01-...</div><input id='attrs-89742fa5-0c35-4ca7-b5fe-1e65862a0587' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-89742fa5-0c35-4ca7-b5fe-1e65862a0587' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5de63ca9-25e8-4cb8-be7e-8bd50c29005b' class='xr-var-data-in' type='checkbox'><label for='data-5de63ca9-25e8-4cb8-be7e-8bd50c29005b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2015, 2, 1, 0, 0, 0, 0, has_year_zero=True),
+        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-02e7649b-0fbc-4073-bed2-e9d9f7055eee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-02e7649b-0fbc-4073-bed2-e9d9f7055eee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ccb0952d-3285-4a36-af73-e3af0d4d7635' class='xr-var-data-in' type='checkbox'><label for='data-ccb0952d-3285-4a36-af73-e3af0d4d7635' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span 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'>2015-02-01 00:00:00 ... 2101-01-...</div><input id='attrs-834f0680-09bd-460c-9392-a1edefeee194' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-834f0680-09bd-460c-9392-a1edefeee194' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4958b039-4880-45d9-9c7c-85e544dee1bd' class='xr-var-data-in' type='checkbox'><label for='data-4958b039-4880-45d9-9c7c-85e544dee1bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2015, 2, 1, 0, 0, 0, 0, has_year_zero=True),
        cftime.DatetimeNoLeap(2015, 3, 1, 0, 0, 0, 0, has_year_zero=True),
        cftime.DatetimeNoLeap(2015, 4, 1, 0, 0, 0, 0, has_year_zero=True), ...,
        cftime.DatetimeNoLeap(2100, 11, 1, 0, 0, 0, 0, has_year_zero=True),
        cftime.DatetimeNoLeap(2100, 12, 1, 0, 0, 0, 0, has_year_zero=True),
        cftime.DatetimeNoLeap(2101, 1, 1, 0, 0, 0, 0, has_year_zero=True)],
-      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e2475a71-8a97-48f0-a104-f15f6905b982' class='xr-section-summary-in' type='checkbox'  ><label for='section-e2475a71-8a97-48f0-a104-f15f6905b982' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-86e46141-c240-472d-b9dc-ef3aaf84df88' class='xr-index-data-in' type='checkbox'/><label for='index-86e46141-c240-472d-b9dc-ef3aaf84df88' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([             -90.0, -89.05759162303664,  -88.1151832460733,
+      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0aa6651b-0aa9-4723-8f93-87603ddcaca1' class='xr-section-summary-in' type='checkbox'  ><label for='section-0aa6651b-0aa9-4723-8f93-87603ddcaca1' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-eef738d8-1119-4844-a273-ffde2824e215' class='xr-index-data-in' type='checkbox'/><label for='index-eef738d8-1119-4844-a273-ffde2824e215' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([             -90.0, -89.05759162303664,  -88.1151832460733,
        -87.17277486910994,  -86.2303664921466, -85.28795811518324,
         -84.3455497382199, -83.40314136125654, -82.46073298429319,
        -81.51832460732984,
@@ -10938,12 +9975,12 @@ <h3>Supplementary Material: Rechunking<a class="headerlink" href="#supplementary
          84.3455497382199,  85.28795811518324,   86.2303664921466,
         87.17277486910994,   88.1151832460733,  89.05759162303664,
                      90.0],
-      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-26f6c2fa-daec-48aa-8120-fa5894a9e862' class='xr-index-data-in' type='checkbox'/><label for='index-26f6c2fa-daec-48aa-8120-fa5894a9e862' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   1.25,    2.5,   3.75,    5.0,   6.25,    7.5,   8.75,   10.0,
+      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-db025c9c-940a-4d12-b3df-e15cfd59fc8b' class='xr-index-data-in' type='checkbox'/><label for='index-db025c9c-940a-4d12-b3df-e15cfd59fc8b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   1.25,    2.5,   3.75,    5.0,   6.25,    7.5,   8.75,   10.0,
         11.25,
        ...
         347.5, 348.75,  350.0, 351.25,  352.5, 353.75,  355.0, 356.25,  357.5,
        358.75],
-      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=288))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f681c5d1-975b-42a4-a827-372ac935275b' class='xr-index-data-in' type='checkbox'/><label for='index-f681c5d1-975b-42a4-a827-372ac935275b' 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([2015-02-01 00:00:00, 2015-03-01 00:00:00, 2015-04-01 00:00:00,
+      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=288))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-76e368c8-9e43-493b-9f53-4ef30fcf09f4' class='xr-index-data-in' type='checkbox'/><label for='index-76e368c8-9e43-493b-9f53-4ef30fcf09f4' 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([2015-02-01 00:00:00, 2015-03-01 00:00:00, 2015-04-01 00:00:00,
              2015-05-01 00:00:00, 2015-06-01 00:00:00, 2015-07-01 00:00:00,
              2015-08-01 00:00:00, 2015-09-01 00:00:00, 2015-10-01 00:00:00,
              2015-11-01 00:00:00,
@@ -10952,7 +9989,7 @@ <h3>Supplementary Material: Rechunking<a class="headerlink" href="#supplementary
              2100-07-01 00:00:00, 2100-08-01 00:00:00, 2100-09-01 00:00:00,
              2100-10-01 00:00:00, 2100-11-01 00:00:00, 2100-12-01 00:00:00,
              2101-01-01 00:00:00],
-            dtype=&#x27;object&#x27;, length=1032, calendar=&#x27;noleap&#x27;, freq=&#x27;MS&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-eaa23426-7f95-450e-832d-56ee8a7a6653' class='xr-section-summary-in' type='checkbox'  checked><label for='section-eaa23426-7f95-450e-832d-56ee8a7a6653' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Reference height temperature</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div></li></ul></div></div></div></div>
+            dtype=&#x27;object&#x27;, length=1032, calendar=&#x27;noleap&#x27;, freq=&#x27;MS&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1bceff38-07ba-4955-a39c-936af9f4af1e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1bceff38-07ba-4955-a39c-936af9f4af1e' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Reference height temperature</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div></li></ul></div></div></div></div>
 </div>
 </div>
 <div class="section" id="supplementary-material-advanced-workflows-and-automatic-parallelization-using-apply-ufunc">
@@ -10964,24 +10001,18 @@ <h3>Supplementary Material: Advanced workflows and automatic parallelization usi
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="c1"># return saturation vapor pressure</span>
-<span class="c1"># using Clausius-Clapeyron equation</span>
+
 <span class="k">def</span> <span class="nf">sat_p</span><span class="p">(</span><span class="n">t</span><span class="p">):</span>
-    <span class="k">return</span> <span class="mf">0.611</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="mf">17.67</span><span class="o">*</span><span class="p">(</span><span class="n">t</span><span class="o">-</span><span class="mf">273.15</span><span class="p">)</span><span class="o">*</span><span class="p">((</span><span class="n">t</span><span class="o">-</span><span class="mf">29.65</span><span class="p">)</span><span class="o">**</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)))</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">es</span><span class="o">=</span><span class="n">xr</span><span class="o">.</span><span class="n">apply_ufunc</span><span class="p">(</span><span class="n">sat_p</span><span class="p">,</span><span class="n">tref</span><span class="p">,</span><span class="n">dask</span><span class="o">=</span><span class="s2">&quot;parallelized&quot;</span><span class="p">,</span><span class="n">output_dtypes</span><span class="o">=</span><span class="p">[</span><span class="nb">float</span><span class="p">])</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Calculate saturation vapor pressure using Clausius-Clapeyron equation&quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="mf">0.611</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="mf">17.67</span> <span class="o">*</span> <span class="p">(</span><span class="n">t</span><span class="o">-</span><span class="mf">273.15</span><span class="p">)</span><span class="o">*</span><span class="p">((</span><span class="n">t</span><span class="o">-</span><span class="mf">29.65</span><span class="p">)</span><span class="o">**</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)))</span>
 </pre></div>
 </div>
 </div>
 </div>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">es</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">es</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">apply_ufunc</span><span class="p">(</span><span class="n">sat_p</span><span class="p">,</span> <span class="n">tref</span><span class="p">,</span> <span class="n">dask</span><span class="o">=</span><span class="s2">&quot;parallelized&quot;</span><span class="p">,</span> <span class="n">output_dtypes</span><span class="o">=</span><span class="p">[</span><span class="nb">float</span><span class="p">])</span>
+<span class="n">es</span>
 </pre></div>
 </div>
 </div>
@@ -11350,11 +10381,11 @@ <h3>Supplementary Material: Advanced workflows and automatic parallelization usi
   fill: currentColor;
 }
 </style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;TREFHT&#x27; (time: 1032, lat: 192, lon: 288)&gt;
-dask.array&lt;chunksize=(90, 192, 288), meta=np.ndarray&gt;
+dask.array&lt;chunksize=(120, 192, 288), meta=np.ndarray&gt;
 Coordinates:
   * lat      (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 87.17 88.12 89.06 90.0
   * lon      (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
-  * time     (time) object 2015-02-01 00:00:00 ... 2101-01-01 00:00:00</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'>'TREFHT'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1032</li><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-17087375-179e-4b17-89bd-50b07afe5ee2' class='xr-array-in' type='checkbox' ><label for='section-17087375-179e-4b17-89bd-50b07afe5ee2' 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=(90, 192, 288), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>
+  * time     (time) object 2015-02-01 00:00:00 ... 2101-01-01 00:00:00</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'>'TREFHT'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1032</li><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-97f1a479-ccd9-4a8a-836a-0a953aae5bcc' class='xr-array-in' type='checkbox' ><label for='section-97f1a479-ccd9-4a8a-836a-0a953aae5bcc' 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=(120, 192, 288), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>
     <tr>
         <td>
             <table style="border-collapse: collapse;">
@@ -11370,17 +10401,17 @@ <h3>Supplementary Material: Advanced workflows and automatic parallelization usi
                     <tr>
                         <th> Bytes </th>
                         <td> 435.38 MiB </td>
-                        <td> 37.97 MiB </td>
+                        <td> 50.62 MiB </td>
                     </tr>
                     
                     <tr>
                         <th> Shape </th>
                         <td> (1032, 192, 288) </td>
-                        <td> (90, 192, 288) </td>
+                        <td> (120, 192, 288) </td>
                     </tr>
                     <tr>
                         <th> Dask graph </th>
-                        <td colspan="2"> 12 chunks in 23 graph layers </td>
+                        <td colspan="2"> 9 chunks in 22 graph layers </td>
                     </tr>
                     <tr>
                         <th> Data type </th>
@@ -11398,17 +10429,14 @@ <h3>Supplementary Material: Advanced workflows and automatic parallelization usi
 
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-  <line x1="16" y1="6" x2="16" y2="47" />
-  <line x1="22" y1="12" x2="22" y2="54" />
-  <line x1="28" y1="18" x2="28" y2="60" />
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   <!-- Colored Rectangle -->
@@ -11416,17 +10444,14 @@ <h3>Supplementary Material: Advanced workflows and automatic parallelization usi
 
   <!-- Horizontal lines -->
   <line x1="10" y1="0" x2="53" y2="0" style="stroke-width:2" />
-  <line x1="16" y1="6" x2="59" y2="6" />
-  <line x1="22" y1="12" x2="65" y2="12" />
-  <line x1="28" y1="18" x2="71" y2="18" />
+  <line x1="18" y1="8" x2="61" y2="8" />
+  <line x1="26" y1="16" x2="69" y2="16" />
   <line x1="34" y1="24" x2="77" y2="24" />
-  <line x1="40" y1="30" x2="84" y2="30" />
-  <line x1="46" y1="36" x2="90" y2="36" />
-  <line x1="53" y1="43" x2="96" y2="43" />
+  <line x1="42" y1="32" x2="86" y2="32" />
+  <line x1="51" y1="41" x2="94" y2="41" />
   <line x1="59" y1="49" x2="102" y2="49" />
-  <line x1="65" y1="55" x2="108" y2="55" />
-  <line x1="71" y1="61" x2="114" y2="61" />
-  <line x1="77" y1="67" x2="121" y2="67" />
+  <line x1="67" y1="57" x2="110" y2="57" />
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@@ -11454,7 +10479,7 @@ <h3>Supplementary Material: Advanced workflows and automatic parallelization usi
 </svg>
         </td>
     </tr>
-</table></div></div></li><li class='xr-section-item'><input id='section-9a17a63f-f6b5-4e32-9925-1eee999e0a26' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9a17a63f-f6b5-4e32-9925-1eee999e0a26' 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 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-e767b4d4-2930-4711-a773-ee98c1423c18' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e767b4d4-2930-4711-a773-ee98c1423c18' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9732a1f3-cf44-460b-aba9-5fef13de8a90' class='xr-var-data-in' type='checkbox'><label for='data-9732a1f3-cf44-460b-aba9-5fef13de8a90' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,
+</table></div></div></li><li class='xr-section-item'><input id='section-ca66e708-a713-4c59-a3d5-6abb58ca2fe4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ca66e708-a713-4c59-a3d5-6abb58ca2fe4' 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 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-de62e579-528f-4e04-8916-e5595c5d3258' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de62e579-528f-4e04-8916-e5595c5d3258' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88e36a8b-45b0-4a20-bbb1-1eca8beec859' class='xr-var-data-in' type='checkbox'><label for='data-88e36a8b-45b0-4a20-bbb1-1eca8beec859' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,
        -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,
        -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,
        -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,
@@ -11485,13 +10510,13 @@ <h3>Supplementary Material: Advanced workflows and automatic parallelization usi
         68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,
         73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,
         79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,
-        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-77f8ab92-f951-4fe7-b1a6-113e25c756ce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-77f8ab92-f951-4fe7-b1a6-113e25c756ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a76c74a-bb4a-4c20-86bd-e2da7a43c0d4' class='xr-var-data-in' type='checkbox'><label for='data-6a76c74a-bb4a-4c20-86bd-e2da7a43c0d4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span 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'>2015-02-01 00:00:00 ... 2101-01-...</div><input id='attrs-b6993b75-8070-4ac3-80e7-38e4321a684b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b6993b75-8070-4ac3-80e7-38e4321a684b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1cef558-a3d4-4151-b545-19c8d89b106d' class='xr-var-data-in' type='checkbox'><label for='data-f1cef558-a3d4-4151-b545-19c8d89b106d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2015, 2, 1, 0, 0, 0, 0, has_year_zero=True),
+        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-e3961ee7-fed4-400c-a8f6-dd3a424a1f1c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e3961ee7-fed4-400c-a8f6-dd3a424a1f1c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0783ddba-133a-4272-a7c7-586e2f4626d2' class='xr-var-data-in' type='checkbox'><label for='data-0783ddba-133a-4272-a7c7-586e2f4626d2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span 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'>2015-02-01 00:00:00 ... 2101-01-...</div><input id='attrs-9410c83d-1894-431d-b906-05028df7e671' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9410c83d-1894-431d-b906-05028df7e671' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09879629-3f61-4a3c-ab52-c01038dd46e2' class='xr-var-data-in' type='checkbox'><label for='data-09879629-3f61-4a3c-ab52-c01038dd46e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2015, 2, 1, 0, 0, 0, 0, has_year_zero=True),
        cftime.DatetimeNoLeap(2015, 3, 1, 0, 0, 0, 0, has_year_zero=True),
        cftime.DatetimeNoLeap(2015, 4, 1, 0, 0, 0, 0, has_year_zero=True), ...,
        cftime.DatetimeNoLeap(2100, 11, 1, 0, 0, 0, 0, has_year_zero=True),
        cftime.DatetimeNoLeap(2100, 12, 1, 0, 0, 0, 0, has_year_zero=True),
        cftime.DatetimeNoLeap(2101, 1, 1, 0, 0, 0, 0, has_year_zero=True)],
-      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f8156a2f-1002-4769-a7fd-633906c3c127' class='xr-section-summary-in' type='checkbox'  ><label for='section-f8156a2f-1002-4769-a7fd-633906c3c127' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-afce4ece-5da6-449f-a8b3-bdac75044dc4' class='xr-index-data-in' type='checkbox'/><label for='index-afce4ece-5da6-449f-a8b3-bdac75044dc4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([             -90.0, -89.05759162303664,  -88.1151832460733,
+      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1aee3e96-db48-49b5-a105-c5e57c6a2f7a' class='xr-section-summary-in' type='checkbox'  ><label for='section-1aee3e96-db48-49b5-a105-c5e57c6a2f7a' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c8b5afa6-5897-452a-a2aa-876460fadf0e' class='xr-index-data-in' type='checkbox'/><label for='index-c8b5afa6-5897-452a-a2aa-876460fadf0e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([             -90.0, -89.05759162303664,  -88.1151832460733,
        -87.17277486910994,  -86.2303664921466, -85.28795811518324,
         -84.3455497382199, -83.40314136125654, -82.46073298429319,
        -81.51832460732984,
@@ -11500,12 +10525,12 @@ <h3>Supplementary Material: Advanced workflows and automatic parallelization usi
          84.3455497382199,  85.28795811518324,   86.2303664921466,
         87.17277486910994,   88.1151832460733,  89.05759162303664,
                      90.0],
-      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8e5783e6-96ac-4336-8943-e8d19fb067da' class='xr-index-data-in' type='checkbox'/><label for='index-8e5783e6-96ac-4336-8943-e8d19fb067da' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   1.25,    2.5,   3.75,    5.0,   6.25,    7.5,   8.75,   10.0,
+      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3d7bb5cb-a499-4aa9-acc5-ad6f4ebbf154' class='xr-index-data-in' type='checkbox'/><label for='index-3d7bb5cb-a499-4aa9-acc5-ad6f4ebbf154' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   1.25,    2.5,   3.75,    5.0,   6.25,    7.5,   8.75,   10.0,
         11.25,
        ...
         347.5, 348.75,  350.0, 351.25,  352.5, 353.75,  355.0, 356.25,  357.5,
        358.75],
-      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=288))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4b47b82d-6e07-4208-9d98-eb649120cb4a' class='xr-index-data-in' type='checkbox'/><label for='index-4b47b82d-6e07-4208-9d98-eb649120cb4a' 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([2015-02-01 00:00:00, 2015-03-01 00:00:00, 2015-04-01 00:00:00,
+      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=288))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-01669dc0-c3f4-4fa2-a177-9f15d4fdea0c' class='xr-index-data-in' type='checkbox'/><label for='index-01669dc0-c3f4-4fa2-a177-9f15d4fdea0c' 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([2015-02-01 00:00:00, 2015-03-01 00:00:00, 2015-04-01 00:00:00,
              2015-05-01 00:00:00, 2015-06-01 00:00:00, 2015-07-01 00:00:00,
              2015-08-01 00:00:00, 2015-09-01 00:00:00, 2015-10-01 00:00:00,
              2015-11-01 00:00:00,
@@ -11514,7 +10539,7 @@ <h3>Supplementary Material: Advanced workflows and automatic parallelization usi
              2100-07-01 00:00:00, 2100-08-01 00:00:00, 2100-09-01 00:00:00,
              2100-10-01 00:00:00, 2100-11-01 00:00:00, 2100-12-01 00:00:00,
              2101-01-01 00:00:00],
-            dtype=&#x27;object&#x27;, length=1032, calendar=&#x27;noleap&#x27;, freq=&#x27;MS&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-505a1192-e4da-4518-b2c8-11894bc0953f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-505a1192-e4da-4518-b2c8-11894bc0953f' 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></div></div>
+            dtype=&#x27;object&#x27;, length=1032, calendar=&#x27;noleap&#x27;, freq=&#x27;MS&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0fa0f3f2-7901-4378-b839-a0af705c7a5f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0fa0f3f2-7901-4378-b839-a0af705c7a5f' 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></div></div>
 </div>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -11891,7 +10916,7 @@ <h3>Supplementary Material: Advanced workflows and automatic parallelization usi
 Coordinates:
   * lat      (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 87.17 88.12 89.06 90.0
   * lon      (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
-  * time     (time) object 2015-02-01 00:00:00 ... 2101-01-01 00:00:00</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'>'TREFHT'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1032</li><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-2eea06d1-76ce-48ba-a233-cfaee713323e' class='xr-array-in' type='checkbox' ><label for='section-2eea06d1-76ce-48ba-a233-cfaee713323e' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.08275 0.08275 0.08275 0.08275 ... 0.5323 0.5323 0.5323 0.5323</span></div><div class='xr-array-data'><pre>array([[[0.08275229, 0.08275241, 0.08275262, ..., 0.08275241,
+  * time     (time) object 2015-02-01 00:00:00 ... 2101-01-01 00:00:00</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'>'TREFHT'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1032</li><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-1172be1e-6330-42ef-af65-ca82431bd12f' class='xr-array-in' type='checkbox' ><label for='section-1172be1e-6330-42ef-af65-ca82431bd12f' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.08275 0.08275 0.08275 0.08275 ... 0.5323 0.5323 0.5323 0.5323</span></div><div class='xr-array-data'><pre>array([[[0.08275229, 0.08275241, 0.08275262, ..., 0.08275241,
          0.08275241, 0.08275241],
         [0.08693817, 0.08662252, 0.08539595, ..., 0.08720139,
          0.08712979, 0.08703589],
@@ -11931,7 +10956,7 @@ <h3>Supplementary Material: Advanced workflows and automatic parallelization usi
         [0.53325725, 0.5332873 , 0.533316  , ..., 0.53317934,
          0.5331973 , 0.5332273 ],
         [0.5323095 , 0.53231186, 0.53231424, ..., 0.5323011 ,
-         0.5323047 , 0.5323071 ]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-43c6851a-12b2-441c-9341-f4af125a1c7c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-43c6851a-12b2-441c-9341-f4af125a1c7c' 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 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-48f3f257-ecd5-4b99-a589-8ccb7bc450e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48f3f257-ecd5-4b99-a589-8ccb7bc450e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d47fe9f7-f672-4813-9a16-c3ed67a33ebb' class='xr-var-data-in' type='checkbox'><label for='data-d47fe9f7-f672-4813-9a16-c3ed67a33ebb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,
+         0.5323047 , 0.5323071 ]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-a97a3237-668f-46a5-a312-c5a065792d54' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a97a3237-668f-46a5-a312-c5a065792d54' 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 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-8865acbc-c38f-4955-86ab-17d638cb3ee5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8865acbc-c38f-4955-86ab-17d638cb3ee5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4bbf8a13-ed71-41a0-9222-ea263e3d95f2' class='xr-var-data-in' type='checkbox'><label for='data-4bbf8a13-ed71-41a0-9222-ea263e3d95f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,
        -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,
        -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,
        -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,
@@ -11962,13 +10987,13 @@ <h3>Supplementary Material: Advanced workflows and automatic parallelization usi
         68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,
         73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,
         79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,
-        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-1c6be297-85d1-495a-950c-d9975e65f516' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c6be297-85d1-495a-950c-d9975e65f516' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-249cfa34-cfad-4645-9fe6-be12f43764ae' class='xr-var-data-in' type='checkbox'><label for='data-249cfa34-cfad-4645-9fe6-be12f43764ae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span 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'>2015-02-01 00:00:00 ... 2101-01-...</div><input id='attrs-bec5fb71-dc07-4e27-893f-8aa271d9a505' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bec5fb71-dc07-4e27-893f-8aa271d9a505' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7016bf1c-fcf2-4dbd-bf20-748d8e5d8022' class='xr-var-data-in' type='checkbox'><label for='data-7016bf1c-fcf2-4dbd-bf20-748d8e5d8022' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2015, 2, 1, 0, 0, 0, 0, has_year_zero=True),
+        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-8e9769b4-2d14-4dce-b1c7-dc514928d643' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8e9769b4-2d14-4dce-b1c7-dc514928d643' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6288f703-5017-45e1-9485-15aed6a6b3a8' class='xr-var-data-in' type='checkbox'><label for='data-6288f703-5017-45e1-9485-15aed6a6b3a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span 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'>2015-02-01 00:00:00 ... 2101-01-...</div><input id='attrs-4de87471-30be-4a19-b98c-50cfd5fb84db' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4de87471-30be-4a19-b98c-50cfd5fb84db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aad7e593-f7b2-4c70-8fbc-c6ef00715341' class='xr-var-data-in' type='checkbox'><label for='data-aad7e593-f7b2-4c70-8fbc-c6ef00715341' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2015, 2, 1, 0, 0, 0, 0, has_year_zero=True),
        cftime.DatetimeNoLeap(2015, 3, 1, 0, 0, 0, 0, has_year_zero=True),
        cftime.DatetimeNoLeap(2015, 4, 1, 0, 0, 0, 0, has_year_zero=True), ...,
        cftime.DatetimeNoLeap(2100, 11, 1, 0, 0, 0, 0, has_year_zero=True),
        cftime.DatetimeNoLeap(2100, 12, 1, 0, 0, 0, 0, has_year_zero=True),
        cftime.DatetimeNoLeap(2101, 1, 1, 0, 0, 0, 0, has_year_zero=True)],
-      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9d94b595-b006-4e05-8663-3a3a0a2ac99f' class='xr-section-summary-in' type='checkbox'  ><label for='section-9d94b595-b006-4e05-8663-3a3a0a2ac99f' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f3b1f1cf-0e60-461f-9c42-a3fd975dfa9c' class='xr-index-data-in' type='checkbox'/><label for='index-f3b1f1cf-0e60-461f-9c42-a3fd975dfa9c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([             -90.0, -89.05759162303664,  -88.1151832460733,
+      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5190138e-fad0-4566-a0e2-8b783dd44b1e' class='xr-section-summary-in' type='checkbox'  ><label for='section-5190138e-fad0-4566-a0e2-8b783dd44b1e' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a54dacad-2eec-49fc-836e-ad7fa7d52301' class='xr-index-data-in' type='checkbox'/><label for='index-a54dacad-2eec-49fc-836e-ad7fa7d52301' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([             -90.0, -89.05759162303664,  -88.1151832460733,
        -87.17277486910994,  -86.2303664921466, -85.28795811518324,
         -84.3455497382199, -83.40314136125654, -82.46073298429319,
        -81.51832460732984,
@@ -11977,12 +11002,12 @@ <h3>Supplementary Material: Advanced workflows and automatic parallelization usi
          84.3455497382199,  85.28795811518324,   86.2303664921466,
         87.17277486910994,   88.1151832460733,  89.05759162303664,
                      90.0],
-      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-be25a14c-521c-466c-94bc-6fff84a9f963' class='xr-index-data-in' type='checkbox'/><label for='index-be25a14c-521c-466c-94bc-6fff84a9f963' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   1.25,    2.5,   3.75,    5.0,   6.25,    7.5,   8.75,   10.0,
+      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-406b87a0-e285-4a00-870f-0ae01341ac8d' class='xr-index-data-in' type='checkbox'/><label for='index-406b87a0-e285-4a00-870f-0ae01341ac8d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   1.25,    2.5,   3.75,    5.0,   6.25,    7.5,   8.75,   10.0,
         11.25,
        ...
         347.5, 348.75,  350.0, 351.25,  352.5, 353.75,  355.0, 356.25,  357.5,
        358.75],
-      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=288))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-395ac8ea-3ee3-45df-8c8a-d63ca3bb580d' class='xr-index-data-in' type='checkbox'/><label for='index-395ac8ea-3ee3-45df-8c8a-d63ca3bb580d' 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([2015-02-01 00:00:00, 2015-03-01 00:00:00, 2015-04-01 00:00:00,
+      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=288))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4ac44724-f23a-4d9e-b74b-b428b5729077' class='xr-index-data-in' type='checkbox'/><label for='index-4ac44724-f23a-4d9e-b74b-b428b5729077' 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([2015-02-01 00:00:00, 2015-03-01 00:00:00, 2015-04-01 00:00:00,
              2015-05-01 00:00:00, 2015-06-01 00:00:00, 2015-07-01 00:00:00,
              2015-08-01 00:00:00, 2015-09-01 00:00:00, 2015-10-01 00:00:00,
              2015-11-01 00:00:00,
@@ -11991,7 +11016,7 @@ <h3>Supplementary Material: Advanced workflows and automatic parallelization usi
              2100-07-01 00:00:00, 2100-08-01 00:00:00, 2100-09-01 00:00:00,
              2100-10-01 00:00:00, 2100-11-01 00:00:00, 2100-12-01 00:00:00,
              2101-01-01 00:00:00],
-            dtype=&#x27;object&#x27;, length=1032, calendar=&#x27;noleap&#x27;, freq=&#x27;MS&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-822e223e-92f2-4183-a53d-db3e6261dd87' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-822e223e-92f2-4183-a53d-db3e6261dd87' 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></div></div>
+            dtype=&#x27;object&#x27;, length=1032, calendar=&#x27;noleap&#x27;, freq=&#x27;MS&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c8d7075f-cd41-4374-ae0f-15f5748c0fc6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c8d7075f-cd41-4374-ae0f-15f5748c0fc6' 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></div></div>
 </div>
 <p>The data used for this tutorial is from one ensemble member. What if we want to use multiple ensemble members? So far, we only run on one machine, what if we run an HPC cluster? We will go over this in the next tutorial.</p>
 </div>
@@ -12134,7 +11159,7 @@ <h3>Ask for help<a class="headerlink" href="#ask-for-help" title="Permalink to t
         
           By Negin Sobhani, Brian Vanderwende, Deepak Cherian, and Ben Kirk.
         
-          Last updated on 4 October 2023.
+          Last updated on 9 October 2023.
         </p>
       </div>
     </div>
diff --git a/notebooks/04-dask-cluster.html b/notebooks/04-dask-cluster.html
index a13ef91..2b4be28 100644
--- a/notebooks/04-dask-cluster.html
+++ b/notebooks/04-dask-cluster.html
@@ -513,12 +513,12 @@ <h4>1.2. Threaded scheduler<a class="headerlink" href="#threaded-scheduler" titl
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 10.6 s, sys: 684 ms, total: 11.3 s
-Wall time: 11.3 s
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 12 s, sys: 470 ms, total: 12.4 s
+Wall time: 12.4 s
 </pre></div>
 </div>
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([ 9.99906772,  9.99973757,  9.99947898, ..., 10.00127456,
-       10.00021317,  9.99985475])
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([10.00067831,  9.99945256, 10.00003869, ..., 10.00016619,
+        9.99965519,  9.99949655])
 </pre></div>
 </div>
 </div>
@@ -534,12 +534,12 @@ <h4>1.2. Threaded scheduler<a class="headerlink" href="#threaded-scheduler" titl
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 11 s, sys: 55.5 ms, total: 11 s
-Wall time: 5.84 s
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 12.1 s, sys: 31 ms, total: 12.2 s
+Wall time: 6.33 s
 </pre></div>
 </div>
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([ 9.99971445, 10.00021562,  9.99952092, ..., 10.0008636 ,
-        9.99885944, 10.00058048])
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([ 9.99992315, 10.000385  , 10.00093179, ...,  9.99850353,
+        9.99976664,  9.99914389])
 </pre></div>
 </div>
 </div>
@@ -557,13 +557,13 @@ <h4>1.2. Threaded scheduler<a class="headerlink" href="#threaded-scheduler" titl
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> threading :  5.5805 s
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> threading :  6.1463 s
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> processes :  6.3084 s
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> processes :  6.9923 s
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>      sync :  10.7883 s
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>      sync :  11.9856 s
 </pre></div>
 </div>
 </div>
@@ -607,7 +607,7 @@ <h3 style="margin-bottom: 0px;">HighLevelGraph</h3>
             <h4 style="display: inline;">Layer1: normal</h4>
         </summary>
         <p style="color: var(--jp-ui-font-color2, #5D5851); margin: -0.25em 0px 0px 0px;">
-            normal-29532f99a9b5cc82c7e3d64ba58442e2
+            normal-87888ed8bcd035009a957c5f725c0368
         </p>
 
         <table>
@@ -713,7 +713,7 @@ <h4 style="display: inline;">Layer1: normal</h4>
             <h4 style="display: inline;">Layer2: mean_chunk</h4>
         </summary>
         <p style="color: var(--jp-ui-font-color2, #5D5851); margin: -0.25em 0px 0px 0px;">
-            mean_chunk-3ba7708f376d62f0157c45d019f003a7
+            mean_chunk-9b5faab08effd0aadf63fc0961f0ec76
         </p>
 
         <table>
@@ -765,7 +765,7 @@ <h4 style="display: inline;">Layer2: mean_chunk</h4>
                     
                         <tr>
                             <th style="text-align: left; width: 150px;"> depends on </th>
-                            <td style="text-align: left;">normal-29532f99a9b5cc82c7e3d64ba58442e2</td>
+                            <td style="text-align: left;">normal-87888ed8bcd035009a957c5f725c0368</td>
                         </tr>
                     
                 
@@ -826,7 +826,7 @@ <h4 style="display: inline;">Layer2: mean_chunk</h4>
             <h4 style="display: inline;">Layer3: mean_combine-partial</h4>
         </summary>
         <p style="color: var(--jp-ui-font-color2, #5D5851); margin: -0.25em 0px 0px 0px;">
-            mean_combine-partial-ec04367d6cb479ddf7f4d5bcccd709fa
+            mean_combine-partial-3386d0b32f56b2a174af174e4c6a8833
         </p>
 
         <table>
@@ -878,7 +878,7 @@ <h4 style="display: inline;">Layer3: mean_combine-partial</h4>
                     
                         <tr>
                             <th style="text-align: left; width: 150px;"> depends on </th>
-                            <td style="text-align: left;">mean_chunk-3ba7708f376d62f0157c45d019f003a7</td>
+                            <td style="text-align: left;">mean_chunk-9b5faab08effd0aadf63fc0961f0ec76</td>
                         </tr>
                     
                 
@@ -932,7 +932,7 @@ <h4 style="display: inline;">Layer3: mean_combine-partial</h4>
             <h4 style="display: inline;">Layer4: mean_agg-aggregate</h4>
         </summary>
         <p style="color: var(--jp-ui-font-color2, #5D5851); margin: -0.25em 0px 0px 0px;">
-            mean_agg-aggregate-c32bd57b2d30725ee6599f2140daacac
+            mean_agg-aggregate-386bf4d92b6c9ff73c8caec210b44020
         </p>
 
         <table>
@@ -984,7 +984,7 @@ <h4 style="display: inline;">Layer4: mean_agg-aggregate</h4>
                     
                         <tr>
                             <th style="text-align: left; width: 150px;"> depends on </th>
-                            <td style="text-align: left;">mean_combine-partial-ec04367d6cb479ddf7f4d5bcccd709fa</td>
+                            <td style="text-align: left;">mean_combine-partial-3386d0b32f56b2a174af174e4c6a8833</td>
                         </tr>
                     
                 
@@ -1138,7 +1138,7 @@ <h5>Let’s start by creating a Local Cluster<a class="headerlink" href="#lets-s
     <div style="width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;"> </div>
     <div style="margin-left: 48px;">
         <h3 style="margin-bottom: 0px;">Client</h3>
-        <p style="color: #9D9D9D; margin-bottom: 0px;">Client-1a977e45-62e4-11ee-907f-6045bd7f0ab1</p>
+        <p style="color: #9D9D9D; margin-bottom: 0px;">Client-87904dde-66e4-11ee-8f99-000d3a01f98b</p>
         <table style="width: 100%; text-align: left;">
 
         <tr>
@@ -1173,7 +1173,7 @@ <h3 style="margin-bottom: 0px;">Client</h3>
     </div>
     <div style="margin-left: 48px;">
         <h3 style="margin-bottom: 0px; margin-top: 0px;">LocalCluster</h3>
-        <p style="color: #9D9D9D; margin-bottom: 0px;">d0363b3b</p>
+        <p style="color: #9D9D9D; margin-bottom: 0px;">282f56a3</p>
         <table style="width: 100%; text-align: left;">
             <tr>
                 <td style="text-align: left;">
@@ -1210,11 +1210,11 @@ <h3 style="display: inline;">Scheduler Info</h3>
         <div style="width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;"> </div>
         <div style="margin-left: 48px;">
             <h3 style="margin-bottom: 0px;">Scheduler</h3>
-            <p style="color: #9D9D9D; margin-bottom: 0px;">Scheduler-866597a4-b31b-4bf4-9109-cd7f49547819</p>
+            <p style="color: #9D9D9D; margin-bottom: 0px;">Scheduler-a4b540d3-1a13-4b67-9b3a-b0222e5f5670</p>
             <table style="width: 100%; text-align: left;">
                 <tr>
                     <td style="text-align: left;">
-                        <strong>Comm:</strong> tcp://127.0.0.1:35541
+                        <strong>Comm:</strong> tcp://127.0.0.1:46299
                     </td>
                     <td style="text-align: left;">
                         <strong>Workers:</strong> 2
@@ -1256,7 +1256,7 @@ <h4 style="margin-bottom: 0px; display: inline;">Worker: 0</h4>
                 <table style="width: 100%; text-align: left;">
                     <tr>
                         <td style="text-align: left;">
-                            <strong>Comm: </strong> tcp://127.0.0.1:40147
+                            <strong>Comm: </strong> tcp://127.0.0.1:43913
                         </td>
                         <td style="text-align: left;">
                             <strong>Total threads: </strong> 1
@@ -1264,7 +1264,7 @@ <h4 style="margin-bottom: 0px; display: inline;">Worker: 0</h4>
                     </tr>
                     <tr>
                         <td style="text-align: left;">
-                            <strong>Dashboard: </strong> <a href="http://127.0.0.1:45633/status" target="_blank">http://127.0.0.1:45633/status</a>
+                            <strong>Dashboard: </strong> <a href="http://127.0.0.1:36845/status" target="_blank">http://127.0.0.1:36845/status</a>
                         </td>
                         <td style="text-align: left;">
                             <strong>Memory: </strong> 3.38 GiB
@@ -1272,13 +1272,13 @@ <h4 style="margin-bottom: 0px; display: inline;">Worker: 0</h4>
                     </tr>
                     <tr>
                         <td style="text-align: left;">
-                            <strong>Nanny: </strong> tcp://127.0.0.1:46707
+                            <strong>Nanny: </strong> tcp://127.0.0.1:45461
                         </td>
                         <td style="text-align: left;"></td>
                     </tr>
                     <tr>
                         <td colspan="2" style="text-align: left;">
-                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-dceh_x5k
+                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-m7vmc_d7
                         </td>
                     </tr>
 
@@ -1301,7 +1301,7 @@ <h4 style="margin-bottom: 0px; display: inline;">Worker: 1</h4>
                 <table style="width: 100%; text-align: left;">
                     <tr>
                         <td style="text-align: left;">
-                            <strong>Comm: </strong> tcp://127.0.0.1:44719
+                            <strong>Comm: </strong> tcp://127.0.0.1:34557
                         </td>
                         <td style="text-align: left;">
                             <strong>Total threads: </strong> 1
@@ -1309,7 +1309,7 @@ <h4 style="margin-bottom: 0px; display: inline;">Worker: 1</h4>
                     </tr>
                     <tr>
                         <td style="text-align: left;">
-                            <strong>Dashboard: </strong> <a href="http://127.0.0.1:41093/status" target="_blank">http://127.0.0.1:41093/status</a>
+                            <strong>Dashboard: </strong> <a href="http://127.0.0.1:45487/status" target="_blank">http://127.0.0.1:45487/status</a>
                         </td>
                         <td style="text-align: left;">
                             <strong>Memory: </strong> 3.38 GiB
@@ -1317,13 +1317,13 @@ <h4 style="margin-bottom: 0px; display: inline;">Worker: 1</h4>
                     </tr>
                     <tr>
                         <td style="text-align: left;">
-                            <strong>Nanny: </strong> tcp://127.0.0.1:40713
+                            <strong>Nanny: </strong> tcp://127.0.0.1:34619
                         </td>
                         <td style="text-align: left;"></td>
                     </tr>
                     <tr>
                         <td colspan="2" style="text-align: left;">
-                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-__dyr2ue
+                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-abf8nxe7
                         </td>
                     </tr>
 
@@ -1378,12 +1378,12 @@ <h4 style="margin-bottom: 0px; display: inline;">Worker: 1</h4>
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 767 ms, sys: 56.6 ms, total: 824 ms
-Wall time: 14.5 s
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 630 ms, sys: 114 ms, total: 743 ms
+Wall time: 15.8 s
 </pre></div>
 </div>
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([10.00030163,  9.99996315,  9.99921624, ...,  9.9991636 ,
-        9.99948973,  9.99918791])
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([10.00064446, 10.00152234,  9.99947898, ...,  9.99973685,
+        9.99992731, 10.00063441])
 </pre></div>
 </div>
 </div>
@@ -1395,6 +1395,34 @@ <h4 style="margin-bottom: 0px; display: inline;">Worker: 1</h4>
 </pre></div>
 </div>
 </div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2023-10-09 20:43:56,752 - distributed.worker - ERROR - Failed to communicate with scheduler during heartbeat.
+Traceback (most recent call last):
+  File &quot;/usr/share/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/distributed/comm/tcp.py&quot;, line 224, in read
+    frames_nbytes = await stream.read_bytes(fmt_size)
+tornado.iostream.StreamClosedError: Stream is closed
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File &quot;/usr/share/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/distributed/worker.py&quot;, line 1254, in heartbeat
+    response = await retry_operation(
+  File &quot;/usr/share/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/distributed/utils_comm.py&quot;, line 454, in retry_operation
+    return await retry(
+  File &quot;/usr/share/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/distributed/utils_comm.py&quot;, line 433, in retry
+    return await coro()
+  File &quot;/usr/share/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/distributed/core.py&quot;, line 1347, in send_recv_from_rpc
+    return await send_recv(comm=comm, op=key, **kwargs)
+  File &quot;/usr/share/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/distributed/core.py&quot;, line 1106, in send_recv
+    response = await comm.read(deserializers=deserializers)
+  File &quot;/usr/share/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/distributed/comm/tcp.py&quot;, line 240, in read
+    convert_stream_closed_error(self, e)
+  File &quot;/usr/share/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/distributed/comm/tcp.py&quot;, line 143, in convert_stream_closed_error
+    raise CommClosedError(f&quot;in {obj}: {exc}&quot;) from exc
+distributed.comm.core.CommClosedError: in &lt;TCP (closed) ConnectionPool.heartbeat_worker local=tcp://127.0.0.1:55608 remote=tcp://127.0.0.1:46299&gt;: Stream is closed
+</pre></div>
+</div>
+</div>
 </div>
 </div>
 </div>
@@ -1494,7 +1522,7 @@ <h3>Dask Distributed (Cluster Managers)<a class="headerlink" href="#dask-distrib
         
           By Negin Sobhani, Brian Vanderwende, Deepak Cherian, and Ben Kirk.
         
-          Last updated on 4 October 2023.
+          Last updated on 9 October 2023.
         </p>
       </div>
     </div>
diff --git a/notebooks/how-to-cite.html b/notebooks/how-to-cite.html
index 2e7dc4d..c9519b0 100644
--- a/notebooks/how-to-cite.html
+++ b/notebooks/how-to-cite.html
@@ -421,7 +421,7 @@ <h1>How to Cite This Cookbook<a class="headerlink" href="#how-to-cite-this-cookb
         
           By Negin Sobhani, Brian Vanderwende, Deepak Cherian, and Ben Kirk.
         
-          Last updated on 4 October 2023.
+          Last updated on 9 October 2023.
         </p>
       </div>
     </div>
diff --git a/search.html b/search.html
index 39c833e..bfcab03 100644
--- a/search.html
+++ b/search.html
@@ -396,7 +396,7 @@ <h1 id="search-documentation">Search</h1>
         
           By Negin Sobhani, Brian Vanderwende, Deepak Cherian, and Ben Kirk.
         
-          Last updated on 4 October 2023.
+          Last updated on 9 October 2023.
         </p>
       </div>
     </div>
diff --git a/searchindex.js b/searchindex.js
index 47868da..7c7db94 100644
--- a/searchindex.js
+++ b/searchindex.js
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