diff --git a/docs/source/api_reference.rst b/docs/source/api_reference.rst
index 8eb6a97..bbf46fe 100644
--- a/docs/source/api_reference.rst
+++ b/docs/source/api_reference.rst
@@ -51,3 +51,6 @@ Spatial Components
 
 .. automodule:: profsea.components.spatial.gia
    :members: GIA
+
+.. automodule:: profsea.components.core.local_model
+    :members: Local
\ No newline at end of file
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 8c7f871..de07bb7 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -29,7 +29,7 @@
             
             Get Started
             
-         .. button-link:: [https://github.com/MetOffice/ProFSea-tool](https://github.com/MetOffice/ProFSea-tool)
+         .. button-link:: https://github.com/MetOffice/ProFSea-tool
             :color: secondary
             :shadow:
             :outline:
diff --git a/profsea-env.yml b/profsea-env.yml
index 455cdf6..1850c7f 100644
--- a/profsea-env.yml
+++ b/profsea-env.yml
@@ -12,7 +12,7 @@ dependencies:
   - numcodecs
   - numpy
   - pandas
-  - pandocpyt
+  - pandoc
   - pip 
   - pip:
     - -e .[dev]
diff --git a/profsea/tutorial_notebooks/worked_example.ipynb b/profsea/tutorial_notebooks/worked_example.ipynb
index e1b3908..78866f5 100644
--- a/profsea/tutorial_notebooks/worked_example.ipynb
+++ b/profsea/tutorial_notebooks/worked_example.ipynb
@@ -32,7 +32,7 @@
    "source": [
     "#### Importing components\n",
     "\n",
-    "The next step is to import all the model components you want to run simulations with - a full list is available in the documention [here](https://metoffice.github.io/ProFSea-tool/). This can be any combination of available `global` and `spatial` components, how you build your model is up to you!\n",
+    "The next step is to import all the model components you want to run simulations with - a full list is available in the documention [here](https://metoffice.github.io/ProFSea-tool/api_reference.html). This can be any combination of available `global` and `spatial` components, how you build your model is up to you!\n",
     "\n",
     "To generate a comprehensive set of sea-level projections, we'll run with all the components that contribute to sea-level change: \n",
     "- thermal expansion\n",
@@ -753,7 +753,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "profsea",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -767,7 +767,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.13"
+   "version": "3.14.6"
   }
  },
  "nbformat": 4,
diff --git a/profsea/tutorial_notebooks/zarr_output_files.ipynb b/profsea/tutorial_notebooks/zarr_output_files.ipynb
new file mode 100644
index 0000000..24a80f4
--- /dev/null
+++ b/profsea/tutorial_notebooks/zarr_output_files.ipynb
@@ -0,0 +1,1690 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e897f81e-bce2-40c3-8fd4-4196703e9bee",
+   "metadata": {},
+   "source": [
+    "#### Working with `.zarr` output files\n",
+    "\n",
+    "This notebook demonstrates how to work with the `.zarr` output files generated by the standard ProFSea workflow. [Zarr](https://zarr.readthedocs.io/en/latest/) is a powerful python library designed for the efficient storage of large-scale scientific data, allowing for the fast reading and writing of n-dimensional arrays. In the following, we outline some key functionalities of the `.zarr` output file format, and how this can be converted to other commonly used formats.  \n",
+    "\n",
+    "In `worked_example.ipynb`, spatial relative sea-level rise projections were saved to `stabilisation_projection.zarr` as\n",
+    "\n",
+    "```python\n",
+    "spatial_model.save_components(\n",
+    "    spatial_model.results,\n",
+    "    scenario_name=\"stabilisation\",\n",
+    "    output_format=\"zarr\",\n",
+    ")\n",
+    "```\n",
+    "\n",
+    "which saves the relative sea-level rise projections as an `xarray.Dataset` containing gridded projections for each component of the spatial model (see [utils.save_components()](https://github.com/MetOffice/ProFSea-tool/blob/profsea-v3/profsea/utils/utils.py) for more details). To work with this output file, the first step is therefore to activate the `profsea` conda environment and import the `xarray` library:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1d35b494-16cb-4ec0-8efb-4a52b07760de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "993d3dca-6ce7-4ab4-8aad-7ad0d64240aa",
+   "metadata": {},
+   "source": [
+    "#### Reading `.zarr` files\n",
+    "\n",
+    "Reading the `.zarr` file is then straightforward. Below, we print the `xarray` dataset, which outlines:\n",
+    "- the coordinates stored in the file (for example `time`, `lat`, `lon`, and `percentile`);\n",
+    "- the data variables corresponding to each sea-level component (for example `sterodynamic`, `greenland`, `landwater`, `wais`, `eais`, `glacier`, and `gia`);\n",
+    "- the dimensions and shape of each variable;\n",
+    "- dataset-level metadata, such as the ProFSea version, scenario name, and description.\n",
+    "\n",
+    "Having loaded in this dataset, we can see the benefit of using the `.zarr` format for very large data files (here, ~5GB): only the metadata is loaded initially, with the underlying data remaining on disk until needed.. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "15860570-c324-45e9-adab-9b1d79d8cd5c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<xarray.Dataset> Size: 5GB\n",
+      "Dimensions:       (percentile: 5, time: 295, lat: 180, lon: 360)\n",
+      "Coordinates:\n",
+      "  * percentile    (percentile) int64 40B 5 17 50 83 95\n",
+      "  * time          (time) int64 2kB 2006 2007 2008 2009 ... 2297 2298 2299 2300\n",
+      "  * lat           (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n",
+      "  * lon           (lon) float64 3kB -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n",
+      "Data variables:\n",
+      "    eais          (percentile, time, lat, lon) float32 382MB dask.array<chunksize=(5, 295, 10, 10), meta=np.ndarray>\n",
+      "    gia           (percentile, time, lat, lon) float64 765MB dask.array<chunksize=(5, 295, 10, 10), meta=np.ndarray>\n",
+      "    glacier       (percentile, time, lat, lon) float64 765MB dask.array<chunksize=(5, 295, 10, 10), meta=np.ndarray>\n",
+      "    greenland     (percentile, time, lat, lon) float32 382MB dask.array<chunksize=(5, 295, 10, 10), meta=np.ndarray>\n",
+      "    landwater     (percentile, time, lat, lon) float32 382MB dask.array<chunksize=(5, 295, 10, 10), meta=np.ndarray>\n",
+      "    sterodynamic  (percentile, time, lat, lon) float64 765MB dask.array<chunksize=(5, 295, 10, 10), meta=np.ndarray>\n",
+      "    total_rsl     (percentile, time, lat, lon) float64 765MB dask.array<chunksize=(5, 295, 10, 10), meta=np.ndarray>\n",
+      "    wais          (percentile, time, lat, lon) float32 382MB dask.array<chunksize=(5, 295, 10, 10), meta=np.ndarray>\n",
+      "Attributes:\n",
+      "    source:       ProFSea v3.0\n",
+      "    scenario:     stabilisation\n",
+      "    description:  Spatial sea level rise projections \n",
+      "\n",
+      "\n",
+      "Data variables: ['eais', 'gia', 'glacier', 'greenland', 'landwater', 'sterodynamic', 'total_rsl', 'wais']\n"
+     ]
+    }
+   ],
+   "source": [
+    "ds = xr.open_zarr(\n",
+    "    \"./stabilisation_projection.zarr\",\n",
+    "    consolidated=True,\n",
+    ")\n",
+    "\n",
+    "print(ds, '\\n\\n')\n",
+    "\n",
+    "print(f'Data variables: {list(ds.data_vars)}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8cccf48-37ce-4571-b96d-3607c684400e",
+   "metadata": {},
+   "source": [
+    "#### Extracting data\n",
+    "\n",
+    "Having loaded in the `.zarr` output file, we can now extract the data for individual components. To inspect the contribution from GIA is as simple as, \n",
+    "\n",
+    "```python \n",
+    "GIA = ds[\"GIA\"]\n",
+    "```\n",
+    "\n",
+    "Instead of working with the full 4-D array, we can instead select individual percentiles, which reduces the local data volume dramatically (approximately five-fold): "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "33925348-23b2-4965-a04a-dcee2a7826bc",
+   "metadata": {},
+   "outputs": [
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+       "  grid-column-start: 3;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > label::before {\n",
+       "  content: \"📂\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label::before {\n",
+       "  content: \"📁\";\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label {\n",
+       "  padding-bottom: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked ~ .xr-sections {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-ellipsis {\n",
+       "  font-size: 1.4em;\n",
+       "  font-weight: 900;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  letter-spacing: 0.15em;\n",
+       "  cursor: default;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;gia&#x27; (time: 295, lat: 180, lon: 360)&gt; Size: 153MB\n",
+       "dask.array&lt;getitem, shape=(295, 180, 360), dtype=float64, chunksize=(295, 10, 10), chunktype=numpy.ndarray&gt;\n",
+       "Coordinates:\n",
+       "  * time        (time) int64 2kB 2006 2007 2008 2009 ... 2297 2298 2299 2300\n",
+       "  * lat         (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n",
+       "  * lon         (lon) float64 3kB -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n",
+       "    percentile  int64 8B 50\n",
+       "Attributes:\n",
+       "    units:      m\n",
+       "    long_name:  Regional gia sea-level projections\n",
+       "    source:     ProFSea-Climate v0.1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-obj-name'>&#x27;gia&#x27;</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 295</li><li><span class='xr-has-index'>lat</span>: 180</li><li><span class='xr-has-index'>lon</span>: 360</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5e609eb4-e240-4dfc-a595-b37539506544' class='xr-array-in' type='checkbox' checked><label for='section-5e609eb4-e240-4dfc-a595-b37539506544' 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=(295, 10, 10), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 145.84 MiB </td>\n",
+       "                        <td> 230.47 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (295, 180, 360) </td>\n",
+       "                        <td> (295, 10, 10) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 648 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-cd4f6b6c-aa96-462b-bb0c-e200a8c6fcd1' class='xr-section-summary-in' type='checkbox' checked /><label for='section-cd4f6b6c-aa96-462b-bb0c-e200a8c6fcd1' class='xr-section-summary' title='Expand/collapse section'>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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2006 2007 2008 ... 2298 2299 2300</div><input id='attrs-a3dacbbe-930e-449c-ab45-24dba6698aa6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a3dacbbe-930e-449c-ab45-24dba6698aa6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75bca536-6a34-4c5c-82d6-9128e9893751' class='xr-var-data-in' type='checkbox'><label for='data-75bca536-6a34-4c5c-82d6-9128e9893751' 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([2006, 2007, 2008, ..., 2298, 2299, 2300], shape=(295,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.5 -88.5 -87.5 ... 88.5 89.5</div><input id='attrs-3ca1fa9a-9c1e-414f-af6e-96d7dc462a45' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3ca1fa9a-9c1e-414f-af6e-96d7dc462a45' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1904c004-e1cd-4dad-8c2d-081f154b4227' class='xr-var-data-in' type='checkbox'><label for='data-1904c004-e1cd-4dad-8c2d-081f154b4227' 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([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
+       "       -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,\n",
+       "       -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,\n",
+       "       -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,\n",
+       "       -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,\n",
+       "       -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,\n",
+       "       -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,\n",
+       "       -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,\n",
+       "        -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,\n",
+       "         0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
+       "        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,\n",
+       "        20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,\n",
+       "        30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,\n",
+       "        40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,\n",
+       "        50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,\n",
+       "        60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,\n",
+       "        70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,\n",
+       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])</pre></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'>-179.5 -178.5 ... 178.5 179.5</div><input id='attrs-d598fa35-73cc-4bc7-bb23-8bd9030c4802' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d598fa35-73cc-4bc7-bb23-8bd9030c4802' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dcbdd322-423d-474e-8dc9-f0da6ce84b96' class='xr-var-data-in' type='checkbox'><label for='data-dcbdd322-423d-474e-8dc9-f0da6ce84b96' 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([-179.5, -178.5, -177.5, ...,  177.5,  178.5,  179.5], shape=(360,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>percentile</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>50</div><input id='attrs-299cb4f9-be73-4824-a98a-3a74c106d90a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-299cb4f9-be73-4824-a98a-3a74c106d90a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3db76938-5614-42c2-87e7-3fcf37809921' class='xr-var-data-in' type='checkbox'><label for='data-3db76938-5614-42c2-87e7-3fcf37809921' 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(50)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-63c351df-108f-4927-b43c-904636351a8e' class='xr-section-summary-in' type='checkbox' checked /><label for='section-63c351df-108f-4927-b43c-904636351a8e' class='xr-section-summary' title='Expand/collapse section'>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>m</dd><dt><span>long_name :</span></dt><dd>Regional gia sea-level projections</dd><dt><span>source :</span></dt><dd>ProFSea-Climate v0.1</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'gia' (time: 295, lat: 180, lon: 360)> Size: 153MB\n",
+       "dask.array<getitem, shape=(295, 180, 360), dtype=float64, chunksize=(295, 10, 10), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * time        (time) int64 2kB 2006 2007 2008 2009 ... 2297 2298 2299 2300\n",
+       "  * lat         (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n",
+       "  * lon         (lon) float64 3kB -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n",
+       "    percentile  int64 8B 50\n",
+       "Attributes:\n",
+       "    units:      m\n",
+       "    long_name:  Regional gia sea-level projections\n",
+       "    source:     ProFSea-Climate v0.1"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "GIA = ds[\"gia\"].sel(percentile=50)\n",
+    "\n",
+    "GIA"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62216cf2-e25a-4458-b699-9c492185e6a3",
+   "metadata": {},
+   "source": [
+    "At this stage, `GIA` is still lazily loaded and is divided into many smaller chunks that can be accessed independently. We can therefore efficiently extract only the subset of data required for a particular analysis, without first loading the entire dataset into memory. Below, we demonstrate several common visualisation and analysis workflows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8001229d-ae4d-4a3a-b54c-a2b3e3f2a949",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "year = 2300\n",
+    "gia_2300 = GIA.sel(time=year) # extract 50th percentile projection for 2300 at all (lat, lon) grid points\n",
+    "\n",
+    "import cartopy.crs as ccrs\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "fig = plt.figure(figsize=(6, 4), layout=\"constrained\")\n",
+    "\n",
+    "vmax = np.nanmax(np.abs(gia_2300))\n",
+    "vmin = -vmax\n",
+    "\n",
+    "ax = fig.add_subplot(111, projection=ccrs.PlateCarree())\n",
+    "ax.set_title(f\"GIA Contribution (50th Percentile, Year {year})\")\n",
+    "\n",
+    "mesh = ax.pcolormesh(\n",
+    "    gia_2300.lon,\n",
+    "    gia_2300.lat,\n",
+    "    gia_2300,\n",
+    "    transform=ccrs.PlateCarree(),\n",
+    "    cmap=\"RdBu_r\",\n",
+    "    vmin=vmin,\n",
+    "    vmax=vmax,\n",
+    ")\n",
+    "\n",
+    "ax.coastlines()\n",
+    "\n",
+    "fig.colorbar(\n",
+    "    mesh,\n",
+    "    label=\"Relative Sea Level (m)\",\n",
+    "    orientation=\"horizontal\",\n",
+    "    pad=0.02,\n",
+    ")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70673fc8-7f18-4847-a6bc-da93549731b2",
+   "metadata": {},
+   "source": [
+    "Instead of plotting a map for a particular time, we can instead plot the timeseries (of the 50th percentile RSL projection) for particular locations. As can be seen below, the `newlyn` and `north_shields` objects have only a single dimension, `time`, making analysis and visualisation straightfoward. \n",
+    "\n",
+    "After selecting a single grid cell, the spatial dimensions are removed and only the time dimension remains. As a result, the resulting dataset is only ~2 kB, compared to the original ~5 GB `Zarr` archive."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "feb9ee3b-1a32-4aa1-a205-0a639c08cdbe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
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+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "  line-height: 1.6;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "  margin-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-obj-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-group-box-contents > label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input,\n",
+       ".xr-group-box-contents > input,\n",
+       ".xr-array-wrap > input {\n",
+       "  display: block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input + label,\n",
+       ".xr-var-item > input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:enabled + label,\n",
+       ".xr-var-item > input:enabled + label,\n",
+       ".xr-array-wrap > input:enabled + label,\n",
+       ".xr-group-box-contents > input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:focus-visible + label,\n",
+       ".xr-var-item > input:focus-visible + label,\n",
+       ".xr-array-wrap > input:focus-visible + label,\n",
+       ".xr-group-box-contents > input:focus-visible + label {\n",
+       "  outline: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:enabled + label:hover,\n",
+       ".xr-var-item > input:enabled + label:hover,\n",
+       ".xr-array-wrap > input:enabled + label:hover,\n",
+       ".xr-group-box-contents > input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "  white-space: nowrap;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > em {\n",
+       "  font-weight: normal;\n",
+       "}\n",
+       "\n",
+       ".xr-span-grid {\n",
+       "  grid-column-end: -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.6em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details,\n",
+       ".xr-group-box-contents > label {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in ~ .xr-section-details {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-children {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 100%;\n",
+       "  grid-column: 1 / -1;\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 30px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 26px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > label::before {\n",
+       "  content: \"📂\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label::before {\n",
+       "  content: \"📁\";\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label {\n",
+       "  padding-bottom: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked ~ .xr-sections {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-ellipsis {\n",
+       "  font-size: 1.4em;\n",
+       "  font-weight: 900;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  letter-spacing: 0.15em;\n",
+       "  cursor: default;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;gia&#x27; (time: 295)&gt; Size: 2kB\n",
+       "dask.array&lt;getitem, shape=(295,), dtype=float64, chunksize=(295,), chunktype=numpy.ndarray&gt;\n",
+       "Coordinates:\n",
+       "  * time        (time) int64 2kB 2006 2007 2008 2009 ... 2297 2298 2299 2300\n",
+       "    lat         float64 8B 50.5\n",
+       "    lon         float64 8B -5.5\n",
+       "    percentile  int64 8B 50\n",
+       "Attributes:\n",
+       "    units:      m\n",
+       "    long_name:  Regional gia sea-level projections\n",
+       "    source:     ProFSea-Climate v0.1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-obj-name'>&#x27;gia&#x27;</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 295</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-be939600-1870-4991-86cc-119a97834117' class='xr-array-in' type='checkbox' checked><label for='section-be939600-1870-4991-86cc-119a97834117' 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=(295,), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 2.30 kiB </td>\n",
+       "                        <td> 2.30 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (295,) </td>\n",
+       "                        <td> (295,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 4 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >295</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-1eb3ecc0-be75-4b2b-9342-0f853e109409' class='xr-section-summary-in' type='checkbox' checked /><label for='section-1eb3ecc0-be75-4b2b-9342-0f853e109409' class='xr-section-summary' title='Expand/collapse section'>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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2006 2007 2008 ... 2298 2299 2300</div><input id='attrs-3d169e9b-f4a8-462c-b8d7-7c2d8b155a15' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3d169e9b-f4a8-462c-b8d7-7c2d8b155a15' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19a6e2d8-abbd-4566-a182-05b498089e2c' class='xr-var-data-in' type='checkbox'><label for='data-19a6e2d8-abbd-4566-a182-05b498089e2c' 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([2006, 2007, 2008, ..., 2298, 2299, 2300], shape=(295,))</pre></div></li><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'>50.5</div><input id='attrs-406131f1-4ea4-4742-bf13-bf68bf8079f3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-406131f1-4ea4-4742-bf13-bf68bf8079f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-199634c7-692a-4db3-b858-82ae7e7b6355' class='xr-var-data-in' type='checkbox'><label for='data-199634c7-692a-4db3-b858-82ae7e7b6355' 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(50.5)</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'>-5.5</div><input id='attrs-6f0332b5-21dd-4897-afcf-c1fe0a1ce7bd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6f0332b5-21dd-4897-afcf-c1fe0a1ce7bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6a807c2-69f3-430c-bd17-2e684bed39df' class='xr-var-data-in' type='checkbox'><label for='data-c6a807c2-69f3-430c-bd17-2e684bed39df' 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(-5.5)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>percentile</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>50</div><input id='attrs-9d6fcb93-8e89-4ba7-bf39-7a1d38c7cbe1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9d6fcb93-8e89-4ba7-bf39-7a1d38c7cbe1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d8d38ea-53f6-4a9f-8670-b36d149d609b' class='xr-var-data-in' type='checkbox'><label for='data-6d8d38ea-53f6-4a9f-8670-b36d149d609b' 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(50)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f65cfa0b-d4e9-4952-9f1a-90075dbf5f70' class='xr-section-summary-in' type='checkbox' checked /><label for='section-f65cfa0b-d4e9-4952-9f1a-90075dbf5f70' class='xr-section-summary' title='Expand/collapse section'>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>m</dd><dt><span>long_name :</span></dt><dd>Regional gia sea-level projections</dd><dt><span>source :</span></dt><dd>ProFSea-Climate v0.1</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'gia' (time: 295)> Size: 2kB\n",
+       "dask.array<getitem, shape=(295,), dtype=float64, chunksize=(295,), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * time        (time) int64 2kB 2006 2007 2008 2009 ... 2297 2298 2299 2300\n",
+       "    lat         float64 8B 50.5\n",
+       "    lon         float64 8B -5.5\n",
+       "    percentile  int64 8B 50\n",
+       "Attributes:\n",
+       "    units:      m\n",
+       "    long_name:  Regional gia sea-level projections\n",
+       "    source:     ProFSea-Climate v0.1"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# The requested latitude and longitude are matched to the nearest available model grid cell using method=\"nearest\".\n",
+    "newlyn = GIA.sel(\n",
+    "    lat=50.1030,\n",
+    "    lon=-5.5428,\n",
+    "    method=\"nearest\",\n",
+    ")\n",
+    "north_shields = GIA.sel(\n",
+    "    lat=55.016,\n",
+    "    lon=-1.449,\n",
+    "    method=\"nearest\"\n",
+    ")\n",
+    "\n",
+    "vmax = 1.1 * np.maximum(np.nanmax(np.abs(newlyn.values)), np.nanmax(np.abs(north_shields.values)))\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "_ = plt.figure(figsize=(6, 4))\n",
+    "\n",
+    "plt.plot(newlyn.time.values, newlyn.values, label='Newlyn')\n",
+    "plt.plot(north_shields.time.values, north_shields.values, label='North Shields')\n",
+    "\n",
+    "plt.ylim(-vmax, vmax)\n",
+    "\n",
+    "plt.xlabel('Year')\n",
+    "plt.ylabel('Relative Sea Level [m]')\n",
+    "\n",
+    "plt.minorticks_on()\n",
+    "plt.gca().tick_params(which='both', direction='in', left=True, right=True, top=True, bottom=True)\n",
+    "\n",
+    "plt.legend(frameon=False, loc='upper left')\n",
+    "\n",
+    "plt.show()\n",
+    "plt.close()\n",
+    "\n",
+    "newlyn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f73fc780-0f6b-40a2-8ce5-ac9c53727b3f",
+   "metadata": {},
+   "source": [
+    "#### Saving data to other output file formats\n",
+    "\n",
+    "Having extracted only the data we are interested in -- in this example, RSL projections for Newlyn and North Shields -- we can now save this data to standard data formats (having avoided reading the whole data file into memory). We include several examples below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c8adfc73-9b7a-4338-8aa9-98cdbfd7f798",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# NB this converts to a pandas dataframe as intermediate step\n",
+    "newlyn.to_dataframe().to_csv(\n",
+    "    \"newlyn_GIA_RSL_timeseries.csv\"\n",
+    ") # saving 50th percentile GIA RSL projections for Newlyn. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "cd37a921-f1cb-44ec-920a-e7ac6d523c36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gia_2300.to_netcdf(\n",
+    "    \"gia_2300.nc\"\n",
+    ") # saving to netcdf using built-in xarray method for 2D DataArray at year 2300"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "efb557f2-dce5-45f9-ba0d-75d3884a42d2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".conda-profsea Python (Conda)",
+   "language": "python",
+   "name": "conda-env-.conda-profsea-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}