{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 6: Climate Oscillations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This chapter delves into two of the most renowned climate oscillations: the El Niño-Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO). ENSO is an ocean-atmosphere oscillation in the tropical Pacific Ocean with a period spanning 2-7 years, while the NAO operates in the North Atlantic Ocean over a period of 5-10 years. Both these oscillations considerably influence the climate of nearby continents. In this chapter, we'll investigate how to formulate indices that represent the state of these climate oscillations. Additionally, we'll briefly delve into how one might identify teleconnections, using surface temperature teleconnections of ENSO as an illustrative example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Objective:</b> <br>\n",
    "   Craft indices for ENSO and NAO climate oscillations analogous to <a href=\"https://climate.copernicus.eu/climate-indicators/sea-surface-temperature#ptab-0-2-content\">Figure 5</a> in <a href=\"https://climate.copernicus.eu/climate-indicators/sea-surface-temperature\">Climate Indicators/Sea Surface Temperature</a> and Figure 3 in <a href=\"https://climate.copernicus.eu/esotc/2022/atmospheric-circulation\">ESOTC 2022/Atmospheric Circulation</a>. Afterwards, explore the teleconnections of ENSO.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Caution</b>: <br>\n",
    "   This notebook is notably data-heavy and necessitates roughly 20 GB of free disk space. Cells in the notebook that initiate a download will be preceded by a warning cell.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">    \n",
    "<b>NOTE</b>: \n",
    "Before interacting with the following notebook, please ensure you've reviewed the <a href=\"https://ecmwfcode4earth.github.io/sketchbook-earth/how_to_run_notebooks.html\">How to Execute the Notebooks</a> section.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<style>\n",
    "td, th {\n",
    "   border: 1px solid white;\n",
    "   border-collapse: collapse;\n",
    "}\n",
    "</style>\n",
    "<table align=\"left\">\n",
    "  <tr>\n",
    "    <th>Run the tutorial via free cloud platforms: </th>\n",
    "    <th><a href=\"https://mybinder.org/v2/gh/ECMWFCode4Earth/sketchbook-earth/main?labpath=tutorials/03_sea_ice/03_sea_ice.ipynb\">\n",
    "        <img src = \"https://mybinder.org/badge.svg\" alt = \"Binder\"></th>\n",
    "    <th><a href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ECMWFCode4Earth/sketchbook-earth/blob/main/tutorials/03_sea_ice/03_sea_ice.ipynb\">\n",
    "        <img src = \"https://kaggle.com/static/images/open-in-kaggle.svg\" alt = \"Kaggle\"></th>\n",
    "    <th><a href=\"https://colab.research.google.com/github/ECMWFCode4Earth/sketchbook-earth/blob/main/tutorials/03_sea_ice/03_sea_ice.ipynb\">\n",
    "        <img src = \"https://colab.research.google.com/assets/colab-badge.svg\" alt = \"Colab\"></th>\n",
    "  </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Packages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Begin by importing the requisite packages for this tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# API and system utilities\n",
    "import cdsapi\n",
    "import os\n",
    "\n",
    "# Data manipulation and computation\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "import dask\n",
    "import datetime as dt\n",
    "\n",
    "# Data visualization and plotting\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as dates\n",
    "import matplotlib.ticker as ticker\n",
    "import seaborn as sns\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "\n",
    "# EOF analysis\n",
    "import xeofs as xe\n",
    "\n",
    "# Progress bars and diagnostics\n",
    "from tqdm.notebook import tqdm\n",
    "from dask.diagnostics import ProgressBar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Execute the cell below to adopt this book's visualisation style. This ensures consistency in visual presentations throughout the notebook. Note, this solely pertains to visualisation and doesn't affect the underlying calculations. If using GoogleColab, the matplotlib stylesheet won't be unavailable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use(\"../copernicus.mplstyle\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To optimise `dask`'s [performance](https://docs.dask.org/en/latest/understanding-performance.html), avoid generating large `dask` chunks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<dask.config.set at 0x7fee6c2aba00>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dask.config.set(**{'array.slicing.split_large_chunks': True})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting Started"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Establish the reference period which characterises the climatology of our datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "REF_PERIOD = dict(time=slice('1991', '2020'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll also define a set of projections for `cartopy` to be used subsequently."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "proj = {\n",
    "    \"data\": ccrs.PlateCarree(),\n",
    "    \"map_global\": ccrs.Robinson(central_longitude=180),\n",
    "    \"map_north_atlantic\": ccrs.Orthographic(central_longitude=-20, central_latitude=60),\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Moving forward, set up a data directory to neatly store our files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "temp                 --> data/temp/era5_temp.nc\n",
      "temp_anomalies       --> data/temp/era5_temp_anomalies.nc\n",
      "gph                  --> data/gph/era5_gph.nc\n"
     ]
    }
   ],
   "source": [
    "path_to = {}  # dictionary containing [<variable> : <target folder>]\n",
    "\n",
    "path_to.update({\"temp\": \"data/temp/era5_temp.nc\"})\n",
    "path_to.update({\"temp_anomalies\": \"data/temp/era5_temp_anomalies.nc\"})\n",
    "path_to.update({\"gph\": \"data/gph/era5_gph.nc\"})\n",
    "\n",
    "for file, path in path_to.items():\n",
    "    os.makedirs(os.path.dirname(path), exist_ok=True)\n",
    "    print(\"{:<20} --> {}\".format(file, path))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ENSO Index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our first step is to derive the ENSO index using sea surface temperature (SST) data from the ERA5 reanalysis. While there are [multiple methods](https://www.weather.gov/fwd/indices) to derive the ENSO index, our approach utilises the NINO3.4 index. This index denotes the average SST anomalies within the region 5°S-5°N, 170°W-120°W.\n",
    "\n",
    "### Downloading Data\n",
    "Kick off by downloading the data from the [Climate Data Store](https://doi.org/10.24381/cds.f17050d7) (CDS). We're particularly interested in the monthly averaged SST data from ERA5 reanalysis. The CDS API is our tool of choice for this download. Given that the NINO3.4 index is constrained to a specific spatial region, one might consider downloading only this section. Yet, since our aim is to later probe the surface temperature teleconnections of ENSO, we'll encompass global data. This includes downloading the monthly averaged 2m temperature data from ERA5 reanalysis.\n",
    "Enter your CDS API key in the cell below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>New to CDS?</b> Consider the <a href=\"https://ecmwf-projects.github.io/copernicus-training-c3s/cds-tutorial.html\">CDS tutorial</a> for a detailed guide.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please input your CDS API key in the subsequent cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "##### ERA5 reanalysis\n",
    "URL = 'https://cds.climate.copernicus.eu/api/v2'\n",
    "KEY = '##################################' # add your key here the format should be as {uid}:{api-key}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's initiate the data download."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Warning</b>: <br>\n",
    "   The upcoming cell will download approximately 4 GB. Ensure you have sufficient storage space. Depending on your internet speed, this could take several hours.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = cdsapi.Client(url=URL, key=KEY)\n",
    "\n",
    "c.retrieve(\n",
    "  'reanalysis-era5-single-levels-monthly-means',\n",
    "  {\n",
    "    'format': 'netcdf',\n",
    "    'product_type': 'monthly_averaged_reanalysis',\n",
    "    'variable': ['2m_temperature', 'sea_surface_temperature'],\n",
    "    'year': list(range(1940, 2023)),\n",
    "    'month': list(range(1, 13)),\n",
    "    'time': '00:00',\n",
    "  },\n",
    "  path_to['temp']\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll then *lazily* load the data employing `xarray` and `dask`. Our chunks will be set by spatial dimensions rather than time, considering our calculations will operate along the time axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
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       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
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       "  stroke-width: 0;\n",
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       "  fill: currentColor;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (longitude: 1440, latitude: 721, time: 996)\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n",
       "  * latitude   (latitude) float32 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n",
       "  * time       (time) datetime64[ns] 1940-01-01 1940-02-01 ... 2022-12-01\n",
       "Data variables:\n",
       "    t2m        (time, latitude, longitude) float32 dask.array&lt;chunksize=(996, 150, 300), meta=np.ndarray&gt;\n",
       "    sst        (time, latitude, longitude) float32 dask.array&lt;chunksize=(996, 150, 300), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    Conventions:  CF-1.6\n",
       "    history:      2023-09-11 17:33:49 GMT by grib_to_netcdf-2.25.1: /opt/ecmw...</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-39d960ea-ed07-4ce8-8786-46e14d256b3d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-39d960ea-ed07-4ce8-8786-46e14d256b3d' 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'>longitude</span>: 1440</li><li><span class='xr-has-index'>latitude</span>: 721</li><li><span class='xr-has-index'>time</span>: 996</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ee7d768f-a87d-445d-b5c6-a6e7e588d90c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ee7d768f-a87d-445d-b5c6-a6e7e588d90c' 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'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.25 0.5 ... 359.2 359.5 359.8</div><input id='attrs-eb622f96-606a-4cd2-add6-8c4d6f4ce608' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eb622f96-606a-4cd2-add6-8c4d6f4ce608' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae61d78c-2f79-4e81-a16b-7ffcf1c26505' class='xr-var-data-in' type='checkbox'><label for='data-ae61d78c-2f79-4e81-a16b-7ffcf1c26505' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,\n",
       "       3.5975e+02], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>90.0 89.75 89.5 ... -89.75 -90.0</div><input id='attrs-dfca6b56-71b5-4cd2-97c5-b5b57c67b4bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dfca6b56-71b5-4cd2-97c5-b5b57c67b4bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6666498-4b5c-4503-9bba-b1642cb2ee14' class='xr-var-data-in' type='checkbox'><label for='data-f6666498-4b5c-4503-9bba-b1642cb2ee14' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([ 90.  ,  89.75,  89.5 , ..., -89.5 , -89.75, -90.  ], dtype=float32)</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'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1940-01-01 ... 2022-12-01</div><input id='attrs-eac02eea-1c86-4a14-a4f8-9693e6189e67' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eac02eea-1c86-4a14-a4f8-9693e6189e67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-967a7866-a225-43a9-be95-14f7372b9849' class='xr-var-data-in' type='checkbox'><label for='data-967a7866-a225-43a9-be95-14f7372b9849' 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></dl></div><div class='xr-var-data'><pre>array([&#x27;1940-01-01T00:00:00.000000000&#x27;, &#x27;1940-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1940-03-01T00:00:00.000000000&#x27;, ..., &#x27;2022-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-11-01T00:00:00.000000000&#x27;, &#x27;2022-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ed0b38e6-6f97-4eab-a2f1-9eb9c2366668' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ed0b38e6-6f97-4eab-a2f1-9eb9c2366668' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>t2m</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(996, 150, 300), meta=np.ndarray&gt;</div><input id='attrs-2cf58882-6134-424e-bb31-c17530516011' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2cf58882-6134-424e-bb31-c17530516011' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-674f48af-af57-4801-ac58-a993d7ac9057' class='xr-var-data-in' type='checkbox'><label for='data-674f48af-af57-4801-ac58-a993d7ac9057' 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>2 metre temperature</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 3.85 GiB </td>\n",
       "                        <td> 170.97 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (996, 721, 1440) </td>\n",
       "                        <td> (996, 150, 300) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 25 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sst</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(996, 150, 300), meta=np.ndarray&gt;</div><input id='attrs-b46f7492-89aa-4a78-a7b1-e976f16aca29' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b46f7492-89aa-4a78-a7b1-e976f16aca29' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d594c3c0-7424-45f9-967a-8e3cafbc1a84' class='xr-var-data-in' type='checkbox'><label for='data-d594c3c0-7424-45f9-967a-8e3cafbc1a84' 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>Sea surface temperature</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 3.85 GiB </td>\n",
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       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
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       "                        <th> Dask graph </th>\n",
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       "                        <th> Data type </th>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-9f860b44-287c-4a3d-9b53-8cbfd0cc5a1e' class='xr-section-summary-in' type='checkbox'  ><label for='section-9f860b44-287c-4a3d-9b53-8cbfd0cc5a1e' 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>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5b43098b-2435-45fc-9230-b29ffb971fef' class='xr-index-data-in' type='checkbox'/><label for='index-5b43098b-2435-45fc-9230-b29ffb971fef' 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(Float64Index([   0.0,   0.25,    0.5,   0.75,    1.0,   1.25,    1.5,   1.75,\n",
       "                 2.0,   2.25,\n",
       "              ...\n",
       "               357.5, 357.75,  358.0, 358.25,  358.5, 358.75,  359.0, 359.25,\n",
       "               359.5, 359.75],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=1440))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1d54f59f-7b50-42b7-a61c-b67d5d4d62cd' class='xr-index-data-in' type='checkbox'/><label for='index-1d54f59f-7b50-42b7-a61c-b67d5d4d62cd' 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(Float64Index([  90.0,  89.75,   89.5,  89.25,   89.0,  88.75,   88.5,  88.25,\n",
       "                88.0,  87.75,\n",
       "              ...\n",
       "              -87.75,  -88.0, -88.25,  -88.5, -88.75,  -89.0, -89.25,  -89.5,\n",
       "              -89.75,  -90.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=721))</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-27c164c0-32ce-4d4b-9644-0ec10ac2bbe9' class='xr-index-data-in' type='checkbox'/><label for='index-27c164c0-32ce-4d4b-9644-0ec10ac2bbe9' 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(DatetimeIndex([&#x27;1940-01-01&#x27;, &#x27;1940-02-01&#x27;, &#x27;1940-03-01&#x27;, &#x27;1940-04-01&#x27;,\n",
       "               &#x27;1940-05-01&#x27;, &#x27;1940-06-01&#x27;, &#x27;1940-07-01&#x27;, &#x27;1940-08-01&#x27;,\n",
       "               &#x27;1940-09-01&#x27;, &#x27;1940-10-01&#x27;,\n",
       "               ...\n",
       "               &#x27;2022-03-01&#x27;, &#x27;2022-04-01&#x27;, &#x27;2022-05-01&#x27;, &#x27;2022-06-01&#x27;,\n",
       "               &#x27;2022-07-01&#x27;, &#x27;2022-08-01&#x27;, &#x27;2022-09-01&#x27;, &#x27;2022-10-01&#x27;,\n",
       "               &#x27;2022-11-01&#x27;, &#x27;2022-12-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=996, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-70169321-de8b-4774-9aa1-8d387dde9ff3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-70169321-de8b-4774-9aa1-8d387dde9ff3' 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>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>history :</span></dt><dd>2023-09-11 17:33:49 GMT by grib_to_netcdf-2.25.1: /opt/ecmwf/mars-client/bin/grib_to_netcdf.bin -S param -o /cache/data1/adaptor.mars.internal-1694453533.3826513-9338-5-6738052d-b422-49f6-9953-36ae2d000357.nc /cache/tmp/6738052d-b422-49f6-9953-36ae2d000357-adaptor.mars.internal-1694453168.8143947-9338-9-tmp.grib</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (longitude: 1440, latitude: 721, time: 996)\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n",
       "  * latitude   (latitude) float32 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n",
       "  * time       (time) datetime64[ns] 1940-01-01 1940-02-01 ... 2022-12-01\n",
       "Data variables:\n",
       "    t2m        (time, latitude, longitude) float32 dask.array<chunksize=(996, 150, 300), meta=np.ndarray>\n",
       "    sst        (time, latitude, longitude) float32 dask.array<chunksize=(996, 150, 300), meta=np.ndarray>\n",
       "Attributes:\n",
       "    Conventions:  CF-1.6\n",
       "    history:      2023-09-11 17:33:49 GMT by grib_to_netcdf-2.25.1: /opt/ecmw..."
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temperatures = xr.open_mfdataset(path_to[\"temp\"], chunks={\"time\": -1, \"longitude\": 300, \"latitude\": 150})\n",
    "temperatures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For simplicity's sake, rename the `longitude` and `latitude` dimensions to `lon` and `lat`, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "temperatures = temperatures.rename({'longitude': 'lon', 'latitude': 'lat'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute Anomalies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, ascertain the monthly climatologies for both datasets across each grid cell. These climatologies will be instrumental in computing the anomalies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed | 24.15 s\n"
     ]
    }
   ],
   "source": [
    "climatologies = temperatures.sel(REF_PERIOD).groupby(\"time.month\").mean()\n",
    "\n",
    "with ProgressBar():\n",
    "    climatologies = climatologies.compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ensuing cell calculates the temperature anomalies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nrieger/miniconda3/envs/tutorial/lib/python3.10/site-packages/xarray/core/indexing.py:1443: PerformanceWarning: Slicing with an out-of-order index is generating 83 times more chunks\n",
      "  return self.array[key]\n"
     ]
    }
   ],
   "source": [
    "anomalies = temperatures.groupby(\"time.month\") - climatologies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A major advantage of `dask` is its capacity to sequence operations until our processed dataset snugly fits into our memory. Nevertheless, repeatedly computing the same `dask` `DataArray` can be inefficient. To counter this, we'll compute this intermediary result, archive it on our disk, and then lazily fetch it for additional processing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed | 241.66 s\n"
     ]
    }
   ],
   "source": [
    "with ProgressBar():\n",
    "    anomalies.to_netcdf(path_to[\"temp_anomalies\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's reload the temperature anomalies:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "anomalies = xr.open_dataset(path_to[\"temp_anomalies\"], chunks={\"time\": -1, \"lon\": 300, \"lat\": 150})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For ease, extract the anomalies of the global surface temperature at 2m (`t2m`) and the sea surface temperature (`sst`), placing them in distinct variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sst&#x27; (time: 996, lat: 721, lon: 1440)&gt;\n",
       "dask.array&lt;open_dataset-sst, shape=(996, 721, 1440), dtype=float32, chunksize=(996, 150, 300), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 0.0 0.25 0.5 0.75 1.0 ... 359.0 359.2 359.5 359.8\n",
       "  * lat      (lat) float32 90.0 89.75 89.5 89.25 ... -89.25 -89.5 -89.75 -90.0\n",
       "  * time     (time) datetime64[ns] 1940-01-01 1940-02-01 ... 2022-12-01\n",
       "    month    (time) int64 dask.array&lt;chunksize=(996,), meta=np.ndarray&gt;</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'>'sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 996</li><li><span class='xr-has-index'>lat</span>: 721</li><li><span class='xr-has-index'>lon</span>: 1440</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-908ca4d0-47c0-4e1c-8827-dd3c2d58f21e' class='xr-array-in' type='checkbox' checked><label for='section-908ca4d0-47c0-4e1c-8827-dd3c2d58f21e' 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=(996, 150, 300), 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> 3.85 GiB </td>\n",
       "                        <td> 170.97 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (996, 721, 1440) </td>\n",
       "                        <td> (996, 150, 300) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 25 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "  <text x=\"198.823529\" y=\"78.865196\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,198.823529,78.865196)\">721</text>\n",
       "  <text x=\"24.411765\" y=\"104.495098\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,24.411765,104.495098)\">996</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-269a723c-6310-4c28-9c42-88e23446faaa' class='xr-section-summary-in' type='checkbox'  checked><label for='section-269a723c-6310-4c28-9c42-88e23446faaa' 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'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.25 0.5 ... 359.2 359.5 359.8</div><input id='attrs-71765112-2bd2-44bd-ab53-9847dad22db0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71765112-2bd2-44bd-ab53-9847dad22db0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4bc7c3c8-75f8-478d-aa42-14d01f8a6972' class='xr-var-data-in' type='checkbox'><label for='data-4bc7c3c8-75f8-478d-aa42-14d01f8a6972' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,\n",
       "       3.5975e+02], dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>90.0 89.75 89.5 ... -89.75 -90.0</div><input id='attrs-c6253d53-1a62-4203-a012-22016ec5fa1a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c6253d53-1a62-4203-a012-22016ec5fa1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0166b1a9-7e1a-4f00-a83a-7b7ce008d7aa' class='xr-var-data-in' type='checkbox'><label for='data-0166b1a9-7e1a-4f00-a83a-7b7ce008d7aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([ 90.  ,  89.75,  89.5 , ..., -89.5 , -89.75, -90.  ], dtype=float32)</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'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1940-01-01 ... 2022-12-01</div><input id='attrs-e645e288-a1b0-4068-91a6-c3290e9f5d75' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e645e288-a1b0-4068-91a6-c3290e9f5d75' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eedd1e51-38fa-4bec-87cd-bc90ac1003eb' class='xr-var-data-in' type='checkbox'><label for='data-eedd1e51-38fa-4bec-87cd-bc90ac1003eb' 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></dl></div><div class='xr-var-data'><pre>array([&#x27;1940-01-01T00:00:00.000000000&#x27;, &#x27;1940-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1940-03-01T00:00:00.000000000&#x27;, ..., &#x27;2022-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-11-01T00:00:00.000000000&#x27;, &#x27;2022-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</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'>dask.array&lt;chunksize=(996,), meta=np.ndarray&gt;</div><input id='attrs-4f914478-8c9c-4916-a9b9-bf5c2c6a856d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4f914478-8c9c-4916-a9b9-bf5c2c6a856d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89d67438-7d97-49fb-9885-395bf128497f' class='xr-var-data-in' type='checkbox'><label for='data-89d67438-7d97-49fb-9885-395bf128497f' 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>\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> 7.78 kiB </td>\n",
       "                        <td> 7.78 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (996,) </td>\n",
       "                        <td> (996,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> int64 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",
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       "\n",
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       "\n",
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       "\n",
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       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >996</text>\n",
       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-d1314d48-7081-4956-95a3-faea496cfe62' class='xr-section-summary-in' type='checkbox'  ><label for='section-d1314d48-7081-4956-95a3-faea496cfe62' 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>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0365b3db-9bf5-40e6-93ff-9de581cb4b4a' class='xr-index-data-in' type='checkbox'/><label for='index-0365b3db-9bf5-40e6-93ff-9de581cb4b4a' 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(Float64Index([   0.0,   0.25,    0.5,   0.75,    1.0,   1.25,    1.5,   1.75,\n",
       "                 2.0,   2.25,\n",
       "              ...\n",
       "               357.5, 357.75,  358.0, 358.25,  358.5, 358.75,  359.0, 359.25,\n",
       "               359.5, 359.75],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=1440))</pre></div></li><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-8f8c3baf-5cf4-4e6f-8711-780579d32b36' class='xr-index-data-in' type='checkbox'/><label for='index-8f8c3baf-5cf4-4e6f-8711-780579d32b36' 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(Float64Index([  90.0,  89.75,   89.5,  89.25,   89.0,  88.75,   88.5,  88.25,\n",
       "                88.0,  87.75,\n",
       "              ...\n",
       "              -87.75,  -88.0, -88.25,  -88.5, -88.75,  -89.0, -89.25,  -89.5,\n",
       "              -89.75,  -90.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=721))</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-0f343187-b35d-4a13-803c-a40d14811c9e' class='xr-index-data-in' type='checkbox'/><label for='index-0f343187-b35d-4a13-803c-a40d14811c9e' 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(DatetimeIndex([&#x27;1940-01-01&#x27;, &#x27;1940-02-01&#x27;, &#x27;1940-03-01&#x27;, &#x27;1940-04-01&#x27;,\n",
       "               &#x27;1940-05-01&#x27;, &#x27;1940-06-01&#x27;, &#x27;1940-07-01&#x27;, &#x27;1940-08-01&#x27;,\n",
       "               &#x27;1940-09-01&#x27;, &#x27;1940-10-01&#x27;,\n",
       "               ...\n",
       "               &#x27;2022-03-01&#x27;, &#x27;2022-04-01&#x27;, &#x27;2022-05-01&#x27;, &#x27;2022-06-01&#x27;,\n",
       "               &#x27;2022-07-01&#x27;, &#x27;2022-08-01&#x27;, &#x27;2022-09-01&#x27;, &#x27;2022-10-01&#x27;,\n",
       "               &#x27;2022-11-01&#x27;, &#x27;2022-12-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=996, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-55ea43a4-477a-49ea-8c30-ed45307699ec' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-55ea43a4-477a-49ea-8c30-ed45307699ec' 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>"
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       "<xarray.DataArray 'sst' (time: 996, lat: 721, lon: 1440)>\n",
       "dask.array<open_dataset-sst, shape=(996, 721, 1440), dtype=float32, chunksize=(996, 150, 300), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 0.0 0.25 0.5 0.75 1.0 ... 359.0 359.2 359.5 359.8\n",
       "  * lat      (lat) float32 90.0 89.75 89.5 89.25 ... -89.25 -89.5 -89.75 -90.0\n",
       "  * time     (time) datetime64[ns] 1940-01-01 1940-02-01 ... 2022-12-01\n",
       "    month    (time) int64 dask.array<chunksize=(996,), meta=np.ndarray>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t2m = anomalies['t2m']\n",
    "sst = anomalies['sst']\n",
    "sst"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "Now, computing the NINO3.4 index is straightforward. We simply average the **weighted** SST anomalies in the region 5°S-5°N, 170°W-120°W. Note that as the longitude dimensions ranges from 0 to 360°E, the region defintion translates to 190°E-240°E."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "nino34 = sst.sel(lon=slice(190, 240), lat=slice(5, -5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we've demonstrated in the previous notebooks, we compute the spatial average by weighting the grid cells by the cosine of their latitude. This is to account for the convergence of the meridians at the poles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-4f79e2d4-3618-4e2b-8a01-497760ecec56' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4f79e2d4-3618-4e2b-8a01-497760ecec56' 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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1940-01-01 ... 2022-12-01</div><input id='attrs-bde0bb7e-bd35-448d-9634-4e3ddb6317a3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bde0bb7e-bd35-448d-9634-4e3ddb6317a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c55f09a2-10c3-46bf-b458-5a1d9ed29ac4' class='xr-var-data-in' type='checkbox'><label for='data-c55f09a2-10c3-46bf-b458-5a1d9ed29ac4' 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([&#x27;1940-01-01T00:00:00.000000000&#x27;, &#x27;1940-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1940-03-01T00:00:00.000000000&#x27;, ..., &#x27;2022-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-11-01T00:00:00.000000000&#x27;, &#x27;2022-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</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'>dask.array&lt;chunksize=(996,), meta=np.ndarray&gt;</div><input id='attrs-a4d08068-154b-4f9e-bbd3-82fb80bc2ea1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a4d08068-154b-4f9e-bbd3-82fb80bc2ea1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8727f02-157d-4f95-900a-d90ec824c4ea' class='xr-var-data-in' type='checkbox'><label for='data-a8727f02-157d-4f95-900a-d90ec824c4ea' 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>\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> 7.78 kiB </td>\n",
       "                        <td> 7.78 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (996,) </td>\n",
       "                        <td> (996,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> int64 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=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >996</text>\n",
       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-e11e2983-890d-430d-9fd3-a41b9daae8b8' class='xr-section-summary-in' type='checkbox'  ><label for='section-e11e2983-890d-430d-9fd3-a41b9daae8b8' 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-a470773e-81ea-47ba-8f8f-ebdb033f5d16' class='xr-index-data-in' type='checkbox'/><label for='index-a470773e-81ea-47ba-8f8f-ebdb033f5d16' 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(DatetimeIndex([&#x27;1940-01-01&#x27;, &#x27;1940-02-01&#x27;, &#x27;1940-03-01&#x27;, &#x27;1940-04-01&#x27;,\n",
       "               &#x27;1940-05-01&#x27;, &#x27;1940-06-01&#x27;, &#x27;1940-07-01&#x27;, &#x27;1940-08-01&#x27;,\n",
       "               &#x27;1940-09-01&#x27;, &#x27;1940-10-01&#x27;,\n",
       "               ...\n",
       "               &#x27;2022-03-01&#x27;, &#x27;2022-04-01&#x27;, &#x27;2022-05-01&#x27;, &#x27;2022-06-01&#x27;,\n",
       "               &#x27;2022-07-01&#x27;, &#x27;2022-08-01&#x27;, &#x27;2022-09-01&#x27;, &#x27;2022-10-01&#x27;,\n",
       "               &#x27;2022-11-01&#x27;, &#x27;2022-12-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=996, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d089a0c2-9f53-40b1-854f-4653ae4f0237' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d089a0c2-9f53-40b1-854f-4653ae4f0237' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'sst' (time: 996)>\n",
       "dask.array<truediv, shape=(996,), dtype=float32, chunksize=(996,), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1940-01-01 1940-02-01 ... 2022-12-01\n",
       "    month    (time) int64 dask.array<chunksize=(996,), meta=np.ndarray>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights = np.cos(np.deg2rad(nino34.lat))\n",
    "nino34 = nino34.weighted(weights).mean(dim=['lon', 'lat'])\n",
    "nino34"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The index's compact size ensures it comfortably fits in our memory. The cell below manages the actual computation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed | 203.16 ms\n"
     ]
    }
   ],
   "source": [
    "with ProgressBar():\n",
    "    nino34 = nino34.compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, illustrate the NINO3.4 index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 4))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.fill_between(nino34.time, nino34, 0, where=nino34 > 0, color='.8')\n",
    "ax.fill_between(nino34.time, nino34, 0, where=nino34 < 0, color='.8')\n",
    "ax.fill_between(nino34.time, nino34, 0.5, where=nino34 > 0.5, color='tomato')\n",
    "ax.fill_between(nino34.time, nino34, -0.5, where=nino34 < -0.5, color='dodgerblue')\n",
    "ax.axhline(0.5, color='k', linewidth=0.5, ls=':')\n",
    "ax.axhline(-0.5, color='k', linewidth=0.5, ls=':')\n",
    "ax.axhline(0, color='k', linewidth=0.5)\n",
    "ax.set_xlabel(\"\")\n",
    "ax.set_ylabel(\"Temperature Anomaly (°C)\")\n",
    "ax.set_ylim(-3, 3)\n",
    "ax.set_title(\"Nino 3.4 Index\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Teleconnections"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Teleconnections describe the influence of a climate oscillation on the climate of a distant region. In this section, we'll delve into the teleconnections of ENSO on continental surface temperature, using the NINO3.4 index as a representative for the ENSO state. For this purpose, we'll utilise the monthly averaged 2m temperature data from the ERA5 reanalysis.\n",
    "\n",
    "A practical method to explore these teleconnections is through correlation analysis. Specifically, we'll calculate the Pearson correlation coefficient between the NINO3.4 index and the continental surface temperatures. Typically, areas with high correlation coefficients highlight regions potentially influenced by ENSO on their surface temperatures. However, it's crucial to remember that a strong correlation doesn't necessarily imply a direct cause-and-effect relationship; spurious correlations may exist. For the sake of brevity and simplicity in this tutorial, we'll treat the Pearson correlation coefficient as an initial approximation to pinpoint possible teleconnection regions. Fortunately, `xarray` offers a convenient built-in `corr` method, enabling us to conduct this analysis seamlessly in a single line of code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed | 105.12 s\n"
     ]
    }
   ],
   "source": [
    "corr_nino34_t2m = xr.corr(nino34, t2m, dim=\"time\")\n",
    "\n",
    "with ProgressBar():\n",
    "    corr_nino34_t2m = corr_nino34_t2m.compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a reference, let's define a rectangular box that encompasses the NINO3.4 region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "nino34_region = dict(\n",
    "    lon=[190, 240, 240, 190, 190],\n",
    "    lat=[-5, -5, 5, 5, -5]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we plot the correlation coefficients together with the NINO3.4 index region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 4))\n",
    "ax = fig.add_subplot(111, projection=proj[\"map_global\"])\n",
    "corr_nino34_t2m.plot(ax=ax, transform=proj[\"data\"], cmap=\"icefire\", vmin=-1, vmax=1, levels=[-1, -.75, -.45, -.15, .15, .45, .75, 1], cbar_kwargs={\"shrink\": 0.8})\n",
    "ax.coastlines(lw=.5, color=\"w\")\n",
    "# Add Nino 3.4 region\n",
    "ax.plot(nino34_region[\"lon\"], nino34_region[\"lat\"], transform=proj[\"data\"], color=\"k\", lw=1)\n",
    "ax.fill(nino34_region[\"lon\"], nino34_region[\"lat\"], transform=proj[\"data\"], color=\"k\", alpha=0.2)\n",
    "ax.set_title(\"Correlation Nino 3.4 - 2m Temperature\", loc=\"center\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NAO Index\n",
    "\n",
    "The North Atlantic Oscillation (NAO) is a significant fluctuation in atmospheric pressure between the subtropical high-pressure system near the Azores in the Atlantic Ocean and the sub-polar low-pressure system near Iceland. It's a pivotal determinant of winter climate patterns in Europe. The NAO quantifies the strength of the westerly winds over the North Atlantic. While numerous ways exist to define the NAO index, the variations between them are generally slight.\n",
    "\n",
    "In this section, we'll adopt the definition found in the [ESOTC 2022](https://climate.copernicus.eu/esotc/2022/about-data#NorthAtlanticOscillationIndex). \n",
    "\n",
    "Succinctly, the NAO index is computed as follows:\n",
    "\n",
    "1. Empirical Orthogonal Function (EOF) analysis is conducted on daily area-weighted 500 hPa geopotential height (Z500) anomalies from the ERA5 reanalysis covering the Euro-Atlantic region (30°N to 88.5°N, 80°W to 40°E) from 1980–2008. Only data from October–April are utilised in this phase. The NAO pattern is characterised by the first mode derived from this EOF analysis.\n",
    "2. A daily NAO index time series is then produced by projecting the daily Z500 anomalies from ERA5 onto the NAO pattern established in the first step. This series is subsequently standardised using its standard deviation from 1991–2020. Notably, the daily anomalies deployed in the second step are deduced from the daily climatology spanning 1991–2020."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Info</b>: <br>\n",
    "   For ESOTC 2022, the ERA-Interim dataset is used instead of ERA5.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Downloading the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To suppress the output, we instantiate a new `cdsapi` Client."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = cdsapi.Client(quiet=True, progress=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given our intent to fetch daily data, it's prudent to cluster months in batches, enhancing the download efficiency. Thus, we designate the years and months for download."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "YEARS = [\"{:04d}\".format(y) for y in range(1970, 2022)]\n",
    "MONTHS = [\"{:02d}\".format(m) for m in range(1, 13)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [CDS](https://cds.climate.copernicus.eu/cdsapp#!/software/app-c3s-daily-era5-statistics?tab=overview) hosts an application allowing daily ERA5 statistics derivation. A comprehensive manual on this application's operation can be found [here](https://datastore.copernicus-climate.eu/documents/app-c3s-daily-era5-statistics/C3S_Application-Documentation_ERA5-daily-statistics-v2.pdf)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Warning</b>: <br>\n",
    "   The upcoming cell will download approximately 9 GB. Ensure you have sufficient storage space. Depending on your internet speed, this could take several hours.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for year in tqdm(YEARS, desc=\"Overall progress\"):\n",
    "    for month in tqdm(MONTHS, desc=\"Year {}\".format(year), leave=False):\n",
    "        # Define the filename and the path to save the file\n",
    "        path_to_file = f\"data/gph/era5_z500_{year}_{month}.nc\"\n",
    "        # Check if the extracted files already exist\n",
    "        file_exits = os.path.exists(path_to_file)\n",
    "        # Download the file only if it doesn\"t exist\n",
    "        if not file_exits:\n",
    "            result = c.service(\n",
    "                \"tool.toolbox.orchestrator.workflow\",\n",
    "                params={\n",
    "                    \"realm\": \"user-apps\",\n",
    "                    \"project\": \"app-c3s-daily-era5-statistics\",\n",
    "                    \"version\": \"master\",\n",
    "                    \"kwargs\": {\n",
    "                        \"dataset\": \"reanalysis-era5-pressure-levels\",\n",
    "                        \"product_type\": \"reanalysis\",\n",
    "                        \"variable\": \"geopotential\",\n",
    "                        \"statistic\": \"daily_mean\",\n",
    "                        \"year\": year,\n",
    "                        \"month\": month,\n",
    "                        \"time_zone\": \"UTC+00:00\",\n",
    "                        \"frequency\": \"1-hourly\",\n",
    "                        \"grid\": \"0.25/0.25\",\n",
    "                        \"pressure_level\": \"500\",\n",
    "                        \"area\": {\"lat\": [30, 88.5], \"lon\": [-80, 40]}\n",
    "                    },\n",
    "                    \"workflow_name\": \"application\"\n",
    "            })\n",
    "            c.download(result, targets=[path_to_file])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we access the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (time: 19358, lat: 235, lon: 481)\n",
       "Coordinates:\n",
       "  * time         (time) datetime64[ns] 1970-01-01 1970-01-02 ... 2022-12-31\n",
       "    realization  int64 0\n",
       "    plev         float64 5e+04\n",
       "  * lat          (lat) float64 30.0 30.25 30.5 30.75 ... 87.75 88.0 88.25 88.5\n",
       "  * lon          (lon) float64 -80.0 -79.75 -79.5 -79.25 ... 39.5 39.75 40.0\n",
       "Data variables:\n",
       "    z            (time, lat, lon) float32 dask.array&lt;chunksize=(31, 235, 481), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    Conventions:  CF-1.7\n",
       "    institution:  European Centre for Medium-Range Weather Forecasts\n",
       "    history:      2023-09-13T15:03 GRIB to CDM+CF via cfgrib-0.9.9.1/ecCodes-...\n",
       "    source:       ECMWF</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-e60ab42c-53cb-490c-bf10-ad0a5e4e1895' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e60ab42c-53cb-490c-bf10-ad0a5e4e1895' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 19358</li><li><span class='xr-has-index'>lat</span>: 235</li><li><span class='xr-has-index'>lon</span>: 481</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e74b2cbb-c525-404e-935c-5b21ddf3408e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e74b2cbb-c525-404e-935c-5b21ddf3408e' class='xr-section-summary' >Coordinates: <span>(5)</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'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1970-01-01 ... 2022-12-31</div><input id='attrs-d38f85dd-c599-4e3a-90f0-e1783eda7de8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d38f85dd-c599-4e3a-90f0-e1783eda7de8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a0f3399-e57c-4b00-93d0-3a8b359dbd8e' class='xr-var-data-in' type='checkbox'><label for='data-1a0f3399-e57c-4b00-93d0-3a8b359dbd8e' 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>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>stored_direction :</span></dt><dd>increasing</dd><dt><span>type :</span></dt><dd>double</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1970-01-01T00:00:00.000000000&#x27;, &#x27;1970-01-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;1970-01-03T00:00:00.000000000&#x27;, ..., &#x27;2022-12-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-12-30T00:00:00.000000000&#x27;, &#x27;2022-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>realization</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-44f2d242-69c3-41ed-8a6f-27208c1fb34f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-44f2d242-69c3-41ed-8a6f-27208c1fb34f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8ddfe05-72f7-4b20-b7bb-b442c9cef7e7' class='xr-var-data-in' type='checkbox'><label for='data-f8ddfe05-72f7-4b20-b7bb-b442c9cef7e7' 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>realization</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>realization</dd><dt><span>stored_direction :</span></dt><dd>increasing</dd><dt><span>type :</span></dt><dd>integer</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>plev</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5e+04</div><input id='attrs-d5b7f681-179b-4974-aa89-8d7d7538e088' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5b7f681-179b-4974-aa89-8d7d7538e088' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f359162-a169-4582-80fb-25a04f51edd8' class='xr-var-data-in' type='checkbox'><label for='data-6f359162-a169-4582-80fb-25a04f51edd8' 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>pressure</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd><dt><span>standard_name :</span></dt><dd>air_pressure</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>type :</span></dt><dd>double</dd></dl></div><div class='xr-var-data'><pre>array(50000.)</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'>30.0 30.25 30.5 ... 88.0 88.25 88.5</div><input id='attrs-8839bc38-51a6-40c9-9fdd-9fdeb537feb7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8839bc38-51a6-40c9-9fdd-9fdeb537feb7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c22ece6-06d1-4b1b-bc9d-0c5da0671073' class='xr-var-data-in' type='checkbox'><label for='data-0c22ece6-06d1-4b1b-bc9d-0c5da0671073' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd></dl></div><div class='xr-var-data'><pre>array([30.  , 30.25, 30.5 , ..., 88.  , 88.25, 88.5 ])</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'>-80.0 -79.75 -79.5 ... 39.75 40.0</div><input id='attrs-ae83a7dd-33dd-4e85-91bf-304d21e8d61c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae83a7dd-33dd-4e85-91bf-304d21e8d61c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27161b1d-c442-4255-a1f5-7eb91c14bf8d' class='xr-var-data-in' type='checkbox'><label for='data-27161b1d-c442-4255-a1f5-7eb91c14bf8d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>valid_max :</span></dt><dd>360.0</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd></dl></div><div class='xr-var-data'><pre>array([-80.  , -79.75, -79.5 , ...,  39.5 ,  39.75,  40.  ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-98762951-ce5c-4a82-b7af-bd11a284b202' class='xr-section-summary-in' type='checkbox'  checked><label for='section-98762951-ce5c-4a82-b7af-bd11a284b202' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>z</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=(31, 235, 481), meta=np.ndarray&gt;</div><input id='attrs-487d9e16-1291-416d-b778-e2bb80ab6e91' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-487d9e16-1291-416d-b778-e2bb80ab6e91' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2f46ced-726a-4bdf-90ae-9ea5184e87d3' class='xr-var-data-in' type='checkbox'><label for='data-d2f46ced-726a-4bdf-90ae-9ea5184e87d3' 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>Geopotential</dd><dt><span>units :</span></dt><dd>m2 s-2</dd><dt><span>standard_name :</span></dt><dd>geopotential</dd><dt><span>comment :</span></dt><dd>Geopotential is the sum of the specific gravitational potential energy relative to the geoid and the specific centripetal potential energy.</dd><dt><span>cds_magics_style_name :</span></dt><dd>turbo_1000_3000</dd><dt><span>type :</span></dt><dd>real</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 8.15 GiB </td>\n",
       "                        <td> 13.37 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (19358, 235, 481) </td>\n",
       "                        <td> (31, 235, 481) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 636 chunks in 1273 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-6eedbe41-6da1-490f-9ccb-d62720ebf1ee' class='xr-section-summary-in' type='checkbox'  ><label for='section-6eedbe41-6da1-490f-9ccb-d62720ebf1ee' 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>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2fe7cbfa-9251-475d-bc4d-0d7e0880076d' class='xr-index-data-in' type='checkbox'/><label for='index-2fe7cbfa-9251-475d-bc4d-0d7e0880076d' 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(DatetimeIndex([&#x27;1970-01-01&#x27;, &#x27;1970-01-02&#x27;, &#x27;1970-01-03&#x27;, &#x27;1970-01-04&#x27;,\n",
       "               &#x27;1970-01-05&#x27;, &#x27;1970-01-06&#x27;, &#x27;1970-01-07&#x27;, &#x27;1970-01-08&#x27;,\n",
       "               &#x27;1970-01-09&#x27;, &#x27;1970-01-10&#x27;,\n",
       "               ...\n",
       "               &#x27;2022-12-22&#x27;, &#x27;2022-12-23&#x27;, &#x27;2022-12-24&#x27;, &#x27;2022-12-25&#x27;,\n",
       "               &#x27;2022-12-26&#x27;, &#x27;2022-12-27&#x27;, &#x27;2022-12-28&#x27;, &#x27;2022-12-29&#x27;,\n",
       "               &#x27;2022-12-30&#x27;, &#x27;2022-12-31&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=19358, freq=None))</pre></div></li><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-ce4b608b-6318-4998-999f-f113dd4d6504' class='xr-index-data-in' type='checkbox'/><label for='index-ce4b608b-6318-4998-999f-f113dd4d6504' 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(Float64Index([ 30.0, 30.25,  30.5, 30.75,  31.0, 31.25,  31.5, 31.75,  32.0,\n",
       "              32.25,\n",
       "              ...\n",
       "              86.25,  86.5, 86.75,  87.0, 87.25,  87.5, 87.75,  88.0, 88.25,\n",
       "               88.5],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=235))</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-71003378-a167-4b8d-af18-9cfb798af6be' class='xr-index-data-in' type='checkbox'/><label for='index-71003378-a167-4b8d-af18-9cfb798af6be' 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(Float64Index([ -80.0, -79.75,  -79.5, -79.25,  -79.0, -78.75,  -78.5, -78.25,\n",
       "               -78.0, -77.75,\n",
       "              ...\n",
       "               37.75,   38.0,  38.25,   38.5,  38.75,   39.0,  39.25,   39.5,\n",
       "               39.75,   40.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=481))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bf888d9f-737f-405b-aeb2-71f2dee3983f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bf888d9f-737f-405b-aeb2-71f2dee3983f' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>institution :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>history :</span></dt><dd>2023-09-13T15:03 GRIB to CDM+CF via cfgrib-0.9.9.1/ecCodes-2.27.0 with {&quot;source&quot;: &quot;/nfs/compute-0018/data9/adaptor.mars.internal-1694617383.266881-4329-7-c6153a93-bac4-4bb8-9b38-e2f54805658b.grib&quot;, &quot;filter_by_keys&quot;: {}, &quot;encode_cf&quot;: [&quot;parameter&quot;, &quot;time&quot;, &quot;geography&quot;, &quot;vertical&quot;]}</dd><dt><span>source :</span></dt><dd>ECMWF</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (time: 19358, lat: 235, lon: 481)\n",
       "Coordinates:\n",
       "  * time         (time) datetime64[ns] 1970-01-01 1970-01-02 ... 2022-12-31\n",
       "    realization  int64 0\n",
       "    plev         float64 5e+04\n",
       "  * lat          (lat) float64 30.0 30.25 30.5 30.75 ... 87.75 88.0 88.25 88.5\n",
       "  * lon          (lon) float64 -80.0 -79.75 -79.5 -79.25 ... 39.5 39.75 40.0\n",
       "Data variables:\n",
       "    z            (time, lat, lon) float32 dask.array<chunksize=(31, 235, 481), meta=np.ndarray>\n",
       "Attributes:\n",
       "    Conventions:  CF-1.7\n",
       "    institution:  European Centre for Medium-Range Weather Forecasts\n",
       "    history:      2023-09-13T15:03 GRIB to CDM+CF via cfgrib-0.9.9.1/ecCodes-...\n",
       "    source:       ECMWF"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z500 = xr.open_mfdataset(\"data/gph/*.nc\")\n",
    "z500"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With just a singular variable at our disposal, it's straightforward to isolate it and utilise the relevant `xarray.DataArray`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;z&#x27; (time: 19358, lat: 235, lon: 481)&gt;\n",
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       "Coordinates:\n",
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       "    realization  int64 0\n",
       "    plev         float64 5e+04\n",
       "  * lat          (lat) float64 30.0 30.25 30.5 30.75 ... 87.75 88.0 88.25 88.5\n",
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       "Attributes:\n",
       "    long_name:              Geopotential\n",
       "    units:                  m2 s-2\n",
       "    standard_name:          geopotential\n",
       "    comment:                Geopotential is the sum of the specific gravitati...\n",
       "    cds_magics_style_name:  turbo_1000_3000\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-11cf82f2-df14-4cea-8082-05eb228a803e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-11cf82f2-df14-4cea-8082-05eb228a803e' class='xr-section-summary' >Coordinates: <span>(5)</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'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1970-01-01 ... 2022-12-31</div><input id='attrs-9f2937ac-f79d-444a-90d1-33bc9ffc97cb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f2937ac-f79d-444a-90d1-33bc9ffc97cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fcece646-66a9-4d27-af4d-18494957cbf6' class='xr-var-data-in' type='checkbox'><label for='data-fcece646-66a9-4d27-af4d-18494957cbf6' 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>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>stored_direction :</span></dt><dd>increasing</dd><dt><span>type :</span></dt><dd>double</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1970-01-01T00:00:00.000000000&#x27;, &#x27;1970-01-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;1970-01-03T00:00:00.000000000&#x27;, ..., &#x27;2022-12-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-12-30T00:00:00.000000000&#x27;, &#x27;2022-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>realization</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-ad868d63-73ee-436d-b184-c473c644bdff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad868d63-73ee-436d-b184-c473c644bdff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcac67d9-1d06-41e0-abea-a553e7dc47b9' class='xr-var-data-in' type='checkbox'><label for='data-bcac67d9-1d06-41e0-abea-a553e7dc47b9' 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>realization</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>realization</dd><dt><span>stored_direction :</span></dt><dd>increasing</dd><dt><span>type :</span></dt><dd>integer</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>plev</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5e+04</div><input id='attrs-ef05f08f-8262-48eb-91de-b6e1a49e92a1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef05f08f-8262-48eb-91de-b6e1a49e92a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ddfdf6d-7511-48ff-9f75-694a0301fa6e' class='xr-var-data-in' type='checkbox'><label for='data-9ddfdf6d-7511-48ff-9f75-694a0301fa6e' 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>pressure</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd><dt><span>standard_name :</span></dt><dd>air_pressure</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>type :</span></dt><dd>double</dd></dl></div><div class='xr-var-data'><pre>array(50000.)</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'>30.0 30.25 30.5 ... 88.0 88.25 88.5</div><input id='attrs-f35292a0-5ff9-412c-990d-f731e403606e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f35292a0-5ff9-412c-990d-f731e403606e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90df7f5a-6c45-48f6-b812-49fccc511551' class='xr-var-data-in' type='checkbox'><label for='data-90df7f5a-6c45-48f6-b812-49fccc511551' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd></dl></div><div class='xr-var-data'><pre>array([30.  , 30.25, 30.5 , ..., 88.  , 88.25, 88.5 ])</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'>-80.0 -79.75 -79.5 ... 39.75 40.0</div><input id='attrs-05fc1c15-b859-4d63-aa75-63993d3d6098' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-05fc1c15-b859-4d63-aa75-63993d3d6098' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5fd15017-c821-471e-8905-98811c3a1fcc' class='xr-var-data-in' type='checkbox'><label for='data-5fd15017-c821-471e-8905-98811c3a1fcc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>valid_max :</span></dt><dd>360.0</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd></dl></div><div class='xr-var-data'><pre>array([-80.  , -79.75, -79.5 , ...,  39.5 ,  39.75,  40.  ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-81fbe581-191e-4f4c-9b6b-8fd137811dcc' class='xr-section-summary-in' type='checkbox'  ><label for='section-81fbe581-191e-4f4c-9b6b-8fd137811dcc' 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>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-27781179-d4ec-4501-ae72-03932c8a6bb5' class='xr-index-data-in' type='checkbox'/><label for='index-27781179-d4ec-4501-ae72-03932c8a6bb5' 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(DatetimeIndex([&#x27;1970-01-01&#x27;, &#x27;1970-01-02&#x27;, &#x27;1970-01-03&#x27;, &#x27;1970-01-04&#x27;,\n",
       "               &#x27;1970-01-05&#x27;, &#x27;1970-01-06&#x27;, &#x27;1970-01-07&#x27;, &#x27;1970-01-08&#x27;,\n",
       "               &#x27;1970-01-09&#x27;, &#x27;1970-01-10&#x27;,\n",
       "               ...\n",
       "               &#x27;2022-12-22&#x27;, &#x27;2022-12-23&#x27;, &#x27;2022-12-24&#x27;, &#x27;2022-12-25&#x27;,\n",
       "               &#x27;2022-12-26&#x27;, &#x27;2022-12-27&#x27;, &#x27;2022-12-28&#x27;, &#x27;2022-12-29&#x27;,\n",
       "               &#x27;2022-12-30&#x27;, &#x27;2022-12-31&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=19358, freq=None))</pre></div></li><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-d582f576-2c7d-430c-b67a-627a365643c0' class='xr-index-data-in' type='checkbox'/><label for='index-d582f576-2c7d-430c-b67a-627a365643c0' 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(Float64Index([ 30.0, 30.25,  30.5, 30.75,  31.0, 31.25,  31.5, 31.75,  32.0,\n",
       "              32.25,\n",
       "              ...\n",
       "              86.25,  86.5, 86.75,  87.0, 87.25,  87.5, 87.75,  88.0, 88.25,\n",
       "               88.5],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=235))</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-db6a81f7-acff-4a41-b2c3-542dd869c855' class='xr-index-data-in' type='checkbox'/><label for='index-db6a81f7-acff-4a41-b2c3-542dd869c855' 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(Float64Index([ -80.0, -79.75,  -79.5, -79.25,  -79.0, -78.75,  -78.5, -78.25,\n",
       "               -78.0, -77.75,\n",
       "              ...\n",
       "               37.75,   38.0,  38.25,   38.5,  38.75,   39.0,  39.25,   39.5,\n",
       "               39.75,   40.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=481))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2590e79a-8085-4e5b-bcde-9926ba699102' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2590e79a-8085-4e5b-bcde-9926ba699102' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Geopotential</dd><dt><span>units :</span></dt><dd>m2 s-2</dd><dt><span>standard_name :</span></dt><dd>geopotential</dd><dt><span>comment :</span></dt><dd>Geopotential is the sum of the specific gravitational potential energy relative to the geoid and the specific centripetal potential energy.</dd><dt><span>cds_magics_style_name :</span></dt><dd>turbo_1000_3000</dd><dt><span>type :</span></dt><dd>real</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'z' (time: 19358, lat: 235, lon: 481)>\n",
       "dask.array<concatenate, shape=(19358, 235, 481), dtype=float32, chunksize=(31, 235, 481), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time         (time) datetime64[ns] 1970-01-01 1970-01-02 ... 2022-12-31\n",
       "    realization  int64 0\n",
       "    plev         float64 5e+04\n",
       "  * lat          (lat) float64 30.0 30.25 30.5 30.75 ... 87.75 88.0 88.25 88.5\n",
       "  * lon          (lon) float64 -80.0 -79.75 -79.5 -79.25 ... 39.5 39.75 40.0\n",
       "Attributes:\n",
       "    long_name:              Geopotential\n",
       "    units:                  m2 s-2\n",
       "    standard_name:          geopotential\n",
       "    comment:                Geopotential is the sum of the specific gravitati...\n",
       "    cds_magics_style_name:  turbo_1000_3000\n",
       "    type:                   real"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z500 = z500['z']\n",
    "z500"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Computing Daily Anomalies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The initial task for computing daily anomalies is establishing the daily climatology. The predefined reference period (1991-2020) will serve as our benchmark."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed | 41.90 s\n"
     ]
    }
   ],
   "source": [
    "daily_climatoloy = z500.sel(REF_PERIOD).groupby(\"time.dayofyear\").mean()\n",
    "with ProgressBar():\n",
    "    daily_climatoloy = daily_climatoloy.compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now proceed to calculate the daily anomalies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nrieger/miniconda3/envs/tutorial/lib/python3.10/site-packages/xarray/core/indexing.py:1443: PerformanceWarning: Slicing with an out-of-order index is generating 53 times more chunks\n",
      "  return self.array[key]\n"
     ]
    }
   ],
   "source": [
    "z500_anomalies = z500.groupby(\"time.dayofyear\") - daily_climatoloy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deriving the NAO Index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As previously outlined, the NAO index is based on the first EOF mode of daily Z500 anomalies from October through April, spanning 1980 to 2008. First, we select this specific time period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nrieger/miniconda3/envs/tutorial/lib/python3.10/site-packages/xarray/core/accessor_dt.py:72: FutureWarning: Index.ravel returning ndarray is deprecated; in a future version this will return a view on self.\n",
      "  values_as_series = pd.Series(values.ravel(), copy=False)\n"
     ]
    },
    {
     "data": {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;z&#x27; (time: 6156, lat: 235, lon: 481)&gt;\n",
       "dask.array&lt;getitem, shape=(6156, 235, 481), dtype=float32, chunksize=(1, 235, 481), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * time         (time) datetime64[ns] 1980-01-01 1980-01-02 ... 2008-12-31\n",
       "    realization  (time) int64 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 0\n",
       "    plev         (time) float64 5e+04 5e+04 5e+04 5e+04 ... 5e+04 5e+04 5e+04\n",
       "  * lat          (lat) float64 30.0 30.25 30.5 30.75 ... 87.75 88.0 88.25 88.5\n",
       "  * lon          (lon) float64 -80.0 -79.75 -79.5 -79.25 ... 39.5 39.75 40.0\n",
       "    dayofyear    (time) int64 1 2 3 4 5 6 7 8 ... 360 361 362 363 364 365 366</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'>'z'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 6156</li><li><span class='xr-has-index'>lat</span>: 235</li><li><span class='xr-has-index'>lon</span>: 481</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-3fd8ac80-d344-4f81-8f68-80195976882c' class='xr-array-in' type='checkbox' checked><label for='section-3fd8ac80-d344-4f81-8f68-80195976882c' 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, 235, 481), 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> 2.59 GiB </td>\n",
       "                        <td> 441.54 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
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       "                        <td> (6156, 235, 481) </td>\n",
       "                        <td> (1, 235, 481) </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 6156 chunks in 1279 graph layers </td>\n",
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       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "  <text x=\"99.182635\" y=\"123.436671\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >481</text>\n",
       "  <text x=\"137.777035\" y=\"87.012453\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,137.777035,87.012453)\">235</text>\n",
       "  <text x=\"35.294118\" y=\"88.142553\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,88.142553)\">6156</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-645ffe6e-5e23-4bcf-8ffb-a4ee962f0b8c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-645ffe6e-5e23-4bcf-8ffb-a4ee962f0b8c' 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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1980-01-01 ... 2008-12-31</div><input id='attrs-aface832-7f37-4cac-901a-5d313c7101ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aface832-7f37-4cac-901a-5d313c7101ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33b818ad-85b6-4182-9dbc-47fa8ced85ff' class='xr-var-data-in' type='checkbox'><label for='data-33b818ad-85b6-4182-9dbc-47fa8ced85ff' 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>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>stored_direction :</span></dt><dd>increasing</dd><dt><span>type :</span></dt><dd>double</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1980-01-01T00:00:00.000000000&#x27;, &#x27;1980-01-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-01-03T00:00:00.000000000&#x27;, ..., &#x27;2008-12-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2008-12-30T00:00:00.000000000&#x27;, &#x27;2008-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>realization</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-82abb3e1-aaa2-4e92-bfac-cc0f1e332596' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-82abb3e1-aaa2-4e92-bfac-cc0f1e332596' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9dc851ea-1c0d-49fb-a2aa-409c23a01a24' class='xr-var-data-in' type='checkbox'><label for='data-9dc851ea-1c0d-49fb-a2aa-409c23a01a24' 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>realization</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>realization</dd><dt><span>stored_direction :</span></dt><dd>increasing</dd><dt><span>type :</span></dt><dd>integer</dd></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>plev</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5e+04 5e+04 5e+04 ... 5e+04 5e+04</div><input id='attrs-7855ca9f-db21-47b2-8741-29e3cf987f26' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7855ca9f-db21-47b2-8741-29e3cf987f26' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed1c6ad9-f3da-45c7-a44e-fd8a60db0756' class='xr-var-data-in' type='checkbox'><label for='data-ed1c6ad9-f3da-45c7-a44e-fd8a60db0756' 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>pressure</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd><dt><span>standard_name :</span></dt><dd>air_pressure</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>type :</span></dt><dd>double</dd></dl></div><div class='xr-var-data'><pre>array([50000., 50000., 50000., ..., 50000., 50000., 50000.])</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'>30.0 30.25 30.5 ... 88.0 88.25 88.5</div><input id='attrs-6954e1f3-4b0d-4949-8fbf-57c5efeae305' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6954e1f3-4b0d-4949-8fbf-57c5efeae305' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4844340f-4abe-48eb-a117-e9541116aa5c' class='xr-var-data-in' type='checkbox'><label for='data-4844340f-4abe-48eb-a117-e9541116aa5c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd></dl></div><div class='xr-var-data'><pre>array([30.  , 30.25, 30.5 , ..., 88.  , 88.25, 88.5 ])</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'>-80.0 -79.75 -79.5 ... 39.75 40.0</div><input id='attrs-ada58362-de0a-417a-aaf3-3420b76d1eeb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ada58362-de0a-417a-aaf3-3420b76d1eeb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-add59434-3d9c-4e38-8690-32b1fbce8862' class='xr-var-data-in' type='checkbox'><label for='data-add59434-3d9c-4e38-8690-32b1fbce8862' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>valid_max :</span></dt><dd>360.0</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd></dl></div><div class='xr-var-data'><pre>array([-80.  , -79.75, -79.5 , ...,  39.5 ,  39.75,  40.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dayofyear</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 ... 362 363 364 365 366</div><input id='attrs-fb290334-e133-44b1-abaf-5ac1a03ae516' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fb290334-e133-44b1-abaf-5ac1a03ae516' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ebe3f866-6ad4-4498-97a1-78ab2fa8b224' class='xr-var-data-in' type='checkbox'><label for='data-ebe3f866-6ad4-4498-97a1-78ab2fa8b224' 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([  1,   2,   3, ..., 364, 365, 366])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1ab1c3ef-826d-4df6-b7af-06c3920b0d34' class='xr-section-summary-in' type='checkbox'  ><label for='section-1ab1c3ef-826d-4df6-b7af-06c3920b0d34' 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>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ab4bacd9-6665-4c50-95ba-8e2b421c2fc9' class='xr-index-data-in' type='checkbox'/><label for='index-ab4bacd9-6665-4c50-95ba-8e2b421c2fc9' 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(DatetimeIndex([&#x27;1980-01-01&#x27;, &#x27;1980-01-02&#x27;, &#x27;1980-01-03&#x27;, &#x27;1980-01-04&#x27;,\n",
       "               &#x27;1980-01-05&#x27;, &#x27;1980-01-06&#x27;, &#x27;1980-01-07&#x27;, &#x27;1980-01-08&#x27;,\n",
       "               &#x27;1980-01-09&#x27;, &#x27;1980-01-10&#x27;,\n",
       "               ...\n",
       "               &#x27;2008-12-22&#x27;, &#x27;2008-12-23&#x27;, &#x27;2008-12-24&#x27;, &#x27;2008-12-25&#x27;,\n",
       "               &#x27;2008-12-26&#x27;, &#x27;2008-12-27&#x27;, &#x27;2008-12-28&#x27;, &#x27;2008-12-29&#x27;,\n",
       "               &#x27;2008-12-30&#x27;, &#x27;2008-12-31&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=6156, freq=None))</pre></div></li><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-edd2f780-b59f-4e6a-afb5-e44838a5c599' class='xr-index-data-in' type='checkbox'/><label for='index-edd2f780-b59f-4e6a-afb5-e44838a5c599' 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(Float64Index([ 30.0, 30.25,  30.5, 30.75,  31.0, 31.25,  31.5, 31.75,  32.0,\n",
       "              32.25,\n",
       "              ...\n",
       "              86.25,  86.5, 86.75,  87.0, 87.25,  87.5, 87.75,  88.0, 88.25,\n",
       "               88.5],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=235))</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-500597c6-2fcd-4ecb-9f05-558cb637e0c2' class='xr-index-data-in' type='checkbox'/><label for='index-500597c6-2fcd-4ecb-9f05-558cb637e0c2' 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(Float64Index([ -80.0, -79.75,  -79.5, -79.25,  -79.0, -78.75,  -78.5, -78.25,\n",
       "               -78.0, -77.75,\n",
       "              ...\n",
       "               37.75,   38.0,  38.25,   38.5,  38.75,   39.0,  39.25,   39.5,\n",
       "               39.75,   40.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=481))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d354fc41-5ae8-4b26-9335-bef105879a44' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d354fc41-5ae8-4b26-9335-bef105879a44' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'z' (time: 6156, lat: 235, lon: 481)>\n",
       "dask.array<getitem, shape=(6156, 235, 481), dtype=float32, chunksize=(1, 235, 481), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time         (time) datetime64[ns] 1980-01-01 1980-01-02 ... 2008-12-31\n",
       "    realization  (time) int64 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 0\n",
       "    plev         (time) float64 5e+04 5e+04 5e+04 5e+04 ... 5e+04 5e+04 5e+04\n",
       "  * lat          (lat) float64 30.0 30.25 30.5 30.75 ... 87.75 88.0 88.25 88.5\n",
       "  * lon          (lon) float64 -80.0 -79.75 -79.5 -79.25 ... 39.5 39.75 40.0\n",
       "    dayofyear    (time) int64 1 2 3 4 5 6 7 8 ... 360 361 362 363 364 365 366"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_winter_month = z500_anomalies.time.dt.month.isin([10,11,12,1,2,3,4])\n",
    "z500_nao_ref = z500_anomalies.isel(time=is_winter_month).sel(time=slice(\"1980-01-01\", \"2008-12-31\"))\n",
    "z500_nao_ref"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step involves conducting the EOF analysis using the `xeofs` package. To initialise the EOF model, we need to specify the number of modes to compute. As we're primarily interested in the first mode, we set this value to 1. We also have to weight the individual grid cells to account for the convergence of the meridians the poles which we do by setting the `use_coslat` parameter to `True`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "eof_model = xe.models.EOF(n_modes=1, use_coslat=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we simply fit the model to our data. Crucially, we need to specify the dimension for the EOF analysis, which in our case is the time dimension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "eof_model.fit(z500_nao_ref, dim=\"time\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Post this, we can capture the EOF pattern and the affiliated principal component (PC). Given our input was sourced from `dask` arrays, both the EOF pattern and PC echo this format. Prior to visualisation, we therefore need to compute the results. Thankfully, `xeofs` furnishes a useful method, computing all results simultaneously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed | 11m 41s\n",
      "[########################################] | 100% Completed | 10m 48s\n",
      "[########################################] | 100% Completed | 11m 37s\n",
      "[########################################] | 100% Completed | 75.40 s\n"
     ]
    }
   ],
   "source": [
    "eof_model.compute(verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Subsequently, we extract the spatial patterns (components)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "components = eof_model.components()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Examining the first EOF mode of the Z500 anomalies reveals the evident NAO pattern. Note, however, that the sign of the pattern is inverse to the NAO pattern typically presented in literature. This is because the EOF analysis is agnostic to the sign of the pattern. Thus, we can simply flip the sign of the pattern to match the conventional NAO pattern."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 3))\n",
    "ax = fig.add_subplot(121, projection=proj[\"map_north_atlantic\"])\n",
    "ax.coastlines(lw=.5)\n",
    "ax.add_feature(cfeature.LAND, color=\".6\")\n",
    "ax.add_feature(cfeature.OCEAN, color=\".3\")\n",
    "gl = ax.gridlines(draw_labels=True, color='.1', ls=':', lw=.25)\n",
    "gl.top_labels = False\n",
    "gl.right_labels = False\n",
    "components.sel(mode=1).plot(transform=ccrs.PlateCarree(), ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following step, the NAO index's daily time series is obtained by projecting the ERA5 daily Z500 anomalies onto the previously delineated NAO pattern. This projection is executed via the `transform` method within the `xeofs` model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "pseudo_pcs = eof_model.transform(z500_anomalies)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first mode, embodying the NAO index, is selected. As mentioned before, we flip the sign of the index to match the conventional NAO index definition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "nao_index = -pseudo_pcs.sel(mode=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We compute the NAO index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed | 98.63 s\n"
     ]
    }
   ],
   "source": [
    "with ProgressBar():\n",
    "    nao_index = nao_index.compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The concluding step entails standardising the NAO index using its 1991–2020 standard deviation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "nao_index = nao_index / nao_index.sel(REF_PERIOD).std(\"time\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Time to visualise the NAO index. To echo the ESOTC 2022's depiction, we craft a helper function positioning the labels aptly between the x-ticks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def center_labels_between_xticks(ax):\n",
    "    # set y-ticks as integers\n",
    "    ax.yaxis.set_major_locator(ticker.MaxNLocator(integer=True))\n",
    "\n",
    "    # Set major x-ticks every 1 month\n",
    "    ax.xaxis.set_major_locator(dates.MonthLocator())\n",
    "\n",
    "\n",
    "    # Centering labels between ticks\n",
    "    # 16 is a slight approximation since months differ in number of days.\n",
    "    ax.xaxis.set_minor_locator(dates.MonthLocator(bymonthday=16))\n",
    "    ax.xaxis.set_major_formatter(ticker.NullFormatter())\n",
    "    ax.xaxis.set_minor_formatter(dates.DateFormatter(\"%b\"))\n",
    "\n",
    "    # Remove the minor tick lines\n",
    "    ax.tick_params(axis=\"x\", which=\"minor\", tick1On=False, tick2On=False)\n",
    "\n",
    "    # Align the minor tick label\n",
    "    for label in ax.get_xticklabels(minor=True):\n",
    "        label.set_horizontalalignment(\"center\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Moreover, a 7-day rolling mean is applied, lending a smoother edge to the NAO index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "nao_index_smooth = nao_index.rolling(time=7, center=True).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, we plot the NAO index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "year = 2022\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 2))\n",
    "ax.fill_between(nao_index_smooth.time, 0, nao_index_smooth, where=nao_index_smooth >= 0, color=\"red\", alpha=0.5)\n",
    "ax.fill_between(nao_index_smooth.time, 0, nao_index_smooth, where=nao_index_smooth <= 0, color=\"blue\", alpha=0.5)\n",
    "ax.axhline(0, color=\"k\", linewidth=0.5)\n",
    "ax.set_title(\"North Atlantic Osciallation (NAO) Index - {}\".format(year))\n",
    "ax.set_ylabel(\"\")\n",
    "ax.set_xlabel(\"\")\n",
    "ax.set_ylim(-3, 3)\n",
    "\n",
    "xlim1 = dt.datetime.strptime(f\"{year}-01-01\", \"%Y-%m-%d\")\n",
    "xlim2 = dt.datetime.strptime(f\"{year}-12-31\", \"%Y-%m-%d\")\n",
    "ax.set_xlim(xlim1, xlim2)\n",
    "center_labels_between_xticks(ax)\n",
    "sns.despine(ax=ax, offset=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Note</b>: <br>\n",
    "   Looking carefully you will spot minor differences between the NAO index presented here and the one in the ESOTC 2022. This is due to the usage of ERA-Interim in the ESOTC 2022 for defining the NAO pattern instead of ERA5.\n",
    "</div>"
   ]
  },
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   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial illuminated the process of extracting both the ENSO and NAO indices from ERA5 data. We also touched upon the methodology of investigating teleconnections, demonstrated with an surface temperature teleconnections of ENSO."
   ]
  }
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