{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 2: Taking Earth's Temperature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PART III: Visualising Recent Temperature Anomalies<a class=\"anchor\" id=\"chapter-one\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imagine you're a seasoned mariner, navigating the vast oceans with nothing but a worn-out map and a compass. You can sense the subtle changes in temperature and weather patterns as you drift from one sea to another. In this journey of \"Taking Earth's Temperature,\" we will be your modern navigational tools, guiding you through the uncharted waters of global temperature anomalies.\n",
    "\n",
    "This tutorial is Part III of the trilogy “Taking Earth’s Temperature”:\n",
    "\n",
    "1. **Long-Term Development** of Global Earth Temperature Since 1850\n",
    "2. **Comparing Reanalysis with Observations since 1950**  \n",
    "3. **Visualising Recent Temperature Anomalies** ([this notebook](#part-two))\n",
    "\n",
    "\n",
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Objective</b> <br>\n",
    "    The final part of this trilogy will focus on visualizing recent temperature anomalies across various regions of the Earth. By the end of this chapter, not only will you be able to reproduce Climate Bulletin graphs, but you'll also create them for your chosen regions, just as a mariner charts new territories.\n",
    " </a>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Here's what we will explore**:\n",
    "\n",
    "- **Calculating spatial averages**: Understanding how to average temperature data across different regions.\n",
    "- **Visualizing spatial data with various map projections**: Learning to represent global temperatures using different visual perspectives."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To do this, we will use the temperature data from the ERA5 re-analysis:\n",
    "\n",
    "| Dataset | Spatial Coverage | Spatial Resolution | Temporal Coverage | Temporal resolution |Provider |\n",
    "|---------|:-------------:|:------:|:------:|:------:|:------:|\n",
    "|  [ERA5](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels-monthly-means?tab=overview) |  Global | 0.25º x 0.25º | 1940 - today | Monthly | C3S/ECMWF |"
   ]
  },
  {
   "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/02_temperature/02c_temperature.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/02_temperature/02c_temperature.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/02_temperature/02c_temperature.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": [
    "#### Getting Set Up"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Importing the Necessary Packages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll start by importing all the required packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Python Standard Libraries\n",
    "import os\n",
    "import datetime as dt\n",
    "import calendar\n",
    "from string import ascii_lowercase as ABC\n",
    "\n",
    "# Data Manipulation Libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import dask\n",
    "\n",
    "# Visualization Libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib.gridspec import GridSpec\n",
    "import matplotlib.dates as mdates\n",
    "import cartopy.crs as ccrs\n",
    "from dask.diagnostics.progress import ProgressBar\n",
    "\n",
    "# Climate Data Store API for retrieving climate data\n",
    "import cdsapi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cell ensures a consistent figure layout. If this notebook is run on any cloud platforms, the default stylesheet may not be available. You can either upload the file to the cloud server or ignore the following cell, as it won't impact the calculation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use( \"../copernicus.mplstyle\")  # Set the visual style of the plots; not necessary for the tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we instruct `dask` to avoid creating large chunks that may arise during different calculations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<dask.config.set at 0x7fefac177e80>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dask.config.set(**{\"array.slicing.split_large_chunks\": True})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Defining Global Parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we'll define some global parameters that we'll refer to throughout the voyage. Depending on your interest, these can be adjusted. For most of the regions we followed the definitions used in the [C3S Climate Intelligence reports](https://climate.copernicus.eu/esotc/2022/about-data#Regiondefinitions). Feel free to add your own regions of interest!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "REGIONS = {\n",
    "    \"Global\": {\"lon\": slice(-180, 180), \"lat\": slice(-90, 90)},\n",
    "    \"Northern Hemisphere\": {\"lon\": slice(-180, 180), \"lat\": slice(0, 90)},\n",
    "    \"Southern Hemisphere\": {\"lon\": slice(-180, 180), \"lat\": slice(-90, 0)},\n",
    "    \"Europe\": {\"lon\": slice(-25, 40), \"lat\": slice(34, 72)},\n",
    "    \"Arctic\": {\"lon\": slice(-180, 180), \"lat\": slice(66.6, 90)},\n",
    "    \"Antarctica\": {\"lon\": slice(-180, 180), \"lat\": slice(-90, -66.6)},\n",
    "    \"Southern Africa\": {\"lon\": slice(0, 60), \"lat\": slice(-35, 0)},\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we want to spatially represent various areas on the globe, we must often tailor the map to our specific region. To automate this process, we'll use a helper function that gives us the extent for each region we've defined, which we can later use for plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_extent_from_region(region):\n",
    "    x0 = REGIONS[region]['lon'].start\n",
    "    x1 = REGIONS[region]['lon'].stop\n",
    "    y0 = REGIONS[region]['lat'].start\n",
    "    y1 = REGIONS[region]['lat'].stop\n",
    "    return x0, x1, y0, y1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Choosing Projections with `cartopy`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll also define the necessary projections we need for visualizing spatial data. This is where we use the package `cartopy`, specially designed for geospatial data processing to produce maps. It's like selecting the right lens to view our world.\n",
    "\n",
    "Here's a crucial distinction in using `cartopy`:\n",
    "\n",
    "- `projection` refers to the visualization representation of the data, i.e., the actual look of the map.\n",
    "- `transform` refers to the projection in which the data is represented.\n",
    "\n",
    "Whether you're interested in the whole globe or more polar areas, you can select the appropriate projection such as `EqualEarth()`, `Robinson()`, `NorthPolarStereo()`, or `SouthPolarStereo()` (see the [full list of projections](https://scitools.org.uk/cartopy/docs/latest/reference/projections.html) supported by `cartopy`). Each projection from 3D onto 2D is always associated with an inherent simplication. As a general rule, most projection can be grouped in one of the following two *distinct* categories:\n",
    "\n",
    "- **Equal-area projections** ensure that areas appearing the same size on the map are indeed the same size.\n",
    "- **Conformal projections** maintain the shape of objects regardless of their location on the map.\n",
    "\n",
    "\n",
    "Remember, no projection is universally right or wrong; it depends on your specific needs. In the following we set the projections for our regions, based on what we think is an appropriate choice, but you may prefer other projections. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "PROJS = {\n",
    "    \"Global\": ccrs.EqualEarth(),\n",
    "    \"Northern Hemisphere\": ccrs.NorthPolarStereo(),\n",
    "    \"Southern Hemisphere\": ccrs.SouthPolarStereo(),\n",
    "    \"Arctic\": ccrs.NorthPolarStereo(),\n",
    "    \"Antarctica\": ccrs.SouthPolarStereo(),\n",
    "    \"Europe\": ccrs.TransverseMercator(central_longitude=7.5, central_latitude=52),\n",
    "    \"Southern Africa\": ccrs.Orthographic(central_longitude=25, central_latitude=-25),\n",
    "    \"Data\": ccrs.PlateCarree(),\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Selecting the Reference Time Period\n",
    "\n",
    "Finally, we'll choose the reference period for calculating climatology. Here, we'll use 1991-2020."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "REF_PERIOD = {\"time\": slice(\"1991\", \"2020\")}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Preparing Data Structure and Helper Functions\n",
    "\n",
    "Following the lead of Parts I and II, we'll create a folder structure for our data, even though it seems unnecessary here since we're only working with ERA5 data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'era5': 'data/era5/temperature_era5.nc'}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_name = {}  # dictionary containing [data source : file name]\n",
    "\n",
    "# Add the data sources and file names\n",
    "file_name.update({\"era5\": \"temperature_era5.nc\"})\n",
    "\n",
    "# Create the paths to the files\n",
    "path_to = {\n",
    "    source: os.path.join(f\"data/{source}/\", file) for source, file in file_name.items()\n",
    "}\n",
    "\n",
    "# Create necessary directories if they do not exist\n",
    "for path in path_to.values():\n",
    "    os.makedirs(\n",
    "        os.path.dirname(path), exist_ok=True\n",
    "    )  # create the folder if not available\n",
    "\n",
    "path_to"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our helper function streamline_coords is a copy from the first two parts. It ensures that ERA5 maintains the same format as in the previous chapters. Here's a summary of the standards this function imposes on the data:\n",
    "\n",
    "- Dimension names are (`\"time\"`, `\"lon\"`, `\"lat\"`)\n",
    "- The `time` coordinate is in `datetime` format and fixed to the beginning of the month for monthly resolution.\n",
    "- The `lon` and `lat` coordinates are sorted by size.\n",
    "- The `lon` coordinate is defined from -180 to +180º (as opposed to 0 to 360º)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def streamline_coords(da):\n",
    "    \"\"\"Streamline the coordinates of a DataArray.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    da : xr.DataArray\n",
    "        The DataArray to streamline.\n",
    "    \"\"\"\n",
    "\n",
    "    # Ensure that time coordinate is fixed to the first day of the month\n",
    "    if \"time\" in da.coords:\n",
    "        da.coords[\"time\"] = da[\"time\"].to_index().to_period(\"M\").to_timestamp()\n",
    "\n",
    "    # Ensure that spatial coordinates are called 'lon' and 'lat'\n",
    "    if \"longitude\" in da.coords:\n",
    "        da = da.rename({\"longitude\": \"lon\"})\n",
    "    if \"latitude\" in da.coords:\n",
    "        da = da.rename({\"latitude\": \"lat\"})\n",
    "\n",
    "    # Ensure that lon/lat are sorted in ascending order\n",
    "    da = da.sortby(\"lat\")\n",
    "    da = da.sortby(\"lon\")\n",
    "\n",
    "    # Ensure that lon is in the range [-180, 180]\n",
    "    lon_min = da[\"lon\"].min()\n",
    "    lon_max = da[\"lon\"].max()\n",
    "    if lon_min < -180 or lon_max > 180:\n",
    "        da.coords[\"lon\"] = (da.coords[\"lon\"] + 180) % 360 - 180\n",
    "        da = da.sortby(da.lon)\n",
    "\n",
    "    return da"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Downloading the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we'll load ERA5 data from the [Climate Data Store (CDS)](https://doi.org/10.24381/cds.f17050d7) using the `cdsapi`, including the land-sea mask. To do this, provide your CDS API key."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": [
    "<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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-18 23:37:09,436 INFO Welcome to the CDS\n",
      "2023-07-18 23:37:09,437 INFO Sending request to https://cds.climate.copernicus.eu/api/v2/resources/reanalysis-era5-single-levels-monthly-means\n",
      "2023-07-18 23:37:09,532 INFO Request is queued\n",
      "2023-07-18 23:37:10,588 INFO Request is running\n",
      "2023-07-18 23:47:29,182 INFO Request is completed\n",
      "2023-07-18 23:47:29,184 INFO Downloading https://download-0014-clone.copernicus-climate.eu/cache-compute-0014/cache/data9/adaptor.mars.internal-1689716608.4304297-30290-1-58d02187-6545-4a99-83e3-91816a555a27.nc to data/era5/temperature_era5.nc (3.4G)\n",
      "2023-07-19 00:26:09,727 INFO Download rate 1.5M/s     \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Result(content_length=3638006464,content_type=application/x-netcdf,location=https://download-0014-clone.copernicus-climate.eu/cache-compute-0014/cache/data9/adaptor.mars.internal-1689716608.4304297-30290-1-58d02187-6545-4a99-83e3-91816a555a27.nc)"
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   "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', 'land_sea_mask'],\n",
    "    'year': list(range(1950, 2023)),\n",
    "    'month': list(range(1, 13)),\n",
    "    'time': '00:00',\n",
    "    # 'grid': [0.25, 0.25],\n",
    "  },\n",
    "  path_to['era5']\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After downloading the ERA5 data, we'll conduct the following operations:\n",
    "\n",
    "1. Streamlining coordinates\n",
    "2. Conversion from Kelvin to Celsius\n",
    "3. Calculating monthly climatology"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (lon: 1440, lat: 721, time: 876)\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 -180.0 -179.8 -179.5 -179.2 ... 179.2 179.5 179.8\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] 1950-01-01 1950-02-01 ... 2022-12-01\n",
       "    month    (time) int64 1 2 3 4 5 6 7 8 9 10 11 ... 2 3 4 5 6 7 8 9 10 11 12\n",
       "Data variables:\n",
       "    t2m      (time, lat, lon) float32 dask.array&lt;chunksize=(876, 27, 720), meta=np.ndarray&gt;\n",
       "    lsm      (time, lat, lon) float32 dask.array&lt;chunksize=(876, 27, 720), meta=np.ndarray&gt;\n",
       "    anom     (time, lat, lon) float32 dask.array&lt;chunksize=(1, 27, 720), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    Conventions:  CF-1.6\n",
       "    history:      2023-07-18 21:45:16 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-0e329c77-1ce5-4197-8a5f-f90f8263f9b4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0e329c77-1ce5-4197-8a5f-f90f8263f9b4' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lon</span>: 1440</li><li><span class='xr-has-index'>lat</span>: 721</li><li><span class='xr-has-index'>time</span>: 876</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-281c1b93-1837-4796-860e-0e3b2ade6961' class='xr-section-summary-in' type='checkbox'  checked><label for='section-281c1b93-1837-4796-860e-0e3b2ade6961' 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'>-180.0 -179.8 ... 179.5 179.8</div><input id='attrs-e0450c4b-5c0c-4cd2-969e-c3b00112cd4e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e0450c4b-5c0c-4cd2-969e-c3b00112cd4e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37291652-9d0a-453e-85be-b1fa7364939e' class='xr-var-data-in' type='checkbox'><label for='data-37291652-9d0a-453e-85be-b1fa7364939e' 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([-180.  , -179.75, -179.5 , ...,  179.25,  179.5 ,  179.75],\n",
       "      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-63082238-4087-4f6f-8c10-e1aff1d6a164' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-63082238-4087-4f6f-8c10-e1aff1d6a164' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df310188-3437-4f08-8181-8867b9afa0a4' class='xr-var-data-in' type='checkbox'><label for='data-df310188-3437-4f08-8181-8867b9afa0a4' 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'>1950-01-01 ... 2022-12-01</div><input id='attrs-6b8e3268-b455-425c-8590-6e8c9f5c4db0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6b8e3268-b455-425c-8590-6e8c9f5c4db0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8e4e322-15a5-4879-aa8f-32bbe69dceb2' class='xr-var-data-in' type='checkbox'><label for='data-f8e4e322-15a5-4879-aa8f-32bbe69dceb2' 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;1950-01-01T00:00:00.000000000&#x27;, &#x27;1950-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1950-03-01T00:00:00.000000000&#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'>1 2 3 4 5 6 7 ... 6 7 8 9 10 11 12</div><input id='attrs-0862a74e-3142-4d6f-9324-a2e30051cbb0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0862a74e-3142-4d6f-9324-a2e30051cbb0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6973f38-1cc8-4321-8c58-8c7689804e3f' class='xr-var-data-in' type='checkbox'><label for='data-c6973f38-1cc8-4321-8c58-8c7689804e3f' 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, ..., 10, 11, 12])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d760f995-bf03-4e1b-8cc0-43bdc55b86e9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d760f995-bf03-4e1b-8cc0-43bdc55b86e9' class='xr-section-summary' >Data variables: <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>t2m</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=(876, 27, 720), meta=np.ndarray&gt;</div><input id='attrs-0b262a54-b900-4b7b-bd3a-df9605f447fb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0b262a54-b900-4b7b-bd3a-df9605f447fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86e5f0eb-9eaa-419d-bd5c-7b0260b88c15' class='xr-var-data-in' type='checkbox'><label for='data-86e5f0eb-9eaa-419d-bd5c-7b0260b88c15' 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> 3.39 GiB </td>\n",
       "                        <td> 64.96 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (876, 721, 1440) </td>\n",
       "                        <td> (876, 27, 720) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 54 chunks in 5 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"222\" height=\"153\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lsm</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=(876, 27, 720), meta=np.ndarray&gt;</div><input id='attrs-6d1fe739-75aa-4550-a847-603cdb9c4448' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6d1fe739-75aa-4550-a847-603cdb9c4448' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d613acbf-2971-4314-a5df-d6c6fd34203e' class='xr-var-data-in' type='checkbox'><label for='data-d613acbf-2971-4314-a5df-d6c6fd34203e' 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>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Land-sea mask</dd><dt><span>standard_name :</span></dt><dd>land_binary_mask</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 3.39 GiB </td>\n",
       "                        <td> 64.96 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (876, 721, 1440) </td>\n",
       "                        <td> (876, 27, 720) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
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       "                        <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>anom</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=(1, 27, 720), meta=np.ndarray&gt;</div><input id='attrs-9451151f-9c5b-47a2-b36f-a28b477e91d2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9451151f-9c5b-47a2-b36f-a28b477e91d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84871c2a-c8b9-42fc-a260-ac20225ce3b3' class='xr-var-data-in' type='checkbox'><label for='data-84871c2a-c8b9-42fc-a260-ac20225ce3b3' 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",
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       "                        <th> Array </th>\n",
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       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 3.39 GiB </td>\n",
       "                        <td> 75.94 kiB </td>\n",
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       "                        <th> Data type </th>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-29c834bc-d4b5-48de-a166-87ab7cdbdf9c' class='xr-section-summary-in' type='checkbox'  ><label for='section-29c834bc-d4b5-48de-a166-87ab7cdbdf9c' 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-b94557cb-43c0-47f4-b6df-1d5509cce730' class='xr-index-data-in' type='checkbox'/><label for='index-b94557cb-43c0-47f4-b6df-1d5509cce730' 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([ -180.0, -179.75,  -179.5, -179.25,  -179.0, -178.75,  -178.5,\n",
       "              -178.25,  -178.0, -177.75,\n",
       "              ...\n",
       "                177.5,  177.75,   178.0,  178.25,   178.5,  178.75,   179.0,\n",
       "               179.25,   179.5,  179.75],\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-79acf068-0903-4dd9-93d4-cc6f19d3394c' class='xr-index-data-in' type='checkbox'/><label for='index-79acf068-0903-4dd9-93d4-cc6f19d3394c' 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-ce2358cd-52f8-4dd6-8728-556a1aa74458' class='xr-index-data-in' type='checkbox'/><label for='index-ce2358cd-52f8-4dd6-8728-556a1aa74458' 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;1950-01-01&#x27;, &#x27;1950-02-01&#x27;, &#x27;1950-03-01&#x27;, &#x27;1950-04-01&#x27;,\n",
       "               &#x27;1950-05-01&#x27;, &#x27;1950-06-01&#x27;, &#x27;1950-07-01&#x27;, &#x27;1950-08-01&#x27;,\n",
       "               &#x27;1950-09-01&#x27;, &#x27;1950-10-01&#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=876, freq=&#x27;MS&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-372b761b-d9a2-4dcc-97be-96d9c406d121' class='xr-section-summary-in' type='checkbox'  checked><label for='section-372b761b-d9a2-4dcc-97be-96d9c406d121' 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>2023-07-18 21:45:16 GMT by grib_to_netcdf-2.25.1: /opt/ecmwf/mars-client/bin/grib_to_netcdf.bin -S param -o /cache/data9/adaptor.mars.internal-1689716608.4304297-30290-1-58d02187-6545-4a99-83e3-91816a555a27.nc /cache/tmp/58d02187-6545-4a99-83e3-91816a555a27-adaptor.mars.internal-1689716230.1355975-30290-1-tmp.grib</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lon: 1440, lat: 721, time: 876)\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 -180.0 -179.8 -179.5 -179.2 ... 179.2 179.5 179.8\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] 1950-01-01 1950-02-01 ... 2022-12-01\n",
       "    month    (time) int64 1 2 3 4 5 6 7 8 9 10 11 ... 2 3 4 5 6 7 8 9 10 11 12\n",
       "Data variables:\n",
       "    t2m      (time, lat, lon) float32 dask.array<chunksize=(876, 27, 720), meta=np.ndarray>\n",
       "    lsm      (time, lat, lon) float32 dask.array<chunksize=(876, 27, 720), meta=np.ndarray>\n",
       "    anom     (time, lat, lon) float32 dask.array<chunksize=(1, 27, 720), meta=np.ndarray>\n",
       "Attributes:\n",
       "    Conventions:  CF-1.6\n",
       "    history:      2023-07-18 21:45:16 GMT by grib_to_netcdf-2.25.1: /opt/ecmw..."
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with xr.open_mfdataset(path_to[\"era5\"]) as era5:\n",
    "    pass\n",
    "\n",
    "# Streamline the coordinates\n",
    "era5 = streamline_coords(era5)\n",
    "\n",
    "# Convert temperatures from Kelvin to Celsius\n",
    "era5[\"t2m\"] = era5[\"t2m\"] - 273.15\n",
    "\n",
    "# Compute monthly climatology\n",
    "era5_monthly_climatology = era5[\"t2m\"].sel(REF_PERIOD).groupby(\"time.month\").mean()\n",
    "era5[\"anom\"] = era5[\"t2m\"].groupby(\"time.month\") - era5_monthly_climatology\n",
    "\n",
    "era5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualising Temperature Anomalies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As an example, we'll take the temperature anomalies from December 2022. Here are the steps:\n",
    "\n",
    "1. **Loading Data into Memory**: First, load the `Dask` `DataArray`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed | 36.37 s\n"
     ]
    }
   ],
   "source": [
    "# Compute the most recent anomaly\n",
    "with ProgressBar():\n",
    "    era5_in_2022 = era5[\"anom\"].sel(time='2022-12').compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Interested in climate oscillations like ENSO, NAO, etc.?</b> Check our notebook on Climate Indices.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. **Setting Visualization Parameters**: Define a dictionary with display settings for reuse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "levels = np.arange(-6, 6.5, 1)\n",
    "kwargs = dict(\n",
    "    levels=levels,\n",
    "    transform=PROJS[\"Data\"],\n",
    "    cmap=\"RdBu_r\",\n",
    "    cbar_kwargs=dict(label=\"Temperature anomaly (ºC)\"),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. **Creating the Visualization**: Similar to figures in the [Climate Bulletins](https://climate.copernicus.eu/surface-air-temperature-december-2022), we'll display the temperature anomaly of a month both globally and for a specific region. To do this, we'll use `GridSpec` from the `matplotlib` module, allowing more visual fine-tuning. You'll need 3 columns (2x Anomalies + 1x Colorbar) and 3 rows to provide a header and footer for additional information like title, reference period, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x360 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "region = \"Southern Africa\"\n",
    "\n",
    "\n",
    "# Recreate the figures similar to the C3S Monthly Bulletins\n",
    "fig = plt.figure(figsize=(8, 3), dpi=120)\n",
    "gs = GridSpec(\n",
    "    3, 3, figure=fig, width_ratios=[1.5, 1, 0.05], height_ratios=[0.05, 1, 0.05]\n",
    ")\n",
    "\n",
    "# Add the axes\n",
    "tax = fig.add_subplot(gs[0, 0])\n",
    "bax = fig.add_subplot(gs[2, 0])\n",
    "ax1 = fig.add_subplot(gs[1, 0], projection=PROJS[\"Global\"])\n",
    "ax2 = fig.add_subplot(gs[1, 1], projection=PROJS[region])\n",
    "cax = fig.add_subplot(gs[1, 2])\n",
    "\n",
    "# Set the extent of the maps\n",
    "ax1.set_global()\n",
    "ax2.set_extent(get_extent_from_region(region), crs=PROJS[\"Data\"])\n",
    "\n",
    "# Add coastlines and anomlies\n",
    "for ax in [ax1, ax2]:\n",
    "    ax.coastlines(lw=0.5, color=\".3\")\n",
    "    era5_in_2022.plot(ax=ax, cbar_ax=cax, **kwargs)\n",
    "\n",
    "# Add titles, labels and text\n",
    "ax1.set_title(\"A | Global\")\n",
    "ax2.set_title(\"B | {}\".format(region))\n",
    "\n",
    "# Get month and add to title\n",
    "month_number = era5_in_2022.time.dt.month.item()\n",
    "title = f\"Surface air temperature anomaly for {calendar.month_name[month_number]} 2022\"\n",
    "tax.set_title(title)\n",
    "# Add units to subtitle\n",
    "tax.text(0, 0, \"in °C\", ha=\"left\", va=\"bottom\", transform=tax.transAxes)\n",
    "tax.axis(\"off\")\n",
    "\n",
    "# Get the reference period and add to the bottom left\n",
    "bax.axis(\"off\")\n",
    "ref_start = REF_PERIOD[\"time\"].start\n",
    "ref_end = REF_PERIOD[\"time\"].stop\n",
    "bax.text(\n",
    "    0,\n",
    "    0,\n",
    "    f\"Reference period: {ref_start}-{ref_end}\",\n",
    "    ha=\"left\",\n",
    "    va=\"bottom\",\n",
    "    transform=bax.transAxes,\n",
    ")\n",
    "\n",
    "# Tweak the layout\n",
    "fig.subplots_adjust(bottom=0.0, hspace=0.02)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Global View: You'll notice a rather heterogeneous pattern of positive and negative anomalies. In our target region, Southern Africa, positive anomalies appear in southwestern Africa, while the remaining areas show negative anomalies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Analysing Anomalies in Context"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How do these anomalies fit within global warming? We'll use spatial averages over our target region to show temporal trends. Here's how:\n",
    "\n",
    "1. **Calculate Spatial Averages**: Use a helper function for this calculation, which we've previously used in Part I and II."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def weighted_spatial_average(da, region, land_mask=None):\n",
    "    \"\"\"Calculate the weighted spatial average of a DataArray.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    da : xr.DataArray\n",
    "        The DataArray to average.\n",
    "    weights : xr.DataArray, optional\n",
    "        A DataArray with the same dimensions as `da` containing the weights.\n",
    "    \"\"\"\n",
    "    da = da.sel(**region)\n",
    "\n",
    "    # Area weighting: calculate the area of each grid cell\n",
    "    weights = np.cos(np.deg2rad(da.lat))\n",
    "\n",
    "    # Additional user-specified weights, e.g. land-sea mask\n",
    "    if land_mask is not None:\n",
    "        land_mask = land_mask.sel(**region)\n",
    "        weights = weights * land_mask.fillna(0)\n",
    "\n",
    "    return da.weighted(weights).mean((\"lat\", \"lon\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. **Include Other Regions as Reference**: To view the temperature anomalies in Southern Africa in both temporal and spatial contexts, calculate the spatial average not only for Southern Africa but also the global average and the Arctic, warming at [almost 4 times the global average](https://doi.org/10.1038/s43247-022-00498-).\n",
    "\n",
    "3. **Focus on Satellite-era Data**: We'll only look at temperature anomalies of the satellite era (from 1979) due to greater uncertainty in the temperatures of previous years, [especially outside Europe](https://doi.org/10.21957/ly5vbtbfd)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed | 158.81 s\n",
      "[########################################] | 100% Completed | 90.09 s\n",
      "[########################################] | 100% Completed | 60.68 s\n"
     ]
    }
   ],
   "source": [
    "# Running twelve-month averages of global-mean, South Africa-mean, and Arctic-mean\n",
    "# surface air temperature anomalies relative to 1991-2020\n",
    "# -----------------------------------------------------------------------------\n",
    "era5_anoms = era5[\"anom\"].sel(time=slice(\"1979\", None))\n",
    "era5_global_mean = weighted_spatial_average(\n",
    "    era5_anoms, REGIONS[\"Global\"], land_mask=None\n",
    ")\n",
    "era5_sa_mean = weighted_spatial_average(\n",
    "    era5_anoms, REGIONS[\"Southern Africa\"], era5[\"lsm\"]\n",
    ")\n",
    "era5_arctic_mean = weighted_spatial_average(\n",
    "    era5_anoms, REGIONS[\"Arctic\"], era5[\"lsm\"]\n",
    ")\n",
    "\n",
    "with ProgressBar():\n",
    "    era5_global_mean = era5_global_mean.compute()\n",
    "    era5_sa_mean = era5_sa_mean.compute()\n",
    "    era5_arctic_mean = era5_arctic_mean.compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. **Applying a 12-Month Running Average for Visualization**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "era5_global_mean_smooth = era5_global_mean.rolling(time=12, center=True).mean()\n",
    "era5_sa_mean_smooth = era5_sa_mean.rolling(time=12, center=True).mean()\n",
    "era5_arctic_mean_smooth = era5_arctic_mean.rolling(time=12, center=True).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. **Plotting Comparisons**: The following cell will plot a summary of the temperature anomaly development in the 3 target regions over the last ~40 years. Note the strong temperature rise during an extreme El-Niño in 2015/16, influencing various climate variables even in remote parts of the world like the Arctic or Southern Africa due to ENSO's [teleconnections](https://www.noaa.gov/jetstream/enso_impacts)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1440 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(9, 12), dpi=120)\n",
    "gs = GridSpec(4, 1, figure=fig, height_ratios=[0.05, 1, 1, 1], hspace=0.25)\n",
    "\n",
    "tax = fig.add_subplot(gs[0, 0])\n",
    "axes = [fig.add_subplot(gs[i, 0]) for i in range(1, 4)]\n",
    "\n",
    "\n",
    "def add_time_series(da, ax):\n",
    "    ax.fill_between(\n",
    "        da.time,\n",
    "        da.where(da > 0, 0).values,\n",
    "        color=\"red\",\n",
    "        alpha=0.5,\n",
    "    )\n",
    "    ax.fill_between(\n",
    "        da.time,\n",
    "        da.where(da < 0, 0).values,\n",
    "        color=\"blue\",\n",
    "        alpha=0.5,\n",
    "    )\n",
    "\n",
    "\n",
    "add_time_series(era5_global_mean_smooth, axes[0])\n",
    "add_time_series(era5_sa_mean_smooth, axes[1])\n",
    "add_time_series(era5_arctic_mean_smooth, axes[2])\n",
    "\n",
    "# Add annotations describing the data\n",
    "desc_global = \"Record high global \\ntemperatures in 2015/16 \\nwere partly due to \\nstrong El Nino conditions.\"\n",
    "desc_artic = \"Arctic amplification leads to a warming rate of almost four times the global average.\\nOne of the reasons is the ice-albedo feedack.\"\n",
    "axes[0].annotate(\n",
    "    desc_global,\n",
    "    xy=(dt.datetime(2016, 1, 1), 0.4),\n",
    "    xytext=(0.05, 0.9),\n",
    "    textcoords=axes[0].transAxes,\n",
    "    ha=\"left\",\n",
    "    va=\"top\",\n",
    "    arrowprops=dict(arrowstyle=\"->\", color=\".4\", connectionstyle=\"arc3,rad=-0.2\"),\n",
    ")\n",
    "axes[2].text(0.05, 0.9, desc_artic, transform=axes[2].transAxes, ha=\"left\", va=\"top\")\n",
    "\n",
    "titles = [\"Global\", \"Southern Africa\", \"Arctic\"]\n",
    "for i, ax in enumerate(axes):\n",
    "    ax.set_ylim(-3, 2)\n",
    "    ax.axhline(0, color=\"k\", ls=\"--\", lw=0.5)\n",
    "    ax.set_title(f\"{ABC[i]} | {titles[i]}\")\n",
    "    ax.xaxis.set_major_formatter(mdates.DateFormatter(\"%Y\"))\n",
    "tax.axis(\"off\")\n",
    "tax.set_title(\"Surface air temperature anomalies in different regions\", fontsize=\"x-large\")\n",
    "tax.text(0, 0, \"in °C\", ha=\"left\", va=\"bottom\", transform=tax.transAxes)\n",
    "ref_start = REF_PERIOD[\"time\"].start\n",
    "ref_end = REF_PERIOD[\"time\"].stop\n",
    "tax.text(\n",
    "    1,\n",
    "    0,\n",
    "    f\"Reference period: {ref_start}-{ref_end}\",\n",
    "    ha=\"right\",\n",
    "    va=\"bottom\",\n",
    "    transform=tax.transAxes,\n",
    ")\n",
    "\n",
    "sns.despine(fig, trim=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Welcome to the end of the tutorial, which also marks the conclusion of our trilogy.\n",
    "In this notebook, we've primarily focused on reconstructing images related to temperature anomalies from the monthly [Climate Bulletins](https://climate.copernicus.eu/climate-bulletins). We've built upon the concepts already learned in Parts I and II of Chapter 2.\n",
    "\n",
    "In the next chapters, we'll continue to explore the direct consequences of temperature rise by reconstructing various illustrations on topics such as Sea Ice and Sea Level Rise from the C3S Climate Bulletins and ESTOC reports."
   ]
  }
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