{
 "cells": [
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   "cell_type": "markdown",
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   "source": [
    "# Chapter 2: Taking Earth's Temperature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part I: It’s getting hot(ter) in here: Long-Term Development of Global Earth Temperature Since 1850"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imagine a doctor’s thermometer, but one that’s designed to gauge the health of our planet. As a fever reveals a person’s illness, Earth’s rising temperature exposes a global condition that’s equally concerning. This chapter, much like a medical investigation, unfolds the diagnosis of Earth’s thermal well-being.\n",
    "\n",
    "The previous chapter delved into the enigmatic world of greenhouse gases (GHGs), namely carbon dioxide and methane, elucidating how their concentrations are increasing in our atmosphere. Now, we turn our lens to scrutinise the surface temperature of our Earth, which is also on the rise, a tell-tale symptom of increasing GHGs.\n",
    "\n",
    "We have divided this chapter into three notebooks, each focusing on a distinctive aspect:\n",
    "\n",
    "1. Long-Term Development of Global Earth Temperature Since 1850 ([this notebook](#part-one))\n",
    "2. Comparing Reanalysis with Observations since 1950\n",
    "3. Visualising Recent Temperature Anomalies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Objective:</b> <br>\n",
    "    Our aim here is to perform a historical examination, comparing the global surface temperature from 1850-2022 using various authoritative data sources. We will stitch together a monthly resolved time series that paints the average surface anomaly over the past ~250 years. The grand finale? A common plot that showcases these time series. </a>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's an overview of the datasets we'll employ:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Dataset | Spatial Coverage | Spatial Resolution | Temporal Coverage | Temporal resolution |Provider |\n",
    "|---------|:-------------:|:------:|:------:|:------:|:------:|\n",
    "|  [NOAAGlobalTempv5](https://www.ncei.noaa.gov/products/land-based-station/noaa-global-temp) |  Global | 5º x 5º | 1850 - today | Monthly | NOAA |\n",
    "|  [Berkeley Earth](https://berkeleyearth.org/data/) |  Global | 1º x 1º | 1850 - today | Monthly | Berkeley Earth |\n",
    "|  [GISTEMPv4](https://data.giss.nasa.gov/gistemp/) |  Global | 2º x 2º | 1880 - today | Monthly | NASA |\n",
    "|  [HadCRUT5](https://www.metoffice.gov.uk/hadobs/hadcrut5/) |  Global | 5º x 5º | 1850 - today | Monthly | Met Office Hadley Centre |\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": [
    "Additionally, we'll wield the land-sea mask created by the respective provider to weight the datasets accurately.\n",
    "\n",
    "**Here's what to expect:**\n",
    "\n",
    "- **Downloading, opening, and streamlining datasets**: From providers as diverse as the climate they track.\n",
    "- **Handling moderate data volumes** (~ 5 GB): No worries, [`dask`](https://www.dask.org/) will take care of the work.\n",
    "- **Calculating spatially and temporally correct averages**: Like taking Earth's temperature, but with mathematics.\n",
    "- **Estimating uncertainties**: A vital step in this scientific expedition, achieved through ensemble members."
   ]
  },
  {
   "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>"
   ]
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   "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/02a_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/02a_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/02a_temperature.ipynb\">\n",
    "        <img src = \"https://colab.research.google.com/assets/colab-badge.svg\" alt = \"Colab\"></th>\n",
    "  </tr>\n",
    "</table>"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Getting Set Up: Your Toolkit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prepare for our planetary health check by importing all necessary packages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Python Standard Libraries\n",
    "import os\n",
    "import zipfile\n",
    "import urllib.request\n",
    "\n",
    "# Data Manipulation Libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import regionmask as rm\n",
    "import dask\n",
    "\n",
    "# Visualization Libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\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 you’re running this notebook on one of the cloud platforms, the stylesheet may not be present by default. In this case you can either upload the file or ignore the following cell. It’s about styling, and while it won’t affect your calculations, aesthetics matter, don’t they?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use(\"../copernicus.mplstyle\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additionally, we instruct dask to avoid the creation of large chunks that may arise in different calculations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<dask.config.set at 0x7f6be1dc3af0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dask.config.set(**{\"array.slicing.split_large_chunks\": True})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the `regionmask` package, you'll craft a land-sea mask for any `xr.DataArray`, something you'll need later if our chosen data provider doesn't offer additional weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Boolean land-sea mask\n",
    "lsm = rm.defined_regions.natural_earth_v5_0_0.land_110"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's where your creativity shines. Define the `REGIONS` that captivate your curiosity. Since the data sets (except ERA5) are relatively coarse, make your regions generous enough to ensure sufficient data. While we followed the definitions used in the [C3S Climate Intelligence reports](https://climate.copernicus.eu/esotc/2022/about-data#Regiondefinitions), feel free to change them or to add your own regions of interest! Your playground is as vast as the planet itself:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the stage where you pick your time frame for defining the period used as climatology. For instance, you could use the currently valid period of 1991-2020."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "REF_PERIOD = {\"time\": slice(\"1991\", \"2020\")}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we are tapping into different data sources that all come in their format and peculiarities, let's organize the data in folders for a better overview."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_name = {}  # dictionary containing [data source : file name]\n",
    "\n",
    "# Berkeley Earth\n",
    "file_name.update({\"berkeley\": \"temperature_berkeley.nc\"})  \n",
    "\n",
    "# GISTEMP\n",
    "file_name.update({\"gistemp_250km\": \"temperature_gistemp_250km.gz\"})  # higher resolution\n",
    "file_name.update({\"gistemp_1200km\": \"temperature_gistemp_1200km.gz\"})  # lower resolution\n",
    "file_name.update({\"gistemp_lsm\": \"temperature_gistemp_lsm.txt\"})  # land sea mask\n",
    "\n",
    "# HadCRUT\n",
    "file_name.update({\"hadcrut\": \"temperature_hadcrut.nc\"})\n",
    "file_name.update({\"hadcrut_lsm\": \"temperature_hadcrut_lsm.nc\"})  # land sea mask\n",
    "file_name.update({\"hadcrut_members\": \"temperature_hadcrut_ensemble_members.zip\"})  # ensemble members\n",
    "\n",
    "# ERA5\n",
    "file_name.update({\"era5\": \"temperature_era5.nc\"})\n",
    "\n",
    "# Create the paths to the files\n",
    "path_to = {}\n",
    "for source, file in file_name.items():\n",
    "    root = \"data/{:}/\".format(source.split(\"_\")[0])\n",
    "    path_to.update({source: os.path.join(root, file)})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create necessary directories if they don't exist:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "berkeley        --> data/berkeley/temperature_berkeley.nc\n",
      "gistemp_250km   --> data/gistemp/temperature_gistemp_250km.gz\n",
      "gistemp_1200km  --> data/gistemp/temperature_gistemp_1200km.gz\n",
      "gistemp_lsm     --> data/gistemp/temperature_gistemp_lsm.txt\n",
      "hadcrut         --> data/hadcrut/temperature_hadcrut.nc\n",
      "hadcrut_lsm     --> data/hadcrut/temperature_hadcrut_lsm.nc\n",
      "hadcrut_members --> data/hadcrut/temperature_hadcrut_ensemble_members.zip\n",
      "era5            --> data/era5/temperature_era5.nc\n"
     ]
    }
   ],
   "source": [
    "for file, path in path_to.items():\n",
    "    os.makedirs(os.path.dirname(path), exist_ok=True)\n",
    "    print(\"{:<15} --> {}\".format(file, path))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we will see, the various data also come with different conventions regarding dimension names and coordinates. We make our work much easier by ensuring at the beginning that all data is in the same format. In our case, we want to streamline datasets as follows:\n",
    "- Dimension names are (`time`, `lon`, `lat`)\n",
    "- The monthly resolved time coordinate is in `datetime` format and fixed to the beginning of the month\n",
    "- The `lon` and `lat` coordinates are sorted by their values\n",
    "- The `lon` coordinate is defined from -180 to +180º (as opposed to 0 to 360º)\n",
    "\n",
    "The following function `streamline_coords` will take care of these operations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def streamline_coords(da):\n",
    "    \"\"\"Streamline the dimensions and 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",
    "        # if already datetime, just ensure that it is the first day of the month\n",
    "        if pd.api.types.is_datetime64_any_dtype(da.time):\n",
    "            da.coords[\"time\"] = da[\"time\"].to_index().to_period(\"M\").to_timestamp()\n",
    "        # if float, convert to datetime\n",
    "        elif da.time.dtype == float:\n",
    "            first_year = int(da.time.values[0])\n",
    "            time_coord = xr.cftime_range(start=f\"{first_year}-01-01\", periods=da.time.size, freq=\"MS\").to_datetimeindex()\n",
    "            da.coords[\"time\"] = time_coord\n",
    "        # else ¯\\_(ツ)_/¯\n",
    "        else:\n",
    "            raise ValueError(\n",
    "                f\"Time coordinate is of type {da.time.dtype}, but must be either datetime or float.\"\n",
    "            )\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": [
    "#### Download and streamline the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Embarking on the setup and organization of our data, we're now venturing into the realms of real-world numbers and figures. Here, patience is a virtue, as the time to request and download the data may vary. Typically, this process should take less than 30 minutes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### NOAA GlobalTemp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The National Oceanic and Atmospheric Administration (NOAA) of the United States is our first destination. Start by opening NOAA's data website of [NOAAGlobalTemp](https://www.ncei.noaa.gov/products/land-based-station/noaa-global-temp), then access the data through THREDDS. Click on the link of the data file, which will bring you to an overview page showing different ways to access the data. Here we select the OPeNDAP protocol by copying the shown link, which we provide to `xarray`'s `open_dataset`, turning what could be a complex task into a simple two-liner."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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       "\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:  (time: 2083, lat: 36, lon: 72, z: 1)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1850-01-01 1850-02-01 ... 2023-07-01\n",
       "  * lat      (lat) float32 -87.5 -82.5 -77.5 -72.5 -67.5 ... 72.5 77.5 82.5 87.5\n",
       "  * lon      (lon) float32 2.5 7.5 12.5 17.5 22.5 ... 342.5 347.5 352.5 357.5\n",
       "  * z        (z) float32 0.0\n",
       "Data variables:\n",
       "    anom     (time, z, lat, lon) float32 ...\n",
       "Attributes: (12/66)\n",
       "    Conventions:                     CF-1.6, ACDD-1.3\n",
       "    title:                           NOAA Merged Land Ocean Global Surface Te...\n",
       "    summary:                         NOAAGlobalTemp is a merged land-ocean su...\n",
       "    institution:                     DOC/NOAA/NESDIS/National Centers for Env...\n",
       "    id:                               gov.noaa.ncdc:C00934 \n",
       "    naming_authority:                 gov.noaa.ncei \n",
       "    ...                              ...\n",
       "    time_coverage_duration:          P173Y7M\n",
       "    references:                      Vose, R. S., et al., 2012: NOAAs merged ...\n",
       "    climatology:                     Climatology is based on 1971-2000 monthl...\n",
       "    acknowledgment:                  The NOAA Global Surface Temperature Data...\n",
       "    date_modified:                   2023-08-08T15:26:56Z\n",
       "    date_issued:                     2023-08-08T15:26:56Z</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-d2091039-38bf-4c7f-83a1-f2cb99b02c92' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d2091039-38bf-4c7f-83a1-f2cb99b02c92' 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>: 2083</li><li><span class='xr-has-index'>lat</span>: 36</li><li><span class='xr-has-index'>lon</span>: 72</li><li><span class='xr-has-index'>z</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c00cd07c-7939-4d5a-bf7b-b1e4920620c3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c00cd07c-7939-4d5a-bf7b-b1e4920620c3' 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'>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'>1850-01-01 ... 2023-07-01</div><input id='attrs-f6edfca0-4e49-4702-8192-b4e80b19e8a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f6edfca0-4e49-4702-8192-b4e80b19e8a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35d28d8f-8caa-4cca-8b0e-35d16431c5c3' class='xr-var-data-in' type='checkbox'><label for='data-35d28d8f-8caa-4cca-8b0e-35d16431c5c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>reference time of global temperature anomalies</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1850-01-01T00:00:00.000000000&#x27;, &#x27;1850-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1850-03-01T00:00:00.000000000&#x27;, ..., &#x27;2023-05-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-06-01T00:00:00.000000000&#x27;, &#x27;2023-07-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span 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'>-87.5 -82.5 -77.5 ... 82.5 87.5</div><input id='attrs-7751bb92-f9d0-4449-a209-e1482a8570cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7751bb92-f9d0-4449-a209-e1482a8570cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92e55090-894e-48d1-9a43-5dd44755cac7' class='xr-var-data-in' type='checkbox'><label for='data-92e55090-894e-48d1-9a43-5dd44755cac7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>grids :</span></dt><dd>Uniform grid from -87.5 to 87.5 by 5</dd><dt><span>valid_min :</span></dt><dd>-87.5</dd><dt><span>valid_max :</span></dt><dd>87.5</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lat</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([-87.5, -82.5, -77.5, -72.5, -67.5, -62.5, -57.5, -52.5, -47.5, -42.5,\n",
       "       -37.5, -32.5, -27.5, -22.5, -17.5, -12.5,  -7.5,  -2.5,   2.5,   7.5,\n",
       "        12.5,  17.5,  22.5,  27.5,  32.5,  37.5,  42.5,  47.5,  52.5,  57.5,\n",
       "        62.5,  67.5,  72.5,  77.5,  82.5,  87.5], dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>2.5 7.5 12.5 ... 347.5 352.5 357.5</div><input id='attrs-1e10925e-644e-4e6c-b220-ed9069f9ec92' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1e10925e-644e-4e6c-b220-ed9069f9ec92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0a5788d-aa6a-48b6-8d2d-e09d580a4021' class='xr-var-data-in' type='checkbox'><label for='data-f0a5788d-aa6a-48b6-8d2d-e09d580a4021' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>grids :</span></dt><dd>Uniform grid from 2.5 to 357.5 by 5</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>valid_min :</span></dt><dd>2.5</dd><dt><span>valid_max :</span></dt><dd>357.5</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lon</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([  2.5,   7.5,  12.5,  17.5,  22.5,  27.5,  32.5,  37.5,  42.5,  47.5,\n",
       "        52.5,  57.5,  62.5,  67.5,  72.5,  77.5,  82.5,  87.5,  92.5,  97.5,\n",
       "       102.5, 107.5, 112.5, 117.5, 122.5, 127.5, 132.5, 137.5, 142.5, 147.5,\n",
       "       152.5, 157.5, 162.5, 167.5, 172.5, 177.5, 182.5, 187.5, 192.5, 197.5,\n",
       "       202.5, 207.5, 212.5, 217.5, 222.5, 227.5, 232.5, 237.5, 242.5, 247.5,\n",
       "       252.5, 257.5, 262.5, 267.5, 272.5, 277.5, 282.5, 287.5, 292.5, 297.5,\n",
       "       302.5, 307.5, 312.5, 317.5, 322.5, 327.5, 332.5, 337.5, 342.5, 347.5,\n",
       "       352.5, 357.5], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z</span></div><div class='xr-var-dims'>(z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-e5e7ea79-5ff4-4198-aff0-8fc407502793' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e5e7ea79-5ff4-4198-aff0-8fc407502793' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf8d0d15-3330-4368-bc33-a098998d14cf' class='xr-var-data-in' type='checkbox'><label for='data-bf8d0d15-3330-4368-bc33-a098998d14cf' 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>height of surface temperature</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>geospatial_bounds_vertical_crs :</span></dt><dd>EPSG:5703</dd><dt><span>units :</span></dt><dd>meters</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>0.0</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Height</dd><dt><span>comment :</span></dt><dd>Actual measurement depth of in situ sea surface temperature varies from 0.2 to 10 m, but corrected to the nominal depth of buoy at 0.2 m; for air temperature over land is 2m.</dd></dl></div><div class='xr-var-data'><pre>array([0.], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-48cb864e-0188-4893-9404-06007fcfc6c5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-48cb864e-0188-4893-9404-06007fcfc6c5' 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>anom</span></div><div class='xr-var-dims'>(time, z, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-82c84d79-62eb-4ff4-a65f-ff731a5bce97' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-82c84d79-62eb-4ff4-a65f-ff731a5bce97' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45d41c27-6d30-41f1-8067-4eabd09f64df' class='xr-var-data-in' type='checkbox'><label for='data-45d41c27-6d30-41f1-8067-4eabd09f64df' 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>Global Temperature Anomalies</dd><dt><span>standard_name :</span></dt><dd>surface_temperature_anomaly</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>degrees C</dd><dt><span>valid_min :</span></dt><dd>-40.0</dd><dt><span>valid_max :</span></dt><dd>40.0</dd></dl></div><div class='xr-var-data'><pre>[5399136 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-600b791e-25f7-4cad-b0c1-269b37401242' class='xr-section-summary-in' type='checkbox'  ><label for='section-600b791e-25f7-4cad-b0c1-269b37401242' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-078fe4c8-f6f0-49da-8947-463ea6355984' class='xr-index-data-in' type='checkbox'/><label for='index-078fe4c8-f6f0-49da-8947-463ea6355984' 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;1850-01-01&#x27;, &#x27;1850-02-01&#x27;, &#x27;1850-03-01&#x27;, &#x27;1850-04-01&#x27;,\n",
       "               &#x27;1850-05-01&#x27;, &#x27;1850-06-01&#x27;, &#x27;1850-07-01&#x27;, &#x27;1850-08-01&#x27;,\n",
       "               &#x27;1850-09-01&#x27;, &#x27;1850-10-01&#x27;,\n",
       "               ...\n",
       "               &#x27;2022-10-01&#x27;, &#x27;2022-11-01&#x27;, &#x27;2022-12-01&#x27;, &#x27;2023-01-01&#x27;,\n",
       "               &#x27;2023-02-01&#x27;, &#x27;2023-03-01&#x27;, &#x27;2023-04-01&#x27;, &#x27;2023-05-01&#x27;,\n",
       "               &#x27;2023-06-01&#x27;, &#x27;2023-07-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=2083, 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-c3c54b42-3f4e-4326-a005-2d77299500df' class='xr-index-data-in' type='checkbox'/><label for='index-c3c54b42-3f4e-4326-a005-2d77299500df' 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([-87.5, -82.5, -77.5, -72.5, -67.5, -62.5, -57.5, -52.5, -47.5,\n",
       "              -42.5, -37.5, -32.5, -27.5, -22.5, -17.5, -12.5,  -7.5,  -2.5,\n",
       "                2.5,   7.5,  12.5,  17.5,  22.5,  27.5,  32.5,  37.5,  42.5,\n",
       "               47.5,  52.5,  57.5,  62.5,  67.5,  72.5,  77.5,  82.5,  87.5],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0256807c-3849-49de-a02f-c6bd91ff7a97' class='xr-index-data-in' type='checkbox'/><label for='index-0256807c-3849-49de-a02f-c6bd91ff7a97' 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([  2.5,   7.5,  12.5,  17.5,  22.5,  27.5,  32.5,  37.5,  42.5,\n",
       "               47.5,  52.5,  57.5,  62.5,  67.5,  72.5,  77.5,  82.5,  87.5,\n",
       "               92.5,  97.5, 102.5, 107.5, 112.5, 117.5, 122.5, 127.5, 132.5,\n",
       "              137.5, 142.5, 147.5, 152.5, 157.5, 162.5, 167.5, 172.5, 177.5,\n",
       "              182.5, 187.5, 192.5, 197.5, 202.5, 207.5, 212.5, 217.5, 222.5,\n",
       "              227.5, 232.5, 237.5, 242.5, 247.5, 252.5, 257.5, 262.5, 267.5,\n",
       "              272.5, 277.5, 282.5, 287.5, 292.5, 297.5, 302.5, 307.5, 312.5,\n",
       "              317.5, 322.5, 327.5, 332.5, 337.5, 342.5, 347.5, 352.5, 357.5],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>z</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-affc7094-b2a0-4a0f-ab94-2acb45741b5c' class='xr-index-data-in' type='checkbox'/><label for='index-affc7094-b2a0-4a0f-ab94-2acb45741b5c' 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], dtype=&#x27;float64&#x27;, name=&#x27;z&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5ee7b0e2-7815-4d34-bdb8-58938183c5b3' class='xr-section-summary-in' type='checkbox'  ><label for='section-5ee7b0e2-7815-4d34-bdb8-58938183c5b3' class='xr-section-summary' >Attributes: <span>(66)</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, ACDD-1.3</dd><dt><span>title :</span></dt><dd>NOAA Merged Land Ocean Global Surface Temperature Analysis (NOAAGlobalTemp)</dd><dt><span>summary :</span></dt><dd>NOAAGlobalTemp is a merged land-ocean surface temperature analysis, formerly abbreviated as MLOST. The SST dataset is the Extended Reconstructed Sea Surface Temperature (ERSST) and the land surface air temperature dataset is from the Global Historical Climatology Network Monthly (GHCN-M) database.</dd><dt><span>institution :</span></dt><dd>DOC/NOAA/NESDIS/National Centers for Environmental Information(NCEI)</dd><dt><span>id :</span></dt><dd> gov.noaa.ncdc:C00934 </dd><dt><span>naming_authority :</span></dt><dd> gov.noaa.ncei </dd><dt><span>project :</span></dt><dd> NOAAGlobalTemp </dd><dt><span>platform :</span></dt><dd>Ships, moored buoys, surface drifting buoys, Argo floats, and weather stations</dd><dt><span>instrument :</span></dt><dd>Conventional thermometers</dd><dt><span>history :</span></dt><dd>Version 5.1.0, blending ERSST V5 and GHCN-M V4.</dd><dt><span>comment :</span></dt><dd>Merged land ocean surface temperature anomalies.</dd><dt><span>product_version :</span></dt><dd>5.1.0</dd><dt><span>source :</span></dt><dd>NOAAGlobalTemp combines a global sea surface temperature with a global land surface air temperature data</dd><dt><span>ncei_template_version :</span></dt><dd>NCEI_NetCDF_Grid_Template_v2.0</dd><dt><span>Metadata_Conventions :</span></dt><dd>Unidata Dataset Discovery v1.0</dd><dt><span>license :</span></dt><dd>These data are available for use without restriction.</dd><dt><span>creator_name :</span></dt><dd>Boyin Huang, Xungang Yin, Ph.D</dd><dt><span>creator_email :</span></dt><dd>Boyin.Huang@noaa.gov, Xungang.Yin@noaa.gov</dd><dt><span>creator_type :</span></dt><dd>group</dd><dt><span>creator_institution :</span></dt><dd>US DOC/NOAA/NESDIS/National Centers for Environmental Information(NCEI)</dd><dt><span>contributor_name :</span></dt><dd>Huai-min Zhang, Ph.D</dd><dt><span>contributor_role :</span></dt><dd>Chief, NOAA/NESDIS/NCEI/CCOG/OSB Ocean Surface Section</dd><dt><span>publisher_name :</span></dt><dd>NCEI</dd><dt><span>publisher_institution :</span></dt><dd>NCEI</dd><dt><span>publisher_url :</span></dt><dd>https://www.ncdc.noaa.gov/</dd><dt><span>publisher_email :</span></dt><dd>ncei.info@noaa.gov</dd><dt><span>publisher_type :</span></dt><dd>institution</dd><dt><span>keywords :</span></dt><dd>Earth Science &gt; Oceans &gt; Ocean Temperature &gt; Sea Surface Temperature, Earth Science &gt; Atmosphere &gt; Atmospheric Temperature &gt; Surface Temperature &gt; Air Temperature</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>standard_name_vocabulary :</span></dt><dd>Climate and Forecast (CF) Standard Name Table (Version 46, 25 July 2017)</dd><dt><span>date_created :</span></dt><dd>2023-08-08T15:26:56Z</dd><dt><span>necdf_version_id :</span></dt><dd>4.2.1.1</dd><dt><span>netcdf_creator_name :</span></dt><dd>Yongsheng Zhang, Ph.D</dd><dt><span>netcdf_creator_email :</span></dt><dd>yongsheng.zhang@noaa.gov</dd><dt><span>dataset_citation_product :</span></dt><dd>NOAA Merged Land Ocean Global Surface Temperature Analysis (NOAAGlobalTemp)</dd><dt><span>dataset_citation_version :</span></dt><dd>5.1.0</dd><dt><span>dataset_citation_institution :</span></dt><dd>NOAA/NESDIS/NCEI, Asheville, NC, USA</dd><dt><span>dataset_citation_url :</span></dt><dd>https://doi.org/10.25921/9qth-2p70</dd><dt><span>metadata_link :</span></dt><dd>https://doi.org/10.25921/9qth-2p70</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>processing_level :</span></dt><dd>NOAA Processing Level 4</dd><dt><span>geospatial_lon_min :</span></dt><dd>2.5</dd><dt><span>geospatial_lon_max :</span></dt><dd>357.5</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>5.0 degree</dd><dt><span>geospatial_lat_min :</span></dt><dd>-87.5</dd><dt><span>geospatial_lat_max :</span></dt><dd>87.5</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>5.0 degree</dd><dt><span>geospatial_bounds :</span></dt><dd>POLYGON ((2.5 -87.5, 2.5 87.5, 357.5 87.5, 357.5 -87.5, 2.5 -87.5))</dd><dt><span>geospatial_bounds_crs :</span></dt><dd>EPSG:4326</dd><dt><span>geospatial_vertical_min :</span></dt><dd>0.0</dd><dt><span>geospatial_vertical_max :</span></dt><dd>0.0</dd><dt><span>geospatial_vertical_units :</span></dt><dd>meters</dd><dt><span>geospatial_vertical_resolution :</span></dt><dd>0.f</dd><dt><span>geospatial_vertical_positive :</span></dt><dd>up</dd><dt><span>time_coverage_start :</span></dt><dd>1850-01</dd><dt><span>time_coverage_end :</span></dt><dd>2023-07</dd><dt><span>time_coverage_resolution :</span></dt><dd>P1M</dd><dt><span>time_coverage_duration :</span></dt><dd>P173Y7M</dd><dt><span>references :</span></dt><dd>Vose, R. S., et al., 2012: NOAAs merged land-ocean surface temperature analysis. Bulletin of the American Meteorological Society, 93, 1677-1685. doi: 10.1175/BAMS-D-11-00241.1. Huang, B., Peter W. Thorne, et. al, 2017: Extended Reconstructed Sea Surface Temperature version 5 (ERSSTv5), Upgrades, validations, and intercomparisons. J. Climate, 30, 8179-8205. doi: 10.1175/JCLI-D-16-0836.1</dd><dt><span>climatology :</span></dt><dd>Climatology is based on 1971-2000 monthly climatology</dd><dt><span>acknowledgment :</span></dt><dd>The NOAA Global Surface Temperature Dataset is provided by the NOAA National Centers for Environmental Information(NCEI)</dd><dt><span>date_modified :</span></dt><dd>2023-08-08T15:26:56Z</dd><dt><span>date_issued :</span></dt><dd>2023-08-08T15:26:56Z</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (time: 2083, lat: 36, lon: 72, z: 1)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1850-01-01 1850-02-01 ... 2023-07-01\n",
       "  * lat      (lat) float32 -87.5 -82.5 -77.5 -72.5 -67.5 ... 72.5 77.5 82.5 87.5\n",
       "  * lon      (lon) float32 2.5 7.5 12.5 17.5 22.5 ... 342.5 347.5 352.5 357.5\n",
       "  * z        (z) float32 0.0\n",
       "Data variables:\n",
       "    anom     (time, z, lat, lon) float32 ...\n",
       "Attributes: (12/66)\n",
       "    Conventions:                     CF-1.6, ACDD-1.3\n",
       "    title:                           NOAA Merged Land Ocean Global Surface Te...\n",
       "    summary:                         NOAAGlobalTemp is a merged land-ocean su...\n",
       "    institution:                     DOC/NOAA/NESDIS/National Centers for Env...\n",
       "    id:                               gov.noaa.ncdc:C00934 \n",
       "    naming_authority:                 gov.noaa.ncei \n",
       "    ...                              ...\n",
       "    time_coverage_duration:          P173Y7M\n",
       "    references:                      Vose, R. S., et al., 2012: NOAAs merged ...\n",
       "    climatology:                     Climatology is based on 1971-2000 monthl...\n",
       "    acknowledgment:                  The NOAA Global Surface Temperature Data...\n",
       "    date_modified:                   2023-08-08T15:26:56Z\n",
       "    date_issued:                     2023-08-08T15:26:56Z"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url_to_noaa = \"https://www.ncei.noaa.gov/thredds/dodsC/noaa-global-temp-v5.1/NOAAGlobalTemp_v5.1.0_gridded_s185001_e202307_c20230808T112655.nc\"\n",
    "noaa = xr.open_dataset(url_to_noaa)\n",
    "noaa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the data comes with an additional `z` dimension which contains only one coordiante representing the surface level. Let's select the surface level and drop the redundant dimension before we streamline our dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "  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:  (time: 2083, lat: 36, lon: 72)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1850-01-01 1850-02-01 ... 2023-07-01\n",
       "  * lat      (lat) float32 -87.5 -82.5 -77.5 -72.5 -67.5 ... 72.5 77.5 82.5 87.5\n",
       "  * lon      (lon) float32 -177.5 -172.5 -167.5 -162.5 ... 167.5 172.5 177.5\n",
       "Data variables:\n",
       "    anom     (time, lat, lon) float32 ...\n",
       "Attributes: (12/66)\n",
       "    Conventions:                     CF-1.6, ACDD-1.3\n",
       "    title:                           NOAA Merged Land Ocean Global Surface Te...\n",
       "    summary:                         NOAAGlobalTemp is a merged land-ocean su...\n",
       "    institution:                     DOC/NOAA/NESDIS/National Centers for Env...\n",
       "    id:                               gov.noaa.ncdc:C00934 \n",
       "    naming_authority:                 gov.noaa.ncei \n",
       "    ...                              ...\n",
       "    time_coverage_duration:          P173Y7M\n",
       "    references:                      Vose, R. S., et al., 2012: NOAAs merged ...\n",
       "    climatology:                     Climatology is based on 1971-2000 monthl...\n",
       "    acknowledgment:                  The NOAA Global Surface Temperature Data...\n",
       "    date_modified:                   2023-08-08T15:26:56Z\n",
       "    date_issued:                     2023-08-08T15:26:56Z</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-5d1fdff5-66a7-4e87-8f09-acbd6f132b29' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5d1fdff5-66a7-4e87-8f09-acbd6f132b29' 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>: 2083</li><li><span class='xr-has-index'>lat</span>: 36</li><li><span class='xr-has-index'>lon</span>: 72</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-dae3d425-c167-4f07-9e2b-8696dd3ddf1d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dae3d425-c167-4f07-9e2b-8696dd3ddf1d' 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'>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'>1850-01-01 ... 2023-07-01</div><input id='attrs-825efca5-b35b-45a1-aeb1-44c1480e8082' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-825efca5-b35b-45a1-aeb1-44c1480e8082' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c504911a-2dc6-4460-bdae-7fe57f3fd9a4' class='xr-var-data-in' type='checkbox'><label for='data-c504911a-2dc6-4460-bdae-7fe57f3fd9a4' 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;1850-01-01T00:00:00.000000000&#x27;, &#x27;1850-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1850-03-01T00:00:00.000000000&#x27;, ..., &#x27;2023-05-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-06-01T00:00:00.000000000&#x27;, &#x27;2023-07-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span 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'>-87.5 -82.5 -77.5 ... 82.5 87.5</div><input id='attrs-91dc6ce7-201e-488d-91e3-5e0ca72070fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-91dc6ce7-201e-488d-91e3-5e0ca72070fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-462477fe-88f6-468d-9abc-086796da36f8' class='xr-var-data-in' type='checkbox'><label for='data-462477fe-88f6-468d-9abc-086796da36f8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>grids :</span></dt><dd>Uniform grid from -87.5 to 87.5 by 5</dd><dt><span>valid_min :</span></dt><dd>-87.5</dd><dt><span>valid_max :</span></dt><dd>87.5</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lat</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([-87.5, -82.5, -77.5, -72.5, -67.5, -62.5, -57.5, -52.5, -47.5, -42.5,\n",
       "       -37.5, -32.5, -27.5, -22.5, -17.5, -12.5,  -7.5,  -2.5,   2.5,   7.5,\n",
       "        12.5,  17.5,  22.5,  27.5,  32.5,  37.5,  42.5,  47.5,  52.5,  57.5,\n",
       "        62.5,  67.5,  72.5,  77.5,  82.5,  87.5], dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>-177.5 -172.5 ... 172.5 177.5</div><input id='attrs-07a35c0f-f3e7-4ac3-87a7-28c0faf062d6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-07a35c0f-f3e7-4ac3-87a7-28c0faf062d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa7968da-b154-44d3-b9de-635922e62570' class='xr-var-data-in' type='checkbox'><label for='data-aa7968da-b154-44d3-b9de-635922e62570' 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([-177.5, -172.5, -167.5, -162.5, -157.5, -152.5, -147.5, -142.5, -137.5,\n",
       "       -132.5, -127.5, -122.5, -117.5, -112.5, -107.5, -102.5,  -97.5,  -92.5,\n",
       "        -87.5,  -82.5,  -77.5,  -72.5,  -67.5,  -62.5,  -57.5,  -52.5,  -47.5,\n",
       "        -42.5,  -37.5,  -32.5,  -27.5,  -22.5,  -17.5,  -12.5,   -7.5,   -2.5,\n",
       "          2.5,    7.5,   12.5,   17.5,   22.5,   27.5,   32.5,   37.5,   42.5,\n",
       "         47.5,   52.5,   57.5,   62.5,   67.5,   72.5,   77.5,   82.5,   87.5,\n",
       "         92.5,   97.5,  102.5,  107.5,  112.5,  117.5,  122.5,  127.5,  132.5,\n",
       "        137.5,  142.5,  147.5,  152.5,  157.5,  162.5,  167.5,  172.5,  177.5],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5c54bf0f-f950-48f2-8b95-10032cbb1e79' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5c54bf0f-f950-48f2-8b95-10032cbb1e79' 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>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'>...</div><input id='attrs-b9b09f67-b307-48bb-a885-bb99693c8de7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b9b09f67-b307-48bb-a885-bb99693c8de7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-444b841f-c02a-4778-ba6d-fd81f2cd6a5e' class='xr-var-data-in' type='checkbox'><label for='data-444b841f-c02a-4778-ba6d-fd81f2cd6a5e' 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>Global Temperature Anomalies</dd><dt><span>standard_name :</span></dt><dd>surface_temperature_anomaly</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>degrees C</dd><dt><span>valid_min :</span></dt><dd>-40.0</dd><dt><span>valid_max :</span></dt><dd>40.0</dd></dl></div><div class='xr-var-data'><pre>[5399136 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d8aae05d-69d9-4236-93eb-6a81b1b425f7' class='xr-section-summary-in' type='checkbox'  ><label for='section-d8aae05d-69d9-4236-93eb-6a81b1b425f7' 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-1f6608ea-274b-4660-b1b1-1d073fb9f5dc' class='xr-index-data-in' type='checkbox'/><label for='index-1f6608ea-274b-4660-b1b1-1d073fb9f5dc' 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;1850-01-01&#x27;, &#x27;1850-02-01&#x27;, &#x27;1850-03-01&#x27;, &#x27;1850-04-01&#x27;,\n",
       "               &#x27;1850-05-01&#x27;, &#x27;1850-06-01&#x27;, &#x27;1850-07-01&#x27;, &#x27;1850-08-01&#x27;,\n",
       "               &#x27;1850-09-01&#x27;, &#x27;1850-10-01&#x27;,\n",
       "               ...\n",
       "               &#x27;2022-10-01&#x27;, &#x27;2022-11-01&#x27;, &#x27;2022-12-01&#x27;, &#x27;2023-01-01&#x27;,\n",
       "               &#x27;2023-02-01&#x27;, &#x27;2023-03-01&#x27;, &#x27;2023-04-01&#x27;, &#x27;2023-05-01&#x27;,\n",
       "               &#x27;2023-06-01&#x27;, &#x27;2023-07-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=2083, freq=&#x27;MS&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b0f26450-4ec3-48d9-b0ba-9efcbd0527c6' class='xr-index-data-in' type='checkbox'/><label for='index-b0f26450-4ec3-48d9-b0ba-9efcbd0527c6' 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([-87.5, -82.5, -77.5, -72.5, -67.5, -62.5, -57.5, -52.5, -47.5,\n",
       "              -42.5, -37.5, -32.5, -27.5, -22.5, -17.5, -12.5,  -7.5,  -2.5,\n",
       "                2.5,   7.5,  12.5,  17.5,  22.5,  27.5,  32.5,  37.5,  42.5,\n",
       "               47.5,  52.5,  57.5,  62.5,  67.5,  72.5,  77.5,  82.5,  87.5],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9452f4b2-135b-42ef-a37d-9ced883c5508' class='xr-index-data-in' type='checkbox'/><label for='index-9452f4b2-135b-42ef-a37d-9ced883c5508' 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([-177.5, -172.5, -167.5, -162.5, -157.5, -152.5, -147.5, -142.5,\n",
       "              -137.5, -132.5, -127.5, -122.5, -117.5, -112.5, -107.5, -102.5,\n",
       "               -97.5,  -92.5,  -87.5,  -82.5,  -77.5,  -72.5,  -67.5,  -62.5,\n",
       "               -57.5,  -52.5,  -47.5,  -42.5,  -37.5,  -32.5,  -27.5,  -22.5,\n",
       "               -17.5,  -12.5,   -7.5,   -2.5,    2.5,    7.5,   12.5,   17.5,\n",
       "                22.5,   27.5,   32.5,   37.5,   42.5,   47.5,   52.5,   57.5,\n",
       "                62.5,   67.5,   72.5,   77.5,   82.5,   87.5,   92.5,   97.5,\n",
       "               102.5,  107.5,  112.5,  117.5,  122.5,  127.5,  132.5,  137.5,\n",
       "               142.5,  147.5,  152.5,  157.5,  162.5,  167.5,  172.5,  177.5],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5104c418-82a4-473e-b4f9-fd54348264a9' class='xr-section-summary-in' type='checkbox'  ><label for='section-5104c418-82a4-473e-b4f9-fd54348264a9' class='xr-section-summary' >Attributes: <span>(66)</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, ACDD-1.3</dd><dt><span>title :</span></dt><dd>NOAA Merged Land Ocean Global Surface Temperature Analysis (NOAAGlobalTemp)</dd><dt><span>summary :</span></dt><dd>NOAAGlobalTemp is a merged land-ocean surface temperature analysis, formerly abbreviated as MLOST. The SST dataset is the Extended Reconstructed Sea Surface Temperature (ERSST) and the land surface air temperature dataset is from the Global Historical Climatology Network Monthly (GHCN-M) database.</dd><dt><span>institution :</span></dt><dd>DOC/NOAA/NESDIS/National Centers for Environmental Information(NCEI)</dd><dt><span>id :</span></dt><dd> gov.noaa.ncdc:C00934 </dd><dt><span>naming_authority :</span></dt><dd> gov.noaa.ncei </dd><dt><span>project :</span></dt><dd> NOAAGlobalTemp </dd><dt><span>platform :</span></dt><dd>Ships, moored buoys, surface drifting buoys, Argo floats, and weather stations</dd><dt><span>instrument :</span></dt><dd>Conventional thermometers</dd><dt><span>history :</span></dt><dd>Version 5.1.0, blending ERSST V5 and GHCN-M V4.</dd><dt><span>comment :</span></dt><dd>Merged land ocean surface temperature anomalies.</dd><dt><span>product_version :</span></dt><dd>5.1.0</dd><dt><span>source :</span></dt><dd>NOAAGlobalTemp combines a global sea surface temperature with a global land surface air temperature data</dd><dt><span>ncei_template_version :</span></dt><dd>NCEI_NetCDF_Grid_Template_v2.0</dd><dt><span>Metadata_Conventions :</span></dt><dd>Unidata Dataset Discovery v1.0</dd><dt><span>license :</span></dt><dd>These data are available for use without restriction.</dd><dt><span>creator_name :</span></dt><dd>Boyin Huang, Xungang Yin, Ph.D</dd><dt><span>creator_email :</span></dt><dd>Boyin.Huang@noaa.gov, Xungang.Yin@noaa.gov</dd><dt><span>creator_type :</span></dt><dd>group</dd><dt><span>creator_institution :</span></dt><dd>US DOC/NOAA/NESDIS/National Centers for Environmental Information(NCEI)</dd><dt><span>contributor_name :</span></dt><dd>Huai-min Zhang, Ph.D</dd><dt><span>contributor_role :</span></dt><dd>Chief, NOAA/NESDIS/NCEI/CCOG/OSB Ocean Surface Section</dd><dt><span>publisher_name :</span></dt><dd>NCEI</dd><dt><span>publisher_institution :</span></dt><dd>NCEI</dd><dt><span>publisher_url :</span></dt><dd>https://www.ncdc.noaa.gov/</dd><dt><span>publisher_email :</span></dt><dd>ncei.info@noaa.gov</dd><dt><span>publisher_type :</span></dt><dd>institution</dd><dt><span>keywords :</span></dt><dd>Earth Science &gt; Oceans &gt; Ocean Temperature &gt; Sea Surface Temperature, Earth Science &gt; Atmosphere &gt; Atmospheric Temperature &gt; Surface Temperature &gt; Air Temperature</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>standard_name_vocabulary :</span></dt><dd>Climate and Forecast (CF) Standard Name Table (Version 46, 25 July 2017)</dd><dt><span>date_created :</span></dt><dd>2023-08-08T15:26:56Z</dd><dt><span>necdf_version_id :</span></dt><dd>4.2.1.1</dd><dt><span>netcdf_creator_name :</span></dt><dd>Yongsheng Zhang, Ph.D</dd><dt><span>netcdf_creator_email :</span></dt><dd>yongsheng.zhang@noaa.gov</dd><dt><span>dataset_citation_product :</span></dt><dd>NOAA Merged Land Ocean Global Surface Temperature Analysis (NOAAGlobalTemp)</dd><dt><span>dataset_citation_version :</span></dt><dd>5.1.0</dd><dt><span>dataset_citation_institution :</span></dt><dd>NOAA/NESDIS/NCEI, Asheville, NC, USA</dd><dt><span>dataset_citation_url :</span></dt><dd>https://doi.org/10.25921/9qth-2p70</dd><dt><span>metadata_link :</span></dt><dd>https://doi.org/10.25921/9qth-2p70</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>processing_level :</span></dt><dd>NOAA Processing Level 4</dd><dt><span>geospatial_lon_min :</span></dt><dd>2.5</dd><dt><span>geospatial_lon_max :</span></dt><dd>357.5</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>5.0 degree</dd><dt><span>geospatial_lat_min :</span></dt><dd>-87.5</dd><dt><span>geospatial_lat_max :</span></dt><dd>87.5</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>5.0 degree</dd><dt><span>geospatial_bounds :</span></dt><dd>POLYGON ((2.5 -87.5, 2.5 87.5, 357.5 87.5, 357.5 -87.5, 2.5 -87.5))</dd><dt><span>geospatial_bounds_crs :</span></dt><dd>EPSG:4326</dd><dt><span>geospatial_vertical_min :</span></dt><dd>0.0</dd><dt><span>geospatial_vertical_max :</span></dt><dd>0.0</dd><dt><span>geospatial_vertical_units :</span></dt><dd>meters</dd><dt><span>geospatial_vertical_resolution :</span></dt><dd>0.f</dd><dt><span>geospatial_vertical_positive :</span></dt><dd>up</dd><dt><span>time_coverage_start :</span></dt><dd>1850-01</dd><dt><span>time_coverage_end :</span></dt><dd>2023-07</dd><dt><span>time_coverage_resolution :</span></dt><dd>P1M</dd><dt><span>time_coverage_duration :</span></dt><dd>P173Y7M</dd><dt><span>references :</span></dt><dd>Vose, R. S., et al., 2012: NOAAs merged land-ocean surface temperature analysis. Bulletin of the American Meteorological Society, 93, 1677-1685. doi: 10.1175/BAMS-D-11-00241.1. Huang, B., Peter W. Thorne, et. al, 2017: Extended Reconstructed Sea Surface Temperature version 5 (ERSSTv5), Upgrades, validations, and intercomparisons. J. Climate, 30, 8179-8205. doi: 10.1175/JCLI-D-16-0836.1</dd><dt><span>climatology :</span></dt><dd>Climatology is based on 1971-2000 monthly climatology</dd><dt><span>acknowledgment :</span></dt><dd>The NOAA Global Surface Temperature Dataset is provided by the NOAA National Centers for Environmental Information(NCEI)</dd><dt><span>date_modified :</span></dt><dd>2023-08-08T15:26:56Z</dd><dt><span>date_issued :</span></dt><dd>2023-08-08T15:26:56Z</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (time: 2083, lat: 36, lon: 72)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1850-01-01 1850-02-01 ... 2023-07-01\n",
       "  * lat      (lat) float32 -87.5 -82.5 -77.5 -72.5 -67.5 ... 72.5 77.5 82.5 87.5\n",
       "  * lon      (lon) float32 -177.5 -172.5 -167.5 -162.5 ... 167.5 172.5 177.5\n",
       "Data variables:\n",
       "    anom     (time, lat, lon) float32 ...\n",
       "Attributes: (12/66)\n",
       "    Conventions:                     CF-1.6, ACDD-1.3\n",
       "    title:                           NOAA Merged Land Ocean Global Surface Te...\n",
       "    summary:                         NOAAGlobalTemp is a merged land-ocean su...\n",
       "    institution:                     DOC/NOAA/NESDIS/National Centers for Env...\n",
       "    id:                               gov.noaa.ncdc:C00934 \n",
       "    naming_authority:                 gov.noaa.ncei \n",
       "    ...                              ...\n",
       "    time_coverage_duration:          P173Y7M\n",
       "    references:                      Vose, R. S., et al., 2012: NOAAs merged ...\n",
       "    climatology:                     Climatology is based on 1971-2000 monthl...\n",
       "    acknowledgment:                  The NOAA Global Surface Temperature Data...\n",
       "    date_modified:                   2023-08-08T15:26:56Z\n",
       "    date_issued:                     2023-08-08T15:26:56Z"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noaa = noaa.isel(z=0, drop=True)\n",
    "noaa = streamline_coords(noaa)\n",
    "noaa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "As the NOAA doesn't offer any land-sea weights for the dataset, we'll employ the ingenious functionality of `regionmask` to create a suitable land-sea mask for our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "}\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:  (time: 2083, lat: 36, lon: 72)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1850-01-01 1850-02-01 ... 2023-07-01\n",
       "  * lat      (lat) float32 -87.5 -82.5 -77.5 -72.5 -67.5 ... 72.5 77.5 82.5 87.5\n",
       "  * lon      (lon) float32 -177.5 -172.5 -167.5 -162.5 ... 167.5 172.5 177.5\n",
       "Data variables:\n",
       "    anom     (time, lat, lon) float32 ...\n",
       "    lsm      (lat, lon) bool True True True True ... False False False False\n",
       "Attributes: (12/66)\n",
       "    Conventions:                     CF-1.6, ACDD-1.3\n",
       "    title:                           NOAA Merged Land Ocean Global Surface Te...\n",
       "    summary:                         NOAAGlobalTemp is a merged land-ocean su...\n",
       "    institution:                     DOC/NOAA/NESDIS/National Centers for Env...\n",
       "    id:                               gov.noaa.ncdc:C00934 \n",
       "    naming_authority:                 gov.noaa.ncei \n",
       "    ...                              ...\n",
       "    time_coverage_duration:          P173Y7M\n",
       "    references:                      Vose, R. S., et al., 2012: NOAAs merged ...\n",
       "    climatology:                     Climatology is based on 1971-2000 monthl...\n",
       "    acknowledgment:                  The NOAA Global Surface Temperature Data...\n",
       "    date_modified:                   2023-08-08T15:26:56Z\n",
       "    date_issued:                     2023-08-08T15:26:56Z</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-22618178-b240-4a5c-9104-23bc8e3f2b6e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-22618178-b240-4a5c-9104-23bc8e3f2b6e' 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>: 2083</li><li><span class='xr-has-index'>lat</span>: 36</li><li><span class='xr-has-index'>lon</span>: 72</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-721b162a-2bcc-4ff8-a41b-2bd61a88c1bb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-721b162a-2bcc-4ff8-a41b-2bd61a88c1bb' 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'>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'>1850-01-01 ... 2023-07-01</div><input id='attrs-a50ad356-8fd1-409c-9ea1-a73ec619de02' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a50ad356-8fd1-409c-9ea1-a73ec619de02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb946ce2-4d80-4e04-9544-b6f24060f76e' class='xr-var-data-in' type='checkbox'><label for='data-cb946ce2-4d80-4e04-9544-b6f24060f76e' 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;1850-01-01T00:00:00.000000000&#x27;, &#x27;1850-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1850-03-01T00:00:00.000000000&#x27;, ..., &#x27;2023-05-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-06-01T00:00:00.000000000&#x27;, &#x27;2023-07-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span 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'>-87.5 -82.5 -77.5 ... 82.5 87.5</div><input id='attrs-1f2810a1-6313-4dd8-bab0-2b20eb74a62a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1f2810a1-6313-4dd8-bab0-2b20eb74a62a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37d342e9-d655-4836-aa4d-b3a12fbdcf63' class='xr-var-data-in' type='checkbox'><label for='data-37d342e9-d655-4836-aa4d-b3a12fbdcf63' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>grids :</span></dt><dd>Uniform grid from -87.5 to 87.5 by 5</dd><dt><span>valid_min :</span></dt><dd>-87.5</dd><dt><span>valid_max :</span></dt><dd>87.5</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lat</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([-87.5, -82.5, -77.5, -72.5, -67.5, -62.5, -57.5, -52.5, -47.5, -42.5,\n",
       "       -37.5, -32.5, -27.5, -22.5, -17.5, -12.5,  -7.5,  -2.5,   2.5,   7.5,\n",
       "        12.5,  17.5,  22.5,  27.5,  32.5,  37.5,  42.5,  47.5,  52.5,  57.5,\n",
       "        62.5,  67.5,  72.5,  77.5,  82.5,  87.5], dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>-177.5 -172.5 ... 172.5 177.5</div><input id='attrs-3a1843bb-c774-42d0-8f13-6e87dfc9dd95' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3a1843bb-c774-42d0-8f13-6e87dfc9dd95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5a2a6dc-cb16-4089-927d-90c2e9919f3e' class='xr-var-data-in' type='checkbox'><label for='data-e5a2a6dc-cb16-4089-927d-90c2e9919f3e' 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([-177.5, -172.5, -167.5, -162.5, -157.5, -152.5, -147.5, -142.5, -137.5,\n",
       "       -132.5, -127.5, -122.5, -117.5, -112.5, -107.5, -102.5,  -97.5,  -92.5,\n",
       "        -87.5,  -82.5,  -77.5,  -72.5,  -67.5,  -62.5,  -57.5,  -52.5,  -47.5,\n",
       "        -42.5,  -37.5,  -32.5,  -27.5,  -22.5,  -17.5,  -12.5,   -7.5,   -2.5,\n",
       "          2.5,    7.5,   12.5,   17.5,   22.5,   27.5,   32.5,   37.5,   42.5,\n",
       "         47.5,   52.5,   57.5,   62.5,   67.5,   72.5,   77.5,   82.5,   87.5,\n",
       "         92.5,   97.5,  102.5,  107.5,  112.5,  117.5,  122.5,  127.5,  132.5,\n",
       "        137.5,  142.5,  147.5,  152.5,  157.5,  162.5,  167.5,  172.5,  177.5],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-26031831-501b-4da9-8bc2-259d5481be25' class='xr-section-summary-in' type='checkbox'  checked><label for='section-26031831-501b-4da9-8bc2-259d5481be25' 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>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'>...</div><input id='attrs-8de50536-ebb1-4dce-90e1-2d0cd1c73113' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8de50536-ebb1-4dce-90e1-2d0cd1c73113' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a158fc6d-3d27-4ca2-9f39-b44b0d72967b' class='xr-var-data-in' type='checkbox'><label for='data-a158fc6d-3d27-4ca2-9f39-b44b0d72967b' 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>Global Temperature Anomalies</dd><dt><span>standard_name :</span></dt><dd>surface_temperature_anomaly</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>degrees C</dd><dt><span>valid_min :</span></dt><dd>-40.0</dd><dt><span>valid_max :</span></dt><dd>40.0</dd></dl></div><div class='xr-var-data'><pre>[5399136 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lsm</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>True True True ... False False</div><input id='attrs-aa77c566-220c-4182-b28d-36db448ca3d2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aa77c566-220c-4182-b28d-36db448ca3d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d97bfdd3-4476-4911-836c-2bcae874ac6a' class='xr-var-data-in' type='checkbox'><label for='data-d97bfdd3-4476-4911-836c-2bcae874ac6a' 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([[ True,  True,  True, ...,  True,  True,  True],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       ...,\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7e22dea0-589b-450e-b177-193a9e81028d' class='xr-section-summary-in' type='checkbox'  ><label for='section-7e22dea0-589b-450e-b177-193a9e81028d' 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-edf61e51-88f7-4c50-955d-0556f7f41644' class='xr-index-data-in' type='checkbox'/><label for='index-edf61e51-88f7-4c50-955d-0556f7f41644' 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;1850-01-01&#x27;, &#x27;1850-02-01&#x27;, &#x27;1850-03-01&#x27;, &#x27;1850-04-01&#x27;,\n",
       "               &#x27;1850-05-01&#x27;, &#x27;1850-06-01&#x27;, &#x27;1850-07-01&#x27;, &#x27;1850-08-01&#x27;,\n",
       "               &#x27;1850-09-01&#x27;, &#x27;1850-10-01&#x27;,\n",
       "               ...\n",
       "               &#x27;2022-10-01&#x27;, &#x27;2022-11-01&#x27;, &#x27;2022-12-01&#x27;, &#x27;2023-01-01&#x27;,\n",
       "               &#x27;2023-02-01&#x27;, &#x27;2023-03-01&#x27;, &#x27;2023-04-01&#x27;, &#x27;2023-05-01&#x27;,\n",
       "               &#x27;2023-06-01&#x27;, &#x27;2023-07-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=2083, freq=&#x27;MS&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6219d9c7-7fc7-4eca-b603-0540ea80a9a2' class='xr-index-data-in' type='checkbox'/><label for='index-6219d9c7-7fc7-4eca-b603-0540ea80a9a2' 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([-87.5, -82.5, -77.5, -72.5, -67.5, -62.5, -57.5, -52.5, -47.5,\n",
       "              -42.5, -37.5, -32.5, -27.5, -22.5, -17.5, -12.5,  -7.5,  -2.5,\n",
       "                2.5,   7.5,  12.5,  17.5,  22.5,  27.5,  32.5,  37.5,  42.5,\n",
       "               47.5,  52.5,  57.5,  62.5,  67.5,  72.5,  77.5,  82.5,  87.5],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7421ead9-4488-4159-8ff3-32b58c4e95a0' class='xr-index-data-in' type='checkbox'/><label for='index-7421ead9-4488-4159-8ff3-32b58c4e95a0' 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([-177.5, -172.5, -167.5, -162.5, -157.5, -152.5, -147.5, -142.5,\n",
       "              -137.5, -132.5, -127.5, -122.5, -117.5, -112.5, -107.5, -102.5,\n",
       "               -97.5,  -92.5,  -87.5,  -82.5,  -77.5,  -72.5,  -67.5,  -62.5,\n",
       "               -57.5,  -52.5,  -47.5,  -42.5,  -37.5,  -32.5,  -27.5,  -22.5,\n",
       "               -17.5,  -12.5,   -7.5,   -2.5,    2.5,    7.5,   12.5,   17.5,\n",
       "                22.5,   27.5,   32.5,   37.5,   42.5,   47.5,   52.5,   57.5,\n",
       "                62.5,   67.5,   72.5,   77.5,   82.5,   87.5,   92.5,   97.5,\n",
       "               102.5,  107.5,  112.5,  117.5,  122.5,  127.5,  132.5,  137.5,\n",
       "               142.5,  147.5,  152.5,  157.5,  162.5,  167.5,  172.5,  177.5],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-57f5ef9b-3ef7-4dde-8777-c39426e77f6d' class='xr-section-summary-in' type='checkbox'  ><label for='section-57f5ef9b-3ef7-4dde-8777-c39426e77f6d' class='xr-section-summary' >Attributes: <span>(66)</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, ACDD-1.3</dd><dt><span>title :</span></dt><dd>NOAA Merged Land Ocean Global Surface Temperature Analysis (NOAAGlobalTemp)</dd><dt><span>summary :</span></dt><dd>NOAAGlobalTemp is a merged land-ocean surface temperature analysis, formerly abbreviated as MLOST. The SST dataset is the Extended Reconstructed Sea Surface Temperature (ERSST) and the land surface air temperature dataset is from the Global Historical Climatology Network Monthly (GHCN-M) database.</dd><dt><span>institution :</span></dt><dd>DOC/NOAA/NESDIS/National Centers for Environmental Information(NCEI)</dd><dt><span>id :</span></dt><dd> gov.noaa.ncdc:C00934 </dd><dt><span>naming_authority :</span></dt><dd> gov.noaa.ncei </dd><dt><span>project :</span></dt><dd> NOAAGlobalTemp </dd><dt><span>platform :</span></dt><dd>Ships, moored buoys, surface drifting buoys, Argo floats, and weather stations</dd><dt><span>instrument :</span></dt><dd>Conventional thermometers</dd><dt><span>history :</span></dt><dd>Version 5.1.0, blending ERSST V5 and GHCN-M V4.</dd><dt><span>comment :</span></dt><dd>Merged land ocean surface temperature anomalies.</dd><dt><span>product_version :</span></dt><dd>5.1.0</dd><dt><span>source :</span></dt><dd>NOAAGlobalTemp combines a global sea surface temperature with a global land surface air temperature data</dd><dt><span>ncei_template_version :</span></dt><dd>NCEI_NetCDF_Grid_Template_v2.0</dd><dt><span>Metadata_Conventions :</span></dt><dd>Unidata Dataset Discovery v1.0</dd><dt><span>license :</span></dt><dd>These data are available for use without restriction.</dd><dt><span>creator_name :</span></dt><dd>Boyin Huang, Xungang Yin, Ph.D</dd><dt><span>creator_email :</span></dt><dd>Boyin.Huang@noaa.gov, Xungang.Yin@noaa.gov</dd><dt><span>creator_type :</span></dt><dd>group</dd><dt><span>creator_institution :</span></dt><dd>US DOC/NOAA/NESDIS/National Centers for Environmental Information(NCEI)</dd><dt><span>contributor_name :</span></dt><dd>Huai-min Zhang, Ph.D</dd><dt><span>contributor_role :</span></dt><dd>Chief, NOAA/NESDIS/NCEI/CCOG/OSB Ocean Surface Section</dd><dt><span>publisher_name :</span></dt><dd>NCEI</dd><dt><span>publisher_institution :</span></dt><dd>NCEI</dd><dt><span>publisher_url :</span></dt><dd>https://www.ncdc.noaa.gov/</dd><dt><span>publisher_email :</span></dt><dd>ncei.info@noaa.gov</dd><dt><span>publisher_type :</span></dt><dd>institution</dd><dt><span>keywords :</span></dt><dd>Earth Science &gt; Oceans &gt; Ocean Temperature &gt; Sea Surface Temperature, Earth Science &gt; Atmosphere &gt; Atmospheric Temperature &gt; Surface Temperature &gt; Air Temperature</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>standard_name_vocabulary :</span></dt><dd>Climate and Forecast (CF) Standard Name Table (Version 46, 25 July 2017)</dd><dt><span>date_created :</span></dt><dd>2023-08-08T15:26:56Z</dd><dt><span>necdf_version_id :</span></dt><dd>4.2.1.1</dd><dt><span>netcdf_creator_name :</span></dt><dd>Yongsheng Zhang, Ph.D</dd><dt><span>netcdf_creator_email :</span></dt><dd>yongsheng.zhang@noaa.gov</dd><dt><span>dataset_citation_product :</span></dt><dd>NOAA Merged Land Ocean Global Surface Temperature Analysis (NOAAGlobalTemp)</dd><dt><span>dataset_citation_version :</span></dt><dd>5.1.0</dd><dt><span>dataset_citation_institution :</span></dt><dd>NOAA/NESDIS/NCEI, Asheville, NC, USA</dd><dt><span>dataset_citation_url :</span></dt><dd>https://doi.org/10.25921/9qth-2p70</dd><dt><span>metadata_link :</span></dt><dd>https://doi.org/10.25921/9qth-2p70</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>processing_level :</span></dt><dd>NOAA Processing Level 4</dd><dt><span>geospatial_lon_min :</span></dt><dd>2.5</dd><dt><span>geospatial_lon_max :</span></dt><dd>357.5</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>5.0 degree</dd><dt><span>geospatial_lat_min :</span></dt><dd>-87.5</dd><dt><span>geospatial_lat_max :</span></dt><dd>87.5</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>5.0 degree</dd><dt><span>geospatial_bounds :</span></dt><dd>POLYGON ((2.5 -87.5, 2.5 87.5, 357.5 87.5, 357.5 -87.5, 2.5 -87.5))</dd><dt><span>geospatial_bounds_crs :</span></dt><dd>EPSG:4326</dd><dt><span>geospatial_vertical_min :</span></dt><dd>0.0</dd><dt><span>geospatial_vertical_max :</span></dt><dd>0.0</dd><dt><span>geospatial_vertical_units :</span></dt><dd>meters</dd><dt><span>geospatial_vertical_resolution :</span></dt><dd>0.f</dd><dt><span>geospatial_vertical_positive :</span></dt><dd>up</dd><dt><span>time_coverage_start :</span></dt><dd>1850-01</dd><dt><span>time_coverage_end :</span></dt><dd>2023-07</dd><dt><span>time_coverage_resolution :</span></dt><dd>P1M</dd><dt><span>time_coverage_duration :</span></dt><dd>P173Y7M</dd><dt><span>references :</span></dt><dd>Vose, R. S., et al., 2012: NOAAs merged land-ocean surface temperature analysis. Bulletin of the American Meteorological Society, 93, 1677-1685. doi: 10.1175/BAMS-D-11-00241.1. Huang, B., Peter W. Thorne, et. al, 2017: Extended Reconstructed Sea Surface Temperature version 5 (ERSSTv5), Upgrades, validations, and intercomparisons. J. Climate, 30, 8179-8205. doi: 10.1175/JCLI-D-16-0836.1</dd><dt><span>climatology :</span></dt><dd>Climatology is based on 1971-2000 monthly climatology</dd><dt><span>acknowledgment :</span></dt><dd>The NOAA Global Surface Temperature Dataset is provided by the NOAA National Centers for Environmental Information(NCEI)</dd><dt><span>date_modified :</span></dt><dd>2023-08-08T15:26:56Z</dd><dt><span>date_issued :</span></dt><dd>2023-08-08T15:26:56Z</dd></dl></div></li></ul></div></div>"
      ],
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       "<xarray.Dataset>\n",
       "Dimensions:  (time: 2083, lat: 36, lon: 72)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1850-01-01 1850-02-01 ... 2023-07-01\n",
       "  * lat      (lat) float32 -87.5 -82.5 -77.5 -72.5 -67.5 ... 72.5 77.5 82.5 87.5\n",
       "  * lon      (lon) float32 -177.5 -172.5 -167.5 -162.5 ... 167.5 172.5 177.5\n",
       "Data variables:\n",
       "    anom     (time, lat, lon) float32 ...\n",
       "    lsm      (lat, lon) bool True True True True ... False False False False\n",
       "Attributes: (12/66)\n",
       "    Conventions:                     CF-1.6, ACDD-1.3\n",
       "    title:                           NOAA Merged Land Ocean Global Surface Te...\n",
       "    summary:                         NOAAGlobalTemp is a merged land-ocean su...\n",
       "    institution:                     DOC/NOAA/NESDIS/National Centers for Env...\n",
       "    id:                               gov.noaa.ncdc:C00934 \n",
       "    naming_authority:                 gov.noaa.ncei \n",
       "    ...                              ...\n",
       "    time_coverage_duration:          P173Y7M\n",
       "    references:                      Vose, R. S., et al., 2012: NOAAs merged ...\n",
       "    climatology:                     Climatology is based on 1971-2000 monthl...\n",
       "    acknowledgment:                  The NOAA Global Surface Temperature Data...\n",
       "    date_modified:                   2023-08-08T15:26:56Z\n",
       "    date_issued:                     2023-08-08T15:26:56Z"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noaa[\"lsm\"] = lsm.mask(noaa).notnull()  # Create a boolean land-sea mask (1=land, 0=sea)\n",
    "noaa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Note</b> <br>\n",
    "    The mask from regionmask is based on <a gref=\"https://www.naturalearthdata.com/\">NaturalEarth</a> shapefiles and is always boolean (consists only of 0s and 1s). In reality, grid cells (especially on the coast) consist of a certain proportion of water and land, which ideally should be reflected. Therefore, it is advisable to use the official land-sea mask of a dataset if possible. In our application, the differences due to this simplification are negligible.</a>\n",
    "</div>"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Berkeley Earth\n",
    "With the NOAA GlobalTemp data in our hands, we move to download data from [Berkeley Earth](https://berkeleyearth.org/data/). This dataset is available as a netCDF file, making it compatible with our process.\n",
    "\n",
    "First, specify the URL and download the Berkeley Earth data to our designated path."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "url_berkeley = 'https://berkeley-earth-temperature.s3.us-west-1.amazonaws.com/Global/Gridded/Land_and_Ocean_LatLong1.nc'\n",
    "urllib.request.urlretrieve(url_berkeley, path_to['berkeley'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, open the dataset and call the `streamline_coords` function to structure the coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
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       "}\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: 360, lat: 180, time: 2079, month_number: 12)\n",
       "Coordinates:\n",
       "  * lon          (lon) float32 -179.5 -178.5 -177.5 -176.5 ... 177.5 178.5 179.5\n",
       "  * lat          (lat) float32 -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5\n",
       "  * time         (time) datetime64[ns] 1850-01-01 1850-02-01 ... 2023-03-01\n",
       "Dimensions without coordinates: month_number\n",
       "Data variables:\n",
       "    land_mask    (lat, lon) float64 ...\n",
       "    temperature  (time, lat, lon) float32 ...\n",
       "    climatology  (month_number, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    Conventions:           Berkeley Earth Internal Convention (based on CF-1.5)\n",
       "    title:                 Native Format Berkeley Earth Surface Temperature A...\n",
       "    history:               20-Apr-2023 07:02:14\n",
       "    institution:           Berkeley Earth Surface Temperature Project\n",
       "    land_source_history:   05-Apr-2023 08:20:01\n",
       "    ocean_source_history:  20-Apr-2023 05:22:16\n",
       "    comment:               This file contains Berkeley Earth surface temperat...</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-f89a39eb-6fa8-4d77-b967-a5e732a9a7e8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f89a39eb-6fa8-4d77-b967-a5e732a9a7e8' 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>: 360</li><li><span class='xr-has-index'>lat</span>: 180</li><li><span class='xr-has-index'>time</span>: 2079</li><li><span>month_number</span>: 12</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f4a62444-eac3-4026-8498-1455b7a9c716' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f4a62444-eac3-4026-8498-1455b7a9c716' 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'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-179.5 -178.5 ... 178.5 179.5</div><input id='attrs-d62f919d-03f7-4604-8575-3ded96201454' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d62f919d-03f7-4604-8575-3ded96201454' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a263f388-8de6-43d9-9b7c-a742da19b02d' class='xr-var-data-in' type='checkbox'><label for='data-a263f388-8de6-43d9-9b7c-a742da19b02d' 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></dl></div><div class='xr-var-data'><pre>array([-179.5, -178.5, -177.5, ...,  177.5,  178.5,  179.5], 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'>-89.5 -88.5 -87.5 ... 88.5 89.5</div><input id='attrs-dff98427-0209-4bd9-8598-177d7ce87095' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dff98427-0209-4bd9-8598-177d7ce87095' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea7497ab-bf15-4997-9872-b1e9f002f6ed' class='xr-var-data-in' type='checkbox'><label for='data-ea7497ab-bf15-4997-9872-b1e9f002f6ed' 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></dl></div><div class='xr-var-data'><pre>array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
       "       -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,\n",
       "       -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,\n",
       "       -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,\n",
       "       -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,\n",
       "       -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,\n",
       "       -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,\n",
       "       -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,\n",
       "        -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,\n",
       "         0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
       "        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,\n",
       "        20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,\n",
       "        30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,\n",
       "        40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,\n",
       "        50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,\n",
       "        60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,\n",
       "        70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,\n",
       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],\n",
       "      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'>1850-01-01 ... 2023-03-01</div><input id='attrs-835fd503-1875-4a6c-a959-654571f285f8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-835fd503-1875-4a6c-a959-654571f285f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20f902a5-da84-4bf7-9633-ce2b7b68a068' class='xr-var-data-in' type='checkbox'><label for='data-20f902a5-da84-4bf7-9633-ce2b7b68a068' 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;1850-01-01T00:00:00.000000000&#x27;, &#x27;1850-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1850-03-01T00:00:00.000000000&#x27;, ..., &#x27;2023-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-02-01T00:00:00.000000000&#x27;, &#x27;2023-03-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8cec21a7-5d7e-475b-aa87-98033351e235' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8cec21a7-5d7e-475b-aa87-98033351e235' 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>land_mask</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d729e61f-4642-4ee5-a1c7-94494239891d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d729e61f-4642-4ee5-a1c7-94494239891d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6604171a-6f25-4487-b348-3d43c6a898fe' class='xr-var-data-in' type='checkbox'><label for='data-6604171a-6f25-4487-b348-3d43c6a898fe' 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>none</dd><dt><span>standard_name :</span></dt><dd>land_mask</dd><dt><span>long_name :</span></dt><dd>Land Mask</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1.0</dd></dl></div><div class='xr-var-data'><pre>[64800 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-87c7dd07-d79e-4cc0-983a-cd9c29bd98c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-87c7dd07-d79e-4cc0-983a-cd9c29bd98c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c203a607-f345-4d96-998a-9b7a0e3fa9d3' class='xr-var-data-in' type='checkbox'><label for='data-c203a607-f345-4d96-998a-9b7a0e3fa9d3' 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>degree C</dd><dt><span>standard_name :</span></dt><dd>surface_temperature_anomaly</dd><dt><span>long_name :</span></dt><dd>Air Surface Temperature Anomaly</dd><dt><span>valid_min :</span></dt><dd>-20.139212068541624</dd><dt><span>valid_max :</span></dt><dd>25.70646898051551</dd></dl></div><div class='xr-var-data'><pre>[134719200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>climatology</span></div><div class='xr-var-dims'>(month_number, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f4a7451e-96b5-4062-8f79-7f290d3b2ef4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4a7451e-96b5-4062-8f79-7f290d3b2ef4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0bf16b8f-3c55-433a-8d7c-ceea0ad89bcc' class='xr-var-data-in' type='checkbox'><label for='data-0bf16b8f-3c55-433a-8d7c-ceea0ad89bcc' 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>degree C</dd><dt><span>standard_name :</span></dt><dd>surface_temperature_climatology</dd><dt><span>long_name :</span></dt><dd>Air Surface Temperature Climatology (Jan 1951 - Dec 1980)</dd><dt><span>valid_min :</span></dt><dd>-69.07067576592691</dd><dt><span>valid_max :</span></dt><dd>38.231822261999675</dd></dl></div><div class='xr-var-data'><pre>[777600 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-29635a49-710d-4fcf-8488-cf2bc7651237' class='xr-section-summary-in' type='checkbox'  ><label for='section-29635a49-710d-4fcf-8488-cf2bc7651237' 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-1bdfee31-c072-44c3-a5f3-40201053c0c8' class='xr-index-data-in' type='checkbox'/><label for='index-1bdfee31-c072-44c3-a5f3-40201053c0c8' 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([-179.5, -178.5, -177.5, -176.5, -175.5, -174.5, -173.5, -172.5,\n",
       "              -171.5, -170.5,\n",
       "              ...\n",
       "               170.5,  171.5,  172.5,  173.5,  174.5,  175.5,  176.5,  177.5,\n",
       "               178.5,  179.5],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=360))</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-58005318-de7a-4c23-8fdb-ed40080946c4' class='xr-index-data-in' type='checkbox'/><label for='index-58005318-de7a-4c23-8fdb-ed40080946c4' 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([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5,\n",
       "              -80.5,\n",
       "              ...\n",
       "               80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,\n",
       "               89.5],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=180))</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-2456903e-067e-48cc-9340-152755192eff' class='xr-index-data-in' type='checkbox'/><label for='index-2456903e-067e-48cc-9340-152755192eff' 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;1850-01-01&#x27;, &#x27;1850-02-01&#x27;, &#x27;1850-03-01&#x27;, &#x27;1850-04-01&#x27;,\n",
       "               &#x27;1850-05-01&#x27;, &#x27;1850-06-01&#x27;, &#x27;1850-07-01&#x27;, &#x27;1850-08-01&#x27;,\n",
       "               &#x27;1850-09-01&#x27;, &#x27;1850-10-01&#x27;,\n",
       "               ...\n",
       "               &#x27;2022-06-01&#x27;, &#x27;2022-07-01&#x27;, &#x27;2022-08-01&#x27;, &#x27;2022-09-01&#x27;,\n",
       "               &#x27;2022-10-01&#x27;, &#x27;2022-11-01&#x27;, &#x27;2022-12-01&#x27;, &#x27;2023-01-01&#x27;,\n",
       "               &#x27;2023-02-01&#x27;, &#x27;2023-03-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=2079, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-98f65ebd-5ab6-4ae6-8ef8-2830d6cbca36' class='xr-section-summary-in' type='checkbox'  checked><label for='section-98f65ebd-5ab6-4ae6-8ef8-2830d6cbca36' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>Berkeley Earth Internal Convention (based on CF-1.5)</dd><dt><span>title :</span></dt><dd>Native Format Berkeley Earth Surface Temperature Anomaly Field</dd><dt><span>history :</span></dt><dd>20-Apr-2023 07:02:14</dd><dt><span>institution :</span></dt><dd>Berkeley Earth Surface Temperature Project</dd><dt><span>land_source_history :</span></dt><dd>05-Apr-2023 08:20:01</dd><dt><span>ocean_source_history :</span></dt><dd>20-Apr-2023 05:22:16</dd><dt><span>comment :</span></dt><dd>This file contains Berkeley Earth surface temperature anomaly field in our native equal-area format.</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (lon: 360, lat: 180, time: 2079, month_number: 12)\n",
       "Coordinates:\n",
       "  * lon          (lon) float32 -179.5 -178.5 -177.5 -176.5 ... 177.5 178.5 179.5\n",
       "  * lat          (lat) float32 -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5\n",
       "  * time         (time) datetime64[ns] 1850-01-01 1850-02-01 ... 2023-03-01\n",
       "Dimensions without coordinates: month_number\n",
       "Data variables:\n",
       "    land_mask    (lat, lon) float64 ...\n",
       "    temperature  (time, lat, lon) float32 ...\n",
       "    climatology  (month_number, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    Conventions:           Berkeley Earth Internal Convention (based on CF-1.5)\n",
       "    title:                 Native Format Berkeley Earth Surface Temperature A...\n",
       "    history:               20-Apr-2023 07:02:14\n",
       "    institution:           Berkeley Earth Surface Temperature Project\n",
       "    land_source_history:   05-Apr-2023 08:20:01\n",
       "    ocean_source_history:  20-Apr-2023 05:22:16\n",
       "    comment:               This file contains Berkeley Earth surface temperat..."
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "berkeley = xr.open_dataset(path_to[\"berkeley\"])\n",
    "berkeley = streamline_coords(berkeley)\n",
    "berkeley"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Note</b> <br>\n",
    "    Berkeley Earth data is also available in the <a href=\"https://cds.climate.copernicus.eu/cdsapp#!/dataset/insitu-gridded-observations-global-and-regional?tab=overview\">Climate Data Store (CDS)</a>, but with ocean values masked. If you need land-only averages, the version in CDS can be used. However, note that the CDS does not provide a land-sea mask.\n",
    "</div>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### GISTEMP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As our temperature exploration continues, we now turn to [GISTEMP](https://data.giss.nasa.gov/gistemp/) (Goddard Institute for Space Studies Surface Temperature Analysis). Here, we will handle two different versions of the data: the 250km smoothing version and the 1200km version. The former reveals detailed patterns, while the latter offers a smoother view and greater spatial coverage. Following the approach of [Simmons et al. (2016)](https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/qj.2949), we'll use the 250km version as our foundation and fill in missing values from the 1200km version.\n",
    "\n",
    "1. **Downloading the GISTEMP Data and land-sea mask**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('data/gistemp/temperature_gistemp_lsm.txt',\n",
       " <http.client.HTTPMessage at 0x7f6b4468d4e0>)"
      ]
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     "execution_count": 20,
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     "output_type": "execute_result"
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   ],
   "source": [
    "url_gistemp_1200km = 'https://data.giss.nasa.gov/pub/gistemp/gistemp1200_GHCNv4_ERSSTv5.nc.gz'\n",
    "url_gistemp_250km = 'https://data.giss.nasa.gov/pub/gistemp/gistemp250_GHCNv4.nc.gz'\n",
    "url_gistemp_land_sea_mask = 'https://data.giss.nasa.gov/pub/gistemp/landmask.2degx2deg.txt'\n",
    "\n",
    "urllib.request.urlretrieve(url_gistemp_1200km, path_to['gistemp_1200km'])\n",
    "urllib.request.urlretrieve(url_gistemp_250km, path_to['gistemp_250km'])\n",
    "urllib.request.urlretrieve(url_gistemp_land_sea_mask, path_to['gistemp_lsm'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. **Loading and Combining the Datasets**\n",
    "\n",
    "Open both versions of the dataset, and combine them, replacing missing values from the 250km version with the 1200km version."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "with xr.open_dataset(path_to['gistemp_1200km']) as gistemp_1200:\n",
    "    gistemp_1200 = gistemp_1200[\"tempanomaly\"]\n",
    "with xr.open_dataset(path_to['gistemp_250km']) as gistemp_250:\n",
    "    gistemp_250 = gistemp_250[\"tempanomaly\"]\n",
    "gistemp = gistemp_250.where(gistemp_250.notnull(), other=gistemp_1200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. **Downloading the Land-Sea Mask and Streamlining Coordinates**\n",
    "\n",
    "Get the land-sea mask and streamline the coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (lat: 90, lon: 180, time: 1723)\n",
       "Coordinates:\n",
       "  * lat          (lat) float32 -89.0 -87.0 -85.0 -83.0 ... 83.0 85.0 87.0 89.0\n",
       "  * lon          (lon) float32 -179.0 -177.0 -175.0 -173.0 ... 175.0 177.0 179.0\n",
       "  * time         (time) datetime64[ns] 1880-01-01 1880-02-01 ... 2023-07-01\n",
       "Data variables:\n",
       "    tempanomaly  (time, lat, lon) float32 nan nan nan nan ... 0.24 0.24 0.24\n",
       "    mask         (lat, lon) float64 1.0 1.0 1.0 1.0 1.0 ... 0.0 0.0 0.0 0.0 0.0\n",
       "Attributes:\n",
       "    long_name:     Surface temperature anomaly\n",
       "    units:         K\n",
       "    cell_methods:  time: mean</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-2f7bba8e-b08e-4ebe-b4d1-9eed475619a7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2f7bba8e-b08e-4ebe-b4d1-9eed475619a7' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 90</li><li><span class='xr-has-index'>lon</span>: 180</li><li><span class='xr-has-index'>time</span>: 1723</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-585be21d-96a0-4944-8bf2-f25cba9a7cc2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-585be21d-96a0-4944-8bf2-f25cba9a7cc2' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-89.0 -87.0 -85.0 ... 87.0 89.0</div><input id='attrs-84c43d91-bb8e-44f9-b7c9-b9187da9d1d9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84c43d91-bb8e-44f9-b7c9-b9187da9d1d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d97b14b0-ea80-4ebd-90a5-82e23fc8af40' class='xr-var-data-in' type='checkbox'><label for='data-d97b14b0-ea80-4ebd-90a5-82e23fc8af40' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-89., -87., -85., -83., -81., -79., -77., -75., -73., -71., -69., -67.,\n",
       "       -65., -63., -61., -59., -57., -55., -53., -51., -49., -47., -45., -43.,\n",
       "       -41., -39., -37., -35., -33., -31., -29., -27., -25., -23., -21., -19.,\n",
       "       -17., -15., -13., -11.,  -9.,  -7.,  -5.,  -3.,  -1.,   1.,   3.,   5.,\n",
       "         7.,   9.,  11.,  13.,  15.,  17.,  19.,  21.,  23.,  25.,  27.,  29.,\n",
       "        31.,  33.,  35.,  37.,  39.,  41.,  43.,  45.,  47.,  49.,  51.,  53.,\n",
       "        55.,  57.,  59.,  61.,  63.,  65.,  67.,  69.,  71.,  73.,  75.,  77.,\n",
       "        79.,  81.,  83.,  85.,  87.,  89.], dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>-179.0 -177.0 ... 177.0 179.0</div><input id='attrs-05afbbd3-bba2-4cdd-9dd5-c209e86bab5d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-05afbbd3-bba2-4cdd-9dd5-c209e86bab5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07667dde-1241-4a9a-87f1-98a1c78a4e9e' class='xr-var-data-in' type='checkbox'><label for='data-07667dde-1241-4a9a-87f1-98a1c78a4e9e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-179., -177., -175., -173., -171., -169., -167., -165., -163., -161.,\n",
       "       -159., -157., -155., -153., -151., -149., -147., -145., -143., -141.,\n",
       "       -139., -137., -135., -133., -131., -129., -127., -125., -123., -121.,\n",
       "       -119., -117., -115., -113., -111., -109., -107., -105., -103., -101.,\n",
       "        -99.,  -97.,  -95.,  -93.,  -91.,  -89.,  -87.,  -85.,  -83.,  -81.,\n",
       "        -79.,  -77.,  -75.,  -73.,  -71.,  -69.,  -67.,  -65.,  -63.,  -61.,\n",
       "        -59.,  -57.,  -55.,  -53.,  -51.,  -49.,  -47.,  -45.,  -43.,  -41.,\n",
       "        -39.,  -37.,  -35.,  -33.,  -31.,  -29.,  -27.,  -25.,  -23.,  -21.,\n",
       "        -19.,  -17.,  -15.,  -13.,  -11.,   -9.,   -7.,   -5.,   -3.,   -1.,\n",
       "          1.,    3.,    5.,    7.,    9.,   11.,   13.,   15.,   17.,   19.,\n",
       "         21.,   23.,   25.,   27.,   29.,   31.,   33.,   35.,   37.,   39.,\n",
       "         41.,   43.,   45.,   47.,   49.,   51.,   53.,   55.,   57.,   59.,\n",
       "         61.,   63.,   65.,   67.,   69.,   71.,   73.,   75.,   77.,   79.,\n",
       "         81.,   83.,   85.,   87.,   89.,   91.,   93.,   95.,   97.,   99.,\n",
       "        101.,  103.,  105.,  107.,  109.,  111.,  113.,  115.,  117.,  119.,\n",
       "        121.,  123.,  125.,  127.,  129.,  131.,  133.,  135.,  137.,  139.,\n",
       "        141.,  143.,  145.,  147.,  149.,  151.,  153.,  155.,  157.,  159.,\n",
       "        161.,  163.,  165.,  167.,  169.,  171.,  173.,  175.,  177.,  179.],\n",
       "      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'>1880-01-01 ... 2023-07-01</div><input id='attrs-481689f9-b71a-4aa6-a4da-82658a752b97' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-481689f9-b71a-4aa6-a4da-82658a752b97' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a412b91-ad00-4e66-a994-6423e2046f83' class='xr-var-data-in' type='checkbox'><label for='data-8a412b91-ad00-4e66-a994-6423e2046f83' 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;1880-01-01T00:00:00.000000000&#x27;, &#x27;1880-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1880-03-01T00:00:00.000000000&#x27;, ..., &#x27;2023-05-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-06-01T00:00:00.000000000&#x27;, &#x27;2023-07-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-024811b1-c8b8-430c-a0f7-6e6a1c07926e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-024811b1-c8b8-430c-a0f7-6e6a1c07926e' 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>tempanomaly</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'>nan nan nan nan ... 0.24 0.24 0.24</div><input id='attrs-188b0510-17b0-44bd-87dd-03463757e430' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-188b0510-17b0-44bd-87dd-03463757e430' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db05d4ca-a815-4707-bad6-05e3c4dd4de8' class='xr-var-data-in' type='checkbox'><label for='data-db05d4ca-a815-4707-bad6-05e3c4dd4de8' 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>Surface temperature anomaly</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><pre>array([[[  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
       "        ...,\n",
       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan]],\n",
       "\n",
       "       [[  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
       "        ...,\n",
       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan]],\n",
       "\n",
       "       [[  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [ 0.83,  0.83,  0.83, ...,  0.83,  0.83,  0.83],\n",
       "        [ 0.83,  0.83,  0.83, ...,  0.83,  0.83,  0.83],\n",
       "        [ 0.83,  0.83,  0.83, ...,  0.83,  0.83,  0.83]],\n",
       "\n",
       "       [[ 1.05,  1.05,  1.05, ...,  1.05,  1.05,  1.05],\n",
       "        [ 1.05,  1.05,  1.05, ...,  1.05,  1.05,  1.05],\n",
       "        [ 1.05,  1.05,  1.05, ...,  1.05,  1.05,  1.05],\n",
       "        ...,\n",
       "        [ 1.15,  1.15,  1.15, ...,  1.15,  1.15,  1.15],\n",
       "        [ 1.15,  1.15,  1.15, ...,  1.15,  1.15,  1.15],\n",
       "        [ 1.15,  1.15,  1.15, ...,  1.15,  1.15,  1.15]],\n",
       "\n",
       "       [[ 1.42,  1.42,  1.42, ...,  1.42,  1.42,  1.42],\n",
       "        [ 1.42,  1.42,  1.42, ...,  1.42,  1.42,  1.42],\n",
       "        [ 1.42,  1.42,  1.42, ...,  1.42,  1.42,  1.42],\n",
       "        ...,\n",
       "        [ 0.24,  0.24,  0.24, ...,  0.24,  0.24,  0.24],\n",
       "        [ 0.24,  0.24,  0.24, ...,  0.24,  0.24,  0.24],\n",
       "        [ 0.24,  0.24,  0.24, ...,  0.24,  0.24,  0.24]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mask</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.0 1.0 1.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-add687ad-267a-4d03-bd7f-b616a842f450' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-add687ad-267a-4d03-bd7f-b616a842f450' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb2ab977-05bc-477d-b847-4b36ec350c17' class='xr-var-data-in' type='checkbox'><label for='data-fb2ab977-05bc-477d-b847-4b36ec350c17' 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., 1., 1., ..., 1., 1., 1.],\n",
       "       [1., 1., 1., ..., 1., 1., 1.],\n",
       "       [1., 1., 1., ..., 1., 1., 1.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6b5e57f9-a069-4282-9d42-ef46c07681c2' class='xr-section-summary-in' type='checkbox'  ><label for='section-6b5e57f9-a069-4282-9d42-ef46c07681c2' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a9281d6a-2d00-49ae-bcd3-c208c2d39092' class='xr-index-data-in' type='checkbox'/><label for='index-a9281d6a-2d00-49ae-bcd3-c208c2d39092' 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([-89.0, -87.0, -85.0, -83.0, -81.0, -79.0, -77.0, -75.0, -73.0,\n",
       "              -71.0, -69.0, -67.0, -65.0, -63.0, -61.0, -59.0, -57.0, -55.0,\n",
       "              -53.0, -51.0, -49.0, -47.0, -45.0, -43.0, -41.0, -39.0, -37.0,\n",
       "              -35.0, -33.0, -31.0, -29.0, -27.0, -25.0, -23.0, -21.0, -19.0,\n",
       "              -17.0, -15.0, -13.0, -11.0,  -9.0,  -7.0,  -5.0,  -3.0,  -1.0,\n",
       "                1.0,   3.0,   5.0,   7.0,   9.0,  11.0,  13.0,  15.0,  17.0,\n",
       "               19.0,  21.0,  23.0,  25.0,  27.0,  29.0,  31.0,  33.0,  35.0,\n",
       "               37.0,  39.0,  41.0,  43.0,  45.0,  47.0,  49.0,  51.0,  53.0,\n",
       "               55.0,  57.0,  59.0,  61.0,  63.0,  65.0,  67.0,  69.0,  71.0,\n",
       "               73.0,  75.0,  77.0,  79.0,  81.0,  83.0,  85.0,  87.0,  89.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a3630734-ab42-4474-b5bc-d40429149751' class='xr-index-data-in' type='checkbox'/><label for='index-a3630734-ab42-4474-b5bc-d40429149751' 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([-179.0, -177.0, -175.0, -173.0, -171.0, -169.0, -167.0, -165.0,\n",
       "              -163.0, -161.0,\n",
       "              ...\n",
       "               161.0,  163.0,  165.0,  167.0,  169.0,  171.0,  173.0,  175.0,\n",
       "               177.0,  179.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=180))</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-78b9430f-96bb-4efc-ab82-7e742f6723a4' class='xr-index-data-in' type='checkbox'/><label for='index-78b9430f-96bb-4efc-ab82-7e742f6723a4' 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;1880-01-01&#x27;, &#x27;1880-02-01&#x27;, &#x27;1880-03-01&#x27;, &#x27;1880-04-01&#x27;,\n",
       "               &#x27;1880-05-01&#x27;, &#x27;1880-06-01&#x27;, &#x27;1880-07-01&#x27;, &#x27;1880-08-01&#x27;,\n",
       "               &#x27;1880-09-01&#x27;, &#x27;1880-10-01&#x27;,\n",
       "               ...\n",
       "               &#x27;2022-10-01&#x27;, &#x27;2022-11-01&#x27;, &#x27;2022-12-01&#x27;, &#x27;2023-01-01&#x27;,\n",
       "               &#x27;2023-02-01&#x27;, &#x27;2023-03-01&#x27;, &#x27;2023-04-01&#x27;, &#x27;2023-05-01&#x27;,\n",
       "               &#x27;2023-06-01&#x27;, &#x27;2023-07-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=1723, freq=&#x27;MS&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0ba1b2ea-f134-479f-a035-27c4199458de' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0ba1b2ea-f134-479f-a035-27c4199458de' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Surface temperature anomaly</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (lat: 90, lon: 180, time: 1723)\n",
       "Coordinates:\n",
       "  * lat          (lat) float32 -89.0 -87.0 -85.0 -83.0 ... 83.0 85.0 87.0 89.0\n",
       "  * lon          (lon) float32 -179.0 -177.0 -175.0 -173.0 ... 175.0 177.0 179.0\n",
       "  * time         (time) datetime64[ns] 1880-01-01 1880-02-01 ... 2023-07-01\n",
       "Data variables:\n",
       "    tempanomaly  (time, lat, lon) float32 nan nan nan nan ... 0.24 0.24 0.24\n",
       "    mask         (lat, lon) float64 1.0 1.0 1.0 1.0 1.0 ... 0.0 0.0 0.0 0.0 0.0\n",
       "Attributes:\n",
       "    long_name:     Surface temperature anomaly\n",
       "    units:         K\n",
       "    cell_methods:  time: mean"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gistemp_lsm = pd.read_csv(\n",
    "    path_to[\"gistemp_lsm\"],\n",
    "    sep=\"\\s+\",\n",
    "    header=1,\n",
    "    names=[\"lon\", \"lat\", \"mask\"],\n",
    ")\n",
    "gistemp_lsm = gistemp_lsm.set_index([\"lat\", \"lon\"])\n",
    "gistemp_lsm = gistemp_lsm.to_xarray()\n",
    "gistemp = xr.merge([gistemp, gistemp_lsm[\"mask\"]])\n",
    "gistemp = streamline_coords(gistemp)\n",
    "gistemp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. **Inspecting the Differences**\n",
    "\n",
    "Plot the differences between the 250km and 1200km versions to visually understand the nuances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1300x350 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, figsize=(13, 3.5))\n",
    "gistemp_250.isel(time=0).plot(ax=axes[0])\n",
    "gistemp_1200.isel(time=0).plot(ax=axes[1])\n",
    "axes[0].set_title('GISTEMP 250 km')\n",
    "axes[1].set_title('GISTEMP 1200 km')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### HadCRUT5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [HadCRUT5](https://www.metoffice.gov.uk/hadobs/hadcrut5/data/current/download.html) dataset provided by the Met Office allows us to examine global climate anomalies and uncertainties. It includes:\n",
    "\n",
    "- A gridded (mean) version\n",
    "- A land-sea mask\n",
    "- Individual ensemble members, which helps to understand model uncertainties."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. **Download the Data**\n",
    "\n",
    "On the website of HadCRUT5, you'll find two main versions:\n",
    "1. HadCRUT5 analysis time series\n",
    "2. HadCRUT5 analysis gridded data\n",
    "\n",
    "Since we're interested in computing the time series ourselves in order to adapt them to our region of interest, we choose the gridded data. Let's copy the corresponding URLs and download the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('data/hadcrut/temperature_hadcrut_ensemble_members.zip',\n",
       " <http.client.HTTPMessage at 0x7f9c05e605b0>)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url_hadcrut = 'https://www.metoffice.gov.uk/hadobs/hadcrut5/data/current/analysis/HadCRUT.5.0.1.0.analysis.anomalies.ensemble_mean.nc'\n",
    "url_hadcrut_weights = 'https://www.metoffice.gov.uk/hadobs/hadcrut5/data/current/analysis/HadCRUT.5.0.1.0.analysis.weights.nc'\n",
    "url_hadcrut_ens_members = 'https://www.metoffice.gov.uk/hadobs/hadcrut5/data/current/analysis/HadCRUT.5.0.1.0.analysis.anomalies.1_to_10_netcdf.zip'\n",
    "urllib.request.urlretrieve(url_hadcrut, path_to['hadcrut'])\n",
    "urllib.request.urlretrieve(url_hadcrut_weights, path_to['hadcrut_lsm'])\n",
    "urllib.request.urlretrieve(url_hadcrut_ens_members, path_to['hadcrut_members'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. **Unzip the Ensemble Members**\n",
    "\n",
    "We'll need to extract the downloaded files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "with zipfile.ZipFile('data/hadcrut/temperature_hadcrut_ensemble_members.zip') as z:\n",
    "    z.extractall('data/hadcrut/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. **Load and Organize the Data**\n",
    "\n",
    "Load the datasets and streamline them for further analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "  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:      (time: 2082, lat: 36, lon: 72, realization: 10)\n",
       "Coordinates:\n",
       "  * time         (time) datetime64[ns] 1850-01-01 1850-02-01 ... 2023-06-01\n",
       "  * lat          (lat) float64 -87.5 -82.5 -77.5 -72.5 ... 72.5 77.5 82.5 87.5\n",
       "  * lon          (lon) float64 -177.5 -172.5 -167.5 -162.5 ... 167.5 172.5 177.5\n",
       "  * realization  (realization) int64 1 10 2 3 4 5 6 7 8 9\n",
       "Data variables:\n",
       "    mean         (time, lat, lon) float64 ...\n",
       "    weights      (time, lat, lon) float64 ...\n",
       "    ensemble     (realization, time, lat, lon) float64 nan nan ... 0.3561 0.3569</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-868ae0aa-8556-44a2-8c92-062ff9dbfbeb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-868ae0aa-8556-44a2-8c92-062ff9dbfbeb' 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>: 2082</li><li><span class='xr-has-index'>lat</span>: 36</li><li><span class='xr-has-index'>lon</span>: 72</li><li><span class='xr-has-index'>realization</span>: 10</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f2a89879-fa2f-4947-81d6-fda09b60231d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f2a89879-fa2f-4947-81d6-fda09b60231d' 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'>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'>1850-01-01 ... 2023-06-01</div><input id='attrs-2d5249e9-c4d4-4133-becf-a1fc1c6a2d22' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2d5249e9-c4d4-4133-becf-a1fc1c6a2d22' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93d30204-3391-43a7-9f51-9b811525aed9' class='xr-var-data-in' type='checkbox'><label for='data-93d30204-3391-43a7-9f51-9b811525aed9' 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;1850-01-01T00:00:00.000000000&#x27;, &#x27;1850-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1850-03-01T00:00:00.000000000&#x27;, ..., &#x27;2023-04-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-05-01T00:00:00.000000000&#x27;, &#x27;2023-06-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span 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'>-87.5 -82.5 -77.5 ... 82.5 87.5</div><input id='attrs-a3456752-f50f-4676-b1c6-fe3067da39a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3456752-f50f-4676-b1c6-fe3067da39a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a95a2b5-e62b-4843-905f-a1ea2498fa33' class='xr-var-data-in' type='checkbox'><label for='data-6a95a2b5-e62b-4843-905f-a1ea2498fa33' 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>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>latitude_bnds</dd><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></dl></div><div class='xr-var-data'><pre>array([-87.5, -82.5, -77.5, -72.5, -67.5, -62.5, -57.5, -52.5, -47.5, -42.5,\n",
       "       -37.5, -32.5, -27.5, -22.5, -17.5, -12.5,  -7.5,  -2.5,   2.5,   7.5,\n",
       "        12.5,  17.5,  22.5,  27.5,  32.5,  37.5,  42.5,  47.5,  52.5,  57.5,\n",
       "        62.5,  67.5,  72.5,  77.5,  82.5,  87.5])</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'>-177.5 -172.5 ... 172.5 177.5</div><input id='attrs-33fa9aa2-4a58-46bb-94a6-30eafef7fbec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-33fa9aa2-4a58-46bb-94a6-30eafef7fbec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b991d4cc-a981-4311-95b3-4d2d5aeb4cc0' class='xr-var-data-in' type='checkbox'><label for='data-b991d4cc-a981-4311-95b3-4d2d5aeb4cc0' 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>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>longitude_bnds</dd><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></dl></div><div class='xr-var-data'><pre>array([-177.5, -172.5, -167.5, -162.5, -157.5, -152.5, -147.5, -142.5, -137.5,\n",
       "       -132.5, -127.5, -122.5, -117.5, -112.5, -107.5, -102.5,  -97.5,  -92.5,\n",
       "        -87.5,  -82.5,  -77.5,  -72.5,  -67.5,  -62.5,  -57.5,  -52.5,  -47.5,\n",
       "        -42.5,  -37.5,  -32.5,  -27.5,  -22.5,  -17.5,  -12.5,   -7.5,   -2.5,\n",
       "          2.5,    7.5,   12.5,   17.5,   22.5,   27.5,   32.5,   37.5,   42.5,\n",
       "         47.5,   52.5,   57.5,   62.5,   67.5,   72.5,   77.5,   82.5,   87.5,\n",
       "         92.5,   97.5,  102.5,  107.5,  112.5,  117.5,  122.5,  127.5,  132.5,\n",
       "        137.5,  142.5,  147.5,  152.5,  157.5,  162.5,  167.5,  172.5,  177.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>realization</span></div><div class='xr-var-dims'>(realization)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 10 2 3 4 5 6 7 8 9</div><input id='attrs-36c514cb-3014-4b0f-9376-5c95a08e887e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36c514cb-3014-4b0f-9376-5c95a08e887e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72e8b2b8-e615-41c6-baf1-db6aa55d71b5' class='xr-var-data-in' type='checkbox'><label for='data-72e8b2b8-e615-41c6-baf1-db6aa55d71b5' 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>1</dd><dt><span>standard_name :</span></dt><dd>realization</dd></dl></div><div class='xr-var-data'><pre>array([ 1, 10,  2,  3,  4,  5,  6,  7,  8,  9])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7b1f432d-12d2-4091-b465-8209cee560c4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7b1f432d-12d2-4091-b465-8209cee560c4' 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>mean</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-495a5520-3bd3-4ca5-a9fd-c354f5df4ab3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-495a5520-3bd3-4ca5-a9fd-c354f5df4ab3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b59e6a76-cd33-4593-8d20-2af2c81afa07' class='xr-var-data-in' type='checkbox'><label for='data-b59e6a76-cd33-4593-8d20-2af2c81afa07' 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>blended air_temperature_anomaly over land with sea_water_temperature_anomaly</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>area: mean (interval: 5.0 degrees_north 5.0 degrees_east) time: mean (interval: 1 month) realization: mean</dd></dl></div><div class='xr-var-data'><pre>[5396544 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>weights</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ba0595b1-cd81-44bd-b0fb-a6ae0732327b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba0595b1-cd81-44bd-b0fb-a6ae0732327b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84ad1e2f-1650-49d6-bd41-f4fa230d5567' class='xr-var-data-in' type='checkbox'><label for='data-84ad1e2f-1650-49d6-bd41-f4fa230d5567' 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>fractional weight for land data in blending with marine observations</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[5396544 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ensemble</span></div><div class='xr-var-dims'>(realization, time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 0.3561 0.3569</div><input id='attrs-9dd4fc19-f6ca-48cf-94a8-4fb08adeb1d7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9dd4fc19-f6ca-48cf-94a8-4fb08adeb1d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd7cd37e-b099-4ad1-8c75-3de90d482d61' class='xr-var-data-in' type='checkbox'><label for='data-fd7cd37e-b099-4ad1-8c75-3de90d482d61' 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>blended air_temperature_anomaly over land with sea_water_temperature_anomaly</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>area: mean (interval: 5.0 degrees_north 5.0 degrees_east) time: mean (interval: 1 month)</dd></dl></div><div class='xr-var-data'><pre>array([[[[            nan,             nan,             nan, ...,\n",
       "                      nan,             nan,             nan],\n",
       "         [            nan,             nan,             nan, ...,\n",
       "                      nan,             nan,             nan],\n",
       "         [            nan,             nan,             nan, ...,\n",
       "                      nan,             nan,             nan],\n",
       "         ...,\n",
       "         [            nan,             nan,             nan, ...,\n",
       "                      nan,             nan,             nan],\n",
       "         [            nan,             nan,             nan, ...,\n",
       "                      nan,             nan,             nan],\n",
       "         [            nan,             nan,             nan, ...,\n",
       "                      nan,             nan,             nan]],\n",
       "\n",
       "        [[            nan,             nan,             nan, ...,\n",
       "                      nan,             nan,             nan],\n",
       "         [            nan,             nan,             nan, ...,\n",
       "                      nan,             nan,             nan],\n",
       "         [            nan,             nan,             nan, ...,\n",
       "                      nan,             nan,             nan],\n",
       "...\n",
       "         [ 1.54616375e+00,  1.97285720e+00,  2.45976022e+00, ...,\n",
       "           5.55669028e-01,  8.88993989e-01,  1.17421258e+00],\n",
       "         [ 9.68492519e-01,  1.21256566e+00,  1.50853323e+00, ...,\n",
       "           4.93693133e-01,  6.35988328e-01,  8.14193135e-01],\n",
       "         [ 1.08255246e+00,  1.11975513e+00,  1.15723724e+00, ...,\n",
       "           1.00441405e+00,  1.01708203e+00,  1.04346370e+00]],\n",
       "\n",
       "        [[ 1.57407473e+00,  1.60624493e+00,  1.65689972e+00, ...,\n",
       "           1.58375375e+00,  1.56412859e+00,  1.56145613e+00],\n",
       "         [ 2.22426256e+00,  2.26907019e+00,  2.34663866e+00, ...,\n",
       "           1.92821146e+00,  2.02252304e+00,  2.14819245e+00],\n",
       "         [ 2.29607263e+00,  2.51979724e+00,  2.53330857e+00, ...,\n",
       "           1.44358581e+00,  1.65066217e+00,  1.98293981e+00],\n",
       "         ...,\n",
       "         [ 2.17719447e-01,  2.70125834e-01,  4.04586054e-01, ...,\n",
       "           5.39498786e-01,  4.04716454e-01,  2.50956939e-01],\n",
       "         [-1.70602919e-01, -1.34105214e-01, -3.91553878e-02, ...,\n",
       "          -2.31120082e-02, -8.34816267e-02, -1.14405931e-01],\n",
       "         [ 3.70798748e-01,  3.83807088e-01,  3.98897261e-01, ...,\n",
       "           3.68470977e-01,  3.56069785e-01,  3.56940381e-01]]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3486583e-336e-4548-bb48-51ea3b2b1c3f' class='xr-section-summary-in' type='checkbox'  ><label for='section-3486583e-336e-4548-bb48-51ea3b2b1c3f' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-40559871-6ef2-4979-abd2-ec5287979b63' class='xr-index-data-in' type='checkbox'/><label for='index-40559871-6ef2-4979-abd2-ec5287979b63' 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;1850-01-01&#x27;, &#x27;1850-02-01&#x27;, &#x27;1850-03-01&#x27;, &#x27;1850-04-01&#x27;,\n",
       "               &#x27;1850-05-01&#x27;, &#x27;1850-06-01&#x27;, &#x27;1850-07-01&#x27;, &#x27;1850-08-01&#x27;,\n",
       "               &#x27;1850-09-01&#x27;, &#x27;1850-10-01&#x27;,\n",
       "               ...\n",
       "               &#x27;2022-09-01&#x27;, &#x27;2022-10-01&#x27;, &#x27;2022-11-01&#x27;, &#x27;2022-12-01&#x27;,\n",
       "               &#x27;2023-01-01&#x27;, &#x27;2023-02-01&#x27;, &#x27;2023-03-01&#x27;, &#x27;2023-04-01&#x27;,\n",
       "               &#x27;2023-05-01&#x27;, &#x27;2023-06-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=2082, freq=&#x27;MS&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-07f12426-c5ff-44a1-90a8-058d620a2ccf' class='xr-index-data-in' type='checkbox'/><label for='index-07f12426-c5ff-44a1-90a8-058d620a2ccf' 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([-87.5, -82.5, -77.5, -72.5, -67.5, -62.5, -57.5, -52.5, -47.5,\n",
       "              -42.5, -37.5, -32.5, -27.5, -22.5, -17.5, -12.5,  -7.5,  -2.5,\n",
       "                2.5,   7.5,  12.5,  17.5,  22.5,  27.5,  32.5,  37.5,  42.5,\n",
       "               47.5,  52.5,  57.5,  62.5,  67.5,  72.5,  77.5,  82.5,  87.5],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-af952166-8a11-4bb6-9138-177c5d03e4d3' class='xr-index-data-in' type='checkbox'/><label for='index-af952166-8a11-4bb6-9138-177c5d03e4d3' 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([-177.5, -172.5, -167.5, -162.5, -157.5, -152.5, -147.5, -142.5,\n",
       "              -137.5, -132.5, -127.5, -122.5, -117.5, -112.5, -107.5, -102.5,\n",
       "               -97.5,  -92.5,  -87.5,  -82.5,  -77.5,  -72.5,  -67.5,  -62.5,\n",
       "               -57.5,  -52.5,  -47.5,  -42.5,  -37.5,  -32.5,  -27.5,  -22.5,\n",
       "               -17.5,  -12.5,   -7.5,   -2.5,    2.5,    7.5,   12.5,   17.5,\n",
       "                22.5,   27.5,   32.5,   37.5,   42.5,   47.5,   52.5,   57.5,\n",
       "                62.5,   67.5,   72.5,   77.5,   82.5,   87.5,   92.5,   97.5,\n",
       "               102.5,  107.5,  112.5,  117.5,  122.5,  127.5,  132.5,  137.5,\n",
       "               142.5,  147.5,  152.5,  157.5,  162.5,  167.5,  172.5,  177.5],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>realization</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d84555a4-a88d-4665-8b68-6862ed70d99a' class='xr-index-data-in' type='checkbox'/><label for='index-d84555a4-a88d-4665-8b68-6862ed70d99a' 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(Int64Index([1, 10, 2, 3, 4, 5, 6, 7, 8, 9], dtype=&#x27;int64&#x27;, name=&#x27;realization&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e503573b-82d7-44b8-bd66-8b190f6e191c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e503573b-82d7-44b8-bd66-8b190f6e191c' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (time: 2082, lat: 36, lon: 72, realization: 10)\n",
       "Coordinates:\n",
       "  * time         (time) datetime64[ns] 1850-01-01 1850-02-01 ... 2023-06-01\n",
       "  * lat          (lat) float64 -87.5 -82.5 -77.5 -72.5 ... 72.5 77.5 82.5 87.5\n",
       "  * lon          (lon) float64 -177.5 -172.5 -167.5 -162.5 ... 167.5 172.5 177.5\n",
       "  * realization  (realization) int64 1 10 2 3 4 5 6 7 8 9\n",
       "Data variables:\n",
       "    mean         (time, lat, lon) float64 ...\n",
       "    weights      (time, lat, lon) float64 ...\n",
       "    ensemble     (realization, time, lat, lon) float64 nan nan ... 0.3561 0.3569"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with xr.open_dataset(path_to[\"hadcrut\"]) as hadcrut:\n",
    "    pass\n",
    "with xr.open_dataset(path_to[\"hadcrut_lsm\"]) as hadcrut_weights:\n",
    "    pass\n",
    "hadcrut_members = xr.open_mfdataset(\"data/hadcrut/*analysis*.nc\", combine=\"nested\", concat_dim=\"realization\")\n",
    "hadcrut_members = hadcrut_members.load()  # load members into memory\n",
    "hadcrut = xr.Dataset(\n",
    "    {\n",
    "        \"mean\": hadcrut[\"tas_mean\"],\n",
    "        \"weights\": hadcrut_weights[\"weights\"],\n",
    "        \"ensemble\": hadcrut_members[\"tas\"],\n",
    "    }\n",
    ")\n",
    "hadcrut = streamline_coords(hadcrut)\n",
    "hadcrut"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Note</b> <br>\n",
    "    The <b>uncertainty estimation</b> doesn't consider the imprecision in the spatial mean due to limited spatial coverage. Be aware of this when interpreting the results, especially for early time periods.</a>\n",
    "</div>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Note</b> <br>\n",
    "    The HadCRUT5 model has 200 ensemble members, but we're using only the first 20 here. While this reduces processing time, be aware that it may lead to an underestimation of uncertainty.</a>\n",
    "</div>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### ERA5 reanalysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we load ERA5 from the [Climate Data Store (CDS)](https://doi.org/10.24381/cds.f17050d7) using the `cdsapi`, including the land-sea mask. \n",
    "\n",
    "1. **Setup Your CDS API Key**\n",
    "\n",
    "You'll need a specific key to access the CDS programmatically:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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": "markdown",
   "metadata": {},
   "source": [
    "2. **Retrieve the Data**\n",
    "\n",
    "Use the following code to pull the data from CDS:"
   ]
  },
  {
   "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', 'land_sea_mask'],\n",
    "    'year': list(range(1950, 2023)),\n",
    "    'month': list(range(1, 13)),\n",
    "    'time': '00:00',\n",
    "  },\n",
    "  path_to['era5']\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unlike other datasets, ERA5 provides temperatures in Kelvin. Convert them to Celsius for easier interpretation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 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",
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       "}\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",
       "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",
       "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-9c91527f-4ace-4232-a8b2-17c14009f8d5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9c91527f-4ace-4232-a8b2-17c14009f8d5' 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-9fd2f209-741d-4dde-a92d-3d1c614b73dc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9fd2f209-741d-4dde-a92d-3d1c614b73dc' 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'>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-99475f96-b598-4f35-9fb9-3998de54e9aa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-99475f96-b598-4f35-9fb9-3998de54e9aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29f191ad-29b4-4e49-855f-1e200954ef44' class='xr-var-data-in' type='checkbox'><label for='data-29f191ad-29b4-4e49-855f-1e200954ef44' 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-697d18f1-2a38-49a6-8cfe-de400de4139f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-697d18f1-2a38-49a6-8cfe-de400de4139f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd6dc8e8-4c44-4073-b4cd-9b7504375eaa' class='xr-var-data-in' type='checkbox'><label for='data-cd6dc8e8-4c44-4073-b4cd-9b7504375eaa' 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-8385e2c2-1a43-4c5d-a61a-ba8ee4809c54' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8385e2c2-1a43-4c5d-a61a-ba8ee4809c54' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4bb1a6cb-0ee3-43b0-a257-023cd7d53469' class='xr-var-data-in' type='checkbox'><label for='data-4bb1a6cb-0ee3-43b0-a257-023cd7d53469' 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></ul></div></li><li class='xr-section-item'><input id='section-5ee0e7ca-7f70-43ac-98df-7d61ae99fc05' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5ee0e7ca-7f70-43ac-98df-7d61ae99fc05' 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, 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-8f544afc-0a68-4292-ab95-4b67ab1b3a10' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8f544afc-0a68-4292-ab95-4b67ab1b3a10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-747b134c-6108-4329-9765-e8bb65164580' class='xr-var-data-in' type='checkbox'><label for='data-747b134c-6108-4329-9765-e8bb65164580' 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",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"52\" y2=\"42\" style=\"stroke-width:2\" />\n",
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       "        </td>\n",
       "    </tr>\n",
       "</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-a9abc191-262b-45f1-9607-6f6788512aad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a9abc191-262b-45f1-9607-6f6788512aad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78b0615c-7c2b-44c0-aa79-f09eadec8581' class='xr-var-data-in' type='checkbox'><label for='data-78b0615c-7c2b-44c0-aa79-f09eadec8581' 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",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 54 chunks in 4 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></ul></div></li><li class='xr-section-item'><input id='section-896e568c-5a88-493a-9099-5c62b247826b' class='xr-section-summary-in' type='checkbox'  ><label for='section-896e568c-5a88-493a-9099-5c62b247826b' 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-f272695e-c82e-424b-86cd-35ea56393195' class='xr-index-data-in' type='checkbox'/><label for='index-f272695e-c82e-424b-86cd-35ea56393195' 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-384dad7c-2d51-4981-b423-07cffc0da713' class='xr-index-data-in' type='checkbox'/><label for='index-384dad7c-2d51-4981-b423-07cffc0da713' 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-f537c6d3-cc08-43d1-962d-8914dba28778' class='xr-index-data-in' type='checkbox'/><label for='index-f537c6d3-cc08-43d1-962d-8914dba28778' 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-3746f809-6123-4a6b-a243-f3d8632d41ab' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3746f809-6123-4a6b-a243-f3d8632d41ab' 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",
       "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",
       "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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with xr.open_mfdataset(path_to[\"era5\"]) as era5:\n",
    "    # convert from Kelvin to Celsius\n",
    "    era5[\"t2m\"] = era5[\"t2m\"] - 273.15\n",
    "era5 = streamline_coords(era5)\n",
    "era5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculation of Spatial Averages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we'll delve into the process of calculating spatial averages from different datasets, specifically accounting for two essential factors:\n",
    "\n",
    "- **Longitude-Latitude Grid Representation**: The data is represented on a regular grid, which can lead to inflation of areas towards the poles.\n",
    "- **Land Proportion Weighting**: When averaging over land, it's necessary to weigh the grid points according to the proportion of land in each cell.\n",
    "\n",
    "These considerations are integral to accurately representing spatial patterns and trends. Here's how we'll proceed:\n",
    "\n",
    "**Step 1: Define the Weighted Spatial Average Function**\n",
    "\n",
    "First, we need a function that takes into account the above factors and can calculate the spatial average, even for subregions, by specifying a land-sea mask."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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",
    "    # Optionally, apply land-sea mask\n",
    "    if land_mask is not None:\n",
    "        land_mask = land_mask.sel(**region)\n",
    "        # fill up nan values with 0 so that they don't affect the weighted mean\n",
    "        land_mask = land_mask.fillna(0)\n",
    "        # combine land mask with weights\n",
    "        weights = weights * land_mask\n",
    "\n",
    "    # Compute the weighted mean\n",
    "    return da.weighted(weights).mean((\"lat\", \"lon\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 2: Prepare Dictionaries for Datasets and Land-Sea Masks**\n",
    "\n",
    "Before we can compute the averages, we need to define dictionaries that associate the data with the corresponding land-sea masks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "temps = {\n",
    "    \"HadCRUT\": hadcrut[\"mean\"],\n",
    "    \"HadCRUT_ensemble\": hadcrut[\"ensemble\"],\n",
    "    \"Berkeley\": berkeley[\"temperature\"],\n",
    "    \"GISTEMP\": gistemp[\"tempanomaly\"],\n",
    "    \"NOAA\": noaa[\"anom\"],\n",
    "    \"ERA5\": era5[\"t2m\"],\n",
    "}\n",
    "land_masks = {\n",
    "    \"HadCRUT\": hadcrut[\"weights\"],\n",
    "    \"HadCRUT_ensemble\": hadcrut[\"weights\"],\n",
    "    \"Berkeley\": berkeley[\"land_mask\"],\n",
    "    \"GISTEMP\": gistemp[\"mask\"],\n",
    "    \"NOAA\": noaa[\"lsm\"],\n",
    "    \"ERA5\": era5[\"lsm\"],\n",
    "}"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 3: Iterate Over Datasets to Calculate Mean Temperatures**\n",
    "\n",
    "Finally, we'll loop through our datasets and compute the mean temperatures for a specified `region`, such as the Arctic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed | 118.59 s\n"
     ]
    },
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:           (time: 2082, realization: 10)\n",
       "Coordinates:\n",
       "  * time              (time) datetime64[ns] 1850-01-01 1850-02-01 ... 2023-06-01\n",
       "  * realization       (realization) int64 1 10 2 3 4 5 6 7 8 9\n",
       "Data variables:\n",
       "    HadCRUT           (time) float64 -1.962 -0.6203 -1.374 ... 2.094 2.331 1.309\n",
       "    HadCRUT_ensemble  (realization, time) float64 -2.02 -0.3699 ... 2.168 1.19\n",
       "    Berkeley          (time) float64 -1.055 0.07 0.6092 0.2195 ... nan nan nan\n",
       "    GISTEMP           (time) float64 nan nan nan nan ... 2.965 2.102 2.159 1.558\n",
       "    NOAA              (time) float64 -1.497 -0.4415 -1.032 ... 1.795 2.349 1.141\n",
       "    ERA5              (time) float32 nan nan nan nan nan ... nan nan nan nan nan</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-0735385a-1cee-4a93-82e3-66f90a629afa' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0735385a-1cee-4a93-82e3-66f90a629afa' 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>: 2082</li><li><span class='xr-has-index'>realization</span>: 10</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0daaa8e7-4335-4fbe-812f-e78e6afe7fa0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0daaa8e7-4335-4fbe-812f-e78e6afe7fa0' 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'>1850-01-01 ... 2023-06-01</div><input id='attrs-a6f69578-02b1-4357-91d4-b468d21d82d4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a6f69578-02b1-4357-91d4-b468d21d82d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c194630-9144-4caf-8fe4-108f2066c777' class='xr-var-data-in' type='checkbox'><label for='data-4c194630-9144-4caf-8fe4-108f2066c777' 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;1850-01-01T00:00:00.000000000&#x27;, &#x27;1850-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1850-03-01T00:00:00.000000000&#x27;, ..., &#x27;2023-04-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-05-01T00:00:00.000000000&#x27;, &#x27;2023-06-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>realization</span></div><div class='xr-var-dims'>(realization)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 10 2 3 4 5 6 7 8 9</div><input id='attrs-ac873369-3fde-480c-86db-8df38ad77aaf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ac873369-3fde-480c-86db-8df38ad77aaf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-854de94b-ef6c-40b4-b840-53f29a4ec409' class='xr-var-data-in' type='checkbox'><label for='data-854de94b-ef6c-40b4-b840-53f29a4ec409' 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, 10,  2,  3,  4,  5,  6,  7,  8,  9])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-65689aaa-f981-4696-81ef-b668c2eed5cb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-65689aaa-f981-4696-81ef-b668c2eed5cb' class='xr-section-summary' >Data variables: <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>HadCRUT</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.962 -0.6203 ... 2.331 1.309</div><input id='attrs-1291fe82-3dd1-4a83-83a8-f8cbffac392a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1291fe82-3dd1-4a83-83a8-f8cbffac392a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-afdd2c08-3060-493f-8a43-69ed4239110a' class='xr-var-data-in' type='checkbox'><label for='data-afdd2c08-3060-493f-8a43-69ed4239110a' 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([-1.9617599 , -0.62034782, -1.37401519, ...,  2.0942024 ,\n",
       "        2.33117465,  1.30903893])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HadCRUT_ensemble</span></div><div class='xr-var-dims'>(realization, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-2.02 -0.3699 -1.611 ... 2.168 1.19</div><input id='attrs-08ab02b2-2d6a-43cb-a522-54ab795e3be3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-08ab02b2-2d6a-43cb-a522-54ab795e3be3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b71ec81-957b-4e43-8a74-c466ca09e581' class='xr-var-data-in' type='checkbox'><label for='data-1b71ec81-957b-4e43-8a74-c466ca09e581' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-2.01999589, -0.36993824, -1.61123542, ...,  2.14151082,\n",
       "         2.36646759,  1.27600144],\n",
       "       [-1.91245621, -0.63479389, -1.52671185, ...,  2.08580137,\n",
       "         2.2903865 ,  1.28526516],\n",
       "       [-2.34974854, -0.59273105, -1.74163133, ...,  2.14700645,\n",
       "         2.20714585,  1.39895018],\n",
       "       ...,\n",
       "       [-1.80512702, -0.65093705, -1.20507997, ...,  2.19351863,\n",
       "         2.56555828,  1.54639039],\n",
       "       [-2.4662412 , -1.3800114 , -1.47008776, ...,  2.3676275 ,\n",
       "         2.12674524,  1.25311858],\n",
       "       [-2.63635961, -1.45741242, -1.33025937, ...,  1.97067019,\n",
       "         2.1679846 ,  1.18965592]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Berkeley</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.055 0.07 0.6092 ... nan nan nan</div><input id='attrs-68e27325-0ebc-440c-9728-c6bef5838b42' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-68e27325-0ebc-440c-9728-c6bef5838b42' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb13de75-c959-410e-b3a2-06f8d1e25448' class='xr-var-data-in' type='checkbox'><label for='data-bb13de75-c959-410e-b3a2-06f8d1e25448' 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([-1.0548457 ,  0.07000187,  0.60923286, ...,         nan,\n",
       "               nan,         nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>GISTEMP</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 2.102 2.159 1.558</div><input id='attrs-646244b0-47bd-4319-a36e-fd00b8e88501' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-646244b0-47bd-4319-a36e-fd00b8e88501' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e876181-7744-403b-9531-bf029e4afb55' class='xr-var-data-in' type='checkbox'><label for='data-0e876181-7744-403b-9531-bf029e4afb55' 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([       nan,        nan,        nan, ..., 2.10194436, 2.15933854,\n",
       "       1.55775116])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NOAA</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.497 -0.4415 ... 2.349 1.141</div><input id='attrs-1a641dff-64da-4eba-86df-e31929614d7a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1a641dff-64da-4eba-86df-e31929614d7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35d85d40-f0c2-419e-9d22-292fb79e411f' class='xr-var-data-in' type='checkbox'><label for='data-35d85d40-f0c2-419e-9d22-292fb79e411f' 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([-1.49688411, -0.44154584, -1.03150738, ...,  1.79532774,\n",
       "        2.34930796,  1.14132655])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ERA5</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-6189d35b-d39a-448d-a2f5-8b26a09f0afd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6189d35b-d39a-448d-a2f5-8b26a09f0afd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a93c179-2e19-4e9e-89b7-3290bbbe3ed1' class='xr-var-data-in' type='checkbox'><label for='data-9a93c179-2e19-4e9e-89b7-3290bbbe3ed1' 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([nan, nan, nan, ..., nan, nan, nan], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-350ac903-aca1-4391-b74a-cecf1bd76e86' class='xr-section-summary-in' type='checkbox'  ><label for='section-350ac903-aca1-4391-b74a-cecf1bd76e86' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-904aa90c-84c8-4296-ad75-93d6c2faa7cd' class='xr-index-data-in' type='checkbox'/><label for='index-904aa90c-84c8-4296-ad75-93d6c2faa7cd' 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;1850-01-01&#x27;, &#x27;1850-02-01&#x27;, &#x27;1850-03-01&#x27;, &#x27;1850-04-01&#x27;,\n",
       "               &#x27;1850-05-01&#x27;, &#x27;1850-06-01&#x27;, &#x27;1850-07-01&#x27;, &#x27;1850-08-01&#x27;,\n",
       "               &#x27;1850-09-01&#x27;, &#x27;1850-10-01&#x27;,\n",
       "               ...\n",
       "               &#x27;2022-09-01&#x27;, &#x27;2022-10-01&#x27;, &#x27;2022-11-01&#x27;, &#x27;2022-12-01&#x27;,\n",
       "               &#x27;2023-01-01&#x27;, &#x27;2023-02-01&#x27;, &#x27;2023-03-01&#x27;, &#x27;2023-04-01&#x27;,\n",
       "               &#x27;2023-05-01&#x27;, &#x27;2023-06-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=2082, freq=&#x27;MS&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>realization</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5662d8d4-ab6c-4c03-95d6-8e470c721c73' class='xr-index-data-in' type='checkbox'/><label for='index-5662d8d4-ab6c-4c03-95d6-8e470c721c73' 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(Int64Index([1, 10, 2, 3, 4, 5, 6, 7, 8, 9], dtype=&#x27;int64&#x27;, name=&#x27;realization&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-77a9ce9d-763e-4d87-a1cc-f60dc3c02229' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-77a9ce9d-763e-4d87-a1cc-f60dc3c02229' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:           (time: 2082, realization: 10)\n",
       "Coordinates:\n",
       "  * time              (time) datetime64[ns] 1850-01-01 1850-02-01 ... 2023-06-01\n",
       "  * realization       (realization) int64 1 10 2 3 4 5 6 7 8 9\n",
       "Data variables:\n",
       "    HadCRUT           (time) float64 -1.962 -0.6203 -1.374 ... 2.094 2.331 1.309\n",
       "    HadCRUT_ensemble  (realization, time) float64 -2.02 -0.3699 ... 2.168 1.19\n",
       "    Berkeley          (time) float64 -1.055 0.07 0.6092 0.2195 ... nan nan nan\n",
       "    GISTEMP           (time) float64 nan nan nan nan ... 2.965 2.102 2.159 1.558\n",
       "    NOAA              (time) float64 -1.497 -0.4415 -1.032 ... 1.795 2.349 1.141\n",
       "    ERA5              (time) float32 nan nan nan nan nan ... nan nan nan nan nan"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "region = \"Arctic\"\n",
    "\n",
    "temp_evolution = {}\n",
    "for source in temps:\n",
    "    spatial_average = weighted_spatial_average(\n",
    "        temps[source], REGIONS[region], h\n",
    "        temp_evolution[source] = spatial_average.compute()\n",
    "temp_evolution = xr.Dataset(temp_evolution)\n",
    "temp_evolution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Note</b>: By changing the `region`, you can easily calculate averages for any other pre-defined region, allowing for versatile analysis and comparisons.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 4: Aligning Datasets to a Common Reference Period**\n",
    "\n",
    "Let's take note that while all datasets except ERA5 are already represented as anomalies, they have different reference periods. This means that we cannot directly compare them with each other. To align them, we'll calculate anomalies relative to the period 1991-2020."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Show anomalies with respect to the 1991-2020 climatology\n",
    "temp_evolution = temp_evolution - temp_evolution.sel(REF_PERIOD).mean(\"time\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 5: Calculate Anomalies Relative to the Pre-Industrial Era**\n",
    "\n",
    "Next, we'll analyze the temperature increase relative to the pre-industrial era, defined here as 1850-1900. We calculate the reference value as the average of the anomalies of all datasets within this time frame, excluding the HadCRUT ensemble members."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "anom_1850_1900 = temp_evolution.drop_vars(\"HadCRUT_ensemble\").sel(\n",
    "    time=slice(\"1850\", \"1900\")\n",
    ")\n",
    "mean_1850_1900 = anom_1850_1900.to_array().mean()\n",
    "mean_1850_1900"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 6: Smooth the Time Series Data**\n",
    "\n",
    "To provide a clearer picture, we'll calculate the 60-month (5-year) centered moving average of the time series. This helps reduce inter-annual climate fluctuations, such as those due to the El Niño-Southern Oscillation (ENSO)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_evolution_smooth = temp_evolution.rolling(time=60, center=True).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 7: Assess the Uncertainty of the HadCRUT Model**\n",
    "\n",
    "Understanding uncertainty is crucial. Here, we calculate the confidence interval for the HadCRUT model as the minimum/maximum value of the ensemble members for each time step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nrieger/miniconda3/envs/tutorial/lib/python3.10/site-packages/numpy/lib/nanfunctions.py:1577: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n"
     ]
    }
   ],
   "source": [
    "confidence_interval = temp_evolution_smooth[\"HadCRUT_ensemble\"].quantile([0.0, 1.0], dim=\"realization\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Et voilà! Visualization Time**\n",
    "\n",
    "With the calculations complete, we're now ready to compare all the time series in a single graph. This will give us a comprehensive view of how temperature anomalies have evolved over different periods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 3))\n",
    "ax = fig.add_subplot(111)\n",
    "ax2 = ax.twinx()\n",
    "line_HadCRUT, = temp_evolution_smooth[\"HadCRUT\"].plot(ax=ax, label=\"HadCRUT\")\n",
    "line_Berkeley, = temp_evolution_smooth[\"Berkeley\"].plot(ax=ax, label=\"Berkeley\")\n",
    "line_GISTEMP, = temp_evolution_smooth[\"GISTEMP\"].plot(ax=ax, label=\"GISTEMP\")\n",
    "line_NOAA, = temp_evolution_smooth[\"NOAA\"].plot(ax=ax, label=\"NOAA\")\n",
    "line_ERA5, = temp_evolution_smooth[\"ERA5\"].plot(ax=ax, label=\"ERA5\")\n",
    "area_HadCRUT = ax.fill_between(\n",
    "    confidence_interval.time,\n",
    "    confidence_interval.sel(quantile=0.0),\n",
    "    confidence_interval.sel(quantile=1.0),\n",
    "    color=plt.rcParams['axes.prop_cycle'].by_key()['color'][0], # same color (1st in the order) as the mean data from the same dataset (HadCRUT)\n",
    "    alpha=0.3,\n",
    "    lw=0,\n",
    "    zorder=-1,\n",
    ")\n",
    "\n",
    "ax.legend(\n",
    "    [(line_HadCRUT, area_HadCRUT), # add in the same handle the line and patch for the HadCRUT mean and ensembles\n",
    "      line_Berkeley, line_GISTEMP, line_NOAA, line_ERA5], \n",
    "    ['HadCRUT', 'Berkeley', 'GISTEMP', 'NOAA', 'ERA5'],\n",
    "    ncols=5, frameon=False, loc=\"upper center\"\\\n",
    "    )\n",
    "\n",
    "\n",
    "ax2.spines[\"right\"].set_visible(True)\n",
    "ax2.spines[\"top\"].set_visible(True)\n",
    "ax.xaxis.set_major_locator(mdates.YearLocator(20))\n",
    "\n",
    "ax.set_xlabel(\"\")\n",
    "ax.set_ylabel(\"\")\n",
    "ax.set_title(f\"{region} Mean Temperature Anomaly (in ºC) since 1850\")\n",
    "ax.text(\n",
    "    -0.02,\n",
    "    1,\n",
    "    \"Relative to \\n1991-2020\",\n",
    "    rotation=0,\n",
    "    ha=\"right\",\n",
    "    va=\"top\",\n",
    "    transform=ax.transAxes,\n",
    ")\n",
    "ax.text(\n",
    "    1.02,\n",
    "    1,\n",
    "    \"Increase above \\n1850-1900\\n reference level\",\n",
    "    rotation=0,\n",
    "    ha=\"left\",\n",
    "    va=\"top\",\n",
    "    transform=ax.transAxes,\n",
    ")\n",
    "ax.axhline(mean_1850_1900, color=\".5\", lw=0.5, ls=\"--\")\n",
    "\n",
    "yticks = np.arange(-10, 10, 1)\n",
    "ax2_yticks = yticks + mean_1850_1900.item()\n",
    "ax2.set_yticks(ax2_yticks)\n",
    "ax2.set_yticklabels(yticks)\n",
    "ax.set_ylim(-4.1, 1.8)\n",
    "ax2.set_ylim(-4.1, 1.8)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Understanding Model Discrepancies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's kick off by observing that datasets from the mid-last century onwards generally exhibit a **high degree of agreement**. But to truly quantify this, we're going to calculate the spread, which is the range between the highest and lowest values for each time point. Note how the differences between datasets tend to grow as we delve further back in time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nrieger/miniconda3/envs/tutorial/lib/python3.10/site-packages/numpy/lib/nanfunctions.py:1577: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "qs = temp_evolution_smooth.drop_vars([\"HadCRUT_ensemble\"]).to_array().quantile([0, 1], 'variable')\n",
    "plt.figure(figsize=(10, 3))\n",
    "qs.diff('quantile').plot.line(x='time', add_legend=False)\n",
    "plt.ylim(0, 2.0)\n",
    "plt.ylabel(\"Model spread (ºC)\")\n",
    "plt.xlabel(\"\")\n",
    "plt.title(\"Disagreement between models decreases over time\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why do these differences occur? There's a plethora of reasons, but one significant factor stands out: as we move further back in time, there were fewer observation stations, leading to limited spatial coverage. Let's illustrate this using **BerkeleyEarth** as our muse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 5))\n",
    "ax = [plt.subplot(1, 3, i+1, projection=ccrs.Robinson()) for i in range(3)]\n",
    "kwargs = dict(transform=ccrs.PlateCarree(), cmap='RdBu_r', vmin=-8, vmax=8, add_colorbar=False, zorder=3)\n",
    "berkeley['temperature'].sel(time='1850-01').plot(ax=ax[0], **kwargs)\n",
    "berkeley['temperature'].sel(time='1900-01').plot(ax=ax[1], **kwargs)\n",
    "berkeley['temperature'].sel(time='1950-01').plot(ax=ax[2], **kwargs)\n",
    "for a in ax:\n",
    "    a.add_feature(cfeature.LAND, lw=.5, color='.3', zorder=1)\n",
    "    a.add_feature(cfeature.OCEAN, lw=.5, color='.7', zorder=2)\n",
    "    a.coastlines(lw=.5, color='.3', zorder=4)\n",
    "    a.set_global()\n",
    "    a.set_title('')\n",
    "ax[0].set_title('1850', loc='center')\n",
    "ax[1].set_title('1900', loc='center')\n",
    "ax[2].set_title('1950', loc='center')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reiterating an essential point: the uncertainty displayed for the HadCRUT model seems on the lower side. While a part of this can be attributed to a reduced ensemble size which we used here, another part stems from the limited spatial coverage at the beginning of the time series."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Wrapping Up with Earth's Temperature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we're going to address the burning question: Just how warm is our dear Earth?\n",
    "\n",
    "By using absolute temperatures from **ERA5**, let’s compute the average temperature for the reference period (1991-2020) over a region of our choice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed | 95.00 s\n"
     ]
    }
   ],
   "source": [
    "# Taking Earth's mean temperature\n",
    "region = \"Arctic\"  # <--- define the region here\n",
    "\n",
    "land_mask = era5[\"lsm\"]\n",
    "clim_temp_era5 = era5[\"t2m\"].sel(REF_PERIOD)  # consider the reference period\n",
    "clim_temp_era5 = weighted_spatial_average(clim_temp_era5, REGIONS[region], land_mask=land_mask)\n",
    "\n",
    "with ProgressBar():\n",
    "    clim_temp_era5 = clim_temp_era5.compute()  # compute the result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have monthly anomalies for our reference period, computing the temporal average correctly requires to weight each month by the respective number of days. Let's do this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "days_in_month = clim_temp_era5.time.dt.days_in_month\n",
    "clim_temp_era5 = (clim_temp_era5 * days_in_month).sum() / days_in_month.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arctic mean temperature between 1991 and 2020 was -12.7 °C.\n"
     ]
    }
   ],
   "source": [
    "print(f'{region} mean temperature between 1991 and 2020 was {clim_temp_era5.item():.1f} °C.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### In Retrospect..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And there you have it! From discerning Earth's surface temperature changes since 1850, grappling with an array of datasets using handy tools like `dask` and `regionmask`, to calculating weighted averages spatially and temporally, we've covered a lot of ground together.\n",
    "\n",
    "In the [following part](https://ecmwfcode4earth.github.io/sketchbook-earth/02_temperature/02b_temperature.html), we focus on comparing ERA5 surface temperatures in Europe with observational data. "
   ]
  }
 ],
 "metadata": {
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