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 "cells": [
  {
   "cell_type": "markdown",
   "id": "087b4239",
   "metadata": {
    "tags": []
   },
   "source": [
    "![logo](./img/LogoLine_horizon_CAMS.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "778aaeb6",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Plot Time Series of CAMS Data\n",
    "\n",
    "This tutorial demonstrates how to plot time series of data from the [Copernicus Atmosphere Monitoring Service (CAMS)](https://atmosphere.copernicus.eu/). The example focusses on CO2, and we will visualise the \"Keeling Curve\" of global average increases in CO2 from the last decades."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f385cb1f",
   "metadata": {},
   "source": [
    "<style>\n",
    "td, th {\n",
    "   border: 1px solid white;\n",
    "   border-collapse: collapse;\n",
    "}\n",
    "</style>\n",
    "<table align=\"left\">\n",
    "  <tr>\n",
    "    <th>Run the tutorial via free cloud platforms: </th>\n",
    "    <th><a href=\"https://mybinder.org/v2/gh/ecmwf-projects/copernicus-training-cams/master?labpath=vis-time-series.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/ecmwf-projects/copernicus-training-cams/blob/master/vis-time-series.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/ecmwf-projects/copernicus-training-cams/blob/master/vis-time-series.ipynb\">\n",
    "        <img src = \"https://colab.research.google.com/assets/colab-badge.svg\" alt = \"Colab\"></th>\n",
    "  </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "077c3bf5",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "824e0914",
   "metadata": {},
   "source": [
    "## Install and import packages"
   ]
  },
  {
   "cell_type": "code",
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   "id": "02c581e6",
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    "execution": {
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   "outputs": [],
   "source": [
    "!pip install cdsapi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c38cfcb6",
   "metadata": {
    "execution": {
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   },
   "outputs": [],
   "source": [
    "# CDS API\n",
    "import cdsapi\n",
    "\n",
    "# Libraries for reading and working with multidimensional arrays\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "\n",
    "# Library for plotting and visualising data\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Disable warnings for data download via API\n",
    "import urllib3 \n",
    "urllib3.disable_warnings()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90547eb1",
   "metadata": {},
   "source": [
    "## Data access and preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b8f25f4",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Download CAMS global reanalysis data\n",
    "\n",
    "Copy your API key into the code cell below, replacing `#######` with your key. (Remember, to access data from the ADS, you will need first to register/login https://ads-beta.atmosphere.copernicus.eu/ and obtain an API key from https://ads-beta.atmosphere.copernicus.eu/how-to-api.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9160a3b7",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "URL = 'https://ads-beta.atmosphere.copernicus.eu/api'\n",
    "\n",
    "# Replace the hashtags with your key:\n",
    "KEY = '##################################'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f194461",
   "metadata": {
    "tags": []
   },
   "source": [
    "Here we specify a data directory into which we will download our data and all output files that we will generate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "12704039",
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2024-09-12T14:09:14.430008Z"
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   "outputs": [],
   "source": [
    "DATADIR = '.'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b9bfaf4",
   "metadata": {},
   "source": [
    "For this tutorial, we will use *CAMS global greenhouse gas reanalysis (EGG4)* data, monthly averaged fields version. The code below shows the subset characteristics that we will extract from this dataset as an API request."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88be4210",
   "metadata": {},
   "source": [
    "```{note}\n",
    "Before running this code, ensure that you have **accepted the terms and conditions**. This is something you only need to do once for each CAMS dataset. You will find the option to do this by selecting the dataset in the ADS, then scrolling to the end of the *Download data* tab.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cb8785ef",
   "metadata": {
    "execution": {
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   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-09-12 14:14:30,224 INFO Request ID is f2070345-f2f8-46ea-9638-96715107bffd\n",
      "2024-09-12 14:14:30,375 INFO status has been updated to accepted\n",
      "2024-09-12 14:14:32,030 INFO status has been updated to running\n",
      "2024-09-12 14:14:50,916 INFO Creating download object as as_source with files:\n",
      "['data_allhours_sfc.nc']\n",
      "2024-09-12 14:14:50,917 INFO status has been updated to successful\n"
     ]
    },
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       "5245c36ce942f9848218193a0bf40472.nc:   0%|          | 0.00/42.3M [00:00<?, ?B/s]"
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      "text/plain": [
       "'./CO2_2003-2020.nc'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = \"cams-global-ghg-reanalysis-egg4-monthly\"\n",
    "request = {\n",
    "    'variable': ['co2_column_mean_molar_fraction'],\n",
    "    'year': ['2003', '2004', '2005', '2006', \n",
    "             '2007', '2008', '2009', '2010', \n",
    "             '2011', '2012', '2013', '2014', \n",
    "             '2015', '2016', '2017', '2018', \n",
    "             '2019', '2020'],\n",
    "    'month': ['01', '02', '03', '04', '05', '06', \n",
    "              '07', '08', '09', '10', '11', '12'],\n",
    "    'product_type': ['monthly_mean'],\n",
    "    'data_format': 'netcdf'\n",
    "}\n",
    "\n",
    "client = cdsapi.Client(url=URL, key=KEY)\n",
    "client.retrieve(dataset, request).download(\n",
    "    f'{DATADIR}/CO2_2003-2020.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eeab0b7a",
   "metadata": {},
   "source": [
    "### Read and inspect data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e86254e",
   "metadata": {
    "tags": []
   },
   "source": [
    "Read the data into an Xarray dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e278d834",
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    "execution": {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 100MB\n",
       "Dimensions:     (valid_time: 216, latitude: 241, longitude: 480)\n",
       "Coordinates:\n",
       "  * valid_time  (valid_time) datetime64[ns] 2kB 2003-01-01 ... 2020-12-01\n",
       "  * latitude    (latitude) float64 2kB 90.0 89.25 88.5 ... -88.5 -89.25 -90.0\n",
       "  * longitude   (longitude) float64 4kB 0.0 0.75 1.5 2.25 ... 357.8 358.5 359.2\n",
       "Data variables:\n",
       "    tcco2       (valid_time, latitude, longitude) float32 100MB ...\n",
       "Attributes:\n",
       "    GRIB_centre:             ecmf\n",
       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
       "    GRIB_subCentre:          0\n",
       "    Conventions:             CF-1.7\n",
       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
       "    history:                 2024-09-12T14:14 GRIB to CDM+CF via cfgrib-0.9.1...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-4f25bb63-213c-4558-bd37-480036344825' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4f25bb63-213c-4558-bd37-480036344825' 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'>valid_time</span>: 216</li><li><span class='xr-has-index'>latitude</span>: 241</li><li><span class='xr-has-index'>longitude</span>: 480</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9ff38a5a-a777-45cf-a75a-a60aac55077a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9ff38a5a-a777-45cf-a75a-a60aac55077a' 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'>valid_time</span></div><div class='xr-var-dims'>(valid_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2003-01-01 ... 2020-12-01</div><input id='attrs-d0b3f28c-434c-4c74-bf8e-1bc60b32c4a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d0b3f28c-434c-4c74-bf8e-1bc60b32c4a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7be9747c-72c3-4e30-8ef2-e3d07333826f' class='xr-var-data-in' type='checkbox'><label for='data-7be9747c-72c3-4e30-8ef2-e3d07333826f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2003-01-01T00:00:00.000000000&#x27;, &#x27;2003-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2003-03-01T00:00:00.000000000&#x27;, ..., &#x27;2020-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-11-01T00:00:00.000000000&#x27;, &#x27;2020-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>90.0 89.25 88.5 ... -89.25 -90.0</div><input id='attrs-bf767c1f-1ae8-42fc-ac8b-4f42e73dc72a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bf767c1f-1ae8-42fc-ac8b-4f42e73dc72a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60cd78af-c47d-48a2-aaad-e41d2b642f3f' class='xr-var-data-in' type='checkbox'><label for='data-60cd78af-c47d-48a2-aaad-e41d2b642f3f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd></dl></div><div class='xr-var-data'><pre>array([ 90.  ,  89.25,  88.5 , ..., -88.5 , -89.25, -90.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.75 1.5 ... 357.8 358.5 359.2</div><input id='attrs-0b761dd0-d2b1-4f48-b8a4-90ee8f3d7539' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0b761dd0-d2b1-4f48-b8a4-90ee8f3d7539' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90668eeb-c67c-4ed0-873e-9877fd34d391' class='xr-var-data-in' type='checkbox'><label for='data-90668eeb-c67c-4ed0-873e-9877fd34d391' 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([  0.  ,   0.75,   1.5 , ..., 357.75, 358.5 , 359.25])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5bd3b535-1960-4466-b996-f9f40c8327c4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5bd3b535-1960-4466-b996-f9f40c8327c4' 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>tcco2</span></div><div class='xr-var-dims'>(valid_time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0fee7fc4-bad2-4788-85a6-71dd75364c58' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0fee7fc4-bad2-4788-85a6-71dd75364c58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cbf30d1b-0a21-409f-bcd3-46e57e1ab3ae' class='xr-var-data-in' type='checkbox'><label for='data-cbf30d1b-0a21-409f-bcd3-46e57e1ab3ae' 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>GRIB_paramId :</span></dt><dd>210064</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>115680</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>avgfc</dd><dt><span>GRIB_gridType :</span></dt><dd>regular_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_Nx :</span></dt><dd>480</dd><dt><span>GRIB_Ny :</span></dt><dd>241</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcco2</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_iDirectionIncrementInDegrees :</span></dt><dd>0.75</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_jDirectionIncrementInDegrees :</span></dt><dd>0.75</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>0</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>90.0</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-90.0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.25</dd><dt><span>GRIB_missingValue :</span></dt><dd>3.4028234663852886e+38</dd><dt><span>GRIB_name :</span></dt><dd>CO2 column-mean molar fraction</dd><dt><span>GRIB_shortName :</span></dt><dd>tcco2</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_units :</span></dt><dd>ppm</dd><dt><span>long_name :</span></dt><dd>CO2 column-mean molar fraction</dd><dt><span>units :</span></dt><dd>ppm</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>GRIB_number :</span></dt><dd>0</dd><dt><span>GRIB_surface :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[24986880 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-018279df-1583-41a4-a8aa-c84cd2502684' class='xr-section-summary-in' type='checkbox'  ><label for='section-018279df-1583-41a4-a8aa-c84cd2502684' 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>valid_time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b64399be-3fda-4e55-8e9b-b92030f0ff99' class='xr-index-data-in' type='checkbox'/><label for='index-b64399be-3fda-4e55-8e9b-b92030f0ff99' 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;2003-01-01&#x27;, &#x27;2003-02-01&#x27;, &#x27;2003-03-01&#x27;, &#x27;2003-04-01&#x27;,\n",
       "               &#x27;2003-05-01&#x27;, &#x27;2003-06-01&#x27;, &#x27;2003-07-01&#x27;, &#x27;2003-08-01&#x27;,\n",
       "               &#x27;2003-09-01&#x27;, &#x27;2003-10-01&#x27;,\n",
       "               ...\n",
       "               &#x27;2020-03-01&#x27;, &#x27;2020-04-01&#x27;, &#x27;2020-05-01&#x27;, &#x27;2020-06-01&#x27;,\n",
       "               &#x27;2020-07-01&#x27;, &#x27;2020-08-01&#x27;, &#x27;2020-09-01&#x27;, &#x27;2020-10-01&#x27;,\n",
       "               &#x27;2020-11-01&#x27;, &#x27;2020-12-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;valid_time&#x27;, length=216, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b7469846-a55a-44ed-8816-e80da1a58841' class='xr-index-data-in' type='checkbox'/><label for='index-b7469846-a55a-44ed-8816-e80da1a58841' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  90.0,  89.25,   88.5,  87.75,   87.0,  86.25,   85.5,  84.75,   84.0,\n",
       "        83.25,\n",
       "       ...\n",
       "       -83.25,  -84.0, -84.75,  -85.5, -86.25,  -87.0, -87.75,  -88.5, -89.25,\n",
       "        -90.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=241))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-10abb7f6-f68e-48ed-8bcf-90d6b4bf42d5' class='xr-index-data-in' type='checkbox'/><label for='index-10abb7f6-f68e-48ed-8bcf-90d6b4bf42d5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   0.75,    1.5,   2.25,    3.0,   3.75,    4.5,   5.25,    6.0,\n",
       "         6.75,\n",
       "       ...\n",
       "        352.5, 353.25,  354.0, 354.75,  355.5, 356.25,  357.0, 357.75,  358.5,\n",
       "       359.25],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=480))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6bbaa3e3-08ca-4428-aaba-f913f69ec8ae' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6bbaa3e3-08ca-4428-aaba-f913f69ec8ae' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>institution :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>history :</span></dt><dd>2024-09-12T14:14 GRIB to CDM+CF via cfgrib-0.9.14.0/ecCodes-2.36.0 with {&quot;source&quot;: &quot;data.grib&quot;, &quot;filter_by_keys&quot;: {&quot;marsStream&quot;: &quot;moda&quot;, &quot;typeOfLevel&quot;: &quot;surface&quot;}, &quot;encode_cf&quot;: [&quot;parameter&quot;, &quot;time&quot;, &quot;geography&quot;, &quot;vertical&quot;]}</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 100MB\n",
       "Dimensions:     (valid_time: 216, latitude: 241, longitude: 480)\n",
       "Coordinates:\n",
       "  * valid_time  (valid_time) datetime64[ns] 2kB 2003-01-01 ... 2020-12-01\n",
       "  * latitude    (latitude) float64 2kB 90.0 89.25 88.5 ... -88.5 -89.25 -90.0\n",
       "  * longitude   (longitude) float64 4kB 0.0 0.75 1.5 2.25 ... 357.8 358.5 359.2\n",
       "Data variables:\n",
       "    tcco2       (valid_time, latitude, longitude) float32 100MB ...\n",
       "Attributes:\n",
       "    GRIB_centre:             ecmf\n",
       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
       "    GRIB_subCentre:          0\n",
       "    Conventions:             CF-1.7\n",
       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
       "    history:                 2024-09-12T14:14 GRIB to CDM+CF via cfgrib-0.9.1..."
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fn = f'{DATADIR}/CO2_2003-2020.nc'\n",
    "ds = xr.open_dataset(fn)\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad885ceb",
   "metadata": {},
   "source": [
    "### Spatial aggregation\n",
    "\n",
    "We would like to visualise this data not in maps, but as a one dimensional time series of global average values. To do this, we will first need to aggregate the data spatially to create a single global average at each time step."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "898b9f20",
   "metadata": {},
   "source": [
    "In order to aggregate over the latitudinal dimension, we need to take into account the variation in area as a function of latitude. We will do this using the cosine of the latitude as a proxy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d44b7911",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-12T14:15:10.861054Z",
     "iopub.status.busy": "2024-09-12T14:15:10.860249Z",
     "iopub.status.idle": "2024-09-12T14:15:10.872905Z",
     "shell.execute_reply": "2024-09-12T14:15:10.871525Z",
     "shell.execute_reply.started": "2024-09-12T14:15:10.860997Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "weights = np.cos(np.deg2rad(ds.latitude))\n",
    "weights.name = \"weights\"\n",
    "ds_weighted = ds.weighted(weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "841c3633",
   "metadata": {},
   "source": [
    "Now we can average over both the longitude and latitude dimensions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "66144931",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-12T14:15:14.230238Z",
     "iopub.status.busy": "2024-09-12T14:15:14.229790Z",
     "iopub.status.idle": "2024-09-12T14:15:15.614036Z",
     "shell.execute_reply": "2024-09-12T14:15:15.612781Z",
     "shell.execute_reply.started": "2024-09-12T14:15:14.230192Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Average (mean) over the latitudinal axis\n",
    "co_ds = ds_weighted.mean(dim=[\"latitude\", \"longitude\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82fb4c2f",
   "metadata": {},
   "source": [
    "### Create xarray Data Array from Dataset object"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "874bff78",
   "metadata": {},
   "source": [
    "Here we create an Xarray Data Array object containing the CO2 variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "80fc8a5e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-12T14:15:18.290222Z",
     "iopub.status.busy": "2024-09-12T14:15:18.289763Z",
     "iopub.status.idle": "2024-09-12T14:15:18.295689Z",
     "shell.execute_reply": "2024-09-12T14:15:18.294385Z",
     "shell.execute_reply.started": "2024-09-12T14:15:18.290179Z"
    }
   },
   "outputs": [],
   "source": [
    "co = co_ds['tcco2']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a2cdc6a",
   "metadata": {},
   "source": [
    "## Plot time series of global CO\n",
    "\n",
    "Now we can plot the time series of globally averaged CO2 data over time."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd55bc7e",
   "metadata": {},
   "source": [
    "### Simple plot using xarray\n",
    "\n",
    "The easiest way to plot data in an Xarray Data Array object is to use Xarray's own plotting functionality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fb7430cf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-12T14:15:21.605171Z",
     "iopub.status.busy": "2024-09-12T14:15:21.604747Z",
     "iopub.status.idle": "2024-09-12T14:15:22.090912Z",
     "shell.execute_reply": "2024-09-12T14:15:22.089597Z",
     "shell.execute_reply.started": "2024-09-12T14:15:21.605132Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x790bc8eeabf0>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "co.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29cf7178",
   "metadata": {},
   "source": [
    "In this plot of monthly averaged CO2 we can see the seasonal variation and yearly increase in global CO2 over the last decades."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7b888f5",
   "metadata": {},
   "source": [
    "### Customised plot using matplotlib\n",
    "\n",
    "In this example we use the Matplotlib library to create the same plot as above, with a few customisations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "09edaeb7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-12T14:15:56.721689Z",
     "iopub.status.busy": "2024-09-12T14:15:56.721178Z",
     "iopub.status.idle": "2024-09-12T14:15:57.380060Z",
     "shell.execute_reply": "2024-09-12T14:15:57.379005Z",
     "shell.execute_reply.started": "2024-09-12T14:15:56.721644Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize = (9, 6)) # Set size and dimensions of figure\n",
    "\n",
    "ax.set_title('CO2 global time series', fontsize=12) # Set figure title\n",
    "ax.set_ylabel('CO2 column-mean molar fraction (ppm)') # Set Y axis title\n",
    "ax.set_xlabel('Year') # Set X axis title\n",
    "ax.grid(linewidth=1, color='gray', alpha=0.5, linestyle='--') # Include gridlines\n",
    "ax.plot(co.valid_time, co) # Plot the data\n",
    "\n",
    "fig.savefig(f'{DATADIR}/CAMS_CO2_reanalysis.png') # Save the figure"
   ]
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "none",
   "dataSources": [],
   "dockerImageVersionId": 30761,
   "isGpuEnabled": false,
   "isInternetEnabled": true,
   "language": "python",
   "sourceType": "notebook"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}