notebooks/regional-scatter-plots_Figure-Atlas-13_R.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Atlas notebooks\n",
    "\n",
    "***\n",
    "\n",
    "> This notebook is an extension of the `regional-scatter-plots_R.ipynb` notebook (available in the same repository https:/IPCC-WG1/Atlas/notebooks/) which provides a clear demonstration of the specific steps and parameters required to accurately replicate **Figure Atlas. 13** of the AR6-WGI Atlas Chapter. It is part of a notebook collection available at https:/IPCC-WG1/Atlas for reproducibility and reusability purposes. This work is licensed under a [Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0).\n",
    ">\n",
    "> ![Creative Commons License >](https://i.creativecommons.org/l/by/4.0/88x31.png)\n",
    ">\n",
    "> To cite this notebook please refere to the main notebook: `regional-scatter-plots_R.ipynb`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "## Computing and visualizing regional climate change (temperature vs precipitation) for reference regions: Figure Atlas.13\n",
    "\n",
    "24/6/2021\n",
    "\n",
    "**M. Iturbide** (Santander Meteorology Group. Institute of Physics of Cantabria, CSIC-UC, Santander, Spain).\n",
    "\n",
    "This notebook reproduces **Figure Atlas.13** of the AR6-WGI Atlas chapter. This is, the scatter plots of global climate change temperature vs. precipitation using CMIP5 and CMIP6 datasets. This notebook builds on other sections of the repository: 1) auxiliary scripts from [reproducibility](https:/IPCC-WG1/Atlas/tree/main/reproducibility), and 2) CMIP5/6 - CORDEX [aggregated datasets](https:/IPCC-WG1/Atlas/tree/main/datasets-aggregated-regionally) for the WGI reference regions.\n",
    "\n",
    "The legend and axis label information of the panels generated here is the one shown in the AR6-WGI Atlas chapter figures. The first column shows the annual temperature delta changes. The second and third columns, the scatterplots of the seasonal temperature and precipitation changes. Each row corresponds to a different reference region or, as in this case, to global results (World).\n",
    "\n",
    "Before starting, the size of the graphic space of this notebook is defined to generate figures of an appropriate size:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(repr)\n",
    "# Change plot size \n",
    "options(repr.plot.width=14, repr.plot.height=6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Easy and direct production of the figure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Load packages and functions\n",
    "This notebook is based on the R programming language and requires packages:\n",
    "\n",
    " * `magrittr` to pipe (`%>%`) sequences of operations\n",
    " * `httr` to handle URLs and HTTP\n",
    " * `lattice` and `latticeExtra` to produce the figures\n",
    " * `gridExtra` to produce the final panel of plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "library(magrittr)\n",
    "library(httr)\n",
    "library(lattice)\n",
    "library(latticeExtra)\n",
    "library(gridExtra)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main function to generate the boxplots and scatterplots is `computeFigures`, which internally uses functions `computeDeltas` and `computeOffset`, all available in this repository. To load these functions in the working environment use the `source` R base function as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "source(\"../datasets-aggregated-regionally/scripts/computeDeltas.R\")\n",
    "source(\"../datasets-aggregated-regionally/scripts/computeFigures.R\")\n",
    "source(\"../datasets-aggregated-regionally/scripts/computeOffset.R\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Parameter setting \n",
    "\n",
    "We define the parameters:\n",
    "- The seasons to show in the Precipitation vs Temperature scatterplots (E.g. 1:12 for annual and c(12, 1, 2) for the boreal winter)\n",
    "- The baseline period (e.g. AR6 reference period 1995-2014). Available years in the datasets are 1850-1900 and 1950-2100.\n",
    "- The area, i.e. \"land\", \"sea\" or \"landsea\"\n",
    "- The reference regions (see reference-regions in this repository). Use `world` to generate global results.\n",
    "- Select a CORDEX domain from the following options: SAM, CAM, NAM, AFR, WAS, EAS, AUS, ANT, ARC, SEA and EUR (use FALSE to exclude CORDEX)\n",
    "- Figure axes ranges (ylim for temperature, xlim for precipitation percentage). Use `y <- NULL` and `x <- NULL` for automatic axes ranges.\n",
    "\n",
    "This is the parameter setting for reproducing the first half of **Figure Atlas. 13**, the one corresponding to **land-sea results**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "scatter.seasons <- list(1:12, c(12, 1, 2), 6:8)\n",
    "ref.period <- 1995:2014\n",
    "area <- \"landsea\"\n",
    "regions <- c(\"world\")\n",
    "cordex.domain <- FALSE\n",
    "xlim <- c(-2, 12)\n",
    "ylim <- c(-2, 7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compute delta changes and figures\n",
    "\n",
    "We are ready to apply the `computeFigures` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2023-06-27 10:18:45] Computing annual delta changes for the Boxplot of region world\n",
      "\n",
      "[2023-06-27 10:18:45] Computing CMIP5..\n",
      "\n",
      "[2023-06-27 10:18:49] Computing CMIP6..\n",
      "\n",
      "[2023-06-27 10:18:52] Computing CORDEX..\n",
      "\n",
      "[2023-06-27 10:19:31] Computing seasonal delta changes for the Scatterplots of region world\n",
      "\n",
      "[2023-06-27 10:19:31] Computing CMIP5..\n",
      "\n",
      "[2023-06-27 10:19:46] Computing CMIP6..\n",
      "\n",
      "[2023-06-27 10:20:02] Computing CORDEX..\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fig_landsea <- computeFigures(regions = regions,\n",
    "                      cordex.domain = cordex.domain,\n",
    "                      area = area, \n",
    "                      ref.period = ref.period, \n",
    "                      scatter.seasons = scatter.seasons,\n",
    "                      xlim = xlim,\n",
    "                      ylim = ylim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output of the above call (`fig_landsea`) is a list of `trellis` class objects that can be easily arranged and displayed using the `grid.arrange` function from the `gridExtra` library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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XxegIu3/961/333//b3/7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\na7pHj512HXroV9NYD0A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SMibS22+qrt/TqzT0FBQaveqYk/UT97u87ERBAMSq4b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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 840
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "do.call(\"grid.arrange\", c(fig_landsea, top =  area))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The legend and axis label information of these panels is the one shown in the AR6-WGI Atlas chapter figures. The first column shows the annual temperature delta changes. The second and third columns, the scatterplots of the seasonal temperature and precipitation changes. Each row corresponds to a different reference region or, as in this case, to global results (World). The legend is also available in this repository as a `png` file under the [notebooks/auxiliary-material]((https:/IPCC-WG1/Atlas/tree/main/notebooks/auxiliary-material) folder:"
   ]
  },
  {
   "attachments": {
    "9b586287-56ea-4162-9850-65cef62e6b12.png": {
     "image/png": 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ff/2+h1Qi/CeNU2hoKMOGDWP58uXY2NiQlZVFeHg4wcHB+Pr6Mnv2bDHhYHh4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\nrEdERFeHixMREV2dJ1oOKmETbOMnAAAAAElFTkSuQmCC\n"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Legend_Figures-Atlas_13-16-17-21-22-24-26-28-29.png](attachment:9b586287-56ea-4162-9850-65cef62e6b12.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now export the Figure as PDF. There are other export options such as `png` (type `?pdf` for help). The `Cairo` package provides other export options. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "outfilename <- sprintf(\"ATvsAP_%s_%s_cordex:%s_baseperiod:%s.pdf\",\n",
    "  paste(regions, collapse = \"-\"), area, cordex.domain, paste(range(ref.period), collapse = \"-\")\n",
    ")\n",
    "\n",
    "pdf(outfilename, width = (length(scatter.seasons)+1)*5*0.85, height = length(regions)*5*0.85)\n",
    "  do.call(\"grid.arrange\", fig_landsea)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'ATvsAP_world_landsea_cordex:FALSE_baseperiod:1995-2014.pdf'"
      ],
      "text/latex": [
       "'ATvsAP\\_world\\_landsea\\_cordex:FALSE\\_baseperiod:1995-2014.pdf'"
      ],
      "text/markdown": [
       "'ATvsAP_world_landsea_cordex:FALSE_baseperiod:1995-2014.pdf'"
      ],
      "text/plain": [
       "[1] \"ATvsAP_world_landsea_cordex:FALSE_baseperiod:1995-2014.pdf\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "outfilename"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change the parameter for the `area` to produce the second half of **Figure Atlas. 13**, the one corresponding to **only land results**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "area <- \"land\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2023-06-27 10:20:14] Computing annual delta changes for the Boxplot of region world\n",
      "\n",
      "[2023-06-27 10:20:14] Computing CMIP5..\n",
      "\n",
      "[2023-06-27 10:20:17] Computing CMIP6..\n",
      "\n",
      "[2023-06-27 10:20:20] Computing CORDEX..\n",
      "\n",
      "[2023-06-27 10:20:57] Computing seasonal delta changes for the Scatterplots of region world\n",
      "\n",
      "[2023-06-27 10:20:57] Computing CMIP5..\n",
      "\n",
      "[2023-06-27 10:21:11] Computing CMIP6..\n",
      "\n",
      "[2023-06-27 10:21:28] Computing CORDEX..\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fig_land <- computeFigures(regions = regions,\n",
    "                      cordex.domain = cordex.domain,\n",
    "                      area = area, \n",
    "                      ref.period = ref.period, \n",
    "                      scatter.seasons = scatter.seasons,\n",
    "                      xlim = xlim,\n",
    "                      ylim = ylim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Z2NXmfvnXPvet0WOmNgBwAAAACRJbADAAAAgAgR2AEAAABAhAjsAAAAACBCBHYA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\njDcBgAhiFBQ3mzqOkm8yMQwprcOp1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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 840
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "do.call(\"grid.arrange\", c(fig_land, top =  area))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Export the Figure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "outfilename <- sprintf(\"ATvsAP_%s_%s_cordex:%s_baseperiod:%s.pdf\",\n",
    "  paste(regions, collapse = \"-\"), area, cordex.domain, paste(range(ref.period), collapse = \"-\")\n",
    ")\n",
    "\n",
    "pdf(outfilename, width = (length(scatter.seasons)+1)*5*0.85, height = length(regions)*5*0.85)\n",
    "  do.call(\"grid.arrange\", fig_land)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Session Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R version 3.6.3 (2020-02-29)\n",
       "Platform: x86_64-conda-linux-gnu (64-bit)\n",
       "Running under: Ubuntu 20.04.2 LTS\n",
       "\n",
       "Matrix products: default\n",
       "BLAS/LAPACK: /home/maialen/miniconda3/envs/Atlas/lib/libopenblasp-r0.3.17.so\n",
       "\n",
       "locale:\n",
       " [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n",
       " [3] LC_TIME=es_ES.UTF-8        LC_COLLATE=en_US.UTF-8    \n",
       " [5] LC_MONETARY=es_ES.UTF-8    LC_MESSAGES=en_US.UTF-8   \n",
       " [7] LC_PAPER=es_ES.UTF-8       LC_NAME=C                 \n",
       " [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n",
       "[11] LC_MEASUREMENT=es_ES.UTF-8 LC_IDENTIFICATION=C       \n",
       "\n",
       "attached base packages:\n",
       "[1] stats     graphics  grDevices utils     datasets  methods   base     \n",
       "\n",
       "other attached packages:\n",
       "[1] gridExtra_2.3       latticeExtra_0.6-29 lattice_0.20-44    \n",
       "[4] httr_1.4.2          magrittr_2.0.1      repr_1.1.3         \n",
       "\n",
       "loaded via a namespace (and not attached):\n",
       " [1] uuid_0.1-4         R6_2.5.0           jpeg_0.1-8.1       rlang_0.4.11      \n",
       " [5] fansi_0.4.2        tools_3.6.3        grid_3.6.3         gtable_0.3.0      \n",
       " [9] png_0.1-7          utf8_1.2.1         htmltools_0.5.1.1  ellipsis_0.3.2    \n",
       "[13] digest_0.6.27      lifecycle_1.0.0    crayon_1.4.1       IRdisplay_1.0     \n",
       "[17] RColorBrewer_1.1-2 base64enc_0.1-3    vctrs_0.3.8        IRkernel_1.2      \n",
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