man/notebooks/climate4R.ipynb

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   "source": [
    "# `Climate4` Jupyter Hub ([Santander Meteorolgoy Group](https://www.meteo.unican.es))\n",
    "\n",
    "## Code of the paper Gutiérrez et al. (2018) *climate research reproducibility with the climate4R R-based framework*, in 8th International Workshop on Climate Informatics, 61-64, C. Chen et al. Editors. doi: [10.5065/D6BZ64XQ](https://dx.doi.org/10.5065/D6BZ64XQ)"
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   "source": [
    "This notebook includes the code used in the paper to illustrate the basic functionality of the [climate4R framework](https://www.meteo.unican.es/climate4R) (formed by the core packages `loadeR`, `transformeR`, `downscaleR`, and `visualizeR`, as well as some wrappers to exiting packages, such as `climate4R.climdex`, which allows the calculation of the [ETCCDI core indices](https://etccdi.pacificclimate.org/list_27_indices.shtml)). The example illustrates how to load, transform and downscale (bias adjust in this case) some representative (EURO-)CORDEX data for a Southern European region (the E-OBS gridded observational datasets is used as reference) in order to compute bias corrected projections of a climate index (summer days, SU). A more detailed description of climate4R and more comprehensive examples are provided in the  reference paper, published in *Environmental Modelling & Software* journal (https://doi.org/10.1016/j.envsoft.2018.09.009).  "
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   "source": [
    "All operations hereinafter are performed with the climate4R framework, except the creation of the color palettes used in map visualization, for which the package `RColorBrewer` is used:"
   ]
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   "source": [
    "library(RColorBrewer)\n",
    "colSU <- rev(brewer.pal(n = 9, \"RdYlBu\"))\n",
    "coldelta <- brewer.pal(n = 9, \"Reds\")\n",
    "colbias <- brewer.pal(n = 9, \"PiYG\")\n",
    "coltx <- rev(brewer.pal(n = 9, \"Spectral\"))"
   ]
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   "source": [
    "### 1. Loading the climate4R framework packages"
   ]
  },
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   "source": [
    "The packages forming the climate4R framework are already installed in the climate4R jupyter hub, so they can be loaded into the current session (user guidance for the different packages, including instructions for local installation, is available at the corresponding GitHub wikis, e.g. https://github.com/SantanderMetGroup/loadeR/wiki)."
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     "text": [
      "Loading required package: loadeR\n",
      "Loading required package: rJava\n",
      "Loading required package: loadeR.java\n",
      "Java version 10x amd64 by Oracle Corporation detected\n",
      "NetCDF Java Library v4.6.0-SNAPSHOT (23 Apr 2015) loaded and ready\n",
      "loadeR version 1.4.9 (2018-12-17) is loaded\n",
      "Please use 'citation(\"loadeR\")' to cite this package.\n",
      "Loading required package: transformeR\n",
      "transformeR version 1.4.7 (2018-12-17) is loaded\n",
      "Please see 'citation(\"transformeR\")' to cite this package.\n",
      "Loading required package: downscaleR\n",
      "Loading required package: deepnet\n",
      "Loading required package: glmnet\n",
      "Loading required package: Matrix\n",
      "Loading required package: foreach\n",
      "Loaded glmnet 2.0-16\n",
      "\n",
      "downscaleR version 3.0.5 (2018-12-17) is loaded\n",
      "Please use 'citation(\"downscaleR\")' to cite this package.\n",
      "Loading required package: visualizeR\n",
      "Loading required package: sm\n",
      "Warning message:\n",
      "“no DISPLAY variable so Tk is not available”Package 'sm', version 2.2-5.6: type help(sm) for summary information\n",
      "visualizeR version 1.3.1 (2018-12-17) is loaded\n",
      "Please see 'citation(\"visualizeR\")' to cite this package.\n",
      "\n",
      "Attaching package: ‘visualizeR’\n",
      "\n",
      "The following objects are masked from ‘package:transformeR’:\n",
      "\n",
      "    clim2sgdf, map.lines, map.stippling\n",
      "\n",
      "Loading required package: climate4R.climdex\n",
      "Loading required package: climdex.pcic\n",
      "Loading required package: PCICt\n",
      "climate4R.climdex version 0.1.4 (2018-08-15) is loaded\n",
      "WARNING: Your current version of climate4R.climdex (v0.1.4) is not up-to-date\n",
      "Get the latest stable version (0.2.0) using <devtools::install_github('SantanderMetGroup/climate4R.climdex')>\n",
      "Use 'climdexShow()' for an overview of the available ETCDDI indices\n"
     ]
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   "source": [
    "climate4R <- list(\"loadeR\", \"transformeR\", \"downscaleR\", \"visualizeR\", \"climate4R.climdex\")\n",
    "lapply(climate4R, require, character.only = TRUE) "
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   "source": [
    "### 2. Setting the geografic domain and season"
   ]
  },
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   "source": [
    "Southern Europe is selected as the target region, focusing on annual data (note that any particular season can be selected for analysis, e.g. DJF `seas <- c(12,1,2)`).  "
   ]
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   "source": [
    "lon <- c(-10,20); lat <- c(35,46); seas <- 1:12"
   ]
  },
  {
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   "source": [
    "This example illustrates different possibilities to access climate data using climate4R, specifying the above parameters together with the time period: \n",
    "1. Local data (using self-generated aggregated catalogs, via [NcML](https://www.unidata.ucar.edu/software/thredds/v4.3/netcdf-java/ncml/v2.2/Tutorial.html) files), \n",
    "2. Remote data from any OpenDAP repository, \n",
    "3. Remote data from the climate4R User Data Gateway (UDG), a [THREDDS](https://www.unidata.ucar.edu/software/thredds/current/tds/) Data Server for climate4R integrated within the jupyter hub infrastructure (near data computing). "
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   "source": [
    "### 3. Loading (remote) E-OBS observations and summer days (SU) calculation"
   ]
  },
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   "source": [
    "E-OBS data is loaded from the remote OpenDAP repository at KNMI. Please, note that when new versions become available the link is no longer valid since the URL is updated with the version number. Therefore, if the code below doesn't work please check the current E-OBS version and update the URL (https://www.ecad.eu/download/ensembles/download.php). "
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   "outputs": [],
   "source": [
    "eobs <- \"https://opendap.knmi.nl/knmi/thredds/dodsC/e-obs_0.25regular/tx_0.25deg_reg_v17.0.nc\""
   ]
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   "source": [
    "The [`climate4R.climdex`](https://github.com/SantanderMetGroup/climate4R.climdex) package is a wrapper of the R package [climdex.pcic](https://github.com/pacificclimate/climdex.pcic) allowing for a seamless integration with the climate4R data structures for the calculation of the ETCCDI core indices, and providing support for parallel computing. In particular, the example below computes Number_of_summer_days (code [SU](https://etccdi.pacificclimate.org/list_27_indices.shtml), climdex.su function) for E-OBS daily maximum temperature for the period 1971-2000 (as obtained by `loadGridData`, the main function of `loadeR` together with a counterpart for station-based data: [`loadStationData`](https://github.com/SantanderMetGroup/loadeR/wiki), not shown in this example). The `visualizeR` funcion `spatialPlot` allows to display regular and (pole-)rotated gridded products, the index climatology in this case (additionally the function `temporalPlot` allows to display multiple time series, as shown later). "
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     "text": [
      "[2018-12-20 19:03:01] Opening dataset...\n",
      "[2018-12-20 19:03:03] The dataset was successfuly opened\n",
      "[2018-12-20 19:03:03] Defining geo-location parameters\n",
      "[2018-12-20 19:03:03] Defining time selection parameters\n",
      "[2018-12-20 19:03:03] Retrieving data subset ...\n",
      "[2018-12-20 19:14:33] Done\n",
      "[2018-12-20 19:14:38] Calculating SU ...\n",
      "[2018-12-20 19:15:40] Done\n",
      "[2018-12-20 19:15:40] - Computing climatology...\n",
      "[2018-12-20 19:15:40] - Done.\n"
     ]
    },
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YMGmZmZf/zxx7lz5/r27aupVCcIQmrq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NRBydylo8iJGxBr/1UqyoqFjgvODz58/R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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "obs.tx <- loadGridData(eobs, var = \"tx\",\n",
    "                       years = 1971:2000, season = seas,\n",
    "                       lonLim = lon, latLim = lat)\n",
    "obs.SU <- climdexGrid(tx = obs.tx, index.code = \"SU\")\n",
    "spatialPlot(climatology(obs.SU), col.regions = colorRampPalette(colSU), backdrop.theme = \"coastline\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### 4. Loading (local) historical CORDEX data and SU calculation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Illustrative historical and RCP8.5 simulations for a representative CORDEX model (the RCA4 SMHI model, for the 0.44 European domain) are available in the shared folder of the climate4R jupyter hub (in the folder \"local_nc_data\", with two subfolders \"historical\" and \"rcp85\" each with six files containing data for 5-year periods). \n",
    "The function `dataInventory` allows to explore the content of a particular file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2018-12-20 19:15:52] Doing inventory ...\n",
      "[2018-12-20 19:15:52] Retrieving info for 'tasmax' (0 vars remaining)\n",
      "[2018-12-20 19:15:52] Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$Description</dt>\n",
       "\t\t<dd>'Daily Maximum Near-Surface Air Temperature'</dd>\n",
       "\t<dt>$DataType</dt>\n",
       "\t\t<dd>'float'</dd>\n",
       "\t<dt>$Units</dt>\n",
       "\t\t<dd>'K'</dd>\n",
       "\t<dt>$Dimensions</dt>\n",
       "\t\t<dd><dl>\n",
       "\t<dt>$time</dt>\n",
       "\t\t<dd><dl>\n",
       "\t<dt>$Type</dt>\n",
       "\t\t<dd>'Time'</dd>\n",
       "\t<dt>$TimeStep</dt>\n",
       "\t\t<dd>'1.0 days'</dd>\n",
       "\t<dt>$Units</dt>\n",
       "\t\t<dd>'days since 1949-12-01 00:00:00'</dd>\n",
       "\t<dt>$Date_range</dt>\n",
       "\t\t<dd>'1971-01-01T12:00:00Z - 1975-12-31T12:00:00Z'</dd>\n",
       "</dl>\n",
       "</dd>\n",
       "\t<dt>$lat</dt>\n",
       "\t\t<dd><dl>\n",
       "\t<dt>$Type</dt>\n",
       "\t\t<dd>'GeoY'</dd>\n",
       "\t<dt>$Units</dt>\n",
       "\t\t<dd>'degrees'</dd>\n",
       "\t<dt>$Values</dt>\n",
       "\t\t<dd><ol class=list-inline>\n",
       "\t<li>-23.21</li>\n",
       "\t<li>-22.77</li>\n",
       "\t<li>-22.33</li>\n",
       "\t<li>-21.89</li>\n",
       "\t<li>-21.45</li>\n",
       "\t<li>-21.01</li>\n",
       "\t<li>-20.57</li>\n",
       "\t<li>-20.13</li>\n",
       "\t<li>-19.69</li>\n",
       "\t<li>-19.25</li>\n",
       "\t<li>-18.81</li>\n",
       "\t<li>-18.37</li>\n",
       "\t<li>-17.93</li>\n",
       "\t<li>-17.49</li>\n",
       "\t<li>-17.05</li>\n",
       "\t<li>-16.61</li>\n",
       "\t<li>-16.17</li>\n",
       "\t<li>-15.73</li>\n",
       "\t<li>-15.29</li>\n",
       "\t<li>-14.85</li>\n",
       "\t<li>-14.41</li>\n",
       "\t<li>-13.97</li>\n",
       "\t<li>-13.53</li>\n",
       "\t<li>-13.09</li>\n",
       "\t<li>-12.65</li>\n",
       "\t<li>-12.21</li>\n",
       "\t<li>-11.77</li>\n",
       "\t<li>-11.33</li>\n",
       "\t<li>-10.89</li>\n",
       "\t<li>-10.45</li>\n",
       "\t<li>-10.01</li>\n",
       "\t<li>-9.57</li>\n",
       "\t<li>-9.13</li>\n",
       "\t<li>-8.69</li>\n",
       "\t<li>-8.25</li>\n",
       "\t<li>-7.81</li>\n",
       "\t<li>-7.37</li>\n",
       "\t<li>-6.93</li>\n",
       "\t<li>-6.49</li>\n",
       "\t<li>-6.05</li>\n",
       "\t<li>-5.61</li>\n",
       "\t<li>-5.17</li>\n",
       "\t<li>-4.73</li>\n",
       "\t<li>-4.29</li>\n",
       "\t<li>-3.85</li>\n",
       "\t<li>-3.41</li>\n",
       "\t<li>-2.97</li>\n",
       "\t<li>-2.53</li>\n",
       "\t<li>-2.09</li>\n",
       "\t<li>-1.65</li>\n",
       "\t<li>-1.21</li>\n",
       "\t<li>-0.77</li>\n",
       "\t<li>-0.330000000000002</li>\n",
       "\t<li>0.109999999999999</li>\n",
       "\t<li>0.550000000000001</li>\n",
       "\t<li>0.989999999999998</li>\n",
       "\t<li>1.43</li>\n",
       "\t<li>1.87</li>\n",
       "\t<li>2.31</li>\n",
       "\t<li>2.75</li>\n",
       "\t<li>3.19</li>\n",
       "\t<li>3.63</li>\n",
       "\t<li>4.07</li>\n",
       "\t<li>4.51</li>\n",
       "\t<li>4.95</li>\n",
       "\t<li>5.39</li>\n",
       "\t<li>5.83</li>\n",
       "\t<li>6.27</li>\n",
       "\t<li>6.71</li>\n",
       "\t<li>7.15</li>\n",
       "\t<li>7.59</li>\n",
       "\t<li>8.03</li>\n",
       "\t<li>8.47</li>\n",
       "\t<li>8.91</li>\n",
       "\t<li>9.35</li>\n",
       "\t<li>9.79</li>\n",
       "\t<li>10.23</li>\n",
       "\t<li>10.67</li>\n",
       "\t<li>11.11</li>\n",
       "\t<li>11.55</li>\n",
       "\t<li>11.99</li>\n",
       "\t<li>12.43</li>\n",
       "\t<li>12.87</li>\n",
       "\t<li>13.31</li>\n",
       "\t<li>13.75</li>\n",
       "\t<li>14.19</li>\n",
       "\t<li>14.63</li>\n",
       "\t<li>15.07</li>\n",
       "\t<li>15.51</li>\n",
       "\t<li>15.95</li>\n",
       "\t<li>16.39</li>\n",
       "\t<li>16.83</li>\n",
       "\t<li>17.27</li>\n",
       "\t<li>17.71</li>\n",
       "\t<li>18.15</li>\n",
       "\t<li>18.59</li>\n",
       "\t<li>19.03</li>\n",
       "\t<li>19.47</li>\n",
       "\t<li>19.91</li>\n",
       "\t<li>20.35</li>\n",
       "\t<li>20.79</li>\n",
       "\t<li>21.23</li>\n",
       "\t<li>21.67</li>\n",
       "</ol>\n",
       "</dd>\n",
       "</dl>\n",
       "</dd>\n",
       "\t<dt>$lon</dt>\n",
       "\t\t<dd><dl>\n",
       "\t<dt>$Type</dt>\n",
       "\t\t<dd>'GeoX'</dd>\n",
       "\t<dt>$Units</dt>\n",
       "\t\t<dd>'degrees'</dd>\n",
       "\t<dt>$Values</dt>\n",
       "\t\t<dd><ol class=list-inline>\n",
       "\t<li>-28.21</li>\n",
       "\t<li>-27.77</li>\n",
       "\t<li>-27.33</li>\n",
       "\t<li>-26.89</li>\n",
       "\t<li>-26.45</li>\n",
       "\t<li>-26.01</li>\n",
       "\t<li>-25.57</li>\n",
       "\t<li>-25.13</li>\n",
       "\t<li>-24.69</li>\n",
       "\t<li>-24.25</li>\n",
       "\t<li>-23.81</li>\n",
       "\t<li>-23.37</li>\n",
       "\t<li>-22.93</li>\n",
       "\t<li>-22.49</li>\n",
       "\t<li>-22.05</li>\n",
       "\t<li>-21.61</li>\n",
       "\t<li>-21.17</li>\n",
       "\t<li>-20.73</li>\n",
       "\t<li>-20.29</li>\n",
       "\t<li>-19.85</li>\n",
       "\t<li>-19.41</li>\n",
       "\t<li>-18.97</li>\n",
       "\t<li>-18.53</li>\n",
       "\t<li>-18.09</li>\n",
       "\t<li>-17.65</li>\n",
       "\t<li>-17.21</li>\n",
       "\t<li>-16.77</li>\n",
       "\t<li>-16.33</li>\n",
       "\t<li>-15.89</li>\n",
       "\t<li>-15.45</li>\n",
       "\t<li>-15.01</li>\n",
       "\t<li>-14.57</li>\n",
       "\t<li>-14.13</li>\n",
       "\t<li>-13.69</li>\n",
       "\t<li>-13.25</li>\n",
       "\t<li>-12.81</li>\n",
       "\t<li>-12.37</li>\n",
       "\t<li>-11.93</li>\n",
       "\t<li>-11.49</li>\n",
       "\t<li>-11.05</li>\n",
       "\t<li>-10.61</li>\n",
       "\t<li>-10.17</li>\n",
       "\t<li>-9.73</li>\n",
       "\t<li>-9.29</li>\n",
       "\t<li>-8.85</li>\n",
       "\t<li>-8.41</li>\n",
       "\t<li>-7.97</li>\n",
       "\t<li>-7.53</li>\n",
       "\t<li>-7.09</li>\n",
       "\t<li>-6.65</li>\n",
       "\t<li>-6.21</li>\n",
       "\t<li>-5.77</li>\n",
       "\t<li>-5.33</li>\n",
       "\t<li>-4.89</li>\n",
       "\t<li>-4.45</li>\n",
       "\t<li>-4.01</li>\n",
       "\t<li>-3.57</li>\n",
       "\t<li>-3.13</li>\n",
       "\t<li>-2.69</li>\n",
       "\t<li>-2.25</li>\n",
       "\t<li>-1.81</li>\n",
       "\t<li>-1.37</li>\n",
       "\t<li>-0.93</li>\n",
       "\t<li>-0.490000000000002</li>\n",
       "\t<li>-0.0500000000000007</li>\n",
       "\t<li>0.390000000000001</li>\n",
       "\t<li>0.829999999999998</li>\n",
       "\t<li>1.27</li>\n",
       "\t<li>1.71</li>\n",
       "\t<li>2.15</li>\n",
       "\t<li>2.59</li>\n",
       "\t<li>3.03</li>\n",
       "\t<li>3.47</li>\n",
       "\t<li>3.91</li>\n",
       "\t<li>4.35</li>\n",
       "\t<li>4.79</li>\n",
       "\t<li>5.23</li>\n",
       "\t<li>5.67</li>\n",
       "\t<li>6.11</li>\n",
       "\t<li>6.55</li>\n",
       "\t<li>6.99</li>\n",
       "\t<li>7.43</li>\n",
       "\t<li>7.87</li>\n",
       "\t<li>8.31</li>\n",
       "\t<li>8.75</li>\n",
       "\t<li>9.19</li>\n",
       "\t<li>9.63</li>\n",
       "\t<li>10.07</li>\n",
       "\t<li>10.51</li>\n",
       "\t<li>10.95</li>\n",
       "\t<li>11.39</li>\n",
       "\t<li>11.83</li>\n",
       "\t<li>12.27</li>\n",
       "\t<li>12.71</li>\n",
       "\t<li>13.15</li>\n",
       "\t<li>13.59</li>\n",
       "\t<li>14.03</li>\n",
       "\t<li>14.47</li>\n",
       "\t<li>14.91</li>\n",
       "\t<li>15.35</li>\n",
       "\t<li>15.79</li>\n",
       "\t<li>16.23</li>\n",
       "\t<li>16.67</li>\n",
       "\t<li>17.11</li>\n",
       "\t<li>17.55</li>\n",
       "\t<li>17.99</li>\n",
       "</ol>\n",
       "</dd>\n",
       "</dl>\n",
       "</dd>\n",
       "</dl>\n",
       "</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$Description] 'Daily Maximum Near-Surface Air Temperature'\n",
       "\\item[\\$DataType] 'float'\n",
       "\\item[\\$Units] 'K'\n",
       "\\item[\\$Dimensions] \\begin{description}\n",
       "\\item[\\$time] \\begin{description}\n",
       "\\item[\\$Type] 'Time'\n",
       "\\item[\\$TimeStep] '1.0 days'\n",
       "\\item[\\$Units] 'days since 1949-12-01 00:00:00'\n",
       "\\item[\\$Date\\_range] '1971-01-01T12:00:00Z - 1975-12-31T12:00:00Z'\n",
       "\\end{description}\n",
       "\n",
       "\\item[\\$lat] \\begin{description}\n",
       "\\item[\\$Type] 'GeoY'\n",
       "\\item[\\$Units] 'degrees'\n",
       "\\item[\\$Values] \\begin{enumerate*}\n",
       "\\item -23.21\n",
       "\\item -22.77\n",
       "\\item -22.33\n",
       "\\item -21.89\n",
       "\\item -21.45\n",
       "\\item -21.01\n",
       "\\item -20.57\n",
       "\\item -20.13\n",
       "\\item -19.69\n",
       "\\item -19.25\n",
       "\\item -18.81\n",
       "\\item -18.37\n",
       "\\item -17.93\n",
       "\\item -17.49\n",
       "\\item -17.05\n",
       "\\item -16.61\n",
       "\\item -16.17\n",
       "\\item -15.73\n",
       "\\item -15.29\n",
       "\\item -14.85\n",
       "\\item -14.41\n",
       "\\item -13.97\n",
       "\\item -13.53\n",
       "\\item -13.09\n",
       "\\item -12.65\n",
       "\\item -12.21\n",
       "\\item -11.77\n",
       "\\item -11.33\n",
       "\\item -10.89\n",
       "\\item -10.45\n",
       "\\item -10.01\n",
       "\\item -9.57\n",
       "\\item -9.13\n",
       "\\item -8.69\n",
       "\\item -8.25\n",
       "\\item -7.81\n",
       "\\item -7.37\n",
       "\\item -6.93\n",
       "\\item -6.49\n",
       "\\item -6.05\n",
       "\\item -5.61\n",
       "\\item -5.17\n",
       "\\item -4.73\n",
       "\\item -4.29\n",
       "\\item -3.85\n",
       "\\item -3.41\n",
       "\\item -2.97\n",
       "\\item -2.53\n",
       "\\item -2.09\n",
       "\\item -1.65\n",
       "\\item -1.21\n",
       "\\item -0.77\n",
       "\\item -0.330000000000002\n",
       "\\item 0.109999999999999\n",
       "\\item 0.550000000000001\n",
       "\\item 0.989999999999998\n",
       "\\item 1.43\n",
       "\\item 1.87\n",
       "\\item 2.31\n",
       "\\item 2.75\n",
       "\\item 3.19\n",
       "\\item 3.63\n",
       "\\item 4.07\n",
       "\\item 4.51\n",
       "\\item 4.95\n",
       "\\item 5.39\n",
       "\\item 5.83\n",
       "\\item 6.27\n",
       "\\item 6.71\n",
       "\\item 7.15\n",
       "\\item 7.59\n",
       "\\item 8.03\n",
       "\\item 8.47\n",
       "\\item 8.91\n",
       "\\item 9.35\n",
       "\\item 9.79\n",
       "\\item 10.23\n",
       "\\item 10.67\n",
       "\\item 11.11\n",
       "\\item 11.55\n",
       "\\item 11.99\n",
       "\\item 12.43\n",
       "\\item 12.87\n",
       "\\item 13.31\n",
       "\\item 13.75\n",
       "\\item 14.19\n",
       "\\item 14.63\n",
       "\\item 15.07\n",
       "\\item 15.51\n",
       "\\item 15.95\n",
       "\\item 16.39\n",
       "\\item 16.83\n",
       "\\item 17.27\n",
       "\\item 17.71\n",
       "\\item 18.15\n",
       "\\item 18.59\n",
       "\\item 19.03\n",
       "\\item 19.47\n",
       "\\item 19.91\n",
       "\\item 20.35\n",
       "\\item 20.79\n",
       "\\item 21.23\n",
       "\\item 21.67\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\end{description}\n",
       "\n",
       "\\item[\\$lon] \\begin{description}\n",
       "\\item[\\$Type] 'GeoX'\n",
       "\\item[\\$Units] 'degrees'\n",
       "\\item[\\$Values] \\begin{enumerate*}\n",
       "\\item -28.21\n",
       "\\item -27.77\n",
       "\\item -27.33\n",
       "\\item -26.89\n",
       "\\item -26.45\n",
       "\\item -26.01\n",
       "\\item -25.57\n",
       "\\item -25.13\n",
       "\\item -24.69\n",
       "\\item -24.25\n",
       "\\item -23.81\n",
       "\\item -23.37\n",
       "\\item -22.93\n",
       "\\item -22.49\n",
       "\\item -22.05\n",
       "\\item -21.61\n",
       "\\item -21.17\n",
       "\\item -20.73\n",
       "\\item -20.29\n",
       "\\item -19.85\n",
       "\\item -19.41\n",
       "\\item -18.97\n",
       "\\item -18.53\n",
       "\\item -18.09\n",
       "\\item -17.65\n",
       "\\item -17.21\n",
       "\\item -16.77\n",
       "\\item -16.33\n",
       "\\item -15.89\n",
       "\\item -15.45\n",
       "\\item -15.01\n",
       "\\item -14.57\n",
       "\\item -14.13\n",
       "\\item -13.69\n",
       "\\item -13.25\n",
       "\\item -12.81\n",
       "\\item -12.37\n",
       "\\item -11.93\n",
       "\\item -11.49\n",
       "\\item -11.05\n",
       "\\item -10.61\n",
       "\\item -10.17\n",
       "\\item -9.73\n",
       "\\item -9.29\n",
       "\\item -8.85\n",
       "\\item -8.41\n",
       "\\item -7.97\n",
       "\\item -7.53\n",
       "\\item -7.09\n",
       "\\item -6.65\n",
       "\\item -6.21\n",
       "\\item -5.77\n",
       "\\item -5.33\n",
       "\\item -4.89\n",
       "\\item -4.45\n",
       "\\item -4.01\n",
       "\\item -3.57\n",
       "\\item -3.13\n",
       "\\item -2.69\n",
       "\\item -2.25\n",
       "\\item -1.81\n",
       "\\item -1.37\n",
       "\\item -0.93\n",
       "\\item -0.490000000000002\n",
       "\\item -0.0500000000000007\n",
       "\\item 0.390000000000001\n",
       "\\item 0.829999999999998\n",
       "\\item 1.27\n",
       "\\item 1.71\n",
       "\\item 2.15\n",
       "\\item 2.59\n",
       "\\item 3.03\n",
       "\\item 3.47\n",
       "\\item 3.91\n",
       "\\item 4.35\n",
       "\\item 4.79\n",
       "\\item 5.23\n",
       "\\item 5.67\n",
       "\\item 6.11\n",
       "\\item 6.55\n",
       "\\item 6.99\n",
       "\\item 7.43\n",
       "\\item 7.87\n",
       "\\item 8.31\n",
       "\\item 8.75\n",
       "\\item 9.19\n",
       "\\item 9.63\n",
       "\\item 10.07\n",
       "\\item 10.51\n",
       "\\item 10.95\n",
       "\\item 11.39\n",
       "\\item 11.83\n",
       "\\item 12.27\n",
       "\\item 12.71\n",
       "\\item 13.15\n",
       "\\item 13.59\n",
       "\\item 14.03\n",
       "\\item 14.47\n",
       "\\item 14.91\n",
       "\\item 15.35\n",
       "\\item 15.79\n",
       "\\item 16.23\n",
       "\\item 16.67\n",
       "\\item 17.11\n",
       "\\item 17.55\n",
       "\\item 17.99\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\end{description}\n",
       "\n",
       "\\end{description}\n",
       "\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$Description\n",
       ":   'Daily Maximum Near-Surface Air Temperature'\n",
       "$DataType\n",
       ":   'float'\n",
       "$Units\n",
       ":   'K'\n",
       "$Dimensions\n",
       ":   $time\n",
       ":   $Type\n",
       ":   'Time'\n",
       "$TimeStep\n",
       ":   '1.0 days'\n",
       "$Units\n",
       ":   'days since 1949-12-01 00:00:00'\n",
       "$Date_range\n",
       ":   '1971-01-01T12:00:00Z - 1975-12-31T12:00:00Z'\n",
       "\n",
       "\n",
       "\n",
       "$lat\n",
       ":   $Type\n",
       ":   'GeoY'\n",
       "$Units\n",
       ":   'degrees'\n",
       "$Values\n",
       ":   1. -23.21\n",
       "2. -22.77\n",
       "3. -22.33\n",
       "4. -21.89\n",
       "5. -21.45\n",
       "6. -21.01\n",
       "7. -20.57\n",
       "8. -20.13\n",
       "9. -19.69\n",
       "10. -19.25\n",
       "11. -18.81\n",
       "12. -18.37\n",
       "13. -17.93\n",
       "14. -17.49\n",
       "15. -17.05\n",
       "16. -16.61\n",
       "17. -16.17\n",
       "18. -15.73\n",
       "19. -15.29\n",
       "20. -14.85\n",
       "21. -14.41\n",
       "22. -13.97\n",
       "23. -13.53\n",
       "24. -13.09\n",
       "25. -12.65\n",
       "26. -12.21\n",
       "27. -11.77\n",
       "28. -11.33\n",
       "29. -10.89\n",
       "30. -10.45\n",
       "31. -10.01\n",
       "32. -9.57\n",
       "33. -9.13\n",
       "34. -8.69\n",
       "35. -8.25\n",
       "36. -7.81\n",
       "37. -7.37\n",
       "38. -6.93\n",
       "39. -6.49\n",
       "40. -6.05\n",
       "41. -5.61\n",
       "42. -5.17\n",
       "43. -4.73\n",
       "44. -4.29\n",
       "45. -3.85\n",
       "46. -3.41\n",
       "47. -2.97\n",
       "48. -2.53\n",
       "49. -2.09\n",
       "50. -1.65\n",
       "51. -1.21\n",
       "52. -0.77\n",
       "53. -0.330000000000002\n",
       "54. 0.109999999999999\n",
       "55. 0.550000000000001\n",
       "56. 0.989999999999998\n",
       "57. 1.43\n",
       "58. 1.87\n",
       "59. 2.31\n",
       "60. 2.75\n",
       "61. 3.19\n",
       "62. 3.63\n",
       "63. 4.07\n",
       "64. 4.51\n",
       "65. 4.95\n",
       "66. 5.39\n",
       "67. 5.83\n",
       "68. 6.27\n",
       "69. 6.71\n",
       "70. 7.15\n",
       "71. 7.59\n",
       "72. 8.03\n",
       "73. 8.47\n",
       "74. 8.91\n",
       "75. 9.35\n",
       "76. 9.79\n",
       "77. 10.23\n",
       "78. 10.67\n",
       "79. 11.11\n",
       "80. 11.55\n",
       "81. 11.99\n",
       "82. 12.43\n",
       "83. 12.87\n",
       "84. 13.31\n",
       "85. 13.75\n",
       "86. 14.19\n",
       "87. 14.63\n",
       "88. 15.07\n",
       "89. 15.51\n",
       "90. 15.95\n",
       "91. 16.39\n",
       "92. 16.83\n",
       "93. 17.27\n",
       "94. 17.71\n",
       "95. 18.15\n",
       "96. 18.59\n",
       "97. 19.03\n",
       "98. 19.47\n",
       "99. 19.91\n",
       "100. 20.35\n",
       "101. 20.79\n",
       "102. 21.23\n",
       "103. 21.67\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "$lon\n",
       ":   $Type\n",
       ":   'GeoX'\n",
       "$Units\n",
       ":   'degrees'\n",
       "$Values\n",
       ":   1. -28.21\n",
       "2. -27.77\n",
       "3. -27.33\n",
       "4. -26.89\n",
       "5. -26.45\n",
       "6. -26.01\n",
       "7. -25.57\n",
       "8. -25.13\n",
       "9. -24.69\n",
       "10. -24.25\n",
       "11. -23.81\n",
       "12. -23.37\n",
       "13. -22.93\n",
       "14. -22.49\n",
       "15. -22.05\n",
       "16. -21.61\n",
       "17. -21.17\n",
       "18. -20.73\n",
       "19. -20.29\n",
       "20. -19.85\n",
       "21. -19.41\n",
       "22. -18.97\n",
       "23. -18.53\n",
       "24. -18.09\n",
       "25. -17.65\n",
       "26. -17.21\n",
       "27. -16.77\n",
       "28. -16.33\n",
       "29. -15.89\n",
       "30. -15.45\n",
       "31. -15.01\n",
       "32. -14.57\n",
       "33. -14.13\n",
       "34. -13.69\n",
       "35. -13.25\n",
       "36. -12.81\n",
       "37. -12.37\n",
       "38. -11.93\n",
       "39. -11.49\n",
       "40. -11.05\n",
       "41. -10.61\n",
       "42. -10.17\n",
       "43. -9.73\n",
       "44. -9.29\n",
       "45. -8.85\n",
       "46. -8.41\n",
       "47. -7.97\n",
       "48. -7.53\n",
       "49. -7.09\n",
       "50. -6.65\n",
       "51. -6.21\n",
       "52. -5.77\n",
       "53. -5.33\n",
       "54. -4.89\n",
       "55. -4.45\n",
       "56. -4.01\n",
       "57. -3.57\n",
       "58. -3.13\n",
       "59. -2.69\n",
       "60. -2.25\n",
       "61. -1.81\n",
       "62. -1.37\n",
       "63. -0.93\n",
       "64. -0.490000000000002\n",
       "65. -0.0500000000000007\n",
       "66. 0.390000000000001\n",
       "67. 0.829999999999998\n",
       "68. 1.27\n",
       "69. 1.71\n",
       "70. 2.15\n",
       "71. 2.59\n",
       "72. 3.03\n",
       "73. 3.47\n",
       "74. 3.91\n",
       "75. 4.35\n",
       "76. 4.79\n",
       "77. 5.23\n",
       "78. 5.67\n",
       "79. 6.11\n",
       "80. 6.55\n",
       "81. 6.99\n",
       "82. 7.43\n",
       "83. 7.87\n",
       "84. 8.31\n",
       "85. 8.75\n",
       "86. 9.19\n",
       "87. 9.63\n",
       "88. 10.07\n",
       "89. 10.51\n",
       "90. 10.95\n",
       "91. 11.39\n",
       "92. 11.83\n",
       "93. 12.27\n",
       "94. 12.71\n",
       "95. 13.15\n",
       "96. 13.59\n",
       "97. 14.03\n",
       "98. 14.47\n",
       "99. 14.91\n",
       "100. 15.35\n",
       "101. 15.79\n",
       "102. 16.23\n",
       "103. 16.67\n",
       "104. 17.11\n",
       "105. 17.55\n",
       "106. 17.99\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$Description\n",
       "[1] \"Daily Maximum Near-Surface Air Temperature\"\n",
       "\n",
       "$DataType\n",
       "[1] \"float\"\n",
       "\n",
       "$Units\n",
       "[1] \"K\"\n",
       "\n",
       "$Dimensions\n",
       "$Dimensions$time\n",
       "$Dimensions$time$Type\n",
       "[1] \"Time\"\n",
       "\n",
       "$Dimensions$time$TimeStep\n",
       "[1] \"1.0 days\"\n",
       "\n",
       "$Dimensions$time$Units\n",
       "[1] \"days since 1949-12-01 00:00:00\"\n",
       "\n",
       "$Dimensions$time$Date_range\n",
       "[1] \"1971-01-01T12:00:00Z - 1975-12-31T12:00:00Z\"\n",
       "\n",
       "\n",
       "$Dimensions$lat\n",
       "$Dimensions$lat$Type\n",
       "[1] \"GeoY\"\n",
       "\n",
       "$Dimensions$lat$Units\n",
       "[1] \"degrees\"\n",
       "\n",
       "$Dimensions$lat$Values\n",
       "  [1] -23.21 -22.77 -22.33 -21.89 -21.45 -21.01 -20.57 -20.13 -19.69 -19.25\n",
       " [11] -18.81 -18.37 -17.93 -17.49 -17.05 -16.61 -16.17 -15.73 -15.29 -14.85\n",
       " [21] -14.41 -13.97 -13.53 -13.09 -12.65 -12.21 -11.77 -11.33 -10.89 -10.45\n",
       " [31] -10.01  -9.57  -9.13  -8.69  -8.25  -7.81  -7.37  -6.93  -6.49  -6.05\n",
       " [41]  -5.61  -5.17  -4.73  -4.29  -3.85  -3.41  -2.97  -2.53  -2.09  -1.65\n",
       " [51]  -1.21  -0.77  -0.33   0.11   0.55   0.99   1.43   1.87   2.31   2.75\n",
       " [61]   3.19   3.63   4.07   4.51   4.95   5.39   5.83   6.27   6.71   7.15\n",
       " [71]   7.59   8.03   8.47   8.91   9.35   9.79  10.23  10.67  11.11  11.55\n",
       " [81]  11.99  12.43  12.87  13.31  13.75  14.19  14.63  15.07  15.51  15.95\n",
       " [91]  16.39  16.83  17.27  17.71  18.15  18.59  19.03  19.47  19.91  20.35\n",
       "[101]  20.79  21.23  21.67\n",
       "\n",
       "\n",
       "$Dimensions$lon\n",
       "$Dimensions$lon$Type\n",
       "[1] \"GeoX\"\n",
       "\n",
       "$Dimensions$lon$Units\n",
       "[1] \"degrees\"\n",
       "\n",
       "$Dimensions$lon$Values\n",
       "  [1] -28.21 -27.77 -27.33 -26.89 -26.45 -26.01 -25.57 -25.13 -24.69 -24.25\n",
       " [11] -23.81 -23.37 -22.93 -22.49 -22.05 -21.61 -21.17 -20.73 -20.29 -19.85\n",
       " [21] -19.41 -18.97 -18.53 -18.09 -17.65 -17.21 -16.77 -16.33 -15.89 -15.45\n",
       " [31] -15.01 -14.57 -14.13 -13.69 -13.25 -12.81 -12.37 -11.93 -11.49 -11.05\n",
       " [41] -10.61 -10.17  -9.73  -9.29  -8.85  -8.41  -7.97  -7.53  -7.09  -6.65\n",
       " [51]  -6.21  -5.77  -5.33  -4.89  -4.45  -4.01  -3.57  -3.13  -2.69  -2.25\n",
       " [61]  -1.81  -1.37  -0.93  -0.49  -0.05   0.39   0.83   1.27   1.71   2.15\n",
       " [71]   2.59   3.03   3.47   3.91   4.35   4.79   5.23   5.67   6.11   6.55\n",
       " [81]   6.99   7.43   7.87   8.31   8.75   9.19   9.63  10.07  10.51  10.95\n",
       " [91]  11.39  11.83  12.27  12.71  13.15  13.59  14.03  14.47  14.91  15.35\n",
       "[101]  15.79  16.23  16.67  17.11  17.55  17.99\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "di <- dataInventory(\"data/historical/tasmax_EUR-44_ICHEC-EC-EARTH_historical_r12i1p1_SMHI-RCA4_v1_day_19710101-19751231.nc\")\n",
    "di$tasmax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "The function `makeAggregatedDataset` scans all the information contained in the files of a particular directory, creating a catalog for data access in the form of a NcML file (see [unidata tutorial](https://www.unidata.ucar.edu/software/thredds/v4.3/netcdf-java/ncml/v2.2/Tutorial.html) and the [loadeR GitHub wiki](https://github.com/SantanderMetGroup/loadeR/wiki/Dataset-definition-and-loading-local-grid-data)), which constitutes a single entry point for the whole dataset. This makes data access more efficient through a unique entry point, independent of the particular division in files. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2018-12-20 19:15:55] Creating dataset from 6 files\n",
      "[2018-12-20 19:15:55] Scanning file 1 out of 1\n",
      "[2018-12-20 19:15:55] Defining aggregating dimension length\n",
      "This process may be slow but will significantly speed-up data retrieval...\n",
      "[2018-12-20 19:15:55] Dimension length defined\n",
      "[2018-12-20 19:15:55] NcML file \"data/historical.ncml\" created from 6 files corresponding to 1 variables\n",
      "Use 'dataInventory' to obtain a description of the dataset\n"
     ]
    }
   ],
   "source": [
    "makeAggregatedDataset(source.dir = \"data/historical/\", \n",
    "                       ncml.file = \"data/historical.ncml\", verbose = TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "The historical simulations are loaded and transformed from Kelvin (the original units of the dataset, as shown above) to Celsius degrees (as in E-OBS). This is done using `gridArithmethics`, which performs arithmetic operations on the data while preserving the  metadata and structures. Note that unit conversion can be more efficiently performed using [`convertR`](https://github.com/SantanderMetGroup/convertR) involving metadata conversion. However, we want to keep this example as simple as possible and, therefore, unit conversion is manually performed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2018-12-20 19:16:04] Defining geo-location parameters\n",
      "[2018-12-20 19:16:04] Defining time selection parameters\n",
      "[2018-12-20 19:16:04] Retrieving data subset ...\n",
      "[2018-12-20 19:16:09] Done\n",
      "[2018-12-20 19:16:15] Calculating SU ...\n",
      "[2018-12-20 19:16:52] Done\n",
      "[2018-12-20 19:16:52] - Computing climatology...\n",
      "[2018-12-20 19:16:52] - Done.\n"
     ]
    },
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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tIulxHSpoT0gy91/U8h7fYWIQlJSHvN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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcm.tx.K <- loadGridData(dataset=\"data/historical.ncml\", \n",
    "                       var = \"tasmax\",years = 1971:2000, season = seas,\n",
    "                       lonLim = lon, latLim = lat)\n",
    "rcm.tx <- gridArithmetics(rcm.tx.K, 273, operator = \"-\")\n",
    "rcm.su <- climdexGrid(tx = rcm.tx, index.code = \"SU\")\n",
    "spatialPlot(climatology(rcm.su), col.regions = colorRampPalette(colSU))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Note that the resulting data is diplayed in the original rotated coordinates and, therefore, some regridding is necessary to harmonize the (rotated) CORDEX and the (regular) E-OBS datasets used in this illustrative example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### 5. Regridding CORDEX data to the E-OBS grid "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "The function `interpGrid` performs regridding from a particular climate4R grid towards a target spatial grid (e.g. obtained with `getGrid`). Standard bilineal and nearest neighbor interpolation methods are defined building on standard implementations from the \"akima\" and \"fields\" packages (`?interpGrid` for more details)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "NOTE: argument 'bilin.method' ignored for nearest neighbour interpolation\n",
      "[2018-12-20 19:16:56] Calculating nearest neighbors...\n",
      "[2018-12-20 19:16:56] Performing nearest interpolation... may take a while\n",
      "[2018-12-20 19:16:56] Done\n",
      "[2018-12-20 19:16:56] - Computing climatology...\n",
      "[2018-12-20 19:16:56] - Done.\n"
     ]
    },
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IbERw99y1bQdRWAquLuRM8Z1EMikXy194h7iZIaH9/S2sajYohHxZDkR3/HXTp8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5V+fPn9fQ0BhwUV1dnbhld3c3AGD69OkB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\nfXnw8TvjnDiMC1n5z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i27ft1qN7ZLTl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\nRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcm.SU <- interpGrid(grid = rcm.su, new.coordinates = getGrid(obs.SU))\n",
    "spatialPlot(climatology(rcm.SU), col.regions = colorRampPalette(colSU), backdrop.theme = \"coastline\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### 6. Calculating (historical) CORDEX SU biases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "By comparing the two SU plots above (for E-OBS and the historical CORDEX) it can be seen that the CORDEX model underestimates the number of summer days (SU). The bias is calculated and plotted below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2018-12-20 19:17:00] - Computing climatology...\n",
      "[2018-12-20 19:17:00] - Done.\n"
     ]
    },
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vldShTz8ildPte/ndKDulhZmDvaW+tx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\nEMIOACCI/wd77GUgy9PNpwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bias <- gridArithmetics(rcm.SU, obs.SU, operator = \"-\")\n",
    "spatialPlot(climatology(bias), at = seq(-100,100,10),\n",
    "            col.regions = colorRampPalette(colbias), backdrop.theme = \"coastline\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### 7. Loading and bias correcting (local) CORDEX future projections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2018-12-20 19:18:27] Creating dataset from 6 files\n",
      "[2018-12-20 19:18:27] Scanning file 1 out of 1\n",
      "[2018-12-20 19:18:27] Defining aggregating dimension length\n",
      "This process may be slow but will significantly speed-up data retrieval...\n",
      "[2018-12-20 19:18:27] Dimension length defined\n",
      "[2018-12-20 19:18:27] NcML file \"data/rcp85.ncml\" created from 6 files corresponding to 1 variables\n",
      "Use 'dataInventory' to obtain a description of the dataset\n",
      "[2018-12-20 19:18:27] Defining geo-location parameters\n",
      "[2018-12-20 19:18:27] Defining time selection parameters\n",
      "[2018-12-20 19:18:27] Retrieving data subset ...\n",
      "[2018-12-20 19:18:31] Done\n",
      "[2018-12-20 19:18:37] Calculating SU ...\n",
      "[2018-12-20 19:19:14] Done\n",
      "NOTE: argument 'bilin.method' ignored for nearest neighbour interpolation\n",
      "[2018-12-20 19:19:14] Calculating nearest neighbors...\n",
      "[2018-12-20 19:19:15] Performing nearest interpolation... may take a while\n",
      "[2018-12-20 19:19:15] Done\n"
     ]
    }
   ],
   "source": [
    "makeAggregatedDataset(source.dir = \"data/rcp85/\", \n",
    "                      ncml.file = \"data/rcp85.ncml\", verbose = TRUE)\n",
    "rcp85.tx.K <- loadGridData(dataset=\"data/rcp85.ncml\", \n",
    "                       var = \"tasmax\",years = 2071:2100, season = seas,\n",
    "                       lonLim = lon, latLim = lat)\n",
    "rcp85.tx <- gridArithmetics(rcp85.tx.K, 273, operator = \"-\")\n",
    "\n",
    "rcp85.su <- climdexGrid(tx = rcp85.tx , index.code = \"SU\")\n",
    "rcp85.SU <- interpGrid(rcp85.su, getGrid(obs.SU))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Bias correction is performed by the `biasCorrection` function, from the [`downscaleR`](https://github.com/SantanderMetGroup/downscaleR/wiki) package. This function includes a number of standard bias correction methods:\n",
    "* local (mean/variance) scaling, \n",
    "* parametric (binomial+gamma) quantile mapping (PQM).\n",
    "* PQM with generalized Pareto for the extremes (GPQM). \n",
    "* empirical quantile mapping, with different extrapolation options (EQM). \n",
    "\n",
    "All methods can be applied separately to each calendar day (or block of days) using a moving window (e.g. `window = c(30, 1)` for a 30-day moving window). The methods PQM, GPQM and EQM have contributed to the VALUE intercomparisson initiative and are described and validated in *Gutiérrez et al. 2019 DOI: [10.1002/joc.5462](https://doi.org/10.1002/joc.5462)* (see `?biasCorrection` or the [wiki](https://github.com/SantanderMetGroup/downscaleR/wiki/bias-correction) for more details on this function).\n",
    "In the following example we apply the standard EQM method; note that no moving window is applied in this case to reduce the computation time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2018-12-20 19:19:46] Argument precipitation is set as FALSE, please ensure that this matches your data.\n",
      "[2018-12-20 19:20:10] Number of windows considered: 1...\n",
      "[2018-12-20 19:20:12] Bias-correcting 1 members separately...\n",
      "[2018-12-20 19:23:47] Done.\n",
      "[2018-12-20 19:23:49] Calculating SU ...\n",
      "[2018-12-20 19:24:50] Done\n"
     ]
    }
   ],
   "source": [
    "bc <- biasCorrection(y = obs.tx, x = rcm.tx,\n",
    "                     newdata = rcp85.tx, method = \"eqm\")\n",
    "bc.SU <- climdexGrid(tx = bc , index.code = \"SU\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2018-12-20 19:25:29] - Computing climatology...\n",
      "[2018-12-20 19:25:29] - Done.\n"
     ]
    },
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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ia6yLjIAlRqt5/mfxH3+JuEFAcz2Wg3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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "delta <- gridArithmetics(bc.SU, obs.SU, operator = \"-\")\n",
    "spatialPlot(climatology(delta), at = seq(0, 80, 5),  \n",
    "            col.regions = colorRampPalette(coldelta), backdrop.theme = \"coastline\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "The function `temporalPlot` allows to easily display the temporal time series for the different datasets, before and after bias correction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "pad applied on the interval: year\n",
      "pad applied on the interval: year\n",
      "pad applied on the interval: year\n",
      "pad applied on the interval: year\n"
     ]
    },
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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kl+zr0VS0REi8Px44BBs7dS9fUByG3FNjWhrQ2HD8PnK+11IhGdA3awOHnC74fL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\n9PYeB9RFKA7+8z//87rrrquoqHjttde01pKCt956q7a29p1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TyyN27979bB6dEQAAD2xJREFUi1/8glXui1sQbDbbrFmzNBWYkM7OTrYt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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temporalPlot(\"E-OBS\" = obs.SU, \"SU_hist\" = rcm.SU, \"SU_rcp85\" = rcp85.SU, \"Adjusted\" = bc.SU,\n",
    "             latLim = 41.64, lonLim = -0.89,\n",
    "             cols = c(\"black\", \"red\", \"red\", \"blue\")) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Most of the climate4R functions can transparently work with ensembles, for which an extra dimension (`member`) in the climate4R data structure is defined. A detailed description and worked out examples are provided in the climate4R reference paper, published in *Environmental Modelling & Software* journal (https://doi.org/10.1016/j.envsoft.2018.09.009).  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### ANNEX. Loading observations and CORDEX data via the climate4R UDG"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "The above example can be fully reproduced accesing the data transparently from the climate4R User Data Gateway (UDG). This data service provides harmonized data for a number of popular datasets in climate studies (such as gridded observations, reanalysis, or climate change projections from CORDEX and CMIP). The function `UDG.datasets` provides the full list of available datasets (the optional parameter `pattern` allows filtering, displaying only those matching the specified regular pattern). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'E-OBS_v14_0.50regular'</li>\n",
       "\t<li>'E-OBS_v14_0.44rotated'</li>\n",
       "\t<li>'E-OBS_v14_0.25regular'</li>\n",
       "\t<li>'E-OBS_v14_0.22rotated'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'E-OBS\\_v14\\_0.50regular'\n",
       "\\item 'E-OBS\\_v14\\_0.44rotated'\n",
       "\\item 'E-OBS\\_v14\\_0.25regular'\n",
       "\\item 'E-OBS\\_v14\\_0.22rotated'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'E-OBS_v14_0.50regular'\n",
       "2. 'E-OBS_v14_0.44rotated'\n",
       "3. 'E-OBS_v14_0.25regular'\n",
       "4. 'E-OBS_v14_0.22rotated'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"E-OBS_v14_0.50regular\" \"E-OBS_v14_0.44rotated\" \"E-OBS_v14_0.25regular\"\n",
       "[4] \"E-OBS_v14_0.22rotated\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "UDG.datasets(pattern = \"E-OBS\")$name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "These datasets can be loaded just indicating the dataset name, as provided by `UDG.datasets`. However, registration (https://www.meteo.unican.es/udg-tap) is required in order to obtain an authorized user and password. All public datasets are available with the role \"PUBLIC\" which is automatically granted upon request (after accepting the different acknowledgement terms and licences holding on the datasets). The function `loginUDG` sets authorization credentials for the current R session."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2018-12-20 19:26:27] Setting credentials...\n",
      "[2018-12-20 19:26:27] Success!\n",
      "Go to <https://www.meteo.unican.es/udg-tap/home> for details on your authorized groups and datasets\n"
     ]
    }
   ],
   "source": [
    "loginUDG(\"UDGusr\", \"UDGpswrd\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "NOTE: Accessing harmonized data from a public UDG dataset\n",
      "[2018-12-20 19:27:22] Defining harmonization parameters for variable \"tasmax\"\n",
      "[2018-12-20 19:27:22] Opening dataset...\n",
      "[2018-12-20 19:27:23] The dataset was successfuly opened\n",
      "[2018-12-20 19:27:23] Defining geo-location parameters\n",
      "[2018-12-20 19:27:24] Defining time selection parameters\n",
      "[2018-12-20 19:27:24] Retrieving data subset ...\n",
      "[2018-12-20 19:28:30] Done\n",
      "[2018-12-20 19:28:31] - Computing climatology...\n",
      "[2018-12-20 19:28:31] - Done.\n"
     ]
    },
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3Y9XZTCxNhwy6Jew77wbdNv7Y/h58UuB2KSy+UxMTHjx49/t8XOzu7Fixd5/5+a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\nJ7TE9X8AAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "obs.tx <- loadGridData(\"E-OBS_v14_0.25regular\", var = \"tasmax\",\n",
    "                       years = 1971:2000, season = seas,\n",
    "                       lonLim = lon, latLim = lat)\n",
    "spatialPlot(climatology(obs.tx), at = seq(-2, 32, 2),\n",
    "            col.regions = colorRampPalette(coltx), backdrop.theme = \"coastline\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [1] \"CORDEX-EUR44_EC-EARTH_r12i1p1_historical_RCA4_v1\"       \n",
      " [2] \"CORDEX-EUR44_CNRM-CM5_r1i1p1_historical_RCA4_v1\"        \n",
      " [3] \"CORDEX-EUR44_EC-EARTH_r1i1p1_historical_RACMO22E_v1\"    \n",
      " [4] \"CORDEX-EUR44_EC-EARTH_r3i1p1_historical_HIRHAM5_v1\"     \n",
      " [5] \"CORDEX-EUR44_IPSL-CM5A-MR_r1i1p1_historical_RCA4_v1\"    \n",
      " [6] \"CORDEX-EUR44_HadGEM2-ES_r1i1p1_historical_RCA4_v1\"      \n",
      " [7] \"CORDEX-EUR44_HadGEM2-ES_r1i1p1_historical_RACMO22E_v1\"  \n",
      " [8] \"CORDEX-EUR44_MPI-ESM-LR_r1i1p1_historical_CCLM4-8-17_v1\"\n",
      " [9] \"CORDEX-EUR44_MPI-ESM-LR_r1i1p1_historical_RCA4_v1\"      \n",
      "[10] \"CORDEX-EUR44_IPSL-CM5A-MR_r1i1p1_historical_WRF331F_v1\" \n",
      "[11] \"CORDEX-EUR44_MPI-ESM-LR_r1i1p1_historical_REMO2009_v1\"  \n",
      "[12] \"CORDEX-EUR44_MPI-ESM-LR_r2i1p1_historical_REMO2009_v1\"  \n"
     ]
    }
   ],
   "source": [
    "historical <- UDG.datasets(pattern = \"EUR44.*historical\")$name \n",
    "print(historical)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "NOTE: Accessing harmonized data from a public UDG dataset\n",
      "[2018-12-20 19:28:43] Defining harmonization parameters for variable \"tasmax\"\n",
      "[2018-12-20 19:28:43] Opening dataset...\n",
      "[2018-12-20 19:28:49] The dataset was successfuly opened\n",
      "[2018-12-20 19:28:49] Defining geo-location parameters\n",
      "[2018-12-20 19:28:50] Defining time selection parameters\n",
      "[2018-12-20 19:28:50] Retrieving data subset ...\n",
      "[2018-12-20 19:29:19] Done\n",
      "[2018-12-20 19:29:19] - Computing climatology...\n",
      "[2018-12-20 19:29:19] - Done.\n"
     ]
    },
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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tyDxEKFJCQhLe5BYqFCEpKQFvcgsVAhCUlIi3uQWKiQhCSkxT1ILFRIQhLS4h4k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\nASFBASFBgf8PLbmPtmp44HwAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcm.tx <- loadGridData(historical[1], var = \"tasmax\",\n",
    "                       years = 1971:2000, season = seas,\n",
    "                       lonLim = lon, latLim = lat)\n",
    "spatialPlot(climatology(rcm.tx), at = seq(-2, 32, 2),\n",
    "            col.regions = colorRampPalette(coltx), backdrop.theme = \"coastline\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [1] \"CORDEX-EUR44_EC-EARTH_r12i1p1_rcp85_RCA4_v1\"       \n",
      " [2] \"CORDEX-EUR44_CNRM-CM5_r1i1p1_rcp85_RCA4_v1\"        \n",
      " [3] \"CORDEX-EUR44_EC-EARTH_r1i1p1_rcp85_RACMO22E_v1\"    \n",
      " [4] \"CORDEX-EUR44_EC-EARTH_r3i1p1_rcp85_HIRHAM5_v1\"     \n",
      " [5] \"CORDEX-EUR44_IPSL-CM5A-MR_r1i1p1_rcp85_RCA4_v1\"    \n",
      " [6] \"CORDEX-EUR44_HadGEM2-ES_r1i1p1_rcp85_RCA4_v1\"      \n",
      " [7] \"CORDEX-EUR44_HadGEM2-ES_r1i1p1_rcp85_RACMO22E_v1\"  \n",
      " [8] \"CORDEX-EUR44_MPI-ESM-LR_r1i1p1_rcp85_CCLM4-8-17_v1\"\n",
      " [9] \"CORDEX-EUR44_MPI-ESM-LR_r1i1p1_rcp85_RCA4_v1\"      \n",
      "[10] \"CORDEX-EUR44_IPSL-CM5A-MR_r1i1p1_rcp85_WRF331F_v1\" \n",
      "[11] \"CORDEX-EUR44_MPI-ESM-LR_r1i1p1_rcp85_REMO2009_v1\"  \n",
      "[12] \"CORDEX-EUR44_MPI-ESM-LR_r2i1p1_rcp85_REMO2009_v1\"  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "NOTE: Accessing harmonized data from a public UDG dataset\n",
      "[2018-12-20 19:30:24] Defining harmonization parameters for variable \"tasmax\"\n",
      "[2018-12-20 19:30:24] Opening dataset...\n",
      "[2018-12-20 19:30:24] The dataset was successfuly opened\n",
      "[2018-12-20 19:30:24] Defining geo-location parameters\n",
      "[2018-12-20 19:30:24] Defining time selection parameters\n",
      "[2018-12-20 19:30:25] Retrieving data subset ...\n",
      "[2018-12-20 19:30:54] Done\n",
      "[2018-12-20 19:30:54] - Computing climatology...\n",
      "[2018-12-20 19:30:54] - Done.\n"
     ]
    },
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8/xUOUBWg54S1Utyl9a/EWvd8ng01oebfCthxKSkKqVv1RIQhJStfKXCklIQqpW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\nQEgQICQIEBIECAkC/j/dP7v1TtQX0gAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcp85 <- UDG.datasets(pattern = \"EUR44.*rcp85\")$name\n",
    "print(rcp85)\n",
    "rcp85.tx <- loadGridData(rcp85[1], var = \"tasmax\",\n",
    "                         years = 2071:2100, season = seas,\n",
    "                         lonLim = lon, latLim = lat)\n",
    "spatialPlot(climatology(rcp85.tx), at = seq(-2, 32, 2),\n",
    "            col.regions = colorRampPalette(coltx), backdrop.theme = \"coastline\")  "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}