{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n# Global Average Annual Temperature Maps\n\nProduces maps of global temperature forecasts from the A1B and E1 scenarios.\n\nThe data used comes from the HadGEM2-AO model simulations for the A1B and E1\nscenarios, both of which were derived using the IMAGE Integrated Assessment\nModel (Johns et al. 2011; Lowe et al. 2009).\n\n## References\n\n Johns T.C., et al. (2011) Climate change under aggressive mitigation: the\n ENSEMBLES multi-model experiment. Climate Dynamics, Vol 37, No. 9-10,\n doi:10.1007/s00382-011-1005-5.\n\n Lowe J.A., C.D. Hewitt, D.P. Van Vuuren, T.C. Johns, E. Stehfest, J-F.\n Royer, and P. van der Linden, 2009. New Study For Climate Modeling,\n Analyses, and Scenarios. Eos Trans. AGU, Vol 90, No. 21,\n doi:10.1029/2009EO210001.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os.path\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nimport iris\nimport iris.coords as coords\nimport iris.plot as iplt\n\n\ndef cop_metadata_callback(cube, field, filename):\n \"\"\"\n A function which adds an \"Experiment\" coordinate which comes from the\n filename.\n \"\"\"\n\n # Extract the experiment name (such as a1b or e1) from the filename (in\n # this case it is just the parent folder's name)\n containing_folder = os.path.dirname(filename)\n experiment_label = os.path.basename(containing_folder)\n\n # Create a coordinate with the experiment label in it\n exp_coord = coords.AuxCoord(\n experiment_label, long_name=\"Experiment\", units=\"no_unit\"\n )\n\n # and add it to the cube\n cube.add_aux_coord(exp_coord)\n\n\ndef main():\n # Load e1 and a1 using the callback to update the metadata\n e1 = iris.load_cube(\n iris.sample_data_path(\"E1.2098.pp\"), callback=cop_metadata_callback\n )\n a1b = iris.load_cube(\n iris.sample_data_path(\"A1B.2098.pp\"), callback=cop_metadata_callback\n )\n\n # Load the global average data and add an 'Experiment' coord it\n global_avg = iris.load_cube(iris.sample_data_path(\"pre-industrial.pp\"))\n\n # Define evenly spaced contour levels: -2.5, -1.5, ... 15.5, 16.5 with the\n # specific colours\n levels = np.arange(20) - 2.5\n red = (\n np.array(\n [\n 0,\n 0,\n 221,\n 239,\n 229,\n 217,\n 239,\n 234,\n 228,\n 222,\n 205,\n 196,\n 161,\n 137,\n 116,\n 89,\n 77,\n 60,\n 51,\n ]\n )\n / 256.0\n )\n green = (\n np.array(\n [\n 16,\n 217,\n 242,\n 243,\n 235,\n 225,\n 190,\n 160,\n 128,\n 87,\n 72,\n 59,\n 33,\n 21,\n 29,\n 30,\n 30,\n 29,\n 26,\n ]\n )\n / 256.0\n )\n blue = (\n np.array(\n [\n 255,\n 255,\n 243,\n 169,\n 99,\n 51,\n 63,\n 37,\n 39,\n 21,\n 27,\n 23,\n 22,\n 26,\n 29,\n 28,\n 27,\n 25,\n 22,\n ]\n )\n / 256.0\n )\n\n # Put those colours into an array which can be passed to contourf as the\n # specific colours for each level\n colors = np.array([red, green, blue]).T\n\n # Subtract the global\n\n # Iterate over each latitude longitude slice for both e1 and a1b scenarios\n # simultaneously\n for e1_slice, a1b_slice in zip(\n e1.slices([\"latitude\", \"longitude\"]),\n a1b.slices([\"latitude\", \"longitude\"]),\n ):\n\n time_coord = a1b_slice.coord(\"time\")\n\n # Calculate the difference from the mean\n delta_e1 = e1_slice - global_avg\n delta_a1b = a1b_slice - global_avg\n\n # Make a wider than normal figure to house two maps side-by-side\n fig = plt.figure(figsize=(12, 5))\n\n # Get the time datetime from the coordinate\n time = time_coord.units.num2date(time_coord.points[0])\n # Set a title for the entire figure, giving the time in a nice format\n # of \"MonthName Year\". Also, set the y value for the title so that it\n # is not tight to the top of the plot.\n fig.suptitle(\n \"Annual Temperature Predictions for \" + time.strftime(\"%Y\"),\n y=0.9,\n fontsize=18,\n )\n\n # Add the first subplot showing the E1 scenario\n plt.subplot(121)\n plt.title(\"HadGEM2 E1 Scenario\", fontsize=10)\n iplt.contourf(delta_e1, levels, colors=colors, extend=\"both\")\n plt.gca().coastlines()\n # get the current axes' subplot for use later on\n plt1_ax = plt.gca()\n\n # Add the second subplot showing the A1B scenario\n plt.subplot(122)\n plt.title(\"HadGEM2 A1B-Image Scenario\", fontsize=10)\n contour_result = iplt.contourf(\n delta_a1b, levels, colors=colors, extend=\"both\"\n )\n plt.gca().coastlines()\n # get the current axes' subplot for use later on\n plt2_ax = plt.gca()\n\n # Now add a colourbar who's leftmost point is the same as the leftmost\n # point of the left hand plot and rightmost point is the rightmost\n # point of the right hand plot\n\n # Get the positions of the 2nd plot and the left position of the 1st\n # plot\n left, bottom, width, height = plt2_ax.get_position().bounds\n first_plot_left = plt1_ax.get_position().bounds[0]\n\n # the width of the colorbar should now be simple\n width = left - first_plot_left + width\n\n # Add axes to the figure, to place the colour bar\n colorbar_axes = fig.add_axes([first_plot_left, 0.18, width, 0.03])\n\n # Add the colour bar\n cbar = plt.colorbar(\n contour_result, colorbar_axes, orientation=\"horizontal\"\n )\n\n # Label the colour bar and add ticks\n cbar.set_label(e1_slice.units)\n cbar.ax.tick_params(length=0)\n\n iplt.show()\n\n\nif __name__ == \"__main__\":\n main()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 0 }