From 98ccfe1b9ce5202c654a30d914225ab76d76ac72 Mon Sep 17 00:00:00 2001
From: Aiko Voigt <aiko.voigt@univie.ac.at>
Date: Thu, 11 Mar 2021 23:57:40 +0100
Subject: [PATCH] Notebook to reproduce Fig. 2 of JAMES 2016 intro paper using
 Pangeo cloud data

---
 pangeo/james2016_figure2.ipynb | 249 +++++++++++++++++++++++++++++++++
 1 file changed, 249 insertions(+)
 create mode 100644 pangeo/james2016_figure2.ipynb

diff --git a/pangeo/james2016_figure2.ipynb b/pangeo/james2016_figure2.ipynb
new file mode 100644
index 0000000..6fc2380
--- /dev/null
+++ b/pangeo/james2016_figure2.ipynb
@@ -0,0 +1,249 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Reproduce Figure 2 of the 2016 JAMES Tracmip introduction paper\n",
+    "\n",
+    "We use approach 1 to access the Pangeo data in the Google Cloud. See load_data_from_pangeo.iypnb in the same folder."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import xarray as xr\n",
+    "import zarr\n",
+    "import gcsfs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Wrapper function to load data. Output is a dictionary of xarray data arrays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_data(freq, var, exp):\n",
+    "    df = pd.read_csv('https://storage.googleapis.com/cmip6/tracmip.csv')\n",
+    "    # a somewhat cumbersome way to query the dataframe ... \n",
+    "    df_var = df.query(\"frequency == \\'\"+freq+\"\\'\").query(\"variable == \\'\"+var+\"\\'\").query(\"experiment == \\'\"+exp+\"\\'\")\n",
+    "    gcs = gcsfs.GCSFileSystem(token='anon')\n",
+    "    datadict = dict()\n",
+    "    for zstore in df_var.source.values:\n",
+    "        mapper = gcs.get_mapper(zstore)\n",
+    "        ds = xr.open_zarr(mapper, consolidated=True)\n",
+    "        # write only variable of interest to dictionary, so this becomes a data array\n",
+    "        datadict[ds.attrs['model_id']] = ds[var] \n",
+    "    return datadict"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load clear-sky radiative fluxes at the surface, based on which we then calculate surface albedo, as well as surface temperature."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rsdscs = load_data('Amon', 'rsdscs', 'aquaControl')\n",
+    "rsuscs = load_data('Amon', 'rsuscs', 'aquaControl')\n",
+    "ts     = load_data('Amon', 'ts'    , 'aquaControl')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Restrict data to last 20 years and average over these as well as over the spatial domain. Note that this will overwrite the dictionaries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def timemean_globalmean(datadict):\n",
+    "    for model in datadict.keys():\n",
+    "        ds = datadict[model]\n",
+    "        # select only last 20 years and average over them\n",
+    "        ntime = ds.time.size # number of timesteps\n",
+    "        ds = datadict[model].isel(time=slice(ntime-20*12, ntime)).mean('time')\n",
+    "        # spatial mean\n",
+    "        weights = np.cos(np.deg2rad(ds.lat))\n",
+    "        weights.name = \"weights\"\n",
+    "        ds = ds.weighted(weights).mean(['lat', 'lon'])\n",
+    "        # overwrite dictionary entry with time-mean spatial-mean\n",
+    "        datadict[model] = ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "timemean_globalmean(rsdscs)\n",
+    "timemean_globalmean(rsuscs)\n",
+    "timemean_globalmean(ts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculate surface albedo."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "alpha = dict()\n",
+    "for model in rsdscs.keys():\n",
+    "    alpha[model] = rsuscs[model].values / rsdscs[model].values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that we set CALTECH surface albedo to 0.06 for the purpose of plotting. In fact, rsdscs and rsuscs are not provided by CALTECH because there are no cloud-radiative effects in CALTECH and the model has a much higher surface albedo of around 0.3 (as can be obtained from the all-sky radiative fluxes) to compensate for the lack of cloud-radiative effects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "alpha['CALTECH'] = 0.06"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plotting of figure 2 of JAMES 2016 paper."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dictionary for model names, model numbers and model colors used in TRACMIP plots\n",
+    "plotdict = {'AM21'        : {'color': np.array([255,204,153])/255, 'nbr':  '1', 'name': 'AM2.1'       },\n",
+    "            'CAM3'        : {'color': np.array([128,128,128])/255, 'nbr':  '2', 'name': 'CAM3'        },\n",
+    "            'CAM4'        : {'color': np.array([148,255,181])/255, 'nbr':  '3', 'name': 'CAM4'        },\n",
+    "            'CAM5Nor'     : {'color': np.array([194,  0,136])/255, 'nbr':  '4', 'name': 'CAM5Nor'     },\n",
+    "            'CNRM-AM5'    : {'color': np.array([  0, 51,128])/255, 'nbr':  '5', 'name': 'CNRM-AM5'    },\n",
+    "            'ECHAM61'     : {'color': np.array([  0,117,220])/255, 'nbr':  '6', 'name': 'ECHAM6.1'    },\n",
+    "            'ECHAM63'     : {'color': np.array([153, 63,  0])/255, 'nbr':  '7', 'name': 'ECHAM6.3'    },\n",
+    "            'GISS-ModelE2': {'color': np.array([157,204,  0])/255, 'nbr':  '8', 'name': 'GISS-ModelE2'},\n",
+    "            'LMDZ5A'      : {'color': np.array([ 76,  0, 92])/255, 'nbr':  '9', 'name': 'LMDZ5A'      },\n",
+    "            'MetUM-CTL'   : {'color': np.array([ 25, 25, 25])/255, 'nbr': '10', 'name': 'MetM-CTL'    },\n",
+    "            'MetUM-ENT'   : {'color': np.array([  0, 92, 49])/255, 'nbr': '11', 'name': 'MetUM-ENT'   },\n",
+    "            'MIROC5'      : {'color': np.array([ 43,206, 72])/255, 'nbr': '12', 'name': 'MIROC5'      },\n",
+    "            'MPAS'        : {'color': np.array([143,124,  0])/255, 'nbr': '13', 'name': 'MPAS'        },\n",
+    "            'CALTECH'     : {'color': np.array([255,164,  5])/255, 'nbr': '14', 'name': 'CALTECH'     }}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 960x320 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure( figsize=(12, 4), dpi=80, facecolor='w', edgecolor='k' )  \n",
+    "\n",
+    "ax = plt.subplot(1, 2, 1) \n",
+    "ax.spines['right'].set_color('none')\n",
+    "ax.spines['top'].set_color('none')\n",
+    "ax.xaxis.set_ticks_position('bottom')\n",
+    "ax.yaxis.set_ticks_position('left') \n",
+    "for model in ts.keys():\n",
+    "    plt.text(alpha[model], ts[model].values, plotdict[model]['nbr'], color=plotdict[model]['color'], fontsize=14, \n",
+    "    fontweight='normal', ha='center', va='center', backgroundcolor='none')\n",
+    "plt.xlim(0.05, 0.105), plt.ylim(290, 302)\n",
+    "plt.title('AquaControl', fontsize=14)\n",
+    "plt.xlabel('Global surface albedo', fontsize=12)\n",
+    "plt.ylabel('Global surface temperature (K)', fontsize=12)\n",
+    "ax.xaxis.set_ticks([0.06, 0.08, 0.1])\n",
+    "ax.xaxis.set_ticklabels([0.06, 0.08, 0.10], fontsize=10)\n",
+    "ax.yaxis.set_ticks([290, 294, 298, 302])\n",
+    "ax.yaxis.set_ticklabels([290, 294, 298, 302], fontsize=10)\n",
+    "\n",
+    "ax = plt.subplot(1, 2, 2)\n",
+    "plt.xlim(0, 1), plt.ylim(0, 1)\n",
+    "plt.axis('off')\n",
+    "for model in ts.keys():\n",
+    "    plt.text(0.0, 1.06-0.08*np.float(plotdict[model]['nbr']), plotdict[model]['nbr'] , color=plotdict[model]['color'], fontsize=14)\n",
+    "    plt.text(0.1, 1.06-0.08*np.float(plotdict[model]['nbr']), plotdict[model]['name'], color=plotdict[model]['color'], fontsize=14)\n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  }
+ ],
+ "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
-- 
GitLab