diff --git a/pangeo/james2016_figure12.ipynb b/pangeo/james2016_figure12.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Reproduce Figure 12 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": [
+    "## Data loading, and then do time mean and spatial mean over last 20 years"
+   ]
+  },
+  {
+   "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": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ts_aqct = load_data('Amon', 'ts', 'aquaControl')\n",
+    "ts_aq4x = load_data('Amon', 'ts', 'aqua4xCO2'  )\n",
+    "ts_ldct = load_data('Amon', 'ts', 'landControl')\n",
+    "ts_ld4x = load_data('Amon', 'ts', 'land4xCO2'  )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pr_aqct = load_data('Amon', 'pr', 'aquaControl')\n",
+    "pr_aq4x = load_data('Amon', 'pr', 'aqua4xCO2'  )\n",
+    "pr_ldct = load_data('Amon', 'pr', 'landControl')\n",
+    "pr_ld4x = load_data('Amon', 'pr', 'land4xCO2'  )"
+   ]
+  },
+  {
+   "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": 5,
+   "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').compute()\n",
+    "        # spatial mean\n",
+    "        weights = np.cos(np.deg2rad(ds.lat))\n",
+    "        weights.name = \"weights\"\n",
+    "        ds = ds.weighted(weights).mean(['lat', 'lon']).compute()\n",
+    "        # overwrite dictionary entry with time-mean spatial-mean\n",
+    "        datadict[model] = ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "timemean_globalmean(ts_aqct)\n",
+    "timemean_globalmean(ts_aq4x)\n",
+    "timemean_globalmean(ts_ldct)\n",
+    "timemean_globalmean(ts_ld4x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "timemean_globalmean(pr_aqct)\n",
+    "timemean_globalmean(pr_aq4x)\n",
+    "timemean_globalmean(pr_ldct)\n",
+    "timemean_globalmean(pr_ld4x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import cm"
+   ]
+  },
+  {
+   "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"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1280x320 with 1 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",
+    "\n",
+    "for model in ts_aq4x.keys():\n",
+    "    plt.text(ts_aqct[model], 0.5*(ts_aq4x[model] - ts_aqct[model]), plotdict[model]['nbr'], fontweight='normal', color=plotdict[model]['color'], \n",
+    "             ha='center', va='center', fontsize=14)\n",
+    "for model in ts_ld4x.keys():\n",
+    "    plt.text(ts_ldct[model], 0.5*(ts_ld4x[model] - ts_ldct[model]), plotdict[model]['nbr'], fontweight='normal', color=plotdict[model]['color'],\n",
+    "             ha='center', va='center', fontsize=14)             \n",
+    "    plt.text(ts_ldct[model], 0.5*(ts_ld4x[model] - ts_ldct[model]) - 0.17, '_', fontweight='normal', \n",
+    "             color=plotdict[model]['color'], ha='center', va='bottom', fontsize=14)\n",
+    "plt.xlim(288, 302), plt.ylim(0.5, 5.7)\n",
+    "\n",
+    "plt.xlabel('Global surface temperature (K)', fontsize=12)\n",
+    "plt.ylabel('Climate sensitivity (K)', fontsize=12)\n",
+    "ax.xaxis.set_ticks([289, 291, 293, 295, 297, 299, 301])\n",
+    "ax.xaxis.set_ticklabels([289, 291, 293, 295, 297, 299, 301], fontsize=10)\n",
+    "ax.yaxis.set_ticks([1, 2, 3, 4, 5])\n",
+    "ax.yaxis.set_ticklabels([1, 2, 3, 4, 5], fontsize=10)\n",
+    "plt.text(0.02, 0.98, 'a)', fontsize=14, ha='left', va='center', transform=ax.transAxes)\n",
+    "\n",
+    "ax = plt.subplot(1, 2, 2)\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",
+    "\n",
+    "xfit = np.linspace(0, 15, 100)\n",
+    "yfit = 2.1*xfit; plt.plot(xfit, yfit, 'k')\n",
+    "for model in ts_aq4x.keys():\n",
+    "    plt.text((ts_aq4x[model]-ts_aqct[model]), 100*(pr_aq4x[model]-pr_aqct[model])/pr_aqct[model], \n",
+    "             plotdict[model]['nbr'], fontweight='normal', color=plotdict[model]['color'], ha='center', va='center', fontsize=14)\n",
+    "for model in ts_ld4x.keys():\n",
+    "    plt.text((ts_ld4x[model]-ts_ldct[model]), 100*(pr_ld4x[model]-pr_ldct[model])/pr_ldct[model], \n",
+    "             plotdict[model]['nbr'], fontweight='normal', color=plotdict[model]['color'], ha='center', va='center', fontsize=14)\n",
+    "    plt.text((ts_ld4x[model]-ts_ldct[model]), 100*(pr_ld4x[model]-pr_ldct[model])/pr_ldct[model] - 0.7, '_', \n",
+    "             fontweight='normal', color=plotdict[model]['color'], ha='center', va='bottom', fontsize=14)\n",
+    "plt.xlim(0, 10); plt.ylim(0, 22)\n",
+    "\n",
+    "plt.xlabel('Global surface temperature increase (K)', fontsize=12)\n",
+    "plt.ylabel('Precipitation increase (%)', fontsize=12)\n",
+    "ax.xaxis.set_ticks([0, 2, 4, 6, 8, 10])\n",
+    "ax.xaxis.set_ticklabels([0, 2, 4, 6, 8, 10], fontsize=10)\n",
+    "ax.yaxis.set_ticks([0, 5, 10, 15, 20])\n",
+    "ax.yaxis.set_ticklabels([0, 5, 10, 15, 20], fontsize=10)\n",
+    "plt.text(0.02, 0.98, 'b)', fontsize=14, ha='left', va='center', transform=ax.transAxes)\n",
+    "\n",
+    "plt.text(8.1, 21, '2.1%/K', fontsize=12, fontstyle='italic', rotation=90/2.5)\n",
+    "\n",
+    "# plot model names and numbers\n",
+    "plt.figure( figsize=(16, 4), dpi=80, facecolor='w', edgecolor='k' )\n",
+    "plt.xlim(0, 1), plt.ylim(0, 1)\n",
+    "plt.axis('off')\n",
+    "ystart=1.0\n",
+    "delta=0.0666\n",
+    "for model in ts_aqct.keys():\n",
+    "    if np.int(plotdict[model]['nbr'])<4:\n",
+    "        plt.text(0.1 , ystart-delta*np.float(plotdict[model]['nbr']), plotdict[model]['nbr'], color=plotdict[model]['color'], fontsize=14)\n",
+    "        plt.text(0.15, ystart-delta*np.float(plotdict[model]['nbr']), plotdict[model]['name'], color=plotdict[model]['color'], fontsize=14)\n",
+    "    elif np.int(plotdict[model]['nbr'])<7:\n",
+    "        plt.text(0.5 , ystart-delta*(np.float(plotdict[model]['nbr'])-3), plotdict[model]['nbr'], color=plotdict[model]['color'], fontsize=14)\n",
+    "        plt.text(0.55, ystart-delta*(np.float(plotdict[model]['nbr'])-3), plotdict[model]['name'], color=plotdict[model]['color'], fontsize=14)\n",
+    "    elif np.int(plotdict[model]['nbr'])<10:\n",
+    "        plt.text(0.9 , ystart-delta*(np.float(plotdict[model]['nbr'])-6), plotdict[model]['nbr'], color=plotdict[model]['color'], fontsize=14)\n",
+    "        plt.text(0.95, ystart-delta*(np.float(plotdict[model]['nbr'])-6), plotdict[model]['name'], color=plotdict[model]['color'], fontsize=14)\n",
+    "    elif np.int(plotdict[model]['nbr'])<13:\n",
+    "        plt.text(1.3 , ystart-delta*(np.float(plotdict[model]['nbr'])-9), plotdict[model]['nbr'], color=plotdict[model]['color'], fontsize=14)\n",
+    "        plt.text(1.4 , ystart-delta*(np.float(plotdict[model]['nbr'])-9), plotdict[model]['name'], color=plotdict[model]['color'], fontsize=14)\n",
+    "    elif np.int(plotdict[model]['nbr'])<15:\n",
+    "        plt.text(1.7 , ystart-delta*(np.float(plotdict[model]['nbr'])-12), plotdict[model]['nbr'], color=plotdict[model]['color'], fontsize=14)\n",
+    "        plt.text(1.8 , ystart-delta*(np.float(plotdict[model]['nbr'])-12), 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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}