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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Reproduce a revised Figure 16 of the 2016 JAMES Tracmip introduction paper\n",
+    "\n",
+    "Attention: We only use those models that have correctly implemented land, plus CALTECH, which has delivered its landOrbit simulations later. Thus the figure generated here can differ from the Fig. 16 of the 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 climatolgical 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",
+    "        ntime = ds.time.size # number of timestep\n",
+    "        ds_clim = ds.isel(time=slice(ntime-20*12, ntime)).groupby('time.month').mean('time')\n",
+    "         # write only variable of interest to dictionary, so this becomes a data array\n",
+    "        datadict[ds.attrs['model_id']] = ds_clim[var] \n",
+    "    return datadict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pr_ldct = load_data('Amon', 'pr', 'landControl')\n",
+    "pr_ldor = load_data('Amon', 'pr', 'landOrbit')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note: not all models have the landOrbit simulations. We are identifying those models that provide both the landControl and the landOrbit simulations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Models that have both landControl and landOrbit simulations:\n",
+      " ['CALTECH', 'CAM3', 'CAM4', 'CNRM-AM5', 'ECHAM61', 'ECHAM63', 'LMDZ5A', 'MIROC5', 'MPAS', 'MetUM-CTL', 'MetUM-ENT']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Models that have both landControl and landOrbit simulations:\\n\", [k for k in pr_ldct if k in pr_ldor])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we filter the landControl models for those that have the landOrbit simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pr_ldct = { model: pr_ldct[model] for model in pr_ldor.keys() }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And we are removing those models that have an error in their implementation of land: ECHAM6.3, LMDZ5A, MetUM-CTL and MetUM-ENT."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for model in ['ECHAM63', 'LMDZ5A', 'MetUM-CTL', 'MetUM-ENT']:\n",
+    "    pr_ldct.pop(model)\n",
+    "    pr_ldor.pop(model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Interpolation to a common latitude-longitute 1deg x 1deg grid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# latintp is the latiudes on which data will be interpolated, likewise for lonintp\n",
+    "latintp  = np.linspace(-89.5, 89.5, 180)     \n",
+    "nlatintp = latintp.size\n",
+    "lonintp  = np.linspace(-179.0, 179.0, 180)     \n",
+    "nlonintp = lonintp.size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_latlon_interpolation(orig):\n",
+    "    from scipy.interpolate import griddata\n",
+    "    intp = np.zeros((12, nlatintp, nlonintp)) + np.nan\n",
+    "    orig = orig.roll(lon=(orig['lon'].size//2), roll_coords=True)\n",
+    "    auxlon = orig['lon'].values\n",
+    "    auxlon[0:orig['lon'].size//2] -= 360\n",
+    "    orig['lon'] = auxlon\n",
+    "    lat = orig['lat'].values\n",
+    "    lon = orig['lon'].values\n",
+    "    # grid of original model data      \n",
+    "    x, y   = np.meshgrid(lon, lat)\n",
+    "    # grid on which we interpolate\n",
+    "    xintp, yintp = np.meshgrid(lonintp, latintp)\n",
+    "    # interpolate data\n",
+    "    for mon in range(12):\n",
+    "        intp[mon] = griddata((x.ravel(), y.ravel()), orig[mon].values.ravel(), (xintp, yintp))\n",
+    "    return intp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We first interpolate landControl."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pr_ldct_intp = dict()\n",
+    "for model in pr_ldct.keys():\n",
+    "    pr_ldct_intp[model] = make_latlon_interpolation(pr_ldct[model])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now interpolate landOrbit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pr_ldor_intp = dict()\n",
+    "for model in pr_ldor.keys():\n",
+    "    pr_ldor_intp[model] = make_latlon_interpolation(pr_ldor[model])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Build zonal-mean precipitation over land and ocean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "We verify that lonintp[90:112] is the longitudes over land: [ 1.  3.  5.  7.  9. 11. 13. 15. 17. 19. 21. 23. 25. 27. 29. 31. 33. 35.\n",
+      " 37. 39. 41. 43.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('We verify that lonintp[90:112] is the longitudes over land:', lonintp[90:112])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pr_ldct_intp_land = dict()\n",
+    "pr_ldct_intp_ocea = dict()\n",
+    "pr_ldor_intp_land = dict()\n",
+    "pr_ldor_intp_ocea = dict()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/fd8940/anaconda3/lib/python3.7/site-packages/numpy/lib/nanfunctions.py:1116: RuntimeWarning: All-NaN slice encountered\n",
+      "  overwrite_input=overwrite_input)\n"
+     ]
+    }
+   ],
+   "source": [
+    "for model in pr_ldct_intp.keys():\n",
+    "    pr_ldct_intp_land[model] = np.nanmedian(pr_ldct_intp[model][:, :, 90:112], axis=2)\n",
+    "    pr_ldct_intp_ocea[model] = np.nanmedian(\n",
+    "        np.concatenate((pr_ldct_intp[model][:, :, 0:90], pr_ldct_intp[model][:, :, 112:]), axis=2), axis=2)\n",
+    "    \n",
+    "for model in pr_ldor_intp.keys():\n",
+    "    pr_ldor_intp_land[model] = np.nanmedian(pr_ldor_intp[model][:, :, 90:112], axis=2)\n",
+    "    pr_ldor_intp_ocea[model] = np.nanmedian(\n",
+    "        np.concatenate((pr_ldor_intp[model][:, :, 0:90], pr_ldor_intp[model][:, :, 112:]), axis=2), axis=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Calculate monthly-mean ITCZ position for land and ocean regions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calculate itcz position based on precip centroid between latboundary\n",
+    "def get_itczposition(pr, lat, latboundary, dlat):\n",
+    "    # pr: precipitation\n",
+    "    # lat: latitudes\n",
+    "    # latboundary: deg N/S that are used to calculate the precip centroid\n",
+    "    # dlat: latitude spacing of the fine interpolated grid\n",
+    "    area  = np.cos(lat*np.pi/180)\n",
+    "    xi    = np.arange(-latboundary, latboundary, dlat)\n",
+    "    # need to make sure that lat increases from SP to NP\n",
+    "    if lat[0]>lat[1]:\n",
+    "        lat = lat[::-1]\n",
+    "        pr  = pr [::-1]\n",
+    "    yi    = np.interp(xi, lat, pr)\n",
+    "    areai = np.interp(xi, lat, area)\n",
+    "    # area-integrated precip (up to constant factor)\n",
+    "    itcz = np.NaN\n",
+    "    nxi = len(xi)\n",
+    "    tot = np.sum(yi*areai)\n",
+    "    yiareai_int = np.zeros(nxi) + np.nan\n",
+    "    for j in range(0, nxi):\n",
+    "        yiareai_int[j] = np.sum(np.multiply(yi[0:j+1], areai[0:j+1]))\n",
+    "    ixi = np.argmin(np.abs(yiareai_int - 0.5*tot))\n",
+    "    itcz = xi[ixi]\n",
+    "    return itcz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "month  = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])\n",
+    "nmonth = month.size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "itcz_ldct_land = dict()\n",
+    "for model in pr_ldct_intp_land.keys():\n",
+    "    temp = np.zeros(nmonth) + np.nan\n",
+    "    for t in range(nmonth):\n",
+    "        temp[t] = get_itczposition(pr_ldct_intp_land[model][t], latintp, 30, 0.1)\n",
+    "    itcz_ldct_land[model] = temp\n",
+    "    del temp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "itcz_ldct_ocea = dict()\n",
+    "for model in pr_ldct_intp_ocea.keys():\n",
+    "    temp = np.zeros(nmonth) + np.nan\n",
+    "    for t in range(nmonth):\n",
+    "        temp[t] = get_itczposition(pr_ldct_intp_ocea[model][t], latintp, 30, 0.1)\n",
+    "    itcz_ldct_ocea[model] = temp\n",
+    "    del temp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "itcz_ldor_land = dict()\n",
+    "for model in pr_ldor_intp_land.keys():\n",
+    "    temp = np.zeros(nmonth) + np.nan\n",
+    "    for t in range(nmonth):\n",
+    "        temp[t] = get_itczposition(pr_ldor_intp_land[model][t], latintp, 30, 0.1)\n",
+    "    itcz_ldor_land[model] = temp\n",
+    "    del temp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "itcz_ldor_ocea = dict()\n",
+    "for model in pr_ldor_intp_ocea.keys():\n",
+    "    temp = np.zeros(nmonth) + np.nan\n",
+    "    for t in range(nmonth):\n",
+    "        temp[t] = get_itczposition(pr_ldor_intp_ocea[model][t], latintp, 30, 0.1)\n",
+    "    itcz_ldor_ocea[model] = temp\n",
+    "    del temp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import cm\n",
+    "from matplotlib.patches import Rectangle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "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": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_niceplot(ax, modelname):\n",
+    "    ax.xaxis.set_ticks([-120, -60, 0, 60, 120])\n",
+    "    ax.xaxis.set_ticklabels([''], fontsize=11)\n",
+    "    ax.yaxis.set_ticks([-0.5, 0, 0.5])\n",
+    "    ax.yaxis.set_ticklabels([''], fontsize=11) \n",
+    "    plt.text(0.03, 0.93, modelname, fontsize=15, ha='left', va='center', \\\n",
+    "             transform=ax.transAxes, backgroundcolor='white')\n",
+    "    plt.xlim(-175, 175), plt.ylim(-0.6, 0.6) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sinlat = np.sin(latintp*np.pi/180.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 960x960 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12, 12), dpi=80, facecolor='w', edgecolor='k')\n",
+    "clev = np.array([-1.8, -1.4, -1.0, -0.6, -0.2, 0.2, 0.6, 1.0, 1.4, 1.8])\n",
+    "\n",
+    "ax = plt.subplot(2, 2, 1)\n",
+    "# calculate model medians\n",
+    "dpr_ocea = 86400*np.nanmedian([(pr_ldor_intp_ocea[model][:]-pr_ldct_intp_ocea[model][:]) \n",
+    "                               for model in pr_ldor_intp_ocea.keys()], axis=0)\n",
+    "itcz_ocea = np.nanmedian([itcz_ldct_ocea[model][:] for model in itcz_ldct_ocea.keys()], axis=0)\n",
+    "c = plt.contourf(month, sinlat, np.transpose(dpr_ocea), clev, extend='both', cmap=cm.BrBG)\n",
+    "plt.plot([-200, 200], [0, 0], 'k--')\n",
+    "plt.plot(month, np.sin(itcz_ocea*np.pi/180), 'k', linewidth=3)\n",
+    "plt.title('Over ocean', fontsize=14)\n",
+    "plt.xlim(1, 12), plt.ylim(-0.6, 0.6)\n",
+    "ax.xaxis.set_ticks(month)\n",
+    "ax.xaxis.set_ticklabels(['Jan', '', '', 'Apr', '', '', 'Jul', '', '' ,'Oct', '', ''], fontsize=10)\n",
+    "ax.yaxis.set_ticks([-0.5, 0, 0.5])\n",
+    "ax.yaxis.set_ticklabels(['30S', 'Eq', '30N'], fontsize=10) \n",
+    "plt.text(0.02, 0.92, 'a)', fontsize=14, ha='left', va='center', transform=ax.transAxes, backgroundcolor='white')\n",
+    "cbar = plt.colorbar(c, ticks=[-1.8, -1.0, -0.2, 0.2, 1.0, 1.8], orientation='vertical', aspect=30)\n",
+    "cbar.ax.tick_params(labelsize=10)\n",
+    "ax.text(1, -0.07, 'mm/day', fontsize=10, transform=ax.transAxes)\n",
+    "\n",
+    "ax = plt.subplot(2, 2, 2)\n",
+    "# calculate model medians\n",
+    "dpr_land = 86400*np.nanmedian([(pr_ldor_intp_land[model][:]-pr_ldct_intp_land[model][:]) \n",
+    "                               for model in pr_ldor_intp_ocea.keys()], axis=0)\n",
+    "itcz_land = np.nanmedian([itcz_ldct_land[model][:] for model in itcz_ldct_land.keys()], axis=0)\n",
+    "plt.contourf(month, sinlat, np.transpose(dpr_land), clev, extend='both', cmap=cm.BrBG)\n",
+    "plt.plot([-200, 200], [0, 0], 'k--')\n",
+    "plt.plot(month, np.sin(itcz_land*np.pi/180), 'k', linewidth=3)\n",
+    "plt.title('Over land', fontsize=14)\n",
+    "plt.xlim(1, 12), plt.ylim(-0.6, 0.6)\n",
+    "ax.xaxis.set_ticks(month)\n",
+    "ax.xaxis.set_ticklabels(['Jan', '', '', 'Apr', '', '', 'Jul', '', '' ,'Oct', '', ''], fontsize=10)\n",
+    "ax.yaxis.set_ticks([-0.5, 0, 0.5])\n",
+    "ax.yaxis.set_ticklabels(['30S', 'Eq', '30N'], fontsize=10) \n",
+    "plt.text(0.02, 0.92, 'b)', fontsize=14, ha='left', va='center', transform=ax.transAxes, backgroundcolor='white')\n",
+    "cbar = plt.colorbar(c, ticks=[-1.8, -1.0, -0.2, 0.2, 1.0, 1.8], orientation='vertical', aspect=30)\n",
+    "cbar.ax.tick_params(labelsize=10)\n",
+    "ax.text(1, -0.07, 'mm/day', fontsize=10, transform=ax.transAxes)\n",
+    "\n",
+    "ax = plt.subplot(2, 2, 3)\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",
+    "plt.plot([-100, 100], [0, 0], 'k--')\n",
+    "# model median\n",
+    "ditcz_ocea = np.nanmedian([(itcz_ldor_ocea[model][:]-itcz_ldct_ocea[model][:]) for model in itcz_ldct_ocea.keys()], axis=0)\n",
+    "for model in itcz_ldor_ocea.keys():\n",
+    "    plt.plot(month, (itcz_ldor_ocea[model]-itcz_ldct_ocea[model]), color=plotdict[model]['color'])\n",
+    "plt.plot(month, ditcz_ocea, 'k', linewidth=3)\n",
+    "plt.xlabel('month')\n",
+    "plt.ylabel('ITCZ shift (deg lat)')\n",
+    "plt.xlim(1, 12), plt.ylim(-3, 3)\n",
+    "ax.xaxis.set_ticks(month)\n",
+    "ax.xaxis.set_ticklabels(['Jan', '', '', 'Apr', '', '', 'Jul', '', '' ,'Oct', '', ''], fontsize=10)\n",
+    "ax.yaxis.set_ticks([-3, -2, -1, 0, 1, 2, 3])\n",
+    "ax.yaxis.set_ticklabels([-3, -2, -1, 0, 1, 2, 3], fontsize=10) \n",
+    "plt.text(0.02, 0.95, 'c)', fontsize=14, ha='left', va='center', transform=ax.transAxes, backgroundcolor='white')\n",
+    "\n",
+    "ax = plt.subplot(2, 2, 4)\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",
+    "plt.plot([-100, 100], [0, 0], 'k--')\n",
+    "# model median\n",
+    "ditcz_land = np.nanmedian([(itcz_ldor_land[model][:]-itcz_ldct_land[model][:]) for model in itcz_ldct_land.keys()], axis=0)\n",
+    "for model in itcz_ldor_land.keys():\n",
+    "    plt.plot(month, (itcz_ldor_land[model]-itcz_ldct_land[model]), color=plotdict[model]['color'])\n",
+    "plt.plot(month, ditcz_land, 'k', linewidth=3)\n",
+    "plt.xlabel('month')\n",
+    "plt.xlim(1, 12), plt.ylim(-3, 3)\n",
+    "ax.xaxis.set_ticks(month)\n",
+    "ax.xaxis.set_ticklabels(['Jan', '', '', 'Apr', '', '', 'Jul', '', '' ,'Oct', '', ''], fontsize=10)\n",
+    "ax.yaxis.set_ticks([-3, -2, -1, 0, 1, 2, 3])\n",
+    "ax.yaxis.set_ticklabels([-3, -2, -1, 0, 1, 2, 3], fontsize=10) \n",
+    "plt.text(0.02, 0.95, 'd)', fontsize=14, ha='left', va='center', transform=ax.transAxes, backgroundcolor='white')\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
+}
diff --git a/pangeo/james2016_figure16.ipynb b/pangeo/james2016_figure16.ipynb
deleted file mode 100644
index cf5ef264f1e3853e4181439c2ef0156ae3f28e44..0000000000000000000000000000000000000000
--- a/pangeo/james2016_figure16.ipynb
+++ /dev/null
@@ -1,649 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Reproduce Figure 16 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 climatolgical 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",
-    "        ntime = ds.time.size # number of timestep\n",
-    "        ds_clim = ds.isel(time=slice(ntime-20*12, ntime)).groupby('time.month').mean('time')\n",
-    "         # write only variable of interest to dictionary, so this becomes a data array\n",
-    "        datadict[ds.attrs['model_id']] = ds_clim[var] \n",
-    "    return datadict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pr_ldct = load_data('Amon', 'pr', 'landControl')\n",
-    "pr_ldor = load_data('Amon', 'pr', 'landOrbit')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note: not all models have the landOrbit simulations. We are identifying those models that provide both the landControl and the landOrbit simulations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Models that have both landControl and landOrbit simulations:\n",
-      " ['CALTECH', 'CAM3', 'CAM4', 'CNRM-AM5', 'ECHAM61', 'ECHAM63', 'LMDZ5A', 'MIROC5', 'MPAS', 'MetUM-CTL', 'MetUM-ENT']\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Models that have both landControl and landOrbit simulations:\\n\", [k for k in pr_ldct if k in pr_ldor])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we filter the landControl models for those that have the landOrbit simulation. We also ommitt the CALTECH model, because it was not included in Fig. 16 of the JAMES paper."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pr_ldct = { model: pr_ldct[model] for model in pr_ldor.keys() }\n",
-    "pr_ldct.pop('CALTECH', None);\n",
-    "pr_ldor.pop('CALTECH', None);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Interpolation to a common latitude-longitute 1deg x 1deg grid"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# latintp is the latiudes on which data will be interpolated, likewise for lonintp\n",
-    "latintp  = np.linspace(-89.5, 89.5, 180)     \n",
-    "nlatintp = latintp.size\n",
-    "lonintp  = np.linspace(-179.0, 179.0, 180)     \n",
-    "nlonintp = lonintp.size"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_latlon_interpolation(orig):\n",
-    "    from scipy.interpolate import griddata\n",
-    "    intp = np.zeros((12, nlatintp, nlonintp)) + np.nan\n",
-    "    orig = orig.roll(lon=(orig['lon'].size//2), roll_coords=True)\n",
-    "    auxlon = orig['lon'].values\n",
-    "    auxlon[0:orig['lon'].size//2] -= 360\n",
-    "    orig['lon'] = auxlon\n",
-    "    lat = orig['lat'].values\n",
-    "    lon = orig['lon'].values\n",
-    "    # grid of original model data      \n",
-    "    x, y   = np.meshgrid(lon, lat)\n",
-    "    # grid on which we interpolate\n",
-    "    xintp, yintp = np.meshgrid(lonintp, latintp)\n",
-    "    # interpolate data\n",
-    "    for mon in range(12):\n",
-    "        intp[mon] = griddata((x.ravel(), y.ravel()), orig[mon].values.ravel(), (xintp, yintp))\n",
-    "    return intp"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We first interpolate landControl."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pr_ldct_intp = dict()\n",
-    "for model in pr_ldct.keys():\n",
-    "    pr_ldct_intp[model] = make_latlon_interpolation(pr_ldct[model])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We now interpolate landOrbit."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pr_ldor_intp = dict()\n",
-    "for model in pr_ldor.keys():\n",
-    "    pr_ldor_intp[model] = make_latlon_interpolation(pr_ldor[model])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Build zonal-mean precipitation over land and ocean"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "We verify that lonintp[90:112] is the longitudes over land: [ 1.  3.  5.  7.  9. 11. 13. 15. 17. 19. 21. 23. 25. 27. 29. 31. 33. 35.\n",
-      " 37. 39. 41. 43.]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print('We verify that lonintp[90:112] is the longitudes over land:', lonintp[90:112])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pr_ldct_intp_land = dict()\n",
-    "pr_ldct_intp_ocea = dict()\n",
-    "pr_ldor_intp_land = dict()\n",
-    "pr_ldor_intp_ocea = dict()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/fd8940/anaconda3/lib/python3.7/site-packages/numpy/lib/nanfunctions.py:1116: RuntimeWarning: All-NaN slice encountered\n",
-      "  overwrite_input=overwrite_input)\n"
-     ]
-    }
-   ],
-   "source": [
-    "for model in pr_ldct_intp.keys():\n",
-    "    pr_ldct_intp_land[model] = np.nanmedian(pr_ldct_intp[model][:, :, 90:112], axis=2)\n",
-    "    pr_ldct_intp_ocea[model] = np.nanmedian(\n",
-    "        np.concatenate((pr_ldct_intp[model][:, :, 0:90], pr_ldct_intp[model][:, :, 112:]), axis=2), axis=2)\n",
-    "    \n",
-    "for model in pr_ldor_intp.keys():\n",
-    "    pr_ldor_intp_land[model] = np.nanmedian(pr_ldor_intp[model][:, :, 90:112], axis=2)\n",
-    "    pr_ldor_intp_ocea[model] = np.nanmedian(\n",
-    "        np.concatenate((pr_ldor_intp[model][:, :, 0:90], pr_ldor_intp[model][:, :, 112:]), axis=2), axis=2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Calculate monthly-mean ITCZ position for land and ocean regions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# calculate itcz position based on precip centroid between latboundary\n",
-    "def get_itczposition(pr, lat, latboundary, dlat):\n",
-    "    # pr: precipitation\n",
-    "    # lat: latitudes\n",
-    "    # latboundary: deg N/S that are used to calculate the precip centroid\n",
-    "    # dlat: latitude spacing of the fine interpolated grid\n",
-    "    area  = np.cos(lat*np.pi/180)\n",
-    "    xi    = np.arange(-latboundary, latboundary, dlat)\n",
-    "    # need to make sure that lat increases from SP to NP\n",
-    "    if lat[0]>lat[1]:\n",
-    "        lat = lat[::-1]\n",
-    "        pr  = pr [::-1]\n",
-    "    yi    = np.interp(xi, lat, pr)\n",
-    "    areai = np.interp(xi, lat, area)\n",
-    "    # area-integrated precip (up to constant factor)\n",
-    "    itcz = np.NaN\n",
-    "    nxi = len(xi)\n",
-    "    tot = np.sum(yi*areai)\n",
-    "    yiareai_int = np.zeros(nxi) + np.nan\n",
-    "    for j in range(0, nxi):\n",
-    "        yiareai_int[j] = np.sum(np.multiply(yi[0:j+1], areai[0:j+1]))\n",
-    "    ixi = np.argmin(np.abs(yiareai_int - 0.5*tot))\n",
-    "    itcz = xi[ixi]\n",
-    "    return itcz"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "month  = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])\n",
-    "nmonth = month.size"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "itcz_ldct_land = dict()\n",
-    "for model in pr_ldct_intp_land.keys():\n",
-    "    temp = np.zeros(nmonth) + np.nan\n",
-    "    for t in range(nmonth):\n",
-    "        temp[t] = get_itczposition(pr_ldct_intp_land[model][t], latintp, 30, 0.1)\n",
-    "    itcz_ldct_land[model] = temp\n",
-    "    del temp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "itcz_ldct_ocea = dict()\n",
-    "for model in pr_ldct_intp_ocea.keys():\n",
-    "    temp = np.zeros(nmonth) + np.nan\n",
-    "    for t in range(nmonth):\n",
-    "        temp[t] = get_itczposition(pr_ldct_intp_ocea[model][t], latintp, 30, 0.1)\n",
-    "    itcz_ldct_ocea[model] = temp\n",
-    "    del temp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "itcz_ldor_land = dict()\n",
-    "for model in pr_ldor_intp_land.keys():\n",
-    "    temp = np.zeros(nmonth) + np.nan\n",
-    "    for t in range(nmonth):\n",
-    "        temp[t] = get_itczposition(pr_ldor_intp_land[model][t], latintp, 30, 0.1)\n",
-    "    itcz_ldor_land[model] = temp\n",
-    "    del temp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "itcz_ldor_ocea = dict()\n",
-    "for model in pr_ldor_intp_ocea.keys():\n",
-    "    temp = np.zeros(nmonth) + np.nan\n",
-    "    for t in range(nmonth):\n",
-    "        temp[t] = get_itczposition(pr_ldor_intp_ocea[model][t], latintp, 30, 0.1)\n",
-    "    itcz_ldor_ocea[model] = temp\n",
-    "    del temp"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plotting"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "from matplotlib import cm\n",
-    "from matplotlib.patches import Rectangle"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "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": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_niceplot(ax, modelname):\n",
-    "    ax.xaxis.set_ticks([-120, -60, 0, 60, 120])\n",
-    "    ax.xaxis.set_ticklabels([''], fontsize=11)\n",
-    "    ax.yaxis.set_ticks([-0.5, 0, 0.5])\n",
-    "    ax.yaxis.set_ticklabels([''], fontsize=11) \n",
-    "    plt.text(0.03, 0.93, modelname, fontsize=15, ha='left', va='center', \\\n",
-    "             transform=ax.transAxes, backgroundcolor='white')\n",
-    "    plt.xlim(-175, 175), plt.ylim(-0.6, 0.6) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sinlat = np.sin(latintp*np.pi/180.0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 960x960 with 6 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(12, 12), dpi=80, facecolor='w', edgecolor='k')\n",
-    "clev = np.array([-1.8, -1.4, -1.0, -0.6, -0.2, 0.2, 0.6, 1.0, 1.4, 1.8])\n",
-    "\n",
-    "ax = plt.subplot(2, 2, 1)\n",
-    "# calculate model medians\n",
-    "dpr_ocea = 86400*np.nanmedian([(pr_ldor_intp_ocea[model][:]-pr_ldct_intp_ocea[model][:]) \n",
-    "                               for model in pr_ldor_intp_ocea.keys()], axis=0)\n",
-    "itcz_ocea = np.nanmedian([itcz_ldct_ocea[model][:] for model in itcz_ldct_ocea.keys()], axis=0)\n",
-    "c = plt.contourf(month, sinlat, np.transpose(dpr_ocea), clev, extend='both', cmap=cm.BrBG)\n",
-    "plt.plot([-200, 200], [0, 0], 'k--')\n",
-    "plt.plot(month, np.sin(itcz_ocea*np.pi/180), 'k', linewidth=3)\n",
-    "plt.title('Over ocean', fontsize=14)\n",
-    "plt.xlim(1, 12), plt.ylim(-0.6, 0.6)\n",
-    "ax.xaxis.set_ticks(month)\n",
-    "ax.xaxis.set_ticklabels(['Jan', '', '', 'Apr', '', '', 'Jul', '', '' ,'Oct', '', ''], fontsize=10)\n",
-    "ax.yaxis.set_ticks([-0.5, 0, 0.5])\n",
-    "ax.yaxis.set_ticklabels(['30S', 'Eq', '30N'], fontsize=10) \n",
-    "plt.text(0.02, 0.92, 'a)', fontsize=14, ha='left', va='center', transform=ax.transAxes, backgroundcolor='white')\n",
-    "cbar = plt.colorbar(c, ticks=[-1.8, -1.0, -0.2, 0.2, 1.0, 1.8], orientation='vertical', aspect=30)\n",
-    "cbar.ax.tick_params(labelsize=10)\n",
-    "ax.text(1, -0.07, 'mm/day', fontsize=10, transform=ax.transAxes)\n",
-    "\n",
-    "ax = plt.subplot(2, 2, 2)\n",
-    "# calculate model medians\n",
-    "dpr_land = 86400*np.nanmedian([(pr_ldor_intp_land[model][:]-pr_ldct_intp_land[model][:]) \n",
-    "                               for model in pr_ldor_intp_ocea.keys()], axis=0)\n",
-    "itcz_land = np.nanmedian([itcz_ldct_land[model][:] for model in itcz_ldct_land.keys()], axis=0)\n",
-    "plt.contourf(month, sinlat, np.transpose(dpr_land), clev, extend='both', cmap=cm.BrBG)\n",
-    "plt.plot([-200, 200], [0, 0], 'k--')\n",
-    "plt.plot(month, np.sin(itcz_land*np.pi/180), 'k', linewidth=3)\n",
-    "plt.title('Over land', fontsize=14)\n",
-    "plt.xlim(1, 12), plt.ylim(-0.6, 0.6)\n",
-    "ax.xaxis.set_ticks(month)\n",
-    "ax.xaxis.set_ticklabels(['Jan', '', '', 'Apr', '', '', 'Jul', '', '' ,'Oct', '', ''], fontsize=10)\n",
-    "ax.yaxis.set_ticks([-0.5, 0, 0.5])\n",
-    "ax.yaxis.set_ticklabels(['30S', 'Eq', '30N'], fontsize=10) \n",
-    "plt.text(0.02, 0.92, 'b)', fontsize=14, ha='left', va='center', transform=ax.transAxes, backgroundcolor='white')\n",
-    "cbar = plt.colorbar(c, ticks=[-1.8, -1.0, -0.2, 0.2, 1.0, 1.8], orientation='vertical', aspect=30)\n",
-    "cbar.ax.tick_params(labelsize=10)\n",
-    "ax.text(1, -0.07, 'mm/day', fontsize=10, transform=ax.transAxes)\n",
-    "\n",
-    "ax = plt.subplot(2, 2, 3)\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",
-    "plt.plot([-100, 100], [0, 0], 'k--')\n",
-    "# model median\n",
-    "ditcz_ocea = np.nanmedian([(itcz_ldor_ocea[model][:]-itcz_ldct_ocea[model][:]) for model in itcz_ldct_ocea.keys()], axis=0)\n",
-    "for model in itcz_ldor_ocea.keys():\n",
-    "    plt.plot(month, (itcz_ldor_ocea[model]-itcz_ldct_ocea[model]), color=plotdict[model]['color'])\n",
-    "plt.plot(month, ditcz_ocea, 'k', linewidth=3)\n",
-    "plt.xlabel('month')\n",
-    "plt.ylabel('ITCZ shift (deg lat)')\n",
-    "plt.xlim(1, 12), plt.ylim(-3, 3)\n",
-    "ax.xaxis.set_ticks(month)\n",
-    "ax.xaxis.set_ticklabels(['Jan', '', '', 'Apr', '', '', 'Jul', '', '' ,'Oct', '', ''], fontsize=10)\n",
-    "ax.yaxis.set_ticks([-3, -2, -1, 0, 1, 2, 3])\n",
-    "ax.yaxis.set_ticklabels([-3, -2, -1, 0, 1, 2, 3], fontsize=10) \n",
-    "plt.text(0.02, 0.95, 'c)', fontsize=14, ha='left', va='center', transform=ax.transAxes, backgroundcolor='white')\n",
-    "\n",
-    "ax = plt.subplot(2, 2, 4)\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",
-    "plt.plot([-100, 100], [0, 0], 'k--')\n",
-    "# model median\n",
-    "ditcz_land = np.nanmedian([(itcz_ldor_land[model][:]-itcz_ldct_land[model][:]) for model in itcz_ldct_land.keys()], axis=0)\n",
-    "for model in itcz_ldor_land.keys():\n",
-    "    plt.plot(month, (itcz_ldor_land[model]-itcz_ldct_land[model]), color=plotdict[model]['color'])\n",
-    "plt.plot(month, ditcz_land, 'k', linewidth=3)\n",
-    "plt.xlabel('month')\n",
-    "plt.xlim(1, 12), plt.ylim(-3, 3)\n",
-    "ax.xaxis.set_ticks(month)\n",
-    "ax.xaxis.set_ticklabels(['Jan', '', '', 'Apr', '', '', 'Jul', '', '' ,'Oct', '', ''], fontsize=10)\n",
-    "ax.yaxis.set_ticks([-3, -2, -1, 0, 1, 2, 3])\n",
-    "ax.yaxis.set_ticklabels([-3, -2, -1, 0, 1, 2, 3], fontsize=10) \n",
-    "plt.text(0.02, 0.95, 'd)', fontsize=14, ha='left', va='center', transform=ax.transAxes, backgroundcolor='white')\n",
-    "\n",
-    "plt.tight_layout;"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x7fb2c5bf5050>"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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yrJpPW/6/KjT9G+rY+tOph1a1CPTydKnRFW1U9kkvV5Z7o9ff0O+aaV0uobTf2LQSqqPchmEwQB51thDK/KfeEmgXM7ZF0AS9KLeqNd1+1JDrqHkn5de08u9Q1fjOVAb1+8oaAW8N9J9WtQiGjTYohjprpElzMbonomXF7+UebaYJWZsunzItzV5kraslMOwtsbbhhqBH+vHClmmml30Rel2IrhMvbgyaVlZVyfotvZZPPG1e3LbuKRGnH4Mz3B3UXtwQtJQ6/M9l+it6zW9Y6cew1KKjm9o0CiqLOuSqqvzdLTQY3BCUZJAvbVINMun+beyMHVV6aUnMNPpd83e3UP24IShIG8au93rdcQaFu3+GEx81lEObRrs4+9JZnMyVz+CJl73/DxGSVkjaIWmnpHUJ1yXpinD9XknL89JKOkLSZkkPhe/DQ/hbJX1D0vclfbrrPreFvO4On8RtguN4iyAFV/7tpSmFE79vG3zX3eXQL5n6Ud7f+1G9fnzIo6V2qG0lkmYBVxLtvTIF3C5p0sy+E4u2ElgSPicBVwMn5aRdB9xqZuuDgVgHXAS8DHwCOD58uvklM9taVP469iw+BrgOOBp4DZgws8slHQF8AVgE7AI+YmbPV73fIBhFI5D2sucpl7pmh1ZVNv1auz5NrqTwphVxryt09tuwxpV+2vWyxmB66Tg8VUWq2jkR2GlmjwBIuh5YBcQNwSrgurCJ/RZJc8M2vosy0q4CTgnpryXa7+UiM/sB8E1J76hD+DpaBHuB3zWzOyUdAtwhaTPwqyRbMmeAZNUaiyiAOpXEIGrydRqEsvL2YkzrLpOs/A57+JV9jEUv985S6t3KPM8AdMctagxqXbpk+gB2TR1ZNPo8SfFa9oSZTYTjhcDjsWtTRLX+OElxFuakPcrMdgOE/d9z3TyBv5b0KvAl4NJgfFKpbAiCkB1BvyfpAaIflmjJqt6vCYq84L0ooDqUQPf98vIcFT9uVQPYTZpSK6K82lLmHTnKylNUoZdR/Gnpu8uzZfsQ7DGzE1KuJf347ocjLU6RtGX4JTN7IlTMvwT8CpHXJpVa+wgkLQJ+EvgWvVuyVtBrbXnQL31d90t6iYsouSoKspd8i5B0717LqYhbo8j9e6WTf1Ke3feu29deVbEn8cqPTO8XNvuxsX3u2XJjkMYUcEzsfBx4smCcsYy0T0taEHToAuCZPEHM7Inw/T1J/4fIbTUYQyDpYCLr89tm9qJU7CGStAZYAzDnTQfXJY5TkKyXvYoi6IcS6fXeZRVkEdnjCi2uyIqmL0uRPPtZ5kkKvO68O+U4pMbgdmCJpMXAE8DZwC92xZkE1oY+gJOAF4KCfzYj7SSwGlgfvr+aJYSkA4C5ZrZH0oHAh4Bb8oSvxRCEG34J+LyZfTkEF7Jkwcc2AXDYgfMbHz4wvXS8NU35ftCkgu6FrBpkXu2yQ6+/uajyy4uXJFNeXmXS5OXXS179VPwAi8af3c83H//tSS2hNhsDM9sraS2wCZgFXGNm2yRdEK5vADYCZwA7gZeA87LShqzXAzdIOh94DDirc09Ju4BDgTFJZwKnA48Cm4JOnkVkBP4qT/46Rg0J+CzwgJn9RexSKUvWNFkdUFnN817pVTlVkaFOI5BVIy6TPi1tliIqc62sbP1QgL3kWaccRQ1Vv5V/h0Xjz+7zDaR22Ha3DlpuDDYSKft42IbYsQEXFk0bwp8DPpCSZlGKKO8pJvEb1NEi+Gmizoj7JN0dwn6fDEvWNpKMQC9D2opSp8ulDhmrKoAs5ZuXd7+VT5X844qqV0qMSKmdjvx5MqSVUR2/vyhxWfNcRa+32G8bmHgznjpGDX2T5F5vSLFkbaHIMLRelXZaJ2LdrpmiE3MG6RLql3Ivqph2TR2Z6HroJa+q9Os+3b8t6z5Fat5F8hkE8f+t21U0EyaetZWRm1lcdgxyr8q7k6774e2cD5uvfpBUVUZJroe6OO3o7Ynhm59aWvu9sqjrtzWt+JPoNuKdfo5+uGidiFYZAptTTwdZnDomn/T64GWl65dB6Pdwxl7Iq513x+0naYq8X/kO2kDk0fkv2mgA4nS3YuKd3l6Jqp9WGQKovg5/XbMOB6U84/fp1wPe1IsTf5mL+Kt7UU79Uux1cdrR21tnDPpN5z+us9WS1Hfg1EfrDAEM105OdVJ3K6EffRJFSVMC3eFNdqb+50Pv3C/sxheXJ8TMT5uVrkxrISlunYakU951KOu8/y7telUDMajRTaNEKw1Bh5mq8PMoMou0jjQdZj821tjLVUUp9NoaSDIAg0yfRtrvqatVkaSYmzTEZYi7tIZF5mGi1YagDcQX6mpyolm8dt993KHfE6faxuanlvZkDG58cXlPyrxfBqBDR9l3/6YiRmCYlGOvrZFuV+OjtUo12rTKELw6p12dQJ2JK/Hvpo1B0jGMTgdaR0l2lOMgjEFevG630CD6BKoq/k4rsB/+9qIVi/hvKGMU2t7RPYy0yhBAu2cOtolRUfzdJCnZKsagzngdWaqSlkevyj9N2fer0zUp3zzjUHQk0yh2vg+C1hkC6J8xKLtU9CgYpWF1C3XTrRwGPZqon8opywD0cwRN0ZFzRSol3XImPXdlJsmddvR2/qGQdE4RWmkI2kTbFqAb1ZZAWeKKOW4U0vzwVfJPoh8++6pKv19DontxUxZpNQzDfIeZwkgZgmGv3ffDCPRrxNCgXuIi90lzJ9V1/35SRvk3PXGwQ5ocec9vUquhux/B3UL9YaQMgTNYyjT1y+RT9noaefIMeiROntIflKIvUmHqpaVcdi2sJoc1jxpuCIaIfk0QK1Lr7J7V2csL2rYhjnWMvOkndSr+ulvDZbdIzSNtGHS8jHcRm0fQ8hnlw4YbgiGjjDGoc6Zyt9LrZR+BOpjJywtUVfxNuj7LDMDII+u5bVtlYqbQSkMw7L78flNWYfTqs+2Fmayo66KKwh/2dyNL/m4jsX85uauoX7TSEDiDoYxCatNopbZ0ivaTYVf4vRD/zW0brTfT6bshkLQCuJxo/8zPmNn6rPh5L0DRlUnT1ilKSj+qaxqVYRSUb1O0Xel33plBvidps/g7HcjR6KGbBiZPEfJ0XdjW93KifYtfAn7VzO7MSivpCOALwCJgF/ARM3te0luBG4H3An9jZmtD/DcDXwSOBV4FvmZm6/Jk76shkDQLuBI4DZgCbpc0aWbfSYo/6+VkZdPLstRVlrJ2ZhZ1Kdq0Wmre0iP9UvSDfsbL3q+q4cgyBrSs7lZQ160EloTPScDVwEk5adcBt5rZeknrwvlFwMvAJ4DjwyfOp8zsG5LGgFslrTSzr2fJ3+8WwYnATjN7BEDS9cAqINEQdDOoB31s+9Q+D23TawpVpVv+KoooT8F1X0+7V6/lWedvqUrWveuUa6ZUYrJ+R1Ej0SnXF46dvc9AiRZ2GhfRdauA68Im9lskzZW0gKi2n5Z2FXBKSH8t0U7NF5nZD4BvSnpHXAgzewn4RjielnQnBcxmvw3BQuDx2PkUkSV8HUlrgDUAs2fPfT28idpOtzEowqAMRllFU5diysun6H2qyNN210kVZorSL0v3+5ZHSypn8yRtjZ1PmNlEOM7VdSlxFuakPcrMdgOY2W5J84sKK2ku8PNELqdM+m0IknoY9/H/hIKcADj0kPFGHdG9+ELzlFRbarNO84yq0k+jrDGAN4ZP1zU6TdOl8tpjZiekZZUQ1q3P0uIUSVsKSQcAfwtc0WlpZNFvQzAFHBM7HweeLJKw6paVbcGV//BRh0ujSF5ONWPQMorourQ4Yxlpn5a0ILQGFgDPFJRnAnjIzP6ySOR+G4LbgSWSFgNPAGcDv1g0cVPGIH5PH1E0syn7fLlir59ejEELKaLrJoG1oQ/gJOCFoOCfzUg7CawG1ofvr+YJIulS4DDgvxYVvq+GwMz2SloLbCIaFnWNmW0rk0fe/sX9pup9k+SPP/RF8i8bP56uiaF/deKKdzQYdmOQpuskXRCubwA2Eg0d3Uk0fPS8rLQh6/XADZLOBx4DzurcU9Iu4FBgTNKZwOnAi8AlwHbgzmjEKp82s89kya+oA7sdHHrIuL33hAtTr7tSGC56NWDO6JJnDOJ9bnd89nfvyPDZF2LOwmPsRy/4WKG4D/7Bxyrfr60M1czimdJvMCr4f+WUxd2yzfCmpgUoiz8cjjMaJFUkfPBFfxg6QwBuDBxnVBjbPrWfQXBjUD9DaQggMgZuEBxnNHA3Y38ZWkPQwY2B44wGcWPgrYJ6GXpDAG4MHMdxqjAjDAG4MXCcUcBdRP1hxhgCx3FGAzcG9eOGwHGcocONQb202hAMeiMMx3GcUaRVM4v18vR+yn/Y18pxHMdpO61uEcRJmljiOI7jVGdoDEEHNwaO4zj1MnSGANwYOI7j1MlQGgJwY+A4jlMXlQyBpD+TtF3SvZK+EjZL7ly7WNJOSTskfbC6qPm4cXAcxylP1RbBZuB4M3sX8CBwMYCkZUTbrb0TWAFcJWlWxXvtR7fi7yxEF/84juM42VQyBGb2d2a2N5xuIdp0GWAVcL2ZvWJm3yXamu3EKvdKI280kRsEx3GcbOrsI/g14OvheCHweOzaVAhrDDcGjuP0E0krgit8p6R1Cdcl6Ypw/V5Jy/PSSjpC0mZJD4Xvw2PXEt3vkv5LyH+bpE8WkT3XEEi6RdL9CZ9VsTiXAHuBz3eCErJK3BxZ0hpJWyVtnX7t34rI3BPef+A4Tr8Iru8rgZXAMuCc4CKPsxJYEj5rgKsLpF0H3GpmS4Bbw3mq+13SW4E/Az5gZu8EjpL0gTz5c2cWm9mpWdclrQY+FG7cUfZTwDGxaOPAkyn5TwATAIcdOD/RWBRhbPuU1/odx2mKE4GdZvYIgKTriVzk34nFWQVcF/TkFklzJS0AFmWkXQWcEtJfC9wGXETM/Q58V1LH/b4XeNDMng1pbgE+TGREUqk6amhFEOoXzOyl2KVJ4GxJsyUtJrKA365yL8dxnIaZ1/FehM+a2LUi7vC0OFlpjzKz3QDhe35OXjuBpZIWSToAOJN9K+WJVF1r6NPAbGCzJIAtZnaBmW2TdAORRdsLXGhmr1a8l+M4Tq3MmoZDHi3siNhjZiekXCviDk+LU9iVnpeXmT0v6aPAF4DXgP8HvD0nr2qGwMzekXHtMuCyKvmXJc09lDeqqEg8x3GcDIq4w9PijGWkfVrSAjPbHdxIz+Tdz8y+BnwNoj5YILcSPrQzi9Moo8y9T8FxnJq4HVgiabGkMaKO3MmuOJPAuWH00MnAC8Hdk5V2ElgdjlcDX42FJ7rfJc0P34cDvw58Jk/4Vi1D3S+ml44XMhBF4zmO48Qxs72S1gKbgFnANcFFfkG4vgHYCJxB5Md/CTgvK23Iej1wg6TzgceAs0KaLPf75ZJ+Ihz/dzN7ME/+GWkIklxESUreRxo5jlMXZraRSNnHwzbEjg24sGjaEP4ckDj8M839bmbnlBKcGega6tBrzd4Ng+M4o8aMbBE4zkygaKXE3ZlOVWa0IXDXj9Mkec9e0dFsVe/TqwzO6DCjDQHkP+j+Ijj9oIhy7o7TRMWlcz9/D0abGW8IHKcqg1LOTbZe3SCMNm4IHKeLUXYnZg2hLmos3KgMH24InJGke0b5KCv/bvLKomhZJZWxG4d2MmOHjzpOEkkbFbkR6D+dMvaybifeInCGjqRO1qRwp514y6B9uCFwhoIsJe8GYPhIM+ZOM8w4Q+APWLMU6Sh0xe1046sAN8uMMQRpyqWXEQxZimpUHtKyyrq7w9WVvdMrHdeRG4fBMSMMQZnJO0kPVL9ncTb9EA9CKbvid+okq0O/6fdpJlKLIZD0e0QbJh9pZntC2MXA+USbIvymmW2q414delU8TSgsV5KOUx/e2Vw/lQ2BpGOA04jWyu6ELSPaXOGdwNuAWyQdV9d2la5YHWe0mV46Dk81LcXMoY4Wwf8EPs4bO+cArAKuN7NXgO9K2gmcCPxz0Uxd2TuO4wyGSoZA0i8AT5jZPWHz+g4LgS2x86kQlpTHGmANwOzZc90AOI7jDJhcQyDpFuDohEuXAL8PnJ6ULCHMkvI3swlgAuDQQ8YT4ziO4zj9I3eJCTM71cyO7/4AjwCLgXsk7QLGgTslHXz7Pd0AAAydSURBVE3UAjgmls048GT94juO47QDSSsk7ZC0U9K6hOuSdEW4fq+k5XlpJR0habOkh8L34bFrF4f4OyR9MBY+JmlC0oOStkv6cJ7sPa81ZGb3mdl8M1tkZouIlP9yM3sKmATOljRb0mJgCfDtXu/lOI7TZiTNAq4EVgLLgHPCoJk4K4l04RIid/jVBdKuA241syXAreG8e0DOCuCqkA9E3ppnzOy4kN8/5Mnfl0XnzGwbcAPwHeBm4MK6Rgw5juO0kBOBnWb2iJlNA9cTDZqJswq4ziK2AHMlLchJuwq4NhxfC5wZC7/ezF4xs+8CnQE5AL8G/A8AM3utM6Q/i9oMQWgZ7ImdX2Zmx5rZj5nZ1+u6j+M4TkPMk7Q19lkTu7YQeDx2njRAJi1OVtqjzGw3QPien5WXpLnh/E8k3Snpi5KOyvthM2JmseM4Ti/Metk47OFXikbfY2YnpFwrMkAmLU7hwTUF8jqAqE/2n8zsY5I+BnwK+JWszHw/AsdxnOoUGSCTFicr7dPBfUT4fiYnr+eAl4CvhPAvAsvJwQ2B42Qwtn3q9Y/jZHA7sETSYkljRB25k11xJoFzw+ihk4EXgrsnK+0ksDocr+aNibuJA3LMzICvAaeEeB8g6qvNxF1DjpNAkuIvagySVs90ZjZmtlfSWmATMAu4xsy2SbogXN8AbATOIOrYfQk4LyttyHo9cIOk84mW8TkrpNkmqTMgZy/7Dsi5CPicpL8Enu3cJws3BM7IUEQ511Hz7+ThxmC0MLONRMo+HrYhdmzAhUXThvDniGr1SWkuAy5LCH8UeF8Z2d0QOENJGSUbV+5p21r2y/UTz7cfRsGNjVMHbgicoSJJqXdTZDOiJnz+de+tHG951JmvM3q4IXBaR1Ul3faO3TrkS1L63jpwesVHDTmtwUfnFKdtrR1nuPEWgVOJunzgrrzqwcvR6QU3BE7PdCudbqPgSmlwdJd3kpuozPBXZ7RwQ1Az/R4lUpY0v3GSUuhlFE6VOE59VJn34DhuCGog7YWr60WsOtSxYwzy0mV1NrpSGR0G1emcN+rLGRxuCDJoi/Krc5JTE/d2ho9+KukqFRKnP7TaEJRRQvEHx5VX78RfQi9Hp5siz0SWEvdnqp20yhDo5WmvubYAL0unCnW2YL1lMBgqGwJJvwGsJVr46CYz+3gIvxg4H3gV+E0z21T1Xo7jjBZeKRkMlQyBpJ8j2jLtXWb2iqT5ITy+n+bbgFskHefbVTqO47SPqjOLPwqsN7NXAMyss2lC1n6ajuM4TouoagiOA35W0rck/YOk94bwIvt3AiBpTWcP0OnX/q2iOI7jOE5Zcl1Dkm4Bjk64dElIfzhwMvBeog0U3k6JPTjNbAKYADjswPl5+3Q6juM4NZNrCMzs1LRrkj4KfDlsuPBtSa8B8yi2f6fjOI7TAqq6hv4v8H4ASccBY8AeUvbTrHgvx3Ecpw9UNQTXAG+XdD9wPbDaIrYBnf00b2bf/TQdx3FmHJJWSNohaaekdQnXJemKcP1eScvz0ko6QtJmSQ+F78Nj1y4O8XdI+mAs/GZJ90jaJmmDpFl5slcyBGY2bWa/bGbHm9lyM/v72LXLzOxYM/sxM/t6lfs4juO0maBsrwRWAsuAc8Iw+jgribwjS4A1wNUF0q4DbjWzJcCt4bx7iP4K4KqYwv+Imf0EcDxwJGHD+yx8YxrHcZzqnAjsNLNHzGyayEOyqivOKuC64DXZAsyVtCAn7Srg2nB8LXBmLDxxiL6ZvRjiHEDkrs8dhOOGwHEcpzpFhsynxclKe5SZ7QYI3/OL3E/SJuAZ4HvAjXnCt2qtIcdxnEFScn2zeZK2xs4nwvB3KDZkPi1O4eH2BfKKDsw+KGkO8HmiAT2bszJzQ+A4jlOMPWZ2Qsq1IkPm0+KMZaR9WtICM9sd3Eid1Rty72dmL0uaJHIjZRoCdw05juNU53ZgiaTFksaIOnInu+JMAueG0UMnAy8Ed09W2klgdTheDXw1Fr7fEH1JBweDgaQDgDOA7XnCe4vAcRynIma2V9JaYBMwC7jGzLZJuiBc3wBsJFLMO4GXgPOy0oas1xOt2HA+8BhhBFDIuzNEfy9hiL6ktwCTkmaHvP4e2JAnvxsCx3GcGjCzjUTKPh62IXZswIVF04bw54APpKS5DLisK+xpouV+SuGuIcdxnBHHDYHjOM6I44bAcRxnxHFD4DiOM+K4IXAcxxlx3BA4juOMOG4IHMdxRhw3BI7jOCNOJUMg6d2Stki6O2xAf2LsWuKmCY7jOE67qDqz+JPAH5vZ1yWdEc5P6do04W3ALZKO813KHMdx2kdV15ABh4bjw3hj9bvUTRMcx3GcdlG1RfDbwCZJnyIyKj8VwhcCW2LxkjZpAEDSGqJt25jzpoMriuM4juOUJdcQSLoFODrh0iVEiyH9jpl9SdJHgM8Cp1Jio4WwscMEwGEHzs/dUs1xHMepl1xDYGanpl2TdB3wW+H0i8BnwnGRTRocx3GcFlC1j+BJ4N+H4/cDD4XjxE0TKt7LcRzH6QNV+wj+G3B52AnnZYKvP23ThIr3chzHcfpAJUNgZt8E3pNybb9NExzHcZz24TOLHcdxRhw3BI7jOCOOGwLHcZwakLQiLKmzU9K6hOuSdEW4fq+k5XlpJR0habOkh8L34bFricv4SHqPpPvCtSskJQ3n3wc3BI7jOBWRNAu4ElgJLAPOCUvtxFlJNIJyCdHAmqsLpF0H3GpmS4Bbwzldy/isAK4K+RDyXRO714o8+d0QOI7jVOdEYKeZPWJm08D1REvtxFkFXGcRW4C5khbkpF0FXBuOrwXOjIXvt4xPyO9QM/tnMzPguliaVKoOH62VF/c++/2bn7pqR9NyJDAP2NO0EF20USZop1wuU3HaKFeaTD9aNeMX9z676eanrppXMPocSVtj5xNhZQSIltB5PHZtCjipK31SnIU5aY8ys90AZrZb0vxYXknL+PwwHHeHZ9IqQwDsMLMTmhaiG0lb2yZXG2WCdsrlMhWnjXL1UyYzy3WbFKTIsjppcQovydOnvNw15DiOUwNFltVJi5OV9ung7iF8P1Mgr/EcOfbDDYHjOE51bgeWSFosaYyoI3eyK84kcG4YPXQy8EJw+2SlnQRWh+PVwFdj4fst4xPy+56kk8NooXNjaVJpm2toIj9KI7RRrjbKBO2Uy2UqThvlaqNM+2BmeyWtBTYBs4BrwlI7F4TrG4CNwBlEHbsvAedlpQ1ZrwdukHQ+8BhwVkiTtYzPR4G/AQ4Cvh4+mSjqWHYcx3FGFXcNOY7jjDhuCBzHcUac1hiCvOnZA5RjV5iefXdnzHDWNO8+ynGNpGck3R8LKz3dfAAy/ZGkJ0J53S3pjAHLdIykb0h6QNI2Sb8VwhsrqwyZmi6rOZK+LemeINcfh/AmyypNpkbLauQws8Y/RB0kDwNvB8aAe4BlDcmyC5jXFfZJYF04Xgf86QDkeB+wHLg/Tw6iaen3ALOBxaEsZw1Ipj8Cfi8h7qBkWgAsD8eHAA+GezdWVhkyNV1WAg4OxwcC3wJObris0mRqtKxG7dOWFkGR6dlNkjbNu2+Y2T8C/1JQjsTp5gOSKY1BybTbzO4Mx98DHiCaSdlYWWXIlMagysrM7Pvh9MDwMZotqzSZ0hhIWY0abTEEaVOvm8CAv5N0h6Q1IWyfad7A/NTU/SVNjqbLb62i1RSvibkVBi6TpEXATxLVKltRVl0yQcNlJWmWpLuJJiZtNrPGyypFJmjJczUKtMUQ9DQtuk/8tJktJ1oJ8EJJ72tIjjI0WX5XA8cC7wZ2A3/ehEySDga+BPy2mb2YFTUhrC9yJcjUeFmZ2atm9m6iGacnSjo+I/pA5EqRqfGyGiXaYgiKTM8eCGb2ZPh+BvgKUbMzbZr3oCk73bzvmNnT4UV+Dfgr3mimD0wmSQcSKdzPm9mXQ3CjZZUkUxvKqoOZ/StwG9ESxa14ruIytamsRoG2GIIi07P7jqS3SDqkcwycDtxP+jTvQVNquvkgBOookMB/JCqvgckkScBngQfM7C9ilxorqzSZWlBWR0qaG44PAk4FttNsWSXK1HRZjRxN91Z3PkRTrx8kGgVwSUMyvJ1oRMI9wLaOHMBbiTaFeCh8HzEAWf6WqEncWVb2/Cw5gEtC2e0AVg5Qps8B9wH3Er2kCwYs088QuQbuBe4OnzOaLKsMmZouq3cBd4X73w/8Qd7zPYCySpOp0bIatY8vMeE4jjPitMU15DiO4zSEGwLHcZwRxw2B4zjOiOOGwHEcZ8RxQ+A4jjPiuCFwHMcZcdwQOI7jjDj/H0kW4a7FiM4pAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.contourf(pr_ldct['ECHAM63'].lon, pr_ldct['ECHAM63'].lat, pr_ldor['ECHAM63'][10])\n",
-    "plt.colorbar()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x7fb2bf66cb90>"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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krbNfRmBQqfofdzoMue571qor7a6sOstpSitolBkJQ5A2kSYePipGoEXSw1eHbJ0YgWHqJO7W/9tL916RVnGRZ6pKmU5vGVpDUOcIjaJvZ4Pm+6xLpm4NDXUOp6635zrrY3urwRk8htYQdJO8Ch9/WIf54ejlLOGm08v/Oc8Y9OOFZJjr+SjghqBLDPuD4UYgol//c9HO2FHbJczpjIEYNeRKp//sP3H2IR9nMBRra2TOIMg66EhaLekBSVOSNiacl6Srw/l7JK3ISyvpGEnbJD0Yvo8O4W+Q9BVJ35P0kbZybgt53R0+idsEx2lsi6Bd2XS6Ds0w0Itr75dyH7T/1BWqk4SkWcA1RBvMTwO3S5o0s/ti0dYAS8PnVOBa4NSctBuB7Wa2KRiIjcClwIvAB4BTwqedXzOznUXlb6QhSFNK3VKITVwCIckQVpUjKY+6DMB3f0yZ5498NHk76kEy8KNoBOpYLjyrjuXlNUCtz1XAlJk9DCDpRmAtEDcEa4Ebwib2OyTNC9v4Ls5IuxY4PaS/nmi/l0vN7PvAVyWdVIfwdWxefwJwA3A88BIwYWZXSToG+DTRRe4B3mtmz2Xl9aM5KrRCJdSnlMtUtE4fivZ0VZVwWTmS4hctu6Xg0xR5ngGIx0vLIwkfW959sl4usupHnco57XnulQGYmTmCPdPHFo0+X1L8LXvCzCbC8SLg8di5aaK3/jhJcRblpD3OzPYChP3fc908gb+V9CPgs8AVwfikUkeL4CDwX83sTklHAndI2gb8JslNmloo8obcSWXKU3x1lNGNSl41zzyFXlTh5+XhxqAYaS8PSefaSXoeir5g9Yt+l1+QfWa2MuVc0gPSXtnT4hRJW4ZfM7Mngj7+LPAbRC/rqVQ2BMFatSzWdyXdT2ThEps0efkVUTjtyqQbSjBLjjLKrOnUoeDbOfCmmUN+z35s7JDykv6/NGPeNGNQpa5VaQlWbblWIV5HytT99ro1TM9NAtPACbHf48CTBeOMZaR9StLC0BpYCDydJ4iZPRG+vyvpfxO5rbprCOJIWgz8NPB1CjZpJK0H1gMccdTRhcrptGKm5dHLtE2nXYm3M/uxsdw4aXm2DEJZY9AE+jXrul+k1fGkZ6+Me3CIuR1YKmkJ8ARwHvCrbXEmgQ2hD+BUYH/Qjc9kpJ0E1gGbwvcXsoSQdAQwz8z2SXo18C7g1jzhazMEkl5P1Az5fTN7Xir2pwcf2wTAnEUnJGr1A2+aOeStMk5S5TryUStV6coqtjRZBo2y191pmva0sx8bS3TBpRmDprQKBkmRd0JZRT3kir0UZnZQ0gZgKzALuM7Mdkm6OJzfDGwBzgamgBeAC7PShqw3ATdJugh4DDi3VaakPcBcYEzSOcBZwKPA1mAEZhEZgY/lyV+LIQiFfhb4pJl9LgSXb9KMWaqiSQtPUspFKmgdCm1QaL9H3ZZ/8fgzh4XFO+Tihr3dINQ9GKAOejlaqBPlWtXlMkgKvcnPnpltIVL28bDNsWMDLimaNoQ/C7wjJc3iFFHeVkziV6hj1JCAvwHuN7O/ip0q1aTplKoVI0lpZdFSaIvHnykz2qCvZN2jstefxZ7pY1Pza4W37lmeu6gJrqI8A1B2YEG3qMNVmkZW3Ym/YBR5Dnv9QuIUp44Wwc8T9UrfK+nuEPbHZDRpipCloDpVwHUovXgeWW++ZcpKUqBJ11gkz6L3pk4DUCbPdgPa7i5qgjHIMgBlRz71mrIdtFmtgTxF3WlfkdM86hg19FWShz9BSpMmjbGxg4WVSbc58/jdLx9v+86ywuk6kS0pTfwNukyeaS2VXtyzoiTJ2HIXJbmKemUMOnEBlTUKvW5FNN1wlaVJ9XiYaOTM4jqIK/Ju5VXGQHRKp4alrAHpNXHZkgxXLxRYVd9/J/LlpSk70KEKg9Q30OS6PAwMhCHIU+rbvrOsVsVflFaZvTAIZehF30WdhqZluNpHh8WNQdWRQ3V1+Pbi7bq9jLom82WV0WuKuEKd3tF4Q1BEwReJ8ytz7wTg5udX5MQ8PE1eurIthqT43TAmdbcK2h/WtIe3SismqRMZIvdQ3i5dw7oWUNnx+kXy61drIG8wQYt43fLWQPdpvCGoQlyRV037K3PvLGVE+kGSYu7Hm1anBijed9BuEOIMq8LPo5OWQtKb/yC4hFz595ahNQRpRiBPoVcxHt1mkJrPVYxBK32TOeqhA32fYJb3Zl93S8IZXhpvCDr1/9/8/IrSSj0rfrvxqOLKab+mtLy6qQzTxoAXnTVdZChgleb9oa6iMY56qFTyrtIaxVRmIbhuUWV4qOO0aLwhgFcUZVmDUNaVUzR+Hf78upV/lWUvOkmblCbLOHQyvyLuKmrCBLMmtAKK4MrfKctAGIIW7cqz1yOFujk6KMsA9HJtoyo+5SIzR9uvs4xh6PbGREU3SWmCUUqiTgPgk79Gi4EyBO2ktRTqGk6apfg7eXMv6v+uqvjrHhrY6XDGooYhyxgsHn+GPRzLkY++ulCZndCJUm9ay8BbAU4VBtoQtEhS2HW+vdflq0/Lp6zi7/cY8E53K0szDD5UsBrdMAKdLDdehKZPdBxVGmUISm4bdwhJb9udVLhuj1bJU/q9VPJlt7fMo2zHZeteuBuicwaxJVDFPeh0h0YZgip0awx9t/3zdSr+Ov3WnWx3mEf8WuMKLG4Q/I2xHN0cIppX95N2ouvEqDd9qPAo0EhDMCwbvyRRVfH3s5OySNlFjUWSAmspEjcG5enHnIGk57TsaDKnGTTKEGhGQ2MEqij8Jo5IKUpZd1N7KyHprTKqEzbQ96VXNHGTmmF5poeZRhmCOP3uEO0Fo6bY8iZgJc2U3TN9LM0anzPcVNmc3hlcum4IJK0GriLaP/PjZrYpLe6smeE1AE1X+mO7p3u6hk/rfqS5ktr9xnn3r8jKpFnX155+VNczKsOwPqudkqfrwm6OVxHtW/wC8JtmdmdWWknHAJ8GFgN7gPea2XOS3gDcDPwM8HdmtiHEfy3wGeBE4EfAF81sY57sXTUEkmYB1wBnAtPA7ZImzey+usuqQ9F22hnaTSXfy03by5ZVh7JMmq0bdw9luRXKylsmfq8NozPYFNR1a4Cl4XMqcC1wak7ajcB2M9skaWP4fSnwIvAB4JTwifNhM/uKpDFgu6Q1ZnZLlvzdbhGsAqbM7GEASTcCa4FMQ9Cvt+del9tLJd8NsuQvo0TjxqDlHoobgPY3z17dt3ZjMChLTKQRb4VVqetV70Fea3BAKaLr1gI3hE3sd0iaJ2kh0dt+Wtq1wOkh/fXAbcClZvZ94KuSTooLYWYvAF8JxzOS7gRyH8ZuG4JFwOOx39NElvBlJK0H1gPMnj2v8S6UThh0hd8JrWsuahCKKtle38skY1CEXim5Tp6Xqs9YXc/oAD7r8yXtjP2eMLOJcJyr61LiLMpJe5yZ7QUws72SFhQVVtI84N1ELqdMum0IknqTDnm9CzdyAmDukeND4XQcRcWfRhkXS8sYxDuNm+CHLmvUoPhQ2wFUhkNFyZGK+8xsZVpWCWHtlTctTpG0pZB0BPAp4OpWSyOLbhuCaeCE2O9x4Mkul1mIutwarvTzqdPfXnXLyibhRmCoKKLr0uKMZaR9StLC0BpYCDxdUJ4J4EEz++sikbttCG4HlkpaAjwBnAf8apfLPIROlMawKJpBJqsl0C9jEC/TO5KdNorouklgQ+gDOBXYHxT8MxlpJ4F1wKbw/YU8QSRdARwF/OeiwnfVEJjZQUkbgK1Ew6KuM7NdafH14owr4SGlE/dKFnn7F3ebbpVbxsjF72UVedyoVSdN10m6OJzfDGwhGjo6RTR89MKstCHrTcBNki4CHgPObZUpaQ8wFxiTdA5wFvA8cDmwG7gzGrHKR8zs41nyd30egZltIboBjpPrJio7MmeYXEVQfohrr8t00knSdcEAtI4NuKRo2hD+LPCOlDSLU0QpPdPvVWUTOE5VxnZP16p8/I3WcarhhsDpG2kGoZNOVDcGjtM5bgicvpPXOihqGGaWjbtBcJwOcEPgNIL21kGVoZVuDBynHG4InMZy1EMHOjYIbgwcpzhuCJxG4Z3IjtN73BA4jcONgeP0FjcETiPxse2O0zvcEDiNxY2B4/QGNwROo6nDGLh7yHGyaeyexY7Toq6VYh3HScZbBM5AU3S5CjcYjpOOGwJnKPD+BMfpHDcEztCQZQzcUDhOOm4InKHCFb7jlKeSIZD0F5J2S7pH0ufDZsmtc5dJmpL0gKR3VhfVcYrRbgxaeyD4onSOk0zVFsE24BQz+wng28BlAJKWE2239lZgNfBRSbMqluU4hcnaVtINguMcSiVDYGb/YGYHw88dRJsuA6wFbjSzA2b2CNHWbKuqlOU4ZclzE7kxcJyIOvsIfgu4JRwvAh6PnZsOYYchab2knZJ2zrz0gxrFcRzH6R2SVgdX+JSkjQnnJenqcP4eSSvy0ko6RtI2SQ+G76Nj5xLd75L+U8h/l6QPFZE91xBIulXStxI+a2NxLgcOAp9sBSVkZUn5m9mEma00s5Vjr3pNEZkdx3EaRXB9XwOsAZYD5wcXeZw1wNLwWQ9cWyDtRmC7mS0Ftoffqe53SW8A/gJ4h5m9FThOUuKex3FyZxab2RlZ5yWtA94VCm4p+2nghFi0ceDJvLIcp25aHcWO02VWAVNm9jCApBuJXOT3xeKsBW4IenKHpHmSFgKLM9KuBU4P6a8HbgMuJeZ+Bx6R1HK/HwS+bWbPhDS3Au8hMiKpVB01tDoI9ctm9kLs1CRwnqTZkpYQWcBvVCnLcRynz8xvubHDZ33sXBF3eFqcrLTHmdlegPC9ICevKWCZpMWSjgDO4dCX8kSqrjX0EWA2sE0SwA4zu9jMdkm6iciiHQQuMbMfVSzLcToirVXgcw6cWTNw5KOJXusk9pnZypRzRdzhaXEKu9Lz8jKz5yS9D/g08BLwf4E35+RVzRCY2UkZ564ErqySv+P0i7jhcIPhFKCIOzwtzlhG2qckLTSzvcGN9HReeWb2ReCLEA3GAXJfwn1msTMSlFHm3qfgdMDtwFJJSySNEXXkTrbFmQQuCKOHTgP2B3dPVtpJYF04Xgd8IRae6H6XtCB8Hw38NvDxPOHdEDgjQ7sxKKrw3TA4eYT5VBuArcD9wE3BRX6xpItDtC3Aw0R+/I8RKenUtCHNJuBMSQ8CZ4bfhPMt9/uXOdT9fpWk+4CvAZvM7Nt58vt+BI7Tho80cjrBzLYQKft42ObYsQGXFE0bwp8FEod/prnfzez8UoLjLYLG4Mse9AZvFTjO4XiLoCY66VxMUi5pCqdsh2UrH+/odBwnDzcEFSmqzDt9E+00fns6NwivUORetLuH/P45w4wbghJUcQ/027Uws2zclVlJ/H45o8LIG4J+K+heUnfrwI2L4wwHI2kIRkn5J1H0+pOUfNLa/mlxHccZDIbaEIy6wq9Kmfvn/nTHGVwG0hBkvYW68u8/7jJynMGikYbAx3YPPu3/jRsGx2kujZpQZnPGXLkPKf6/OlXwCZfdpZEtAmc46Vc/Ql2T9NrzGvVWThkXYFILsRPF7sagO7ghcPpCncq5k3Li54qUWWTi4LAbhjIz4TvJy+kfbgicRlF1OGqnb5nx8nqp3Oo0Hlnlt8pxBewkUYshkPRHRBsmH2tm+0LYZcBFRJsi/K6Zba2jLGc06GTk0SDO/O5VuW4AnCwqGwJJJxCtk/1YLGw50eYKbwXeCNwq6WTfrtIpQ17rwP31jlMPdbQI/gfwfl7ZOQdgLXCjmR0AHpE0BawC/qWG8pwRo8jbrL/xOk7nVBo+KumXgSfM7JttpxYBj8d+T4ewpDzWS9opaefMD79fRRzHcRynA3JbBJJuBY5POHU58MfAWUnJEsIsKX8zmwAmAOYeOZ4Yx3Ecx+keuS0CMzvDzE5p/xDtvbkE+KakPcA4cKek44laACfEshkHnqxffMdxnGYgabWkByRNSdqYcF6Srg7n75G0Ii+tpGMkbZP0YPg+OnbushD/AUnvjIWPSZqQ9G1JuyW9J0/2jl1DZnavmS0ws8VmtphI+a8ws+8Ak8B5kmZLWgIsBb7RaVmO4zhNRtIs4BpgDbAcOD8MmomzhkgXLgXWA1VFADQAAAw6SURBVNcWSLsR2G5mS4Ht4Xf7gJzVwEdDPhB5a542s5NDfv+UJ39Xlpgws13ATcB9wJeBS3zEkOM4Q8wqYMrMHjazGeBGokEzcdYCN1jEDmCepIU5adcC14fj64FzYuE3mtkBM3sEaA3IAfgt4L8DmNlLrSH9WdRmCELLYF/s95VmdqKZvcXMbqmrHMdxnD4xvzWwJXzWx84VGSCTFicr7XFmthcgfC/IykvSvPD7g5LulPQZScflXZjPLHYcZ2SZ9aJx1EMHikbfZ2YrU84VGSCTFqfw4JoCeR1B1Cf7NTP7Q0l/CHwY+I2szBq1+mi/8MlIjuNUpMgAmbQ4WWmfCu4jwvfTOXk9C7wAfD6EfwZYQQ4jbQjGdk+/bARax/FPVnzHcZwYtwNLJS2RNEbUkTvZFmcSuCCMHjoN2B/cPVlpJ4F14Xgdr0zcTRyQY2YGfBE4PcR7B1FfbSYj6xoqotDT4tSxQJnjOMODmR2UtAHYCswCrjOzXZIuDuc3A1uAs4k6dl8ALsxKG7LeBNwk6SKiZXzODWl2SWoNyDnIoQNyLgU+IemvgWda5WQxVIagiIKu+43eV3V0HAfAzLYQKft42ObYsQGXFE0bwp8leqtPSnMlcGVC+KPAL5aRvbGGoF1hF1liNymsV/vndquVUOY+OI7jdEKjDIFenCnkjilDP3z6RZV3Umui1wbMcRynUYZgWMlT6FktmrRwbxk4o4S/FHWXkR41NMj4g+E0nTrqqI/U6w3eIhgyOt1/13HqIl4H09yfvqlQs3BDMMBU6TdxY+Bk0akLsmgfnyv/ZuGGYETphzEYxf6Nbim8uoZH520HmldPXKEPB24IRphuGoMsBTEMLZJ+K8C6yi86sTLp/+r3PXDqww3BiJP1MLc//EXe6HulHJLKaX+7raq83I/9CnFjMOr3YhhxQ+CkUvecjvY8yrQKOlkSpKqcrvAOxe/H8FLZEEj6HWAD0XoXXzKz94fwy4CLgB8Bv2tmW6uW5QwXrlgcpxlUMgSSfolop5yfMLMDkhaE8Pg2am8EbpV0su9S5jiO0zyqTih7H7DJzA4AmFlrreysbdQcx3GcBlHVEJwM/IKkr0v6J0k/E8KLbNsGgKT1ra3fZl76QUVxHMdxnLLkuoYk3Qocn3Dq8pD+aOA04GeI1s1+MyW2XjOzCWAC4KhXL8jbns1xHMepmVxDYGZnpJ2T9D7gc2Gd7W9IegmYT7Ft2xzHcZwGUNU19PfA2wEknQyMAftI2UatYlmO4zhOF6g6fPQ64DpJ3wJmgHWhdZC1jZrjOI7TICq1CMxsxsx+3cxOMbMVZvaPsXNXmtmJZvYWM7uluqiO4zjNRdJqSQ9ImpK0MeG8JF0dzt8jaUVeWknHSNom6cHwfXTs3GUh/gOS3hkL/7Kkb0raJWmzpFl5svt+BI7jOBUJyvYaYA2wHDg/zKeKs4bITb4UWA9cWyDtRmC7mS0Ftoff7XO1VgMfjSn895rZTwKnAMcSNrzPwg2B4zhOdVYBU2b2sJnNADcSzaeKsxa4wSJ2APMkLcxJuxa4PhxfD5wTC0+cq2Vmz4c4RxD12+aOxnRD4DiOU50ic6fS4mSlPc7M9gKE7wVFypO0FXga+C5wc57wvuic4zgji16cKbPm1XxJO2O/J8I8KCg2dyotTuF5VwXyig7M3ilpDvBJopGd27Iyc0PgOI5TjH1mtjLlXJG5U2lxxjLSPiVpoZntDW6k1jI+ueWZ2YuSJoncSJmGwF1DjuM41bkdWCppiaQxoo7cybY4k8AFYfTQacD+4O7JSjsJrAvH64AvxMIPm6sl6fXBYCDpCOBsYHee8N4icBzHqYiZHZS0AdgKzAKuM7Ndki4O5zcDW4gU8xTwAnBhVtqQ9SaipXsuAh4jjAAKeR82V0vS64BJSbNDXv8IbM6T3w2B4zhODZjZFiJlHw/bHDs24JKiaUP4s8A7UtJcCVzZFvYU0bpvpXDXkOM4zojjhsBxHGfEcUPgOI4z4rghcBzHGXHcEDiO44w4bggcx3FGHDcEjuM4I04lQyDppyTtkHR32IB+Vexc4lrZjuM4TrOoOqHsQ8Cfmdktks4Ov09vWyv7jcCtkk72Xcocx3GaR1XXkAFzw/FRvLLoUepa2Y7jOE6zqNoi+H1gq6QPExmVnwvhi4AdsXhJa3MDIGk90W49zHnV6yuK4ziO45Ql1xBIuhU4PuHU5URrYPyBmX1W0nuBvwHOoMT62mE97wmAo169IHcnHcdxHKdecg2BmZ2Rdk7SDcDvhZ+fAT4ejousze04juM0gKp9BE8C/y4cvx14MBwnrpVdsSzHcRynC1TtI/gvwFVhA4QXCb7+tLWyK5blOI7jdIFKhsDMvgq8LeXcYWtlO47jOM3DZxY7juOMOG4IHMdxRhw3BI7jOCOOGwLHcZwakLQ6rK02JWljwnlJujqcv0fSiry0ko6RtE3Sg+H76Ni5xPXcJL1N0r3h3NWSkuZ1HYIbAsdxnIpImgVcA6wBlgPnhzXX4qwhGkq/lGiE5bUF0m4EtpvZUmB7+E3bem6rgY+GfAj5ro+VtTpP/qrDR2tl6U++iVt2XtNvMRzHccqyCpgys4cBJN1ItObafbE4a4EbzMyAHZLmSVoILM5IuxY4PaS/HrgNuJTYem7AI5KmgFWS9gBzzexfQl43AOcAt2QJ3yhDcMcdd+yT9GgXsp4P7OtCvlVookzQTLmaKBM0U65RkunHqmbw/MFntn75Ox+dXzD6HEk7Y78nwhI5EK2l9njs3DRwalv6pDiLctIeZ2Z7Acxsr6QFsbyS1nP7YThuD8+kUYbAzI7tRr6SdprZym7k3SlNlAmaKVcTZYJmyuUylcPMct0mBSmyvlpanMJrs3UpL+8jcBzHqYEi66ulxclK+1RwHxG+ny6Q13iOHIfhhsBxHKc6twNLJS2RNEbUkTvZFmcSuCCMHjoN2B/cPllpJ4F14Xgd8IVY+GHruYX8vivptDBa6IJYmlQa5RrqIhP5UXpOE2WCZsrVRJmgmXK5TH3AzA5K2gBsBWYB14U11y4O5zcDW4CziTbqegG4MCttyHoTcJOki4DHgHNDmqz13N4H/B3wGqJO4syOYgBFHdiO4zjOqOKuIcdxnBHHDYHjOM6IM1SGQNK5knZJeknSylj4Ykk/kHR3+GyOnSs9HbsuucK52qaJV5DvTyU9Ebs/Z+fJ1yvypu33UI494f+4uzWWPGv6fxfluE7S05K+FQsrvQxBD2RqbJ1yEjCzofkA/wZ4C9Hsu5Wx8MXAt1LSfAP4WaLxt7cAa3oo13Lgm8BsYAnwEDCrV3LF5PhT4I8SwlPl69H/OSuU+WZgLMiyvE91aw8wvy3sQ8DGcLwR+PMeyPGLwIp4fU6To1f/X4pMjaxT/kn+DFWLwMzuN7MHisYP43Lnmtm/WFRLW9OxeyXXy9PEzewRotEEq3olVwES5eth+S9P2zezGaA19b4prCWa9k/47vp/ZGb/DPxrQTl68v+lyJRGv+uUk8BQGYIclki6S9I/SfqFELaIDqZj10jWlPNey7VB0YqI18VcC2ny9Yp+lx/HgH+QdIek9SHskOn/wILU1N0lTY5+378m1ikngYGbRyDpVuD4hFOXm1naxIm9wJvM7FlJbwP+XtJb6XA6do1y1TpNPIss+YhWK/xgKOODwF8Cv9UNOUrS7/Lj/LyZPRnWetkmaXef5ChDP+9fU+uUk8DAGQIzO6ODNAeAA+H4DkkPASfT4XTsuuSi5mniWRSVT9LHgP+TI1+v6Hf5L2NmT4bvpyV9nsid8ZSkhRYtBhaf/t9r0uTo2/0zs6daxw2rU04CI+EaknSswlrdkt5MNB37YetwOnaN1DpNvFOC8mjxH4DW6I9E+bolRwJFpu13HUmvk3Rk6xg4i+gepU3/7zWlliHohUANrlNOEv3ura7zQ1Thpone/p8Ctobw9wC7iEYr3Am8O5ZmJVElfQj4CGG2dS/kCucuD2U/QGxkUC/kipX1CeBe4B6iB3Vhnnw9/E/PBr4dZLi8T/XqzaHufDPUo8tD+BuINgt5MHwf0wNZPkXk6mwtN3xRlhy9+P9SZGpsnfLP4R9fYsJxHGfEGQnXkOM4jpOOGwLHcZwRxw2B4zjOiOOGwHEcZ8RxQ+A4jjPiuCFwHMcZcdwQOI7jjDj/H+QioCp8+cGEAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.contourf(lonintp, latintp, pr_ldor_intp['ECHAM63'][10])\n",
-    "plt.colorbar()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['CAM3', 'CAM4', 'CNRM-AM5', 'ECHAM61', 'ECHAM63', 'LMDZ5A', 'MIROC5', 'MPAS', 'MetUM-CTL', 'MetUM-ENT'])"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "itcz_ldct_land.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
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