sst_bias-spinup7.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SST Biases\n",
    "based on https://github.com/COSIMA/ACCESS-OM2-1-025-010deg-report/blob/master/figures/temperature_biases/sst_bias.ipynb but also includes 01deg_jra55v13_ryf8485_spinup7 and 01deg_jra55v13_ryf9091.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "netcdf_index loaded.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/config.py:168: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.\n",
      "  data = yaml.load(f.read()) or {}\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/distributed/config.py:20: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.\n",
      "  defaults = yaml.load(f)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available exptdata keys:  ['1deg', '025deg', '01deg']\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import cosima_cookbook as cc\n",
    "import matplotlib.pyplot as plt\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import IPython.display\n",
    "import cmocean as cm\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cft\n",
    "from dask.distributed import Client, progress\n",
    "\n",
    "\n",
    "import sys, os\n",
    "sys.path.append(os.path.join(os.getcwd(), '..'))  # so we can import ../exptdata\n",
    "import exptdata\n",
    "print('Available exptdata keys: ', [k for k in exptdata.exptdict.keys()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "exptdata.exptdict['spinup7'] = \\\n",
    "              {'model': 'access-om2-01',\n",
    "               'expt': '01deg_jra55v13_ryf8485_spinup7',\n",
    "               'desc': 'ACCESS-OM2-01 spinup 7',\n",
    "               'n_files': None,\n",
    "#                'time_units': 'days since 0001-01-01',\n",
    "               'time_units': 'days since 1988-01-01', # so .sel(time=slice(pd.datetime(1998,1,1),None)) removes first 10 years\n",
    "               'offset': None,\n",
    "               'exptdir': '/g/data3/hh5/tmp/cosima/access-om2-01/01deg_jra55v13_ryf8485_spinup7'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "exptdata.exptdict['01deg_jra55v13_ryf9091'] = \\\n",
    "              {'model': 'access-om2-01',\n",
    "               'expt': '01deg_jra55v13_ryf9091/',\n",
    "               'desc': 'ACCESS-OM2-01 RYF 1990-91',\n",
    "               'n_files': None,\n",
    "#                'time_units': 'days since 0001-01-01',\n",
    "               'time_units': 'days since 1988-01-01', # so .sel(time=slice(pd.datetime(1998,1,1),None)) removes first 10 years\n",
    "               'offset': None,\n",
    "               'exptdir': '/g/data3/hh5/tmp/cosima/access-om2-01/01deg_jra55v13_ryf9091/'}"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# use common start and end dates for all runs\n",
    "tstart = exptdata.clim_tstart\n",
    "tend = exptdata.clim_tend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "tstart = pd.datetime(1998,1,1)  # must be 1 Jan so we don't average ove rpartial years\n",
    "# tend = None # unused"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "figdir = ''\n",
    "def savefigure(fname):\n",
    "    plt.savefig(os.path.join(figdir, fname+'.png'),dpi=300, bbox_inches=\"tight\")  # comment out to disable saving\n",
    "    plt.savefig(os.path.join(figdir, fname+'.pdf'),dpi=300, bbox_inches=\"tight\")  # comment out to disable saving\n",
    "    return"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "client = Client('tcp://10.0.64.18:8786', local_dir='/local/g40/amh157')\n",
    "client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "for ekey in exptdata.exptdict.keys():\n",
    "    e = exptdata.exptdict[ekey]\n",
    "    e['WOA13expt'] = 'woa13/01'  # redefined below for 1deg, 025deg\n",
    "    e['WOA13file'] = 'woa13_ts_??_mom01.nc'  # redefined below for 1deg, 025deg\n",
    "exptdata.exptdict['1deg']['WOA13expt'] = 'woa13/10_KDS50'\n",
    "exptdata.exptdict['1deg']['WOA13file'] = 'woa13_ts_??_mom10.nc'   \n",
    "exptdata.exptdict['025deg']['WOA13expt'] = 'woa13/025_KDS50'\n",
    "exptdata.exptdict['025deg']['WOA13file'] = 'woa13_ts_??_mom025.nc'   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using database sqlite:////g/data3/hh5/tmp/cosima/cosima-cookbook/cosima-cookbook.db\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1deg: 1998-2016\n",
      "Using database sqlite:////g/data3/hh5/tmp/cosima/cosima-cookbook/cosima-cookbook.db\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/array/numpy_compat.py:28: RuntimeWarning: invalid value encountered in true_divide\n",
      "  x = np.divide(x1, x2, out)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using database sqlite:////g/data3/hh5/tmp/cosima/cosima-cookbook/cosima-cookbook.db\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "025deg: 1998-2016\n",
      "Using database sqlite:////g/data3/hh5/tmp/cosima/cosima-cookbook/cosima-cookbook.db\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/array/numpy_compat.py:28: RuntimeWarning: invalid value encountered in true_divide\n",
      "  x = np.divide(x1, x2, out)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using database sqlite:////g/data3/hh5/tmp/cosima/cosima-cookbook/cosima-cookbook.db\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "01deg: 1998-2016\n",
      "Using database sqlite:////g/data3/hh5/tmp/cosima/cosima-cookbook/cosima-cookbook.db\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/array/numpy_compat.py:28: RuntimeWarning: invalid value encountered in true_divide\n",
      "  x = np.divide(x1, x2, out)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using database sqlite:////g/data3/hh5/tmp/cosima/cosima-cookbook/cosima-cookbook.db\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "spinup7: 1998-2007\n",
      "Using database sqlite:////g/data3/hh5/tmp/cosima/cosima-cookbook/cosima-cookbook.db\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/array/numpy_compat.py:28: RuntimeWarning: invalid value encountered in true_divide\n",
      "  x = np.divide(x1, x2, out)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using database sqlite:////g/data3/hh5/tmp/cosima/cosima-cookbook/cosima-cookbook.db\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "01deg_jra55v13_ryf9091: 1998-2033\n",
      "Using database sqlite:////g/data3/hh5/tmp/cosima/cosima-cookbook/cosima-cookbook.db\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/compatibility.py:93: FutureWarning: The autoclose argument is no longer used by xarray.open_dataset() and is now ignored; it will be removed in a future version of xarray. If necessary, you can control the maximum number of simultaneous open files with xarray.set_options(file_cache_maxsize=...).\n",
      "  return func(*args, **kwargs)\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/array/numpy_compat.py:28: RuntimeWarning: invalid value encountered in true_divide\n",
      "  x = np.divide(x1, x2, out)\n"
     ]
    }
   ],
   "source": [
    "for ekey in exptdata.exptdict.keys():\n",
    "    e = exptdata.exptdict[ekey]\n",
    "    surface_temp = cc.get_nc_variable(e['expt'], 'ocean.nc', 'temp',\n",
    "                            chunks={'st_ocean': None}, n=e['n_files'],\n",
    "                            time_units=e['time_units'], offset=e['offset']).isel(st_ocean=0)\n",
    "    lastyear = surface_temp.time[-1].values.astype('datetime64[Y]') # ensure we stop at Jan 1\n",
    "    surface_temp = surface_temp.sel(time=slice(tstart,lastyear))\n",
    "    e['yearrange'] = str(tstart.year)+'-'+str(lastyear-1)\n",
    "    print(ekey+': '+e['yearrange'])\n",
    "    e['temp_WOA13'] = cc.get_nc_variable(e['WOA13expt'], e['WOA13file'], 'temp').isel(ZT=0).mean('time')\n",
    "    temp_anom_tmp = surface_temp.mean('time') - e['temp_WOA13'].values - 273.15\n",
    "    e['temp_anom'] = temp_anom_tmp.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,14))\n",
    "clev = np.arange(-3,3.01,0.3)\n",
    "land_50m = cft.NaturalEarthFeature('physical', 'land', '50m',\n",
    "                                        edgecolor='black',\n",
    "                                        facecolor='gray',linewidth=0.5)\n",
    "\n",
    "for i, ekey in enumerate(exptdata.exptdict.keys()):\n",
    "    e = exptdata.exptdict[ekey]\n",
    "    ax1 = plt.subplot(1+len(exptdata.exptdict)//2,2,i+1,projection=ccrs.Robinson(central_longitude=-100))\n",
    "    ax1.coastlines(resolution='50m')\n",
    "    ax1.add_feature(land_50m)\n",
    "    pn = e['temp_anom'].plot.contourf(cmap=cm.cm.balance,levels=clev,add_colorbar=False,transform=ccrs.PlateCarree())\n",
    "    plt.title('('+chr(i+ord('a'))+') ' + e['desc'] + ', ' + e['yearrange'])\n",
    "    if i==0:\n",
    "        p0 = pn\n",
    "\n",
    "i = i+1\n",
    "e = exptdata.exptdict['01deg']\n",
    "ax1 = plt.subplot(1+len(exptdata.exptdict)//2,2,i+1,projection=ccrs.Robinson(central_longitude=-100))\n",
    "ax1.coastlines(resolution='50m')\n",
    "ax1.add_feature(land_50m)\n",
    "pn = e['temp_WOA13'].plot.contourf(cmap=cm.cm.thermal,levels=np.arange(-2,30.01,1),add_colorbar=False,transform=ccrs.PlateCarree())\n",
    "plt.title('('+chr(i+ord('a'))+') ' + 'WOA13')\n",
    "\n",
    "ax5 = plt.axes([0.92,0.52,0.01,0.33])\n",
    "cb = plt.colorbar(p0,cax=ax5,orientation='vertical')\n",
    "cb.ax.set_ylabel('SST anomaly (°C)')\n",
    "\n",
    "ax6 = plt.axes([0.92,0.13,0.01,0.33])\n",
    "cb = plt.colorbar(pn,cax=ax6,orientation='vertical')\n",
    "cb.ax.set_ylabel('SST (°C)')\n",
    "\n",
    "savefigure('global_sst_bias')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:analysis3-19.01]",
   "language": "python",
   "name": "conda-env-analysis3-19.01-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}