{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 3, analyzing the Baltic Sea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import xarray as xr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: b'/Users/boergel/Documents/work/climateoftheocean/data/ocean_day3d.nc'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m~/miniconda3/envs/xarray/lib/python3.10/site-packages/xarray/backends/file_manager.py:199\u001b[0m, in \u001b[0;36mCachingFileManager._acquire_with_cache_info\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m    198\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 199\u001b[0m     file \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_cache[\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_key]\n\u001b[1;32m    200\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n",
      "File \u001b[0;32m~/miniconda3/envs/xarray/lib/python3.10/site-packages/xarray/backends/lru_cache.py:53\u001b[0m, in \u001b[0;36mLRUCache.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m     52\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock:\n\u001b[0;32m---> 53\u001b[0m     value \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_cache[key]\n\u001b[1;32m     54\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_cache\u001b[39m.\u001b[39mmove_to_end(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: [<class 'netCDF4._netCDF4.Dataset'>, ('/Users/boergel/Documents/work/climateoftheocean/data/ocean_day3d.nc',), 'r', (('clobber', True), ('diskless', False), ('format', 'NETCDF4'), ('persist', False))]",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m/Users/boergel/Documents/work/climateoftheocean/2022-01-20-climate-of-the-ocean-h3.ipynb Zelle 3\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/boergel/Documents/work/climateoftheocean/2022-01-20-climate-of-the-ocean-h3.ipynb#W2sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m ds \u001b[39m=\u001b[39m xr\u001b[39m.\u001b[39;49mopen_dataset(\u001b[39m\"\u001b[39;49m\u001b[39mdata/ocean_day3d.nc\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n",
      "File \u001b[0;32m~/miniconda3/envs/xarray/lib/python3.10/site-packages/xarray/backends/api.py:495\u001b[0m, in \u001b[0;36mopen_dataset\u001b[0;34m(filename_or_obj, engine, chunks, cache, decode_cf, mask_and_scale, decode_times, decode_timedelta, use_cftime, concat_characters, decode_coords, drop_variables, backend_kwargs, *args, **kwargs)\u001b[0m\n\u001b[1;32m    483\u001b[0m decoders \u001b[39m=\u001b[39m _resolve_decoders_kwargs(\n\u001b[1;32m    484\u001b[0m     decode_cf,\n\u001b[1;32m    485\u001b[0m     open_backend_dataset_parameters\u001b[39m=\u001b[39mbackend\u001b[39m.\u001b[39mopen_dataset_parameters,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    491\u001b[0m     decode_coords\u001b[39m=\u001b[39mdecode_coords,\n\u001b[1;32m    492\u001b[0m )\n\u001b[1;32m    494\u001b[0m overwrite_encoded_chunks \u001b[39m=\u001b[39m kwargs\u001b[39m.\u001b[39mpop(\u001b[39m\"\u001b[39m\u001b[39moverwrite_encoded_chunks\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m)\n\u001b[0;32m--> 495\u001b[0m backend_ds \u001b[39m=\u001b[39m backend\u001b[39m.\u001b[39;49mopen_dataset(\n\u001b[1;32m    496\u001b[0m     filename_or_obj,\n\u001b[1;32m    497\u001b[0m     drop_variables\u001b[39m=\u001b[39;49mdrop_variables,\n\u001b[1;32m    498\u001b[0m     \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mdecoders,\n\u001b[1;32m    499\u001b[0m     \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs,\n\u001b[1;32m    500\u001b[0m )\n\u001b[1;32m    501\u001b[0m ds \u001b[39m=\u001b[39m _dataset_from_backend_dataset(\n\u001b[1;32m    502\u001b[0m     backend_ds,\n\u001b[1;32m    503\u001b[0m     filename_or_obj,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    510\u001b[0m     \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs,\n\u001b[1;32m    511\u001b[0m )\n\u001b[1;32m    512\u001b[0m \u001b[39mreturn\u001b[39;00m ds\n",
      "File \u001b[0;32m~/miniconda3/envs/xarray/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:550\u001b[0m, in \u001b[0;36mNetCDF4BackendEntrypoint.open_dataset\u001b[0;34m(self, filename_or_obj, mask_and_scale, decode_times, concat_characters, decode_coords, drop_variables, use_cftime, decode_timedelta, group, mode, format, clobber, diskless, persist, lock, autoclose)\u001b[0m\n\u001b[1;32m    529\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mopen_dataset\u001b[39m(\n\u001b[1;32m    530\u001b[0m     \u001b[39mself\u001b[39m,\n\u001b[1;32m    531\u001b[0m     filename_or_obj,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    546\u001b[0m     autoclose\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m,\n\u001b[1;32m    547\u001b[0m ):\n\u001b[1;32m    549\u001b[0m     filename_or_obj \u001b[39m=\u001b[39m _normalize_path(filename_or_obj)\n\u001b[0;32m--> 550\u001b[0m     store \u001b[39m=\u001b[39m NetCDF4DataStore\u001b[39m.\u001b[39;49mopen(\n\u001b[1;32m    551\u001b[0m         filename_or_obj,\n\u001b[1;32m    552\u001b[0m         mode\u001b[39m=\u001b[39;49mmode,\n\u001b[1;32m    553\u001b[0m         \u001b[39mformat\u001b[39;49m\u001b[39m=\u001b[39;49m\u001b[39mformat\u001b[39;49m,\n\u001b[1;32m    554\u001b[0m         group\u001b[39m=\u001b[39;49mgroup,\n\u001b[1;32m    555\u001b[0m         clobber\u001b[39m=\u001b[39;49mclobber,\n\u001b[1;32m    556\u001b[0m         diskless\u001b[39m=\u001b[39;49mdiskless,\n\u001b[1;32m    557\u001b[0m         persist\u001b[39m=\u001b[39;49mpersist,\n\u001b[1;32m    558\u001b[0m         lock\u001b[39m=\u001b[39;49mlock,\n\u001b[1;32m    559\u001b[0m         autoclose\u001b[39m=\u001b[39;49mautoclose,\n\u001b[1;32m    560\u001b[0m     )\n\u001b[1;32m    562\u001b[0m     store_entrypoint \u001b[39m=\u001b[39m StoreBackendEntrypoint()\n\u001b[1;32m    563\u001b[0m     \u001b[39mwith\u001b[39;00m close_on_error(store):\n",
      "File \u001b[0;32m~/miniconda3/envs/xarray/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:379\u001b[0m, in \u001b[0;36mNetCDF4DataStore.open\u001b[0;34m(cls, filename, mode, format, group, clobber, diskless, persist, lock, lock_maker, autoclose)\u001b[0m\n\u001b[1;32m    373\u001b[0m kwargs \u001b[39m=\u001b[39m \u001b[39mdict\u001b[39m(\n\u001b[1;32m    374\u001b[0m     clobber\u001b[39m=\u001b[39mclobber, diskless\u001b[39m=\u001b[39mdiskless, persist\u001b[39m=\u001b[39mpersist, \u001b[39mformat\u001b[39m\u001b[39m=\u001b[39m\u001b[39mformat\u001b[39m\n\u001b[1;32m    375\u001b[0m )\n\u001b[1;32m    376\u001b[0m manager \u001b[39m=\u001b[39m CachingFileManager(\n\u001b[1;32m    377\u001b[0m     netCDF4\u001b[39m.\u001b[39mDataset, filename, mode\u001b[39m=\u001b[39mmode, kwargs\u001b[39m=\u001b[39mkwargs\n\u001b[1;32m    378\u001b[0m )\n\u001b[0;32m--> 379\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mcls\u001b[39;49m(manager, group\u001b[39m=\u001b[39;49mgroup, mode\u001b[39m=\u001b[39;49mmode, lock\u001b[39m=\u001b[39;49mlock, autoclose\u001b[39m=\u001b[39;49mautoclose)\n",
      "File \u001b[0;32m~/miniconda3/envs/xarray/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:327\u001b[0m, in \u001b[0;36mNetCDF4DataStore.__init__\u001b[0;34m(self, manager, group, mode, lock, autoclose)\u001b[0m\n\u001b[1;32m    325\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_group \u001b[39m=\u001b[39m group\n\u001b[1;32m    326\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_mode \u001b[39m=\u001b[39m mode\n\u001b[0;32m--> 327\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mformat \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mds\u001b[39m.\u001b[39mdata_model\n\u001b[1;32m    328\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_filename \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mds\u001b[39m.\u001b[39mfilepath()\n\u001b[1;32m    329\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mis_remote \u001b[39m=\u001b[39m is_remote_uri(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_filename)\n",
      "File \u001b[0;32m~/miniconda3/envs/xarray/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:388\u001b[0m, in \u001b[0;36mNetCDF4DataStore.ds\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    386\u001b[0m \u001b[39m@property\u001b[39m\n\u001b[1;32m    387\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mds\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[0;32m--> 388\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_acquire()\n",
      "File \u001b[0;32m~/miniconda3/envs/xarray/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:382\u001b[0m, in \u001b[0;36mNetCDF4DataStore._acquire\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m    381\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_acquire\u001b[39m(\u001b[39mself\u001b[39m, needs_lock\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m):\n\u001b[0;32m--> 382\u001b[0m     \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_manager\u001b[39m.\u001b[39macquire_context(needs_lock) \u001b[39mas\u001b[39;00m root:\n\u001b[1;32m    383\u001b[0m         ds \u001b[39m=\u001b[39m _nc4_require_group(root, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_group, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_mode)\n\u001b[1;32m    384\u001b[0m     \u001b[39mreturn\u001b[39;00m ds\n",
      "File \u001b[0;32m~/miniconda3/envs/xarray/lib/python3.10/contextlib.py:135\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    133\u001b[0m \u001b[39mdel\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39margs, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mkwds, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfunc\n\u001b[1;32m    134\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 135\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mnext\u001b[39;49m(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mgen)\n\u001b[1;32m    136\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mStopIteration\u001b[39;00m:\n\u001b[1;32m    137\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mgenerator didn\u001b[39m\u001b[39m'\u001b[39m\u001b[39mt yield\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39m\n",
      "File \u001b[0;32m~/miniconda3/envs/xarray/lib/python3.10/site-packages/xarray/backends/file_manager.py:187\u001b[0m, in \u001b[0;36mCachingFileManager.acquire_context\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m    184\u001b[0m \u001b[39m@contextlib\u001b[39m\u001b[39m.\u001b[39mcontextmanager\n\u001b[1;32m    185\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39macquire_context\u001b[39m(\u001b[39mself\u001b[39m, needs_lock\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m):\n\u001b[1;32m    186\u001b[0m     \u001b[39m\"\"\"Context manager for acquiring a file.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 187\u001b[0m     file, cached \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_acquire_with_cache_info(needs_lock)\n\u001b[1;32m    188\u001b[0m     \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m    189\u001b[0m         \u001b[39myield\u001b[39;00m file\n",
      "File \u001b[0;32m~/miniconda3/envs/xarray/lib/python3.10/site-packages/xarray/backends/file_manager.py:205\u001b[0m, in \u001b[0;36mCachingFileManager._acquire_with_cache_info\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m    203\u001b[0m     kwargs \u001b[39m=\u001b[39m kwargs\u001b[39m.\u001b[39mcopy()\n\u001b[1;32m    204\u001b[0m     kwargs[\u001b[39m\"\u001b[39m\u001b[39mmode\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_mode\n\u001b[0;32m--> 205\u001b[0m file \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_opener(\u001b[39m*\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_args, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m    206\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_mode \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mw\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[1;32m    207\u001b[0m     \u001b[39m# ensure file doesn't get overriden when opened again\u001b[39;00m\n\u001b[1;32m    208\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_mode \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39ma\u001b[39m\u001b[39m\"\u001b[39m\n",
      "File \u001b[0;32msrc/netCDF4/_netCDF4.pyx:2353\u001b[0m, in \u001b[0;36mnetCDF4._netCDF4.Dataset.__init__\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32msrc/netCDF4/_netCDF4.pyx:1963\u001b[0m, in \u001b[0;36mnetCDF4._netCDF4._ensure_nc_success\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: b'/Users/boergel/Documents/work/climateoftheocean/data/ocean_day3d.nc'"
     ]
    }
   ],
   "source": [
    "ds = xr.open_dataset(\"data/ocean_day3d.nc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you start your work at IOW you will start by reading literature about the dynamics of the Baltic Sea. Soon you will notice that nearly every article starts with a paragraph similar to:\n",
    "\n",
    "> \"The hydrography of the Baltic Sea depends on the water exchange with the world ocean which is restricted by the narrows and sills of the Danish Straits and on river runoff into the Baltic [Meier and Kauker, 2003].\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at this figure ([Meier and Kauker, 2003](https://agupubs.onlinelibrary.wiley.com/cms/asset/144f12b6-8897-474b-b4e7-4dc1ad3c8faa/)) and zoom on the connection between the world ocean and the Baltic Sea.\n",
    "\n",
    "The coordinates are : `lon=8-17, lat=53-59`\n",
    "\n",
    "![](https://agupubs.onlinelibrary.wiley.com/cms/asset/144f12b6-8897-474b-b4e7-4dc1ad3c8faa/jgrc9306-fig-0001.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check you data first. In MOM we use different names for lon, lat and depth:\n",
    "\n",
    "lon = xt_ocean\n",
    "lat = yt_ocean\n",
    "depth = st_ocean\n",
    "\n",
    "xarray allows you to select areas using\n",
    "\n",
    "```python\n",
    "\n",
    "ds.sel(xt_ocean=slice(lon1, lon2), yt_ocean=slice(lat1, lat2))\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Frozen({'xt_ocean': 91, 'yt_ocean': 102, 'time': 11, 'nv': 2, 'xu_ocean': 91, 'yu_ocean': 102, 'st_ocean': 100, 'st_edges_ocean': 101, 'sw_ocean': 100, 'sw_edges_ocean': 101})"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.dims"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "danish_straits = ds.sel(xt_ocean=slice(,), yt_ocean=slice(,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fafbf7ec110>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instead of selecting coordinate, we can also use \n",
    "# index selction using .isel\n",
    "# we are selecting the surface and the first timestep of \n",
    "# the variable salt and plot it\n",
    "\n",
    "danish_straits.salt.isel(st_ocean=0, time = 0).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 1:** What is the first thing you notice, when you compare to the realistic bathymetry above?\n",
    "\n",
    "Answer: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 2:** Look at the colobar. What role play the Danish Straits for the salinity of the Baltic Sea?\n",
    "\n",
    "Answer:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following on common Baltic Sea introductions, you will find something similar to:\n",
    "\n",
    "> The inflow of freshwater by river runoff and a positive net precipitation cause a positive water balance with respect to the North Sea. The positive water balance leads to strong gradients in salinity and ecosystem variables (Reckermann et al., 2008)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So let's look at the mean surface salinity!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7faf43e89090>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "level = [0,2,4,6,8,10,15,20,30,35]\n",
    "\n",
    "f, ax = plt.subplots(1)\n",
    "\n",
    "ds.salt.isel(st_ocean=0).mean(\"time\").plot(levels=level, cmap=plt.cm.jet)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following Markus second sentence of his paper,\n",
    "\n",
    "> In the long-term mean, high-saline water from the Kattegat enters the Baltic proper and low-saline water leaves the Baltic because of the freshwater surplus.\n",
    "\n",
    "Think about the exchange flow of the Baltic Sea. How would the profile of a transect a 16°E look like? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We first select the latitude range 53-57, then longitude at 16°E\n",
    "# note that by using `method=\"nearest\"` we will search for the nearest lon coordinate to 13\n",
    "\n",
    "transect = ds.sel(yt_ocean=slice(53, 57)).sel(xt_ocean=16, method=\"nearest\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This leaves us with the dimensions: time, depth and latitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('time', 'st_ocean', 'yt_ocean')"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transect.salt.dims"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Markus talks about the long-term mean in his paper. So start by averaging over the time dimension.\n",
    "\n",
    "using \n",
    "\n",
    "```python\n",
    ".mean(\"time\")\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "transect_mean_time = transect.mean(\"time\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can average over the latitude, to give us a depth profile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "transect_mean_time_latitude = transect_mean_time.mean(\"yt_ocean\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(1)\n",
    "transect_mean_time_latitude.salt.plot(ax=ax, y=\"st_ocean\")\n",
    "ax.set_ylabel(\"Depth [m]\")\n",
    "ax.set_xlabel(\"Salinity [g/kg]\")\n",
    "ax.invert_yaxis()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following along with Markus Paper:\n",
    "\n",
    "> The bottom water in the deep subbasins is ventilated mainly by large perturbations, so-called major Baltic saltwater inflows [Matthäus and Franck, 1992; Fischer and Matthäus, 1996]. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this year we have no strong inflow. However, we can notice inflows of high saline water analyzing the station located in the Arkona Basin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "by2 = ds.sel(xt_ocean = 16.2, yt_ocean = 55.5, method=\"nearest\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x864 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = (by2.salt - by2.salt.mean(\"time\")).plot(col=\"time\", col_wrap = 3, y=\"st_ocean\")\n",
    "\n",
    "for ax in g.axes[0]:\n",
    "    ax.invert_yaxis()\n",
    "    ax.set_xlabel(\"Salinity[g/kg]\")\n",
    "    ax.set_ylabel(\"Depth [m]\")\n",
    "\n",
    "g.fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 3:** Focus on the month of February. What is happening?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These saline inflows are important for the oxygen supply of the deeper layers, since due to the strong stratification in the Baltic Sea only layers above the permanent halocline are directly influenced by the atmosphere and therefore supplied with oxygen (Mohrholz et al., 2015)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Seasonal cycle of the temperature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now look at the depth-averaged seasonal cycle of the Baltic Sea."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_temp_season = ds.temp.resample(time=\"1M\").mean(\"time\").mean(\"st_ocean\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ds_temp_season' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m/Users/boergel/Documents/work/climateoftheocean/2022-01-20-climate-of-the-ocean-h3.ipynb Zelle 33\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/boergel/Documents/work/climateoftheocean/2022-01-20-climate-of-the-ocean-h3.ipynb#X44sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m (ds_temp_season\u001b[39m-\u001b[39mds_temp_season\u001b[39m.\u001b[39mmean(\u001b[39m\"\u001b[39m\u001b[39mtime\u001b[39m\u001b[39m\"\u001b[39m))\u001b[39m.\u001b[39mplot(col\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mtime\u001b[39m\u001b[39m\"\u001b[39m, col_wrap \u001b[39m=\u001b[39m\u001b[39m3\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'ds_temp_season' is not defined"
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   ],
   "source": [
    "(ds_temp_season-ds_temp_season.mean(\"time\")).plot(col=\"time\", col_wrap =3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 4:** Above you see the deviation from the mean temperature of the Baltic Sea for every single month. Try to discuss the differences for every month."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sea Ice\n",
    "\n",
    "During winter time parts of the Baltic Sea are covered with sea ice. The sea ice influences the air-sea interaction. Sea ice directly influences temperature, salinity, but also the transfer of momentum into the ocean. The annual ice cover varies from only being present in the Bothnian Bay to a nearly fully covered Baltic Sea. Therefore, the Baltic Sea is exposed to great variation in sea ice cover."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "ice = xr.open_dataset(\"data/ice_day.nc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start by analyzing the seasonal sea ice cover."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
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       "Dimensions:      (xt: 91, xb: 92, yt: 102, yb: 103, time: 11, nv: 2, ct: 5, xv: 91, yv: 102)\n",
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       "    MI           (time, yt, xt) float32 ...\n",
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       "    HS           (time, yt, xt) float32 ...\n",
       "    TS           (time, yt, xt) float32 ...\n",
       "    ...           ...\n",
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       "    time_bounds  (time, nv) timedelta64[ns] 0 days 28 days ... 303 days 334 days\n",
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       "    filename:   ice_day.nc\n",
       "    title:      ERGOM-MOM510 8 n.m. Baltic Hiresaff 1850-2009\n",
       "    grid_type:  regular\n",
       "    grid_tile:  N/A</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-71210d18-2df6-4c25-b435-c3e88b702b1a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-71210d18-2df6-4c25-b435-c3e88b702b1a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>xt</span>: 91</li><li><span class='xr-has-index'>xb</span>: 92</li><li><span class='xr-has-index'>yt</span>: 102</li><li><span class='xr-has-index'>yb</span>: 103</li><li><span class='xr-has-index'>time</span>: 11</li><li><span class='xr-has-index'>nv</span>: 2</li><li><span class='xr-has-index'>ct</span>: 5</li><li><span class='xr-has-index'>xv</span>: 91</li><li><span class='xr-has-index'>yv</span>: 102</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-66bf27ee-866e-44b2-814f-152c8696070b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-66bf27ee-866e-44b2-814f-152c8696070b' class='xr-section-summary' >Coordinates: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xt</span></div><div class='xr-var-dims'>(xt)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>8.12 8.36 8.6 ... 29.24 29.48 29.72</div><input id='attrs-fb1c7752-9db7-445a-9b5d-0c4d917f5f35' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fb1c7752-9db7-445a-9b5d-0c4d917f5f35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da3853fa-b20c-4eef-ad9d-e0a02cf4f156' class='xr-var-data-in' type='checkbox'><label for='data-da3853fa-b20c-4eef-ad9d-e0a02cf4f156' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd><dt><span>edges :</span></dt><dd>xb</dd></dl></div><div class='xr-var-data'><pre>array([ 8.12,  8.36,  8.6 ,  8.84,  9.08,  9.32,  9.56,  9.8 , 10.04, 10.28,\n",
       "       10.52, 10.76, 11.  , 11.24, 11.48, 11.72, 11.96, 12.2 , 12.44, 12.68,\n",
       "       12.92, 13.16, 13.4 , 13.64, 13.88, 14.12, 14.36, 14.6 , 14.84, 15.08,\n",
       "       15.32, 15.56, 15.8 , 16.04, 16.28, 16.52, 16.76, 17.  , 17.24, 17.48,\n",
       "       17.72, 17.96, 18.2 , 18.44, 18.68, 18.92, 19.16, 19.4 , 19.64, 19.88,\n",
       "       20.12, 20.36, 20.6 , 20.84, 21.08, 21.32, 21.56, 21.8 , 22.04, 22.28,\n",
       "       22.52, 22.76, 23.  , 23.24, 23.48, 23.72, 23.96, 24.2 , 24.44, 24.68,\n",
       "       24.92, 25.16, 25.4 , 25.64, 25.88, 26.12, 26.36, 26.6 , 26.84, 27.08,\n",
       "       27.32, 27.56, 27.8 , 28.04, 28.28, 28.52, 28.76, 29.  , 29.24, 29.48,\n",
       "       29.72])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xb</span></div><div class='xr-var-dims'>(xb)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>8.0 8.24 8.48 ... 29.36 29.6 29.84</div><input id='attrs-b227a747-0644-483b-a6e9-f13281ea09db' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b227a747-0644-483b-a6e9-f13281ea09db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-756dedad-035b-412d-b5a9-b1499210e942' class='xr-var-data-in' type='checkbox'><label for='data-756dedad-035b-412d-b5a9-b1499210e942' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([ 8.  ,  8.24,  8.48,  8.72,  8.96,  9.2 ,  9.44,  9.68,  9.92, 10.16,\n",
       "       10.4 , 10.64, 10.88, 11.12, 11.36, 11.6 , 11.84, 12.08, 12.32, 12.56,\n",
       "       12.8 , 13.04, 13.28, 13.52, 13.76, 14.  , 14.24, 14.48, 14.72, 14.96,\n",
       "       15.2 , 15.44, 15.68, 15.92, 16.16, 16.4 , 16.64, 16.88, 17.12, 17.36,\n",
       "       17.6 , 17.84, 18.08, 18.32, 18.56, 18.8 , 19.04, 19.28, 19.52, 19.76,\n",
       "       20.  , 20.24, 20.48, 20.72, 20.96, 21.2 , 21.44, 21.68, 21.92, 22.16,\n",
       "       22.4 , 22.64, 22.88, 23.12, 23.36, 23.6 , 23.84, 24.08, 24.32, 24.56,\n",
       "       24.8 , 25.04, 25.28, 25.52, 25.76, 26.  , 26.24, 26.48, 26.72, 26.96,\n",
       "       27.2 , 27.44, 27.68, 27.92, 28.16, 28.4 , 28.64, 28.88, 29.12, 29.36,\n",
       "       29.6 , 29.84])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yt</span></div><div class='xr-var-dims'>(yt)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>53.64 53.76 53.88 ... 65.64 65.76</div><input id='attrs-580a1c5a-4561-426b-ad0f-c6091dcb2904' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-580a1c5a-4561-426b-ad0f-c6091dcb2904' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d2a0960-03a1-4dbb-a78b-30526775da80' class='xr-var-data-in' type='checkbox'><label for='data-5d2a0960-03a1-4dbb-a78b-30526775da80' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd><dt><span>edges :</span></dt><dd>yb</dd></dl></div><div class='xr-var-data'><pre>array([53.64, 53.76, 53.88, 54.  , 54.12, 54.24, 54.36, 54.48, 54.6 , 54.72,\n",
       "       54.84, 54.96, 55.08, 55.2 , 55.32, 55.44, 55.56, 55.68, 55.8 , 55.92,\n",
       "       56.04, 56.16, 56.28, 56.4 , 56.52, 56.64, 56.76, 56.88, 57.  , 57.12,\n",
       "       57.24, 57.36, 57.48, 57.6 , 57.72, 57.84, 57.96, 58.08, 58.2 , 58.32,\n",
       "       58.44, 58.56, 58.68, 58.8 , 58.92, 59.04, 59.16, 59.28, 59.4 , 59.52,\n",
       "       59.64, 59.76, 59.88, 60.  , 60.12, 60.24, 60.36, 60.48, 60.6 , 60.72,\n",
       "       60.84, 60.96, 61.08, 61.2 , 61.32, 61.44, 61.56, 61.68, 61.8 , 61.92,\n",
       "       62.04, 62.16, 62.28, 62.4 , 62.52, 62.64, 62.76, 62.88, 63.  , 63.12,\n",
       "       63.24, 63.36, 63.48, 63.6 , 63.72, 63.84, 63.96, 64.08, 64.2 , 64.32,\n",
       "       64.44, 64.56, 64.68, 64.8 , 64.92, 65.04, 65.16, 65.28, 65.4 , 65.52,\n",
       "       65.64, 65.76])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yb</span></div><div class='xr-var-dims'>(yb)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>53.58 53.7 53.82 ... 65.7 65.82</div><input id='attrs-c92c0d95-bae5-445b-9981-c1b10a2f4c6a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c92c0d95-bae5-445b-9981-c1b10a2f4c6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a8a4a96-4992-4a60-9a50-3c6946c9546f' class='xr-var-data-in' type='checkbox'><label for='data-3a8a4a96-4992-4a60-9a50-3c6946c9546f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([53.58, 53.7 , 53.82, 53.94, 54.06, 54.18, 54.3 , 54.42, 54.54, 54.66,\n",
       "       54.78, 54.9 , 55.02, 55.14, 55.26, 55.38, 55.5 , 55.62, 55.74, 55.86,\n",
       "       55.98, 56.1 , 56.22, 56.34, 56.46, 56.58, 56.7 , 56.82, 56.94, 57.06,\n",
       "       57.18, 57.3 , 57.42, 57.54, 57.66, 57.78, 57.9 , 58.02, 58.14, 58.26,\n",
       "       58.38, 58.5 , 58.62, 58.74, 58.86, 58.98, 59.1 , 59.22, 59.34, 59.46,\n",
       "       59.58, 59.7 , 59.82, 59.94, 60.06, 60.18, 60.3 , 60.42, 60.54, 60.66,\n",
       "       60.78, 60.9 , 61.02, 61.14, 61.26, 61.38, 61.5 , 61.62, 61.74, 61.86,\n",
       "       61.98, 62.1 , 62.22, 62.34, 62.46, 62.58, 62.7 , 62.82, 62.94, 63.06,\n",
       "       63.18, 63.3 , 63.42, 63.54, 63.66, 63.78, 63.9 , 64.02, 64.14, 64.26,\n",
       "       64.38, 64.5 , 64.62, 64.74, 64.86, 64.98, 65.1 , 65.22, 65.34, 65.46,\n",
       "       65.58, 65.7 , 65.82])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1961-02-15 00:00:00 ... 1961-12-...</div><input id='attrs-703a8f71-0381-4add-999f-36a341562c8f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-703a8f71-0381-4add-999f-36a341562c8f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e45369fd-5143-4320-9f5e-22c2423dc110' class='xr-var-data-in' type='checkbox'><label for='data-e45369fd-5143-4320-9f5e-22c2423dc110' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>cartesian_axis :</span></dt><dd>T</dd><dt><span>calendar_type :</span></dt><dd>JULIAN</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeJulian(1961, 2, 15, 0, 0, 0, 0),\n",
       "       cftime.DatetimeJulian(1961, 3, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeJulian(1961, 4, 16, 0, 0, 0, 0),\n",
       "       cftime.DatetimeJulian(1961, 5, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeJulian(1961, 6, 16, 0, 0, 0, 0),\n",
       "       cftime.DatetimeJulian(1961, 7, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeJulian(1961, 8, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeJulian(1961, 9, 16, 0, 0, 0, 0),\n",
       "       cftime.DatetimeJulian(1961, 10, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeJulian(1961, 11, 16, 0, 0, 0, 0),\n",
       "       cftime.DatetimeJulian(1961, 12, 16, 12, 0, 0, 0)], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>nv</span></div><div class='xr-var-dims'>(nv)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 2.0</div><input id='attrs-cd594a11-d1c3-4919-8b6b-8b5633bbf080' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd594a11-d1c3-4919-8b6b-8b5633bbf080' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f864a84a-875d-4a44-ae7a-af4248fa9322' class='xr-var-data-in' type='checkbox'><label for='data-f864a84a-875d-4a44-ae7a-af4248fa9322' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>vertex number</dd><dt><span>units :</span></dt><dd>none</dd><dt><span>cartesian_axis :</span></dt><dd>N</dd></dl></div><div class='xr-var-data'><pre>array([1., 2.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ct</span></div><div class='xr-var-dims'>(ct)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.1 0.3 0.7 1.1</div><input id='attrs-470476cb-f13b-4c84-83f0-5186ab879f3b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-470476cb-f13b-4c84-83f0-5186ab879f3b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c22165f9-36d1-4a84-b372-2fde1d805e04' class='xr-var-data-in' type='checkbox'><label for='data-c22165f9-36d1-4a84-b372-2fde1d805e04' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>thickness</dd><dt><span>units :</span></dt><dd>meters</dd><dt><span>cartesian_axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>array([0. , 0.1, 0.3, 0.7, 1.1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xv</span></div><div class='xr-var-dims'>(xv)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>8.24 8.48 8.72 ... 29.36 29.6 29.84</div><input id='attrs-042fee8f-8ff6-4d3f-a14e-dc3925ea3748' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-042fee8f-8ff6-4d3f-a14e-dc3925ea3748' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bdbe9eba-8753-4304-b10c-f044f526babb' class='xr-var-data-in' type='checkbox'><label for='data-bdbe9eba-8753-4304-b10c-f044f526babb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([ 8.24,  8.48,  8.72,  8.96,  9.2 ,  9.44,  9.68,  9.92, 10.16, 10.4 ,\n",
       "       10.64, 10.88, 11.12, 11.36, 11.6 , 11.84, 12.08, 12.32, 12.56, 12.8 ,\n",
       "       13.04, 13.28, 13.52, 13.76, 14.  , 14.24, 14.48, 14.72, 14.96, 15.2 ,\n",
       "       15.44, 15.68, 15.92, 16.16, 16.4 , 16.64, 16.88, 17.12, 17.36, 17.6 ,\n",
       "       17.84, 18.08, 18.32, 18.56, 18.8 , 19.04, 19.28, 19.52, 19.76, 20.  ,\n",
       "       20.24, 20.48, 20.72, 20.96, 21.2 , 21.44, 21.68, 21.92, 22.16, 22.4 ,\n",
       "       22.64, 22.88, 23.12, 23.36, 23.6 , 23.84, 24.08, 24.32, 24.56, 24.8 ,\n",
       "       25.04, 25.28, 25.52, 25.76, 26.  , 26.24, 26.48, 26.72, 26.96, 27.2 ,\n",
       "       27.44, 27.68, 27.92, 28.16, 28.4 , 28.64, 28.88, 29.12, 29.36, 29.6 ,\n",
       "       29.84])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yv</span></div><div class='xr-var-dims'>(yv)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>53.7 53.82 53.94 ... 65.7 65.82</div><input id='attrs-0fe5336a-1948-4273-b15d-42de07109228' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0fe5336a-1948-4273-b15d-42de07109228' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-744b4aee-ca02-4d75-85b8-c016651ae2b5' class='xr-var-data-in' type='checkbox'><label for='data-744b4aee-ca02-4d75-85b8-c016651ae2b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([53.7 , 53.82, 53.94, 54.06, 54.18, 54.3 , 54.42, 54.54, 54.66, 54.78,\n",
       "       54.9 , 55.02, 55.14, 55.26, 55.38, 55.5 , 55.62, 55.74, 55.86, 55.98,\n",
       "       56.1 , 56.22, 56.34, 56.46, 56.58, 56.7 , 56.82, 56.94, 57.06, 57.18,\n",
       "       57.3 , 57.42, 57.54, 57.66, 57.78, 57.9 , 58.02, 58.14, 58.26, 58.38,\n",
       "       58.5 , 58.62, 58.74, 58.86, 58.98, 59.1 , 59.22, 59.34, 59.46, 59.58,\n",
       "       59.7 , 59.82, 59.94, 60.06, 60.18, 60.3 , 60.42, 60.54, 60.66, 60.78,\n",
       "       60.9 , 61.02, 61.14, 61.26, 61.38, 61.5 , 61.62, 61.74, 61.86, 61.98,\n",
       "       62.1 , 62.22, 62.34, 62.46, 62.58, 62.7 , 62.82, 62.94, 63.06, 63.18,\n",
       "       63.3 , 63.42, 63.54, 63.66, 63.78, 63.9 , 64.02, 64.14, 64.26, 64.38,\n",
       "       64.5 , 64.62, 64.74, 64.86, 64.98, 65.1 , 65.22, 65.34, 65.46, 65.58,\n",
       "       65.7 , 65.82])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-61cae77a-00e9-4f47-918d-c50c1cd7eb3b' class='xr-section-summary-in' type='checkbox'  ><label for='section-61cae77a-00e9-4f47-918d-c50c1cd7eb3b' class='xr-section-summary' >Data variables: <span>(26)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>FRAZIL</span></div><div class='xr-var-dims'>(time, yt, xt)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6f9ada58-4ded-400d-a2e8-7a2d2a9e4dd2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f9ada58-4ded-400d-a2e8-7a2d2a9e4dd2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d16db8d-185d-4276-94f5-77d8cab12696' class='xr-var-data-in' type='checkbox'><label for='data-9d16db8d-185d-4276-94f5-77d8cab12696' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>energy flux of frazil formation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd></dl></div><div class='xr-var-data'><pre>[102102 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CN</span></div><div class='xr-var-dims'>(time, ct, yt, xt)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f291f5ba-6f28-4d94-814e-03937384f02b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f291f5ba-6f28-4d94-814e-03937384f02b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee237ec7-2b65-48dc-98fc-8530848307c1' class='xr-var-data-in' type='checkbox'><label for='data-ee237ec7-2b65-48dc-98fc-8530848307c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>ice concentration</dd><dt><span>units :</span></dt><dd>0-1</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd></dl></div><div class='xr-var-data'><pre>[510510 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MI</span></div><div class='xr-var-dims'>(time, yt, xt)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-22fcc61b-9806-4f93-bd0f-02d4059b2b63' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-22fcc61b-9806-4f93-bd0f-02d4059b2b63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f964547-4b04-4e3f-b672-243ed1399bfd' class='xr-var-data-in' type='checkbox'><label for='data-0f964547-4b04-4e3f-b672-243ed1399bfd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>ice mass</dd><dt><span>units :</span></dt><dd>kg/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd></dl></div><div class='xr-var-data'><pre>[102102 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HI</span></div><div class='xr-var-dims'>(time, yt, xt)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b94091fc-b773-4fad-ad65-0cab975ce7c0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b94091fc-b773-4fad-ad65-0cab975ce7c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac4481c4-694e-4956-bb33-b1188bd055d4' class='xr-var-data-in' type='checkbox'><label for='data-ac4481c4-694e-4956-bb33-b1188bd055d4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>ice thickness</dd><dt><span>units :</span></dt><dd>m-ice</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd></dl></div><div class='xr-var-data'><pre>[102102 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HS</span></div><div class='xr-var-dims'>(time, yt, xt)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-432abffd-c9db-4969-8dbb-de563c1bda71' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-432abffd-c9db-4969-8dbb-de563c1bda71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8cd7d6a4-2f82-45d3-b1c1-69097cb0f472' class='xr-var-data-in' type='checkbox'><label for='data-8cd7d6a4-2f82-45d3-b1c1-69097cb0f472' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>snow thickness</dd><dt><span>units :</span></dt><dd>m-snow</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd></dl></div><div class='xr-var-data'><pre>[102102 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TS</span></div><div class='xr-var-dims'>(time, yt, xt)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3721ea44-273c-44f6-b087-023115344bc0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3721ea44-273c-44f6-b087-023115344bc0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6a7d57e-c31d-422a-955c-c3cc6a0c9fdc' class='xr-var-data-in' type='checkbox'><label for='data-b6a7d57e-c31d-422a-955c-c3cc6a0c9fdc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>surface temperature</dd><dt><span>units :</span></dt><dd>C</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd></dl></div><div class='xr-var-data'><pre>[102102 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>T1</span></div><div class='xr-var-dims'>(time, yt, xt)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-389cc0eb-47ac-420c-ab16-92ac3596d3f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-389cc0eb-47ac-420c-ab16-92ac3596d3f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f662b3a-d87e-4ec7-8be9-172fac96552e' class='xr-var-data-in' type='checkbox'><label for='data-0f662b3a-d87e-4ec7-8be9-172fac96552e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>upper ice layer temperature</dd><dt><span>units :</span></dt><dd>C</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd></dl></div><div class='xr-var-data'><pre>[102102 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>T2</span></div><div class='xr-var-dims'>(time, yt, xt)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3e024c7b-8620-4e52-80c8-240b2674f2fc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3e024c7b-8620-4e52-80c8-240b2674f2fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29ee03df-8a68-48ce-ac7c-1fc8c25cc062' class='xr-var-data-in' type='checkbox'><label for='data-29ee03df-8a68-48ce-ac7c-1fc8c25cc062' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>lower ice layer temperature</dd><dt><span>units :</span></dt><dd>C</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd></dl></div><div class='xr-var-data'><pre>[102102 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALB</span></div><div class='xr-var-dims'>(time, yt, xt)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bf265f13-ea2a-4545-9ed3-74ec33f40367' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bf265f13-ea2a-4545-9ed3-74ec33f40367' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be70035e-d069-40a6-b5a2-f7265ef540fd' class='xr-var-data-in' type='checkbox'><label for='data-be70035e-d069-40a6-b5a2-f7265ef540fd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>surface albedo</dd><dt><span>units :</span></dt><dd>0-1</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd></dl></div><div class='xr-var-data'><pre>[102102 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SW</span></div><div class='xr-var-dims'>(time, yt, xt)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4866d0ae-ab64-4eef-8426-0810d486eadc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4866d0ae-ab64-4eef-8426-0810d486eadc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d9b33ab-2a7c-4010-8e3a-2172bbbaf97b' class='xr-var-data-in' type='checkbox'><label for='data-0d9b33ab-2a7c-4010-8e3a-2172bbbaf97b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>short wave heat flux</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd></dl></div><div class='xr-var-data'><pre>[102102 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LW</span></div><div class='xr-var-dims'>(time, yt, xt)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-66d767a1-fb8c-48dc-945f-164b87b50aa5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66d767a1-fb8c-48dc-945f-164b87b50aa5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a396aec0-9ae6-4df2-ac56-8356ee6cd2ba' class='xr-var-data-in' type='checkbox'><label for='data-a396aec0-9ae6-4df2-ac56-8356ee6cd2ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>long wave heat flux over ice</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd></dl></div><div class='xr-var-data'><pre>[102102 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TMELT</span></div><div class='xr-var-dims'>(time, yt, xt)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-eef77bba-5763-4bc2-8e79-f9c217c2eff8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eef77bba-5763-4bc2-8e79-f9c217c2eff8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2ab2c29-c63c-48d0-9f9c-f7d76cb48aea' class='xr-var-data-in' type='checkbox'><label for='data-b2ab2c29-c63c-48d0-9f9c-f7d76cb48aea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>upper surface melting energy flux</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd></dl></div><div class='xr-var-data'><pre>[102102 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>BMELT</span></div><div class='xr-var-dims'>(time, yt, xt)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bcc616eb-c5ce-4412-816e-8ddb908a6339' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bcc616eb-c5ce-4412-816e-8ddb908a6339' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62a46cf9-a1d0-400d-9ee0-0009592b2ed5' class='xr-var-data-in' type='checkbox'><label for='data-62a46cf9-a1d0-400d-9ee0-0009592b2ed5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>bottom surface melting energy flux</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd></dl></div><div class='xr-var-data'><pre>[102102 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UI</span></div><div class='xr-var-dims'>(time, yv, xv)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-858dd630-d513-4d5a-b59a-725042026dc8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-858dd630-d513-4d5a-b59a-725042026dc8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3fe8e16b-5fa5-4c38-9046-a169dc6433c6' class='xr-var-data-in' type='checkbox'><label for='data-3fe8e16b-5fa5-4c38-9046-a169dc6433c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>ice velocity - x component</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd></dl></div><div class='xr-var-data'><pre>[102102 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>VI</span></div><div class='xr-var-dims'>(time, yv, xv)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-93a2939b-aa20-41d7-a58e-ea4f72fa54d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-93a2939b-aa20-41d7-a58e-ea4f72fa54d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4af4e55e-77f4-4caa-9aae-5025b891c5c0' class='xr-var-data-in' type='checkbox'><label for='data-4af4e55e-77f4-4caa-9aae-5025b891c5c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>ice velocity - y component</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd></dl></div><div class='xr-var-data'><pre>[102102 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SLP</span></div><div class='xr-var-dims'>(time, yt, xt)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c8e3c14c-b443-4d4f-b857-9d61e4ee2880' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c8e3c14c-b443-4d4f-b857-9d61e4ee2880' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8dd9c8e5-8b4d-4c7e-ba8f-71b78df6987c' class='xr-var-data-in' type='checkbox'><label for='data-8dd9c8e5-8b4d-4c7e-ba8f-71b78df6987c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>sea level pressure</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd></dl></div><div class='xr-var-data'><pre>[102102 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SST</span></div><div class='xr-var-dims'>(time, yt, xt)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-922b125c-d3a8-4bc9-a7d2-5c49fc4063ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-922b125c-d3a8-4bc9-a7d2-5c49fc4063ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0c765a6-6c26-4519-8b3e-f89528884513' class='xr-var-data-in' type='checkbox'><label for='data-f0c765a6-6c26-4519-8b3e-f89528884513' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>sea surface temperature</dd><dt><span>units :</span></dt><dd>deg-C</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd></dl></div><div class='xr-var-data'><pre>[102102 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SSS</span></div><div class='xr-var-dims'>(time, yt, xt)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c5c5dc5e-0e1f-482f-b31f-452b60b11ca4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c5c5dc5e-0e1f-482f-b31f-452b60b11ca4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d24efd1d-b7eb-4547-80d7-5756c74fe1c1' class='xr-var-data-in' type='checkbox'><label for='data-d24efd1d-b7eb-4547-80d7-5756c74fe1c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>sea surface salinity</dd><dt><span>units :</span></dt><dd>psu</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd></dl></div><div class='xr-var-data'><pre>[102102 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SNOWFL</span></div><div class='xr-var-dims'>(time, yt, xt)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0013a8cb-0339-4cde-a868-ee5a0bd643f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0013a8cb-0339-4cde-a868-ee5a0bd643f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d7e2ba5-f35c-4743-b5a0-cc2ceeb6cfcf' class='xr-var-data-in' type='checkbox'><label for='data-5d7e2ba5-f35c-4743-b5a0-cc2ceeb6cfcf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>rate of snow fall</dd><dt><span>units :</span></dt><dd>kg/(m^2*s)</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd></dl></div><div class='xr-var-data'><pre>[102102 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RAIN</span></div><div class='xr-var-dims'>(time, yt, xt)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-247096fc-2693-46fb-81f0-9df877cc73d8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-247096fc-2693-46fb-81f0-9df877cc73d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52bcb9f6-16af-4ad7-9622-f858b6046afc' class='xr-var-data-in' type='checkbox'><label for='data-52bcb9f6-16af-4ad7-9622-f858b6046afc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>rate of rain fall</dd><dt><span>units :</span></dt><dd>kg/(m^2*s)</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd></dl></div><div class='xr-var-data'><pre>[102102 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EVAP</span></div><div class='xr-var-dims'>(time, yt, xt)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0eaf7c39-e2e8-47ac-a139-0a326920354d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0eaf7c39-e2e8-47ac-a139-0a326920354d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9bfb04df-f20b-40be-a55a-63f46669b949' class='xr-var-data-in' type='checkbox'><label for='data-9bfb04df-f20b-40be-a55a-63f46669b949' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>evaporation</dd><dt><span>units :</span></dt><dd>kg/(m^2*s)</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd></dl></div><div class='xr-var-data'><pre>[102102 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RUNOFF</span></div><div class='xr-var-dims'>(time, yt, xt)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f92ce0bf-91e3-4f54-abbe-74082510b898' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f92ce0bf-91e3-4f54-abbe-74082510b898' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b2cf9b3-1014-4e51-a653-e2ef898b4388' class='xr-var-data-in' type='checkbox'><label for='data-8b2cf9b3-1014-4e51-a653-e2ef898b4388' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>liquid runoff</dd><dt><span>units :</span></dt><dd>kg/(m^2*s)</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd></dl></div><div class='xr-var-data'><pre>[102102 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>average_T1</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-749c7721-ab01-484e-bc93-fc381f4e00e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-749c7721-ab01-484e-bc93-fc381f4e00e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16f2a28b-bc96-432a-acbc-5f82ef604316' class='xr-var-data-in' type='checkbox'><label for='data-16f2a28b-bc96-432a-acbc-5f82ef604316' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Start time for average period</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1961-02-01T00:00:00.000000000&#x27;, &#x27;1961-03-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1961-04-01T00:00:00.000000000&#x27;, &#x27;1961-05-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1961-06-01T00:00:00.000000000&#x27;, &#x27;1961-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1961-08-01T00:00:00.000000000&#x27;, &#x27;1961-09-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1961-10-01T00:00:00.000000000&#x27;, &#x27;1961-11-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1961-12-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>average_T2</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e2b84a61-cd22-4547-a3d2-590462c5ef86' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2b84a61-cd22-4547-a3d2-590462c5ef86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e1dc1f1-aeef-472f-abb0-6a74341822e0' class='xr-var-data-in' type='checkbox'><label for='data-9e1dc1f1-aeef-472f-abb0-6a74341822e0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>End time for average period</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1961-03-01T00:00:00.000000000&#x27;, &#x27;1961-04-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1961-05-01T00:00:00.000000000&#x27;, &#x27;1961-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1961-07-01T00:00:00.000000000&#x27;, &#x27;1961-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1961-09-01T00:00:00.000000000&#x27;, &#x27;1961-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1961-11-01T00:00:00.000000000&#x27;, &#x27;1961-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1962-01-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>average_DT</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-15868ee0-3cd7-4209-b0a2-36f28bc12d91' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-15868ee0-3cd7-4209-b0a2-36f28bc12d91' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f38b53a-bfcd-42dd-877b-19555f437ed8' class='xr-var-data-in' type='checkbox'><label for='data-8f38b53a-bfcd-42dd-877b-19555f437ed8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Length of average period</dd></dl></div><div class='xr-var-data'><pre>array([2419200000000000, 2678400000000000, 2592000000000000, 2678400000000000,\n",
       "       2592000000000000, 2678400000000000, 2678400000000000, 2592000000000000,\n",
       "       2678400000000000, 2592000000000000, 2678400000000000],\n",
       "      dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bounds</span></div><div class='xr-var-dims'>(time, nv)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a4cb09af-9d98-472c-878e-9fd682639aa2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a4cb09af-9d98-472c-878e-9fd682639aa2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20692173-1767-4dbf-bce5-44341449aeea' class='xr-var-data-in' type='checkbox'><label for='data-20692173-1767-4dbf-bce5-44341449aeea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time axis boundaries</dd><dt><span>calendar :</span></dt><dd>JULIAN</dd></dl></div><div class='xr-var-data'><pre>array([[                0,  2419200000000000],\n",
       "       [ 2419200000000000,  5097600000000000],\n",
       "       [ 5097600000000000,  7689600000000000],\n",
       "       [ 7689600000000000, 10368000000000000],\n",
       "       [10368000000000000, 12960000000000000],\n",
       "       [12960000000000000, 15638400000000000],\n",
       "       [15638400000000000, 18316800000000000],\n",
       "       [18316800000000000, 20908800000000000],\n",
       "       [20908800000000000, 23587200000000000],\n",
       "       [23587200000000000, 26179200000000000],\n",
       "       [26179200000000000, 28857600000000000]], dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-df2eef28-c027-4ed0-8c76-01bc868ccfdc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-df2eef28-c027-4ed0-8c76-01bc868ccfdc' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>filename :</span></dt><dd>ice_day.nc</dd><dt><span>title :</span></dt><dd>ERGOM-MOM510 8 n.m. Baltic Hiresaff 1850-2009</dd><dt><span>grid_type :</span></dt><dd>regular</dd><dt><span>grid_tile :</span></dt><dd>N/A</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (xt: 91, xb: 92, yt: 102, yb: 103, time: 11, nv: 2, ct: 5, xv: 91, yv: 102)\n",
       "Coordinates:\n",
       "  * xt           (xt) float64 8.12 8.36 8.6 8.84 9.08 ... 29.0 29.24 29.48 29.72\n",
       "  * xb           (xb) float64 8.0 8.24 8.48 8.72 8.96 ... 29.12 29.36 29.6 29.84\n",
       "  * yt           (yt) float64 53.64 53.76 53.88 54.0 ... 65.4 65.52 65.64 65.76\n",
       "  * yb           (yb) float64 53.58 53.7 53.82 53.94 ... 65.46 65.58 65.7 65.82\n",
       "  * time         (time) object 1961-02-15 00:00:00 ... 1961-12-16 12:00:00\n",
       "  * nv           (nv) float64 1.0 2.0\n",
       "  * ct           (ct) float64 0.0 0.1 0.3 0.7 1.1\n",
       "  * xv           (xv) float64 8.24 8.48 8.72 8.96 9.2 ... 29.12 29.36 29.6 29.84\n",
       "  * yv           (yv) float64 53.7 53.82 53.94 54.06 ... 65.46 65.58 65.7 65.82\n",
       "Data variables: (12/26)\n",
       "    FRAZIL       (time, yt, xt) float32 ...\n",
       "    CN           (time, ct, yt, xt) float32 ...\n",
       "    MI           (time, yt, xt) float32 ...\n",
       "    HI           (time, yt, xt) float32 ...\n",
       "    HS           (time, yt, xt) float32 ...\n",
       "    TS           (time, yt, xt) float32 ...\n",
       "    ...           ...\n",
       "    EVAP         (time, yt, xt) float32 ...\n",
       "    RUNOFF       (time, yt, xt) float32 ...\n",
       "    average_T1   (time) datetime64[ns] ...\n",
       "    average_T2   (time) datetime64[ns] ...\n",
       "    average_DT   (time) timedelta64[ns] ...\n",
       "    time_bounds  (time, nv) timedelta64[ns] ...\n",
       "Attributes:\n",
       "    filename:   ice_day.nc\n",
       "    title:      ERGOM-MOM510 8 n.m. Baltic Hiresaff 1850-2009\n",
       "    grid_type:  regular\n",
       "    grid_tile:  N/A"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.plot.facetgrid.FacetGrid at 0x7faf3e711f10>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 936x648 with 13 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ice.MI.resample(time=\"1M\").mean().plot(col=\"time\", col_wrap=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 5:** Why do we find sea ice in the North but not in the central Baltic Sea?"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  },
  "vscode": {
   "interpreter": {
    "hash": "85c5783586d30ff72e98f5d52ef95e9c172daa6cb2628b0e9aeb01c271dd5883"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}