nblibrary/ice/Hemis_seaice_visual_compare_contour.ipynb

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   "source": [
    "### Sea Ice Diagnostics for two CESM3 runs"
   ]
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   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "import yaml\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
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    "tags": [
     "parameters",
     "hide-input"
    ]
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   "source": [
    "# These parameters are needed when running interactively\n",
    "\n",
    "CESM_output_dir = \"\"  # \"/glade/campaign/cesm/development/cross-wg/diagnostic_framework/CESM_output_for_testing\"\n",
    "\n",
    "case_name = \"\"  # \"b.e30_beta04.BLT1850.ne30_t232_wgx3.121congel\"\n",
    "base_case_name = \"\"  # \"b.e30_beta04.BLT1850.ne30_t232_wgx3.121\"\n",
    "\n",
    "start_date = \"\"  # \"0001-01-01\"\n",
    "end_date = \"\"  # \"0100-01-01\"\n",
    "base_start_date = \"\"  # \"0001-01-01\"\n",
    "base_end_date = \"\"  # \"0100-01-01\"\n",
    "\n",
    "obs_data_dir = \"\"  # \"/glade/campaign/cesm/development/cross-wg/diagnostic_framework/CUPiD_obs_data\"\n",
    "grid_file = \"\"  # \"/glade/campaign/cesm/community/omwg/grids/tx2_3v2_grid.nc\"\n",
    "climo_nyears = 35\n",
    "\n",
    "serial = False  # use dask LocalCluster\n",
    "\n",
    "lc_kwargs = {}"
   ]
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    "# When running interactively, cupid_run should be set to 0 for\n",
    "# a DASK cluster\n",
    "\n",
    "cupid_run = 1\n",
    "\n",
    "if cupid_run == 1:\n",
    "\n",
    "    from dask.distributed import Client, LocalCluster\n",
    "\n",
    "    # Spin up cluster (if running in parallel)\n",
    "    client = None\n",
    "    if not serial:\n",
    "        cluster = LocalCluster(**lc_kwargs)\n",
    "        client = Client(cluster)\n",
    "\n",
    "else:\n",
    "\n",
    "    from dask.distributed import Client\n",
    "    from dask_jobqueue import PBSCluster\n",
    "\n",
    "    cluster = PBSCluster(\n",
    "        cores=16,\n",
    "        processes=16,\n",
    "        memory=\"100GB\",\n",
    "        account=\"P93300065\",\n",
    "        queue=\"casper\",\n",
    "        walltime=\"02:00:00\",\n",
    "    )\n",
    "\n",
    "    client = Client(cluster)\n",
    "\n",
    "    cluster.scale(1)\n",
    "\n",
    "    print(cluster)\n",
    "print(client)"
   ]
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   "source": [
    "# Read in two cases. The ADF timeseries are needed here.\n",
    "\n",
    "ds1 = xr.open_mfdataset(\n",
    "    CESM_output_dir\n",
    "    + \"/\"\n",
    "    + case_name\n",
    "    + \"/ice/proc/tseries/\"\n",
    "    + case_name\n",
    "    + \".cice.h.\"\n",
    "    + \"*.nc\",\n",
    "    data_vars=\"minimal\",\n",
    "    compat=\"override\",\n",
    "    coords=\"minimal\",\n",
    ").sel(time=slice(start_date, end_date))\n",
    "ds2 = xr.open_mfdataset(\n",
    "    CESM_output_dir\n",
    "    + \"/\"\n",
    "    + base_case_name\n",
    "    + \"/ice/proc/tseries/\"\n",
    "    + base_case_name\n",
    "    + \".cice.h.\"\n",
    "    + \"*.nc\",\n",
    "    data_vars=\"minimal\",\n",
    "    compat=\"override\",\n",
    "    coords=\"minimal\",\n",
    ").sel(time=slice(base_start_date, base_end_date))\n",
    "\n",
    "ds_grid = xr.open_dataset(grid_file)\n",
    "TLAT = ds_grid[\"TLAT\"]\n",
    "TLON = ds_grid[\"TLONG\"]\n",
    "tarea = ds_grid[\"TAREA\"] * 1.0e-4\n",
    "angle = ds_grid[\"ANGLE\"]\n",
    "\n",
    "ds1_ann = ds1.resample(time=\"YS\").mean(dim=\"time\")\n",
    "ds2_ann = ds2.resample(time=\"YS\").mean(dim=\"time\")\n",
    "\n",
    "with open(\"cice_masks.yml\", \"r\") as file:\n",
    "    cice_masks = yaml.safe_load(file)\n",
    "\n",
    "with open(\"cice_vars.yml\", \"r\") as file:\n",
    "    cice_vars = yaml.safe_load(file)\n",
    "\n",
    "path_HadleyOI = os.path.join(obs_data_dir, \"ice\", \"analysis_datasets\", \"1x1d/\")"
   ]
  },
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   "outputs": [],
   "source": [
    "from plot_diff import plot_diff\n",
    "from vect_diff import vect_diff"
   ]
  },
  {
   "cell_type": "code",
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   "id": "28a0da20-6bad-45d7-a61a-43d55ea2acd2",
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   "source": [
    "for var in cice_vars:\n",
    "    vmin = cice_vars[var][0][\"levels\"][0]\n",
    "    vmax = cice_vars[var][0][\"levels\"][-1]\n",
    "    levels = np.array(cice_vars[var][0][\"levels\"])\n",
    "    title = cice_vars[var][1][\"title\"]\n",
    "    field1 = ds1[var].isel(time=slice(-climo_nyears * 12, None))\n",
    "    field2 = ds2[var].isel(time=slice(-climo_nyears * 12, None))\n",
    "    plot_diff(\n",
    "        field1,\n",
    "        field2,\n",
    "        levels,\n",
    "        case_name,\n",
    "        base_case_name,\n",
    "        title,\n",
    "        \"N\",\n",
    "        TLAT,\n",
    "        TLON,\n",
    "        path_HadleyOI,\n",
    "    )"
   ]
  },
  {
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   "source": [
    "for var in cice_vars:\n",
    "    vmin = cice_vars[var][0][\"levels\"][0]\n",
    "    vmax = cice_vars[var][0][\"levels\"][1]\n",
    "    levels = np.array(cice_vars[var][0][\"levels\"])\n",
    "    title = cice_vars[var][1][\"title\"]\n",
    "    field1 = ds1[var].isel(time=slice(-climo_nyears * 12, None))\n",
    "    field2 = ds2[var].isel(time=slice(-climo_nyears * 12, None))\n",
    "    plot_diff(\n",
    "        field1,\n",
    "        field2,\n",
    "        levels,\n",
    "        case_name,\n",
    "        base_case_name,\n",
    "        title,\n",
    "        \"S\",\n",
    "        TLAT,\n",
    "        TLON,\n",
    "        path_HadleyOI,\n",
    "    )"
   ]
  },
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   "outputs": [],
   "source": [
    "uvel1 = ds1_ann[\"uvel\"].isel(time=slice(-climo_nyears, None)).mean(\"time\").squeeze()\n",
    "vvel1 = ds1_ann[\"vvel\"].isel(time=slice(-climo_nyears, None)).mean(\"time\").squeeze()\n",
    "uvel2 = ds2_ann[\"uvel\"].isel(time=slice(-climo_nyears, None)).mean(\"time\").squeeze()\n",
    "vvel2 = ds2_ann[\"vvel\"].isel(time=slice(-climo_nyears, None)).mean(\"time\").squeeze()\n",
    "\n",
    "vect_diff(uvel1, vvel1, uvel2, vvel2, angle, \"N\", case_name, base_case_name, TLAT, TLON)"
   ]
  },
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   "source": [
    "vect_diff(uvel1, vvel1, uvel2, vvel2, angle, \"S\", case_name, base_case_name, TLAT, TLON)"
   ]
  },
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