diff --git a/conda-env/dev.yml b/conda-env/dev.yml
index 0a14f0395..539bac09e 100644
--- a/conda-env/dev.yml
+++ b/conda-env/dev.yml
@@ -29,6 +29,7 @@ dependencies:
     - rasterio=1.3.6
     - shapely=2.0.1
     - numdifftools
+    - nc-time-axis
     # ==================
     # Testing
     # ==================
diff --git a/doc/jupyter/Demo/Demo_9_seaIceExtent_ivanova.ipynb b/doc/jupyter/Demo/Demo_9_seaIceExtent_ivanova.ipynb
new file mode 100644
index 000000000..bdf9775bf
--- /dev/null
+++ b/doc/jupyter/Demo/Demo_9_seaIceExtent_ivanova.ipynb
@@ -0,0 +1,2766 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "acb8d42e",
+   "metadata": {},
+   "source": [
+    "# Sea Ice Demo"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "848c69e5",
+   "metadata": {},
+   "source": [
+    "**Summary**  \n",
+    "The PCMDI Metrics sea ice driver produces metrics that compare modeled and observed sea ice extent. This notebook demonstrates how to run the PCMDI Metrics sea ice code.\n",
+    "\n",
+    "**Authors**   \n",
+    "Ana Ordonez, Jiwoo Lee, Paul Durack, Peter Gleckler (PCMDI, Lawrence Livermore National Laboratory)\n",
+    "\n",
+    "**Reference**  \n",
+    "Ivanova, D. P., P. J. Gleckler, K. E. Taylor, P. J. Durack, and K. D. Marvel, 2016: Moving beyond the Total Sea Ice Extent in Gauging Model Biases. J. Climate, 29, 8965–8987, https://doi.org/10.1175/JCLI-D-16-0026.1. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bfd3b73",
+   "metadata": {},
+   "source": [
+    "## Demo data\n",
+    "This demo uses three CMIP6 models. The 'siconc' and 'areacello' variables are needed and can be found in the following directories. In addition, six other models are available that can be added to the analyses in this demo:\n",
+    "```\n",
+    "/p/user_pub/pmp/demo/sea-ice/links_siconc  \n",
+    "/p/user_pub/pmp/demo/sea-ice/links_area\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00d48042",
+   "metadata": {},
+   "source": [
+    "The observation dataset provided is a satellite derived sea ice concentration dataset from EUMETSAT OSI SAF. More information about this data can be found at the [osi-450-a product page](https://osi-saf.eumetsat.int/products/osi-450-a). The path to this data is:\n",
+    "```\n",
+    "/p/user_pub/pmp/demo/sea-ice/EUMETSAT\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b854017",
+   "metadata": {},
+   "source": [
+    "## Sectors\n",
+    "This code block produces maps that show the different regions used in the analysis along with the mean observed sea ice concentration. The code to generate these figures can be found in the script `sea_ice_sector_plots.py`.\n",
+    "\n",
+    "Below process will take about 30 seconds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b6d75e4e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Creating Arctic map\n",
+      "Creating Antarctic map\n",
+      "CPU times: user 8.72 ms, sys: 4.33 ms, total: 13 ms\n",
+      "Wall time: 27.3 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "%%bash\n",
+    "python sea_ice_sector_plots.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a82ee330",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To open and display one of the graphics\n",
+    "from IPython.display import display_png, JSON, Image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6a7eb6da",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "a = Image(\"Arctic_regions.png\")\n",
+    "b = Image(\"Antarctic_regions.png\")\n",
+    "display_png(a,b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5294910f",
+   "metadata": {},
+   "source": [
+    "## Basic example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f316897b",
+   "metadata": {},
+   "source": [
+    "This first case will work with sea ice concentration ouput from a single model, E3SM-1-0. Two overview plots are shown below to visualize the Arctic sea ice in this model.\n",
+    "\n",
+    "For this demo, we start the OSI-SAF satellite data in 1988 as that avoids missing data in earlier parts of the record.\n",
+    "\n",
+    "The code to generate these figures can be found in  `sea_ice_line_plots.py`.\n",
+    "\n",
+    "Below process will take about 15 seconds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a6cb929f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-01-25 22:44:28,596 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 6.37 ms, sys: 1.71 ms, total: 8.08 ms\n",
+      "Wall time: 15.1 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "%%bash\n",
+    "python sea_ice_line_plots.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3120f819",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "a = Image(\"Arctic_tseries.png\")\n",
+    "b = Image(\"Arctic_clim.png\")\n",
+    "display_png(a,b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2540cd5d",
+   "metadata": {},
+   "source": [
+    "The PMP drivers can all read user arguments from parameter files. We provide a demo parameter file, which is shown below. Comments (beginning with a '#') explain each of the parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6e4fa38d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# Sea ice metrics parameter file\n",
+      "\n",
+      "# List of models to include in analysis\n",
+      "test_data_set = [\n",
+      "    \"E3SM-1-0\",\n",
+      "]\n",
+      "\n",
+      "# realization can be a single realization, a list of realizations, or \"*\" for all realizations\n",
+      "realization = \"r1i2p2f1\"\n",
+      "\n",
+      "# test_data_path is a template for the model data parent directory\n",
+      "test_data_path = \"/p/user_pub/pmp/demo/sea-ice/links_siconc/%(model)/historical/%(realization)/siconc/\"\n",
+      "\n",
+      "# filename_template is a template for the model data file name\n",
+      "# combine it with test_data_path to get complete data path\n",
+      "filename_template = \"siconc_SImon_%(model)_historical_%(realization)_*_*.nc\"\n",
+      "\n",
+      "# The name of the sea ice variable in the model data\n",
+      "var = \"siconc\"\n",
+      "\n",
+      "# Start and end years for model data\n",
+      "msyear = 1981\n",
+      "meyear = 2010\n",
+      "\n",
+      "# Factor for adjusting model data to decimal rather than percent units\n",
+      "ModUnitsAdjust = (True, \"multiply\", 1e-2)\n",
+      "\n",
+      "# Template for the grid area file\n",
+      "area_template = \"/p/user_pub/pmp/demo/sea-ice/links_area/%(model)/*.nc\"\n",
+      "\n",
+      "# Area variable name; likely 'areacello' or 'areacella' for CMIP6\n",
+      "area_var = \"areacello\"\n",
+      "\n",
+      "# Factor to convert area units to km-2\n",
+      "AreaUnitsAdjust = (True, \"multiply\", 1e-6)\n",
+      "\n",
+      "# Directory for writing outputs\n",
+      "case_id = \"ex1\"\n",
+      "metrics_output_path = \"sea_ice_demo/%(case_id)/\"\n",
+      "\n",
+      "# Settings for the observational data\n",
+      "reference_data_path_nh = \"/p/user_pub/pmp/demo/sea-ice/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/ice_conc_nh_ease2-250_cdr-v3p0_198801-202012.nc\"\n",
+      "reference_data_path_sh = \"/p/user_pub/pmp/demo/sea-ice/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/ice_conc_sh_ease2-250_cdr-v3p0_198801-202012.nc\"\n",
+      "ObsUnitsAdjust = (True, \"multiply\", 1e-2)\n",
+      "reference_data_set = \"OSI-SAF\"\n",
+      "osyear = 1988\n",
+      "oeyear = 2020\n",
+      "obs_var = \"ice_conc\"\n",
+      "ObsAreaUnitsAdjust = (False, 0, 0)\n",
+      "obs_area_template = None\n",
+      "obs_area_var = None\n",
+      "obs_cell_area = 625  # km 2\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open(\"sea_ice_param.py\") as f:\n",
+    "    print(f.read())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38dbe853",
+   "metadata": {},
+   "source": [
+    "To see all of the parameters available for the sea ice metrics, run the --help command as shown here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9d6c1fbf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "usage: sea_ice_driver.py [-h] [--parameters PARAMETERS]\n",
+      "                         [--diags OTHER_PARAMETERS [OTHER_PARAMETERS ...]]\n",
+      "                         [--case_id CASE_ID] [-v VAR] [--obs_var OBS_VAR]\n",
+      "                         [--area_var AREA_VAR] [--obs_area_var OBS_AREA_VAR]\n",
+      "                         [-r REFERENCE_DATA_SET [REFERENCE_DATA_SET ...]]\n",
+      "                         [--reference_data_path_nh REFERENCE_DATA_PATH_NH]\n",
+      "                         [--reference_data_path_sh REFERENCE_DATA_PATH_SH]\n",
+      "                         [-t TEST_DATA_SET [TEST_DATA_SET ...]]\n",
+      "                         [--test_data_path TEST_DATA_PATH]\n",
+      "                         [--realization REALIZATION]\n",
+      "                         [--filename_template FILENAME_TEMPLATE]\n",
+      "                         [--metrics_output_path METRICS_OUTPUT_PATH]\n",
+      "                         [--filename_output_template FILENAME_OUTPUT_TEMPLATE]\n",
+      "                         [--area_template AREA_TEMPLATE]\n",
+      "                         [--obs_area_template_nh OBS_AREA_TEMPLATE_NH]\n",
+      "                         [--obs_area_template_sh OBS_AREA_TEMPLATE_SH]\n",
+      "                         [--obs_cell_area OBS_CELL_AREA]\n",
+      "                         [--output_json_template OUTPUT_JSON_TEMPLATE]\n",
+      "                         [--debug] [--plots] [--osyear OSYEAR]\n",
+      "                         [--msyear MSYEAR] [--oeyear OEYEAR] [--meyear MEYEAR]\n",
+      "                         [--ObsUnitsAdjust OBSUNITSADJUST]\n",
+      "                         [--ModUnitsAdjust MODUNITSADJUST]\n",
+      "                         [--AreaUnitsAdjust AREAUNITSADJUST]\n",
+      "                         [--ObsAreaUnitsAdjust OBSAREAUNITSADJUST]\n",
+      "\n",
+      "options:\n",
+      "  -h, --help            show this help message and exit\n",
+      "  --parameters PARAMETERS, -p PARAMETERS\n",
+      "  --diags OTHER_PARAMETERS [OTHER_PARAMETERS ...], -d OTHER_PARAMETERS [OTHER_PARAMETERS ...]\n",
+      "                        Path to other user-defined parameter file. (default:\n",
+      "                        None)\n",
+      "  --case_id CASE_ID     Defines a subdirectory to the metrics output, so\n",
+      "                        multiplecases can be compared (default: None)\n",
+      "  -v VAR, --var VAR     Name of model sea ice concentration variable (default:\n",
+      "                        None)\n",
+      "  --obs_var OBS_VAR     Name of obs sea ice concentration variable (default:\n",
+      "                        None)\n",
+      "  --area_var AREA_VAR   Name of model area variable (default: None)\n",
+      "  --obs_area_var OBS_AREA_VAR\n",
+      "                        Name of reference data area variable (default: None)\n",
+      "  -r REFERENCE_DATA_SET [REFERENCE_DATA_SET ...], --reference_data_set REFERENCE_DATA_SET [REFERENCE_DATA_SET ...]\n",
+      "                        List of observations or models that are used as a\n",
+      "                        reference against the test_data_set (default: None)\n",
+      "  --reference_data_path_nh REFERENCE_DATA_PATH_NH\n",
+      "                        Path for the reference climatologies for southern\n",
+      "                        hemisphere (default: None)\n",
+      "  --reference_data_path_sh REFERENCE_DATA_PATH_SH\n",
+      "                        Path for the reference climatologies for northern\n",
+      "                        hemisphere (default: None)\n",
+      "  -t TEST_DATA_SET [TEST_DATA_SET ...], --test_data_set TEST_DATA_SET [TEST_DATA_SET ...]\n",
+      "                        List of observations or models to test against the\n",
+      "                        reference_data_set (default: None)\n",
+      "  --test_data_path TEST_DATA_PATH\n",
+      "                        Path for the test climitologies (default: None)\n",
+      "  --realization REALIZATION\n",
+      "                        A simulation parameter (default: None)\n",
+      "  --filename_template FILENAME_TEMPLATE\n",
+      "                        Template for climatology files (default: None)\n",
+      "  --metrics_output_path METRICS_OUTPUT_PATH\n",
+      "                        Directory of where to put the results (default: None)\n",
+      "  --filename_output_template FILENAME_OUTPUT_TEMPLATE\n",
+      "                        Filename for the interpolated test climatologies\n",
+      "                        (default: None)\n",
+      "  --area_template AREA_TEMPLATE\n",
+      "                        Filename template for model grid area (default: None)\n",
+      "  --obs_area_template_nh OBS_AREA_TEMPLATE_NH\n",
+      "                        Filename template for obs grid area in Northern\n",
+      "                        Hemisphere (default: None)\n",
+      "  --obs_area_template_sh OBS_AREA_TEMPLATE_SH\n",
+      "                        Filename template for obs grid area in Southern\n",
+      "                        Hemisphere (default: None)\n",
+      "  --obs_cell_area OBS_CELL_AREA\n",
+      "                        For equal area grids, the cell area in km (default:\n",
+      "                        None)\n",
+      "  --output_json_template OUTPUT_JSON_TEMPLATE\n",
+      "                        Filename template for results json files (default:\n",
+      "                        None)\n",
+      "  --debug               Turn on debugging mode by printing more information to\n",
+      "                        track progress (default: False)\n",
+      "  --plots               Set to True to generate figures. (default: False)\n",
+      "  --osyear OSYEAR       Start year for reference data set (default: None)\n",
+      "  --msyear MSYEAR       Start year for model data set (default: None)\n",
+      "  --oeyear OEYEAR       End year for reference data set (default: None)\n",
+      "  --meyear MEYEAR       End year for model data set (default: None)\n",
+      "  --ObsUnitsAdjust OBSUNITSADJUST\n",
+      "                        Factor to convert obs sea ice concentration to\n",
+      "                        decimal. For example: - (True, 'divide', 100.0) #\n",
+      "                        percentage to decimal - (False, 0, 0) # No adjustment\n",
+      "                        (default) (default: (False, 0, 0))\n",
+      "  --ModUnitsAdjust MODUNITSADJUST\n",
+      "                        Factor to convert model sea ice concentration to\n",
+      "                        decimal. For example: - (True, 'divide', 100.0) #\n",
+      "                        percentage to decimal - (False, 0, 0) # No adjustment\n",
+      "                        (default) (default: (False, 0, 0))\n",
+      "  --AreaUnitsAdjust AREAUNITSADJUST\n",
+      "                        Factor to convert area data to km^2. For example: -\n",
+      "                        (True, 'multiply', 1e-6) # m^2 to km^2 - (False, 0, 0)\n",
+      "                        # No adjustment (default) (default: (False, 0, 0))\n",
+      "  --ObsAreaUnitsAdjust OBSAREAUNITSADJUST\n",
+      "                        Factor to convert area data to km^2. For example: -\n",
+      "                        (True, 'multiply', 1e-6) # m^2 to km^2 - (False, 0, 0)\n",
+      "                        # No adjustment (default) (default: (False, 0, 0))\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "sea_ice_driver.py --help"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9bfa9c97",
+   "metadata": {},
+   "source": [
+    "The PMP drivers are run on the command line. In this Jupyter Notebook, we use the bash cell magic function %%bash to run command line functions from the notebook.\n",
+    "\n",
+    "The PMP sea ice metrics driver call follows the basic format:\n",
+    "ice_driver.py -p parameter_file.py --additional arguments\n",
+    "\n",
+    "The following cell runs the driver with the demo parameter file we saw above.\n",
+    "\n",
+    "Below process will take about 3 minutes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d6ff0052",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['E3SM-1-0']\n",
+      "Find all realizations: False\n",
+      "OBS: Arctic\n",
+      "Converting units by  multiply 0.01\n",
+      "OBS: Antarctic\n",
+      "Converting units by  multiply 0.01\n",
+      "Model list: ['E3SM-1-0']\n",
+      "/p/user_pub/pmp/demo/sea-ice/links_area/E3SM-1-0/*.nc\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-01-25 22:45:49,104 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Converting units by  multiply 1e-06\n",
+      "\n",
+      "-----------------------\n",
+      "model, run, variable: E3SM-1-0 r1i2p2f1 siconc\n",
+      "test_data (model in this case) full_path:\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_185001-185912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_186001-186912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_187001-187912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_188001-188912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_189001-189912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_190001-190912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_191001-191912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_192001-192912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_193001-193912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_194001-194912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_195001-195912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_196001-196912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_197001-197912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_198001-198912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_199001-199912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_200001-200912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_201001-201112.nc\n",
+      "Converting units by  multiply 0.01\n",
+      "\n",
+      "-------------------------------------------\n",
+      "Calculating model regional average metrics \n",
+      "for  E3SM-1-0\n",
+      "--------------------------------------------\n",
+      "arctic\n",
+      "ca\n",
+      "na\n",
+      "np\n",
+      "antarctic\n",
+      "sp\n",
+      "sa\n",
+      "io\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO::2024-01-25 22:46::pcmdi_metrics:: Results saved to a json file: /home/lee1043/git/pcmdi_metrics_20240125/pcmdi_metrics/doc/jupyter/Demo/sea_ice_demo/ex1/sea_ice_metrics.json\n",
+      "2024-01-25 22:46:57,691 [INFO]: base.py(write:251) >> Results saved to a json file: /home/lee1043/git/pcmdi_metrics_20240125/pcmdi_metrics/doc/jupyter/Demo/sea_ice_demo/ex1/sea_ice_metrics.json\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 43.3 ms, sys: 12.8 ms, total: 56.1 ms\n",
+      "Wall time: 2min 15s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "%%bash\n",
+    "sea_ice_driver.py -p sea_ice_param.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "084440aa",
+   "metadata": {},
+   "source": [
+    "One of the primary outputs of the PMP is a JSON file containing the metrics values. In this case, the metrics are the mean square errors of the time mean and monthly mean ice extent. Ice extent is defined as the total area covered by sea ice concentration of >= 15%. The metrics are organized by model, realization, and reference dataset.\n",
+    "\n",
+    "The metrics JSON from this run is displayed below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "9a46fb89",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\n",
+      "    \"DIMENSIONS\": {\n",
+      "        \"index\": {\n",
+      "            \"monthly_clim\": \"Monthly climatology of extent\",\n",
+      "            \"total_extent\": \"Sum of ice coverage where concentration > 15%\"\n",
+      "        },\n",
+      "        \"json_structure\": [\n",
+      "            \"model\",\n",
+      "            \"realization\",\n",
+      "            \"obs\",\n",
+      "            \"region\",\n",
+      "            \"index\",\n",
+      "            \"statistic\"\n",
+      "        ],\n",
+      "        \"model\": [\n",
+      "            \"E3SM-1-0\"\n",
+      "        ],\n",
+      "        \"region\": {},\n",
+      "        \"statistic\": {\n",
+      "            \"mse\": \"Mean Square Error (10^12 km^4)\"\n",
+      "        }\n",
+      "    },\n",
+      "    \"RESULTS\": {\n",
+      "        \"E3SM-1-0\": {\n",
+      "            \"antarctic\": {\n",
+      "                \"model_mean\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.4635192339671928\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.139646926848\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r1i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.4635192339671928\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.139646926848\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                }\n",
+      "            },\n",
+      "            \"arctic\": {\n",
+      "                \"model_mean\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"5.476181000101471\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"3.628078727168\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r1i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"5.476181000101471\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"3.628078727168\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                }\n",
+      "            },\n",
+      "            \"ca\": {\n",
+      "                \"model_mean\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.05045644169895609\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.007755424768\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r1i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.05045644169895609\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.007755424768\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                }\n",
+      "            },\n",
+      "            \"io\": {\n",
+      "                \"model_mean\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.04955696515353039\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.00991997952\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r1i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.04955696515353039\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.00991997952\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                }\n",
+      "            },\n",
+      "            \"na\": {\n",
+      "                \"model_mean\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"2.3482121752568643\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"1.576847409152\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r1i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"2.3482121752568643\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"1.576847409152\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                }\n",
+      "            },\n",
+      "            \"np\": {\n",
+      "                \"model_mean\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.6264518797177615\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.287947685888\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r1i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.6264518797177615\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.287947685888\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                }\n",
+      "            },\n",
+      "            \"sa\": {\n",
+      "                \"model_mean\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.3797729615722766\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.297013608448\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r1i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.3797729615722766\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.297013608448\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                }\n",
+      "            },\n",
+      "            \"sp\": {\n",
+      "                \"model_mean\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.6767107661262813\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.078223351808\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r1i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.6767107661262813\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.078223351808\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                }\n",
+      "            }\n",
+      "        }\n",
+      "    },\n",
+      "    \"json_structure\": [\n",
+      "        \"model\",\n",
+      "        \"realization\",\n",
+      "        \"obs\",\n",
+      "        \"region\",\n",
+      "        \"index\",\n",
+      "        \"statistic\"\n",
+      "    ],\n",
+      "    \"json_version\": 3.0,\n",
+      "    \"model_year_range\": {\n",
+      "        \"E3SM-1-0\": [\n",
+      "            \"1981\",\n",
+      "            \"2010\"\n",
+      "        ]\n",
+      "    },\n",
+      "    \"provenance\": {\n",
+      "        \"commandLine\": \"/home/lee1043/.conda/envs/pcmdi_metrics_dev_20231129/bin/sea_ice_driver.py -p sea_ice_param.py\",\n",
+      "        \"conda\": {\n",
+      "            \"Platform\": \"linux-64\",\n",
+      "            \"PythonVersion\": \"3.10.12.final.0\",\n",
+      "            \"Version\": \"23.3.1\",\n",
+      "            \"buildVersion\": \"not installed\"\n",
+      "        },\n",
+      "        \"date\": \"2024-01-25 22:46:38\",\n",
+      "        \"openGL\": {\n",
+      "            \"GLX\": {\n",
+      "                \"client\": {\n",
+      "                    \"vendor\": \"Mesa Project and SGI\",\n",
+      "                    \"version\": \"1.4\"\n",
+      "                },\n",
+      "                \"server\": {\n",
+      "                    \"vendor\": \"SGI\",\n",
+      "                    \"version\": \"1.4\"\n",
+      "                },\n",
+      "                \"version\": \"1.4\"\n",
+      "            },\n",
+      "            \"renderer\": \"llvmpipe (LLVM 7.0, 256 bits)\",\n",
+      "            \"shading language version\": \"1.20\",\n",
+      "            \"vendor\": \"VMware, Inc.\",\n",
+      "            \"version\": \"2.1 Mesa 18.3.4\"\n",
+      "        },\n",
+      "        \"osAccess\": false,\n",
+      "        \"packages\": {\n",
+      "            \"PMP\": \"v3.0.2-11-g06b151f\",\n",
+      "            \"PMPObs\": \"See 'References' key below, for detailed obs provenance information.\",\n",
+      "            \"blas\": \"0.3.25\",\n",
+      "            \"cdat_info\": \"8.2.1\",\n",
+      "            \"cdms\": \"3.1.5\",\n",
+      "            \"cdp\": \"1.7.0\",\n",
+      "            \"cdtime\": \"3.1.4\",\n",
+      "            \"cdutil\": \"8.2.1\",\n",
+      "            \"clapack\": null,\n",
+      "            \"esmf\": \"0.8.2\",\n",
+      "            \"esmpy\": \"8.4.2\",\n",
+      "            \"genutil\": \"8.2.1\",\n",
+      "            \"lapack\": \"3.9.0\",\n",
+      "            \"matplotlib\": \"3.7.1\",\n",
+      "            \"mesalib\": null,\n",
+      "            \"numpy\": \"1.23.5\",\n",
+      "            \"python\": \"3.10.10\",\n",
+      "            \"scipy\": \"1.11.4\",\n",
+      "            \"uvcdat\": null,\n",
+      "            \"vcs\": null,\n",
+      "            \"vtk\": null,\n",
+      "            \"xarray\": \"2023.11.0\",\n",
+      "            \"xcdat\": \"0.5.0\"\n",
+      "        },\n",
+      "        \"platform\": {\n",
+      "            \"Name\": \"gates.llnl.gov\",\n",
+      "            \"OS\": \"Linux\",\n",
+      "            \"Version\": \"3.10.0-1160.71.1.el7.x86_64\"\n",
+      "        },\n",
+      "        \"userId\": \"lee1043\"\n",
+      "    }\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open(\"sea_ice_demo/ex1/sea_ice_metrics.json\") as f:\n",
+    "    print(f.read())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d74b6752",
+   "metadata": {},
+   "source": [
+    "This driver also outputs a bar chart that visualizes the mean square error between the model and observations. Since there is only one model and one realization in this instance, the bar chart looks very simple. The red bar indicates the mean square error for the time mean ice extent, and the blue bar indicates the mean square error for the climatological ice extent."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "c6dfa7a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sea_ice_demo/ex1/MSE_bar_chart.png\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls {\"sea_ice_demo/ex1/MSE_bar_chart.png\"}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "d14e933a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "a = Image(\"sea_ice_demo/ex1/MSE_bar_chart.png\")\n",
+    "display_png(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a9b323ec",
+   "metadata": {},
+   "source": [
+    "## Working with multiple realizations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0c427a07",
+   "metadata": {},
+   "source": [
+    "The sea ice driver can generate metrics based on an average of all available realizations. To do so, provide an asterisk \\* as the value to the --realization argument on the command line. Options passed on the command line will supercede arguments in the parameter file. \n",
+    "\n",
+    "In addition, we set the --case_id value to 'ex2' to save results in a new directory.\n",
+    "\n",
+    "Below process will take about 5 minutes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "5f8174e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['E3SM-1-0']\n",
+      "Find all realizations: True\n",
+      "OBS: Arctic\n",
+      "Converting units by  multiply 0.01\n",
+      "OBS: Antarctic\n",
+      "Converting units by  multiply 0.01\n",
+      "Model list: ['E3SM-1-0']\n",
+      "\n",
+      "=================================\n",
+      "model, runs: E3SM-1-0 ['r1i2p2f1', 'r2i2p2f1', 'r3i2p2f1', 'r4i2p2f1']\n",
+      "/p/user_pub/pmp/demo/sea-ice/links_area/E3SM-1-0/*.nc\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-01-25 22:48:06,512 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Converting units by  multiply 1e-06\n",
+      "\n",
+      "-----------------------\n",
+      "model, run, variable: E3SM-1-0 r1i2p2f1 siconc\n",
+      "test_data (model in this case) full_path:\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_185001-185912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_186001-186912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_187001-187912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_188001-188912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_189001-189912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_190001-190912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_191001-191912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_192001-192912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_193001-193912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_194001-194912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_195001-195912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_196001-196912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_197001-197912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_198001-198912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_199001-199912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_200001-200912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_201001-201112.nc\n",
+      "Converting units by  multiply 0.01\n",
+      "\n",
+      "-----------------------\n",
+      "model, run, variable: E3SM-1-0 r2i2p2f1 siconc\n",
+      "test_data (model in this case) full_path:\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_185001-185912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_186001-186912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_187001-187912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_188001-188912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_189001-189912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_190001-190912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_191001-191912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_192001-192912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_193001-193912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_194001-194912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_195001-195912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_196001-196912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_197001-197912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_198001-198912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_199001-199912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_200001-200912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_201001-201312.nc\n",
+      "Converting units by  multiply 0.01\n",
+      "\n",
+      "-----------------------\n",
+      "model, run, variable: E3SM-1-0 r3i2p2f1 siconc\n",
+      "test_data (model in this case) full_path:\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_185001-185912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_186001-186912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_187001-187912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_188001-188912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_189001-189912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_190001-190912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_191001-191912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_192001-192912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_193001-193912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_194001-194912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_195001-195912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_196001-196912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_197001-197912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_198001-198912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_199001-199912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_200001-200912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_201001-201412.nc\n",
+      "Converting units by  multiply 0.01\n",
+      "\n",
+      "-----------------------\n",
+      "model, run, variable: E3SM-1-0 r4i2p2f1 siconc\n",
+      "test_data (model in this case) full_path:\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_185001-185912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_186001-186912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_187001-187912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_188001-188912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_189001-189912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_190001-190912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_191001-191912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_192001-192912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_193001-193912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_194001-194912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_195001-195912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_196001-196912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_197001-197912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_198001-198912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_199001-199912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_200001-200912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_201001-201412.nc\n",
+      "Converting units by  multiply 0.01\n",
+      "\n",
+      "-------------------------------------------\n",
+      "Calculating model regional average metrics \n",
+      "for  E3SM-1-0\n",
+      "--------------------------------------------\n",
+      "arctic\n",
+      "ca\n",
+      "na\n",
+      "np\n",
+      "antarctic\n",
+      "sp\n",
+      "sa\n",
+      "io\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO::2024-01-25 22:50::pcmdi_metrics:: Results saved to a json file: /home/lee1043/git/pcmdi_metrics_20240125/pcmdi_metrics/doc/jupyter/Demo/sea_ice_demo/ex2/sea_ice_metrics.json\n",
+      "2024-01-25 22:50:52,832 [INFO]: base.py(write:251) >> Results saved to a json file: /home/lee1043/git/pcmdi_metrics_20240125/pcmdi_metrics/doc/jupyter/Demo/sea_ice_demo/ex2/sea_ice_metrics.json\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 92.6 ms, sys: 36 ms, total: 129 ms\n",
+      "Wall time: 3min 55s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "%%bash\n",
+    "sea_ice_driver.py -p sea_ice_param.py --realization '*' --case_id \"ex2\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cadb1306",
+   "metadata": {},
+   "source": [
+    "Since we have averaged four different realizations, the resulting statistics are different than seen in example 1. The bar chart now contains markers showing the overall spread among the realizations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "d6cb5f07",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "a = Image(\"sea_ice_demo/ex2/MSE_bar_chart.png\")\n",
+    "display_png(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "499d3935",
+   "metadata": {},
+   "source": [
+    "# Working with multiple models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51b008db",
+   "metadata": {},
+   "source": [
+    "Along with using multiple realizations, we can include multiple models in a single analysis. The model data must all follow a single filename template. All model inputs must use the same name and units for the sea ice variable.\n",
+    "\n",
+    "The example below shows how to use three models in the analysis, with all available realizations. The models are listed as inputs to the --test_data_set flag.\n",
+    "\n",
+    "Want to add more models? Six other model sea ice datasets are available in the directories linked in the notebook introduction.\n",
+    "\n",
+    "Below process will take about 10 minutes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "679d7289",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "bash: line 1: fg: no job control\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['E3SM-1-0', 'CanESM5', 'MIROC6']\n",
+      "Find all realizations: True\n",
+      "OBS: Arctic\n",
+      "Converting units by  multiply 0.01\n",
+      "OBS: Antarctic\n",
+      "Converting units by  multiply 0.01\n",
+      "Model list: ['CanESM5', 'E3SM-1-0', 'MIROC6']\n",
+      "\n",
+      "=================================\n",
+      "model, runs: CanESM5 ['r2i1p1f1', 'r1i1p1f1', 'r3i1p1f1']\n",
+      "/p/user_pub/pmp/demo/sea-ice/links_area/CanESM5/*.nc\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-01-25 22:52:02,672 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Converting units by  multiply 1e-06\n",
+      "\n",
+      "-----------------------\n",
+      "model, run, variable: CanESM5 r2i1p1f1 siconc\n",
+      "test_data (model in this case) full_path:\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/CanESM5/historical/r2i1p1f1/siconc/siconc_SImon_CanESM5_historical_r2i1p1f1_gn_185001-201412.nc\n",
+      "Converting units by  multiply 0.01\n",
+      "\n",
+      "-----------------------\n",
+      "model, run, variable: CanESM5 r1i1p1f1 siconc\n",
+      "test_data (model in this case) full_path:\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/CanESM5/historical/r1i1p1f1/siconc/siconc_SImon_CanESM5_historical_r1i1p1f1_gn_185001-201412.nc\n",
+      "Converting units by  multiply 0.01\n",
+      "\n",
+      "-----------------------\n",
+      "model, run, variable: CanESM5 r3i1p1f1 siconc\n",
+      "test_data (model in this case) full_path:\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/CanESM5/historical/r3i1p1f1/siconc/siconc_SImon_CanESM5_historical_r3i1p1f1_gn_185001-201412.nc\n",
+      "Converting units by  multiply 0.01\n",
+      "\n",
+      "-------------------------------------------\n",
+      "Calculating model regional average metrics \n",
+      "for  CanESM5\n",
+      "--------------------------------------------\n",
+      "arctic\n",
+      "ca\n",
+      "na\n",
+      "np\n",
+      "antarctic\n",
+      "sp\n",
+      "sa\n",
+      "io\n",
+      "\n",
+      "=================================\n",
+      "model, runs: E3SM-1-0 ['r1i2p2f1', 'r2i2p2f1', 'r3i2p2f1', 'r4i2p2f1']\n",
+      "/p/user_pub/pmp/demo/sea-ice/links_area/E3SM-1-0/*.nc\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-01-25 22:52:53,710 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Converting units by  multiply 1e-06\n",
+      "\n",
+      "-----------------------\n",
+      "model, run, variable: E3SM-1-0 r1i2p2f1 siconc\n",
+      "test_data (model in this case) full_path:\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_185001-185912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_186001-186912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_187001-187912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_188001-188912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_189001-189912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_190001-190912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_191001-191912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_192001-192912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_193001-193912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_194001-194912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_195001-195912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_196001-196912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_197001-197912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_198001-198912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_199001-199912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_200001-200912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_201001-201112.nc\n",
+      "Converting units by  multiply 0.01\n",
+      "\n",
+      "-----------------------\n",
+      "model, run, variable: E3SM-1-0 r2i2p2f1 siconc\n",
+      "test_data (model in this case) full_path:\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_185001-185912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_186001-186912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_187001-187912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_188001-188912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_189001-189912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_190001-190912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_191001-191912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_192001-192912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_193001-193912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_194001-194912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_195001-195912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_196001-196912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_197001-197912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_198001-198912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_199001-199912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_200001-200912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_201001-201312.nc\n",
+      "Converting units by  multiply 0.01\n",
+      "\n",
+      "-----------------------\n",
+      "model, run, variable: E3SM-1-0 r3i2p2f1 siconc\n",
+      "test_data (model in this case) full_path:\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_185001-185912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_186001-186912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_187001-187912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_188001-188912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_189001-189912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_190001-190912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_191001-191912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_192001-192912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_193001-193912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_194001-194912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_195001-195912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_196001-196912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_197001-197912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_198001-198912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_199001-199912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_200001-200912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_201001-201412.nc\n",
+      "Converting units by  multiply 0.01\n",
+      "\n",
+      "-----------------------\n",
+      "model, run, variable: E3SM-1-0 r4i2p2f1 siconc\n",
+      "test_data (model in this case) full_path:\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_185001-185912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_186001-186912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_187001-187912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_188001-188912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_189001-189912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_190001-190912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_191001-191912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_192001-192912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_193001-193912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_194001-194912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_195001-195912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_196001-196912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_197001-197912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_198001-198912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_199001-199912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_200001-200912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_201001-201412.nc\n",
+      "Converting units by  multiply 0.01\n",
+      "\n",
+      "-------------------------------------------\n",
+      "Calculating model regional average metrics \n",
+      "for  E3SM-1-0\n",
+      "--------------------------------------------\n",
+      "arctic\n",
+      "ca\n",
+      "na\n",
+      "np\n",
+      "antarctic\n",
+      "sp\n",
+      "sa\n",
+      "io\n",
+      "\n",
+      "=================================\n",
+      "model, runs: MIROC6 ['r2i1p1f1', 'r1i1p1f1', 'r4i1p1f1', 'r3i1p1f1']\n",
+      "/p/user_pub/pmp/demo/sea-ice/links_area/MIROC6/*.nc\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-01-25 22:55:31,709 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Converting units by  multiply 1e-06\n",
+      "\n",
+      "-----------------------\n",
+      "model, run, variable: MIROC6 r2i1p1f1 siconc\n",
+      "test_data (model in this case) full_path:\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r2i1p1f1/siconc/siconc_SImon_MIROC6_historical_r2i1p1f1_gn_185001-194912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r2i1p1f1/siconc/siconc_SImon_MIROC6_historical_r2i1p1f1_gn_195001-201412.nc\n",
+      "Converting units by  multiply 0.01\n",
+      "\n",
+      "-----------------------\n",
+      "model, run, variable: MIROC6 r1i1p1f1 siconc\n",
+      "test_data (model in this case) full_path:\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r1i1p1f1/siconc/siconc_SImon_MIROC6_historical_r1i1p1f1_gn_185001-194912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r1i1p1f1/siconc/siconc_SImon_MIROC6_historical_r1i1p1f1_gn_195001-201412.nc\n",
+      "Converting units by  multiply 0.01\n",
+      "\n",
+      "-----------------------\n",
+      "model, run, variable: MIROC6 r4i1p1f1 siconc\n",
+      "test_data (model in this case) full_path:\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r4i1p1f1/siconc/siconc_SImon_MIROC6_historical_r4i1p1f1_gn_185001-194912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r4i1p1f1/siconc/siconc_SImon_MIROC6_historical_r4i1p1f1_gn_195001-201412.nc\n",
+      "Converting units by  multiply 0.01\n",
+      "\n",
+      "-----------------------\n",
+      "model, run, variable: MIROC6 r3i1p1f1 siconc\n",
+      "test_data (model in this case) full_path:\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r3i1p1f1/siconc/siconc_SImon_MIROC6_historical_r3i1p1f1_gn_185001-194912.nc\n",
+      "   /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r3i1p1f1/siconc/siconc_SImon_MIROC6_historical_r3i1p1f1_gn_195001-201412.nc\n",
+      "Converting units by  multiply 0.01\n",
+      "\n",
+      "-------------------------------------------\n",
+      "Calculating model regional average metrics \n",
+      "for  MIROC6\n",
+      "--------------------------------------------\n",
+      "arctic\n",
+      "ca\n",
+      "na\n",
+      "np\n",
+      "antarctic\n",
+      "sp\n",
+      "sa\n",
+      "io\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO::2024-01-25 22:59::pcmdi_metrics:: Results saved to a json file: /home/lee1043/git/pcmdi_metrics_20240125/pcmdi_metrics/doc/jupyter/Demo/sea_ice_demo/ex3/sea_ice_metrics.json\n",
+      "2024-01-25 22:59:27,749 [INFO]: base.py(write:251) >> Results saved to a json file: /home/lee1043/git/pcmdi_metrics_20240125/pcmdi_metrics/doc/jupyter/Demo/sea_ice_demo/ex3/sea_ice_metrics.json\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "%%time\n",
+    "sea_ice_driver.py -p sea_ice_param.py \\\n",
+    "--test_data_set \"E3SM-1-0\" \"CanESM5\" \"MIROC6\" \\\n",
+    "--realization '*' \\\n",
+    "--case_id \"ex3\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a17ffee",
+   "metadata": {},
+   "source": [
+    "The output JSON now includes metrics for all three models."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "b07dbb8b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\n",
+      "    \"DIMENSIONS\": {\n",
+      "        \"index\": {\n",
+      "            \"monthly_clim\": \"Monthly climatology of extent\",\n",
+      "            \"total_extent\": \"Sum of ice coverage where concentration > 15%\"\n",
+      "        },\n",
+      "        \"json_structure\": [\n",
+      "            \"model\",\n",
+      "            \"realization\",\n",
+      "            \"obs\",\n",
+      "            \"region\",\n",
+      "            \"index\",\n",
+      "            \"statistic\"\n",
+      "        ],\n",
+      "        \"model\": [\n",
+      "            \"CanESM5\",\n",
+      "            \"E3SM-1-0\",\n",
+      "            \"MIROC6\"\n",
+      "        ],\n",
+      "        \"region\": {},\n",
+      "        \"statistic\": {\n",
+      "            \"mse\": \"Mean Square Error (10^12 km^4)\"\n",
+      "        }\n",
+      "    },\n",
+      "    \"RESULTS\": {\n",
+      "        \"CanESM5\": {\n",
+      "            \"antarctic\": {\n",
+      "                \"model_mean\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"5.1043444982100254\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"4.816687734317558\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r1i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"3.8203905542158574\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"3.551903219690635\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r2i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"5.408472768567934\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"5.10073354794071\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r3i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"6.255511442006537\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"5.95826796378333\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                }\n",
+      "            },\n",
+      "            \"arctic\": {\n",
+      "                \"model_mean\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"2.6739701578200408\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"2.5552395000296997\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r1i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"1.9601839559074323\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"1.8071711277770932\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r2i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"2.8686219657630323\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"2.7646000598233362\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r3i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"3.306431955856059\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"3.1987918127469728\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                }\n",
+      "            },\n",
+      "            \"ca\": {\n",
+      "                \"model_mean\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.12445176930055403\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.0752818530752368\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r1i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.06261975386075735\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.04065017565672462\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r2i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.18876746901985617\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.11135594838591391\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r3i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.14126431682827864\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.08283366771548334\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                }\n",
+      "            },\n",
+      "            \"io\": {\n",
+      "                \"model_mean\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.3902350350581252\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.3411097649596542\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r1i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.27378096542718267\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.23052580580517984\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r2i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.39222062635127936\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.34482149125771394\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r3i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.5287500850978069\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.4689404665141464\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                }\n",
+      "            },\n",
+      "            \"na\": {\n",
+      "                \"model_mean\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"1.8575586124643404\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"1.6617817141384847\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r1i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"1.5264155067552119\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"1.3050483466111835\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r2i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"1.7640984838802416\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"1.5843839089856835\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r3i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"2.3388720869665747\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"2.1497244832528395\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                }\n",
+      "            },\n",
+      "            \"np\": {\n",
+      "                \"model_mean\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.0063419603431157535\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.001033088420302666\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r1i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.005949894526659108\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"1.7412310294637067e-06\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r2i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.01271835367014484\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.004687148872326894\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r3i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.005275638631907463\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.0008574285177782025\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                }\n",
+      "            },\n",
+      "            \"sa\": {\n",
+      "                \"model_mean\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.4618851114415225\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.21877947801248515\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r1i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.3604933562263525\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.16135560774807228\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r2i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.48465097876034335\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.18590427961007985\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r3i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.565345935295451\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.32530874506699275\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                }\n",
+      "            },\n",
+      "            \"sp\": {\n",
+      "                \"model_mean\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"1.3466206749703824\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"1.3264114860024545\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r1i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"1.0412143157666585\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"1.0233677214618289\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r2i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"1.5844213803502167\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"1.5597222118436824\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r3i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"1.4483893228104396\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"1.4270545631414926\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                }\n",
+      "            }\n",
+      "        },\n",
+      "        \"E3SM-1-0\": {\n",
+      "            \"antarctic\": {\n",
+      "                \"model_mean\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.7772427941035078\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.512854523904\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r1i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.4635192339671928\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.139646926848\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r2i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.7917153708317476\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.5296078848\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r3i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.9431708933066041\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.709116624896\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r4i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"1.1123064886611145\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.8482918891519999\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                }\n",
+      "            },\n",
+      "            \"arctic\": {\n",
+      "                \"model_mean\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"5.271005131039172\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"3.602193842176\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r1i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"5.476181000101471\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"3.628078727168\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r2i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"4.798813326297904\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"3.0712725504\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r3i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"5.695229471419496\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"4.135149109248\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r4i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"5.16787172788022\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"3.6138642309119997\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                }\n",
+      "            },\n",
+      "            \"ca\": {\n",
+      "                \"model_mean\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.06682122096680175\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.014511187968\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r1i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.05045644169895609\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.007755424768\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r2i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.04953964308899206\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.007533873664\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r3i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.09545969211386617\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.026321457152\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r4i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.08158619730649973\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.020952242176\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                }\n",
+      "            },\n",
+      "            \"io\": {\n",
+      "                \"model_mean\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.08859447654792228\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.033486426112\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r1i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.04955696515353039\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.00991997952\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r2i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.0709290381850532\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.020307523584\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r3i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.13171857892467173\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.064746631168\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r4i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.12394583688994158\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.055420383232\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                }\n",
+      "            },\n",
+      "            \"na\": {\n",
+      "                \"model_mean\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"2.2353377826268255\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"1.514922442752\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r1i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"2.3482121752568643\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"1.576847409152\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r2i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"1.986686713962093\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"1.273763069952\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r3i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"2.5126581069696856\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"1.781503885312\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r4i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"2.120027257004436\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"1.450146136064\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                }\n",
+      "            },\n",
+      "            \"np\": {\n",
+      "                \"model_mean\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.5951950421264879\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.268423725056\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r1i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.6264518797177615\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.287947685888\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r2i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.5857836656186229\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.258358591488\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r3i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.5653155943768037\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.255321079808\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r4i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.605146184785239\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.272687104\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                }\n",
+      "            },\n",
+      "            \"sa\": {\n",
+      "                \"model_mean\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.4924799868799379\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.406647668736\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r1i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.3797729615722766\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.297013608448\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r2i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.43324236598783966\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.3584606208\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r3i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.54670122730152\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.455321387008\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r4i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.6355585206799742\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.53622751232\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                }\n",
+      "            },\n",
+      "            \"sp\": {\n",
+      "                \"model_mean\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.5282094877035928\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.01284434432\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r1i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.6767107661262813\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.078223351808\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r2i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.4522165451285096\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.000495858944\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r3i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.5318409136802855\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.009201968127999999\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r4i2p2f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.501167141217253\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.003074912256\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                }\n",
+      "            }\n",
+      "        },\n",
+      "        \"MIROC6\": {\n",
+      "            \"antarctic\": {\n",
+      "                \"model_mean\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"83.57711925460697\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"68.05560229888\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r1i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"83.38600579613097\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"67.918356283392\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r2i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"83.83210652837262\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"68.251656650752\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r3i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"83.79334319896631\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"68.22144507904\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r4i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"83.29824611560043\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"67.831450304512\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                }\n",
+      "            },\n",
+      "            \"arctic\": {\n",
+      "                \"model_mean\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.7964690037128367\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.35166486528\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r1i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.897558208169598\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.338398609408\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r2i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.6413023948192471\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.211103809536\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r3i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.77767869088113\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.374499180544\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r4i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.9160660976325624\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.516833574912\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                }\n",
+      "            },\n",
+      "            \"ca\": {\n",
+      "                \"model_mean\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.08012887394477156\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.021300850688\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r1i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.09644034149447794\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.009676776448\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r2i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.08312628758340265\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.019284629504\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r3i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.07526427644741965\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.0323789824\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r4i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.07474956943685433\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.027760095232\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                }\n",
+      "            },\n",
+      "            \"io\": {\n",
+      "                \"model_mean\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"2.481295899016718\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"1.642160586752\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r1i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"2.4925219153664493\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"1.646709702656\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r2i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"2.4959466246800366\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"1.6535963566079999\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r3i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"2.4778961139985274\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"1.6392122531839999\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r4i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"2.4589450695378665\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"1.629173710848\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                }\n",
+      "            },\n",
+      "            \"na\": {\n",
+      "                \"model_mean\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.09909765129397402\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.051428544512\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r1i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.1197780144247023\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.064398516224\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r2i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.06350924181643114\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.014681411584\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r3i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.07872592575887577\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.037655158784\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r4i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.16268848865282248\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.114331983872\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                }\n",
+      "            },\n",
+      "            \"np\": {\n",
+      "                \"model_mean\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.1074685296351375\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.043079524352\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r1i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.11746168062866781\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.046970937344\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r2i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.08798398576956203\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.03501553664\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r3i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.1243555932581142\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.050896572416\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r4i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"0.10222095624994212\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"0.040308391936\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                }\n",
+      "            },\n",
+      "            \"sa\": {\n",
+      "                \"model_mean\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"12.832756905129132\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"10.706316427264\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r1i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"12.968499777649717\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"10.810716848128\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r2i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"12.732441134907969\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"10.638712635392\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r3i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"12.91876551982416\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"10.769082089472\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r4i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"12.712614806291402\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"10.607433613312\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                }\n",
+      "            },\n",
+      "            \"sp\": {\n",
+      "                \"model_mean\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"16.158254589965146\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"13.595991605247999\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r1i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"15.90437524234963\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"13.40484878336\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r2i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"16.338122498955823\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"13.727326797824\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r3i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"16.167353763780575\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"13.60793174016\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                },\n",
+      "                \"r4i1p1f1\": {\n",
+      "                    \"OSI-SAF\": {\n",
+      "                        \"monthly_clim\": {\n",
+      "                            \"mse\": \"16.22520331188068\"\n",
+      "                        },\n",
+      "                        \"total_extent\": {\n",
+      "                            \"mse\": \"13.644895092736\"\n",
+      "                        }\n",
+      "                    }\n",
+      "                }\n",
+      "            }\n",
+      "        }\n",
+      "    },\n",
+      "    \"json_structure\": [\n",
+      "        \"model\",\n",
+      "        \"realization\",\n",
+      "        \"obs\",\n",
+      "        \"region\",\n",
+      "        \"index\",\n",
+      "        \"statistic\"\n",
+      "    ],\n",
+      "    \"json_version\": 3.0,\n",
+      "    \"model_year_range\": {\n",
+      "        \"CanESM5\": [\n",
+      "            \"1981\",\n",
+      "            \"2010\"\n",
+      "        ],\n",
+      "        \"E3SM-1-0\": [\n",
+      "            \"1981\",\n",
+      "            \"2010\"\n",
+      "        ],\n",
+      "        \"MIROC6\": [\n",
+      "            \"1981\",\n",
+      "            \"2010\"\n",
+      "        ]\n",
+      "    },\n",
+      "    \"provenance\": {\n",
+      "        \"commandLine\": \"/home/lee1043/.conda/envs/pcmdi_metrics_dev_20231129/bin/sea_ice_driver.py -p sea_ice_param.py --test_data_set E3SM-1-0 CanESM5 MIROC6 --realization * --case_id ex3\",\n",
+      "        \"conda\": {\n",
+      "            \"Platform\": \"linux-64\",\n",
+      "            \"PythonVersion\": \"3.10.12.final.0\",\n",
+      "            \"Version\": \"23.3.1\",\n",
+      "            \"buildVersion\": \"not installed\"\n",
+      "        },\n",
+      "        \"date\": \"2024-01-25 22:59:08\",\n",
+      "        \"openGL\": {\n",
+      "            \"GLX\": {\n",
+      "                \"client\": {\n",
+      "                    \"vendor\": \"Mesa Project and SGI\",\n",
+      "                    \"version\": \"1.4\"\n",
+      "                },\n",
+      "                \"server\": {\n",
+      "                    \"vendor\": \"SGI\",\n",
+      "                    \"version\": \"1.4\"\n",
+      "                },\n",
+      "                \"version\": \"1.4\"\n",
+      "            },\n",
+      "            \"renderer\": \"llvmpipe (LLVM 7.0, 256 bits)\",\n",
+      "            \"shading language version\": \"1.20\",\n",
+      "            \"vendor\": \"VMware, Inc.\",\n",
+      "            \"version\": \"2.1 Mesa 18.3.4\"\n",
+      "        },\n",
+      "        \"osAccess\": false,\n",
+      "        \"packages\": {\n",
+      "            \"PMP\": \"v3.0.2-11-g06b151f\",\n",
+      "            \"PMPObs\": \"See 'References' key below, for detailed obs provenance information.\",\n",
+      "            \"blas\": \"0.3.25\",\n",
+      "            \"cdat_info\": \"8.2.1\",\n",
+      "            \"cdms\": \"3.1.5\",\n",
+      "            \"cdp\": \"1.7.0\",\n",
+      "            \"cdtime\": \"3.1.4\",\n",
+      "            \"cdutil\": \"8.2.1\",\n",
+      "            \"clapack\": null,\n",
+      "            \"esmf\": \"0.8.2\",\n",
+      "            \"esmpy\": \"8.4.2\",\n",
+      "            \"genutil\": \"8.2.1\",\n",
+      "            \"lapack\": \"3.9.0\",\n",
+      "            \"matplotlib\": \"3.7.1\",\n",
+      "            \"mesalib\": null,\n",
+      "            \"numpy\": \"1.23.5\",\n",
+      "            \"python\": \"3.10.10\",\n",
+      "            \"scipy\": \"1.11.4\",\n",
+      "            \"uvcdat\": null,\n",
+      "            \"vcs\": null,\n",
+      "            \"vtk\": null,\n",
+      "            \"xarray\": \"2023.11.0\",\n",
+      "            \"xcdat\": \"0.5.0\"\n",
+      "        },\n",
+      "        \"platform\": {\n",
+      "            \"Name\": \"gates.llnl.gov\",\n",
+      "            \"OS\": \"Linux\",\n",
+      "            \"Version\": \"3.10.0-1160.71.1.el7.x86_64\"\n",
+      "        },\n",
+      "        \"userId\": \"lee1043\"\n",
+      "    }\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open(\"sea_ice_demo/ex3/sea_ice_metrics.json\") as f:\n",
+    "    print(f.read())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f48b3856",
+   "metadata": {},
+   "source": [
+    "Now the resulting bar chart shows three different models with their spread."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "41aa14a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "a = Image(\"sea_ice_demo/ex3/MSE_bar_chart.png\")\n",
+    "display_png(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc43281a",
+   "metadata": {},
+   "source": [
+    "# Further exploration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19abc98d",
+   "metadata": {},
+   "source": [
+    "Maybe you want to compare more models, or take a closer look at the model data? Here are links to the data for further exploration.\n",
+    "\n",
+    "As a reminder, data for nine models is available here:\n",
+    "```\n",
+    "/p/user_pub/pmp/demo/sea-ice/links_siconc  \n",
+    "/p/user_pub/pmp/demo/sea-ice/links_area\n",
+    "```\n",
+    "\n",
+    "The observational time series can be found at:\n",
+    "```\n",
+    "/p/user_pub/pmp/demo/sea-ice/EUMETSAT\n",
+    "```\n",
+    "\n",
+    "For some example plotting code using xcdat and matplotlib, see the scripts that were used to generate the introductory figures:\n",
+    "\n",
+    "```\n",
+    "sea_ice_sector_plots.py\n",
+    "sea_ice_line_plots.py\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f1161f29",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pcmdi_metrics_dev_20231129",
+   "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": "3.10.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/doc/jupyter/Demo/sea_ice_line_plots.py b/doc/jupyter/Demo/sea_ice_line_plots.py
new file mode 100644
index 000000000..c3c01132a
--- /dev/null
+++ b/doc/jupyter/Demo/sea_ice_line_plots.py
@@ -0,0 +1,61 @@
+import cftime
+import matplotlib.pyplot as plt
+import xarray as xr
+import xcdat as xc
+
+ds = xc.open_mfdataset(
+    "/p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_*_*.nc"
+)
+area = xc.open_dataset(
+    "/p/user_pub/pmp/demo/sea-ice/links_area/E3SM-1-0/areacello_Ofx_E3SM-1-0_historical_r1i1p1f1_gr.nc"
+)
+
+arctic = (ds.where(ds.lat > 0) * 1e-2 * area.areacello * 1e-6).sum(("lat", "lon"))
+
+f_os_n = "/p/user_pub/pmp/demo/sea-ice/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/ice_conc_nh_ease2-250_cdr-v3p0_198801-202012.nc"
+obs = xc.open_dataset(f_os_n)
+obs_area = 625
+obs_arctic = (obs.ice_conc.where(obs.lat > 0) * 1e-2 * obs_area).sum(("xc", "yc"))
+
+# Time series plot
+arctic.siconc.sel({"time": slice("1981-01-01", "2010-12-31")}).plot(label="E3SM-1-0")
+obs_arctic.plot(label="OSI-SAF")
+plt.title("Arctic monthly sea ice extent")
+plt.ylabel("Extent (km${^2}$)")
+plt.xlabel("time")
+plt.xlim(
+    [
+        cftime.DatetimeNoLeap(1981, 1, 16, 12, 0, 0, 0, has_year_zero=True),
+        cftime.DatetimeNoLeap(2010, 12, 16, 12, 0, 0, 0, has_year_zero=True),
+    ]
+)
+plt.legend(loc="upper right", fontsize=9)
+plt.savefig("Arctic_tseries.png")
+plt.close()
+
+# Climatology plot
+arctic_ds = xr.Dataset(
+    data_vars={"siconc": arctic.siconc, "time_bnds": ds.time_bnds},
+    coords={"time": ds.time},
+)
+arctic_clim = arctic_ds.sel(
+    {"time": slice("1981-01-01", "2010-12-31")}
+).temporal.climatology("siconc", freq="month")
+arctic_clim["time"] = [x for x in range(1, 13)]
+
+obs_arc_ds = xr.Dataset(
+    data_vars={"ice_conc": obs_arctic, "time_bnds": obs.time_bnds},
+    coords={"time": obs.time},
+)
+obs_clim = obs_arc_ds.temporal.climatology("ice_conc", freq="month")
+obs_clim["time"] = [x for x in range(1, 13)]
+
+arctic_clim.siconc.plot(label="E3SM-1-0")
+obs_clim.ice_conc.plot(label="OSI-SAF")
+plt.title("Arctic climatological sea ice extent\n1981-2010")
+plt.xlabel("month")
+plt.ylabel("Extent (km${^2}$)")
+plt.xlim([1, 12])
+plt.legend(loc="upper right", fontsize=9)
+plt.savefig("Arctic_clim.png")
+plt.close()
diff --git a/doc/jupyter/Demo/sea_ice_param.py b/doc/jupyter/Demo/sea_ice_param.py
new file mode 100644
index 000000000..fcff3da45
--- /dev/null
+++ b/doc/jupyter/Demo/sea_ice_param.py
@@ -0,0 +1,52 @@
+# Sea ice metrics parameter file
+
+# List of models to include in analysis
+test_data_set = [
+    "E3SM-1-0",
+]
+
+# realization can be a single realization, a list of realizations, or "*" for all realizations
+realization = "r1i2p2f1"
+
+# test_data_path is a template for the model data parent directory
+test_data_path = "/p/user_pub/pmp/demo/sea-ice/links_siconc/%(model)/historical/%(realization)/siconc/"
+
+# filename_template is a template for the model data file name
+# combine it with test_data_path to get complete data path
+filename_template = "siconc_SImon_%(model)_historical_%(realization)_*_*.nc"
+
+# The name of the sea ice variable in the model data
+var = "siconc"
+
+# Start and end years for model data
+msyear = 1981
+meyear = 2010
+
+# Factor for adjusting model data to decimal rather than percent units
+ModUnitsAdjust = (True, "multiply", 1e-2)
+
+# Template for the grid area file
+area_template = "/p/user_pub/pmp/demo/sea-ice/links_area/%(model)/*.nc"
+
+# Area variable name; likely 'areacello' or 'areacella' for CMIP6
+area_var = "areacello"
+
+# Factor to convert area units to km-2
+AreaUnitsAdjust = (True, "multiply", 1e-6)
+
+# Directory for writing outputs
+case_id = "ex1"
+metrics_output_path = "sea_ice_demo/%(case_id)/"
+
+# Settings for the observational data
+reference_data_path_nh = "/p/user_pub/pmp/demo/sea-ice/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/ice_conc_nh_ease2-250_cdr-v3p0_198801-202012.nc"
+reference_data_path_sh = "/p/user_pub/pmp/demo/sea-ice/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/ice_conc_sh_ease2-250_cdr-v3p0_198801-202012.nc"
+ObsUnitsAdjust = (True, "multiply", 1e-2)
+reference_data_set = "OSI-SAF"
+osyear = 1988
+oeyear = 2020
+obs_var = "ice_conc"
+ObsAreaUnitsAdjust = (False, 0, 0)
+obs_area_template = None
+obs_area_var = None
+obs_cell_area = 625  # km 2
diff --git a/doc/jupyter/Demo/sea_ice_sector_plots.py b/doc/jupyter/Demo/sea_ice_sector_plots.py
new file mode 100644
index 000000000..d9f65b790
--- /dev/null
+++ b/doc/jupyter/Demo/sea_ice_sector_plots.py
@@ -0,0 +1,156 @@
+import cartopy.crs as ccrs
+import matplotlib.colors as colors
+import matplotlib.pyplot as plt
+import numpy as np
+import regionmask
+import xcdat as xc
+
+from pcmdi_metrics.utils import create_land_sea_mask
+
+# ----------
+# Arctic
+# ----------
+print("Creating Arctic map")
+# Load and process data
+f_os_n = "/p/user_pub/pmp/demo/sea-ice/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/ice_conc_nh_ease2-250_cdr-v3p0_198801-202012.nc"
+obs = xc.open_dataset(f_os_n)
+obs = obs.mean("time")
+mask = create_land_sea_mask(obs, lon_key="lon", lat_key="lat")
+obs["ice_conc"] = obs["ice_conc"].where(mask < 1)
+ds = obs.assign_coords(
+    xc=obs["lon"], yc=obs["lat"]
+)  # Assign these variables to Coordinates, which were originally data variables
+
+# Set up regions
+region_NA = np.array([[-120, 45], [-120, 80], [90, 80], [90, 45]])
+region_NP = np.array([[90, 45], [90, 65], [240, 65], [240, 45]])
+names = ["North_Atlantic", "North_Pacific"]
+abbrevs = ["NA", "NP"]
+arctic_regions = regionmask.Regions(
+    [region_NA, region_NP], names=names, abbrevs=abbrevs, name="arctic"
+)
+
+# Do plotting
+cmap = colors.LinearSegmentedColormap.from_list("", [[0, 85 / 255, 182 / 255], "white"])
+proj = ccrs.NorthPolarStereo()
+ax = plt.subplot(111, projection=proj)
+ax.set_global()
+ds.ice_conc.plot.pcolormesh(
+    ax=ax,
+    x="xc",
+    y="yc",
+    transform=ccrs.PlateCarree(),
+    cmap=cmap,
+    cbar_kwargs={"label": "ice concentration (%)"},
+)
+arctic_regions.plot_regions(
+    ax=ax,
+    add_label=False,
+    label="abbrev",
+    line_kws={"color": [0.2, 0.2, 0.25], "linewidth": 3},
+)
+ax.set_extent([-180, 180, 43, 90], ccrs.PlateCarree())
+ax.coastlines(color=[0.3, 0.3, 0.3])
+plt.annotate(
+    "North Atlantic",
+    (0.5, 0.2),
+    xycoords="axes fraction",
+    horizontalalignment="right",
+    verticalalignment="bottom",
+    color="white",
+)
+plt.annotate(
+    "North Pacific",
+    (0.65, 0.88),
+    xycoords="axes fraction",
+    horizontalalignment="right",
+    verticalalignment="bottom",
+    color="white",
+)
+plt.annotate(
+    "Central\nArctic ",
+    (0.56, 0.56),
+    xycoords="axes fraction",
+    horizontalalignment="right",
+    verticalalignment="bottom",
+)
+ax.set_facecolor([0.55, 0.55, 0.6])
+plt.title("Arctic regions with mean\nOSI-SAF ice concentration\n1988-2020")
+plt.savefig("Arctic_regions.png")
+plt.close()
+obs.close()
+
+# ----------
+# Antarctic
+# ----------
+print("Creating Antarctic map")
+# Load and process data
+f_os_s = "/p/user_pub/pmp/demo/sea-ice/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/ice_conc_sh_ease2-250_cdr-v3p0_198801-202012.nc"
+obs = xc.open_dataset(f_os_s)
+obs = obs.mean("time")
+mask = create_land_sea_mask(obs, lon_key="lon", lat_key="lat")
+obs["ice_conc"] = obs["ice_conc"].where(mask < 1)
+ds = obs.assign_coords(
+    xc=obs["lon"], yc=obs["lat"]
+)  # Assign these variables to Coordinates, which were originally data variables
+
+# Set up regions
+region_IO = np.array([[20, -90], [90, -90], [90, -55], [20, -55]])
+region_SA = np.array([[20, -90], [-60, -90], [-60, -55], [20, -55]])
+region_SP = np.array([[90, -90], [300, -90], [300, -55], [90, -55]])
+names = ["Indian Ocean", "South Atlantic", "South Pacific"]
+abbrevs = ["IO", "SA", "SP"]
+arctic_regions = regionmask.Regions(
+    [region_IO, region_SA, region_SP], names=names, abbrevs=abbrevs, name="antarctic"
+)
+
+# Do plotting
+cmap = colors.LinearSegmentedColormap.from_list("", [[0, 85 / 255, 182 / 255], "white"])
+proj = ccrs.SouthPolarStereo()
+ax = plt.subplot(111, projection=proj)
+ax.set_global()
+ds.ice_conc.plot.pcolormesh(
+    ax=ax,
+    x="xc",
+    y="yc",
+    transform=ccrs.PlateCarree(),
+    cmap=cmap,
+    cbar_kwargs={"label": "ice concentration (%)"},
+)
+arctic_regions.plot_regions(
+    ax=ax,
+    add_label=False,
+    label="abbrev",
+    line_kws={"color": [0.2, 0.2, 0.25], "linewidth": 3},
+)
+ax.set_extent([-180, 180, -53, -90], ccrs.PlateCarree())
+ax.coastlines(color=[0.3, 0.3, 0.3])
+plt.annotate(
+    "South Pacific",
+    (0.50, 0.2),
+    xycoords="axes fraction",
+    horizontalalignment="right",
+    verticalalignment="bottom",
+    color="black",
+)
+plt.annotate(
+    "Indian\nOcean",
+    (0.89, 0.66),
+    xycoords="axes fraction",
+    horizontalalignment="right",
+    verticalalignment="bottom",
+    color="black",
+)
+plt.annotate(
+    "South Atlantic",
+    (0.54, 0.82),
+    xycoords="axes fraction",
+    horizontalalignment="right",
+    verticalalignment="bottom",
+    color="black",
+)
+ax.set_facecolor([0.55, 0.55, 0.6])
+plt.title("Antarctic regions with mean\nOSI-SAF ice concentration\n1988-2020")
+plt.savefig("Antarctic_regions.png")
+plt.close()
+obs.close()
diff --git a/pcmdi_metrics/sea_ice/README.md b/pcmdi_metrics/sea_ice/README.md
new file mode 100644
index 000000000..947570024
--- /dev/null
+++ b/pcmdi_metrics/sea_ice/README.md
@@ -0,0 +1,61 @@
+# Sea Ice
+
+## Summary
+
+## Demo
+
+* Link to notebook
+
+## Inputs
+
+### Sectors
+
+## Run
+
+The PMP sea ice metrics can be controlled via an input parameter file, the command line, or both. With the command line only it is executed via:
+
+```
+sea_ice_driver.py -p parameter_file.py
+```
+
+or as a combination of an input parameter file and the command line, e.g.:
+
+```
+sea_ice_driver.py -p parameter_file.py --msyear 1991 --meyear 2020
+```
+
+## Outputs
+
+The driver produces a JSON file containing mean square error metrics for all input models and realizations relative to the reference data set. It also produces a bar chart displaying these metrics.
+
+## Parameters
+
+* **case_id**: Save JSON and figure files into this subdirectory so that results from multiple tests can be readily organized.
+* **test_data_set**: List of model names.
+* **realization**: List of realizations.
+* **test_data_path**: File path to directory containing model/test data.
+* **filename_template**: File name template for test data, e.g., "CMIP5.historical.%(model_version).r1i1p1.mon.%(variable).19810-200512.AC.v2019022512.nc" where "model_version" and "variable" will be analyzed for each of the entries in test_data_set and vars.
+* **var**: Name of model sea ice variable
+* **msyear**: Start year for test data set.
+* **meyear**: End year for test data set.
+* **ModUnitsAdjust**: Factor to convert model sea ice data to fraction of 1. Uses format (flag (bool), operation (str), value (float)). Operation can be "add", "subtract", "multiply", or "divide". For example, use (True, 'multiply', 1e-2) to convert from percent concentration to decimal concentration.
+* **area_template**: File path of model grid area data.
+* **area_var**: Name of model area variable, e.g. "areacello"
+* **AreaUnitsAdjust**: Factor to convert model area data to units of km2. Uses format (flag (bool), operation (str), value (float)). Operation can be "add", "subtract", "multiply", or "divide". For example, use (True, 'multiply', 1e6) to convert from m2 to km2.
+* **metrics_output_path**: Directory path for metrics output in JSON files, e.g., '~/demo_data/PMP_metrics/'. The %(case_id) variable can be used here. If exists, should be empty before run.
+* **reference_data_path_nh**: The reference data file path for the northern hemisphere. If data is global, provide same path for nh and sh.
+* **reference_data_path_sh**: The reference data file path for the southern hemisphere. If data is global, provide same path for nh and sh.
+* **ObsUnitsAdjust**: Factor to convert reference sea ice data to fraction of 1. Uses format (flag (bool), operation (str), value (float)). Operation can be "add", "subtract", "multiply", or "divide". For example, use (True, 'multiply', 1e-2) to convert from percent concentration to decimal concentration.
+* **reference_data_set**: A short name describing the reference dataset, e.g. "OSI-SAF".
+* **osyear**: Start year for reference data set.
+* **oeyear**: End year for reference data set.
+* **obs_var**: Name of reference sea ice variable.
+* **ObsAreaUnitsAdjust**: Factor to convert model area data to units of km2. Uses format (flag (bool), operation (str), value (float)). Operation can be "add", "subtract", "multiply", or "divide". For example, use (True, 'multiply', 1e6) to convert from m2 to km2.
+* **obs_area_template**: File path of grid area data. If unavailalbe, skip and use "obs_cell_area".
+* **obs_area_var**: Name of reference area variable, if available. If unavailable, skip and use "obs_cell_area".
+* **obs_cell_area**: For equal area grids, the area of a single grid cell in units of km2.
+
+
+## Reference
+
+Ivanova, D. P., P. J. Gleckler, K. E. Taylor, P. J. Durack, and K. D. Marvel, 2016: Moving beyond the Total Sea Ice Extent in Gauging Model Biases. J. Climate, 29, 8965–8987, https://doi.org/10.1175/JCLI-D-16-0026.1. 
\ No newline at end of file
diff --git a/pcmdi_metrics/sea_ice/__init__.py b/pcmdi_metrics/sea_ice/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/pcmdi_metrics/sea_ice/lib/__init__.py b/pcmdi_metrics/sea_ice/lib/__init__.py
new file mode 100644
index 000000000..06f0911d4
--- /dev/null
+++ b/pcmdi_metrics/sea_ice/lib/__init__.py
@@ -0,0 +1 @@
+from .sea_ice_parser import create_sea_ice_parser
diff --git a/pcmdi_metrics/sea_ice/lib/sea_ice_parser.py b/pcmdi_metrics/sea_ice/lib/sea_ice_parser.py
new file mode 100644
index 000000000..bc5ae5f6c
--- /dev/null
+++ b/pcmdi_metrics/sea_ice/lib/sea_ice_parser.py
@@ -0,0 +1,220 @@
+#!/usr/bin/env python
+from pcmdi_metrics.mean_climate.lib import pmp_parser
+
+
+def create_sea_ice_parser():
+    parser = pmp_parser.PMPMetricsParser()
+    parser.add_argument(
+        "--case_id",
+        dest="case_id",
+        help="Defines a subdirectory to the metrics output, so multiple"
+        + "cases can be compared",
+        required=False,
+    )
+
+    parser.add_argument(
+        "-v",
+        "--var",
+        type=str,
+        dest="var",
+        help="Name of model sea ice concentration variable",
+        required=False,
+    )
+
+    parser.add_argument(
+        "--obs_var",
+        type=str,
+        dest="obs_var",
+        help="Name of obs sea ice concentration variable",
+        required=False,
+    )
+
+    parser.add_argument(
+        "--area_var",
+        type=str,
+        dest="area_var",
+        help="Name of model area variable",
+        required=False,
+    )
+
+    parser.add_argument(
+        "--obs_area_var",
+        type=str,
+        dest="obs_area_var",
+        help="Name of reference data area variable",
+        required=False,
+        default=None,
+    )
+
+    parser.add_argument(
+        "-r",
+        "--reference_data_set",
+        default=None,
+        type=str,
+        nargs="+",
+        dest="reference_data_set",
+        help="List of observations or models that are used as a "
+        + "reference against the test_data_set",
+        required=False,
+    )
+
+    parser.add_argument(
+        "--reference_data_path_nh",
+        default=None,
+        dest="reference_data_path_nh",
+        help="Path for the reference climatologies for southern hemisphere",
+        required=False,
+    )
+
+    parser.add_argument(
+        "--reference_data_path_sh",
+        default=None,
+        dest="reference_data_path_sh",
+        help="Path for the reference climatologies for northern hemisphere",
+        required=False,
+    )
+
+    parser.add_argument(
+        "-t",
+        "--test_data_set",
+        type=str,
+        nargs="+",
+        dest="test_data_set",
+        help="List of observations or models to test "
+        + "against the reference_data_set",
+        required=False,
+    )
+
+    parser.add_argument(
+        "--test_data_path",
+        dest="test_data_path",
+        help="Path for the test climitologies",
+        required=False,
+    )
+
+    parser.add_argument(
+        "--realization",
+        dest="realization",
+        help="A simulation parameter",
+        required=False,
+    )
+
+    parser.add_argument(
+        "--filename_template",
+        dest="filename_template",
+        help="Template for climatology files",
+        required=False,
+    )
+
+    parser.add_argument(
+        "--metrics_output_path",
+        dest="metrics_output_path",
+        default=None,
+        help="Directory of where to put the results",
+        required=False,
+    )
+
+    parser.add_argument(
+        "--filename_output_template",
+        dest="filename_output_template",
+        help="Filename for the interpolated test climatologies",
+        required=False,
+    )
+
+    parser.add_argument(
+        "--area_template",
+        dest="area_template",
+        help="Filename template for model grid area",
+        required=False,
+    )
+
+    parser.add_argument(
+        "--obs_area_template_nh",
+        dest="obs_area_template_nh",
+        help="Filename template for obs grid area in Northern Hemisphere",
+        required=False,
+        default=None,
+    )
+
+    parser.add_argument(
+        "--obs_area_template_sh",
+        dest="obs_area_template_sh",
+        help="Filename template for obs grid area in Southern Hemisphere",
+        required=False,
+        default=None,
+    )
+
+    parser.add_argument(
+        "--obs_cell_area",
+        dest="obs_cell_area",
+        help="For equal area grids, the cell area in km",
+        required=False,
+        default=None,
+    )
+
+    parser.add_argument(
+        "--output_json_template",
+        help="Filename template for results json files",
+        required=False,
+    )
+
+    parser.add_argument(
+        "--debug",
+        dest="debug",
+        action="store_true",
+        help="Turn on debugging mode by printing more information to track progress",
+        required=False,
+    )
+    parser.add_argument(
+        "--plots",
+        action="store_true",
+        help="Set to True to generate figures.",
+        required=False,
+    )
+    parser.add_argument(
+        "--osyear", dest="osyear", type=int, help="Start year for reference data set"
+    )
+    parser.add_argument(
+        "--msyear", dest="msyear", type=int, help="Start year for model data set"
+    )
+    parser.add_argument(
+        "--oeyear", dest="oeyear", type=int, help="End year for reference data set"
+    )
+    parser.add_argument(
+        "--meyear", dest="meyear", type=int, help="End year for model data set"
+    )
+    parser.add_argument(
+        "--ObsUnitsAdjust",
+        type=tuple,
+        default=(False, 0, 0),
+        help="Factor to convert obs sea ice concentration to decimal. For example:\n"
+        "- (True, 'divide', 100.0)  # percentage to decimal\n"
+        "- (False, 0, 0) # No adjustment (default)",
+    )
+    parser.add_argument(
+        "--ModUnitsAdjust",
+        type=tuple,
+        default=(False, 0, 0),
+        help="Factor to convert model sea ice concentration to decimal. For example:\n"
+        "- (True, 'divide', 100.0)  # percentage to decimal\n"
+        "- (False, 0, 0) # No adjustment (default)",
+    )
+    parser.add_argument(
+        "--AreaUnitsAdjust",
+        type=tuple,
+        default=(False, 0, 0),
+        help="Factor to convert area data to km^2. For example:\n"
+        "- (True, 'multiply', 1e-6)  # m^2 to km^2\n"
+        "- (False, 0, 0) # No adjustment (default)",
+    )
+
+    parser.add_argument(
+        "--ObsAreaUnitsAdjust",
+        type=tuple,
+        default=(False, 0, 0),
+        help="Factor to convert area data to km^2. For example:\n"
+        "- (True, 'multiply', 1e-6)  # m^2 to km^2\n"
+        "- (False, 0, 0) # No adjustment (default)",
+    )
+
+    return parser
diff --git a/pcmdi_metrics/sea_ice/param/parameter_file.py b/pcmdi_metrics/sea_ice/param/parameter_file.py
new file mode 100644
index 000000000..367619106
--- /dev/null
+++ b/pcmdi_metrics/sea_ice/param/parameter_file.py
@@ -0,0 +1,44 @@
+# CMIP6
+# =======
+case_id = "cmip6_osi-saf"
+test_data_set = [
+    "E3SM-1-0",
+    "CanESM5",
+    "CAS-ESM2-0",
+    "GFDL-ESM4",
+    "E3SM-2-0",
+    "MIROC6",
+    "ACCESS-CM2",
+    "ACCESS-ESM1-5",
+]
+realization = "*"
+test_data_path = "links_siconc/%(model)/historical/%(realization)/siconc/"
+filename_template = "siconc_SImon_%(model)_historical_%(realization)_*_*.nc"
+var = "siconc"
+msyear = 1981
+meyear = 2000
+ModUnitsAdjust = (True, "multiply", 1e-2)
+
+area_template = "links_area/%(model)/*.nc"
+area_var = "areacello"
+AreaUnitsAdjust = (True, "multiply", 1e-6)
+
+metrics_output_path = "demo/%(case_id)/"
+
+
+# OSI-SAF data
+reference_data_path_nh = (
+    "/p/user_pub/PCMDIobs/obs4MIPs_input/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/*nh*"
+)
+reference_data_path_sh = (
+    "/p/user_pub/PCMDIobs/obs4MIPs_input/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/*sh*"
+)
+ObsUnitsAdjust = (True, "multiply", 1e-2)
+reference_data_set = "OSI-SAF"
+osyear = 1981
+oeyear = 2010
+obs_var = "ice_conc"
+ObsAreaUnitsAdjust = (False, 0, 0)
+obs_area_template = None  # km2
+obs_area_var = None
+obs_cell_area = 625
diff --git a/pcmdi_metrics/sea_ice/sea_ice_driver.py b/pcmdi_metrics/sea_ice/sea_ice_driver.py
new file mode 100644
index 000000000..85306087c
--- /dev/null
+++ b/pcmdi_metrics/sea_ice/sea_ice_driver.py
@@ -0,0 +1,916 @@
+#!/usr/bin/env python
+
+import datetime
+import glob
+import json
+import os
+import sys
+
+import dask
+import matplotlib.pyplot as plt
+import numpy as np
+import xarray as xr
+import xcdat as xc
+
+from pcmdi_metrics.io import xcdat_openxml
+from pcmdi_metrics.io.base import Base
+from pcmdi_metrics.sea_ice.lib import create_sea_ice_parser
+from pcmdi_metrics.utils import create_land_sea_mask
+
+
+class MetadataFile:
+    # This class organizes the contents for the CMEC
+    # metadata file called output.json, which describes
+    # the other files in the output bundle.
+
+    def __init__(self, metrics_output_path):
+        self.outfile = os.path.join(metrics_output_path, "output.json")
+        self.json = {
+            "provenance": {
+                "environment": "",
+                "modeldata": "",
+                "obsdata": "",
+                "log": "",
+            },
+            "metrics": {},
+            "data": {},
+            "plots": {},
+        }
+
+    def update_metrics(self, kw, filename, longname, desc):
+        tmp = {"filename": filename, "longname": longname, "description": desc}
+        self.json["metrics"].update({kw: tmp})
+        return
+
+    def update_data(self, kw, filename, longname, desc):
+        tmp = {"filename": filename, "longname": longname, "description": desc}
+        self.json["data"].update({kw: tmp})
+        return
+
+    def update_plots(self, kw, filename, longname, desc):
+        tmp = {"filename": filename, "longname": longname, "description": desc}
+        self.json["plots"].update({kw: tmp})
+
+    def update_provenance(self, kw, data):
+        self.json["provenance"].update({kw: data})
+        return
+
+    def update_index(self, val):
+        self.json["index"] = val
+        return
+
+    def write(self):
+        with open(self.outfile, "w") as f:
+            json.dump(self.json, f, indent=4)
+
+
+def sea_ice_regions(ds, var, xvar, yvar):
+    # Two sets of region definitions are provided, one for
+    # -180:180 and one for 0:360 longitude ranges
+    data_arctic = ds[var].where(ds[yvar] > 0, 0)
+    data_antarctic = ds[var].where(ds[yvar] < 0, 0)
+    if (ds[xvar] > 180).any():  # 0 to 360
+        data_ca1 = ds[var].where(
+            (
+                (ds[yvar] > 80)
+                & (ds[yvar] <= 87.2)
+                & ((ds[xvar] > 240) | (ds[xvar] <= 90))
+            ),
+            0,
+        )
+        data_ca2 = ds[var].where(
+            ((ds[yvar] > 65) & (ds[yvar] < 87.2))
+            & ((ds[xvar] > 90) & (ds[xvar] <= 240)),
+            0,
+        )
+        data_ca = data_ca1 + data_ca2
+        data_np = ds[var].where(
+            (ds[yvar] > 35) & (ds[yvar] <= 65) & ((ds[xvar] > 90) & (ds[xvar] <= 240)),
+            0,
+        )
+        data_na = ds[var].where(
+            (ds[yvar] > 45) & (ds[yvar] <= 80) & ((ds[xvar] > 240) | (ds[xvar] <= 90)),
+            0,
+        )
+        data_na = data_na - data_na.where(
+            (ds[yvar] > 45) & (ds[yvar] <= 50) & (ds[xvar] > 30) & (ds[xvar] <= 60),
+            0,
+        )
+        data_sa = ds[var].where(
+            (ds[yvar] > -90)
+            & (ds[yvar] <= -40)
+            & ((ds[xvar] > 300) | (ds[xvar] <= 20)),
+            0,
+        )
+        data_sp = ds[var].where(
+            (ds[yvar] > -90)
+            & (ds[yvar] <= -40)
+            & ((ds[xvar] > 90) & (ds[xvar] <= 300)),
+            0,
+        )
+        data_io = ds[var].where(
+            (ds[yvar] > -90) & (ds[yvar] <= -40) & (ds[xvar] > 20) & (ds[xvar] <= 90),
+            0,
+        )
+    else:  # -180 to 180
+        data_ca1 = ds[var].where(
+            (
+                (ds[yvar] > 80)
+                & (ds[yvar] <= 87.2)
+                & (ds[xvar] > -120)
+                & (ds[xvar] <= 90)
+            ),
+            0,
+        )
+        data_ca2 = ds[var].where(
+            ((ds[yvar] > 65) & (ds[yvar] < 87.2))
+            & ((ds[xvar] > 90) | (ds[xvar] <= -120)),
+            0,
+        )
+        data_ca = data_ca1 + data_ca2
+        data_np = ds[var].where(
+            (ds[yvar] > 35) & (ds[yvar] <= 65) & ((ds[xvar] > 90) | (ds[xvar] <= -120)),
+            0,
+        )
+        data_na = ds[var].where(
+            (ds[yvar] > 45) & (ds[yvar] <= 80) & (ds[xvar] > -120) & (ds[xvar] <= 90),
+            0,
+        )
+        data_na = data_na - data_na.where(
+            (ds[yvar] > 45) & (ds[yvar] <= 50) & (ds[xvar] > 30) & (ds[xvar] <= 60),
+            0,
+        )
+        data_sa = ds[var].where(
+            (ds[yvar] > -90) & (ds[yvar] <= -55) & (ds[xvar] > -60) & (ds[xvar] <= 20),
+            0,
+        )
+        data_sp = ds[var].where(
+            (ds[yvar] > -90)
+            & (ds[yvar] <= -55)
+            & ((ds[xvar] > 90) | (ds[xvar] <= -60)),
+            0,
+        )
+        data_io = ds[var].where(
+            (ds[yvar] > -90) & (ds[yvar] <= -55) & (ds[xvar] > 20) & (ds[xvar] <= 90),
+            0,
+        )
+
+    regions_dict = {
+        "arctic": data_arctic.copy(deep=True),
+        "ca": data_ca.copy(deep=True),
+        "np": data_np.copy(deep=True),
+        "na": data_na.copy(deep=True),
+        "antarctic": data_antarctic.copy(deep=True),
+        "sa": data_sa.copy(deep=True),
+        "sp": data_sp.copy(deep=True),
+        "io": data_io.copy(deep=True),
+    }
+    return regions_dict
+
+
+def find_lon(ds):
+    for key in ds.coords:
+        if key in ["lon", "longitude"]:
+            return key
+    for key in ds.keys():
+        if key in ["lon", "longitude"]:
+            return key
+    return None
+
+
+def find_lat(ds):
+    for key in ds.coords:
+        if key in ["lat", "latitude"]:
+            return key
+    for key in ds.keys():
+        if key in ["lat", "latitude"]:
+            return key
+    return None
+
+
+def mse_t(dm, do, weights=None):
+    """Computes mse"""
+    if dm is None and do is None:  # just want the doc
+        return {
+            "Name": "Temporal Mean Square Error",
+            "Abstract": "Compute Temporal Mean Square Error",
+            "Contact": "pcmdi-metrics@llnl.gov",
+        }
+    if weights is None:
+        stat = np.sum(((dm.data - do.data) ** 2)) / len(dm, axis=0)
+    else:
+        stat = np.sum(((dm.data - do.data) ** 2) * weights, axis=0)
+    if isinstance(stat, dask.array.core.Array):
+        stat = stat.compute()
+    return stat
+
+
+def mse_model(dm, do, var=None):
+    """Computes mse"""
+    if dm is None and do is None:  # just want the doc
+        return {
+            "Name": "Mean Square Error",
+            "Abstract": "Compute Mean Square Error",
+            "Contact": "pcmdi-metrics@llnl.gov",
+        }
+    if var is not None:  # dataset
+        stat = (dm[var].data - do[var].data) ** 2
+    else:  # dataarray
+        stat = (dm - do) ** 2
+    if isinstance(stat, dask.array.core.Array):
+        stat = stat.compute()
+    return stat
+
+
+def to_ice_con_ds(da, ds, obs_var):
+    # Convert sea ice data array to dataset using
+    # coordinates from another dataset
+    ds = xr.Dataset(
+        data_vars={obs_var: da, "time_bnds": ds.time_bnds}, coords={"time": ds.time}
+    )
+    return ds
+
+
+def adjust_units(ds, adjust_tuple):
+    action_dict = {"multiply": "*", "divide": "/", "add": "+", "subtract": "-"}
+    if adjust_tuple[0]:
+        print("Converting units by ", adjust_tuple[1], adjust_tuple[2])
+        cmd = " ".join(["ds", str(action_dict[adjust_tuple[1]]), str(adjust_tuple[2])])
+        ds = eval(cmd)
+    return ds
+
+
+def verify_output_path(metrics_output_path, case_id):
+    if metrics_output_path is None:
+        metrics_output_path = datetime.datetime.now().strftime("v%Y%m%d")
+    if case_id is not None:
+        metrics_output_path = metrics_output_path.replace("%(case_id)", case_id)
+    if not os.path.exists(metrics_output_path):
+        print("\nMetrics output path not found.")
+        print("Creating metrics output directory", metrics_output_path)
+        try:
+            os.makedirs(metrics_output_path)
+        except Exception as e:
+            print("\nError: Could not create metrics output path", metrics_output_path)
+            print(e)
+            print("Exiting.")
+            sys.exit()
+    return metrics_output_path
+
+
+def verify_years(start_year, end_year, msg="Error: Invalid start or end year"):
+    if start_year is None and end_year is None:
+        return
+    elif start_year is None or end_year is None:
+        # If only one of the two is set, exit.
+        print(msg)
+        print("Exiting")
+        sys.exit()
+
+
+def set_up_realizations(realization):
+    find_all_realizations = False
+    if realization is None:
+        realization = ""
+        realizations = [realization]
+    elif isinstance(realization, str):
+        if realization.lower() in ["all", "*"]:
+            find_all_realizations = True
+            realizations = [""]
+        else:
+            realizations = [realization]
+    elif isinstance(realization, list):
+        realizations = realization
+
+    return find_all_realizations, realizations
+
+
+def load_dataset(filepath):
+    # Load an xarray dataset from the given filepath.
+    # If list of netcdf files, opens mfdataset.
+    # If list of xmls, open last file in list.
+    if filepath[-1].endswith(".xml"):
+        # Final item of sorted list would have most recent version date
+        ds = xcdat_openxml.xcdat_openxml(filepath[-1])
+    elif len(filepath) > 1:
+        ds = xc.open_mfdataset(filepath, chunks=None)
+    else:
+        ds = xc.open_dataset(filepath[0])
+    return ds
+
+
+def replace_multi(string, rdict):
+    # Replace multiple keyworks in a string template
+    # based on key-value pairs in 'rdict'.
+    for k in rdict.keys():
+        string = string.replace(k, rdict[k])
+    return string
+
+
+def get_xy_coords(ds, xvar):
+    if len(ds[xvar].dims) == 2:
+        lon_j, lon_i = ds[xvar].dims
+    elif len(ds[xvar].dims) == 1:
+        lon_j = find_lon(ds)
+        lon_i = find_lat(ds)
+    return lon_i, lon_j
+
+
+if __name__ == "__main__":
+    parser = create_sea_ice_parser()
+    parameter = parser.get_parameter(argparse_vals_only=False)
+
+    # Parameters
+    # I/O settings
+    case_id = parameter.case_id
+    realization = parameter.realization
+    var = parameter.var
+    filename_template = parameter.filename_template
+    test_data_path = parameter.test_data_path
+    model_list = parameter.test_data_set
+    reference_data_path_nh = parameter.reference_data_path_nh
+    reference_data_path_sh = parameter.reference_data_path_sh
+    reference_data_set = parameter.reference_data_set
+    metrics_output_path = parameter.metrics_output_path
+    area_template = parameter.area_template
+    area_var = parameter.area_var
+    AreaUnitsAdjust = parameter.AreaUnitsAdjust
+    obs_area_var = parameter.obs_area_var
+    obs_var = parameter.obs_var
+    obs_area_template_nh = parameter.obs_area_template_nh
+    obs_area_template_sh = parameter.obs_area_template_sh
+    obs_cell_area = parameter.obs_cell_area
+    ObsAreaUnitsAdjust = parameter.ObsAreaUnitsAdjust
+    ModUnitsAdjust = parameter.ModUnitsAdjust
+    ObsUnitsAdjust = parameter.ObsUnitsAdjust
+    msyear = parameter.msyear
+    meyear = parameter.meyear
+    osyear = parameter.osyear
+    oeyear = parameter.oeyear
+
+    print(model_list)
+    model_list.sort()
+    # Verifying output directory
+    metrics_output_path = verify_output_path(metrics_output_path, case_id)
+
+    if isinstance(reference_data_set, list):
+        # Fix a command line issue
+        reference_data_set = reference_data_set[0]
+
+    # Verify years
+    ok_mod = verify_years(
+        msyear,
+        meyear,
+        msg="Error: Model msyear and meyear must both be set or both be None (unset).",
+    )
+    ok_obs = verify_years(
+        osyear,
+        oeyear,
+        msg="Error: Obs osyear and oeyear must both be set or both be None (unset).",
+    )
+
+    # Initialize output.json file
+    meta = MetadataFile(metrics_output_path)
+
+    # Setting up model realization list
+    find_all_realizations, realizations = set_up_realizations(realization)
+    print("Find all realizations:", find_all_realizations)
+
+    #### Do Obs part
+    arctic_clims = {}
+    arctic_means = {}
+
+    print("OBS: Arctic")
+    nh_files = glob.glob(reference_data_path_nh)
+    obs = load_dataset(nh_files)
+    xvar = find_lon(obs)
+    yvar = find_lat(obs)
+    coord_i, coord_j = get_xy_coords(obs, xvar)
+    if osyear is not None:
+        obs = obs.sel(
+            {
+                "time": slice(
+                    "{0}-01-01".format(osyear),
+                    "{0}-12-31".format(oeyear),
+                )
+            }
+        ).compute()  # TODO: won't always need to compute
+    obs[obs_var] = adjust_units(obs[obs_var], ObsUnitsAdjust)
+    if obs_area_var is not None:
+        obs[obs_area_var] = adjust_units(obs[obs_area_var], ObsAreaUnitsAdjust)
+        area_val = obs[obs_area_var]
+    else:
+        area_val = obs_cell_area
+    # Remove land areas (including lakes)
+    mask = create_land_sea_mask(obs, lon_key=xvar, lat_key=yvar)
+    obs[obs_var] = obs[obs_var].where(mask < 1)
+    # Get regions
+    rgn_dict = sea_ice_regions(obs, obs_var, xvar, yvar)
+
+    # Get ice extent
+    total_extent_arctic_obs = (
+        rgn_dict["arctic"].where(rgn_dict["arctic"] > 0.15) * area_val
+    ).sum((coord_i, coord_j), skipna=True)
+    total_extent_ca_obs = (rgn_dict["ca"].where(rgn_dict["ca"] > 0.15) * area_val).sum(
+        (coord_i, coord_j), skipna=True
+    )
+    total_extent_np_obs = (rgn_dict["np"].where(rgn_dict["np"] > 0.15) * area_val).sum(
+        (coord_i, coord_j), skipna=True
+    )
+    total_extent_na_obs = (rgn_dict["na"].where(rgn_dict["na"] > 0.15) * area_val).sum(
+        (coord_i, coord_j), skipna=True
+    )
+
+    clim_arctic_obs = to_ice_con_ds(
+        total_extent_arctic_obs, obs, obs_var
+    ).temporal.climatology(obs_var, freq="month")
+    clim_ca_obs = to_ice_con_ds(total_extent_ca_obs, obs, obs_var).temporal.climatology(
+        obs_var, freq="month"
+    )
+    clim_np_obs = to_ice_con_ds(total_extent_np_obs, obs, obs_var).temporal.climatology(
+        obs_var, freq="month"
+    )
+    clim_na_obs = to_ice_con_ds(total_extent_na_obs, obs, obs_var).temporal.climatology(
+        obs_var, freq="month"
+    )
+
+    arctic_clims = {
+        "arctic": clim_arctic_obs,
+        "ca": clim_ca_obs,
+        "np": clim_np_obs,
+        "na": clim_na_obs,
+    }
+
+    arctic_means = {
+        "arctic": total_extent_arctic_obs.mean("time", skipna=True).data.item(),
+        "ca": total_extent_ca_obs.mean("time", skipna=True).data.item(),
+        "np": total_extent_np_obs.mean("time", skipna=True).data.item(),
+        "na": total_extent_na_obs.mean("time", skipna=True).data.item(),
+    }
+    obs.close()
+
+    antarctic_clims = {}
+    antarctic_means = {}
+    print("OBS: Antarctic")
+    sh_files = glob.glob(reference_data_path_sh)
+    obs = load_dataset(sh_files)
+    xvar = find_lon(obs)
+    yvar = find_lat(obs)
+    coord_i, coord_j = get_xy_coords(obs, xvar)
+    if osyear is not None:
+        obs = obs.sel(
+            {
+                "time": slice(
+                    "{0}-01-01".format(osyear),
+                    "{0}-12-31".format(oeyear),
+                )
+            }
+        ).compute()
+    obs[obs_var] = adjust_units(obs[obs_var], ObsUnitsAdjust)
+    if obs_area_var is not None:
+        obs[obs_area_var] = adjust_units(obs[obs_area_var], ObsAreaUnitsAdjust)
+        area_val = obs[obs_area_var]
+    else:
+        area_val = obs_cell_area
+    # Remove land areas (including lakes)
+    mask = create_land_sea_mask(obs, lon_key="lon", lat_key="lat")
+    obs[obs_var] = obs[obs_var].where(mask < 1)
+    rgn_dict = sea_ice_regions(obs, obs_var, "lon", "lat")
+
+    total_extent_antarctic_obs = (
+        rgn_dict["antarctic"].where(rgn_dict["antarctic"] > 0.15) * area_val
+    ).sum((coord_i, coord_j), skipna=True)
+    total_extent_sa_obs = (rgn_dict["sa"].where(rgn_dict["sa"] > 0.15) * area_val).sum(
+        (coord_i, coord_j), skipna=True
+    )
+    total_extent_sp_obs = (rgn_dict["sp"].where(rgn_dict["sp"] > 0.15) * area_val).sum(
+        (coord_i, coord_j), skipna=True
+    )
+    total_extent_io_obs = (rgn_dict["io"].where(rgn_dict["io"] > 0.15) * area_val).sum(
+        (coord_i, coord_j), skipna=True
+    )
+
+    clim_antarctic_obs = to_ice_con_ds(
+        total_extent_antarctic_obs, obs, obs_var
+    ).temporal.climatology(obs_var, freq="month")
+    clim_sa_obs = to_ice_con_ds(total_extent_sa_obs, obs, obs_var).temporal.climatology(
+        obs_var, freq="month"
+    )
+    clim_sp_obs = to_ice_con_ds(total_extent_sp_obs, obs, obs_var).temporal.climatology(
+        obs_var, freq="month"
+    )
+    clim_io_obs = to_ice_con_ds(total_extent_io_obs, obs, obs_var).temporal.climatology(
+        obs_var, freq="month"
+    )
+
+    antarctic_clims = {
+        "antarctic": clim_antarctic_obs,
+        "io": clim_io_obs,
+        "sp": clim_sp_obs,
+        "sa": clim_sa_obs,
+    }
+
+    antarctic_means = {
+        "antarctic": total_extent_antarctic_obs.mean("time", skipna=True).data.item(),
+        "io": total_extent_io_obs.mean("time", skipna=True).compute().data.item(),
+        "sp": total_extent_sp_obs.mean("time", skipna=True).compute().data.item(),
+        "sa": total_extent_sa_obs.mean("time", skipna=True).compute().data.item(),
+    }
+    obs.close()
+
+    obs_clims = {reference_data_set: {}}
+    obs_means = {reference_data_set: {}}
+    for item in antarctic_clims:
+        obs_clims[reference_data_set][item] = antarctic_clims[item]
+        obs_means[reference_data_set][item] = antarctic_means[item]
+    for item in arctic_clims:
+        obs_clims[reference_data_set][item] = arctic_clims[item]
+        obs_means[reference_data_set][item] = arctic_means[item]
+
+    #### Do model part
+
+    # Needs to weigh months by length for metrics later
+    clim_wts = [31.0, 28.0, 31.0, 30.0, 31.0, 30.0, 31.0, 31.0, 30.0, 31.0, 30.0, 31.0]
+    clim_wts = [x / 365 for x in clim_wts]
+    # Initialize JSON data
+    mse = {}
+    metrics = {
+        "DIMENSIONS": {
+            "json_structure": [
+                "model",
+                "realization",
+                "obs",
+                "region",
+                "index",
+                "statistic",
+            ],
+            "region": {},
+            "index": {
+                "monthly_clim": "Monthly climatology of extent",
+                "total_extent": "Sum of ice coverage where concentration > 15%",
+            },
+            "statistic": {"mse": "Mean Square Error (10^12 km^4)"},
+            "model": model_list,
+        },
+        "RESULTS": {},
+        "model_year_range": {},
+    }
+    print("Model list:", model_list)
+
+    # Loop over models and realizations to generate metrics
+    for model in model_list:
+        start_year = msyear
+        end_year = meyear
+
+        real_dict = {
+            "arctic": {"model_mean": 0},
+            "ca": {"model_mean": 0},
+            "na": {"model_mean": 0},
+            "np": {"model_mean": 0},
+            "antarctic": {"model_mean": 0},
+            "sp": {"model_mean": 0},
+            "sa": {"model_mean": 0},
+            "io": {"model_mean": 0},
+        }
+        mse[model] = {
+            "arctic": {"model_mean": {reference_data_set: {}}},
+            "ca": {"model_mean": {reference_data_set: {}}},
+            "na": {"model_mean": {reference_data_set: {}}},
+            "np": {"model_mean": {reference_data_set: {}}},
+            "antarctic": {"model_mean": {reference_data_set: {}}},
+            "sp": {"model_mean": {reference_data_set: {}}},
+            "sa": {"model_mean": {reference_data_set: {}}},
+            "io": {"model_mean": {reference_data_set: {}}},
+        }
+
+        tags = {
+            "%(variable)": var,
+            "%(model)": model,
+            "%(model_version)": model,
+            "%(realization)": "*",
+        }
+        if find_all_realizations:
+            test_data_full_path_tmp = os.path.join(test_data_path, filename_template)
+            test_data_full_path_tmp = replace_multi(test_data_full_path_tmp, tags)
+            ncfiles = glob.glob(test_data_full_path_tmp)
+            realizations = []
+            for ncfile in ncfiles:
+                basename = ncfile.split("/")[-1]
+                if len(basename.split(".")) <= 2:
+                    if basename.split("_")[4] not in realizations:
+                        realizations.append(basename.split("_")[4])
+                else:
+                    if basename.split(".")[3] not in realizations:
+                        realizations.append(basename.split(".")[3])
+
+            print("\n=================================")
+            print("model, runs:", model, realizations)
+            list_of_runs = realizations
+        else:
+            list_of_runs = realizations
+
+        # Model grid area
+        print(replace_multi(area_template, tags))
+        area = xc.open_dataset(glob.glob(replace_multi(area_template, tags))[0])
+        area[area_var] = adjust_units(area[area_var], AreaUnitsAdjust)
+
+        if len(list_of_runs) > 0:
+            # Loop over realizations
+            for run_ind, run in enumerate(list_of_runs):
+                # Find model data, determine number of files, check if they exist
+                tags = {
+                    "%(variable)": var,
+                    "%(model)": model,
+                    "%(model_version)": model,
+                    "%(realization)": run,
+                }
+                test_data_full_path = os.path.join(test_data_path, filename_template)
+                test_data_full_path = replace_multi(test_data_full_path, tags)
+                test_data_full_path = glob.glob(test_data_full_path)
+                test_data_full_path.sort()
+                if len(test_data_full_path) == 0:
+                    print("")
+                    print("-----------------------")
+                    print("Not found: model, run, variable:", model, run, var)
+                    break
+                else:
+                    print("")
+                    print("-----------------------")
+                    print("model, run, variable:", model, run, var)
+                    print("test_data (model in this case) full_path:")
+                    for t in test_data_full_path:
+                        print("  ", t)
+
+                # Load and prep data
+                ds = load_dataset(test_data_full_path)
+                ds[var] = adjust_units(ds[var], ModUnitsAdjust)
+                xvar = find_lon(ds)
+                yvar = find_lat(ds)
+                if xvar is None or yvar is None:
+                    print("Could not get latitude or longitude variables")
+                    break
+                if (ds[xvar] < -180).any():
+                    ds[xvar] = ds[xvar].where(ds[xvar] >= -180, ds[xvar] + 360)
+
+                # Get time slice if year parameters exist
+                if start_year is not None:
+                    ds = ds.sel(
+                        {
+                            "time": slice(
+                                "{0}-01-01".format(start_year),
+                                "{0}-12-31".format(end_year),
+                            )
+                        }
+                    )
+                    yr_range = [str(start_year), str(end_year)]
+                else:
+                    # Get labels for start/end years from dataset
+                    yr_range = [
+                        str(int(ds.time.dt.year[0])),
+                        str(int(ds.time.dt.year[-1])),
+                    ]
+
+                # Get regions
+                regions_dict = sea_ice_regions(ds, var, xvar, yvar)
+
+                ds.close()
+                # Running sum of all realizations
+                for rgn in regions_dict:
+                    data = regions_dict[rgn]
+                    # coordinates aren't always the same as lat/lon names,
+                    # especially if lat/lon are 2D
+                    lon_i, lon_j = get_xy_coords(data, xvar)
+                    # area data doesn't always use same coordinates as siconc data in CMIP6
+                    # so we multiply by area.data, dropping the coordinates
+                    rgn_total = (data.where(data > 0.15, 0) * area[area_var].data).sum(
+                        (lon_j, lon_i), skipna=True
+                    )
+                    real_dict[rgn][run] = rgn_total
+                    real_dict[rgn]["model_mean"] = (
+                        real_dict[rgn]["model_mean"] + rgn_total
+                    )
+
+            print("\n-------------------------------------------")
+            print("Calculating model regional average metrics \nfor ", model)
+            print("--------------------------------------------")
+            for rgn in real_dict:
+                print(rgn)
+
+                # Average all realizations, fix bounds, get climatologies and totals
+                # total_rgn = (totals_dict[rgn] / len(list_of_runs)).to_dataset(name=var)
+                real_dict[rgn]["model_mean"] = real_dict[rgn]["model_mean"] / len(
+                    list_of_runs
+                )
+
+                for run in real_dict[rgn]:
+                    # Set up metrics dictionary
+                    if run not in mse[model][rgn]:
+                        mse[model][rgn][run] = {}
+                    mse[model][rgn][run].update(
+                        {
+                            reference_data_set: {
+                                "monthly_clim": {"mse": None},
+                                "total_extent": {"mse": None},
+                            }
+                        }
+                    )
+
+                    run_data = real_dict[rgn][run].to_dataset(name=var)
+                    run_data = run_data.bounds.add_missing_bounds()
+                    clim_extent = run_data.temporal.climatology(var, freq="month")
+                    total = run_data.mean("time")[var].data
+
+                    # Get errors, convert to 1e12 km^-4
+                    mse[model][rgn][run][reference_data_set]["monthly_clim"][
+                        "mse"
+                    ] = str(
+                        mse_t(
+                            clim_extent[var],
+                            obs_clims[reference_data_set][rgn][obs_var],
+                            weights=clim_wts,
+                        )
+                        * 1e-12
+                    )
+                    mse[model][rgn][run][reference_data_set]["total_extent"][
+                        "mse"
+                    ] = str(
+                        mse_model(total, obs_means[reference_data_set][rgn]) * 1e-12
+                    )
+
+            # Update year list
+            metrics["model_year_range"][model] = [str(start_year), str(end_year)]
+        else:
+            for rgn in mse[model]:
+                # Set up metrics dictionary
+                mse[model][rgn]["model_mean"][reference_data_set] = {
+                    "monthly_clim": {"mse": None},
+                    "total_extent": {"mse": None},
+                }
+                metrics["model_year_range"][model] = ["", ""]
+
+    # -----------------
+    # Update metrics
+    # -----------------
+    metrics["RESULTS"] = mse
+
+    metricsfile = os.path.join(metrics_output_path, "sea_ice_metrics.json")
+    JSON = Base(metrics_output_path, "sea_ice_metrics.json")
+    json_structure = metrics["DIMENSIONS"]["json_structure"]
+    JSON.write(
+        metrics,
+        json_structure=json_structure,
+        sort_keys=True,
+        indent=4,
+        separators=(",", ": "),
+    )
+    meta.update_metrics(
+        "metrics",
+        metricsfile,
+        "metrics_JSON",
+        "JSON file containig regional sea ice metrics",
+    )
+
+    # ----------------
+    # Make figure
+    # ----------------
+    sector_list = [
+        "Central Arctic Sector",
+        "North Atlantic Sector",
+        "North Pacific Sector",
+        "Indian Ocean Sector",
+        "South Atlantic Sector",
+        "South Pacific Sector",
+    ]
+    sector_short = ["ca", "na", "np", "io", "sa", "sp"]
+    fig7, ax7 = plt.subplots(6, 1, figsize=(5, 9))
+    # mlabels = model_list + ["bootstrap"]
+    mlabels = model_list
+    ind = np.arange(len(mlabels))  # the x locations for the groups
+    # ind = np.arange(len(mods)+1)  # the x locations for the groups
+    width = 0.3
+    # n = len(ind) - 1
+    n = len(ind)
+    for inds, sector in enumerate(sector_list):
+        # Assemble data
+        mse_clim = []
+        mse_ext = []
+        clim_range = []
+        ext_range = []
+        clim_err_x = []
+        clim_err_y = []
+        ext_err_y = []
+        rgn = sector_short[inds]
+        for nmod, model in enumerate(model_list):
+            mse_clim.append(
+                float(
+                    metrics["RESULTS"][model][rgn]["model_mean"][reference_data_set][
+                        "monthly_clim"
+                    ]["mse"]
+                )
+            )
+            mse_ext.append(
+                float(
+                    metrics["RESULTS"][model][rgn]["model_mean"][reference_data_set][
+                        "total_extent"
+                    ]["mse"]
+                )
+            )
+            # Get spread, only if there are multiple realizations
+            if len(metrics["RESULTS"][model][rgn].keys()) > 2:
+                for r in metrics["RESULTS"][model][rgn]:
+                    if r != "model_mean":
+                        clim_err_x.append(ind[nmod])
+                        clim_err_y.append(
+                            float(
+                                metrics["RESULTS"][model][rgn][r][reference_data_set][
+                                    "monthly_clim"
+                                ]["mse"]
+                            )
+                        )
+                        ext_err_y.append(
+                            float(
+                                metrics["RESULTS"][model][rgn][r][reference_data_set][
+                                    "total_extent"
+                                ]["mse"]
+                            )
+                        )
+
+        # plot data
+        if len(model_list) < 4:
+            mark_size = 9
+        elif len(model_list) < 12:
+            mark_size = 3
+        else:
+            mark_size = 1
+        ax7[inds].bar(ind - width / 2.0, mse_clim, width, color="b", label="Ann. Cycle")
+        ax7[inds].bar(ind, mse_ext, width, color="r", label="Ann. Mean")
+        if len(clim_err_x) > 0:
+            ax7[inds].scatter(
+                [x - width / 2.0 for x in clim_err_x],
+                clim_err_y,
+                marker="D",
+                s=mark_size,
+                color="k",
+            )
+            ax7[inds].scatter(clim_err_x, ext_err_y, marker="D", s=mark_size, color="k")
+        # xticks
+        if inds == len(sector_list) - 1:
+            ax7[inds].set_xticks(ind + width / 2.0, mlabels, rotation=90, size=7)
+        else:
+            ax7[inds].set_xticks(ind + width / 2.0, labels="")
+        # yticks
+        if len(clim_err_y) > 0:
+            datamax = np.max(np.array(clim_err_y))
+        else:
+            datamax = np.max(np.array(mse_clim))
+        ymax = (datamax) * 1.3
+        ax7[inds].set_ylim(0.0, ymax)
+        if ymax < 0.1:
+            ticks = np.linspace(0, 0.1, 6)
+            labels = [str(round(x, 3)) for x in ticks]
+        elif ymax < 1:
+            ticks = np.linspace(0, 1, 5)
+            labels = [str(round(x, 1)) for x in ticks]
+        elif ymax < 4:
+            ticks = np.linspace(0, round(ymax), num=round(ymax / 2) * 2 + 1)
+            labels = [str(round(x, 1)) for x in ticks]
+        elif ymax > 10:
+            ticks = range(0, round(ymax), 5)
+            labels = [str(round(x, 0)) for x in ticks]
+        else:
+            ticks = range(0, round(ymax))
+            labels = [str(round(x, 0)) for x in ticks]
+
+        ax7[inds].set_yticks(ticks, labels, fontsize=8)
+        # labels etc
+        ax7[inds].set_ylabel("10${^1}{^2}$km${^4}$", size=8)
+        ax7[inds].grid(True, linestyle=":")
+        ax7[inds].annotate(
+            sector,
+            (0.35, 0.8),
+            xycoords="axes fraction",
+            size=9,
+        )
+    # Add legend, save figure
+    ax7[0].legend(loc="upper right", fontsize=6)
+    plt.suptitle("Mean Square Error relative to " + reference_data_set, y=0.91)
+    figfile = os.path.join(metrics_output_path, "MSE_bar_chart.png")
+    plt.savefig(figfile)
+    meta.update_plots(
+        "bar_chart", figfile, "regional_bar_chart", "Bar chart of regional MSE"
+    )
+
+    # Update and write metadata file
+    try:
+        with open(os.path.join(metricsfile), "r") as f:
+            tmp = json.load(f)
+        meta.update_provenance("environment", tmp["provenance"])
+    except Exception:
+        # Skip provenance if there's an issue
+        print("Error: Could not get provenance from metrics json for output.json.")
+
+    meta.update_provenance("modeldata", test_data_path)
+    if reference_data_path_nh is not None:
+        meta.update_provenance("obsdata_nh", reference_data_path_nh)
+        meta.update_provenance("obsdata_sh", reference_data_path_sh)
+    meta.write()
diff --git a/pcmdi_metrics/sea_ice/sea_ice_parallel.py b/pcmdi_metrics/sea_ice/sea_ice_parallel.py
new file mode 100644
index 000000000..4565b711d
--- /dev/null
+++ b/pcmdi_metrics/sea_ice/sea_ice_parallel.py
@@ -0,0 +1,122 @@
+import glob
+import os
+
+import xsearch as xs
+
+from pcmdi_metrics.mean_climate.lib.pmp_parser import PMPParser
+from pcmdi_metrics.misc.scripts import parallel_submitter
+from pcmdi_metrics.precip_variability.lib import AddParserArgument
+
+num_cpus = 20
+
+# Read parameters
+P = PMPParser()
+P = AddParserArgument(P)
+param = P.get_parameter()
+mip = "cmip6"
+exp = "historical"
+var = param.var
+mod = None
+frq = "mon"
+
+if mod is None:
+    pathDict = xs.findPaths(exp, var, frq, mip_era=mip.upper())
+else:
+    pathDict = xs.findPaths(exp, var, frq, mip_era=mip.upper(), model=mod)
+# Get which area variable needed
+print("Reading external variable attribute")
+# pathDB =  xs.addAttribute(pathDict, 'external_variables')
+areacello = xs.findPaths("historical", "areacello", "fx", cmipTable="Ofx")
+areacella = xs.findPaths("historical", "areacella", "fx")
+path_list = sorted(list(pathDict.keys()))
+print("Number of datasets:", len(path_list))
+
+cmd_list = []
+log_list = []
+model_list = xs.getGroupValues(pathDict, "model")
+print(model_list)
+for model in model_list:
+    skip = False
+    path = xs.getValuesForFacet(pathDict, "model", model)[0]
+    basename = os.path.basename(glob.glob(os.path.join(path, "*"))[0])
+
+    dir_template = (
+        "/".join(path.split("/")[0:9])
+        + "/%(realization)/"
+        + "/".join(path.split("/")[10:13])
+        + "/*/"
+    )
+    file_template = (
+        "_".join(basename.split("_")[0:4])
+        + "_%(realization)_"
+        + basename.split("_")[5]
+        + "_*-*.nc"
+    )
+
+    single = xs.getValuesForFacet(pathDict, "model", model)
+    empty = [{} for item in single]
+    d1 = zip(single, empty)
+    db = dict(d1)
+    db = xs.addAttribute(db, "external_variables")
+
+    # area_var = pathDB[path]["external_variables"]
+    # try:
+    area_var = db[single[0]]["external_variables"]
+    # except:
+    #    print("No external variables")
+    #    print("Guessing areacello")
+    #    area_var = "areacello"
+    if area_var == "areacello":  # Same for all realizations
+        # try:
+        apath = xs.getValuesForFacet(areacello, "model", model)
+        abase = os.path.basename(glob.glob(os.path.join(apath[0], "*"))[0])
+        area_path = os.path.join(apath[0], abase)
+        # except:
+        #    print("No areacello for model ", model)
+        #    print(apath)
+        #    skip = True
+        ## Make filename template
+        # area_path = "/".join(apath[0].split("/")[0:9]) + "/%(realization)/" + "/".join(apath[0].split("/")[10:])
+    elif area_var == "areacella":  # Different for each realization
+        apath = xs.getValuesForFacet(areacella, "model", model)
+        abase = os.path.basename(glob.glob(os.path.join(apath[0], "*"))[0])
+        abase = (
+            "_".join(abase.split("_")[0:4])
+            + "_%(realization)_"
+            + "_".join(abase.split("_")[5:])
+        )
+        # Make filename template
+        area_dir = (
+            "/".join(apath[0].split("/")[0:9])
+            + "/%(realization)/"
+            + "/".join(apath[0].split("/")[10:])
+        )
+        area_path = os.path.join(area_dir, abase)
+    else:
+        "Area variable not found."
+        skip = True
+
+    if not skip:
+        cmd_list.append(
+            "python -u ice_driver.py -p parameter_file.py --case_id "
+            + model
+            + " --test_data_set '"
+            + model
+            + "' --test_data_path '"
+            + dir_template
+            + "' --filename_template '"
+            + file_template
+            + "' --area_template '"
+            + area_path
+            + "' --area_var "
+            + area_var
+        )
+        log_list.append("log_" + mip + "_" + var + "_" + model)
+
+
+parallel_submitter(
+    cmd_list,
+    log_dir="./log",
+    logfilename_list=log_list,
+    num_workers=num_cpus,
+)
diff --git a/setup.py b/setup.py
index 1e0544425..2d5f2ea3a 100644
--- a/setup.py
+++ b/setup.py
@@ -40,6 +40,7 @@
     "pcmdi_metrics/precip_distribution/precip_distribution_driver.py",
     "pcmdi_metrics/cloud_feedback/cloud_feedback_driver.py",
     "pcmdi_metrics/extremes/extremes_driver.py",
+    "pcmdi_metrics/sea_ice/sea_ice_driver.py",
 ]
 
 entry_points = {