diff --git a/ocean/runoff_conservation.ipynb b/ocean/runoff_conservation.ipynb
new file mode 100644
index 0000000..0a24c62
--- /dev/null
+++ b/ocean/runoff_conservation.ipynb
@@ -0,0 +1,3768 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "4e7979a0-79be-4ff4-b594-fe7cd1881dc1",
+   "metadata": {},
+   "source": [
+    "# Runoff in OM3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8a198ca-cf8a-4d67-8e64-5ba6fd09230b",
+   "metadata": {},
+   "source": [
+    "In ACCESS-OM3 runoff is prescribed from the DROF (Data Run Off) component and passed through to mediator to MOM6 (the ocean model). We can lose or gain water at each of these steps, but the goal is the total volume of runoff entering MOM6 is the same as the total volume of run whcih originated in the DROF."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2e75b29b-6a28-4aed-a5a3-00efda594aef",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:17:43.114003Z",
+     "iopub.status.busy": "2025-01-09T04:17:43.113842Z",
+     "iopub.status.idle": "2025-01-09T04:17:43.119984Z",
+     "shell.execute_reply": "2025-01-09T04:17:43.119051Z",
+     "shell.execute_reply.started": "2025-01-09T04:17:43.113986Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "EARTH_R = 3.71229E6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "10d9c451-80cc-4d06-9a1a-ccca65a5f947",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:17:43.121899Z",
+     "iopub.status.busy": "2025-01-09T04:17:43.121419Z",
+     "iopub.status.idle": "2025-01-09T04:17:43.138282Z",
+     "shell.execute_reply": "2025-01-09T04:17:43.137158Z",
+     "shell.execute_reply.started": "2025-01-09T04:17:43.121853Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ROF_SOURCE_F = [\n",
+    "    '/g/data/vk83/configurations/inputs/JRA-55/RYF/v1-4/data/RYF.licalvf.1990_1991.nc', \n",
+    "    '/g/data/vk83/configurations/inputs/JRA-55/RYF/v1-4/data/RYF.friver.1990_1991.nc',\n",
+    "    '/g/data/qv56/replicas/input4MIPs/CMIP6/OMIP/MRI/MRI-JRA55-do-1-4-0/ocean/fx/areacello/gr/v20190429/areacello_input4MIPs_atmosphericState_OMIP_MRI-JRA55-do-1-4-0_gr.nc' # area\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "de5ec267-5123-4e99-96d9-0c3d9c9cfbc1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:17:43.140383Z",
+     "iopub.status.busy": "2025-01-09T04:17:43.139809Z",
+     "iopub.status.idle": "2025-01-09T04:17:43.150899Z",
+     "shell.execute_reply": "2025-01-09T04:17:43.149853Z",
+     "shell.execute_reply.started": "2025-01-09T04:17:43.140336Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ROF_MESH_F = '/g/data/vk83/configurations/inputs/access-om3/share/meshes/share/2024.09.16/JRA55do-drof-ESMFmesh.nc'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c00a412d-e89e-4e30-9fcf-1c9ca97daac8",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:17:43.273645Z",
+     "iopub.status.busy": "2025-01-09T04:17:43.272652Z",
+     "iopub.status.idle": "2025-01-09T04:17:43.278349Z",
+     "shell.execute_reply": "2025-01-09T04:17:43.277282Z",
+     "shell.execute_reply.started": "2025-01-09T04:17:43.273595Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "MODEL_MESH_F = '/g/data/vk83/experiments/inputs/access-om3/share/meshes/global.1deg/2024.01.25/access-om2-1deg-ESMFmesh.nc'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1571cb4-6a45-4e3c-bab1-9f0868cbdd04",
+   "metadata": {},
+   "source": [
+    "The experiment output used for demonstration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "2f97d414-4096-44ee-8701-e06553523621",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:17:43.981945Z",
+     "iopub.status.busy": "2025-01-09T04:17:43.980863Z",
+     "iopub.status.idle": "2025-01-09T04:17:43.986384Z",
+     "shell.execute_reply": "2025-01-09T04:17:43.985293Z",
+     "shell.execute_reply.started": "2025-01-09T04:17:43.981896Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ARCHIVE_DIR = '/g/data/tm70/as2285/payu/MOM6-CICE6/archive/'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "f1436268-3ce8-471f-9fc1-66de75802834",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:17:44.365092Z",
+     "iopub.status.busy": "2025-01-09T04:17:44.364186Z",
+     "iopub.status.idle": "2025-01-09T04:17:44.374288Z",
+     "shell.execute_reply": "2025-01-09T04:17:44.373185Z",
+     "shell.execute_reply.started": "2025-01-09T04:17:44.365042Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/scratch/tm70/as2285/access-om3/archive/MOM6-CICE6\n"
+     ]
+    }
+   ],
+   "source": [
+    "cd {ARCHIVE_DIR}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "525b3c95-68d4-434c-beb6-af8e09e82a84",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:18:52.858073Z",
+     "iopub.status.busy": "2025-01-09T04:18:52.856172Z",
+     "iopub.status.idle": "2025-01-09T04:18:52.947935Z",
+     "shell.execute_reply": "2025-01-09T04:18:52.946947Z",
+     "shell.execute_reply.started": "2025-01-09T04:18:52.858002Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import xarray as xr\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import esmpy\n",
+    "import cf_xarray\n",
+    "\n",
+    "from intake import open_esm_datastore\n",
+    "from copy import copy\n",
+    "from dask.distributed import Client"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "266d4544-a75f-4898-a55d-ef6082012f86",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:17:49.004802Z",
+     "iopub.status.busy": "2025-01-09T04:17:49.004331Z",
+     "iopub.status.idle": "2025-01-09T04:17:51.830282Z",
+     "shell.execute_reply": "2025-01-09T04:17:51.828104Z",
+     "shell.execute_reply.started": "2025-01-09T04:17:49.004781Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.04/lib/python3.10/site-packages/distributed/node.py:182: UserWarning: Port 8787 is already in use.\n",
+      "Perhaps you already have a cluster running?\n",
+      "Hosting the HTTP server on port 35085 instead\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "client = Client(threads_per_worker=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "2a0e1eb5-8acb-4f62-b8d7-8f0948450117",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:17:51.833264Z",
+     "iopub.status.busy": "2025-01-09T04:17:51.832624Z",
+     "iopub.status.idle": "2025-01-09T04:17:51.992303Z",
+     "shell.execute_reply": "2025-01-09T04:17:51.988063Z",
+     "shell.execute_reply.started": "2025-01-09T04:17:51.833213Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
+       "    <div style=\"margin-left: 48px;\">\n",
+       "        <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
+       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-af89eef8-ce40-11ef-aaaa-00000080fe80</p>\n",
+       "        <table style=\"width: 100%; text-align: left;\">\n",
+       "\n",
+       "        <tr>\n",
+       "        \n",
+       "            <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
+       "            <td style=\"text-align: left;\"><strong>Cluster type:</strong> distributed.LocalCluster</td>\n",
+       "        \n",
+       "        </tr>\n",
+       "\n",
+       "        \n",
+       "            <tr>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Dashboard: </strong> <a href=\"/proxy/35085/status\" target=\"_blank\">/proxy/35085/status</a>\n",
+       "                </td>\n",
+       "                <td style=\"text-align: left;\"></td>\n",
+       "            </tr>\n",
+       "        \n",
+       "\n",
+       "        </table>\n",
+       "\n",
+       "        \n",
+       "            <button style=\"margin-bottom: 12px;\" data-commandlinker-command=\"dask:populate-and-launch-layout\" data-commandlinker-args='{\"url\": \"/proxy/35085/status\" }'>\n",
+       "                Launch dashboard in JupyterLab\n",
+       "            </button>\n",
+       "        \n",
+       "\n",
+       "        \n",
+       "            <details>\n",
+       "            <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
+       "            <div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
+       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\">\n",
+       "    </div>\n",
+       "    <div style=\"margin-left: 48px;\">\n",
+       "        <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">LocalCluster</h3>\n",
+       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">e71aa9e3</p>\n",
+       "        <table style=\"width: 100%; text-align: left;\">\n",
+       "            <tr>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Dashboard:</strong> <a href=\"/proxy/35085/status\" target=\"_blank\">/proxy/35085/status</a>\n",
+       "                </td>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Workers:</strong> 12\n",
+       "                </td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Total threads:</strong> 12\n",
+       "                </td>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Total memory:</strong> 46.00 GiB\n",
+       "                </td>\n",
+       "            </tr>\n",
+       "            \n",
+       "            <tr>\n",
+       "    <td style=\"text-align: left;\"><strong>Status:</strong> running</td>\n",
+       "    <td style=\"text-align: left;\"><strong>Using processes:</strong> True</td>\n",
+       "</tr>\n",
+       "\n",
+       "            \n",
+       "        </table>\n",
+       "\n",
+       "        <details>\n",
+       "            <summary style=\"margin-bottom: 20px;\">\n",
+       "                <h3 style=\"display: inline;\">Scheduler Info</h3>\n",
+       "            </summary>\n",
+       "\n",
+       "            <div style=\"\">\n",
+       "    <div>\n",
+       "        <div style=\"width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;\"> </div>\n",
+       "        <div style=\"margin-left: 48px;\">\n",
+       "            <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n",
+       "            <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-4d3afa30-2490-460e-b488-7d9929be34b5</p>\n",
+       "            <table style=\"width: 100%; text-align: left;\">\n",
+       "                <tr>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Comm:</strong> tcp://127.0.0.1:45539\n",
+       "                    </td>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Workers:</strong> 12\n",
+       "                    </td>\n",
+       "                </tr>\n",
+       "                <tr>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Dashboard:</strong> <a href=\"/proxy/35085/status\" target=\"_blank\">/proxy/35085/status</a>\n",
+       "                    </td>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Total threads:</strong> 12\n",
+       "                    </td>\n",
+       "                </tr>\n",
+       "                <tr>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Started:</strong> Just now\n",
+       "                    </td>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Total memory:</strong> 46.00 GiB\n",
+       "                    </td>\n",
+       "                </tr>\n",
+       "            </table>\n",
+       "        </div>\n",
+       "    </div>\n",
+       "\n",
+       "    <details style=\"margin-left: 48px;\">\n",
+       "        <summary style=\"margin-bottom: 20px;\">\n",
+       "            <h3 style=\"display: inline;\">Workers</h3>\n",
+       "        </summary>\n",
+       "\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 0</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:45287\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/44895/status\" target=\"_blank\">/proxy/44895/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 3.83 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:32961\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /jobfs/132027242.gadi-pbs/dask-scratch-space/worker-jd0i7gp7\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 1</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:37319\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/44099/status\" target=\"_blank\">/proxy/44099/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 3.83 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:33531\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /jobfs/132027242.gadi-pbs/dask-scratch-space/worker-m1z7on5w\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 2</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:35715\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/43871/status\" target=\"_blank\">/proxy/43871/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 3.83 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:34215\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /jobfs/132027242.gadi-pbs/dask-scratch-space/worker-onsz6zio\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 3</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:44465\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/40157/status\" target=\"_blank\">/proxy/40157/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 3.83 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:39761\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /jobfs/132027242.gadi-pbs/dask-scratch-space/worker-k3x4b8_v\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 4</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:45361\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/37057/status\" target=\"_blank\">/proxy/37057/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 3.83 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:43425\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /jobfs/132027242.gadi-pbs/dask-scratch-space/worker-1pyhjr67\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 5</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:34781\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/44063/status\" target=\"_blank\">/proxy/44063/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 3.83 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:46641\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /jobfs/132027242.gadi-pbs/dask-scratch-space/worker-itxgueom\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 6</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:35863\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/35811/status\" target=\"_blank\">/proxy/35811/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 3.83 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:44867\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /jobfs/132027242.gadi-pbs/dask-scratch-space/worker-gplpis7j\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 7</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:35121\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/35347/status\" target=\"_blank\">/proxy/35347/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 3.83 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:33423\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /jobfs/132027242.gadi-pbs/dask-scratch-space/worker-sr9cht63\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 8</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:41843\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/46355/status\" target=\"_blank\">/proxy/46355/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 3.83 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:35171\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /jobfs/132027242.gadi-pbs/dask-scratch-space/worker-e5nxy781\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 9</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:35047\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/45925/status\" target=\"_blank\">/proxy/45925/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 3.83 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:42561\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /jobfs/132027242.gadi-pbs/dask-scratch-space/worker-rjzug1c0\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 10</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:35985\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/39121/status\" target=\"_blank\">/proxy/39121/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 3.83 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:43805\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /jobfs/132027242.gadi-pbs/dask-scratch-space/worker-wg1uuacw\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 11</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:40229\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/43573/status\" target=\"_blank\">/proxy/43573/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 3.83 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:46221\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /jobfs/132027242.gadi-pbs/dask-scratch-space/worker-zzy2dvd5\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "\n",
+       "    </details>\n",
+       "</div>\n",
+       "\n",
+       "        </details>\n",
+       "    </div>\n",
+       "</div>\n",
+       "            </details>\n",
+       "        \n",
+       "\n",
+       "    </div>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "<Client: 'tcp://127.0.0.1:45539' processes=12 threads=12, memory=46.00 GiB>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "client"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b77e3e6-053e-4c5e-be23-1a443dbd799d",
+   "metadata": {},
+   "source": [
+    "Open the DROF source data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "15828e26-76cd-430b-a328-51b267a1afb5",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:17:52.001727Z",
+     "iopub.status.busy": "2025-01-09T04:17:52.000825Z",
+     "iopub.status.idle": "2025-01-09T04:18:09.057176Z",
+     "shell.execute_reply": "2025-01-09T04:18:09.055612Z",
+     "shell.execute_reply.started": "2025-01-09T04:17:52.001687Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ryf_runoff_ds = xr.open_mfdataset(ROF_SOURCE_F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "589ece9e-e573-4d84-99d1-d332f33ed9df",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:18:09.060335Z",
+     "iopub.status.busy": "2025-01-09T04:18:09.058727Z",
+     "iopub.status.idle": "2025-01-09T04:18:09.139165Z",
+     "shell.execute_reply": "2025-01-09T04:18:09.137832Z",
+     "shell.execute_reply.started": "2025-01-09T04:18:09.060301Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
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+       " */\n",
+       "\n",
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+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
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+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
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+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
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+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 3GB\n",
+       "Dimensions:    (lat: 720, bnds: 2, lon: 1440, time: 365)\n",
+       "Coordinates:\n",
+       "  * lat        (lat) float64 6kB -89.88 -89.62 -89.38 ... 89.38 89.62 89.88\n",
+       "  * lon        (lon) float64 12kB 0.125 0.375 0.625 0.875 ... 359.4 359.6 359.9\n",
+       "  * time       (time) object 3kB 1900-01-01 00:00:00 ... 1900-12-31 00:00:00\n",
+       "Dimensions without coordinates: bnds\n",
+       "Data variables:\n",
+       "    lat_bnds   (lat, bnds) float64 12kB dask.array&lt;chunksize=(720, 2), meta=np.ndarray&gt;\n",
+       "    lon_bnds   (lon, bnds) float64 23kB dask.array&lt;chunksize=(1440, 2), meta=np.ndarray&gt;\n",
+       "    areacello  (lat, lon) float32 4MB dask.array&lt;chunksize=(720, 1440), meta=np.ndarray&gt;\n",
+       "    friver     (time, lat, lon) float32 2GB dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;\n",
+       "    licalvf    (time, lat, lon) float32 2GB dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;\n",
+       "Attributes: (12/34)\n",
+       "    Conventions:         CF-1.7 CMIP-6.2\n",
+       "    activity_id:         input4MIPs\n",
+       "    comment:             Based on JRA-55 reanalysis (1958-01 to 2019-01)\n",
+       "    contact:             Hiroyuki Tsujino (htsujino@mri-jma.go.jp)\n",
+       "    creation_date:       2019-03-08T11:48:52Z\n",
+       "    data_specs_version:  01.00.29\n",
+       "    ...                  ...\n",
+       "    target_mip:          OMIP\n",
+       "    title:               MRI JRA55-do 1.4.0 dataset prepared for input4MIPs\n",
+       "    tracking_id:         hdl:21.14100/29996f42-a99e-4762-8f11-24888d857e61\n",
+       "    variable_id:         areacello\n",
+       "    license:             OMIP boundary condition data produced by MRI is lice...\n",
+       "    cmor_version:        3.4.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-41e0b822-0b93-4400-b511-cd620aaecd51' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-41e0b822-0b93-4400-b511-cd620aaecd51' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 720</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lon</span>: 1440</li><li><span class='xr-has-index'>time</span>: 365</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-cd9e2503-ac27-46fa-ade7-ef9753da7938' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cd9e2503-ac27-46fa-ade7-ef9753da7938' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.88 -89.62 ... 89.62 89.88</div><input id='attrs-885ca523-04b4-42c5-a0b1-59bb6eb4a3b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-885ca523-04b4-42c5-a0b1-59bb6eb4a3b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae70e913-3e3e-48a4-9901-7218065a4ba7' class='xr-var-data-in' type='checkbox'><label for='data-ae70e913-3e3e-48a4-9901-7218065a4ba7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-89.875, -89.625, -89.375, ...,  89.375,  89.625,  89.875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.125 0.375 0.625 ... 359.6 359.9</div><input id='attrs-529525ab-5982-46b2-96f3-7a5ef4734840' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-529525ab-5982-46b2-96f3-7a5ef4734840' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01e5af62-aa72-492d-9674-adea727e9311' class='xr-var-data-in' type='checkbox'><label for='data-01e5af62-aa72-492d-9674-adea727e9311' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([1.25000e-01, 3.75000e-01, 6.25000e-01, ..., 3.59375e+02, 3.59625e+02,\n",
+       "       3.59875e+02])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1900-01-01 00:00:00 ... 1900-12-...</div><input id='attrs-0ee1bcf2-b1c2-474c-8859-c45324033a80' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ee1bcf2-b1c2-474c-8859-c45324033a80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3a11a1c-7aa4-47fb-967b-e9e47d337d24' class='xr-var-data-in' type='checkbox'><label for='data-e3a11a1c-7aa4-47fb-967b-e9e47d337d24' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cartesian_axis :</span></dt><dd>T</dd><dt><span>modulo :</span></dt><dd> </dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1900, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(1900, 1, 2, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(1900, 1, 3, 0, 0, 0, 0, has_year_zero=True), ...,\n",
+       "       cftime.DatetimeNoLeap(1900, 12, 29, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(1900, 12, 30, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(1900, 12, 31, 0, 0, 0, 0, has_year_zero=True)],\n",
+       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9b33adb5-5634-4303-81fc-210b2515caef' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9b33adb5-5634-4303-81fc-210b2515caef' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(720, 2), meta=np.ndarray&gt;</div><input id='attrs-5d5c902e-7a1e-4171-b98e-e69d108d65ef' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5d5c902e-7a1e-4171-b98e-e69d108d65ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4c14ae6-6e34-460f-90fb-8b1567959189' class='xr-var-data-in' type='checkbox'><label for='data-f4c14ae6-6e34-460f-90fb-8b1567959189' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 11.25 kiB </td>\n",
+       "                        <td> 11.25 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (720, 2) </td>\n",
+       "                        <td> (720, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 10 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
+       "  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">720</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440, 2), meta=np.ndarray&gt;</div><input id='attrs-1e91808a-4022-4f68-ae17-ee43a773a735' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1e91808a-4022-4f68-ae17-ee43a773a735' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50f57ff5-5994-47d5-98d1-75c51ea8b657' class='xr-var-data-in' type='checkbox'><label for='data-50f57ff5-5994-47d5-98d1-75c51ea8b657' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 22.50 kiB </td>\n",
+       "                        <td> 22.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440, 2) </td>\n",
+       "                        <td> (1440, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 10 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
+       "  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">1440</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>areacello</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(720, 1440), meta=np.ndarray&gt;</div><input id='attrs-4e13da8e-8d65-4dc7-8e1a-4980e5795104' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e13da8e-8d65-4dc7-8e1a-4980e5795104' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7cde551e-e838-4436-b855-3b7fe0517b26' class='xr-var-data-in' type='checkbox'><label for='data-7cde551e-e838-4436-b855-3b7fe0517b26' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>cell_area</dd><dt><span>long_name :</span></dt><dd>Grid-Cell Area for Ocean Variables</dd><dt><span>comment :</span></dt><dd>Horizontal area of ocean grid cells</dd><dt><span>units :</span></dt><dd>m2</dd><dt><span>cell_methods :</span></dt><dd>area: sum</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 3.96 MiB </td>\n",
+       "                        <td> 3.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (720, 1440) </td>\n",
+       "                        <td> (720, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"110\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.0 0.0,60.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.000000\" y=\"30.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.000000)\">720</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>friver</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;</div><input id='attrs-d3193f59-43e6-47b5-8710-752b50722847' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d3193f59-43e6-47b5-8710-752b50722847' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13c50c29-72f9-4e40-950c-0672734b0c47' class='xr-var-data-in' type='checkbox'><label for='data-13c50c29-72f9-4e40-950c-0672734b0c47' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>water_flux_into_sea_water_from_rivers</dd><dt><span>long_name :</span></dt><dd>Water Flux into Sea Water from Rivers</dd><dt><span>comment :</span></dt><dd>computed as the river flux of liquid water into the ocean divided by the area of the ocean portion of the grid cell, input4MIPs_table_comment: computed as the river flux of water into the ocean divided by the area of the ocean portion of the grid cell</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>cell_methods :</span></dt><dd>area: mean where sea time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacello</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.41 GiB </td>\n",
+       "                        <td> 3.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (365, 720, 1440) </td>\n",
+       "                        <td> (1, 720, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 365 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"205\" height=\"135\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"60\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"62\" />\n",
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+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"65\" />\n",
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+       "  <line x1=\"19\" y1=\"9\" x2=\"19\" y2=\"69\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"70\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>licalvf</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;</div><input id='attrs-6c3c5dfd-3049-452a-9bc2-515588851f5a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c3c5dfd-3049-452a-9bc2-515588851f5a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b05302ad-d280-4e7e-8bbf-0c2a9da40df0' class='xr-var-data-in' type='checkbox'><label for='data-b05302ad-d280-4e7e-8bbf-0c2a9da40df0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>land_ice_specific_mass_flux_due_to_calving</dd><dt><span>long_name :</span></dt><dd>Land Ice Calving Flux</dd><dt><span>comment :</span></dt><dd>Computed as the flux of solid ice into the ocean divided by the area of the land portion of the grid cell</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean where ice_sheet</dd><dt><span>cell_measures :</span></dt><dd>area: areacello</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.41 GiB </td>\n",
+       "                        <td> 3.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (365, 720, 1440) </td>\n",
+       "                        <td> (1, 720, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 365 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "\n",
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+       "  <text x=\"12.632406\" y=\"92.632406\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,12.632406,92.632406)\">365</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-03e6b281-2427-4347-a8ad-5d0953bb1c30' class='xr-section-summary-in' type='checkbox'  ><label for='section-03e6b281-2427-4347-a8ad-5d0953bb1c30' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2468d5d5-17d4-4c46-820c-c0a3da3c6b65' class='xr-index-data-in' type='checkbox'/><label for='index-2468d5d5-17d4-4c46-820c-c0a3da3c6b65' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-89.875, -89.625, -89.375, -89.125, -88.875, -88.625, -88.375, -88.125,\n",
+       "       -87.875, -87.625,\n",
+       "       ...\n",
+       "        87.625,  87.875,  88.125,  88.375,  88.625,  88.875,  89.125,  89.375,\n",
+       "        89.625,  89.875],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=720))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-263b2c1a-1803-4900-941d-9f2284d8f9fd' class='xr-index-data-in' type='checkbox'/><label for='index-263b2c1a-1803-4900-941d-9f2284d8f9fd' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0.125,   0.375,   0.625,   0.875,   1.125,   1.375,   1.625,   1.875,\n",
+       "         2.125,   2.375,\n",
+       "       ...\n",
+       "       357.625, 357.875, 358.125, 358.375, 358.625, 358.875, 359.125, 359.375,\n",
+       "       359.625, 359.875],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=1440))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1e84bc64-da78-4a0d-abe3-71f9389d868b' class='xr-index-data-in' type='checkbox'/><label for='index-1e84bc64-da78-4a0d-abe3-71f9389d868b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([1900-01-01 00:00:00, 1900-01-02 00:00:00, 1900-01-03 00:00:00,\n",
+       "             1900-01-04 00:00:00, 1900-01-05 00:00:00, 1900-01-06 00:00:00,\n",
+       "             1900-01-07 00:00:00, 1900-01-08 00:00:00, 1900-01-09 00:00:00,\n",
+       "             1900-01-10 00:00:00,\n",
+       "             ...\n",
+       "             1900-12-22 00:00:00, 1900-12-23 00:00:00, 1900-12-24 00:00:00,\n",
+       "             1900-12-25 00:00:00, 1900-12-26 00:00:00, 1900-12-27 00:00:00,\n",
+       "             1900-12-28 00:00:00, 1900-12-29 00:00:00, 1900-12-30 00:00:00,\n",
+       "             1900-12-31 00:00:00],\n",
+       "            dtype=&#x27;object&#x27;, length=365, calendar=&#x27;noleap&#x27;, freq=&#x27;D&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d31308e9-57e9-492d-a9dc-5e88aaf6d827' class='xr-section-summary-in' type='checkbox'  ><label for='section-d31308e9-57e9-492d-a9dc-5e88aaf6d827' class='xr-section-summary' >Attributes: <span>(34)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>input4MIPs</dd><dt><span>comment :</span></dt><dd>Based on JRA-55 reanalysis (1958-01 to 2019-01)</dd><dt><span>contact :</span></dt><dd>Hiroyuki Tsujino (htsujino@mri-jma.go.jp)</dd><dt><span>creation_date :</span></dt><dd>2019-03-08T11:48:52Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.29</dd><dt><span>dataset_category :</span></dt><dd>atmosphericState</dd><dt><span>frequency :</span></dt><dd>fx</dd><dt><span>further_info_url :</span></dt><dd>http://climate.mri-jma.go.jp/~htsujino/jra55do.html</dd><dt><span>grid :</span></dt><dd>0.25x0.25 degree latitude x longitude</dd><dt><span>grid_label :</span></dt><dd>gr</dd><dt><span>history :</span></dt><dd>2019-03-08T11:48:52Z; CMOR rewrote data to be consistent with input4MIPs, CMIP6, and CF-1.7 CMIP-6.2 standards</dd><dt><span>institution :</span></dt><dd>Meteorological Research Institute, Tsukuba, Ibaraki 305-0052, Japan</dd><dt><span>institution_id :</span></dt><dd>MRI</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>nominal_resolution :</span></dt><dd>25 km</dd><dt><span>product :</span></dt><dd>reanalysis</dd><dt><span>realm :</span></dt><dd>ocean</dd><dt><span>references :</span></dt><dd>Tsujino et al., 2018: JRA-55 based surface dataset for driving ocean-sea-ice models (JRA55-do), Ocean Modelling, 130(1), pp 79-139. https://doi.org/10.1016/j.ocemod.2018.07.002</dd><dt><span>region :</span></dt><dd>global_ocean</dd><dt><span>release_year :</span></dt><dd>2019</dd><dt><span>source :</span></dt><dd>MRI JRA55-do 1.4.0: Atmospheric state generated for OMIP based on the JRA-55 reanalysis</dd><dt><span>source_description :</span></dt><dd>Atmospheric state and terrestrial runoff datasets produced by MRI for the OMIP experiment of CMIP6</dd><dt><span>source_id :</span></dt><dd>MRI-JRA55-do-1-4-0</dd><dt><span>source_type :</span></dt><dd>satellite_blended</dd><dt><span>source_version :</span></dt><dd>1.4.0</dd><dt><span>table_id :</span></dt><dd>input4MIPs_Ofx</dd><dt><span>table_info :</span></dt><dd>Creation Date:(06 March 2019) MD5:b14ca57e92343d575b84b8ab3624b3ed</dd><dt><span>target_mip :</span></dt><dd>OMIP</dd><dt><span>title :</span></dt><dd>MRI JRA55-do 1.4.0 dataset prepared for input4MIPs</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/29996f42-a99e-4762-8f11-24888d857e61</dd><dt><span>variable_id :</span></dt><dd>areacello</dd><dt><span>license :</span></dt><dd>OMIP boundary condition data produced by MRI is licensed under a Creative Commons Attribution-[NonCommercial-]ShareAlike 4.0 International License (https://creativecommons.org/licenses). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing input4MIPs output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file). The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>cmor_version :</span></dt><dd>3.4.0</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 3GB\n",
+       "Dimensions:    (lat: 720, bnds: 2, lon: 1440, time: 365)\n",
+       "Coordinates:\n",
+       "  * lat        (lat) float64 6kB -89.88 -89.62 -89.38 ... 89.38 89.62 89.88\n",
+       "  * lon        (lon) float64 12kB 0.125 0.375 0.625 0.875 ... 359.4 359.6 359.9\n",
+       "  * time       (time) object 3kB 1900-01-01 00:00:00 ... 1900-12-31 00:00:00\n",
+       "Dimensions without coordinates: bnds\n",
+       "Data variables:\n",
+       "    lat_bnds   (lat, bnds) float64 12kB dask.array<chunksize=(720, 2), meta=np.ndarray>\n",
+       "    lon_bnds   (lon, bnds) float64 23kB dask.array<chunksize=(1440, 2), meta=np.ndarray>\n",
+       "    areacello  (lat, lon) float32 4MB dask.array<chunksize=(720, 1440), meta=np.ndarray>\n",
+       "    friver     (time, lat, lon) float32 2GB dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n",
+       "    licalvf    (time, lat, lon) float32 2GB dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n",
+       "Attributes: (12/34)\n",
+       "    Conventions:         CF-1.7 CMIP-6.2\n",
+       "    activity_id:         input4MIPs\n",
+       "    comment:             Based on JRA-55 reanalysis (1958-01 to 2019-01)\n",
+       "    contact:             Hiroyuki Tsujino (htsujino@mri-jma.go.jp)\n",
+       "    creation_date:       2019-03-08T11:48:52Z\n",
+       "    data_specs_version:  01.00.29\n",
+       "    ...                  ...\n",
+       "    target_mip:          OMIP\n",
+       "    title:               MRI JRA55-do 1.4.0 dataset prepared for input4MIPs\n",
+       "    tracking_id:         hdl:21.14100/29996f42-a99e-4762-8f11-24888d857e61\n",
+       "    variable_id:         areacello\n",
+       "    license:             OMIP boundary condition data produced by MRI is lice...\n",
+       "    cmor_version:        3.4.0"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ryf_runoff_ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fcc7eef-f06e-4359-bcc0-ad943ed639b5",
+   "metadata": {},
+   "source": [
+    "To make this demonstration easier, some extra diagnostics were turned on.\n",
+    "\n",
+    "These are:\n",
+    "\n",
+    "- daily mom output of friver and ficeberg fields in the `diag_table`\n",
+    "- daily averages of mediator/coupler history for both rof and ocn components \n",
+    "\n",
+    "The configuration ran is https://github.com/ACCESS-NRI/access-om3-configs/commit/c0a05327bdc384ce3d9c625f6f485aab12e26c1c"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "197b612d-e456-4289-8d0f-def56a1c60b7",
+   "metadata": {},
+   "source": [
+    "For completeness, we'll also use the cell areas calculated by ESMF, although they should be the same as the mom cell areas:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "271d6f5e-a672-471a-b84a-3f66d10b6c92",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:18:09.142444Z",
+     "iopub.status.busy": "2025-01-09T04:18:09.141243Z",
+     "iopub.status.idle": "2025-01-09T04:18:18.957189Z",
+     "shell.execute_reply": "2025-01-09T04:18:18.955466Z",
+     "shell.execute_reply.started": "2025-01-09T04:18:09.142409Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "--------------------------------------------------------------------------\n",
+      "WARNING: There was an error initializing an OpenFabrics device.\n",
+      "\n",
+      "  Local host:   gadi-cpu-clx-0438\n",
+      "  Local device: mlx5_0\n",
+      "--------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "rof_mesh = esmpy.Mesh(\n",
+    "    filename = ROF_MESH_F ,\n",
+    "    filetype=esmpy.FileFormat.ESMFMESH\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1bee958a-4b32-4eb8-962f-2c29a6966c44",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:18:18.959388Z",
+     "iopub.status.busy": "2025-01-09T04:18:18.959167Z",
+     "iopub.status.idle": "2025-01-09T04:18:19.151517Z",
+     "shell.execute_reply": "2025-01-09T04:18:19.150334Z",
+     "shell.execute_reply.started": "2025-01-09T04:18:18.959369Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "rof_fld = esmpy.Field(\n",
+    "    rof_mesh,\n",
+    "    meshloc=esmpy.MeshLoc.ELEMENT\n",
+    ")\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "069485a5-d56c-4afb-9bef-f09efdc72cac",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:18:19.152799Z",
+     "iopub.status.busy": "2025-01-09T04:18:19.152588Z",
+     "iopub.status.idle": "2025-01-09T04:18:19.697177Z",
+     "shell.execute_reply": "2025-01-09T04:18:19.696036Z",
+     "shell.execute_reply.started": "2025-01-09T04:18:19.152781Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "rof_fld.get_area()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "136199de-9de6-40b9-a1ac-f44484441b85",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:18:19.700919Z",
+     "iopub.status.busy": "2025-01-09T04:18:19.700729Z",
+     "iopub.status.idle": "2025-01-09T04:18:19.710596Z",
+     "shell.execute_reply": "2025-01-09T04:18:19.709384Z",
+     "shell.execute_reply.started": "2025-01-09T04:18:19.700902Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "rof_areas = copy(rof_fld.data.reshape(720,1440)*6.37122E6*6.37122E6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "d285fe29-7006-4239-a3c0-63dced3b64a0",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:18:19.714553Z",
+     "iopub.status.busy": "2025-01-09T04:18:19.714193Z",
+     "iopub.status.idle": "2025-01-09T04:18:20.600505Z",
+     "shell.execute_reply": "2025-01-09T04:18:20.599264Z",
+     "shell.execute_reply.started": "2025-01-09T04:18:19.714534Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "model_mesh = esmpy.Mesh(\n",
+    "    filename= MODEL_MESH_F, \n",
+    "    filetype=esmpy.FileFormat.ESMFMESH,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "a360cabe-d9d3-44ac-a426-e78b75a88870",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:18:20.601873Z",
+     "iopub.status.busy": "2025-01-09T04:18:20.601679Z",
+     "iopub.status.idle": "2025-01-09T04:18:20.623561Z",
+     "shell.execute_reply": "2025-01-09T04:18:20.622706Z",
+     "shell.execute_reply.started": "2025-01-09T04:18:20.601855Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "med_fld = esmpy.Field(\n",
+    "    model_mesh,\n",
+    "    meshloc=esmpy.MeshLoc.ELEMENT\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "7250b3de-a3df-4f7b-b87b-78a3392d6d41",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:18:20.624858Z",
+     "iopub.status.busy": "2025-01-09T04:18:20.624668Z",
+     "iopub.status.idle": "2025-01-09T04:18:20.682372Z",
+     "shell.execute_reply": "2025-01-09T04:18:20.681129Z",
+     "shell.execute_reply.started": "2025-01-09T04:18:20.624841Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "med_fld.get_area()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "1930d5e5-73b3-43c2-ab41-a7ef59c57bfa",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:18:20.683506Z",
+     "iopub.status.busy": "2025-01-09T04:18:20.683294Z",
+     "iopub.status.idle": "2025-01-09T04:18:20.688011Z",
+     "shell.execute_reply": "2025-01-09T04:18:20.687189Z",
+     "shell.execute_reply.started": "2025-01-09T04:18:20.683489Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "med_areas = copy(med_fld.data.reshape(300,360)*6.37122E6*6.37122E6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e2dcdd66-55bb-4358-bae6-b76413dd1b7c",
+   "metadata": {},
+   "source": [
+    "# DROF to Mediator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a5dca4d-bc1a-42d9-83a5-edc96830250e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T01:17:57.080199Z",
+     "iopub.status.busy": "2025-01-09T01:17:57.077767Z",
+     "iopub.status.idle": "2025-01-09T01:17:57.091269Z",
+     "shell.execute_reply": "2025-01-09T01:17:57.089800Z",
+     "shell.execute_reply.started": "2025-01-09T01:17:57.080122Z"
+    }
+   },
+   "source": [
+    "If we start from the source, want to compare the incoming run off from JRA to the runoff received by the mediator. This remaps from the JRA grid to the mediator grid (the same as the ocean grid in this case).\n",
+    "\n",
+    "The mesh files are configured through the `meshfile` in `drof.streams.xml` for the DROF mesh, and `model_meshfile` in `drof_in` for the mediator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "d6c9764f-451d-46f6-97cb-74c73f658d12",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:18:20.688957Z",
+     "iopub.status.busy": "2025-01-09T04:18:20.688773Z",
+     "iopub.status.idle": "2025-01-09T04:18:26.435987Z",
+     "shell.execute_reply": "2025-01-09T04:18:26.434965Z",
+     "shell.execute_reply.started": "2025-01-09T04:18:20.688941Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "rof_med_files = ! ls output*/access-om3.cpl.ha.rof.*\n",
+    "\n",
+    "rof_med_ds = xr.open_mfdataset(rof_med_files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "e41c57fd-6b65-4754-bd2c-275d8ea74ac9",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:18:57.147203Z",
+     "iopub.status.busy": "2025-01-09T04:18:57.144518Z",
+     "iopub.status.idle": "2025-01-09T04:18:57.194198Z",
+     "shell.execute_reply": "2025-01-09T04:18:57.193215Z",
+     "shell.execute_reply.started": "2025-01-09T04:18:57.147135Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "jra_runoff = ((ryf_runoff_ds.friver+ryf_runoff_ds.licalvf)*rof_areas).cf.sum(['X','Y'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "209bcfb0-b0b4-47c3-b26c-d804edf0b28d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:18:58.461639Z",
+     "iopub.status.busy": "2025-01-09T04:18:58.461084Z",
+     "iopub.status.idle": "2025-01-09T04:18:58.493992Z",
+     "shell.execute_reply": "2025-01-09T04:18:58.492523Z",
+     "shell.execute_reply.started": "2025-01-09T04:18:58.461592Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "jra_licalvf_runoff = ((ryf_runoff_ds.licalvf)*rof_areas).cf.sum(['X','Y'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "9652d066-ac50-43be-a9ba-2c8a1edf794a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:18:58.834096Z",
+     "iopub.status.busy": "2025-01-09T04:18:58.832941Z",
+     "iopub.status.idle": "2025-01-09T04:18:58.865507Z",
+     "shell.execute_reply": "2025-01-09T04:18:58.864197Z",
+     "shell.execute_reply.started": "2025-01-09T04:18:58.834040Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "jra_friver_runoff = ((ryf_runoff_ds.friver)*rof_areas).cf.sum(['X','Y'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "21423e44-6408-429b-84b3-712bc08c935c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "e9874d92-ed02-4ffa-b1bc-a31fb4fa0500",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:19:03.448955Z",
+     "iopub.status.busy": "2025-01-09T04:19:03.447570Z",
+     "iopub.status.idle": "2025-01-09T04:19:08.879874Z",
+     "shell.execute_reply": "2025-01-09T04:19:08.878699Z",
+     "shell.execute_reply.started": "2025-01-09T04:19:03.448903Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x14ac45746bc0>]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "jra_friver_runoff.plot(label='jra55do')\n",
+    "(rof_med_ds.rofImp_Forr_rofl*med_areas).sum(['rofImp_ny','rofImp_nx']).plot()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "fd310b97-4404-44db-837b-bbfaefd8c26b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:19:08.881601Z",
+     "iopub.status.busy": "2025-01-09T04:19:08.881397Z",
+     "iopub.status.idle": "2025-01-09T04:19:13.312560Z",
+     "shell.execute_reply": "2025-01-09T04:19:13.311414Z",
+     "shell.execute_reply.started": "2025-01-09T04:19:08.881583Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x14ac45210b80>]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "jra_licalvf_runoff.plot(label='jra55do')\n",
+    "(rof_med_ds.rofImp_Forr_rofi*med_areas).sum(['rofImp_ny','rofImp_nx']).plot()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a5e40d54-6976-42f7-94bd-b85e9743c796",
+   "metadata": {},
+   "source": [
+    "You can see we have less liquid run-off entering the mediator (orange-line) than exists in the DROF. This is because the access-om2 1degee grid starts at 78S however the DROF product has meltwater south of this location:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "2ed310f1-e669-4d92-82f8-a5b13645710f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:19:13.313739Z",
+     "iopub.status.busy": "2025-01-09T04:19:13.313554Z",
+     "iopub.status.idle": "2025-01-09T04:19:17.951245Z",
+     "shell.execute_reply": "2025-01-09T04:19:17.950175Z",
+     "shell.execute_reply.started": "2025-01-09T04:19:13.313723Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x14ad0e50d7e0>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12,4))\n",
+    "plt.subplot(1,2,1)\n",
+    "ryf_runoff_ds.sum('time').friver.sel(lat=slice(None,-78)).plot()\n",
+    "plt.subplot(1,2,2)\n",
+    "ryf_runoff_ds.sum('time').licalvf.sel(lat=slice(None,-78)).plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65fb093e-cb1c-4c7a-be40-d30f0d97af73",
+   "metadata": {},
+   "source": [
+    "If we remove the meltwater south of 78S, then the match is closer:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "034cb6e3-c299-480b-ae98-2e833b8a52d8",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:19:17.953446Z",
+     "iopub.status.busy": "2025-01-09T04:19:17.953243Z",
+     "iopub.status.idle": "2025-01-09T04:19:17.979452Z",
+     "shell.execute_reply": "2025-01-09T04:19:17.975725Z",
+     "shell.execute_reply.started": "2025-01-09T04:19:17.953428Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "jra_licalvf_runoff = ((ryf_runoff_ds.licalvf)*rof_areas).sel(lat=slice(-78,None)).cf.sum(['X','Y'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "fa27011e-d52d-4d87-a008-1336b1f387ee",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:19:17.980586Z",
+     "iopub.status.busy": "2025-01-09T04:19:17.980400Z",
+     "iopub.status.idle": "2025-01-09T04:19:18.014219Z",
+     "shell.execute_reply": "2025-01-09T04:19:18.012365Z",
+     "shell.execute_reply.started": "2025-01-09T04:19:17.980569Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "jra_friver_runoff = ((ryf_runoff_ds.friver)*rof_areas).sel(lat=slice(-78,None)).cf.sum(['X','Y'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "f253e7c7-19b1-48b0-a227-cbfe84153a53",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:19:18.017181Z",
+     "iopub.status.busy": "2025-01-09T04:19:18.016490Z",
+     "iopub.status.idle": "2025-01-09T04:19:21.963436Z",
+     "shell.execute_reply": "2025-01-09T04:19:21.962287Z",
+     "shell.execute_reply.started": "2025-01-09T04:19:18.017133Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x14ac3c19df00>]"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "jra_friver_runoff.plot(label='jra55do')\n",
+    "(rof_med_ds.rofImp_Forr_rofl*med_areas).sum(['rofImp_ny','rofImp_nx']).plot()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "3eefdb4c-788b-4287-b0e3-245be2debba4",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:19:21.965329Z",
+     "iopub.status.busy": "2025-01-09T04:19:21.964939Z",
+     "iopub.status.idle": "2025-01-09T04:19:26.397384Z",
+     "shell.execute_reply": "2025-01-09T04:19:26.396454Z",
+     "shell.execute_reply.started": "2025-01-09T04:19:21.965309Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x14ac3bc5d870>]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "jra_licalvf_runoff.plot(label='jra55do')\n",
+    "(rof_med_ds.rofImp_Forr_rofi*med_areas).sum(['rofImp_ny','rofImp_nx']).plot()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a19c4d1f-3f4f-43c1-9e0c-f70627ba605e",
+   "metadata": {},
+   "source": [
+    "Whilst we are here, we can compare the cell areas used by the mediator to those provided by the data product:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "9cc5b0e1-4b5d-4076-b326-e95dfcd11e68",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:19:26.398938Z",
+     "iopub.status.busy": "2025-01-09T04:19:26.398747Z",
+     "iopub.status.idle": "2025-01-09T04:19:34.947320Z",
+     "shell.execute_reply": "2025-01-09T04:19:34.946369Z",
+     "shell.execute_reply.started": "2025-01-09T04:19:26.398920Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.colorbar.Colorbar at 0x14ac0a9c22f0>"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.pcolor(rof_areas-ryf_runoff_ds.areacello.values)\n",
+    "plt.colorbar()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "223af40e-59b4-422b-9981-4c52a72511aa",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:19:34.948946Z",
+     "iopub.status.busy": "2025-01-09T04:19:34.948569Z",
+     "iopub.status.idle": "2025-01-09T04:19:42.528610Z",
+     "shell.execute_reply": "2025-01-09T04:19:42.527653Z",
+     "shell.execute_reply.started": "2025-01-09T04:19:34.948927Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.colorbar.Colorbar at 0x14abe4192da0>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.pcolor((rof_areas-ryf_runoff_ds.areacello.values)/rof_areas)\n",
+    "plt.colorbar()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8e8c8b2-5bc9-4348-a72a-d326fcc9bb70",
+   "metadata": {},
+   "source": [
+    "They are not totally identical. The ones in the data product are about 0.12% smaller. Which should be inconsequential."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0e07aeb-b48a-4a92-a73f-aae196914cff",
+   "metadata": {},
+   "source": [
+    "# Mediator to mediator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ee98f65-377d-4803-aebb-06aefebcda35",
+   "metadata": {},
+   "source": [
+    "Note the configuration here on the DROF doesn't have any awareness of the ocean/land mask. It just maps from any cell to any cell. We use the mediator remapping step to moved all runoff into ocean cells, so the ocn model receives the same overall volume of runoff that is received by the mediator.\n",
+    "\n",
+    "(The mediator remapping step is a little strange in our case. The mediator uses the same mesh as the ocean, so there is nothing to regrid, however we use the remapping weights to move an runoff going to land cells to go to ocean cells.)\n",
+    "\n",
+    "The mesh files are `mesh_rof` in `nuopc.runconfig` for the incoming field, and `mesh_ocn` in the same file for the outgoing field."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "9a95f23b-f9f0-46ec-9826-4835cd532d51",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:19:53.050861Z",
+     "iopub.status.busy": "2025-01-09T04:19:53.048986Z",
+     "iopub.status.idle": "2025-01-09T04:20:07.417066Z",
+     "shell.execute_reply": "2025-01-09T04:20:07.415932Z",
+     "shell.execute_reply.started": "2025-01-09T04:19:53.050805Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ocn_med_files = ! ls output*/access-om3.cpl.ha.ocn.*\n",
+    "\n",
+    "ocn_med_ds = xr.open_mfdataset(ocn_med_files)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f923ae3-f8fa-4b35-bccd-ece584d3303f",
+   "metadata": {},
+   "source": [
+    "First check the overall volumes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "c8f881c9-7852-4941-abff-4f73843107f4",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:20:07.421014Z",
+     "iopub.status.busy": "2025-01-09T04:20:07.420357Z",
+     "iopub.status.idle": "2025-01-09T04:20:14.119601Z",
+     "shell.execute_reply": "2025-01-09T04:20:14.118525Z",
+     "shell.execute_reply.started": "2025-01-09T04:20:07.420991Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x14ac372a11b0>]"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "(rof_med_ds.rofImp_Forr_rofl*med_areas).sum(['rofImp_ny','rofImp_nx']).plot()\n",
+    "(ocn_med_ds.ocnExp_Foxx_rofl*med_areas).sum(['ocnExp_ny','ocnExp_nx']).plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "3349eb60-d983-408d-93e4-f5e6b0906492",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:20:14.121938Z",
+     "iopub.status.busy": "2025-01-09T04:20:14.121261Z",
+     "iopub.status.idle": "2025-01-09T04:20:19.857206Z",
+     "shell.execute_reply": "2025-01-09T04:20:19.856193Z",
+     "shell.execute_reply.started": "2025-01-09T04:20:14.121919Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x14ac390986a0>]"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "(rof_med_ds.rofImp_Forr_rofi*med_areas).sum(['rofImp_ny','rofImp_nx']).plot()\n",
+    "(ocn_med_ds.ocnExp_Foxx_rofi*med_areas).sum(['ocnExp_ny','ocnExp_nx']).plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e2f1a24e-0bb7-44c4-8b89-5c03b97f2a7e",
+   "metadata": {},
+   "source": [
+    "And the difference in location of the outgoing vs incoming runoff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "622c3b8f-831e-4331-b2e0-a0d813e81b79",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:20:19.858633Z",
+     "iopub.status.busy": "2025-01-09T04:20:19.858406Z",
+     "iopub.status.idle": "2025-01-09T04:20:19.872389Z",
+     "shell.execute_reply": "2025-01-09T04:20:19.871709Z",
+     "shell.execute_reply.started": "2025-01-09T04:20:19.858612Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "mod_mesh_ds = xr.open_dataset(MODEL_MESH_F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "5c74b1d2-39cd-43ca-9f7c-9bb13e35926a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:28:45.795276Z",
+     "iopub.status.busy": "2025-01-09T04:28:45.793546Z",
+     "iopub.status.idle": "2025-01-09T04:28:50.704071Z",
+     "shell.execute_reply": "2025-01-09T04:28:50.703121Z",
+     "shell.execute_reply.started": "2025-01-09T04:28:45.795222Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x14abc8a2dea0>"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.pcolor(ocn_med_ds.ocnExp_Foxx_rofl.sum('time').values - rof_med_ds.rofImp_Forr_rofl.sum('time').values, vmin = -1e-5, vmax = 1e-5, cmap='viridis')\n",
+    "plt.colorbar()\n",
+    "plt.pcolormesh(mod_mesh_ds.elementMask.values.reshape(300,360), cmap='grey', alpha=0.1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "bf1468c4-755e-4ad5-bc91-4515c09c631f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:29:29.630712Z",
+     "iopub.status.busy": "2025-01-09T04:29:29.628922Z",
+     "iopub.status.idle": "2025-01-09T04:29:34.730105Z",
+     "shell.execute_reply": "2025-01-09T04:29:34.727793Z",
+     "shell.execute_reply.started": "2025-01-09T04:29:29.630647Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x14abcfef00a0>"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.pcolor(ocn_med_ds.ocnExp_Foxx_rofi.sum('time').values - rof_med_ds.rofImp_Forr_rofi.sum('time').values, vmin = -1e-5, vmax = 1e-5, cmap='viridis')\n",
+    "plt.colorbar()\n",
+    "plt.pcolormesh(mod_mesh_ds.elementMask.values.reshape(300,360), cmap='grey', alpha=0.1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8f71d142-4128-4cdb-9a7c-25feda47042c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c675516-d445-4b11-bfdf-368c80c7a870",
+   "metadata": {},
+   "source": [
+    "# Mediator to OCN"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d020389d-62bc-44fa-9cc1-2de5921477d6",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T03:12:59.395313Z",
+     "iopub.status.busy": "2025-01-09T03:12:59.392072Z",
+     "iopub.status.idle": "2025-01-09T03:12:59.409761Z",
+     "shell.execute_reply": "2025-01-09T03:12:59.408092Z",
+     "shell.execute_reply.started": "2025-01-09T03:12:59.395207Z"
+    }
+   },
+   "source": [
+    "There is no remapping at this step, it relies on the `ocn_mesh` file and the MOM grid being the same (with the exception that the MOM6 Nuopc cap will correct for differences in cell area between the ocn_mesh and MOM grid)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "b77778fc-4ffc-4939-b4cd-700b9d854d26",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:20:30.766971Z",
+     "iopub.status.busy": "2025-01-09T04:20:30.766794Z",
+     "iopub.status.idle": "2025-01-09T04:20:30.800046Z",
+     "shell.execute_reply": "2025-01-09T04:20:30.795295Z",
+     "shell.execute_reply.started": "2025-01-09T04:20:30.766953Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "om3_ds = open_esm_datastore(\n",
+    "    f'{ARCHIVE_DIR}/intake_esm_ds.json', \n",
+    "    columns_with_iterables=['variable','variable_long_name','variable_standard_name'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "5ddf911b-9995-4bdd-9865-79c680bcd50f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:20:30.803066Z",
+     "iopub.status.busy": "2025-01-09T04:20:30.802596Z",
+     "iopub.status.idle": "2025-01-09T04:20:39.076103Z",
+     "shell.execute_reply": "2025-01-09T04:20:39.072710Z",
+     "shell.execute_reply.started": "2025-01-09T04:20:30.803046Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "mom_frof_ds = om3_ds.search(variable=['friver', 'ficeberg']).to_dask()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "74fdb805-93c0-47ac-ae1c-3263aa9de4c1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:20:39.079446Z",
+     "iopub.status.busy": "2025-01-09T04:20:39.078612Z",
+     "iopub.status.idle": "2025-01-09T04:20:39.151039Z",
+     "shell.execute_reply": "2025-01-09T04:20:39.149949Z",
+     "shell.execute_reply.started": "2025-01-09T04:20:39.079376Z"
+    }
+   },
+   "outputs": [
+    {
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+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 315MB\n",
+       "Dimensions:   (time: 365, yh: 300, xh: 360)\n",
+       "Coordinates:\n",
+       "  * time      (time) object 3kB 1900-01-01 12:00:00 ... 1900-12-31 12:00:00\n",
+       "  * yh        (yh) float64 2kB -77.88 -77.63 -77.38 -77.13 ... 88.87 89.32 89.77\n",
+       "  * xh        (xh) float64 3kB -279.5 -278.5 -277.5 -276.5 ... 77.5 78.5 79.5\n",
+       "Data variables:\n",
+       "    ficeberg  (time, yh, xh) float32 158MB dask.array&lt;chunksize=(1, 300, 360), meta=np.ndarray&gt;\n",
+       "    friver    (time, yh, xh) float32 158MB dask.array&lt;chunksize=(1, 300, 360), meta=np.ndarray&gt;\n",
+       "Attributes: (12/16)\n",
+       "    NumFilesInSet:                            1\n",
+       "    title:                                    MOM6 diagnostic fields table\n",
+       "    associated_files:                         areacello: access-om3.mom6.h.st...\n",
+       "    grid_type:                                regular\n",
+       "    grid_tile:                                N/A\n",
+       "    intake_esm_vars:                          [&#x27;ficeberg&#x27;, &#x27;friver&#x27;]\n",
+       "    ...                                       ...\n",
+       "    intake_esm_attrs:variable_standard_name:  sea_water_salinity,sea_water_po...\n",
+       "    intake_esm_attrs:variable_cell_methods:   [&#x27;time: mean&#x27;, &#x27;time: mean&#x27;, &#x27;z...\n",
+       "    intake_esm_attrs:variable_units:          [&#x27;psu&#x27;, &#x27;degC&#x27;, &#x27;m3 s-1&#x27;, &#x27;m3 s...\n",
+       "    intake_esm_attrs:file_id:                 access_om3_mom6_h_nativeXXXX_XX\n",
+       "    intake_esm_attrs:_data_format_:           netcdf\n",
+       "    intake_esm_dataset_key:                   access_om3_mom6_h_nativeXXXX_XX...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-2278bb3b-9a1c-445b-be7a-17dd9d2a2447' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2278bb3b-9a1c-445b-be7a-17dd9d2a2447' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 365</li><li><span class='xr-has-index'>yh</span>: 300</li><li><span class='xr-has-index'>xh</span>: 360</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-33deb910-cafc-4e0d-9099-7f0401387950' class='xr-section-summary-in' type='checkbox'  checked><label for='section-33deb910-cafc-4e0d-9099-7f0401387950' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1900-01-01 12:00:00 ... 1900-12-...</div><input id='attrs-100883cf-949a-4767-8542-598755690484' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-100883cf-949a-4767-8542-598755690484' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c27ead6-5b1c-4d9b-9487-b48e70703bc3' class='xr-var-data-in' type='checkbox'><label for='data-0c27ead6-5b1c-4d9b-9487-b48e70703bc3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>calendar_type :</span></dt><dd>NOLEAP</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1900, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(1900, 1, 2, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(1900, 1, 3, 12, 0, 0, 0, has_year_zero=True), ...,\n",
+       "       cftime.DatetimeNoLeap(1900, 12, 29, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(1900, 12, 30, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(1900, 12, 31, 12, 0, 0, 0, has_year_zero=True)],\n",
+       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yh</span></div><div class='xr-var-dims'>(yh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-77.88 -77.63 ... 89.32 89.77</div><input id='attrs-45adc08f-1500-40d8-a15c-55c706b68ddd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45adc08f-1500-40d8-a15c-55c706b68ddd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0304fdf8-bf8a-4c00-98a6-9c2eb742e520' class='xr-var-data-in' type='checkbox'><label for='data-0304fdf8-bf8a-4c00-98a6-9c2eb742e520' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>h point nominal latitude</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-77.876623, -77.629713, -77.381707, ...,  88.872933,  89.324006,\n",
+       "        89.774476])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xh</span></div><div class='xr-var-dims'>(xh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.5 -278.5 -277.5 ... 78.5 79.5</div><input id='attrs-e223aa73-a3e3-4229-a4d6-3aacd6639dae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e223aa73-a3e3-4229-a4d6-3aacd6639dae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ef98aa0-583f-45ca-9bfa-3f6ee26c740b' class='xr-var-data-in' type='checkbox'><label for='data-1ef98aa0-583f-45ca-9bfa-3f6ee26c740b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>h point nominal longitude</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-279.5, -278.5, -277.5, ...,   77.5,   78.5,   79.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4cf2b0f3-817d-4047-8411-6d291da9a51b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4cf2b0f3-817d-4047-8411-6d291da9a51b' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>ficeberg</span></div><div class='xr-var-dims'>(time, yh, xh)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 300, 360), meta=np.ndarray&gt;</div><input id='attrs-01c7f6de-0527-465e-8966-095d6568566d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01c7f6de-0527-465e-8966-095d6568566d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-999c9a10-7649-411a-9265-7e00f695b0bb' class='xr-var-data-in' type='checkbox'><label for='data-999c9a10-7649-411a-9265-7e00f695b0bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>long_name :</span></dt><dd>Water Flux into Seawater from Icebergs</dd><dt><span>cell_methods :</span></dt><dd>area:mean yh:mean xh:mean time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacello</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd><dt><span>standard_name :</span></dt><dd>water_flux_into_sea_water_from_icebergs</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 150.38 MiB </td>\n",
+       "                        <td> 421.88 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (365, 300, 360) </td>\n",
+       "                        <td> (1, 300, 360) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 365 chunks in 25 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>friver</span></div><div class='xr-var-dims'>(time, yh, xh)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 300, 360), meta=np.ndarray&gt;</div><input id='attrs-19c83823-2804-4d84-be48-26eb4dd88a08' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-19c83823-2804-4d84-be48-26eb4dd88a08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24e75f74-70d1-495d-87f8-f293d749ccf8' class='xr-var-data-in' type='checkbox'><label for='data-24e75f74-70d1-495d-87f8-f293d749ccf8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>long_name :</span></dt><dd>Water Flux into Sea Water From Rivers</dd><dt><span>cell_methods :</span></dt><dd>area:mean yh:mean xh:mean time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacello</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd><dt><span>standard_name :</span></dt><dd>water_flux_into_sea_water_from_rivers</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 150.38 MiB </td>\n",
+       "                        <td> 421.88 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (365, 300, 360) </td>\n",
+       "                        <td> (1, 300, 360) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 365 chunks in 25 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-63e0298c-2c46-455d-a65d-0a7fc3bd9f22' class='xr-section-summary-in' type='checkbox'  ><label for='section-63e0298c-2c46-455d-a65d-0a7fc3bd9f22' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5e4d612b-249d-4360-bd44-73128a1a8b2a' class='xr-index-data-in' type='checkbox'/><label for='index-5e4d612b-249d-4360-bd44-73128a1a8b2a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([1900-01-01 12:00:00, 1900-01-02 12:00:00, 1900-01-03 12:00:00,\n",
+       "             1900-01-04 12:00:00, 1900-01-05 12:00:00, 1900-01-06 12:00:00,\n",
+       "             1900-01-07 12:00:00, 1900-01-08 12:00:00, 1900-01-09 12:00:00,\n",
+       "             1900-01-10 12:00:00,\n",
+       "             ...\n",
+       "             1900-12-22 12:00:00, 1900-12-23 12:00:00, 1900-12-24 12:00:00,\n",
+       "             1900-12-25 12:00:00, 1900-12-26 12:00:00, 1900-12-27 12:00:00,\n",
+       "             1900-12-28 12:00:00, 1900-12-29 12:00:00, 1900-12-30 12:00:00,\n",
+       "             1900-12-31 12:00:00],\n",
+       "            dtype=&#x27;object&#x27;, length=365, calendar=&#x27;noleap&#x27;, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>yh</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a883d9c3-fe55-4d1a-bc7d-e812bbb76795' class='xr-index-data-in' type='checkbox'/><label for='index-a883d9c3-fe55-4d1a-bc7d-e812bbb76795' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-77.87662337662337, -77.62971340045422, -77.38170670967813,\n",
+       "       -77.13229193169715, -76.88053325031572, -76.62612553775392,\n",
+       "       -76.36814128806307,  -76.1062857353073,  -75.8396437697329,\n",
+       "        -75.5679350353177,\n",
+       "       ...\n",
+       "        85.71622733564506,  86.16718145415204,  86.61861799525872,\n",
+       "        87.06938121176299,  87.52064932837074,  87.97126714724561,\n",
+       "        88.42241336735029,  88.87293318844945,   89.3240056291542,\n",
+       "        89.77447612988227],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;yh&#x27;, length=300))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>xh</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-dba9734f-9c7a-4bc2-bd99-178d3296eba7' class='xr-index-data-in' type='checkbox'/><label for='index-dba9734f-9c7a-4bc2-bd99-178d3296eba7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-279.5, -278.5, -277.5, -276.5, -275.5, -274.5, -273.5, -272.5, -271.5,\n",
+       "       -270.5,\n",
+       "       ...\n",
+       "         70.5,   71.5,   72.5,   73.5,   74.5,   75.5,   76.5,   77.5,   78.5,\n",
+       "         79.5],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;xh&#x27;, length=360))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-56727e31-3b4f-454b-8cc4-9856a2722f3d' class='xr-section-summary-in' type='checkbox'  ><label for='section-56727e31-3b4f-454b-8cc4-9856a2722f3d' class='xr-section-summary' >Attributes: <span>(16)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>NumFilesInSet :</span></dt><dd>1</dd><dt><span>title :</span></dt><dd>MOM6 diagnostic fields table</dd><dt><span>associated_files :</span></dt><dd>areacello: access-om3.mom6.h.static.nc</dd><dt><span>grid_type :</span></dt><dd>regular</dd><dt><span>grid_tile :</span></dt><dd>N/A</dd><dt><span>intake_esm_vars :</span></dt><dd>[&#x27;ficeberg&#x27;, &#x27;friver&#x27;]</dd><dt><span>intake_esm_attrs:realm :</span></dt><dd>ocean</dd><dt><span>intake_esm_attrs:variable :</span></dt><dd>soga,thetaoga,uh,vh,vhbt,uhbt,agessc,T_ady_2d,T_adx_2d,T_diffy_2d,T_diffx_2d,diftrelo,diftrblo,difmxybo,difmxylo,volcello,vmo,vhGM,vhml,umo,uhGM,uhml,uo,vo,h,e,thetao,so,KE,rhopot0,oml,tauuo,tauvo,friver,prsn,prlq,evs,hfsso,rlntds,hfsnthermds,sfdsi,rsntds,hfds,ustar,hfsifrazil,wfo,vprec,ficeberg,fsitherm,hflso,pso,seaice_melt_heat,Heat_PmE,salt_flux_added,heat_content_lrunoff,heat_content_frunoff,heat_content_lprec,heat_content_fprec,heat_content_vprec,heat_content_cond,heat_content_evap,SSH,tos,sos,SSU,SSV,mass_wt,opottempmint,somint,Rd_dx,speed,mlotst,average_T1,average_T2,average_DT,time_bnds</dd><dt><span>intake_esm_attrs:frequency :</span></dt><dd>1day</dd><dt><span>intake_esm_attrs:variable_long_name :</span></dt><dd>Global Mean Ocean Salinity,Global Mean Ocean Potential Temperature,Zonal Thickness Flux,Meridional Thickness Flux,Barotropic meridional transport averaged over a baroclinic step,Barotropic zonal transport averaged over a baroclinic step,Ideal Age Tracer,Vertically Integrated Advective Meridional Flux of Heat,Vertically Integrated Advective Zonal Flux of Heat,Vertically Integrated Diffusive Meridional Flux of Heat,Vertically Integrated Diffusive Zonal Flux of Heat,Ocean Tracer Epineutral Laplacian Diffusivity,Ocean Tracer Diffusivity due to Parameterized Mesoscale Advection,Ocean lateral biharmonic viscosity,Ocean lateral Laplacian viscosity,Ocean grid-cell volume,Ocean Mass Y Transport,Time Mean Diffusive Meridional Thickness Flux,Meridional Thickness Flux to Restratify Mixed Layer,Ocean Mass X Transport,Time Mean Diffusive Zonal Thickness Flux,Zonal Thickness Flux to Restratify Mixed Layer,Sea Water X Velocity,Sea Water Y Velocity,Layer Thickness,Interface Height Relative to Mean Sea Level,Sea Water Potential Temperature,Sea Water Salinity,Layer kinetic energy per unit mass,Potential density referenced to surface,Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP,Surface Downward X Stress,Surface Downward Y Stress,Water Flux into Sea Water From Rivers,Snowfall Flux where Ice Free Ocean over Sea,Rainfall Flux where Ice Free Ocean over Sea,Water Evaporation Flux Where Ice Free Ocean over Sea,Surface Downward Sensible Heat Flux,Surface Net Downward Longwave Radiation,Latent Heat to Melt Frozen Precipitation,Downward Sea Ice Basal Salt Flux,Net Downward Shortwave Radiation at Sea Water Surface,Surface ocean heat flux from SW+LW+latent+sensible+masstransfer+frazil+seaice_melt_heat,Surface friction velocity = [(gustiness + tau_magnitude)/rho0]^(1/2),Heat Flux into Sea Water due to Frazil Ice Formation,Water Flux Into Sea Water,Virtual liquid precip into ocean due to SSS restoring,Water Flux into Seawater from Icebergs,water flux to ocean from sea ice melt(&gt; 0) or form(&lt; 0),Surface Downward Latent Heat Flux due to Evap + Melt Snow/Ice,Sea Water Pressure at Sea Water Surface,Heat flux into ocean due to snow and sea ice melt/freeze,Heat flux into ocean from mass flux into ocean,Salt flux into ocean at surface due to restoring or flux adjustment,Heat content (relative to 0C) of liquid runoff into ocean,Heat content (relative to 0C) of solid runoff into ocean,Heat content (relative to 0degC) of liquid precip entering ocean,Heat content (relative to 0degC) of frozen prec entering ocean,Heat content (relative to 0degC) of virtual precip entering ocean,Heat content (relative to 0degC) of water condensing into ocean,Heat content (relative to 0degC) of water evaporating from ocean,Sea Surface Height,Sea Surface Temperature,Sea Surface Salinity,Sea Surface Zonal Velocity,Sea Surface Meridional Velocity,The column mass for calculating mass-weighted average properties,integral_wrt_depth_of_product_of_sea_water_density_and_potential_temperature,integral_wrt_depth_of_product_of_sea_water_density_and_salinity,Ratio between deformation radius and grid spacing,Sea Surface Speed,Ocean Mixed Layer Thickness Defined by Sigma T,Start time for average period,End time for average period,Length of average period,time axis boundaries</dd><dt><span>intake_esm_attrs:variable_standard_name :</span></dt><dd>sea_water_salinity,sea_water_potential_temperature,,,,,ideal_age_tracer,,,,,ocean_tracer_epineutral_laplacian_diffusivity,ocean_tracer_diffusivity_due_to_parameterized_mesoscale_advection,ocean_momentum_xy_biharmonic_diffusivity,ocean_momentum_xy_laplacian_diffusivity,ocean_volume,ocean_mass_y_transport,,,ocean_mass_x_transport,,,sea_water_x_velocity,sea_water_y_velocity,,,sea_water_potential_temperature,sea_water_salinity,,,,surface_downward_x_stress,surface_downward_y_stress,water_flux_into_sea_water_from_rivers,snowfall_flux,rainfall_flux,water_evaporation_flux,surface_downward_sensible_heat_flux,surface_net_downward_longwave_flux,heat_flux_into_sea_water_due_to_snow_thermodynamics,downward_sea_ice_basal_salt_flux,net_downward_shortwave_flux_at_sea_water_surface,surface_downward_heat_flux_in_sea_water,,heat_flux_into_sea_water_due_to_frazil_ice_formation,water_flux_into_sea_water,,water_flux_into_sea_water_from_icebergs,water_flux_into_sea_water_due_to_sea_ice_thermodynamics,surface_downward_latent_heat_flux,sea_water_pressure_at_sea_water_surface,snow_ice_melt_heat_flux,,,temperature_flux_due_to_runoff_expressed_as_heat_flux_into_sea_water,temperature_flux_due_to_solid_runoff_expressed_as_heat_flux_into_sea_water,,,,,,,sea_surface_temperature,sea_surface_salinity,,,,Depth integrated density times potential temperature,Depth integrated density times salinity,,,ocean_mixed_layer_thickness_defined_by_sigma_t,,,,</dd><dt><span>intake_esm_attrs:variable_cell_methods :</span></dt><dd>[&#x27;time: mean&#x27;, &#x27;time: mean&#x27;, &#x27;zl:sum yh:sum xq:point time: mean&#x27;, &#x27;zl:sum yq:point xh:sum time: mean&#x27;, &#x27;yq:point xh:mean time: mean&#x27;, &#x27;yh:mean xq:point time: mean&#x27;, &#x27;area:mean zl:mean yh:mean xh:mean time: mean&#x27;, &#x27;yq:point xh:sum time: mean&#x27;, &#x27;yh:sum xq:point time: mean&#x27;, &#x27;yq:point xh:sum time: mean&#x27;, &#x27;yh:sum xq:point time: mean&#x27;, &#x27;area:mean zi:point yh:mean xh:mean time: mean&#x27;, &#x27;area:mean zl:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean zl:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean zl:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:sum zl:sum yh:sum xh:sum time: mean&#x27;, &#x27;zl:sum yq:point xh:sum time: mean&#x27;, &#x27;zl:sum yq:point xh:sum time: mean&#x27;, &#x27;zl:sum yq:point xh:sum time: mean&#x27;, &#x27;zl:sum yh:sum xq:point time: mean&#x27;, &#x27;zl:sum yh:sum xq:point time: mean&#x27;, &#x27;zl:sum yh:sum xq:point time: mean&#x27;, &#x27;zl:mean yh:mean xq:point time: mean&#x27;, &#x27;zl:mean yq:point xh:mean time: mean&#x27;, &#x27;area:mean zl:sum yh:mean xh:mean time: mean&#x27;, &#x27;area:mean zi:point yh:mean xh:mean time: mean&#x27;, &#x27;area:mean zl:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean zl:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean zl:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean zl:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;yh:mean xq:point time: mean&#x27;, &#x27;yq:point xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;yh:mean xq:point time: mean&#x27;, &#x27;yq:point xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;]</dd><dt><span>intake_esm_attrs:variable_units :</span></dt><dd>[&#x27;psu&#x27;, &#x27;degC&#x27;, &#x27;m3 s-1&#x27;, &#x27;m3 s-1&#x27;, &#x27;m3 s-1&#x27;, &#x27;m3 s-1&#x27;, &#x27;yr&#x27;, &#x27;W&#x27;, &#x27;W&#x27;, &#x27;W&#x27;, &#x27;W&#x27;, &#x27;m2 s-1&#x27;, &#x27;m2 s-1&#x27;, &#x27;m4 s-1&#x27;, &#x27;m2 s-1&#x27;, &#x27;m3&#x27;, &#x27;kg s-1&#x27;, &#x27;kg s-1&#x27;, &#x27;kg s-1&#x27;, &#x27;kg s-1&#x27;, &#x27;kg s-1&#x27;, &#x27;kg s-1&#x27;, &#x27;m s-1&#x27;, &#x27;m s-1&#x27;, &#x27;m&#x27;, &#x27;m&#x27;, &#x27;degC&#x27;, &#x27;psu&#x27;, &#x27;m2 s-2&#x27;, &#x27;kg m-3&#x27;, &#x27;meter&#x27;, &#x27;N m-2&#x27;, &#x27;N m-2&#x27;, &#x27;kg m-2 s-1&#x27;, &#x27;kg m-2 s-1&#x27;, &#x27;kg m-2 s-1&#x27;, &#x27;kg m-2 s-1&#x27;, &#x27;W m-2&#x27;, &#x27;W m-2&#x27;, &#x27;W m-2&#x27;, &#x27;kg m-2 s-1&#x27;, &#x27;W m-2&#x27;, &#x27;W m-2&#x27;, &#x27;m s-1&#x27;, &#x27;W m-2&#x27;, &#x27;kg m-2 s-1&#x27;, &#x27;kg m-2 s-1&#x27;, &#x27;kg m-2 s-1&#x27;, &#x27;kg m-2 s-1&#x27;, &#x27;W m-2&#x27;, &#x27;Pa&#x27;, &#x27;W m-2&#x27;, &#x27;W m-2&#x27;, &#x27;kg m-2 s-1&#x27;, &#x27;W m-2&#x27;, &#x27;W m-2&#x27;, &#x27;W m-2&#x27;, &#x27;W m-2&#x27;, &#x27;W m-2&#x27;, &#x27;W m-2&#x27;, &#x27;W m-2&#x27;, &#x27;m&#x27;, &#x27;degC&#x27;, &#x27;psu&#x27;, &#x27;m s-1&#x27;, &#x27;m s-1&#x27;, &#x27;kg m-2&#x27;, &#x27;degC kg m-2&#x27;, &#x27;psu kg m-2&#x27;, &#x27;m m-1&#x27;, &#x27;m s-1&#x27;, &#x27;m&#x27;, &#x27;days since 0001-01-01 00:00:00&#x27;, &#x27;days since 0001-01-01 00:00:00&#x27;, &#x27;days&#x27;, &#x27;days since 0001-01-01 00:00:00&#x27;]</dd><dt><span>intake_esm_attrs:file_id :</span></dt><dd>access_om3_mom6_h_nativeXXXX_XX</dd><dt><span>intake_esm_attrs:_data_format_ :</span></dt><dd>netcdf</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>access_om3_mom6_h_nativeXXXX_XX.1day</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 315MB\n",
+       "Dimensions:   (time: 365, yh: 300, xh: 360)\n",
+       "Coordinates:\n",
+       "  * time      (time) object 3kB 1900-01-01 12:00:00 ... 1900-12-31 12:00:00\n",
+       "  * yh        (yh) float64 2kB -77.88 -77.63 -77.38 -77.13 ... 88.87 89.32 89.77\n",
+       "  * xh        (xh) float64 3kB -279.5 -278.5 -277.5 -276.5 ... 77.5 78.5 79.5\n",
+       "Data variables:\n",
+       "    ficeberg  (time, yh, xh) float32 158MB dask.array<chunksize=(1, 300, 360), meta=np.ndarray>\n",
+       "    friver    (time, yh, xh) float32 158MB dask.array<chunksize=(1, 300, 360), meta=np.ndarray>\n",
+       "Attributes: (12/16)\n",
+       "    NumFilesInSet:                            1\n",
+       "    title:                                    MOM6 diagnostic fields table\n",
+       "    associated_files:                         areacello: access-om3.mom6.h.st...\n",
+       "    grid_type:                                regular\n",
+       "    grid_tile:                                N/A\n",
+       "    intake_esm_vars:                          ['ficeberg', 'friver']\n",
+       "    ...                                       ...\n",
+       "    intake_esm_attrs:variable_standard_name:  sea_water_salinity,sea_water_po...\n",
+       "    intake_esm_attrs:variable_cell_methods:   ['time: mean', 'time: mean', 'z...\n",
+       "    intake_esm_attrs:variable_units:          ['psu', 'degC', 'm3 s-1', 'm3 s...\n",
+       "    intake_esm_attrs:file_id:                 access_om3_mom6_h_nativeXXXX_XX\n",
+       "    intake_esm_attrs:_data_format_:           netcdf\n",
+       "    intake_esm_dataset_key:                   access_om3_mom6_h_nativeXXXX_XX..."
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mom_frof_ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "4461c0de-b151-4837-bca9-96fde45030c5",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:20:39.153117Z",
+     "iopub.status.busy": "2025-01-09T04:20:39.152609Z",
+     "iopub.status.idle": "2025-01-09T04:20:39.192400Z",
+     "shell.execute_reply": "2025-01-09T04:20:39.191521Z",
+     "shell.execute_reply.started": "2025-01-09T04:20:39.153083Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "mom_area = xr.open_dataset(om3_ds.search(variable='areacello').df.path[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "c7a7fbf9-e230-4092-b5da-9463cc9e654f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:29:55.707340Z",
+     "iopub.status.busy": "2025-01-09T04:29:55.706511Z",
+     "iopub.status.idle": "2025-01-09T04:30:01.177992Z",
+     "shell.execute_reply": "2025-01-09T04:30:01.175822Z",
+     "shell.execute_reply.started": "2025-01-09T04:29:55.707275Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x14abbc4b2860>]"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "(mom_frof_ds.friver * mom_area.areacello).sum(['xh','yh']).plot()\n",
+    "(ocn_med_ds.ocnExp_Foxx_rofl*med_areas).sum(['ocnExp_ny','ocnExp_nx']).plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "43269d61-aaa4-4162-b37e-c04f301804fc",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:30:08.925568Z",
+     "iopub.status.busy": "2025-01-09T04:30:08.924883Z",
+     "iopub.status.idle": "2025-01-09T04:30:12.662246Z",
+     "shell.execute_reply": "2025-01-09T04:30:12.661273Z",
+     "shell.execute_reply.started": "2025-01-09T04:30:08.925541Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x14abc0d60bb0>]"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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IxZGIFyld9ToAO6InEBLR8rzHnZlzZDSjtprT4eT7V37FqMKPcZoGu0Y8Q+Lon1gd66KSRk6lmGBiOcL2tV9bHUdEakHFkYiXyD+US9/ipQBEj/nlBY+tmnNkNo/iyDRNvpv3EGOPuDZU3D7wT/SYcLvFqWrHPzCEXS1GA1Cy4T2L04hIbag4EvESGV+8RqBRyR6/BOL7jLzwwadHjprLUv6lC//MuLzXANjU40F6XXWPxYnqJqjvDQAkHPmGiooKi9OIyMWoOBLxAg6Hk45Zrgm7RT2nX/wFp+cc4Wz6c1hW/fstLsl8BoC0rneR8pPfW5yo7pJGXE0hYbTmONtWfWF1HBG5CBVHIl5g84qP6WjmUUIwyRP+30WPr9oEsom31YqLi+i05lEAUmNvoO8tcyxOVD92/0B2txwLwMnUf1obRkQuSsWRiBeoXOOaiL2z9USCQiMv/oJmMiE79Z9PEMdh8o1oet723Pm3NvABof1drbXux76lrLzM4jQiciEqjkQslpe7j74nXPfeirn0whOxzzBtZ0aOmu5SfqfDSbfsDwDIG/A/BISEW5yoYRKHTuIYEbSkmG0rPrU6johcgIojEYvt/vL/8DccZPgn0yF5SO1e1AzurbYjdTlx5HPSDKT7uFusjtNgNj9/9rQaB0D5pvctTiMiF6LiSMRCFZWVdM12/aI82ee2Wr/OOD1y1JR3yD6+1rXsPT1yBIHBvj1qdEbEINe+TMnHl1FaesriNCJyPiqORCy0aekS4siniFB6XPbT2r/QaNptNdPppHP+VwD49Z5qcRr3SRg4gQKiiDROsHX5R1bHEZHzUHEkYiFjwxsAZMROJiA4tNavM6tGjppmW23vzjTiyKfM9Cdh+DVWx3Ebw+5HVsxlADi2fGBxGhE5HxVHIhbJ3beblJOrAYgb96u6vdjWtOccHdz4GQC7g3sTFBphcRr3ihp0IwA9i1Zw4kSJxWlE5FxUHIlYJPOr/8PPcLIzsDdxCX3r9FqjibfVQnOWAnCywxhLc3hCl/6XkG9EE2acYtvyJVbHEZFzUHEkYoHy8nIS9rvaKmV9Z9T59UYTbqudKCkmqXQzALEDJlucxv0Mm53s2MtdX2xVcSTijVQciVgg7dv3iOEox4ig56W1uF3Ifzkz58jWBIujjHX/IdgoJ59o2if2tzqOR7Qaerq1VrKS4uJCi9OIyH9TcSRiAf/UBQDsbjcFv8DgOr/eOH1vtaY4cnRq+38AyG45FMPWNP+K6tR7FHlGDKFGGduXac8jEW/TNP/mEfFie3akkVK6HoCO4+s4EfuMJrxDdtsjKwHwS7zM4iSeY9hs7G/naq3Zt39ocRoR+W8qjkQa2cH/PIPNMNkSOoyY+B71O4nRNOcc5WbtorMzG4dp0HVI05tvVF3MMFc7tdeJ1Rw/dtTiNCJSnYojkUaUl5vNgKP/BiBk7H31Ps+PE7Kb1shR9nrXPcf2BCQRHtXG4jSe1bHHEPbb4ggyKti5bLHVcUSkGhVHIo1o77sPEWRUsMu/O10HTqj3eQx705yQ7Zf5HQDH40ZbnKQRGAZ57ScCELDzXxaHEZHqVByJNJJNq79mRNHnAPhNnAOGUe9zNcUJ2eXl5SSWuOZiRadcaXGaxtFupOuGur1OreNoQb7FaUTkDBVHIo2gqLiIyC/vASC15RV07n9pw054pq1G02mrZaQuI9I4QSFhdO4zyuo4jSIusT/77J0IMBzsXKrWmoi3UHEkbrFzyzpW/PV6vpn/Bw4eyrM6jtdJe+M+4s1cCowoEm59scHna4qbQBZuds3F2hs+EJufn8VpGk9+R9coWcgutdZEvIWKI2kwp8OJ+dFMRp74inHZL1Lyf1dQontGVVn3zRJGH3XtZXN03LOEuWGisVG1CWTTGTmKPrgCALPrOIuTNK4Oo1yr1nqWpnL40AGL04gIqDgSN1j31TskO9Krvu5mZrFx/v0WJvIehw8fosP3DwCQ2uZaEkde65bznplz1FQmZBfk55FQmQFA/JCrLE7TuGK79GavXxf8DQcZS9+2Oo6IoOJI3CB83QsArG8/g51j5wIw+shi1n3XvDe3M02T3QvuIpYj5Nra0nPGC247t2E/PSG7icw52rPmU+yGSZa9Ey3bdrY6TqM7Gu/a0yl8zycWJxERUHEkDbRvzw56OHbiMA0SpvwP3cf+hNTW1wDQYdn9HM4/aG1AC638eB7DTnyDwzSonPJ/BISEu+3cRlO7t9rubwDIbzPC4iDWiB99urVWtomDudkWpxERFUfSIDkr3gEgPSiFyNbtAehx+wvk2toSyxH2LPgFpmlaGdES+7Mz6bnxEQA2db6DTilj3Xp+WxNareZ0OOlcuAaA8J6XW5zGGq06dme3fyJ2w2TPcrXWRKym4kgapHW2a9+eUwk/3uohMCQSxzV/p9K0MfTkUlYsedWqeJZwOJzkL/oZLYwS9vp1I2X6HLdfoylNyN69bR1tOMopM6BBG2P6uuNdXH+GItVaE7GciiOpt317dpDk2IXDNOg25qYa3+vYZwybu/4cgJTNfyZrT/q5TtEkrVj8V/qXr6fM9Cd42jzs/gFuv4bt9JwjG77fVjuc+hkAu0P6EhAUYnEa63QefSsAPSq2kbtvj8VpRJo3FUdSbzkrXMP/1Vtq1fW9+TF2B3QnwjhJ8Tv/j/LyisaO2Oh279jEoIxnANje8ze0TejnkeucmZDdFEaOwnOXAVDaaay1QSwW3a4LGQE9sBkmmcv/YXUckWZNxZHUW+ts16Z9pxLPvfTa5udP5PQFnCSQ3pVbWbnokUZM1/jKysuoeP9OQowydgT1o+/1D3nsWlVtNR8fOSoqOk73sq0AtB/UvJbwn0txt6sBiM761OIkIs2b1xRHc+bMwTAM7r333gse9/LLL5OcnExwcDBJSUksXLjwrGOOHz/OzJkzadu2LUFBQSQnJ/P55597KHnztG/P9qqWWsLom897XOtOyezu9wcAhu97la0bVzRWxEa3euHDJDvSKSGEmNterypgPMHWREaOdq35ggCjkoNGG9p26W11HMt1HTMdp2mQ7Egne89Oq+OINFteURytW7eO1157jT59+lzwuFdffZXZs2fzyCOPsG3bNh599FFmzpzJJ5/8OIGxvLyc8ePHk5WVxfvvv096ejpz586lXbt2nn4bzUrVKrXgFCJax13w2D5X/5ot4SMJMByEfPJLiouLGiNio9qybhnDc+YBkDX4EVrGdfXo9X4cOfLt4qgs/SsA9kcPb9CNeJuKFjEdSQ9y/T2YvUKtNRGrWF4clZSUMH36dObOnUtUVNQFj33rrbf4xS9+wbRp0+jSpQs33ngjd9xxB0899VTVMfPnz+fo0aN89NFHjBgxgk6dOjFy5EhSUlI8/VaalaqWWkItWiGGQefb53GEFnQxc0h94z4Pp2tcxSXFhH0+E3/DweaIMfSa+HOPX9N2ZodsHy6OTNOk/ZGVAAR0v8ziNN7jVOIUANpka7RbxCqWF0czZ85k0qRJXHbZxf9yLCsrIygoqMZzwcHBrF27looK12Tfjz/+mGHDhjFz5kxiYmLo1asXTzzxBA7H+edmlJWVUVRUVOMh55e1u3YtterCWralYNyzAIw++j5rv37fkxEbVeobv6GzmeMq/ma81igjIGduzGr34TlH2Xt30NE8QKVpo9vgSVbH8RoJl9xMpWkj0bGbzIzNVscRaZYsLY4WL17Mxo0bmTOndvvAXH755cybN48NGzZgmibr169n/vz5VFRUUFBQAMDevXt5//33cTgcfP755/zhD3/gmWee4fHHHz/veefMmUNkZGTVo0OHDm55f03V/h9q31KrLmnUdaTGXAdA/IoHyD+U55F8jWnddx8x+sh7ABSMe4awlrGNcl1bE2ir5a5zTTreHdiDkIiWFqfxHuEt25Ie7FrlmLtCG0KKWMGy4ignJ4dZs2axaNGis0aDzufhhx9m4sSJDB06FH9/f6ZMmcKMGTMAsNtdvyycTidt2rThtddeY8CAAdx44438/ve/59VXz78R4ezZsyksLKx65OTkNPj9NWVtqjZ+rPvqop4zXiDH1p42HCNrwZ04Hb77y/3IkcN0WOa6wW5qm2tIGnV9o13bZvf94iho33cAFLUbbXES71Pe/RoA2u7/d7PcYV7EapYVRxs2bCA/P58BAwbg5+eHn58fy5Yt44UXXsDPz++cbbDg4GDmz5/PyZMnycrKIjs7m/j4eMLDw2nVqhUAbdu2JTExsapYAkhOTubgwYOUl5efM0tgYCARERE1HnJuWbu3k+jYXaeWWnUBwWFw7d+pMO0MPvU9K5a85IGUnmeaJulv3EUsBRywtaWHG28qWxtnVqvZfXS1WmlpKYknUwFo3V8ttf+WMPYmKkw7XZ1Z7Nq+0eo4Is2OZcXRuHHj2LJlC2lpaVWPgQMHMn36dNLS0moUN//N39+f9u3bY7fbWbx4MZMnT8Zmc72VESNGsHv3bpzOH39pZGRk0LZtWwIC3L9TcXOz/8zGj3VsqVXXoddItiTcBUD/rU+wd9d2t+VrLCs/eYPhJV/hMA3Kr3qFwJDIRr3+jztk+2ZxlL7+G8KMUxwlgview6yO43XCWrRmZ+hAAA6tfMfiNCLNj2XFUXh4OL169arxCA0NJTo6ml69egGudtdtt91W9ZqMjAwWLVrErl27WLt2LTfeeCNbt27liSeeqDrmrrvu4siRI8yaNYuMjAw+++wznnjiCWbOnNno77EpapNzZpXa1Q06T7+bHmFXQA/CjFOcfPcOn9o9Ozcnkx4b/gjApvjbie93aaNnqBo58tEJ2SXbvgAgK3KwR/eD8mWO5KkAdDjwOabTN4tgEV9l+Wq1C8nLyyM7O7vqa4fDwTPPPENKSgrjx4+ntLSUlStXEh8fX3VMhw4d+M9//sO6devo06cP99xzD7NmzeK3v/2tBe+gaanZUrvp4i+4AMPuT9Qtb3CCIHpVbmflW390U0rPcjicHFp0J1FGMXv9upJyy5OW5LDbzhRHvvlLs82hHwAwumkJ//kkjbmRctOPeDOX9C1rrI4j0qz4WR2guqVLl9b4esGCBTW+Tk5OJjU19aLnGTZsGKtXr3ZjMgFXSy0e2Bncl571bKlV16pjd7YMfJje63/PiOy/s3ndFfQZNKbB5/WkH957mtFl607fVPZ17P6BluQwTred7YaJ6XRi2Lz63zk1HDqQTYLTdWPVzkN0y5DzCY6IYnP4EPqU/EDBqncgRe1HkcbiO3+jiuVan26pldZjldr59J40ky3ho/E3HER8fhdFxYVuO7e77d65mQE7nwZge49ZHrupbG3Y7T/+u8bpYy2XzDWuJfx7/LrSos3ZNyyWanpeC0D8wS99emWniK9RcSS1krVrK0luaqnVYBh0/n/zOEIU8WYum+bPct+53aisvJyyf/6cUKOMnUEp9L3hd5bmObMJJEClw3fmawHY9n4LwJGYkRYn8X5JY27glBlAew6yM/V7q+OINBsqjqRW9v+wGHC11Oq7Su18wqJiODrhOQBGHfuQ1f95163nd4dVC/9IT8cOSgih9a3zLZ9EbK92fWdlpYVJ6sbhcNCt2DV/JrL3FRan8X6BIZGkR7jaacfWet+fC5GmSsWR1EqbqpZaw1apnU/C8KlsjP0JAF1XPsjBg/s9cp362LphBcNzXgMgc9AfiW7XzeJEYPfzr/p/h8N3iqOMTStpSREnCKJr/8Zf5eeL/Pq4NhftnP8fHGqtiTQKFUdyUVm7tv64Sm2sG1tq/6X3jL+Rbe9Aa46T/ebPvWKORcmJEkI+/SUBhoMt4aPofeUvrY4E/LiUH7jgfQO9zZE01+7qu0P74xdQu53xm7vEkddxgiDiOMz2dd9aHUekWVBxJBd15l5qO4P7EhHd1mPX8Q8KxbhuLuWmncGnfuD795/32LVqa+Mb99Pl9E1lO98+t1FuKlsb1Sdkmz40chSVtxyAis4aNaqtgOBQMiJHAVC4Xq01kcag4kguqk2Oa8O+0kTPtNSq69BjGNuS7gZgwPYn2ZOxxePXPJ/1Sz9h5GHXL6PDl/6VsJaeKwzrylZt6X6ljxRHx48dIbF8BwAdB2kJf10E9HXdsDmh4GsqfWiOmYivUnEkF3SmpVZp2kgY47mWWnV9p/2R9MDehBmllL57J2XlZY1y3eqOHikgbul92AyT1FZX0330Txo9w4UYNhsO0zWKZfpIW23Xms/wNxzk2trSplN3q+P4lKTh11BMCDEcZevq/1gdR6TJU3EkF3SmpZYRnOLRllp1ht2PVre+QQnB9HTsYPWbf2iU655hmiY73vgVcRwmz4ghecaLjXr92nLgWrHm8JGl/JUZXwNwoNUIi5P4Hr/AYHZFuTZIPbHxPYvTiDR9Ko7kgs601E41Qkutuuj2CWQNfhSA4ftfZ/OaxpuIuvKzNxlR8iVO06D0qpcJCmvRaNeuC8fpP75OHxg5Mp1OOh1dBUBw8gSL0/im4H43AND96LeUl5dbnEakaVNxJOeVaUFLrbpeE3/O5shL8DccRH4xk8Ljxz1+zdz9+0he5xqpSut4G537j/f4NevLefqPr+n0/jkomRlbiCOfctOPboO1v1F9JA67ikLCiKaQbSs/szqOSJOm4kjOK/d0Sy3dw6vUzssw6Hr7XA4bLelkHmDzgns8ejmnw0neW3fS0igmy68zKbf+xaPXayiH4frj6wv7HOVtcN0yZFdQL4JCIy1O45vs/gHsjr4EgNK09y1OI9K0qTiS84rJPr3xY6J1K4tCW7Tm+IQXABh1/F+s/uJtj11rxT+fZWDZGspNPwJumIfdy/fhcfpQWy00ZykAJzt4942FvV34QNfCgORjSyktLbU4jUjTpeJIzikrYwsJzj2WtdSqSxh2FRvbujIkrP4teQey3X6NvRlb6b/jrwBsS55FXNJAt1/D3c5MyDa9fEL2yZMlJJ7aBEBM/8kWp/Ft3QZN5BgRtDBK2PbDx1bHEWmyVBzJOe23uqX2X3rPeJZ99k5EU0juwjvdunt2eXkFp969kzCjlPTAPpbfVLa2fGXkKGPtV4QYZRQQRYfu3l90ejObnz97W18GQMWmDyxOI9J0qTiSc4o5vUqtzMKWWnX+gSH43TCPctOPgaWr+f6fz7rt3Cvf+l96OrZTQjDRt76OUW33aW9WVRw5vbs4OrHdtS9PVtQwDJv+ymmoyEHTAOhRuJyTJ09YnEakadLfVHKW6i21bha31Kpr130w25JnATBwx1/YtWNTg8+5PXUFw7P/D4DMgX+gVfvEBp+zsfy4Ws27i6PYgh8A8EscZ3GSpqHrgMs4bLQkwjjJ1u8/sjqOSJOk4kjO4m0tter6/uT37AxKIdQoo/L9n1FaVv/ds0+cOEHgx786fVPZkfSeNNONST3PaZxpq3nvarUD2Xvo6tyH0zToOtQ7RiF9nWH3Y1+Ma4sJc8sSi9OINE0qjuQsjXkvtboybHba3PYGxYSQ7MhgzZv1nx+0fsEDdDX3cZRI4md4z01la8t5ekK2NxdH+9a5lvDvCUgkPCrG4jRNR8shNwLQq3gFJSXFFqcRaXpUHEkNWRlbSPSSVWrn0zKuK9lD/wzAiNz5pK36us7n2Lj8U0blu0bIDo39C+HRcW7N2BicxpnVat7bVvPb69rZ/FjsKIuTNC2dU8ZyyGhNqFHKtmWamC3ibiqOpIYfW2r9iIiOtTjN+fW84mdsanEZfoaT6C/v5vjxo7V+7dGjR4j59sxNZSeTPPZGDyb1HG/fIbuiooLEknUAtOw70eI0TYths5HT9nIAbNvVWhNxNxVHUkNMjvUbP9ZWwu1/J9+IpgN5bH3j15imedHXuG4qO5N25JNntCF5xkuNkNQzzsw58tYJ2ekblxNpnKCYELqkaPNHd2szzDWy27NkNYWFx60NI9LEqDiSKpkZW0hw7vXqllp1IZGtKLriJZymwcjCT1n977cu+prVn7/FiOJ/4zQNTk16maCwqEZI6hmml+9zVLjFVWjvCR+Izc/f4jRNT8deIzhgiyXEKGPH0netjiPSpKg4kiq5PtJSq67bkCtJaz8dgMQ1vyd3/77zHpt3IJvEtb8HIK3DrXQZ6Nt3h6+ac+SlI0ctD34PgKOzlvB7hGFwoJ2rXem/818WhxFpWlQcSZUzLbWyJO9vqVXX57anybLHE20UcfCtn+E4x+7ZToeT/Qt/TrRRRJY9nj5eflPZ2jizWs0b5xwdKThEYkU6AJ2G+NbnyZe0HXEzAL1OruXo0SMWpxFpOlQcCQCZGZt9qqVWnV9gMAE/eZ1y048BZWtZsfivZx3z/fvPM6h0FRWmHf8b5uIXGGxBUvcyDe+dkL1n9afYDZN9to60atfV6jhNVrukQeTY2hNoVLBz6WKr44g0GSqOBKjZUgtv6RstterikgayvcdvABic8TQZ2zZWfW/vru0M2P4UAFu7/5p23QdbktHdvHlCtnPXNwAcajPC4iRNnGFwqIOrtRaU/pG1WUSaEBVHAlS7l1qS9238WFspN/yWHcH9CTbKMT+4k9LSUsrLKzj57s8IM06RHtibvj952OqYbmN66Zwjp8NJ58JVAIT1vNziNE1fu5GuOXe9SjdQcPiQxWlEmgYVR/JfLTXf3PMHXLtnx/70DYoIJcm5mzULfsuqRY/Sq3IbJwgi+hbfualsbXjrJpB7tq8nhqOUmv508/FJ776gbUI/suzxBBgOMpa+bXUckSZBxZH4fEutuqjYeHJGPAHAyLwFDN/3CgB7+v+BVh2SrIzmdqaXbgJ5KPVzAHaHpBAQHGpxmuahoNMkAEJ2fWxxEpGmQcWREFu1Ss13W2rV9Rw/g01tpmA3TPwNB1vDR9LnqrutjuV2Z9pqeFlbLXz/MgBOdbzE4iTNR8cxtwDQqyyNQ3n7LU4j4vuaTo9B6iUzYzPdnJk+31L7bym/WsjRzE2UnThOcuIQn7upbG2YXjghu7i4kO5lW8CAuIGTrY7TbLTp1IO9ft3oUrmbXcveIebG/7E6kohP08hRM3fmXmo7Q3y/pfbfWnZOoW2vMdgDgqyO4hleOHK0a80XBBoVHDJa0a5bitVxmpWjnV3FaOQetdZEGkrFUTNmmiZtT7fUyptIS6058caRo9KdXwGQ03J4kxyt82bxY1yr1nqWb+HA/kyL04j4tlq31V544QV+/vOfExQUxAsvvHDBY++5554GBxPPy0yv3lLzrY0fpdqcI9M7iiPTNIk7shKAgKTxFqdpflq1T2SXf3cSKnayd9nbxE3/vdWRRHxWrYuj5557junTpxMUFMRzzz133uMMw1Bx5CMOrFxMF1wttV5RMVbHkTo6M3LkLcXR/swM4s1cKk0bXYdMsjpOs1TU9SrYuZOWmZ8CKo5E6qvWxVFaWhqRkZEAZGZqyNbXmaZJzP4zLbUpFqeR+vC21Wo56z6mA7A7MJnukdFWx2mWuoy9BXb+lR6V28nJzKBD50SrI4n4pFrPOWrZsiX5+fkAXHrppRw/ftxTmaQRZKZvJqEJrlJrTrxth+zAfUsBKGo32togzVhUbDzpAb0A2Pf9PyxOI+K7al0chYWFceSI667PS5cupaKiwmOhxPMOrHTdpHJnSH/C1VLzTV7UVisrKyXxxAYAWqVcaXGa5u1EgmtxRat9n1ucRMR31bqtdtlll3HJJZeQnJwMwNSpUwkICDjnsd9++6170olH1GypaZWarzJtZ9pqTmuDAOkbltLHOMVxwunce7jVcZq1bmOm49g6h+6ODLJ2byO+W0+rI4n4nFoXR4sWLeLNN99kz549LFu2jJ49exISEuLJbOIhaqk1EV60Wq14q6vY3hsxhP5N6P51viiiTXt2BKeQXJpGzvdvE9/tcasjificWv8tFhwczC9/+UsA1q9fz1NPPUWLFi08lUs86MDKd06vUhugVWq+7ExbzQvmHLU+tML1P90utTaIAFCaNAU2pRGb8zmm+RiG9pwSqZN6bQL53XffVRVGpmlimqY7M4kHmaZJ7P4vAChPusriNNIQ3rLPUf7B/XSr3ANA5yH6THmDhDE3u0aGnXvZk77Z6jgiPqfeO2QvXLiQ3r17ExwcTHBwMH369OGtt95yZzbxgL3pm+jmzKTCtKul5utOjxwZFo8c7V3zKTbDZK+9M1ExHS3NIi5hLWNJDxkAQN4PWrUmUlf1Ko6effZZ7rrrLq688kree+893n33Xa644gp++ctfXnCDSLFe3krXvdTStUrN51VNyDatnZBt7PkGgIKYkZbmkJoqkq8BoN3+f2t0X6SO6jVz8sUXX+TVV1/ltttuq3puypQp9OzZk0ceeYT77rvPbQHFfVwttS8BqNAqNZ9nVC3lt644cjgcdC1aA0Bk74mW5ZCzJY65kfINf6QL2WRsXUdi78FWRxLxGfUaOcrLy2P48LOX6w4fPpy8vLwGhxLPqN5S6zZmmtVxpKFs1s852rV5Fa0o5CSBdB0wzrIccraQyFakh7kKovxV71icRsS31Ks46tatG++9995Zz7/77rskJCQ0OJR4hlpqTYwXTMg+kuZawr87pB9+AUGW5ZBzc/aYCkDHvC8wvWA/LBFfUa+22qOPPsq0adNYvnw5I0aMwDAMVqxYwTfffHPOokmsV72lpo0fm4Yzq9UMC9tqkQeWA1AeryX83ihpzE8oW/t7OnKA7Wkr6dFf88JEaqNeI0fXXXcda9eupVWrVnz00UcsWbKEVq1asXbtWqZOnerujOIGWqXWBNmsXa1WeOwoieXbAGg/WEv4vVFQWBQ7w4cCcHTtYovTiPiOOo8cVVRU8POf/5yHH36YRYsWeSKTeEDeynfoiutear2j2lgdR9zBsHa12q61/2ag4SDXiKVdfA9LMsjF2XpfC6u+p/PBL3E6nNjs9d7BRaTZqPOfEn9/fz788EO3B5kzZw6GYXDvvfde8LiXX36Z5ORkgoODSUpKYuHChTW+v2DBAgzDOOtRWlrq9sy+wjRN2p7e+FGr1JoOw3amrWbNyFF5+lcAHGile6l5s6RRN3CKQNqRz/YNS62OI+IT6vVPiKlTp/LRRx+5LcS6det47bXX6NOnzwWPe/XVV5k9ezaPPPII27Zt49FHH2XmzJl88sknNY6LiIggLy+vxiMoqPlOFt27M42uziy11JoaC4sj0zTpeHQlAMHJExr9+lJ7ASHhpEe4Ctjj6zQnVKQ26jUhu1u3bvz5z39m5cqVDBgwgNDQ0Brfv+eee2p9rpKSEqZPn87cuXN57LHHLnjsW2+9xS9+8QumTXMtQ+/SpQurV6/mqaee4qqrfpzzYBgGsbGxdXhHTZtaak2UhROy9+3aQjyHKDftdBus/Y28nX/K9fD9d3Q7/BWVlZX4+enmwCIXUq8/IfPmzaNFixZs2LCBDRs21PieYRh1Ko5mzpzJpEmTuOyyyy5aHJWVlZ01AhQcHMzatWupqKjA398fcBVcnTp1wuFw0LdvX/785z/Tr1+/C563rKys6uuioqJa5/d2pmnSNte1Sq2yu1pqTYmVbbXc9Z8RD+wO6kWPsBaNfn2pm8QRUyn5/gFiKWDzum/oM+xyqyOJeLV6FUeZmZluufjixYvZuHEj69atq9Xxl19+OfPmzeOaa66hf//+bNiwgfnz51NRUUFBQQFt27ale/fuLFiwgN69e1NUVMTzzz/PiBEj2LRp03n3YJozZw6PPvqoW96Tt6nZUrvJ6jjiThbePiQk+zsAStqPafRrS935B4WytcVo+h3/kuL174GKI5EL8uiyhYiICPbu3XvO7+Xk5DBr1iwWLVpU6/lADz/8MBMnTmTo0KH4+/szZcoUZsyYAYDd7vpFMXToUG655RZSUlIYNWoU7733HomJibz44ovnPe/s2bMpLCyseuTk5NTtjXqx6hs/hrVobXEacSvDmpGjUydPkngqDYCY/pMa9dpSf0F9rwcg4cg3VFRUWJxGxLt5tDi60M0ON2zYQH5+PgMGDMDPzw8/Pz+WLVvGCy+8gJ+fHw7H2X/hBwcHM3/+fE6ePElWVhbZ2dnEx8cTHh5Oq1atznkdm83GoEGD2LVr13mzBAYGEhERUePRFFRvqVV0n2JxGnG3H9tqjTtylL7uK0KNMo7Qgo7Jgxr12lJ/iSOmUEQobTjG1tVfWB1HxKtZtuHFuHHj2LJlC2lpaVWPgQMHMn36dNLS0qpGgs7F39+f9u3bY7fbWbx4MZMnT8ZmO/dbMU2TtLQ02rZt66m34rW0Sq1pM85sAknjFkcntv8HgKwWQ6sKNPF+dv9AdrccC8Cpjf+0NoyIl7NsyUJ4eDi9evWq8VxoaCjR0dFVz8+ePZvc3NyqvYwyMjJYu3YtQ4YM4dixYzz77LNs3bqVN998s+ocjz76KEOHDiUhIYGioiJeeOEF0tLSePnllxvvzXmJM6vU0kP600sttSbHqpGjmMM/AGBP0I1mfU1o/xvg689IOvYdZeVlBAYEWh1JxCt59VapeXl5ZGdnV33tcDh45plnSElJYfz48ZSWlrJy5Uri4+Orjjl+/Dg///nPSU5OZsKECeTm5rJ8+XIGDx5swTuwjquldnrjR7XUmiQrVqsd2J9JN2cmTtOgy1DdMsTXJAyZxHHCiaaIrSs+szqOiNfy6MiRYRh1On7p0qU1vl6wYEGNr5OTk0lNTb3gOZ577jmee+65Ol23Kdq7I5Wuzn1qqTVlp4sjWyOOHO1b8ylxwF7/bnSLbn6tal9n8w9gT6tLGVDwL8o3/RMuvdbqSCJeybIJ2eJZeatcN5ncGTJAq9SaKsP1b5vGnHPkl/kNAEfbjmq0a4p7RQx0baLb4/gySktPWZxGxDvVqzg6der8f6Dy8vKq/v/f//437dq1q88lpAGqt9S08WPTdeYGoo3VVquoqKBb8XoAovpc2SjXFPfrNuhyjtCCSOMEW5f/y+o4Il6pXsVRv3792Lhx41nPv//++zXujzZy5EgCAzXhr7HtOd1SK9fGj02b0bhttV2bVhBlFFNMMF36avNHX2XY/ciKuQyAyi0fWJxGxDvVqzgaP348w4cP58knn8Q0TUpKSpgxYwY//elP+eMf/+jujFJHB0+31NJDBhDW4tz7P4nvM+yN21Y7tunfAOwJG4TdP6BRrimeETXI1VrrWfQ9J06UWJxGxPvUa0L2iy++yKRJk7j99tv57LPPOHDgABEREaxbt44ePXq4O6PUgVapNR9G1YTsxmmrReV9D0Bl50sa5XriOZ37j+PwZ9G05ghrli9hyMTbrI4k4lXqPSF7woQJXHvttfzwww/k5OTw5JNPqjDyAtVbaolapdak2c4s5W+EkaNjRw6TWLETgPghWsLv6wybnX2xE1xfbP3Q2jAiXqhexdGePXsYNmwYn376KV9++SUPPvggU6ZM4cEHH9Q9eyx2cNXpe6mFqqXW1BmNuJR/1+pP8TOcZNva06r9uW/gLL6l1VDXfMReJT9QVFxocRoR71Kv4qhv37507tyZTZs2MX78eB577DG+/fZblixZ0uw2W/QmpmkSd+ZeaklqqTV1hs3VFbc1wsiRY9fXABxqPcLj15LG0an3KPKMNoQaZWxf+r7VcUS8Sr2Ko1deeYXFixfTokWLqueGDx9Oamoq/fv3d1c2qaM92zfSRS21ZsOwN86cI9PppPPx1QCE9rzco9eSxmPYbOTGXQGAfYdaayLV1as4uvXWW8/5fHh4OPPmzWtQIKm/H1tqA9VSawZ+nHPk2c1W9+xIJZYCykx/ug6a4NFrSeOKGTEdgN4nVnP82FGL04h4j3oXRyUlZy//zMrKYvTo0Q0OJXVnmiZxB1x3S69I0saPzYFhc/3x9XRb7dDGTwHYFdyHwOBwj15LGleH5CHst7UjyKhg57LFVscR8Rr1Ko62b99O7969+eGHH6qee/PNN0lJSSEmJsZt4aT2dm/foJZaM2M7vc+Rp9tqYfuXAXCq41iPXkcsYBjkdXDtdh648yNrs4h4kXoVR2vWrGHatGlceuml/O53v+OGG27g7rvv5rnnnuP99zWxzwqHzmz8qJZas1G1Ws2DI0cnSopJKt0MQNsBkz12HbFOu5Gu1lrPU+s5cvigxWlEvEO9NoH08/PjySefJDAwkD//+c/4+fmxbNkyhg0b5u58UgvVV6lVauPHZqMxVqulr/2S/kYF+UY07RL6euw6Yp24hH5k2eOJd2SRsfRtht3wG6sjiViuXiNHFRUV3H///Tz11FPMnj2bYcOGMXXqVD7//HN355Na2L19A13MbFdLbfQ0q+NII7HZPT9yVLrDNY8tO2pY1RwnaXoOx7tGBUN2fWJxEhHvUK+/7QYOHMjHH3/M0qVLefzxx1m6dCn33Xcf1157Lb/61a/cnVEuonpLLVQttWbD1ghttbYFKwHwT7zMY9cQ63UafQsAvcpSOZSXbXEaEevVuzhKS0tj6NChABiGwUMPPcTq1atZvny5WwPKhZmmSbuqlto11oaRRuXpkaOczAw6mzk4TIOuQ3XLkKasTadkdvsnYjdMdi992+o4IparV3H0+uuvExoaetbzffv2ZcOGDQ0OJbW3e/sGOp9pqY1RS605ObNaze6h24fkrHct4d8d0F2T/JuB411crbUWez+2OImI9eo9iWDZsmVcddVVdOvWjYSEBK6++mq+//57AgMD3ZlPLuJMS21n6CBCI6MtTiONydMTsgMyvwWgsJ32LmsOOo92be6bXL6d3H27LU4jYq16FUeLFi3isssuIyQkhHvuuYe7776b4OBgxo0bx9tva0i2sVRvqTm0Sq3ZOdNWs3ugOCovLyfxhGsUODrlSrefX7xPdLsupAf0wmaYZC7/h9VxRCxVr6X8jz/+OH/5y1+47777qp6bNWsWzz77LH/+85+5+eab3RZQzm/3tvUkqKXWbNntnhs5St/wHb2NkxwnjM69R7r9/OKdShKuhm1baZX1KfC/VscRsUy9Ro727t3LVVedPUHz6quvJjMzs8GhpHYOrX4XUEutuTqzCaSf4f7iqGjrFwBkRgzG5levf0OJD+o2djoO06C7I4OsXVutjiNimXoVRx06dOCbb7456/lvvvmGDh06NDiUXFz1jR/VUmuezowcATgd7r2FSPTBFQCYXS5163nFu0W2bk96cF8A9q/QFAlpvur1T8L777+fe+65h7S0NIYPH45hGKxYsYIFCxbw/PPPuzujnINaamI/PXIE4HA6quYgNdThQ7kkVu4CA+KH6ibGzU1p92sgLZXYnM8xzccxDMPqSCKNrl7F0V133UVsbCzPPPMM7733HgDJycm8++67TJmiUYzGkL/mnyTgaqn1UUutWTKqFUOOykr8/QPcct69az6jtWGSaY+nc2wnt5xTfEfCmJupSP0T3ZyZ7N6+kW49B1gdSaTR1XsywdSpU5k6dao7s0gdtDj4AwDlXSdYnESsYvf7sThyOivddl5zt6tlfrjNCDq77aziK8Kj2rAldCC9T67h4Mq3VRxJs1SnOUcdO3bkyJEjVV+/9NJLFBUVuT2UXFhR0TESyncA0H6Allk3Vzab++ccORxOuhatASCi90S3nFN8j6PHtQC0P/BvTKfnbk8j4q3qVBzt378fR7W/hH/3u99RUFDg9lByYbvW/ocAw8EBI4bY+GSr44hFbNUmZDvcVBzt3rqG1hzjpBlI1wG6n1pzlTRmGmWmP/FmLumbV1sdR6TRNeg226ZpuiuH1EFZuqvtcaDlEIuTiJWqr1YzHe5pqxWkfgbA7tC++AcGu+Wc4nuCw6PYGT4MgCOrtWpNmp8GFUdijdZH1gHg122stUHEUjbbj398HW4qjiIOuG4cXdbpErecT3yX0dvVWut88EucDrXWpHmp84TsefPmERYWBkBlZSULFiygVauaN6W855573JNOzlJUdIwujkwwoEOK9qBpzgybDYdpYDdMTDe01YqKjpFYtg0MaD9wshsSii9LGnUdJ1c9RBz5bN2wlF6D9feNNB91Ko46duzI3Llzq76OjY3lrbfeqnGMYRgqjjwoc9P3pBgmh4xWxMRpLVFz58CGHQcON6xW27X63wwwKjlgxBDXpZcb0okvCwyJIDVyFP0Kv6Zw3WJQcSTNSJ2Ko6ysLA/FkNoq2bUSgNzwPsRYnEWs58QGOHC6oa1Wlv4VALnRw4nTxn8C+KdcD8u/JuHwV1RWVuKnW8lIM+HROUe9e/cmJyfHk5dodkLyNwLgiBtocRLxBs7Tf4QbOifE6XDS6Yhr76yg7uMbnEuahu4jrqGYENpwlG2rv7Q6jkij8WhxlJWVRUVFhScv0aw4HU46n9oGQMvuoyxOI97AcaY4amBbbWfq97TjEKfMABKGnX1TaWme/AKDyYgaC8CJje9ZmkWkMWm1mg/J2rWZFkYJpaY/nXoMtjqOeAGncWbkqGETso+texeA9IjhBIVGNDiXNB0h/X8CQPej31JeXm5xGpHGoeLIh+RvWwZAZmASfgFBFqcRb+DEdQsR01n/4sjpcNL5kGu+kf308m2RMxKHTuIYEbSkiG0/fGp1HJFGoeLIl+x37W9U3Lq/xUHEWzhxTZxuyITsTSs/I458ThJI4igVR1KT3T+APa3GAVCW9k+L04g0DhVHPiSmcBMAwZ2HWZxEvEXVhOwGjBxVrnZtz7Gj1RUEBoe7JZc0LRGDXK215OPLKC09ZXEaEc9TceQjjh05TGdnNgAdU8ZYnEa8xZniqL5ttQP7M+lbsgKANpf+ym25pGlJGDiBAqKINE6wdfmHVscR8TiPFkd///vfiYnRbjzukLXJNd8o14glsnU7i9OIt2hocbTny1fxNxxkBCTTocdQd0aTJsSw+5EZOwEA55YPLE4j4nn1Lo6++eYbJk+eTNeuXenWrRuTJ0/m66+/rnHMzTffTGhoaINDCpzc49r88WBkisVJxJv8uFqt7nOOysrLSMh5H4BTKbe7NZc0PS0H3wRAz6IVlJQUW5xGxLPqVRy99NJLXHHFFYSHhzNr1izuueceIiIiuPLKK3nppZfcnVGA8IJUAMx2gyxOIt6kIavV0r55l1iOcIwIelx2q7ujSRPTpe8YDhptCDVK2b7sfavjiHhUvfaCnzNnDs899xx333131XP33HMPI0aM4PHHH6/xvDRcZUUFXUp3gAGte4y2Oo54EadhA7N++xwFpr4BwO521zAoMMTd0aSJMWw2cuKuIDZ3IbZtS2CSRhul6arXyFFRURFXXHHFWc9PmDCBoqKiBoeSmjJ3biTMOMUJguiQNMDqOOJFzKo5R3Vrq+1J30Tf8o04TYP4CTM9EU2aoJhhNwPQ68QqCo8dtTiNiOfUqzi6+uqr+fDDs1cs/Otf/+Kqq3TrAXcr2L4cgKzAZGy68aNUU98J2XlfvwrA9tBBtO7U3e25pGnq2HMo+23tCDIq2LHsXavjiHhMvX7TJicn8/jjj7N06VKGDXPtubN69Wp++OEH7r//fl544YWqY++55x73JG3GbLmuzR9L2mjUSGpyGqfnHNWhrVZSUkyv/E/AANvgOz0VTZoiwyCv/UTaZ88jYOdHwF1WJxLxiHoVR6+//jpRUVFs376d7du3Vz3fokULXn/99aqvDcNQceQGccWbAQjrqs0fpSbz9Go106x9W23zl28w3CjhoNGGZO2ILXXUbtQt8I959Dq1jiMFh4hupe1apOmpV3GUmZnp7hxyHocP7qeDmQdAx77a/FFqqmqrOZy1Ot40TaK2LwIgp8s0Yu1q00rdxCX0I8seT7wji/Sl7zD8+nutjiTidvWac3Tq1Pm3j8/Ly6t3GDlb9mbX5o/7bB0Ib9Ha4jTibczTbbXa3j5kx8bvSXakU27aSbpCO2JL/RzuNBmA0Ix/WZxExDPqVRz169ePjRs3nvX8+++/T58+fRocSn5UuncVAIdbaPNHOZt5+sazmLUrjo4v/z8AtrW4hIjWcZ6KJU1cx9G3ANCzLI38vByL04i4X72Ko/HjxzN8+HCefPJJTNOkpKSEGTNm8NOf/pQ//vGP9QoyZ84cDMPg3nvvveBxL7/8MsnJyQQHB5OUlMTChQvPe+zixYsxDINrrrmmXpm8QeSRNACMDoOtDSJeyazaIfvixdGRgnz6Hf8KgIhRv/BoLmnaYuKT2e2XgJ/hZNeyt62OI+J29Zpw8OKLLzJp0iRuv/12PvvsMw4cOEBERATr1q2jR48edT7funXreO211y466vTqq68ye/Zs5s6dy6BBg1i7di133nknUVFRZ20hsG/fPh544AFGjRpV5zzeoqyslK7l6WBAbE/NN5KznVmtVpuRo+1f/J1RRjlZ9k50HTDew8mkqTve9SpIf5bIPR8DD1kdR8St6n1vtQkTJnDttdfyww8/kJOTw5NPPlmvwqikpITp06czd+5coqKiLnjsW2+9xS9+8QumTZtGly5duPHGG7njjjt46qmnahzncDiYPn06jz76KF26dKlzJm+xd+tago1yigglrltvq+OIFzqzCSQXmXPkcDjpsOcdAI4m3wqG4elo0sR1Hu265UyP8m0cyN5jcRoR96pXcbRnzx6GDRvGp59+ypdffsmDDz7IlClTePDBB6moqKjTuWbOnMmkSZO47LLLLnpsWVkZQUFBNZ4LDg5m7dq1Na77pz/9idatW3PHHXfUKYu3OZb+PQD7gnti2OwWpxFvdGZC9sU2gdy04hPizVxOEESPK37WGNGkiYtu14WdAT2xGSZ7l/3D6jgiblWv4qhv37507tyZTZs2MX78eB577DG+/fZblixZwuDBtZ8bs3jxYjZu3MicOXNqdfzll1/OvHnz2LBhA6Zpsn79eubPn09FRQUFBQUA/PDDD7z++uvMnTu31jnKysooKiqq8fAG/gdcmz+ejBlocRLxVmfmHF105GjNPAB2tp5IUNiFR2hFautEt6sBaJX1qcVJRNyrXsXRK6+8wuLFi2nRokXVc8OHDyc1NZX+/fvX6hw5OTnMmjWLRYsWnTUadD4PP/wwEydOZOjQofj7+zNlyhRmzJgBgN1up7i4mFtuuYW5c+fSqlWrWr+fOXPmEBkZWfXo0KFDrV/rKaZp0q5kKwARCdr8Uc7NWYuRo9zsvfQ7sQKAmHG6j5q4T7ex03GYBt0d6WTt3n7xF4j4iHoVR7feeus5nw8PD6+xQ/aFbNiwgfz8fAYMGICfnx9+fn4sW7aMF154AT8/PxznWH0THBzM/PnzOXnyJFlZWWRnZxMfH094eDitWrViz549ZGVlcdVVV1Wdc+HChXz88cf4+fmxZ8+5++KzZ8+msLCw6pGTY/3S1Lz9mcRxGIdpEN9ntNVxxFsZF7+32t7/vIKf4SQ9oBftuw9qrGTSDES26cDO4L4A7P9+kbVhRNyoTsXRr371K0pKSqq+fuutt2p8ffz4ca688spanWvcuHFs2bKFtLS0qsfAgQOZPn06aWlp2O3nn2Pj7+9P+/btsdvtLF68mMmTJ2Oz2ejevftZ57z66qu55JJLSEtLO++IUGBgIBERETUeVsvd4rrZ7D6/eILDW1gbRrxWVVvtPKvVysrLSNz/AQClfWc0UippTsqSrgEgJudzTNO0NoyIm9RpKf/f//53HnnkEcLCwgDXZOoRI0ZUfV1WVsaXX35Zq3OFh4fTq1evGs+FhoYSHR1d9fzs2bPJzc2t2ssoIyODtWvXMmTIEI4dO8azzz7L1q1befPNNwEICgo665xnWn///by3q8hybf54JKovvrveTjztYhOyN329mMEc5SgR9Bx3S2NGk2YiYezNVKT9iQRnJnt2pNK1R+2mVoh4szqNHP33vwo8/a+EvLw8srOzq752OBw888wzpKSkMH78eEpLS1m5ciXx8fEezWGFqKNpAPh1GmJtEPFqZ4qj803IDkx7A4A97a/FLzC4sWJJMxIe1Yadoa5FI3krtSGkNA1eddfJpUuX1vh6wYIFNb5OTk4mNTW1Tuf873P4gpMnT9ClYjcY0Lb3WKvjiBe7UFttz440UspTcZoG8ZdrIrZ4TmWPqbB+De1zP8d0/gXDVu8t9ES8gj7BXmjvlpUEGpUcJZKYjklWxxFvVtVWc571rbxvXgFgW+gQWndIbNRY0rwkjZlGmelPvJlL+pY1VscRabA6jxz98Y9/JCQkBIDy8nIef/xxIiMjATh58qR70zVThRmuZdc5ob1oqX+ByQWcb+SouLiQ3oc/BQPsQ7Xpo3hWSHhLNoUPJaXke46sehtStP2I+LY6FUejR48mPT296uvhw4ezd+/es46RhgnKWw9AWVtt/igXcZ5NILd8+QbDjRPkGW1IHjHVgmDS3Bi9r4NV3xN/8EucDic2u/5hJ76rTsXRf88JEvcznU46nnRt/hiZOMLiNOLtfpyQ/WNbzTRNone49pzJ7XoTbe1eNbVQmqikUddxctVDtOMQ2zYspefgS62OJFJvKu29zP6sDFpznArTTnxvFUdyEadHjoxqbbXt65eR5NhFuelH4sS7rEomzUxgSAQ7I0cBcHzduxanEWmYev2T0uFwsGDBAr755hvy8/Nx/tdk0G+//dYt4ZqjA1uX0QHI8u9KQnCY1XHEy1Xtc1StOCpa8XcAtkVdQr/otpbkkuYpIOU6WP413Q7/h8rKSvz8NGopvqlen9xZs2axYMECJk2aRK9evTAMw925mi3nPtdKj2PR/SxOIj7BVrOtVnD4EH2Pfw0GRI76pYXBpDlKGjGV4uX3E8NRNq/5ij4jJlodSaRe6lUcLV68mPfee6/WtwqR2os+vgmAgPihFicRn3B65OhMW23HF39nlFFOpr0zXfqPszKZNEP+gcFsjhrLgGOfU7LhXVBxJD6qXnOOAgIC6Natm7uzNHvFRcfpUula/de+zxiL04gvqJqQbTpwOJx02vsOAMd63Aoa0RULBPf/CQBJR7+lvLzc4jQi9VOv4uj+++/n+eef100G3Sxz8wr8DCeHjGhatetqdRzxBbYfl/KnLf8XHc0DlBBMj8vvsDaXNFtJQydxnHCiKWTbyk+tjiNSL/Vqq61YsYLvvvuOf//73/Ts2RN/f/8a31+yZIlbwjU3xbtWAnAgrDcxFmcR32BUa6uZ6+YBkN7mSgaEtbAwlTRndv8Adrcax8CCjyhLfR/GXmt1JJE6q1dx1KJFC6ZO1cZy7hZyaAMAlXGDLE4ivsI8PSHbv+QAfU6tBQNix+k+amKt8IHT4IuPSD6+lNLSUwQF6abH4lvqVRy98cYb7s7R7DkdTuJLtwHQsvtIi9OIzzi9z1HKqTXYDZOdgb3pnjTA4lDS3CUOmkDBF1G0Mo6xfvlHDJxwk9WRROqkXnOOMjMz2bVr11nP79q1i6ysrIZmapb27d5CFMWUmf506qmValJLp0eO7IZr/l9539utTCMCgGH3IzN2AgDOLR9YnEak7upVHM2YMYOVK1ee9fyaNWuYMWNGQzM1S4e2LQcgMzARv4Agi9OIrzgz5wjgKJH0GDfdwjQiP2o5+EYAehZ9T0lJscVpROqmXsVRamoqI0acfWuLoUOHkpaW1tBMzVOOa/PHolba/FHqwPZjcbSnw7UqrMVrdOk7loNGG0KNUrYve9/qOCJ1Uq/iyDAMiovP/pdAYWEhDofjHK+Qi2lduBmAoC7DLE4iPuX0nCOHadD5Ck3EFu9h2GzktL3c9f/btYJZfEu9iqNRo0YxZ86cGoWQw+Fgzpw5jBypycR1dexoAZ0d2QB07DPW2jDiU4Kj2wOwLWIkrdolWJxGpKaY4a42b++SVRw/dtTiNCK1V6/Van/5y18YPXo0SUlJjBrlugvz999/T1FRkW46Ww9Zm5fTzzA5YMQS16a91XHEh/S+7FZ2B4eQNPAKq6OInKVjz6FkL2lPR+d+Nn69gOE3/MbqSCK1Uq+Rox49erB582amTZtGfn4+xcXF3HbbbezcuZNevXq5O2OTd3K3a3L7wYjeFicRX2Pz86fbmBsJDG1hdRSRsxkGh7peB0BU+rsWhxGpvXoVRwAhISG0bNmStm3bEhUVRVhYGHa7/eIvlLOEHd4IgKP9YIuTiIi4V8JlP6PStJFcuZOMLeusjiNSK/UqjtavX0/Xrl157rnnOHr0KAUFBTz33HN07dqVjRs3ujtjk1ZZWUmX0u0AtEkeZXEaERH3ahHTke3hroUmR7980uI0IrVTr+Lovvvu4+qrryYrK4slS5bw4YcfkpmZyeTJk7n33nvdHLFpy9yxkXDjFCcJpEP3gVbHERFxuxZX/B6AwcXfkL5lrcVpRC6u3iNHDz30EH5+P87n9vPz48EHH2T9+vVuC9ccFOz4HoCswGRsfv4XOVpExPd07DWCzeGjsRkmpz55CKfDaXUkkQuqV3EUERFBdnb2Wc/n5OQQHh7e4FDNiS3X9a+o4jb9LU4iIuI5MdfOocz0p2/5Rlb+6/+sjiNyQfUqjqZNm8Ydd9zBu+++S05ODvv372fx4sX87Gc/46abdIPBuogt2gJAaNfhFicREfGcmM692Nbt5wAkb5pD/qEDFicSOb967XP09NNPYxgGt912G5WVlQD4+/tz11138eSTmnBXW4cP5dLJzAWgU8oYi9OIiHhWyo3/y74nP6WTYx8rF91Hm/u1vF+8U71GjgICAnj++ec5duwYaWlppKamcvToUZ577jkCAwPdnbHJyt7kutlstq094VFtLE4jIuJZdv9AzMnP4zQNhhd/wfrvPrQ6ksg51XufI3DtddS7d2/69OlDSEiIuzI1G6WZqwDIj+xjcRIRkcYR3+8S0mKvBSBm2WyKi4ssTiRytgYVR9IwkQWprv/pMMTaICIijajHLc9w2GhJB/JIXfR7q+OInEXFkUXKy8vpUp4OQGzP0RanERFpPEHhURSMegyAYQf/wfa0VRYnEqlJxZFF9m5dQ4hRRjEhtEtIsTqOiEijSr50OpvDR+FvODA+mUVFRYXVkUSqqDiyyJGdKwDICu6JYdM96USk+ekw/WVKCCbZkc6qd/9idRyRKiqOLBKQ57oB46kYbf4oIs1TVGwndve5H4D+u14gOzPD4kQiLiqOLGCaJnHFrs0fw7qNsDiNiIh1Uq75DbsCehBmlJL/7q8xnbq1iFhPxZEFDh7YRzvycZoGnVM0GVtEmi/DZifs+lcoN+0MLF3Nqs8WWB1JRMWRFfZvWgrAPr94gsOjrA0jImKxton92BJ/OwDdNvyJIwX5FieS5k7FkQXKs9YAcCRKq9RERABSbv4zubY42nCMHYvutzqONHMqjizQ8qhr80dbR23+KCIC4BcYQukVzwIw8vjHpK743OJE0pypOGpkpadO0qViNwBxvTTfSETkjK6DJ5La6ioAWnzzP5w8ecLiRNJcqThqZHu2rCLQqOAYEcTE97A6joiIV0m85TmOEklncz/r/vGI1XGkmVJx1MiOp7s2f8wO7YVh049fRKS60BatyRv2CABD988nY9sGawNJs6Tfzo0s8OB6AMpiB1icRETEO/WccDvbQoYQaFRS/uGvqaystDqSNDMqjhqR6XTS8cRWACITR1qcRkTESxkGMdNf5hSB9Krcxqr3/2Z1ImlmVBw1ov37dtGGo1SYduL7aGdsEZHzadUugZ3J9wDQZ8ez5OZkWRtImhUVR40oqkUU2/s+zPYutxMYHG51HBERr9b3+ofY459ApHGC/e/cg2maVkeSZkLFUSMKi2pDj2seIOWnz1gdRUTE6xl2fwKmvkSlaWPIyWWs+fJtqyNJM6HiSEREvFaHHkPZ1H46APGr/8jx40ctTiTNgYojERHxar2nzyHPiCGWAra89aDVccQNUtf9QOGJU1bHOC8VRyIi4tUCQsIpGf8UAMML3mfzmm8tTiQNcTB3H/0+uxLbXzpTerLE6jjnpOJIRES8XsLwqaRFTcBumIR8+RtKS0utjiT1tH/zUgAK/GIICgmzNsx5qDgSERGf0PWW5ykkjG7OTNa8/Wer40g9lWetBqCgRYrFSc7Pa4qjOXPmYBgG99577wWPe/nll0lOTiY4OJikpCQWLlxY4/tLlixh4MCBtGjRgtDQUPr27ctbb73lweQiItIYwqPj2DfwDwAM3vcae9O3WJxI6qPFkTQA7J2GWhvkAvysDgCwbt06XnvtNfr06XPB41599VVmz57N3LlzGTRoEGvXruXOO+8kKiqKq65y3cm5ZcuW/P73v6d79+4EBATw6aefcvvtt9OmTRsuv/zyxng7IiLiIX0m/ZIdW98luTSVovd/jfO332Kze82/8+UiSk+dpGvFLjCgba/RVsc5L8s/USUlJUyfPp25c+cSFRV1wWPfeustfvGLXzBt2jS6dOnCjTfeyB133MFTTz1VdczYsWOZOnUqycnJdO3alVmzZtGnTx9WrFjh6bciIiKeZhhE3/QKpaY/fStSWfnRy1YnkjrYu2UVgUYFx4ggNr6H1XHOy/LiaObMmUyaNInLLrvsoseWlZURFBRU47ng4GDWrl1LRUXFWcebpsk333xDeno6o0efv0ItKyujqKioxkNERLxTm0492J54FwA9Nz/FwYP7LU4ktXU8wzVQkR3SE8NmeQlyXpYmW7x4MRs3bmTOnDm1Ov7yyy9n3rx5bNiwAdM0Wb9+PfPnz6eiooKCgoKq4woLCwkLCyMgIIBJkybx4osvMn78+POed86cOURGRlY9OnTo0OD3JiIinpPyk4fJsscTZRSzd9G9VseRWgrIWw9AWexAi5NcmGXFUU5ODrNmzWLRokVnjQadz8MPP8zEiRMZOnQo/v7+TJkyhRkzZgBgt9urjgsPDyctLY1169bx+OOP85vf/IalS5ee97yzZ8+msLCw6pGTk9OQtyYiIh5m9w/AuPoFnKbB8JKvWPfNEqsjyUWYTicdT2wFIDJpuMVpLswwLbqT30cffcTUqVNrFDUOhwPDMLDZbJSVldX4XnUVFRUcOnSItm3b8tprr/HQQw9x/PhxbOcZovvZz35GTk4OX375Za2yFRUVERkZSWFhIREREXV/cyIi0ig2/t+d9D/4HvuJIeL+dUSER1odSc5jf1Y67RcMptK0UflQNkEh7r8Bu7t+f1s2cjRu3Di2bNlCWlpa1WPgwIFMnz6dtLS08xZGAP7+/rRv3x673c7ixYuZPHnyeQsjcM09Kisr88TbEBERC/W45a/kG9G05xBpb822Oo5cwIGtywHI9O/qkcLInSxbyh8eHk6vXr1qPBcaGkp0dHTV87NnzyY3N7dqL6OMjAzWrl3LkCFDOHbsGM8++yxbt27lzTffrDrHnDlzGDhwIF27dqW8vJzPP/+chQsX8uqrrzbemxMRkUYRFNaCzDFP0GbpLxh+6B22bbyZnv1HWh1LzqHy9OaPx1r2tTZILXjFPkfnk5eXR3Z2dtXXDoeDZ555hvT0dPz9/bnkkktYuXIl8fHxVcecOHGCX/3qV+zfv5/g4GC6d+/OokWLmDZtmgXvQEREPC157I1s3vgOfYqWYv/0Xsp7rSIgwN/qWPJfWh3bBEBAvPdu/niGZXOOvJnmHImI+JbC/GxsrwwhnJMs73I/o2/7o9WRpJqSkiIC/xqPv+Hg8M/W07p9gkeu4/NzjkRERNwlsk1H9qT8DwAD9rzEvr3pFieS6jI3rXAVRrSkdbtuVse5KBVHIiLSJKRMmUV6YC9CjTIK3r0bp8NpdSQ5rWjXDwDsD+sFhmFxmotTcSQiIk2CYbMTccPLlJt2BpStZdVnr1sdSU4LPrQBgIq4QRYnqR0VRyIi0mS07daXrV1+BkDSxj9TcPiQxYnE6XASf2obAC27+8ZKQhVHIiLSpPS58VFybO1pRSHpi+6zOk6zl7N3Ky0potz0o1PPYVbHqRUVRyIi0qT4BQZTceVzAIwo/IyNyz+1OFHzdvDM5o8BifgHBlucpnZUHImISJPTZeAEUltfA0D0dw9y4sQJawM1Y86ctQAUtuprbZA6UHEkIiJNUtKtz1JAFJ3MXNYvetjqOM1Wm+OuzR8DO/tGSw1UHImISBMVEhHNoRGPAjDswAJ2bl5rcaLmp/DYUeId+wDo0GestWHqQMWRiIg0WT0vu42tocMIMBw4/jWLispKqyM1K5mbl2E3TPKMNrSM7Wh1nFpTcSQiIk2XYRA3/RVOEERPx3ZWvfeM1YmalRO7VwGQF97b4iR1o+JIRESatJZxXcjoeS8A/dKfI3ffXmsDNSNh+a7NHx3tB1ucpG5UHImISJPX99r/Ybd/IuHGKXIX34Puue55DoeDzqXbAWjVfZTFaepGxZGIiDR5ht2P4OteptK0MfjU96z+91tWR2ryMnemEmGc5KQZSMceGjkSERHxOu26D2ZTx1sB6LL2fzl69IjFiZq2w9tdmz9mBXXH7udvcZq6UXEkIiLNRp/pczhga0sMR9m26H+sjtOk2favA6C4dT+Lk9SdiiMREWk2/INCOTH+rwCMOLKE1B++tDhR0xVbtBmAkK7DLU5SdyqORESkWUkYdhVpLSdiM0xiv/oVBw/kWB2pyTly+CCdzP0AdOozxuI0dafiSEREmp3ut79Kri2OthRw9PXryM8/aHWkJmXfpmUA5NjaEREda3GaulNxJCIizU5QeBS2m96mkDB6ONIpevVycvfvszpWk3Fqr2vzx0ORfSxOUj8qjkREpFlqm9CPUzd/zBEjim5mFs55E8jcvcPqWE1CREGq6398bPPHM1QciYhIsxWbOABu/4KDRhs6cJCQRVeSvmWd1bF8Wnl5OV3KXEVmmx6jLU5TPyqORESkWYvu2J2gX3xNjr0DMRyl9ftT2bxumdWxfFbm9nWEGmUUE0z7RN9bxg8qjkRERGgR24mou79hj38iLY1iOn86jY3LP7U6lk86suN7APYF9cBmt1ucpn5UHImIiABhUTG0m/UfdgalEG6cosc3M1jz5TtWx/I5fgfWA3CyTX+Lk9SfiiMREZHTgsKi6Hrvv9kSNpwgo4L+K2fyw0d/tzqWT2lX7Nr8MTRhhMVJ6k/FkYiISDX+QaH0nPUv0lpMwN9wMCz1Ib5/5ymrY/mEQweyacchnKZBvA9u/niGiiMREZH/YvMPIOWexWyIuR6bYTIq/QmWz/8dpmlaHc2r5Wx2TWTP9utIaGRLi9PUn4ojERGRczBsdgb8ch7rO94BwOjsl1nx6t04HU6Lk3mv8kzX5o+HW/S1NkgDqTgSERE5H8Ng4P97lg1JvwFgVP4iVr/4UyorKiwO5p1aHEkDwN7RNzd/PEPFkYiIyEUMuOl/Se37J5ymwfDjH7PxbzdQWlpqdSyvUlp6iq4VGQC07eW7841AxZGIiEit9LtmFluH/41y087gE9+x429XcaKkyOpYXiNzyyoCjQqOE05s555Wx2kQFUciIiK11OfyGeweN5dTZgD9SteS9fwVHD9WYHUsr3AsfQUA2SE9MWy+XV74dnoREZFG1mP0deyf/DZFhNCzYhuHX5rA4UP7rY5luYA81+aPpTEDLE7ScCqORERE6ihh0HiOXv8hR4kgwbGHk/93OXnZu6yOZRnTNGl/YisAkYkjLU7TcCqORERE6iG+11BO3fIph4xWdDL3w/wr2Jex2epYljiwbzexHKHStNGpj4ojERGRZqtdtxSM//clObZ2tKWAsLcns3vzKqtjNbrcra7NH/f5dyEoNMLiNA2n4khERKQB2nToRtgvv2KPvQvRFNLmg+vYsfYrq2M1qsqs1QAcieprbRA3UXEkIiLSQFFt2tH611+xw78nEcYJOn02nU1LP7A6VqOJPrYJAP/4IRYncQ8VRyIiIm4Q0aIV8fd+yeagQYQYZSR/dycb//2G1bE87kRJMV0q9wDQrrdvb/54hoojERERNwkODaf7fZ+yIWwsAYaDlNX3sW7J81bH8qi9m1fgbzgooAVtOiRaHcctVByJiIi4UUBgEH3v/YA1UVdhN0wGbf4ja/7xJ6tjeUzRrh8A2B/WGwzD4jTuoeJIRETEzex+fgz+9UJWxU4HYMiuZ1g97z5Mp9PiZO4XfHADAOVtB1qcxH1UHImIiHiAYbMx9OcvsSp+JgBD989n3at34nQ4LE7mPk6Hk06ntgEQleT7+xudoeJIRETEQwybjWEznmBV99/hNA0GH36fjS/cRGVFudXR3CJ773aiKaTc9KNTr+FWx3EbFUciIiIeNuzGh1jf/0kqTRsDC79k69+uoaz0hNWxGuzgtuUAZAV0IyAoxOI07qPiSEREpBEMnvJLNo14hVLTn74nfmD3c1dysviY1bEaxMxeA8Cx6H4WJ3EvFUciIiKNZMCEm9g1fgEnzCB6lqWx//nLKTp6yOpY9dbmuGvzx6DOQy1O4l4qjkRERBpR75GTyb76PY4RTmJlOkdfuoyjB/dZHavOCo8fJd6RBUCHlEusDeNmKo5EREQaWfKAMRy54SPyaUm8M5vSv4/n0L6dVseqk8xNy7EbJgeN1rSM7WR1HLdScSQiImKBbj0HUnrrZ+wnljjzEPY3riBn53qrY9XaiT2rADgQ3tviJO7nNcXRnDlzMAyDe++994LHvfzyyyQnJxMcHExSUhILFy6s8f25c+cyatQooqKiiIqK4rLLLmPt2rUeTC4iIlI/Hbv2wH7nl+yxdaIVxwhfPIW9acusjlUrofmuzR8dcYMsTuJ+XlEcrVu3jtdee40+ffpc8LhXX32V2bNn88gjj7Bt2zYeffRRZs6cySeffFJ1zNKlS7npppv47rvvWLVqFR07dmTChAnk5uZ6+m2IiIjUWdt28bS46z/ssCfRghJiPvwJq7/6J6ZpWh3tvBwOB51PbQcgOnmUxWnczzAt/umXlJTQv39/XnnlFR577DH69u3L3/72t3MeO3z4cEaMGMFf//rXqufuvfde1q9fz4oVK875GofDQVRUFC+99BK33XZbrTIVFRURGRlJYWEhERERdX5PIiIidVVYeIzsV6bSuywVgBWh42k1+X/pnux9bas92zfQ9b1LOWUGEPCHXOz+AVZHAtz3+9vykaOZM2cyadIkLrvssoseW1ZWRlBQUI3ngoODWbt2LRUVFed8zcmTJ6moqKBly5YXPG9RUVGNh4iISGOKjIyi+2/+zaY21wAw8sRXdFs8mhV/vZ6tmzdYG+6/HN5+evPHwCSvKYzcydLiaPHixWzcuJE5c+bU6vjLL7+cefPmsWHDBkzTZP369cyfP5+KigoKCgrO+Zrf/va3tGvX7oLF15w5c4iMjKx6dOjQoV7vR0REpCH8A4NJ+dWb5F73MTtDB+FnOBl54iuSPxjHyqemsGnjKqsjuuxfB0Bx6/4WB/EMy4qjnJwcZs2axaJFi84aDTqfhx9+mIkTJzJ06FD8/f2ZMmUKM2bMAMBut591/F/+8hfeeecdlixZcsFrzJ49m8LCwqpHTk5Ovd6TiIiIO7TrPYbu//M1B274jO3hw7EbJsNPLaX3vyayes4kNq5ZZumcpNjC05s/dhlmWQZPsmzO0UcffcTUqVNrFDUOhwPDMLDZbJSVlZ2z4AGoqKjg0KFDtG3bltdee42HHnqI48ePY7P9WOs9/fTTPPbYY3z99dcMHDiwTtk050hERLzJwZ1rKPj34/Qq/HEl29qAIRhj/oeBwy/DMIxGy3K04BAtX0oEoOjunUS0atto174Yd/3+9nNjpjoZN24cW7ZsqfHc7bffTvfu3XnooYfOWxgB+Pv70759e8DVmps8eXKNwuivf/0rjz32GF9++WWdCyMRERFvE9t9CLHdP6ZgTyp5nz5Gz6PfMLh8DXx1PRuW9qdixAMMHn0lNpvni6SsTctoCey3taW9FxVG7mRZcRQeHk6vXr1qPBcaGkp0dHTV87NnzyY3N7dqL6OMjAzWrl3LkCFDOHbsGM8++yxbt27lzTffrDrHX/7yFx5++GHefvtt4uPjOXjwIABhYWGEhYU10rsTERFxv1Zd+9Fq1gcc2beVAx8/TnLBFwyo2AhLbybt+z6cHHY/Qy65Grvdc7NmSveuBOBQRArtPXYVa1m+Wu1C8vLyyM7Orvra4XDwzDPPkJKSwvjx4yktLWXlypXEx8dXHfPKK69QXl7O9ddfT9u2baseTz/9tAXvQERExP2iO/Wi96/foeTna9jcZgoVpp2+js0MX/FTtj8xghVfvEtlpcMj1w477NpqwGzf9DZ/PMPyfY68keYciYiILyk6mEnmvx4jOe8jAqgEYLstgYL+sxh6+c0E+J9/qkpdVFRUUP5Ye0KNUrJ/8h869hjilvO6S5PZ50hEREQaJiK2Mym/eJ3ymalsan8zpwigh3MXo9ffTeYTA1n60euUnWc/wLrI3L6OUKOUEoJpnzjADcm9k4ojERGRJiKsdUdSfvYq5qzNbOo0g5MEkWTuZWzab9j/RD++e/9VTpWW1/v8BTu+ByArKBmbn2XTlj1OxZGIiEgTExLVlpTbn8d231Y2dbmTEkLoauZwydbfcujJFL5993lOnCqt83n9DqwH4GSbpjtqBCqOREREmqygyNak3PY0/g9sY3PCTIoII54DXLrjjxx9qg/f/uOvFJ04UevzxRVvBiC0W9Pc/PEMFUciIiJNXGBYS/pMf4LgB7ezuft9HDMi6cAhLt31GCV/7cM3Cx/n+EXuK5qfl0N707U9TqeUsY2Q2joqjkRERJoJ/5BI+tz4COEPbmNLr4coMFoSRwHj9v6Fsmf68M0b/8uRY8fO+drsza7duffZOhIWGd2YsRudiiMREZFmxi84nN7X/46Ws7ezre/D5ButiTGOMW7f3zD/lsI3c3/H4f+6oXt5puumt4dbpFgRuVGpOBIREWmmbAHB9LzmAVr/fhvbB/yZg7YYWhmFjMt9Gb8XU/jm7w+Qd+gQ5eUVxB36DgCjo3ftbeQJ2gTyHLQJpIiINEdmZTk7v5pPxPoXaOfIBaDIDGFnUF8Gl62kkDD8f7OZkAjvbKtpE0gRERFxK8MvgOSJvyTud5vJGPEcOX4diTBOMrjMdT+13Ul3em1h5E4aOToHjRyJiIgATie7l7+DsepFHLZAOs/6HP+gUKtTnZe7fn833e0tRUREpGFsNrqNnQ5jp1udpFGprSYiIiJSjYojERERkWpUHImIiIhUo+JIREREpBoVRyIiIiLVqDgSERERqUbFkYiIiEg1Ko5EREREqlFxJCIiIlKNiiMRERGRalQciYiIiFSj4khERESkGhVHIiIiItWoOBIRERGpxs/qAN7INE0AioqKLE4iIiIitXXm9/aZ3+P1peLoHIqLiwHo0KGDxUlERESkroqLi4mMjKz36w2zoeVVE+R0Ojlw4ADh4eEYhuG28xYVFdGhQwdycnKIiIhw23ml+dJnSjxJny/xFE99tkzTpLi4mLi4OGy2+s8c0sjROdhsNtq3b++x80dEROgvGnErfabEk/T5Ek/xxGerISNGZ2hCtoiIiEg1Ko5EREREqlFx1IgCAwP53//9XwIDA62OIk2EPlPiSfp8iad4+2dLE7JFREREqtHIkYiIiEg1Ko5EREREqlFxJCIiIlKNiiNg+fLlXHXVVcTFxWEYBh999FGN7x86dIgZM2YQFxdHSEgIV1xxBbt27apxTFlZGb/+9a9p1aoVoaGhXH311ezfv7/GMceOHePWW28lMjKSyMhIbr31Vo4fP37RfFu2bGHMmDEEBwfTrl07/vSnP9XYGj0vL4+bb76ZpKQkbDYb9957b31/FOIGvv55Wrp0KYZhnPXYuXNnvX8m4l6+/hkDePnll0lOTiY4OJikpCQWLlxYr5+FuFdjfbYef/xxhg8fTkhICC1atKh1vsb6fajiCDhx4gQpKSm89NJLZ33PNE2uueYa9u7dy7/+9S9SU1Pp1KkTl112GSdOnKg67t577+XDDz9k8eLFrFixgpKSEiZPnozD4ag65uabbyYtLY0vvviCL774grS0NG699dYLZisqKmL8+PHExcWxbt06XnzxRZ5++mmeffbZqmPKyspo3bo1v//970lJSXHDT0Qawtc/T2ekp6eTl5dX9UhISGjAT0Xcydc/Y6+++iqzZ8/mkUceYdu2bTz66KPMnDmTTz75xA0/HWmIxvpslZeXc8MNN3DXXXfVOluj/j40pQbA/PDDD6u+Tk9PNwFz69atVc9VVlaaLVu2NOfOnWuapmkeP37c9Pf3NxcvXlx1TG5urmmz2cwvvvjCNE3T3L59uwmYq1evrjpm1apVJmDu3LnzvHleeeUVMzIy0iwtLa16bs6cOWZcXJzpdDrPOn7MmDHmrFmz6vy+xTN88fP03XffmYB57NixBr13aRy++BkbNmyY+cADD9R43axZs8wRI0bU4ycgnuKpz1Z1b7zxhhkZGVmrPI35+1AjRxdRVlYGQFBQUNVzdrudgIAAVqxYAcCGDRuoqKhgwoQJVcfExcXRq1cvVq5cCcCqVauIjIxkyJAhVccMHTqUyMjIqmPOZdWqVYwZM6bGXhCXX345Bw4cICsryy3vURqPL32e+vXrR9u2bRk3bhzfffdd/d+0NCpf+IyVlZXVyAcQHBzM2rVrqaioqOc7F09z12ervhrz96GKo4vo3r07nTp1Yvbs2Rw7dozy8nKefPJJDh48SF5eHgAHDx4kICCAqKioGq+NiYnh4MGDVce0adPmrPO3adOm6phzOXjwIDExMWed98z3xLf4wuepbdu2vPbaa3zwwQcsWbKEpKQkxo0bx/Lly+v/xqXR+MJn7PLLL2fevHls2LAB0zRZv3498+fPp6KigoKCgvq/efEod3226qsxfx+qOLoIf39/PvjgAzIyMmjZsiUhISEsXbqUiRMnYrfbL/ha0zQxDKPq6+r/f65jevbsSVhYGGFhYUycOPG8rzNPTz471/nEu/nC5ykpKYk777yT/v37M2zYMF555RUmTZrE008/Xb83LY3KFz5jDz/8MBMnTmTo0KH4+/szZcoUZsyYAXDRjGIdd362Lsbq34d+bj1bEzVgwADS0tIoLCykvLyc1q1bM2TIEAYOHAhAbGws5eXlHDt2rEa1nJ+fz/Dhw6uOOXTo0FnnPnz4cFXl+/nnn1cNKQcHB1e97r8r4vz8fICzKmjxDb74eRo6dCiLFi2q71uWRubtn7Hg4GDmz5/P3//+dw4dOlQ1WhkeHk6rVq3c+aMQN3PHZ6s2rP59qJGjOoiMjKR169bs2rWL9evXM2XKFMD1YfH39+err76qOjYvL4+tW7dWfRiGDRtGYWEha9eurTpmzZo1FBYWVh3TqVMnunXrRrdu3WjXrl3V65YvX055eXnV6/7zn/8QFxdHfHy8p9+yeJAvfZ5SU1Np27at2967NA5v/4z5+/vTvn177HY7ixcvZvLkydhs+rXkCxry2aoNy38f1msadxNTXFxspqammqmpqSZgPvvss2Zqaqq5b98+0zRN87333jO/++47c8+ePeZHH31kdurUybz22mtrnOOXv/yl2b59e/Prr782N27caF566aVmSkqKWVlZWXXMFVdcYfbp08dctWqVuWrVKrN3797m5MmTL5jt+PHjZkxMjHnTTTeZW7ZsMZcsWWJGRESYTz/9dI3jzuQfMGCAefPNN5upqanmtm3b3PQTkrrw9c/Tc889Z3744YdmRkaGuXXrVvO3v/2tCZgffPCBG39K0hC+/hlLT08333rrLTMjI8Ncs2aNOW3aNLNly5ZmZmam+35IUi+N9dnat2+fmZqaaj766KNmWFhY1TWLi4vPm60xfx+qODJ/XLr834+f/vSnpmma5vPPP2+2b9/e9Pf3Nzt27Gj+4Q9/MMvKymqc49SpU+bdd99ttmzZ0gwODjYnT55sZmdn1zjmyJEj5vTp083w8HAzPDzcnD59eq2WS2/evNkcNWqUGRgYaMbGxpqPPPLIWcsWz5W/U6dODfmxSD35+ufpqaeeMrt27WoGBQWZUVFR5siRI83PPvuswT8XcR9f/4xt377d7Nu3rxkcHGxGRESYU6ZMueD2ANJ4Guuz9dOf/vSc1/nuu+8umK+xfh8ap08kIiIiImjOkYiIiEgNKo5EREREqlFxJCIiIlKNiiMRERGRalQciYiIiFSj4khERESkGhVHIiIiItWoOBIRERGpRsWRiDQZS5cuxTAMjh8/bnUUEfFh2iFbRHzW2LFj6du3L3/7298AKC8v5+jRo8TExGAYhrXhRMRn+VkdQETEXQICAoiNjbU6hoj4OLXVRMQnzZgxg2XLlvH8889jGAaGYbBgwYIabbUFCxbQokULPv30U5KSkggJCeH666/nxIkTvPnmm8THxxMVFcWvf/1rHA5H1bnLy8t58MEHadeuHaGhoQwZMoSlS5da80ZFpNFp5EhEfNLzzz9PRkYGvXr14k9/+hMA27ZtO+u4kydP8sILL7B48WKKi4u59tprufbaa2nRogWff/45e/fu5brrrmPkyJFMmzYNgNtvv52srCwWL15MXFwcH374IVdccQVbtmwhISGhUd+niDQ+FUci4pMiIyMJCAggJCSkqpW2c+fOs46rqKjg1VdfpWvXrgBcf/31vPXWWxw6dIiwsDB69OjBJZdcwnfffce0adPYs2cP77zzDvv37ycuLg6ABx54gC+++II33niDJ554ovHepIhYQsWRiDRpISEhVYURQExMDPHx8YSFhdV4Lj8/H4CNGzdimiaJiYk1zlNWVkZ0dHTjhBYRS6k4EpEmzd/fv8bXhmGc8zmn0wmA0+nEbrezYcMG7HZ7jeOqF1Qi0nSpOBIRnxUQEFBjIrU79OvXD4fDQX5+PqNGjXLruUXEN2i1moj4rPj4eNasWUNWVhYFBQVVoz8NkZiYyPTp07nttttYsmQJmZmZrFu3jqeeeorPP//cDalFxNupOBIRn/XAAw9gt9vp0aMHrVu3Jjs72y3nfeONN7jtttu4//77SUpK4uqrr2bNmjV06NDBLecXEe+mHbJFREREqtHIkYiIiEg1Ko5EREREqlFxJCIiIlKNiiMRERGRalQciYiIiFSj4khERESkGhVHIiIiItWoOBIRERGpRsWRiIiISDUqjkRERESqUXEkIiIiUo2KIxEREZFq/j/fNPL5ZBNBLwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "(mom_frof_ds.ficeberg * mom_area.areacello).sum(['xh','yh']).plot()\n",
+    "(ocn_med_ds.ocnExp_Foxx_rofi*med_areas).sum(['ocnExp_ny','ocnExp_nx']).plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "87cbfcdf-7582-4220-af43-06ec110db2a2",
+   "metadata": {},
+   "source": [
+    "And compare areas, shows only minor differences at the geographic north pole:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "117deb51-f704-4f63-b841-083330c055a8",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:30:12.713144Z",
+     "iopub.status.busy": "2025-01-09T04:30:12.711641Z",
+     "iopub.status.idle": "2025-01-09T04:30:13.611067Z",
+     "shell.execute_reply": "2025-01-09T04:30:13.610140Z",
+     "shell.execute_reply.started": "2025-01-09T04:30:12.713089Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.colorbar.Colorbar at 0x14abb88bfdc0>"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.pcolor(med_areas - mom_area.areacello.values)\n",
+    "plt.colorbar()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a4a2a079-b9d3-4e8c-98be-5caf9d78144c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c214d9b-bdb6-427e-a226-6e833b3045bf",
+   "metadata": {},
+   "source": [
+    "# And end to end ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "60d32121-fa42-4e27-8fe4-a69b5f2ddac6",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:30:26.812468Z",
+     "iopub.status.busy": "2025-01-09T04:30:26.811676Z",
+     "iopub.status.idle": "2025-01-09T04:30:26.847270Z",
+     "shell.execute_reply": "2025-01-09T04:30:26.846186Z",
+     "shell.execute_reply.started": "2025-01-09T04:30:26.812414Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "mom_frunin_total = ((mom_frof_ds.friver+mom_frof_ds.ficeberg) * mom_area.areacello).sum(['xh','yh'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "04aa04e1-d897-44e8-ba55-a59f528b18b2",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:30:28.553270Z",
+     "iopub.status.busy": "2025-01-09T04:30:28.551587Z",
+     "iopub.status.idle": "2025-01-09T04:30:33.313285Z",
+     "shell.execute_reply": "2025-01-09T04:30:33.312328Z",
+     "shell.execute_reply.started": "2025-01-09T04:30:28.553205Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x14abb8154430>]"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "jra_runoff.plot(label='jra55do')\n",
+    "mom_frunin_total.plot(label='mom6')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "daa24c6f-262c-4a0b-9296-274329c124a6",
+   "metadata": {},
+   "source": [
+    "And excluding the runoff south of 78S:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "67fc3b02-9025-4ab9-8421-9cb973046e61",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:31:46.163889Z",
+     "iopub.status.busy": "2025-01-09T04:31:46.162735Z",
+     "iopub.status.idle": "2025-01-09T04:31:53.928387Z",
+     "shell.execute_reply": "2025-01-09T04:31:53.927235Z",
+     "shell.execute_reply.started": "2025-01-09T04:31:46.163832Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x14ac45875e70>]"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "(jra_friver_runoff+jra_licalvf_runoff).plot(label='jra55do')\n",
+    "mom_frunin_total.plot(label='mom6')"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "664f0b57-fc1f-40e7-ab9d-e3509587e534",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "c79ce737-1596-4c1a-9dd2-5b01dc2171db",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T04:32:45.461196Z",
+     "iopub.status.busy": "2025-01-09T04:32:45.459084Z",
+     "iopub.status.idle": "2025-01-09T04:32:47.401156Z",
+     "shell.execute_reply": "2025-01-09T04:32:47.399714Z",
+     "shell.execute_reply.started": "2025-01-09T04:32:45.461110Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "client.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b82d3b39-8b0b-4607-ae68-c472fcd92204",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:analysis3-24.04] *",
+   "language": "python",
+   "name": "conda-env-analysis3-24.04-py"
+  },
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+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
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+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
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+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
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+ "nbformat": 4,
+ "nbformat_minor": 5
+}