diff --git a/Recipes/Nearest_Neighbour_Distance.ipynb b/Recipes/Nearest_Neighbour_Distance.ipynb
index 74355f13..572bbe68 100644
--- a/Recipes/Nearest_Neighbour_Distance.ipynb
+++ b/Recipes/Nearest_Neighbour_Distance.ipynb
@@ -31,10 +31,12 @@
    },
    "outputs": [],
    "source": [
-    "import cosima_cookbook as cc\n",
+    "import intake\n",
+    "\n",
     "import xarray as xr\n",
     "import numpy as np\n",
     "import datetime as dt\n",
+    "\n",
     "from sklearn.neighbors import BallTree\n",
     "import matplotlib.pyplot as plt"
    ]
@@ -54,9 +56,508 @@
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "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-b18081f6-6732-11ef-9685-0000018ffe80</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/8787/status\" target=\"_blank\">/proxy/8787/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/8787/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;\">088c4d50</p>\n",
+       "        <table style=\"width: 100%; text-align: left;\">\n",
+       "            <tr>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Dashboard:</strong> <a href=\"/proxy/8787/status\" target=\"_blank\">/proxy/8787/status</a>\n",
+       "                </td>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Workers:</strong> 8\n",
+       "                </td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Total threads:</strong> 48\n",
+       "                </td>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Total memory:</strong> 188.56 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-86643f04-3935-4853-843e-f153c59b926b</p>\n",
+       "            <table style=\"width: 100%; text-align: left;\">\n",
+       "                <tr>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Comm:</strong> tcp://127.0.0.1:45827\n",
+       "                    </td>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Workers:</strong> 8\n",
+       "                    </td>\n",
+       "                </tr>\n",
+       "                <tr>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Dashboard:</strong> <a href=\"/proxy/8787/status\" target=\"_blank\">/proxy/8787/status</a>\n",
+       "                    </td>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Total threads:</strong> 48\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> 188.56 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://127.0.0.1:34301\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 6\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/38597/status\" target=\"_blank\">/proxy/38597/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 23.57 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:46039\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/123969626.gadi-pbs/dask-scratch-space/worker-irj2wqfd\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://127.0.0.1:33457\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 6\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/35013/status\" target=\"_blank\">/proxy/35013/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 23.57 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:40171\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/123969626.gadi-pbs/dask-scratch-space/worker-xbghgbmy\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://127.0.0.1:36755\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 6\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/34885/status\" target=\"_blank\">/proxy/34885/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 23.57 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:35943\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/123969626.gadi-pbs/dask-scratch-space/worker-0eacf3et\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://127.0.0.1:34425\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 6\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/41651/status\" target=\"_blank\">/proxy/41651/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 23.57 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:39671\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/123969626.gadi-pbs/dask-scratch-space/worker-jp_mftlo\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://127.0.0.1:38547\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 6\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/35225/status\" target=\"_blank\">/proxy/35225/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 23.57 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:38939\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/123969626.gadi-pbs/dask-scratch-space/worker-4w4x9avg\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://127.0.0.1:43999\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 6\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/45311/status\" target=\"_blank\">/proxy/45311/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 23.57 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:37339\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/123969626.gadi-pbs/dask-scratch-space/worker-_3jmgkk5\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://127.0.0.1:40331\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 6\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/34639/status\" target=\"_blank\">/proxy/34639/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 23.57 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:37009\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/123969626.gadi-pbs/dask-scratch-space/worker-42j0sofv\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://127.0.0.1:34523\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 6\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/40891/status\" target=\"_blank\">/proxy/40891/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 23.57 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:43513\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/123969626.gadi-pbs/dask-scratch-space/worker-ttwwfcbl\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://127.0.0.1:45827' processes=8 threads=48, memory=188.56 GiB>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "session = cc.database.create_session()"
+    "from dask.distributed import Client\n",
+    "client = Client()\n",
+    "client"
    ]
   },
   {
@@ -65,39 +566,32 @@
    "metadata": {},
    "source": [
     "## Accessing ACCESS-OM2-01 data\n",
-    "We will use monthly sea ice outputs from cycle four. For this example, we will load only two months of sea ice data. Note the use of `decode_coords = False`. This avoids lengthly delays in accessing sea ice data. See more information [here](https://forum.access-hive.org.au/t/issues-loading-access-om2-01-data-from-cycle-4/418/3)."
+    "\n",
+    "First we load the ACCESS-NRI default intake catalog."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "b1de38e7-862a-4974-9a64-0b1046391571",
-   "metadata": {
-    "tags": []
-   },
+   "id": "06213890-26f2-4f14-871f-523078d6c9a5",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "var_ice = cc.querying.getvar('01deg_jra55v140_iaf_cycle4', 'aice_m', session, \n",
-    "                             start_time='1978-01', end_time='1978-03',\n",
-    "                             decode_coords=False)"
+    "catalog = intake.cat.access_nri"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "ce17902e-7eae-4a71-aa8a-94703a1bb4ad",
+   "id": "50cbd6c4-b6ed-437a-8b82-37751cd66e6b",
    "metadata": {},
    "source": [
-    "The sea ice outputs need some processing before we can start our calculations. You can check this [example](IcePlottingExample.ipynb) for a guide on how to load and plot sea ice data.  \n",
-    "  \n",
-    "We will follow these processing steps:\n",
-    "1. Correct time dimension values by subtracting 12 hours,\n",
-    "2. Attach the ocean model grid so we can calculate distances."
+    "We use monthly sea ice outputs. Here, we load only two months of sea ice data."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "5f3c2530-9225-4b59-ba62-22f374698b00",
+   "id": "b1de38e7-862a-4974-9a64-0b1046391571",
    "metadata": {
     "tags": []
    },
@@ -136,6 +630,7 @@
        "}\n",
        "\n",
        "html[theme=dark],\n",
+       "html[data-theme=dark],\n",
        "body[data-theme=dark],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -468,28 +963,24 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;aice_m&#x27; (time: 3, yt_ocean: 713, xt_ocean: 3600)&gt;\n",
-       "dask.array&lt;getitem, shape=(3, 713, 3600), dtype=float32, chunksize=(1, 270, 360), chunktype=numpy.ndarray&gt;\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;aice_m&#x27; (time: 3, nj: 2700, ni: 3600)&gt; Size: 117MB\n",
+       "dask.array&lt;getitem, shape=(3, 2700, 3600), dtype=float32, chunksize=(1, 270, 360), chunktype=numpy.ndarray&gt;\n",
        "Coordinates:\n",
-       "  * time      (time) datetime64[ns] 1977-12-31T12:00:00 ... 1978-02-28T12:00:00\n",
-       "  * xt_ocean  (xt_ocean) float64 -279.9 -279.8 -279.7 ... 79.75 79.85 79.95\n",
-       "  * yt_ocean  (yt_ocean) float64 -79.97 -79.93 -79.88 ... -45.18 -45.11 -45.04\n",
+       "  * time     (time) datetime64[ns] 24B 1978-01-01 1978-02-01 1978-03-01\n",
+       "    TLON     (nj, ni) float32 39MB dask.array&lt;chunksize=(270, 360), meta=np.ndarray&gt;\n",
+       "    TLAT     (nj, ni) float32 39MB dask.array&lt;chunksize=(270, 360), meta=np.ndarray&gt;\n",
+       "    ULON     (nj, ni) float32 39MB dask.array&lt;chunksize=(270, 360), meta=np.ndarray&gt;\n",
+       "    ULAT     (nj, ni) float32 39MB dask.array&lt;chunksize=(270, 360), meta=np.ndarray&gt;\n",
+       "Dimensions without coordinates: nj, ni\n",
        "Attributes:\n",
        "    units:          1\n",
        "    long_name:      ice area  (aggregate)\n",
-       "    coordinates:    TLON TLAT time\n",
        "    cell_measures:  area: tarea\n",
        "    cell_methods:   time: mean\n",
-       "    time_rep:       averaged\n",
-       "    ncfiles:        [&#x27;/g/data/cj50/access-om2/raw-output/access-om2-01/01deg_...\n",
-       "    contact:        Andrew Kiss\n",
-       "    email:          andrew.kiss@anu.edu.au\n",
-       "    created:        2022-04-27\n",
-       "    description:    0.1 degree ACCESS-OM2 global model configuration under in...\n",
-       "    notes:          Run configuration and history: https://github.com/COSIMA/...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'aice_m'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 3</li><li><span class='xr-has-index'>yt_ocean</span>: 713</li><li><span class='xr-has-index'>xt_ocean</span>: 3600</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-05d37150-7292-4c56-afba-ec2569a2ef28' class='xr-array-in' type='checkbox' checked><label for='section-05d37150-7292-4c56-afba-ec2569a2ef28' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 243, 360), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "    time_rep:       averaged</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'aice_m'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 3</li><li><span>nj</span>: 2700</li><li><span>ni</span>: 3600</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b8585803-b20d-4385-8b79-92904e4bd84e' class='xr-array-in' type='checkbox' checked><label for='section-b8585803-b20d-4385-8b79-92904e4bd84e' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 270, 360), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
-       "            <table>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
        "                <thead>\n",
        "                    <tr>\n",
        "                        <td> </td>\n",
@@ -501,45 +992,50 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 29.37 MiB </td>\n",
+       "                        <td> 111.24 MiB </td>\n",
        "                        <td> 379.69 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (3, 713, 3600) </td>\n",
+       "                        <td> (3, 2700, 3600) </td>\n",
        "                        <td> (1, 270, 360) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 693 Tasks </td>\n",
-       "                        <td> 90 Chunks </td>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 300 chunks in 1466 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\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=\"194\" height=\"106\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "        <svg width=\"194\" height=\"154\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"10\" y1=\"14\" x2=\"24\" y2=\"29\" />\n",
-       "  <line x1=\"10\" y1=\"30\" x2=\"24\" y2=\"45\" />\n",
-       "  <line x1=\"10\" y1=\"41\" x2=\"24\" y2=\"56\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"10\" y1=\"90\" x2=\"24\" y2=\"104\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
-       "  <line x1=\"19\" y1=\"9\" x2=\"19\" y2=\"51\" />\n",
-       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"90\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"94\" />\n",
+       "  <line x1=\"19\" y1=\"9\" x2=\"19\" y2=\"99\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"104\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"10.0,0.0 24.9485979497544,14.948597949754403 24.9485979497544,56.91709675182327 10.0,41.96849880206887\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"10.0,0.0 24.9485979497544,14.948597949754403 24.9485979497544,104.9485979497544 10.0,90.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
@@ -564,88 +1060,1197 @@
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        "\n",
        "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
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-       "  <line x1=\"24\" y1=\"56\" x2=\"144\" y2=\"56\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"24\" y1=\"23\" x2=\"144\" y2=\"23\" />\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"104\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"84.948598\" y=\"124.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3600</text>\n",
+       "  <text x=\"164.948598\" y=\"59.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,164.948598,59.948598)\">2700</text>\n",
+       "  <text x=\"7.474299\" y=\"117.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,117.474299)\">3</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-c0011cc6-a948-492a-8698-7406b8a5d73d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c0011cc6-a948-492a-8698-7406b8a5d73d' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1978-01-01 1978-02-01 1978-03-01</div><input id='attrs-0542d6b5-68c6-46aa-8513-ffc40504a0c1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0542d6b5-68c6-46aa-8513-ffc40504a0c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a019865a-850b-4d49-aad9-698cc612c1ef' class='xr-var-data-in' type='checkbox'><label for='data-a019865a-850b-4d49-aad9-698cc612c1ef' 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>model time</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1978-01-01T00:00:00.000000000&#x27;, &#x27;1978-02-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;1978-03-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TLON</span></div><div class='xr-var-dims'>(nj, ni)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(270, 360), meta=np.ndarray&gt;</div><input id='attrs-13f5696d-59b5-4833-8ceb-9ceab7c86598' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-13f5696d-59b5-4833-8ceb-9ceab7c86598' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8751dbad-0150-4b36-ac1f-3c46ede226c0' class='xr-var-data-in' type='checkbox'><label for='data-8751dbad-0150-4b36-ac1f-3c46ede226c0' 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>T grid center longitude</dd><dt><span>units :</span></dt><dd>degrees_east</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> 37.08 MiB </td>\n",
+       "                        <td> 379.69 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2700, 3600) </td>\n",
+       "                        <td> (270, 360) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 100 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=\"140\" 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",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"90\" style=\"stroke-width:2\" />\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>TLAT</span></div><div class='xr-var-dims'>(nj, ni)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(270, 360), meta=np.ndarray&gt;</div><input id='attrs-b2fa04d9-dc67-41f9-a0b9-67e8baf3b917' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b2fa04d9-dc67-41f9-a0b9-67e8baf3b917' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63a49214-3574-43aa-b21c-d77034757fed' class='xr-var-data-in' type='checkbox'><label for='data-63a49214-3574-43aa-b21c-d77034757fed' 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>T grid center latitude</dd><dt><span>units :</span></dt><dd>degrees_north</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> 37.08 MiB </td>\n",
+       "                        <td> 379.69 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2700, 3600) </td>\n",
+       "                        <td> (270, 360) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 100 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=\"140\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"90\" style=\"stroke-width:2\" />\n",
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+       "  <!-- Colored Rectangle -->\n",
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+       "  <!-- Text -->\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>ULON</span></div><div class='xr-var-dims'>(nj, ni)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(270, 360), meta=np.ndarray&gt;</div><input id='attrs-6e23d40d-5004-4416-85a4-81e99a60c712' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e23d40d-5004-4416-85a4-81e99a60c712' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7395fa50-a489-461a-b730-26958719fb0d' class='xr-var-data-in' type='checkbox'><label for='data-7395fa50-a489-461a-b730-26958719fb0d' 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>U grid center longitude</dd><dt><span>units :</span></dt><dd>degrees_east</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> 37.08 MiB </td>\n",
+       "                        <td> 379.69 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2700, 3600) </td>\n",
+       "                        <td> (270, 360) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 100 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=\"140\" 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",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"90\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,90.0 0.0,90.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"110.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3600</text>\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>ULAT</span></div><div class='xr-var-dims'>(nj, ni)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(270, 360), meta=np.ndarray&gt;</div><input id='attrs-d9fffc49-ebd5-4d64-b083-db67a826bb01' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9fffc49-ebd5-4d64-b083-db67a826bb01' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c8f7ab4-8385-4ea2-ba48-b65155041995' class='xr-var-data-in' type='checkbox'><label for='data-1c8f7ab4-8385-4ea2-ba48-b65155041995' 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>U grid center latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>comment :</span></dt><dd>Latitude of NE corner of T grid cell</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> 37.08 MiB </td>\n",
+       "                        <td> 379.69 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2700, 3600) </td>\n",
+       "                        <td> (270, 360) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 100 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=\"140\" 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",
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+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
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+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"90\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"90\" />\n",
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+       "  <line x1=\"72\" y1=\"0\" x2=\"72\" y2=\"90\" />\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"90\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,90.0 0.0,90.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"110.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3600</text>\n",
+       "  <text x=\"140.000000\" y=\"45.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,45.000000)\">2700</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-1912d1cb-21ee-4ecb-b725-66cd527d4a24' class='xr-section-summary-in' type='checkbox'  ><label for='section-1912d1cb-21ee-4ecb-b725-66cd527d4a24' class='xr-section-summary' >Indexes: <span>(1)</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-96819e71-f0af-40d6-b669-4ef618e8c2bc' class='xr-index-data-in' type='checkbox'/><label for='index-96819e71-f0af-40d6-b669-4ef618e8c2bc' 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(DatetimeIndex([&#x27;1978-01-01&#x27;, &#x27;1978-02-01&#x27;, &#x27;1978-03-01&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d0ed7234-ed1a-423f-9146-5d960eb54ec1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d0ed7234-ed1a-423f-9146-5d960eb54ec1' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>ice area  (aggregate)</dd><dt><span>cell_measures :</span></dt><dd>area: tarea</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_rep :</span></dt><dd>averaged</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'aice_m' (time: 3, nj: 2700, ni: 3600)> Size: 117MB\n",
+       "dask.array<getitem, shape=(3, 2700, 3600), dtype=float32, chunksize=(1, 270, 360), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 24B 1978-01-01 1978-02-01 1978-03-01\n",
+       "    TLON     (nj, ni) float32 39MB dask.array<chunksize=(270, 360), meta=np.ndarray>\n",
+       "    TLAT     (nj, ni) float32 39MB dask.array<chunksize=(270, 360), meta=np.ndarray>\n",
+       "    ULON     (nj, ni) float32 39MB dask.array<chunksize=(270, 360), meta=np.ndarray>\n",
+       "    ULAT     (nj, ni) float32 39MB dask.array<chunksize=(270, 360), meta=np.ndarray>\n",
+       "Dimensions without coordinates: nj, ni\n",
+       "Attributes:\n",
+       "    units:          1\n",
+       "    long_name:      ice area  (aggregate)\n",
+       "    cell_measures:  area: tarea\n",
+       "    cell_methods:   time: mean\n",
+       "    time_rep:       averaged"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cat_subset = catalog['01deg_jra55v140_iaf_cycle4']\n",
+    "ds = cat_subset.search(variable='aice_m',\n",
+    ").to_dask(\n",
+    "    xarray_combine_by_coords_kwargs=dict(compat=\"override\",\n",
+    "                                         data_vars=\"minimal\",\n",
+    "                                         coords=\"minimal\")\n",
+    ")\n",
+    "\n",
+    "var_ice = ds['aice_m']\n",
+    "var_ice = var_ice.sel(time=slice('1978-01', '1978-03'))\n",
+    "var_ice"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce17902e-7eae-4a71-aa8a-94703a1bb4ad",
+   "metadata": {},
+   "source": [
+    "The sea ice outputs need some processing before we can start our calculations. You can check this [example](IcePlottingExample.ipynb) for a guide on how to load and plot sea ice data.  \n",
+    "  \n",
+    "We will follow these processing steps:\n",
+    "1. Correct time dimension values by subtracting 12 hours,\n",
+    "2. Attach the ocean model grid so we can calculate distances."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5f3c2530-9225-4b59-ba62-22f374698b00",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;aice_m&#x27; (time: 3, yt_ocean: 713, xt_ocean: 3600)&gt; Size: 31MB\n",
+       "dask.array&lt;getitem, shape=(3, 713, 3600), dtype=float32, chunksize=(1, 270, 360), chunktype=numpy.ndarray&gt;\n",
+       "Coordinates:\n",
+       "    TLON      (yt_ocean, xt_ocean) float32 10MB dask.array&lt;chunksize=(243, 360), meta=np.ndarray&gt;\n",
+       "    TLAT      (yt_ocean, xt_ocean) float32 10MB dask.array&lt;chunksize=(243, 360), meta=np.ndarray&gt;\n",
+       "    ULON      (yt_ocean, xt_ocean) float32 10MB dask.array&lt;chunksize=(243, 360), meta=np.ndarray&gt;\n",
+       "    ULAT      (yt_ocean, xt_ocean) float32 10MB dask.array&lt;chunksize=(243, 360), meta=np.ndarray&gt;\n",
+       "  * time      (time) datetime64[ns] 24B 1977-12-31T12:00:00 ... 1978-02-28T12...\n",
+       "  * xt_ocean  (xt_ocean) float64 29kB -279.9 -279.8 -279.7 ... 79.75 79.85 79.95\n",
+       "  * yt_ocean  (yt_ocean) float64 6kB -79.97 -79.93 -79.88 ... -45.11 -45.04\n",
+       "Attributes:\n",
+       "    units:          1\n",
+       "    long_name:      ice area  (aggregate)\n",
+       "    cell_measures:  area: tarea\n",
+       "    cell_methods:   time: mean\n",
+       "    time_rep:       averaged</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'aice_m'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 3</li><li><span class='xr-has-index'>yt_ocean</span>: 713</li><li><span class='xr-has-index'>xt_ocean</span>: 3600</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-14e54ac1-e263-434c-baf9-53da28c0de28' class='xr-array-in' type='checkbox' checked><label for='section-14e54ac1-e263-434c-baf9-53da28c0de28' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 243, 360), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "    <tr>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 29.37 MiB </td>\n",
+       "                        <td> 379.69 kiB </td>\n",
+       "                    </tr>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 90 chunks in 1467 graph layers </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-51112e9c-d1f4-4982-ab70-5507c5e69050' class='xr-section-summary-in' type='checkbox'  checked><label for='section-51112e9c-d1f4-4982-ab70-5507c5e69050' class='xr-section-summary' >Coordinates: <span>(7)</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>TLON</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(243, 360), meta=np.ndarray&gt;</div><input id='attrs-dee864ae-55a4-4587-b50c-b24ee3db5668' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dee864ae-55a4-4587-b50c-b24ee3db5668' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd400e31-a8e6-4881-8438-3fffcea05043' class='xr-var-data-in' type='checkbox'><label for='data-dd400e31-a8e6-4881-8438-3fffcea05043' 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>T grid center longitude</dd><dt><span>units :</span></dt><dd>degrees_east</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> 9.79 MiB </td>\n",
+       "                        <td> 379.69 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (713, 3600) </td>\n",
+       "                        <td> (270, 360) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 30 chunks in 3 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",
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+       "  <!-- Text -->\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>TLAT</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(243, 360), meta=np.ndarray&gt;</div><input id='attrs-abd38dd8-9588-4325-8d33-01d1efc5079f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-abd38dd8-9588-4325-8d33-01d1efc5079f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1ed30a1-0335-4faa-baec-43bfb214161e' class='xr-var-data-in' type='checkbox'><label for='data-e1ed30a1-0335-4faa-baec-43bfb214161e' 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>T grid center latitude</dd><dt><span>units :</span></dt><dd>degrees_north</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> 9.79 MiB </td>\n",
+       "                        <td> 379.69 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (713, 3600) </td>\n",
+       "                        <td> (270, 360) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 30 chunks in 3 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",
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+       "  <!-- Text -->\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>ULON</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(243, 360), meta=np.ndarray&gt;</div><input id='attrs-fd871cd5-ea62-4dcc-93ab-511ea1b51f28' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fd871cd5-ea62-4dcc-93ab-511ea1b51f28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05b2f757-8b4c-40f7-8b38-05f62d40aaa2' class='xr-var-data-in' type='checkbox'><label for='data-05b2f757-8b4c-40f7-8b38-05f62d40aaa2' 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>U grid center longitude</dd><dt><span>units :</span></dt><dd>degrees_east</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> 9.79 MiB </td>\n",
+       "                        <td> 379.69 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (713, 3600) </td>\n",
+       "                        <td> (270, 360) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 30 chunks in 3 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",
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+       "  <!-- Text -->\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>ULAT</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(243, 360), meta=np.ndarray&gt;</div><input id='attrs-d473d816-a122-4bba-9f92-6f965a69e266' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d473d816-a122-4bba-9f92-6f965a69e266' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e4b2f39-e81f-4e3e-ba94-c0143755058a' class='xr-var-data-in' type='checkbox'><label for='data-3e4b2f39-e81f-4e3e-ba94-c0143755058a' 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>U grid center latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>comment :</span></dt><dd>Latitude of NE corner of T grid cell</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> 9.79 MiB </td>\n",
+       "                        <td> 379.69 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (713, 3600) </td>\n",
+       "                        <td> (270, 360) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 30 chunks in 3 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",
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-       "  <line x1=\"120\" y1=\"14\" x2=\"120\" y2=\"56\" />\n",
-       "  <line x1=\"132\" y1=\"14\" x2=\"132\" y2=\"56\" />\n",
-       "  <line x1=\"144\" y1=\"14\" x2=\"144\" y2=\"56\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"41\" />\n",
+       "  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"41\" />\n",
+       "  <line x1=\"36\" y1=\"0\" x2=\"36\" y2=\"41\" />\n",
+       "  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"41\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"41\" />\n",
+       "  <line x1=\"72\" y1=\"0\" x2=\"72\" y2=\"41\" />\n",
+       "  <line x1=\"84\" y1=\"0\" x2=\"84\" y2=\"41\" />\n",
+       "  <line x1=\"96\" y1=\"0\" x2=\"96\" y2=\"41\" />\n",
+       "  <line x1=\"108\" y1=\"0\" x2=\"108\" y2=\"41\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"41\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"24.9485979497544,14.948597949754403 144.9485979497544,14.948597949754403 144.9485979497544,56.91709675182327 24.9485979497544,56.91709675182327\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,41.96849880206887 0.0,41.96849880206887\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"84.948598\" y=\"76.917097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3600</text>\n",
-       "  <text x=\"164.948598\" y=\"35.932847\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,164.948598,35.932847)\">713</text>\n",
-       "  <text x=\"7.474299\" y=\"69.442798\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,69.442798)\">3</text>\n",
+       "  <text x=\"60.000000\" y=\"61.968499\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3600</text>\n",
+       "  <text x=\"140.000000\" y=\"20.984249\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,20.984249)\">713</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></div></li><li class='xr-section-item'><input id='section-78c3e3b3-a80e-4720-b762-50186181e0a5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-78c3e3b3-a80e-4720-b762-50186181e0a5' 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'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1977-12-31T12:00:00 ... 1978-02-...</div><input id='attrs-11c42800-1aad-4c70-9ee0-624fe0870a69' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-11c42800-1aad-4c70-9ee0-624fe0870a69' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e995fdf8-4096-4294-af23-23ee62da7fa3' class='xr-var-data-in' type='checkbox'><label for='data-e995fdf8-4096-4294-af23-23ee62da7fa3' 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'><pre>array([&#x27;1977-12-31T12:00:00.000000000&#x27;, &#x27;1978-01-31T12:00:00.000000000&#x27;,\n",
-       "       &#x27;1978-02-28T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.9 -279.8 ... 79.85 79.95</div><input id='attrs-90f56550-e557-44ab-80e0-0091bbe4d63e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-90f56550-e557-44ab-80e0-0091bbe4d63e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f421be0-80bf-429e-928f-ec1a4c71d4e6' class='xr-var-data-in' type='checkbox'><label for='data-4f421be0-80bf-429e-928f-ec1a4c71d4e6' 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'><pre>array([-279.95, -279.85, -279.75, ...,   79.75,   79.85,   79.95])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-79.97 -79.93 ... -45.11 -45.04</div><input id='attrs-812e2db6-62df-4138-b84a-c70a3d93de07' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-812e2db6-62df-4138-b84a-c70a3d93de07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f528d470-17fe-4f1c-891e-12211bbdaed8' class='xr-var-data-in' type='checkbox'><label for='data-f528d470-17fe-4f1c-891e-12211bbdaed8' 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'><pre>array([-79.968169, -79.92593 , -79.88369 , ..., -45.177229, -45.106694,\n",
-       "       -45.036071])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3334ffbb-d94d-41cb-9c9d-061c7a17ae7f' class='xr-section-summary-in' type='checkbox'  ><label for='section-3334ffbb-d94d-41cb-9c9d-061c7a17ae7f' 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-4c60df6d-4120-46e2-9bd3-db93e3bd241f' class='xr-index-data-in' type='checkbox'/><label for='index-4c60df6d-4120-46e2-9bd3-db93e3bd241f' 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(DatetimeIndex([&#x27;1977-12-31 12:00:00&#x27;, &#x27;1978-01-31 12:00:00&#x27;,\n",
+       "</table></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'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1977-12-31T12:00:00 ... 1978-02-...</div><input id='attrs-d1dd2ff9-c266-487d-af1e-84ae58679bf8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d1dd2ff9-c266-487d-af1e-84ae58679bf8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bad1629b-11ca-42fe-95ee-a2554a74f725' class='xr-var-data-in' type='checkbox'><label for='data-bad1629b-11ca-42fe-95ee-a2554a74f725' 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'><pre>array([&#x27;1977-12-31T12:00:00.000000000&#x27;, &#x27;1978-01-31T12:00:00.000000000&#x27;,\n",
+       "       &#x27;1978-02-28T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.9 -279.8 ... 79.85 79.95</div><input id='attrs-6d8b5325-6187-4f6a-b536-3986c3e8464a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6d8b5325-6187-4f6a-b536-3986c3e8464a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42a21595-9aa5-4774-866b-03e634feccaa' class='xr-var-data-in' type='checkbox'><label for='data-42a21595-9aa5-4774-866b-03e634feccaa' 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'><pre>array([-279.95, -279.85, -279.75, ...,   79.75,   79.85,   79.95])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-79.97 -79.93 ... -45.11 -45.04</div><input id='attrs-36f67e65-1781-4619-af0c-67e9abe0a053' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-36f67e65-1781-4619-af0c-67e9abe0a053' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9c0414c-563a-4cfd-b9bc-a511da48589e' class='xr-var-data-in' type='checkbox'><label for='data-e9c0414c-563a-4cfd-b9bc-a511da48589e' 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'><pre>array([-79.968169, -79.92593 , -79.88369 , ..., -45.177229, -45.106694,\n",
+       "       -45.036071])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-014127bc-3e68-4e0b-97f0-14f8157f2cd4' class='xr-section-summary-in' type='checkbox'  ><label for='section-014127bc-3e68-4e0b-97f0-14f8157f2cd4' 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-21191541-b449-4b2d-a073-db10ec4fc944' class='xr-index-data-in' type='checkbox'/><label for='index-21191541-b449-4b2d-a073-db10ec4fc944' 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(DatetimeIndex([&#x27;1977-12-31 12:00:00&#x27;, &#x27;1978-01-31 12:00:00&#x27;,\n",
        "               &#x27;1978-02-28 12:00:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>xt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6266583c-f163-4382-83f2-f7157822796b' class='xr-index-data-in' type='checkbox'/><label for='index-6266583c-f163-4382-83f2-f7157822796b' 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(Float64Index([            -279.95, -279.84999999999997, -279.74999999999994,\n",
-       "               -279.6499999999999,  -279.5499999999999,  -279.4499999999999,\n",
-       "              -279.34999999999985, -279.24999999999983,  -279.1499999999998,\n",
-       "               -279.0499999999998,\n",
-       "              ...\n",
-       "                79.04999999999492,   79.14999999999495,   79.24999999999491,\n",
-       "                79.34999999999494,    79.4499999999949,   79.54999999999492,\n",
-       "                79.64999999999489,   79.74999999999491,   79.84999999999488,\n",
-       "                 79.9499999999949],\n",
-       "             dtype=&#x27;float64&#x27;, name=&#x27;xt_ocean&#x27;, length=3600))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>yt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f00c12a0-7b72-4475-a99f-2f63f1903813' class='xr-index-data-in' type='checkbox'/><label for='index-f00c12a0-7b72-4475-a99f-2f63f1903813' 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(Float64Index([ -79.96816911008175,  -79.92592975570126,  -79.88369040132078,\n",
-       "               -79.84145104694029,  -79.79921169255981,  -79.75697233817932,\n",
-       "               -79.71473298379884,  -79.67249362941835,  -79.63025427503787,\n",
-       "               -79.58801492065739,\n",
-       "              ...\n",
-       "              -45.668532621945744,  -45.59860823054044,  -45.52859648283897,\n",
-       "               -45.45849759219382, -45.388311337648716,  -45.31803793305094,\n",
-       "              -45.247677159023404, -45.177229229918716,  -45.10669392794424,\n",
-       "              -45.036071467968426],\n",
-       "             dtype=&#x27;float64&#x27;, name=&#x27;yt_ocean&#x27;, length=713))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3cc4246a-21e8-4b7f-abdd-0230a31ff66b' class='xr-section-summary-in' type='checkbox'  ><label for='section-3cc4246a-21e8-4b7f-abdd-0230a31ff66b' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>ice area  (aggregate)</dd><dt><span>coordinates :</span></dt><dd>TLON TLAT time</dd><dt><span>cell_measures :</span></dt><dd>area: tarea</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_rep :</span></dt><dd>averaged</dd><dt><span>ncfiles :</span></dt><dd>[&#x27;/g/data/cj50/access-om2/raw-output/access-om2-01/01deg_jra55v140_iaf_cycle4/output827/ice/OUTPUT/iceh.1977-12.nc&#x27;, &#x27;/g/data/cj50/access-om2/raw-output/access-om2-01/01deg_jra55v140_iaf_cycle4/output828/ice/OUTPUT/iceh.1978-01.nc&#x27;, &#x27;/g/data/cj50/access-om2/raw-output/access-om2-01/01deg_jra55v140_iaf_cycle4/output828/ice/OUTPUT/iceh.1978-02.nc&#x27;]</dd><dt><span>contact :</span></dt><dd>Andrew Kiss</dd><dt><span>email :</span></dt><dd>andrew.kiss@anu.edu.au</dd><dt><span>created :</span></dt><dd>2022-04-27</dd><dt><span>description :</span></dt><dd>0.1 degree ACCESS-OM2 global model configuration under interannual forcing. The configuration is based on that described in Kiss et al. (2020), https://doi.org/10.5194/gmd-13-401-2020, but with many improvements and coupled biogeochemistry in the ocean and sea ice. This is cycle 4 of the IAF spinup.  Configuration physics is identical to 01deg_jra55v140_iaf_cycle3 but executables and diagnostics have changed, and sea ice and ocean coupled BGC is included. 1 Jan 1958 initial conditions are from final 1 Jan 2019 restart from 01deg_jra55v140_iaf_cycle3 and BGC tracers generated by https://github.com/COSIMA/input_om2-bgc/tree/5ec65b4 with my_choice == &#x27;01deg_jra55v140_iaf_cycle4&#x27;, except for oxygen, which is initialised at 1 Jan 1979 from csiro_bgc.res.nc generated by https://github.com/COSIMA/input_om2-bgc/tree/6868e9b. BGC tracers have no effect on the physical state, and oxygen has no effect on other BGC tracers. Run with JRA55-do v1.4.0 interannually-varying forcing with all solid runoff  converted to liquid runoff with no heat transfer. 61-year spin up, from 1 Jan 1958 to 1 Jan 2019. This run is extended from 1 Jan 2019 to the present day in 01deg_jra55v140_iaf_cycle4_jra55v150_extension.</dd><dt><span>notes :</span></dt><dd>Run configuration and history: https://github.com/COSIMA/01deg_jra55_iaf/tree/01deg_jra55v140_iaf_cycle4 Sea ice data was accidentally deleted from April 2002 to the end of 2018, apart from 6-hourly ice area from 2014-2016 inclusive. This was re-generated by https://github.com/COSIMA/01deg_jra55_iaf/tree/01deg_jra55v140_iaf_cycle4_rerun_from_2002 which also added daily aicen, vicen and 3-hourly uvel, vvel, divu, shear for 2016. This re-generated data (including the extra 2016 outputs) has been added to this experiment&#x27;s output on cj50. 2d and 3d BGC data has 2, 3, or 4 decimal digits of precision - see https://github.com/COSIMA/01deg_jra55_iaf/blob/386771b/reduce_sigfig.sh#L13-L93 Source code: https://github.com/COSIMA/access-om2 License: Attribution-NonCommercial-NoDerivatives 4.0 International (http://creativecommons.org/licenses/by-nc-nd/4.0/legalcode) Conditions of use: We request that users of this or other ACCESS-OM2 model code or output data: (a) consider citing Kiss et al. (2020) [http://doi.org/10.5194/gmd-13-401-2020] (b) include an acknowledgement such as the following: &quot;The authors thank the Consortium for Ocean-Sea Ice Modelling in Australia (COSIMA; http://www.cosima.org.au) for making the ACCESS-OM2 suite of models available at https://github.com/COSIMA/access-om2. Model runs were undertaken with the assistance of resources from the National Computational Infrastructure (NCI), which is supported by the Australian Government.&quot; (c) let us know of any publications which use these models or data so we can add them to our list: https://scholar.google.com/citations?hl=en&amp;user=inVqu_4AAAAJ</dd></dl></div></li></ul></div></div>"
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>xt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cff0ae0b-5d9c-4b2d-89c9-7edc280371c7' class='xr-index-data-in' type='checkbox'/><label for='index-cff0ae0b-5d9c-4b2d-89c9-7edc280371c7' 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([            -279.95, -279.84999999999997, -279.74999999999994,\n",
+       "        -279.6499999999999,  -279.5499999999999,  -279.4499999999999,\n",
+       "       -279.34999999999985, -279.24999999999983,  -279.1499999999998,\n",
+       "        -279.0499999999998,\n",
+       "       ...\n",
+       "         79.04999999999492,   79.14999999999495,   79.24999999999491,\n",
+       "         79.34999999999494,    79.4499999999949,   79.54999999999492,\n",
+       "         79.64999999999489,   79.74999999999491,   79.84999999999488,\n",
+       "          79.9499999999949],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;xt_ocean&#x27;, length=3600))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>yt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-179aa223-e6d7-4ee4-ba80-b677e1f39bed' class='xr-index-data-in' type='checkbox'/><label for='index-179aa223-e6d7-4ee4-ba80-b677e1f39bed' 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([ -79.96816911008175,  -79.92592975570126,  -79.88369040132078,\n",
+       "        -79.84145104694029,  -79.79921169255981,  -79.75697233817932,\n",
+       "        -79.71473298379884,  -79.67249362941835,  -79.63025427503787,\n",
+       "        -79.58801492065739,\n",
+       "       ...\n",
+       "       -45.668532621945744,  -45.59860823054044,  -45.52859648283897,\n",
+       "        -45.45849759219382, -45.388311337648716,  -45.31803793305094,\n",
+       "       -45.247677159023404, -45.177229229918716,  -45.10669392794424,\n",
+       "       -45.036071467968426],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;yt_ocean&#x27;, length=713))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-52b38d08-b527-4618-be73-27a77b8f76ba' class='xr-section-summary-in' type='checkbox'  checked><label for='section-52b38d08-b527-4618-be73-27a77b8f76ba' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>ice area  (aggregate)</dd><dt><span>cell_measures :</span></dt><dd>area: tarea</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_rep :</span></dt><dd>averaged</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.DataArray 'aice_m' (time: 3, yt_ocean: 713, xt_ocean: 3600)>\n",
+       "<xarray.DataArray 'aice_m' (time: 3, yt_ocean: 713, xt_ocean: 3600)> Size: 31MB\n",
        "dask.array<getitem, shape=(3, 713, 3600), dtype=float32, chunksize=(1, 270, 360), chunktype=numpy.ndarray>\n",
        "Coordinates:\n",
-       "  * time      (time) datetime64[ns] 1977-12-31T12:00:00 ... 1978-02-28T12:00:00\n",
-       "  * xt_ocean  (xt_ocean) float64 -279.9 -279.8 -279.7 ... 79.75 79.85 79.95\n",
-       "  * yt_ocean  (yt_ocean) float64 -79.97 -79.93 -79.88 ... -45.18 -45.11 -45.04\n",
+       "    TLON      (yt_ocean, xt_ocean) float32 10MB dask.array<chunksize=(243, 360), meta=np.ndarray>\n",
+       "    TLAT      (yt_ocean, xt_ocean) float32 10MB dask.array<chunksize=(243, 360), meta=np.ndarray>\n",
+       "    ULON      (yt_ocean, xt_ocean) float32 10MB dask.array<chunksize=(243, 360), meta=np.ndarray>\n",
+       "    ULAT      (yt_ocean, xt_ocean) float32 10MB dask.array<chunksize=(243, 360), meta=np.ndarray>\n",
+       "  * time      (time) datetime64[ns] 24B 1977-12-31T12:00:00 ... 1978-02-28T12...\n",
+       "  * xt_ocean  (xt_ocean) float64 29kB -279.9 -279.8 -279.7 ... 79.75 79.85 79.95\n",
+       "  * yt_ocean  (yt_ocean) float64 6kB -79.97 -79.93 -79.88 ... -45.11 -45.04\n",
        "Attributes:\n",
        "    units:          1\n",
        "    long_name:      ice area  (aggregate)\n",
-       "    coordinates:    TLON TLAT time\n",
        "    cell_measures:  area: tarea\n",
        "    cell_methods:   time: mean\n",
-       "    time_rep:       averaged\n",
-       "    ncfiles:        ['/g/data/cj50/access-om2/raw-output/access-om2-01/01deg_...\n",
-       "    contact:        Andrew Kiss\n",
-       "    email:          andrew.kiss@anu.edu.au\n",
-       "    created:        2022-04-27\n",
-       "    description:    0.1 degree ACCESS-OM2 global model configuration under in...\n",
-       "    notes:          Run configuration and history: https://github.com/COSIMA/..."
+       "    time_rep:       averaged"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Loading area_t to get ocean grid\n",
-    "area_t = cc.querying.getvar('01deg_jra55v140_iaf_cycle4', 'area_t', session, n=1)\n",
+    "var_search = catalog['01deg_jra55v140_iaf_cycle4'].search(variable='area_t')\n",
+    "var_search = var_search.search(path=var_search.df['path'][0])\n",
+    "ds = var_search.to_dask()\n",
+    "area_t = ds['area_t']\n",
     "\n",
     "# Apply time correction so data appears in the middle (12:00) of the day rather than at the beginning of the day (00:00)\n",
     "var_ice['time'] = var_ice.time.to_pandas() - dt.timedelta(hours = 12)\n",
@@ -680,7 +2285,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "06538e50-5575-4e5a-906e-1604f0ab7275",
    "metadata": {
     "tags": []
@@ -689,23 +2294,21 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x14c666a25eb0>"
+       "<matplotlib.collections.QuadMesh at 0x1487035e7e80>"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
+       "<Figure size 640x480 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -727,7 +2330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "1ff776bb-82ac-41cb-8b32-8f6cd40ce82b",
    "metadata": {
     "tags": []
@@ -736,23 +2339,21 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.contour.QuadContourSet at 0x14c66488e5b0>"
+       "<matplotlib.contour.QuadContourSet at 0x1487021bf0a0>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
+       "<Figure size 640x480 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -780,7 +2381,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "304d6a4b-11bd-4fd9-ab78-f113014a0fd1",
    "metadata": {
     "tags": []
@@ -798,7 +2399,7 @@
        "       [ -45.03607147,   79.95      ]])"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -827,7 +2428,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "86cf7d0e-cc59-459e-9ecb-2a1e4f5a0fd7",
    "metadata": {
     "tags": []
@@ -845,7 +2446,7 @@
        "       [ -61.84325357,   79.95      ]])"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -871,7 +2472,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "09edbc2f-c5a0-48fc-bbdd-2b13da68ce69",
    "metadata": {
     "tags": []
@@ -881,8 +2482,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 1.65 ms, sys: 1.38 ms, total: 3.03 ms\n",
-      "Wall time: 6.81 ms\n"
+      "CPU times: user 5.68 ms, sys: 0 ns, total: 5.68 ms\n",
+      "Wall time: 5.34 ms\n"
      ]
     }
    ],
@@ -895,7 +2496,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "b39e0332-8172-493a-8dbc-c4e716432980",
    "metadata": {
     "tags": []
@@ -905,8 +2506,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 23 s, sys: 0 ns, total: 23 s\n",
-      "Wall time: 23 s\n"
+      "CPU times: user 22.8 s, sys: 342 ms, total: 23.2 s\n",
+      "Wall time: 20.1 s\n"
      ]
     }
    ],
@@ -930,7 +2531,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "ee5eb05a-8e86-4423-859f-366a4a4e067e",
    "metadata": {
     "tags": []
@@ -970,6 +2571,7 @@
        "}\n",
        "\n",
        "html[theme=dark],\n",
+       "html[data-theme=dark],\n",
        "body[data-theme=dark],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -1302,7 +2904,7 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (yt_ocean: 713, xt_ocean: 3600)&gt;\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (yt_ocean: 713, xt_ocean: 3600)&gt; Size: 21MB\n",
        "array([[ 805.00144028,  803.19226827,  801.38249999, ...,  810.42536522,\n",
        "         808.61799012,  806.81001469],\n",
        "       [ 806.69292563,  804.88000362,  803.06648432, ...,  812.12809432,\n",
@@ -1317,8 +2919,8 @@
        "       [1623.26793377, 1622.06347241, 1620.88580274, ..., 1627.04144284,\n",
        "        1625.75698268, 1624.4991249 ]])\n",
        "Coordinates:\n",
-       "  * yt_ocean  (yt_ocean) float64 -79.97 -79.93 -79.88 ... -45.18 -45.11 -45.04\n",
-       "  * xt_ocean  (xt_ocean) float64 -279.9 -279.8 -279.7 ... 79.75 79.85 79.95</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>yt_ocean</span>: 713</li><li><span class='xr-has-index'>xt_ocean</span>: 3600</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-63b5e161-0a4e-458b-9e9d-e4e398ca2138' class='xr-array-in' type='checkbox' checked><label for='section-63b5e161-0a4e-458b-9e9d-e4e398ca2138' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>805.0 803.2 801.4 799.6 ... 1.628e+03 1.627e+03 1.626e+03 1.624e+03</span></div><div class='xr-array-data'><pre>array([[ 805.00144028,  803.19226827,  801.38249999, ...,  810.42536522,\n",
+       "  * yt_ocean  (yt_ocean) float64 6kB -79.97 -79.93 -79.88 ... -45.11 -45.04\n",
+       "  * xt_ocean  (xt_ocean) float64 29kB -279.9 -279.8 -279.7 ... 79.75 79.85 79.95</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>yt_ocean</span>: 713</li><li><span class='xr-has-index'>xt_ocean</span>: 3600</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-296b2a4c-1a06-460e-93ed-69745d2fef6a' class='xr-array-in' type='checkbox' checked><label for='section-296b2a4c-1a06-460e-93ed-69745d2fef6a' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>805.0 803.2 801.4 799.6 ... 1.628e+03 1.627e+03 1.626e+03 1.624e+03</span></div><div class='xr-array-data'><pre>array([[ 805.00144028,  803.19226827,  801.38249999, ...,  810.42536522,\n",
        "         808.61799012,  806.81001469],\n",
        "       [ 806.69292563,  804.88000362,  803.06648432, ...,  812.12809432,\n",
        "         810.31697234,  808.50524899],\n",
@@ -1330,29 +2932,29 @@
        "       [1615.51041181, 1614.30178279, 1613.12003095, ..., 1619.29692977,\n",
        "        1618.0080497 , 1616.74585538],\n",
        "       [1623.26793377, 1622.06347241, 1620.88580274, ..., 1627.04144284,\n",
-       "        1625.75698268, 1624.4991249 ]])</pre></div></div></li><li class='xr-section-item'><input id='section-01a2ee58-36d8-4b6a-8965-47c0f6316fa6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-01a2ee58-36d8-4b6a-8965-47c0f6316fa6' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-79.97 -79.93 ... -45.11 -45.04</div><input id='attrs-8555c61c-46e9-4c06-9439-49644fb4a46c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8555c61c-46e9-4c06-9439-49644fb4a46c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ac38123-6da5-4329-9093-0dcf1b1ae364' class='xr-var-data-in' type='checkbox'><label for='data-4ac38123-6da5-4329-9093-0dcf1b1ae364' 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'><pre>array([-79.968169, -79.92593 , -79.88369 , ..., -45.177229, -45.106694,\n",
-       "       -45.036071])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.9 -279.8 ... 79.85 79.95</div><input id='attrs-f813a14d-6db3-474b-910b-5169b3bd3349' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f813a14d-6db3-474b-910b-5169b3bd3349' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e862f6c-9c68-4ca4-bbb5-e22e98039708' class='xr-var-data-in' type='checkbox'><label for='data-7e862f6c-9c68-4ca4-bbb5-e22e98039708' 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'><pre>array([-279.95, -279.85, -279.75, ...,   79.75,   79.85,   79.95])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-338ac119-b75d-4515-b126-a92f392c8559' class='xr-section-summary-in' type='checkbox'  ><label for='section-338ac119-b75d-4515-b126-a92f392c8559' class='xr-section-summary' >Indexes: <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-index-name'><div>yt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8a9396c2-c873-48ad-8bfb-1c6b145fb102' class='xr-index-data-in' type='checkbox'/><label for='index-8a9396c2-c873-48ad-8bfb-1c6b145fb102' 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(Float64Index([ -79.96816911008175,  -79.92592975570126,  -79.88369040132078,\n",
-       "               -79.84145104694029,  -79.79921169255981,  -79.75697233817932,\n",
-       "               -79.71473298379884,  -79.67249362941835,  -79.63025427503787,\n",
-       "               -79.58801492065739,\n",
-       "              ...\n",
-       "              -45.668532621945744,  -45.59860823054044,  -45.52859648283897,\n",
-       "               -45.45849759219382, -45.388311337648716,  -45.31803793305094,\n",
-       "              -45.247677159023404, -45.177229229918716,  -45.10669392794424,\n",
-       "              -45.036071467968426],\n",
-       "             dtype=&#x27;float64&#x27;, name=&#x27;yt_ocean&#x27;, length=713))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>xt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5e942a88-3b0d-4a70-9f9f-610ab16e6de4' class='xr-index-data-in' type='checkbox'/><label for='index-5e942a88-3b0d-4a70-9f9f-610ab16e6de4' 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(Float64Index([            -279.95, -279.84999999999997, -279.74999999999994,\n",
-       "               -279.6499999999999,  -279.5499999999999,  -279.4499999999999,\n",
-       "              -279.34999999999985, -279.24999999999983,  -279.1499999999998,\n",
-       "               -279.0499999999998,\n",
-       "              ...\n",
-       "                79.04999999999492,   79.14999999999495,   79.24999999999491,\n",
-       "                79.34999999999494,    79.4499999999949,   79.54999999999492,\n",
-       "                79.64999999999489,   79.74999999999491,   79.84999999999488,\n",
-       "                 79.9499999999949],\n",
-       "             dtype=&#x27;float64&#x27;, name=&#x27;xt_ocean&#x27;, length=3600))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4c9dd4db-2d23-4746-9f89-52ab67dee5a5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4c9dd4db-2d23-4746-9f89-52ab67dee5a5' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "        1625.75698268, 1624.4991249 ]])</pre></div></div></li><li class='xr-section-item'><input id='section-0872ef85-dd4c-4b89-adee-3aa789e017ff' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0872ef85-dd4c-4b89-adee-3aa789e017ff' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-79.97 -79.93 ... -45.11 -45.04</div><input id='attrs-aba0fd89-87a6-4af5-8db8-3fb8af41b78e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aba0fd89-87a6-4af5-8db8-3fb8af41b78e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9bcd99e-77a8-413c-b56e-c4386db4b27a' class='xr-var-data-in' type='checkbox'><label for='data-c9bcd99e-77a8-413c-b56e-c4386db4b27a' 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'><pre>array([-79.968169, -79.92593 , -79.88369 , ..., -45.177229, -45.106694,\n",
+       "       -45.036071])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.9 -279.8 ... 79.85 79.95</div><input id='attrs-65a00c8d-5547-4d96-9752-95f0007195e9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-65a00c8d-5547-4d96-9752-95f0007195e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d60d8a24-125e-4a9e-ab73-ecc8c45e2cf5' class='xr-var-data-in' type='checkbox'><label for='data-d60d8a24-125e-4a9e-ab73-ecc8c45e2cf5' 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'><pre>array([-279.95, -279.85, -279.75, ...,   79.75,   79.85,   79.95])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a6b50c83-c300-4b31-a957-0558ed5209c9' class='xr-section-summary-in' type='checkbox'  ><label for='section-a6b50c83-c300-4b31-a957-0558ed5209c9' class='xr-section-summary' >Indexes: <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-index-name'><div>yt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-245e5527-42d0-48fd-8654-b7304f97c540' class='xr-index-data-in' type='checkbox'/><label for='index-245e5527-42d0-48fd-8654-b7304f97c540' 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([ -79.96816911008175,  -79.92592975570126,  -79.88369040132078,\n",
+       "        -79.84145104694029,  -79.79921169255981,  -79.75697233817932,\n",
+       "        -79.71473298379884,  -79.67249362941835,  -79.63025427503787,\n",
+       "        -79.58801492065739,\n",
+       "       ...\n",
+       "       -45.668532621945744,  -45.59860823054044,  -45.52859648283897,\n",
+       "        -45.45849759219382, -45.388311337648716,  -45.31803793305094,\n",
+       "       -45.247677159023404, -45.177229229918716,  -45.10669392794424,\n",
+       "       -45.036071467968426],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;yt_ocean&#x27;, length=713))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>xt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5605e8cf-6f63-41ba-8753-35b0aff38034' class='xr-index-data-in' type='checkbox'/><label for='index-5605e8cf-6f63-41ba-8753-35b0aff38034' 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([            -279.95, -279.84999999999997, -279.74999999999994,\n",
+       "        -279.6499999999999,  -279.5499999999999,  -279.4499999999999,\n",
+       "       -279.34999999999985, -279.24999999999983,  -279.1499999999998,\n",
+       "        -279.0499999999998,\n",
+       "       ...\n",
+       "         79.04999999999492,   79.14999999999495,   79.24999999999491,\n",
+       "         79.34999999999494,    79.4499999999949,   79.54999999999492,\n",
+       "         79.64999999999489,   79.74999999999491,   79.84999999999488,\n",
+       "          79.9499999999949],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;xt_ocean&#x27;, length=3600))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7ee35bf8-f827-4370-b746-cf3bc5a741c7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7ee35bf8-f827-4370-b746-cf3bc5a741c7' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.DataArray (yt_ocean: 713, xt_ocean: 3600)>\n",
+       "<xarray.DataArray (yt_ocean: 713, xt_ocean: 3600)> Size: 21MB\n",
        "array([[ 805.00144028,  803.19226827,  801.38249999, ...,  810.42536522,\n",
        "         808.61799012,  806.81001469],\n",
        "       [ 806.69292563,  804.88000362,  803.06648432, ...,  812.12809432,\n",
@@ -1367,11 +2969,11 @@
        "       [1623.26793377, 1622.06347241, 1620.88580274, ..., 1627.04144284,\n",
        "        1625.75698268, 1624.4991249 ]])\n",
        "Coordinates:\n",
-       "  * yt_ocean  (yt_ocean) float64 -79.97 -79.93 -79.88 ... -45.18 -45.11 -45.04\n",
-       "  * xt_ocean  (xt_ocean) float64 -279.9 -279.8 -279.7 ... 79.75 79.85 79.95"
+       "  * yt_ocean  (yt_ocean) float64 6kB -79.97 -79.93 -79.88 ... -45.11 -45.04\n",
+       "  * xt_ocean  (xt_ocean) float64 29kB -279.9 -279.8 -279.7 ... 79.75 79.85 79.95"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1399,32 +3001,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "a1a74720-552f-4dec-85cb-8bb4eea1b895",
    "metadata": {
     "tags": []
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.07/lib/python3.10/site-packages/distributed/client.py:3362: UserWarning: Sending large graph of size 19.61 MiB.\n",
+      "This may cause some slowdown.\n",
+      "Consider loading the data with Dask directly\n",
+      " or using futures or delayed objects to embed the data into the graph without repetition.\n",
+      "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.\n",
+      "  warnings.warn(\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x14c664500970>"
+       "<matplotlib.collections.QuadMesh at 0x1487030ac3d0>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
+       "<Figure size 640x480 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -1444,7 +3056,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "f2de3cdc-351c-4b73-8786-57b71c436915",
    "metadata": {
     "tags": []
@@ -1479,32 +3091,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "d14e67c2-d1c8-4ba3-9d75-2aae21d2ebf0",
    "metadata": {
     "tags": []
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.07/lib/python3.10/site-packages/distributed/client.py:3362: UserWarning: Sending large graph of size 19.61 MiB.\n",
+      "This may cause some slowdown.\n",
+      "Consider loading the data with Dask directly\n",
+      " or using futures or delayed objects to embed the data into the graph without repetition.\n",
+      "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.\n",
+      "  warnings.warn(\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x14c666b674c0>"
+       "<matplotlib.collections.QuadMesh at 0x148704edeb60>"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
+       "<Figure size 640x480 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -1524,7 +3146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "916f236d-6607-4812-a658-64ca364296ac",
    "metadata": {
     "tags": []
@@ -1564,6 +3186,7 @@
        "}\n",
        "\n",
        "html[theme=dark],\n",
+       "html[data-theme=dark],\n",
        "body[data-theme=dark],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -1896,15 +3519,19 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (time: 2, yt_ocean: 713, xt_ocean: 3600)&gt;\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (time: 2, yt_ocean: 713, xt_ocean: 3600)&gt; Size: 41MB\n",
        "dask.array&lt;concatenate, shape=(2, 713, 3600), dtype=float64, chunksize=(1, 270, 360), chunktype=numpy.ndarray&gt;\n",
        "Coordinates:\n",
-       "  * yt_ocean  (yt_ocean) float64 -79.97 -79.93 -79.88 ... -45.18 -45.11 -45.04\n",
-       "  * xt_ocean  (xt_ocean) float64 -279.9 -279.8 -279.7 ... 79.75 79.85 79.95\n",
-       "  * time      (time) datetime64[ns] 1977-12-31T12:00:00 1978-01-31T12:00:00</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 2</li><li><span class='xr-has-index'>yt_ocean</span>: 713</li><li><span class='xr-has-index'>xt_ocean</span>: 3600</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-0f894180-2a76-4cd4-87fb-92426e859d3a' class='xr-array-in' type='checkbox' checked><label for='section-0f894180-2a76-4cd4-87fb-92426e859d3a' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 243, 360), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "  * yt_ocean  (yt_ocean) float64 6kB -79.97 -79.93 -79.88 ... -45.11 -45.04\n",
+       "  * xt_ocean  (xt_ocean) float64 29kB -279.9 -279.8 -279.7 ... 79.75 79.85 79.95\n",
+       "    TLON      (yt_ocean, xt_ocean) float32 10MB dask.array&lt;chunksize=(243, 360), meta=np.ndarray&gt;\n",
+       "    TLAT      (yt_ocean, xt_ocean) float32 10MB dask.array&lt;chunksize=(243, 360), meta=np.ndarray&gt;\n",
+       "    ULON      (yt_ocean, xt_ocean) float32 10MB dask.array&lt;chunksize=(243, 360), meta=np.ndarray&gt;\n",
+       "    ULAT      (yt_ocean, xt_ocean) float32 10MB dask.array&lt;chunksize=(243, 360), meta=np.ndarray&gt;\n",
+       "  * time      (time) datetime64[ns] 16B 1977-12-31T12:00:00 1978-01-31T12:00:00</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 2</li><li><span class='xr-has-index'>yt_ocean</span>: 713</li><li><span class='xr-has-index'>xt_ocean</span>: 3600</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-f19d0513-1e68-4d6f-829d-8aabd46c6a0d' class='xr-array-in' type='checkbox' checked><label for='section-f19d0513-1e68-4d6f-829d-8aabd46c6a0d' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 243, 360), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
-       "            <table>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
        "                <thead>\n",
        "                    <tr>\n",
        "                        <td> </td>\n",
@@ -1926,14 +3553,12 @@
        "                        <td> (1, 270, 360) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 1115 Tasks </td>\n",
-       "                        <td> 60 Chunks </td>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 1480 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float64 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
        "                    </tr>\n",
        "                </tbody>\n",
        "            </table>\n",
@@ -2005,38 +3630,306 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></div></li><li class='xr-section-item'><input id='section-bc711550-4406-432b-b99f-914d9daa3d84' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bc711550-4406-432b-b99f-914d9daa3d84' 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'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-79.97 -79.93 ... -45.11 -45.04</div><input id='attrs-c49af4bf-979a-4158-977f-1e123d7dfa86' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c49af4bf-979a-4158-977f-1e123d7dfa86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-758e94c7-8e95-4476-b644-c3c2278af1af' class='xr-var-data-in' type='checkbox'><label for='data-758e94c7-8e95-4476-b644-c3c2278af1af' 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'><pre>array([-79.968169, -79.92593 , -79.88369 , ..., -45.177229, -45.106694,\n",
-       "       -45.036071])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.9 -279.8 ... 79.85 79.95</div><input id='attrs-597d63a6-a7f8-48ab-a7e0-5e44f43a6e0b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-597d63a6-a7f8-48ab-a7e0-5e44f43a6e0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b22f0a53-e2ea-4b3f-96ab-ed0d1452216e' class='xr-var-data-in' type='checkbox'><label for='data-b22f0a53-e2ea-4b3f-96ab-ed0d1452216e' 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'><pre>array([-279.95, -279.85, -279.75, ...,   79.75,   79.85,   79.95])</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'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1977-12-31T12:00:00 1978-01-31T1...</div><input id='attrs-b643ed52-6f1e-4f40-83bd-fab889c598b9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b643ed52-6f1e-4f40-83bd-fab889c598b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-021a8720-91ae-4228-bd5d-5521ea0bba9c' class='xr-var-data-in' type='checkbox'><label for='data-021a8720-91ae-4228-bd5d-5521ea0bba9c' 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'><pre>array([&#x27;1977-12-31T12:00:00.000000000&#x27;, &#x27;1978-01-31T12:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5b3b25bb-d13e-4a61-9309-f7091ccdf9ef' class='xr-section-summary-in' type='checkbox'  ><label for='section-5b3b25bb-d13e-4a61-9309-f7091ccdf9ef' 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>yt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5671f8ab-0b5f-4fff-ab7a-69f4d4cfb527' class='xr-index-data-in' type='checkbox'/><label for='index-5671f8ab-0b5f-4fff-ab7a-69f4d4cfb527' 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(Float64Index([ -79.96816911008175,  -79.92592975570126,  -79.88369040132078,\n",
-       "               -79.84145104694029,  -79.79921169255981,  -79.75697233817932,\n",
-       "               -79.71473298379884,  -79.67249362941835,  -79.63025427503787,\n",
-       "               -79.58801492065739,\n",
-       "              ...\n",
-       "              -45.668532621945744,  -45.59860823054044,  -45.52859648283897,\n",
-       "               -45.45849759219382, -45.388311337648716,  -45.31803793305094,\n",
-       "              -45.247677159023404, -45.177229229918716,  -45.10669392794424,\n",
-       "              -45.036071467968426],\n",
-       "             dtype=&#x27;float64&#x27;, name=&#x27;yt_ocean&#x27;, length=713))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>xt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-dcd50a7d-1749-4f53-8b85-5a7144162928' class='xr-index-data-in' type='checkbox'/><label for='index-dcd50a7d-1749-4f53-8b85-5a7144162928' 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(Float64Index([            -279.95, -279.84999999999997, -279.74999999999994,\n",
-       "               -279.6499999999999,  -279.5499999999999,  -279.4499999999999,\n",
-       "              -279.34999999999985, -279.24999999999983,  -279.1499999999998,\n",
-       "               -279.0499999999998,\n",
-       "              ...\n",
-       "                79.04999999999492,   79.14999999999495,   79.24999999999491,\n",
-       "                79.34999999999494,    79.4499999999949,   79.54999999999492,\n",
-       "                79.64999999999489,   79.74999999999491,   79.84999999999488,\n",
-       "                 79.9499999999949],\n",
-       "             dtype=&#x27;float64&#x27;, name=&#x27;xt_ocean&#x27;, length=3600))</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-e90244c4-455c-44a1-bdf8-a9ba8d299d2c' class='xr-index-data-in' type='checkbox'/><label for='index-e90244c4-455c-44a1-bdf8-a9ba8d299d2c' 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(DatetimeIndex([&#x27;1977-12-31 12:00:00&#x27;, &#x27;1978-01-31 12:00:00&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-caedbb43-7290-4e8d-b565-c5aec3d12501' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-caedbb43-7290-4e8d-b565-c5aec3d12501' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "</table></div></div></li><li class='xr-section-item'><input id='section-cb1d117b-f690-40e5-a09d-cdf3a97a94d1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cb1d117b-f690-40e5-a09d-cdf3a97a94d1' class='xr-section-summary' >Coordinates: <span>(7)</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'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-79.97 -79.93 ... -45.11 -45.04</div><input id='attrs-ca516d0d-548f-4e86-9773-0c7ea645174a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ca516d0d-548f-4e86-9773-0c7ea645174a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e620f930-65c2-4fbe-9866-d32bdd8aafc5' class='xr-var-data-in' type='checkbox'><label for='data-e620f930-65c2-4fbe-9866-d32bdd8aafc5' 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'><pre>array([-79.968169, -79.92593 , -79.88369 , ..., -45.177229, -45.106694,\n",
+       "       -45.036071])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.9 -279.8 ... 79.85 79.95</div><input id='attrs-33f17de7-e3bc-4ab4-a341-0136a1206782' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-33f17de7-e3bc-4ab4-a341-0136a1206782' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06958713-4a05-454d-926f-6e09c20294a5' class='xr-var-data-in' type='checkbox'><label for='data-06958713-4a05-454d-926f-6e09c20294a5' 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'><pre>array([-279.95, -279.85, -279.75, ...,   79.75,   79.85,   79.95])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TLON</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(243, 360), meta=np.ndarray&gt;</div><input id='attrs-f58fe8d0-d1d9-4cc6-9356-0873e72cf639' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f58fe8d0-d1d9-4cc6-9356-0873e72cf639' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8e8570e-9cd0-4fbc-a71a-0f54282a6ac4' class='xr-var-data-in' type='checkbox'><label for='data-e8e8570e-9cd0-4fbc-a71a-0f54282a6ac4' 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>T grid center longitude</dd><dt><span>units :</span></dt><dd>degrees_east</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> 9.79 MiB </td>\n",
+       "                        <td> 379.69 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (713, 3600) </td>\n",
+       "                        <td> (270, 360) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 30 chunks in 3 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=\"91\" 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=\"14\" x2=\"120\" y2=\"14\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"41\" x2=\"120\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"41\" />\n",
+       "  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"41\" />\n",
+       "  <line x1=\"36\" y1=\"0\" x2=\"36\" y2=\"41\" />\n",
+       "  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"41\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"41\" />\n",
+       "  <line x1=\"72\" y1=\"0\" x2=\"72\" y2=\"41\" />\n",
+       "  <line x1=\"84\" y1=\"0\" x2=\"84\" y2=\"41\" />\n",
+       "  <line x1=\"96\" y1=\"0\" x2=\"96\" y2=\"41\" />\n",
+       "  <line x1=\"108\" y1=\"0\" x2=\"108\" y2=\"41\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,41.96849880206887 0.0,41.96849880206887\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"61.968499\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3600</text>\n",
+       "  <text x=\"140.000000\" y=\"20.984249\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,20.984249)\">713</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TLAT</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(243, 360), meta=np.ndarray&gt;</div><input id='attrs-830d2483-33b2-4a25-ab00-22aa43be022b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-830d2483-33b2-4a25-ab00-22aa43be022b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-348ef174-c76b-498f-92f3-66ad17ba8749' class='xr-var-data-in' type='checkbox'><label for='data-348ef174-c76b-498f-92f3-66ad17ba8749' 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>T grid center latitude</dd><dt><span>units :</span></dt><dd>degrees_north</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> 9.79 MiB </td>\n",
+       "                        <td> 379.69 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (713, 3600) </td>\n",
+       "                        <td> (270, 360) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 30 chunks in 3 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=\"91\" 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=\"14\" x2=\"120\" y2=\"14\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"41\" x2=\"120\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"41\" />\n",
+       "  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"41\" />\n",
+       "  <line x1=\"36\" y1=\"0\" x2=\"36\" y2=\"41\" />\n",
+       "  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"41\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"41\" />\n",
+       "  <line x1=\"72\" y1=\"0\" x2=\"72\" y2=\"41\" />\n",
+       "  <line x1=\"84\" y1=\"0\" x2=\"84\" y2=\"41\" />\n",
+       "  <line x1=\"96\" y1=\"0\" x2=\"96\" y2=\"41\" />\n",
+       "  <line x1=\"108\" y1=\"0\" x2=\"108\" y2=\"41\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,41.96849880206887 0.0,41.96849880206887\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"61.968499\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3600</text>\n",
+       "  <text x=\"140.000000\" y=\"20.984249\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,20.984249)\">713</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ULON</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(243, 360), meta=np.ndarray&gt;</div><input id='attrs-20f180e2-ae22-43f0-bb74-4f933617dbe7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20f180e2-ae22-43f0-bb74-4f933617dbe7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72a07ffe-d9d9-4038-a759-e3b491aba13b' class='xr-var-data-in' type='checkbox'><label for='data-72a07ffe-d9d9-4038-a759-e3b491aba13b' 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>U grid center longitude</dd><dt><span>units :</span></dt><dd>degrees_east</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> 9.79 MiB </td>\n",
+       "                        <td> 379.69 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (713, 3600) </td>\n",
+       "                        <td> (270, 360) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 30 chunks in 3 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=\"91\" 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=\"14\" x2=\"120\" y2=\"14\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"41\" x2=\"120\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"41\" />\n",
+       "  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"41\" />\n",
+       "  <line x1=\"36\" y1=\"0\" x2=\"36\" y2=\"41\" />\n",
+       "  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"41\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"41\" />\n",
+       "  <line x1=\"72\" y1=\"0\" x2=\"72\" y2=\"41\" />\n",
+       "  <line x1=\"84\" y1=\"0\" x2=\"84\" y2=\"41\" />\n",
+       "  <line x1=\"96\" y1=\"0\" x2=\"96\" y2=\"41\" />\n",
+       "  <line x1=\"108\" y1=\"0\" x2=\"108\" y2=\"41\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,41.96849880206887 0.0,41.96849880206887\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"61.968499\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3600</text>\n",
+       "  <text x=\"140.000000\" y=\"20.984249\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,20.984249)\">713</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ULAT</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(243, 360), meta=np.ndarray&gt;</div><input id='attrs-e1da21f3-602e-4e32-89dc-7fd0e3472dd4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e1da21f3-602e-4e32-89dc-7fd0e3472dd4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4282b6a7-0646-4119-bd28-eba29d3a758d' class='xr-var-data-in' type='checkbox'><label for='data-4282b6a7-0646-4119-bd28-eba29d3a758d' 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>U grid center latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>comment :</span></dt><dd>Latitude of NE corner of T grid cell</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> 9.79 MiB </td>\n",
+       "                        <td> 379.69 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (713, 3600) </td>\n",
+       "                        <td> (270, 360) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 30 chunks in 3 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=\"91\" 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=\"14\" x2=\"120\" y2=\"14\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"41\" x2=\"120\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"41\" />\n",
+       "  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"41\" />\n",
+       "  <line x1=\"36\" y1=\"0\" x2=\"36\" y2=\"41\" />\n",
+       "  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"41\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"41\" />\n",
+       "  <line x1=\"72\" y1=\"0\" x2=\"72\" y2=\"41\" />\n",
+       "  <line x1=\"84\" y1=\"0\" x2=\"84\" y2=\"41\" />\n",
+       "  <line x1=\"96\" y1=\"0\" x2=\"96\" y2=\"41\" />\n",
+       "  <line x1=\"108\" y1=\"0\" x2=\"108\" y2=\"41\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,41.96849880206887 0.0,41.96849880206887\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"61.968499\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3600</text>\n",
+       "  <text x=\"140.000000\" y=\"20.984249\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,20.984249)\">713</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></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'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1977-12-31T12:00:00 1978-01-31T1...</div><input id='attrs-3132944c-1187-452c-9902-348848bdb60c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3132944c-1187-452c-9902-348848bdb60c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-940af11a-4d27-4bfd-b75e-548690b02d91' class='xr-var-data-in' type='checkbox'><label for='data-940af11a-4d27-4bfd-b75e-548690b02d91' 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'><pre>array([&#x27;1977-12-31T12:00:00.000000000&#x27;, &#x27;1978-01-31T12:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-56cda8f5-a97a-4fa9-abb4-4063fe3c947b' class='xr-section-summary-in' type='checkbox'  ><label for='section-56cda8f5-a97a-4fa9-abb4-4063fe3c947b' 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>yt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-296f9270-63f5-4220-a078-a6340dd74b44' class='xr-index-data-in' type='checkbox'/><label for='index-296f9270-63f5-4220-a078-a6340dd74b44' 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([ -79.96816911008175,  -79.92592975570126,  -79.88369040132078,\n",
+       "        -79.84145104694029,  -79.79921169255981,  -79.75697233817932,\n",
+       "        -79.71473298379884,  -79.67249362941835,  -79.63025427503787,\n",
+       "        -79.58801492065739,\n",
+       "       ...\n",
+       "       -45.668532621945744,  -45.59860823054044,  -45.52859648283897,\n",
+       "        -45.45849759219382, -45.388311337648716,  -45.31803793305094,\n",
+       "       -45.247677159023404, -45.177229229918716,  -45.10669392794424,\n",
+       "       -45.036071467968426],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;yt_ocean&#x27;, length=713))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>xt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-203adf52-0da4-4481-9da5-ec80cfe11d4a' class='xr-index-data-in' type='checkbox'/><label for='index-203adf52-0da4-4481-9da5-ec80cfe11d4a' 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([            -279.95, -279.84999999999997, -279.74999999999994,\n",
+       "        -279.6499999999999,  -279.5499999999999,  -279.4499999999999,\n",
+       "       -279.34999999999985, -279.24999999999983,  -279.1499999999998,\n",
+       "        -279.0499999999998,\n",
+       "       ...\n",
+       "         79.04999999999492,   79.14999999999495,   79.24999999999491,\n",
+       "         79.34999999999494,    79.4499999999949,   79.54999999999492,\n",
+       "         79.64999999999489,   79.74999999999491,   79.84999999999488,\n",
+       "          79.9499999999949],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;xt_ocean&#x27;, length=3600))</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-2f088aa7-ead6-4ac2-b780-3345657861fd' class='xr-index-data-in' type='checkbox'/><label for='index-2f088aa7-ead6-4ac2-b780-3345657861fd' 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(DatetimeIndex([&#x27;1977-12-31 12:00:00&#x27;, &#x27;1978-01-31 12:00:00&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-013ec407-9ab6-48ef-9f89-201f629dbcc3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-013ec407-9ab6-48ef-9f89-201f629dbcc3' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.DataArray (time: 2, yt_ocean: 713, xt_ocean: 3600)>\n",
+       "<xarray.DataArray (time: 2, yt_ocean: 713, xt_ocean: 3600)> Size: 41MB\n",
        "dask.array<concatenate, shape=(2, 713, 3600), dtype=float64, chunksize=(1, 270, 360), chunktype=numpy.ndarray>\n",
        "Coordinates:\n",
-       "  * yt_ocean  (yt_ocean) float64 -79.97 -79.93 -79.88 ... -45.18 -45.11 -45.04\n",
-       "  * xt_ocean  (xt_ocean) float64 -279.9 -279.8 -279.7 ... 79.75 79.85 79.95\n",
-       "  * time      (time) datetime64[ns] 1977-12-31T12:00:00 1978-01-31T12:00:00"
+       "  * yt_ocean  (yt_ocean) float64 6kB -79.97 -79.93 -79.88 ... -45.11 -45.04\n",
+       "  * xt_ocean  (xt_ocean) float64 29kB -279.9 -279.8 -279.7 ... 79.75 79.85 79.95\n",
+       "    TLON      (yt_ocean, xt_ocean) float32 10MB dask.array<chunksize=(243, 360), meta=np.ndarray>\n",
+       "    TLAT      (yt_ocean, xt_ocean) float32 10MB dask.array<chunksize=(243, 360), meta=np.ndarray>\n",
+       "    ULON      (yt_ocean, xt_ocean) float32 10MB dask.array<chunksize=(243, 360), meta=np.ndarray>\n",
+       "    ULAT      (yt_ocean, xt_ocean) float32 10MB dask.array<chunksize=(243, 360), meta=np.ndarray>\n",
+       "  * time      (time) datetime64[ns] 16B 1977-12-31T12:00:00 1978-01-31T12:00:00"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2048,32 +3941,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "id": "6b9c254a-dc1c-4d7e-9c78-18bc2099d936",
    "metadata": {
     "tags": []
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.07/lib/python3.10/site-packages/distributed/client.py:3362: UserWarning: Sending large graph of size 39.21 MiB.\n",
+      "This may cause some slowdown.\n",
+      "Consider loading the data with Dask directly\n",
+      " or using futures or delayed objects to embed the data into the graph without repetition.\n",
+      "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.\n",
+      "  warnings.warn(\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "<xarray.plot.facetgrid.FacetGrid at 0x14c666b0ac70>"
+       "<xarray.plot.facetgrid.FacetGrid at 0x1487026fbe50>"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 504x216 with 3 Axes>"
+       "<Figure size 700x300 with 3 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -2086,15 +3989,25 @@
    "id": "d0057e34-7fc6-469c-a850-1b07f2da8239",
    "metadata": {},
    "source": [
-    "You are done! You should be able to follow the same workflow to calculate distances to any line/point of your interest."
+    "We are done! We should be able to follow the same workflow to calculate distances to any line/point of your interest."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "b3d128e2-e3fc-4f1b-be87-38066338e6fb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "client.close()"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:analysis3-22.10] *",
+   "display_name": "Python [conda env:analysis3-24.07]",
    "language": "python",
-   "name": "conda-env-analysis3-22.10-py"
+   "name": "conda-env-analysis3-24.07-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -2106,7 +4019,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.15"
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,