From 7ae7b588931edd49bf18cca177a6c0ceea834aa6 Mon Sep 17 00:00:00 2001
From: max <u5190335@anu.edu.au>
Date: Tue, 28 Nov 2023 11:35:47 +1100
Subject: [PATCH 1/7] converting from cc to intake

---
 .../Cross-contour_transport.ipynb             | 937 ++++++++----------
 1 file changed, 396 insertions(+), 541 deletions(-)

diff --git a/DocumentedExamples/Cross-contour_transport.ipynb b/DocumentedExamples/Cross-contour_transport.ipynb
index b8d36ecb..0ab49d73 100644
--- a/DocumentedExamples/Cross-contour_transport.ipynb
+++ b/DocumentedExamples/Cross-contour_transport.ipynb
@@ -15,8 +15,6 @@
     "\n",
     "**Note:** updated 4Sep23 to modify the calculation of the transport masks to include cases where the contour borders a grid cell on three sides. Previously these options were not included in the calculation and thus there was missing transport.\n",
     "\n",
-    "**Alert:** After including the additional cases the contour number doesn't always monotonically increase along the contour. At the moment, the two indices that are set at the same time are adjacent numbers, whereas if you were following the contour you'd expect their numbers to be 2 apart with the other coordinate in between.\n",
-    "\n",
     "First, we load useful packages:"
    ]
   },
@@ -29,7 +27,9 @@
    "source": [
     "%matplotlib inline\n",
     "\n",
-    "import cosima_cookbook as cc\n",
+    "import pandas as pd\n",
+    "import intake\n",
+    "catalog = intake.cat.access_nri\n",
     "import matplotlib.pyplot as plt\n",
     "import netCDF4 as nc\n",
     "import xarray as xr\n",
@@ -51,20 +51,20 @@
        "    <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-aca314ec-5cfa-11ee-b703-00000755fe80</p>\n",
+       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-f4ae6b00-836e-11ee-a3df-0000018efe80</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",
+       "            <td style=\"text-align: left;\"><strong>Connection method:</strong> Direct</td>\n",
+       "            <td style=\"text-align: left;\"></td>\n",
        "        \n",
        "        </tr>\n",
        "\n",
        "        \n",
        "            <tr>\n",
        "                <td style=\"text-align: left;\">\n",
-       "                    <strong>Dashboard: </strong> <a href=\"/proxy/45237/status\" target=\"_blank\">/proxy/45237/status</a>\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",
@@ -73,76 +73,43 @@
        "        </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;\">5b7e03f0</p>\n",
-       "        <table style=\"width: 100%; text-align: left;\">\n",
-       "            <tr>\n",
-       "                <td style=\"text-align: left;\">\n",
-       "                    <strong>Dashboard:</strong> <a href=\"/proxy/45237/status\" target=\"_blank\">/proxy/45237/status</a>\n",
-       "                </td>\n",
-       "                <td style=\"text-align: left;\">\n",
-       "                    <strong>Workers:</strong> 7\n",
-       "                </td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                <td style=\"text-align: left;\">\n",
-       "                    <strong>Total threads:</strong> 28\n",
-       "                </td>\n",
-       "                <td style=\"text-align: left;\">\n",
-       "                    <strong>Total memory:</strong> 251.20 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",
+       "            <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Scheduler Info</h3></summary>\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-dd6a1ad9-6aaa-4029-bd9d-7eaba4703d33</p>\n",
+       "            <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-7547f1e1-4c20-48f5-98ca-661eaefedcf0</p>\n",
        "            <table style=\"width: 100%; text-align: left;\">\n",
        "                <tr>\n",
        "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Comm:</strong> tcp://127.0.0.1:34743\n",
+       "                        <strong>Comm:</strong> tcp://10.6.22.43:8786\n",
        "                    </td>\n",
        "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Workers:</strong> 7\n",
+       "                        <strong>Workers:</strong> 1\n",
        "                    </td>\n",
        "                </tr>\n",
        "                <tr>\n",
        "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Dashboard:</strong> <a href=\"/proxy/45237/status\" target=\"_blank\">/proxy/45237/status</a>\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> 28\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",
+       "                        <strong>Started:</strong> 2 hours ago\n",
        "                    </td>\n",
        "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Total memory:</strong> 251.20 GiB\n",
+       "                        <strong>Total memory:</strong> 188.56 GiB\n",
        "                    </td>\n",
        "                </tr>\n",
        "            </table>\n",
@@ -160,309 +127,80 @@
        "            <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://127.0.0.1:35735\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 4\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"/proxy/45533/status\" target=\"_blank\">/proxy/45533/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 35.89 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:41743\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/96251678.gadi-pbs/dask-worker-space/worker-v7og2hcm\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://127.0.0.1:39941\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 4\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"/proxy/41975/status\" target=\"_blank\">/proxy/41975/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 35.89 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:45627\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/96251678.gadi-pbs/dask-worker-space/worker-bw79v_pr\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://127.0.0.1:36985\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 4\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"/proxy/36543/status\" target=\"_blank\">/proxy/36543/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 35.89 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:40093\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/96251678.gadi-pbs/dask-worker-space/worker-q290gfz7\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",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: tcp://10.6.22.43:37571</h4>\n",
        "                </summary>\n",
        "                <table style=\"width: 100%; text-align: left;\">\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:39267\n",
+       "                            <strong>Comm: </strong> tcp://10.6.22.43:37571\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 4\n",
+       "                            <strong>Total threads: </strong> 48\n",
        "                        </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"/proxy/34769/status\" target=\"_blank\">/proxy/34769/status</a>\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/33801/status\" target=\"_blank\">/proxy/33801/status</a>\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 35.89 GiB\n",
+       "                            <strong>Memory: </strong> 188.56 GiB\n",
        "                        </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:45385\n",
+       "                            <strong>Nanny: </strong> tcp://10.6.22.43:44673\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/96251678.gadi-pbs/dask-worker-space/worker-q3bpyg71\n",
+       "                            <strong>Local directory: </strong> /scratch/iq82/mp7041/dasktmp/dask-scratch-space/worker-efhnz2dp\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://127.0.0.1:36977\n",
+       "                            <strong>Tasks executing: </strong> \n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 4\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"/proxy/34033/status\" target=\"_blank\">/proxy/34033/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 35.89 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:34169\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/96251678.gadi-pbs/dask-worker-space/worker-djn9kgj0\n",
+       "                            <strong>Tasks in memory: </strong> \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://127.0.0.1:37401\n",
+       "                            <strong>Tasks ready: </strong> \n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 4\n",
+       "                            <strong>Tasks in flight: </strong>\n",
        "                        </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"/proxy/34175/status\" target=\"_blank\">/proxy/34175/status</a>\n",
+       "                            <strong>CPU usage:</strong> 104.7%\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 35.89 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:40483\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/96251678.gadi-pbs/dask-worker-space/worker-dcu0ksho\n",
+       "                            <strong>Last seen: </strong> Just now\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://127.0.0.1:32887\n",
+       "                            <strong>Memory usage: </strong> 1.66 GiB\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 4\n",
+       "                            <strong>Spilled bytes: </strong> 0 B\n",
        "                        </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"/proxy/33281/status\" target=\"_blank\">/proxy/33281/status</a>\n",
+       "                            <strong>Read bytes: </strong> 74.78 kiB\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 35.89 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:38353\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/96251678.gadi-pbs/dask-worker-space/worker-ych_5d5u\n",
+       "                            <strong>Write bytes: </strong> 115.25 kiB\n",
        "                        </td>\n",
        "                    </tr>\n",
-       "\n",
-       "                    \n",
-       "\n",
        "                    \n",
        "\n",
        "                </table>\n",
@@ -473,10 +211,6 @@
        "\n",
        "    </details>\n",
        "</div>\n",
-       "\n",
-       "        </details>\n",
-       "    </div>\n",
-       "</div>\n",
        "            </details>\n",
        "        \n",
        "\n",
@@ -484,7 +218,7 @@
        "</div>"
       ],
       "text/plain": [
-       "<Client: 'tcp://127.0.0.1:34743' processes=7 threads=28, memory=251.20 GiB>"
+       "<Client: 'tcp://10.6.22.43:8786' processes=1 threads=48, memory=188.56 GiB>"
       ]
      },
      "execution_count": 2,
@@ -493,10 +227,18 @@
     }
    ],
    "source": [
-    "client = Client()\n",
+    "client = Client(\"tcp://10.6.22.43:8786\")\n",
     "client"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6b0b052d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "id": "0fc07fcc-6e1d-40c0-be5b-c887266a20c7",
@@ -507,13 +249,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "142873eb-d834-40c2-aa5c-7b8f750f83d0",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "session = cc.database.create_session()"
-   ]
+   "source": []
   },
   {
    "cell_type": "markdown",
@@ -525,7 +265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "eed34fea-7cdd-4439-9668-3f543b9a4766",
    "metadata": {},
    "outputs": [],
@@ -543,7 +283,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "900d12f3-548f-4854-8686-5dc9f3d037c7",
    "metadata": {},
    "outputs": [],
@@ -565,41 +305,294 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 14,
+   "id": "e28cf315-ef31-4ce1-a526-b56b73ae2edb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['ANGLE',\n",
+       " 'ANGLET',\n",
+       " 'HTE',\n",
+       " 'HTN',\n",
+       " 'NCAT',\n",
+       " 'TLAT',\n",
+       " 'TLON',\n",
+       " 'Tsfc_m',\n",
+       " 'ULAT',\n",
+       " 'ULON',\n",
+       " 'age_global',\n",
+       " 'aice_m',\n",
+       " 'aicen_m',\n",
+       " 'alidf_ai_m',\n",
+       " 'alidr_ai_m',\n",
+       " 'alvdf_ai_m',\n",
+       " 'alvdr_ai_m',\n",
+       " 'area_t',\n",
+       " 'area_u',\n",
+       " 'average_DT',\n",
+       " 'average_T1',\n",
+       " 'average_T2',\n",
+       " 'bih_fric_u',\n",
+       " 'bih_fric_v',\n",
+       " 'blkmask',\n",
+       " 'buoyfreq2_wt',\n",
+       " 'congel_m',\n",
+       " 'divu_m',\n",
+       " 'drag_coeff',\n",
+       " 'dxt',\n",
+       " 'dxu',\n",
+       " 'dyt',\n",
+       " 'dyu',\n",
+       " 'dzt',\n",
+       " 'eta_global',\n",
+       " 'eta_t',\n",
+       " 'evap',\n",
+       " 'evap_heat',\n",
+       " 'flatn_ai_m',\n",
+       " 'fmeltt_ai_m',\n",
+       " 'fmelttn_ai_m',\n",
+       " 'fprec',\n",
+       " 'fprec_melt_heat',\n",
+       " 'frazil_3d',\n",
+       " 'frazil_3d_int_z',\n",
+       " 'frazil_m',\n",
+       " 'frz_onset_m',\n",
+       " 'fsalt_ai_m',\n",
+       " 'fsalt_m',\n",
+       " 'fswup_m',\n",
+       " 'geolat_c',\n",
+       " 'geolat_t',\n",
+       " 'geolon_c',\n",
+       " 'geolon_t',\n",
+       " 'hi_m',\n",
+       " 'hs_m',\n",
+       " 'ht',\n",
+       " 'hu',\n",
+       " 'ke_tot',\n",
+       " 'kmt',\n",
+       " 'kmu',\n",
+       " 'lprec',\n",
+       " 'lw_heat',\n",
+       " 'melt',\n",
+       " 'mld',\n",
+       " 'mlt_onset_m',\n",
+       " 'net_sfc_heating',\n",
+       " 'opening_m',\n",
+       " 'passive_adelie',\n",
+       " 'passive_prydz',\n",
+       " 'passive_ross',\n",
+       " 'passive_weddell',\n",
+       " 'pbot_t',\n",
+       " 'pe_tot',\n",
+       " 'pme_net',\n",
+       " 'pme_river',\n",
+       " 'pot_rho_0',\n",
+       " 'pot_rho_1',\n",
+       " 'pot_rho_2',\n",
+       " 'pot_temp',\n",
+       " 'rho',\n",
+       " 'rhoave',\n",
+       " 'river',\n",
+       " 'runoff',\n",
+       " 'salt',\n",
+       " 'salt_global_ave',\n",
+       " 'salt_surface_ave',\n",
+       " 'sea_level',\n",
+       " 'sea_levelsq',\n",
+       " 'sens_heat',\n",
+       " 'sfc_hflux_coupler',\n",
+       " 'sfc_hflux_from_runoff',\n",
+       " 'sfc_hflux_pme',\n",
+       " 'sfc_salt_flux_coupler',\n",
+       " 'sfc_salt_flux_ice',\n",
+       " 'sfc_salt_flux_restore',\n",
+       " 'shear_m',\n",
+       " 'sig1_m',\n",
+       " 'sig2_m',\n",
+       " 'sss_m',\n",
+       " 'sst_m',\n",
+       " 'strairx_m',\n",
+       " 'strairy_m',\n",
+       " 'strength_m',\n",
+       " 'surface_salt',\n",
+       " 'surface_temp',\n",
+       " 'sw_heat',\n",
+       " 'swflx',\n",
+       " 'tarea',\n",
+       " 'tau_x',\n",
+       " 'tau_y',\n",
+       " 'temp',\n",
+       " 'temp_advection',\n",
+       " 'temp_global_ave',\n",
+       " 'temp_nonlocal_KPP',\n",
+       " 'temp_rivermix',\n",
+       " 'temp_submeso',\n",
+       " 'temp_surface_ave',\n",
+       " 'temp_tendency',\n",
+       " 'temp_vdiffuse_diff_cbt_conv',\n",
+       " 'temp_vdiffuse_impl',\n",
+       " 'temp_xflux_adv',\n",
+       " 'temp_yflux_adv',\n",
+       " 'time_bounds',\n",
+       " 'tmask',\n",
+       " 'total_net_sfc_heating',\n",
+       " 'total_ocean_calving',\n",
+       " 'total_ocean_calving_heat',\n",
+       " 'total_ocean_calving_melt_heat',\n",
+       " 'total_ocean_evap',\n",
+       " 'total_ocean_evap_heat',\n",
+       " 'total_ocean_fprec',\n",
+       " 'total_ocean_fprec_melt_heat',\n",
+       " 'total_ocean_heat',\n",
+       " 'total_ocean_hflux_coupler',\n",
+       " 'total_ocean_hflux_evap',\n",
+       " 'total_ocean_hflux_prec',\n",
+       " 'total_ocean_lprec',\n",
+       " 'total_ocean_lw_heat',\n",
+       " 'total_ocean_melt',\n",
+       " 'total_ocean_pme_river',\n",
+       " 'total_ocean_river',\n",
+       " 'total_ocean_river_heat',\n",
+       " 'total_ocean_runoff',\n",
+       " 'total_ocean_runoff_heat',\n",
+       " 'total_ocean_salt',\n",
+       " 'total_ocean_sens_heat',\n",
+       " 'total_ocean_sfc_salt_flux_coupler',\n",
+       " 'total_ocean_swflx',\n",
+       " 'total_ocean_swflx_vis',\n",
+       " 'tx_trans',\n",
+       " 'tx_trans_int_z',\n",
+       " 'tx_trans_rho',\n",
+       " 'tx_trans_submeso',\n",
+       " 'ty_trans',\n",
+       " 'ty_trans_int_z',\n",
+       " 'ty_trans_nrho_submeso',\n",
+       " 'ty_trans_rho',\n",
+       " 'ty_trans_submeso',\n",
+       " 'u',\n",
+       " 'u_dot_grad_vert_pv',\n",
+       " 'uarea',\n",
+       " 'uatm_m',\n",
+       " 'uhrho_et',\n",
+       " 'uocn_m',\n",
+       " 'usurf',\n",
+       " 'uvel_m',\n",
+       " 'v',\n",
+       " 'vatm_m',\n",
+       " 'vert_pv',\n",
+       " 'vhrho_nt',\n",
+       " 'vicen_m',\n",
+       " 'vocn_m',\n",
+       " 'vsurf',\n",
+       " 'vvel_m',\n",
+       " 'wfiform',\n",
+       " 'wfimelt',\n",
+       " 'wt']"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cat_subset = catalog[experiment]\n",
+    "sorted(set().union(*cat_subset.df['variable']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
    "id": "ab5a694c-b2a2-4f1d-9366-9a9facbf3fe5",
    "metadata": {
     "tags": []
    },
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "CPU times: user 10.9 s, sys: 2.41 s, total: 13.3 s\n",
-      "Wall time: 1min 5s\n"
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.07/lib/python3.10/site-packages/intake_esm/cat.py:270: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
+      "  .applymap(type)\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.07/lib/python3.10/site-packages/intake_esm/cat.py:270: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
+      "  .applymap(type)\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.07/lib/python3.10/site-packages/intake_esm/cat.py:270: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
+      "  .applymap(type)\n"
+     ]
+    },
+    {
+     "ename": "KeyError",
+     "evalue": "0",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.07/lib/python3.10/site-packages/pandas/core/indexes/range.py:414\u001b[0m, in \u001b[0;36mRangeIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    413\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 414\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_range\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnew_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    415\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
+      "\u001b[0;31mValueError\u001b[0m: 0 is not in range",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "File \u001b[0;32m<timed exec>:3\u001b[0m\n",
+      "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.07/lib/python3.10/site-packages/pandas/core/series.py:1040\u001b[0m, in \u001b[0;36mSeries.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1037\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[key]\n\u001b[1;32m   1039\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m key_is_scalar:\n\u001b[0;32m-> 1040\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_value\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1042\u001b[0m \u001b[38;5;66;03m# Convert generator to list before going through hashable part\u001b[39;00m\n\u001b[1;32m   1043\u001b[0m \u001b[38;5;66;03m# (We will iterate through the generator there to check for slices)\u001b[39;00m\n\u001b[1;32m   1044\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n",
+      "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.07/lib/python3.10/site-packages/pandas/core/series.py:1156\u001b[0m, in \u001b[0;36mSeries._get_value\u001b[0;34m(self, label, takeable)\u001b[0m\n\u001b[1;32m   1153\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[label]\n\u001b[1;32m   1155\u001b[0m \u001b[38;5;66;03m# Similar to Index.get_value, but we do not fall back to positional\u001b[39;00m\n\u001b[0;32m-> 1156\u001b[0m loc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1158\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(loc):\n\u001b[1;32m   1159\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[loc]\n",
+      "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.07/lib/python3.10/site-packages/pandas/core/indexes/range.py:416\u001b[0m, in \u001b[0;36mRangeIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_range\u001b[38;5;241m.\u001b[39mindex(new_key)\n\u001b[1;32m    415\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m--> 416\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m    417\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Hashable):\n\u001b[1;32m    418\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key)\n",
+      "\u001b[0;31mKeyError\u001b[0m: 0"
      ]
     }
    ],
    "source": [
     "%%time\n",
     "\n",
-    "yt_ocean = cc.querying.getvar(experiment, 'yt_ocean', session, n=1)\n",
+    "cat_subset = catalog[experiment]\n",
+    "var_search = cat_subset.search(variable='yt_ocean')\n",
+    "var_search = var_search.search(path=var_search.df['path'][0])\n",
+    "darray = var_search.to_dask()\n",
+    "darray = darray['yt_ocean']\n",
+    "yt_ocean = darray\n",
     "yt_ocean = yt_ocean.sel(yt_ocean = lat_range)\n",
     "\n",
-    "xt_ocean = cc.querying.getvar(experiment, 'xt_ocean', session, n=1)\n",
-    "\n",
-    "yu_ocean = cc.querying.getvar(experiment, 'yu_ocean', session, n=1)\n",
+    "cat_subset = catalog[experiment]\n",
+    "var_search = cat_subset.search(variable='xt_ocean')\n",
+    "var_search = var_search.search(path=var_search.df['path'][0])\n",
+    "darray = var_search.to_dask()\n",
+    "darray = darray['xt_ocean']\n",
+    "xt_ocean = darray\n",
+    "\n",
+    "cat_subset = catalog[experiment]\n",
+    "var_search = cat_subset.search(variable='yu_ocean')\n",
+    "var_search = var_search.search(path=var_search.df['path'][0])\n",
+    "darray = var_search.to_dask()\n",
+    "darray = darray['yu_ocean']\n",
+    "yu_ocean = darray\n",
     "yu_ocean = yu_ocean.sel(yu_ocean = lat_range)\n",
     "\n",
-    "xu_ocean = cc.querying.getvar(experiment, 'xu_ocean', session, n=1)"
+    "cat_subset = catalog[experiment]\n",
+    "var_search = cat_subset.search(variable='xu_ocean')\n",
+    "var_search = var_search.search(path=var_search.df['path'][0])\n",
+    "darray = var_search.to_dask()\n",
+    "darray = darray['xu_ocean']\n",
+    "xu_ocean = darray"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "577743fb-7cd1-4a43-b4f2-8dcef92aaaac",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'yt_ocean' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[43myt_ocean\u001b[49m) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(yu_ocean):\n\u001b[1;32m      2\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhelp! y different size\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m yt_ocean\u001b[38;5;241m.\u001b[39mmin(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124myt_ocean\u001b[39m\u001b[38;5;124m'\u001b[39m)\u001b[38;5;241m>\u001b[39m yu_ocean\u001b[38;5;241m.\u001b[39mmin(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124myu_ocean\u001b[39m\u001b[38;5;124m'\u001b[39m):\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'yt_ocean' is not defined"
+     ]
+    }
+   ],
    "source": [
     "if len(yt_ocean) != len(yu_ocean):\n",
     "    print('help! y different size')\n",
@@ -624,7 +617,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "21118fd1-2274-444d-80c2-30bf6cc55ce1",
    "metadata": {},
    "outputs": [],
@@ -632,7 +625,12 @@
     "start_time = '2170-01-01'\n",
     "end_time = '2170-12-31'\n",
     "time_slice = slice(start_time, end_time)\n",
-    "ssh = cc.querying.getvar(experiment,'sea_level', session, start_time = start_time, end_time = end_time)\n",
+    "cat_subset = catalog[experiment]\n",
+    "var_search = cat_subset.search(variable='sea_level')\n",
+    "darray = var_search.to_dask()\n",
+    "darray = darray['sea_level']\n",
+    "darray = darray.sel(time=slice(start_time, end_time))\n",
+    "ssh = darray\n",
     "\n",
     "# select one year and latitude range\n",
     "ssh = ssh.sel(yt_ocean = lat_range, time = time_slice)\n",
@@ -653,7 +651,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "2cc4a6dc-393b-45e7-923a-f543f681344c",
    "metadata": {},
    "outputs": [],
@@ -671,23 +669,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "c000a8da-6309-4d9b-a6e6-f2f4518a2de9",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig = plt.figure(figsize = (10, 4))\n",
     "\n",
@@ -698,7 +683,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "e2ca6843-4f28-4b5b-805f-99f858cf482e",
    "metadata": {},
    "outputs": [],
@@ -711,7 +696,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "1a1ca948-228b-4e97-a3e7-874932ece34e",
    "metadata": {},
    "outputs": [],
@@ -735,23 +720,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "76fbe005-ed16-43d3-b443-a16819602fac",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig = plt.figure(figsize = (10, 4))\n",
     "count = 0\n",
@@ -783,7 +755,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "bdb41806-ce8d-447b-825c-9020ffe6b276",
    "metadata": {},
    "outputs": [],
@@ -801,7 +773,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "5ecb5141-0e70-4dd7-b209-db2f260994bb",
    "metadata": {},
    "outputs": [],
@@ -815,7 +787,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "a95b0816-b795-4242-968f-58cfb86e85c6",
    "metadata": {},
    "outputs": [],
@@ -836,23 +808,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "910fea81-3d88-4c41-84d1-1ef8bcefeb52",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig = plt.figure(figsize=(10, 5))\n",
     "\n",
@@ -870,7 +829,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "id": "c78519e6-0b37-4b9a-bf38-518da3e66f25",
    "metadata": {},
    "outputs": [],
@@ -895,7 +854,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "fd86bf32-0a0d-485a-8abb-49e05db24bed",
    "metadata": {},
    "outputs": [],
@@ -906,7 +865,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "id": "a345555f-6878-433a-8a9e-78e8785ecd16",
    "metadata": {},
    "outputs": [],
@@ -925,7 +884,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "id": "dce023e5-a888-4bd4-87a1-9725cc306628",
    "metadata": {},
    "outputs": [],
@@ -942,7 +901,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "id": "a6a007ad-69ed-4897-b6ab-7e3d0ad258d1",
    "metadata": {},
    "outputs": [],
@@ -955,23 +914,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "id": "b84dab94-95a5-4846-8002-c101c79199e4",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.figure(1, figsize=(16, 8))\n",
     "contour_mask.plot(extend='both');"
@@ -1000,7 +946,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "id": "e77f5ab9-6322-46b9-99ac-f76c20218e1d",
    "metadata": {},
    "outputs": [],
@@ -1051,23 +997,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "id": "fd9d5bcb-7cb4-4fa5-b054-5941c848edb1",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.figure(1, figsize=(16, 8))\n",
     "\n",
@@ -1088,7 +1021,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "id": "44a8c118-640e-44c2-89c2-55f4e5bf1b7a",
    "metadata": {},
    "outputs": [],
@@ -1131,8 +1064,7 @@
     "        mask_y_transport[index_j-1, index_i] = 1\n",
     "        mask_y_transport[index_j, index_i] = -1        \n",
     "        mask_y_transport_numbered[index_j-1, index_i] = new_number_count\n",
-    "        mask_y_transport_numbered[index_j, index_i] = new_number_count+1\n",
-    "        new_number_count += 2\n",
+    "        new_number_count += 1\n",
     "    # if point to right is towards Antarctica and point to left is away from Antarctica:\n",
     "    # zonal indices increased by 1 due to halos\n",
     "    # take transport grid point on right of t grid:\n",
@@ -1151,29 +1083,15 @@
     "        mask_x_transport[index_j, index_i-1] = 1\n",
     "        mask_x_transport[index_j, index_i] = -1        \n",
     "        mask_x_transport_numbered[index_j, index_i-1] = new_number_count\n",
-    "        mask_x_transport_numbered[index_j, index_i] = new_number_count+1\n",
-    "        new_number_count += 2"
+    "        new_number_count += 1"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "id": "bd99120e-ff5a-423e-a9af-cf8bbf8082f6",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.figure(1, figsize=(16, 8))\n",
     "\n",
@@ -1195,7 +1113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "id": "e60e7a18-e049-496e-9077-c7758372ab56",
    "metadata": {},
    "outputs": [],
@@ -1223,23 +1141,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "id": "439dc694-1436-4843-85c4-25f6853b02e9",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.figure(1, figsize=(16, 8))\n",
     "\n",
@@ -1256,7 +1161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "id": "a8538013-8bd3-4cc8-804a-528ae89d6c37",
    "metadata": {},
    "outputs": [],
@@ -1288,15 +1193,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "id": "c8043e3a-d4b2-4ee7-969d-db569336e4be",
    "metadata": {},
    "outputs": [],
    "source": [
-    "ty_trans = cc.querying.getvar(experiment, 'ty_trans', session, start_time = start_time, end_time = end_time)\n",
+    "cat_subset = catalog[experiment]\n",
+    "var_search = cat_subset.search(variable='ty_trans')\n",
+    "darray = var_search.to_dask()\n",
+    "darray = darray['ty_trans']\n",
+    "darray = darray.sel(time=slice(start_time, end_time))\n",
+    "ty_trans = darray\n",
     "ty_trans = ty_trans.sel(yu_ocean = lat_range, time = time_slice)\n",
     "\n",
-    "tx_trans = cc.querying.getvar(experiment, 'tx_trans', session, start_time = start_time, end_time = end_time)\n",
+    "cat_subset = catalog[experiment]\n",
+    "var_search = cat_subset.search(variable='tx_trans')\n",
+    "darray = var_search.to_dask()\n",
+    "darray = darray['tx_trans']\n",
+    "darray = darray.sel(time=slice(start_time, end_time))\n",
+    "tx_trans = darray\n",
     "tx_trans = tx_trans.sel(yt_ocean = lat_range, time = time_slice)\n",
     "\n",
     "ty_trans = ty_trans.rename({'yu_ocean': 'y_ocean', 'xt_ocean': 'x_ocean'})\n",
@@ -1313,19 +1228,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "id": "c9d42b7d-967a-4d47-bac2-e664032f6ecb",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 25.8 s, sys: 3.52 s, total: 29.4 s\n",
-      "Wall time: 55 s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "# weighed time mean by month length\n",
@@ -1349,7 +1255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "id": "7f0b74fb-b530-4dbb-94ba-5b40f5b2803e",
    "metadata": {},
    "outputs": [],
@@ -1370,19 +1276,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "id": "e7cb043a-e3c2-4fb1-9dd6-82e2f92ba645",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 4 s, sys: 219 ms, total: 4.22 s\n",
-      "Wall time: 4.17 s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "## We could also loop in time if we didn't want the time average. Initialise a data array and fill in data by looping in time.\n",
@@ -1402,23 +1299,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "id": "6b0aba54-80b5-4812-9c70-e2963137db41",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, ax = plt.subplots(figsize = (10, 4))\n",
     "\n",
@@ -1436,7 +1320,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "id": "f726f72d-baeb-4230-9e6e-988245694331",
    "metadata": {},
    "outputs": [],
@@ -1470,25 +1354,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "id": "05958ea6-d747-4dff-868e-529930e7a0cb",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 305 ms, sys: 39.4 ms, total: 345 ms\n",
-      "Wall time: 923 ms\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
-    "dxu = cc.querying.getvar(experiment, 'dxu', session, ncfile = 'ocean_grid.nc', n=1)\n",
+    "cat_subset = catalog[experiment]\n",
+    "var_search = cat_subset.search(variable='dxu')\n",
+    "var_search = var_search.search(path=var_search.df['path'][0])\n",
+    "darray = var_search.to_dask()\n",
+    "darray = darray['dxu']\n",
+    "dxu = darray\n",
     "dxu = dxu.sel(yu_ocean = lat_range)\n",
     "\n",
-    "dyt = cc.querying.getvar(experiment, 'dyt', session, ncfile = 'ocean_grid.nc', n=1)\n",
+    "cat_subset = catalog[experiment]\n",
+    "var_search = cat_subset.search(variable='dyt')\n",
+    "var_search = var_search.search(path=var_search.df['path'][0])\n",
+    "darray = var_search.to_dask()\n",
+    "darray = darray['dyt']\n",
+    "dyt = darray\n",
     "dyt = dyt.sel(yt_ocean = lat_range)\n",
     "\n",
     "num_points = len(lat_along_contour)"
@@ -1496,19 +1381,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "id": "616aad75-af80-44bf-8bf3-c591035cd469",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 5min 13s, sys: 17.9 s, total: 5min 31s\n",
-      "Wall time: 10min 9s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "# if there is a bend in the contour, add the distance using the half-length of the diagonal\n",
@@ -1583,7 +1459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
    "id": "c31b7d6b-b0ce-427a-b4ef-7097326a9d0b",
    "metadata": {},
    "outputs": [],
@@ -1623,7 +1499,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": null,
    "id": "5c992571-c6fd-48d9-92e2-481c813c3424",
    "metadata": {},
    "outputs": [],
@@ -1633,23 +1509,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "id": "58dec990-919e-4ec7-8ff4-cc18e763a527",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, axes = plt.subplots(nrows = 2, figsize = (10, 8))\n",
     "\n",
@@ -1677,21 +1540,13 @@
    "source": [
     "We can see that there is a net northward transport across the Antarctic Circumpolar Current in the top 500m - this is the Ekman wind-driven transport. We could then choose to extract the density (or salt and temperature) along this same path, do this by interpolating density to the north and eastern edge of t-cells. Then we could bin the transports in each depth level into the corresponding density, to determine the transport across the contour in density space. An example of this calculation can be found in https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Save_and_bin_along_contours.ipynb. "
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d47d6e63-e147-4c7f-876a-b2057ec59d4a",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:analysis3-22.07]",
+   "display_name": "Python [conda env:analysis3-23.07] *",
    "language": "python",
-   "name": "conda-env-analysis3-22.07-py"
+   "name": "conda-env-analysis3-23.07-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1703,7 +1558,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.15"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,

From 35205a44568cb968f50a783cb8a0db8a9b898c74 Mon Sep 17 00:00:00 2001
From: Taimoor Sohail <taimoorsohail@gmail.com>
Date: Mon, 8 Jul 2024 17:51:54 +1000
Subject: [PATCH 2/7] Added code to do simple along-isobath averaging

---
 .../Along_Isobath_Average.ipynb               | 1811 +++++++++++++++++
 1 file changed, 1811 insertions(+)
 create mode 100644 ContributedExamples/Along_Isobath_Average.ipynb

diff --git a/ContributedExamples/Along_Isobath_Average.ipynb b/ContributedExamples/Along_Isobath_Average.ipynb
new file mode 100644
index 00000000..684ed649
--- /dev/null
+++ b/ContributedExamples/Along_Isobath_Average.ipynb
@@ -0,0 +1,1811 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "723a3b22-c5f9-4a34-b278-d36e29e0c296",
+   "metadata": {},
+   "source": [
+    "## <center> This code shows how to do an along-isobath averging for scalar properties (and vector properties not rotated to along- and across-contour) in ACCESS-OM2-01\n",
+    "\n",
+    "### <center> This code is relevant for Antarctic shelf analyses where an average over a section of cross-shelf regions is needed "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7c9575fe-e29f-4649-95fe-328c2aaf563d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Some caveats: \n",
+    "\n",
+    "# 1) If there are deep valleys or troughs around the coastline, those will be averaged with \n",
+    "# deeper isobaths further offshore. Careful adjustment of the mask is necessary to ensure your \n",
+    "# results are interpretable. \n",
+    "\n",
+    "# Please also inspect the ht_coarse and hu_coarse variables derived from the variables of interest. \n",
+    "\n",
+    "# 2) More caveats should be added here! \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a476d924-8b6d-4c03-801b-386e07944e2f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load necessary modules\n",
+    "import cosima_cookbook as cc\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import xarray as xr\n",
+    "from xhistogram.xarray import histogram\n",
+    "from dask.distributed import Client"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e3cc23c9-8622-4a9a-9b5f-2313a8553955",
+   "metadata": {},
+   "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-178f8365-3ce6-11ef-9d62-00000792fe80</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;\">2d40c3ce</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> 7\n",
+       "                </td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Total threads:</strong> 28\n",
+       "                </td>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Total memory:</strong> 251.18 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-e7dfc6d9-4c4d-48fc-a28a-1f34694fe362</p>\n",
+       "            <table style=\"width: 100%; text-align: left;\">\n",
+       "                <tr>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Comm:</strong> tcp://127.0.0.1:40319\n",
+       "                    </td>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Workers:</strong> 7\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> 28\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> 251.18 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://127.0.0.1:39895\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 4\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/35499/status\" target=\"_blank\">/proxy/35499/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 35.88 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:43033\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/119946825.gadi-pbs/dask-scratch-space/worker-t02y4tr9\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://127.0.0.1:35735\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 4\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/33933/status\" target=\"_blank\">/proxy/33933/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 35.88 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:39603\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/119946825.gadi-pbs/dask-scratch-space/worker-14xntlxh\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://127.0.0.1:42027\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 4\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/36063/status\" target=\"_blank\">/proxy/36063/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 35.88 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:34129\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/119946825.gadi-pbs/dask-scratch-space/worker-b9qivda0\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://127.0.0.1:42289\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 4\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/36099/status\" target=\"_blank\">/proxy/36099/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 35.88 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:41595\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/119946825.gadi-pbs/dask-scratch-space/worker-zyooigd0\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://127.0.0.1:34779\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 4\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/34901/status\" target=\"_blank\">/proxy/34901/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 35.88 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:40997\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/119946825.gadi-pbs/dask-scratch-space/worker-3ondu3xa\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://127.0.0.1:40755\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 4\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/35179/status\" target=\"_blank\">/proxy/35179/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 35.88 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:35463\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/119946825.gadi-pbs/dask-scratch-space/worker-oehf4b1g\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://127.0.0.1:34305\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 4\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/46203/status\" target=\"_blank\">/proxy/46203/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 35.88 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:34645\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/119946825.gadi-pbs/dask-scratch-space/worker-1gvrgi9b\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://127.0.0.1:40319' processes=7 threads=28, memory=251.18 GiB>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "client = Client()\n",
+    "client"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "665b7147-a240-47f2-b0ad-2b45b7d8dc96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "session = cc.database.create_session()\n",
+    "# Define experiment in database - here we choose the RYF90-91 experiment #\n",
+    "expt = '01deg_jra55v13_ryf9091'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "fefd4fa9-9208-45d9-ae1b-a19f8062a715",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define the latitude bounds and the start and end times #\n",
+    "lat_min, lat_max = -80, -59\n",
+    "lat_slice = slice(lat_min, lat_max)\n",
+    "start_time, end_time = '2170-01-01', '2180-12-31'\n",
+    "\n",
+    "## Note that the bathymetry provided by ht and hu is not discretised into the same bins as st_ocean(_edges)\n",
+    "## The histogram function in this code thus yields uneven bins if we bin ht by st_ocean_edges bins\n",
+    "## As a result, we use ht and hu for our masking, but we obtain bottom bathymetry from the variable itself,\n",
+    "## which IS discretised into st_ocean bins \n",
+    "\n",
+    "# Import bathymetry - t-grid #\n",
+    "ht = cc.querying.getvar(expt, 'ht', session, n=1)\n",
+    "ht = ht.sel(yt_ocean=lat_slice)\n",
+    "# Import bathymetry - u-grid #\n",
+    "hu = cc.querying.getvar(expt, 'hu', session, n=1)\n",
+    "hu = hu.sel(yu_ocean=lat_slice)\n",
+    "\n",
+    "# Import properties to average along isobaths #\n",
+    "# This code works for scalar properties and for vectors oriented along and across the model grid (not along- and cross-isobath)\n",
+    "\n",
+    "temp = cc.querying.getvar(expt, 'temp', session, start_time=start_time, end_time=end_time, frequency='1 monthly')\n",
+    "temp = temp.sel(time=slice(start_time, end_time)).sel(yt_ocean=lat_slice)\n",
+    "salt = cc.querying.getvar(expt, 'salt', session, start_time=start_time, end_time=end_time, frequency='1 monthly')\n",
+    "salt = salt.sel(time=slice(start_time, end_time)).sel(yt_ocean=lat_slice)\n",
+    "density = cc.querying.getvar(expt, 'pot_rho_0', session, start_time=start_time, end_time=end_time, frequency='1 monthly')\n",
+    "density = density.sel(time=slice(start_time,end_time)).sel(yt_ocean=lat_slice)\n",
+    "u = cc.querying.getvar(expt, 'u', session, start_time=start_time, end_time=end_time, frequency='1 monthly')\n",
+    "u = u.sel(time=slice(start_time, end_time)).sel(yu_ocean=lat_slice)\n",
+    "v = cc.querying.getvar(expt, 'v', session, start_time=start_time, end_time=end_time, frequency='1 monthly')\n",
+    "v = v.sel(time=slice(start_time, end_time)).sel(yu_ocean=lat_slice)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "aba39329-3227-4429-9d96-061fdfcee5c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 18.4 s, sys: 9.45 s, total: 27.8 s\n",
+      "Wall time: 29.8 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "## Calulating a Volume and area for weighted averaging using xhistogram:\n",
+    "## Note that the thickness here is NOT time-varying, use\n",
+    "## dzt for time-varying thickness if necessary.\n",
+    "\n",
+    "xt_ocean = cc.querying.getvar(expt,'xt_ocean' , session,n=1)\n",
+    "yt_ocean = cc.querying.getvar(expt,'yt_ocean' , session,n=1)\n",
+    "yt_ocean = yt_ocean.sel(yt_ocean=lat_slice)\n",
+    "# import edges of st_ocean and add lat/lon dimensions:\n",
+    "st_edges_ocean = cc.querying.getvar(expt, 'st_edges_ocean', session, n=1)\n",
+    "st_edges_array = st_edges_ocean.expand_dims({'yt_ocean':yt_ocean,'xt_ocean':xt_ocean}, axis=[1,2])\n",
+    "\n",
+    "st_edges_with_partial = st_edges_array.where(st_edges_array<ht, other=ht)\n",
+    "thickness = st_edges_with_partial.diff(dim='st_edges_ocean')\n",
+    "\n",
+    "# change coordinate of thickness to st_ocean (needed for multipling with other variables):\n",
+    "st_ocean = cc.querying.getvar(expt, 'st_ocean', session, n=1)\n",
+    "thickness['st_edges_ocean'] = st_ocean.values\n",
+    "thickness = thickness.rename(({'st_edges_ocean':'st_ocean'}))\n",
+    "\n",
+    "# area for area averaging:\n",
+    "area_t = cc.querying.getvar(expt,'area_t',session,n=1)\n",
+    "area_t = area_t.sel(yt_ocean=lat_slice)\n",
+    "\n",
+    "volume = area_t*thickness\n",
+    "volume = volume.load()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "fe6137e2-0405-4f3f-a96c-d2893830bccf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 15.2 s, sys: 8.92 s, total: 24.1 s\n",
+      "Wall time: 25 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# As above, for the u-grid #\n",
+    "\n",
+    "xu_ocean = cc.querying.getvar(expt,'xu_ocean' , session,n=1)\n",
+    "yu_ocean = cc.querying.getvar(expt,'yu_ocean' , session,n=1)\n",
+    "yu_ocean = yu_ocean.sel(yu_ocean=lat_slice)\n",
+    "# import edges of st_ocean and add lat/lon dimensions:\n",
+    "st_edges_ocean = cc.querying.getvar(expt, 'st_edges_ocean', session, n=1)\n",
+    "st_edges_array = st_edges_ocean.expand_dims({'yu_ocean':yu_ocean,'xu_ocean':xu_ocean}, axis=[1,2])\n",
+    "\n",
+    "st_edges_with_partial = st_edges_array.where(st_edges_array<hu, other=hu)\n",
+    "thickness_u = st_edges_with_partial.diff(dim='st_edges_ocean')\n",
+    "\n",
+    "# change coordinate of thickness to st_ocean (needed for multipling with other variables):\n",
+    "st_ocean = cc.querying.getvar(expt, 'st_ocean', session, n=1)\n",
+    "thickness_u['st_edges_ocean'] = st_ocean.values\n",
+    "thickness_u = thickness_u.rename(({'st_edges_ocean':'st_ocean'}))\n",
+    "\n",
+    "# area for area averaging:\n",
+    "area_u = cc.querying.getvar(expt,'area_u',session,n=1)\n",
+    "area_u = area_u.sel(yu_ocean=lat_slice)\n",
+    "\n",
+    "volume_u = area_u*thickness_u\n",
+    "volume_u = volume_u.load()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "db710d5a-108e-4f1f-9f57-6a9b9e24df88",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "### DEFINE AND MASK YOUR REGION OF INTEREST ###\n",
+    "\n",
+    "# As a simple example, we mask out everything #\n",
+    "# but the Ross Sea shallower than 1000m #\n",
+    "\n",
+    "# As an alternative, you can define any polygon as your mask #\n",
+    "\n",
+    "lon_slice = slice(-200,-150)\n",
+    "mask_depth = 1000\n",
+    "\n",
+    "mask_t = ht.sel(xt_ocean=lon_slice) < mask_depth\n",
+    "mask_u = hu.sel(xu_ocean=lon_slice) < mask_depth\n",
+    "mask_t = mask_t.where(mask_t!=0)\n",
+    "mask_u = mask_u.where(mask_u!=0)\n",
+    "# Plot the mask that will be used #\n",
+    "mask_t.plot()\n",
+    "\n",
+    "# Mask out the variables of interest using the above mask\n",
+    "hu_masked = hu*mask_u\n",
+    "ht_masked = ht*mask_t\n",
+    "\n",
+    "volume_masked = volume*mask_t\n",
+    "volume_u_masked = volume_u*mask_u\n",
+    "area_t_masked = area_t*mask_t\n",
+    "area_u_masked = area_u*mask_u\n",
+    "temp_masked = temp*mask_t\n",
+    "salt_masked = salt*mask_t\n",
+    "density_masked = density*mask_t\n",
+    "u_masked = u*mask_u"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "f5e6b45f-5f55-45a2-8868-7b2b2fac241f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 4min 22s, sys: 8.94 s, total: 4min 31s\n",
+      "Wall time: 4min 52s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "## As mentioned above, we can't use ht and hu for the histogram binning below, as our isobath bins are st_edges_ocean, \n",
+    "## while ht and hu have much finer resolution, leading to uneven bins. \n",
+    "\n",
+    "## Below, we isolate the bottom st_ocean of temp (as ht_coarse) and u (as hu_coarse) that isn't NaN. This is the variable over\n",
+    "## which we perform the binning. Note that we assume that NaN grid cells are NaN at all times (i.e., ht_coarse and hu_coarse don't change over time)\n",
+    "\n",
+    "###### T-GRID ######\n",
+    "\n",
+    "# Create an empty DataArray with the same shape as 'temp_avg'\n",
+    "temp_avg = temp_masked.mean('time')\n",
+    "full_st_ocean_t = xr.full_like(temp_avg, np.nan)\n",
+    "\n",
+    "# Assign the 1D st_ocean coordinate to the new DataArray, broadcasting along the other dimensions\n",
+    "full_st_ocean_t.data = np.broadcast_to(temp_avg.st_ocean.values[:, np.newaxis, np.newaxis], temp_avg.shape)\n",
+    "full_st_ocean_t_masked = full_st_ocean_t*temp_avg/temp_avg # Here, we mask by temp_avg to ensure the Nans are preserved \n",
+    "\n",
+    "# Now 'full_st_ocean_t_masked' is a 3D DataArray with 'st_ocean' values broadcasted along yt_ocean and xt_ocean\n",
+    "# and masked by temp_avg\n",
+    "\n",
+    "# Find the bottom depth for each (yt_ocean, xt_ocean) coordinate\n",
+    "ht_coarse = full_st_ocean_t_masked.max(dim='st_ocean').compute().rename('ht_coarse')\n",
+    "\n",
+    "###### U-GRID ######\n",
+    "\n",
+    "# Create an empty DataArray with the same shape as 'u_avg'\n",
+    "u_avg = u_masked.mean('time')\n",
+    "full_st_ocean_u = xr.full_like(u_avg, np.nan)\n",
+    "\n",
+    "# Assign the 1D st_ocean coordinate to the new DataArray, broadcasting along the other dimensions\n",
+    "full_st_ocean_u.data = np.broadcast_to(u_avg.st_ocean.values[:, np.newaxis, np.newaxis], u_avg.shape)\n",
+    "full_st_ocean_u_masked = full_st_ocean_u*u_avg/u_avg # Here, we mask by u_avg to ensure the Nans are preserved \n",
+    "\n",
+    "# Now 'full_st_ocean_u_masked' is a 3D DataArray with 'st_ocean' values broadcasted along yu_ocean and xu_ocean\n",
+    "# and masked by u_avg\n",
+    "\n",
+    "# Find the bottom depth for each (yu_ocean, xu_ocean) coordinate\n",
+    "hu_coarse = full_st_ocean_u_masked.max(dim='st_ocean').compute().rename('hu_coarse')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "f1e6ed2b-c349-4246-a1cd-8bf61c47614d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'Longitude')"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "(ht_coarse-ht_masked).plot()\n",
+    "plt.title('Difference between the bottom bathymetry\\n in ht and actual variables [m]')\n",
+    "plt.ylabel('Latitude')\n",
+    "plt.xlabel('Longitude')\n",
+    "plt.show()\n",
+    "(hu_coarse-hu_masked).plot()\n",
+    "plt.title('Difference between the bottom bathymetry\\n in hu and actual variables [m]')\n",
+    "plt.ylabel('Latitude')\n",
+    "plt.xlabel('Longitude')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "2c90d8f7-c9d8-4580-a87a-af4ad37014ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n",
+      "  return func(*(_execute_task(a, cache) for a in args))\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n",
+      "  return func(*(_execute_task(a, cache) for a in args))\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n",
+      "  return func(*(_execute_task(a, cache) for a in args))\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n",
+      "  return func(*(_execute_task(a, cache) for a in args))\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n",
+      "  return func(*(_execute_task(a, cache) for a in args))\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n",
+      "  return func(*(_execute_task(a, cache) for a in args))\n",
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n",
+      "  return func(*(_execute_task(a, cache) for a in args))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 2min 38s, sys: 10.4 s, total: 2min 48s\n",
+      "Wall time: 3min 37s\n"
+     ]
+    },
+    {
+     "data": {
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+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;histogram_hu_coarse&#x27; (st_ocean: 75, time: 120,\n",
+       "                                         hu_coarse_bin: 46)&gt; Size: 3MB\n",
+       "array([[[        nan,         nan,         nan, ..., -0.05329067,\n",
+       "         -0.0545278 , -0.04532973],\n",
+       "        [        nan,         nan,         nan, ..., -0.07846384,\n",
+       "         -0.07439158, -0.06675977],\n",
+       "        [        nan,         nan,         nan, ..., -0.06968889,\n",
+       "         -0.06318065, -0.03509349],\n",
+       "        ...,\n",
+       "        [        nan,         nan,         nan, ..., -0.01335827,\n",
+       "         -0.01355833, -0.02252559],\n",
+       "        [        nan,         nan,         nan, ..., -0.01091509,\n",
+       "         -0.01020199, -0.01440421],\n",
+       "        [        nan,         nan,         nan, ..., -0.04878291,\n",
+       "         -0.04536789, -0.04028598]],\n",
+       "\n",
+       "       [[        nan,         nan,         nan, ..., -0.04996552,\n",
+       "         -0.05102612, -0.04109836],\n",
+       "        [        nan,         nan,         nan, ..., -0.07459088,\n",
+       "         -0.07073072, -0.06223585],\n",
+       "        [        nan,         nan,         nan, ..., -0.07006387,\n",
+       "         -0.06542959, -0.03951225],\n",
+       "...\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan]],\n",
+       "\n",
+       "       [[        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        ...,\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan]]])\n",
+       "Coordinates:\n",
+       "  * hu_coarse_bin  (hu_coarse_bin) float64 368B 0.5413 1.681 ... 843.0 926.6\n",
+       "Dimensions without coordinates: st_ocean, time</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'>'histogram_hu_coarse'</div><ul class='xr-dim-list'><li><span>st_ocean</span>: 75</li><li><span>time</span>: 120</li><li><span class='xr-has-index'>hu_coarse_bin</span>: 46</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-74f169e3-8cb6-495d-80c6-839337dbea75' class='xr-array-in' type='checkbox' checked><label for='section-74f169e3-8cb6-495d-80c6-839337dbea75' 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>nan nan nan nan nan nan -0.003532 ... nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[[        nan,         nan,         nan, ..., -0.05329067,\n",
+       "         -0.0545278 , -0.04532973],\n",
+       "        [        nan,         nan,         nan, ..., -0.07846384,\n",
+       "         -0.07439158, -0.06675977],\n",
+       "        [        nan,         nan,         nan, ..., -0.06968889,\n",
+       "         -0.06318065, -0.03509349],\n",
+       "        ...,\n",
+       "        [        nan,         nan,         nan, ..., -0.01335827,\n",
+       "         -0.01355833, -0.02252559],\n",
+       "        [        nan,         nan,         nan, ..., -0.01091509,\n",
+       "         -0.01020199, -0.01440421],\n",
+       "        [        nan,         nan,         nan, ..., -0.04878291,\n",
+       "         -0.04536789, -0.04028598]],\n",
+       "\n",
+       "       [[        nan,         nan,         nan, ..., -0.04996552,\n",
+       "         -0.05102612, -0.04109836],\n",
+       "        [        nan,         nan,         nan, ..., -0.07459088,\n",
+       "         -0.07073072, -0.06223585],\n",
+       "        [        nan,         nan,         nan, ..., -0.07006387,\n",
+       "         -0.06542959, -0.03951225],\n",
+       "...\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan]],\n",
+       "\n",
+       "       [[        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        ...,\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan]]])</pre></div></div></li><li class='xr-section-item'><input id='section-ac425447-a37a-4640-85a5-43c7d4a31bea' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ac425447-a37a-4640-85a5-43c7d4a31bea' class='xr-section-summary' >Coordinates: <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-var-name'><span class='xr-has-index'>hu_coarse_bin</span></div><div class='xr-var-dims'>(hu_coarse_bin)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5413 1.681 2.94 ... 843.0 926.6</div><input id='attrs-2d4f2358-8625-41d2-8eb7-200c2ef35510' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2d4f2358-8625-41d2-8eb7-200c2ef35510' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b90cbd75-0748-4373-8a97-c66755fd3b8a' class='xr-var-data-in' type='checkbox'><label for='data-b90cbd75-0748-4373-8a97-c66755fd3b8a' 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([5.412808e-01, 1.680735e+00, 2.939953e+00, 4.331521e+00, 5.869350e+00,\n",
+       "       7.568810e+00, 9.446885e+00, 1.152234e+01, 1.381593e+01, 1.635055e+01,\n",
+       "       1.915154e+01, 2.224687e+01, 2.566746e+01, 2.944746e+01, 3.362460e+01,\n",
+       "       3.824057e+01, 4.334140e+01, 4.897796e+01, 5.520640e+01, 6.208874e+01,\n",
+       "       6.969342e+01, 7.809601e+01, 8.737988e+01, 9.763700e+01, 1.089687e+02,\n",
+       "       1.214869e+02, 1.353144e+02, 1.505868e+02, 1.674530e+02, 1.860765e+02,\n",
+       "       2.066365e+02, 2.293296e+02, 2.543701e+02, 2.819920e+02, 3.124492e+02,\n",
+       "       3.460166e+02, 3.829906e+02, 4.236883e+02, 4.684475e+02, 5.176242e+02,\n",
+       "       5.715899e+02, 6.307275e+02, 6.954248e+02, 7.660668e+02, 8.430255e+02,\n",
+       "       9.266482e+02])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d9b4019c-25f0-4723-8d60-fb3c1fffaf5f' class='xr-section-summary-in' type='checkbox'  ><label for='section-d9b4019c-25f0-4723-8d60-fb3c1fffaf5f' 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>hu_coarse_bin</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8e9b19ce-a682-4eef-9818-d584ee09ef79' class='xr-index-data-in' type='checkbox'/><label for='index-8e9b19ce-a682-4eef-9818-d584ee09ef79' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.5412807653916101,  1.680734679831433,  2.939952648914465,\n",
+       "        4.331521485149508, 5.8693504240540255, 7.5688099200502155,\n",
+       "        9.446884959648713, 11.522344392803275, 13.815927932333222,\n",
+       "       16.350552632935347, 19.151540835805992,  22.24687175218868,\n",
+       "       25.667459058670023, 29.447457087455206,  33.62459841385048,\n",
+       "        38.24056586677453, 43.341402211041604,  48.97796096317406,\n",
+       "        55.20640199365842, 62.088735720175855,  69.69341978534455,\n",
+       "        78.09601210263148,  87.37988400213499,  97.63699685168024,\n",
+       "       108.96874488209758, 121.48686590184016, 135.31441999260392,\n",
+       "        150.5868339523135, 167.45300594349283, 186.07646019149857,\n",
+       "       206.63653528423634,  229.3295811897483,  254.3701289430592,\n",
+       "       281.99198245469614, 312.44916351368306,  346.0166182176396,\n",
+       "        382.9905653992972,  423.6883357450478, 468.44751540395634,\n",
+       "        517.6241725363747,  571.5899153226626,  630.7275131613291,\n",
+       "        695.4248214962124,  766.0667992097763,  843.0255127651847,\n",
+       "         926.648198508397],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;hu_coarse_bin&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d9b651a8-262d-432d-b5d6-08116969168a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d9b651a8-262d-432d-b5d6-08116969168a' 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 'histogram_hu_coarse' (st_ocean: 75, time: 120,\n",
+       "                                         hu_coarse_bin: 46)> Size: 3MB\n",
+       "array([[[        nan,         nan,         nan, ..., -0.05329067,\n",
+       "         -0.0545278 , -0.04532973],\n",
+       "        [        nan,         nan,         nan, ..., -0.07846384,\n",
+       "         -0.07439158, -0.06675977],\n",
+       "        [        nan,         nan,         nan, ..., -0.06968889,\n",
+       "         -0.06318065, -0.03509349],\n",
+       "        ...,\n",
+       "        [        nan,         nan,         nan, ..., -0.01335827,\n",
+       "         -0.01355833, -0.02252559],\n",
+       "        [        nan,         nan,         nan, ..., -0.01091509,\n",
+       "         -0.01020199, -0.01440421],\n",
+       "        [        nan,         nan,         nan, ..., -0.04878291,\n",
+       "         -0.04536789, -0.04028598]],\n",
+       "\n",
+       "       [[        nan,         nan,         nan, ..., -0.04996552,\n",
+       "         -0.05102612, -0.04109836],\n",
+       "        [        nan,         nan,         nan, ..., -0.07459088,\n",
+       "         -0.07073072, -0.06223585],\n",
+       "        [        nan,         nan,         nan, ..., -0.07006387,\n",
+       "         -0.06542959, -0.03951225],\n",
+       "...\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan]],\n",
+       "\n",
+       "       [[        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        ...,\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan]]])\n",
+       "Coordinates:\n",
+       "  * hu_coarse_bin  (hu_coarse_bin) float64 368B 0.5413 1.681 ... 843.0 926.6\n",
+       "Dimensions without coordinates: st_ocean, time"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "### This section bins variables of interest by isobath using xhistogram #\n",
+    "\n",
+    "# First, we define the edges of the isobath bins - it makes sense for these to be the same as st_edges_ocean #\n",
+    "# because that is the vertical resolution of the ACCESS-OM2-01 model #\n",
+    "bins_isobath = st_edges_ocean.where(st_edges_ocean<=mask_depth).dropna(dim='st_edges_ocean').values\n",
+    "\n",
+    "# Next, we sum the volume-weighted variable of interest along that isobath using xhistogram\n",
+    "V_sum = histogram(ht_coarse, bins = bins_isobath, dim = ['yt_ocean', 'xt_ocean'], weights = volume_masked)\n",
+    "Vu_sum = histogram(hu_coarse, bins = bins_isobath, dim = ['yu_ocean', 'xu_ocean'], weights = volume_u_masked)\n",
+    "\n",
+    "A_sum = histogram(ht_coarse, bins = bins_isobath, dim = ['yt_ocean', 'xt_ocean'], weights = area_t_masked)\n",
+    "Au_sum = histogram(hu_coarse, bins = bins_isobath, dim = ['yu_ocean', 'xu_ocean'], weights = area_u_masked)\n",
+    "\n",
+    "TV_sum = histogram(ht_coarse, bins = bins_isobath, dim = ['yt_ocean', 'xt_ocean'], weights = volume_masked*temp_masked)\n",
+    "SV_sum = histogram(ht_coarse, bins = bins_isobath, dim = ['yt_ocean', 'xt_ocean'], weights = volume_masked*salt_masked)\n",
+    "rhoV_sum = histogram(ht_coarse, bins = bins_isobath, dim = ['yt_ocean', 'xt_ocean'], weights = volume_masked*density_masked)\n",
+    "uV_sum = histogram(hu_coarse, bins = bins_isobath, dim = ['yu_ocean', 'xu_ocean'], weights = volume_u_masked*u_masked)\n",
+    "\n",
+    "# Finally, we divide the summed, volume-weighted variable of interest by volume, to get a mean along an isobath\n",
+    "\n",
+    "T_mean = TV_sum/V_sum\n",
+    "S_mean = SV_sum/V_sum\n",
+    "rho_mean = rhoV_sum/V_sum\n",
+    "u_mean = uV_sum/Vu_sum\n",
+    "\n",
+    "T_mean.load()\n",
+    "rho_mean.load()\n",
+    "S_mean.load()\n",
+    "u_mean.load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "ddcb8196-587e-4bfc-98a9-4ef992ea20fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## The binning above creates an averaged field as a function of isobath ##\n",
+    "# Let's plot this to see what it looks like \n",
+    "\n",
+    "plt.pcolor(T_mean.ht_coarse_bin, st_ocean, T_mean.mean('time')-273.15)\n",
+    "plt.gca().invert_yaxis()\n",
+    "plt.ylim(1000,0)\n",
+    "plt.xlabel('Isobath [m]')\n",
+    "plt.ylabel('Depth [m]')\n",
+    "plt.colorbar(label = 'Temperature [$^\\circ$C]')\n",
+    "plt.show()\n",
+    "\n",
+    "## Each variable is averaged along isobaths, so the bathymetry looks diagonal in the plot #\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "4d16836d-bfe0-4824-8e23-e2fe2ef63619",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## We can rescale the isobath (x-axis) to be scaled by the cumulative \n",
+    "## surface area covered by each isobath, from shallowest to deepest.\n",
+    "\n",
+    "## This produces a 'normalised area' or pseduo-latitude x-axis ##\n",
+    "\n",
+    "normalised_area_cumsum = A_sum.cumsum('ht_coarse_bin')/np.nansum(A_sum)\n",
+    "normalised_area_cumsum_u = Au_sum.cumsum('hu_coarse_bin')/np.nansum(Au_sum)\n",
+    "\n",
+    "lat_max, lat_min = abs(mask_t.yt_ocean.max()), abs(mask_t.yt_ocean.min())\n",
+    "\n",
+    "pseudo_lat = -1*(lat_min-(lat_min-lat_max)*normalised_area_cumsum)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c2c4084f-9c14-48eb-aeec-79cd8fac6b0f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Here, we plot the averaged quantities as a function of normalised area (between 0 and 1)\n",
+    "## and pseduo-latitude\n",
+    "\n",
+    "\n",
+    "fig, axs = plt.subplots(2,2, figsize=(24*0.5,10), facecolor='w', edgecolor='k')\n",
+    "fig.subplots_adjust(hspace = 0.15, wspace=0.2)\n",
+    "axs = axs.ravel()\n",
+    "\n",
+    "fontsize = 13\n",
+    "\n",
+    "\n",
+    "im1 = axs[0].contourf(normalised_area_cumsum, st_ocean, T_mean.mean('time')-273.15, levels=500, \\\n",
+    "                      vmin=-2, vmax=2, cmap=plt.cm.bwr)\n",
+    "im2 = axs[1].contourf(normalised_area_cumsum, st_ocean, S_mean.mean('time'), levels=500, vmin=34.1,  \\\n",
+    "                      vmax = 34.7, cmap=plt.cm.viridis)\n",
+    "im3 = axs[2].contourf(normalised_area_cumsum, st_ocean, rho_mean.mean('time')-1027, levels=500, \\\n",
+    "                      vmin=0.2, vmax=0.87, cmap=plt.cm.plasma)\n",
+    "im4 = axs[3].contourf(normalised_area_cumsum_u, st_ocean, u_mean.mean('time'), levels=500, \\\n",
+    "                      vmin=-0.06, vmax=0.06, cmap=plt.cm.bwr)\n",
+    "\n",
+    "density_contours = np.linspace(1027.7, 1028, 5)\n",
+    "cont=axs[0].contour(normalised_area_cumsum_u,st_ocean,rho_mean.mean('time'),density_contours,\n",
+    "                 colors='gray',zorder=2 ,fontsize=fontsize)\n",
+    "cont=axs[1].contour(normalised_area_cumsum_u,st_ocean,rho_mean.mean('time'),density_contours,\n",
+    "                 colors='gray',zorder=2 ,fontsize=fontsize)\n",
+    "cont=axs[2].contour(normalised_area_cumsum_u,st_ocean,rho_mean.mean('time'),density_contours,\n",
+    "                 colors='gray',zorder=2 ,fontsize=fontsize)\n",
+    "cont=axs[3].contour(normalised_area_cumsum_u,st_ocean,rho_mean.mean('time'),density_contours,\n",
+    "                 colors='gray',zorder=2 ,fontsize=fontsize)\n",
+    "\n",
+    "\n",
+    "for i in range(4):\n",
+    "    axs[i].invert_yaxis()\n",
+    "    axs[i].set_xlabel('Normalised distance from Antarctica')\n",
+    "    axs[i].set_ylim(mask_depth, 0)\n",
+    "\n",
+    "axs[0].set_ylabel('Depth [m]')\n",
+    "\n",
+    "cb1 = plt.colorbar(im1, ax=axs[0], fraction=0.03, pad=0.25, orientation = 'horizontal')\n",
+    "cb2 = plt.colorbar(im2, ax=axs[1], fraction=0.03, pad=0.25, orientation = 'horizontal')\n",
+    "cb3 = plt.colorbar(im3, ax=axs[2], fraction=0.03, pad=0.25, orientation = 'horizontal')\n",
+    "cb4 = plt.colorbar(im4, ax=axs[3], fraction=0.03, pad=0.25, orientation = 'horizontal')\n",
+    "\n",
+    "cb1.set_label('Time-mean Temp ($^\\circ$C)')\n",
+    "cb2.set_label('Time-mean Salinity (g/kg)')\n",
+    "cb3.set_label('Time-mean Density (+1027 kg/m$^3$)')\n",
+    "cb4.set_label('Time-mean Zonal Velocity (m/s)')\n",
+    "\n",
+    "cb1.set_ticks(np.linspace(np.min(T_mean.mean('time')-273.15), np.max(T_mean.mean('time')-273.15),5))\n",
+    "cb2.set_ticks(np.linspace(np.min(S_mean.mean('time')), np.max(S_mean.mean('time')),5))\n",
+    "cb3.set_ticks(np.linspace(np.min(rho_mean.mean('time')-1027), np.max(rho_mean.mean('time')-1027),5))\n",
+    "cb4.set_ticks(np.linspace(np.min(u_mean.mean('time')), np.max(u_mean.mean('time')),4))\n",
+    "\n",
+    "plt.suptitle('Isobath-averaged properties')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "0b3eae77-5079-454b-9fd3-acbea0fc269c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(2,2, figsize=(24*0.5,10), facecolor='w', edgecolor='k')\n",
+    "fig.subplots_adjust(hspace = 0.15, wspace=0.2)\n",
+    "axs = axs.ravel()\n",
+    "\n",
+    "fontsize = 13\n",
+    "\n",
+    "\n",
+    "im1 = axs[0].contourf(pseudo_lat, st_ocean, T_mean.mean('time')-273.15, levels=500, \\\n",
+    "                      vmin=-2, vmax=2, cmap=plt.cm.bwr)\n",
+    "im2 = axs[1].contourf(pseudo_lat, st_ocean, S_mean.mean('time'), levels=500, vmin=34.1,  \\\n",
+    "                      vmax = 34.7, cmap=plt.cm.viridis)\n",
+    "im3 = axs[2].contourf(pseudo_lat, st_ocean, rho_mean.mean('time')-1027, levels=500, \\\n",
+    "                      vmin=0.2, vmax=0.87, cmap=plt.cm.plasma)\n",
+    "im4 = axs[3].contourf(pseudo_lat, st_ocean, u_mean.mean('time'), levels=500, \\\n",
+    "                      vmin=-0.06, vmax=0.06, cmap=plt.cm.bwr)\n",
+    "\n",
+    "density_contours = np.linspace(1027.7, 1028, 5)\n",
+    "cont=axs[0].contour(pseudo_lat,st_ocean,rho_mean.mean('time'),density_contours,\n",
+    "                 colors='gray',zorder=2 ,fontsize=fontsize)\n",
+    "cont=axs[1].contour(pseudo_lat,st_ocean,rho_mean.mean('time'),density_contours,\n",
+    "                 colors='gray',zorder=2 ,fontsize=fontsize)\n",
+    "cont=axs[2].contour(pseudo_lat,st_ocean,rho_mean.mean('time'),density_contours,\n",
+    "                 colors='gray',zorder=2 ,fontsize=fontsize)\n",
+    "cont=axs[3].contour(pseudo_lat,st_ocean,rho_mean.mean('time'),density_contours,\n",
+    "                 colors='gray',zorder=2 ,fontsize=fontsize)\n",
+    "\n",
+    "\n",
+    "for i in range(4):\n",
+    "    axs[i].invert_yaxis()\n",
+    "    axs[i].set_xlabel('Representative latitude ($^\\circ$S)')\n",
+    "    axs[i].set_ylim(mask_depth, 0)\n",
+    "\n",
+    "axs[0].set_ylabel('Depth [m]')\n",
+    "\n",
+    "cb1 = plt.colorbar(im1, ax=axs[0], fraction=0.03, pad=0.25, orientation = 'horizontal')\n",
+    "cb2 = plt.colorbar(im2, ax=axs[1], fraction=0.03, pad=0.25, orientation = 'horizontal')\n",
+    "cb3 = plt.colorbar(im3, ax=axs[2], fraction=0.03, pad=0.25, orientation = 'horizontal')\n",
+    "cb4 = plt.colorbar(im4, ax=axs[3], fraction=0.03, pad=0.25, orientation = 'horizontal')\n",
+    "\n",
+    "cb1.set_label('Time-mean Temp ($^\\circ$C)')\n",
+    "cb2.set_label('Time-mean Salinity (g/kg)')\n",
+    "cb3.set_label('Time-mean Density (+1027 kg/m$^3$)')\n",
+    "cb4.set_label('Time-mean Zonal Velocity (m/s)')\n",
+    "\n",
+    "cb1.set_ticks(np.linspace(np.min(T_mean.mean('time')-273.15), np.max(T_mean.mean('time')-273.15),5))\n",
+    "cb2.set_ticks(np.linspace(np.min(S_mean.mean('time')), np.max(S_mean.mean('time')),5))\n",
+    "cb3.set_ticks(np.linspace(np.min(rho_mean.mean('time')-1027), np.max(rho_mean.mean('time')-1027),5))\n",
+    "cb4.set_ticks(np.linspace(np.min(u_mean.mean('time')), np.max(u_mean.mean('time')),4))\n",
+    "\n",
+    "plt.suptitle('Isobath-averaged properties')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "xwmb_venv",
+   "language": "python",
+   "name": "xwmb_venv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 0e3b7a71d614fef5afafa0e26be2250fd878c674 Mon Sep 17 00:00:00 2001
From: Taimoor Sohail <taimoorsohail@gmail.com>
Date: Mon, 8 Jul 2024 17:55:26 +1000
Subject: [PATCH 3/7] Changed to Corss-contour transport

---
 ContributedExamples/Untitled.ipynb | 64 ++++++++++++++++++++++++++++++
 1 file changed, 64 insertions(+)
 create mode 100644 ContributedExamples/Untitled.ipynb

diff --git a/ContributedExamples/Untitled.ipynb b/ContributedExamples/Untitled.ipynb
new file mode 100644
index 00000000..e3281029
--- /dev/null
+++ b/ContributedExamples/Untitled.ipynb
@@ -0,0 +1,64 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "29563dac-f631-416e-b3f7-28c163c13be7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xwmb\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5a6b3f7d-89cc-4601-a392-cb21af927350",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.1.1'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xwmb.__version__\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "36b424cd-4cc6-46fb-95ca-ce732593493a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "xwmb_venv",
+   "language": "python",
+   "name": "xwmb_venv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From c603f0e829d0a6beab49a390d1436c293f00dc4f Mon Sep 17 00:00:00 2001
From: Taimoor Sohail <taimoorsohail@gmail.com>
Date: Mon, 8 Jul 2024 17:56:45 +1000
Subject: [PATCH 4/7] Updated Cross-contour transport

---
 .../Cross-contour_transport.ipynb             | 562 +++++++++++++-----
 1 file changed, 405 insertions(+), 157 deletions(-)

diff --git a/DocumentedExamples/Cross-contour_transport.ipynb b/DocumentedExamples/Cross-contour_transport.ipynb
index 0ab49d73..8fac4a2a 100644
--- a/DocumentedExamples/Cross-contour_transport.ipynb
+++ b/DocumentedExamples/Cross-contour_transport.ipynb
@@ -25,8 +25,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
-    "\n",
     "import pandas as pd\n",
     "import intake\n",
     "catalog = intake.cat.access_nri\n",
@@ -51,13 +49,13 @@
        "    <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-f4ae6b00-836e-11ee-a3df-0000018efe80</p>\n",
+       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-d4f28e73-3765-11ef-a23d-000007a3fe80</p>\n",
        "        <table style=\"width: 100%; text-align: left;\">\n",
        "\n",
        "        <tr>\n",
        "        \n",
-       "            <td style=\"text-align: left;\"><strong>Connection method:</strong> Direct</td>\n",
-       "            <td style=\"text-align: left;\"></td>\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",
@@ -80,20 +78,57 @@
        "\n",
        "        \n",
        "            <details>\n",
-       "            <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Scheduler Info</h3></summary>\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;\">c1f41846</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> 7\n",
+       "                </td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Total threads:</strong> 28\n",
+       "                </td>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Total memory:</strong> 251.18 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-7547f1e1-4c20-48f5-98ca-661eaefedcf0</p>\n",
+       "            <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-66a42456-46a1-4e8a-ae77-1d8fdf394881</p>\n",
        "            <table style=\"width: 100%; text-align: left;\">\n",
        "                <tr>\n",
        "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Comm:</strong> tcp://10.6.22.43:8786\n",
+       "                        <strong>Comm:</strong> tcp://127.0.0.1:41207\n",
        "                    </td>\n",
        "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Workers:</strong> 1\n",
+       "                        <strong>Workers:</strong> 7\n",
        "                    </td>\n",
        "                </tr>\n",
        "                <tr>\n",
@@ -101,15 +136,15 @@
        "                        <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",
+       "                        <strong>Total threads:</strong> 28\n",
        "                    </td>\n",
        "                </tr>\n",
        "                <tr>\n",
        "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Started:</strong> 2 hours ago\n",
+       "                        <strong>Started:</strong> Just now\n",
        "                    </td>\n",
        "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Total memory:</strong> 188.56 GiB\n",
+       "                        <strong>Total memory:</strong> 251.18 GiB\n",
        "                    </td>\n",
        "                </tr>\n",
        "            </table>\n",
@@ -127,80 +162,309 @@
        "            <div style=\"margin-left: 48px;\">\n",
        "            <details>\n",
        "                <summary>\n",
-       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: tcp://10.6.22.43:37571</h4>\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://127.0.0.1:35635\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 4\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/42673/status\" target=\"_blank\">/proxy/42673/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 35.88 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:43421\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/119464434.gadi-pbs/dask-scratch-space/worker-oqigcvra\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://10.6.22.43:37571\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:46285\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 48\n",
+       "                            <strong>Total threads: </strong> 4\n",
        "                        </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"/proxy/33801/status\" target=\"_blank\">/proxy/33801/status</a>\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/42309/status\" target=\"_blank\">/proxy/42309/status</a>\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 188.56 GiB\n",
+       "                            <strong>Memory: </strong> 35.88 GiB\n",
        "                        </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://10.6.22.43:44673\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:36997\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> /scratch/iq82/mp7041/dasktmp/dask-scratch-space/worker-efhnz2dp\n",
+       "                            <strong>Local directory: </strong> /jobfs/119464434.gadi-pbs/dask-scratch-space/worker-2j_ngx7a\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>Tasks executing: </strong> \n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:40769\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 4\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/42759/status\" target=\"_blank\">/proxy/42759/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 35.88 GiB\n",
        "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Tasks in memory: </strong> \n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:41175\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/119464434.gadi-pbs/dask-scratch-space/worker-obtzrqnx\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://127.0.0.1:36049\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 4\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/32787/status\" target=\"_blank\">/proxy/32787/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 35.88 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:34551\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/119464434.gadi-pbs/dask-scratch-space/worker-uosv_xbw\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://127.0.0.1:45969\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 4\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/42147/status\" target=\"_blank\">/proxy/42147/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 35.88 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:46761\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/119464434.gadi-pbs/dask-scratch-space/worker-vx3d790r\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>Tasks ready: </strong> \n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:36517\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Tasks in flight: </strong>\n",
+       "                            <strong>Total threads: </strong> 4\n",
        "                        </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>CPU usage:</strong> 104.7%\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/38571/status\" target=\"_blank\">/proxy/38571/status</a>\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Last seen: </strong> Just now\n",
+       "                            <strong>Memory: </strong> 35.88 GiB\n",
        "                        </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory usage: </strong> 1.66 GiB\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:34347\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/119464434.gadi-pbs/dask-scratch-space/worker-247a6p0f\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://127.0.0.1:34463\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Spilled bytes: </strong> 0 B\n",
+       "                            <strong>Total threads: </strong> 4\n",
        "                        </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Read bytes: </strong> 74.78 kiB\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/41363/status\" target=\"_blank\">/proxy/41363/status</a>\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Write bytes: </strong> 115.25 kiB\n",
+       "                            <strong>Memory: </strong> 35.88 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:42449\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/119464434.gadi-pbs/dask-scratch-space/worker-q7hmr1i1\n",
        "                        </td>\n",
        "                    </tr>\n",
+       "\n",
+       "                    \n",
+       "\n",
        "                    \n",
        "\n",
        "                </table>\n",
@@ -211,6 +475,10 @@
        "\n",
        "    </details>\n",
        "</div>\n",
+       "\n",
+       "        </details>\n",
+       "    </div>\n",
+       "</div>\n",
        "            </details>\n",
        "        \n",
        "\n",
@@ -218,7 +486,7 @@
        "</div>"
       ],
       "text/plain": [
-       "<Client: 'tcp://10.6.22.43:8786' processes=1 threads=48, memory=188.56 GiB>"
+       "<Client: 'tcp://127.0.0.1:41207' processes=7 threads=28, memory=251.18 GiB>"
       ]
      },
      "execution_count": 2,
@@ -227,7 +495,7 @@
     }
    ],
    "source": [
-    "client = Client(\"tcp://10.6.22.43:8786\")\n",
+    "client = Client()\n",
     "client"
    ]
   },
@@ -305,7 +573,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 5,
    "id": "e28cf315-ef31-4ce1-a526-b56b73ae2edb",
    "metadata": {},
    "outputs": [
@@ -492,7 +760,7 @@
        " 'wt']"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -502,111 +770,6 @@
     "sorted(set().union(*cat_subset.df['variable']))"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "ab5a694c-b2a2-4f1d-9366-9a9facbf3fe5",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.07/lib/python3.10/site-packages/intake_esm/cat.py:270: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
-      "  .applymap(type)\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.07/lib/python3.10/site-packages/intake_esm/cat.py:270: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
-      "  .applymap(type)\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.07/lib/python3.10/site-packages/intake_esm/cat.py:270: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
-      "  .applymap(type)\n"
-     ]
-    },
-    {
-     "ename": "KeyError",
-     "evalue": "0",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.07/lib/python3.10/site-packages/pandas/core/indexes/range.py:414\u001b[0m, in \u001b[0;36mRangeIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    413\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 414\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_range\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnew_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    415\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
-      "\u001b[0;31mValueError\u001b[0m: 0 is not in range",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "File \u001b[0;32m<timed exec>:3\u001b[0m\n",
-      "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.07/lib/python3.10/site-packages/pandas/core/series.py:1040\u001b[0m, in \u001b[0;36mSeries.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1037\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[key]\n\u001b[1;32m   1039\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m key_is_scalar:\n\u001b[0;32m-> 1040\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_value\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1042\u001b[0m \u001b[38;5;66;03m# Convert generator to list before going through hashable part\u001b[39;00m\n\u001b[1;32m   1043\u001b[0m \u001b[38;5;66;03m# (We will iterate through the generator there to check for slices)\u001b[39;00m\n\u001b[1;32m   1044\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n",
-      "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.07/lib/python3.10/site-packages/pandas/core/series.py:1156\u001b[0m, in \u001b[0;36mSeries._get_value\u001b[0;34m(self, label, takeable)\u001b[0m\n\u001b[1;32m   1153\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[label]\n\u001b[1;32m   1155\u001b[0m \u001b[38;5;66;03m# Similar to Index.get_value, but we do not fall back to positional\u001b[39;00m\n\u001b[0;32m-> 1156\u001b[0m loc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1158\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(loc):\n\u001b[1;32m   1159\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[loc]\n",
-      "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.07/lib/python3.10/site-packages/pandas/core/indexes/range.py:416\u001b[0m, in \u001b[0;36mRangeIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_range\u001b[38;5;241m.\u001b[39mindex(new_key)\n\u001b[1;32m    415\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m--> 416\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m    417\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Hashable):\n\u001b[1;32m    418\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key)\n",
-      "\u001b[0;31mKeyError\u001b[0m: 0"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "\n",
-    "cat_subset = catalog[experiment]\n",
-    "var_search = cat_subset.search(variable='yt_ocean')\n",
-    "var_search = var_search.search(path=var_search.df['path'][0])\n",
-    "darray = var_search.to_dask()\n",
-    "darray = darray['yt_ocean']\n",
-    "yt_ocean = darray\n",
-    "yt_ocean = yt_ocean.sel(yt_ocean = lat_range)\n",
-    "\n",
-    "cat_subset = catalog[experiment]\n",
-    "var_search = cat_subset.search(variable='xt_ocean')\n",
-    "var_search = var_search.search(path=var_search.df['path'][0])\n",
-    "darray = var_search.to_dask()\n",
-    "darray = darray['xt_ocean']\n",
-    "xt_ocean = darray\n",
-    "\n",
-    "cat_subset = catalog[experiment]\n",
-    "var_search = cat_subset.search(variable='yu_ocean')\n",
-    "var_search = var_search.search(path=var_search.df['path'][0])\n",
-    "darray = var_search.to_dask()\n",
-    "darray = darray['yu_ocean']\n",
-    "yu_ocean = darray\n",
-    "yu_ocean = yu_ocean.sel(yu_ocean = lat_range)\n",
-    "\n",
-    "cat_subset = catalog[experiment]\n",
-    "var_search = cat_subset.search(variable='xu_ocean')\n",
-    "var_search = var_search.search(path=var_search.df['path'][0])\n",
-    "darray = var_search.to_dask()\n",
-    "darray = darray['xu_ocean']\n",
-    "xu_ocean = darray"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "577743fb-7cd1-4a43-b4f2-8dcef92aaaac",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'yt_ocean' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[43myt_ocean\u001b[49m) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(yu_ocean):\n\u001b[1;32m      2\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhelp! y different size\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m yt_ocean\u001b[38;5;241m.\u001b[39mmin(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124myt_ocean\u001b[39m\u001b[38;5;124m'\u001b[39m)\u001b[38;5;241m>\u001b[39m yu_ocean\u001b[38;5;241m.\u001b[39mmin(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124myu_ocean\u001b[39m\u001b[38;5;124m'\u001b[39m):\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'yt_ocean' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "if len(yt_ocean) != len(yu_ocean):\n",
-    "    print('help! y different size')\n",
-    "\n",
-    "if yt_ocean.min('yt_ocean')> yu_ocean.min('yu_ocean'):\n",
-    "    print('help! wrong order')\n",
-    "\n",
-    "if len(xt_ocean) != len(xu_ocean):\n",
-    "    print('help! x different size')\n",
-    "\n",
-    "if xt_ocean.min('xt_ocean')> xu_ocean.min('xu_ocean'):\n",
-    "    print('help! x wrong order')"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "207b7d83-c992-4b5f-896c-44cc7ff291c2",
@@ -617,7 +780,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "21118fd1-2274-444d-80c2-30bf6cc55ce1",
    "metadata": {},
    "outputs": [],
@@ -651,7 +814,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "2cc4a6dc-393b-45e7-923a-f543f681344c",
    "metadata": {},
    "outputs": [],
@@ -669,10 +832,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "c000a8da-6309-4d9b-a6e6-f2f4518a2de9",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig = plt.figure(figsize = (10, 4))\n",
     "\n",
@@ -683,7 +857,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "e2ca6843-4f28-4b5b-805f-99f858cf482e",
    "metadata": {},
    "outputs": [],
@@ -696,7 +870,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
+   "id": "ead8a493-7df4-425d-b5e0-2283f5105ce5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xt_ocean = ssh.xt_ocean\n",
+    "yt_ocean = ssh.yt_ocean\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
    "id": "1a1ca948-228b-4e97-a3e7-874932ece34e",
    "metadata": {},
    "outputs": [],
@@ -720,10 +905,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "76fbe005-ed16-43d3-b443-a16819602fac",
+   "execution_count": 15,
+   "id": "9de8c6b5-be92-4756-b220-05703744c978",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig = plt.figure(figsize = (10, 4))\n",
     "count = 0\n",
@@ -755,7 +951,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "bdb41806-ce8d-447b-825c-9020ffe6b276",
    "metadata": {},
    "outputs": [],
@@ -773,7 +969,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
+   "id": "dec96425-9f73-4b48-b1f7-9ad08cae4a20",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_contour"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
    "id": "5ecb5141-0e70-4dd7-b209-db2f260994bb",
    "metadata": {},
    "outputs": [],
@@ -787,7 +1004,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "id": "a95b0816-b795-4242-968f-58cfb86e85c6",
    "metadata": {},
    "outputs": [],
@@ -808,10 +1025,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "id": "910fea81-3d88-4c41-84d1-1ef8bcefeb52",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig = plt.figure(figsize=(10, 5))\n",
     "\n",
@@ -829,7 +1057,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "id": "c78519e6-0b37-4b9a-bf38-518da3e66f25",
    "metadata": {},
    "outputs": [],
@@ -854,10 +1082,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "id": "fd86bf32-0a0d-485a-8abb-49e05db24bed",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "IndexError",
+     "evalue": "index 0 is out of bounds for axis 0 with size 0",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[32], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m lat_along_contour \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mappend(lat_along_contour, \u001b[43mlat_along_contour\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m)\n\u001b[1;32m      2\u001b[0m lon_along_contour \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mappend(lon_along_contour, lon_along_contour[\u001b[38;5;241m0\u001b[39m])\n",
+      "\u001b[0;31mIndexError\u001b[0m: index 0 is out of bounds for axis 0 with size 0"
+     ]
+    }
+   ],
    "source": [
     "lat_along_contour = np.append(lat_along_contour, lat_along_contour[0])\n",
     "lon_along_contour = np.append(lon_along_contour, lon_along_contour[0])"
@@ -1540,13 +1780,21 @@
    "source": [
     "We can see that there is a net northward transport across the Antarctic Circumpolar Current in the top 500m - this is the Ekman wind-driven transport. We could then choose to extract the density (or salt and temperature) along this same path, do this by interpolating density to the north and eastern edge of t-cells. Then we could bin the transports in each depth level into the corresponding density, to determine the transport across the contour in density space. An example of this calculation can be found in https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Save_and_bin_along_contours.ipynb. "
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "18818f5b-b957-4472-a704-5342c6b8aa1c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:analysis3-23.07] *",
+   "display_name": "Python [conda env:analysis3-23.10]",
    "language": "python",
-   "name": "conda-env-analysis3-23.07-py"
+   "name": "conda-env-analysis3-23.10-py"
   },
   "language_info": {
    "codemirror_mode": {

From 9200900cf4049729d030ebb07ebc58af34a54b48 Mon Sep 17 00:00:00 2001
From: "Navid C. Constantinou" <navidcy@users.noreply.github.com>
Date: Tue, 9 Jul 2024 05:38:22 +0100
Subject: [PATCH 5/7] Delete ContributedExamples/Untitled.ipynb

---
 ContributedExamples/Untitled.ipynb | 64 ------------------------------
 1 file changed, 64 deletions(-)
 delete mode 100644 ContributedExamples/Untitled.ipynb

diff --git a/ContributedExamples/Untitled.ipynb b/ContributedExamples/Untitled.ipynb
deleted file mode 100644
index e3281029..00000000
--- a/ContributedExamples/Untitled.ipynb
+++ /dev/null
@@ -1,64 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "29563dac-f631-416e-b3f7-28c163c13be7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import xwmb\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "5a6b3f7d-89cc-4601-a392-cb21af927350",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'0.1.1'"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xwmb.__version__\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "36b424cd-4cc6-46fb-95ca-ce732593493a",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "xwmb_venv",
-   "language": "python",
-   "name": "xwmb_venv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

From 4c318e6a833b4ddfd47b1853a3468d8d9ee43280 Mon Sep 17 00:00:00 2001
From: "Navid C. Constantinou" <navidcy@users.noreply.github.com>
Date: Tue, 9 Jul 2024 05:45:17 +0100
Subject: [PATCH 6/7] Delete ContributedExamples/Along_Isobath_Average.ipynb

---
 .../Along_Isobath_Average.ipynb               | 1811 -----------------
 1 file changed, 1811 deletions(-)
 delete mode 100644 ContributedExamples/Along_Isobath_Average.ipynb

diff --git a/ContributedExamples/Along_Isobath_Average.ipynb b/ContributedExamples/Along_Isobath_Average.ipynb
deleted file mode 100644
index 684ed649..00000000
--- a/ContributedExamples/Along_Isobath_Average.ipynb
+++ /dev/null
@@ -1,1811 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "723a3b22-c5f9-4a34-b278-d36e29e0c296",
-   "metadata": {},
-   "source": [
-    "## <center> This code shows how to do an along-isobath averging for scalar properties (and vector properties not rotated to along- and across-contour) in ACCESS-OM2-01\n",
-    "\n",
-    "### <center> This code is relevant for Antarctic shelf analyses where an average over a section of cross-shelf regions is needed "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "7c9575fe-e29f-4649-95fe-328c2aaf563d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### Some caveats: \n",
-    "\n",
-    "# 1) If there are deep valleys or troughs around the coastline, those will be averaged with \n",
-    "# deeper isobaths further offshore. Careful adjustment of the mask is necessary to ensure your \n",
-    "# results are interpretable. \n",
-    "\n",
-    "# Please also inspect the ht_coarse and hu_coarse variables derived from the variables of interest. \n",
-    "\n",
-    "# 2) More caveats should be added here! \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "a476d924-8b6d-4c03-801b-386e07944e2f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load necessary modules\n",
-    "import cosima_cookbook as cc\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import xarray as xr\n",
-    "from xhistogram.xarray import histogram\n",
-    "from dask.distributed import Client"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "e3cc23c9-8622-4a9a-9b5f-2313a8553955",
-   "metadata": {},
-   "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-178f8365-3ce6-11ef-9d62-00000792fe80</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;\">2d40c3ce</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> 7\n",
-       "                </td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                <td style=\"text-align: left;\">\n",
-       "                    <strong>Total threads:</strong> 28\n",
-       "                </td>\n",
-       "                <td style=\"text-align: left;\">\n",
-       "                    <strong>Total memory:</strong> 251.18 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-e7dfc6d9-4c4d-48fc-a28a-1f34694fe362</p>\n",
-       "            <table style=\"width: 100%; text-align: left;\">\n",
-       "                <tr>\n",
-       "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Comm:</strong> tcp://127.0.0.1:40319\n",
-       "                    </td>\n",
-       "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Workers:</strong> 7\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> 28\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> 251.18 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://127.0.0.1:39895\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 4\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"/proxy/35499/status\" target=\"_blank\">/proxy/35499/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 35.88 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:43033\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/119946825.gadi-pbs/dask-scratch-space/worker-t02y4tr9\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://127.0.0.1:35735\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 4\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"/proxy/33933/status\" target=\"_blank\">/proxy/33933/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 35.88 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:39603\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/119946825.gadi-pbs/dask-scratch-space/worker-14xntlxh\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://127.0.0.1:42027\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 4\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"/proxy/36063/status\" target=\"_blank\">/proxy/36063/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 35.88 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:34129\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/119946825.gadi-pbs/dask-scratch-space/worker-b9qivda0\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://127.0.0.1:42289\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 4\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"/proxy/36099/status\" target=\"_blank\">/proxy/36099/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 35.88 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:41595\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/119946825.gadi-pbs/dask-scratch-space/worker-zyooigd0\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://127.0.0.1:34779\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 4\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"/proxy/34901/status\" target=\"_blank\">/proxy/34901/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 35.88 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:40997\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/119946825.gadi-pbs/dask-scratch-space/worker-3ondu3xa\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://127.0.0.1:40755\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 4\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"/proxy/35179/status\" target=\"_blank\">/proxy/35179/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 35.88 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:35463\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/119946825.gadi-pbs/dask-scratch-space/worker-oehf4b1g\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://127.0.0.1:34305\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 4\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"/proxy/46203/status\" target=\"_blank\">/proxy/46203/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 35.88 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:34645\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/119946825.gadi-pbs/dask-scratch-space/worker-1gvrgi9b\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://127.0.0.1:40319' processes=7 threads=28, memory=251.18 GiB>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "client = Client()\n",
-    "client"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "665b7147-a240-47f2-b0ad-2b45b7d8dc96",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "session = cc.database.create_session()\n",
-    "# Define experiment in database - here we choose the RYF90-91 experiment #\n",
-    "expt = '01deg_jra55v13_ryf9091'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "fefd4fa9-9208-45d9-ae1b-a19f8062a715",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Define the latitude bounds and the start and end times #\n",
-    "lat_min, lat_max = -80, -59\n",
-    "lat_slice = slice(lat_min, lat_max)\n",
-    "start_time, end_time = '2170-01-01', '2180-12-31'\n",
-    "\n",
-    "## Note that the bathymetry provided by ht and hu is not discretised into the same bins as st_ocean(_edges)\n",
-    "## The histogram function in this code thus yields uneven bins if we bin ht by st_ocean_edges bins\n",
-    "## As a result, we use ht and hu for our masking, but we obtain bottom bathymetry from the variable itself,\n",
-    "## which IS discretised into st_ocean bins \n",
-    "\n",
-    "# Import bathymetry - t-grid #\n",
-    "ht = cc.querying.getvar(expt, 'ht', session, n=1)\n",
-    "ht = ht.sel(yt_ocean=lat_slice)\n",
-    "# Import bathymetry - u-grid #\n",
-    "hu = cc.querying.getvar(expt, 'hu', session, n=1)\n",
-    "hu = hu.sel(yu_ocean=lat_slice)\n",
-    "\n",
-    "# Import properties to average along isobaths #\n",
-    "# This code works for scalar properties and for vectors oriented along and across the model grid (not along- and cross-isobath)\n",
-    "\n",
-    "temp = cc.querying.getvar(expt, 'temp', session, start_time=start_time, end_time=end_time, frequency='1 monthly')\n",
-    "temp = temp.sel(time=slice(start_time, end_time)).sel(yt_ocean=lat_slice)\n",
-    "salt = cc.querying.getvar(expt, 'salt', session, start_time=start_time, end_time=end_time, frequency='1 monthly')\n",
-    "salt = salt.sel(time=slice(start_time, end_time)).sel(yt_ocean=lat_slice)\n",
-    "density = cc.querying.getvar(expt, 'pot_rho_0', session, start_time=start_time, end_time=end_time, frequency='1 monthly')\n",
-    "density = density.sel(time=slice(start_time,end_time)).sel(yt_ocean=lat_slice)\n",
-    "u = cc.querying.getvar(expt, 'u', session, start_time=start_time, end_time=end_time, frequency='1 monthly')\n",
-    "u = u.sel(time=slice(start_time, end_time)).sel(yu_ocean=lat_slice)\n",
-    "v = cc.querying.getvar(expt, 'v', session, start_time=start_time, end_time=end_time, frequency='1 monthly')\n",
-    "v = v.sel(time=slice(start_time, end_time)).sel(yu_ocean=lat_slice)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "aba39329-3227-4429-9d96-061fdfcee5c9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 18.4 s, sys: 9.45 s, total: 27.8 s\n",
-      "Wall time: 29.8 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "## Calulating a Volume and area for weighted averaging using xhistogram:\n",
-    "## Note that the thickness here is NOT time-varying, use\n",
-    "## dzt for time-varying thickness if necessary.\n",
-    "\n",
-    "xt_ocean = cc.querying.getvar(expt,'xt_ocean' , session,n=1)\n",
-    "yt_ocean = cc.querying.getvar(expt,'yt_ocean' , session,n=1)\n",
-    "yt_ocean = yt_ocean.sel(yt_ocean=lat_slice)\n",
-    "# import edges of st_ocean and add lat/lon dimensions:\n",
-    "st_edges_ocean = cc.querying.getvar(expt, 'st_edges_ocean', session, n=1)\n",
-    "st_edges_array = st_edges_ocean.expand_dims({'yt_ocean':yt_ocean,'xt_ocean':xt_ocean}, axis=[1,2])\n",
-    "\n",
-    "st_edges_with_partial = st_edges_array.where(st_edges_array<ht, other=ht)\n",
-    "thickness = st_edges_with_partial.diff(dim='st_edges_ocean')\n",
-    "\n",
-    "# change coordinate of thickness to st_ocean (needed for multipling with other variables):\n",
-    "st_ocean = cc.querying.getvar(expt, 'st_ocean', session, n=1)\n",
-    "thickness['st_edges_ocean'] = st_ocean.values\n",
-    "thickness = thickness.rename(({'st_edges_ocean':'st_ocean'}))\n",
-    "\n",
-    "# area for area averaging:\n",
-    "area_t = cc.querying.getvar(expt,'area_t',session,n=1)\n",
-    "area_t = area_t.sel(yt_ocean=lat_slice)\n",
-    "\n",
-    "volume = area_t*thickness\n",
-    "volume = volume.load()\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "fe6137e2-0405-4f3f-a96c-d2893830bccf",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 15.2 s, sys: 8.92 s, total: 24.1 s\n",
-      "Wall time: 25 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "# As above, for the u-grid #\n",
-    "\n",
-    "xu_ocean = cc.querying.getvar(expt,'xu_ocean' , session,n=1)\n",
-    "yu_ocean = cc.querying.getvar(expt,'yu_ocean' , session,n=1)\n",
-    "yu_ocean = yu_ocean.sel(yu_ocean=lat_slice)\n",
-    "# import edges of st_ocean and add lat/lon dimensions:\n",
-    "st_edges_ocean = cc.querying.getvar(expt, 'st_edges_ocean', session, n=1)\n",
-    "st_edges_array = st_edges_ocean.expand_dims({'yu_ocean':yu_ocean,'xu_ocean':xu_ocean}, axis=[1,2])\n",
-    "\n",
-    "st_edges_with_partial = st_edges_array.where(st_edges_array<hu, other=hu)\n",
-    "thickness_u = st_edges_with_partial.diff(dim='st_edges_ocean')\n",
-    "\n",
-    "# change coordinate of thickness to st_ocean (needed for multipling with other variables):\n",
-    "st_ocean = cc.querying.getvar(expt, 'st_ocean', session, n=1)\n",
-    "thickness_u['st_edges_ocean'] = st_ocean.values\n",
-    "thickness_u = thickness_u.rename(({'st_edges_ocean':'st_ocean'}))\n",
-    "\n",
-    "# area for area averaging:\n",
-    "area_u = cc.querying.getvar(expt,'area_u',session,n=1)\n",
-    "area_u = area_u.sel(yu_ocean=lat_slice)\n",
-    "\n",
-    "volume_u = area_u*thickness_u\n",
-    "volume_u = volume_u.load()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "db710d5a-108e-4f1f-9f57-6a9b9e24df88",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "### DEFINE AND MASK YOUR REGION OF INTEREST ###\n",
-    "\n",
-    "# As a simple example, we mask out everything #\n",
-    "# but the Ross Sea shallower than 1000m #\n",
-    "\n",
-    "# As an alternative, you can define any polygon as your mask #\n",
-    "\n",
-    "lon_slice = slice(-200,-150)\n",
-    "mask_depth = 1000\n",
-    "\n",
-    "mask_t = ht.sel(xt_ocean=lon_slice) < mask_depth\n",
-    "mask_u = hu.sel(xu_ocean=lon_slice) < mask_depth\n",
-    "mask_t = mask_t.where(mask_t!=0)\n",
-    "mask_u = mask_u.where(mask_u!=0)\n",
-    "# Plot the mask that will be used #\n",
-    "mask_t.plot()\n",
-    "\n",
-    "# Mask out the variables of interest using the above mask\n",
-    "hu_masked = hu*mask_u\n",
-    "ht_masked = ht*mask_t\n",
-    "\n",
-    "volume_masked = volume*mask_t\n",
-    "volume_u_masked = volume_u*mask_u\n",
-    "area_t_masked = area_t*mask_t\n",
-    "area_u_masked = area_u*mask_u\n",
-    "temp_masked = temp*mask_t\n",
-    "salt_masked = salt*mask_t\n",
-    "density_masked = density*mask_t\n",
-    "u_masked = u*mask_u"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "f5e6b45f-5f55-45a2-8868-7b2b2fac241f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 4min 22s, sys: 8.94 s, total: 4min 31s\n",
-      "Wall time: 4min 52s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "## As mentioned above, we can't use ht and hu for the histogram binning below, as our isobath bins are st_edges_ocean, \n",
-    "## while ht and hu have much finer resolution, leading to uneven bins. \n",
-    "\n",
-    "## Below, we isolate the bottom st_ocean of temp (as ht_coarse) and u (as hu_coarse) that isn't NaN. This is the variable over\n",
-    "## which we perform the binning. Note that we assume that NaN grid cells are NaN at all times (i.e., ht_coarse and hu_coarse don't change over time)\n",
-    "\n",
-    "###### T-GRID ######\n",
-    "\n",
-    "# Create an empty DataArray with the same shape as 'temp_avg'\n",
-    "temp_avg = temp_masked.mean('time')\n",
-    "full_st_ocean_t = xr.full_like(temp_avg, np.nan)\n",
-    "\n",
-    "# Assign the 1D st_ocean coordinate to the new DataArray, broadcasting along the other dimensions\n",
-    "full_st_ocean_t.data = np.broadcast_to(temp_avg.st_ocean.values[:, np.newaxis, np.newaxis], temp_avg.shape)\n",
-    "full_st_ocean_t_masked = full_st_ocean_t*temp_avg/temp_avg # Here, we mask by temp_avg to ensure the Nans are preserved \n",
-    "\n",
-    "# Now 'full_st_ocean_t_masked' is a 3D DataArray with 'st_ocean' values broadcasted along yt_ocean and xt_ocean\n",
-    "# and masked by temp_avg\n",
-    "\n",
-    "# Find the bottom depth for each (yt_ocean, xt_ocean) coordinate\n",
-    "ht_coarse = full_st_ocean_t_masked.max(dim='st_ocean').compute().rename('ht_coarse')\n",
-    "\n",
-    "###### U-GRID ######\n",
-    "\n",
-    "# Create an empty DataArray with the same shape as 'u_avg'\n",
-    "u_avg = u_masked.mean('time')\n",
-    "full_st_ocean_u = xr.full_like(u_avg, np.nan)\n",
-    "\n",
-    "# Assign the 1D st_ocean coordinate to the new DataArray, broadcasting along the other dimensions\n",
-    "full_st_ocean_u.data = np.broadcast_to(u_avg.st_ocean.values[:, np.newaxis, np.newaxis], u_avg.shape)\n",
-    "full_st_ocean_u_masked = full_st_ocean_u*u_avg/u_avg # Here, we mask by u_avg to ensure the Nans are preserved \n",
-    "\n",
-    "# Now 'full_st_ocean_u_masked' is a 3D DataArray with 'st_ocean' values broadcasted along yu_ocean and xu_ocean\n",
-    "# and masked by u_avg\n",
-    "\n",
-    "# Find the bottom depth for each (yu_ocean, xu_ocean) coordinate\n",
-    "hu_coarse = full_st_ocean_u_masked.max(dim='st_ocean').compute().rename('hu_coarse')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "f1e6ed2b-c349-4246-a1cd-8bf61c47614d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 0, 'Longitude')"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "(ht_coarse-ht_masked).plot()\n",
-    "plt.title('Difference between the bottom bathymetry\\n in ht and actual variables [m]')\n",
-    "plt.ylabel('Latitude')\n",
-    "plt.xlabel('Longitude')\n",
-    "plt.show()\n",
-    "(hu_coarse-hu_masked).plot()\n",
-    "plt.title('Difference between the bottom bathymetry\\n in hu and actual variables [m]')\n",
-    "plt.ylabel('Latitude')\n",
-    "plt.xlabel('Longitude')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "2c90d8f7-c9d8-4580-a87a-af4ad37014ae",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n",
-      "  return func(*(_execute_task(a, cache) for a in args))\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n",
-      "  return func(*(_execute_task(a, cache) for a in args))\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n",
-      "  return func(*(_execute_task(a, cache) for a in args))\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n",
-      "  return func(*(_execute_task(a, cache) for a in args))\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n",
-      "  return func(*(_execute_task(a, cache) for a in args))\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n",
-      "  return func(*(_execute_task(a, cache) for a in args))\n",
-      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n",
-      "  return func(*(_execute_task(a, cache) for a in args))\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 2min 38s, sys: 10.4 s, total: 2min 48s\n",
-      "Wall time: 3min 37s\n"
-     ]
-    },
-    {
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-       "  --xr-background-color: var(--jp-layout-color0, white);\n",
-       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
-       "}\n",
-       "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
-       "body.vscode-dark {\n",
-       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
-       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
-       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
-       "  --xr-disabled-color: #515151;\n",
-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
-       "}\n",
-       "\n",
-       ".xr-wrap {\n",
-       "  display: block !important;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
-       "}\n",
-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;histogram_hu_coarse&#x27; (st_ocean: 75, time: 120,\n",
-       "                                         hu_coarse_bin: 46)&gt; Size: 3MB\n",
-       "array([[[        nan,         nan,         nan, ..., -0.05329067,\n",
-       "         -0.0545278 , -0.04532973],\n",
-       "        [        nan,         nan,         nan, ..., -0.07846384,\n",
-       "         -0.07439158, -0.06675977],\n",
-       "        [        nan,         nan,         nan, ..., -0.06968889,\n",
-       "         -0.06318065, -0.03509349],\n",
-       "        ...,\n",
-       "        [        nan,         nan,         nan, ..., -0.01335827,\n",
-       "         -0.01355833, -0.02252559],\n",
-       "        [        nan,         nan,         nan, ..., -0.01091509,\n",
-       "         -0.01020199, -0.01440421],\n",
-       "        [        nan,         nan,         nan, ..., -0.04878291,\n",
-       "         -0.04536789, -0.04028598]],\n",
-       "\n",
-       "       [[        nan,         nan,         nan, ..., -0.04996552,\n",
-       "         -0.05102612, -0.04109836],\n",
-       "        [        nan,         nan,         nan, ..., -0.07459088,\n",
-       "         -0.07073072, -0.06223585],\n",
-       "        [        nan,         nan,         nan, ..., -0.07006387,\n",
-       "         -0.06542959, -0.03951225],\n",
-       "...\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan]],\n",
-       "\n",
-       "       [[        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        ...,\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan]]])\n",
-       "Coordinates:\n",
-       "  * hu_coarse_bin  (hu_coarse_bin) float64 368B 0.5413 1.681 ... 843.0 926.6\n",
-       "Dimensions without coordinates: st_ocean, time</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'>'histogram_hu_coarse'</div><ul class='xr-dim-list'><li><span>st_ocean</span>: 75</li><li><span>time</span>: 120</li><li><span class='xr-has-index'>hu_coarse_bin</span>: 46</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-74f169e3-8cb6-495d-80c6-839337dbea75' class='xr-array-in' type='checkbox' checked><label for='section-74f169e3-8cb6-495d-80c6-839337dbea75' 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>nan nan nan nan nan nan -0.003532 ... nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[[        nan,         nan,         nan, ..., -0.05329067,\n",
-       "         -0.0545278 , -0.04532973],\n",
-       "        [        nan,         nan,         nan, ..., -0.07846384,\n",
-       "         -0.07439158, -0.06675977],\n",
-       "        [        nan,         nan,         nan, ..., -0.06968889,\n",
-       "         -0.06318065, -0.03509349],\n",
-       "        ...,\n",
-       "        [        nan,         nan,         nan, ..., -0.01335827,\n",
-       "         -0.01355833, -0.02252559],\n",
-       "        [        nan,         nan,         nan, ..., -0.01091509,\n",
-       "         -0.01020199, -0.01440421],\n",
-       "        [        nan,         nan,         nan, ..., -0.04878291,\n",
-       "         -0.04536789, -0.04028598]],\n",
-       "\n",
-       "       [[        nan,         nan,         nan, ..., -0.04996552,\n",
-       "         -0.05102612, -0.04109836],\n",
-       "        [        nan,         nan,         nan, ..., -0.07459088,\n",
-       "         -0.07073072, -0.06223585],\n",
-       "        [        nan,         nan,         nan, ..., -0.07006387,\n",
-       "         -0.06542959, -0.03951225],\n",
-       "...\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan]],\n",
-       "\n",
-       "       [[        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        ...,\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan]]])</pre></div></div></li><li class='xr-section-item'><input id='section-ac425447-a37a-4640-85a5-43c7d4a31bea' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ac425447-a37a-4640-85a5-43c7d4a31bea' class='xr-section-summary' >Coordinates: <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-var-name'><span class='xr-has-index'>hu_coarse_bin</span></div><div class='xr-var-dims'>(hu_coarse_bin)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5413 1.681 2.94 ... 843.0 926.6</div><input id='attrs-2d4f2358-8625-41d2-8eb7-200c2ef35510' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2d4f2358-8625-41d2-8eb7-200c2ef35510' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b90cbd75-0748-4373-8a97-c66755fd3b8a' class='xr-var-data-in' type='checkbox'><label for='data-b90cbd75-0748-4373-8a97-c66755fd3b8a' 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([5.412808e-01, 1.680735e+00, 2.939953e+00, 4.331521e+00, 5.869350e+00,\n",
-       "       7.568810e+00, 9.446885e+00, 1.152234e+01, 1.381593e+01, 1.635055e+01,\n",
-       "       1.915154e+01, 2.224687e+01, 2.566746e+01, 2.944746e+01, 3.362460e+01,\n",
-       "       3.824057e+01, 4.334140e+01, 4.897796e+01, 5.520640e+01, 6.208874e+01,\n",
-       "       6.969342e+01, 7.809601e+01, 8.737988e+01, 9.763700e+01, 1.089687e+02,\n",
-       "       1.214869e+02, 1.353144e+02, 1.505868e+02, 1.674530e+02, 1.860765e+02,\n",
-       "       2.066365e+02, 2.293296e+02, 2.543701e+02, 2.819920e+02, 3.124492e+02,\n",
-       "       3.460166e+02, 3.829906e+02, 4.236883e+02, 4.684475e+02, 5.176242e+02,\n",
-       "       5.715899e+02, 6.307275e+02, 6.954248e+02, 7.660668e+02, 8.430255e+02,\n",
-       "       9.266482e+02])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d9b4019c-25f0-4723-8d60-fb3c1fffaf5f' class='xr-section-summary-in' type='checkbox'  ><label for='section-d9b4019c-25f0-4723-8d60-fb3c1fffaf5f' 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>hu_coarse_bin</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8e9b19ce-a682-4eef-9818-d584ee09ef79' class='xr-index-data-in' type='checkbox'/><label for='index-8e9b19ce-a682-4eef-9818-d584ee09ef79' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.5412807653916101,  1.680734679831433,  2.939952648914465,\n",
-       "        4.331521485149508, 5.8693504240540255, 7.5688099200502155,\n",
-       "        9.446884959648713, 11.522344392803275, 13.815927932333222,\n",
-       "       16.350552632935347, 19.151540835805992,  22.24687175218868,\n",
-       "       25.667459058670023, 29.447457087455206,  33.62459841385048,\n",
-       "        38.24056586677453, 43.341402211041604,  48.97796096317406,\n",
-       "        55.20640199365842, 62.088735720175855,  69.69341978534455,\n",
-       "        78.09601210263148,  87.37988400213499,  97.63699685168024,\n",
-       "       108.96874488209758, 121.48686590184016, 135.31441999260392,\n",
-       "        150.5868339523135, 167.45300594349283, 186.07646019149857,\n",
-       "       206.63653528423634,  229.3295811897483,  254.3701289430592,\n",
-       "       281.99198245469614, 312.44916351368306,  346.0166182176396,\n",
-       "        382.9905653992972,  423.6883357450478, 468.44751540395634,\n",
-       "        517.6241725363747,  571.5899153226626,  630.7275131613291,\n",
-       "        695.4248214962124,  766.0667992097763,  843.0255127651847,\n",
-       "         926.648198508397],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;hu_coarse_bin&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d9b651a8-262d-432d-b5d6-08116969168a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d9b651a8-262d-432d-b5d6-08116969168a' 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 'histogram_hu_coarse' (st_ocean: 75, time: 120,\n",
-       "                                         hu_coarse_bin: 46)> Size: 3MB\n",
-       "array([[[        nan,         nan,         nan, ..., -0.05329067,\n",
-       "         -0.0545278 , -0.04532973],\n",
-       "        [        nan,         nan,         nan, ..., -0.07846384,\n",
-       "         -0.07439158, -0.06675977],\n",
-       "        [        nan,         nan,         nan, ..., -0.06968889,\n",
-       "         -0.06318065, -0.03509349],\n",
-       "        ...,\n",
-       "        [        nan,         nan,         nan, ..., -0.01335827,\n",
-       "         -0.01355833, -0.02252559],\n",
-       "        [        nan,         nan,         nan, ..., -0.01091509,\n",
-       "         -0.01020199, -0.01440421],\n",
-       "        [        nan,         nan,         nan, ..., -0.04878291,\n",
-       "         -0.04536789, -0.04028598]],\n",
-       "\n",
-       "       [[        nan,         nan,         nan, ..., -0.04996552,\n",
-       "         -0.05102612, -0.04109836],\n",
-       "        [        nan,         nan,         nan, ..., -0.07459088,\n",
-       "         -0.07073072, -0.06223585],\n",
-       "        [        nan,         nan,         nan, ..., -0.07006387,\n",
-       "         -0.06542959, -0.03951225],\n",
-       "...\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan]],\n",
-       "\n",
-       "       [[        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        ...,\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan]]])\n",
-       "Coordinates:\n",
-       "  * hu_coarse_bin  (hu_coarse_bin) float64 368B 0.5413 1.681 ... 843.0 926.6\n",
-       "Dimensions without coordinates: st_ocean, time"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "### This section bins variables of interest by isobath using xhistogram #\n",
-    "\n",
-    "# First, we define the edges of the isobath bins - it makes sense for these to be the same as st_edges_ocean #\n",
-    "# because that is the vertical resolution of the ACCESS-OM2-01 model #\n",
-    "bins_isobath = st_edges_ocean.where(st_edges_ocean<=mask_depth).dropna(dim='st_edges_ocean').values\n",
-    "\n",
-    "# Next, we sum the volume-weighted variable of interest along that isobath using xhistogram\n",
-    "V_sum = histogram(ht_coarse, bins = bins_isobath, dim = ['yt_ocean', 'xt_ocean'], weights = volume_masked)\n",
-    "Vu_sum = histogram(hu_coarse, bins = bins_isobath, dim = ['yu_ocean', 'xu_ocean'], weights = volume_u_masked)\n",
-    "\n",
-    "A_sum = histogram(ht_coarse, bins = bins_isobath, dim = ['yt_ocean', 'xt_ocean'], weights = area_t_masked)\n",
-    "Au_sum = histogram(hu_coarse, bins = bins_isobath, dim = ['yu_ocean', 'xu_ocean'], weights = area_u_masked)\n",
-    "\n",
-    "TV_sum = histogram(ht_coarse, bins = bins_isobath, dim = ['yt_ocean', 'xt_ocean'], weights = volume_masked*temp_masked)\n",
-    "SV_sum = histogram(ht_coarse, bins = bins_isobath, dim = ['yt_ocean', 'xt_ocean'], weights = volume_masked*salt_masked)\n",
-    "rhoV_sum = histogram(ht_coarse, bins = bins_isobath, dim = ['yt_ocean', 'xt_ocean'], weights = volume_masked*density_masked)\n",
-    "uV_sum = histogram(hu_coarse, bins = bins_isobath, dim = ['yu_ocean', 'xu_ocean'], weights = volume_u_masked*u_masked)\n",
-    "\n",
-    "# Finally, we divide the summed, volume-weighted variable of interest by volume, to get a mean along an isobath\n",
-    "\n",
-    "T_mean = TV_sum/V_sum\n",
-    "S_mean = SV_sum/V_sum\n",
-    "rho_mean = rhoV_sum/V_sum\n",
-    "u_mean = uV_sum/Vu_sum\n",
-    "\n",
-    "T_mean.load()\n",
-    "rho_mean.load()\n",
-    "S_mean.load()\n",
-    "u_mean.load()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "ddcb8196-587e-4bfc-98a9-4ef992ea20fa",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "## The binning above creates an averaged field as a function of isobath ##\n",
-    "# Let's plot this to see what it looks like \n",
-    "\n",
-    "plt.pcolor(T_mean.ht_coarse_bin, st_ocean, T_mean.mean('time')-273.15)\n",
-    "plt.gca().invert_yaxis()\n",
-    "plt.ylim(1000,0)\n",
-    "plt.xlabel('Isobath [m]')\n",
-    "plt.ylabel('Depth [m]')\n",
-    "plt.colorbar(label = 'Temperature [$^\\circ$C]')\n",
-    "plt.show()\n",
-    "\n",
-    "## Each variable is averaged along isobaths, so the bathymetry looks diagonal in the plot #\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "4d16836d-bfe0-4824-8e23-e2fe2ef63619",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## We can rescale the isobath (x-axis) to be scaled by the cumulative \n",
-    "## surface area covered by each isobath, from shallowest to deepest.\n",
-    "\n",
-    "## This produces a 'normalised area' or pseduo-latitude x-axis ##\n",
-    "\n",
-    "normalised_area_cumsum = A_sum.cumsum('ht_coarse_bin')/np.nansum(A_sum)\n",
-    "normalised_area_cumsum_u = Au_sum.cumsum('hu_coarse_bin')/np.nansum(Au_sum)\n",
-    "\n",
-    "lat_max, lat_min = abs(mask_t.yt_ocean.max()), abs(mask_t.yt_ocean.min())\n",
-    "\n",
-    "pseudo_lat = -1*(lat_min-(lat_min-lat_max)*normalised_area_cumsum)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c2c4084f-9c14-48eb-aeec-79cd8fac6b0f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## Here, we plot the averaged quantities as a function of normalised area (between 0 and 1)\n",
-    "## and pseduo-latitude\n",
-    "\n",
-    "\n",
-    "fig, axs = plt.subplots(2,2, figsize=(24*0.5,10), facecolor='w', edgecolor='k')\n",
-    "fig.subplots_adjust(hspace = 0.15, wspace=0.2)\n",
-    "axs = axs.ravel()\n",
-    "\n",
-    "fontsize = 13\n",
-    "\n",
-    "\n",
-    "im1 = axs[0].contourf(normalised_area_cumsum, st_ocean, T_mean.mean('time')-273.15, levels=500, \\\n",
-    "                      vmin=-2, vmax=2, cmap=plt.cm.bwr)\n",
-    "im2 = axs[1].contourf(normalised_area_cumsum, st_ocean, S_mean.mean('time'), levels=500, vmin=34.1,  \\\n",
-    "                      vmax = 34.7, cmap=plt.cm.viridis)\n",
-    "im3 = axs[2].contourf(normalised_area_cumsum, st_ocean, rho_mean.mean('time')-1027, levels=500, \\\n",
-    "                      vmin=0.2, vmax=0.87, cmap=plt.cm.plasma)\n",
-    "im4 = axs[3].contourf(normalised_area_cumsum_u, st_ocean, u_mean.mean('time'), levels=500, \\\n",
-    "                      vmin=-0.06, vmax=0.06, cmap=plt.cm.bwr)\n",
-    "\n",
-    "density_contours = np.linspace(1027.7, 1028, 5)\n",
-    "cont=axs[0].contour(normalised_area_cumsum_u,st_ocean,rho_mean.mean('time'),density_contours,\n",
-    "                 colors='gray',zorder=2 ,fontsize=fontsize)\n",
-    "cont=axs[1].contour(normalised_area_cumsum_u,st_ocean,rho_mean.mean('time'),density_contours,\n",
-    "                 colors='gray',zorder=2 ,fontsize=fontsize)\n",
-    "cont=axs[2].contour(normalised_area_cumsum_u,st_ocean,rho_mean.mean('time'),density_contours,\n",
-    "                 colors='gray',zorder=2 ,fontsize=fontsize)\n",
-    "cont=axs[3].contour(normalised_area_cumsum_u,st_ocean,rho_mean.mean('time'),density_contours,\n",
-    "                 colors='gray',zorder=2 ,fontsize=fontsize)\n",
-    "\n",
-    "\n",
-    "for i in range(4):\n",
-    "    axs[i].invert_yaxis()\n",
-    "    axs[i].set_xlabel('Normalised distance from Antarctica')\n",
-    "    axs[i].set_ylim(mask_depth, 0)\n",
-    "\n",
-    "axs[0].set_ylabel('Depth [m]')\n",
-    "\n",
-    "cb1 = plt.colorbar(im1, ax=axs[0], fraction=0.03, pad=0.25, orientation = 'horizontal')\n",
-    "cb2 = plt.colorbar(im2, ax=axs[1], fraction=0.03, pad=0.25, orientation = 'horizontal')\n",
-    "cb3 = plt.colorbar(im3, ax=axs[2], fraction=0.03, pad=0.25, orientation = 'horizontal')\n",
-    "cb4 = plt.colorbar(im4, ax=axs[3], fraction=0.03, pad=0.25, orientation = 'horizontal')\n",
-    "\n",
-    "cb1.set_label('Time-mean Temp ($^\\circ$C)')\n",
-    "cb2.set_label('Time-mean Salinity (g/kg)')\n",
-    "cb3.set_label('Time-mean Density (+1027 kg/m$^3$)')\n",
-    "cb4.set_label('Time-mean Zonal Velocity (m/s)')\n",
-    "\n",
-    "cb1.set_ticks(np.linspace(np.min(T_mean.mean('time')-273.15), np.max(T_mean.mean('time')-273.15),5))\n",
-    "cb2.set_ticks(np.linspace(np.min(S_mean.mean('time')), np.max(S_mean.mean('time')),5))\n",
-    "cb3.set_ticks(np.linspace(np.min(rho_mean.mean('time')-1027), np.max(rho_mean.mean('time')-1027),5))\n",
-    "cb4.set_ticks(np.linspace(np.min(u_mean.mean('time')), np.max(u_mean.mean('time')),4))\n",
-    "\n",
-    "plt.suptitle('Isobath-averaged properties')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "0b3eae77-5079-454b-9fd3-acbea0fc269c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x1000 with 8 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axs = plt.subplots(2,2, figsize=(24*0.5,10), facecolor='w', edgecolor='k')\n",
-    "fig.subplots_adjust(hspace = 0.15, wspace=0.2)\n",
-    "axs = axs.ravel()\n",
-    "\n",
-    "fontsize = 13\n",
-    "\n",
-    "\n",
-    "im1 = axs[0].contourf(pseudo_lat, st_ocean, T_mean.mean('time')-273.15, levels=500, \\\n",
-    "                      vmin=-2, vmax=2, cmap=plt.cm.bwr)\n",
-    "im2 = axs[1].contourf(pseudo_lat, st_ocean, S_mean.mean('time'), levels=500, vmin=34.1,  \\\n",
-    "                      vmax = 34.7, cmap=plt.cm.viridis)\n",
-    "im3 = axs[2].contourf(pseudo_lat, st_ocean, rho_mean.mean('time')-1027, levels=500, \\\n",
-    "                      vmin=0.2, vmax=0.87, cmap=plt.cm.plasma)\n",
-    "im4 = axs[3].contourf(pseudo_lat, st_ocean, u_mean.mean('time'), levels=500, \\\n",
-    "                      vmin=-0.06, vmax=0.06, cmap=plt.cm.bwr)\n",
-    "\n",
-    "density_contours = np.linspace(1027.7, 1028, 5)\n",
-    "cont=axs[0].contour(pseudo_lat,st_ocean,rho_mean.mean('time'),density_contours,\n",
-    "                 colors='gray',zorder=2 ,fontsize=fontsize)\n",
-    "cont=axs[1].contour(pseudo_lat,st_ocean,rho_mean.mean('time'),density_contours,\n",
-    "                 colors='gray',zorder=2 ,fontsize=fontsize)\n",
-    "cont=axs[2].contour(pseudo_lat,st_ocean,rho_mean.mean('time'),density_contours,\n",
-    "                 colors='gray',zorder=2 ,fontsize=fontsize)\n",
-    "cont=axs[3].contour(pseudo_lat,st_ocean,rho_mean.mean('time'),density_contours,\n",
-    "                 colors='gray',zorder=2 ,fontsize=fontsize)\n",
-    "\n",
-    "\n",
-    "for i in range(4):\n",
-    "    axs[i].invert_yaxis()\n",
-    "    axs[i].set_xlabel('Representative latitude ($^\\circ$S)')\n",
-    "    axs[i].set_ylim(mask_depth, 0)\n",
-    "\n",
-    "axs[0].set_ylabel('Depth [m]')\n",
-    "\n",
-    "cb1 = plt.colorbar(im1, ax=axs[0], fraction=0.03, pad=0.25, orientation = 'horizontal')\n",
-    "cb2 = plt.colorbar(im2, ax=axs[1], fraction=0.03, pad=0.25, orientation = 'horizontal')\n",
-    "cb3 = plt.colorbar(im3, ax=axs[2], fraction=0.03, pad=0.25, orientation = 'horizontal')\n",
-    "cb4 = plt.colorbar(im4, ax=axs[3], fraction=0.03, pad=0.25, orientation = 'horizontal')\n",
-    "\n",
-    "cb1.set_label('Time-mean Temp ($^\\circ$C)')\n",
-    "cb2.set_label('Time-mean Salinity (g/kg)')\n",
-    "cb3.set_label('Time-mean Density (+1027 kg/m$^3$)')\n",
-    "cb4.set_label('Time-mean Zonal Velocity (m/s)')\n",
-    "\n",
-    "cb1.set_ticks(np.linspace(np.min(T_mean.mean('time')-273.15), np.max(T_mean.mean('time')-273.15),5))\n",
-    "cb2.set_ticks(np.linspace(np.min(S_mean.mean('time')), np.max(S_mean.mean('time')),5))\n",
-    "cb3.set_ticks(np.linspace(np.min(rho_mean.mean('time')-1027), np.max(rho_mean.mean('time')-1027),5))\n",
-    "cb4.set_ticks(np.linspace(np.min(u_mean.mean('time')), np.max(u_mean.mean('time')),4))\n",
-    "\n",
-    "plt.suptitle('Isobath-averaged properties')\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "xwmb_venv",
-   "language": "python",
-   "name": "xwmb_venv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

From 942c97d1075a24fc7502ad450c4841fe55a3556c Mon Sep 17 00:00:00 2001
From: Marc White <marc.white@anu.edu.au>
Date: Mon, 16 Sep 2024 16:23:10 +1000
Subject: [PATCH 7/7] Remove stray note & update runtimes for 14 CPU

---
 Recipes/Cross-contour_transport.ipynb | 531 +++++++++++++++++++++-----
 1 file changed, 428 insertions(+), 103 deletions(-)

diff --git a/Recipes/Cross-contour_transport.ipynb b/Recipes/Cross-contour_transport.ipynb
index b2462a5d..a0e354d2 100644
--- a/Recipes/Cross-contour_transport.ipynb
+++ b/Recipes/Cross-contour_transport.ipynb
@@ -11,7 +11,7 @@
     "\n",
     "This notebook calculates the transport across an arbitrary contour. We do this by first creating the contour, such as sea surface height, and extracting the coordinates using `matplotlib`'s `Path` class. We then create some masks to indicate which direction is across the contour at each position along the contour. We then load the transport data and compute the transport, resulting in data with dimensions depth and along contour index. \n",
     "\n",
-    "Computation times shown used conda environment `analysis3-24.01` on 28 broadwell cpus.\n",
+    "Computation times shown used conda environment `analysis3-24.07` on 14 broadwell cpus.\n",
     "\n",
     "**Alert:** After including the additional cases the contour number doesn't always monotonically increase along the contour. At the moment, the two indices that are set at the same time are adjacent numbers, whereas if you were following the contour you'd expect their numbers to be 2 apart with the other coordinate in between. See  https://github.com/COSIMA/cosima-recipes/issues/383.\n",
     "\n",
@@ -44,6 +44,16 @@
    "id": "789a9952-1884-4633-aa20-60c05cce513b",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.07/lib/python3.10/site-packages/distributed/node.py:182: UserWarning: Port 8787 is already in use.\n",
+      "Perhaps you already have a cluster running?\n",
+      "Hosting the HTTP server on port 37157 instead\n",
+      "  warnings.warn(\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -51,7 +61,7 @@
        "    <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-c60e2b19-73e3-11ef-a086-000007dffe80</p>\n",
+       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-616fabdc-73f3-11ef-ab52-000007b6fe80</p>\n",
        "        <table style=\"width: 100%; text-align: left;\">\n",
        "\n",
        "        <tr>\n",
@@ -64,7 +74,7 @@
        "        \n",
        "            <tr>\n",
        "                <td style=\"text-align: left;\">\n",
-       "                    <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
+       "                    <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:37157/status\" target=\"_blank\">http://127.0.0.1:37157/status</a>\n",
        "                </td>\n",
        "                <td style=\"text-align: left;\"></td>\n",
        "            </tr>\n",
@@ -73,7 +83,7 @@
        "        </table>\n",
        "\n",
        "        \n",
-       "            <button style=\"margin-bottom: 12px;\" data-commandlinker-command=\"dask:populate-and-launch-layout\" data-commandlinker-args='{\"url\": \"http://127.0.0.1:8787/status\" }'>\n",
+       "            <button style=\"margin-bottom: 12px;\" data-commandlinker-command=\"dask:populate-and-launch-layout\" data-commandlinker-args='{\"url\": \"http://127.0.0.1:37157/status\" }'>\n",
        "                Launch dashboard in JupyterLab\n",
        "            </button>\n",
        "        \n",
@@ -86,22 +96,22 @@
        "    </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;\">15655ac2</p>\n",
+       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">85170904</p>\n",
        "        <table style=\"width: 100%; text-align: left;\">\n",
        "            <tr>\n",
        "                <td style=\"text-align: left;\">\n",
-       "                    <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
+       "                    <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:37157/status\" target=\"_blank\">http://127.0.0.1:37157/status</a>\n",
        "                </td>\n",
        "                <td style=\"text-align: left;\">\n",
-       "                    <strong>Workers:</strong> 7\n",
+       "                    <strong>Workers:</strong> 14\n",
        "                </td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td style=\"text-align: left;\">\n",
-       "                    <strong>Total threads:</strong> 7\n",
+       "                    <strong>Total threads:</strong> 14\n",
        "                </td>\n",
        "                <td style=\"text-align: left;\">\n",
-       "                    <strong>Total memory:</strong> 251.19 GiB\n",
+       "                    <strong>Total memory:</strong> 125.20 GiB\n",
        "                </td>\n",
        "            </tr>\n",
        "            \n",
@@ -123,22 +133,22 @@
        "        <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-fb7a0057-1cc0-4abf-a827-320022aac1da</p>\n",
+       "            <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-e47c7a03-3756-4afd-a97e-f860c9485a32</p>\n",
        "            <table style=\"width: 100%; text-align: left;\">\n",
        "                <tr>\n",
        "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Comm:</strong> tcp://127.0.0.1:34261\n",
+       "                        <strong>Comm:</strong> tcp://127.0.0.1:39671\n",
        "                    </td>\n",
        "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Workers:</strong> 7\n",
+       "                        <strong>Workers:</strong> 14\n",
        "                    </td>\n",
        "                </tr>\n",
        "                <tr>\n",
        "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
+       "                        <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:37157/status\" target=\"_blank\">http://127.0.0.1:37157/status</a>\n",
        "                    </td>\n",
        "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Total threads:</strong> 7\n",
+       "                        <strong>Total threads:</strong> 14\n",
        "                    </td>\n",
        "                </tr>\n",
        "                <tr>\n",
@@ -146,7 +156,7 @@
        "                        <strong>Started:</strong> Just now\n",
        "                    </td>\n",
        "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Total memory:</strong> 251.19 GiB\n",
+       "                        <strong>Total memory:</strong> 125.20 GiB\n",
        "                    </td>\n",
        "                </tr>\n",
        "            </table>\n",
@@ -169,7 +179,7 @@
        "                <table style=\"width: 100%; text-align: left;\">\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:37351\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:40077\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
        "                            <strong>Total threads: </strong> 1\n",
@@ -177,21 +187,21 @@
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:39313/status\" target=\"_blank\">http://127.0.0.1:39313/status</a>\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:33903/status\" target=\"_blank\">http://127.0.0.1:33903/status</a>\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 35.88 GiB\n",
+       "                            <strong>Memory: </strong> 8.94 GiB\n",
        "                        </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:43301\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:37695\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> /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-honrolm1\n",
+       "                            <strong>Local directory: </strong> /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-yxij9jn4\n",
        "                        </td>\n",
        "                    </tr>\n",
        "\n",
@@ -214,7 +224,7 @@
        "                <table style=\"width: 100%; text-align: left;\">\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:44715\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:46383\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
        "                            <strong>Total threads: </strong> 1\n",
@@ -222,21 +232,21 @@
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:38385/status\" target=\"_blank\">http://127.0.0.1:38385/status</a>\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:39477/status\" target=\"_blank\">http://127.0.0.1:39477/status</a>\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 35.88 GiB\n",
+       "                            <strong>Memory: </strong> 8.94 GiB\n",
        "                        </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:45897\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:39125\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> /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-phultux2\n",
+       "                            <strong>Local directory: </strong> /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-pncqp44q\n",
        "                        </td>\n",
        "                    </tr>\n",
        "\n",
@@ -259,7 +269,7 @@
        "                <table style=\"width: 100%; text-align: left;\">\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:41667\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:35603\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
        "                            <strong>Total threads: </strong> 1\n",
@@ -267,21 +277,21 @@
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:45233/status\" target=\"_blank\">http://127.0.0.1:45233/status</a>\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:33535/status\" target=\"_blank\">http://127.0.0.1:33535/status</a>\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 35.88 GiB\n",
+       "                            <strong>Memory: </strong> 8.94 GiB\n",
        "                        </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:44869\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:34695\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> /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-g8s0kvud\n",
+       "                            <strong>Local directory: </strong> /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-353lt3ib\n",
        "                        </td>\n",
        "                    </tr>\n",
        "\n",
@@ -304,7 +314,7 @@
        "                <table style=\"width: 100%; text-align: left;\">\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:39041\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:36681\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
        "                            <strong>Total threads: </strong> 1\n",
@@ -312,21 +322,21 @@
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:40641/status\" target=\"_blank\">http://127.0.0.1:40641/status</a>\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:34593/status\" target=\"_blank\">http://127.0.0.1:34593/status</a>\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 35.88 GiB\n",
+       "                            <strong>Memory: </strong> 8.94 GiB\n",
        "                        </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:33535\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:33617\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> /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-bepdwr3v\n",
+       "                            <strong>Local directory: </strong> /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-9tej1r25\n",
        "                        </td>\n",
        "                    </tr>\n",
        "\n",
@@ -349,7 +359,7 @@
        "                <table style=\"width: 100%; text-align: left;\">\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:45353\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:44839\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
        "                            <strong>Total threads: </strong> 1\n",
@@ -357,21 +367,21 @@
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:44497/status\" target=\"_blank\">http://127.0.0.1:44497/status</a>\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:46345/status\" target=\"_blank\">http://127.0.0.1:46345/status</a>\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 35.88 GiB\n",
+       "                            <strong>Memory: </strong> 8.94 GiB\n",
        "                        </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:38497\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:35231\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> /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-fyzvp63z\n",
+       "                            <strong>Local directory: </strong> /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-872pkm1k\n",
        "                        </td>\n",
        "                    </tr>\n",
        "\n",
@@ -394,7 +404,7 @@
        "                <table style=\"width: 100%; text-align: left;\">\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:34045\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:37765\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
        "                            <strong>Total threads: </strong> 1\n",
@@ -402,21 +412,21 @@
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:38183/status\" target=\"_blank\">http://127.0.0.1:38183/status</a>\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:43961/status\" target=\"_blank\">http://127.0.0.1:43961/status</a>\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 35.88 GiB\n",
+       "                            <strong>Memory: </strong> 8.94 GiB\n",
        "                        </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:45053\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:33801\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> /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-aymd6dgl\n",
+       "                            <strong>Local directory: </strong> /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-vi_84_9m\n",
        "                        </td>\n",
        "                    </tr>\n",
        "\n",
@@ -439,7 +449,322 @@
        "                <table style=\"width: 100%; text-align: left;\">\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:38313\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:46505\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:36903/status\" target=\"_blank\">http://127.0.0.1:36903/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 8.94 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:43265\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> /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-wksev_uw\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://127.0.0.1:42893\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:39315/status\" target=\"_blank\">http://127.0.0.1:39315/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 8.94 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:42805\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> /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-p0nkpghf\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 8</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:39803\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:37591/status\" target=\"_blank\">http://127.0.0.1:37591/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 8.94 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:36883\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> /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-smh6_vj5\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 9</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:43769\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:37475/status\" target=\"_blank\">http://127.0.0.1:37475/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 8.94 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:33883\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> /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-kb1jchge\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 10</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:43601\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:34537/status\" target=\"_blank\">http://127.0.0.1:34537/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 8.94 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:43447\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> /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-2sspwgds\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 11</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:41001\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:43991/status\" target=\"_blank\">http://127.0.0.1:43991/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 8.94 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:39465\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> /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-8_vsshfo\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: 12</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:44541\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:35379/status\" target=\"_blank\">http://127.0.0.1:35379/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 8.94 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:37177\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> /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-599815et\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: 13</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:34383\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
        "                            <strong>Total threads: </strong> 1\n",
@@ -447,21 +772,21 @@
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:36685/status\" target=\"_blank\">http://127.0.0.1:36685/status</a>\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:39987/status\" target=\"_blank\">http://127.0.0.1:39987/status</a>\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 35.88 GiB\n",
+       "                            <strong>Memory: </strong> 8.94 GiB\n",
        "                        </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:45777\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:33319\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> /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-0x3156dn\n",
+       "                            <strong>Local directory: </strong> /scratch/tm70/mcw120/tmp/dask-scratch-space/worker-96mcw_mz\n",
        "                        </td>\n",
        "                    </tr>\n",
        "\n",
@@ -488,7 +813,7 @@
        "</div>"
       ],
       "text/plain": [
-       "<Client: 'tcp://127.0.0.1:34261' processes=7 threads=7, memory=251.19 GiB>"
+       "<Client: 'tcp://127.0.0.1:39671' processes=14 threads=14, memory=125.20 GiB>"
       ]
      },
      "execution_count": 2,
@@ -1147,7 +1472,7 @@
        "    units:          m\n",
        "    valid_range:    [-1.e+09  1.e+09]\n",
        "    cell_methods:   time: point\n",
-       "    standard_name:  sea_floor_depth_below_geoid</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'>'ht'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>yt_ocean</span>: 510</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-b8a1378e-d3c8-4421-bd6c-6170493c52c5' class='xr-array-in' type='checkbox' checked><label for='section-b8a1378e-d3c8-4421-bd6c-6170493c52c5' 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=(510, 900), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "    standard_name:  sea_floor_depth_below_geoid</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'>'ht'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>yt_ocean</span>: 510</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-98c71032-b360-4c98-baaa-fe135b928e54' class='xr-array-in' type='checkbox' checked><label for='section-98c71032-b360-4c98-baaa-fe135b928e54' 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=(510, 900), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1205,8 +1530,8 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></div></li><li class='xr-section-item'><input id='section-8da09610-a442-4b41-935b-1e6eea31de4e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8da09610-a442-4b41-935b-1e6eea31de4e' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xt_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-8c2c2f7d-fd93-4135-8cb7-ebec883db4bc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8c2c2f7d-fd93-4135-8cb7-ebec883db4bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a94d993c-dbc2-4e5d-b4f6-3c469b313937' class='xr-var-data-in' type='checkbox'><label for='data-a94d993c-dbc2-4e5d-b4f6-3c469b313937' 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>tcell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-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'>-81.11 -81.07 ... -59.08 -59.03</div><input id='attrs-ad26115a-94b3-41bb-a802-14dd7a19cce2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad26115a-94b3-41bb-a802-14dd7a19cce2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d34b9fdf-b667-4447-a32b-e74cfc765705' class='xr-var-data-in' type='checkbox'><label for='data-d34b9fdf-b667-4447-a32b-e74cfc765705' 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>tcell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-81.108632, -81.066392, -81.024153, ..., -59.13588 , -59.084541,\n",
-       "       -59.033125])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolon_t</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=(510, 900), meta=np.ndarray&gt;</div><input id='attrs-07103c97-5288-43a2-9d47-355617409528' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-07103c97-5288-43a2-9d47-355617409528' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-674697ba-10b6-4956-945e-89a6175f1104' class='xr-var-data-in' type='checkbox'><label for='data-674697ba-10b6-4956-945e-89a6175f1104' 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>tracer longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>valid_range :</span></dt><dd>[-281.  361.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-4d220edc-e462-4c2f-8429-f7c48e58604c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4d220edc-e462-4c2f-8429-f7c48e58604c' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xt_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-ddab6a1e-514a-454e-8583-c04ea68c141d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ddab6a1e-514a-454e-8583-c04ea68c141d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d977c19-e152-4725-8a63-645fe87afc6d' class='xr-var-data-in' type='checkbox'><label for='data-8d977c19-e152-4725-8a63-645fe87afc6d' 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>tcell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-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'>-81.11 -81.07 ... -59.08 -59.03</div><input id='attrs-884a39a3-4e95-418e-9a9a-a4c9b6e6a75d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-884a39a3-4e95-418e-9a9a-a4c9b6e6a75d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff97dd43-2a70-46e9-8ba7-0847150f7eee' class='xr-var-data-in' type='checkbox'><label for='data-ff97dd43-2a70-46e9-8ba7-0847150f7eee' 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>tcell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-81.108632, -81.066392, -81.024153, ..., -59.13588 , -59.084541,\n",
+       "       -59.033125])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolon_t</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=(510, 900), meta=np.ndarray&gt;</div><input id='attrs-b9ae529f-b71d-45a9-9ade-68de567cdc9a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b9ae529f-b71d-45a9-9ade-68de567cdc9a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-98688e44-070b-4d69-bea7-ecabd4e405d5' class='xr-var-data-in' type='checkbox'><label for='data-98688e44-070b-4d69-bea7-ecabd4e405d5' 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>tracer longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>valid_range :</span></dt><dd>[-281.  361.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1264,7 +1589,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolat_t</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=(510, 900), meta=np.ndarray&gt;</div><input id='attrs-3455989b-a0d9-46e2-96b9-3b08bf1fb79d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3455989b-a0d9-46e2-96b9-3b08bf1fb79d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-430f3115-7577-4f62-8946-29879ec9fce4' class='xr-var-data-in' type='checkbox'><label for='data-430f3115-7577-4f62-8946-29879ec9fce4' 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>tracer latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>valid_range :</span></dt><dd>[-91.  91.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolat_t</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=(510, 900), meta=np.ndarray&gt;</div><input id='attrs-9f519316-98c0-477e-a291-fb907b82e918' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f519316-98c0-477e-a291-fb907b82e918' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19412bc5-6317-41dc-a8fe-c957a5948074' class='xr-var-data-in' type='checkbox'><label for='data-19412bc5-6317-41dc-a8fe-c957a5948074' 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>tracer latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>valid_range :</span></dt><dd>[-91.  91.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1322,7 +1647,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-1f01c31d-4db2-4ade-b1a8-f2e1f3f08d37' class='xr-section-summary-in' type='checkbox'  ><label for='section-1f01c31d-4db2-4ade-b1a8-f2e1f3f08d37' 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>xt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fe0a8901-957d-40de-a0a3-ded7ab2f5130' class='xr-index-data-in' type='checkbox'/><label for='index-fe0a8901-957d-40de-a0a3-ded7ab2f5130' 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",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-a51e7b7c-566a-4259-b7da-16d29545e213' class='xr-section-summary-in' type='checkbox'  ><label for='section-a51e7b7c-566a-4259-b7da-16d29545e213' 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>xt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a59adc4d-a166-42c1-b9fe-29fc1ecd8747' class='xr-index-data-in' type='checkbox'/><label for='index-a59adc4d-a166-42c1-b9fe-29fc1ecd8747' 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",
@@ -1331,7 +1656,7 @@
        "         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-be141e9b-80a4-481b-8a79-17d9f4f20f1b' class='xr-index-data-in' type='checkbox'/><label for='index-be141e9b-80a4-481b-8a79-17d9f4f20f1b' 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([ -81.10863167835483,  -81.06639232397434,  -81.02415296959386,\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-051cff60-2df8-4c19-b0ee-c90c6025e87f' class='xr-index-data-in' type='checkbox'/><label for='index-051cff60-2df8-4c19-b0ee-c90c6025e87f' 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([ -81.10863167835483,  -81.06639232397434,  -81.02415296959386,\n",
        "        -80.98191361521337,  -80.93967426083289,   -80.8974349064524,\n",
        "        -80.85519555207192,  -80.81295619769143,  -80.77071684331095,\n",
        "        -80.72847748893047,\n",
@@ -1340,7 +1665,7 @@
        "         -59.3404671310413,  -59.28943522766847,  -59.23832683955227,\n",
        "       -59.187141594328104,  -59.13587971534102,  -59.08454083138616,\n",
        "        -59.03312516518795],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;yt_ocean&#x27;, length=510))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4c7c6c4f-a668-4c4d-b838-66445531975c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4c7c6c4f-a668-4c4d-b838-66445531975c' 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>long_name :</span></dt><dd>ocean depth on t-cells</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_range :</span></dt><dd>[-1.e+09  1.e+09]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>standard_name :</span></dt><dd>sea_floor_depth_below_geoid</dd></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;float64&#x27;, name=&#x27;yt_ocean&#x27;, length=510))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0b7637a0-9ade-4c47-8261-664b2b896b64' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0b7637a0-9ade-4c47-8261-664b2b896b64' 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>long_name :</span></dt><dd>ocean depth on t-cells</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_range :</span></dt><dd>[-1.e+09  1.e+09]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>standard_name :</span></dt><dd>sea_floor_depth_below_geoid</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.DataArray 'ht' (yt_ocean: 510, xt_ocean: 3600)> Size: 7MB\n",
@@ -1759,7 +2084,7 @@
        "    long_name:     ocean depth on u-cells\n",
        "    units:         m\n",
        "    valid_range:   [-1.e+09  1.e+09]\n",
-       "    cell_methods:  time: point</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'>'hu'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>yu_ocean</span>: 510</li><li><span class='xr-has-index'>xu_ocean</span>: 3600</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-4d489b03-bc13-4d71-994b-cdaa4878ad6d' class='xr-array-in' type='checkbox' checked><label for='section-4d489b03-bc13-4d71-994b-cdaa4878ad6d' 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=(510, 900), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "    cell_methods:  time: point</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'>'hu'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>yu_ocean</span>: 510</li><li><span class='xr-has-index'>xu_ocean</span>: 3600</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b55c9ad8-6181-4b4d-9134-071ba5a48abd' class='xr-array-in' type='checkbox' checked><label for='section-b55c9ad8-6181-4b4d-9134-071ba5a48abd' 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=(510, 900), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1817,8 +2142,8 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></div></li><li class='xr-section-item'><input id='section-a4c4ea06-cda0-460d-88fa-f654e608230f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a4c4ea06-cda0-460d-88fa-f654e608230f' class='xr-section-summary' >Coordinates: <span>(4)</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'>xu_ocean</span></div><div class='xr-var-dims'>(xu_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.9 -279.8 -279.7 ... 79.9 80.0</div><input id='attrs-b710ad46-8989-4fb3-9186-ec3aa7c96694' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b710ad46-8989-4fb3-9186-ec3aa7c96694' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b35d25b2-e82e-4b26-b300-222fd1a70fb6' class='xr-var-data-in' type='checkbox'><label for='data-b35d25b2-e82e-4b26-b300-222fd1a70fb6' 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>ucell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-279.9, -279.8, -279.7, ...,   79.8,   79.9,   80. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yu_ocean</span></div><div class='xr-var-dims'>(yu_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-81.09 -81.05 ... -59.06 -59.01</div><input id='attrs-6204d1bf-fa22-414d-a007-d57b5be3147e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6204d1bf-fa22-414d-a007-d57b5be3147e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-566a7e2f-d4f3-4ad6-a05a-b2d9b433dd8e' class='xr-var-data-in' type='checkbox'><label for='data-566a7e2f-d4f3-4ad6-a05a-b2d9b433dd8e' 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>ucell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-81.087512, -81.045273, -81.003033, ..., -59.11021 , -59.058833,\n",
-       "       -59.007379])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolon_c</span></div><div class='xr-var-dims'>(yu_ocean, xu_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(510, 900), meta=np.ndarray&gt;</div><input id='attrs-de736137-f174-4b56-a7f4-ec968028a1b1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de736137-f174-4b56-a7f4-ec968028a1b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da3e9bfb-3036-41b3-a038-03175da625fb' class='xr-var-data-in' type='checkbox'><label for='data-da3e9bfb-3036-41b3-a038-03175da625fb' 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>uv longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>valid_range :</span></dt><dd>[-281.  361.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-1718c1c6-2c82-4823-bc7a-f8e1c07e4f6c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1718c1c6-2c82-4823-bc7a-f8e1c07e4f6c' class='xr-section-summary' >Coordinates: <span>(4)</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'>xu_ocean</span></div><div class='xr-var-dims'>(xu_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.9 -279.8 -279.7 ... 79.9 80.0</div><input id='attrs-5225a184-548f-40e1-93b7-4799af3c79dc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5225a184-548f-40e1-93b7-4799af3c79dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45143a91-f2ba-4ced-a2e2-255e8f140385' class='xr-var-data-in' type='checkbox'><label for='data-45143a91-f2ba-4ced-a2e2-255e8f140385' 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>ucell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-279.9, -279.8, -279.7, ...,   79.8,   79.9,   80. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yu_ocean</span></div><div class='xr-var-dims'>(yu_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-81.09 -81.05 ... -59.06 -59.01</div><input id='attrs-367d5456-76a2-4650-a786-1eed82c62061' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-367d5456-76a2-4650-a786-1eed82c62061' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32c22e77-0171-497b-a9f8-1b4f5f1cc362' class='xr-var-data-in' type='checkbox'><label for='data-32c22e77-0171-497b-a9f8-1b4f5f1cc362' 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>ucell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-81.087512, -81.045273, -81.003033, ..., -59.11021 , -59.058833,\n",
+       "       -59.007379])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolon_c</span></div><div class='xr-var-dims'>(yu_ocean, xu_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(510, 900), meta=np.ndarray&gt;</div><input id='attrs-54259432-cd9b-4893-9232-7060d04841a9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-54259432-cd9b-4893-9232-7060d04841a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68f07eac-7df7-476a-8b95-411f3a50dafb' class='xr-var-data-in' type='checkbox'><label for='data-68f07eac-7df7-476a-8b95-411f3a50dafb' 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>uv longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>valid_range :</span></dt><dd>[-281.  361.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1876,7 +2201,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolat_c</span></div><div class='xr-var-dims'>(yu_ocean, xu_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(510, 900), meta=np.ndarray&gt;</div><input id='attrs-603e5c88-89f0-4a8f-9d64-d1b6c74cbf8a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-603e5c88-89f0-4a8f-9d64-d1b6c74cbf8a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3344b8b2-9bd9-44e4-96ef-af25b7e06ddc' class='xr-var-data-in' type='checkbox'><label for='data-3344b8b2-9bd9-44e4-96ef-af25b7e06ddc' 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>uv latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>valid_range :</span></dt><dd>[-91.  91.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolat_c</span></div><div class='xr-var-dims'>(yu_ocean, xu_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(510, 900), meta=np.ndarray&gt;</div><input id='attrs-0deec6f0-5730-426f-b783-1de576053fa7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0deec6f0-5730-426f-b783-1de576053fa7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95f3ecbd-4b6d-43cc-b418-327db72ae827' class='xr-var-data-in' type='checkbox'><label for='data-95f3ecbd-4b6d-43cc-b418-327db72ae827' 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>uv latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>valid_range :</span></dt><dd>[-91.  91.]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1934,7 +2259,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-9cbd07d8-fd5b-40e3-ab28-0167a2400b6b' class='xr-section-summary-in' type='checkbox'  ><label for='section-9cbd07d8-fd5b-40e3-ab28-0167a2400b6b' 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>xu_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-eb1785ba-1a40-4448-9dd8-809a5e000e62' class='xr-index-data-in' type='checkbox'/><label for='index-eb1785ba-1a40-4448-9dd8-809a5e000e62' 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([             -279.9, -279.79999999999995, -279.69999999999993,\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-914381b3-d027-4e02-9647-e7696e5ba636' class='xr-section-summary-in' type='checkbox'  ><label for='section-914381b3-d027-4e02-9647-e7696e5ba636' 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>xu_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-99a5ee8b-1f4c-4c2e-be18-5030ec163fca' class='xr-index-data-in' type='checkbox'/><label for='index-99a5ee8b-1f4c-4c2e-be18-5030ec163fca' 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([             -279.9, -279.79999999999995, -279.69999999999993,\n",
        "        -279.5999999999999,  -279.4999999999999, -279.39999999999986,\n",
        "       -279.29999999999984,  -279.1999999999998,  -279.0999999999998,\n",
        "        -278.9999999999998,\n",
@@ -1943,7 +2268,7 @@
        "         79.39999999999492,   79.49999999999491,    79.5999999999949,\n",
        "          79.6999999999949,    79.7999999999949,   79.89999999999489,\n",
        "                      80.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;xu_ocean&#x27;, length=3600))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>yu_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4af4757c-fe4e-4e70-83a8-90bece6b0110' class='xr-index-data-in' type='checkbox'/><label for='index-4af4757c-fe4e-4e70-83a8-90bece6b0110' 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([ -81.08751200116458,   -81.0452726467841,  -81.00303329240361,\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;xu_ocean&#x27;, length=3600))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>yu_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4ecf9744-7ab9-4f86-9e9b-03dd5b1a5ced' class='xr-index-data-in' type='checkbox'/><label for='index-4ecf9744-7ab9-4f86-9e9b-03dd5b1a5ced' 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([ -81.08751200116458,   -81.0452726467841,  -81.00303329240361,\n",
        "        -80.96079393802313,  -80.91855458364265,  -80.87631522926216,\n",
        "        -80.83407587488168,  -80.79183652050119,  -80.74959716612071,\n",
        "        -80.70735781174022,\n",
@@ -1952,7 +2277,7 @@
        "       -59.314951179354885, -59.263881033610375,  -59.21273421694019,\n",
        "       -59.161510654834565,  -59.11021027336359,  -59.05883299828705,\n",
        "       -59.007378755945275],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;yu_ocean&#x27;, length=510))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6f878728-3277-44d9-919d-5c992a9e0a0d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6f878728-3277-44d9-919d-5c992a9e0a0d' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>ocean depth on u-cells</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_range :</span></dt><dd>[-1.e+09  1.e+09]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;float64&#x27;, name=&#x27;yu_ocean&#x27;, length=510))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3acef397-8aac-4812-8601-f59d06d863e5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3acef397-8aac-4812-8601-f59d06d863e5' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>ocean depth on u-cells</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_range :</span></dt><dd>[-1.e+09  1.e+09]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.DataArray 'hu' (yu_ocean: 510, xu_ocean: 3600)> Size: 7MB\n",
@@ -2350,8 +2675,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 32.2 s, sys: 412 ms, total: 32.6 s\n",
-      "Wall time: 32.3 s\n"
+      "CPU times: user 29.5 s, sys: 340 ms, total: 29.8 s\n",
+      "Wall time: 29.5 s\n"
      ]
     }
    ],
@@ -2411,7 +2736,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x7fbc79133040>"
+       "<matplotlib.colorbar.Colorbar at 0x7f20846d3700>"
       ]
      },
      "execution_count": 24,
@@ -2457,8 +2782,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 58.9 s, sys: 745 ms, total: 59.7 s\n",
-      "Wall time: 54.4 s\n"
+      "CPU times: user 1min 8s, sys: 1.43 s, total: 1min 10s\n",
+      "Wall time: 56.5 s\n"
      ]
     }
    ],
@@ -3048,7 +3373,7 @@
        "    valid_range:    [-1.e+20  1.e+20]\n",
        "    cell_methods:   time: mean\n",
        "    time_avg_info:  average_T1,average_T2,average_DT\n",
-       "    standard_name:  ocean_mass_y_transport</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'>'ty_trans'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 12</li><li><span class='xr-has-index'>st_ocean</span>: 75</li><li><span class='xr-has-index'>yu_ocean</span>: 510</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-c2d293d2-b08d-40e6-a635-b20508de235c' class='xr-array-in' type='checkbox' checked><label for='section-c2d293d2-b08d-40e6-a635-b20508de235c' 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, 7, 300, 400), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "    standard_name:  ocean_mass_y_transport</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'>'ty_trans'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 12</li><li><span class='xr-has-index'>st_ocean</span>: 75</li><li><span class='xr-has-index'>yu_ocean</span>: 510</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-c2207e86-d214-47a1-ac88-e915c0027302' class='xr-array-in' type='checkbox' checked><label for='section-c2207e86-d214-47a1-ac88-e915c0027302' 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, 7, 300, 400), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3191,7 +3516,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></div></li><li class='xr-section-item'><input id='section-2d9dd759-c631-473c-ae41-597020170120' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2d9dd759-c631-473c-ae41-597020170120' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xt_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-10bc79f7-b91c-4ebe-be99-0887c2f5de68' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-10bc79f7-b91c-4ebe-be99-0887c2f5de68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7cf19679-4c1f-40de-b69b-1a47a56aaf90' class='xr-var-data-in' type='checkbox'><label for='data-7cf19679-4c1f-40de-b69b-1a47a56aaf90' 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>tcell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-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'>st_ocean</span></div><div class='xr-var-dims'>(st_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5413 1.681 ... 5.709e+03</div><input id='attrs-de4ab340-04ef-4eae-9503-b82d8af504fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de4ab340-04ef-4eae-9503-b82d8af504fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e95bd0c2-cc05-4545-aad4-6586a2b2dffd' class='xr-var-data-in' type='checkbox'><label for='data-e95bd0c2-cc05-4545-aad4-6586a2b2dffd' 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>tcell zstar depth</dd><dt><span>units :</span></dt><dd>meters</dd><dt><span>cartesian_axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>edges :</span></dt><dd>st_edges_ocean</dd></dl></div><div class='xr-var-data'><pre>array([5.412808e-01, 1.680735e+00, 2.939953e+00, 4.331521e+00, 5.869350e+00,\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-66efd1b5-7c27-4377-a039-0c4b7c55d43f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-66efd1b5-7c27-4377-a039-0c4b7c55d43f' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xt_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-19ae1b91-014d-4723-aa12-78721f0da65c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-19ae1b91-014d-4723-aa12-78721f0da65c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-392a77a1-b601-47b8-836b-d0880f6604ba' class='xr-var-data-in' type='checkbox'><label for='data-392a77a1-b601-47b8-836b-d0880f6604ba' 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>tcell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-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'>st_ocean</span></div><div class='xr-var-dims'>(st_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5413 1.681 ... 5.709e+03</div><input id='attrs-08f4c639-4021-40ac-8de9-03c7f54661b8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08f4c639-4021-40ac-8de9-03c7f54661b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b80fcaeb-3497-4cc9-a353-5dbdc36bfa23' class='xr-var-data-in' type='checkbox'><label for='data-b80fcaeb-3497-4cc9-a353-5dbdc36bfa23' 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>tcell zstar depth</dd><dt><span>units :</span></dt><dd>meters</dd><dt><span>cartesian_axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>edges :</span></dt><dd>st_edges_ocean</dd></dl></div><div class='xr-var-data'><pre>array([5.412808e-01, 1.680735e+00, 2.939953e+00, 4.331521e+00, 5.869350e+00,\n",
        "       7.568810e+00, 9.446885e+00, 1.152234e+01, 1.381593e+01, 1.635055e+01,\n",
        "       1.915154e+01, 2.224687e+01, 2.566746e+01, 2.944746e+01, 3.362460e+01,\n",
        "       3.824057e+01, 4.334140e+01, 4.897796e+01, 5.520640e+01, 6.208874e+01,\n",
@@ -3205,7 +3530,7 @@
        "       2.161913e+03, 2.322601e+03, 2.488533e+03, 2.659189e+03, 2.834054e+03,\n",
        "       3.012631e+03, 3.194453e+03, 3.379089e+03, 3.566145e+03, 3.755274e+03,\n",
        "       3.946166e+03, 4.138551e+03, 4.332197e+03, 4.526903e+03, 4.722497e+03,\n",
-       "       4.918835e+03, 5.115794e+03, 5.313270e+03, 5.511177e+03, 5.709443e+03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2170-01-16 12:00:00 ... 2170-12-...</div><input id='attrs-d5c94306-5147-4076-97fd-82ad219a28b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5c94306-5147-4076-97fd-82ad219a28b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6cd73d97-9604-45eb-a4e9-33f415d2954f' class='xr-var-data-in' type='checkbox'><label for='data-6cd73d97-9604-45eb-a4e9-33f415d2954f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>cartesian_axis :</span></dt><dd>T</dd><dt><span>calendar_type :</span></dt><dd>NOLEAP</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2170, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       4.918835e+03, 5.115794e+03, 5.313270e+03, 5.511177e+03, 5.709443e+03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2170-01-16 12:00:00 ... 2170-12-...</div><input id='attrs-96443d01-6374-4667-88df-5c115ad7eba2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-96443d01-6374-4667-88df-5c115ad7eba2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9b19f1f-ae6e-47b9-96e2-0be032cef6d9' class='xr-var-data-in' type='checkbox'><label for='data-b9b19f1f-ae6e-47b9-96e2-0be032cef6d9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>cartesian_axis :</span></dt><dd>T</dd><dt><span>calendar_type :</span></dt><dd>NOLEAP</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2170, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.DatetimeNoLeap(2170, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.DatetimeNoLeap(2170, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.DatetimeNoLeap(2170, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
@@ -3217,8 +3542,8 @@
        "       cftime.DatetimeNoLeap(2170, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.DatetimeNoLeap(2170, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.DatetimeNoLeap(2170, 12, 16, 12, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yu_ocean</span></div><div class='xr-var-dims'>(yu_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-81.09 -81.05 ... -59.06 -59.01</div><input id='attrs-e3a4d86b-1623-4726-a967-f88a516a18a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e3a4d86b-1623-4726-a967-f88a516a18a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1651508f-7169-48cc-812a-0ec16d9068e1' class='xr-var-data-in' type='checkbox'><label for='data-1651508f-7169-48cc-812a-0ec16d9068e1' 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>ucell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-81.087512, -81.045273, -81.003033, ..., -59.11021 , -59.058833,\n",
-       "       -59.007379])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4d057140-af2e-4653-96c6-6e4d6a7f7260' class='xr-section-summary-in' type='checkbox'  ><label for='section-4d057140-af2e-4653-96c6-6e4d6a7f7260' class='xr-section-summary' >Indexes: <span>(4)</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>xt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9f2ae9ed-96bf-46ee-a355-3492001af11c' class='xr-index-data-in' type='checkbox'/><label for='index-9f2ae9ed-96bf-46ee-a355-3492001af11c' 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",
+       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yu_ocean</span></div><div class='xr-var-dims'>(yu_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-81.09 -81.05 ... -59.06 -59.01</div><input id='attrs-5b11b071-e860-41a9-a10e-63fb8c2dc85c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b11b071-e860-41a9-a10e-63fb8c2dc85c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6baebdc1-753b-49d9-b801-992337fa5886' class='xr-var-data-in' type='checkbox'><label for='data-6baebdc1-753b-49d9-b801-992337fa5886' 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>ucell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-81.087512, -81.045273, -81.003033, ..., -59.11021 , -59.058833,\n",
+       "       -59.007379])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-81e154cb-b136-41e3-aaf8-2c856dbc7cc5' class='xr-section-summary-in' type='checkbox'  ><label for='section-81e154cb-b136-41e3-aaf8-2c856dbc7cc5' class='xr-section-summary' >Indexes: <span>(4)</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>xt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-470afae2-5636-4442-aa00-881b928911d7' class='xr-index-data-in' type='checkbox'/><label for='index-470afae2-5636-4442-aa00-881b928911d7' 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",
@@ -3227,7 +3552,7 @@
        "         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>st_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6ade60c8-f31a-4584-90d3-b2d9a9c50aa5' class='xr-index-data-in' type='checkbox'/><label for='index-6ade60c8-f31a-4584-90d3-b2d9a9c50aa5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.5412807653916101,  1.680734679831433,  2.939952648914465,\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>st_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f7bd847d-a0f1-495f-a2a8-31166416371b' class='xr-index-data-in' type='checkbox'/><label for='index-f7bd847d-a0f1-495f-a2a8-31166416371b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.5412807653916101,  1.680734679831433,  2.939952648914465,\n",
        "        4.331521485149508, 5.8693504240540255, 7.5688099200502155,\n",
        "        9.446884959648713, 11.522344392803275, 13.815927932333222,\n",
        "       16.350552632935347, 19.151540835805992,  22.24687175218868,\n",
@@ -3252,11 +3577,11 @@
        "        4138.551439998412,  4332.197126591926,  4526.902649987421,\n",
        "        4722.496976663542,  4918.834912408163,  5115.793747063695,\n",
        "        5313.270158218243,  5511.177417673949,  5709.442914225761],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;st_ocean&#x27;))</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-1e67a52f-79e1-4097-a88a-4ec51a5198d0' class='xr-index-data-in' type='checkbox'/><label for='index-1e67a52f-79e1-4097-a88a-4ec51a5198d0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([2170-01-16 12:00:00, 2170-02-15 00:00:00, 2170-03-16 12:00:00,\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;st_ocean&#x27;))</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-af22b4de-d8dc-41ec-91a5-9647344b5ec9' class='xr-index-data-in' type='checkbox'/><label for='index-af22b4de-d8dc-41ec-91a5-9647344b5ec9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([2170-01-16 12:00:00, 2170-02-15 00:00:00, 2170-03-16 12:00:00,\n",
        "             2170-04-16 00:00:00, 2170-05-16 12:00:00, 2170-06-16 00:00:00,\n",
        "             2170-07-16 12:00:00, 2170-08-16 12:00:00, 2170-09-16 00:00:00,\n",
        "             2170-10-16 12:00:00, 2170-11-16 00:00:00, 2170-12-16 12:00:00],\n",
-       "            dtype=&#x27;object&#x27;, length=12, calendar=&#x27;noleap&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>yu_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9e057c94-450b-4c0f-937c-6ef1d86e76fc' class='xr-index-data-in' type='checkbox'/><label for='index-9e057c94-450b-4c0f-937c-6ef1d86e76fc' 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([ -81.08751200116458,   -81.0452726467841,  -81.00303329240361,\n",
+       "            dtype=&#x27;object&#x27;, length=12, calendar=&#x27;noleap&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>yu_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ce67d678-ddfa-4615-b016-7e972e83a81b' class='xr-index-data-in' type='checkbox'/><label for='index-ce67d678-ddfa-4615-b016-7e972e83a81b' 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([ -81.08751200116458,   -81.0452726467841,  -81.00303329240361,\n",
        "        -80.96079393802313,  -80.91855458364265,  -80.87631522926216,\n",
        "        -80.83407587488168,  -80.79183652050119,  -80.74959716612071,\n",
        "        -80.70735781174022,\n",
@@ -3265,7 +3590,7 @@
        "       -59.314951179354885, -59.263881033610375,  -59.21273421694019,\n",
        "       -59.161510654834565,  -59.11021027336359,  -59.05883299828705,\n",
        "       -59.007378755945275],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;yu_ocean&#x27;, length=510))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6c11b989-49e6-47ca-8aa9-8f9729a29469' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6c11b989-49e6-47ca-8aa9-8f9729a29469' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>T-cell j-mass transport</dd><dt><span>units :</span></dt><dd>kg/s</dd><dt><span>valid_range :</span></dt><dd>[-1.e+20  1.e+20]</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd><dt><span>standard_name :</span></dt><dd>ocean_mass_y_transport</dd></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;float64&#x27;, name=&#x27;yu_ocean&#x27;, length=510))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3dd80683-a3d1-49e0-93c0-14e8d517ff69' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3dd80683-a3d1-49e0-93c0-14e8d517ff69' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>T-cell j-mass transport</dd><dt><span>units :</span></dt><dd>kg/s</dd><dt><span>valid_range :</span></dt><dd>[-1.e+20  1.e+20]</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd><dt><span>standard_name :</span></dt><dd>ocean_mass_y_transport</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.DataArray 'ty_trans' (time: 12, st_ocean: 75, yu_ocean: 510,\n",
@@ -3697,7 +4022,7 @@
        "    valid_range:    [-1.e+20  1.e+20]\n",
        "    cell_methods:   time: mean\n",
        "    time_avg_info:  average_T1,average_T2,average_DT\n",
-       "    standard_name:  ocean_mass_y_transport</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'>'ty_trans'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 12</li><li><span class='xr-has-index'>st_ocean</span>: 75</li><li><span class='xr-has-index'>yu_ocean</span>: 510</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-3cb3e8b5-1aec-4b7d-bd5e-fae49d85b224' class='xr-array-in' type='checkbox' checked><label for='section-3cb3e8b5-1aec-4b7d-bd5e-fae49d85b224' 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=(12, 75, 102, 360), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "    standard_name:  ocean_mass_y_transport</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'>'ty_trans'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 12</li><li><span class='xr-has-index'>st_ocean</span>: 75</li><li><span class='xr-has-index'>yu_ocean</span>: 510</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-d1a1ccd0-74c0-4600-98cb-27242a83c013' class='xr-array-in' type='checkbox' checked><label for='section-d1a1ccd0-74c0-4600-98cb-27242a83c013' 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=(12, 75, 102, 360), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3817,7 +4142,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></div></li><li class='xr-section-item'><input id='section-c723d8d8-6f12-4a20-8176-360a335693be' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c723d8d8-6f12-4a20-8176-360a335693be' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xt_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-ac271e3a-00d0-423b-8d31-38d3cacad527' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac271e3a-00d0-423b-8d31-38d3cacad527' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8bec632b-4ea7-42e7-b4aa-7b8867503f16' class='xr-var-data-in' type='checkbox'><label for='data-8bec632b-4ea7-42e7-b4aa-7b8867503f16' 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>tcell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-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'>st_ocean</span></div><div class='xr-var-dims'>(st_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5413 1.681 ... 5.709e+03</div><input id='attrs-41167714-808c-4789-8583-64695ff82b93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41167714-808c-4789-8583-64695ff82b93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7ab16a7-cbe0-448f-94a0-09a495004522' class='xr-var-data-in' type='checkbox'><label for='data-b7ab16a7-cbe0-448f-94a0-09a495004522' 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>tcell zstar depth</dd><dt><span>units :</span></dt><dd>meters</dd><dt><span>cartesian_axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>edges :</span></dt><dd>st_edges_ocean</dd></dl></div><div class='xr-var-data'><pre>array([5.412808e-01, 1.680735e+00, 2.939953e+00, 4.331521e+00, 5.869350e+00,\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-a05f86c2-c9df-4eb3-bca7-eb2ba618d3f4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a05f86c2-c9df-4eb3-bca7-eb2ba618d3f4' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xt_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-c075544f-1b7c-4177-9edf-dff46c16d4d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c075544f-1b7c-4177-9edf-dff46c16d4d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e327831-d95b-40f6-ac09-c722205fcef7' class='xr-var-data-in' type='checkbox'><label for='data-9e327831-d95b-40f6-ac09-c722205fcef7' 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>tcell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-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'>st_ocean</span></div><div class='xr-var-dims'>(st_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5413 1.681 ... 5.709e+03</div><input id='attrs-979bd25e-7a2d-4986-a7fd-93fe71596e06' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-979bd25e-7a2d-4986-a7fd-93fe71596e06' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-610568cc-6862-48d6-9dc7-0b51f4ecbbbd' class='xr-var-data-in' type='checkbox'><label for='data-610568cc-6862-48d6-9dc7-0b51f4ecbbbd' 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>tcell zstar depth</dd><dt><span>units :</span></dt><dd>meters</dd><dt><span>cartesian_axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>edges :</span></dt><dd>st_edges_ocean</dd></dl></div><div class='xr-var-data'><pre>array([5.412808e-01, 1.680735e+00, 2.939953e+00, 4.331521e+00, 5.869350e+00,\n",
        "       7.568810e+00, 9.446885e+00, 1.152234e+01, 1.381593e+01, 1.635055e+01,\n",
        "       1.915154e+01, 2.224687e+01, 2.566746e+01, 2.944746e+01, 3.362460e+01,\n",
        "       3.824057e+01, 4.334140e+01, 4.897796e+01, 5.520640e+01, 6.208874e+01,\n",
@@ -3831,7 +4156,7 @@
        "       2.161913e+03, 2.322601e+03, 2.488533e+03, 2.659189e+03, 2.834054e+03,\n",
        "       3.012631e+03, 3.194453e+03, 3.379089e+03, 3.566145e+03, 3.755274e+03,\n",
        "       3.946166e+03, 4.138551e+03, 4.332197e+03, 4.526903e+03, 4.722497e+03,\n",
-       "       4.918835e+03, 5.115794e+03, 5.313270e+03, 5.511177e+03, 5.709443e+03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2170-01-16 12:00:00 ... 2170-12-...</div><input id='attrs-04412ca2-5ecf-4ba4-a1c1-d494d9039349' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-04412ca2-5ecf-4ba4-a1c1-d494d9039349' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e9e2029-1401-4e13-8fbf-928290c7bc94' class='xr-var-data-in' type='checkbox'><label for='data-6e9e2029-1401-4e13-8fbf-928290c7bc94' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>cartesian_axis :</span></dt><dd>T</dd><dt><span>calendar_type :</span></dt><dd>NOLEAP</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2170, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       4.918835e+03, 5.115794e+03, 5.313270e+03, 5.511177e+03, 5.709443e+03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2170-01-16 12:00:00 ... 2170-12-...</div><input id='attrs-5d5c650e-9b90-4ada-ab6f-0be2b84e3b78' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5d5c650e-9b90-4ada-ab6f-0be2b84e3b78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2bca83af-40e2-484d-9c72-21eab0e94fc5' class='xr-var-data-in' type='checkbox'><label for='data-2bca83af-40e2-484d-9c72-21eab0e94fc5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>cartesian_axis :</span></dt><dd>T</dd><dt><span>calendar_type :</span></dt><dd>NOLEAP</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2170, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.DatetimeNoLeap(2170, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.DatetimeNoLeap(2170, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.DatetimeNoLeap(2170, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
@@ -3843,8 +4168,8 @@
        "       cftime.DatetimeNoLeap(2170, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.DatetimeNoLeap(2170, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.DatetimeNoLeap(2170, 12, 16, 12, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yu_ocean</span></div><div class='xr-var-dims'>(yu_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-81.09 -81.05 ... -59.06 -59.01</div><input id='attrs-3ea4d1d1-f1d8-41ee-9774-3f6b222cc578' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ea4d1d1-f1d8-41ee-9774-3f6b222cc578' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-004dfc50-8d1c-4797-a414-f8b4d1b9cb16' class='xr-var-data-in' type='checkbox'><label for='data-004dfc50-8d1c-4797-a414-f8b4d1b9cb16' 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>ucell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-81.087512, -81.045273, -81.003033, ..., -59.11021 , -59.058833,\n",
-       "       -59.007379])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9dd3c013-dec8-4f5b-ab60-00db633b6ebe' class='xr-section-summary-in' type='checkbox'  ><label for='section-9dd3c013-dec8-4f5b-ab60-00db633b6ebe' class='xr-section-summary' >Indexes: <span>(4)</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>xt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-79d8220b-78e4-40ef-a0d9-3c8f34d7540d' class='xr-index-data-in' type='checkbox'/><label for='index-79d8220b-78e4-40ef-a0d9-3c8f34d7540d' 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",
+       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yu_ocean</span></div><div class='xr-var-dims'>(yu_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-81.09 -81.05 ... -59.06 -59.01</div><input id='attrs-a235a9c6-c2a3-4acd-9a3a-a6c3abe1cca6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a235a9c6-c2a3-4acd-9a3a-a6c3abe1cca6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a47ef068-62a1-40e6-8feb-050a126b686c' class='xr-var-data-in' type='checkbox'><label for='data-a47ef068-62a1-40e6-8feb-050a126b686c' 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>ucell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-81.087512, -81.045273, -81.003033, ..., -59.11021 , -59.058833,\n",
+       "       -59.007379])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b3ccd543-f088-4e92-a9f0-a21d3b2114cb' class='xr-section-summary-in' type='checkbox'  ><label for='section-b3ccd543-f088-4e92-a9f0-a21d3b2114cb' class='xr-section-summary' >Indexes: <span>(4)</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>xt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1a3c1232-2de3-4037-9fa4-82c6c980ad46' class='xr-index-data-in' type='checkbox'/><label for='index-1a3c1232-2de3-4037-9fa4-82c6c980ad46' 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",
@@ -3853,7 +4178,7 @@
        "         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>st_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d9d70b2a-89c6-4b10-a8fc-9fb267e9ed69' class='xr-index-data-in' type='checkbox'/><label for='index-d9d70b2a-89c6-4b10-a8fc-9fb267e9ed69' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.5412807653916101,  1.680734679831433,  2.939952648914465,\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>st_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8fc3e34a-0f45-4c44-ba61-4d9845f2ba77' class='xr-index-data-in' type='checkbox'/><label for='index-8fc3e34a-0f45-4c44-ba61-4d9845f2ba77' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.5412807653916101,  1.680734679831433,  2.939952648914465,\n",
        "        4.331521485149508, 5.8693504240540255, 7.5688099200502155,\n",
        "        9.446884959648713, 11.522344392803275, 13.815927932333222,\n",
        "       16.350552632935347, 19.151540835805992,  22.24687175218868,\n",
@@ -3878,11 +4203,11 @@
        "        4138.551439998412,  4332.197126591926,  4526.902649987421,\n",
        "        4722.496976663542,  4918.834912408163,  5115.793747063695,\n",
        "        5313.270158218243,  5511.177417673949,  5709.442914225761],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;st_ocean&#x27;))</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-72ca7975-014c-49fe-85ec-d2120efe61ef' class='xr-index-data-in' type='checkbox'/><label for='index-72ca7975-014c-49fe-85ec-d2120efe61ef' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([2170-01-16 12:00:00, 2170-02-15 00:00:00, 2170-03-16 12:00:00,\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;st_ocean&#x27;))</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-eea97d06-08e4-4daa-8665-c6410cb88d7b' class='xr-index-data-in' type='checkbox'/><label for='index-eea97d06-08e4-4daa-8665-c6410cb88d7b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([2170-01-16 12:00:00, 2170-02-15 00:00:00, 2170-03-16 12:00:00,\n",
        "             2170-04-16 00:00:00, 2170-05-16 12:00:00, 2170-06-16 00:00:00,\n",
        "             2170-07-16 12:00:00, 2170-08-16 12:00:00, 2170-09-16 00:00:00,\n",
        "             2170-10-16 12:00:00, 2170-11-16 00:00:00, 2170-12-16 12:00:00],\n",
-       "            dtype=&#x27;object&#x27;, length=12, calendar=&#x27;noleap&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>yu_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-81fdfdc3-5a79-4b05-8a5f-cc13a0fb69b0' class='xr-index-data-in' type='checkbox'/><label for='index-81fdfdc3-5a79-4b05-8a5f-cc13a0fb69b0' 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([ -81.08751200116458,   -81.0452726467841,  -81.00303329240361,\n",
+       "            dtype=&#x27;object&#x27;, length=12, calendar=&#x27;noleap&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>yu_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-496f5eca-4455-4fce-af5c-7c2b7bef06f4' class='xr-index-data-in' type='checkbox'/><label for='index-496f5eca-4455-4fce-af5c-7c2b7bef06f4' 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([ -81.08751200116458,   -81.0452726467841,  -81.00303329240361,\n",
        "        -80.96079393802313,  -80.91855458364265,  -80.87631522926216,\n",
        "        -80.83407587488168,  -80.79183652050119,  -80.74959716612071,\n",
        "        -80.70735781174022,\n",
@@ -3891,7 +4216,7 @@
        "       -59.314951179354885, -59.263881033610375,  -59.21273421694019,\n",
        "       -59.161510654834565,  -59.11021027336359,  -59.05883299828705,\n",
        "       -59.007378755945275],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;yu_ocean&#x27;, length=510))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a9a4f7fc-29c4-4890-a13a-ecb0dd15f425' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a9a4f7fc-29c4-4890-a13a-ecb0dd15f425' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>T-cell j-mass transport</dd><dt><span>units :</span></dt><dd>kg/s</dd><dt><span>valid_range :</span></dt><dd>[-1.e+20  1.e+20]</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd><dt><span>standard_name :</span></dt><dd>ocean_mass_y_transport</dd></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;float64&#x27;, name=&#x27;yu_ocean&#x27;, length=510))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dce69172-8cf5-4472-aaaa-565b6f2f33fc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dce69172-8cf5-4472-aaaa-565b6f2f33fc' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>T-cell j-mass transport</dd><dt><span>units :</span></dt><dd>kg/s</dd><dt><span>valid_range :</span></dt><dd>[-1.e+20  1.e+20]</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd><dt><span>standard_name :</span></dt><dd>ocean_mass_y_transport</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.DataArray 'ty_trans' (time: 12, st_ocean: 75, yu_ocean: 510,\n",
@@ -4316,7 +4641,7 @@
        "    valid_range:    [-1.e+20  1.e+20]\n",
        "    cell_methods:   time: mean\n",
        "    time_avg_info:  average_T1,average_T2,average_DT\n",
-       "    standard_name:  ocean_mass_x_transport</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'>'tx_trans'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 12</li><li><span class='xr-has-index'>st_ocean</span>: 75</li><li><span class='xr-has-index'>latitude</span>: 510</li><li><span class='xr-has-index'>longitude</span>: 3600</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-3d5fe10e-5622-4268-b8e7-d15839978727' class='xr-array-in' type='checkbox' checked><label for='section-3d5fe10e-5622-4268-b8e7-d15839978727' 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=(12, 75, 102, 360), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "    standard_name:  ocean_mass_x_transport</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'>'tx_trans'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 12</li><li><span class='xr-has-index'>st_ocean</span>: 75</li><li><span class='xr-has-index'>latitude</span>: 510</li><li><span class='xr-has-index'>longitude</span>: 3600</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ac837877-60f3-42c0-8674-9a7296962215' class='xr-array-in' type='checkbox' checked><label for='section-ac837877-60f3-42c0-8674-9a7296962215' 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=(12, 75, 102, 360), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4436,8 +4761,8 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></div></li><li class='xr-section-item'><input id='section-1a6e724f-04d5-4aba-bc08-7b3e16cd0681' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1a6e724f-04d5-4aba-bc08-7b3e16cd0681' class='xr-section-summary' >Coordinates: <span>(4)</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'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-81.11 -81.07 ... -59.08 -59.03</div><input id='attrs-cb2eea14-d831-40ab-bf90-dec0e0d4e0e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb2eea14-d831-40ab-bf90-dec0e0d4e0e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf59844e-af79-4fcc-aa04-b54e0de562f7' class='xr-var-data-in' type='checkbox'><label for='data-cf59844e-af79-4fcc-aa04-b54e0de562f7' 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>tcell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-81.108632, -81.066392, -81.024153, ..., -59.13588 , -59.084541,\n",
-       "       -59.033125])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>st_ocean</span></div><div class='xr-var-dims'>(st_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5413 1.681 ... 5.709e+03</div><input id='attrs-39f319f3-c133-483e-8d10-484becf83a39' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-39f319f3-c133-483e-8d10-484becf83a39' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99dd0036-e0c2-47ab-9739-24060e16a110' class='xr-var-data-in' type='checkbox'><label for='data-99dd0036-e0c2-47ab-9739-24060e16a110' 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>tcell zstar depth</dd><dt><span>units :</span></dt><dd>meters</dd><dt><span>cartesian_axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>edges :</span></dt><dd>st_edges_ocean</dd></dl></div><div class='xr-var-data'><pre>array([5.412808e-01, 1.680735e+00, 2.939953e+00, 4.331521e+00, 5.869350e+00,\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-9a52726b-0364-4c63-9c1b-c493a838f729' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9a52726b-0364-4c63-9c1b-c493a838f729' class='xr-section-summary' >Coordinates: <span>(4)</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'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-81.11 -81.07 ... -59.08 -59.03</div><input id='attrs-1eae441f-560a-4e48-abed-9b6a4f877ae8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1eae441f-560a-4e48-abed-9b6a4f877ae8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-caf70f0c-985b-4e52-b7cf-c7ccb848864c' class='xr-var-data-in' type='checkbox'><label for='data-caf70f0c-985b-4e52-b7cf-c7ccb848864c' 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>tcell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-81.108632, -81.066392, -81.024153, ..., -59.13588 , -59.084541,\n",
+       "       -59.033125])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>st_ocean</span></div><div class='xr-var-dims'>(st_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5413 1.681 ... 5.709e+03</div><input id='attrs-3deaa01b-2822-4c40-8137-1d26aa7e11e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3deaa01b-2822-4c40-8137-1d26aa7e11e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca7308ff-a902-4b7f-8ddc-9682230406cd' class='xr-var-data-in' type='checkbox'><label for='data-ca7308ff-a902-4b7f-8ddc-9682230406cd' 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>tcell zstar depth</dd><dt><span>units :</span></dt><dd>meters</dd><dt><span>cartesian_axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>edges :</span></dt><dd>st_edges_ocean</dd></dl></div><div class='xr-var-data'><pre>array([5.412808e-01, 1.680735e+00, 2.939953e+00, 4.331521e+00, 5.869350e+00,\n",
        "       7.568810e+00, 9.446885e+00, 1.152234e+01, 1.381593e+01, 1.635055e+01,\n",
        "       1.915154e+01, 2.224687e+01, 2.566746e+01, 2.944746e+01, 3.362460e+01,\n",
        "       3.824057e+01, 4.334140e+01, 4.897796e+01, 5.520640e+01, 6.208874e+01,\n",
@@ -4451,7 +4776,7 @@
        "       2.161913e+03, 2.322601e+03, 2.488533e+03, 2.659189e+03, 2.834054e+03,\n",
        "       3.012631e+03, 3.194453e+03, 3.379089e+03, 3.566145e+03, 3.755274e+03,\n",
        "       3.946166e+03, 4.138551e+03, 4.332197e+03, 4.526903e+03, 4.722497e+03,\n",
-       "       4.918835e+03, 5.115794e+03, 5.313270e+03, 5.511177e+03, 5.709443e+03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2170-01-16 12:00:00 ... 2170-12-...</div><input id='attrs-41d3f88b-99d8-4692-ad0b-0aec6ee21bfe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41d3f88b-99d8-4692-ad0b-0aec6ee21bfe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-899e9228-f353-42fd-88b5-cda8c7e3a6f6' class='xr-var-data-in' type='checkbox'><label for='data-899e9228-f353-42fd-88b5-cda8c7e3a6f6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>cartesian_axis :</span></dt><dd>T</dd><dt><span>calendar_type :</span></dt><dd>NOLEAP</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2170, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       4.918835e+03, 5.115794e+03, 5.313270e+03, 5.511177e+03, 5.709443e+03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2170-01-16 12:00:00 ... 2170-12-...</div><input id='attrs-757f6215-b436-4c77-85d1-27f8f1f4501e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-757f6215-b436-4c77-85d1-27f8f1f4501e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f74d41c2-d857-4b6c-b222-705a3254fcdc' class='xr-var-data-in' type='checkbox'><label for='data-f74d41c2-d857-4b6c-b222-705a3254fcdc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>cartesian_axis :</span></dt><dd>T</dd><dt><span>calendar_type :</span></dt><dd>NOLEAP</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2170, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.DatetimeNoLeap(2170, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.DatetimeNoLeap(2170, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.DatetimeNoLeap(2170, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
@@ -4463,7 +4788,7 @@
        "       cftime.DatetimeNoLeap(2170, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.DatetimeNoLeap(2170, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.DatetimeNoLeap(2170, 12, 16, 12, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.9 -279.8 -279.7 ... 79.9 80.0</div><input id='attrs-60a976b3-44a7-4dd8-92ec-90d6c01bd04a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-60a976b3-44a7-4dd8-92ec-90d6c01bd04a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-efc6d5fb-3503-4a9f-8113-7da768f8a39e' class='xr-var-data-in' type='checkbox'><label for='data-efc6d5fb-3503-4a9f-8113-7da768f8a39e' 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>ucell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-279.9, -279.8, -279.7, ...,   79.8,   79.9,   80. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9c6bb9a9-a3e8-481a-9e3f-83193318c6ce' class='xr-section-summary-in' type='checkbox'  ><label for='section-9c6bb9a9-a3e8-481a-9e3f-83193318c6ce' class='xr-section-summary' >Indexes: <span>(4)</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>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b595b97a-5481-495c-bd10-2e618cb0e292' class='xr-index-data-in' type='checkbox'/><label for='index-b595b97a-5481-495c-bd10-2e618cb0e292' 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([ -81.10863167835483,  -81.06639232397434,  -81.02415296959386,\n",
+       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.9 -279.8 -279.7 ... 79.9 80.0</div><input id='attrs-d677358c-cdf5-42be-bd90-c4f79405a34e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d677358c-cdf5-42be-bd90-c4f79405a34e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f697cd1-56a5-4053-871f-9f3d118bbb00' class='xr-var-data-in' type='checkbox'><label for='data-5f697cd1-56a5-4053-871f-9f3d118bbb00' 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>ucell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-279.9, -279.8, -279.7, ...,   79.8,   79.9,   80. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-397aa764-e42e-4565-bca8-2432abded26c' class='xr-section-summary-in' type='checkbox'  ><label for='section-397aa764-e42e-4565-bca8-2432abded26c' class='xr-section-summary' >Indexes: <span>(4)</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>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d71a74fb-3969-48d0-9bc9-c63d3d1dbb7e' class='xr-index-data-in' type='checkbox'/><label for='index-d71a74fb-3969-48d0-9bc9-c63d3d1dbb7e' 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([ -81.10863167835483,  -81.06639232397434,  -81.02415296959386,\n",
        "        -80.98191361521337,  -80.93967426083289,   -80.8974349064524,\n",
        "        -80.85519555207192,  -80.81295619769143,  -80.77071684331095,\n",
        "        -80.72847748893047,\n",
@@ -4472,7 +4797,7 @@
        "         -59.3404671310413,  -59.28943522766847,  -59.23832683955227,\n",
        "       -59.187141594328104,  -59.13587971534102,  -59.08454083138616,\n",
        "        -59.03312516518795],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=510))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>st_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-213fa73f-3c9a-4af1-92ae-956d16ec1de1' class='xr-index-data-in' type='checkbox'/><label for='index-213fa73f-3c9a-4af1-92ae-956d16ec1de1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.5412807653916101,  1.680734679831433,  2.939952648914465,\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=510))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>st_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e2f12077-c75b-4b85-a877-37714c64a37a' class='xr-index-data-in' type='checkbox'/><label for='index-e2f12077-c75b-4b85-a877-37714c64a37a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.5412807653916101,  1.680734679831433,  2.939952648914465,\n",
        "        4.331521485149508, 5.8693504240540255, 7.5688099200502155,\n",
        "        9.446884959648713, 11.522344392803275, 13.815927932333222,\n",
        "       16.350552632935347, 19.151540835805992,  22.24687175218868,\n",
@@ -4497,11 +4822,11 @@
        "        4138.551439998412,  4332.197126591926,  4526.902649987421,\n",
        "        4722.496976663542,  4918.834912408163,  5115.793747063695,\n",
        "        5313.270158218243,  5511.177417673949,  5709.442914225761],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;st_ocean&#x27;))</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-6bcbbbd6-1129-4279-ae2a-c7ef4a7309a6' class='xr-index-data-in' type='checkbox'/><label for='index-6bcbbbd6-1129-4279-ae2a-c7ef4a7309a6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([2170-01-16 12:00:00, 2170-02-15 00:00:00, 2170-03-16 12:00:00,\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;st_ocean&#x27;))</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-0d3a1b27-5f8a-4fe1-9e95-61506db24a9b' class='xr-index-data-in' type='checkbox'/><label for='index-0d3a1b27-5f8a-4fe1-9e95-61506db24a9b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([2170-01-16 12:00:00, 2170-02-15 00:00:00, 2170-03-16 12:00:00,\n",
        "             2170-04-16 00:00:00, 2170-05-16 12:00:00, 2170-06-16 00:00:00,\n",
        "             2170-07-16 12:00:00, 2170-08-16 12:00:00, 2170-09-16 00:00:00,\n",
        "             2170-10-16 12:00:00, 2170-11-16 00:00:00, 2170-12-16 12:00:00],\n",
-       "            dtype=&#x27;object&#x27;, length=12, calendar=&#x27;noleap&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9f6db243-17e1-47c3-8d59-a8f493deaa34' class='xr-index-data-in' type='checkbox'/><label for='index-9f6db243-17e1-47c3-8d59-a8f493deaa34' 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([             -279.9, -279.79999999999995, -279.69999999999993,\n",
+       "            dtype=&#x27;object&#x27;, length=12, calendar=&#x27;noleap&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fe49d977-74c4-4c8f-9014-d2c6071db7fa' class='xr-index-data-in' type='checkbox'/><label for='index-fe49d977-74c4-4c8f-9014-d2c6071db7fa' 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([             -279.9, -279.79999999999995, -279.69999999999993,\n",
        "        -279.5999999999999,  -279.4999999999999, -279.39999999999986,\n",
        "       -279.29999999999984,  -279.1999999999998,  -279.0999999999998,\n",
        "        -278.9999999999998,\n",
@@ -4510,7 +4835,7 @@
        "         79.39999999999492,   79.49999999999491,    79.5999999999949,\n",
        "          79.6999999999949,    79.7999999999949,   79.89999999999489,\n",
        "                      80.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=3600))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ebd35cba-4f03-4682-afc5-9c4bb0e565cf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ebd35cba-4f03-4682-afc5-9c4bb0e565cf' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>T-cell i-mass transport</dd><dt><span>units :</span></dt><dd>kg/s</dd><dt><span>valid_range :</span></dt><dd>[-1.e+20  1.e+20]</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd><dt><span>standard_name :</span></dt><dd>ocean_mass_x_transport</dd></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=3600))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a7750e1c-2c4c-40dc-b431-d904a1d1535f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a7750e1c-2c4c-40dc-b431-d904a1d1535f' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>T-cell i-mass transport</dd><dt><span>units :</span></dt><dd>kg/s</dd><dt><span>valid_range :</span></dt><dd>[-1.e+20  1.e+20]</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd><dt><span>standard_name :</span></dt><dd>ocean_mass_x_transport</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.DataArray 'tx_trans' (time: 12, st_ocean: 75, latitude: 510,\n",
@@ -4569,8 +4894,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 24.4 s, sys: 3.54 s, total: 27.9 s\n",
-      "Wall time: 58.2 s\n"
+      "CPU times: user 28.8 s, sys: 3.85 s, total: 32.6 s\n",
+      "Wall time: 37 s\n"
      ]
     }
    ],
@@ -4627,7 +4952,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/scratch/tm70/mcw120/tmp/ipykernel_123014/2814091809.py:13: FutureWarning: updating coordinate 'contour_index' with a PandasMultiIndex would leave the multi-index level coordinates ['latitude', 'longitude'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['contour_index', 'latitude', 'longitude'])` before assigning new coordinate values.\n",
+      "/scratch/tm70/mcw120/tmp/ipykernel_551762/2814091809.py:13: FutureWarning: updating coordinate 'contour_index' with a PandasMultiIndex would leave the multi-index level coordinates ['latitude', 'longitude'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['contour_index', 'latitude', 'longitude'])` before assigning new coordinate values.\n",
       "  vol_trans_across_contour.coords['contour_index'] = contour_index_array\n"
      ]
     }
@@ -4700,9 +5025,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/scratch/tm70/mcw120/tmp/ipykernel_123014/3395851563.py:11: FutureWarning: updating coordinate 'contour_index' with a PandasMultiIndex would leave the multi-index level coordinates ['latitude', 'longitude'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['contour_index', 'latitude', 'longitude'])` before assigning new coordinate values.\n",
+      "/scratch/tm70/mcw120/tmp/ipykernel_551762/3395851563.py:11: FutureWarning: updating coordinate 'contour_index' with a PandasMultiIndex would leave the multi-index level coordinates ['latitude', 'longitude'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['contour_index', 'latitude', 'longitude'])` before assigning new coordinate values.\n",
       "  lat_along_contour.coords['contour_index'] = contour_index_array\n",
-      "/scratch/tm70/mcw120/tmp/ipykernel_123014/3395851563.py:12: FutureWarning: updating coordinate 'contour_index' with a PandasMultiIndex would leave the multi-index level coordinates ['latitude', 'longitude'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['contour_index', 'latitude', 'longitude'])` before assigning new coordinate values.\n",
+      "/scratch/tm70/mcw120/tmp/ipykernel_551762/3395851563.py:12: FutureWarning: updating coordinate 'contour_index' with a PandasMultiIndex would leave the multi-index level coordinates ['latitude', 'longitude'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['contour_index', 'latitude', 'longitude'])` before assigning new coordinate values.\n",
       "  lon_along_contour.coords['contour_index'] = contour_index_array\n"
      ]
     }
@@ -4842,7 +5167,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },