Fig_winds_anomaly.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "eea07a1c-9d38-4b23-ae6b-ee6b503a8a92",
   "metadata": {},
   "source": [
    "### This code merges code Fig5_old to Fig2c_and_Selecting_peak and Fig4 to create the means\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "67308e62-21ce-4ef4-81cd-7e36604332a6",
   "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-acfd14fe-5f90-11ef-8e07-0000082dfe80</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/35683/status\" target=\"_blank\">/proxy/35683/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/35683/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;\">4225c02c</p>\n",
       "        <table style=\"width: 100%; text-align: left;\">\n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Dashboard:</strong> <a href=\"/proxy/35683/status\" target=\"_blank\">/proxy/35683/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> 125.19 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-a41979a8-9718-49e7-8372-a74e13465918</p>\n",
       "            <table style=\"width: 100%; text-align: left;\">\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Comm:</strong> tcp://127.0.0.1:36769\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/35683/status\" target=\"_blank\">/proxy/35683/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> 125.19 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://127.0.0.1:40213\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/38941/status\" target=\"_blank\">/proxy/38941/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 17.88 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:36661\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/123358034.gadi-pbs/dask-scratch-space/worker-lg7nq4xr\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://127.0.0.1:45367\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/44333/status\" target=\"_blank\">/proxy/44333/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 17.88 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:34245\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/123358034.gadi-pbs/dask-scratch-space/worker-wc97oi7h\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://127.0.0.1:36937\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/35411/status\" target=\"_blank\">/proxy/35411/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 17.88 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:41873\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/123358034.gadi-pbs/dask-scratch-space/worker-7oxybuia\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://127.0.0.1:34831\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/42145/status\" target=\"_blank\">/proxy/42145/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 17.88 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:42545\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/123358034.gadi-pbs/dask-scratch-space/worker-oak_p_4v\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://127.0.0.1:35937\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/32789/status\" target=\"_blank\">/proxy/32789/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 17.88 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:35777\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/123358034.gadi-pbs/dask-scratch-space/worker-gnklrlrd\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://127.0.0.1:35769\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/35789/status\" target=\"_blank\">/proxy/35789/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 17.88 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:45581\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/123358034.gadi-pbs/dask-scratch-space/worker-oqfj75t0\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://127.0.0.1:40877\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/43287/status\" target=\"_blank\">/proxy/43287/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 17.88 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:39897\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/123358034.gadi-pbs/dask-scratch-space/worker-s81cxros\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://127.0.0.1:36769' processes=7 threads=28, memory=125.19 GiB>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "import cmocean\n",
    "import cosima_cookbook as cc\n",
    "import dask.distributed as dsk\n",
    "import glob\n",
    "import matplotlib.gridspec as gs\n",
    "import matplotlib.path as mpath\n",
    "import matplotlib.patches as mpatches\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mticker\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n",
    "from cartopy.util import add_cyclic_point\n",
    "import matplotlib.colors as mcolors\n",
    "\n",
    "import warnings # ignore these warnings\n",
    "warnings.filterwarnings(\"ignore\", category = FutureWarning)\n",
    "warnings.filterwarnings(\"ignore\", category = UserWarning)\n",
    "warnings.filterwarnings(\"ignore\", category = RuntimeWarning)\n",
    "\n",
    "figdir = '/home/561/jn8053/g_e14/figures-tmp/easterlies-collaborative/'\n",
    "figdir = '/g/data/v45/akm157/figures/easterlies/paper_figures/'\n",
    "\n",
    "clnt = dsk.Client()\n",
    "clnt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44e9309d-9ba0-4a93-b933-d6f2abe1fb62",
   "metadata": {},
   "source": [
    "## Creating the wind variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b28d1337-db1e-4dab-b844-eef1badd2189",
   "metadata": {},
   "outputs": [],
   "source": [
    "#session_ryf = cc.database.create_session()\n",
    "master_session = cc.database.create_session('/g/data/ik11/databases/cosima_master.db')\n",
    "control = '01deg_jra55v13_ryf9091'\n",
    "iaf_cycle3 = '01deg_jra55v140_iaf_cycle3'\n",
    "\n",
    "first_year = '1958'\n",
    "last_year = '2018'\n",
    "\n",
    "start_time=first_year+'-01-01'\n",
    "end_time=last_year+'-12-31'\n",
    "\n",
    "time_period = str(int(start_time[:4]))+'-'+str(int(end_time[:4]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6dcafe4-f95e-4f17-85eb-eb6bb1655480",
   "metadata": {},
   "source": [
    "### Prepare winds (taux)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "202c85ed-4c7e-4855-a2ae-de87651147b1",
   "metadata": {},
   "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",
      "/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": [
    "lat_slice  = slice(-75.5,-60)\n",
    "lon_slice  = slice(-256, -246)\n",
    "\n",
    "windX_con = cc.querying.getvar(iaf_cycle3,'tau_x', master_session,frequency='1 monthly',start_time=start_time, end_time=end_time)\n",
    "windX_con = windX_con.sel(yu_ocean=lat_slice).sel(xu_ocean=lon_slice)                            \n",
    "windX_con = windX_con.load()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc8e84d7-b1c9-4ae1-85a3-827f3c10c115",
   "metadata": {},
   "source": [
    "### Prepare winds (tauy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1b8d36d4-b9fb-414a-83dd-abbd1bf25917",
   "metadata": {},
   "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",
      "/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",
      "/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": [
    "lat_slice  = slice(-75.5,-60)\n",
    "lon_slice  = slice(-256, -246)\n",
    "\n",
    "windY_con = cc.querying.getvar(iaf_cycle3,'tau_y', master_session,frequency='1 monthly',start_time=start_time, end_time=end_time)\n",
    "windY_con = windY_con.sel(yu_ocean=lat_slice).sel(xu_ocean=lon_slice)                            \n",
    "windY_con = windY_con.load()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "996e0f66-da77-4a8b-8b74-dc3d0a73b3d1",
   "metadata": {},
   "source": [
    "### Opening arrays that select the picks for low and high intrusion months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d11acfd5-f49d-42d0-9a84-3d4002f55e97",
   "metadata": {},
   "outputs": [],
   "source": [
    "outpath = '/g/data/v45/ns3783/top_index.nc'\n",
    "profile_data = xr.open_dataset(outpath)\n",
    "top_id = profile_data.top_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a72db748-d043-4213-a1c7-0ac609e32e5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "outpath = '/g/data/v45/ns3783/bottom_index.nc'\n",
    "profile_data = xr.open_dataset(outpath)\n",
    "bt_id = profile_data.bt_id"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6b078f8-7d93-4cd2-8362-b2b9c0d88edd",
   "metadata": {},
   "source": [
    "### creating the averages for peaks and lows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2885afde-cd48-439b-b447-a983f07eaa2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#full mean\n",
    "windX_avg = windX_con.mean('time')\n",
    "windY_avg = windY_con.mean('time')\n",
    "\n",
    "#mean of all high peaks together\n",
    "windX_high_avg = windX_con[top_id].mean('time')\n",
    "windY_high_avg = windY_con[top_id].mean('time')\n",
    "\n",
    "#mean of all low peaks together\n",
    "windX_low_avg = windX_con[bt_id].mean('time')\n",
    "windY_low_avg = windY_con[bt_id].mean('time')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89a2aad6-c031-4559-b94d-e3a7b1a1b11c",
   "metadata": {},
   "source": [
    "### load the averages for picks and lows and time average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "417113de-d606-40e5-9db4-57d64a6cc5b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "windX_avg = windX_avg.load()\n",
    "windY_avg = windY_avg.load()\n",
    "\n",
    "windX_high_avg = windX_high_avg.load()\n",
    "windY_high_avg = windY_high_avg.load()\n",
    "\n",
    "windX_low_avg = windX_low_avg.load()\n",
    "windY_low_avg = windY_low_avg.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52c07791-1195-4530-b80e-454d80907693",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1228f9c0-3058-4ed7-a611-6b784f28f74a",
   "metadata": {},
   "source": [
    "### Load bathymetry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3fe14211-aff3-4e22-9f10-2f8195221421",
   "metadata": {},
   "outputs": [],
   "source": [
    "#have to load this again\n",
    "lat_slice  = slice(-75.5,-60)\n",
    "lon_slice  = slice(-275, -230)\n",
    "\n",
    "# import bathymetry and the land/ocean mask to add to plot:\n",
    "ht = cc.querying.getvar(control,'ht' , master_session,n=1)\n",
    "ht = ht.sel(yt_ocean=lat_slice).sel(xt_ocean=lon_slice)\n",
    "# create land mask from the bathymetry:\n",
    "land_mask = np.squeeze(ht.values)*0\n",
    "land_mask[np.isnan(land_mask)]=1\n",
    "land_mask = np.where(land_mask==1,land_mask,np.nan)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "3a715ac4-73f8-4df7-9e50-e101b13ee72c",
   "metadata": {},
   "source": [
    "fontsize=20\n",
    "\n",
    "fig=plt.figure(figsize=(24, 26))\n",
    "fig.subplots_adjust(top=0.88)\n",
    "\n",
    "ax0=plt.subplot(4,3,1) \n",
    "pc = plt.pcolormesh(windX_high_avg.xu_ocean, windX_high_avg.yu_ocean,windX_high_avg,cmap = cmocean.cm.balance, \n",
    "                        vmin=-0.25,vmax=0.05)\n",
    "plt.title('WindX stress (str intrusion mths)',fontsize=fontsize)\n",
    "        #plot contours\n",
    "plt.contour(ht.xt_ocean,ht.yt_ocean,ht,levels=[1000,3000],colors='k',linewidth=.5)\n",
    "        # plot the land:\n",
    "plt.contourf(ht.xt_ocean,ht.yt_ocean,land_mask,colors='grey')\n",
    "        # labels etc:\n",
    "        #plt.xlabel('Longitude',fontsize=fontsize)\n",
    "plt.ylabel('Latitude [$^\\circ$S]',fontsize=fontsize)\n",
    "#plt.xlabel('Longitude [$^\\circ$E]',fontsize=fontsize)\n",
    "\n",
    "plt.tick_params(labelsize=fontsize)\n",
    "        #lim\n",
    "plt.ylim((-67, -64))\n",
    "plt.xlim((-255, -246))\n",
    "        # colorbar:\n",
    "cbar=plt.colorbar(pc,orientation='vertical',extend='neither')\n",
    "cbar.ax.locator_params(nbins=8)\n",
    "#cbar.set_label(r'(N/m$^{2}$)', fontsize = 20,labelpad=5)\n",
    "cbar.ax.tick_params(labelsize=20)\n",
    "ax0.set_xticklabels([-255+359,-254+359,-252+359,-250+359,-248+359,-246+359])\n",
    "\n",
    "ax0=plt.subplot(4,3,2) \n",
    "pc = plt.pcolormesh(windX_low_avg.xu_ocean, windX_low_avg.yu_ocean,windX_low_avg,cmap = cmocean.cm.balance, \n",
    "                        vmin=-0.25,vmax=0.05)\n",
    "plt.title('WindX stress (wk intrusion mths)',fontsize=fontsize)\n",
    "        #plot contours\n",
    "plt.contour(ht.xt_ocean,ht.yt_ocean,ht,levels=[1000,3000],colors='k',linewidth=.5)\n",
    "        # plot the land:\n",
    "plt.contourf(ht.xt_ocean,ht.yt_ocean,land_mask,colors='grey')\n",
    "        # labels etc:\n",
    "        #plt.xlabel('Longitude',fontsize=fontsize)\n",
    "#plt.ylabel('Latitude [$^\\circ$S]',fontsize=fontsize)\n",
    "#plt.xlabel('Longitude [$^\\circ$E]',fontsize=fontsize)\n",
    "\n",
    "plt.tick_params(labelsize=fontsize)\n",
    "        #lim\n",
    "plt.ylim((-67, -64))\n",
    "plt.xlim((-255, -246))\n",
    "        # colorbar:\n",
    "cbar=plt.colorbar(pc,orientation='vertical',extend='neither')\n",
    "cbar.ax.locator_params(nbins=8)\n",
    "#cbar.set_label(r'(N/m$^{2}$)', fontsize = 20,labelpad=5)\n",
    "cbar.ax.tick_params(labelsize=20)\n",
    "ax0.set_xticklabels([-255+359,-254+359,-252+359,-250+359,-248+359,-246+359])\n",
    "\n",
    "\n",
    "ax0=plt.subplot(4,3,3)\n",
    "pc = plt.pcolormesh(windX_avg.xu_ocean, windX_avg.yu_ocean,windX_avg,cmap = cmocean.cm.balance, \n",
    "                        vmin=-0.25,vmax=0.05)\n",
    "plt.title('WindX stress (avg)',fontsize=fontsize)\n",
    "        #plot contours\n",
    "plt.contour(ht.xt_ocean,ht.yt_ocean,ht,levels=[1000,3000],colors='k',linewidth=.5)\n",
    "        # plot the land:\n",
    "plt.contourf(ht.xt_ocean,ht.yt_ocean,land_mask,colors='grey')\n",
    "        # labels etc:\n",
    "        #plt.xlabel('Longitude',fontsize=fontsize)\n",
    "#plt.ylabel('Latitude [$^\\circ$S]',fontsize=fontsize)\n",
    "#plt.xlabel('Longitude [$^\\circ$E]',fontsize=fontsize)\n",
    "\n",
    "plt.tick_params(labelsize=fontsize)\n",
    "        #lim\n",
    "plt.ylim((-67, -64))\n",
    "plt.xlim((-255, -246))\n",
    "        # colorbar:\n",
    "cbar=plt.colorbar(pc,orientation='vertical',extend='neither')\n",
    "cbar.ax.locator_params(nbins=8)\n",
    "cbar.set_label(r'(N/m$^{2}$)', fontsize = 20,labelpad=5)\n",
    "cbar.ax.tick_params(labelsize=20)\n",
    "ax0.set_xticklabels([-255+359,-254+359,-252+359,-250+359,-248+359,-246+359])\n",
    "\n",
    "ax0=plt.subplot(4,3,4) \n",
    "pc = plt.pcolormesh(windY_high_avg.xu_ocean, windY_high_avg.yu_ocean,windY_high_avg,cmap = cmocean.cm.balance, \n",
    "                        vmin=-0.05,vmax=0.25)\n",
    "plt.title('WindY stress (str intrusion mths)',fontsize=fontsize)\n",
    "        #plot contours\n",
    "plt.contour(ht.xt_ocean,ht.yt_ocean,ht,levels=[1000,3000],colors='k',linewidth=.5)\n",
    "        # plot the land:\n",
    "plt.contourf(ht.xt_ocean,ht.yt_ocean,land_mask,colors='grey')\n",
    "        # labels etc:\n",
    "        #plt.xlabel('Longitude',fontsize=fontsize)\n",
    "plt.ylabel('Latitude [$^\\circ$S]',fontsize=fontsize)\n",
    "plt.xlabel('Longitude [$^\\circ$E]',fontsize=fontsize)\n",
    "\n",
    "plt.tick_params(labelsize=fontsize)\n",
    "        #lim\n",
    "plt.ylim((-67, -64))\n",
    "plt.xlim((-255, -246))\n",
    "        # colorbar:\n",
    "cbar=plt.colorbar(pc,orientation='vertical',extend='neither')\n",
    "cbar.ax.locator_params(nbins=8)\n",
    "#cbar.set_label(r'(N/m$^{2}$)', fontsize = 20,labelpad=5)\n",
    "cbar.ax.tick_params(labelsize=20)\n",
    "ax0.set_xticklabels([-255+359,-254+359,-252+359,-250+359,-248+359,-246+359])\n",
    "\n",
    "ax0=plt.subplot(4,3,5) \n",
    "pc = plt.pcolormesh(windY_low_avg.xu_ocean, windY_low_avg.yu_ocean,windY_low_avg,cmap = cmocean.cm.balance, \n",
    "                        vmin=-0.05,vmax=0.25)\n",
    "plt.title('WindY stress (wk intrusion mths)',fontsize=fontsize)\n",
    "        #plot contours\n",
    "plt.contour(ht.xt_ocean,ht.yt_ocean,ht,levels=[1000,3000],colors='k',linewidth=.5)\n",
    "        # plot the land:\n",
    "plt.contourf(ht.xt_ocean,ht.yt_ocean,land_mask,colors='grey')\n",
    "        # labels etc:\n",
    "        #plt.xlabel('Longitude',fontsize=fontsize)\n",
    "#plt.ylabel('Latitude [$^\\circ$S]',fontsize=fontsize)\n",
    "plt.xlabel('Longitude [$^\\circ$E]',fontsize=fontsize)\n",
    "\n",
    "plt.tick_params(labelsize=fontsize)\n",
    "        #lim\n",
    "plt.ylim((-67, -64))\n",
    "plt.xlim((-255, -246))\n",
    "        # colorbar:\n",
    "cbar=plt.colorbar(pc,orientation='vertical',extend='neither')\n",
    "cbar.ax.locator_params(nbins=8)\n",
    "#cbar.set_label(r'(N/m$^{2}$)', fontsize = 20,labelpad=5)\n",
    "cbar.ax.tick_params(labelsize=20)\n",
    "ax0.set_xticklabels([-255+359,-254+359,-252+359,-250+359,-248+359,-246+359])\n",
    "\n",
    "\n",
    "ax0=plt.subplot(4,3,6) \n",
    "pc = plt.pcolormesh(windY_avg.xu_ocean, windY_avg.yu_ocean,windY_avg,cmap = cmocean.cm.balance, \n",
    "                        vmin=-0.05,vmax=0.25)\n",
    "#plt.title('Anomaly for Str Heat Flux (Y) yrs',fontsize=fontsize)\n",
    "plt.title('WindY stress (avg)',fontsize=fontsize)\n",
    "        #plot contours\n",
    "plt.contour(ht.xt_ocean,ht.yt_ocean,ht,levels=[1000,3000],colors='k',linewidth=.5)\n",
    "        # plot the land:\n",
    "plt.contourf(ht.xt_ocean,ht.yt_ocean,land_mask,colors='grey')\n",
    "        # labels etc:\n",
    "        #plt.xlabel('Longitude',fontsize=fontsize)\n",
    "#plt.ylabel('Latitude [$^\\circ$S]',fontsize=fontsize)\n",
    "plt.xlabel('Longitude [$^\\circ$E]',fontsize=fontsize)\n",
    "\n",
    "plt.tick_params(labelsize=fontsize)\n",
    "        #lim\n",
    "plt.ylim((-67, -64))\n",
    "plt.xlim((-255, -246))\n",
    "        # colorbar:\n",
    "cbar=plt.colorbar(pc,orientation='vertical',extend='neither')\n",
    "cbar.ax.locator_params(nbins=8)\n",
    "cbar.set_label(r'(N/m$^{2}$)', fontsize = 20,labelpad=5)\n",
    "cbar.ax.tick_params(labelsize=20)\n",
    "ax0.set_xticklabels([-255+359,-254+359,-252+359,-250+359,-248+359,-246+359])\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "save_results_to = '/g/data/v45/ns3783/ScriptsChapter3/Figures/'\n",
    "plt.savefig(save_results_to + 'WindStress_free_limits.png', dpi=120,bbox_inches='tight', pad_inches=0.1)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "ab694697-34f4-49bd-86cc-8f8982dd202e",
   "metadata": {},
   "source": [
    "fontsize=20\n",
    "\n",
    "fig=plt.figure(figsize=(24, 26))\n",
    "fig.subplots_adjust(top=0.88)\n",
    "\n",
    "ax0=plt.subplot(4,3,1) \n",
    "pc = plt.pcolormesh(windX_high_avg.xu_ocean, windX_high_avg.yu_ocean,windX_high_avg,cmap = cmocean.cm.balance, \n",
    "                        vmin=-0.3,vmax=0.3)\n",
    "plt.title('WindX stress (str intrusion mths)',fontsize=fontsize)\n",
    "        #plot contours\n",
    "plt.contour(ht.xt_ocean,ht.yt_ocean,ht,levels=[1000,3000],colors='k',linewidth=.5)\n",
    "        # plot the land:\n",
    "plt.contourf(ht.xt_ocean,ht.yt_ocean,land_mask,colors='grey')\n",
    "        # labels etc:\n",
    "        #plt.xlabel('Longitude',fontsize=fontsize)\n",
    "plt.ylabel('Latitude [$^\\circ$S]',fontsize=fontsize)\n",
    "#plt.xlabel('Longitude [$^\\circ$E]',fontsize=fontsize)\n",
    "\n",
    "plt.tick_params(labelsize=fontsize)\n",
    "        #lim\n",
    "plt.ylim((-67, -64))\n",
    "plt.xlim((-255, -246))\n",
    "        # colorbar:\n",
    "cbar=plt.colorbar(pc,orientation='vertical',extend='neither')\n",
    "cbar.ax.locator_params(nbins=8)\n",
    "#cbar.set_label(r'(N/m$^{2}$)', fontsize = 20,labelpad=5)\n",
    "cbar.ax.tick_params(labelsize=20)\n",
    "ax0.set_xticklabels([-255+359,-254+359,-252+359,-250+359,-248+359,-246+359])\n",
    "\n",
    "ax0=plt.subplot(4,3,2) \n",
    "pc = plt.pcolormesh(windX_low_avg.xu_ocean, windX_low_avg.yu_ocean,windX_low_avg,cmap = cmocean.cm.balance, \n",
    "                        vmin=-0.3,vmax=0.3)\n",
    "plt.title('WindX stress (wk intrusion mths)',fontsize=fontsize)\n",
    "        #plot contours\n",
    "plt.contour(ht.xt_ocean,ht.yt_ocean,ht,levels=[1000,3000],colors='k',linewidth=.5)\n",
    "        # plot the land:\n",
    "plt.contourf(ht.xt_ocean,ht.yt_ocean,land_mask,colors='grey')\n",
    "        # labels etc:\n",
    "        #plt.xlabel('Longitude',fontsize=fontsize)\n",
    "#plt.ylabel('Latitude [$^\\circ$S]',fontsize=fontsize)\n",
    "#plt.xlabel('Longitude [$^\\circ$E]',fontsize=fontsize)\n",
    "\n",
    "plt.tick_params(labelsize=fontsize)\n",
    "        #lim\n",
    "plt.ylim((-67, -64))\n",
    "plt.xlim((-255, -246))\n",
    "        # colorbar:\n",
    "cbar=plt.colorbar(pc,orientation='vertical',extend='neither')\n",
    "cbar.ax.locator_params(nbins=8)\n",
    "#cbar.set_label(r'(N/m$^{2}$)', fontsize = 20,labelpad=5)\n",
    "cbar.ax.tick_params(labelsize=20)\n",
    "ax0.set_xticklabels([-255+359,-254+359,-252+359,-250+359,-248+359,-246+359])\n",
    "\n",
    "\n",
    "ax0=plt.subplot(4,3,3)\n",
    "pc = plt.pcolormesh(windX_avg.xu_ocean, windX_avg.yu_ocean,windX_avg,cmap = cmocean.cm.balance, \n",
    "                        vmin=-0.3,vmax=0.3)\n",
    "plt.title('WindX stress (avg)',fontsize=fontsize)\n",
    "        #plot contours\n",
    "plt.contour(ht.xt_ocean,ht.yt_ocean,ht,levels=[1000,3000],colors='k',linewidth=.5)\n",
    "        # plot the land:\n",
    "plt.contourf(ht.xt_ocean,ht.yt_ocean,land_mask,colors='grey')\n",
    "        # labels etc:\n",
    "        #plt.xlabel('Longitude',fontsize=fontsize)\n",
    "#plt.ylabel('Latitude [$^\\circ$S]',fontsize=fontsize)\n",
    "#plt.xlabel('Longitude [$^\\circ$E]',fontsize=fontsize)\n",
    "\n",
    "plt.tick_params(labelsize=fontsize)\n",
    "        #lim\n",
    "plt.ylim((-67, -64))\n",
    "plt.xlim((-255, -246))\n",
    "        # colorbar:\n",
    "cbar=plt.colorbar(pc,orientation='vertical',extend='neither')\n",
    "cbar.ax.locator_params(nbins=8)\n",
    "cbar.set_label(r'(N/m$^{2}$)', fontsize = 20,labelpad=5)\n",
    "cbar.ax.tick_params(labelsize=20)\n",
    "ax0.set_xticklabels([-255+359,-254+359,-252+359,-250+359,-248+359,-246+359])\n",
    "\n",
    "ax0=plt.subplot(4,3,4) \n",
    "pc = plt.pcolormesh(windY_high_avg.xu_ocean, windY_high_avg.yu_ocean,windY_high_avg,cmap = cmocean.cm.balance, \n",
    "                        vmin=-0.3,vmax=0.3)\n",
    "plt.title('WindY stress (str intrusion mths)',fontsize=fontsize)\n",
    "        #plot contours\n",
    "plt.contour(ht.xt_ocean,ht.yt_ocean,ht,levels=[1000,3000],colors='k',linewidth=.5)\n",
    "        # plot the land:\n",
    "plt.contourf(ht.xt_ocean,ht.yt_ocean,land_mask,colors='grey')\n",
    "        # labels etc:\n",
    "        #plt.xlabel('Longitude',fontsize=fontsize)\n",
    "plt.ylabel('Latitude [$^\\circ$S]',fontsize=fontsize)\n",
    "plt.xlabel('Longitude [$^\\circ$E]',fontsize=fontsize)\n",
    "\n",
    "plt.tick_params(labelsize=fontsize)\n",
    "        #lim\n",
    "plt.ylim((-67, -64))\n",
    "plt.xlim((-255, -246))\n",
    "        # colorbar:\n",
    "cbar=plt.colorbar(pc,orientation='vertical',extend='neither')\n",
    "cbar.ax.locator_params(nbins=8)\n",
    "#cbar.set_label(r'(N/m$^{2}$)', fontsize = 20,labelpad=5)\n",
    "cbar.ax.tick_params(labelsize=20)\n",
    "ax0.set_xticklabels([-255+359,-254+359,-252+359,-250+359,-248+359,-246+359])\n",
    "\n",
    "ax0=plt.subplot(4,3,5) \n",
    "pc = plt.pcolormesh(windY_low_avg.xu_ocean, windY_low_avg.yu_ocean,windY_low_avg,cmap = cmocean.cm.balance, \n",
    "                        vmin=-0.3,vmax=0.3)\n",
    "plt.title('WindY stress (wk intrusion mths)',fontsize=fontsize)\n",
    "        #plot contours\n",
    "plt.contour(ht.xt_ocean,ht.yt_ocean,ht,levels=[1000,3000],colors='k',linewidth=.5)\n",
    "        # plot the land:\n",
    "plt.contourf(ht.xt_ocean,ht.yt_ocean,land_mask,colors='grey')\n",
    "        # labels etc:\n",
    "        #plt.xlabel('Longitude',fontsize=fontsize)\n",
    "#plt.ylabel('Latitude [$^\\circ$S]',fontsize=fontsize)\n",
    "plt.xlabel('Longitude [$^\\circ$E]',fontsize=fontsize)\n",
    "\n",
    "plt.tick_params(labelsize=fontsize)\n",
    "        #lim\n",
    "plt.ylim((-67, -64))\n",
    "plt.xlim((-255, -246))\n",
    "        # colorbar:\n",
    "cbar=plt.colorbar(pc,orientation='vertical',extend='neither')\n",
    "cbar.ax.locator_params(nbins=8)\n",
    "#cbar.set_label(r'(N/m$^{2}$)', fontsize = 20,labelpad=5)\n",
    "cbar.ax.tick_params(labelsize=20)\n",
    "ax0.set_xticklabels([-255+359,-254+359,-252+359,-250+359,-248+359,-246+359])\n",
    "\n",
    "\n",
    "ax0=plt.subplot(4,3,6) \n",
    "pc = plt.pcolormesh(windY_avg.xu_ocean, windY_avg.yu_ocean,windY_avg,cmap = cmocean.cm.balance, \n",
    "                        vmin=-0.3,vmax=0.3)\n",
    "#plt.title('Anomaly for Str Heat Flux (Y) yrs',fontsize=fontsize)\n",
    "plt.title('WindY stress (avg)',fontsize=fontsize)\n",
    "        #plot contours\n",
    "plt.contour(ht.xt_ocean,ht.yt_ocean,ht,levels=[1000,3000],colors='k',linewidth=.5)\n",
    "        # plot the land:\n",
    "plt.contourf(ht.xt_ocean,ht.yt_ocean,land_mask,colors='grey')\n",
    "        # labels etc:\n",
    "        #plt.xlabel('Longitude',fontsize=fontsize)\n",
    "#plt.ylabel('Latitude [$^\\circ$S]',fontsize=fontsize)\n",
    "plt.xlabel('Longitude [$^\\circ$E]',fontsize=fontsize)\n",
    "\n",
    "plt.tick_params(labelsize=fontsize)\n",
    "        #lim\n",
    "plt.ylim((-67, -64))\n",
    "plt.xlim((-255, -246))\n",
    "        # colorbar:\n",
    "cbar=plt.colorbar(pc,orientation='vertical',extend='neither')\n",
    "cbar.ax.locator_params(nbins=8)\n",
    "cbar.set_label(r'(N/m$^{2}$)', fontsize = 20,labelpad=5)\n",
    "cbar.ax.tick_params(labelsize=20)\n",
    "ax0.set_xticklabels([-255+359,-254+359,-252+359,-250+359,-248+359,-246+359])\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "save_results_to = '/g/data/v45/ns3783/ScriptsChapter3/Figures/'\n",
    "plt.savefig(save_results_to + 'WindStress_fixed_limits.png', dpi=120,bbox_inches='tight', pad_inches=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "15819636-2bc0-48d9-87b0-dfefc78b16cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2400x2600 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize=20\n",
    "\n",
    "fig=plt.figure(figsize=(24, 26))\n",
    "fig.subplots_adjust(top=0.88)\n",
    "\n",
    "wX_high = windX_high_avg-windX_avg;\n",
    "wX_low = windX_low_avg-windX_avg;\n",
    "wY_high = windY_high_avg-windY_avg;\n",
    "wY_low = windY_low_avg-windY_avg;\n",
    "ax0=plt.subplot(4,3,1) \n",
    "pc = plt.pcolormesh(wX_high.xu_ocean, wX_high.yu_ocean,wX_high,cmap = cmocean.cm.balance, \n",
    "                        vmin=-0.035,vmax=0.035)\n",
    "plt.title('WindX stress anom (str intrusion mths)',fontsize=fontsize)\n",
    "        #plot contours\n",
    "plt.contour(ht.xt_ocean,ht.yt_ocean,ht,levels=[1000,3000],colors='k',linewidth=.5)\n",
    "        # plot the land:\n",
    "plt.contourf(ht.xt_ocean,ht.yt_ocean,land_mask,colors='grey')\n",
    "        # labels etc:\n",
    "        #plt.xlabel('Longitude',fontsize=fontsize)\n",
    "plt.ylabel('Latitude ($^\\circ$S)',fontsize=fontsize)\n",
    "#plt.xlabel('Longitude [$^\\circ$E]',fontsize=fontsize)\n",
    "\n",
    "plt.tick_params(labelsize=fontsize)\n",
    "        #lim\n",
    "plt.ylim((-67, -64))\n",
    "plt.xlim((-255, -246))\n",
    "        # colorbar:\n",
    "cbar=plt.colorbar(pc,orientation='vertical',extend='neither')\n",
    "cbar.ax.locator_params(nbins=8)\n",
    "#cbar.set_label(r'(N/m$^{2}$)', fontsize = 20,labelpad=5)\n",
    "cbar.ax.tick_params(labelsize=20)\n",
    "ax0.set_xticklabels([-255+359,-254+359,-252+359,-250+359,-248+359,-246+359])\n",
    " # Add subplot indexes (a), b), c)) - first argument moves it horizontally, second argument moves it vertically\n",
    "\n",
    "ax0=plt.subplot(4,3,2) \n",
    "pc = plt.pcolormesh(wX_low.xu_ocean, wX_low.yu_ocean,wX_low,cmap = cmocean.cm.balance, \n",
    "                        vmin=-0.035,vmax=0.035)\n",
    "plt.title('WindX stress anom (wk intrusion mths)',fontsize=fontsize)\n",
    "        #plot contours\n",
    "plt.contour(ht.xt_ocean,ht.yt_ocean,ht,levels=[1000,3000],colors='k',linewidth=.5)\n",
    "        # plot the land:\n",
    "plt.contourf(ht.xt_ocean,ht.yt_ocean,land_mask,colors='grey')\n",
    "        # labels etc:\n",
    "        #plt.xlabel('Longitude',fontsize=fontsize)\n",
    "#plt.ylabel('Latitude [$^\\circ$S]',fontsize=fontsize)\n",
    "#plt.xlabel('Longitude [$^\\circ$E]',fontsize=fontsize)\n",
    "\n",
    "plt.tick_params(labelsize=fontsize)\n",
    "        #lim\n",
    "plt.ylim((-67, -64))\n",
    "plt.xlim((-255, -246))\n",
    "        # colorbar:\n",
    "cbar=plt.colorbar(pc,orientation='vertical',extend='neither')\n",
    "cbar.ax.locator_params(nbins=8)\n",
    "#cbar.set_label(r'(N/m$^{2}$)', fontsize = 20,labelpad=5)\n",
    "cbar.ax.tick_params(labelsize=20)\n",
    "ax0.set_xticklabels([-255+359,-254+359,-252+359,-250+359,-248+359,-246+359])\n",
    "\n",
    "\n",
    "ax0=plt.subplot(4,3,3)\n",
    "pc = plt.pcolormesh(windX_avg.xu_ocean, windX_avg.yu_ocean,windX_avg,cmap = cmocean.cm.balance, \n",
    "                        vmin=-0.25,vmax=0.05)\n",
    "plt.title('WindX stress (avg)',fontsize=fontsize)\n",
    "        #plot contours\n",
    "plt.contour(ht.xt_ocean,ht.yt_ocean,ht,levels=[1000,3000],colors='k',linewidth=.5)\n",
    "        # plot the land:\n",
    "plt.contourf(ht.xt_ocean,ht.yt_ocean,land_mask,colors='grey')\n",
    "        # labels etc:\n",
    "        #plt.xlabel('Longitude',fontsize=fontsize)\n",
    "#plt.ylabel('Latitude [$^\\circ$S]',fontsize=fontsize)\n",
    "#plt.xlabel('Longitude [$^\\circ$E]',fontsize=fontsize)\n",
    "\n",
    "plt.tick_params(labelsize=fontsize)\n",
    "        #lim\n",
    "plt.ylim((-67, -64))\n",
    "plt.xlim((-255, -246))\n",
    "        # colorbar:\n",
    "cbar=plt.colorbar(pc,orientation='vertical',extend='neither')\n",
    "cbar.ax.locator_params(nbins=8)\n",
    "cbar.set_label(r'(N/m$^{2}$)', fontsize = 20,labelpad=5)\n",
    "cbar.ax.tick_params(labelsize=20)\n",
    "ax0.set_xticklabels([-255+359,-254+359,-252+359,-250+359,-248+359,-246+359])\n",
    "\n",
    "ax0=plt.subplot(4,3,4) \n",
    "pc = plt.pcolormesh(wY_high.xu_ocean, wY_high.yu_ocean,wY_high,cmap = cmocean.cm.balance, \n",
    "                        vmin=-0.025,vmax=0.005)\n",
    "plt.title('WindY stress anom (str intrusion mths)',fontsize=fontsize)\n",
    "        #plot contours\n",
    "plt.contour(ht.xt_ocean,ht.yt_ocean,ht,levels=[1000,3000],colors='k',linewidth=.5)\n",
    "        # plot the land:\n",
    "plt.contourf(ht.xt_ocean,ht.yt_ocean,land_mask,colors='grey')\n",
    "        # labels etc:\n",
    "        #plt.xlabel('Longitude',fontsize=fontsize)\n",
    "plt.ylabel('Latitude [$^\\circ$S]',fontsize=fontsize)\n",
    "plt.xlabel('Longitude [$^\\circ$E]',fontsize=fontsize)\n",
    "\n",
    "plt.tick_params(labelsize=fontsize)\n",
    "        #lim\n",
    "plt.ylim((-67, -64))\n",
    "plt.xlim((-255, -246))\n",
    "        # colorbar:\n",
    "cbar=plt.colorbar(pc,orientation='vertical',extend='neither')\n",
    "cbar.ax.locator_params(nbins=8)\n",
    "#cbar.set_label(r'(N/m$^{2}$)', fontsize = 20,labelpad=5)\n",
    "cbar.ax.tick_params(labelsize=20)\n",
    "ax0.set_xticklabels([-255+359,-254+359,-252+359,-250+359,-248+359,-246+359])\n",
    "\n",
    "ax0=plt.subplot(4,3,5) \n",
    "pc = plt.pcolormesh(wY_low.xu_ocean, wY_low.yu_ocean,wY_low,cmap = cmocean.cm.balance, \n",
    "                        vmin=-0.025,vmax=0.005)\n",
    "plt.title('WindY stress anom (wk intrusion mths)',fontsize=fontsize)\n",
    "        #plot contours\n",
    "plt.contour(ht.xt_ocean,ht.yt_ocean,ht,levels=[1000,3000],colors='k',linewidth=.5)\n",
    "        # plot the land:\n",
    "plt.contourf(ht.xt_ocean,ht.yt_ocean,land_mask,colors='grey')\n",
    "        # labels etc:\n",
    "        #plt.xlabel('Longitude',fontsize=fontsize)\n",
    "#plt.ylabel('Latitude [$^\\circ$S]',fontsize=fontsize)\n",
    "plt.xlabel('Longitude ($^\\circ$E)',fontsize=fontsize)\n",
    "\n",
    "plt.tick_params(labelsize=fontsize)\n",
    "        #lim\n",
    "plt.ylim((-67, -64))\n",
    "plt.xlim((-255, -246))\n",
    "        # colorbar:\n",
    "cbar=plt.colorbar(pc,orientation='vertical',extend='neither')\n",
    "cbar.ax.locator_params(nbins=8)\n",
    "#cbar.set_label(r'(N/m$^{2}$)', fontsize = 20,labelpad=5)\n",
    "cbar.ax.tick_params(labelsize=20)\n",
    "ax0.set_xticklabels([-255+359,-254+359,-252+359,-250+359,-248+359,-246+359])\n",
    "\n",
    "\n",
    "ax0=plt.subplot(4,3,6) \n",
    "pc = plt.pcolormesh(windY_avg.xu_ocean, windY_avg.yu_ocean,windY_avg,cmap = cmocean.cm.balance, \n",
    "                        vmin=-0.05,vmax=0.25)\n",
    "#plt.title('Anomaly for Str Heat Flux (Y) yrs',fontsize=fontsize)\n",
    "plt.title('WindY stress (avg)',fontsize=fontsize)\n",
    "        #plot contours\n",
    "plt.contour(ht.xt_ocean,ht.yt_ocean,ht,levels=[1000,3000],colors='k',linewidth=.5)\n",
    "        # plot the land:\n",
    "plt.contourf(ht.xt_ocean,ht.yt_ocean,land_mask,colors='grey')\n",
    "        # labels etc:\n",
    "        #plt.xlabel('Longitude',fontsize=fontsize)\n",
    "#plt.ylabel('Latitude [$^\\circ$S]',fontsize=fontsize)\n",
    "plt.xlabel('Longitude ($^\\circ$E)',fontsize=fontsize)\n",
    "\n",
    "plt.tick_params(labelsize=fontsize)\n",
    "        #lim\n",
    "plt.ylim((-67, -64))\n",
    "plt.xlim((-255, -246))\n",
    "        # colorbar:\n",
    "cbar=plt.colorbar(pc,orientation='vertical',extend='neither')\n",
    "cbar.ax.locator_params(nbins=8)\n",
    "cbar.set_label(r'(N/m$^{2}$)', fontsize = 20,labelpad=5)\n",
    "cbar.ax.tick_params(labelsize=20)\n",
    "ax0.set_xticklabels([-255+359,-254+359,-252+359,-250+359,-248+359,-246+359])\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "save_results_to = '/g/data/v45/ns3783/ScriptsChapter3/Figures/'\n",
    "plt.savefig(save_results_to + 'WindStressAnom_fixed_limits.png', dpi=120,bbox_inches='tight', pad_inches=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29f7a6c4-3ab2-4df4-aec2-b1f269d49ada",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:analysis3-24.01] *",
   "language": "python",
   "name": "conda-env-analysis3-24.01-py"
  },
  "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
}