From d83595ad96706fa18e4e194b757afcdbb33116ad Mon Sep 17 00:00:00 2001
From: Felicity Chun <felicity.chun@anu.edu.au>
Date: Mon, 9 Feb 2026 16:45:14 +1100
Subject: [PATCH 1/3] diag script, copy CC script, new trends function

---
 data_pre_processing.ipynb     |  211 +-
 trends.py                     |  104 +
 troppac_trends_with_n34.ipynb | 3383 +++++++++++++++++++++++++++++++++
 3 files changed, 3626 insertions(+), 72 deletions(-)
 create mode 100755 trends.py
 create mode 100644 troppac_trends_with_n34.ipynb

diff --git a/data_pre_processing.ipynb b/data_pre_processing.ipynb
index 04fe864..6c3321e 100644
--- a/data_pre_processing.ipynb
+++ b/data_pre_processing.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "7be84f55-a429-467a-b01a-fd64cc949edb",
    "metadata": {},
    "outputs": [],
@@ -60,7 +60,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 15,
    "id": "f4701774-c590-40cd-a550-de635d96ae07",
    "metadata": {},
    "outputs": [],
@@ -111,7 +111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 16,
    "id": "81039646-9085-4061-bbea-65bbfba3c74d",
    "metadata": {},
    "outputs": [],
@@ -138,7 +138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 17,
    "id": "1426df34-d486-4cfe-91a4-8d7d213c459a",
    "metadata": {},
    "outputs": [],
@@ -165,7 +165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 18,
    "id": "9dbbb757-c1b1-4924-a055-178506e23330",
    "metadata": {},
    "outputs": [],
@@ -192,7 +192,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 3,
    "id": "05af817e-923b-4356-8ad3-21bc81c51ae8",
    "metadata": {},
    "outputs": [],
@@ -209,12 +209,63 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 6,
    "id": "14d2024b-13b9-42ae-a063-ac5bc94d6cd5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "masked_array(\n",
+       "  data=[[246.4697 , 246.4697 , 246.4697 , ..., 246.4697 , 246.4697 ,\n",
+       "         246.4697 ],\n",
+       "        [247.39708, 247.33827, 247.28154, ..., 247.57051, 247.51707,\n",
+       "         247.45937],\n",
+       "        [248.11905, 248.00008, 247.88597, ..., 248.44518, 248.33568,\n",
+       "         248.23036],\n",
+       "        ...,\n",
+       "        [242.17198, 242.29324, 242.40303, ..., 241.7135 , 241.89888,\n",
+       "         242.02023],\n",
+       "        [241.2053 , 241.24907, 241.28726, ..., 241.02998, 241.06673,\n",
+       "         241.13202],\n",
+       "        [240.66132, 240.66132, 240.66132, ..., 240.66132, 240.66132,\n",
+       "         240.66132]],\n",
+       "  mask=False,\n",
+       "  fill_value=1e+20,\n",
+       "  dtype=float32)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/iris/fileformats/netcdf/_thread_safe_nc.py:340: UserWarning: WARNING: missing_value not used since it\n",
+      "cannot be safely cast to variable data type\n",
+      "  var = variable[keys]\n",
+      "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/iris/fileformats/netcdf/_thread_safe_nc.py:340: UserWarning: WARNING: missing_value not used since it\n",
+      "cannot be safely cast to variable data type\n",
+      "  var = variable[keys]\n",
+      "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/iris/fileformats/netcdf/_thread_safe_nc.py:340: UserWarning: WARNING: missing_value not used since it\n",
+      "cannot be safely cast to variable data type\n",
+      "  var = variable[keys]\n",
+      "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/iris/fileformats/netcdf/_thread_safe_nc.py:340: UserWarning: WARNING: missing_value not used since it\n",
+      "cannot be safely cast to variable data type\n",
+      "  var = variable[keys]\n",
+      "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/iris/fileformats/netcdf/_thread_safe_nc.py:340: UserWarning: WARNING: missing_value not used since it\n",
+      "cannot be safely cast to variable data type\n",
+      "  var = variable[keys]\n",
+      "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/iris/fileformats/netcdf/_thread_safe_nc.py:340: UserWarning: WARNING: missing_value not used since it\n",
+      "cannot be safely cast to variable data type\n",
+      "  var = variable[keys]\n"
+     ]
+    }
+   ],
    "source": [
-    "# cube_esm1_5"
+    "cube_esm1_5"
    ]
   },
   {
@@ -493,42 +544,74 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "021ad0d0-4338-4bf6-b6a0-accde93b86b4",
+   "execution_count": 4,
+   "id": "9ea9db9a-f637-4d07-967c-a2cdb900d827",
    "metadata": {},
    "outputs": [],
    "source": [
-    "## "
+    "# def calc_trend(xarr, start,end,length):\n",
+    "#     #loop over years\n",
+    "#     for i in np.arange(start,end,1):\n",
+    "#         startyear=i\n",
+    "#         endyear=i+length\n",
+    "#         #extract window\n",
+    "#         arr = xarr.sel(year=slice(str(startyear),str(endyear)))\n",
+    "    \n",
+    "#         timedim=np.arange(0,len(arr.year))\n",
+    "#         arr['year'] = timedim\n",
+    "#         #call polyfit to calculate trend over the window. multiply by 10 so we get units of trend per decade\n",
+    "#         trnd = ((arr.polyfit(dim='year',deg=1,skipna=True)).polyfit_coefficients.isel(degree=0))*10\n",
+    " \n",
+    "#         #if it's the first year, copy the trend to trnarr\n",
+    "#         if i == start:\n",
+    "#             trnarr = trnd\n",
+    "#         #if it's the subsequent years, concatenate the trend to trnarr\n",
+    "#         else:\n",
+    "#             trnarr = xr.concat([trnarr,trnd],dim='year')\n",
+    "\n",
+    "#     trnarr['year'] = np.arange(start,end,1)\n",
+    "\n",
+    "#     return trnarr"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "9ea9db9a-f637-4d07-967c-a2cdb900d827",
+   "execution_count": 7,
+   "id": "021ad0d0-4338-4bf6-b6a0-accde93b86b4",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def calc_trend(xarr, start,end,length):\n",
-    "    #loop over years\n",
-    "    for i in np.arange(start,end,1):\n",
-    "        startyear=i\n",
-    "        endyear=i+length\n",
-    "        #extract window\n",
-    "        arr = xarr.sel(year=slice(str(startyear),str(endyear)))\n",
+    "##  updated calc trend 6/02/26\n",
+    "def calc_trend(xarr, start, end, length):\n",
+    "    \"\"\"\n",
+    "    Compute running linear trends over windows of size `length` (years)\n",
+    "    from `start` to `end - 1`, returning trend per decade.\n",
+    "\n",
+    "    xarr must have a coordinate \"year\".\n",
+    "    \"\"\"\n",
     "    \n",
-    "        timedim=np.arange(0,len(arr.year))\n",
-    "        arr['year'] = timedim\n",
-    "        #call polyfit to calculate trend over the window. multiply by 10 so we get units of trend per decade\n",
-    "        trnd = ((arr.polyfit(dim='year',deg=1,skipna=True)).polyfit_coefficients.isel(degree=0))*10\n",
-    " \n",
-    "        #if it's the first year, copy the trend to trnarr\n",
-    "        if i == start:\n",
-    "            trnarr = trnd\n",
-    "        #if it's the subsequent years, concatenate the trend to trnarr\n",
-    "        else:\n",
-    "            trnarr = xr.concat([trnarr,trnd],dim='year')\n",
+    "    trend_list = []\n",
+    "\n",
+    "    for yr in range(start, end):\n",
+    "        # window\n",
+    "        win = xarr.sel(year=slice(str(yr), str(yr + length-1)))\n",
     "\n",
-    "    trnarr['year'] = np.arange(start,end,1)\n",
+    "        # replace year coordinate with integers 0..N-1 for polyfit\n",
+    "        t = np.arange(win.year.size)\n",
+    "        win = win.assign_coords(year=t)\n",
+    "\n",
+    "        # polyfit: slope * 10 for trend per decade\n",
+    "        tr = (win.polyfit(dim=\"year\", deg=1, skipna=True)\n",
+    "                 .polyfit_coefficients\n",
+    "                 .isel(degree=0) * 10)\n",
+    "        \n",
+    "        trend_list.append(tr)\n",
+    "\n",
+    "    # concatenate trends along a new \"year\" dimension\n",
+    "    trnarr = xr.concat(trend_list, dim=\"year\")\n",
+    "\n",
+    "    # restore actual years\n",
+    "    trnarr = trnarr.assign_coords(year=np.arange(start, end))\n",
     "\n",
     "    return trnarr"
    ]
@@ -548,7 +631,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "id": "714ca1de-2063-4cca-9ee6-1dcb421f4ee7",
    "metadata": {},
    "outputs": [],
@@ -588,12 +671,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 9,
    "id": "8565ae54-f3fa-4bfa-a16c-b10bcb55f755",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/iris/fileformats/netcdf/_thread_safe_nc.py:340: UserWarning: WARNING: missing_value not used since it\n",
+      "cannot be safely cast to variable data type\n",
+      "  var = variable[keys]\n"
+     ]
+    }
+   ],
    "source": [
-    "pre_processing_trend_write(cube_esm1_5,\"ACCESS_ESM1_5\", output_path=\"/g/data/kj13/datasets/visualisation_projects/uncharted_future/trend\")    "
+    "\n",
+    "pre_processing_trend_write(cube_esm1_5,\"ACCESS_ESM1_5\", output_path=\"/scratch/iq82/fc6164/esmvaltool_output/process_data/trend\")\n",
+    "\n",
+    "# pre_processing_trend_write(cube_esm1_5,\"ACCESS_ESM1_5\", output_path=\"/g/data/kj13/datasets/visualisation_projects/uncharted_future/trend\")    "
    ]
   },
   {
@@ -648,43 +744,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "id": "84001a80-ec2e-4503-b725-1bd7afea0a5a",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/iris/fileformats/netcdf/_thread_safe_nc.py:340: UserWarning: WARNING: missing_value not used since it\n",
-      "cannot be safely cast to variable data type\n",
-      "  var = variable[keys]\n",
-      "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/iris/fileformats/netcdf/_thread_safe_nc.py:340: UserWarning: WARNING: missing_value not used since it\n",
-      "cannot be safely cast to variable data type\n",
-      "  var = variable[keys]\n",
-      "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/iris/fileformats/netcdf/_thread_safe_nc.py:340: UserWarning: WARNING: missing_value not used since it\n",
-      "cannot be safely cast to variable data type\n",
-      "  var = variable[keys]\n",
-      "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/iris/fileformats/netcdf/_thread_safe_nc.py:340: UserWarning: WARNING: missing_value not used since it\n",
-      "cannot be safely cast to variable data type\n",
-      "  var = variable[keys]\n",
-      "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/iris/fileformats/netcdf/_thread_safe_nc.py:340: UserWarning: WARNING: missing_value not used since it\n",
-      "cannot be safely cast to variable data type\n",
-      "  var = variable[keys]\n",
-      "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/iris/fileformats/netcdf/_thread_safe_nc.py:340: UserWarning: WARNING: missing_value not used since it\n",
-      "cannot be safely cast to variable data type\n",
-      "  var = variable[keys]\n",
-      "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/iris/fileformats/netcdf/_thread_safe_nc.py:340: UserWarning: WARNING: missing_value not used since it\n",
-      "cannot be safely cast to variable data type\n",
-      "  var = variable[keys]\n",
-      "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/iris/fileformats/netcdf/_thread_safe_nc.py:340: UserWarning: WARNING: missing_value not used since it\n",
-      "cannot be safely cast to variable data type\n",
-      "  var = variable[keys]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "path=\"/g/data/kj13/datasets/visualisation_projects/uncharted_future/trend\"\n",
+    "# path=\"/g/data/kj13/datasets/visualisation_projects/uncharted_future/trend\"\n",
+    "path = \"/scratch/iq82/fc6164/esmvaltool_output/process_data/trend\"\n",
+    "\n",
     "dataset_list={\n",
     "    \"CESM2\":cube_cesm2,\n",
     "    \"NORESM2\":cube_noresm2,\n",
@@ -1490,7 +1557,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:analysis3-25.09] *",
+   "display_name": "Python [conda env:analysis3-25.09]",
    "language": "python",
    "name": "conda-env-analysis3-25.09-py"
   },
diff --git a/trends.py b/trends.py
new file mode 100755
index 0000000..61c6bae
--- /dev/null
+++ b/trends.py
@@ -0,0 +1,104 @@
+"""diagnostic script to plot"""
+
+import iris
+import os
+import logging
+import numpy as np
+import xarray as xr
+
+from esmvaltool.diag_scripts.shared import (
+    run_diagnostic,
+    save_figure,
+    group_metadata,
+    select_metadata,
+)
+from esmvalcore.preprocessor import (
+    climate_statistics,
+    regrid
+)
+
+
+# This part sends debug statements to stdout
+logger = logging.getLogger(os.path.basename(__file__))
+
+
+def calc_trend(xarr, start, end, length):
+    """
+    Compute running linear trends over windows of size `length` (years)
+    from `start` to `end - 1`, returning trend per decade.
+
+    xarr must have a coordinate "year".
+    """
+    
+    trend_list = []
+
+    for yr in range(start, end):
+        # window
+        win = xarr.sel(year=slice(str(yr), str(yr + length-1)))
+
+        # replace year coordinate with integers 0..N-1 for polyfit
+        t = np.arange(win.year.size)
+        win = win.assign_coords(year=t)
+
+        # polyfit: slope * 10 for trend per decade
+        tr = (win.polyfit(dim="year", deg=1, skipna=True)
+                 .polyfit_coefficients
+                 .isel(degree=0) * 10)
+        
+        trend_list.append(tr)
+
+    # concatenate trends along a new "year" dimension
+    trnarr = xr.concat(trend_list, dim="year")
+
+    # restore actual years
+    trnarr = trnarr.assign_coords(year=np.arange(start, end))
+
+    return trnarr
+
+
+def get_provenance_record(caption, ancestor_files):
+    """Create a provenance record describing the diagnostic data and plot."""
+
+    record = {
+        "caption": caption,
+        "statistics": ["other"],
+        "domains": ["polar"],
+        "plot_types": ["map"],
+        "authors": [
+            "chun_felicity",
+        ],
+        "references": [
+            "access-nri",
+        ],
+        "ancestors": ancestor_files,
+    }
+    return record
+
+
+def main(cfg):
+    """run on all datasets"""
+
+    input_data = cfg["input_data"].values()
+
+    for dataset in input_data:
+        # Load the data
+        input_file = dataset['filename']
+        name = dataset['dataset'] + '_' + dataset['exp']
+        cube = iris.load_cube(input_file)
+
+        data=xr.DataArray.from_iris(cube)
+        ds_model_ann=data.groupby('time.year').mean('time')
+
+        ds_model_ann_trend_31 = calc_trend(ds_model_ann, dataset['start_year'], dataset['end_year']-31, 31)
+        
+        # Save output
+        # prov_record = get_provenance_record(
+        #     f'Barotropic streamfunction from {name}.',
+        #     [input_file],
+        # )
+        # get work dir
+        ds_model_ann_trend_31.to_netcdf(os.path.join(cfg["work_dir"], f"{name}_trend_31yr{dataset['start_year']}_{dataset['end_year']-31}.nc"))
+
+if __name__ == "__main__":
+    with run_diagnostic() as config:
+        main(config)
diff --git a/troppac_trends_with_n34.ipynb b/troppac_trends_with_n34.ipynb
new file mode 100644
index 0000000..fb4664b
--- /dev/null
+++ b/troppac_trends_with_n34.ipynb
@@ -0,0 +1,3383 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "556234b8-0af5-4392-8676-0e96b71700fe",
+   "metadata": {},
+   "source": [
+    "# this script calculates\n",
+    "- global SST anomalies\n",
+    "- global SST trends over a sliding window\n",
+    "- \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "25aeaf4d-70c0-4308-9e86-30e867f5c53f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#from clef.code import *\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "import glob\n",
+    "import os\n",
+    "import xarray as xr\n",
+    "# from cmip_catalog import *\n",
+    "from tqdm import tqdm_notebook as tqdm\n",
+    "import dask.bag as dasb\n",
+    "from dask.diagnostics import ProgressBar\n",
+    "from scipy import signal\n",
+    "from scipy.stats import spearmanr, norm\n",
+    "import cartopy.crs as ccrs\n",
+    "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n",
+    "import xskillscore as xskill\n",
+    "\n",
+    "import sys\n",
+    "# sys.path.append('/home/548/cxc548/lib/python/bom-climate-change-variability-and-extreme-toolbox')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "aa67729b-1fe9-44d0-8095-b40545c60e79",
+   "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-74a1e173-0306-11f1-b5fd-000003a6fe80</p>\n",
+       "        <table style=\"width: 100%; text-align: left;\">\n",
+       "\n",
+       "        <tr>\n",
+       "        \n",
+       "            <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
+       "            <td style=\"text-align: left;\"><strong>Cluster type:</strong> distributed.LocalCluster</td>\n",
+       "        \n",
+       "        </tr>\n",
+       "\n",
+       "        \n",
+       "            <tr>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Dashboard: </strong> <a href=\"/proxy/8787/status\" target=\"_blank\">/proxy/8787/status</a>\n",
+       "                </td>\n",
+       "                <td style=\"text-align: left;\"></td>\n",
+       "            </tr>\n",
+       "        \n",
+       "\n",
+       "        </table>\n",
+       "\n",
+       "        \n",
+       "            <button style=\"margin-bottom: 12px;\" data-commandlinker-command=\"dask:populate-and-launch-layout\" data-commandlinker-args='{\"url\": \"/proxy/8787/status\" }'>\n",
+       "                Launch dashboard in JupyterLab\n",
+       "            </button>\n",
+       "        \n",
+       "\n",
+       "        \n",
+       "            <details>\n",
+       "            <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
+       "            <div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
+       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\">\n",
+       "    </div>\n",
+       "    <div style=\"margin-left: 48px;\">\n",
+       "        <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">LocalCluster</h3>\n",
+       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">a6fa77a0</p>\n",
+       "        <table style=\"width: 100%; text-align: left;\">\n",
+       "            <tr>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Dashboard:</strong> <a href=\"/proxy/8787/status\" target=\"_blank\">/proxy/8787/status</a>\n",
+       "                </td>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Workers:</strong> 7\n",
+       "                </td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Total threads:</strong> 7\n",
+       "                </td>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Total memory:</strong> 32.00 GiB\n",
+       "                </td>\n",
+       "            </tr>\n",
+       "            \n",
+       "            <tr>\n",
+       "    <td style=\"text-align: left;\"><strong>Status:</strong> running</td>\n",
+       "    <td style=\"text-align: left;\"><strong>Using processes:</strong> True</td>\n",
+       "</tr>\n",
+       "\n",
+       "            \n",
+       "        </table>\n",
+       "\n",
+       "        <details>\n",
+       "            <summary style=\"margin-bottom: 20px;\">\n",
+       "                <h3 style=\"display: inline;\">Scheduler Info</h3>\n",
+       "            </summary>\n",
+       "\n",
+       "            <div style=\"\">\n",
+       "    <div>\n",
+       "        <div style=\"width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;\"> </div>\n",
+       "        <div style=\"margin-left: 48px;\">\n",
+       "            <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n",
+       "            <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-d4c243b0-88b6-4ac6-9a0e-ed40c0276304</p>\n",
+       "            <table style=\"width: 100%; text-align: left;\">\n",
+       "                <tr>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Comm:</strong> tcp://127.0.0.1:41277\n",
+       "                    </td>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Workers:</strong> 0 \n",
+       "                    </td>\n",
+       "                </tr>\n",
+       "                <tr>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Dashboard:</strong> <a href=\"/proxy/8787/status\" target=\"_blank\">/proxy/8787/status</a>\n",
+       "                    </td>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Total threads:</strong> 0\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> 0 B\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://127.0.0.1:39543\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/35657/status\" target=\"_blank\">/proxy/35657/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 4.57 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:34571\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/159992262.gadi-pbs/dask-scratch-space/worker-m12d0a6s\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://127.0.0.1:46691\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/44813/status\" target=\"_blank\">/proxy/44813/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 4.57 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:37351\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/159992262.gadi-pbs/dask-scratch-space/worker-f9hrw_y1\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://127.0.0.1:37397\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/39739/status\" target=\"_blank\">/proxy/39739/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 4.57 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:42475\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/159992262.gadi-pbs/dask-scratch-space/worker-r5z4tfgt\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://127.0.0.1:34015\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/33147/status\" target=\"_blank\">/proxy/33147/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 4.57 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:41551\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/159992262.gadi-pbs/dask-scratch-space/worker-6ult422y\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://127.0.0.1:43643\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/36955/status\" target=\"_blank\">/proxy/36955/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 4.57 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:34489\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/159992262.gadi-pbs/dask-scratch-space/worker-8exfxtfy\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://127.0.0.1:45151\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/32877/status\" target=\"_blank\">/proxy/32877/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 4.57 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:45353\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/159992262.gadi-pbs/dask-scratch-space/worker-7u8hl8lw\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://127.0.0.1:42951\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/40455/status\" target=\"_blank\">/proxy/40455/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 4.57 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:42337\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/159992262.gadi-pbs/dask-scratch-space/worker-0zfxhzik\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://127.0.0.1:41277' processes=7 threads=7, memory=32.00 GiB>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from dask.distributed import Client\n",
+    "client = Client(threads_per_worker = 1)\n",
+    "client"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "5d87bac1-d70a-4951-8fed-bcbe9da2d8ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#-- Function to return an array of sliding trends starting at 'start', ending at 'end'\n",
+    "# with length 'length'\n",
+    "\n",
+    "# def calc_trend(xarr, start,end,length):\n",
+    "#     #loop over years\n",
+    "#     for i in np.arange(start,end,1):\n",
+    "#         startyear=i\n",
+    "#         endyear=i+length\n",
+    "#         #extract window\n",
+    "#         arr = xarr.sel(year=slice(str(startyear),str(endyear)))\n",
+    "    \n",
+    "#         timedim=np.arange(0,len(arr.year))\n",
+    "#         arr['year'] = timedim\n",
+    "#         #call polyfit to calculate trend over the window. multiply by 10 so we get units of trend per decade\n",
+    "#         trnd = ((arr.polyfit(dim='year',deg=1,skipna=True)).polyfit_coefficients.isel(degree=0))*10\n",
+    " \n",
+    "#         #if it's the first year, copy the trend to trnarr\n",
+    "#         if i == start:\n",
+    "#             trnarr = trnd\n",
+    "#         #if it's the subsequent years, concatenate the trend to trnarr\n",
+    "#         else:\n",
+    "#             trnarr = xr.concat([trnarr,trnd],dim='year')\n",
+    "\n",
+    "#     trnarr['year'] = np.arange(start,end,1)\n",
+    "\n",
+    "#     return trnarr\n",
+    "\n",
+    "def calc_trend(xarr, start, end, length):\n",
+    "    \"\"\"\n",
+    "    Compute running linear trends over windows of size `length` (years)\n",
+    "    from `start` to `end - 1`, returning trend per decade.\n",
+    "\n",
+    "    xarr must have a coordinate \"year\".\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    trend_list = []\n",
+    "\n",
+    "    for yr in range(start, end):\n",
+    "        # window\n",
+    "        win = xarr.sel(year=slice(str(yr), str(yr + length-1)))\n",
+    "\n",
+    "        # replace year coordinate with integers 0..N-1 for polyfit\n",
+    "        t = np.arange(win.year.size)\n",
+    "        win = win.assign_coords(year=t)\n",
+    "\n",
+    "        # polyfit: slope * 10 for trend per decade\n",
+    "        tr = (win.polyfit(dim=\"year\", deg=1, skipna=True)\n",
+    "                 .polyfit_coefficients\n",
+    "                 .isel(degree=0) * 10)\n",
+    "        \n",
+    "        trend_list.append(tr)\n",
+    "\n",
+    "    # concatenate trends along a new \"year\" dimension\n",
+    "    trnarr = xr.concat(trend_list, dim=\"year\")\n",
+    "\n",
+    "    # restore actual years\n",
+    "    trnarr = trnarr.assign_coords(year=np.arange(start, end))\n",
+    "\n",
+    "    return trnarr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "619dc84e-3303-467e-b68e-6f8466c5689f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def detrend_1d(y, deg=1):\n",
+    "    \"\"\"\n",
+    "    Removes polynomial trend from a 1D array while preserving the mean.\n",
+    "\n",
+    "    Parameters:\n",
+    "        y (np.ndarray): Input 1D data array.\n",
+    "        deg (int): Degree of the polynomial to fit (default is 1 for linear detrending).\n",
+    "\n",
+    "    Returns:\n",
+    "        np.ndarray: Detrended data with original mean retained.\n",
+    "    \"\"\"\n",
+    "    x = np.arange(y.size) \n",
+    "    z = np.polyfit(x, y, deg)  \n",
+    "    trend = np.polyval(z, x)  \n",
+    "    return y - trend + np.mean(y)  # Retain mean after detrending\n",
+    "\n",
+    "def detrend_wrap(da, deg=1):\n",
+    "    \"\"\"\n",
+    "    Applies 1D polynomial detrending across the 'time' dimension of an xarray DataArray.\n",
+    "\n",
+    "    Parameters:\n",
+    "        da (xr.DataArray): Input data with a 'time' dimension.\n",
+    "        deg (int): Degree of the polynomial to fit (default is 1).\n",
+    "\n",
+    "    Returns:\n",
+    "        xr.DataArray: Detrended data.\n",
+    "    \"\"\"\n",
+    "    return xr.apply_ufunc(\n",
+    "        detrend_1d, da,\n",
+    "        input_core_dims=[['time']],  \n",
+    "        vectorize=True,  \n",
+    "        dask='parallelized',  \n",
+    "        kwargs={'deg': deg}  \n",
+    "    )\n",
+    "\n",
+    "def detrend_monthly(da, deg=1):\n",
+    "    \"\"\"\n",
+    "    Applies polynomial detrending separately to each calendar month across years.\n",
+    "\n",
+    "    Parameters:\n",
+    "        da (xr.DataArray): Input data with 'time' dimension.\n",
+    "        deg (int): Degree of the polynomial to fit (default is 1).\n",
+    "\n",
+    "    Returns:\n",
+    "        xr.DataArray: Monthly detrended data.\n",
+    "    \"\"\"    \n",
+    "    return da.groupby('time.month').apply(\n",
+    "        lambda x: xr.apply_ufunc(\n",
+    "            detrend_1d, x,\n",
+    "            input_core_dims=[['time']],\n",
+    "            output_core_dims=[['time']],\n",
+    "            vectorize=True,\n",
+    "            dask='parallelized',\n",
+    "            output_dtypes=[float],\n",
+    "            kwargs={'deg': deg}\n",
+    "        )\n",
+    "    )\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3a5d9f5e-7ad4-4554-b530-a8e9e34fe377",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def identify_enso_years_from_nino34(\n",
+    "    nino34: xr.DataArray,\n",
+    "    threshold: float = 0.5,\n",
+    "    runmean_months: int = 3,\n",
+    "    min_consecutive_months: int = 5,\n",
+    "    year_mode: str = \"any\"  # \"any\" or \"peak\"\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Identify El Niño / La Niña events from a monthly Niño3.4 anomaly time series.\n",
+    "\n",
+    "    Rules:\n",
+    "      - Compute a runmean_months (default 3) running mean.\n",
+    "      - El Niño: running mean >= +threshold for >= min_consecutive_months consecutive months.\n",
+    "      - La Niña: running mean <= -threshold for >= min_consecutive_months consecutive months.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    nino34 : xr.DataArray\n",
+    "        Monthly Niño3.4 anomalies with a 'time' coordinate.\n",
+    "    threshold : float\n",
+    "        Threshold in °C (default 0.5).\n",
+    "    runmean_months : int\n",
+    "        Running mean window in months (default 3).\n",
+    "    min_consecutive_months : int\n",
+    "        Minimum consecutive months meeting threshold (default 5).\n",
+    "    year_mode : str\n",
+    "        \"any\"  -> return all calendar years touched by qualifying event months.\n",
+    "        \"peak\" -> return the year of the peak month for each event (max for El Niño, min for La Niña).\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    dict with:\n",
+    "      - \"nino34_rm\": running-mean series\n",
+    "      - \"el_nino_events\": list of dicts with start/end and months\n",
+    "      - \"la_nina_events\": list of dicts with start/end and months\n",
+    "      - \"el_nino_years\": sorted list of years\n",
+    "      - \"la_nina_years\": sorted list of years\n",
+    "    \"\"\"\n",
+    "    if \"time\" not in nino34.dims:\n",
+    "        raise ValueError(\"nino34 must have a 'time' dimension.\")\n",
+    "    if nino34.time.size < runmean_months:\n",
+    "        raise ValueError(\"Not enough data for the requested running mean window.\")\n",
+    "\n",
+    "    # Ensure time is sorted\n",
+    "    nino34 = nino34.sortby(\"time\")\n",
+    "\n",
+    "    # 3-month running mean (centered)\n",
+    "    rm = nino34.rolling(time=runmean_months, center=True).mean()\n",
+    "\n",
+    "    def _find_runs(mask: xr.DataArray):\n",
+    "        \"\"\"Return list of (start_idx, end_idx) inclusive runs where mask is True.\"\"\"\n",
+    "        m = mask.fillna(False).values.astype(bool)\n",
+    "        if m.ndim != 1:\n",
+    "            raise ValueError(\"Expected a 1D time series.\")\n",
+    "\n",
+    "        runs = []\n",
+    "        in_run = False\n",
+    "        start = None\n",
+    "        for i, val in enumerate(m):\n",
+    "            if val and not in_run:\n",
+    "                in_run = True\n",
+    "                start = i\n",
+    "            elif (not val) and in_run:\n",
+    "                end = i - 1\n",
+    "                runs.append((start, end))\n",
+    "                in_run = False\n",
+    "        if in_run:\n",
+    "            runs.append((start, len(m) - 1))\n",
+    "        return runs\n",
+    "\n",
+    "    def _events_from_runs(runs, kind: str):\n",
+    "        events = []\n",
+    "        for s, e in runs:\n",
+    "            length = e - s + 1\n",
+    "            if length < min_consecutive_months:\n",
+    "                continue\n",
+    "\n",
+    "            months = rm.time.isel(time=slice(s, e + 1)).values\n",
+    "            start_time = rm.time.isel(time=s).item()\n",
+    "            end_time = rm.time.isel(time=e).item()\n",
+    "\n",
+    "            # Peak month definition: max for El Niño, min for La Niña\n",
+    "            seg = rm.isel(time=slice(s, e + 1))\n",
+    "            if kind == \"el_nino\":\n",
+    "                peak_idx = int(seg.argmax(\"time\"))\n",
+    "            else:\n",
+    "                peak_idx = int(seg.argmin(\"time\"))\n",
+    "            peak_time = seg.time.isel(time=peak_idx).item()\n",
+    "            peak_value = float(seg.isel(time=peak_idx).values)\n",
+    "\n",
+    "            events.append({\n",
+    "                \"start\": start_time,\n",
+    "                \"end\": end_time,\n",
+    "                \"n_months\": int(length),\n",
+    "                \"peak_time\": peak_time,\n",
+    "                \"peak_value\": peak_value,\n",
+    "                \"months\": months,\n",
+    "            })\n",
+    "        return events\n",
+    "\n",
+    "    el_mask = rm >= threshold\n",
+    "    la_mask = rm <= -threshold\n",
+    "\n",
+    "    el_runs = _find_runs(el_mask)\n",
+    "    la_runs = _find_runs(la_mask)\n",
+    "\n",
+    "    el_events = _events_from_runs(el_runs, \"el_nino\")\n",
+    "    la_events = _events_from_runs(la_runs, \"la_nina\")\n",
+    "\n",
+    "    def _years_from_events(events, mode: str, kind: str):\n",
+    "        years = set()\n",
+    "        if mode == \"any\":\n",
+    "            for ev in events:\n",
+    "                for t in ev[\"months\"]:\n",
+    "                    years.add(np.datetime64(t, \"M\").astype(object).year)\n",
+    "        elif mode == \"peak\":\n",
+    "            for ev in events:\n",
+    "                # peak_time is a python datetime-like object after .item()\n",
+    "                years.add(ev[\"peak_time\"].year)\n",
+    "        else:\n",
+    "            raise ValueError(\"year_mode must be 'any' or 'peak'.\")\n",
+    "        return sorted(years)\n",
+    "\n",
+    "    el_years = _years_from_events(el_events, year_mode, \"el_nino\")\n",
+    "    la_years = _years_from_events(la_events, year_mode, \"la_nina\")\n",
+    "\n",
+    "    return {\n",
+    "        \"nino34_rm\": rm,\n",
+    "        \"el_nino_events\": el_events,\n",
+    "        \"la_nina_events\": la_events,\n",
+    "        \"el_nino_years\": el_years,\n",
+    "        \"la_nina_years\": la_years,\n",
+    "    }\n",
+    "\n",
+    "# --- Example usage ---\n",
+    "# nino34_anom is a DataArray with monthly anomalies and dim 'time'\n",
+    "# out = identify_enso_years_from_nino34(nino34_anom, year_mode=\"any\")\n",
+    "# print(out[\"el_nino_years\"])\n",
+    "# print(out[\"la_nina_years\"])\n",
+    "# out[\"el_nino_events\"][0]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "bb1b61e3-21a9-4a9c-9d7e-9998a0006545",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#read in SSTs\n",
+    "ts_obs = xr.open_dataset('/g/data/ct11/access-nri/replicas/esmvaltool/obsdata-v2/Tier2/HadISST/OBS_HadISST_reanaly_1_Amon_ts_187001-202112.nc', decode_times=True).ts\n",
+    "\n",
+    "\n",
+    "#get annual mean (for trend calculation - not detrended)\n",
+    "ts_obs_ann = ts_obs.groupby('time.year').mean('time')\n",
+    "\n",
+    "#get detrended monthly time series (for nino 3.4 calculation)\n",
+    "ts_obs_dt = detrend_monthly(ts_obs, deg=1)\n",
+    "\n",
+    "#get detrended monthly anomalies\n",
+    "ts_obs_anoms_dt = ts_obs_dt.groupby('time.month') - ts_obs_dt.sel(time=slice('1870','1920')).groupby('time.month').mean('time')\n",
+    "#the above is monthly data, if we wanted to do some smoothing we could maybe do 12-month running mean\n",
+    "ts_anoms_rolling_dt = ts_obs_anoms_dt.rolling(time=5, center=True).mean()\n",
+    "\n",
+    "#now get nino3.4 index from monthly detrended anomalies\n",
+    "n34_obs = ts_obs_anoms_dt.sel(lat=slice(-5,5),lon=slice(190,240)).mean(['lat','lon'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "95a118a4-a927-4cf8-b3a6-7ef36a43df53",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "El Niño years: 61 [1877, 1878, 1888, 1889, 1896, 1897, 1899, 1900, 1902, 1903, 1904, 1905, 1906, 1911, 1912, 1913, 1914, 1915, 1918, 1919, 1923, 1924, 1925, 1926, 1930, 1931, 1940, 1941, 1942, 1951, 1957, 1958, 1963, 1964, 1965, 1966, 1968, 1969, 1972, 1973, 1976, 1977, 1982, 1983, 1986, 1987, 1988, 1991, 1992, 1994, 1995, 1997, 1998, 2002, 2003, 2009, 2010, 2015, 2016, 2018, 2019]\n",
+      "La Niña years: 53 [1870, 1872, 1873, 1874, 1875, 1876, 1879, 1880, 1886, 1887, 1889, 1890, 1892, 1893, 1894, 1909, 1910, 1916, 1917, 1933, 1934, 1942, 1943, 1949, 1950, 1951, 1954, 1955, 1956, 1964, 1965, 1970, 1971, 1973, 1974, 1975, 1976, 1984, 1985, 1988, 1989, 1998, 1999, 2000, 2007, 2008, 2010, 2011, 2012, 2017, 2018, 2020, 2021]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#plot n3.4 just to check\n",
+    "\n",
+    "n34_obs.plot()\n",
+    "out = identify_enso_years_from_nino34(n34_obs, year_mode=\"any\",threshold=0.6)\n",
+    "\n",
+    "n_el_years = len(out[\"el_nino_years\"])\n",
+    "n_la_years = len(out[\"la_nina_years\"])\n",
+    "\n",
+    "\"\"\"\n",
+    "  out is a dict with:\n",
+    "      - \"nino34_rm\": running-mean series\n",
+    "      - \"el_nino_events\": list of dicts with start/end and months\n",
+    "      - \"la_nina_events\": list of dicts with start/end and months\n",
+    "      - \"el_nino_years\": sorted list of years\n",
+    "      - \"la_nina_years\": sorted list of years\n",
+    "      \"\"\"\n",
+    "print(\"El Niño years:\", n_el_years, out[\"el_nino_years\"])\n",
+    "print(\"La Niña years:\", n_la_years, out[\"la_nina_years\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "585d79d4-360b-4749-801d-830d233e2eed",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n",
+      "<class 'xarray.core.dataarray.DataArray'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "obs_m20c_trnarr = calc_trend(ts_obs_ann,1850,1983,31)\n",
+    "\n",
+    "# import matplotlib.animation as animation\n",
+    "# from IPython.display import HTML\n",
+    "# import cartopy.feature as cfeature\n",
+    "\n",
+    "# fig, axes = plt.subplots(1,1,figsize=(20,15),\n",
+    "#                          subplot_kw=dict(projection=ccrs.PlateCarree(central_longitude=180)))\n",
+    "cmap = plt.get_cmap('RdBu_r')\n",
+    "# m, M = -0.2, 0.2\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "43ad8148-0012-4ecb-9225-5b7babb6dc73",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "# Create figure and axes with projection\n",
+    "fig, axes = plt.subplots(\n",
+    "    figsize=(10, 7),\n",
+    "    subplot_kw={'projection': ccrs.PlateCarree(central_longitude=180)}\n",
+    ")\n",
+    "cmap = plt.get_cmap('RdBu_r')\n",
+    "m, M = -0.2, 0.2\n",
+    "\n",
+    "# Plot one slice just to see\n",
+    "maptrn = obs_m20c_trnarr.isel(year=100).plot.contourf(\n",
+    "    ax=axes,\n",
+    "    cmap=cmap,\n",
+    "    vmin=m,\n",
+    "    vmax=M,\n",
+    "    levels=np.linspace(m, M, 21),\n",
+    "    add_colorbar=False,\n",
+    "    transform=ccrs.PlateCarree()\n",
+    ")\n",
+    "\n",
+    "# Axis formatting\n",
+    "axes.set_xticks(np.arange(-180, 181, 30), crs=ccrs.PlateCarree())\n",
+    "axes.set_yticks(np.arange(-90, 91, 30), crs=ccrs.PlateCarree())\n",
+    "\n",
+    "lon_formatter = LongitudeFormatter(zero_direction_label=True, degree_symbol='')\n",
+    "lat_formatter = LatitudeFormatter(degree_symbol='')\n",
+    "axes.xaxis.set_major_formatter(lon_formatter)\n",
+    "axes.yaxis.set_major_formatter(lat_formatter)\n",
+    "\n",
+    "axes.coastlines(resolution='110m')\n",
+    "axes.set_xlabel(\"\")\n",
+    "axes.set_ylabel(\"\")\n",
+    "\n",
+    "# Add colorbar\n",
+    "cbar = fig.colorbar(maptrn, ax=axes, orientation='horizontal', pad=0.05, shrink=0.8)\n",
+    "cbar.set_label('51-yr trend (°C/decade)')\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "40272391-c7f1-45f7-802b-56e58509ad19",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# #calculate a 34 year trend and animate\n",
+    "\n",
+    "# obs_m20c_trnarr = calc_trend(ts_obs_ann,1850,1985,34)\n",
+    "\n",
+    "# import matplotlib.animation as animation\n",
+    "# from IPython.display import HTML\n",
+    "\n",
+    "# fig, axes = plt.subplots(1,1,figsize=(20,15),\n",
+    "#                          subplot_kw=dict(projection=ccrs.PlateCarree(central_longitude=180)))\n",
+    "# cmap = plt.get_cmap('RdBu_r')\n",
+    "# m, M = -0.2, 0.2\n",
+    "\n",
+    "# def animate(frame):\n",
+    "#     axes.clear()\n",
+    "#     obs_m20c_trnarr.isel(year=frame).plot.contourf(\n",
+    "#         ax=axes, cmap=cmap, vmin=m, vmax=M, \n",
+    "#         levels=np.linspace(m, M, 21), \n",
+    "#         add_colorbar=False, transform=ccrs.PlateCarree()\n",
+    "#     )\n",
+    "#     axes.set_title(f\"34-yr trend starting {int(obs_m20c_trnarr.year[frame].values)}\")\n",
+    "#     axes.add_feature(cfeature.BORDERS, linewidth=0.5, edgecolor=\"black\")   # country borders\n",
+    "#     axes.add_feature(cfeature.LAND, facecolor=\"lightgray\", alpha=0.5)\n",
+    "\n",
+    "# ani = animation.FuncAnimation(\n",
+    "#     fig, animate, frames=len(obs_m20c_trnarr.year),\n",
+    "#     interval=200, blit=False\n",
+    "# )\n",
+    "\n",
+    "# ani.save('animation_34yrs.gif', writer='imagemagick', fps=2)\n",
+    "# HTML(ani.to_jshtml())\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "24910606-9901-4e01-8a82-d1cf47d11a21",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1000x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#plot one slice just to see\n",
+    "\n",
+    "\n",
+    "# Create figure and axes with projection\n",
+    "fig, axes = plt.subplots(\n",
+    "    figsize=(10, 7),\n",
+    "    subplot_kw={'projection': ccrs.PlateCarree(central_longitude=180)}\n",
+    ")\n",
+    "\n",
+    "# Plot one slice just to see\n",
+    "maptrn = obs_m20c_trnarr.isel(year=132).plot.contourf(\n",
+    "    ax=axes,\n",
+    "    cmap=cmap,\n",
+    "    vmin=m,\n",
+    "    vmax=M,\n",
+    "    levels=np.linspace(m, M, 21),\n",
+    "    add_colorbar=False,\n",
+    "    transform=ccrs.PlateCarree()\n",
+    ")\n",
+    "\n",
+    "# Axis formatting\n",
+    "axes.set_xticks(np.arange(-180, 181, 30), crs=ccrs.PlateCarree())\n",
+    "axes.set_yticks(np.arange(-90, 91, 30), crs=ccrs.PlateCarree())\n",
+    "\n",
+    "lon_formatter = LongitudeFormatter(zero_direction_label=True, degree_symbol='')\n",
+    "lat_formatter = LatitudeFormatter(degree_symbol='')\n",
+    "axes.xaxis.set_major_formatter(lon_formatter)\n",
+    "axes.yaxis.set_major_formatter(lat_formatter)\n",
+    "\n",
+    "axes.coastlines(resolution='110m')\n",
+    "axes.set_xlabel(\"\")\n",
+    "axes.set_ylabel(\"\")\n",
+    "\n",
+    "# Add colorbar\n",
+    "cbar = fig.colorbar(maptrn, ax=axes, orientation='horizontal', pad=0.05, shrink=0.8)\n",
+    "cbar.set_label('31-yr trend (°C/decade)')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "f465486d-8cd9-4979-ace2-76108d132438",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 40 ts files\n"
+     ]
+    }
+   ],
+   "source": [
+    "#read in ACCESS ESM1.5/CANESM5/CESM2/MIROC6/NORESM2\n",
+    "\n",
+    "import glob\n",
+    "\n",
+    "base = \"/g/data/fs38/publications/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/*/Amon/ts/gn/latest/*.nc\"\n",
+    "\n",
+    "#base = \"/g/data/oi10/replicas/CMIP6/CMIP/CCCma/CanESM5/historical/*/Amon/ts/gn/latest/*.nc\"\n",
+    "\n",
+    "files = sorted(glob.glob(base))\n",
+    "\n",
+    "print(f\"Found {len(files)} ts files\")\n",
+    "ds = xr.open_mfdataset(\n",
+    "    files,\n",
+    "    combine=\"nested\",\n",
+    "    concat_dim=\"member_id\",\n",
+    "    parallel=True,\n",
+    "    chunks= 'auto'\n",
+    ")\n",
+    "\n",
+    "\n",
+    "da = ds['ts']        # extract the variable\n",
+    "da = da.transpose('time', 'lat', 'lon', 'member_id')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "6205f2f3-b5b4-4e8f-9577-1a10d5232aa4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "  line-height: 1.6;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-obj-name,\n",
+       ".xr-group-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name::before {\n",
+       "  content: \"📁\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name,\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "  border: 2px solid transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 20px auto;\n",
+       "  width: 100%;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 20px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;ts&#x27; (time: 1980, lat: 145, lon: 192, member_id: 40)&gt; Size: 9GB\n",
+       "dask.array&lt;transpose, shape=(1980, 145, 192, 40), dtype=float32, chunksize=(1205, 145, 192, 1), chunktype=numpy.ndarray&gt;\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 16kB 1850-01-16T12:00:00 ... 2014-12-16T12...\n",
+       "  * lat      (lat) float64 1kB -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0\n",
+       "  * lon      (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
+       "Dimensions without coordinates: member_id\n",
+       "Attributes:\n",
+       "    standard_name:  surface_temperature\n",
+       "    long_name:      Surface Temperature\n",
+       "    comment:        Temperature of the lower boundary of the atmosphere\n",
+       "    units:          K\n",
+       "    cell_methods:   area: time: mean\n",
+       "    cell_measures:  area: areacella\n",
+       "    history:        2020-06-05T04:06:10Z altered by CMOR: replaced missing va...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-obj-name'>&#x27;ts&#x27;</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1980</li><li><span class='xr-has-index'>lat</span>: 145</li><li><span class='xr-has-index'>lon</span>: 192</li><li><span>member_id</span>: 40</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6a831c17-fa84-4b9a-9c82-e2d1c47c1c91' class='xr-array-in' type='checkbox' checked><label for='section-6a831c17-fa84-4b9a-9c82-e2d1c47c1c91' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1205, 145, 192, 1), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 8.21 GiB </td>\n",
+       "                        <td> 127.97 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1980, 145, 192, 40) </td>\n",
+       "                        <td> (1205, 145, 192, 1) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 80 chunks in 122 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"480\" height=\"110\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <text x=\"190.825698\" y=\"69.251330\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,190.825698,69.251330)\">145</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-3e76366b-1f8d-4e47-b289-8f00741e6946' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3e76366b-1f8d-4e47-b289-8f00741e6946' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1850-01-16T12:00:00 ... 2014-12-...</div><input id='attrs-f272d05a-76db-4bc4-bc99-de1801e82d93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f272d05a-76db-4bc4-bc99-de1801e82d93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2b4f1ce-c6e1-4724-bcec-90c7afd94803' class='xr-var-data-in' type='checkbox'><label for='data-f2b4f1ce-c6e1-4724-bcec-90c7afd94803' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1850-01-16T12:00:00.000000000&#x27;, &#x27;1850-02-15T00:00:00.000000000&#x27;,\n",
+       "       &#x27;1850-03-16T12:00:00.000000000&#x27;, ..., &#x27;2014-10-16T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-11-16T00:00:00.000000000&#x27;, &#x27;2014-12-16T12:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -88.75 -87.5 ... 88.75 90.0</div><input id='attrs-84d8508b-6c57-4a75-b8af-d63753f441e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84d8508b-6c57-4a75-b8af-d63753f441e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c793ecf3-b9e2-4652-97a6-2a385403be1e' class='xr-var-data-in' type='checkbox'><label for='data-c793ecf3-b9e2-4652-97a6-2a385403be1e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-90.  , -88.75, -87.5 , -86.25, -85.  , -83.75, -82.5 , -81.25, -80.  ,\n",
+       "       -78.75, -77.5 , -76.25, -75.  , -73.75, -72.5 , -71.25, -70.  , -68.75,\n",
+       "       -67.5 , -66.25, -65.  , -63.75, -62.5 , -61.25, -60.  , -58.75, -57.5 ,\n",
+       "       -56.25, -55.  , -53.75, -52.5 , -51.25, -50.  , -48.75, -47.5 , -46.25,\n",
+       "       -45.  , -43.75, -42.5 , -41.25, -40.  , -38.75, -37.5 , -36.25, -35.  ,\n",
+       "       -33.75, -32.5 , -31.25, -30.  , -28.75, -27.5 , -26.25, -25.  , -23.75,\n",
+       "       -22.5 , -21.25, -20.  , -18.75, -17.5 , -16.25, -15.  , -13.75, -12.5 ,\n",
+       "       -11.25, -10.  ,  -8.75,  -7.5 ,  -6.25,  -5.  ,  -3.75,  -2.5 ,  -1.25,\n",
+       "         0.  ,   1.25,   2.5 ,   3.75,   5.  ,   6.25,   7.5 ,   8.75,  10.  ,\n",
+       "        11.25,  12.5 ,  13.75,  15.  ,  16.25,  17.5 ,  18.75,  20.  ,  21.25,\n",
+       "        22.5 ,  23.75,  25.  ,  26.25,  27.5 ,  28.75,  30.  ,  31.25,  32.5 ,\n",
+       "        33.75,  35.  ,  36.25,  37.5 ,  38.75,  40.  ,  41.25,  42.5 ,  43.75,\n",
+       "        45.  ,  46.25,  47.5 ,  48.75,  50.  ,  51.25,  52.5 ,  53.75,  55.  ,\n",
+       "        56.25,  57.5 ,  58.75,  60.  ,  61.25,  62.5 ,  63.75,  65.  ,  66.25,\n",
+       "        67.5 ,  68.75,  70.  ,  71.25,  72.5 ,  73.75,  75.  ,  76.25,  77.5 ,\n",
+       "        78.75,  80.  ,  81.25,  82.5 ,  83.75,  85.  ,  86.25,  87.5 ,  88.75,\n",
+       "        90.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.875 3.75 ... 356.2 358.1</div><input id='attrs-640342bb-7218-4470-bb88-f23882b76466' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-640342bb-7218-4470-bb88-f23882b76466' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a01c01d-dce8-4bbb-ad32-c6b1d6c31e99' class='xr-var-data-in' type='checkbox'><label for='data-8a01c01d-dce8-4bbb-ad32-c6b1d6c31e99' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  0.   ,   1.875,   3.75 ,   5.625,   7.5  ,   9.375,  11.25 ,  13.125,\n",
+       "        15.   ,  16.875,  18.75 ,  20.625,  22.5  ,  24.375,  26.25 ,  28.125,\n",
+       "        30.   ,  31.875,  33.75 ,  35.625,  37.5  ,  39.375,  41.25 ,  43.125,\n",
+       "        45.   ,  46.875,  48.75 ,  50.625,  52.5  ,  54.375,  56.25 ,  58.125,\n",
+       "        60.   ,  61.875,  63.75 ,  65.625,  67.5  ,  69.375,  71.25 ,  73.125,\n",
+       "        75.   ,  76.875,  78.75 ,  80.625,  82.5  ,  84.375,  86.25 ,  88.125,\n",
+       "        90.   ,  91.875,  93.75 ,  95.625,  97.5  ,  99.375, 101.25 , 103.125,\n",
+       "       105.   , 106.875, 108.75 , 110.625, 112.5  , 114.375, 116.25 , 118.125,\n",
+       "       120.   , 121.875, 123.75 , 125.625, 127.5  , 129.375, 131.25 , 133.125,\n",
+       "       135.   , 136.875, 138.75 , 140.625, 142.5  , 144.375, 146.25 , 148.125,\n",
+       "       150.   , 151.875, 153.75 , 155.625, 157.5  , 159.375, 161.25 , 163.125,\n",
+       "       165.   , 166.875, 168.75 , 170.625, 172.5  , 174.375, 176.25 , 178.125,\n",
+       "       180.   , 181.875, 183.75 , 185.625, 187.5  , 189.375, 191.25 , 193.125,\n",
+       "       195.   , 196.875, 198.75 , 200.625, 202.5  , 204.375, 206.25 , 208.125,\n",
+       "       210.   , 211.875, 213.75 , 215.625, 217.5  , 219.375, 221.25 , 223.125,\n",
+       "       225.   , 226.875, 228.75 , 230.625, 232.5  , 234.375, 236.25 , 238.125,\n",
+       "       240.   , 241.875, 243.75 , 245.625, 247.5  , 249.375, 251.25 , 253.125,\n",
+       "       255.   , 256.875, 258.75 , 260.625, 262.5  , 264.375, 266.25 , 268.125,\n",
+       "       270.   , 271.875, 273.75 , 275.625, 277.5  , 279.375, 281.25 , 283.125,\n",
+       "       285.   , 286.875, 288.75 , 290.625, 292.5  , 294.375, 296.25 , 298.125,\n",
+       "       300.   , 301.875, 303.75 , 305.625, 307.5  , 309.375, 311.25 , 313.125,\n",
+       "       315.   , 316.875, 318.75 , 320.625, 322.5  , 324.375, 326.25 , 328.125,\n",
+       "       330.   , 331.875, 333.75 , 335.625, 337.5  , 339.375, 341.25 , 343.125,\n",
+       "       345.   , 346.875, 348.75 , 350.625, 352.5  , 354.375, 356.25 , 358.125])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cac39757-d40b-40b0-a05f-a64423c259fd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cac39757-d40b-40b0-a05f-a64423c259fd' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>surface_temperature</dd><dt><span>long_name :</span></dt><dd>Surface Temperature</dd><dt><span>comment :</span></dt><dd>Temperature of the lower boundary of the atmosphere</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>history :</span></dt><dd>2020-06-05T04:06:10Z altered by CMOR: replaced missing value flag (-1.07374e+09) with standard missing value (1e+20).</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'ts' (time: 1980, lat: 145, lon: 192, member_id: 40)> Size: 9GB\n",
+       "dask.array<transpose, shape=(1980, 145, 192, 40), dtype=float32, chunksize=(1205, 145, 192, 1), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 16kB 1850-01-16T12:00:00 ... 2014-12-16T12...\n",
+       "  * lat      (lat) float64 1kB -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0\n",
+       "  * lon      (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
+       "Dimensions without coordinates: member_id\n",
+       "Attributes:\n",
+       "    standard_name:  surface_temperature\n",
+       "    long_name:      Surface Temperature\n",
+       "    comment:        Temperature of the lower boundary of the atmosphere\n",
+       "    units:          K\n",
+       "    cell_methods:   area: time: mean\n",
+       "    cell_measures:  area: areacella\n",
+       "    history:        2020-06-05T04:06:10Z altered by CMOR: replaced missing va..."
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "da"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "3d1f1735-c629-4762-ac8d-fe83e1e324d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "da_ann = da.groupby('time.year').mean('time')\n",
+    "\n",
+    "# da_ann = da_ann.chunk({\"year\": -1})\n",
+    "\n",
+    "esm15_m20c_trnarr = calc_trend(da_ann,1850,1983,31)\n",
+    "esm15_m20c_trnarr_em = esm15_m20c_trnarr.mean('member_id').chunk({\"year\": -1})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "02e91f68-e0de-42b5-afe4-936689689209",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fig, axes = plt.subplots(1,1,figsize=(20,15),\n",
+    "#                          subplot_kw=dict(projection=ccrs.PlateCarree(central_longitude=180)))\n",
+    "# cmap = plt.get_cmap('RdBu_r')\n",
+    "# m, M = -0.2, 0.2\n",
+    "\n",
+    "# def animate(frame):\n",
+    "#     axes.clear()\n",
+    "#     esm15_m20c_trnarr_em.isel(year=frame).plot.contourf(\n",
+    "#         ax=axes, cmap=cmap, vmin=m, vmax=M, \n",
+    "#         levels=np.linspace(m, M, 21), \n",
+    "#         add_colorbar=False, transform=ccrs.PlateCarree()\n",
+    "#     )\n",
+    "#     axes.set_title(f\"31-yr trend starting {int(esm15_m20c_trnarr_em.year[frame].values)}\")\n",
+    "#     axes.add_feature(cfeature.BORDERS, linewidth=0.5, edgecolor=\"black\")   # country borders\n",
+    "#     axes.add_feature(cfeature.LAND, facecolor=\"lightgray\", alpha=0.5)\n",
+    "\n",
+    "# ani = animation.FuncAnimation(\n",
+    "#     fig, animate, frames=len(esm15_m20c_trnarr_em.year),\n",
+    "#     interval=200, blit=False\n",
+    "# )\n",
+    "\n",
+    "# ani.save('esm15_animation_31yrs.gif', writer='imagemagick', fps=2)\n",
+    "# HTML(ani.to_jshtml())\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "693160ac-b1ae-4f32-8e53-9f1ecdf5544a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#plot one slice just to see\n",
+    "\n",
+    "\n",
+    "# # Create figure and axes with projection\n",
+    "# fig, axes = plt.subplots(\n",
+    "#     figsize=(10, 7),\n",
+    "#     subplot_kw={'projection': ccrs.PlateCarree(central_longitude=180)}\n",
+    "# )\n",
+    "# cmap = plt.get_cmap('RdBu_r')\n",
+    "\n",
+    "# m=-1\n",
+    "# M=1\n",
+    "# # Plot one slice just to see\n",
+    "# maptrn = esm15_m20c_trnarr_em.isel(year=132).plot.contourf(\n",
+    "#     ax=axes,\n",
+    "#     cmap=cmap,\n",
+    "#     vmin=m,\n",
+    "#     vmax=M,\n",
+    "#     levels=np.linspace(m, M, 21),\n",
+    "#     add_colorbar=False,\n",
+    "#     transform=ccrs.PlateCarree()\n",
+    "# )\n",
+    "\n",
+    "# # Axis formatting\n",
+    "# axes.set_xticks(np.arange(-180, 181, 30), crs=ccrs.PlateCarree())\n",
+    "# axes.set_yticks(np.arange(-90, 91, 30), crs=ccrs.PlateCarree())\n",
+    "\n",
+    "# lon_formatter = LongitudeFormatter(zero_direction_label=True, degree_symbol='')\n",
+    "# lat_formatter = LatitudeFormatter(degree_symbol='')\n",
+    "# axes.xaxis.set_major_formatter(lon_formatter)\n",
+    "# axes.yaxis.set_major_formatter(lat_formatter)\n",
+    "\n",
+    "# axes.coastlines(resolution='110m')\n",
+    "# axes.set_xlabel(\"\")\n",
+    "# axes.set_ylabel(\"\")\n",
+    "\n",
+    "# # Add colorbar\n",
+    "# cbar = fig.colorbar(maptrn, ax=axes, orientation='horizontal', pad=0.05, shrink=0.8)\n",
+    "# cbar.set_label('ACCESS ESM1.5 EM 31-yr trend (°C/decade)')\n",
+    "\n",
+    "# plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "7cd4eb60-edaf-4701-8c85-188305583b74",
+   "metadata": {},
+   "outputs": [
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+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;polyfit_coefficients&#x27; (lat: 145, lon: 192)&gt; Size: 223kB\n",
+       "dask.array&lt;getitem, shape=(145, 192), dtype=float64, chunksize=(145, 192), chunktype=numpy.ndarray&gt;\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float64 1kB -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0\n",
+       "  * lon      (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
+       "    degree   int64 8B 1\n",
+       "    year     int64 8B 1982</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-obj-name'>&#x27;polyfit_coefficients&#x27;</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 145</li><li><span class='xr-has-index'>lon</span>: 192</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5ee07e4d-8828-486c-b937-803a5228e3bc' class='xr-array-in' type='checkbox' checked><label for='section-5ee07e4d-8828-486c-b937-803a5228e3bc' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(145, 192), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 217.50 kiB </td>\n",
+       "                        <td> 217.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (145, 192) </td>\n",
+       "                        <td> (145, 192) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 1467 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"140\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"90\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"90\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,90.625 0.0,90.625\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"110.625000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >192</text>\n",
+       "  <text x=\"140.000000\" y=\"45.312500\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,45.312500)\">145</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-9bd1f4fd-1b0d-489e-8e53-2f35c06f1acc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9bd1f4fd-1b0d-489e-8e53-2f35c06f1acc' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -88.75 -87.5 ... 88.75 90.0</div><input id='attrs-1922c126-d4eb-4cd7-9bd8-18cbf52c7b39' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1922c126-d4eb-4cd7-9bd8-18cbf52c7b39' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67129c3f-095b-4643-ac17-28f3c192da36' class='xr-var-data-in' type='checkbox'><label for='data-67129c3f-095b-4643-ac17-28f3c192da36' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-90.  , -88.75, -87.5 , -86.25, -85.  , -83.75, -82.5 , -81.25, -80.  ,\n",
+       "       -78.75, -77.5 , -76.25, -75.  , -73.75, -72.5 , -71.25, -70.  , -68.75,\n",
+       "       -67.5 , -66.25, -65.  , -63.75, -62.5 , -61.25, -60.  , -58.75, -57.5 ,\n",
+       "       -56.25, -55.  , -53.75, -52.5 , -51.25, -50.  , -48.75, -47.5 , -46.25,\n",
+       "       -45.  , -43.75, -42.5 , -41.25, -40.  , -38.75, -37.5 , -36.25, -35.  ,\n",
+       "       -33.75, -32.5 , -31.25, -30.  , -28.75, -27.5 , -26.25, -25.  , -23.75,\n",
+       "       -22.5 , -21.25, -20.  , -18.75, -17.5 , -16.25, -15.  , -13.75, -12.5 ,\n",
+       "       -11.25, -10.  ,  -8.75,  -7.5 ,  -6.25,  -5.  ,  -3.75,  -2.5 ,  -1.25,\n",
+       "         0.  ,   1.25,   2.5 ,   3.75,   5.  ,   6.25,   7.5 ,   8.75,  10.  ,\n",
+       "        11.25,  12.5 ,  13.75,  15.  ,  16.25,  17.5 ,  18.75,  20.  ,  21.25,\n",
+       "        22.5 ,  23.75,  25.  ,  26.25,  27.5 ,  28.75,  30.  ,  31.25,  32.5 ,\n",
+       "        33.75,  35.  ,  36.25,  37.5 ,  38.75,  40.  ,  41.25,  42.5 ,  43.75,\n",
+       "        45.  ,  46.25,  47.5 ,  48.75,  50.  ,  51.25,  52.5 ,  53.75,  55.  ,\n",
+       "        56.25,  57.5 ,  58.75,  60.  ,  61.25,  62.5 ,  63.75,  65.  ,  66.25,\n",
+       "        67.5 ,  68.75,  70.  ,  71.25,  72.5 ,  73.75,  75.  ,  76.25,  77.5 ,\n",
+       "        78.75,  80.  ,  81.25,  82.5 ,  83.75,  85.  ,  86.25,  87.5 ,  88.75,\n",
+       "        90.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.875 3.75 ... 356.2 358.1</div><input id='attrs-95e2f324-3409-4e4b-8bd8-2b9eaea20c28' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95e2f324-3409-4e4b-8bd8-2b9eaea20c28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2efd015-a161-469a-92cd-98fb5f46a891' class='xr-var-data-in' type='checkbox'><label for='data-f2efd015-a161-469a-92cd-98fb5f46a891' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  0.   ,   1.875,   3.75 ,   5.625,   7.5  ,   9.375,  11.25 ,  13.125,\n",
+       "        15.   ,  16.875,  18.75 ,  20.625,  22.5  ,  24.375,  26.25 ,  28.125,\n",
+       "        30.   ,  31.875,  33.75 ,  35.625,  37.5  ,  39.375,  41.25 ,  43.125,\n",
+       "        45.   ,  46.875,  48.75 ,  50.625,  52.5  ,  54.375,  56.25 ,  58.125,\n",
+       "        60.   ,  61.875,  63.75 ,  65.625,  67.5  ,  69.375,  71.25 ,  73.125,\n",
+       "        75.   ,  76.875,  78.75 ,  80.625,  82.5  ,  84.375,  86.25 ,  88.125,\n",
+       "        90.   ,  91.875,  93.75 ,  95.625,  97.5  ,  99.375, 101.25 , 103.125,\n",
+       "       105.   , 106.875, 108.75 , 110.625, 112.5  , 114.375, 116.25 , 118.125,\n",
+       "       120.   , 121.875, 123.75 , 125.625, 127.5  , 129.375, 131.25 , 133.125,\n",
+       "       135.   , 136.875, 138.75 , 140.625, 142.5  , 144.375, 146.25 , 148.125,\n",
+       "       150.   , 151.875, 153.75 , 155.625, 157.5  , 159.375, 161.25 , 163.125,\n",
+       "       165.   , 166.875, 168.75 , 170.625, 172.5  , 174.375, 176.25 , 178.125,\n",
+       "       180.   , 181.875, 183.75 , 185.625, 187.5  , 189.375, 191.25 , 193.125,\n",
+       "       195.   , 196.875, 198.75 , 200.625, 202.5  , 204.375, 206.25 , 208.125,\n",
+       "       210.   , 211.875, 213.75 , 215.625, 217.5  , 219.375, 221.25 , 223.125,\n",
+       "       225.   , 226.875, 228.75 , 230.625, 232.5  , 234.375, 236.25 , 238.125,\n",
+       "       240.   , 241.875, 243.75 , 245.625, 247.5  , 249.375, 251.25 , 253.125,\n",
+       "       255.   , 256.875, 258.75 , 260.625, 262.5  , 264.375, 266.25 , 268.125,\n",
+       "       270.   , 271.875, 273.75 , 275.625, 277.5  , 279.375, 281.25 , 283.125,\n",
+       "       285.   , 286.875, 288.75 , 290.625, 292.5  , 294.375, 296.25 , 298.125,\n",
+       "       300.   , 301.875, 303.75 , 305.625, 307.5  , 309.375, 311.25 , 313.125,\n",
+       "       315.   , 316.875, 318.75 , 320.625, 322.5  , 324.375, 326.25 , 328.125,\n",
+       "       330.   , 331.875, 333.75 , 335.625, 337.5  , 339.375, 341.25 , 343.125,\n",
+       "       345.   , 346.875, 348.75 , 350.625, 352.5  , 354.375, 356.25 , 358.125])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>degree</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1</div><input id='attrs-ebd17014-9688-46cc-90fd-a0f3fddc2ebd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ebd17014-9688-46cc-90fd-a0f3fddc2ebd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-277e0310-fdfd-464c-a8c2-5f85fe08e3c4' class='xr-var-data-in' type='checkbox'><label for='data-277e0310-fdfd-464c-a8c2-5f85fe08e3c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(1)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>year</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1982</div><input id='attrs-879899c9-4264-4aeb-8ef3-a00f8dad2f03' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-879899c9-4264-4aeb-8ef3-a00f8dad2f03' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-945b59c4-f560-4a20-8f9d-65caf4d35c2c' class='xr-var-data-in' type='checkbox'><label for='data-945b59c4-f560-4a20-8f9d-65caf4d35c2c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(1982)</pre></div></li></ul></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'polyfit_coefficients' (lat: 145, lon: 192)> Size: 223kB\n",
+       "dask.array<getitem, shape=(145, 192), dtype=float64, chunksize=(145, 192), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float64 1kB -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0\n",
+       "  * lon      (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
+       "    degree   int64 8B 1\n",
+       "    year     int64 8B 1982"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "esm15_m20c_trnarr_em.isel(year=132)\n",
+    "# da.compute()\n",
+    "# da.rechunk() #'auto'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "bf212bcb-7986-45d2-92e1-9a33aa061cd8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "  line-height: 1.6;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-obj-name,\n",
+       ".xr-group-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name::before {\n",
+       "  content: \"📁\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name,\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "  border: 2px solid transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 20px auto;\n",
+       "  width: 100%;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 20px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;polyfit_coefficients&#x27; (year: 133, lat: 145, lon: 192)&gt; Size: 30MB\n",
+       "dask.array&lt;rechunk-merge, shape=(133, 145, 192), dtype=float64, chunksize=(133, 145, 192), chunktype=numpy.ndarray&gt;\n",
+       "Coordinates:\n",
+       "  * year     (year) int64 1kB 1850 1851 1852 1853 1854 ... 1979 1980 1981 1982\n",
+       "  * lat      (lat) float64 1kB -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0\n",
+       "  * lon      (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
+       "    degree   int64 8B 1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-obj-name'>&#x27;polyfit_coefficients&#x27;</div><ul class='xr-dim-list'><li><span class='xr-has-index'>year</span>: 133</li><li><span class='xr-has-index'>lat</span>: 145</li><li><span class='xr-has-index'>lon</span>: 192</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-8869d537-86ed-4bae-bd32-374900e80be9' class='xr-array-in' type='checkbox' checked><label for='section-8869d537-86ed-4bae-bd32-374900e80be9' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(133, 145, 192), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 28.25 MiB </td>\n",
+       "                        <td> 28.25 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (133, 145, 192) </td>\n",
+       "                        <td> (133, 145, 192) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 1466 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "\n",
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+       "  <text x=\"118.897059\" y=\"159.522059\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >192</text>\n",
+       "  <text x=\"198.897059\" y=\"94.209559\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,198.897059,94.209559)\">145</text>\n",
+       "  <text x=\"24.448529\" y=\"135.073529\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,24.448529,135.073529)\">133</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-067eb3a1-60d4-490b-9117-524321596240' class='xr-section-summary-in' type='checkbox'  checked><label for='section-067eb3a1-60d4-490b-9117-524321596240' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1850 1851 1852 ... 1980 1981 1982</div><input id='attrs-a4e1c32c-327b-4ddf-b5cf-ddd724ba7c64' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a4e1c32c-327b-4ddf-b5cf-ddd724ba7c64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c713227-bf55-4471-aba1-425761aa111e' class='xr-var-data-in' type='checkbox'><label for='data-4c713227-bf55-4471-aba1-425761aa111e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1850, 1851, 1852, 1853, 1854, 1855, 1856, 1857, 1858, 1859, 1860, 1861,\n",
+       "       1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873,\n",
+       "       1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 1882, 1883, 1884, 1885,\n",
+       "       1886, 1887, 1888, 1889, 1890, 1891, 1892, 1893, 1894, 1895, 1896, 1897,\n",
+       "       1898, 1899, 1900, 1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909,\n",
+       "       1910, 1911, 1912, 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921,\n",
+       "       1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933,\n",
+       "       1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945,\n",
+       "       1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957,\n",
+       "       1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969,\n",
+       "       1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981,\n",
+       "       1982])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -88.75 -87.5 ... 88.75 90.0</div><input id='attrs-4c268b28-6658-40ab-a909-fe41af614526' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c268b28-6658-40ab-a909-fe41af614526' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-541e42db-9625-4c8f-85d2-6837a7c15377' class='xr-var-data-in' type='checkbox'><label for='data-541e42db-9625-4c8f-85d2-6837a7c15377' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-90.  , -88.75, -87.5 , -86.25, -85.  , -83.75, -82.5 , -81.25, -80.  ,\n",
+       "       -78.75, -77.5 , -76.25, -75.  , -73.75, -72.5 , -71.25, -70.  , -68.75,\n",
+       "       -67.5 , -66.25, -65.  , -63.75, -62.5 , -61.25, -60.  , -58.75, -57.5 ,\n",
+       "       -56.25, -55.  , -53.75, -52.5 , -51.25, -50.  , -48.75, -47.5 , -46.25,\n",
+       "       -45.  , -43.75, -42.5 , -41.25, -40.  , -38.75, -37.5 , -36.25, -35.  ,\n",
+       "       -33.75, -32.5 , -31.25, -30.  , -28.75, -27.5 , -26.25, -25.  , -23.75,\n",
+       "       -22.5 , -21.25, -20.  , -18.75, -17.5 , -16.25, -15.  , -13.75, -12.5 ,\n",
+       "       -11.25, -10.  ,  -8.75,  -7.5 ,  -6.25,  -5.  ,  -3.75,  -2.5 ,  -1.25,\n",
+       "         0.  ,   1.25,   2.5 ,   3.75,   5.  ,   6.25,   7.5 ,   8.75,  10.  ,\n",
+       "        11.25,  12.5 ,  13.75,  15.  ,  16.25,  17.5 ,  18.75,  20.  ,  21.25,\n",
+       "        22.5 ,  23.75,  25.  ,  26.25,  27.5 ,  28.75,  30.  ,  31.25,  32.5 ,\n",
+       "        33.75,  35.  ,  36.25,  37.5 ,  38.75,  40.  ,  41.25,  42.5 ,  43.75,\n",
+       "        45.  ,  46.25,  47.5 ,  48.75,  50.  ,  51.25,  52.5 ,  53.75,  55.  ,\n",
+       "        56.25,  57.5 ,  58.75,  60.  ,  61.25,  62.5 ,  63.75,  65.  ,  66.25,\n",
+       "        67.5 ,  68.75,  70.  ,  71.25,  72.5 ,  73.75,  75.  ,  76.25,  77.5 ,\n",
+       "        78.75,  80.  ,  81.25,  82.5 ,  83.75,  85.  ,  86.25,  87.5 ,  88.75,\n",
+       "        90.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.875 3.75 ... 356.2 358.1</div><input id='attrs-4f7979c0-e02b-43c8-88ff-f99097158c10' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f7979c0-e02b-43c8-88ff-f99097158c10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7afd1364-d411-4af4-a81d-990d4296f911' class='xr-var-data-in' type='checkbox'><label for='data-7afd1364-d411-4af4-a81d-990d4296f911' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  0.   ,   1.875,   3.75 ,   5.625,   7.5  ,   9.375,  11.25 ,  13.125,\n",
+       "        15.   ,  16.875,  18.75 ,  20.625,  22.5  ,  24.375,  26.25 ,  28.125,\n",
+       "        30.   ,  31.875,  33.75 ,  35.625,  37.5  ,  39.375,  41.25 ,  43.125,\n",
+       "        45.   ,  46.875,  48.75 ,  50.625,  52.5  ,  54.375,  56.25 ,  58.125,\n",
+       "        60.   ,  61.875,  63.75 ,  65.625,  67.5  ,  69.375,  71.25 ,  73.125,\n",
+       "        75.   ,  76.875,  78.75 ,  80.625,  82.5  ,  84.375,  86.25 ,  88.125,\n",
+       "        90.   ,  91.875,  93.75 ,  95.625,  97.5  ,  99.375, 101.25 , 103.125,\n",
+       "       105.   , 106.875, 108.75 , 110.625, 112.5  , 114.375, 116.25 , 118.125,\n",
+       "       120.   , 121.875, 123.75 , 125.625, 127.5  , 129.375, 131.25 , 133.125,\n",
+       "       135.   , 136.875, 138.75 , 140.625, 142.5  , 144.375, 146.25 , 148.125,\n",
+       "       150.   , 151.875, 153.75 , 155.625, 157.5  , 159.375, 161.25 , 163.125,\n",
+       "       165.   , 166.875, 168.75 , 170.625, 172.5  , 174.375, 176.25 , 178.125,\n",
+       "       180.   , 181.875, 183.75 , 185.625, 187.5  , 189.375, 191.25 , 193.125,\n",
+       "       195.   , 196.875, 198.75 , 200.625, 202.5  , 204.375, 206.25 , 208.125,\n",
+       "       210.   , 211.875, 213.75 , 215.625, 217.5  , 219.375, 221.25 , 223.125,\n",
+       "       225.   , 226.875, 228.75 , 230.625, 232.5  , 234.375, 236.25 , 238.125,\n",
+       "       240.   , 241.875, 243.75 , 245.625, 247.5  , 249.375, 251.25 , 253.125,\n",
+       "       255.   , 256.875, 258.75 , 260.625, 262.5  , 264.375, 266.25 , 268.125,\n",
+       "       270.   , 271.875, 273.75 , 275.625, 277.5  , 279.375, 281.25 , 283.125,\n",
+       "       285.   , 286.875, 288.75 , 290.625, 292.5  , 294.375, 296.25 , 298.125,\n",
+       "       300.   , 301.875, 303.75 , 305.625, 307.5  , 309.375, 311.25 , 313.125,\n",
+       "       315.   , 316.875, 318.75 , 320.625, 322.5  , 324.375, 326.25 , 328.125,\n",
+       "       330.   , 331.875, 333.75 , 335.625, 337.5  , 339.375, 341.25 , 343.125,\n",
+       "       345.   , 346.875, 348.75 , 350.625, 352.5  , 354.375, 356.25 , 358.125])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>degree</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1</div><input id='attrs-d397d667-8864-4fff-ba55-ed67b12f281e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d397d667-8864-4fff-ba55-ed67b12f281e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9a762ef-f2ef-44bc-b0fe-193e2124379b' class='xr-var-data-in' type='checkbox'><label for='data-f9a762ef-f2ef-44bc-b0fe-193e2124379b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(1)</pre></div></li></ul></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'polyfit_coefficients' (year: 133, lat: 145, lon: 192)> Size: 30MB\n",
+       "dask.array<rechunk-merge, shape=(133, 145, 192), dtype=float64, chunksize=(133, 145, 192), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * year     (year) int64 1kB 1850 1851 1852 1853 1854 ... 1979 1980 1981 1982\n",
+       "  * lat      (lat) float64 1kB -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0\n",
+       "  * lon      (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
+       "    degree   int64 8B 1"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "esm15_m20c_trnarr_em#.isel(year=132)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "119b9ef0-0b98-48ab-8b31-ec2dc63e3090",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<xarray.DataArray 'polyfit_coefficients' (year: 133, lat: 145, lon: 192)> Size: 30MB\n",
+      "dask.array<rechunk-merge, shape=(133, 145, 192), dtype=float64, chunksize=(133, 145, 192), chunktype=numpy.ndarray>\n",
+      "Coordinates:\n",
+      "  * year     (year) int64 1kB 1850 1851 1852 1853 1854 ... 1979 1980 1981 1982\n",
+      "  * lat      (lat) float64 1kB -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0\n",
+      "  * lon      (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
+      "    degree   int64 8B 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(esm15_m20c_trnarr_em)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "c3bf7703-c323-4226-b4ed-a49ba24446ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# print(esm15_m20c_trnarr_em)\n",
+    "arr = esm15_m20c_trnarr_em.chunk({\n",
+    "    \"year\": -1,      # keep full year dimension in one chunk\n",
+    "    \"lat\": 50,       # break 145 → 50 + 50 + 45\n",
+    "    \"lon\": 64        # break 192 → 64 + 64 + 64\n",
+    "})\n",
+    "\n",
+    "# arr.to_netcdf(\"/scratch/eg3/cxc548/ACCESS_ESM15_EM_31yr_trn_TS.nc\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8ff0afa7-4b24-46a6-9193-2b278373683f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arr.to_netcdf(\"/scratch/iq82/fc6164/esmvaltool_output/process_data/ACCESS_ESM15_EM_31yr_trn_TS.nc\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "643b1078-9250-4976-8e66-7bfce3457601",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:analysis3-26.01]",
+   "language": "python",
+   "name": "conda-env-analysis3-26.01-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": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 5c9a4bc76ddfb6d97f0dcacfe354b39b4067eb4f Mon Sep 17 00:00:00 2001
From: Felicity Chun <felicity.chun@anu.edu.au>
Date: Wed, 11 Feb 2026 12:11:06 +1100
Subject: [PATCH 2/3] add esmval recipe

---
 data_pre_processing.ipynb                     | 1787 ++++++++++++++++-
 .../recipe_cmip6_ts_trend.yml                 |   79 +
 trends.py => esmval_recipe_trends/trends.py   |   51 +-
 3 files changed, 1878 insertions(+), 39 deletions(-)
 create mode 100644 esmval_recipe_trends/recipe_cmip6_ts_trend.yml
 rename trends.py => esmval_recipe_trends/trends.py (64%)

diff --git a/data_pre_processing.ipynb b/data_pre_processing.ipynb
index 6c3321e..0d48f0b 100644
--- a/data_pre_processing.ipynb
+++ b/data_pre_processing.ipynb
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 43,
    "id": "8aafdcf1-df25-4b8d-8ec1-6b43a186672a",
    "metadata": {},
    "outputs": [],
@@ -61,7 +61,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "f4701774-c590-40cd-a550-de635d96ae07",
+   "id": "a754c167-ea55-460c-bb56-25e0c9964f3d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -687,9 +687,7 @@
    ],
    "source": [
     "\n",
-    "pre_processing_trend_write(cube_esm1_5,\"ACCESS_ESM1_5\", output_path=\"/scratch/iq82/fc6164/esmvaltool_output/process_data/trend\")\n",
-    "\n",
-    "# pre_processing_trend_write(cube_esm1_5,\"ACCESS_ESM1_5\", output_path=\"/g/data/kj13/datasets/visualisation_projects/uncharted_future/trend\")    "
+    "pre_processing_trend_write(cube_esm1_5,\"ACCESS_ESM1_5\", output_path=\"/g/data/kj13/datasets/visualisation_projects/uncharted_future/trend\")    "
    ]
   },
   {
@@ -749,8 +747,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# path=\"/g/data/kj13/datasets/visualisation_projects/uncharted_future/trend\"\n",
-    "path = \"/scratch/iq82/fc6164/esmvaltool_output/process_data/trend\"\n",
+    "path=\"/g/data/kj13/datasets/visualisation_projects/uncharted_future/trend\"\n",
     "\n",
     "dataset_list={\n",
     "    \"CESM2\":cube_cesm2,\n",
@@ -1553,6 +1550,1782 @@
     "path=\"/g/data/kj13/datasets/visualisation_projects/uncharted_future/Rolling_SSTA/\"\n",
     "pre_processing_SSTA_annually_rolling_write_ear5_monthly(cube_era5, \"ERA5-025\", output_path=path, start_year=\"1959\", end_year=\"2009\")"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6bd1d3b-8cb2-4d96-8d85-bd56e34b8f3b",
+   "metadata": {},
+   "source": [
+    "### mean for multiple model trends"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "5762d47b-17e7-4311-a5bf-2301553342b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<defs>\n",
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+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "  border: 2px solid transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 420MB\n",
+       "Dimensions:                (lon: 360, lat: 180, year: 54)\n",
+       "Coordinates:\n",
+       "  * lon                    (lon) float64 3kB 0.5 1.5 2.5 ... 357.5 358.5 359.5\n",
+       "  * lat                    (lat) float64 1kB -89.5 -88.5 -87.5 ... 88.5 89.5\n",
+       "  * year                   (year) int64 432B 2015 2016 2017 ... 2066 2067 2068\n",
+       "    degree                 int64 8B 1\n",
+       "Data variables: (12/15)\n",
+       "    ACCESS-CM2ssp370       (year, lat, lon) float64 28MB dask.array&lt;chunksize=(54, 180, 360), meta=np.ndarray&gt;\n",
+       "    ACCESS-ESM1-5ssp370    (year, lat, lon) float64 28MB dask.array&lt;chunksize=(54, 180, 360), meta=np.ndarray&gt;\n",
+       "    BCC-CSM2-MRssp370      (year, lat, lon) float64 28MB dask.array&lt;chunksize=(54, 180, 360), meta=np.ndarray&gt;\n",
+       "    CAS-ESM2-0ssp370       (year, lat, lon) float64 28MB dask.array&lt;chunksize=(54, 180, 360), meta=np.ndarray&gt;\n",
+       "    CESM2-WACCMssp370      (year, lat, lon) float64 28MB dask.array&lt;chunksize=(9, 180, 360), meta=np.ndarray&gt;\n",
+       "    CMCC-CM2-SR5ssp370     (year, lat, lon) float64 28MB dask.array&lt;chunksize=(54, 180, 360), meta=np.ndarray&gt;\n",
+       "    ...                     ...\n",
+       "    MIROC6ssp370           (year, lat, lon) float64 28MB dask.array&lt;chunksize=(54, 180, 360), meta=np.ndarray&gt;\n",
+       "    MPI-ESM-1-2-HAMssp370  (year, lat, lon) float64 28MB dask.array&lt;chunksize=(9, 180, 360), meta=np.ndarray&gt;\n",
+       "    MRI-ESM2-0ssp370       (year, lat, lon) float64 28MB dask.array&lt;chunksize=(54, 180, 360), meta=np.ndarray&gt;\n",
+       "    NorESM2-LMssp370       (year, lat, lon) float64 28MB dask.array&lt;chunksize=(8, 180, 360), meta=np.ndarray&gt;\n",
+       "    NorESM2-MMssp370       (year, lat, lon) float64 28MB dask.array&lt;chunksize=(54, 180, 360), meta=np.ndarray&gt;\n",
+       "    TaiESM1ssp370          (year, lat, lon) float64 28MB dask.array&lt;chunksize=(54, 180, 360), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-edf56101-cd33-4826-b4d7-664af53e481c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-edf56101-cd33-4826-b4d7-664af53e481c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lon</span>: 360</li><li><span class='xr-has-index'>lat</span>: 180</li><li><span class='xr-has-index'>year</span>: 54</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8c8a86c5-5b73-4b08-bae8-dc426a35f34e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8c8a86c5-5b73-4b08-bae8-dc426a35f34e' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5 1.5 2.5 ... 357.5 358.5 359.5</div><input id='attrs-c7ac2a91-e6ad-4558-89d2-ba06fdbe68d7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c7ac2a91-e6ad-4558-89d2-ba06fdbe68d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f84b3efc-5cbd-4023-a93e-92992bdf98bd' class='xr-var-data-in' type='checkbox'><label for='data-f84b3efc-5cbd-4023-a93e-92992bdf98bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees</dd></dl></div><div class='xr-var-data'><pre>array([  0.5,   1.5,   2.5, ..., 357.5, 358.5, 359.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.5 -88.5 -87.5 ... 88.5 89.5</div><input id='attrs-287e368b-6ab2-4dfd-9851-4d4ceea7f0ee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-287e368b-6ab2-4dfd-9851-4d4ceea7f0ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a30cf8f5-9007-454b-bea9-153e87364c07' class='xr-var-data-in' type='checkbox'><label for='data-a30cf8f5-9007-454b-bea9-153e87364c07' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees</dd></dl></div><div class='xr-var-data'><pre>array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
+       "       -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,\n",
+       "       -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,\n",
+       "       -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,\n",
+       "       -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,\n",
+       "       -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,\n",
+       "       -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,\n",
+       "       -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,\n",
+       "        -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,\n",
+       "         0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
+       "        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,\n",
+       "        20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,\n",
+       "        30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,\n",
+       "        40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,\n",
+       "        50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,\n",
+       "        60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,\n",
+       "        70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,\n",
+       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2015 2016 2017 ... 2066 2067 2068</div><input id='attrs-7ca0d7c0-7e98-48f2-9c83-edad5ce7e350' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7ca0d7c0-7e98-48f2-9c83-edad5ce7e350' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8fa7edb-908b-4e70-992d-b13a6737ca5c' class='xr-var-data-in' type='checkbox'><label for='data-b8fa7edb-908b-4e70-992d-b13a6737ca5c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026,\n",
+       "       2027, 2028, 2029, 2030, 2031, 2032, 2033, 2034, 2035, 2036, 2037, 2038,\n",
+       "       2039, 2040, 2041, 2042, 2043, 2044, 2045, 2046, 2047, 2048, 2049, 2050,\n",
+       "       2051, 2052, 2053, 2054, 2055, 2056, 2057, 2058, 2059, 2060, 2061, 2062,\n",
+       "       2063, 2064, 2065, 2066, 2067, 2068])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>degree</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1</div><input id='attrs-6df1f4d9-ed99-4d8c-9d1e-f960580977d8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6df1f4d9-ed99-4d8c-9d1e-f960580977d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25289096-bfa1-4dd2-8309-4569e55a7445' class='xr-var-data-in' type='checkbox'><label for='data-25289096-bfa1-4dd2-8309-4569e55a7445' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(1)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6c6523bd-01d6-49da-9cc2-0c50866c7519' class='xr-section-summary-in' type='checkbox'  ><label for='section-6c6523bd-01d6-49da-9cc2-0c50866c7519' class='xr-section-summary' >Data variables: <span>(15)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>ACCESS-CM2ssp370</span></div><div class='xr-var-dims'>(year, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(54, 180, 360), meta=np.ndarray&gt;</div><input id='attrs-70fb7100-bb99-4632-82f6-38111c47e2e1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-70fb7100-bb99-4632-82f6-38111c47e2e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2fafca0b-10f5-4fb6-824b-52cfa66f42f2' class='xr-var-data-in' type='checkbox'><label for='data-2fafca0b-10f5-4fb6-824b-52cfa66f42f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 26.70 MiB </td>\n",
+       "                        <td> 26.70 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (54, 180, 360) </td>\n",
+       "                        <td> (54, 180, 360) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"203\" height=\"133\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
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+       "  <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"83\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"93.944923\" y=\"103.944923\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >360</text>\n",
+       "  <text x=\"173.944923\" y=\"53.944923\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,173.944923,53.944923)\">180</text>\n",
+       "  <text x=\"11.972462\" y=\"91.972462\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,11.972462,91.972462)\">54</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACCESS-ESM1-5ssp370</span></div><div class='xr-var-dims'>(year, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(54, 180, 360), meta=np.ndarray&gt;</div><input id='attrs-0305129f-4a44-4463-bf2e-aa06f00c2aca' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0305129f-4a44-4463-bf2e-aa06f00c2aca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79b14ca8-9a56-4ce1-81f7-7691a546bd6d' class='xr-var-data-in' type='checkbox'><label for='data-79b14ca8-9a56-4ce1-81f7-7691a546bd6d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 26.70 MiB </td>\n",
+       "                        <td> 26.70 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (54, 180, 360) </td>\n",
+       "                        <td> (54, 180, 360) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"203\" height=\"133\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
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+       "\n",
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+       "\n",
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+       "\n",
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+       "  <text x=\"173.944923\" y=\"53.944923\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,173.944923,53.944923)\">180</text>\n",
+       "  <text x=\"11.972462\" y=\"91.972462\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,11.972462,91.972462)\">54</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>BCC-CSM2-MRssp370</span></div><div class='xr-var-dims'>(year, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(54, 180, 360), meta=np.ndarray&gt;</div><input id='attrs-576dfaf5-e3f9-43cb-902b-e01705ad0c43' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-576dfaf5-e3f9-43cb-902b-e01705ad0c43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c1a95d6-f22e-417d-88d0-19ab1e43f43d' class='xr-var-data-in' type='checkbox'><label for='data-6c1a95d6-f22e-417d-88d0-19ab1e43f43d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 26.70 MiB </td>\n",
+       "                        <td> 26.70 MiB </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CAS-ESM2-0ssp370</span></div><div class='xr-var-dims'>(year, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(54, 180, 360), meta=np.ndarray&gt;</div><input id='attrs-97c50541-8403-42c1-a6fc-4dad74191ba9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-97c50541-8403-42c1-a6fc-4dad74191ba9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f14a27c8-53fb-427a-9115-376bb6014ff4' class='xr-var-data-in' type='checkbox'><label for='data-f14a27c8-53fb-427a-9115-376bb6014ff4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 26.70 MiB </td>\n",
+       "                        <td> 26.70 MiB </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (54, 180, 360) </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CESM2-WACCMssp370</span></div><div class='xr-var-dims'>(year, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(9, 180, 360), meta=np.ndarray&gt;</div><input id='attrs-b22329ee-1fc7-4baa-ae7e-787fa5b36bf3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b22329ee-1fc7-4baa-ae7e-787fa5b36bf3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4bec88c-cc05-407f-8e2b-b22f5cfe4d2f' class='xr-var-data-in' type='checkbox'><label for='data-a4bec88c-cc05-407f-8e2b-b22f5cfe4d2f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 26.70 MiB </td>\n",
+       "                        <td> 4.45 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (9, 180, 360) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 6 chunks in 13 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CMCC-CM2-SR5ssp370</span></div><div class='xr-var-dims'>(year, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(54, 180, 360), meta=np.ndarray&gt;</div><input id='attrs-d0b52d5a-d802-475b-8042-68179f73f33a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d0b52d5a-d802-475b-8042-68179f73f33a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fde99552-711f-4e2e-81c1-a1ec3e7af5b1' class='xr-var-data-in' type='checkbox'><label for='data-fde99552-711f-4e2e-81c1-a1ec3e7af5b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 26.70 MiB </td>\n",
+       "                        <td> 26.70 MiB </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CMCC-ESM2ssp370</span></div><div class='xr-var-dims'>(year, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(54, 180, 360), meta=np.ndarray&gt;</div><input id='attrs-616d6aaf-2a01-4ad6-8a20-c2539a161823' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-616d6aaf-2a01-4ad6-8a20-c2539a161823' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cac64b33-ae0b-4e06-b568-a1a7d66ea5c6' class='xr-var-data-in' type='checkbox'><label for='data-cac64b33-ae0b-4e06-b568-a1a7d66ea5c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 26.70 MiB </td>\n",
+       "                        <td> 26.70 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (54, 180, 360) </td>\n",
+       "                        <td> (54, 180, 360) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CanESM5ssp370</span></div><div class='xr-var-dims'>(year, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(54, 180, 360), meta=np.ndarray&gt;</div><input id='attrs-ae532e54-f01f-4ab8-b994-7e64f01e7b5e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ae532e54-f01f-4ab8-b994-7e64f01e7b5e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef4b1e9a-9dc1-4be9-b1b3-9801a3fb3a45' class='xr-var-data-in' type='checkbox'><label for='data-ef4b1e9a-9dc1-4be9-b1b3-9801a3fb3a45' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 26.70 MiB </td>\n",
+       "                        <td> 26.70 MiB </td>\n",
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+       "                        <th> Shape </th>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                    <tr>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FGOALS-g3ssp370</span></div><div class='xr-var-dims'>(year, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(54, 180, 360), meta=np.ndarray&gt;</div><input id='attrs-d76a51aa-e6a9-44da-9c67-e8c82ae0ac9d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d76a51aa-e6a9-44da-9c67-e8c82ae0ac9d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-972a33a9-be71-48e3-80f9-fcd668831a44' class='xr-var-data-in' type='checkbox'><label for='data-972a33a9-be71-48e3-80f9-fcd668831a44' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
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+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 26.70 MiB </td>\n",
+       "                        <td> 26.70 MiB </td>\n",
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+       "                    <tr>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                        <th> Data type </th>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MIROC6ssp370</span></div><div class='xr-var-dims'>(year, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(54, 180, 360), meta=np.ndarray&gt;</div><input id='attrs-ff1960e1-bf25-4ae8-92fb-7f9f9207a93b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ff1960e1-bf25-4ae8-92fb-7f9f9207a93b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2385bf6f-95d8-4213-a8a5-5f26643c5acc' class='xr-var-data-in' type='checkbox'><label for='data-2385bf6f-95d8-4213-a8a5-5f26643c5acc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
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+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 26.70 MiB </td>\n",
+       "                        <td> 26.70 MiB </td>\n",
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+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (54, 180, 360) </td>\n",
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+       "                    <tr>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MPI-ESM-1-2-HAMssp370</span></div><div class='xr-var-dims'>(year, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(9, 180, 360), meta=np.ndarray&gt;</div><input id='attrs-f67bc8f8-4381-4493-86fb-380474e1e72a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f67bc8f8-4381-4493-86fb-380474e1e72a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab28cea7-bb13-4ac3-955c-d1a31e275b69' class='xr-var-data-in' type='checkbox'><label for='data-ab28cea7-bb13-4ac3-955c-d1a31e275b69' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 26.70 MiB </td>\n",
+       "                        <td> 4.45 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (54, 180, 360) </td>\n",
+       "                        <td> (9, 180, 360) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 6 chunks in 13 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MRI-ESM2-0ssp370</span></div><div class='xr-var-dims'>(year, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(54, 180, 360), meta=np.ndarray&gt;</div><input id='attrs-e32d5c60-fb07-4d95-928e-0d2994bc7ca6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e32d5c60-fb07-4d95-928e-0d2994bc7ca6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5268e686-9d5c-4c6c-bea8-0fbaf8ddfbae' class='xr-var-data-in' type='checkbox'><label for='data-5268e686-9d5c-4c6c-bea8-0fbaf8ddfbae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 26.70 MiB </td>\n",
+       "                        <td> 26.70 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (54, 180, 360) </td>\n",
+       "                        <td> (54, 180, 360) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NorESM2-LMssp370</span></div><div class='xr-var-dims'>(year, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(8, 180, 360), meta=np.ndarray&gt;</div><input id='attrs-ea981f8b-87c9-4cc9-ba92-3c332524cf54' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ea981f8b-87c9-4cc9-ba92-3c332524cf54' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71fa88d5-be3b-4ab6-b83f-af99b0bc91fa' class='xr-var-data-in' type='checkbox'><label for='data-71fa88d5-be3b-4ab6-b83f-af99b0bc91fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 26.70 MiB </td>\n",
+       "                        <td> 3.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (54, 180, 360) </td>\n",
+       "                        <td> (8, 180, 360) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7 chunks in 13 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NorESM2-MMssp370</span></div><div class='xr-var-dims'>(year, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(54, 180, 360), meta=np.ndarray&gt;</div><input id='attrs-3a29092e-3c3e-4350-9655-65c415759e60' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3a29092e-3c3e-4350-9655-65c415759e60' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1003b2a5-8a08-4f91-946e-e4c923791889' class='xr-var-data-in' type='checkbox'><label for='data-1003b2a5-8a08-4f91-946e-e4c923791889' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 26.70 MiB </td>\n",
+       "                        <td> 26.70 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (54, 180, 360) </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "                </tbody>\n",
+       "            </table>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TaiESM1ssp370</span></div><div class='xr-var-dims'>(year, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(54, 180, 360), meta=np.ndarray&gt;</div><input id='attrs-1784c864-575c-4260-b806-df14c607f156' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1784c864-575c-4260-b806-df14c607f156' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc7dab35-6870-4db4-8985-a5fdfd06c5c6' class='xr-var-data-in' type='checkbox'><label for='data-dc7dab35-6870-4db4-8985-a5fdfd06c5c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 26.70 MiB </td>\n",
+       "                        <td> 26.70 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (54, 180, 360) </td>\n",
+       "                        <td> (54, 180, 360) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"203\" height=\"133\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n",
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+       "\n",
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+       "\n",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "  <text x=\"11.972462\" y=\"91.972462\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,11.972462,91.972462)\">54</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-7b2bf2fc-ecce-43c6-85c0-3dd0cb49310a' class='xr-section-summary-in' type='checkbox'  ><label for='section-7b2bf2fc-ecce-43c6-85c0-3dd0cb49310a' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-a1bb1e80-a2f6-4f71-9ae7-397704ff0103' class='xr-index-data-in' type='checkbox'/><label for='index-a1bb1e80-a2f6-4f71-9ae7-397704ff0103' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
+       "       ...\n",
+       "       350.5, 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=360))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-b98e13e0-4c39-4dc1-bfa0-a14c50a93cd4' class='xr-index-data-in' type='checkbox'/><label for='index-b98e13e0-4c39-4dc1-bfa0-a14c50a93cd4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
+       "       ...\n",
+       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=180))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>year</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-7369d347-a6a6-4bce-aa42-c93a3108c1e2' class='xr-index-data-in' type='checkbox'/><label for='index-7369d347-a6a6-4bce-aa42-c93a3108c1e2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026,\n",
+       "       2027, 2028, 2029, 2030, 2031, 2032, 2033, 2034, 2035, 2036, 2037, 2038,\n",
+       "       2039, 2040, 2041, 2042, 2043, 2044, 2045, 2046, 2047, 2048, 2049, 2050,\n",
+       "       2051, 2052, 2053, 2054, 2055, 2056, 2057, 2058, 2059, 2060, 2061, 2062,\n",
+       "       2063, 2064, 2065, 2066, 2067, 2068],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;year&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9a8fc20a-1d03-455e-8e3c-3eea7af320b6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9a8fc20a-1d03-455e-8e3c-3eea7af320b6' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 420MB\n",
+       "Dimensions:                (lon: 360, lat: 180, year: 54)\n",
+       "Coordinates:\n",
+       "  * lon                    (lon) float64 3kB 0.5 1.5 2.5 ... 357.5 358.5 359.5\n",
+       "  * lat                    (lat) float64 1kB -89.5 -88.5 -87.5 ... 88.5 89.5\n",
+       "  * year                   (year) int64 432B 2015 2016 2017 ... 2066 2067 2068\n",
+       "    degree                 int64 8B 1\n",
+       "Data variables: (12/15)\n",
+       "    ACCESS-CM2ssp370       (year, lat, lon) float64 28MB dask.array<chunksize=(54, 180, 360), meta=np.ndarray>\n",
+       "    ACCESS-ESM1-5ssp370    (year, lat, lon) float64 28MB dask.array<chunksize=(54, 180, 360), meta=np.ndarray>\n",
+       "    BCC-CSM2-MRssp370      (year, lat, lon) float64 28MB dask.array<chunksize=(54, 180, 360), meta=np.ndarray>\n",
+       "    CAS-ESM2-0ssp370       (year, lat, lon) float64 28MB dask.array<chunksize=(54, 180, 360), meta=np.ndarray>\n",
+       "    CESM2-WACCMssp370      (year, lat, lon) float64 28MB dask.array<chunksize=(9, 180, 360), meta=np.ndarray>\n",
+       "    CMCC-CM2-SR5ssp370     (year, lat, lon) float64 28MB dask.array<chunksize=(54, 180, 360), meta=np.ndarray>\n",
+       "    ...                     ...\n",
+       "    MIROC6ssp370           (year, lat, lon) float64 28MB dask.array<chunksize=(54, 180, 360), meta=np.ndarray>\n",
+       "    MPI-ESM-1-2-HAMssp370  (year, lat, lon) float64 28MB dask.array<chunksize=(9, 180, 360), meta=np.ndarray>\n",
+       "    MRI-ESM2-0ssp370       (year, lat, lon) float64 28MB dask.array<chunksize=(54, 180, 360), meta=np.ndarray>\n",
+       "    NorESM2-LMssp370       (year, lat, lon) float64 28MB dask.array<chunksize=(8, 180, 360), meta=np.ndarray>\n",
+       "    NorESM2-MMssp370       (year, lat, lon) float64 28MB dask.array<chunksize=(54, 180, 360), meta=np.ndarray>\n",
+       "    TaiESM1ssp370          (year, lat, lon) float64 28MB dask.array<chunksize=(54, 180, 360), meta=np.ndarray>"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import xarray as xr\n",
+    "\n",
+    "# dir_trends = '/g/data/kj13/datasets/visualisation_projects/uncharted_future/trend/models/*.nc'\n",
+    "dir_trends = '/g/data/kj13/datasets/visualisation_projects/uncharted_future/trend/models/future/*.nc'\n",
+    "\n",
+    "def _name_var(x):\n",
+    "    name = x.encoding['source'].split('/')[-1]\n",
+    "    name = ''.join(name.split('_')[:2])\n",
+    "    x.assign_coords({'source':name})\n",
+    "    return x.rename_vars({'polyfit_coefficients':name})\n",
+    "\n",
+    "mfds = xr.open_mfdataset(dir_trends, preprocess=_name_var)\n",
+    "mfds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "1194718a-40a9-484f-9a8e-fce91b9df380",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean = mfds.to_array(dim='trend').mean('trend')\n",
+    "start = mean.year[0].values\n",
+    "end = mean.year[-1].values\n",
+    "\n",
+    "output_path = '/g/data/kj13/datasets/visualisation_projects/uncharted_future/trend'\n",
+    "mean.to_netcdf(f\"{output_path}/future_cmip6_model_ts_31years_trend_{start}_{end}.nc\")"
+   ]
   }
  ],
  "metadata": {
diff --git a/esmval_recipe_trends/recipe_cmip6_ts_trend.yml b/esmval_recipe_trends/recipe_cmip6_ts_trend.yml
new file mode 100644
index 0000000..b241521
--- /dev/null
+++ b/esmval_recipe_trends/recipe_cmip6_ts_trend.yml
@@ -0,0 +1,79 @@
+# ESMValTool
+# 
+---
+documentation:
+  description: multi model cmip6 statistics
+  title: CMIP6 ts trends
+  authors:
+    - chun_felicity
+  maintainer:
+    - chun_felicity
+
+# datasets:
+  # - project: CMIP6
+  #   exp: SSP370
+  #   dataset: '*'
+  #   institute: '*'
+  #   ensemble: 'r*i1p1f1'
+  #   grid: gn
+
+overlap: &models
+  - {dataset: ACCESS-ESM1-5, project: CMIP6, ensemble: '*', grid: gn}
+  - {dataset: ACCESS-CM2, project: CMIP6, ensemble: '*', grid: gn}
+  - {dataset: BCC-CSM2-MR, project: CMIP6, ensemble: '*', grid: gn}
+  - {dataset: CAS-ESM2-0, project: CMIP6, ensemble: '*', grid: gn}
+  - {dataset: CESM2-WACCM, project: CMIP6, ensemble: '*', grid: gn} # missing years in ensembles r2,3
+  - {dataset: CMCC-CM2-SR5, project: CMIP6, ensemble: '*', grid: gn}
+  - {dataset: CMCC-ESM2, project: CMIP6, ensemble: '*', grid: gn}
+  - {dataset: CanESM5, project: CMIP6, ensemble: '*', grid: gn}
+  - {dataset: FGOALS-g3, project: CMIP6, ensemble: '*', grid: gn}
+  # - {dataset: IITM-ESM, project: CMIP6, ensemble: '*', grid: gn, start_year: '*', end_year: 2098} ##not in historical
+  - {dataset: MIROC6, project: CMIP6, ensemble: '*', grid: gn}
+  - {dataset: MPI-ESM-1-2-HAM, project: CMIP6, ensemble: '*', grid: gn}
+  - {dataset: MRI-ESM2-0, project: CMIP6, ensemble: '*', grid: gn}
+  - {dataset: NorESM2-LM, project: CMIP6, ensemble: '*', grid: gn} # missing years in ensembles r2,3
+  - {dataset: NorESM2-MM, project: CMIP6, ensemble: '*', grid: gn}
+  - {dataset: TaiESM1, project: CMIP6, ensemble: '*', grid: gn}
+
+
+preprocessors:
+  all_preprocessor:
+    regrid:
+      target_grid: 1x1
+      scheme: linear
+    ensemble_statistics:
+      statistics: [mean]
+
+
+diagnostics:
+  diagnostic_metrics:
+    description: run ts in groups future, historical and obs
+    variables:
+      ts_future:
+        short_name: ts
+        mip: Amon
+        activity: ScenarioMIP
+        exp: ssp370
+        timerange: '*'
+        preprocessor: all_preprocessor
+        additional_datasets: *models
+
+      ts_obs:
+        short_name: ts
+        mip: Amon
+        timerange: '*'
+        additional_datasets:
+          - {dataset: HadISST, project: OBS, type: reanaly, tier: 2, version: 1}
+
+      ts:
+        mip: Amon
+        exp: historical
+        timerange: '*'
+        preprocessor: all_preprocessor
+        additional_datasets: *models
+
+    scripts:
+      trends:
+        script: trends.py #update filepath or run in folder
+
+
diff --git a/trends.py b/esmval_recipe_trends/trends.py
similarity index 64%
rename from trends.py
rename to esmval_recipe_trends/trends.py
index 61c6bae..89eeb4c 100755
--- a/trends.py
+++ b/esmval_recipe_trends/trends.py
@@ -14,7 +14,8 @@
 )
 from esmvalcore.preprocessor import (
     climate_statistics,
-    regrid
+    regrid,
+    concatenate
 )
 
 
@@ -56,48 +57,34 @@ def calc_trend(xarr, start, end, length):
     return trnarr
 
 
-def get_provenance_record(caption, ancestor_files):
-    """Create a provenance record describing the diagnostic data and plot."""
-
-    record = {
-        "caption": caption,
-        "statistics": ["other"],
-        "domains": ["polar"],
-        "plot_types": ["map"],
-        "authors": [
-            "chun_felicity",
-        ],
-        "references": [
-            "access-nri",
-        ],
-        "ancestors": ancestor_files,
-    }
-    return record
-
-
 def main(cfg):
     """run on all datasets"""
 
     input_data = cfg["input_data"].values()
+    ## group by dataset to concatenate future to historical after ensemble means
+    # for dataset, attributes in group_metadata(input_data,"dataset", sort='exp').items():
+    #     in_files = [d['filename'] for d in attributes]
+    #     cubes = iris.load(in_files)
+    #     start_year = min([d['start_year'] for d in attributes]) 
+    #     end_year = max([d['end_year'] for d in attributes])
+        
+    #     logger.info(f"Loaded {len(cubes)} cubes for {dataset}, {start_year} - {end_year}")
+    #     iris.util.equalise_attributes(cubes)
+    #     cube = concatenate(cubes)
 
+    ## keep future variable separate 
     for dataset in input_data:
-        # Load the data
         input_file = dataset['filename']
         name = dataset['dataset'] + '_' + dataset['exp']
         cube = iris.load_cube(input_file)
+        start_year, end_year = dataset['start_year'], dataset['end_year']
+        ##
 
-        data=xr.DataArray.from_iris(cube)
-        ds_model_ann=data.groupby('time.year').mean('time')
+        data = xr.DataArray.from_iris(cube)
+        ds_model_ann = data.groupby('time.year').mean('time')
+        ds_model_ann_trend_31 = calc_trend(ds_model_ann, start_year, end_year-31, 31)
 
-        ds_model_ann_trend_31 = calc_trend(ds_model_ann, dataset['start_year'], dataset['end_year']-31, 31)
-        
-        # Save output
-        # prov_record = get_provenance_record(
-        #     f'Barotropic streamfunction from {name}.',
-        #     [input_file],
-        # )
-        # get work dir
-        ds_model_ann_trend_31.to_netcdf(os.path.join(cfg["work_dir"], f"{name}_trend_31yr{dataset['start_year']}_{dataset['end_year']-31}.nc"))
+        ds_model_ann_trend_31.to_netcdf(os.path.join(cfg["work_dir"], f"{dataset}_trend_31yr{start_year}_{end_year-31}.nc"))
 
 if __name__ == "__main__":
     with run_diagnostic() as config:

From f7c310d0828e58648c75645b9e9d7af0cc2b84bf Mon Sep 17 00:00:00 2001
From: Felicity Chun <felicity.chun@anu.edu.au>
Date: Wed, 11 Feb 2026 15:26:23 +1100
Subject: [PATCH 3/3] remove temp notebook from Christine

---
 troppac_trends_with_n34.ipynb | 3383 ---------------------------------
 1 file changed, 3383 deletions(-)
 delete mode 100644 troppac_trends_with_n34.ipynb

diff --git a/troppac_trends_with_n34.ipynb b/troppac_trends_with_n34.ipynb
deleted file mode 100644
index fb4664b..0000000
--- a/troppac_trends_with_n34.ipynb
+++ /dev/null
@@ -1,3383 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "556234b8-0af5-4392-8676-0e96b71700fe",
-   "metadata": {},
-   "source": [
-    "# this script calculates\n",
-    "- global SST anomalies\n",
-    "- global SST trends over a sliding window\n",
-    "- \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "25aeaf4d-70c0-4308-9e86-30e867f5c53f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#from clef.code import *\n",
-    "import seaborn as sns\n",
-    "\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from matplotlib import pyplot as plt\n",
-    "import glob\n",
-    "import os\n",
-    "import xarray as xr\n",
-    "# from cmip_catalog import *\n",
-    "from tqdm import tqdm_notebook as tqdm\n",
-    "import dask.bag as dasb\n",
-    "from dask.diagnostics import ProgressBar\n",
-    "from scipy import signal\n",
-    "from scipy.stats import spearmanr, norm\n",
-    "import cartopy.crs as ccrs\n",
-    "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n",
-    "import xskillscore as xskill\n",
-    "\n",
-    "import sys\n",
-    "# sys.path.append('/home/548/cxc548/lib/python/bom-climate-change-variability-and-extreme-toolbox')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "aa67729b-1fe9-44d0-8095-b40545c60e79",
-   "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-74a1e173-0306-11f1-b5fd-000003a6fe80</p>\n",
-       "        <table style=\"width: 100%; text-align: left;\">\n",
-       "\n",
-       "        <tr>\n",
-       "        \n",
-       "            <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
-       "            <td style=\"text-align: left;\"><strong>Cluster type:</strong> distributed.LocalCluster</td>\n",
-       "        \n",
-       "        </tr>\n",
-       "\n",
-       "        \n",
-       "            <tr>\n",
-       "                <td style=\"text-align: left;\">\n",
-       "                    <strong>Dashboard: </strong> <a href=\"/proxy/8787/status\" target=\"_blank\">/proxy/8787/status</a>\n",
-       "                </td>\n",
-       "                <td style=\"text-align: left;\"></td>\n",
-       "            </tr>\n",
-       "        \n",
-       "\n",
-       "        </table>\n",
-       "\n",
-       "        \n",
-       "            <button style=\"margin-bottom: 12px;\" data-commandlinker-command=\"dask:populate-and-launch-layout\" data-commandlinker-args='{\"url\": \"/proxy/8787/status\" }'>\n",
-       "                Launch dashboard in JupyterLab\n",
-       "            </button>\n",
-       "        \n",
-       "\n",
-       "        \n",
-       "            <details>\n",
-       "            <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
-       "            <div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
-       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\">\n",
-       "    </div>\n",
-       "    <div style=\"margin-left: 48px;\">\n",
-       "        <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">LocalCluster</h3>\n",
-       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">a6fa77a0</p>\n",
-       "        <table style=\"width: 100%; text-align: left;\">\n",
-       "            <tr>\n",
-       "                <td style=\"text-align: left;\">\n",
-       "                    <strong>Dashboard:</strong> <a href=\"/proxy/8787/status\" target=\"_blank\">/proxy/8787/status</a>\n",
-       "                </td>\n",
-       "                <td style=\"text-align: left;\">\n",
-       "                    <strong>Workers:</strong> 7\n",
-       "                </td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                <td style=\"text-align: left;\">\n",
-       "                    <strong>Total threads:</strong> 7\n",
-       "                </td>\n",
-       "                <td style=\"text-align: left;\">\n",
-       "                    <strong>Total memory:</strong> 32.00 GiB\n",
-       "                </td>\n",
-       "            </tr>\n",
-       "            \n",
-       "            <tr>\n",
-       "    <td style=\"text-align: left;\"><strong>Status:</strong> running</td>\n",
-       "    <td style=\"text-align: left;\"><strong>Using processes:</strong> True</td>\n",
-       "</tr>\n",
-       "\n",
-       "            \n",
-       "        </table>\n",
-       "\n",
-       "        <details>\n",
-       "            <summary style=\"margin-bottom: 20px;\">\n",
-       "                <h3 style=\"display: inline;\">Scheduler Info</h3>\n",
-       "            </summary>\n",
-       "\n",
-       "            <div style=\"\">\n",
-       "    <div>\n",
-       "        <div style=\"width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;\"> </div>\n",
-       "        <div style=\"margin-left: 48px;\">\n",
-       "            <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n",
-       "            <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-d4c243b0-88b6-4ac6-9a0e-ed40c0276304</p>\n",
-       "            <table style=\"width: 100%; text-align: left;\">\n",
-       "                <tr>\n",
-       "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Comm:</strong> tcp://127.0.0.1:41277\n",
-       "                    </td>\n",
-       "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Workers:</strong> 0 \n",
-       "                    </td>\n",
-       "                </tr>\n",
-       "                <tr>\n",
-       "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Dashboard:</strong> <a href=\"/proxy/8787/status\" target=\"_blank\">/proxy/8787/status</a>\n",
-       "                    </td>\n",
-       "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Total threads:</strong> 0\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> 0 B\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://127.0.0.1:39543\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 1\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"/proxy/35657/status\" target=\"_blank\">/proxy/35657/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 4.57 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:34571\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/159992262.gadi-pbs/dask-scratch-space/worker-m12d0a6s\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://127.0.0.1:46691\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 1\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"/proxy/44813/status\" target=\"_blank\">/proxy/44813/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 4.57 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:37351\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/159992262.gadi-pbs/dask-scratch-space/worker-f9hrw_y1\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://127.0.0.1:37397\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 1\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"/proxy/39739/status\" target=\"_blank\">/proxy/39739/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 4.57 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:42475\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/159992262.gadi-pbs/dask-scratch-space/worker-r5z4tfgt\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://127.0.0.1:34015\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 1\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"/proxy/33147/status\" target=\"_blank\">/proxy/33147/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 4.57 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:41551\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/159992262.gadi-pbs/dask-scratch-space/worker-6ult422y\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://127.0.0.1:43643\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 1\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"/proxy/36955/status\" target=\"_blank\">/proxy/36955/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 4.57 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:34489\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/159992262.gadi-pbs/dask-scratch-space/worker-8exfxtfy\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://127.0.0.1:45151\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 1\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"/proxy/32877/status\" target=\"_blank\">/proxy/32877/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 4.57 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:45353\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/159992262.gadi-pbs/dask-scratch-space/worker-7u8hl8lw\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://127.0.0.1:42951\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 1\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"/proxy/40455/status\" target=\"_blank\">/proxy/40455/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 4.57 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:42337\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/159992262.gadi-pbs/dask-scratch-space/worker-0zfxhzik\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://127.0.0.1:41277' processes=7 threads=7, memory=32.00 GiB>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from dask.distributed import Client\n",
-    "client = Client(threads_per_worker = 1)\n",
-    "client"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "5d87bac1-d70a-4951-8fed-bcbe9da2d8ea",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#-- Function to return an array of sliding trends starting at 'start', ending at 'end'\n",
-    "# with length 'length'\n",
-    "\n",
-    "# def calc_trend(xarr, start,end,length):\n",
-    "#     #loop over years\n",
-    "#     for i in np.arange(start,end,1):\n",
-    "#         startyear=i\n",
-    "#         endyear=i+length\n",
-    "#         #extract window\n",
-    "#         arr = xarr.sel(year=slice(str(startyear),str(endyear)))\n",
-    "    \n",
-    "#         timedim=np.arange(0,len(arr.year))\n",
-    "#         arr['year'] = timedim\n",
-    "#         #call polyfit to calculate trend over the window. multiply by 10 so we get units of trend per decade\n",
-    "#         trnd = ((arr.polyfit(dim='year',deg=1,skipna=True)).polyfit_coefficients.isel(degree=0))*10\n",
-    " \n",
-    "#         #if it's the first year, copy the trend to trnarr\n",
-    "#         if i == start:\n",
-    "#             trnarr = trnd\n",
-    "#         #if it's the subsequent years, concatenate the trend to trnarr\n",
-    "#         else:\n",
-    "#             trnarr = xr.concat([trnarr,trnd],dim='year')\n",
-    "\n",
-    "#     trnarr['year'] = np.arange(start,end,1)\n",
-    "\n",
-    "#     return trnarr\n",
-    "\n",
-    "def calc_trend(xarr, start, end, length):\n",
-    "    \"\"\"\n",
-    "    Compute running linear trends over windows of size `length` (years)\n",
-    "    from `start` to `end - 1`, returning trend per decade.\n",
-    "\n",
-    "    xarr must have a coordinate \"year\".\n",
-    "    \"\"\"\n",
-    "    \n",
-    "    trend_list = []\n",
-    "\n",
-    "    for yr in range(start, end):\n",
-    "        # window\n",
-    "        win = xarr.sel(year=slice(str(yr), str(yr + length-1)))\n",
-    "\n",
-    "        # replace year coordinate with integers 0..N-1 for polyfit\n",
-    "        t = np.arange(win.year.size)\n",
-    "        win = win.assign_coords(year=t)\n",
-    "\n",
-    "        # polyfit: slope * 10 for trend per decade\n",
-    "        tr = (win.polyfit(dim=\"year\", deg=1, skipna=True)\n",
-    "                 .polyfit_coefficients\n",
-    "                 .isel(degree=0) * 10)\n",
-    "        \n",
-    "        trend_list.append(tr)\n",
-    "\n",
-    "    # concatenate trends along a new \"year\" dimension\n",
-    "    trnarr = xr.concat(trend_list, dim=\"year\")\n",
-    "\n",
-    "    # restore actual years\n",
-    "    trnarr = trnarr.assign_coords(year=np.arange(start, end))\n",
-    "\n",
-    "    return trnarr"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "619dc84e-3303-467e-b68e-6f8466c5689f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def detrend_1d(y, deg=1):\n",
-    "    \"\"\"\n",
-    "    Removes polynomial trend from a 1D array while preserving the mean.\n",
-    "\n",
-    "    Parameters:\n",
-    "        y (np.ndarray): Input 1D data array.\n",
-    "        deg (int): Degree of the polynomial to fit (default is 1 for linear detrending).\n",
-    "\n",
-    "    Returns:\n",
-    "        np.ndarray: Detrended data with original mean retained.\n",
-    "    \"\"\"\n",
-    "    x = np.arange(y.size) \n",
-    "    z = np.polyfit(x, y, deg)  \n",
-    "    trend = np.polyval(z, x)  \n",
-    "    return y - trend + np.mean(y)  # Retain mean after detrending\n",
-    "\n",
-    "def detrend_wrap(da, deg=1):\n",
-    "    \"\"\"\n",
-    "    Applies 1D polynomial detrending across the 'time' dimension of an xarray DataArray.\n",
-    "\n",
-    "    Parameters:\n",
-    "        da (xr.DataArray): Input data with a 'time' dimension.\n",
-    "        deg (int): Degree of the polynomial to fit (default is 1).\n",
-    "\n",
-    "    Returns:\n",
-    "        xr.DataArray: Detrended data.\n",
-    "    \"\"\"\n",
-    "    return xr.apply_ufunc(\n",
-    "        detrend_1d, da,\n",
-    "        input_core_dims=[['time']],  \n",
-    "        vectorize=True,  \n",
-    "        dask='parallelized',  \n",
-    "        kwargs={'deg': deg}  \n",
-    "    )\n",
-    "\n",
-    "def detrend_monthly(da, deg=1):\n",
-    "    \"\"\"\n",
-    "    Applies polynomial detrending separately to each calendar month across years.\n",
-    "\n",
-    "    Parameters:\n",
-    "        da (xr.DataArray): Input data with 'time' dimension.\n",
-    "        deg (int): Degree of the polynomial to fit (default is 1).\n",
-    "\n",
-    "    Returns:\n",
-    "        xr.DataArray: Monthly detrended data.\n",
-    "    \"\"\"    \n",
-    "    return da.groupby('time.month').apply(\n",
-    "        lambda x: xr.apply_ufunc(\n",
-    "            detrend_1d, x,\n",
-    "            input_core_dims=[['time']],\n",
-    "            output_core_dims=[['time']],\n",
-    "            vectorize=True,\n",
-    "            dask='parallelized',\n",
-    "            output_dtypes=[float],\n",
-    "            kwargs={'deg': deg}\n",
-    "        )\n",
-    "    )\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "3a5d9f5e-7ad4-4554-b530-a8e9e34fe377",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "def identify_enso_years_from_nino34(\n",
-    "    nino34: xr.DataArray,\n",
-    "    threshold: float = 0.5,\n",
-    "    runmean_months: int = 3,\n",
-    "    min_consecutive_months: int = 5,\n",
-    "    year_mode: str = \"any\"  # \"any\" or \"peak\"\n",
-    "):\n",
-    "    \"\"\"\n",
-    "    Identify El Niño / La Niña events from a monthly Niño3.4 anomaly time series.\n",
-    "\n",
-    "    Rules:\n",
-    "      - Compute a runmean_months (default 3) running mean.\n",
-    "      - El Niño: running mean >= +threshold for >= min_consecutive_months consecutive months.\n",
-    "      - La Niña: running mean <= -threshold for >= min_consecutive_months consecutive months.\n",
-    "\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    nino34 : xr.DataArray\n",
-    "        Monthly Niño3.4 anomalies with a 'time' coordinate.\n",
-    "    threshold : float\n",
-    "        Threshold in °C (default 0.5).\n",
-    "    runmean_months : int\n",
-    "        Running mean window in months (default 3).\n",
-    "    min_consecutive_months : int\n",
-    "        Minimum consecutive months meeting threshold (default 5).\n",
-    "    year_mode : str\n",
-    "        \"any\"  -> return all calendar years touched by qualifying event months.\n",
-    "        \"peak\" -> return the year of the peak month for each event (max for El Niño, min for La Niña).\n",
-    "\n",
-    "    Returns\n",
-    "    -------\n",
-    "    dict with:\n",
-    "      - \"nino34_rm\": running-mean series\n",
-    "      - \"el_nino_events\": list of dicts with start/end and months\n",
-    "      - \"la_nina_events\": list of dicts with start/end and months\n",
-    "      - \"el_nino_years\": sorted list of years\n",
-    "      - \"la_nina_years\": sorted list of years\n",
-    "    \"\"\"\n",
-    "    if \"time\" not in nino34.dims:\n",
-    "        raise ValueError(\"nino34 must have a 'time' dimension.\")\n",
-    "    if nino34.time.size < runmean_months:\n",
-    "        raise ValueError(\"Not enough data for the requested running mean window.\")\n",
-    "\n",
-    "    # Ensure time is sorted\n",
-    "    nino34 = nino34.sortby(\"time\")\n",
-    "\n",
-    "    # 3-month running mean (centered)\n",
-    "    rm = nino34.rolling(time=runmean_months, center=True).mean()\n",
-    "\n",
-    "    def _find_runs(mask: xr.DataArray):\n",
-    "        \"\"\"Return list of (start_idx, end_idx) inclusive runs where mask is True.\"\"\"\n",
-    "        m = mask.fillna(False).values.astype(bool)\n",
-    "        if m.ndim != 1:\n",
-    "            raise ValueError(\"Expected a 1D time series.\")\n",
-    "\n",
-    "        runs = []\n",
-    "        in_run = False\n",
-    "        start = None\n",
-    "        for i, val in enumerate(m):\n",
-    "            if val and not in_run:\n",
-    "                in_run = True\n",
-    "                start = i\n",
-    "            elif (not val) and in_run:\n",
-    "                end = i - 1\n",
-    "                runs.append((start, end))\n",
-    "                in_run = False\n",
-    "        if in_run:\n",
-    "            runs.append((start, len(m) - 1))\n",
-    "        return runs\n",
-    "\n",
-    "    def _events_from_runs(runs, kind: str):\n",
-    "        events = []\n",
-    "        for s, e in runs:\n",
-    "            length = e - s + 1\n",
-    "            if length < min_consecutive_months:\n",
-    "                continue\n",
-    "\n",
-    "            months = rm.time.isel(time=slice(s, e + 1)).values\n",
-    "            start_time = rm.time.isel(time=s).item()\n",
-    "            end_time = rm.time.isel(time=e).item()\n",
-    "\n",
-    "            # Peak month definition: max for El Niño, min for La Niña\n",
-    "            seg = rm.isel(time=slice(s, e + 1))\n",
-    "            if kind == \"el_nino\":\n",
-    "                peak_idx = int(seg.argmax(\"time\"))\n",
-    "            else:\n",
-    "                peak_idx = int(seg.argmin(\"time\"))\n",
-    "            peak_time = seg.time.isel(time=peak_idx).item()\n",
-    "            peak_value = float(seg.isel(time=peak_idx).values)\n",
-    "\n",
-    "            events.append({\n",
-    "                \"start\": start_time,\n",
-    "                \"end\": end_time,\n",
-    "                \"n_months\": int(length),\n",
-    "                \"peak_time\": peak_time,\n",
-    "                \"peak_value\": peak_value,\n",
-    "                \"months\": months,\n",
-    "            })\n",
-    "        return events\n",
-    "\n",
-    "    el_mask = rm >= threshold\n",
-    "    la_mask = rm <= -threshold\n",
-    "\n",
-    "    el_runs = _find_runs(el_mask)\n",
-    "    la_runs = _find_runs(la_mask)\n",
-    "\n",
-    "    el_events = _events_from_runs(el_runs, \"el_nino\")\n",
-    "    la_events = _events_from_runs(la_runs, \"la_nina\")\n",
-    "\n",
-    "    def _years_from_events(events, mode: str, kind: str):\n",
-    "        years = set()\n",
-    "        if mode == \"any\":\n",
-    "            for ev in events:\n",
-    "                for t in ev[\"months\"]:\n",
-    "                    years.add(np.datetime64(t, \"M\").astype(object).year)\n",
-    "        elif mode == \"peak\":\n",
-    "            for ev in events:\n",
-    "                # peak_time is a python datetime-like object after .item()\n",
-    "                years.add(ev[\"peak_time\"].year)\n",
-    "        else:\n",
-    "            raise ValueError(\"year_mode must be 'any' or 'peak'.\")\n",
-    "        return sorted(years)\n",
-    "\n",
-    "    el_years = _years_from_events(el_events, year_mode, \"el_nino\")\n",
-    "    la_years = _years_from_events(la_events, year_mode, \"la_nina\")\n",
-    "\n",
-    "    return {\n",
-    "        \"nino34_rm\": rm,\n",
-    "        \"el_nino_events\": el_events,\n",
-    "        \"la_nina_events\": la_events,\n",
-    "        \"el_nino_years\": el_years,\n",
-    "        \"la_nina_years\": la_years,\n",
-    "    }\n",
-    "\n",
-    "# --- Example usage ---\n",
-    "# nino34_anom is a DataArray with monthly anomalies and dim 'time'\n",
-    "# out = identify_enso_years_from_nino34(nino34_anom, year_mode=\"any\")\n",
-    "# print(out[\"el_nino_years\"])\n",
-    "# print(out[\"la_nina_years\"])\n",
-    "# out[\"el_nino_events\"][0]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "bb1b61e3-21a9-4a9c-9d7e-9998a0006545",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#read in SSTs\n",
-    "ts_obs = xr.open_dataset('/g/data/ct11/access-nri/replicas/esmvaltool/obsdata-v2/Tier2/HadISST/OBS_HadISST_reanaly_1_Amon_ts_187001-202112.nc', decode_times=True).ts\n",
-    "\n",
-    "\n",
-    "#get annual mean (for trend calculation - not detrended)\n",
-    "ts_obs_ann = ts_obs.groupby('time.year').mean('time')\n",
-    "\n",
-    "#get detrended monthly time series (for nino 3.4 calculation)\n",
-    "ts_obs_dt = detrend_monthly(ts_obs, deg=1)\n",
-    "\n",
-    "#get detrended monthly anomalies\n",
-    "ts_obs_anoms_dt = ts_obs_dt.groupby('time.month') - ts_obs_dt.sel(time=slice('1870','1920')).groupby('time.month').mean('time')\n",
-    "#the above is monthly data, if we wanted to do some smoothing we could maybe do 12-month running mean\n",
-    "ts_anoms_rolling_dt = ts_obs_anoms_dt.rolling(time=5, center=True).mean()\n",
-    "\n",
-    "#now get nino3.4 index from monthly detrended anomalies\n",
-    "n34_obs = ts_obs_anoms_dt.sel(lat=slice(-5,5),lon=slice(190,240)).mean(['lat','lon'])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "95a118a4-a927-4cf8-b3a6-7ef36a43df53",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "El Niño years: 61 [1877, 1878, 1888, 1889, 1896, 1897, 1899, 1900, 1902, 1903, 1904, 1905, 1906, 1911, 1912, 1913, 1914, 1915, 1918, 1919, 1923, 1924, 1925, 1926, 1930, 1931, 1940, 1941, 1942, 1951, 1957, 1958, 1963, 1964, 1965, 1966, 1968, 1969, 1972, 1973, 1976, 1977, 1982, 1983, 1986, 1987, 1988, 1991, 1992, 1994, 1995, 1997, 1998, 2002, 2003, 2009, 2010, 2015, 2016, 2018, 2019]\n",
-      "La Niña years: 53 [1870, 1872, 1873, 1874, 1875, 1876, 1879, 1880, 1886, 1887, 1889, 1890, 1892, 1893, 1894, 1909, 1910, 1916, 1917, 1933, 1934, 1942, 1943, 1949, 1950, 1951, 1954, 1955, 1956, 1964, 1965, 1970, 1971, 1973, 1974, 1975, 1976, 1984, 1985, 1988, 1989, 1998, 1999, 2000, 2007, 2008, 2010, 2011, 2012, 2017, 2018, 2020, 2021]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#plot n3.4 just to check\n",
-    "\n",
-    "n34_obs.plot()\n",
-    "out = identify_enso_years_from_nino34(n34_obs, year_mode=\"any\",threshold=0.6)\n",
-    "\n",
-    "n_el_years = len(out[\"el_nino_years\"])\n",
-    "n_la_years = len(out[\"la_nina_years\"])\n",
-    "\n",
-    "\"\"\"\n",
-    "  out is a dict with:\n",
-    "      - \"nino34_rm\": running-mean series\n",
-    "      - \"el_nino_events\": list of dicts with start/end and months\n",
-    "      - \"la_nina_events\": list of dicts with start/end and months\n",
-    "      - \"el_nino_years\": sorted list of years\n",
-    "      - \"la_nina_years\": sorted list of years\n",
-    "      \"\"\"\n",
-    "print(\"El Niño years:\", n_el_years, out[\"el_nino_years\"])\n",
-    "print(\"La Niña years:\", n_la_years, out[\"la_nina_years\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "585d79d4-360b-4749-801d-830d233e2eed",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n",
-      "<class 'xarray.core.dataarray.DataArray'>\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "obs_m20c_trnarr = calc_trend(ts_obs_ann,1850,1983,31)\n",
-    "\n",
-    "# import matplotlib.animation as animation\n",
-    "# from IPython.display import HTML\n",
-    "# import cartopy.feature as cfeature\n",
-    "\n",
-    "# fig, axes = plt.subplots(1,1,figsize=(20,15),\n",
-    "#                          subplot_kw=dict(projection=ccrs.PlateCarree(central_longitude=180)))\n",
-    "cmap = plt.get_cmap('RdBu_r')\n",
-    "# m, M = -0.2, 0.2\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "43ad8148-0012-4ecb-9225-5b7babb6dc73",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x700 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "\n",
-    "# Create figure and axes with projection\n",
-    "fig, axes = plt.subplots(\n",
-    "    figsize=(10, 7),\n",
-    "    subplot_kw={'projection': ccrs.PlateCarree(central_longitude=180)}\n",
-    ")\n",
-    "cmap = plt.get_cmap('RdBu_r')\n",
-    "m, M = -0.2, 0.2\n",
-    "\n",
-    "# Plot one slice just to see\n",
-    "maptrn = obs_m20c_trnarr.isel(year=100).plot.contourf(\n",
-    "    ax=axes,\n",
-    "    cmap=cmap,\n",
-    "    vmin=m,\n",
-    "    vmax=M,\n",
-    "    levels=np.linspace(m, M, 21),\n",
-    "    add_colorbar=False,\n",
-    "    transform=ccrs.PlateCarree()\n",
-    ")\n",
-    "\n",
-    "# Axis formatting\n",
-    "axes.set_xticks(np.arange(-180, 181, 30), crs=ccrs.PlateCarree())\n",
-    "axes.set_yticks(np.arange(-90, 91, 30), crs=ccrs.PlateCarree())\n",
-    "\n",
-    "lon_formatter = LongitudeFormatter(zero_direction_label=True, degree_symbol='')\n",
-    "lat_formatter = LatitudeFormatter(degree_symbol='')\n",
-    "axes.xaxis.set_major_formatter(lon_formatter)\n",
-    "axes.yaxis.set_major_formatter(lat_formatter)\n",
-    "\n",
-    "axes.coastlines(resolution='110m')\n",
-    "axes.set_xlabel(\"\")\n",
-    "axes.set_ylabel(\"\")\n",
-    "\n",
-    "# Add colorbar\n",
-    "cbar = fig.colorbar(maptrn, ax=axes, orientation='horizontal', pad=0.05, shrink=0.8)\n",
-    "cbar.set_label('51-yr trend (°C/decade)')\n",
-    "\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "40272391-c7f1-45f7-802b-56e58509ad19",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# #calculate a 34 year trend and animate\n",
-    "\n",
-    "# obs_m20c_trnarr = calc_trend(ts_obs_ann,1850,1985,34)\n",
-    "\n",
-    "# import matplotlib.animation as animation\n",
-    "# from IPython.display import HTML\n",
-    "\n",
-    "# fig, axes = plt.subplots(1,1,figsize=(20,15),\n",
-    "#                          subplot_kw=dict(projection=ccrs.PlateCarree(central_longitude=180)))\n",
-    "# cmap = plt.get_cmap('RdBu_r')\n",
-    "# m, M = -0.2, 0.2\n",
-    "\n",
-    "# def animate(frame):\n",
-    "#     axes.clear()\n",
-    "#     obs_m20c_trnarr.isel(year=frame).plot.contourf(\n",
-    "#         ax=axes, cmap=cmap, vmin=m, vmax=M, \n",
-    "#         levels=np.linspace(m, M, 21), \n",
-    "#         add_colorbar=False, transform=ccrs.PlateCarree()\n",
-    "#     )\n",
-    "#     axes.set_title(f\"34-yr trend starting {int(obs_m20c_trnarr.year[frame].values)}\")\n",
-    "#     axes.add_feature(cfeature.BORDERS, linewidth=0.5, edgecolor=\"black\")   # country borders\n",
-    "#     axes.add_feature(cfeature.LAND, facecolor=\"lightgray\", alpha=0.5)\n",
-    "\n",
-    "# ani = animation.FuncAnimation(\n",
-    "#     fig, animate, frames=len(obs_m20c_trnarr.year),\n",
-    "#     interval=200, blit=False\n",
-    "# )\n",
-    "\n",
-    "# ani.save('animation_34yrs.gif', writer='imagemagick', fps=2)\n",
-    "# HTML(ani.to_jshtml())\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "24910606-9901-4e01-8a82-d1cf47d11a21",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x700 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#plot one slice just to see\n",
-    "\n",
-    "\n",
-    "# Create figure and axes with projection\n",
-    "fig, axes = plt.subplots(\n",
-    "    figsize=(10, 7),\n",
-    "    subplot_kw={'projection': ccrs.PlateCarree(central_longitude=180)}\n",
-    ")\n",
-    "\n",
-    "# Plot one slice just to see\n",
-    "maptrn = obs_m20c_trnarr.isel(year=132).plot.contourf(\n",
-    "    ax=axes,\n",
-    "    cmap=cmap,\n",
-    "    vmin=m,\n",
-    "    vmax=M,\n",
-    "    levels=np.linspace(m, M, 21),\n",
-    "    add_colorbar=False,\n",
-    "    transform=ccrs.PlateCarree()\n",
-    ")\n",
-    "\n",
-    "# Axis formatting\n",
-    "axes.set_xticks(np.arange(-180, 181, 30), crs=ccrs.PlateCarree())\n",
-    "axes.set_yticks(np.arange(-90, 91, 30), crs=ccrs.PlateCarree())\n",
-    "\n",
-    "lon_formatter = LongitudeFormatter(zero_direction_label=True, degree_symbol='')\n",
-    "lat_formatter = LatitudeFormatter(degree_symbol='')\n",
-    "axes.xaxis.set_major_formatter(lon_formatter)\n",
-    "axes.yaxis.set_major_formatter(lat_formatter)\n",
-    "\n",
-    "axes.coastlines(resolution='110m')\n",
-    "axes.set_xlabel(\"\")\n",
-    "axes.set_ylabel(\"\")\n",
-    "\n",
-    "# Add colorbar\n",
-    "cbar = fig.colorbar(maptrn, ax=axes, orientation='horizontal', pad=0.05, shrink=0.8)\n",
-    "cbar.set_label('31-yr trend (°C/decade)')\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "f465486d-8cd9-4979-ace2-76108d132438",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 40 ts files\n"
-     ]
-    }
-   ],
-   "source": [
-    "#read in ACCESS ESM1.5/CANESM5/CESM2/MIROC6/NORESM2\n",
-    "\n",
-    "import glob\n",
-    "\n",
-    "base = \"/g/data/fs38/publications/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/*/Amon/ts/gn/latest/*.nc\"\n",
-    "\n",
-    "#base = \"/g/data/oi10/replicas/CMIP6/CMIP/CCCma/CanESM5/historical/*/Amon/ts/gn/latest/*.nc\"\n",
-    "\n",
-    "files = sorted(glob.glob(base))\n",
-    "\n",
-    "print(f\"Found {len(files)} ts files\")\n",
-    "ds = xr.open_mfdataset(\n",
-    "    files,\n",
-    "    combine=\"nested\",\n",
-    "    concat_dim=\"member_id\",\n",
-    "    parallel=True,\n",
-    "    chunks= 'auto'\n",
-    ")\n",
-    "\n",
-    "\n",
-    "da = ds['ts']        # extract the variable\n",
-    "da = da.transpose('time', 'lat', 'lon', 'member_id')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "6205f2f3-b5b4-4e8f-9577-1a10d5232aa4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "</symbol>\n",
-       "</defs>\n",
-       "</svg>\n",
-       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
-       "\n",
-       ":root {\n",
-       "  --xr-font-color0: var(\n",
-       "    --jp-content-font-color0,\n",
-       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
-       "  );\n",
-       "  --xr-font-color2: var(\n",
-       "    --jp-content-font-color2,\n",
-       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
-       "  );\n",
-       "  --xr-font-color3: var(\n",
-       "    --jp-content-font-color3,\n",
-       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
-       "  );\n",
-       "  --xr-border-color: var(\n",
-       "    --jp-border-color2,\n",
-       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
-       "  );\n",
-       "  --xr-disabled-color: var(\n",
-       "    --jp-layout-color3,\n",
-       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
-       "  );\n",
-       "  --xr-background-color: var(\n",
-       "    --jp-layout-color0,\n",
-       "    var(--pst-color-on-background, white)\n",
-       "  );\n",
-       "  --xr-background-color-row-even: var(\n",
-       "    --jp-layout-color1,\n",
-       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
-       "  );\n",
-       "  --xr-background-color-row-odd: var(\n",
-       "    --jp-layout-color2,\n",
-       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
-       "  );\n",
-       "}\n",
-       "\n",
-       "html[theme=\"dark\"],\n",
-       "html[data-theme=\"dark\"],\n",
-       "body[data-theme=\"dark\"],\n",
-       "body.vscode-dark {\n",
-       "  --xr-font-color0: var(\n",
-       "    --jp-content-font-color0,\n",
-       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
-       "  );\n",
-       "  --xr-font-color2: var(\n",
-       "    --jp-content-font-color2,\n",
-       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
-       "  );\n",
-       "  --xr-font-color3: var(\n",
-       "    --jp-content-font-color3,\n",
-       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
-       "  );\n",
-       "  --xr-border-color: var(\n",
-       "    --jp-border-color2,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
-       "  );\n",
-       "  --xr-disabled-color: var(\n",
-       "    --jp-layout-color3,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
-       "  );\n",
-       "  --xr-background-color: var(\n",
-       "    --jp-layout-color0,\n",
-       "    var(--pst-color-on-background, #111111)\n",
-       "  );\n",
-       "  --xr-background-color-row-even: var(\n",
-       "    --jp-layout-color1,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
-       "  );\n",
-       "  --xr-background-color-row-odd: var(\n",
-       "    --jp-layout-color2,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
-       "  );\n",
-       "}\n",
-       "\n",
-       ".xr-wrap {\n",
-       "  display: block !important;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
-       "  line-height: 1.6;\n",
-       "}\n",
-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-obj-name,\n",
-       ".xr-group-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-group-name::before {\n",
-       "  content: \"📁\";\n",
-       "  padding-right: 0.3em;\n",
-       "}\n",
-       "\n",
-       ".xr-group-name,\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
-       "  margin-block-start: 0;\n",
-       "  margin-block-end: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: inline-block;\n",
-       "  opacity: 0;\n",
-       "  height: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "  border: 2px solid transparent !important;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:focus + label {\n",
-       "  border: 2px solid var(--xr-font-color0) !important;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: \"►\";\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: \"▼\";\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-top: 4px;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-group-box {\n",
-       "  display: inline-grid;\n",
-       "  grid-template-columns: 0px 20px auto;\n",
-       "  width: 100%;\n",
-       "}\n",
-       "\n",
-       ".xr-group-box-vline {\n",
-       "  grid-column-start: 1;\n",
-       "  border-right: 0.2em solid;\n",
-       "  border-color: var(--xr-border-color);\n",
-       "  width: 0px;\n",
-       "}\n",
-       "\n",
-       ".xr-group-box-hline {\n",
-       "  grid-column-start: 2;\n",
-       "  grid-row-start: 1;\n",
-       "  height: 1em;\n",
-       "  width: 20px;\n",
-       "  border-bottom: 0.2em solid;\n",
-       "  border-color: var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-group-box-contents {\n",
-       "  grid-column-start: 3;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: \"(\";\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: \")\";\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: \",\";\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  border-color: var(--xr-background-color-row-odd);\n",
-       "  margin-bottom: 0;\n",
-       "  padding-top: 2px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "  border-color: var(--xr-background-color-row-even);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  border-top: 2px dotted var(--xr-background-color);\n",
-       "  padding-bottom: 20px !important;\n",
-       "  padding-top: 10px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in + label,\n",
-       ".xr-var-data-in + label,\n",
-       ".xr-index-data-in + label {\n",
-       "  padding: 0 1px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > pre,\n",
-       ".xr-index-data > pre,\n",
-       ".xr-var-data > table > tbody > tr {\n",
-       "  background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
-       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
-       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
-       "  color: var(--xr-font-color0);\n",
-       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
-       "  stroke-width: 0.8px;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;ts&#x27; (time: 1980, lat: 145, lon: 192, member_id: 40)&gt; Size: 9GB\n",
-       "dask.array&lt;transpose, shape=(1980, 145, 192, 40), dtype=float32, chunksize=(1205, 145, 192, 1), chunktype=numpy.ndarray&gt;\n",
-       "Coordinates:\n",
-       "  * time     (time) datetime64[ns] 16kB 1850-01-16T12:00:00 ... 2014-12-16T12...\n",
-       "  * lat      (lat) float64 1kB -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0\n",
-       "  * lon      (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
-       "Dimensions without coordinates: member_id\n",
-       "Attributes:\n",
-       "    standard_name:  surface_temperature\n",
-       "    long_name:      Surface Temperature\n",
-       "    comment:        Temperature of the lower boundary of the atmosphere\n",
-       "    units:          K\n",
-       "    cell_methods:   area: time: mean\n",
-       "    cell_measures:  area: areacella\n",
-       "    history:        2020-06-05T04:06:10Z altered by CMOR: replaced missing va...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-obj-name'>&#x27;ts&#x27;</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1980</li><li><span class='xr-has-index'>lat</span>: 145</li><li><span class='xr-has-index'>lon</span>: 192</li><li><span>member_id</span>: 40</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6a831c17-fa84-4b9a-9c82-e2d1c47c1c91' class='xr-array-in' type='checkbox' checked><label for='section-6a831c17-fa84-4b9a-9c82-e2d1c47c1c91' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1205, 145, 192, 1), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 8.21 GiB </td>\n",
-       "                        <td> 127.97 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1980, 145, 192, 40) </td>\n",
-       "                        <td> (1205, 145, 192, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 80 chunks in 122 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"480\" height=\"110\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></div></li><li class='xr-section-item'><input id='section-3e76366b-1f8d-4e47-b289-8f00741e6946' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3e76366b-1f8d-4e47-b289-8f00741e6946' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1850-01-16T12:00:00 ... 2014-12-...</div><input id='attrs-f272d05a-76db-4bc4-bc99-de1801e82d93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f272d05a-76db-4bc4-bc99-de1801e82d93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2b4f1ce-c6e1-4724-bcec-90c7afd94803' class='xr-var-data-in' type='checkbox'><label for='data-f2b4f1ce-c6e1-4724-bcec-90c7afd94803' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1850-01-16T12:00:00.000000000&#x27;, &#x27;1850-02-15T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1850-03-16T12:00:00.000000000&#x27;, ..., &#x27;2014-10-16T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2014-11-16T00:00:00.000000000&#x27;, &#x27;2014-12-16T12:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -88.75 -87.5 ... 88.75 90.0</div><input id='attrs-84d8508b-6c57-4a75-b8af-d63753f441e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84d8508b-6c57-4a75-b8af-d63753f441e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c793ecf3-b9e2-4652-97a6-2a385403be1e' class='xr-var-data-in' type='checkbox'><label for='data-c793ecf3-b9e2-4652-97a6-2a385403be1e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-90.  , -88.75, -87.5 , -86.25, -85.  , -83.75, -82.5 , -81.25, -80.  ,\n",
-       "       -78.75, -77.5 , -76.25, -75.  , -73.75, -72.5 , -71.25, -70.  , -68.75,\n",
-       "       -67.5 , -66.25, -65.  , -63.75, -62.5 , -61.25, -60.  , -58.75, -57.5 ,\n",
-       "       -56.25, -55.  , -53.75, -52.5 , -51.25, -50.  , -48.75, -47.5 , -46.25,\n",
-       "       -45.  , -43.75, -42.5 , -41.25, -40.  , -38.75, -37.5 , -36.25, -35.  ,\n",
-       "       -33.75, -32.5 , -31.25, -30.  , -28.75, -27.5 , -26.25, -25.  , -23.75,\n",
-       "       -22.5 , -21.25, -20.  , -18.75, -17.5 , -16.25, -15.  , -13.75, -12.5 ,\n",
-       "       -11.25, -10.  ,  -8.75,  -7.5 ,  -6.25,  -5.  ,  -3.75,  -2.5 ,  -1.25,\n",
-       "         0.  ,   1.25,   2.5 ,   3.75,   5.  ,   6.25,   7.5 ,   8.75,  10.  ,\n",
-       "        11.25,  12.5 ,  13.75,  15.  ,  16.25,  17.5 ,  18.75,  20.  ,  21.25,\n",
-       "        22.5 ,  23.75,  25.  ,  26.25,  27.5 ,  28.75,  30.  ,  31.25,  32.5 ,\n",
-       "        33.75,  35.  ,  36.25,  37.5 ,  38.75,  40.  ,  41.25,  42.5 ,  43.75,\n",
-       "        45.  ,  46.25,  47.5 ,  48.75,  50.  ,  51.25,  52.5 ,  53.75,  55.  ,\n",
-       "        56.25,  57.5 ,  58.75,  60.  ,  61.25,  62.5 ,  63.75,  65.  ,  66.25,\n",
-       "        67.5 ,  68.75,  70.  ,  71.25,  72.5 ,  73.75,  75.  ,  76.25,  77.5 ,\n",
-       "        78.75,  80.  ,  81.25,  82.5 ,  83.75,  85.  ,  86.25,  87.5 ,  88.75,\n",
-       "        90.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.875 3.75 ... 356.2 358.1</div><input id='attrs-640342bb-7218-4470-bb88-f23882b76466' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-640342bb-7218-4470-bb88-f23882b76466' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a01c01d-dce8-4bbb-ad32-c6b1d6c31e99' class='xr-var-data-in' type='checkbox'><label for='data-8a01c01d-dce8-4bbb-ad32-c6b1d6c31e99' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  0.   ,   1.875,   3.75 ,   5.625,   7.5  ,   9.375,  11.25 ,  13.125,\n",
-       "        15.   ,  16.875,  18.75 ,  20.625,  22.5  ,  24.375,  26.25 ,  28.125,\n",
-       "        30.   ,  31.875,  33.75 ,  35.625,  37.5  ,  39.375,  41.25 ,  43.125,\n",
-       "        45.   ,  46.875,  48.75 ,  50.625,  52.5  ,  54.375,  56.25 ,  58.125,\n",
-       "        60.   ,  61.875,  63.75 ,  65.625,  67.5  ,  69.375,  71.25 ,  73.125,\n",
-       "        75.   ,  76.875,  78.75 ,  80.625,  82.5  ,  84.375,  86.25 ,  88.125,\n",
-       "        90.   ,  91.875,  93.75 ,  95.625,  97.5  ,  99.375, 101.25 , 103.125,\n",
-       "       105.   , 106.875, 108.75 , 110.625, 112.5  , 114.375, 116.25 , 118.125,\n",
-       "       120.   , 121.875, 123.75 , 125.625, 127.5  , 129.375, 131.25 , 133.125,\n",
-       "       135.   , 136.875, 138.75 , 140.625, 142.5  , 144.375, 146.25 , 148.125,\n",
-       "       150.   , 151.875, 153.75 , 155.625, 157.5  , 159.375, 161.25 , 163.125,\n",
-       "       165.   , 166.875, 168.75 , 170.625, 172.5  , 174.375, 176.25 , 178.125,\n",
-       "       180.   , 181.875, 183.75 , 185.625, 187.5  , 189.375, 191.25 , 193.125,\n",
-       "       195.   , 196.875, 198.75 , 200.625, 202.5  , 204.375, 206.25 , 208.125,\n",
-       "       210.   , 211.875, 213.75 , 215.625, 217.5  , 219.375, 221.25 , 223.125,\n",
-       "       225.   , 226.875, 228.75 , 230.625, 232.5  , 234.375, 236.25 , 238.125,\n",
-       "       240.   , 241.875, 243.75 , 245.625, 247.5  , 249.375, 251.25 , 253.125,\n",
-       "       255.   , 256.875, 258.75 , 260.625, 262.5  , 264.375, 266.25 , 268.125,\n",
-       "       270.   , 271.875, 273.75 , 275.625, 277.5  , 279.375, 281.25 , 283.125,\n",
-       "       285.   , 286.875, 288.75 , 290.625, 292.5  , 294.375, 296.25 , 298.125,\n",
-       "       300.   , 301.875, 303.75 , 305.625, 307.5  , 309.375, 311.25 , 313.125,\n",
-       "       315.   , 316.875, 318.75 , 320.625, 322.5  , 324.375, 326.25 , 328.125,\n",
-       "       330.   , 331.875, 333.75 , 335.625, 337.5  , 339.375, 341.25 , 343.125,\n",
-       "       345.   , 346.875, 348.75 , 350.625, 352.5  , 354.375, 356.25 , 358.125])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cac39757-d40b-40b0-a05f-a64423c259fd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cac39757-d40b-40b0-a05f-a64423c259fd' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>surface_temperature</dd><dt><span>long_name :</span></dt><dd>Surface Temperature</dd><dt><span>comment :</span></dt><dd>Temperature of the lower boundary of the atmosphere</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>history :</span></dt><dd>2020-06-05T04:06:10Z altered by CMOR: replaced missing value flag (-1.07374e+09) with standard missing value (1e+20).</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'ts' (time: 1980, lat: 145, lon: 192, member_id: 40)> Size: 9GB\n",
-       "dask.array<transpose, shape=(1980, 145, 192, 40), dtype=float32, chunksize=(1205, 145, 192, 1), chunktype=numpy.ndarray>\n",
-       "Coordinates:\n",
-       "  * time     (time) datetime64[ns] 16kB 1850-01-16T12:00:00 ... 2014-12-16T12...\n",
-       "  * lat      (lat) float64 1kB -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0\n",
-       "  * lon      (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
-       "Dimensions without coordinates: member_id\n",
-       "Attributes:\n",
-       "    standard_name:  surface_temperature\n",
-       "    long_name:      Surface Temperature\n",
-       "    comment:        Temperature of the lower boundary of the atmosphere\n",
-       "    units:          K\n",
-       "    cell_methods:   area: time: mean\n",
-       "    cell_measures:  area: areacella\n",
-       "    history:        2020-06-05T04:06:10Z altered by CMOR: replaced missing va..."
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "da"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "3d1f1735-c629-4762-ac8d-fe83e1e324d9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "da_ann = da.groupby('time.year').mean('time')\n",
-    "\n",
-    "# da_ann = da_ann.chunk({\"year\": -1})\n",
-    "\n",
-    "esm15_m20c_trnarr = calc_trend(da_ann,1850,1983,31)\n",
-    "esm15_m20c_trnarr_em = esm15_m20c_trnarr.mean('member_id').chunk({\"year\": -1})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "02e91f68-e0de-42b5-afe4-936689689209",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# fig, axes = plt.subplots(1,1,figsize=(20,15),\n",
-    "#                          subplot_kw=dict(projection=ccrs.PlateCarree(central_longitude=180)))\n",
-    "# cmap = plt.get_cmap('RdBu_r')\n",
-    "# m, M = -0.2, 0.2\n",
-    "\n",
-    "# def animate(frame):\n",
-    "#     axes.clear()\n",
-    "#     esm15_m20c_trnarr_em.isel(year=frame).plot.contourf(\n",
-    "#         ax=axes, cmap=cmap, vmin=m, vmax=M, \n",
-    "#         levels=np.linspace(m, M, 21), \n",
-    "#         add_colorbar=False, transform=ccrs.PlateCarree()\n",
-    "#     )\n",
-    "#     axes.set_title(f\"31-yr trend starting {int(esm15_m20c_trnarr_em.year[frame].values)}\")\n",
-    "#     axes.add_feature(cfeature.BORDERS, linewidth=0.5, edgecolor=\"black\")   # country borders\n",
-    "#     axes.add_feature(cfeature.LAND, facecolor=\"lightgray\", alpha=0.5)\n",
-    "\n",
-    "# ani = animation.FuncAnimation(\n",
-    "#     fig, animate, frames=len(esm15_m20c_trnarr_em.year),\n",
-    "#     interval=200, blit=False\n",
-    "# )\n",
-    "\n",
-    "# ani.save('esm15_animation_31yrs.gif', writer='imagemagick', fps=2)\n",
-    "# HTML(ani.to_jshtml())\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "693160ac-b1ae-4f32-8e53-9f1ecdf5544a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#plot one slice just to see\n",
-    "\n",
-    "\n",
-    "# # Create figure and axes with projection\n",
-    "# fig, axes = plt.subplots(\n",
-    "#     figsize=(10, 7),\n",
-    "#     subplot_kw={'projection': ccrs.PlateCarree(central_longitude=180)}\n",
-    "# )\n",
-    "# cmap = plt.get_cmap('RdBu_r')\n",
-    "\n",
-    "# m=-1\n",
-    "# M=1\n",
-    "# # Plot one slice just to see\n",
-    "# maptrn = esm15_m20c_trnarr_em.isel(year=132).plot.contourf(\n",
-    "#     ax=axes,\n",
-    "#     cmap=cmap,\n",
-    "#     vmin=m,\n",
-    "#     vmax=M,\n",
-    "#     levels=np.linspace(m, M, 21),\n",
-    "#     add_colorbar=False,\n",
-    "#     transform=ccrs.PlateCarree()\n",
-    "# )\n",
-    "\n",
-    "# # Axis formatting\n",
-    "# axes.set_xticks(np.arange(-180, 181, 30), crs=ccrs.PlateCarree())\n",
-    "# axes.set_yticks(np.arange(-90, 91, 30), crs=ccrs.PlateCarree())\n",
-    "\n",
-    "# lon_formatter = LongitudeFormatter(zero_direction_label=True, degree_symbol='')\n",
-    "# lat_formatter = LatitudeFormatter(degree_symbol='')\n",
-    "# axes.xaxis.set_major_formatter(lon_formatter)\n",
-    "# axes.yaxis.set_major_formatter(lat_formatter)\n",
-    "\n",
-    "# axes.coastlines(resolution='110m')\n",
-    "# axes.set_xlabel(\"\")\n",
-    "# axes.set_ylabel(\"\")\n",
-    "\n",
-    "# # Add colorbar\n",
-    "# cbar = fig.colorbar(maptrn, ax=axes, orientation='horizontal', pad=0.05, shrink=0.8)\n",
-    "# cbar.set_label('ACCESS ESM1.5 EM 31-yr trend (°C/decade)')\n",
-    "\n",
-    "# plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "7cd4eb60-edaf-4701-8c85-188305583b74",
-   "metadata": {},
-   "outputs": [
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-       "}\n",
-       "\n",
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-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
-       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
-       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
-       "  color: var(--xr-font-color0);\n",
-       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
-       "  stroke-width: 0.8px;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;polyfit_coefficients&#x27; (lat: 145, lon: 192)&gt; Size: 223kB\n",
-       "dask.array&lt;getitem, shape=(145, 192), dtype=float64, chunksize=(145, 192), chunktype=numpy.ndarray&gt;\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float64 1kB -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0\n",
-       "  * lon      (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
-       "    degree   int64 8B 1\n",
-       "    year     int64 8B 1982</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-obj-name'>&#x27;polyfit_coefficients&#x27;</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 145</li><li><span class='xr-has-index'>lon</span>: 192</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5ee07e4d-8828-486c-b937-803a5228e3bc' class='xr-array-in' type='checkbox' checked><label for='section-5ee07e4d-8828-486c-b937-803a5228e3bc' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(145, 192), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
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-       "                    <tr>\n",
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-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 217.50 kiB </td>\n",
-       "                        <td> 217.50 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (145, 192) </td>\n",
-       "                        <td> (145, 192) </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1467 graph layers </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"140\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
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-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"90\" style=\"stroke-width:2\" />\n",
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-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"110.625000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >192</text>\n",
-       "  <text x=\"140.000000\" y=\"45.312500\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,45.312500)\">145</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></div></li><li class='xr-section-item'><input id='section-9bd1f4fd-1b0d-489e-8e53-2f35c06f1acc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9bd1f4fd-1b0d-489e-8e53-2f35c06f1acc' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -88.75 -87.5 ... 88.75 90.0</div><input id='attrs-1922c126-d4eb-4cd7-9bd8-18cbf52c7b39' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1922c126-d4eb-4cd7-9bd8-18cbf52c7b39' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67129c3f-095b-4643-ac17-28f3c192da36' class='xr-var-data-in' type='checkbox'><label for='data-67129c3f-095b-4643-ac17-28f3c192da36' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-90.  , -88.75, -87.5 , -86.25, -85.  , -83.75, -82.5 , -81.25, -80.  ,\n",
-       "       -78.75, -77.5 , -76.25, -75.  , -73.75, -72.5 , -71.25, -70.  , -68.75,\n",
-       "       -67.5 , -66.25, -65.  , -63.75, -62.5 , -61.25, -60.  , -58.75, -57.5 ,\n",
-       "       -56.25, -55.  , -53.75, -52.5 , -51.25, -50.  , -48.75, -47.5 , -46.25,\n",
-       "       -45.  , -43.75, -42.5 , -41.25, -40.  , -38.75, -37.5 , -36.25, -35.  ,\n",
-       "       -33.75, -32.5 , -31.25, -30.  , -28.75, -27.5 , -26.25, -25.  , -23.75,\n",
-       "       -22.5 , -21.25, -20.  , -18.75, -17.5 , -16.25, -15.  , -13.75, -12.5 ,\n",
-       "       -11.25, -10.  ,  -8.75,  -7.5 ,  -6.25,  -5.  ,  -3.75,  -2.5 ,  -1.25,\n",
-       "         0.  ,   1.25,   2.5 ,   3.75,   5.  ,   6.25,   7.5 ,   8.75,  10.  ,\n",
-       "        11.25,  12.5 ,  13.75,  15.  ,  16.25,  17.5 ,  18.75,  20.  ,  21.25,\n",
-       "        22.5 ,  23.75,  25.  ,  26.25,  27.5 ,  28.75,  30.  ,  31.25,  32.5 ,\n",
-       "        33.75,  35.  ,  36.25,  37.5 ,  38.75,  40.  ,  41.25,  42.5 ,  43.75,\n",
-       "        45.  ,  46.25,  47.5 ,  48.75,  50.  ,  51.25,  52.5 ,  53.75,  55.  ,\n",
-       "        56.25,  57.5 ,  58.75,  60.  ,  61.25,  62.5 ,  63.75,  65.  ,  66.25,\n",
-       "        67.5 ,  68.75,  70.  ,  71.25,  72.5 ,  73.75,  75.  ,  76.25,  77.5 ,\n",
-       "        78.75,  80.  ,  81.25,  82.5 ,  83.75,  85.  ,  86.25,  87.5 ,  88.75,\n",
-       "        90.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.875 3.75 ... 356.2 358.1</div><input id='attrs-95e2f324-3409-4e4b-8bd8-2b9eaea20c28' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95e2f324-3409-4e4b-8bd8-2b9eaea20c28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2efd015-a161-469a-92cd-98fb5f46a891' class='xr-var-data-in' type='checkbox'><label for='data-f2efd015-a161-469a-92cd-98fb5f46a891' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  0.   ,   1.875,   3.75 ,   5.625,   7.5  ,   9.375,  11.25 ,  13.125,\n",
-       "        15.   ,  16.875,  18.75 ,  20.625,  22.5  ,  24.375,  26.25 ,  28.125,\n",
-       "        30.   ,  31.875,  33.75 ,  35.625,  37.5  ,  39.375,  41.25 ,  43.125,\n",
-       "        45.   ,  46.875,  48.75 ,  50.625,  52.5  ,  54.375,  56.25 ,  58.125,\n",
-       "        60.   ,  61.875,  63.75 ,  65.625,  67.5  ,  69.375,  71.25 ,  73.125,\n",
-       "        75.   ,  76.875,  78.75 ,  80.625,  82.5  ,  84.375,  86.25 ,  88.125,\n",
-       "        90.   ,  91.875,  93.75 ,  95.625,  97.5  ,  99.375, 101.25 , 103.125,\n",
-       "       105.   , 106.875, 108.75 , 110.625, 112.5  , 114.375, 116.25 , 118.125,\n",
-       "       120.   , 121.875, 123.75 , 125.625, 127.5  , 129.375, 131.25 , 133.125,\n",
-       "       135.   , 136.875, 138.75 , 140.625, 142.5  , 144.375, 146.25 , 148.125,\n",
-       "       150.   , 151.875, 153.75 , 155.625, 157.5  , 159.375, 161.25 , 163.125,\n",
-       "       165.   , 166.875, 168.75 , 170.625, 172.5  , 174.375, 176.25 , 178.125,\n",
-       "       180.   , 181.875, 183.75 , 185.625, 187.5  , 189.375, 191.25 , 193.125,\n",
-       "       195.   , 196.875, 198.75 , 200.625, 202.5  , 204.375, 206.25 , 208.125,\n",
-       "       210.   , 211.875, 213.75 , 215.625, 217.5  , 219.375, 221.25 , 223.125,\n",
-       "       225.   , 226.875, 228.75 , 230.625, 232.5  , 234.375, 236.25 , 238.125,\n",
-       "       240.   , 241.875, 243.75 , 245.625, 247.5  , 249.375, 251.25 , 253.125,\n",
-       "       255.   , 256.875, 258.75 , 260.625, 262.5  , 264.375, 266.25 , 268.125,\n",
-       "       270.   , 271.875, 273.75 , 275.625, 277.5  , 279.375, 281.25 , 283.125,\n",
-       "       285.   , 286.875, 288.75 , 290.625, 292.5  , 294.375, 296.25 , 298.125,\n",
-       "       300.   , 301.875, 303.75 , 305.625, 307.5  , 309.375, 311.25 , 313.125,\n",
-       "       315.   , 316.875, 318.75 , 320.625, 322.5  , 324.375, 326.25 , 328.125,\n",
-       "       330.   , 331.875, 333.75 , 335.625, 337.5  , 339.375, 341.25 , 343.125,\n",
-       "       345.   , 346.875, 348.75 , 350.625, 352.5  , 354.375, 356.25 , 358.125])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>degree</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1</div><input id='attrs-ebd17014-9688-46cc-90fd-a0f3fddc2ebd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ebd17014-9688-46cc-90fd-a0f3fddc2ebd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-277e0310-fdfd-464c-a8c2-5f85fe08e3c4' class='xr-var-data-in' type='checkbox'><label for='data-277e0310-fdfd-464c-a8c2-5f85fe08e3c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(1)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>year</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1982</div><input id='attrs-879899c9-4264-4aeb-8ef3-a00f8dad2f03' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-879899c9-4264-4aeb-8ef3-a00f8dad2f03' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-945b59c4-f560-4a20-8f9d-65caf4d35c2c' class='xr-var-data-in' type='checkbox'><label for='data-945b59c4-f560-4a20-8f9d-65caf4d35c2c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(1982)</pre></div></li></ul></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'polyfit_coefficients' (lat: 145, lon: 192)> Size: 223kB\n",
-       "dask.array<getitem, shape=(145, 192), dtype=float64, chunksize=(145, 192), chunktype=numpy.ndarray>\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float64 1kB -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0\n",
-       "  * lon      (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
-       "    degree   int64 8B 1\n",
-       "    year     int64 8B 1982"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "esm15_m20c_trnarr_em.isel(year=132)\n",
-    "# da.compute()\n",
-    "# da.rechunk() #'auto'"
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-   "execution_count": 15,
-   "id": "bf212bcb-7986-45d2-92e1-9a33aa061cd8",
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-       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
-       "  );\n",
-       "}\n",
-       "\n",
-       "html[theme=\"dark\"],\n",
-       "html[data-theme=\"dark\"],\n",
-       "body[data-theme=\"dark\"],\n",
-       "body.vscode-dark {\n",
-       "  --xr-font-color0: var(\n",
-       "    --jp-content-font-color0,\n",
-       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
-       "  );\n",
-       "  --xr-font-color2: var(\n",
-       "    --jp-content-font-color2,\n",
-       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
-       "  );\n",
-       "  --xr-font-color3: var(\n",
-       "    --jp-content-font-color3,\n",
-       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
-       "  );\n",
-       "  --xr-border-color: var(\n",
-       "    --jp-border-color2,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
-       "  );\n",
-       "  --xr-disabled-color: var(\n",
-       "    --jp-layout-color3,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
-       "  );\n",
-       "  --xr-background-color: var(\n",
-       "    --jp-layout-color0,\n",
-       "    var(--pst-color-on-background, #111111)\n",
-       "  );\n",
-       "  --xr-background-color-row-even: var(\n",
-       "    --jp-layout-color1,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
-       "  );\n",
-       "  --xr-background-color-row-odd: var(\n",
-       "    --jp-layout-color2,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
-       "  );\n",
-       "}\n",
-       "\n",
-       ".xr-wrap {\n",
-       "  display: block !important;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
-       "  line-height: 1.6;\n",
-       "}\n",
-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-obj-name,\n",
-       ".xr-group-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-group-name::before {\n",
-       "  content: \"📁\";\n",
-       "  padding-right: 0.3em;\n",
-       "}\n",
-       "\n",
-       ".xr-group-name,\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
-       "  margin-block-start: 0;\n",
-       "  margin-block-end: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: inline-block;\n",
-       "  opacity: 0;\n",
-       "  height: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "  border: 2px solid transparent !important;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:focus + label {\n",
-       "  border: 2px solid var(--xr-font-color0) !important;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: \"►\";\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: \"▼\";\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-top: 4px;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-group-box {\n",
-       "  display: inline-grid;\n",
-       "  grid-template-columns: 0px 20px auto;\n",
-       "  width: 100%;\n",
-       "}\n",
-       "\n",
-       ".xr-group-box-vline {\n",
-       "  grid-column-start: 1;\n",
-       "  border-right: 0.2em solid;\n",
-       "  border-color: var(--xr-border-color);\n",
-       "  width: 0px;\n",
-       "}\n",
-       "\n",
-       ".xr-group-box-hline {\n",
-       "  grid-column-start: 2;\n",
-       "  grid-row-start: 1;\n",
-       "  height: 1em;\n",
-       "  width: 20px;\n",
-       "  border-bottom: 0.2em solid;\n",
-       "  border-color: var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-group-box-contents {\n",
-       "  grid-column-start: 3;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: \"(\";\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: \")\";\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: \",\";\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  border-color: var(--xr-background-color-row-odd);\n",
-       "  margin-bottom: 0;\n",
-       "  padding-top: 2px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "  border-color: var(--xr-background-color-row-even);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  border-top: 2px dotted var(--xr-background-color);\n",
-       "  padding-bottom: 20px !important;\n",
-       "  padding-top: 10px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in + label,\n",
-       ".xr-var-data-in + label,\n",
-       ".xr-index-data-in + label {\n",
-       "  padding: 0 1px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > pre,\n",
-       ".xr-index-data > pre,\n",
-       ".xr-var-data > table > tbody > tr {\n",
-       "  background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
-       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
-       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
-       "  color: var(--xr-font-color0);\n",
-       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
-       "  stroke-width: 0.8px;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;polyfit_coefficients&#x27; (year: 133, lat: 145, lon: 192)&gt; Size: 30MB\n",
-       "dask.array&lt;rechunk-merge, shape=(133, 145, 192), dtype=float64, chunksize=(133, 145, 192), chunktype=numpy.ndarray&gt;\n",
-       "Coordinates:\n",
-       "  * year     (year) int64 1kB 1850 1851 1852 1853 1854 ... 1979 1980 1981 1982\n",
-       "  * lat      (lat) float64 1kB -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0\n",
-       "  * lon      (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
-       "    degree   int64 8B 1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-obj-name'>&#x27;polyfit_coefficients&#x27;</div><ul class='xr-dim-list'><li><span class='xr-has-index'>year</span>: 133</li><li><span class='xr-has-index'>lat</span>: 145</li><li><span class='xr-has-index'>lon</span>: 192</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-8869d537-86ed-4bae-bd32-374900e80be9' class='xr-array-in' type='checkbox' checked><label for='section-8869d537-86ed-4bae-bd32-374900e80be9' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(133, 145, 192), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 28.25 MiB </td>\n",
-       "                        <td> 28.25 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (133, 145, 192) </td>\n",
-       "                        <td> (133, 145, 192) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1466 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
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-       "        </td>\n",
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-       "  <text x=\"118.897059\" y=\"159.522059\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >192</text>\n",
-       "  <text x=\"198.897059\" y=\"94.209559\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,198.897059,94.209559)\">145</text>\n",
-       "  <text x=\"24.448529\" y=\"135.073529\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,24.448529,135.073529)\">133</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></div></li><li class='xr-section-item'><input id='section-067eb3a1-60d4-490b-9117-524321596240' class='xr-section-summary-in' type='checkbox'  checked><label for='section-067eb3a1-60d4-490b-9117-524321596240' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1850 1851 1852 ... 1980 1981 1982</div><input id='attrs-a4e1c32c-327b-4ddf-b5cf-ddd724ba7c64' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a4e1c32c-327b-4ddf-b5cf-ddd724ba7c64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c713227-bf55-4471-aba1-425761aa111e' class='xr-var-data-in' type='checkbox'><label for='data-4c713227-bf55-4471-aba1-425761aa111e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1850, 1851, 1852, 1853, 1854, 1855, 1856, 1857, 1858, 1859, 1860, 1861,\n",
-       "       1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873,\n",
-       "       1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 1882, 1883, 1884, 1885,\n",
-       "       1886, 1887, 1888, 1889, 1890, 1891, 1892, 1893, 1894, 1895, 1896, 1897,\n",
-       "       1898, 1899, 1900, 1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909,\n",
-       "       1910, 1911, 1912, 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921,\n",
-       "       1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933,\n",
-       "       1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945,\n",
-       "       1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957,\n",
-       "       1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969,\n",
-       "       1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981,\n",
-       "       1982])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -88.75 -87.5 ... 88.75 90.0</div><input id='attrs-4c268b28-6658-40ab-a909-fe41af614526' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c268b28-6658-40ab-a909-fe41af614526' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-541e42db-9625-4c8f-85d2-6837a7c15377' class='xr-var-data-in' type='checkbox'><label for='data-541e42db-9625-4c8f-85d2-6837a7c15377' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-90.  , -88.75, -87.5 , -86.25, -85.  , -83.75, -82.5 , -81.25, -80.  ,\n",
-       "       -78.75, -77.5 , -76.25, -75.  , -73.75, -72.5 , -71.25, -70.  , -68.75,\n",
-       "       -67.5 , -66.25, -65.  , -63.75, -62.5 , -61.25, -60.  , -58.75, -57.5 ,\n",
-       "       -56.25, -55.  , -53.75, -52.5 , -51.25, -50.  , -48.75, -47.5 , -46.25,\n",
-       "       -45.  , -43.75, -42.5 , -41.25, -40.  , -38.75, -37.5 , -36.25, -35.  ,\n",
-       "       -33.75, -32.5 , -31.25, -30.  , -28.75, -27.5 , -26.25, -25.  , -23.75,\n",
-       "       -22.5 , -21.25, -20.  , -18.75, -17.5 , -16.25, -15.  , -13.75, -12.5 ,\n",
-       "       -11.25, -10.  ,  -8.75,  -7.5 ,  -6.25,  -5.  ,  -3.75,  -2.5 ,  -1.25,\n",
-       "         0.  ,   1.25,   2.5 ,   3.75,   5.  ,   6.25,   7.5 ,   8.75,  10.  ,\n",
-       "        11.25,  12.5 ,  13.75,  15.  ,  16.25,  17.5 ,  18.75,  20.  ,  21.25,\n",
-       "        22.5 ,  23.75,  25.  ,  26.25,  27.5 ,  28.75,  30.  ,  31.25,  32.5 ,\n",
-       "        33.75,  35.  ,  36.25,  37.5 ,  38.75,  40.  ,  41.25,  42.5 ,  43.75,\n",
-       "        45.  ,  46.25,  47.5 ,  48.75,  50.  ,  51.25,  52.5 ,  53.75,  55.  ,\n",
-       "        56.25,  57.5 ,  58.75,  60.  ,  61.25,  62.5 ,  63.75,  65.  ,  66.25,\n",
-       "        67.5 ,  68.75,  70.  ,  71.25,  72.5 ,  73.75,  75.  ,  76.25,  77.5 ,\n",
-       "        78.75,  80.  ,  81.25,  82.5 ,  83.75,  85.  ,  86.25,  87.5 ,  88.75,\n",
-       "        90.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.875 3.75 ... 356.2 358.1</div><input id='attrs-4f7979c0-e02b-43c8-88ff-f99097158c10' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f7979c0-e02b-43c8-88ff-f99097158c10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7afd1364-d411-4af4-a81d-990d4296f911' class='xr-var-data-in' type='checkbox'><label for='data-7afd1364-d411-4af4-a81d-990d4296f911' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  0.   ,   1.875,   3.75 ,   5.625,   7.5  ,   9.375,  11.25 ,  13.125,\n",
-       "        15.   ,  16.875,  18.75 ,  20.625,  22.5  ,  24.375,  26.25 ,  28.125,\n",
-       "        30.   ,  31.875,  33.75 ,  35.625,  37.5  ,  39.375,  41.25 ,  43.125,\n",
-       "        45.   ,  46.875,  48.75 ,  50.625,  52.5  ,  54.375,  56.25 ,  58.125,\n",
-       "        60.   ,  61.875,  63.75 ,  65.625,  67.5  ,  69.375,  71.25 ,  73.125,\n",
-       "        75.   ,  76.875,  78.75 ,  80.625,  82.5  ,  84.375,  86.25 ,  88.125,\n",
-       "        90.   ,  91.875,  93.75 ,  95.625,  97.5  ,  99.375, 101.25 , 103.125,\n",
-       "       105.   , 106.875, 108.75 , 110.625, 112.5  , 114.375, 116.25 , 118.125,\n",
-       "       120.   , 121.875, 123.75 , 125.625, 127.5  , 129.375, 131.25 , 133.125,\n",
-       "       135.   , 136.875, 138.75 , 140.625, 142.5  , 144.375, 146.25 , 148.125,\n",
-       "       150.   , 151.875, 153.75 , 155.625, 157.5  , 159.375, 161.25 , 163.125,\n",
-       "       165.   , 166.875, 168.75 , 170.625, 172.5  , 174.375, 176.25 , 178.125,\n",
-       "       180.   , 181.875, 183.75 , 185.625, 187.5  , 189.375, 191.25 , 193.125,\n",
-       "       195.   , 196.875, 198.75 , 200.625, 202.5  , 204.375, 206.25 , 208.125,\n",
-       "       210.   , 211.875, 213.75 , 215.625, 217.5  , 219.375, 221.25 , 223.125,\n",
-       "       225.   , 226.875, 228.75 , 230.625, 232.5  , 234.375, 236.25 , 238.125,\n",
-       "       240.   , 241.875, 243.75 , 245.625, 247.5  , 249.375, 251.25 , 253.125,\n",
-       "       255.   , 256.875, 258.75 , 260.625, 262.5  , 264.375, 266.25 , 268.125,\n",
-       "       270.   , 271.875, 273.75 , 275.625, 277.5  , 279.375, 281.25 , 283.125,\n",
-       "       285.   , 286.875, 288.75 , 290.625, 292.5  , 294.375, 296.25 , 298.125,\n",
-       "       300.   , 301.875, 303.75 , 305.625, 307.5  , 309.375, 311.25 , 313.125,\n",
-       "       315.   , 316.875, 318.75 , 320.625, 322.5  , 324.375, 326.25 , 328.125,\n",
-       "       330.   , 331.875, 333.75 , 335.625, 337.5  , 339.375, 341.25 , 343.125,\n",
-       "       345.   , 346.875, 348.75 , 350.625, 352.5  , 354.375, 356.25 , 358.125])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>degree</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1</div><input id='attrs-d397d667-8864-4fff-ba55-ed67b12f281e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d397d667-8864-4fff-ba55-ed67b12f281e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9a762ef-f2ef-44bc-b0fe-193e2124379b' class='xr-var-data-in' type='checkbox'><label for='data-f9a762ef-f2ef-44bc-b0fe-193e2124379b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(1)</pre></div></li></ul></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'polyfit_coefficients' (year: 133, lat: 145, lon: 192)> Size: 30MB\n",
-       "dask.array<rechunk-merge, shape=(133, 145, 192), dtype=float64, chunksize=(133, 145, 192), chunktype=numpy.ndarray>\n",
-       "Coordinates:\n",
-       "  * year     (year) int64 1kB 1850 1851 1852 1853 1854 ... 1979 1980 1981 1982\n",
-       "  * lat      (lat) float64 1kB -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0\n",
-       "  * lon      (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
-       "    degree   int64 8B 1"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "esm15_m20c_trnarr_em#.isel(year=132)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "119b9ef0-0b98-48ab-8b31-ec2dc63e3090",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<xarray.DataArray 'polyfit_coefficients' (year: 133, lat: 145, lon: 192)> Size: 30MB\n",
-      "dask.array<rechunk-merge, shape=(133, 145, 192), dtype=float64, chunksize=(133, 145, 192), chunktype=numpy.ndarray>\n",
-      "Coordinates:\n",
-      "  * year     (year) int64 1kB 1850 1851 1852 1853 1854 ... 1979 1980 1981 1982\n",
-      "  * lat      (lat) float64 1kB -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0\n",
-      "  * lon      (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
-      "    degree   int64 8B 1\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(esm15_m20c_trnarr_em)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "c3bf7703-c323-4226-b4ed-a49ba24446ca",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# print(esm15_m20c_trnarr_em)\n",
-    "arr = esm15_m20c_trnarr_em.chunk({\n",
-    "    \"year\": -1,      # keep full year dimension in one chunk\n",
-    "    \"lat\": 50,       # break 145 → 50 + 50 + 45\n",
-    "    \"lon\": 64        # break 192 → 64 + 64 + 64\n",
-    "})\n",
-    "\n",
-    "# arr.to_netcdf(\"/scratch/eg3/cxc548/ACCESS_ESM15_EM_31yr_trn_TS.nc\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8ff0afa7-4b24-46a6-9193-2b278373683f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "arr.to_netcdf(\"/scratch/iq82/fc6164/esmvaltool_output/process_data/ACCESS_ESM15_EM_31yr_trn_TS.nc\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "643b1078-9250-4976-8e66-7bfce3457601",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python [conda env:analysis3-26.01]",
-   "language": "python",
-   "name": "conda-env-analysis3-26.01-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": "3.11.14"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}