diff --git a/15-ENSO_diversity.ipynb b/15-ENSO_diversity.ipynb
index e3b8d27..d8c8337 100644
--- a/15-ENSO_diversity.ipynb
+++ b/15-ENSO_diversity.ipynb
@@ -23,16 +23,358 @@
     "- **NCEP2 (1979–2018)**: A second-generation reanalysis from NCEP that improves upon earlier reanalysis products and offers global atmospheric and surface data.   "
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "0e694ef5-b014-4e98-a2c6-6d069cb669d3",
+   "metadata": {},
+   "source": [
+    "## Dask Cluster \n",
+    "\n",
+    "Start a cluster with multiple cores "
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
+   "id": "dfdb2537-52da-47a8-8051-fee717c87adf",
+   "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-a89a9713-edb4-11ef-8802-00000183fe80</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",
+       "\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;\">3a0aa1be</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> 4\n",
+       "                </td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Total threads:</strong> 12\n",
+       "                </td>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Total memory:</strong> 46.00 GiB\n",
+       "                </td>\n",
+       "            </tr>\n",
+       "            \n",
+       "            <tr>\n",
+       "    <td style=\"text-align: left;\"><strong>Status:</strong> running</td>\n",
+       "    <td style=\"text-align: left;\"><strong>Using processes:</strong> True</td>\n",
+       "</tr>\n",
+       "\n",
+       "            \n",
+       "        </table>\n",
+       "\n",
+       "        <details>\n",
+       "            <summary style=\"margin-bottom: 20px;\">\n",
+       "                <h3 style=\"display: inline;\">Scheduler Info</h3>\n",
+       "            </summary>\n",
+       "\n",
+       "            <div style=\"\">\n",
+       "    <div>\n",
+       "        <div style=\"width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;\"> </div>\n",
+       "        <div style=\"margin-left: 48px;\">\n",
+       "            <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n",
+       "            <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-9b7a6118-81d8-49f0-b288-00bda3581da7</p>\n",
+       "            <table style=\"width: 100%; text-align: left;\">\n",
+       "                <tr>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Comm:</strong> tcp://127.0.0.1:46173\n",
+       "                    </td>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Workers:</strong> 4\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> 12\n",
+       "                    </td>\n",
+       "                </tr>\n",
+       "                <tr>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Started:</strong> Just now\n",
+       "                    </td>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Total memory:</strong> 46.00 GiB\n",
+       "                    </td>\n",
+       "                </tr>\n",
+       "            </table>\n",
+       "        </div>\n",
+       "    </div>\n",
+       "\n",
+       "    <details style=\"margin-left: 48px;\">\n",
+       "        <summary style=\"margin-bottom: 20px;\">\n",
+       "            <h3 style=\"display: inline;\">Workers</h3>\n",
+       "        </summary>\n",
+       "\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 0</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:41387\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 3\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/33427/status\" target=\"_blank\">/proxy/33427/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 11.50 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:34553\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/135541908.gadi-pbs/dask-scratch-space/worker-op46eqwa\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://127.0.0.1:33117\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 3\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/35387/status\" target=\"_blank\">/proxy/35387/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 11.50 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:40337\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/135541908.gadi-pbs/dask-scratch-space/worker-4jvxmsan\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://127.0.0.1:38293\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 3\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/36333/status\" target=\"_blank\">/proxy/36333/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 11.50 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:42425\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/135541908.gadi-pbs/dask-scratch-space/worker-t_b8_lpv\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://127.0.0.1:34375\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 3\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/proxy/42859/status\" target=\"_blank\">/proxy/42859/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 11.50 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:41597\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/135541908.gadi-pbs/dask-scratch-space/worker-eft2i9tr\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://127.0.0.1:46173' processes=4 threads=12, memory=46.00 GiB>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from dask.distributed import Client, progress\n",
+    "client = Client()\n",
+    "client"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9adc99f1-de29-4e08-969f-23908ef4db1a",
+   "metadata": {},
+   "source": [
+    "## Datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
    "id": "03ca2b4d-aa80-4b22-86e1-bb668f4c832e",
    "metadata": {
     "scrolled": true
    },
    "outputs": [],
    "source": [
-    "from esmvalcore.config import CFG\n",
     "from esmvalcore.dataset import Dataset"
    ]
   },
@@ -46,102 +388,117 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "0d112937-acb8-40c7-8fda-24779cd4c46d",
+   "execution_count": 21,
+   "id": "0e0aefb0-234b-4d6a-98be-8564d468e635",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "obs = Dataset(\n",
-    "    short_name='tos',\n",
-    "    dataset='NOAA-ERSSTv5',\n",
-    "    mip=\"Omon\",\n",
-    "    project='OBS6',\n",
+    "model_datasets = {\n",
+    "\"ACCESS-CM2\": \n",
+    "    Dataset(\n",
+    "    short_name='ts',\n",
+    "    project='CMIP6',\n",
+    "    mip=\"Amon\",\n",
+    "    exp=\"historical\",\n",
+    "    ensemble=\"r1i1p1f1\",\n",
+    "    timerange=\"19000101/20190101\",\n",
+    "    dataset=\"ACCESS-CM2\",\n",
+    "    grid=\"gn\"\n",
+    ")}\n",
+    "\n",
+    "obs_datasets = {\n",
+    "\"HadISST\": \n",
+    "    Dataset(\n",
+    "    short_name='ts',\n",
+    "    dataset='HadISST',\n",
+    "    mip=\"Amon\",\n",
+    "    project='OBS',\n",
     "    type='reanaly',\n",
-    "    timerange=\"1900/2019\",\n",
-    "    tier=2)\n",
-    "# list(obs.from_files())\n",
-    "len(obs.files)"
+    "    tier=2),\n",
+    "# \"ERSSTv5\":\n",
+    "#     Dataset(\n",
+    "#     short_name='tos',\n",
+    "#     dataset='NOAA-ERSSTv5',\n",
+    "#     mip=\"Omon\",\n",
+    "#     project='OBS6',\n",
+    "#     type='reanaly',\n",
+    "#     tier=2),\n",
+    "# \"ERA-Interim\": \n",
+    "#     Dataset(\n",
+    "#     short_name='tos',\n",
+    "#     dataset='ERA-Interim',\n",
+    "#     mip=\"Omon\",\n",
+    "#     project='OBS6',\n",
+    "#     type='reanaly',\n",
+    "#     timerange=\"19790101/20190101\",\n",
+    "#     tier=3)\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1adc46e-b9bb-4a5b-acf6-c3fdb0bed0ba",
+   "metadata": {},
+   "source": [
+    "## Add Ancillary files\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "6c57f395-5f5a-47ec-a714-99d191ec22c6",
-   "metadata": {
-    "scrolled": true
-   },
+   "execution_count": 22,
+   "id": "cce89e9d-72ee-401c-b4ed-ab745992b1a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for name, dataset in model_datasets.items():\n",
+    "    dataset.add_supplementary(short_name='sftlf', mip=\"fx\")\n",
+    "    dataset.add_supplementary(short_name=\"areacella\", mip=\"fx\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "ba7e62d3-98f3-4fa9-b1f4-ff529d93617d",
+   "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "([LocalFile('/g/data/fs38/publications/CMIP6/CMIP/CSIRO-ARCCSS/ACCESS-CM2/historical/r1i1p1f1/Omon/tos/gn/v20191108/tos_Omon_ACCESS-CM2_historical_r1i1p1f1_gn_185001-201412.nc')],\n",
-       " [LocalFile('/g/data/fs38/publications/CMIP6/CMIP/CSIRO-ARCCSS/ACCESS-CM2/historical/r1i1p1f1/Ofx/areacello/gn/v20191108/areacello_Ofx_ACCESS-CM2_historical_r1i1p1f1_gn.nc')])"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:esmvalcore.cmor._fixes.fix.genericfix:Long name changed from 'Percentage of the grid  cell occupied by land (including lakes)' to 'Percentage of the Grid Cell Occupied by Land (Including Lakes)'\n",
+      "(for file /g/data/fs38/publications/CMIP6/CMIP/CSIRO-ARCCSS/ACCESS-CM2/historical/r1i1p1f1/fx/sftlf/gn/v20191108/sftlf_fx_ACCESS-CM2_historical_r1i1p1f1_gn.nc)\n"
+     ]
     }
    ],
    "source": [
-    "## model\n",
-    "var = Dataset(\n",
-    "    short_name='tos',\n",
-    "    mip='Omon',\n",
-    "    project='CMIP6',\n",
-    "    activity='CMIP',\n",
-    "    dataset='ACCESS-CM2',\n",
-    "    ensemble='r1i1p1f1',\n",
-    "    institute='*',\n",
-    "    grid='*',\n",
-    "    exp='historical',\n",
-    "    # timerange='1979/2019',\n",
-    "    # derive='true'\n",
-    ")\n",
-    "\n",
-    "var_noarea = var.copy()\n",
-    "\n",
-    "var.add_supplementary(short_name='areacello', mip='Ofx')\n",
-    "var.find_files()\n",
-    "# list(var.from_files())[0].files\n",
-    "var.files,var.supplementaries[0].files\n"
+    "model_datasets = {name: dataset.load() for name, dataset in model_datasets.items()}\n",
+    "obs_datasets = {name: dataset.load() for name, dataset in obs_datasets.items()}"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "300de51c",
-   "metadata": {
-    "scrolled": true
-   },
+   "execution_count": 6,
+   "id": "b07c8086-b740-490f-96d9-e0521b464bbc",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "import iris\n",
-    "from esmvalcore.preprocessor import (\n",
-    "                detrend, # dimension, method, \n",
-    "                meridional_statistics, # \n",
-    "                zonal_statistics,\n",
-    "                area_statistics,\n",
-    "                regrid, #esmf bilinear\n",
-    "                mask_landsea,\n",
-    "                anomalies,\n",
-    "                extract_month, \n",
-    "                mask_inside_range, mask_above_threshold, mask_below_threshold,\n",
-    "                extract_region, # equatorial pacific #1 - 150°E-90°W, zonal 5°S-5°N average\n",
-    "                climate_statistics,# month mean, standard deviation\n",
-    "                rolling_window_statistics) \n",
+    "from esmvalcore.preprocessor import detrend  # Remove trends from the data\n",
+    "from esmvalcore.preprocessor import meridional_statistics  # Calculate statistics along meridional direction\n",
+    "from esmvalcore.preprocessor import zonal_statistics  # Calculate statistics along zonal direction\n",
+    "from esmvalcore.preprocessor import area_statistics  # Compute area-based statistics\n",
+    "from esmvalcore.preprocessor import regrid  # Regrid data using ESMF bilinear method\n",
+    "from esmvalcore.preprocessor import mask_landsea  # Mask land or sea regions\n",
+    "from esmvalcore.preprocessor import anomalies  # Compute anomalies in the data\n",
+    "from esmvalcore.preprocessor import extract_month  # Extract specific month from the data\n",
+    "from esmvalcore.preprocessor import extract_time\n",
+    "from esmvalcore.preprocessor import mask_inside_range  # Mask values inside a specified range\n",
+    "from esmvalcore.preprocessor import mask_above_threshold  # Mask values above a specified threshold\n",
+    "from esmvalcore.preprocessor import mask_below_threshold  # Mask values below a specified threshold\n",
+    "from esmvalcore.preprocessor import extract_region  # Extract a specific geographical region (e.g., Equatorial Pacific)\n",
+    "from esmvalcore.preprocessor import climate_statistics  # Compute month mean and standard deviation\n",
+    "from esmvalcore.preprocessor import rolling_window_statistics  # Apply rolling window statistics\n",
     "\n",
+    "import iris\n",
     "import numpy as np\n",
     "import iris.quickplot as qplt\n",
     "import matplotlib.pyplot as plt\n",
@@ -153,50 +510,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "784947b3-b662-479a-8a4f-56432f77d324",
    "metadata": {
     "scrolled": true
    },
    "outputs": [],
    "source": [
-    "## \n",
     "def nino3_4ssta(cube):\n",
     "    cube = extract_region(cube, 190, 240, -5, 5) #\n",
-    "    # cube = regrid(cube, target_grid=\"1x1\", scheme=\"linear\")   \n",
-    "    \n",
+    "    cube = extract_time(cube, start_year=1900, start_month=1, start_day=1, end_year=2014, end_month=12, end_day=31)    \n",
+    "    cube = regrid(cube, target_grid=\"1x1\", scheme=\"linear\")   \n",
     "    # triangle weighted- double smoothed\n",
     "    cube = rolling_window_statistics(cube, coordinate='time', operator='mean', window_length=5)\n",
     "    cube = rolling_window_statistics(cube, coordinate='time', operator='mean', window_length=5)\n",
-    "    \n",
     "    cube = area_statistics(cube, 'mean')\n",
     "    cube = extract_month(cube, 12)\n",
     "    cube = anomalies(cube, period='monthly', standardize=True)  #normalise\n",
-    "  \n",
     "    return cube\n",
     "\n",
     "def eq_ssta(cube):\n",
     "    cube = extract_region(cube, 150, 270, -5, 5)\n",
+    "    cube = extract_time(cube, start_year=1900, start_month=1, start_day=1, end_year=2014, end_month=12, end_day=31)    \n",
     "    # season cycle? detrend? #convert units\n",
     "    cube = rolling_window_statistics(cube, coordinate='time', operator='mean', window_length=5)\n",
     "    cube = rolling_window_statistics(cube, coordinate='time', operator='mean', window_length=5)\n",
     "    cube = extract_month(cube, 12)\n",
-    "    cube = anomalies(cube,period='monthly')\n",
+    "    cube = anomalies(cube, period='monthly')\n",
     "    cube = regrid(cube, target_grid=\"1x1\", scheme=\"linear\") \n",
-    "    \n",
     "    cube = meridional_statistics(cube, 'mean') #\n",
     "     # weighted running average 5-degree\n",
     "    cube = rolling_window_statistics(cube, coordinate='longitude', operator='mean', window_length=5)\n",
     "    cube = rolling_window_statistics(cube, coordinate='longitude', operator='mean', window_length=5)\n",
     "    return cube\n",
     "\n",
-    "\n",
     "def enso_events(cube):\n",
-    "    # mean, std = cube.data.mean(), cube.data.std() #normalised, use 0.75\n",
+    "    mean, std = cube.data.mean(), cube.data.std() #normalised, use 0.75\n",
     "    # range1, range2 = mean-0.75*std, mean + 0.75*std \n",
-    "    \n",
-    "    a_events = mask_to_years(mask_above_threshold(cube.copy(), -0.75))\n",
-    "    o_events = mask_to_years(mask_below_threshold(cube.copy(), 0.75))\n",
+    "    a_events = mask_to_years(mask_above_threshold(cube.copy(), -0.75*std))\n",
+    "    o_events = mask_to_years(mask_below_threshold(cube.copy(), 0.75*std))\n",
     "    return {'nina':a_events, 'nino':o_events}\n",
     "\n",
     "def mask_to_years(events):\n",
@@ -223,25 +575,31 @@
     "            loc_ls.append(cube.coord('longitude').points[indx])\n",
     "\n",
     "        res_lon[enso] = loc_ls\n",
-    "\n",
+    "        \n",
+    "    res_lon['enso'] = res_lon['nino'] + res_lon['nina']\n",
     "    return res_lon # return data to plot \n",
     "\n",
     "# IQR and abs((model-ref)/ref)*100\n",
     "def iqr(data):\n",
     "    #calculate interquartile range \n",
     "    q3, q1 = np.percentile(data, [75 ,25])\n",
-    "    print(q3, q1)\n",
     "    iqr = q3 - q1\n",
     "    return iqr\n",
     "    \n",
     "def compute(obs_iqr, mod_iqr):\n",
-    "    return abs((mod_iqr-obs_iqr)/obs_iqr)*100"
+    "    return abs((mod_iqr-obs_iqr)/obs_iqr)*100\n",
+    "\n",
+    "def preproc(dataset):\n",
+    "    nino_res = nino3_4ssta(dataset)\n",
+    "    events = enso_events(nino_res)\n",
+    "    eq_res = eq_ssta(dataset)\n",
+    "    return eq_res, events\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "2302a678-63e9-4fb2-8eb1-e702ff6213f2",
+   "execution_count": 24,
+   "id": "c4045a52-2fe8-405f-9f4d-3c7820557959",
    "metadata": {},
    "outputs": [
     {
@@ -250,14 +608,6 @@
      "text": [
       "/g/data/xp65/public/apps/med_conda/envs/esmvaltool-0.4/lib/python3.11/site-packages/iris/cube.py:4620: IrisIgnoringBoundsWarning: The bounds of coordinate 'time' were ignored in the rolling window operation.\n",
       "  warnings.warn(\n",
-      "/g/data/xp65/public/apps/med_conda/envs/esmvaltool-0.4/lib/python3.11/site-packages/iris/coords.py:2153: IrisVagueMetadataWarning: Cannot check if coordinate is contiguous: Invalid operation for 'cell index along second dimension', with 0 bound(s). Contiguous bounds are only defined for 1D coordinates with 2 bounds. Metadata may not be fully descriptive for 'cell index along second dimension'. Ignoring bounds.\n",
-      "  warnings.warn(\n",
-      "/g/data/xp65/public/apps/med_conda/envs/esmvaltool-0.4/lib/python3.11/site-packages/iris/coords.py:2153: IrisVagueMetadataWarning: Cannot check if coordinate is contiguous: Invalid operation for 'cell index along first dimension', with 0 bound(s). Contiguous bounds are only defined for 1D coordinates with 2 bounds. Metadata may not be fully descriptive for 'cell index along first dimension'. Ignoring bounds.\n",
-      "  warnings.warn(\n",
-      "/g/data/xp65/public/apps/med_conda/envs/esmvaltool-0.4/lib/python3.11/site-packages/iris/coords.py:2140: IrisVagueMetadataWarning: Collapsing a multi-dimensional coordinate. Metadata may not be fully descriptive for 'latitude'.\n",
-      "  warnings.warn(\n",
-      "/g/data/xp65/public/apps/med_conda/envs/esmvaltool-0.4/lib/python3.11/site-packages/iris/coords.py:2140: IrisVagueMetadataWarning: Collapsing a multi-dimensional coordinate. Metadata may not be fully descriptive for 'longitude'.\n",
-      "  warnings.warn(\n",
       "/g/data/xp65/public/apps/med_conda/envs/esmvaltool-0.4/lib/python3.11/site-packages/numpy/ma/core.py:5417: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
       "  ret = super().var(axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
       "/g/data/xp65/public/apps/med_conda/envs/esmvaltool-0.4/lib/python3.11/site-packages/numpy/core/_methods.py:195: RuntimeWarning: invalid value encountered in divide\n",
@@ -269,16 +619,20 @@
       "/g/data/xp65/public/apps/med_conda/envs/esmvaltool-0.4/lib/python3.11/site-packages/iris/cube.py:4620: IrisIgnoringBoundsWarning: The bounds of coordinate 'longitude' were ignored in the rolling window operation.\n",
       "  warnings.warn(\n"
      ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "245.5 219.0\n",
-      "ACCESS-CM2 enso IQR: 26.5\n",
-      "245.5 219.0\n"
-     ]
-    },
+    }
+   ],
+   "source": [
+    "model_datasets_prep = {name: preproc(dataset) for name, dataset in model_datasets.items()}\n",
+    "\n",
+    "model_datasets_res = {name: diversity(*preprocs) for name, preprocs in model_datasets_prep.items()}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "c07ece66-8d9c-41d8-bf84-789461a38985",
+   "metadata": {},
+   "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
@@ -290,47 +644,37 @@
       "/g/data/xp65/public/apps/med_conda/envs/esmvaltool-0.4/lib/python3.11/site-packages/numpy/core/_methods.py:195: RuntimeWarning: invalid value encountered in divide\n",
       "  ret = um.true_divide(\n",
       "/g/data/xp65/public/apps/med_conda/envs/esmvaltool-0.4/lib/python3.11/site-packages/iris/cube.py:4620: IrisIgnoringBoundsWarning: The bounds of coordinate 'time' were ignored in the rolling window operation.\n",
+      "  warnings.warn(\n",
+      "/g/data/xp65/public/apps/med_conda/envs/esmvaltool-0.4/lib/python3.11/site-packages/iris/cube.py:4067: IrisUserWarning: Collapsing spatial coordinate 'latitude' without weighting\n",
+      "  warnings.warn(\n",
+      "/g/data/xp65/public/apps/med_conda/envs/esmvaltool-0.4/lib/python3.11/site-packages/iris/cube.py:4620: IrisIgnoringBoundsWarning: The bounds of coordinate 'longitude' were ignored in the rolling window operation.\n",
       "  warnings.warn(\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "obs_datasets_prep = {name: preproc(dataset) for name, dataset in obs_datasets.items()}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "cd9c6341-575e-44d8-afcb-f461b18923ae",
+   "metadata": {},
+   "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "235.5 194.5\n",
-      "NOAA-ERSSTv5 enso IQR: 41.0\n",
-      "235.5 194.5\n",
-      "metric:  35.36585365853659\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/g/data/xp65/public/apps/med_conda/envs/esmvaltool-0.4/lib/python3.11/site-packages/iris/cube.py:4067: IrisUserWarning: Collapsing spatial coordinate 'latitude' without weighting\n",
-      "  warnings.warn(\n",
-      "/g/data/xp65/public/apps/med_conda/envs/esmvaltool-0.4/lib/python3.11/site-packages/iris/cube.py:4620: IrisIgnoringBoundsWarning: The bounds of coordinate 'longitude' were ignored in the rolling window operation.\n",
-      "  warnings.warn(\n"
+      "metric:  58.69565217391305\n"
      ]
     }
    ],
    "source": [
-    "datasets = [var, obs]\n",
-    "\n",
-    "#plot 1\n",
-    "process = {}\n",
-    "metric = []\n",
-    "for ds in datasets:\n",
-    "    nino_res = nino3_4ssta(ds.load())\n",
-    "    events = enso_events(nino_res)\n",
-    "\n",
-    "    eq_res = eq_ssta(ds.load())\n",
-    "    results_lon = diversity(eq_res, events)\n",
-    "    results_lon['enso'] = results_lon['nino'] + results_lon['nina']\n",
-    "    print(ds['dataset'], 'enso IQR:', iqr(results_lon['enso'])) \n",
-    "    process[ds['dataset']] = results_lon\n",
-    "    metric.append(iqr(results_lon['enso']))#plot with label\n",
+    "obs_datasets_res = {name: diversity(*preprocs) for name, preprocs in obs_datasets_prep.items()}\n",
     "\n",
+    "metric = [iqr(model_datasets_res[\"ACCESS-CM2\"]['enso']), \n",
+    "          iqr(obs_datasets_res[\"HadISST\"]['enso'])]\n",
     "print('metric: ', compute(metric[1],metric[0]))"
    ]
   },
@@ -346,7 +690,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 27,
    "id": "ae5d0b8e-bf8f-4440-bf0e-9457f5dbc7f4",
    "metadata": {},
    "outputs": [
@@ -356,13 +700,13 @@
        "Text(0.75, 0.8, '* observation')"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -410,13 +754,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 28,
    "id": "945b214c-b5b0-402e-b9a3-273eac8cefac",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 2000x700 with 2 Axes>"
       ]
@@ -434,13 +778,14 @@
     "    else:\n",
     "        return f'{int(x)}°E'\n",
     "\n",
-    "\n",
+    "ds_labels = ['ACCESS-CM2','HadISST']\n",
     "fig = plt.figure(figsize=(20, 7))\n",
-    "nina = [process[ds]['nina'] for ds in process.keys()] # subplots for nina and nino\n",
-    "nino = [process[ds]['nino'] for ds in process.keys()]\n",
+    "nina = [model_datasets_res[\"ACCESS-CM2\"]['nina'], obs_datasets_res[\"HadISST\"]['nina']] # subplots for nina and nino\n",
+    "nino = [model_datasets_res[\"ACCESS-CM2\"]['nino'], obs_datasets_res[\"HadISST\"]['nino']]\n",
+    "#another plot for enso = [process[ds]['enso'] for ds in process.keys()]\n",
     "\n",
     "ax1 = plt.subplot(121)\n",
-    "bplt = ax1.boxplot(nina, labels=process.keys(), showmeans=True)\n",
+    "bplt = ax1.boxplot(nina, labels=ds_labels, showmeans=True)\n",
     "#check whiskers to 10th 90th percentile,, whis=(5,95) box-25th to 75th, diamond mean\n",
     "\n",
     "for k in bplt.keys():\n",
@@ -458,7 +803,7 @@
     "plt.grid(linestyle='--', axis='y')\n",
     "\n",
     "ax1 = plt.subplot(122)\n",
-    "bplt = ax1.boxplot(nino, labels=process.keys(), showmeans=True) # whis=(10,90),\n",
+    "bplt = ax1.boxplot(nino, labels=ds_labels, showmeans=True) # whis=(10,90),\n",
     "for k in bplt.keys():\n",
     "    plt.setp(bplt[k], linewidth=2)\n",
     "plt.title('El Nino diversity')\n",
@@ -467,6 +812,36 @@
     "plt.gca().yaxis.set_major_formatter(plt.FuncFormatter(format_longitude))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "f7a5a5d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#plot for \n",
+    "enso = [model_datasets_res[\"ACCESS-CM2\"]['enso'], obs_datasets_res[\"HadISST\"]['enso']]\n",
+    "\n",
+    "bplt = plt.boxplot(enso, labels=ds_labels, showmeans=True) # whis=(10,90),\n",
+    "for k in bplt.keys():\n",
+    "    plt.setp(bplt[k], linewidth=2)\n",
+    "plt.title('ENSO diversity')\n",
+    "plt.ylabel('longitude of min/max SSTA')\n",
+    "plt.grid(linestyle='--', axis='y')\n",
+    "plt.gca().yaxis.set_major_formatter(plt.FuncFormatter(format_longitude))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "b4b4dee0-0d9a-458f-b86b-2ccd9b785d2c",