12-ENSO_seasonality.ipynb

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    "# ENSO_seasonality: Seasonal Variability of SST Anomalies in the Central Equatorial Pacific  \n",
    "\n",
    "Computes the ratio of boreal winter (November–January, NDJ) to spring (March–May, MAM) standard deviation of sea surface temperature anomalies (SSTA) in the Niño3.4 region (horizontal average over 5°S–5°N, 170°W–120°W). This metric evaluates the seasonality of ENSO variability, characterised by maximum variability in winter and minimum variability in spring in the observations.  \n",
    "\n",
    "## Datasets Used for Seasonality Comparison  \n",
    "\n",
    "The following reference datasets are used to evaluate the seasonal variability of ENSO-related SST anomalies in model simulations:  \n",
    "\n",
    "- **TropFlux (1979–2018)**: The primary dataset for comparison, designed for analysing tropical climate variability, including surface fluxes and SST anomalies.  \n",
    "\n",
    "Additional datasets include:  \n",
    "- **20CRv2 (1871–2012)**: A historical reanalysis product providing long-term atmospheric data using surface pressure observations.  \n",
    "- **ERA-Interim (1979–2018)**: A high-resolution reanalysis dataset offering consistent atmospheric and surface variable estimates.  \n",
    "- **ERSSTv5 (1854–2018)**: A globally gridded dataset optimised for climate monitoring, including ENSO-related SST anomaly studies.  \n",
    "- **HadISST (1870–2018)**: A historical dataset combining SST and sea ice observations with reconstructions.  \n",
    "- **NCEP2 (1979–2018)**: A second-generation reanalysis product delivering improved atmospheric and surface data globally.  \n"
   ]
  },
  {
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   "id": "a3a2571a-c2f1-4ac8-a272-d4c731c062cd",
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   "source": [
    "from esmvalcore.dataset import Dataset"
   ]
  },
  {
   "cell_type": "code",
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   "id": "a20485d9-9e93-4564-9fc0-f07c64a3bc36",
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   "source": [
    "model_datasets = {\n",
    "\"ACCESS-ESM1-5\": \n",
    "    Dataset(\n",
    "    short_name='tos',\n",
    "    project='CMIP6',\n",
    "    mip=\"Omon\",\n",
    "    exp=\"historical\",\n",
    "    ensemble=\"r1i1p1f1\",\n",
    "    timerange=\"19790101/20190101\",\n",
    "    dataset=\"ACCESS-ESM1-5\",\n",
    "    grid=\"gn\"\n",
    ")}\n",
    "model_datasets[\"ACCESS-ESM1-5\"].add_supplementary(short_name='areacello', mip='Ofx')\n",
    "\n",
    "obs_datasets = {\n",
    "\"HadISST\": \n",
    "    Dataset(\n",
    "    short_name='tos',\n",
    "    dataset='HadISST',\n",
    "    mip=\"Omon\",\n",
    "    project='OBS',\n",
    "    type='reanaly',\n",
    "    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",
    "}"
   ]
  },
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       "    <tr class=\"iris\">\n",
       "<th class=\"iris iris-word-cell\">Sea Surface Temperature (degC)</th>\n",
       "<th class=\"iris iris-word-cell\">time</th>\n",
       "<th class=\"iris iris-word-cell\">cell index along second dimension</th>\n",
       "<th class=\"iris iris-word-cell\">cell index along first dimension</th>\n",
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       "    <td class=\"iris-inclusion-cell\">dimension</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
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       "    <td class=\"iris-word-cell iris-subheading-cell\">\tlatitude</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
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       "    <td class=\"iris-title iris-word-cell\">Cell measures</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tcell_area</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
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       "    <td class=\"iris-title iris-word-cell\">Cell methods</td>\n",
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       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\t0</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">area: mean where sea</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\t1</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">time: mean</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Attributes</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tConventions</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;CF-1.7 CMIP-6.2&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tactivity_id</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;CMIP&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tbranch_method</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;standard&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tbranch_time_in_child</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">0.0</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tbranch_time_in_parent</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">-616894</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tcmor_version</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;3.4.0&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tdata_specs_version</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;01.00.30&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\texperiment</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;all-forcing simulation of the recent past&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\texperiment_id</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;historical&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\texternal_variables</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;areacello&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tforcing_index</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">1</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tfrequency</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;mon&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tfurther_info_url</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;https://furtherinfo.es-doc.org/CMIP6.CSIRO.ACCESS-ESM1-5.historical.no ...&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tgrid</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;native atmosphere N96 grid (145x192 latxlon)&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tgrid_label</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;gn&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tinitialization_index</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">1</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tinstitution</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;Commonwealth Scientific and Industrial Research Organisation, Aspendale, ...&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tinstitution_id</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;CSIRO&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tlicense</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;CMIP6 model data produced by CSIRO is licensed under a Creative Commons ...&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tmip_era</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;CMIP6&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tnominal_resolution</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;250 km&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tnotes</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&quot;Exp: ESM-historical; Local ID: HI-05; Variable: tos ([&#x27;sst&#x27;])&quot;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tparent_activity_id</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;CMIP&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tparent_experiment_id</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;piControl&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tparent_mip_era</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;CMIP6&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tparent_source_id</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;ACCESS-ESM1-5&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tparent_time_units</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;days since 1850-1-1 00:00:00&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tparent_variant_label</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;r1i1p1f1&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tphysics_index</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">1</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tproduct</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;model-output&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\trealization_index</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">1</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\trealm</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;ocean&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\trun_variant</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;forcing: GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = CO2, N2O, CH4, CFC11, CFC12, ...&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tsource</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;ACCESS-ESM1.5 (2019): \\naerosol: CLASSIC (v1.0)\\natmos: HadGAM2 (r1.1, ...&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tsource_id</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;ACCESS-ESM1-5&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tsource_type</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;AOGCM&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tsub_experiment</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;none&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tsub_experiment_id</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;none&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\ttable_id</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;Omon&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\ttable_info</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;Creation Date:(30 April 2019) MD5:e14f55f257cceafb2523e41244962371&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\ttitle</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;ACCESS-ESM1-5 output prepared for CMIP6&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tvariable_id</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;tos&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tvariant_label</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;r1i1p1f1&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tversion</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;v20191115&#x27;</td>\n",
       "</tr>\n",
       "</table>\n",
       "        "
      ],
      "text/plain": [
       "<iris 'Cube' of sea_surface_temperature / (degC) (time: 432; cell index along second dimension: 300; cell index along first dimension: 360)>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_datasets[\"ACCESS-ESM1-5\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5e048061-f220-4e34-820a-629c7b6b5580",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:esmvalcore.local:'CMIP6' rootpaths '/g/data/zv30/cmip/CMIP6' set in config-user.yml do not exist\n",
      "WARNING:esmvalcore.local:'OBS6' rootpaths '/g/data/nf33/public/data/ESMValTool/obsdata' set in config-user.yml do not exist\n"
     ]
    }
   ],
   "source": [
    "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": 4,
   "id": "3e4b8d63-b98d-4195-a6cb-7a5316c6db3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from esmvalcore.preprocessor import anomalies\n",
    "from esmvalcore.preprocessor import area_statistics\n",
    "from esmvalcore.preprocessor import climate_statistics\n",
    "from esmvalcore.preprocessor import convert_units\n",
    "from esmvalcore.preprocessor import extract_region\n",
    "from esmvalcore.preprocessor import extract_season\n",
    "from esmvalcore.preprocessor import regrid\n",
    "from esmvalcore.preprocessor import detrend\n",
    "from esmvalcore.preprocessor import meridional_statistics\n",
    "from esmvalcore.preprocessor import mask_landsea\n",
    "import iris\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import iris.quickplot as qplt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "32af671f-d71e-4899-becd-cfb6bb28c0f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sst_boreal(cube, season): #season->'NDJ','MAM'\n",
    "    nino34_latext_region = {\"start_longitude\": 190., \"end_longitude\": 240., \"start_latitude\": -5., \"end_latitude\": 5.}\n",
    "    # cube = regrid(cube, target_grid=\"1x1\", scheme=\"linear\")\n",
    "    cube = convert_units(cube, units=\"degC\")\n",
    "    cube = extract_region(cube, **nino34_latext_region)\n",
    "    cube = anomalies(cube,period='monthly')\n",
    "    cube = area_statistics(cube,operator='mean')\n",
    "    cube = extract_season(cube, season) # get NDJ\n",
    "    cube = climate_statistics(cube, operator=\"std_dev\", period=\"full\")\n",
    "    return cube\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ae4b4cb0-54ef-487b-ba08-7368bc8b5fcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sst_std(cube):\n",
    "    nino34_latext_region = {\"start_longitude\": 190., \"end_longitude\": 240., \"start_latitude\": -5., \"end_latitude\": 5.}\n",
    "    # cube = regrid(cube, target_grid=\"1x1\", scheme=\"linear\")\n",
    "    cube = extract_region(cube, **nino34_latext_region)\n",
    "    cube = anomalies(cube,period='monthly')\n",
    "    cube = area_statistics(cube, operator='mean')\n",
    "    cube = climate_statistics(cube, operator=\"std_dev\", period=\"monthly\") #monthly to plot months\n",
    "    return cube\n",
    "def sst_std_3(cube):\n",
    "    nino34_latext_region = {\"start_longitude\": 150., \"end_longitude\": 270., \"start_latitude\": -5., \"end_latitude\": 5.}\n",
    "    cube = regrid(cube, target_grid=\"0.5x0.5\", scheme=\"linear\")\n",
    "    cube = extract_region(cube, **nino34_latext_region)\n",
    "    cube = anomalies(cube,period='monthly')\n",
    "    cube = meridional_statistics(cube, 'mean')\n",
    "    cube = climate_statistics(cube, operator=\"std_dev\", period=\"monthly\") #monthly\n",
    "    return cube"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "821d528c-da59-4b25-9bfa-3d7a53a61a9f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/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",
      "  ret = um.true_divide(\n",
      "/g/data/xp65/public/apps/med_conda/envs/esmvaltool-0.4/lib/python3.11/site-packages/iris/coords.py:2164: IrisVagueMetadataWarning: Collapsing a non-contiguous coordinate. Metadata may not be fully descriptive for 'time'.\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 'season_year', with 0 bound(s). Contiguous bounds are only defined for 1D coordinates with 2 bounds. Metadata may not be fully descriptive for 'season_year'. Ignoring bounds.\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",
      "  ret = um.true_divide(\n",
      "/g/data/xp65/public/apps/med_conda/envs/esmvaltool-0.4/lib/python3.11/site-packages/iris/coords.py:2164: IrisVagueMetadataWarning: Collapsing a non-contiguous coordinate. Metadata may not be fully descriptive for 'time'.\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 'season_year', with 0 bound(s). Contiguous bounds are only defined for 1D coordinates with 2 bounds. Metadata may not be fully descriptive for 'season_year'. Ignoring bounds.\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",
      "  ret = um.true_divide(\n",
      "/g/data/xp65/public/apps/med_conda/envs/esmvaltool-0.4/lib/python3.11/site-packages/iris/coords.py:2164: IrisVagueMetadataWarning: Collapsing a non-contiguous coordinate. Metadata may not be fully descriptive for 'time'.\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 'season_year', with 0 bound(s). Contiguous bounds are only defined for 1D coordinates with 2 bounds. Metadata may not be fully descriptive for 'season_year'. 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 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",
      "  ret = um.true_divide(\n",
      "/g/data/xp65/public/apps/med_conda/envs/esmvaltool-0.4/lib/python3.11/site-packages/iris/coords.py:2164: IrisVagueMetadataWarning: Collapsing a non-contiguous coordinate. Metadata may not be fully descriptive for 'time'.\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 'season_year', with 0 bound(s). Contiguous bounds are only defined for 1D coordinates with 2 bounds. Metadata may not be fully descriptive for 'season_year'. Ignoring bounds.\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",
      "  ret = um.true_divide(\n",
      "/g/data/xp65/public/apps/med_conda/envs/esmvaltool-0.4/lib/python3.11/site-packages/iris/coords.py:2164: IrisVagueMetadataWarning: Collapsing a non-contiguous coordinate. Metadata may not be fully descriptive for 'time'.\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 'season_year', with 0 bound(s). Contiguous bounds are only defined for 1D coordinates with 2 bounds. Metadata may not be fully descriptive for 'season_year'. Ignoring bounds.\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",
      "  ret = um.true_divide(\n",
      "/g/data/xp65/public/apps/med_conda/envs/esmvaltool-0.4/lib/python3.11/site-packages/iris/coords.py:2164: IrisVagueMetadataWarning: Collapsing a non-contiguous coordinate. Metadata may not be fully descriptive for 'time'.\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 'season_year', with 0 bound(s). Contiguous bounds are only defined for 1D coordinates with 2 bounds. Metadata may not be fully descriptive for 'season_year'. Ignoring bounds.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "model_datasets_prep = {name: sst_boreal(dataset, 'NDJ') for name, dataset in model_datasets.items()}\n",
    "obs_datasets_prep = {name: sst_boreal(dataset,'NDJ') for name, dataset in obs_datasets.items()}\n",
    "\n",
    "model_datasets_prep2 = {name: sst_boreal(dataset, 'MAM') for name, dataset in model_datasets.items()}\n",
    "obs_datasets_prep2 = {name: sst_boreal(dataset,'MAM') for name, dataset in obs_datasets.items()}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44abf0d2-1f03-4de4-a33b-e7e6dd6baf44",
   "metadata": {},
   "source": [
    "## Diagnostic Level 1\n",
    "\n",
    "The first level shows the diagnostic used to compute the metric and highlight the difference between the model and the reference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b2d2fae2-9a3b-437a-90b6-2fcb05a55ea2",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_ = {'borealwinter':model_datasets_prep[\"ACCESS-ESM1-5\"], 'borealspring':model_datasets_prep2[\"ACCESS-ESM1-5\"]}\n",
    "\n",
    "obs_ = {'borealwinter':obs_datasets_prep[\"HadISST\"], 'borealspring':obs_datasets_prep2[\"HadISST\"]}\n",
    "\n",
    "mod_seas = model_['borealwinter'].data.item()/model_['borealspring'].data.item()\n",
    "obs_seas = obs_['borealwinter'].data.item()/obs_['borealspring'].data.item()\n",
    "\n",
    "# abs((mod_seas-obs_seas)/obs_seas)*100, mod_seas, obs_seas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "494d04c8-feff-457d-bcf2-c5a0f3ce0bd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.75, 0.8, '* observation')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ssta_std = [mod_seas, obs_seas]\n",
    "plt.scatter(range(len(ssta_std)), ssta_std, c=['blue','black'], marker='D')\n",
    "\n",
    "plt.xlim(-0.5,2)#range(-1,3,1)) #['model','obs']\n",
    "plt.xticks([])\n",
    "\n",
    "plt.ylabel('SSTA std (NDJ/MAM) (°C/°C)')\n",
    "plt.grid(linestyle='--', axis='y')\n",
    "plt.title('ENSO Seasonality')\n",
    "\n",
    "metricval = abs((mod_seas-obs_seas)/obs_seas)*100\n",
    "\n",
    "plt.text(0.95, 0.95, f'Metric: {metricval:.2f} %', fontsize=12, ha='right', transform=plt.gca().transAxes,\n",
    "         bbox=dict(facecolor='white', alpha=0.8, edgecolor='none'))\n",
    "\n",
    "plt.text(0.75,0.85, '* ACCESS-ESM1-5', color='blue',transform=plt.gca().transAxes)\n",
    "plt.text(0.75,0.8, '* observation', color='black',transform=plt.gca().transAxes)\n",
    "# plt.legend(loc='lower right')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d65d58b-7c8f-4ede-92a2-b0be95a135d9",
   "metadata": {},
   "source": [
    "**Figure 1**: Ratio of winter to spring standard deviation of sea surface temperature anomalies (SSTA) in the central equatorial Pacific (Niño 3.4 region average), illustrating the seasonal timing of SSTA. The model typically exhibits weaker variability than observed, indicating that ENSO events occur too frequently during spring in the model. In this case, the seasonal timing is slightly too strong. Black markers represent the reference data, while blue markers represent the model output. The derived metric is the absolute relative difference, calculated as: \\( \\text{abs}((\\text{model} - \\text{ref}) / \\text{ref}) \\times 100 \\)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1413702-1cd3-4797-8c0f-e9c1e99a7090",
   "metadata": {},
   "source": [
    "## Diagnostic Level 2\n",
    "\n",
    "The second level shows the mean annual structure of the ENSO amplitude: the 12 months standard deviation of the anomalies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3fbaa115-5602-40b3-94b9-fa6329a4c4a0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/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",
      "  ret = um.true_divide(\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",
      "  ret = um.true_divide(\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",
      "  ret = um.true_divide(\n"
     ]
    }
   ],
   "source": [
    "model_data_prep = {name: sst_std(dataset) for name, dataset in model_datasets.items()}\n",
    "obs_data_prep = {name: sst_std(dataset) for name, dataset in obs_datasets.items()}\n",
    "\n",
    "data_prep_2 = [model_data_prep[\"ACCESS-ESM1-5\"], obs_data_prep[\"HadISST\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7d4137e0-5df9-40e8-94f1-16fac41c3c78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x150e16b3ea90>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Create the plot\n",
    "plt.figure(figsize=(6, 6))\n",
    "\n",
    "# Plot observation data in black\n",
    "qplt.plot(data_prep_2[1], color='black', label='Observation', linewidth=4)\n",
    "\n",
    "# Plot model data in blue\n",
    "qplt.plot(data_prep_2[0], color='blue', label='ACCESS-ESM1-5', linewidth=4)\n",
    "\n",
    "# Define x-axis labels\n",
    "# months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
    "months = ['Jan', 'May', 'Sep']\n",
    "plt.xticks(range(1,13,4),labels=months)\n",
    "# Set the x and y axis labels\n",
    "plt.xlabel('Months')\n",
    "plt.ylabel('SSTA std (°C)')\n",
    "plt.title('SSTA standard deviation')\n",
    "plt.grid(linestyle='--'); plt.legend()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df1dc35b-4d4b-40ba-b09a-aeaa88b5e3b5",
   "metadata": {},
   "source": [
    "**Figure 2**: Mean annual structure of the standard deviation of sea surface temperature anomalies (SSTA) in the central equatorial Pacific (Niño 3.4 region average), illustrating the seasonal variability. The model typically shows too weak variability in winter and too strong variability in spring. In this case, the model exhibits weak variability during both winter and spring. The black curve represents the reference data, while the blue curve corresponds to the model output."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76b0eef9-cffd-4572-b7a4-318f370f56a0",
   "metadata": {},
   "source": [
    "## Diagnostic Level 3\n",
    "\n",
    "The third level shows the spatio-mean annual structure of the ENSO amplitude: the Hovmöller of the standard deviation of anomalies in the equatorial Pacific."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e1890eb1-afdf-4254-bf88-c3524838a904",
   "metadata": {},
   "outputs": [
    {
     "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/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",
      "  ret = um.true_divide(\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/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",
      "  ret = um.true_divide(\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/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",
      "  ret = um.true_divide(\n"
     ]
    }
   ],
   "source": [
    "model_data_prep3 = {name: sst_std_3(dataset) for name, dataset in model_datasets.items()}\n",
    "obs_data_prep3 = {name: sst_std_3(dataset) for name, dataset in obs_datasets.items()}\n",
    "\n",
    "data_prep_3 = {**model_data_prep3, \"HadISST\":obs_data_prep3[\"HadISST\"]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0c8ae62f-ff72-4499-8a09-06f5edb7c9aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import iris.plot as iplt\n",
    "import cartopy.feature as cfeature\n",
    "import cartopy.crs as ccrs\n",
    "\n",
    "fig = plt.figure(figsize=(15, 7))\n",
    "i =121\n",
    "def format_longitude(x, pos):\n",
    "    if x > 180:\n",
    "        return f'{int(360 - x)}°W'\n",
    "    elif x == 180:\n",
    "        return f'{int(x)}°'\n",
    "    else:\n",
    "        return f'{int(x)}°E'\n",
    "\n",
    "for label, cube in data_prep_3.items():\n",
    "    \n",
    "    ax1 = plt.subplot(i)\n",
    "    c1 = iplt.pcolormesh(cube, cmap='Reds')\n",
    "    ax1.set_yticks([1,5,9])\n",
    "    ax1.set_title(label)\n",
    "    plt.gca().xaxis.set_major_formatter(plt.FuncFormatter(format_longitude))\n",
    "    i+=1\n",
    "\n",
    "\n",
    "# Add a single colorbar at the bottom\n",
    "cax = plt.axes([0.15,-0.08,0.7,0.05])\n",
    "cbar = fig.colorbar(c1, cax=cax, orientation='horizontal', extend='both', ticks=np.arange(0,2,0.5))\n",
    "cbar.set_label('SSTA std (°C)')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba562b29-ef43-4eb0-ab08-6c2a45753b3a",
   "metadata": {},
   "source": [
    "Figure 3: Spatio-mean annual structure of the standard deviation of sea surface temperature anomalies (SSTA) in the equatorial Pacific (averaged between 5°S and 5°N). The figure typically shows too weak variability off the coast of South America and too strong variability in the central Pacific during spring. Additionally, there is too weak variability in the central-eastern Pacific during winter and excessive variability west of the dateline year-round. In this case, while the seasonal timing of ENSO is roughly captured, the variability in the central Pacific is overly strong during autumn. The left map displays the reference data, while the right map shows the model output."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "812d348c-6c62-4cdb-85e9-6d850ab15529",
   "metadata": {},
   "source": [
    "### Dive down level 4:\n",
    "The fourth level shows the zonal structure of the ENSO amplitude during winter and spring: the standard deviation of the anomalies along the equator in the Pacific."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d6e26941-5dc8-4341-b160-a631588def44",
   "metadata": {},
   "outputs": [
    {
     "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: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:4067: IrisUserWarning: Collapsing spatial coordinate 'latitude' without weighting\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "## from level 3 -extract season, full climate statictics\n",
    "def sst_std_3(cube):\n",
    "    nino34_latext_region = {\"start_longitude\": 150., \"end_longitude\": 270., \"start_latitude\": -5., \"end_latitude\": 5.}\n",
    "    cube = regrid(cube, target_grid=\"1x1\", scheme=\"linear\")\n",
    "    cube = convert_units(cube, units=\"degC\")\n",
    "    cube = extract_region(cube, **nino34_latext_region)\n",
    "    cube = anomalies(cube,period='monthly')\n",
    "    cube = meridional_statistics(cube, 'mean')\n",
    "    # cube = climate_statistics(cube, operator=\"std_dev\", period=\"monthly\") # monthly remove from 3\n",
    "    return cube\n",
    "\n",
    "model_data_prep3 = {name: sst_std_3(dataset) for name, dataset in model_datasets.items()}\n",
    "obs_data_prep3 = {name: sst_std_3(dataset) for name, dataset in obs_datasets.items()}\n",
    "data_prep_3 = {**model_data_prep3, \"HadISST\":obs_data_prep3[\"HadISST\"]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "216e8be7-3ce6-4569-96af-288e36fb7406",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/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",
      "  ret = um.true_divide(\n",
      "/g/data/xp65/public/apps/med_conda/envs/esmvaltool-0.4/lib/python3.11/site-packages/iris/coords.py:2164: IrisVagueMetadataWarning: Collapsing a non-contiguous coordinate. Metadata may not be fully descriptive for 'time'.\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 'season_year', with 0 bound(s). Contiguous bounds are only defined for 1D coordinates with 2 bounds. Metadata may not be fully descriptive for 'season_year'. Ignoring bounds.\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",
      "  ret = um.true_divide(\n",
      "/g/data/xp65/public/apps/med_conda/envs/esmvaltool-0.4/lib/python3.11/site-packages/iris/coords.py:2164: IrisVagueMetadataWarning: Collapsing a non-contiguous coordinate. Metadata may not be fully descriptive for 'time'.\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 'season_year', with 0 bound(s). Contiguous bounds are only defined for 1D coordinates with 2 bounds. Metadata may not be fully descriptive for 'season_year'. Ignoring bounds.\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",
      "  ret = um.true_divide(\n",
      "/g/data/xp65/public/apps/med_conda/envs/esmvaltool-0.4/lib/python3.11/site-packages/iris/coords.py:2164: IrisVagueMetadataWarning: Collapsing a non-contiguous coordinate. Metadata may not be fully descriptive for 'time'.\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 'season_year', with 0 bound(s). Contiguous bounds are only defined for 1D coordinates with 2 bounds. Metadata may not be fully descriptive for 'season_year'. Ignoring bounds.\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",
      "  ret = um.true_divide(\n",
      "/g/data/xp65/public/apps/med_conda/envs/esmvaltool-0.4/lib/python3.11/site-packages/iris/coords.py:2164: IrisVagueMetadataWarning: Collapsing a non-contiguous coordinate. Metadata may not be fully descriptive for 'time'.\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 'season_year', with 0 bound(s). Contiguous bounds are only defined for 1D coordinates with 2 bounds. Metadata may not be fully descriptive for 'season_year'. Ignoring bounds.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "        \n",
    "lines = {'ACCESS-ESM1-5': 'solid', 'HadISST': 'dashdot'}\n",
    "for label, cube in data_prep_3.items():\n",
    "    for seas, col in {'NDJ':'red','MAM':'blue'}.items():\n",
    "        cube_plot = extract_season(cube, seas) # get NDJ #{ NDJ, MAM, red, blue\n",
    "        cube_plot = climate_statistics(cube_plot, operator=\"std_dev\", period=\"full\")\n",
    "        qplt.plot(cube_plot, color=col, label=label,linestyle=lines[label], linewidth=4)\n",
    "        \n",
    "##\n",
    "plt.xlabel('longitude')\n",
    "plt.ylabel('SSTA std (°C)')\n",
    "plt.title('SSTA standard deviation')\n",
    "plt.grid(linestyle='--')\n",
    "plt.gca().xaxis.set_major_formatter(plt.FuncFormatter(format_longitude))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56bc3cdf-cc89-4854-99ab-c2b2c2a07d5a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}