14-ENSO_duration.ipynb

{
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   "source": [
    "# ENSO_duration: Duration of SST Anomalies in the Central Equatorial Pacific  \n",
    "\n",
    "Computes the number of months during which sea surface temperature anomalies (SSTA) persist in the Niño3.4 region (horizontal average over 5°S–5°N, 170°W–120°W). This metric evaluates the duration of SST anomalies during the 6 years centred on the peak of ENSO events, comparing model simulations with reference observations.\n",
    "\n",
    "## Datasets Used for Duration Comparison  \n",
    "\n",
    "The following reference datasets are used to evaluate the duration 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.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a3a2571a-c2f1-4ac8-a272-d4c731c062cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from esmvalcore.dataset import Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a20485d9-9e93-4564-9fc0-f07c64a3bc36",
   "metadata": {},
   "outputs": [],
   "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=\"19780101/20190101\", ##rolling window\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='OBS',\n",
    "#     type='reanaly',\n",
    "#     timerange=\"19790101/20190101\",\n",
    "#     tier=3)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5e048061-f220-4e34-820a-629c7b6b5580",
   "metadata": {},
   "outputs": [],
   "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 rolling_window_statistics\n",
    "from esmvalcore.preprocessor import meridional_statistics\n",
    "from esmvalcore.preprocessor import convert_units\n",
    "from esmvalcore.preprocessor import extract_region\n",
    "from esmvalcore.preprocessor import extract_month\n",
    "from esmvalcore.preprocessor import regrid\n",
    "from esmvalcore.preprocessor import detrend\n",
    "from esmvalcore.preprocessor import mask_landsea\n",
    "import iris\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import iris.quickplot as qplt\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import sacpy as scp\n",
    "from esmvalcore.iris_helpers import date2num \n",
    "# [d for d in t_coord.units.num2date(t_coord.points)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e2daff76-44ec-46b3-9157-81e506b032c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sst_enso_n34(cube):\n",
    "    nino34_latext_region = {\"start_longitude\": 190., \"end_longitude\": 240., \"start_latitude\": -5., \"end_latitude\": 5.}\n",
    "    cube = convert_units(cube, units=\"degC\")\n",
    "    # cube = mask_landsea(cube, mask_out=\"land\") #shp or land fraction\n",
    "    cube = extract_region(cube, **nino34_latext_region)\n",
    "    # #remove time mean\n",
    "    cube = anomalies(cube, period='monthly') \n",
    "    cube = rolling_window_statistics(cube, coordinate='time', operator='mean', window_length=5)##rolling window cuts off months?\n",
    "    cube = rolling_window_statistics(cube, coordinate='time', operator='mean', window_length=5)\n",
    "    cube = area_statistics(cube,operator='mean')\n",
    "    # cube = extract_month(cube,12) # select after\n",
    "    # detrend?\n",
    "    return cube\n",
    "\n",
    "def sst_meridional(cube): ##along latitude for area\n",
    "    nino34_latext_region = {\"start_longitude\": 160., \"end_longitude\": 280., \"start_latitude\": -5., \"end_latitude\": 5.}\n",
    "    cube = convert_units(cube, units=\"degC\")\n",
    "    #mask\n",
    "    cube = anomalies(cube, period='monthly')\n",
    "    cube = extract_region(cube, **nino34_latext_region)\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 = regrid(cube, target_grid=\"1x1\", scheme=\"linear\")\n",
    "    cube = meridional_statistics(cube,operator='mean')\n",
    "    return cube\n",
    "\n",
    "#rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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/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/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 '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: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: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 '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"
     ]
    }
   ],
   "source": [
    "model_datasets_prep1 = {name: sst_enso_n34(dataset) for name, dataset in model_datasets.items()}\n",
    "model_datasets_prep2 = {name: sst_meridional(dataset) for name, dataset in model_datasets.items()}\n",
    "\n",
    "obs_datasets_prep1 = {name: sst_enso_n34(dataset) for name, dataset in obs_datasets.items()}\n",
    "obs_datasets_prep2 = {name: sst_meridional(dataset) for name, dataset in obs_datasets.items()}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d4db516-5092-4d0e-9f49-20b3ab5b75e4",
   "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": 7,
   "id": "ea61c6be-21be-4634-81fc-5426ede94a8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# \n",
    "def sst_regressed(n34_dec, n34, n34_area):\n",
    "    leadlagyr = 3 #rolling window cut off, not include first year\n",
    "    n34_dec_ct = n34_dec[leadlagyr:-leadlagyr]\n",
    "    event_years = n34_dec_ct.time.dt.year #\n",
    "    # Ensure that the selected years are not the last or second last year in the n34 dataset\n",
    "    years_of_interest_array = []\n",
    "    \n",
    "    # Fill the array with the years of interest for each event year \n",
    "    for i, year in enumerate(event_years):# 2:-3 for dec        \n",
    "        years_of_interest_array.append([year.values - 2, year.values - 1, year.values, year.values + 1, year.values + 2, year.values + 3])\n",
    "    \n",
    "    n34_selected = []\n",
    "    n34_area_selected = []\n",
    "    for i in range(len(years_of_interest_array)): #creates sst_time_series\n",
    "        # Select the data for the current year and append it to n34_selected #n34 is not dec month only\n",
    "        n34_selected.append(n34.sel(time=n34['time.year'].isin(years_of_interest_array[i])))\n",
    "        n34_area_selected.append(n34_area.sel(time=n34_area['time.year'].isin(years_of_interest_array[i])))\n",
    "\n",
    "    # 1) linear regression of sst_time_series on sst_enso\n",
    "    slope = scp.LinReg(n34_dec_ct.values, n34_selected).slope\n",
    "    slope_area = scp.LinReg(n34_dec_ct.values, n34_area_selected).slope\n",
    "\n",
    "    return slope, slope_area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "617266ca-750f-4dce-8664-21e32ff7c5a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_n34 = model_datasets_prep1[\"ACCESS-ESM1-5\"]\n",
    "model_n34_dec = extract_month(model_datasets_prep1[\"ACCESS-ESM1-5\"],12)\n",
    "model_n34_area = model_datasets_prep2[\"ACCESS-ESM1-5\"]\n",
    "\n",
    "obs_n34 = obs_datasets_prep1[\"HadISST\"]\n",
    "obs_n34_dec = extract_month(obs_datasets_prep1[\"HadISST\"],12)\n",
    "obs_n34_area = obs_datasets_prep2[\"HadISST\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2712bd74-76c6-4f8d-9cc4-756f812024fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "## metric computation - rmse of slopes\n",
    "cb_out = {'nino34':model_n34, 'n34_dec':model_n34_dec, 'n34_area':model_n34_area}\n",
    "darray_dict = {cbname: xr.DataArray.from_iris(cb) for cbname, cb in cb_out.items()}\n",
    "\n",
    "model = sst_regressed(darray_dict['n34_dec'], darray_dict['nino34'], darray_dict['n34_area'])\n",
    "\n",
    "cb_out = {'nino34':obs_n34, 'n34_dec':obs_n34_dec, 'n34_area':obs_n34_area}\n",
    "darray_dict = {cbname: xr.DataArray.from_iris(cb) for cbname, cb in cb_out.items()}\n",
    "obs = sst_regressed(darray_dict['n34_dec'], darray_dict['nino34'], darray_dict['n34_area'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dc2f9415-2b7c-402d-88bd-1ced73a5601e",
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 12,
     "metadata": {},
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   ],
   "source": [
    "obs[1].shape, obs[0].shape, model[1].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e97b1deb-9020-47ad-bd0e-d58d73d8efb1",
   "metadata": {},
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     "execution_count": 13,
     "metadata": {},
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   ],
   "source": [
    "## duration\n",
    "\n",
    "# Plot the slope and correlation\n",
    "# Define the lead/lag months\n",
    "months = np.arange(1, 73) - 36\n",
    "\n",
    "obs_model_count = []\n",
    "# Calculate the number of months where slope > 0.25 in the range -20 to 20\n",
    "within_range = (months >= -30) & (months <= 30)\n",
    "slope_above_025 = model[0][within_range] > 0.25\n",
    "obs_model_count.append(np.sum(slope_above_025))\n",
    "slope_above_025 = obs[0][within_range] > 0.25\n",
    "obs_model_count.append(np.sum(slope_above_025))\n",
    "\n",
    "metric = abs((obs_model_count[1]-obs_model_count[0])/obs_model_count[0])*100\n",
    "metric, obs_model_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "44c391af-110f-4cc4-8500-a17a95434068",
   "metadata": {},
   "outputs": [
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set x_position to represent the model (1, 2, or 3)\n",
    "x_position = [1, 2]  # Change this to 1, 2, or 3 based on your requirement\n",
    "label_array = ['HadISST', 'ACCESS-ESM1-5']\n",
    "\n",
    "# Plot the HadISST duration point in black\n",
    "plt.scatter(x_position[0], obs_model_count[0], color='black', edgecolor='black', s=100, marker='o', label=label_array[0])\n",
    "\n",
    "# Plot the Model 2 duration point in blue\n",
    "plt.scatter(x_position[1], obs_model_count[1], color='blue', edgecolor='black', s=100, marker='o', label=label_array[1])\n",
    "\n",
    "plt.text(0.02, 0.95, f'Metric: {metric:.2f} %', fontsize=12, ha='left', transform=plt.gca().transAxes,\n",
    "         bbox=dict(facecolor='white', alpha=0.8, edgecolor='none'))\n",
    "\n",
    "plt.xlim(0,3)\n",
    "plt.xticks([])\n",
    "\n",
    "# Add labels and title\n",
    "# plt.xlabel('Model')\n",
    "plt.ylabel('Duration (reg > 0.25) (months)')\n",
    "plt.title('ENSO Duration')\n",
    "\n",
    "# Optionally add a grid and legend\n",
    "plt.grid(linestyle='--')\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d79efa98-fb88-4956-aad2-2c79c211de3c",
   "metadata": {},
   "source": [
    "**Figure 1**: Duration of the ENSO life cycle where SSTA exceeds 0.25, illustrating the \"duration\" of the SSTA event. The model typically shows a duration close to the reference, but slightly longer. The black markers represent the reference data, while the 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 \\).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a165cdbf-ef2b-4a1b-8af7-3e474426e95d",
   "metadata": {},
   "source": [
    "## Diagnostic Level 2\n",
    "\n",
    "The second level shows the temporal evolution of SSTA during ENSO: the ENSO life-cycle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b8ca7952-acca-443d-b019-c993210a76a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the slope\n",
    "plt.plot(months, obs[0], label='HadISST', lw=4)\n",
    "plt.plot(months, model[0], label='Model', lw=4)\n",
    "\n",
    "# Draw a black dashed line at y=0.25\n",
    "plt.axhline(y=0.25, color='black', linestyle='--', label='0.25 threshold')\n",
    "\n",
    "# Draw a thicker line at y=0\n",
    "plt.axhline(y=0, color='black', linewidth=2) #, label='Zero Line')\n",
    "\n",
    "# Define custom ticks for the x-axis (every 6 months)\n",
    "xticks = np.arange(1, 73, 6) - 36  # Adjust for lead/lag months\n",
    "xtick_labels = ['Jan', 'Jul'] * (len(xticks) // 2)\n",
    "\n",
    "# Set the labels and titles\n",
    "plt.xlabel('Lead & Lag Months')\n",
    "plt.ylabel('Degree C / C')\n",
    "plt.legend()\n",
    "plt.grid(linestyle='--')\n",
    "\n",
    "# Apply the custom ticks and labels to the x-axis\n",
    "plt.xticks(xticks, xtick_labels)\n",
    "plt.yticks(np.arange(-2,2.5, step=1))\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a4dc6ea-95bf-43bf-b67a-9a1c218e4d55",
   "metadata": {},
   "source": [
    "**Figure 2**: Temporal structure of sea surface temperature anomalies (SSTA) in the central equatorial Pacific (Niño 3.4 region average), illustrating the temporal evolution of SSTA. The model typically exhibits an overly frequent oscillation between negative and positive SSTA phases. The black curve represents the reference data, while the blue curve corresponds to the model output.\n"
   ]
  }
 ],
 "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
}