10-ENSO_lifecycle.ipynb

{
 "cells": [
  {
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   "metadata": {},
   "source": [
    "# ENSO_lifecycle: temporal evolution of SST anomalies in the central equatorial Pacific\n",
    "\n",
    "Computes the temporal root mean square error (RMSE) of sea surface temperature anomalies (SSTA) in the Niño3.4 region (horizontal average over 5°S–5°N, 170°W–120°W) between model and observations. The analysis spans 6 years centred on ENSO peak years to evaluate the ability of the model to capture the full lifecycle of ENSO events, including onset, peak, and decay phases.\n",
    "\n",
    "## Datasets Used for Temporal Evolution Comparison\n",
    "The following reference datasets are employed to evaluate the ENSO lifecycle in model simulations:\n",
    "\n",
    "- **TropFlux** (1979–2018): The primary dataset for comparison, offering accurate surface fluxes and anomalies for tropical climate studies.\n",
    "  \n",
    "## Additional datasets include:\n",
    "\n",
    "- **20CRv2** (1871–2012): A historical reanalysis dataset reconstructing global atmospheric conditions using surface pressure observations.\n",
    "- **ERA-Interim** (1979–2018): A widely used reanalysis product providing consistent, high-resolution estimates of atmospheric and surface variables.\n",
    "- **ERSSTv5** (1854–2018): A globally gridded SST anomaly dataset optimised for long-term climate monitoring and ENSO analysis.\n",
    "- **HadISST** (1870–2018): A comprehensive dataset of SST and sea ice, blending historical observations with reconstructions.\n",
    "- **NCEP2** (1979–2018): A second-generation reanalysis dataset delivering improved global atmospheric and surface data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a3a2571a-c2f1-4ac8-a272-d4c731c062cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from esmvalcore.dataset import Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 4,
   "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": 5,
   "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": 6,
   "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": 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/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": "ba236428-f734-4f31-8cd3-8565c7639375",
   "metadata": {},
   "source": [
    "## Diagnostic Level 1\n",
    "\n",
    "The first level shows the diagnostic used to compute the metric and highlight the main differences between the model and the reference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 9,
   "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",
    "\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": 10,
   "id": "2712bd74-76c6-4f8d-9cc4-756f812024fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "## obs\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",
    "\n",
    "rmse = np.sqrt(np.mean((obs[0] - model[0]) ** 2))\n",
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d1e825b2-709d-42d3-a3b2-2b271ebd2268",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "months = np.arange(1, 73) - 36\n",
    "\n",
    "# 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",
    "# Adding RMSE text on top of the plot\n",
    "plt.text(0.5, 0.95, f'RMSE: {rmse:.2f} °C/°C', fontsize=12, ha='center', transform=plt.gca().transAxes,\n",
    "         bbox=dict(facecolor='white', alpha=0.8, edgecolor='none'))\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e3f9c55-7290-44c6-bad6-cd011191c77e",
   "metadata": {},
   "source": [
    "Figure 1: Temporal evolution of sea surface temperature anomalies (SSTA) in the central equatorial Pacific (Niño 3.4 region average), illustrating the ENSO-associated variability. The model tends to exhibit overly frequent transitions between negative and positive SSTA phases. The black curve represents the reference data, while the blue curve corresponds to the model output. The derived metric is the temporal root mean square error (RMSE) between the model and reference curves."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27cb46ed-b2cd-40d4-9fb6-6236c6386b97",
   "metadata": {},
   "source": [
    "# Diagnostic Level 2\n",
    "\n",
    "The second level shows the broader picture to better understand the spatial-temporal pattern of ENSO: the Hovmöller of the anomalies in the equatorial Pacific."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cf99738e-1338-493f-96a7-b4fa00db8a4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Define custom ticks for the y-axis (every 6 months)\n",
    "yticks = range(1, 73, 6)\n",
    "ytick_labels = ['Jan', 'Jul'] * (len(yticks) // 2)\n",
    "\n",
    "# Define shared color limits for both subplots\n",
    "vmin = -1.2 # min(np.min(slope_area), np.min(obs_slope_area))\n",
    "vmax = 1.2 # max(np.max(slope_area), np.max(obs_slope_area))\n",
    "\n",
    "# Create the subplots\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 10), sharey=True)\n",
    "\n",
    "# First subplot obs[0] - model[0]\n",
    "c1 = ax1.pcolormesh(obs_n34_area.coord('longitude').points, range(1, 73), obs[1], shading='auto', cmap='RdBu_r', vmin=vmin, vmax=vmax)\n",
    "ax1.set_ylabel('Time (Months)')\n",
    "ax1.set_xlabel('Longitude')\n",
    "ax1.set_xlim([160,280])\n",
    "ax1.set_title('HadISST: reg(ENSO SSTA, SSTA)')\n",
    "ax1.set_yticks(yticks)\n",
    "ax1.set_yticklabels(ytick_labels)\n",
    "\n",
    "# Second subplot\n",
    "c2 = ax2.pcolormesh(model_n34_area.coord('longitude').points, range(1, 73), model[1], shading='auto', cmap='RdBu_r', vmin=vmin, vmax=vmax)\n",
    "ax2.set_xlabel('Longitude')\n",
    "ax2.set_title('Model: reg(ENSO SSTA, SSTA)')\n",
    "ax2.set_xlim([160,280])\n",
    "\n",
    "# Adjust the layout to add more space for the colorbar on the right\n",
    "plt.subplots_adjust(right=1.05)\n",
    "\n",
    "# Add a colorbar and position it slightly to the right of the plots\n",
    "cbar = fig.colorbar(c1, ax=[ax1, ax2], label='Slope', orientation='vertical', fraction=0.05, pad=0.04)\n",
    "\n",
    "# Display the plot\n",
    "# plt.tight_layout()\n",
    "#plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87947d04-c5dd-45d3-acf7-51741b8570e3",
   "metadata": {},
   "source": [
    "Figure 2: spatial-temporal structure of sea surface temperature anomalies (SSTA) associated with ENSO in the equatorial Pacific (5°S-5°N average), showing usually the too frequent swing from negative to positive SSTA, along with maximum anomalies during ENSO peak lasting too long. The left and right Hovmöllers show respectively the reference and the model."
   ]
  }
 ],
 "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
}