15-ENSO_diversity.ipynb

{
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   "source": [
    "# ENSO_diversity: Zonal Location of Maximum SST Anomalies in the Equatorial Pacific  \n",
    "\n",
    "Computes the interquartile range (IQR) of the zonal location distribution of the maximum (and minimum) sea surface temperature anomalies (SSTA) in the equatorial Pacific, averaged meridionally over 5°S–5°N. This metric quantifies the diversity in the zonal location of the maximum (during El Niño) and minimum (during La Niña) SST anomalies during boreal winter. The analysis uses December values smoothed with a 5-month triangular-weighted moving average to reduce seasonal variability.  \n",
    "\n",
    "## Datasets Used for Diversity Comparison  \n",
    "\n",
    "The following reference datasets are used to evaluate the spatial diversity of maximum SST anomalies in model simulations:  \n",
    "\n",
    "- **TropFlux (1979–2018)**: The primary dataset for comparison, offering tropical climate variability data, including SST anomalies and fluxes over the tropical oceans.  \n",
    "\n",
    "Additional datasets include:  \n",
    "- **20CRv2 (1871–2012)**: A historical atmospheric reanalysis dataset that provides surface pressure-based global atmospheric data over more than a century.  \n",
    "- **ERA-Interim (1979–2018)**: A reanalysis dataset from the ECMWF, providing high-resolution atmospheric and surface data using modern assimilation techniques.  \n",
    "- **ERSSTv5 (1854–2018)**: A dataset optimised for reconstructing global SST anomalies and capturing climate variability, including ENSO.  \n",
    "- **HadISST (1870–2018)**: A dataset providing global SST reconstructions using in-situ measurements and satellite observations.  \n",
    "- **NCEP2 (1979–2018)**: A second-generation reanalysis from NCEP that improves upon earlier reanalysis products and offers global atmospheric and surface data.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "03ca2b4d-aa80-4b22-86e1-bb668f4c832e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from esmvalcore.config import CFG\n",
    "from esmvalcore.dataset import Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f22e175",
   "metadata": {},
   "source": [
    "### Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0d112937-acb8-40c7-8fda-24779cd4c46d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs = Dataset(\n",
    "    short_name='tos',\n",
    "    dataset='NOAA-ERSSTv5',\n",
    "    mip=\"Omon\",\n",
    "    project='OBS6',\n",
    "    type='reanaly',\n",
    "    timerange=\"1900/2019\",\n",
    "    tier=2)\n",
    "# list(obs.from_files())\n",
    "len(obs.files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6c57f395-5f5a-47ec-a714-99d191ec22c6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([LocalFile('/g/data/fs38/publications/CMIP6/CMIP/CSIRO-ARCCSS/ACCESS-CM2/historical/r1i1p1f1/Omon/tos/gn/v20191108/tos_Omon_ACCESS-CM2_historical_r1i1p1f1_gn_185001-201412.nc')],\n",
       " [LocalFile('/g/data/fs38/publications/CMIP6/CMIP/CSIRO-ARCCSS/ACCESS-CM2/historical/r1i1p1f1/Ofx/areacello/gn/v20191108/areacello_Ofx_ACCESS-CM2_historical_r1i1p1f1_gn.nc')])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## model\n",
    "var = Dataset(\n",
    "    short_name='tos',\n",
    "    mip='Omon',\n",
    "    project='CMIP6',\n",
    "    activity='CMIP',\n",
    "    dataset='ACCESS-CM2',\n",
    "    ensemble='r1i1p1f1',\n",
    "    institute='*',\n",
    "    grid='*',\n",
    "    exp='historical',\n",
    "    # timerange='1979/2019',\n",
    "    # derive='true'\n",
    ")\n",
    "\n",
    "var_noarea = var.copy()\n",
    "\n",
    "var.add_supplementary(short_name='areacello', mip='Ofx')\n",
    "var.find_files()\n",
    "# list(var.from_files())[0].files\n",
    "var.files,var.supplementaries[0].files\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "300de51c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import iris\n",
    "from esmvalcore.preprocessor import (\n",
    "                detrend, # dimension, method, \n",
    "                meridional_statistics, # \n",
    "                zonal_statistics,\n",
    "                area_statistics,\n",
    "                regrid, #esmf bilinear\n",
    "                mask_landsea,\n",
    "                anomalies,\n",
    "                extract_month, \n",
    "                mask_inside_range, mask_above_threshold, mask_below_threshold,\n",
    "                extract_region, # equatorial pacific #1 - 150°E-90°W, zonal 5°S-5°N average\n",
    "                climate_statistics,# month mean, standard deviation\n",
    "                rolling_window_statistics) \n",
    "\n",
    "import numpy as np\n",
    "import iris.quickplot as qplt\n",
    "import matplotlib.pyplot as plt\n",
    "import iris.plot as iplt\n",
    "import matplotlib.colors as mcolors\n",
    "import cartopy.feature as cfeature\n",
    "import cartopy.crs as ccrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "784947b3-b662-479a-8a4f-56432f77d324",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "## \n",
    "def nino3_4ssta(cube):\n",
    "    cube = extract_region(cube, 190, 240, -5, 5) #\n",
    "    # cube = regrid(cube, target_grid=\"1x1\", scheme=\"linear\")   \n",
    "    \n",
    "    # triangle weighted- double smoothed\n",
    "    cube = rolling_window_statistics(cube, coordinate='time', operator='mean', window_length=5)\n",
    "    cube = rolling_window_statistics(cube, coordinate='time', operator='mean', window_length=5)\n",
    "    \n",
    "    cube = area_statistics(cube, 'mean')\n",
    "    cube = extract_month(cube, 12)\n",
    "    cube = anomalies(cube, period='monthly', standardize=True)  #normalise\n",
    "  \n",
    "    return cube\n",
    "\n",
    "def eq_ssta(cube):\n",
    "    cube = extract_region(cube, 150, 270, -5, 5)\n",
    "    # season cycle? detrend? #convert units\n",
    "    cube = rolling_window_statistics(cube, coordinate='time', operator='mean', window_length=5)\n",
    "    cube = rolling_window_statistics(cube, coordinate='time', operator='mean', window_length=5)\n",
    "    cube = extract_month(cube, 12)\n",
    "    cube = anomalies(cube,period='monthly')\n",
    "    cube = regrid(cube, target_grid=\"1x1\", scheme=\"linear\") \n",
    "    \n",
    "    cube = meridional_statistics(cube, 'mean') #\n",
    "     # weighted running average 5-degree\n",
    "    cube = rolling_window_statistics(cube, coordinate='longitude', operator='mean', window_length=5)\n",
    "    cube = rolling_window_statistics(cube, coordinate='longitude', operator='mean', window_length=5)\n",
    "    return cube\n",
    "\n",
    "\n",
    "def enso_events(cube):\n",
    "    # mean, std = cube.data.mean(), cube.data.std() #normalised, use 0.75\n",
    "    # range1, range2 = mean-0.75*std, mean + 0.75*std \n",
    "    \n",
    "    a_events = mask_to_years(mask_above_threshold(cube.copy(), -0.75))\n",
    "    o_events = mask_to_years(mask_below_threshold(cube.copy(), 0.75))\n",
    "    return {'nina':a_events, 'nino':o_events}\n",
    "\n",
    "def mask_to_years(events):\n",
    "    # build time with mask\n",
    "    maskedTime = np.ma.masked_array(events.coord('time').points, mask=events.data.mask)\n",
    "    # print(f'events cube shape: {events.shape}')\n",
    "    # return years\n",
    "    return [events.coord('time').units.num2date(time).year for time in maskedTime.compressed()]\n",
    "\n",
    "# Diversity - get zonal location of max and min\n",
    "def diversity(ssta_cube, events_dict): #2 masks/events list\n",
    "    # each enso year, max/min SSTA, get longitude\n",
    "    res_lon = {}\n",
    "    for enso, events in events_dict.items():\n",
    "        year_enso = iris.Constraint(time=lambda cell: cell.point.year in events)\n",
    "        cube = ssta_cube.extract(year_enso)\n",
    "        if enso == 'nina':\n",
    "            cube = cube * -1\n",
    "        #iterate through cube, each time get max/min value and return lon\n",
    "        loc_ls = []\n",
    "        for yr_slice in cube.slices(['longitude']):\n",
    "\n",
    "            indx = np.argmax(yr_slice.data) # if nina multiply by -1 or min\n",
    "            loc_ls.append(cube.coord('longitude').points[indx])\n",
    "\n",
    "        res_lon[enso] = loc_ls\n",
    "\n",
    "    return res_lon # return data to plot \n",
    "\n",
    "# IQR and abs((model-ref)/ref)*100\n",
    "def iqr(data):\n",
    "    #calculate interquartile range \n",
    "    q3, q1 = np.percentile(data, [75 ,25])\n",
    "    print(q3, q1)\n",
    "    iqr = q3 - q1\n",
    "    return iqr\n",
    "    \n",
    "def compute(obs_iqr, mod_iqr):\n",
    "    return abs((mod_iqr-obs_iqr)/obs_iqr)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2302a678-63e9-4fb2-8eb1-e702ff6213f2",
   "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:4620: IrisIgnoringBoundsWarning: The bounds of coordinate 'time' were ignored in the rolling window operation.\n",
      "  warnings.warn(\n",
      "/g/data/xp65/public/apps/med_conda/envs/esmvaltool-0.4/lib/python3.11/site-packages/iris/coords.py:2153: IrisVagueMetadataWarning: Cannot check if coordinate is contiguous: Invalid operation for 'cell index along second dimension', with 0 bound(s). Contiguous bounds are only defined for 1D coordinates with 2 bounds. Metadata may not be fully descriptive for 'cell index along second dimension'. Ignoring bounds.\n",
      "  warnings.warn(\n",
      "/g/data/xp65/public/apps/med_conda/envs/esmvaltool-0.4/lib/python3.11/site-packages/iris/coords.py:2153: IrisVagueMetadataWarning: Cannot check if coordinate is contiguous: Invalid operation for 'cell index along first dimension', with 0 bound(s). Contiguous bounds are only defined for 1D coordinates with 2 bounds. Metadata may not be fully descriptive for 'cell index along first dimension'. Ignoring bounds.\n",
      "  warnings.warn(\n",
      "/g/data/xp65/public/apps/med_conda/envs/esmvaltool-0.4/lib/python3.11/site-packages/iris/coords.py:2140: IrisVagueMetadataWarning: Collapsing a multi-dimensional coordinate. Metadata may not be fully descriptive for 'latitude'.\n",
      "  warnings.warn(\n",
      "/g/data/xp65/public/apps/med_conda/envs/esmvaltool-0.4/lib/python3.11/site-packages/iris/coords.py:2140: IrisVagueMetadataWarning: Collapsing a multi-dimensional coordinate. Metadata may not be fully descriptive for 'longitude'.\n",
      "  warnings.warn(\n",
      "/g/data/xp65/public/apps/med_conda/envs/esmvaltool-0.4/lib/python3.11/site-packages/numpy/ma/core.py:5417: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = super().var(axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/g/data/xp65/public/apps/med_conda/envs/esmvaltool-0.4/lib/python3.11/site-packages/numpy/core/_methods.py:195: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/g/data/xp65/public/apps/med_conda/envs/esmvaltool-0.4/lib/python3.11/site-packages/iris/cube.py:4620: IrisIgnoringBoundsWarning: The bounds of coordinate 'time' were ignored in the rolling window operation.\n",
      "  warnings.warn(\n",
      "/g/data/xp65/public/apps/med_conda/envs/esmvaltool-0.4/lib/python3.11/site-packages/iris/cube.py:4067: IrisUserWarning: Collapsing spatial coordinate 'latitude' without weighting\n",
      "  warnings.warn(\n",
      "/g/data/xp65/public/apps/med_conda/envs/esmvaltool-0.4/lib/python3.11/site-packages/iris/cube.py:4620: IrisIgnoringBoundsWarning: The bounds of coordinate 'longitude' were ignored in the rolling window operation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "245.5 219.0\n",
      "ACCESS-CM2 enso IQR: 26.5\n",
      "245.5 219.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/g/data/xp65/public/apps/med_conda/envs/esmvaltool-0.4/lib/python3.11/site-packages/iris/cube.py: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/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:4620: IrisIgnoringBoundsWarning: The bounds of coordinate 'time' were ignored in the rolling window operation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "235.5 194.5\n",
      "NOAA-ERSSTv5 enso IQR: 41.0\n",
      "235.5 194.5\n",
      "metric:  35.36585365853659\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/g/data/xp65/public/apps/med_conda/envs/esmvaltool-0.4/lib/python3.11/site-packages/iris/cube.py:4067: IrisUserWarning: Collapsing spatial coordinate 'latitude' without weighting\n",
      "  warnings.warn(\n",
      "/g/data/xp65/public/apps/med_conda/envs/esmvaltool-0.4/lib/python3.11/site-packages/iris/cube.py:4620: IrisIgnoringBoundsWarning: The bounds of coordinate 'longitude' were ignored in the rolling window operation.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "datasets = [var, obs]\n",
    "\n",
    "#plot 1\n",
    "process = {}\n",
    "metric = []\n",
    "for ds in datasets:\n",
    "    nino_res = nino3_4ssta(ds.load())\n",
    "    events = enso_events(nino_res)\n",
    "\n",
    "    eq_res = eq_ssta(ds.load())\n",
    "    results_lon = diversity(eq_res, events)\n",
    "    results_lon['enso'] = results_lon['nino'] + results_lon['nina']\n",
    "    print(ds['dataset'], 'enso IQR:', iqr(results_lon['enso'])) \n",
    "    process[ds['dataset']] = results_lon\n",
    "    metric.append(iqr(results_lon['enso']))#plot with label\n",
    "\n",
    "print('metric: ', compute(metric[1],metric[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e4a3f55-47b4-47c0-b26b-b2a5399d7ddf",
   "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": 9,
   "id": "ae5d0b8e-bf8f-4440-bf0e-9457f5dbc7f4",
   "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": [
    "plt.scatter(range(len(metric)), metric, 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('IQR of min/max SSTA (°long)')\n",
    "plt.grid(linestyle='--', axis='y')\n",
    "plt.title('ENSO diversity')\n",
    "\n",
    "metricval = compute(metric[1],metric[0])\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-CM2', 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": "09078b7d-1dc3-415d-ae0c-c00d7e25619e",
   "metadata": {},
   "source": [
    "**Figure 1**: Width of the zonal location of maximum (minimum) SSTA during all El Niño (La Niña) events, illustrating the \"diversity\" of ENSO events. The model typically shows a smaller zonal width compared to the reference. 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": "49bc5455-39b4-4aea-826b-68d00672bbf9",
   "metadata": {},
   "source": [
    "## Diagnostic Level 2\n",
    "\n",
    "The second level shows the distribution of zonal location of the maximum SSTA during all ENSO events, La Niña events, El Niño events: the ENSO diversity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "945b214c-b5b0-402e-b9a3-273eac8cefac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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",
    "\n",
    "fig = plt.figure(figsize=(20, 7))\n",
    "nina = [process[ds]['nina'] for ds in process.keys()] # subplots for nina and nino\n",
    "nino = [process[ds]['nino'] for ds in process.keys()]\n",
    "\n",
    "ax1 = plt.subplot(121)\n",
    "bplt = ax1.boxplot(nina, labels=process.keys(), showmeans=True)\n",
    "#check whiskers to 10th 90th percentile,, whis=(5,95) box-25th to 75th, diamond mean\n",
    "\n",
    "for k in bplt.keys():\n",
    "    plt.setp(bplt[k], linewidth=2)\n",
    "# colours = ['blue','black']\n",
    "# for k in ['boxes','whiskers','caps']:\n",
    "#     for patch, colour in zip(bplt[k], ):\n",
    "#         # patch.set_color(colour)\n",
    "#         patch.set_linewidth(2)\n",
    "#         if k == 'boxes':\n",
    "#             patch.set_fill(False)\n",
    "ax1.set_title('La Nina diversity')\n",
    "plt.ylabel('longitude of min SSTA')\n",
    "plt.gca().yaxis.set_major_formatter(plt.FuncFormatter(format_longitude))\n",
    "plt.grid(linestyle='--', axis='y')\n",
    "\n",
    "ax1 = plt.subplot(122)\n",
    "bplt = ax1.boxplot(nino, labels=process.keys(), showmeans=True) # whis=(10,90),\n",
    "for k in bplt.keys():\n",
    "    plt.setp(bplt[k], linewidth=2)\n",
    "plt.title('El Nino diversity')\n",
    "plt.ylabel('longitude of max SSTA')\n",
    "plt.grid(linestyle='--', axis='y')\n",
    "plt.gca().yaxis.set_major_formatter(plt.FuncFormatter(format_longitude))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4b4dee0-0d9a-458f-b86b-2ccd9b785d2c",
   "metadata": {},
   "source": [
    "**Figure 2**: Distributions of the zonal location of the maximum SSTA during all ENSO events (top left), La Niña events (top right), and El Niño events (bottom left). The model typically shows a too wide distribution for La Niña events and too narrow for El Niño events. The distributions are also skewed in the wrong direction and shifted eastward for both types of events. The black and blue boxplots represent the reference and the model, respectively. Boxplots: whiskers extend to the 10th and 90th percentiles; boxes encompass the 25th and 75th percentiles; and a diamond marks the mean. La Niña (El Niño) events are defined when the December Niño3.4 SSTA (smoothed with a 5-month triangular-weighted moving average) is below -0.75 (above 0.75) standard deviation.\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
}