13-ENSO_asymmetry.ipynb

{
 "cells": [
  {
   "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": 7,
   "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=\"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",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 23,
   "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 convert_units\n",
    "from esmvalcore.preprocessor import extract_region\n",
    "from esmvalcore.preprocessor import regrid\n",
    "from esmvalcore.preprocessor import detrend\n",
    "from esmvalcore.preprocessor import axis_statistics, 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\n",
    "from scipy.stats import skew\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2daff76-44ec-46b3-9157-81e506b032c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepoc(cube):\n",
    "    nino34_latext_region = {\"start_longitude\": 120., \"end_longitude\": 280., \"start_latitude\": -5., \"end_latitude\": 5.}\n",
    "    cube = convert_units(cube, units=\"degC\")\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 = meridional_statistics(cube, 'mean')\n",
    "    return cube\n",
    "\n",
    "def prepoc3(cube):\n",
    "    nino34_latext_region = {\"start_longitude\": 120., \"end_longitude\": 280., \"start_latitude\": -15., \"end_latitude\": 15.}\n",
    "    cube = convert_units(cube, units=\"degC\")\n",
    "    cube = regrid(cube, target_grid=\"1x1\", scheme=\"linear\")\n",
    "    cube = extract_region(cube, **nino34_latext_region)\n",
    "    cube = anomalies(cube,period='monthly')\n",
    "    skew_array = skew(cube.data, axis=0) ##2d?\n",
    "    return skew_array\n",
    "    \n",
    "def sst_ske(cube): #calc skew outside function\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 = 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",
    "\n",
    "    return cube\n",
    "\n",
    "def compute(obs, mod):\n",
    "    return abs((mod-obs)/obs)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "821d528c-da59-4b25-9bfa-3d7a53a61a9f",
   "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": [
    "model_datasets_prep2 = {name: prepoc(dataset) for name, dataset in model_datasets.items()}\n",
    "obs_datasets_prep2 = {name: prepoc(dataset) for name, dataset in obs_datasets.items()}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bcb71c64-9c89-40ac-b07e-8ff2359230ca",
   "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/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"
     ]
    }
   ],
   "source": [
    "model_datasets_prep = {name: sst_ske(dataset) for name, dataset in model_datasets.items()}\n",
    "obs_datasets_prep = {name: sst_ske(dataset) for name, dataset in obs_datasets.items()}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9d78ca49-24a3-4b34-935a-60a4b04f993f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.75, 0.8, '* observation')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_skew = skew(model_datasets_prep[\"ACCESS-ESM1-5\"].data, axis=0)\n",
    "obs_skew = skew(obs_datasets_prep[\"HadISST\"].data, axis=0)\n",
    "\n",
    "metric = [model_skew,obs_skew]\n",
    "\n",
    "\n",
    "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('SSTA skewness (°C)')\n",
    "plt.grid(linestyle='--', axis='y')\n",
    "plt.title('ENSO skewness')\n",
    "\n",
    "metricval = compute(metric[1],metric[0])\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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b64373db-6a72-4dfd-a994-8cc039fcc925",
   "metadata": {},
   "source": [
    "## diagnostic level 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d4137e0-5df9-40e8-94f1-16fac41c3c78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x149e6be96650>"
      ]
     },
     "execution_count": 29,
     "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": [
    "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",
    "# Calculate the skew\n",
    "model_skew_dev = skew(model_datasets_prep2[\"ACCESS-ESM1-5\"].data, axis=0)\n",
    "obs_skew_dev = skew(obs_datasets_prep2[\"HadISST\"].data, axis=0)\n",
    "\n",
    "# Create the plot\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.plot(model_datasets_prep2[\"ACCESS-ESM1-5\"].coord('longitude').points, model_skew_dev, c='b', linewidth=4, label='model')\n",
    "plt.plot(obs_datasets_prep2[\"HadISST\"].coord('longitude').points, obs_skew_dev, c='k', linewidth=4, label='ref: HadISST')\n",
    "\n",
    "plt.gca().xaxis.set_major_formatter(plt.FuncFormatter(format_longitude))\n",
    "# Set the x and y axis labels\n",
    "plt.xlabel('longitude')\n",
    "plt.ylabel('SSTA skewness (°C)')\n",
    "plt.grid(linestyle='--'); plt.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c46ab161-0723-4b7f-ad54-a45135edf1eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2d?\n",
    "model_datasets_prep3 = {name: prepoc3(dataset) for name, dataset in model_datasets.items()}\n",
    "obs_datasets_prep3 = {name: prepoc3(dataset) for name, dataset in obs_datasets.items()}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc832155",
   "metadata": {},
   "source": [
    "## dive down level 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c8ae62f-ff72-4499-8a09-06f5edb7c9aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import iris.plot as iplt\n",
    "import matplotlib.colors as mcolors\n",
    "import cartopy.feature as cfeature\n",
    "import cartopy.crs as ccrs\n",
    "\n",
    "fig = plt.figure(figsize=(20, 7))\n",
    "proj = ccrs.Orthographic(central_longitude=210.0)\n",
    "i =121\n",
    "process = {**model_datasets_prep3, **obs_datasets_prep3}\n",
    "\n",
    "\n",
    "for label, skew in process.items():\n",
    "    \n",
    "    ax1 = plt.subplot(i,projection=proj)\n",
    "    ax1.add_feature(cfeature.LAND, facecolor='gray')  # Add land feature with gray color\n",
    "    ax1.coastlines()\n",
    "    cf1 = iplt.contourf(skew, levels=np.arange(0,2,0.1), cmap='coolwarm')\n",
    "\n",
    "    ax1.set_extent([130, 290, -20, 20], crs=ccrs.PlateCarree())\n",
    "    ax1.set_title(label)\n",
    "\n",
    "    # Add gridlines for latitude and longitude\n",
    "    gl1 = ax1.gridlines(draw_labels=True, linestyle='--')\n",
    "    gl1.top_labels = False\n",
    "    gl1.right_labels = False\n",
    "\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(cf1, cax=cax, orientation='horizontal', extend='both', ticks=np.arange(0,2,0.5))\n",
    "cbar.set_label('SSTA std (°C)')"
   ]
  }
 ],
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