23-sst_nhf_feedback.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8e444b8f-e137-4087-8abd-48ef10a4adfb",
   "metadata": {},
   "source": [
    "# sst-nhf feedback : \n",
    "\n",
    "Computes net surface heat flux anomalies (NHFA; sum of latent and sensible heat fluxes and longwave and shortwave radiations) regressed onto surface temperature anomalies (SSTA) both in the eastern equatorial Pacific (horizontal Niño3 average).\n",
    "\n",
    "#### Reference observations:\n",
    "TropFlux 1979-2018 (main)\n",
    "\n",
    "NHF: OAFlux-ISCCP 1984-2009, DEEP-C 1985-2016, ERA5 1940-2022, 20CRv3 1836-2015, NCEP2 1979-2023\n",
    "SST: ERSSTv5 1854-2023, HadISST 1870-2023, COBE2 1850-2023, ERA5 1940-2022, 20CRv3 1836-2015, NCEP2 1979-2023\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a3a2571a-c2f1-4ac8-a272-d4c731c062cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from esmvalcore.dataset import Dataset\n",
    "\n",
    "from esmvalcore.config import CFG\n",
    "CFG['rootpath']['OBS6'].append(r'/g/data/kj13/datasets/esmvaltool/obsdata-v2') #NCEP-DOE-R2 tos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "64899e7d-5aac-48bc-ba9e-ae23283c8a1f",
   "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"
     ]
    }
   ],
   "source": [
    "model_ts_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_ts_datasets[\"ACCESS-ESM1-5\"].add_supplementary(short_name='areacello', mip='Ofx')\n",
    "\n",
    "model_ts = {name: dataset.load() for name, dataset in model_ts_datasets.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a4419b4c-dced-4dd1-a091-e0d4f15685bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_hfls_datasets = { \n",
    "\"ACCESS-ESM1-5\": \n",
    "    Dataset(\n",
    "    short_name='hfls',\n",
    "    project='CMIP6',\n",
    "    mip=\"Amon\",\n",
    "    exp=\"historical\",\n",
    "    ensemble=\"r1i1p1f1\",\n",
    "    # timerange=\"19790101/20190101\",\n",
    "    dataset=\"ACCESS-ESM1-5\",\n",
    "    grid=\"gn\"\n",
    ")}\n",
    "\n",
    "model_hfls_datasets[\"ACCESS-ESM1-5\"].add_supplementary(short_name='areacella', mip='fx')\n",
    "\n",
    "#rest variables\n",
    "model_other = {}\n",
    "for cvar in ['hfss','rlds','rlus','rsds','rsus']:\n",
    "    \n",
    "    model_other[cvar] = model_hfls_datasets[\"ACCESS-ESM1-5\"].copy(short_name=cvar)\n",
    "\n",
    "# load cubes, formula nhf\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0b9ecd35-a1ee-4474-ae35-12f4c3438eae",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:esmvalcore.cmor.check:There were warnings in variable hfls:\n",
      " hfls: attribute positive not present\n",
      "loaded from file /g/data/fs38/publications/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r1i1p1f1/Amon/hfls/gn/v20191115/hfls_Amon_ACCESS-ESM1-5_historical_r1i1p1f1_gn_185001-201412.nc\n",
      "WARNING:esmvalcore.cmor.check:There were warnings in variable hfls:\n",
      " hfls: attribute positive not present\n",
      "loaded from file \n",
      "WARNING:esmvalcore.cmor.check:There were warnings in variable hfss:\n",
      " hfss: attribute positive not present\n",
      "loaded from file /g/data/fs38/publications/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r1i1p1f1/Amon/hfss/gn/v20191115/hfss_Amon_ACCESS-ESM1-5_historical_r1i1p1f1_gn_185001-201412.nc\n",
      "WARNING:esmvalcore.cmor.check:There were warnings in variable hfss:\n",
      " hfss: attribute positive not present\n",
      "loaded from file \n",
      "WARNING:esmvalcore.cmor.check:There were warnings in variable rlds:\n",
      " rlds: attribute positive not present\n",
      "loaded from file /g/data/fs38/publications/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r1i1p1f1/Amon/rlds/gn/v20191115/rlds_Amon_ACCESS-ESM1-5_historical_r1i1p1f1_gn_185001-201412.nc\n",
      "WARNING:esmvalcore.cmor.check:There were warnings in variable rlds:\n",
      " rlds: attribute positive not present\n",
      "loaded from file \n",
      "WARNING:esmvalcore.cmor.check:There were warnings in variable rlus:\n",
      " rlus: attribute positive not present\n",
      "loaded from file /g/data/fs38/publications/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r1i1p1f1/Amon/rlus/gn/v20191115/rlus_Amon_ACCESS-ESM1-5_historical_r1i1p1f1_gn_185001-201412.nc\n",
      "WARNING:esmvalcore.cmor.check:There were warnings in variable rlus:\n",
      " rlus: attribute positive not present\n",
      "loaded from file \n",
      "WARNING:esmvalcore.cmor.check:There were warnings in variable rsds:\n",
      " rsds: attribute positive not present\n",
      "loaded from file /g/data/fs38/publications/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r1i1p1f1/Amon/rsds/gn/v20191115/rsds_Amon_ACCESS-ESM1-5_historical_r1i1p1f1_gn_185001-201412.nc\n",
      "WARNING:esmvalcore.cmor.check:There were warnings in variable rsds:\n",
      " rsds: attribute positive not present\n",
      "loaded from file \n",
      "WARNING:esmvalcore.cmor.check:There were warnings in variable rsus:\n",
      " rsus: attribute positive not present\n",
      "loaded from file /g/data/fs38/publications/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r1i1p1f1/Amon/rsus/gn/v20191115/rsus_Amon_ACCESS-ESM1-5_historical_r1i1p1f1_gn_185001-201412.nc\n",
      "WARNING:esmvalcore.cmor.check:There were warnings in variable rsus:\n",
      " rsus: attribute positive not present\n",
      "loaded from file \n"
     ]
    }
   ],
   "source": [
    "model_hfls = {name: dataset.load() for name, dataset in model_hfls_datasets.items()}\n",
    "model_rest = {name: dataset.load() for name, dataset in model_other.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4ffda2ac-8c8c-4478-85a3-c25f3caf63d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "550.3307 69.14513\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(73.619156, -69.14512481133171, -550.3307)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(model_hfls['ACCESS-ESM1-5'].data.max(), model_hfls['ACCESS-ESM1-5'].data.mean())\n",
    "model_rest\n",
    "test = - model_hfls['ACCESS-ESM1-5']\n",
    "test.data.max() , test.data.mean(), test.data.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c79b7fbb-0243-4209-b2f6-ab3da878ae63",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# nhf = -*hfls* -hfss + rlds - rlus + rsds - rsus #if 'd' make positive\n",
    "model_nhf = - model_hfls['ACCESS-ESM1-5'] - model_rest['hfss'] + model_rest['rlds']- model_rest['rlus']+ model_rest['rsds']- model_rest['rsus']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "316dd037-5b03-4b6c-8005-b29d0f58525b",
   "metadata": {},
   "outputs": [],
   "source": [
    "obs_tos_datasets = {\n",
    "\"HadISST\": \n",
    "    Dataset(\n",
    "    short_name='tos',\n",
    "    dataset='HadISST',\n",
    "    mip=\"Omon\",\n",
    "    project='OBS',\n",
    "    type='reanaly',\n",
    "    timerange=\"19790101/20190101\",\n",
    "    tier=2),\n",
    "\"ERA-Interim\":\n",
    "    Dataset(\n",
    "    short_name='ts',\n",
    "    dataset='ERA-Interim',\n",
    "    mip=\"Amon\",\n",
    "    project='OBS6',\n",
    "    type='reanaly',\n",
    "    tier=3)\n",
    "}\n",
    "obs_tauu_datasets = { ##zos\n",
    " \"ERA-Interim\": \n",
    "     Dataset(\n",
    "     short_name='tauu',\n",
    "     dataset='ERA-Interim',\n",
    "     mip=\"Amon\",\n",
    "     project='OBS6',\n",
    "     type='reanaly',\n",
    "     timerange=\"19790101/20190101\",\n",
    "     tier=3)\n",
    "}"
   ]
  },
  {
   "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 seasonal_statistics\n",
    "from esmvalcore.preprocessor import rolling_window_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 extract_month\n",
    "from esmvalcore.preprocessor import detrend\n",
    "from esmvalcore.preprocessor import regrid\n",
    "from esmvalcore.preprocessor import detrend\n",
    "from esmvalcore.preprocessor import mask_landsea\n",
    "from esmvalcore.preprocessor import extract_time\n",
    "import iris\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import iris.quickplot as qplt\n",
    "import numpy as np\n",
    "import scipy.stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "61674b62",
   "metadata": {},
   "outputs": [],
   "source": [
    "def east(cube):\n",
    "    nino3_region = {\"start_longitude\": 210., \"end_longitude\": 270., \"start_latitude\": -5., \"end_latitude\": 5.}\n",
    "\n",
    "    cube = regrid(cube, target_grid=\"1x1\", scheme=\"linear\")\n",
    "    # cube = convert_units(cube, units=\"degC\")\n",
    "    cube = extract_region(cube, **nino3_region)\n",
    "    # cube = detrend(cube, dimension=\"time\", method=\"linear\")\n",
    "    cube = area_statistics(cube, operator='mean')\n",
    "    cube = anomalies(cube, period=\"full\")\n",
    "    \n",
    "    return cube\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "33b76c54-52e7-4f24-8a7d-30b54025fd6f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "<th class=\"iris iris-word-cell\">time</th>\n",
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       "<td class=\"iris-word-cell iris-subheading-cell\">Shape</td>\n",
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       "    <td class=\"iris-word-cell iris-subheading-cell\">\t0</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"1\">area: mean where sea</td>\n",
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       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\t1</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"1\">time: mean</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\t2</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"1\">latitude: longitude: 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",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tConventions</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"1\">&#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=\"1\">&#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=\"1\">&#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=\"1\">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=\"1\">-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=\"1\">&#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=\"1\">&#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=\"1\">&#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=\"1\">&#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=\"1\">&#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=\"1\">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=\"1\">&#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=\"1\">&#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=\"1\">&#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=\"1\">&#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=\"1\">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=\"1\">&#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=\"1\">&#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=\"1\">&#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=\"1\">&#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=\"1\">&#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=\"1\">&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=\"1\">&#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=\"1\">&#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=\"1\">&#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=\"1\">&#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=\"1\">&#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=\"1\">&#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=\"1\">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=\"1\">&#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=\"1\">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=\"1\">&#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=\"1\">&#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=\"1\">&#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=\"1\">&#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=\"1\">&#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=\"1\">&#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=\"1\">&#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=\"1\">&#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=\"1\">&#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=\"1\">&#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=\"1\">&#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=\"1\">&#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=\"1\">&#x27;v20191115&#x27;</td>\n",
       "</tr>\n",
       "</table>\n",
       "        "
      ],
      "text/plain": [
       "<iris 'Cube' of sea_surface_temperature / (degC) (time: 1980)>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_ts_prep['ACCESS-ESM1-5']\n",
    "# qplt.plot(cube), cube.data.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "821d528c-da59-4b25-9bfa-3d7a53a61a9f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model_ts_prep = {name: east(dataset) for name, dataset in model_ts.items()}\n",
    "model_nhf_prep = {'ACCESS-ESM1-5': east(model_nhf)}\n",
    "\n",
    "# obs_tos_prep = {name: sst_east(dataset) for name, dataset in obs_tos_datasets.items()}\n",
    "# obs_tauu_prep = {name: taux_west(dataset) for name, dataset in obs_tauu_datasets.items()}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e8bcc0c-3bca-40be-b77b-ba3a3490f33f",
   "metadata": {},
   "source": [
    "## Calculate Linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "107e496a-6be3-490f-a0d0-73415fb043ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "masked_array(data=[2.3525236 , 0.25193423],\n",
       "             mask=False,\n",
       "       fill_value=1e+20,\n",
       "            dtype=float32)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# scp.LinReg(model_tauu_prep[\"ACCESS-ESM1-5\"].data, model_tos_prep[\"ACCESS-ESM1-5\"].data).intcpt\n",
    "B_data = model_ts_prep[\"ACCESS-ESM1-5\"].data\n",
    "B_with_intercept = np.vstack([B_data, np.ones_like(B_data)]).T\n",
    "coefs, _, _, _ = np.linalg.lstsq(B_with_intercept, model_nhf_prep[\"ACCESS-ESM1-5\"].data, rcond=None)\n",
    "coefs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb1fc647-03a5-472e-8264-41ded88a5a63",
   "metadata": {},
   "source": [
    "# Diagnostic Level 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6400f946-00d7-4750-aaf4-f6d0bd7effed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-3.2507362 -70.46447 2.9968243 67.892944\n",
      "2.3525234806702193 0.25193423895592576\n",
      "(1980,) (1980,)\n",
      "W m-2 degC\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.02, 0.9, 'slope: 2.35 ')"
      ]
     },
     "execution_count": 14,
     "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": [
    "from scipy.stats import linregress\n",
    "xseq = np.linspace(-4, 4, num=10)\n",
    "print(model_ts_prep[\"ACCESS-ESM1-5\"].data.min(), model_nhf_prep[\"ACCESS-ESM1-5\"].data.min(), \n",
    "      model_ts_prep[\"ACCESS-ESM1-5\"].data.max(), model_nhf_prep[\"ACCESS-ESM1-5\"].data.max())\n",
    "# linregress(model_tauu_prep[\"ACCESS-ESM1-5\"].data, model_tos_prep[\"ACCESS-ESM1-5\"].data)\n",
    "slope, intcpt, _,_,_ = linregress(model_ts_prep[\"ACCESS-ESM1-5\"].data, model_nhf_prep[\"ACCESS-ESM1-5\"].data)\n",
    "\n",
    "print(slope, intcpt)\n",
    "mod_slope = slope\n",
    "plt.plot(xseq, intcpt+slope*xseq)\n",
    "# slope, intcpt, _,_,_ = linregress(obs_tos_prep[\"HadISST\"].data, obs_tauu_prep[\"ERA-Interim\"].data)\n",
    "# plt.plot(xseq, intcpt+slope*xseq, color='black')\n",
    "# metric_val = abs((mod_slope-slope)/slope)*100\n",
    "\n",
    "print(model_nhf_prep[\"ACCESS-ESM1-5\"].shape, model_ts_prep[\"ACCESS-ESM1-5\"].shape) #scatter plot\n",
    "print(model_nhf_prep[\"ACCESS-ESM1-5\"].units, model_ts_prep[\"ACCESS-ESM1-5\"].units)\n",
    "plt.scatter(model_ts_prep[\"ACCESS-ESM1-5\"].data, model_nhf_prep[\"ACCESS-ESM1-5\"].data, s=10)\n",
    "yunit = model_nhf_prep[\"ACCESS-ESM1-5\"].units \n",
    "xunit = model_ts_prep[\"ACCESS-ESM1-5\"].units\n",
    "\n",
    "# print(obs_tos_prep[\"HadISST\"].shape, obs_tauu_prep[\"ERA-Interim\"].shape)\n",
    "# plt.scatter(obs_tos_prep[\"HadISST\"].data, obs_tauu_prep[\"ERA-Interim\"].data,s=20, c='black', marker='D')\n",
    "plt.xlim(-5,5)\n",
    "\n",
    "plt.ylabel(f'nino3 NHFA ({yunit})')\n",
    "plt.xlabel(f'nino3 SSTA ({xunit})')\n",
    "\n",
    "## text slope\n",
    "plt.text(0.02, 0.9, f'slope: {mod_slope:.2f} ', fontsize=12, ha='left',\n",
    "         transform=plt.gca().transAxes, bbox=dict(facecolor='white', alpha=0.8, edgecolor='none'))\n",
    "\n",
    "# print(metric_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46d3c2fc-534e-49d8-a576-ca07c4934b8f",
   "metadata": {},
   "source": [
    "# Diagnostic Level 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4d84029-2efd-436c-83aa-ebfb4ac8d5a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# SPLIT BY X<0 AND X>0 (temp)\n",
    "import pandas as pd\n",
    "\n",
    "model_tauu_prep[\"ACCESS-ESM1-5\"].data, model_ssh_prep[\"ACCESS-ESM1-5\"]\n",
    "# matrix/cube? split by tauua 0 -df?time rows, col tauu,ssh create tuple points, zip\n",
    "\n",
    "df = pd.DataFrame({'tauu':model_tauu_prep[\"ACCESS-ESM1-5\"].data, 'ssh':model_ssh_prep[\"ACCESS-ESM1-5\"].data})\n",
    "print(df.shape)\n",
    "df.loc[df['tauu']<0]\n",
    "\n",
    "xseq = np.linspace(-50, 0, num=50)\n",
    "slope, intcpt, _,_,_ = linregress(df.loc[df['tauu']<0, 'tauu'], df.loc[df['tauu']<0, 'ssh'])\n",
    "plt.plot(xseq, intcpt+slope*xseq, linewidth=3)\n",
    "print(slope)\n",
    "xseq = np.linspace(0, 50, num=50)\n",
    "slope, intcpt, _,_,_ = linregress(df.loc[df['tauu']>0, 'tauu'], df.loc[df['tauu']>0, 'ssh'])\n",
    "plt.plot(xseq, intcpt+slope*xseq, color='red', linewidth=3)\n",
    "print(slope)\n",
    "\n",
    "plt.scatter(model_tauu_prep[\"ACCESS-ESM1-5\"].data, model_ssh_prep[\"ACCESS-ESM1-5\"].data, c='k', s=10)\n",
    "xseq = np.linspace(-50, 50, num=50)\n",
    "slope, intcpt, _,_,_ = linregress(df['tauu'], df['ssh'])\n",
    "plt.plot(xseq, intcpt+slope*xseq, c='black')\n",
    "print(slope)\n",
    "plt.xlim(-50,50)\n",
    "plt.ylim(-25,25)\n",
    "plt.grid(linestyle='--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "507266df-42aa-4cd4-99a8-683c9b9e6b77",
   "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
}