{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## *NOTE* Frequency indices T?90, R??mm, N??? have errors in them. Output files deleted.\n", "\n", "Need to address the underlying function to count days above threshold per ensemble member." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This script generates the ETCCDI temperature indices from a single historical run of CESM2-WACCM daily temperature maxima (TX) and temperature minima (TN).\n", "\n", "Outputs one file per index:\n", "\n", "* TXX \n", "* TXN \n", "* TNN \n", "* TNX \n", "* TN90 \n", "* TN10 \n", "* TX90 \n", "* TX10 \n", "* SU \n", "* TR \n", "* ID \n", "* FD \n", "\n", "GSL Growing season length is not computed here. Script yet to be developed." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "tags": [] }, "outputs": [], "source": [ "import warnings\n", "warnings.filterwarnings('ignore')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import numpy as np # data arrays\n", "import xarray as xr # data array manipulation\n", "import pandas as pd\n", "import datetime as dt\n", "import os\n", "import glob" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define the precipitation functions" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from importlib import reload\n", "import config\n", "import tempex_func\n", "import utils #pyclimdex by B Groenks\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Read in each of the datasets in turn, calculate indices, then write the output to netcdf." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "iDir = \"/glade/work/maritye/Data/ARISE-MCB/Dailies/TREFHT/\"\n", "otnDir = \"/glade/work/maritye/Data/ARISE-MCB/ETCCDI/TREFHTMN/\"\n", "otxDir = '/glade/work/maritye/Data/ARISE-MCB/ETCCDI/TREFHTMX/'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create standard information to include with each data set" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "years = np.arange(2035,2070)\n", "dates= '2035-2069'\n", "member=range(10)\n", "lat = np.linspace(-90,90,num=192)\n", "lon = np.linspace(0,358.75,num=288)\n", "dims = ('member','year', 'lat', 'lon')\n", "coords = dict(member = member, year=years, lat=lat, lon=lon)\n", "attribs = dict(description='Temperature Extreme Indices based on ETCCDI definitions. MCB scenarios with 5% global seeding', \n", " history='Created by Mari Tye November 2023.' ),\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "read in thresholds from SSP245 take the mean of the ensemble as the threshold for these runs." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "with xr.open_dataset('/glade/work/maritye/Data/ARISE-SAI/ETCCDI/SSP245/TREFHTMN/b.e21.BWSSP245cmip6.f09_g17.CMIP6-SSP2-4.5-WACCM.cam.h1.QN10.2015-2070.nc') as dsn10:\n", " dn10 = dsn10.mean(dim='members')\n", "\n", "with xr.open_dataset('/glade/work/maritye/Data/ARISE-SAI/ETCCDI/SSP245/TREFHTMN/b.e21.BWSSP245cmip6.f09_g17.CMIP6-SSP2-4.5-WACCM.cam.h1.QN90.2015-2070.nc') as dsn90:\n", " dn90 = dsn90.mean(dim='members')\n", " \n", "with xr.open_dataset('/glade/work/maritye/Data/ARISE-SAI/ETCCDI/SSP245/TREFHTMX/b.e21.BWSSP245cmip6.f09_g17.CMIP6-SSP2-4.5-WACCM.cam.h1.QX10.2015-2070.nc') as dsx10:\n", " dx10 = dsx10.mean(dim='members')\n", "\n", "with xr.open_dataset('/glade/work/maritye/Data/ARISE-SAI/ETCCDI/SSP245/TREFHTMX/b.e21.BWSSP245cmip6.f09_g17.CMIP6-SSP2-4.5-WACCM.cam.h1.QX90.2015-2070.nc') as dsx90:\n", " dx90 = dsx90.mean(dim='members')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Daily minimum temperatures" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create some blank arrays to fill with each index for each ensemble member. Write these out at the end with netcdf" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "tags": [] }, "outputs": [], "source": [ "TNX = xr.DataArray(None, coords=coords, dims=dims, attrs=attribs, name='TNX')\n", "TNN = xr.DataArray(None, coords=coords, dims=dims, attrs=attribs, name='TNN')\n", "TN90 = xr.DataArray(None, coords=coords, dims=dims, attrs=attribs, name='TN90')\n", "TN10 = xr.DataArray(None, coords=coords, dims=dims, attrs=attribs, name='TN10')\n", "TR = xr.DataArray(None, coords=coords, dims=dims, attrs=attribs, name='TR')\n", "FD = xr.DataArray(None, coords=coords, dims=dims, attrs=attribs, name='FD')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "list the files - glob includes the filepath" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "files = glob.glob(iDir + '*.TREFHTMN.*')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "tnx\n", "tnn\n", "tr\n", "fd\n", "TN90\n", "TN10\n" ] } ], "source": [ "for eff in range(1):\n", " print(eff)\n", " fp = files[eff]\n", " ds = xr.open_dataset(fp)\n", " ds = ds.sel(time=slice('2015-01-01', '2069-12-31'))\n", "\n", " ### Fixed index\n", " TNX[eff,:,:,:] = tempex_func.warmest_night(ds) # Warmest night\n", " print('tnx')\n", " TNN[eff,:,:,:] = tempex_func.coolest_night(ds) # Coolest night\n", " print('tnn')\n", "\n", " ## Fixed threshold count of days\n", " TR[eff,:,:,:] = tempex_func.tropical_nights(ds) # Tropical Nights Tn>20C\n", " print('tr')\n", " FD[eff,:,:,:] = tempex_func.frost_days(ds) # Nights with TN