{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Seasonal Mean Response\n", "* Wenchang Yang (wenchang@princeton.edu)\n", "* Department of Geosciences, Princeton University" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2018-05-22T20:46:15.200583Z", "start_time": "2018-05-22T20:46:14.997552Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tue May 22 16:46:15 EDT 2018\n", "[Start] matplotlib settings: ####################\n", "Python: 3.6.5\n", "matplotlib: 2.2.2 backend nbAgg\n", "interactive = True\n", "figure.max_open_warning = 50\n", "hatch.linewidth = 0.5\n", "hatch.color = gray\n", "legend.frameon = False\n", "savefig.bbox = tight\n", "savefig.format = pdf\n", "[End] matplotlib settings. ####################\n" ] } ], "source": [ "%matplotlib notebook\n", "!date\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "# plt.rcParams['hatch.color']='g'\n", "import xarray as xr\n", "from scipy.stats import ttest_1samp\n", "\n", "from plotsetting import *\n", "from geoplots import mapplot, xticksyear, xticksmonth, yticks2lat\n", "import geoxarray" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2018-05-22T20:46:16.824514Z", "start_time": "2018-05-22T20:46:16.821903Z" } }, "outputs": [], "source": [ "# parameters\n", "data_names = ['netrad_toa', 't_surf', 'precip']\n", "figname = f'fig_seasonalMean_JJA.pdf'\n", "prcp_scale = 3600 * 24" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Agung data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2018-05-22T20:46:35.431342Z", "start_time": "2018-05-22T20:46:18.638134Z" }, "code_folding": [ 0 ], "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data loaded from cache/volcano_impact_on_climate_agung.ipynb.netrad_toa.ctl.nc\n", "Data loaded from cache/volcano_impact_on_climate_agung.ipynb.netrad_toa.nc\n", "Data loaded from cache/volcano_impact_on_climate_agung.ipynb.t_surf.ctl.nc\n", "Data loaded from cache/volcano_impact_on_climate_agung.ipynb.t_surf.nc\n", "Data loaded from cache/volcano_impact_on_climate_agung.ipynb.precip.ctl.nc\n", "Data loaded from cache/volcano_impact_on_climate_agung.ipynb.precip.nc\n" ] } ], "source": [ "# Agung data\n", "year_volc = 1963\n", "season = slice(f'{year_volc+1}-06', f'{year_volc+1}-08')\n", "daas_agung = dict()\n", "for data_name in data_names:\n", " from data_CTL1860 import open_ensemble as get_ctl\n", " from data_agung import open_data as get_volcano\n", " volcano_name = 'Agung'\n", " year_volcano = 1963\n", " ens = range(1,31)\n", " nbname = 'volcano_impact_on_climate_agung.ipynb'\n", " new_names = {'grid_xt': 'lon', 'grid_yt': 'lat'}\n", "\n", " ncfile = f'cache/{nbname}.{data_name}.ctl.nc'\n", " try:\n", " da_ctl = xr.open_dataarray(ncfile).load()\n", " print('Data loaded from', ncfile)\n", " except:\n", " da_ctl = get_ctl(data_name, ens=ens, year_volcano=year_volcano).rename(new_names).load()\n", " da_ctl.to_dataset().to_netcdf(ncfile)\n", " print('Data calculated and saved to', ncfile)\n", "\n", " ncfile = f'cache/{nbname}.{data_name}.nc'\n", " try:\n", " da_volcano = xr.open_dataarray(ncfile).load()\n", " print('Data loaded from', ncfile)\n", " except:\n", " da_volcano = get_volcano(data_name, ens=ens).rename(new_names).load()\n", " da_volcano.to_dataset().to_netcdf(ncfile)\n", " print('Data calculated and saved to', ncfile)\n", "\n", " daa = da_volcano - da_ctl\n", " if data_name in ('precip',):\n", " daa = daa * prcp_scale\n", " \n", " daa = daa.sel(time=season).mean('time') # seasonal mean\n", " tt, pp = ttest_1samp(daa, 0)\n", " daa = daa.mean('en').load()\n", " pvalue = xr.DataArray(pp, dims=daa.dims, coords=[daa[dim] for dim in list(daa.dims)])\n", " \n", " daas_agung[data_name] = dict()\n", " daas_agung[data_name]['daa'] = daa\n", " daas_agung[data_name]['pvalue'] = pvalue\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### StMaria data" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2018-05-22T20:46:53.764887Z", "start_time": "2018-05-22T20:46:36.918772Z" }, "code_folding": [ 0 ], "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data loaded from cache/volcano_impact_on_climate_stmaria.ipynb.netrad_toa.ctl.nc\n", "Data loaded from cache/volcano_impact_on_climate_stmaria.ipynb.netrad_toa.nc\n", "Data loaded from cache/volcano_impact_on_climate_stmaria.ipynb.t_surf.ctl.nc\n", "Data loaded from cache/volcano_impact_on_climate_stmaria.ipynb.t_surf.nc\n", "Data loaded from cache/volcano_impact_on_climate_stmaria.ipynb.precip.ctl.nc\n", "Data loaded from cache/volcano_impact_on_climate_stmaria.ipynb.precip.nc\n" ] } ], "source": [ "# St Maria data\n", "year_volc = 1902\n", "season = slice(f'{year_volc+1}-06', f'{year_volc+1}-08')\n", "daas_stmaria = dict()\n", "for data_name in data_names:\n", " from data_CTL1860 import open_ensemble as get_ctl\n", " from data_stmaria import open_data as get_volcano\n", " volcano_name = 'StMaria'\n", " year_volcano = 1902\n", " ens = range(1,30+1)\n", " nbname = 'volcano_impact_on_climate_stmaria.ipynb'\n", " new_names = {'grid_xt': 'lon', 'grid_yt': 'lat'}\n", "\n", " ncfile = f'cache/{nbname}.{data_name}.ctl.nc'\n", " try:\n", " da_ctl = xr.open_dataarray(ncfile).load()\n", " print('Data loaded from', ncfile)\n", " except:\n", " da_ctl = get_ctl(data_name, ens=ens, year_volcano=year_volcano).rename(new_names).load()\n", " da_ctl.to_dataset().to_netcdf(ncfile)\n", " print('Data calculated and saved to', ncfile)\n", "\n", " ncfile = f'cache/{nbname}.{data_name}.nc'\n", " try:\n", " da_volcano = xr.open_dataarray(ncfile).load()\n", " print('Data loaded from', ncfile)\n", " except:\n", " da_volcano = get_volcano(data_name, ens=ens).rename(new_names).load()\n", " da_volcano.to_dataset().to_netcdf(ncfile)\n", " print('Data calculated and saved to', ncfile)\n", "\n", " daa = da_volcano - da_ctl\n", " if data_name in ('precip',):\n", " daa = daa * prcp_scale\n", " \n", " daa = daa.sel(time=season).mean('time') # zonal mean\n", " tt, pp = ttest_1samp(daa, 0)\n", " daa = daa.mean('en').load()\n", " pvalue = xr.DataArray(pp, dims=daa.dims, coords=[daa[dim] for dim in list(daa.dims)])\n", " \n", " daas_stmaria[data_name] = dict()\n", " daas_stmaria[data_name]['daa'] = daa\n", " daas_stmaria[data_name]['pvalue'] = pvalue" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2018-05-22T20:47:02.075292Z", "start_time": "2018-05-22T20:46:56.977101Z" }, "code_folding": [] }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. 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