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ED_figure6.py
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#!/usr/bin/env python3
"""
Author: Victoria McDonald
email: [email protected]
website: http://torimcd.github.com
license: BSD
*** THIS SCRIPT IS CURRENTLY BROKEN AND WILL NOT REPRODUCE ED FIGURE 6.
IT WILL BE UPDATED SOON ***
This script prepares the model output to make a set of 2D histograms of cloud behavior against forcings.
The actual figure is plotted in MATLAB, see /matlab_files/ED_figure6/
"""
import matplotlib
matplotlib.use("Agg")
import os
import sys
import numpy as np
import netCDF4
import operator
from matplotlib import colors
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import processing_functions as pf
# ------------------------------------------------------------------------
# change this section to match where you downloaded the model output files
# ------------------------------------------------------------------------
download_path = '' # enter the path to the directory where you downloaded the archived data, eg '/home/user/Downloads'
filebase = download_path + 'FYSP_clouds_archive/CAM4/'
filebase_c5 = download_path + 'FYSP_clouds_archive/CAM5/'
outfileloc = download_path + 'temp_data/' # this is the location to save the processed netcdf files to
pf.prep_anomaly_histograms(filebase, filebase_c5, outfileloc)
casenames_c4 = {'_10', '_09', '_08'}
casenames_c5 = {'_10', '_09'}
# Calc eis anom and plot v swcf
i = 0
lcl = []
z700 = []
lts = []
qs850 = []
tempsum = []
pal = []
eis = []
swcf = []
ah700 = []
ah1000 = []
ah = []
lcwp = []
cldlow = []
lhflx = []
ts = []
lcl_8 = []
z700_8 = []
lts_8 = []
qs850_8 = []
tempsum_8 = []
pal_8 = []
eis_8 = []
swcf_8 = []
ah700_8 = []
ah1000_8 = []
ah_8 = []
lcwp_8 = []
cldlow_8 = []
lhflx_8 = []
ts_8 = []
lcl_9 = []
z700_9 = []
lts_9 = []
qs850_9 = []
tempsum_9 = []
pal_9 = []
eis_9 = []
swcf_9 = []
ah700_9 = []
ah1000_9 = []
ah_9 = []
lcwp_9 = []
cldlow_9 = []
lhflx_9 = []
ts_9 = []
c4_cldlow_anom_8 = []
c4_lhflx_anom_8 = []
c4_swcf_anom_8 = []
c4_ah_anom_8 = []
c4_eis_anom_8 = []
c4_lcwp_anom_8 = []
c4_ts_anom_8 = []
c4_cldlow_anom_9 = []
c4_lhflx_anom_9 = []
c4_swcf_anom_9 = []
c4_ah_anom_9 = []
c4_eis_anom_9 = []
c4_lcwp_anom_9 = []
c4_ts_anom_9 = []
c5_cldlow_anom_9 = []
c5_lhflx_anom_9 = []
c5_swcf_anom_9 = []
c5_ah_anom_9 = []
c5_eis_anom_9 = []
c5_lcwp_anom_9 = []
c5_ts_anom_9 = []
for CASENAME in casenames_c4:
if CASENAME == '_08':
# GET LCL
dsloc = outfileloc + 'c4_eis_map' + CASENAME + '_lcl.nc'
if os.path.isfile(dsloc):
# open the file and get out the variable
ds = netCDF4.Dataset(dsloc)
lcl_8 = ds.variables['lcl'][:]
ds.close() #close the file
lcl_8 = lcl_8.flatten()
#GET z700
dsloc = outfileloc + 'c4_eis_map' + CASENAME +'_z700.nc'
if os.path.isfile(dsloc):
# open the file and get out the variable
ds = netCDF4.Dataset(dsloc)
z700_8 = ds.variables['Z3'][:]
ds.close() #close the file
z700_8 = z700_8.flatten()
#GET lts
dsloc = outfileloc + 'c4_lts_map' + CASENAME +'_lts.nc'
if os.path.isfile(dsloc):
# open the file and get out the variable
ds = netCDF4.Dataset(dsloc)
lts_8 = ds.variables['lts'][:]
ds.close() #close the file
lts_8 = lts_8.flatten()
#GET qs850
dsloc = outfileloc + 'c4_eis_map'+ CASENAME +'_qs850.nc'
if os.path.isfile(dsloc):
# open the file and get out the variable
ds = netCDF4.Dataset(dsloc)
qs850_8 = ds.variables['smr'][:]
ds.close() #close the file
qs850_8 = qs850_8.flatten()
#GET tempsum
dsloc = outfileloc + 'c4_eis_map' + CASENAME +'_tempsum.nc'
if os.path.isfile(dsloc):
# open the file and get out the variable
ds = netCDF4.Dataset(dsloc)
tempsum_8 = ds.variables['T'][:]
ds.close() #close the file
tempsum_8 = tempsum_8.flatten()
#GET ah700
dsloc = outfileloc + 'c4_700ah' + CASENAME +'.nc'
if os.path.isfile(dsloc):
# open the file and get out the variable
ds = netCDF4.Dataset(dsloc)
ah700_8 = ds.variables['Q'][:]
ds.close() #close the file
ah700_8 = ah700_8.flatten()
#GET ah1000
dsloc = outfileloc + 'c4_1000ah' + CASENAME +'.nc'
if os.path.isfile(dsloc):
# open the file and get out the variable
ds = netCDF4.Dataset(dsloc)
ah1000_8 = ds.variables['QREFHT'][:]
ds.close() #close the file
ah1000_8 = ah1000_8.flatten()
#GET lcwp
dsloc = outfileloc + 'c4_map_wp_low' + CASENAME + '.nc'
if os.path.isfile(dsloc):
# open the file and get out the variable
ds = netCDF4.Dataset(dsloc)
lcwp_8 = ds.variables['ICLDTWP'][:]
ds.close() #close the file
lcwp_8 = lcwp_8.flatten()
#GET swcf
dsloc_swcf = outfileloc + 'c4_swcf'+CASENAME+'.nc'
if os.path.isfile(dsloc_swcf):
#print(dsloc_swcf)
# open the file and get out the variable
dscf = netCDF4.Dataset(dsloc_swcf)
swcf_8 = dscf.variables['SWCF'][:]
dscf.close() #close the file
swcf_8 = swcf_8.flatten()
#GET cldlow
dsloc_cldlow = outfileloc + 'c4_cldlow' + CASENAME+'.nc'
if os.path.isfile(dsloc_cldlow):
# open the file and get out the variable
dscf = netCDF4.Dataset(dsloc_cldlow)
cldlow_8 = dscf.variables['CLDLOW'][:]
dscf.close() #close the file
cldlow_8 =cldlow_8.flatten()
#GET lhflx
dsloc_lhflx = outfileloc + 'c4_lhflx' + CASENAME+'.nc'
if os.path.isfile(dsloc_lhflx):
# open the file and get out the variable
dscf = netCDF4.Dataset(dsloc_lhflx)
lhflx_8 = dscf.variables['LHFLX'][:]
dscf.close() #close the file
lhflx_8 = lhflx_8.flatten()
#GET ts
dsloc = outfileloc + 'c4_ts' + CASENAME+'.nc'
if os.path.isfile(dsloc):
# open the file and get out the variable
ds = netCDF4.Dataset(dsloc)
ts_8 = ds.variables['TS'][:]
ds.close() #close the file
ts_8 = ts_8.flatten()
pal_8 = (9.9)*(1-(1+2450000*qs850_8/(287.058*(tempsum_8/2)))/(1+(2450000**2)*qs850_8/(993*461.4*((tempsum_8/2)**2))))
eis_8 = lts_8 - pal_8*((z700_8/1000)-lcl_8)
ah_8 = list(map(operator.sub, ah1000_8*1000, ah700_8*1000))
elif CASENAME == '_09':
# GET LCL
dsloc = outfileloc + 'c4_eis_map' + CASENAME + '_lcl.nc'
if os.path.isfile(dsloc):
# open the file and get out the variable
ds = netCDF4.Dataset(dsloc)
lcl_9 = ds.variables['lcl'][:]
ds.close() #close the file
lcl_9 = lcl_9.flatten()
#GET z700
dsloc = outfileloc + 'c4_eis_map' + CASENAME +'_z700.nc'
if os.path.isfile(dsloc):
# open the file and get out the variable
ds = netCDF4.Dataset(dsloc)
z700_9 = ds.variables['Z3'][:]
ds.close() #close the file
z700_9 = z700_9.flatten()
#GET lts
dsloc = outfileloc + 'c4_lts_map' + CASENAME +'_lts.nc'
if os.path.isfile(dsloc):
# open the file and get out the variable
ds = netCDF4.Dataset(dsloc)
lts_9 = ds.variables['lts'][:]
ds.close() #close the file
lts_9 = lts_9.flatten()
#GET qs850
dsloc = outfileloc + 'c4_eis_map'+ CASENAME +'_qs850.nc'
if os.path.isfile(dsloc):
# open the file and get out the variable
ds = netCDF4.Dataset(dsloc)
qs850_9 = ds.variables['smr'][:]
ds.close() #close the file
qs850_9 = qs850_9.flatten()
#GET tempsum
dsloc = outfileloc + 'c4_eis_map' + CASENAME +'_tempsum.nc'
if os.path.isfile(dsloc):
# open the file and get out the variable
ds = netCDF4.Dataset(dsloc)
tempsum_9 = ds.variables['T'][:]
ds.close() #close the file
tempsum_9 = tempsum_9.flatten()
#GET ah700
dsloc = outfileloc + 'c4_700ah' + CASENAME +'.nc'
if os.path.isfile(dsloc):
# open the file and get out the variable
ds = netCDF4.Dataset(dsloc)
ah700_9 = ds.variables['Q'][:]
ds.close() #close the file
ah700_9 = ah700_9.flatten()
#GET ah1000
dsloc = outfileloc + 'c4_1000ah' + CASENAME +'.nc'
if os.path.isfile(dsloc):
# open the file and get out the variable
ds = netCDF4.Dataset(dsloc)
ah1000_9 = ds.variables['QREFHT'][:]
ds.close() #close the file
ah1000_9 = ah1000_9.flatten()
#GET lcwp
dsloc = outfileloc + 'c4_map_wp_low' + CASENAME + '.nc'
if os.path.isfile(dsloc):
# open the file and get out the variable
ds = netCDF4.Dataset(dsloc)
lcwp_9 = ds.variables['ICLDTWP'][:]
ds.close() #close the file
lcwp_9 = lcwp_9.flatten()
#GET swcf
dsloc_swcf = outfileloc + 'c4_swcf'+CASENAME+'.nc'
if os.path.isfile(dsloc_swcf):
#print(dsloc_swcf)
# open the file and get out the variable
dscf = netCDF4.Dataset(dsloc_swcf)
swcf_9 = dscf.variables['SWCF'][:]
dscf.close() #close the file
swcf_9 = swcf_9.flatten()
#GET cldlow
dsloc_cldlow = outfileloc + 'c4_cldlow' + CASENAME+'.nc'
if os.path.isfile(dsloc_cldlow):
# open the file and get out the variable
dscf = netCDF4.Dataset(dsloc_cldlow)
cldlow_9 = dscf.variables['CLDLOW'][:]
dscf.close() #close the file
cldlow_9 =cldlow_9.flatten()
#GET lhflx
dsloc_lhflx = outfileloc + 'c4_lhflx' + CASENAME+'.nc'
if os.path.isfile(dsloc_lhflx):
# open the file and get out the variable
dscf = netCDF4.Dataset(dsloc_lhflx)
lhflx_9 = dscf.variables['LHFLX'][:]
dscf.close() #close the file
lhflx_9 = lhflx_9.flatten()
#GET ts
dsloc = outfileloc + 'c4_ts' + CASENAME+'.nc'
if os.path.isfile(dsloc):
# open the file and get out the variable
ds = netCDF4.Dataset(dsloc)
ts_9 = ds.variables['TS'][:]
ds.close() #close the file
ts_9 = ts_9.flatten()
pal_9 = (9.9)*(1-(1+2450000*qs850_9/(287.058*(tempsum_9/2)))/(1+(2450000**2)*qs850_9/(993*461.4*((tempsum_9/2)**2))))
eis_9 = lts_9 - pal_9*((z700_9/1000)-lcl_9)
ah_9 = list(map(operator.sub, ah1000_9*1000, ah700_9*1000))
#mask = swcf_c < (-5)
else:
# GET LCL
dsloc = outfileloc + 'c4_eis_map' + CASENAME + '_lcl.nc'
if os.path.isfile(dsloc):
# open the file and get out the variable
ds = netCDF4.Dataset(dsloc)
lcl = ds.variables['lcl'][:]
ds.close() #close the file
lcl = lcl.flatten()
#GET z700
dsloc = outfileloc + 'c4_eis_map' + CASENAME +'_z700.nc'
if os.path.isfile(dsloc):
# open the file and get out the variable
ds = netCDF4.Dataset(dsloc)
z700 = ds.variables['Z3'][:]
ds.close() #close the file
z700 = z700.flatten()
#GET lts
dsloc = outfileloc + 'c4_lts_map' + CASENAME +'_lts.nc'
if os.path.isfile(dsloc):
# open the file and get out the variable
ds = netCDF4.Dataset(dsloc)
lts = ds.variables['lts'][:]
ds.close() #close the file
lts = lts.flatten()
#GET qs850
dsloc = outfileloc + 'c4_eis_map'+ CASENAME +'_qs850.nc'
if os.path.isfile(dsloc):
# open the file and get out the variable
ds = netCDF4.Dataset(dsloc)
qs850 = ds.variables['smr'][:]
ds.close() #close the file
qs850 = qs850.flatten()
#GET tempsum
dsloc = outfileloc + 'c4_eis_map' + CASENAME +'_tempsum.nc'
if os.path.isfile(dsloc):
# open the file and get out the variable
ds = netCDF4.Dataset(dsloc)
tempsum = ds.variables['T'][:]
ds.close() #close the file
tempsum = tempsum.flatten()
#GET ah700
dsloc = outfileloc + 'c4_700ah' + CASENAME +'.nc'
if os.path.isfile(dsloc):
# open the file and get out the variable
ds = netCDF4.Dataset(dsloc)
ah700 = ds.variables['Q'][:]
ds.close() #close the file
ah700 = ah700.flatten()
#GET ah1000
dsloc = outfileloc + 'c4_1000ah' + CASENAME +'.nc'
if os.path.isfile(dsloc):
# open the file and get out the variable
ds = netCDF4.Dataset(dsloc)
ah1000 = ds.variables['QREFHT'][:]
ds.close() #close the file
ah1000 = ah1000.flatten()
#GET lcwp
dsloc = outfileloc + 'c4_map_wp_low' + CASENAME + '.nc'
if os.path.isfile(dsloc):
# open the file and get out the variable
ds = netCDF4.Dataset(dsloc)
lcwp = ds.variables['ICLDTWP'][:]
ds.close() #close the file
lcwp = lcwp.flatten()
#GET swcf
dsloc_swcf = outfileloc + 'c4_swcf'+CASENAME+'.nc'
if os.path.isfile(dsloc_swcf):
#print(dsloc_swcf)
# open the file and get out the variable
dscf = netCDF4.Dataset(dsloc_swcf)
swcf = dscf.variables['SWCF'][:]
dscf.close() #close the file
swcf = swcf.flatten()
#GET cldlow
dsloc_cldlow = outfileloc + 'c4_cldlow' + CASENAME+'.nc'
if os.path.isfile(dsloc_cldlow):
# open the file and get out the variable
dscf = netCDF4.Dataset(dsloc_cldlow)
cldlow = dscf.variables['CLDLOW'][:]
dscf.close() #close the file
cldlow =cldlow.flatten()
#GET lhflx
dsloc_lhflx = outfileloc + 'c4_lhflx' + CASENAME+'.nc'
if os.path.isfile(dsloc_lhflx):
# open the file and get out the variable
dscf = netCDF4.Dataset(dsloc_lhflx)
lhflx = dscf.variables['LHFLX'][:]
dscf.close() #close the file
lhflx = lhflx.flatten()
#GET ts
dsloc = outfileloc + 'c4_ts' + CASENAME+'.nc'
if os.path.isfile(dsloc):
# open the file and get out the variable
ds = netCDF4.Dataset(dsloc)
ts = ds.variables['TS'][:]
ds.close() #close the file
ts = ts.flatten()
pal = (9.9)*(1-(1+2450000*qs850/(287.058*(tempsum/2)))/(1+(2450000**2)*qs850/(993*461.4*((tempsum/2)**2))))
eis = lts - pal*((z700/1000)-lcl)
ah = list(map(operator.sub,ah1000*1000, ah700*1000))
# eis_anom is 0.8sc - 1.0sc
c4_eis_anom_8 = list(map(operator.sub,eis_8, eis))
c4_ah_anom_8 = list(map(operator.sub, ah_8, ah))
c4_lcwp_anom_8 = list(map(operator.sub,lcwp_8, lcwp))
c4_cldlow_anom_8 = list(map(operator.sub, cldlow_8, cldlow))
c4_swcf_anom_8 = list(map(operator.sub,swcf_8, swcf))
c4_lhflx_anom_8 = list(map(operator.sub,lhflx_8, lhflx))
c4_ts_anom_8 = list(map(operator.sub,ts_8, ts))
#create plot
fig = plt.figure()
gs1 = gridspec.GridSpec(3,4)
gs1.update(wspace=0.05, hspace=0.05)
ax1 = plt.subplot(gs1[0])
ax2 = plt.subplot(gs1[1])
ax3 = plt.subplot(gs1[2])
ax4 = plt.subplot(gs1[3])
ax5 = plt.subplot(gs1[4])
ax6 = plt.subplot(gs1[5])
ax7 = plt.subplot(gs1[6])
ax8 = plt.subplot(gs1[7])
ax9 = plt.subplot(gs1[8])
ax10 = plt.subplot(gs1[9])
ax11 = plt.subplot(gs1[10])
ax12 = plt.subplot(gs1[11])
a = ax1.hist2d(c4_lhflx_anom_8, c4_cldlow_anom_8, bins=50, cmap='Greys', norm=colors.Normalize(), vmin=0, vmax=100, range=[[-0.006, 0.002], [-0.00004, 0.00001]])
ax1.hlines(0, -0.1, 0.1)
ax1.vlines(0, -1, 1)
b = ax2.hist2d(c4_ah_anom_8, c4_cldlow_anom_8, bins=50, cmap='Greys', norm=colors.Normalize(), vmin=0, vmax=100, range=[[-0.0004, 0.0002], [-0.00003, 0.00001]])
ax2.hlines(0, -0.1, 0.1)
ax2.vlines(0, -1, 1)
c = ax3.hist2d(c4_eis_anom_8, c4_cldlow_anom_8, bins=50, cmap='Greys', norm=colors.Normalize(), vmin=0, vmax=100, range=[[-0.005, 0.01], [-0.00003, 0.00001]])
ax3.hlines(0, -0.1, 0.1)
ax3.vlines(0, -1, 1)
d = ax4.hist2d(c4_ts_anom_8, c4_cldlow_anom_8, bins=50, cmap='Greys', norm=colors.Normalize(), vmin=0, vmax=100, range=[[-0.0004, 0.0004], [-0.00003, 0.00001]])
ax4.hlines(0, -0.1, 0.1)
ax4.vlines(0, -1, 1)
e = ax5.hist2d(c4_lhflx_anom_8, c4_lcwp_anom_8, bins=50, cmap='Greys', norm=colors.Normalize(), vmin=0, vmax=100, range=[[-0.006, 0.002], [-0.02, 0.02]])
ax5.hlines(0, -0.1, 0.1)
ax5.vlines(0, -1, 1)
f = ax6.hist2d(c4_ah_anom_8, c4_lcwp_anom_8, bins=50, cmap='Greys', norm=colors.Normalize(), vmin=0, vmax=100, range=[[-0.0004, 0.0002], [-0.02, 0.02]])
ax6.hlines(0, -0.1, 0.1)
ax6.vlines(0, -1, 1)
g = ax7.hist2d(c4_eis_anom_8, c4_lcwp_anom_8, bins=50, cmap='Greys', norm=colors.Normalize(), vmin=0, vmax=100, range=[[-0.005, 0.01], [-0.02, 0.02]])
ax7.hlines(0, -0.1, 0.1)
ax7.vlines(0, -1, 1)
h = ax8.hist2d(c4_ts_anom_8, c4_lcwp_anom_8, bins=50, cmap='Greys', norm=colors.Normalize(), vmin=0, vmax=100, range=[[-0.0004, 0.0004], [-0.02, 0.02]])
ax8.hlines(0, -0.1, 0.1)
ax8.vlines(0, -1, 1)
i = ax9.hist2d(c4_lhflx_anom_8, c4_swcf_anom_8, bins=50, cmap='Greys', norm=colors.Normalize(), vmin=0, vmax=100, range=[[-0.006, 0.002], [-0.001, 0.006]])
ax9.hlines(0, -0.1, 0.1)
ax9.vlines(0, -1, 1)
j = ax10.hist2d(c4_ah_anom_8, c4_swcf_anom_8, bins=50, cmap='Greys', norm=colors.Normalize(), vmin=0, vmax=100, range=[[-0.0004, 0.0002], [-0.001, 0.006]])
ax10.hlines(0, -0.1, 0.1)
ax10.vlines(0, -1, 1)
k = ax11.hist2d(c4_eis_anom_8, c4_swcf_anom_8, bins=50, cmap='Greys', norm=colors.Normalize(), vmin=0, vmax=100, range=[[-0.005, 0.01], [-0.001, 0.006]])
ax11.hlines(0, -0.1, 0.1)
ax11.vlines(0, -1, 1)
l = ax12.hist2d(c4_ts_anom_8, c4_swcf_anom_8, bins=50, cmap='Greys', norm=colors.Normalize(), vmin=0, vmax=100, range=[[-0.0004, 0.0004], [-0.001, 0.006]])
ax12.hlines(0, -0.1, 0.1)
ax12.vlines(0, -1, 1)
ax1.set_ylabel('Low Cloud Fraction', fontsize=6)
#ax1.set_xlabel('Latent Heat Flux')
ax1.set_xticklabels([])
ax5.set_ylabel('Low Cloud Water Path', fontsize=6)
ax9.set_ylabel('SWCF', fontsize=6)
ax9.set_xlabel('Latent Heat Flux', fontsize=6)
ax9.invert_yaxis()
ax10.set_xlabel('Absolute Humidity', fontsize=6)
ax10.invert_yaxis()
ax11.set_xlabel('EIS', fontsize=6)
ax11.invert_yaxis()
ax12.set_xlabel('Surface Temperature', fontsize=6)
ax12.invert_yaxis()
ax2.set_yticklabels([])
ax2.set_xticklabels([])
ax3.set_yticklabels([])
ax3.set_xticklabels([])
ax4.set_yticklabels([])
ax4.set_xticklabels([])
#ax.set_yticklabels([])
ax5.set_xticklabels([])
ax6.set_yticklabels([])
ax6.set_xticklabels([])
ax7.set_yticklabels([])
ax7.set_xticklabels([])
ax8.set_yticklabels([])
ax8.set_xticklabels([])
ax10.set_yticklabels([])
ax11.set_yticklabels([])
ax12.set_yticklabels([])
plt.setp(ax9.get_xticklabels(), rotation=45)
plt.setp(ax10.get_xticklabels(), rotation=45)
plt.setp(ax11.get_xticklabels(), rotation=45)
plt.setp(ax12.get_xticklabels(), rotation=45)
plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
cax = plt.axes([0.85, 0.1, 0.015, 0.8])
plt.colorbar(a[3], cax = cax)
plt.show()
fig.savefig("figures_ED/ED_figure6.pdf", bbox_inches='tight')