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figure4.py
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#!/usr/bin/env python3
"""
Author: Victoria McDonald
email: [email protected]
website: http://torimcd.github.com
license: BSD
"""
import matplotlib
matplotlib.use("Agg")
import os
import sys
import numpy as np
import netCDF4
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from matplotlib import ticker
from mpl_toolkits.basemap import Basemap
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/'
outfileloc = download_path + 'temp_data/' # this is the location to save the processed netcdf files to
# ------------------------------------
# the fields we want to average for our plots - these must not depend on pressure level
fields = 'CLDHGH,CLDLOW,LHFLX,LWCF,PRECT,SHFLX,SWCF,TS'
# process the fields we're plotting
pf.map_annual_average(filebase, outfileloc, 'cam4', fields) # averages fields over years 31-60, retaining location so can be plotted in map view
pf.map_total_waterpath(filebase, outfileloc, 'cam4') # the same as above but for vertical velocity selected at 700 hPa
# cloud climatology
cloudfields= ['CLDHGH','ICLDTWP','LWCF','CLDLOW','ICLDTWP','SWCF']
cloudcmaps=['bone', 'BrBG', 'BuPu', 'BrBG', 'PuRd', 'RdBu_r', 'bone', 'BrBG', 'BuPu', 'BrBG', 'OrRd_r', 'RdBu_r']
cloudfilenames = ['c4_map_annual_average','c4_map_wp_high','c4_map_annual_average','c4_map_annual_average','c4_map_wp_low', 'c4_map_annual_average']
cloudletters = ['a', 'b', 'c', 'd', 'e', 'f']
cloudheadings = ['High Cloud Fraction', 'Total High Cloud Water Path', 'Longwave Cloud Forcing', 'Low Cloud Fraction', 'Total Low Cloud Water Path','Shortwave Cloud Forcing']
cloudaxislabels = [r'$\mathsf{Fraction}$', r'$\mathsf{g/m^2}$', r'$\mathsf{W/m^2}$', r'$\mathsf{Fraction}$', r'$\mathsf{g/m^2}$', r'$\mathsf{W/m^2}$']
cloudvmins = [0,-0.5, 0, -300, 0, -60, 0, -0.5, 0,-300, -110, -60]
cloudvmaxs = [1, 0.5, 600, 300, 100, 60, 1, 0.5, 700, 300, 0, 60]
# creat figure
fig = plt.figure(figsize=(7.08661, 7.86))
# container with 2 rows of 2 columns, first column is grid of absolute value plots, second column is diff plots. First row is cloud climatology, second row is model climatology
outer_grid = gridspec.GridSpec(1, 2, wspace=0.2, hspace=0.1, width_ratios=(2,1))
# first two columns, absolute value plots
cldabsgrid = gridspec.GridSpecFromSubplotSpec(6, 3, subplot_spec=outer_grid[0], wspace=0.0, hspace=0.45, width_ratios=(15,15,1))
# third colum, anomaly plots
clddiffgrid = gridspec.GridSpecFromSubplotSpec(6, 2, subplot_spec=outer_grid[1], wspace=0.0, hspace=0.45, width_ratios=(25,1))
# -------------------------CLOUD CLIMATOLOGY -------------------------
# keep track of which field/row we're on
n=0
# keep track of which gridspace/column we're plotting in for abs val
a = 0
# keep track of which gridspace/column we're plotting in for diff
d = 0
# keep track of which vmin/max we're on
v = 0
present = '_10'
eight = '_08'
for p in cloudfields:
f = cloudfilenames[n]
cloudfield = cloudfields[n]
presentcase = outfileloc + f + present +'.nc'
eightcase = outfileloc + f + eight +'.nc'
#plot the data - PRESENT
ax = fig.add_subplot(cldabsgrid[a])
a=a+1
ds = netCDF4.Dataset(presentcase)
presfld = ds.variables[p][:]
lats = ds.variables['lat'][:]
lons = ds.variables['lon'][:]
ds.close() #close the file
# setup the map
m = Basemap(lat_0=0,lon_0=0, ax=ax)
m.drawcoastlines()
m.drawcountries()
parallels = [-45, 0, 45]
meridians = [-90., 0., 90.]
m.drawparallels(parallels, labels=[True ,False,False, False], fontsize=6)
m.drawmeridians(meridians,labels=[False,False,False,True], fontsize=6)
# Create 2D lat/lon arrays for Basemap
lon2d, lat2d = np.meshgrid(lons, lats)
# Plot the data
cs = m.pcolormesh(lon2d,lat2d,np.squeeze(presfld), cmap=cloudcmaps[v], latlon='True', vmin=cloudvmins[v], vmax=cloudvmaxs[v], rasterized=True)
# This is the fix for the white lines between contour levels
cs.set_edgecolor("face")
# add letter annotation
plt.text(-0.10, 1.0, cloudletters[n], fontsize=6, fontweight="bold", transform=ax.transAxes)
# add heading
plt.text(0.65, 1.05, cloudheadings[n], fontsize=7, transform=ax.transAxes)
#plot the data - EIGHT
ax = fig.add_subplot(cldabsgrid[a])
a=a+1
ds = netCDF4.Dataset(eightcase)
efld = ds.variables[p][:]
lats = ds.variables['lat'][:]
lons = ds.variables['lon'][:]
ds.close() #close the file
# setup the map
m = Basemap(lat_0=0,lon_0=0, ax=ax)
m.drawcoastlines()
m.drawcountries()
parallels = [-45, 0, 45]
meridians = [-90., 0., 90.]
m.drawparallels(parallels, labels=[False ,False,False, False], fontsize=6)
m.drawmeridians(meridians,labels=[False,False,False,True], fontsize=6)
# Create 2D lat/lon arrays for Basemap
lon2d, lat2d = np.meshgrid(lons, lats)
# Plot
cs = m.pcolormesh(lon2d,lat2d,np.squeeze(efld), cmap=cloudcmaps[v], latlon='True', vmin=cloudvmins[v], vmax=cloudvmaxs[v], rasterized=True)
v = v+1
# This is the fix for the white lines between contour levels
cs.set_edgecolor("face")
# plot the colorbar - ABS value
ax = fig.add_subplot(cldabsgrid[a])
a=a+1
cb = plt.colorbar(cs, cax=ax)
cb.ax.tick_params(labelsize=6)
tick_locator = ticker.MaxNLocator(nbins=5)
cb.locator = tick_locator
cb.update_ticks()
#plot the data - DIFF
ax = fig.add_subplot(clddiffgrid[d])
d=d+1
if os.path.isfile(eightcase):
# setup the map
m = Basemap(lat_0=0,lon_0=0, ax=ax)
m.drawcoastlines()
m.drawcountries()
parallels = [-45, 0, 45]
meridians = [-90., 0., 90.]
m.drawparallels(parallels, labels=[True ,False,False, False], fontsize=6)
m.drawmeridians(meridians,labels=[False,False,False,True], fontsize=6)
# Create 2D lat/lon arrays for Basemap
lon2d, lat2d = np.meshgrid(lons, lats)
# Plot
cs = m.pcolormesh(lon2d,lat2d,np.squeeze(efld)-np.squeeze(presfld), cmap=cloudcmaps[v], latlon='True', vmin=cloudvmins[v], vmax=cloudvmaxs[v], rasterized=True)
v = v+1
# This is the fix for the white lines between contour levels
cs.set_edgecolor("face")
# plot the colorbar - DIFF value
ax = fig.add_subplot(clddiffgrid[d])
d=d+1
cb = plt.colorbar(cs, cax=ax)
cb.set_label(label=cloudaxislabels[n], fontsize=6)
cb.ax.tick_params(labelsize=6)
tick_locator = ticker.MaxNLocator(nbins=5)
cb.locator = tick_locator
cb.update_ticks()
# go to next field/row
n=n+1
# -----------------------------
plt.show()
fig.savefig("figures_main/figure4.pdf", format='pdf', bbox_inches='tight')