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tracer_plot.py
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tracer_plot.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Conduct data analysis and plot quantities related to GEOSChem Rn–Pb–Be
tracer simulations
Revision History
15102019 -- initial version created
27022020 -- data analysis to examine the zonal mean jet/tracer relationship
"""
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cfeature
from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter
# To generate colormap, I found these three "midpoint" colors from
# https://learnui.design/tools/data-color-picker.html#palette:
#003f5c, #bc5090, #ffa600
# Then I created a nine color palette using
# https://vis4.net/palettes/#/9|s|00429d,96ffea,ffffe0|ffffe0,ff005e,93003a|1|1
COLORS = ['#003f5c', '#514969', '#80556f', '#a9636c', '#cc7560',
'#e98b47', '#ffa600']
LABELS = ['$\chi_{10-20}$', '$\chi_{20-30}$',
'$\chi_{30-40}$', '$\chi_{40-50}$',
'$\chi_{50-60}$','$\chi_{60-70}$',
'$\chi_{70-80}$']
mpl.rcParams['hatch.linewidth'] = 0.3
mpl.rcParams['xtick.major.width'] = 1
mpl.rcParams['ytick.major.width'] = 1
mpl.rcParams['axes.spines.bottom'] = 1
mpl.rcParams['axes.spines.left'] = 1
mpl.rcParams['axes.spines.right'] = 1
mpl.rcParams['axes.spines.top'] = 1
def fig1(lat, lng, TRAC_10_20_jja, TRAC_20_30_jja, TRAC_30_40_jja,
TRAC_40_50_jja, TRAC_50_60_jja, TRAC_60_70_jja, TRAC_70_80_jja, edj_jja):
"""
Parameters
----------
lat : numpy.ndarray
Latitude coordinates for the Northern Hemisphere, units of degrees
north, [lat,]
lng : numpy.ndarray
Longitude coordinates for the Northern Hemisphere, units of degrees
east, [lng,]
TRAC_10_20_jja : numpy.ndarray
GEOS-Chem CO-like tracer for given pressure levels (n.b., column
average is performed in function) with prescribed emissions from 10-20
deg north for days during JJA 2008-2010, units of volume mixing ratio,
[time, lev, lat, lng]
TRAC_20_30_jja : numpy.ndarray
GEOS-Chem CO-like tracer for given pressure levels (n.b., column
average is performed in function) with prescribed emissions from 20-30
deg north for days during JJA 2008-2010, units of volume mixing ratio,
[time, lev, lat, lng]
TRAC_30_40_jja : numpy.ndarray
GEOS-Chem CO-like tracer for given pressure levels (n.b., column
average is performed in function) with prescribed emissions from 30-40
deg north for days during JJA 2008-2010, units of volume mixing ratio,
[time, lev, lat, lng]
TRAC_40_50_jja : numpy.ndarray
GEOS-Chem CO-like tracer for given pressure levels (n.b., column
average is performed in function) with prescribed emissions from 40-50
deg north for days during JJA 2008-2010, units of volume mixing ratio,
[time, lev, lat, lng]
TRAC_50_60_jja : numpy.ndarray
GEOS-Chem CO-like tracer for given pressure levels (n.b., column
average is performed in function) with prescribed emissions from 50-60
deg north for days during JJA 2008-2010, units of volume mixing ratio,
[time, lev, lat, lng]
TRAC_60_70_jja : numpy.ndarray
GEOS-Chem CO-like tracer for given pressure levels (n.b., column
average is performed in function) with prescribed emissions from 60-70
deg north for days during JJA 2008-2010, units of volume mixing ratio,
[time, lev, lat, lng]
TRAC_70_80_jja : numpy.ndarray
GEOS-Chem CO-like tracer for given pressure levels (n.b., column
average is performed in function) with prescribed emissions from 70-80
deg north for days during JJA 2008-2010, units of volume mixing ratio,
[time, lev, lat, lng]
edj_jja : numpy.ndarray
Latitude of the jet stream for JJA 2008-2010, units of degrees north,
[time, lng]
Returns
-------
None
"""
if lng[-1] != 360:
lng[-1] = 360.
fig = plt.figure(figsize=(10,6))
ax1 = plt.subplot2grid((3,3), (0,0), colspan=1, rowspan=3)
ax2 = plt.subplot2grid((3,3), (0,1), colspan=2,
projection=ccrs.Miller(central_longitude=0.))
ax3 = plt.subplot2grid((3,3), (1,1), colspan=2,
projection=ccrs.Miller(central_longitude=0.))
ax4 = plt.subplot2grid((3,3), (2,1), colspan=2,
projection=ccrs.Miller(central_longitude=0.))
# Zonally-averaged tracer concentrations
ax1.set_title('(a)', fontsize=12, x=0.02, ha='left')
lw = [2.5, 1, 1, 2.5, 1, 1, 2.5]
for i, tracer in enumerate([TRAC_10_20_jja, TRAC_20_30_jja, TRAC_30_40_jja,
TRAC_40_50_jja, TRAC_50_60_jja, TRAC_60_70_jja, TRAC_70_80_jja]):
# Zonal, time, and vertical average; convert to ppm
tracer = np.nanmean(tracer, axis=tuple((0,1,3)))*1e6
ax1.plot(tracer, lat, color=COLORS[i], label=LABELS[i],
lw=lw[i])
ax1.set_xlim([0, 1.6])
ax1.set_xticks([0, 0.4, 0.8, 1.2, 1.6])
ax1.set_xticklabels(['0.0', '', '0.8', '', '1.6'])
ax1.set_ylim([10, 80])
ax1.set_yticks([10, 20, 30, 40, 50, 60, 70, 80])
ax1.set_yticklabels(['10$^{\circ}$N', '20$^{\circ}$N', '30$^{\circ}$N',
'40$^{\circ}$N', '50$^{\circ}$N', '60$^{\circ}$N', '70$^{\circ}$N',
'80$^{\circ}$N'])
ax1.set_xlabel('[ppm]', fontsize=12)
ax1.tick_params(which='major', labelsize=9)
# Add Legend
ax1.legend(bbox_to_anchor=[3.3, -0.07, 0, 0], ncol=4, frameon=False,
fontsize=12)
# Distribution of X70-80
ax2.set_title('(b) $\chi_{70-80}$', fontsize=12, x=0.02, ha='left')
mb = ax2.contourf(lng, lat, np.nanmean(TRAC_70_80_jja,
axis=tuple((0,1)))*1e6, np.linspace(0., 1.6, 9),
cmap=plt.get_cmap('pink_r'), extend='max',
transform=ccrs.PlateCarree(), zorder=2)
# Distribution of X40-50
ax3.set_title('(c) $\chi_{40-50}$', fontsize=12, x=0.02, ha='left')
mb = ax3.contourf(lng, lat,
np.nanmean(TRAC_40_50_jja, axis=tuple((0,1)))*1e6,
np.linspace(0., 1.6, 9), cmap=plt.get_cmap('pink_r'), extend='max',
transform=ccrs.PlateCarree(), zorder=2)
# Distribution of X10-20
ax4.set_title('(d) $\chi_{10-20}$', fontsize=12, x=0.02, ha='left')
mb = ax4.contourf(lng, lat,
np.nanmean(TRAC_10_20_jja, axis=tuple((0,1)))*1e6,
np.linspace(0., 1.6, 9), cmap=plt.get_cmap('pink_r'), extend='max',
transform=ccrs.PlateCarree(), zorder=2)
# Aesthetics
for ax in [ax2, ax3, ax4]:
# Add eddy-driven jet
skiplng = 5
ax.errorbar(lng[::skiplng], np.nanmean(edj_jja, axis=0)[::skiplng],
yerr=np.nanstd(edj_jja, axis=0)[::skiplng], zorder=10, color='k',
markersize=3, elinewidth=1.25, ecolor='k', fmt='o',
transform=ccrs.PlateCarree())
ax.coastlines(lw=0.25, resolution='50m', color='k', zorder=3)
ax.set_extent([lng.min()-180., lng.max()-180., 10, 80])
ax.set_xticks([-180, -120, -60, 0, 60, 120, 180], crs=ccrs.PlateCarree())
lng_formatter = LongitudeFormatter()
ax.xaxis.set_major_formatter(lng_formatter)
ax.get_xaxis().set_ticklabels([])
ax.set_yticks([15, 50, 75], crs=ccrs.PlateCarree())
lat_formatter = LatitudeFormatter()
ax.yaxis.set_major_formatter(lat_formatter)
ax.tick_params(which='major', labelsize=9)
ax4.set_xticks([-180, -120, -60, 0, 60, 120, 180], crs=ccrs.PlateCarree())
lng_formatter = LongitudeFormatter()
ax4.xaxis.set_major_formatter(lng_formatter)
ax4.tick_params(which='major', labelsize=9)
plt.subplots_adjust(top=0.95, right=0.85, bottom=0.18)
# Add colorbar
cbaxes = fig.add_axes([ax4.get_position().x1+0.03, ax4.get_position().y0,
0.02, ax2.get_position().y1-ax4.get_position().y0])
cb = plt.colorbar(mb, cax=cbaxes, orientation='vertical')
cb.set_label(label='[ppm]', size=12)
cb.ax.tick_params(labelsize=9)
# Move ax1 such that its top/bottom line up with ax2 and ax4
pos1 = ax1.get_position() # get the original position
pos1 = [pos1.x0-0.04, ax4.get_position().y0, pos1.width,
(ax2.get_position().y1-ax4.get_position().y0)]
ax1.set_position(pos1) # set a new position
plt.savefig('/Users/ghkerr/phd/tracer/figs/'+'fig1.pdf', dpi=500)
plt.show()
return
def fig2(lat, lng, TRAC_10_20_jja, TRAC_40_50_jja, TRAC_70_80_jja,
TRAC_10_20_djf, TRAC_40_50_djf, TRAC_70_80_djf, edj_dist_jja,
edj_dist_djf, edj_jja, edj_djf):
"""
Parameters
----------
lat : numpy.ndarray
Latitude coordinates for the Northern Hemisphere, units of degrees
north, [lat,]
lng : numpy.ndarray
Longitude coordinates for the Northern Hemisphere, units of degrees
east, [lng,]
TRAC_10_20_jja : numpy.ndarray
GEOS-Chem CO-like tracer for given pressure levels (n.b., column
average is performed in function) with prescribed emissions from 10-20
deg north for days during JJA 2008-2010, units of volume mixing ratio,
[time, lev, lat, lng]
TRAC_40_50_jja : numpy.ndarray
GEOS-Chem CO-like tracer for given pressure levels (n.b., column
average is performed in function) with prescribed emissions from 40-50
deg north for days during JJA 2008-2010, units of volume mixing ratio,
[time, lev, lat, lng]
TRAC_70_80_jja : numpy.ndarray
GEOS-Chem CO-like tracer for given pressure levels (n.b., column
average is performed in function) with prescribed emissions from 70-80
deg north for days during JJA 2008-2010, units of volume mixing ratio,
[time, lev, lat, lng]
TRAC_10_20_djf : numpy.ndarray
GEOS-Chem CO-like tracer for given pressure levels (n.b., column
average is performed in function) with prescribed emissions from 10-20
deg north for days during DJF 2008-2010, units of volume mixing ratio,
[time, lev, lat, lng]
TRAC_40_50_djf : numpy.ndarray
GEOS-Chem CO-like tracer for given pressure levels (n.b., column
average is performed in function) with prescribed emissions from 40-50
deg north for days during DJF 2008-2010, units of volume mixing ratio,
[time, lev, lat, lng]
TRAC_70_80_djf : numpy.ndarray
GEOS-Chem CO-like tracer for given pressure levels (n.b., column
average is performed in function) with prescribed emissions from 70-80
deg north for days during DJF 2008-2010, units of volume mixing ratio,
[time, lev, lat, lng]
edj_dist_jja : numpy.ndarray
Distance from the jet stream for JJA 2008-2010, units of degrees,
[time, lat, lng]
edj_dist_djf : numpy.ndarray
Distance from the jet stream for DJF 2008-2010, units of degrees,
[time, lat, lng]
edj_jja : numpy.ndarray
Latitude of the jet stream for JJA 2008-2010, units of degrees north,
[time, lng]
edj_djf : numpy.ndarray
Latitude of the jet stream for DJF 2008-2010, units of degrees north,
[time, lng]
Returns
-------
None
"""
if lng[-1] != 360:
lng[-1] = 360.
fig = plt.figure(figsize=(11,5.5))
ax1 = plt.subplot2grid((3,4), (0,0), colspan=2,
projection=ccrs.Miller(central_longitude=0.))
ax2 = plt.subplot2grid((3,4), (1,0), colspan=2,
projection=ccrs.Miller(central_longitude=0.))
ax3 = plt.subplot2grid((3,4), (2,0), colspan=2,
projection=ccrs.Miller(central_longitude=0.))
ax4 = plt.subplot2grid((3,4), (0,2), colspan=2,
projection=ccrs.Miller(central_longitude=0.))
ax5 = plt.subplot2grid((3,4), (1,2), colspan=2,
projection=ccrs.Miller(central_longitude=0.))
ax6 = plt.subplot2grid((3,4), (2,2), colspan=2,
projection=ccrs.Miller(central_longitude=0.))
ax1.set_title('(a) JJA $\chi_{70-80}$ (PW$\:$-$\:$EW)', fontsize=12,
x=0.02, ha='left')
ax2.set_title('(c) JJA $\chi_{40-50}$ (PW$\:$-$\:$EW)', fontsize=12,
x=0.02, ha='left')
ax3.set_title('(e) JJA $\chi_{10-20}$ (PW$\:$-$\:$EW)', fontsize=12,
x=0.02, ha='left')
ax4.set_title('(b) DJF $\chi_{70-80}$ (PW$\:$-$\:$EW)', fontsize=12,
x=0.02, ha='left')
ax5.set_title('(d) DJF $\chi_{40-50}$ (PW$\:$-$\:$EW)', fontsize=12,
x=0.02, ha='left')
ax6.set_title('(f) DJF $\chi_{10-20}$ (PW$\:$-$\:$EW)', fontsize=12,
x=0.02, ha='left')
# Loop through JJA axes and plot
axes = [ax1, ax2, ax3]
for i, tracer in enumerate([TRAC_70_80_jja, TRAC_40_50_jja,
TRAC_10_20_jja]):
# Calculate tracer-jet correlation and significance
r_tracerjet = globalo3_calculate.calculate_r(np.nanmean(tracer, axis=1),
edj_dist_jja, lat, lng)
significance_r_tracerjet = globalo3_calculate.calculate_r_significance(
np.nanmean(tracer, axis=1), edj_dist_jja, r_tracerjet, lat, lng)
# Find (PW - EW) jet composites
eqjet_lat, eqjet_lat_var, pwjet_lat, pwjet_lat_var, pwjet_tracer, \
eqjet_tracer = globalo3_calculate.segregate_field_bylat(
np.nanmean(tracer, axis=1), lng, edj_jja,
np.arange(0, len(tracer), 1))
mb = axes[i].contourf(lng, lat, (pwjet_tracer-eqjet_tracer)*1e6,
np.linspace(-0.2, 0.2, 11), cmap=plt.get_cmap('coolwarm'),
extend='both', transform=ccrs.PlateCarree(), zorder=2)
# Add hatching for significance
axes[i].contourf(lng, lat, significance_r_tracerjet,
hatches=['//////'], colors='none', transform=ccrs.PlateCarree(),
zorder= 4)
# Add eddy-driven jet
skiplng = 5
axes[i].errorbar(lng[::skiplng], np.nanmean(edj_jja, axis=0)[::skiplng],
yerr=np.nanstd(edj_jja, axis=0)[::skiplng], zorder=10, color='k',
markersize=3, elinewidth=1.25, ecolor='k', fmt='o',
transform=ccrs.PlateCarree())
axes[i].coastlines(lw=0.25, resolution='50m', color='k', zorder=3)
axes[i].set_extent([lng.min()-180., lng.max()-180., 10., 80.])
axes[i].set_xticks([-180, -120, -60, 0, 60, 120, 180],
crs=ccrs.PlateCarree())
axes[i].get_xaxis().set_ticklabels([])
axes[i].set_yticks([15, 50, 75], crs=ccrs.PlateCarree())
lat_formatter = LatitudeFormatter()
axes[i].yaxis.set_major_formatter(lat_formatter)
axes[i].tick_params(which='major', labelsize=9)
# For DJF
axes = [ax4, ax5, ax6]
for i, tracer in enumerate([TRAC_70_80_djf, TRAC_40_50_djf,
TRAC_10_20_djf]):
r_tracerjet = globalo3_calculate.calculate_r(np.nanmean(tracer, axis=1),
edj_dist_djf, lat, lng)
significance_r_tracerjet = globalo3_calculate.calculate_r_significance(
np.nanmean(tracer, axis=1), edj_dist_djf, r_tracerjet, lat, lng)
eqjet_lat, eqjet_lat_var, pwjet_lat, pwjet_lat_var, pwjet_tracer, \
eqjet_tracer = globalo3_calculate.segregate_field_bylat(
np.nanmean(tracer, axis=1), lng, edj_djf,
np.arange(0, len(tracer), 1))
mb = axes[i].contourf(lng, lat, (pwjet_tracer-eqjet_tracer)*1e6,
np.linspace(-0.2, 0.2, 11), cmap=plt.get_cmap('coolwarm'),
extend='both', transform=ccrs.PlateCarree(), zorder=2)
axes[i].contourf(lng, lat, significance_r_tracerjet,
hatches=['//////'], colors='none', transform=ccrs.PlateCarree(),
zorder= 4)
skiplng = 5
axes[i].errorbar(lng[::skiplng], np.nanmean(edj_djf, axis=0)[::skiplng],
yerr=np.nanstd(edj_djf, axis=0)[::skiplng], zorder=10, color='k',
markersize=3, elinewidth=1.25, ecolor='k', fmt='o',
transform=ccrs.PlateCarree())
axes[i].coastlines(lw=0.25, resolution='50m', color='k', zorder=3)
axes[i].set_extent([lng.min()-180., lng.max()-180., 10., 80.])
axes[i].set_xticks([-180, -120, -60, 0, 60, 120, 180],
crs=ccrs.PlateCarree())
lng_formatter = LongitudeFormatter()
axes[i].xaxis.set_major_formatter(lng_formatter)
axes[i].get_xaxis().set_ticklabels([])
axes[i].set_yticks([15, 50, 75], crs=ccrs.PlateCarree())
lat_formatter = LatitudeFormatter()
axes[i].get_yaxis().set_ticklabels([])
axes[i].tick_params(which='major', labelsize=9)
for ax in [ax3, ax6]:
ax.set_xticks([-180, -120, -60, 0, 60, 120, 180],
crs=ccrs.PlateCarree())
lng_formatter = LongitudeFormatter()
ax.xaxis.set_major_formatter(lng_formatter)
ax.tick_params(which='major', labelsize=9)
plt.subplots_adjust(left=0.05, right=0.86)
# Add colorbar
cbaxes = fig.add_axes([ax4.get_position().x1+0.03, ax6.get_position().y0,
0.02, ax4.get_position().y1-ax6.get_position().y0])
cb = plt.colorbar(mb, cax=cbaxes, orientation='vertical')
cb.set_label(label='[ppm]', size=12)
cb.set_ticks(np.linspace(-0.2, 0.2, 11))
cb.ax.tick_params(labelsize=9)
plt.savefig('/Users/ghkerr/phd/tracer/figs/'+'fig2.pdf', dpi=500)
plt.show()
return
def fig3(lat, lng, TRAC_10_20_jja, TRAC_20_30_jja, TRAC_30_40_jja,
TRAC_40_50_jja, TRAC_50_60_jja, TRAC_60_70_jja, TRAC_70_80_jja,
TRAC_10_20_djf, TRAC_20_30_djf, TRAC_30_40_djf, TRAC_40_50_djf,
TRAC_50_60_djf, TRAC_60_70_djf, TRAC_70_80_djf, V_jja, V_djf, edj_dist_jja,
edj_dist_djf, edj_jja, edj_djf):
"""
"""
import scipy.interpolate
fig = plt.figure(figsize=(7.5,8))
ax1 = plt.subplot2grid((3,1), (0,0))
ax2 = plt.subplot2grid((3,1), (1,0))
ax3 = plt.subplot2grid((3,1), (2,0))
ax1.set_title('(a) JJA r($\chi$, $\phi_{jet}$)', fontsize=12, x=0.02,
ha='left')
ax2.set_title('(b) DJF r($\chi$, $\phi_{jet}$)', fontsize=12, x=0.02,
ha='left')
ax3.set_title('(c) r(V, $\phi_{jet}$)', fontsize=12, x=0.02,
ha='left')
# Draw y = 0 (no correlation) line
ax1.axhline(y=0, xmin=0, xmax=90, lw=0.75, ls='--', color='black')
ax2.axhline(y=0, xmin=0, xmax=90, lw=0.75, ls='--', color='black')
ax3.axhline(y=0, xmin=0, xmax=90, lw=0.75, ls='--', color='black')
# Determine near-surface meridional wind-jet correlation
r_Vjet = globalo3_calculate.calculate_r(np.nanmean(V_jja, axis=1),
edj_dist_jja, lat, lng)
ax3.plot(lat, np.nanmean(r_Vjet, axis=1), ls='solid', color='k', lw=2,
label='JJA')
r_Vjet_djf = globalo3_calculate.calculate_r(np.nanmean(V_djf, axis=1),
edj_dist_djf, lat, lng)
ax3.plot(lat, np.nanmean(r_Vjet_djf, axis=1), ls='dashdot', color='k',
lw=2, label='DJF')
# Loop through tracers for JJA
for i, tracer in enumerate([TRAC_10_20_jja, TRAC_20_30_jja,
TRAC_30_40_jja, TRAC_40_50_jja, TRAC_50_60_jja, TRAC_60_70_jja,
TRAC_70_80_jja]):
tracer = np.nanmean(tracer, axis=1)
# Calculate tracer-jet correlation
r_tracerjet = globalo3_calculate.calculate_r(tracer, edj_dist_jja, lat,
lng)
ax1.plot(lat, np.nanmean(r_tracerjet, axis=1), color=COLORS[i],
label=LABELS[i], lw=2)
# Loop through tracers for DJF
for i, tracer in enumerate([TRAC_10_20_djf, TRAC_20_30_djf,
TRAC_30_40_djf, TRAC_40_50_djf, TRAC_50_60_djf, TRAC_60_70_djf,
TRAC_70_80_djf]):
tracer = np.nanmean(tracer, axis=1)
# Calculate tracer-jet correlation
r_tracerjet = globalo3_calculate.calculate_r(tracer, edj_dist_djf, lat,
lng)
ax2.plot(lat, np.nanmean(r_tracerjet, axis=1), color=COLORS[i],
label=LABELS[i], lw=2)
# Aesthetics
for ax in [ax1, ax2, ax3]:
ax.set_xlim([10, 80])
ax.set_xticks([10, 20, 30, 40, 50, 60, 70, 80])
ax.set_xticklabels([])
ax.set_ylabel('[$\cdot$]', fontsize=12)
ax.tick_params(which='major', labelsize=9)
ax1.set_ylim([-0.28, 0.28])
ax1.set_yticks(np.linspace(-0.28, 0.28, 5))
ax2.set_ylim([-0.36, 0.36])
ax2.set_yticks(np.linspace(-0.36, 0.36, 5))
# ax1.set_ylim([-0.36, 0.36])
# ax1.set_yticks(np.linspace(-0.36, 0.36, 5))
# ax2.set_ylim([-0.44, 0.44])
# ax2.set_yticks(np.linspace(-0.44, 0.44, 5))
ax3.set_ylim([-0.2, 0.2])
ax3.set_yticks(np.linspace(-0.2, 0.2, 5))
ax3.set_xticklabels(['10$^{\circ}$N', '20$^{\circ}$N', '30$^{\circ}$N',
'40$^{\circ}$N', '50$^{\circ}$N', '60$^{\circ}$N', '70$^{\circ}$N',
'80$^{\circ}$N'])
ax3.legend(loc=1, ncol=1, frameon=False, fontsize=12, handlelength=3)
ax2.legend(bbox_to_anchor=[0.95, -1.45, 0, 0], ncol=4, frameon=False,
fontsize=12)
plt.subplots_adjust(hspace=0.3, top=0.95, bottom=0.15)
# Interpolate the zonally-averaged r(V, jet) and find where the
# r(V, jet) = 0.; this is kind of kludgey, so the values are hard-coded in
interp = scipy.interpolate.interp1d(lat, np.nanmean(r_Vjet, axis=-1))
interp_djf = scipy.interpolate.interp1d(lat, np.nanmean(r_Vjet_djf,
axis=-1))
r_Vjet_zm_interp = []
r_Vjet_zm_interp_djf = []
lat_interp = np.linspace(0, lat.max(), 500)
for x in lat_interp:
r_Vjet_zm_interp.append(interp(x))
r_Vjet_zm_interp = np.abs(r_Vjet_zm_interp)
for x in lat_interp:
r_Vjet_zm_interp_djf.append(interp_djf(x))
r_Vjet_zm_interp_djf = np.abs(r_Vjet_zm_interp_djf)
# # Find index of 4 smallest values since r(V, jet) = 0. four times
# r_Vjet_zm_interp.argsort()[:4] # however this has some repeats, so increase
# # to greater values to pull off values
# lat_where_0 = lat_interp[[114, 211, 372, 494]]
# Indicate jet; errorbars represent the zonally-averaged standard deviation
# of the daily variations in the position of the jet
ax1.errorbar(np.nanmean(edj_jja, axis=tuple((0,1))), 0.0,
xerr=np.nanmean(np.nanstd(edj_jja, axis=0)), zorder=10, color='k',
markersize=8, elinewidth=3., ecolor='k', fmt='o')
ax2.errorbar(np.nanmean(edj_djf, axis=tuple((0,1))), 0.0,
xerr=np.nanmean(np.nanstd(edj_djf, axis=0)), zorder=10, color='k',
markersize=8, elinewidth=3., ecolor='k', fmt='o')
lat_where_0 = np.array([20.44689379, 37.84468938, 66.72144289,
88.60320641])
lat_where_0_djf = np.array([6.0981963, 20.8056, 64.2104])
for x in lat_where_0:
ax1.axvline(x=x, c='k', lw=0.75, ls='--', zorder=0)
for x in lat_where_0_djf:
ax2.axvline(x=x, c='k', lw=0.75, ls='--', zorder=0)
plt.savefig('/Users/ghkerr/phd/tracer/figs/'+'fig3.pdf', dpi=500)
plt.show()
return
def fig4(lat, lng, TRAC_40_50_jja, TRAC_40_50_djf, V_jja, V_djf, edj_dist_jja,
edj_dist_djf, edj_jja, edj_djf):
"""
Parameters
----------
lat : numpy.ndarray
Latitude coordinates for the Northern Hemisphere, units of degrees
north, [lat,]
lng : numpy.ndarray
Longitude coordinates for the Northern Hemisphere, units of degrees
east, [lng,]
TRAC_40_50_jja : numpy.ndarray
GEOS-Chem CO-like tracer for given pressure levels (n.b., column
average is performed in function) with prescribed emissions from 40-50
deg north for days during JJA 2008-2010, units of volume mixing ratio,
[time, lev, lat, lng]
TRAC_40_50_djf : numpy.ndarray
GEOS-Chem CO-like tracer for given pressure levels (n.b., column
average is performed in function) with prescribed emissions from 40-50
deg north for days during DJF 2008-2010, units of volume mixing ratio,
[time, lev, lat, lng]
V_jja : numpy.ndarray
Meridional wind for JJA 2008-2010 (note that the levels should match
that of the GEOS-Chem tracers), units of m s-1, [time, lev, lat, lng]
V_djf : numpy.ndarray
Meridional wind for DJF 2008-2010 (note that the levels should match
that of the GEOS-Chem tracers), units of m s-1, [time, lev, lat, lng]
edj_dist_jja : numpy.ndarray
Distance from the jet stream for JJA 2008-2010, units of degrees,
[time, lat, lng]
edj_dist_djf : numpy.ndarray
Distance from the jet stream for DJF 2008-2010, units of degrees,
[time, lat, lng]
edj_jja : numpy.ndarray
Latitude of the jet stream for JJA 2008-2010, units of degrees north,
[time, lng]
edj_djf : numpy.ndarray
Latitude of the jet stream for DJF 2008-2010, units of degrees north,
[time, lng]
Returns
-------
None
"""
from matplotlib.patches import Patch
if lng[-1] != 360:
lng[-1] = 360.
fig = plt.figure(figsize=(11,3.85))
ax1 = plt.subplot2grid((2,4), (0,0), colspan=2,
projection=ccrs.Miller(central_longitude=0.))
ax2 = plt.subplot2grid((2,4), (0,2), colspan=2,
projection=ccrs.Miller(central_longitude=0.))
ax3 = plt.subplot2grid((2,4), (1,0), colspan=2,
projection=ccrs.Miller(central_longitude=0.))
ax4 = plt.subplot2grid((2,4), (1,2), colspan=2,
projection=ccrs.Miller(central_longitude=0.))
ax1.set_title('(a) JJA V (PW$\:$-$\:$EW)', fontsize=12, x=0.02, ha='left')
ax2.set_title('(b) DJF V (PW$\:$-$\:$EW)', fontsize=12, x=0.02, ha='left')
ax3.set_title('(c) JJA r($\chi_{40-50}$, $\phi_{jet}$)',
fontsize=12, x=0.02, ha='left')
ax4.set_title('(d) DJF r($\chi_{40-50}$, $\phi_{jet}$)',
fontsize=12, x=0.02, ha='left')
cmap = plt.get_cmap('coolwarm')
# Determine near-surface meridional V-jet correlation
r_Vjet_jja = globalo3_calculate.calculate_r(np.nanmean(V_jja, axis=1),
edj_dist_jja, lat, lng)
r_Vjet_djf = globalo3_calculate.calculate_r(np.nanmean(V_djf, axis=1),
edj_dist_djf, lat, lng)
# Calculate V-jet significance, and PW/EW composites for
# JJA and DJF
significance_r_Vjet_jja = \
globalo3_calculate.calculate_r_significance(np.nanmean(V_jja, axis=1),
edj_dist_jja, r_Vjet_jja, lat, lng)
significance_r_Vjet_djf = \
globalo3_calculate.calculate_r_significance(np.nanmean(V_djf, axis=1),
edj_dist_djf, r_Vjet_djf, lat, lng)
eqjet_lat_jja, eqjet_lat_var_jja, pwjet_lat_jja, pwjet_lat_var_jja, \
pwjet_V_jja, eqjet_V_jja = globalo3_calculate.segregate_field_bylat(
np.nanmean(V_jja, axis=1), lng, edj_jja, np.arange(0, len(V_jja), 1))
eqjet_lat_djf, eqjet_lat_var_djf, pwjet_lat_djf, pwjet_lat_var_djf, \
pwjet_V_djf, eqjet_V_djf = globalo3_calculate.segregate_field_bylat(
np.nanmean(V_djf, axis=1), lng, edj_djf, np.arange(0, len(V_djf), 1))
# Plot (PW-EW) composites
clevst = np.linspace(-5, 5, 13)
mbt = ax1.contourf(lng, lat, (pwjet_V_jja-eqjet_V_jja),
clevst, cmap=plt.get_cmap('coolwarm'), extend='both',
transform=ccrs.PlateCarree(), zorder=2)
mbt = ax2.contourf(lng, lat, (pwjet_V_djf-eqjet_V_djf),
clevst, cmap=plt.get_cmap('coolwarm'), extend='both',
transform=ccrs.PlateCarree(), zorder=2)
# Hatching for significance
ax1.contourf(lng, lat, significance_r_Vjet_jja,
hatches=['//////'], colors='none', transform=ccrs.PlateCarree(),
zorder=4)
ax2.contourf(lng, lat, significance_r_Vjet_djf,
hatches=['//////'], colors='none', transform=ccrs.PlateCarree(),
zorder=4)
# Contours indicating mean V
CS = ax1.contour(lng, lat, np.nanmean(V_jja, axis=tuple((0,1))),
[-5,5], linewidths=1., colors='k')
CS = ax2.contour(lng, lat, np.nanmean(V_djf, axis=tuple((0,1))),
[-5,5], linewidths=1., colors='k')
# JJA TRAC_40_50-jet correlation
clevsb = np.linspace(-0.5, 0.5, 11)
r_tracerjet = globalo3_calculate.calculate_r(
np.nanmean(TRAC_40_50_jja, axis=1), edj_dist_jja, lat, lng)
# Column- and time-averaged tracer
tracer = np.nanmean(np.nanmean(TRAC_40_50_jja, axis=1), axis=0)
# Loop through each longitude and calculate the latitudinal gradient
grad_tracer = np.empty(shape=tracer.shape)
grad_tracer[:] = np.nan
for spine_i in np.arange(0, len(lng), 1):
# Tracer concentrations for given "spine" of latitudes at a given
# longitude
spine = tracer[:,spine_i]
grad_spine = np.gradient(spine)
grad_tracer[:,spine_i] = grad_spine
grad_tracer = np.array(grad_tracer)
expected = -1.*r_Vjet_jja*grad_tracer
# Find where the expected values are positive/negative
poz = np.empty(grad_tracer.shape)
poz[:] = np.nan
where_poz = np.where(expected > 0.)
poz[where_poz] = 1.
neg = np.empty(grad_tracer.shape)
neg[:] = np.nan
where_neg = np.where(expected < 0.)
neg[where_neg] = 1.
# Plotting
mbb = ax3.contourf(lng, lat, r_tracerjet, clevsb, cmap=cmap,
extend='both', transform=ccrs.PlateCarree(), zorder=1)
ax3.contourf(lng, lat, poz, hatches=['...'], colors='none',
transform=ccrs.PlateCarree())
ax3.contourf(lng, lat, neg, hatches=['///'], colors='none',
transform=ccrs.PlateCarree())
# DJF TRAC_40_50-jet correlation
r_tracerjet = globalo3_calculate.calculate_r(
np.nanmean(TRAC_40_50_djf, axis=1), edj_dist_djf, lat, lng)
# Column- and time-averaged tracer
tracer = np.nanmean(np.nanmean(TRAC_40_50_djf, axis=1), axis=0)
# Loop through each longitude and calculate the latitudinal gradient
grad_tracer = np.empty(shape=tracer.shape)
grad_tracer[:] = np.nan
for spine_i in np.arange(0, len(lng), 1):
# Tracer concentrations for given "spine" of latitudes at a given
# longitude
spine = tracer[:,spine_i]
grad_spine = np.gradient(spine)
grad_tracer[:,spine_i] = grad_spine
grad_tracer = np.array(grad_tracer)
expected = -1.*r_Vjet_djf*grad_tracer
# Find where the expected values are positive/negative
poz = np.empty(grad_tracer.shape)
poz[:] = np.nan
where_poz = np.where(expected > 0.)
poz[where_poz] = 1.
neg = np.empty(grad_tracer.shape)
neg[:] = np.nan
where_neg = np.where(expected < 0.)
neg[where_neg] = 1.
# Plotting
mbb = ax4.contourf(lng, lat, r_tracerjet, clevsb, cmap=cmap,
extend='both', transform=ccrs.PlateCarree(), zorder=1)
ax4.contourf(lng, lat, poz, hatches=['...'], colors='none',
transform=ccrs.PlateCarree())
ax4.contourf(lng, lat, neg, hatches=['///'], colors='none',
transform=ccrs.PlateCarree())
# Denote parallels and meridians
axes = [ax1, ax2, ax3, ax4]
for i in np.arange(0, len(axes), 1):
axes[i].set_xticks([-180, -120, -60, 0, 60, 120, 180],
crs=ccrs.PlateCarree())
lng_formatter = LongitudeFormatter()
axes[i].xaxis.set_major_formatter(lng_formatter)
axes[i].get_xaxis().set_ticklabels([])
axes[i].set_yticks([15, 50, 75], crs=ccrs.PlateCarree())
lat_formatter = LatitudeFormatter()
axes[i].yaxis.set_major_formatter(lat_formatter)
axes[i].get_yaxis().set_ticklabels([])
axes[i].coastlines(lw=0.25, resolution='50m', color='k', zorder=3)
axes[i].set_extent([lng.min()-180., lng.max()-180., 10., 80.])
axes[i].outline_patch.set_zorder(20)
# Add eddy-driven jet
for ax in [ax1, ax3]:
skiplng = 5
ax.errorbar(lng[::skiplng], np.nanmean(edj_jja, axis=0)[::skiplng],
yerr=np.nanstd(edj_jja, axis=0)[::skiplng], zorder=10, color='k',
markersize=3, elinewidth=1.25, ecolor='k', fmt='o',
transform=ccrs.PlateCarree())
for ax in [ax2, ax4]:
skiplng = 5
ax.errorbar(lng[::skiplng], np.nanmean(edj_djf, axis=0)[::skiplng],
yerr=np.nanstd(edj_djf, axis=0)[::skiplng], zorder=10, color='k',
markersize=3, elinewidth=1.25, ecolor='k', fmt='o',
transform=ccrs.PlateCarree())
# Label parallels and meridians, if appropriate
axes = [ax1, ax3]
for i in np.arange(0, len(axes), 1):
axes[i].set_yticks([15, 50, 75], crs=ccrs.PlateCarree())
lat_formatter = LatitudeFormatter()
axes[i].yaxis.set_major_formatter(lat_formatter)
axes[i].tick_params(which='major', labelsize=9)
ax3.set_xticks([-180, -120, -60, 0, 60, 120, 180], crs=ccrs.PlateCarree())
lng_formatter = LongitudeFormatter()
ax3.xaxis.set_major_formatter(lng_formatter)
ax3.tick_params(which='major', labelsize=9)
ax4.set_xticks([-180, -120, -60, 0, 60, 120, 180], crs=ccrs.PlateCarree())
lng_formatter = LongitudeFormatter()
ax4.xaxis.set_major_formatter(lng_formatter)
ax4.tick_params(which='major', labelsize=9)
plt.subplots_adjust(left=0.05, right=0.86)
# Add custom legend for bottom row
patch_1 = Patch(fill=False,
label='sgn[r($\chi_{40-50}$, $\phi_{jet}$)] > 0', hatch='...',
linewidth=0.5)
patch_2 = Patch(fill=False,
label='sgn[r($\chi_{40-50}$, $\phi_{jet}$)] < 0', hatch='///',
linewidth=0.5)
leg = ax3.legend(handles=[patch_1, patch_2], ncol=2, frameon=False,
bbox_to_anchor=[-0.04, -0.16, 0, 0], fontsize=12)
for patch in leg.get_patches():
patch.set_height(12)
patch.set_width(28)
# Add colorbar for V (PW - EW) plots
colorbar_axes = plt.gcf().add_axes([ax2.get_position().x1+0.02,
ax2.get_position().y0, 0.012,
(ax2.get_position().y1-ax2.get_position().y0)])
colorbar = plt.colorbar(mbt, colorbar_axes, orientation='vertical',
ticks=clevst[::3], extend='both')
colorbar.ax.tick_params(labelsize=12)
colorbar.set_label('[m s$^{-1}$]', fontsize=16)
# Add colorbar for correlation plots
colorbar_axes = plt.gcf().add_axes([ax4.get_position().x1+0.02,
ax4.get_position().y0, 0.012,
(ax4.get_position().y1-ax4.get_position().y0)])
colorbar = plt.colorbar(mbb, colorbar_axes, orientation='vertical',
ticks=clevsb[::2], extend='both')
colorbar.ax.tick_params(labelsize=12)
colorbar.set_label('[$\cdot$]', fontsize=16)
plt.savefig('/Users/ghkerr/phd/tracer/figs/'+
'fig4.pdf', dpi=500)
plt.show()
return
def figS2(lat, lng, TRAC_10_20_jja, TRAC_20_30_jja, TRAC_30_40_jja,
TRAC_40_50_jja, TRAC_50_60_jja, TRAC_60_70_jja, TRAC_70_80_jja,
TRAC_10_20_djf, TRAC_20_30_djf, TRAC_30_40_djf, TRAC_40_50_djf,
TRAC_50_60_djf, TRAC_60_70_djf, TRAC_70_80_djf, V_jja, V_djf, edj_dist_jja,
edj_dist_djf, edj_jja, edj_djf):
"""
"""
import scipy.interpolate
fig = plt.figure(figsize=(7.5,8))
ax1 = plt.subplot2grid((3,1), (0,0))
ax2 = plt.subplot2grid((3,1), (1,0))
ax3 = plt.subplot2grid((3,1), (2,0))
ax1.set_title('(a) JJA r($\chi$, $\phi_{jet}$)', fontsize=12, x=0.02,
ha='left')
ax2.set_title('(b) DJF r($\chi$, $\phi_{jet}$)', fontsize=12, x=0.02,
ha='left')
ax3.set_title('(c) r(V, $\phi_{jet}$)', fontsize=12, x=0.02,
ha='left')
# Draw y = 0 (no correlation) line
ax1.axhline(y=0, xmin=0, xmax=90, lw=0.75, ls='--', color='black')
ax2.axhline(y=0, xmin=0, xmax=90, lw=0.75, ls='--', color='black')
ax3.axhline(y=0, xmin=0, xmax=90, lw=0.75, ls='--', color='black')
# Determine near-surface meridional wind-jet correlation
r_Vjet = globalo3_calculate.calculate_r(np.nanmean(V_jja, axis=1),
edj_dist_jja, lat, lng)
ax3.plot(lat, np.nanmean(r_Vjet, axis=1), ls='solid', color='k', lw=2,
label='JJA')
r_Vjet_djf = globalo3_calculate.calculate_r(np.nanmean(V_djf, axis=1),
edj_dist_djf, lat, lng)
ax3.plot(lat, np.nanmean(r_Vjet_djf, axis=1), ls='dashdot', color='k',
lw=2, label='DJF')
# Loop through tracers for JJA
for i, tracer in enumerate([TRAC_10_20_jja, TRAC_20_30_jja,
TRAC_30_40_jja, TRAC_40_50_jja, TRAC_50_60_jja, TRAC_60_70_jja,
TRAC_70_80_jja]):
tracer = np.nanmean(tracer, axis=1)
# Calculate tracer-jet correlation
r_tracerjet = globalo3_calculate.calculate_r(tracer, edj_dist_jja, lat,
lng)
ax1.plot(lat, np.nanmean(r_tracerjet, axis=1), color=COLORS[i],
label=LABELS[i], lw=2)
# Loop through tracers for DJF
for i, tracer in enumerate([TRAC_10_20_djf, TRAC_20_30_djf,
TRAC_30_40_djf, TRAC_40_50_djf, TRAC_50_60_djf, TRAC_60_70_djf,
TRAC_70_80_djf]):
tracer = np.nanmean(tracer, axis=1)
# Calculate tracer-jet correlation
r_tracerjet = globalo3_calculate.calculate_r(tracer, edj_dist_djf, lat,
lng)
ax2.plot(lat, np.nanmean(r_tracerjet, axis=1), color=COLORS[i],
label=LABELS[i], lw=2)
# Aesthetics
for ax in [ax1, ax2, ax3]:
ax.set_xlim([10, 80])
ax.set_xticks([10, 20, 30, 40, 50, 60, 70, 80])
ax.set_xticklabels([])
ax.set_ylabel('[$\cdot$]', fontsize=12)
ax.tick_params(which='major', labelsize=9)
ax1.set_ylim([-0.36, 0.36])
ax1.set_yticks(np.linspace(-0.36, 0.36, 5))
ax2.set_ylim([-0.44, 0.44])
ax2.set_yticks(np.linspace(-0.46, 0.46, 5))
ax3.set_ylim([-0.14, 0.14])
ax3.set_yticks(np.linspace(-0.14, 0.14, 5))
ax3.set_xticklabels(['10$^{\circ}$N', '20$^{\circ}$N', '30$^{\circ}$N',
'40$^{\circ}$N', '50$^{\circ}$N', '60$^{\circ}$N', '70$^{\circ}$N',
'80$^{\circ}$N'])
ax3.legend(loc=1, ncol=1, frameon=False, fontsize=12, handlelength=3)
ax2.legend(bbox_to_anchor=[0.95, -1.45, 0, 0], ncol=4, frameon=False,
fontsize=12)
plt.subplots_adjust(hspace=0.3, top=0.95, bottom=0.15)
# Interpolate the zonally-averaged r(V, jet) and find where the
# r(V, jet) = 0.; this is kind of kludgey, so the values are hard-coded in
interp = scipy.interpolate.interp1d(lat, np.nanmean(r_Vjet, axis=-1))
interp_djf = scipy.interpolate.interp1d(lat, np.nanmean(r_Vjet_djf,
axis=-1))
r_Vjet_zm_interp = []
r_Vjet_zm_interp_djf = []
lat_interp = np.linspace(0, lat.max(), 500)
for x in lat_interp:
r_Vjet_zm_interp.append(interp(x))
r_Vjet_zm_interp = np.abs(r_Vjet_zm_interp)
for x in lat_interp:
r_Vjet_zm_interp_djf.append(interp_djf(x))
r_Vjet_zm_interp_djf = np.abs(r_Vjet_zm_interp_djf)
# # Find index of 4 smallest values since r(V, jet) = 0. four times
r_Vjet_zm_interp.argsort()[:4] # however this has some repeats, so increase
# # to greater values to pull off values
# lat_where_0 = lat_interp[[114, 211, 372, 494]]
# Indicate jet; errorbars represent the zonally-averaged standard deviation
# of the daily variations in the position of the jet
ax1.errorbar(np.nanmean(edj_jja, axis=tuple((0,1))), 0.0,
xerr=np.nanmean(np.nanstd(edj_jja, axis=0)), zorder=10, color='k',
markersize=8, elinewidth=3., ecolor='k', fmt='o')
ax2.errorbar(np.nanmean(edj_djf, axis=tuple((0,1))), 0.0,
xerr=np.nanmean(np.nanstd(edj_djf, axis=0)), zorder=10, color='k',
markersize=8, elinewidth=3., ecolor='k', fmt='o')
lat_where_0 = np.array([21.70240480961924, 43.763527054108216,
67.08016032064128,])
lat_where_0_djf = np.array([32.6432865731463, 65.46593186372746])
for x in lat_where_0:
ax1.axvline(x=x, c='k', lw=0.75, ls='--', zorder=0)
for x in lat_where_0_djf:
ax2.axvline(x=x, c='k', lw=0.75, ls='--', zorder=0)
plt.savefig('/Users/ghkerr/phd/tracer/figs/'+'figS2.pdf', dpi=500)
plt.show()
return
def figS3(lat, lng, TRAC_40_50_jja, TRAC_40_50_djf, V_jja, V_djf, edj_dist_jja,
edj_dist_djf, edj_jja, edj_djf):
"""
Parameters
----------
lat : numpy.ndarray
Latitude coordinates for the Northern Hemisphere, units of degrees
north, [lat,]
lng : numpy.ndarray
Longitude coordinates for the Northern Hemisphere, units of degrees
east, [lng,]
TRAC_40_50_jja : numpy.ndarray
GEOS-Chem CO-like tracer for given pressure levels (n.b., column
average is performed in function) with prescribed emissions from 40-50
deg north for days during JJA 2008-2010, units of volume mixing ratio,
[time, lev, lat, lng]
TRAC_40_50_djf : numpy.ndarray
GEOS-Chem CO-like tracer for given pressure levels (n.b., column
average is performed in function) with prescribed emissions from 40-50
deg north for days during DJF 2008-2010, units of volume mixing ratio,
[time, lev, lat, lng]
V_jja : numpy.ndarray
Meridional wind for JJA 2008-2010 (note that the levels should match
that of the GEOS-Chem tracers), units of m s-1, [time, lev, lat, lng]
V_djf : numpy.ndarray
Meridional wind for DJF 2008-2010 (note that the levels should match
that of the GEOS-Chem tracers), units of m s-1, [time, lev, lat, lng]
edj_dist_jja : numpy.ndarray
Distance from the jet stream for JJA 2008-2010, units of degrees,
[time, lat, lng]
edj_dist_djf : numpy.ndarray
Distance from the jet stream for DJF 2008-2010, units of degrees,
[time, lat, lng]
edj_jja : numpy.ndarray
Latitude of the jet stream for JJA 2008-2010, units of degrees north,
[time, lng]
edj_djf : numpy.ndarray
Latitude of the jet stream for DJF 2008-2010, units of degrees north,
[time, lng]
Returns
-------
None
"""
from matplotlib.patches import Patch
if lng[-1] != 360:
lng[-1] = 360.
fig = plt.figure(figsize=(11,3.85))
ax1 = plt.subplot2grid((2,4), (0,0), colspan=2,
projection=ccrs.Miller(central_longitude=0.))
ax2 = plt.subplot2grid((2,4), (0,2), colspan=2,
projection=ccrs.Miller(central_longitude=0.))
ax3 = plt.subplot2grid((2,4), (1,0), colspan=2,
projection=ccrs.Miller(central_longitude=0.))
ax4 = plt.subplot2grid((2,4), (1,2), colspan=2,
projection=ccrs.Miller(central_longitude=0.))
ax1.set_title('(a) JJA V (PW$\:$-$\:$EW)', fontsize=12, x=0.02, ha='left')
ax2.set_title('(b) DJF V (PW$\:$-$\:$EW)', fontsize=12, x=0.02, ha='left')
ax3.set_title('(c) JJA r($\chi_{40-50}$, $\phi_{jet}$)',
fontsize=12, x=0.02, ha='left')
ax4.set_title('(d) DJF r($\chi_{40-50}$, $\phi_{jet}$)',
fontsize=12, x=0.02, ha='left')
cmap = plt.get_cmap('coolwarm')
# Determine near-surface meridional V-jet correlation
r_Vjet_jja = globalo3_calculate.calculate_r(np.nanmean(V_jja, axis=1),
edj_dist_jja, lat, lng)
r_Vjet_djf = globalo3_calculate.calculate_r(np.nanmean(V_djf, axis=1),
edj_dist_djf, lat, lng)
# Calculate V-jet significance, and PW/EW composites for
# JJA and DJF
significance_r_Vjet_jja = \
globalo3_calculate.calculate_r_significance(np.nanmean(V_jja, axis=1),
edj_dist_jja, r_Vjet_jja, lat, lng)
significance_r_Vjet_djf = \
globalo3_calculate.calculate_r_significance(np.nanmean(V_djf, axis=1),
edj_dist_djf, r_Vjet_djf, lat, lng)
eqjet_lat_jja, eqjet_lat_var_jja, pwjet_lat_jja, pwjet_lat_var_jja, \
pwjet_V_jja, eqjet_V_jja = globalo3_calculate.segregate_field_bylat(
np.nanmean(V_jja, axis=1), lng, edj_jja, np.arange(0, len(V_jja), 1))
eqjet_lat_djf, eqjet_lat_var_djf, pwjet_lat_djf, pwjet_lat_var_djf, \
pwjet_V_djf, eqjet_V_djf = globalo3_calculate.segregate_field_bylat(
np.nanmean(V_djf, axis=1), lng, edj_djf, np.arange(0, len(V_djf), 1))
# Plot (PW-EW) composites
clevst = np.linspace(-5, 5, 13)
mbt = ax1.contourf(lng, lat, (pwjet_V_jja-eqjet_V_jja),
clevst, cmap=plt.get_cmap('coolwarm'), extend='both',
transform=ccrs.PlateCarree(), zorder=2)
mbt = ax2.contourf(lng, lat, (pwjet_V_djf-eqjet_V_djf),
clevst, cmap=plt.get_cmap('coolwarm'), extend='both',
transform=ccrs.PlateCarree(), zorder=2)
# Hatching for significance
ax1.contourf(lng, lat, significance_r_Vjet_jja,
hatches=['//////'], colors='none', transform=ccrs.PlateCarree(),
zorder=4)
ax2.contourf(lng, lat, significance_r_Vjet_djf,
hatches=['//////'], colors='none', transform=ccrs.PlateCarree(),
zorder=4)
# Contours indicating mean V
CS = ax1.contour(lng, lat, np.nanmean(V_jja, axis=tuple((0,1))),
[-5,5], linewidths=1., colors='k')
CS = ax2.contour(lng, lat, np.nanmean(V_djf, axis=tuple((0,1))),
[-5,5], linewidths=1., colors='k')
# JJA TRAC_40_50-jet correlation
clevsb = np.linspace(-0.5, 0.5, 11)
r_tracerjet = globalo3_calculate.calculate_r(
np.nanmean(TRAC_40_50_jja, axis=1), edj_dist_jja, lat, lng)
# Column- and time-averaged tracer
tracer = np.nanmean(np.nanmean(TRAC_40_50_jja, axis=1), axis=0)
# Loop through each longitude and calculate the latitudinal gradient
grad_tracer = np.empty(shape=tracer.shape)
grad_tracer[:] = np.nan
for spine_i in np.arange(0, len(lng), 1):
# Tracer concentrations for given "spine" of latitudes at a given
# longitude
spine = tracer[:,spine_i]
grad_spine = np.gradient(spine)
grad_tracer[:,spine_i] = grad_spine
grad_tracer = np.array(grad_tracer)
expected = -1.*r_Vjet_jja*grad_tracer
# Find where the expected values are positive/negative
poz = np.empty(grad_tracer.shape)
poz[:] = np.nan
where_poz = np.where(expected > 0.)
poz[where_poz] = 1.
neg = np.empty(grad_tracer.shape)
neg[:] = np.nan
where_neg = np.where(expected < 0.)
neg[where_neg] = 1.
# Plotting
mbb = ax3.contourf(lng, lat, r_tracerjet, clevsb, cmap=cmap,
extend='both', transform=ccrs.PlateCarree(), zorder=1)
ax3.contourf(lng, lat, poz, hatches=['...'], colors='none',
transform=ccrs.PlateCarree())
ax3.contourf(lng, lat, neg, hatches=['///'], colors='none',
transform=ccrs.PlateCarree())
# DJF TRAC_40_50-jet correlation
r_tracerjet = globalo3_calculate.calculate_r(
np.nanmean(TRAC_40_50_djf, axis=1), edj_dist_djf, lat, lng)
# Column- and time-averaged tracer
tracer = np.nanmean(np.nanmean(TRAC_40_50_djf, axis=1), axis=0)
# Loop through each longitude and calculate the latitudinal gradient
grad_tracer = np.empty(shape=tracer.shape)
grad_tracer[:] = np.nan
for spine_i in np.arange(0, len(lng), 1):
# Tracer concentrations for given "spine" of latitudes at a given
# longitude
spine = tracer[:,spine_i]
grad_spine = np.gradient(spine)
grad_tracer[:,spine_i] = grad_spine
grad_tracer = np.array(grad_tracer)
expected = -1.*r_Vjet_djf*grad_tracer
# Find where the expected values are positive/negative
poz = np.empty(grad_tracer.shape)
poz[:] = np.nan
where_poz = np.where(expected > 0.)
poz[where_poz] = 1.
neg = np.empty(grad_tracer.shape)
neg[:] = np.nan
where_neg = np.where(expected < 0.)
neg[where_neg] = 1.
# Plotting
mbb = ax4.contourf(lng, lat, r_tracerjet, clevsb, cmap=cmap,
extend='both', transform=ccrs.PlateCarree(), zorder=1)
ax4.contourf(lng, lat, poz, hatches=['...'], colors='none',
transform=ccrs.PlateCarree())
ax4.contourf(lng, lat, neg, hatches=['///'], colors='none',
transform=ccrs.PlateCarree())
# Denote parallels and meridians
axes = [ax1, ax2, ax3, ax4]
for i in np.arange(0, len(axes), 1):
axes[i].set_xticks([-180, -120, -60, 0, 60, 120, 180],
crs=ccrs.PlateCarree())
lng_formatter = LongitudeFormatter()
axes[i].xaxis.set_major_formatter(lng_formatter)
axes[i].get_xaxis().set_ticklabels([])
axes[i].set_yticks([15, 50, 75], crs=ccrs.PlateCarree())
lat_formatter = LatitudeFormatter()
axes[i].yaxis.set_major_formatter(lat_formatter)
axes[i].get_yaxis().set_ticklabels([])
axes[i].coastlines(lw=0.25, resolution='50m', color='k', zorder=3)
axes[i].set_extent([lng.min()-180., lng.max()-180., 10., 80.])
axes[i].outline_patch.set_zorder(20)
# Add eddy-driven jet