def sst_sss_seasonal (mesh_path, file_path1, file_path2, save=False, fig_name=None):
# FESOM parameters
circumpolar=True
mask_cavities=True
# Season names for plot titles
season_names = ['DJF', 'MAM', 'JJA', 'SON']
# Build FESOM mesh
elements, patches = make_patches(mesh_path, circumpolar, mask_cavities)
# Figure out how many 2D nodes there are
file = open(mesh_path + 'nod2d.out', 'r')
file.readline()
n2d = 0
for line in file:
n2d += 1
file.close()
# Get seasonal averages of the 3D FESOM output
temp = seasonal_avg(file_path1, file_path2, 'temp')
salt = seasonal_avg(file_path1, file_path2, 'salt')
# Select the surface layer
sst = temp[:,:n2d]
sss = salt[:,:n2d]
# Plot
fig = figure(figsize=(20,9))
# Loop over seasons
for season in range(4):
# SST
# Build an array of FESOM data values corresponding to each Element
values1 = []
for elm in elements:
# For each element not in an ice shelf cavity, append the mean
# value for the 3 component Nodes
if not elm.cavity:
values1.append(mean([sst[season,elm.nodes[0].id], sst[season,elm.nodes[1].id], sst[season,elm.nodes[2].id]]))
ax = fig.add_subplot(2, 4, season+1, aspect='equal')
img = PatchCollection(patches, cmap='RdBu_r' )#jet)
img.set_array(array(values1))
#img.set_clim(vmin=-2, vmax=10)
img.set_clim(vmin=-4, vmax=4)
img.set_edgecolor('face')
ax.add_collection(img)
xlim([-35, 35])
ylim([-33, 37])
axis('off')
if season == 0:
text(-39, 0, r'SST ($^{\circ}$C)', fontsize=21, ha='right')
title(season_names[season], fontsize=24)
if season == 3:
cbaxes1 = fig.add_axes([0.92, 0.55, 0.01, 0.3])
#cbar1 = colorbar(img, ticks=arange(-2,10+4,4), cax=cbaxes1)
cbar1 = colorbar(img, ticks=arange(-4,4+2,2), cax=cbaxes1)
cbar1.ax.tick_params(labelsize=16)
# SSS
values2 = []
for elm in elements:
# For each element not in an ice shelf cavity, append the mean
# value for the 3 component Nodes
if not elm.cavity:
values2.append(mean([sss[season,elm.nodes[0].id], sss[season,elm.nodes[1].id], sss[season,elm.nodes[2].id]]))
ax = fig.add_subplot(2, 4, season+5, aspect='equal')
img = PatchCollection(patches, cmap='RdBu_r') #jet)
img.set_array(array(values2))
#img.set_clim(vmin=33, vmax=35)
img.set_clim(vmin=-0.5, vmax=0.5)
img.set_edgecolor('face')
ax.add_collection(img)
xlim([-35, 35])
ylim([-33, 37])
axis('off')
if season == 0:
text(-39, 0, 'SSS (psu)', fontsize=21, ha='right')
if season == 3:
cbaxes2 = fig.add_axes([0.92, 0.15, 0.01, 0.3])
#cbar2 = colorbar(img, ticks=arange(33,35+0.5,0.5), cax=cbaxes2)
cbar2 = colorbar(img, ticks=arange(-0.5,0.5+0.25,0.25), cax=cbaxes2)
cbar2.ax.tick_params(labelsize=16)
# Decrease space between plots
subplots_adjust(wspace=0.025,hspace=0.025)
# Finished
if save:
fig.savefig(fig_name)
else:
fig.show()
def sose_fesom_seasonal(elements,
file_path1,
file_path2,
var_name,
lon0,
depth_min,
save=False,
fig_name=None):
# Path to SOSE seasonal climatology file
sose_file = '/short/m68/kaa561/SOSE_seasonal_climatology.nc'
lat_max = -60 #-30
season_names = ['DJF', 'MAM', 'JJA', 'SON']
# Bounds on colour scale
if var_name == 'temp':
var_min = -2.5
var_max = 3.5 #7.5
var_ticks = 1
elif var_name == 'salt':
var_min = 33.8
var_max = 34.8
var_ticks = 0.2
else:
print 'Unknown variable ' + var_name
return
# Choose what to write on the title about the variable
if var_name == 'temp':
var_string = r'Temperature ($^{\circ}$C)'
elif var_name == 'salt':
var_string = 'Salinity (psu)'
# Choose what to write on the title about longitude
if lon0 < 0:
lon_string = ' at ' + str(int(round(-lon0))) + r'$^{\circ}$W'
else:
lon_string = ' at ' + str(int(round(lon0))) + r'$^{\circ}$E'
print 'Processing SOSE data'
# Read grid and 3D data (already seasonally averaged)
id = Dataset(sose_file, 'r')
lon_sose = id.variables['longitude'][0, :]
lat_sose = id.variables['latitude'][:, 0]
z_sose = id.variables['depth'][:]
var_3d_sose = id.variables[var_name][:, :, :, :]
# Calculate zonal slices for each season
var_sose = ma.empty([4, size(z_sose), size(lat_sose, 0)])
var_sose[:, :, :] = 0.0
for season in range(4):
print 'Calculating zonal slices for ' + season_names[season]
var_sose[season, :, :] = interp_lon_sose(var_3d_sose[season, :, :, :],
lon_sose, lon0)
# Get seasonal averages of the FESOM output
fesom_data = seasonal_avg(file_path1, file_path2, var_name)
# Set colour levels
lev = linspace(var_min, var_max, num=50)
# Choose southern boundary based on extent of SOSE grid
lat_min = amin(lat_sose)
# Plot
fig = figure(figsize=(20, 9))
for season in range(4):
# FESOM
print 'Calculating zonal slices for ' + season_names[season]
patches, values, tmp = side_patches(elements, lat_max, lon0,
fesom_data[season, :])
ax = fig.add_subplot(2, 4, season + 1)
img = PatchCollection(patches, cmap=jet)
img.set_array(array(values))
img.set_edgecolor('face')
img.set_clim(vmin=var_min, vmax=var_max)
ax.add_collection(img)
xlim([lat_min, lat_max])
ylim([depth_min, 0])
title('FESOM (' + season_names[season] + ')', fontsize=24)
if season == 0:
ylabel('depth (m)', fontsize=18)
# SOSE
fig.add_subplot(2, 4, season + 5)
pcolormesh(lat_sose,
z_sose,
var_sose[season, :, :],
vmin=var_min,
vmax=var_max,
cmap='jet')
xlim([lat_min, lat_max])
ylim([depth_min, 0])
title('SOSE (' + season_names[season] + ')', fontsize=24)
xlabel('Latitude', fontsize=18)
if season == 0:
ylabel('depth (m)', fontsize=18)
# Add colorbar
cbaxes = fig.add_axes([0.93, 0.2, 0.015, 0.6])
cbar = colorbar(img,
cax=cbaxes,
ticks=arange(var_min, var_max + var_ticks, var_ticks))
cbar.ax.tick_params(labelsize=16)
# Add the main title
suptitle(var_string + lon_string, fontsize=30)
# Finished
if save:
fig.savefig(fig_name)
else:
fig.show()
def aice_hi_seasonal(mesh_path,
file_path1,
file_path2,
save=False,
fig_name=None):
# FESOM parameters
circumpolar = True
mask_cavities = True
# Season names for plot titles
season_names = ['DJF', 'MAM', 'JJA', 'SON']
# Build FESOM mesh
elements, patches = make_patches(mesh_path, circumpolar, mask_cavities)
# Get seasonal averages of the FESOM output
aice = seasonal_avg(file_path1, file_path2, 'area')
hi = seasonal_avg(file_path1, file_path2, 'hice')
# Plot
fig = figure(figsize=(20, 9))
# Loop over seasons
for season in range(4):
# aice
# Build an array of FESOM data values corresponding to each Element
values1 = []
for elm in elements:
# For each element not in an ice shelf cavity, append the mean
# value for the 3 component Nodes
if not elm.cavity:
values1.append(
mean([
aice[season, elm.nodes[0].id],
aice[season, elm.nodes[1].id], aice[season,
elm.nodes[2].id]
]))
ax = fig.add_subplot(2, 4, season + 1, aspect='equal')
img = PatchCollection(patches, cmap=jet)
img.set_array(array(values1))
#img.set_clim(vmin=-1, vmax=1)
img.set_clim(vmin=0, vmax=1)
img.set_edgecolor('face')
ax.add_collection(img)
xlim([-35, 35])
ylim([-33, 37])
axis('off')
if season == 0:
text(-39, 0, 'aice (%)', fontsize=21, ha='right')
title(season_names[season], fontsize=24)
if season == 3:
cbaxes1 = fig.add_axes([0.92, 0.55, 0.01, 0.3])
#cbar1 = colorbar(img, ticks=arange(-1,1+0.5,0.5), cax=cbaxes1)
cbar1 = colorbar(img, ticks=arange(0, 1 + 0.25, 0.25), cax=cbaxes1)
cbar1.ax.tick_params(labelsize=16)
# hi
values2 = []
for elm in elements:
# For each element not in an ice shelf cavity, append the mean
# value for the 3 component Nodes
if not elm.cavity:
values2.append(
mean([
hi[season, elm.nodes[0].id],
hi[season, elm.nodes[1].id], hi[season,
elm.nodes[2].id]
]))
ax = fig.add_subplot(2, 4, season + 5, aspect='equal')
img = PatchCollection(patches, cmap=jet)
img.set_array(array(values2))
#img.set_clim(vmin=-0.5, vmax=0.5)
img.set_clim(vmin=0, vmax=1.5)
img.set_edgecolor('face')
ax.add_collection(img)
xlim([-35, 35])
ylim([-33, 37])
axis('off')
if season == 0:
text(-39, 0, 'hi (m)', fontsize=21, ha='right')
if season == 3:
cbaxes2 = fig.add_axes([0.92, 0.15, 0.01, 0.3])
#cbar2 = colorbar(img, ticks=arange(-0.5,0.5+0.25,0.25), cax=cbaxes2)
cbar2 = colorbar(img, ticks=arange(0, 1.5 + 0.5, 0.5), cax=cbaxes2)
cbar2.ax.tick_params(labelsize=16)
# Decrease space between plots
subplots_adjust(wspace=0.025, hspace=0.025)
# Finished
if save:
fig.savefig(fig_name)
else:
fig.show()
def nsidc_aice_seasonal(mesh_path,
file_path1,
file_path2,
save=False,
fig_name=None):
# FESOM parameters
circumpolar = True
mask_cavities = True
# Season names for plot titles
season_names = ['DJF', 'MAM', 'JJA', 'SON']
# NSIDC file paths
nsidc_head = '/short/m68/kaa561/nsidc_aice/seaice_conc_monthly_sh'
nsidc_head_0 = nsidc_head + '_f11_'
nsidc_head_1 = nsidc_head + '_f13_'
nsidc_tail = '_v02r00.nc'
# Degrees to radians conversion factor
deg2rad = pi / 180.0
# Number of days per month (just for NSIDC)
ndays_month = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
# Build FESOM mesh
elements, patches = make_patches(mesh_path, circumpolar, mask_cavities)
# Get seasonal averages of the FESOM output
fesom_data = seasonal_avg(file_path1, file_path2, 'area')
# Read NSIDC grid from the January file
id = Dataset(nsidc_head_0 + '199501' + nsidc_tail, 'r')
nsidc_lon = id.variables['longitude'][:, :]
nsidc_lat = id.variables['latitude'][:, :]
id.close()
# Initialise seasonal averages of NSIDC data
nsidc_data = ma.empty([4, size(nsidc_lon, 0), size(nsidc_lon, 1)])
nsidc_data[:, :] = 0.0
# Process one season at a time
for season in range(4):
# Figure out which months we care about
if season == 0:
# DJF
nsidc_months = [12, 1, 2]
elif season == 1:
# MAM
nsidc_months = [3, 4, 5]
elif season == 2:
# JJA
nsidc_months = [6, 7, 8]
elif season == 3:
# SON
nsidc_months = [9, 10, 11]
season_days = 0 # Number of days in season; this will be incremented
# Process one month at a time
for month in nsidc_months:
# Construct NSIDC file path
if month < 10:
nsidc_file = nsidc_head_0 + '19950' + str(month) + nsidc_tail
else:
nsidc_file = nsidc_head_1 + '1995' + str(month) + nsidc_tail
# Read concentration data
id = Dataset(nsidc_file, 'r')
nsidc_data_raw = id.variables['seaice_conc_monthly_cdr'][0, :, :]
# Read std just for the mask
nsidc_mask = id.variables['stdev_of_seaice_conc_monthly_cdr'][
0, :, :]
id.close()
# Set land mask
nsidc_data_tmp = ma.empty(shape(nsidc_data_raw))
nsidc_data_tmp[:, :] = 0.0
nsidc_data_tmp[~nsidc_mask.mask] = nsidc_data_raw[~nsidc_mask.mask]
nsidc_data_tmp[nsidc_mask.mask] = ma.masked
# Accumulate master array, weighted with number of days per month
nsidc_data[season, :, :] += nsidc_data_tmp * ndays_month[month - 1]
season_days += ndays_month[month - 1]
# Convert from sum to average
nsidc_data[season, :, :] /= season_days
# Convert to spherical coordinates
nsidc_x = -(nsidc_lat + 90) * cos(nsidc_lon * deg2rad + pi / 2)
nsidc_y = (nsidc_lat + 90) * sin(nsidc_lon * deg2rad + pi / 2)
# Find boundaries for each side of plot based on extent of NSIDC grid
bdry1 = amax(nsidc_x[:, 0])
bdry2 = amin(nsidc_x[:, -1])
bdry3 = amin(nsidc_y[:, 0])
bdry4 = amax(nsidc_y[:, -1])
# Set consistent colour levels
lev = linspace(0, 1, num=50)
# Plot
fig = figure(figsize=(20, 9))
# Loop over seasons
for season in range(4):
# NSIDC
ax = fig.add_subplot(2, 4, season + 1, aspect='equal')
contourf(nsidc_x, nsidc_y, nsidc_data[season, :, :], lev)
if season == 0:
text(-39, 0, 'NSIDC', fontsize=24, ha='right')
title(season_names[season], fontsize=24)
xlim([bdry1, bdry2])
ylim([bdry3, bdry4])
axis('off')
# Build an array of FESOM data values corresponding to each Element
values = []
for elm in elements:
# For each element not in an ice shelf cavity, append the mean
# value for the 3 component Nodes
if not elm.cavity:
values.append(
mean([
fesom_data[season, elm.nodes[0].id],
fesom_data[season, elm.nodes[1].id],
fesom_data[season, elm.nodes[2].id]
]))
# Plot FESOM data
ax = fig.add_subplot(2, 4, season + 5, aspect='equal')
img = PatchCollection(patches, cmap=jet)
img.set_array(array(values))
img.set_clim(vmin=0, vmax=1)
img.set_edgecolor('face')
ax.add_collection(img)
xlim([bdry1, bdry2])
ylim([bdry3, bdry4])
axis('off')
if season == 0:
text(-39, 0, 'FESOM', fontsize=24, ha='right')
# Add a horizontal colorbar at the bottom
cbaxes = fig.add_axes([0.25, 0.04, 0.5, 0.02])
cbar = colorbar(img,
orientation='horizontal',
ticks=arange(0, 1 + 0.25, 0.25),
cax=cbaxes)
cbar.ax.tick_params(labelsize=16)
# Add the main title
suptitle('Sea ice concentration', fontsize=30)
# Decrease space between plots
subplots_adjust(wspace=0.025, hspace=0.025)
# Finished
if save:
fig.savefig(fig_name)
else:
fig.show()
def mld_jja_diff(mesh_path,
file_path_beg,
file_path_end,
save=False,
fig_name=None,
limit=None):
# Definition of mixed layer depth: where potential density exceeds
# surface density by this amount (kg/m^3) as in Sallee et al 2013
density_anom = 0.03
# Plotting parameters
circumpolar = True
mask_cavities = True
lat_max = -30 + 90
font_sizes = [30, 24, 20]
print 'Building grid'
elements, patches = make_patches(mesh_path, circumpolar, mask_cavities)
print 'Reading data'
# First 10 years
# Read temperature and salinity at each node, seasonally averaged over JJA
tmp = seasonal_avg(file_path_beg, file_path_beg, 'temp')
temp_beg = tmp[2, :]
tmp = seasonal_avg(file_path_beg, file_path_beg, 'salt')
salt_beg = tmp[2, :]
# Last 10 years
tmp = seasonal_avg(file_path_beg, file_path_end, 'temp')
temp_end = tmp[2, :]
tmp = seasonal_avg(file_path_beg, file_path_end, 'salt')
salt_end = tmp[2, :]
# Calculate potential density (depth 0)
print 'Calculating density'
density_beg = unesco(temp_beg, salt_beg, zeros(shape(temp_beg)))
density_end = unesco(temp_end, salt_end, zeros(shape(temp_end)))
# Calculate mixed layer depth at each element
print 'Calculating mixed layer depth'
# First 10 years
mld_beg = []
for elm in elements:
if (mask_cavities and not elm.cavity) or (not mask_cavities):
# Get mixed layer depth at each node
mld_nodes = []
# Make sure we exclude ice shelf cavity nodes from element mean
# (an Element can be a non-cavity element and still have up to 2
# cavity nodes)
for i in range(3):
if (mask_cavities
and not elm.cavity_nodes[i]) or (not mask_cavities):
node = elm.nodes[i]
density_sfc = density_beg[node.id]
temp_depth = node.depth
curr_node = node.below
while True:
if curr_node is None:
# Reached bottom
mld_nodes.append(temp_depth)
break
if density_beg[
curr_node.id] >= density_sfc + density_anom:
# Reached critical density anomaly
mld_nodes.append(curr_node.depth)
break
temp_depth = curr_node.depth
curr_node = curr_node.below
# For this element, save the mean mixed layer depth across
# non-cavity nodes (up to 3)
mld_beg.append(mean(array(mld_nodes)))
# Last 10 years
mld_end = []
for elm in elements:
if (mask_cavities and not elm.cavity) or (not mask_cavities):
# Get mixed layer depth at each node
mld_nodes = []
# Make sure we exclude ice shelf cavity nodes from element mean
# (an Element can be a non-cavity element and still have up to 2
# cavity nodes)
for i in range(3):
if (mask_cavities
and not elm.cavity_nodes[i]) or (not mask_cavities):
node = elm.nodes[i]
density_sfc = density_end[node.id]
temp_depth = node.depth
curr_node = node.below
while True:
if curr_node is None:
mld_nodes.append(temp_depth)
break
if density_end[
curr_node.id] >= density_sfc + density_anom:
mld_nodes.append(curr_node.depth)
break
temp_depth = curr_node.depth
curr_node = curr_node.below
# For this element, save the mean mixed layer depth across
# non-cavity nodes (up to 3)
mld_end.append(mean(array(mld_nodes)))
# Calculate change in mixed layer depth
mld_change = array(mld_end) - array(mld_beg)
if mask_cavities:
# Get mask array of patches for ice shelf cavity elements
mask_patches = iceshelf_mask(elements)
# Choose colour bounds
if limit is not None:
bound = limit
else:
bound = amax(array(mld_change))
print 'Plotting'
# Set up plot
fig = figure(figsize=(16, 12))
ax = fig.add_subplot(1, 1, 1, aspect='equal')
# Set colourmap for patches, and refer it to the values array
img = PatchCollection(patches, cmap='RdBu_r')
img.set_array(array(mld_change))
img.set_edgecolor('face')
# Add patches to plot
ax.add_collection(img)
if mask_cavities:
# Set colour to light grey for patches in mask
overlay = PatchCollection(mask_patches, facecolor=(0.6, 0.6, 0.6))
overlay.set_edgecolor('face')
# Add mask to plot
ax.add_collection(overlay)
# Configure plot
xlim([-lat_max, lat_max])
ylim([-lat_max, lat_max])
ax.get_xaxis().set_ticks([])
ax.get_yaxis().set_ticks([])
axis('off')
title('Change in JJA mixed layer depth (m)\n2091-2100 vs 2006-2015',
fontsize=font_sizes[0])
cbar = colorbar(img)
cbar.ax.tick_params(labelsize=font_sizes[2])
img.set_clim(vmin=-bound, vmax=bound)
if save:
fig.savefig(fig_name)
else:
fig.show()
def temp_salt_seasonal (elements, file_path1, file_path2, lon0, depth_min, save=False, fig_name=None):
# Northern boundary for plot
lat_max = -30
# Season names for titles
season_names = ['DJF', 'MAM', 'JJA', 'SON']
# Bounds on colour scales for temperature and salinity
var_min = [-2.5, 33.8]
var_max = [3.5, 34.8]
var_ticks = [1, 0.2]
# Choose what to write on the title about longitude
if lon0 < 0:
lon_string = str(int(round(-lon0))) + r'$^{\circ}$W'
else:
lon_string = str(int(round(lon0))) + r'$^{\circ}$E'
# Get seasonal averages of temperature and salinity
temp_data = seasonal_avg(file_path1, file_path2, 'temp')
salt_data = seasonal_avg(file_path1, file_path2, 'salt')
# Set colour levels
lev1 = linspace(var_min[0], var_max[0], num=50)
lev2 = linspace(var_min[1], var_max[1], num=50)
# Plot
fig = figure(figsize=(20,9))
# Loop over seasons
for season in range(4):
print 'Calculating zonal slices for ' + season_names[season]
# Interpolate temperature to lon0 and get plotting patches
patches, values, lat_min = side_patches(elements, lat_max, lon0, temp_data[season,:])
ax = fig.add_subplot(2, 4, season+1)
img = PatchCollection(patches, cmap=jet)
img.set_array(array(values))
img.set_edgecolor('face')
img.set_clim(vmin=var_min[0], vmax=var_max[0])
ax.add_collection(img)
xlim([lat_min, lat_max])
ylim([depth_min, 0])
title('Temperature (' + season_names[season] + ')', fontsize=24)
if season == 0:
# Add depth label on the left
ylabel('depth (m)', fontsize=18)
if season == 3:
# Add a colourbar on the right
cbaxes = fig.add_axes([0.93, 0.6, 0.015, 0.3])
cbar1 = colorbar(img, cax=cbaxes, ticks=arange(var_min[0], var_max[0]+var_ticks[0], var_ticks[0]))
cbar1.ax.tick_params(labelsize=16)
# Repeat for salinity
patches, values, lat_min = side_patches(elements, lat_max, lon0, salt_data[season,:])
ax = fig.add_subplot(2, 4, season+5)
img = PatchCollection(patches, cmap=jet)
img.set_array(array(values))
img.set_edgecolor('face')
img.set_clim(vmin=var_min[1], vmax=var_max[1])
ax.add_collection(img)
xlim([lat_min, lat_max])
ylim([depth_min, 0])
title('Salinity (' + season_names[season] + ')', fontsize=24)
if season == 0:
ylabel('Depth (m)', fontsize=18)
xlabel('Latitude', fontsize=18)
if season == 3:
cbaxes = fig.add_axes([0.93, 0.1, 0.015, 0.3])
cbar2 = colorbar(img, cax=cbaxes, ticks=arange(var_min[1], var_max[1]+var_ticks[1], var_ticks[1]))
cbar2.ax.tick_params(labelsize=16)
suptitle(lon_string, fontsize=30)
# Finished
if save:
fig.savefig(fig_name)
else:
fig.show()
def ismr_seasonal (mesh_path, file_path1, file_path2, save=False, fig_name=None):
# Plotting parameters
lat_max = -63 + 90
circumpolar = True
mask_cavities = True
# Seconds per year
sec_per_year = 365.25*24*3600
# Season names for plot titles
season_names = ['DJF', 'MAM', 'JJA', 'SON']
# Set colour map
# Values for change points
cmap_vals = array([-0.1, 0, 1, 2, 5, 8])
# Colours for change points
# (blue, white, yellow-orange, red-orange, dark red, purple)
cmap_colors = [(0.26, 0.45, 0.86), (1, 1, 1), (1, 0.9, 0.4), (0.99, 0.59, 0.18), (0.5, 0.0, 0.08), (0.96, 0.17, 0.89)]
# Map to 0-1
cmap_vals_norm = (cmap_vals + 0.1)/(8 + 0.1)
# Combine into a list
cmap_list = []
for i in range(size(cmap_vals)):
cmap_list.append((cmap_vals_norm[i], cmap_colors[i]))
# Make colour map
mf_cmap = LinearSegmentedColormap.from_list('melt_freeze', cmap_list)
# Build FESOM mesh
# Get separate patches for the open ocean elements so we can mask them out
elements, mask_patches = make_patches(mesh_path, circumpolar, mask_cavities)
patches = iceshelf_mask(elements)
# Get seasonal averages of freshwater flux
ismr = seasonal_avg(file_path1, file_path2, 'wnet')*sec_per_year
# Select ice shelf nodes
values = []
# Set up a grey square covering the domain, anything that isn't covered
# up later is land
x_reg, y_reg = meshgrid(linspace(-lat_max, lat_max, num=100), linspace(-lat_max, lat_max, num=100))
land_square = zeros(shape(x_reg))
# Plot
fig = figure(figsize=(20,6))
# Loop over seasons
for season in range(4):
# Build an array of ismr values corresponding to each ice shelf Element
values = []
# Loop over elements
for elm in elements:
# For each element in an ice shelf cavity, append the mean value
# for the 3 component Nodes
if elm.cavity:
values.append(mean([ismr[season,elm.nodes[0].id], ismr[season,elm.nodes[1].id], ismr[season,elm.nodes[2].id]]))
ax = fig.add_subplot(1, 4, season+1, aspect='equal')
# Start with grey square background for land
contourf(x_reg, y_reg, land_square, 1, colors=(('0.6', '0.6', '0.6')))
img = PatchCollection(patches, cmap='RdBu_r') #mf_cmap)
img.set_array(array(values))
img.set_edgecolor('face')
img.set_clim(vmin=-3, vmax=3)
#img.set_clim(vmin=-0.1, vmax=8)
ax.add_collection(img)
# Mask out the open ocean in white
overlay = PatchCollection(mask_patches, facecolor=(1,1,1))
overlay.set_edgecolor('face')
ax.add_collection(overlay)
# Contour ice shelf fronts
contour_lines = []
for elm in elements:
# Select elements where exactly 2 of the 3 nodes are in a cavity
if count_nonzero(elm.cavity_nodes) == 2:
# Save the coastal flags and x- and y- coordinates of these 2
coast_tmp = []
x_tmp = []
y_tmp = []
for i in range(3):
if elm.cavity_nodes[i]:
coast_tmp.append(elm.coast_nodes[i])
x_tmp.append(elm.x[i])
y_tmp.append(elm.y[i])
# Select elements where at most 1 of these 2 nodes are coastal
if count_nonzero(coast_tmp) < 2:
# Draw a line between the 2 nodes
contour_lines.append([(x_tmp[0], y_tmp[0]), (x_tmp[1], y_tmp[1])])
# Add all the lines to the plot
contours = LineCollection(contour_lines, edgecolor='black', linewidth=1)
ax.add_collection(contours)
# Configure plot
xlim([-lat_max, lat_max])
ylim([-lat_max, lat_max])
axis('off')
title(season_names[season], fontsize=24)
if season == 3:
cbaxes = fig.add_axes([0.92, 0.2, 0.01, 0.6])
cbar = colorbar(img, cax=cbaxes)
cbar.ax.tick_params(labelsize=16)
suptitle('Ice shelf melt rate (m/y)', fontsize=30)
# Decrease space between plots
subplots_adjust(wspace=0.025)
# Finished
if save:
fig.savefig(fig_name)
else:
fig.show()