def avg_zeta (file_path):
# Read time and grid variables
file = Dataset(file_path, 'r')
time = file.variables['ocean_time'][:]
# Convert time from seconds to years
time = time/(365*24*60*60)
lon = file.variables['lon_rho'][:-15,1:-1]
lat = file.variables['lat_rho'][:-15,1:-1]
mask = file.variables['mask_rho'][:-15,1:-1]
avg_zeta = []
# Calculate dx and dy in another script
dx, dy = cartesian_grid_2d(lon, lat)
# Calculate dA and mask with land mask
dA = ma.masked_where(mask==0, dx*dy)
for l in range(size(time)):
print 'Processing timestep ' + str(l+1) + ' of ' + str(size(time))
# Read zeta at this timestep
zeta = file.variables['zeta'][l,:-15,1:-1]
# Calculate area-weighted average
avg_zeta.append(sum(zeta*dA)/sum(dA))
file.close()
# Plot results
clf()
plot(time, avg_zeta)
xlabel('Years')
ylabel('Average sea surface height (m)')
show()
def paul_holland_hack(grid_file):
total_fw = 1500 # Gt/y
nbdry = -60 # Apply the freshwater evenly south of here
sec_per_year = 365.25 * 24 * 60 * 60
# Read grid and masks, making sure to get rid of the overlapping periodic
# boundary cells that are double-counted
id = Dataset(grid_file, 'r')
lat = id.variables['lat_rho'][:, 1:-1]
lon = id.variables['lon_rho'][:, 1:-1]
mask_zice = id.variables['mask_zice'][:, 1:-1]
mask_rho = id.variables['mask_rho'][:, 1:-1]
id.close()
# Mask out land and ice shelves
mask = mask_rho - mask_zice
# Get differentials
dx, dy = cartesian_grid_2d(lon, lat)
# Open ocean cells
ocn_flag = mask == 1
# Cells south of 60S
loc_flag = lat < nbdry
# Total area of all open ocean cells
total_area = sum(dx * dy * ocn_flag)
print 'Total area = ' + str(total_area) + ' m^2'
# Total area of open ocean cells south of 60S
target_area = sum(dx * dy * ocn_flag * loc_flag)
print 'Area south of 60S = ' + str(target_area) + ' m^2'
# Multiply by 1e12 to convert from Gt/y to kg/y
# Divide by sec_per_year to convert from kg/y to kg/s
# Divide by target area to get kg/m^2/s
fw_flux = total_fw * 1e12 / target_area / sec_per_year
print 'Freshwater flux to add = ' + str(fw_flux) + 'kg/m^2/s'
def calc_grid(file_path):
# Read grid variables
id = Dataset(file_path, 'r')
lon = id.variables['lon_rho'][:, :]
lat = id.variables['lat_rho'][:, :]
lon_u = id.variables['lon_u'][:, :]
lat_u = id.variables['lat_u'][:, :]
lon_v = id.variables['lon_v'][:, :]
lat_v = id.variables['lat_v'][:, :]
zice = id.variables['zice'][:, :]
mask_rho = id.variables['mask_rho'][:, :]
id.close()
# Calculate dx and dy in another script
dx, dy = cartesian_grid_2d(lon_u, lat_u, lon_v, lat_v)
# Calculate dA and mask with zice
zice_masked = zice * mask_rho
dA = ma.masked_where(zice_masked == 0, dx * dy)
# Save dimensions
#num_lat = size(lon, 0)
#num_lon = size(lon, 1)
# Make longitude values go from -180 to 180, not 0 to 360
#index = lon > 180
#lon[index] = lon[index] - 360
return dA, lon, lat
def cartesian_grid_3d (lon, lat, h, zice, theta_s, theta_b, hc, N, zeta=None):
# Calculate 2D dx and dy in another script
dx, dy = cartesian_grid_2d(lon, lat)
# Copy into 3D arrays, same at each depth level
dx = tile(dx, (N,1,1))
dy = tile(dy, (N,1,1))
# Save horizontal dimensions
num_lat = size(lon, 0)
num_lon = size(lon, 1)
# Get a 3D array of z-coordinates; sc_r and Cs_r are unused
z, sc_r, Cs_r = calc_z(h, zice, theta_s, theta_b, hc, N, zeta)
# We have z at the midpoint of each cell, now find it on the top and
# bottom edges of each cell
z_edges = zeros((N+1, num_lat, num_lon))
z_edges[1:-1,:,:] = 0.5*(z[0:-1,:,:] + z[1:,:,:])
# At surface, z=zice
z_edges[-1,:,:] = zice[:,:]
# Add zeta if it exists
if zeta is not None:
z_edges[-1,:,:] += zeta[:,:]
# At bottom, extrapolate
z_edges[0,:,:] = 2*z[0,:,:] - z_edges[1,:,:]
# Now find dz
dz = z_edges[1:,:,:] - z_edges[0:-1,:,:]
return dx, dy, dz, z
def total_iceshelf_area(roms_grid_file, fesom_mesh_path_lr,
fesom_mesh_path_hr):
id = Dataset(roms_grid_file, 'r')
lon = id.variables['lon_rho'][:-15, 1:-1]
lat = id.variables['lat_rho'][:-15, 1:-1]
zice = id.variables['zice'][:-15, 1:-1]
id.close()
dx, dy = cartesian_grid_2d(lon, lat)
dA = ma.masked_where(zice == 0, dx * dy)
print 'MetROMS: ' + str(sum(dA)) + ' m^2'
elements_lr = fesom_grid(fesom_mesh_path_lr,
circumpolar=True,
cross_180=False)
area_elm_lr = zeros(len(elements_lr))
for i in range(len(elements_lr)):
elm = elements_lr[i]
if elm.cavity:
area_elm_lr[i] = elm.area()
print 'FESOM (low-res): ' + str(sum(area_elm_lr)) + ' m^2'
elements_hr = fesom_grid(fesom_mesh_path_hr,
circumpolar=True,
cross_180=False)
area_elm_hr = zeros(len(elements_hr))
for i in range(len(elements_hr)):
elm = elements_hr[i]
if elm.cavity:
area_elm_hr[i] = elm.area()
print 'FESOM (high-res): ' + str(sum(area_elm_hr)) + ' m^2'
def cartesian_grid_3d(lon, lat, h, zice, theta_s, theta_b, hc, N, zeta=None):
# Calculate 2D dx and dy in another script
dx, dy = cartesian_grid_2d(lon, lat)
# Copy into 3D arrays, same at each depth level
dx = tile(dx, (N, 1, 1))
dy = tile(dy, (N, 1, 1))
# Save horizontal dimensions
num_lat = size(lon, 0)
num_lon = size(lon, 1)
# Get a 3D array of z-coordinates; sc_r and Cs_r are unused
z, sc_r, Cs_r = calc_z(h, zice, theta_s, theta_b, hc, N, zeta)
# We have z at the midpoint of each cell, now find it on the top and
# bottom edges of each cell
z_edges = zeros((N + 1, num_lat, num_lon))
z_edges[1:-1, :, :] = 0.5 * (z[0:-1, :, :] + z[1:, :, :])
# At surface, z=zice
z_edges[-1, :, :] = zice[:, :]
# Add zeta if it exists
if zeta is not None:
z_edges[-1, :, :] += zeta[:, :]
# At bottom, extrapolate
z_edges[0, :, :] = 2 * z[0, :, :] - z_edges[1, :, :]
# Now find dz
dz = z_edges[1:, :, :] - z_edges[0:-1, :, :]
return dx, dy, dz, z
def avg_zeta(file_path):
# Read time and grid variables
file = Dataset(file_path, 'r')
time = file.variables['ocean_time'][:]
# Convert time from seconds to years
time = time / (365 * 24 * 60 * 60)
lon = file.variables['lon_rho'][:-15, 1:-1]
lat = file.variables['lat_rho'][:-15, 1:-1]
mask = file.variables['mask_rho'][:-15, 1:-1]
avg_zeta = []
# Calculate dx and dy in another script
dx, dy = cartesian_grid_2d(lon, lat)
# Calculate dA and mask with land mask
dA = ma.masked_where(mask == 0, dx * dy)
for l in range(size(time)):
print 'Processing timestep ' + str(l + 1) + ' of ' + str(size(time))
# Read zeta at this timestep
zeta = file.variables['zeta'][l, :-15, 1:-1]
# Calculate area-weighted average
avg_zeta.append(sum(zeta * dA) / sum(dA))
file.close()
# Plot results
clf()
plot(time, avg_zeta)
xlabel('Years')
ylabel('Average sea surface height (m)')
show()
def grid_res (grid_path, save=False, fig_name=None):
# Degrees to radians conversion factor
deg2rad = pi/180
# Read grid
id = Dataset(grid_path, 'r')
lon = id.variables['lon_rho'][:-15,:-1]
lat = id.variables['lat_rho'][:-15,:-1]
mask = id.variables['mask_rho'][:-15,:-1]
id.close()
# Get differentials
dx, dy = cartesian_grid_2d(lon, lat)
# Calculate resolution: square root of the area, converted to km
res = sqrt(dx*dy)*1e-3
# Apply land mask
res = ma.masked_where(mask==0, res)
# Polar coordinates for plotting
x = -(lat+90)*cos(lon*deg2rad+pi/2)
y = (lat+90)*sin(lon*deg2rad+pi/2)
# Colour levels
lev = linspace(0, 20, num=50)
# Plot
fig = figure(figsize=(16,12))
fig.add_subplot(1,1,1, aspect='equal')
contourf(x, y, res, lev, extend='both')
cbar = colorbar()
cbar.ax.tick_params(labelsize=20)
title('Grid resolution (km)', fontsize=30)
axis('off')
if save:
fig.savefig(fig_name)
else:
fig.show()
def grid_res (grid_path, save=False, fig_name=None):
# Degrees to radians conversion factor
deg2rad = pi/180
# Read grid
id = Dataset(grid_path, 'r')
lon = id.variables['lon_rho'][:-15,:-1]
lat = id.variables['lat_rho'][:-15,:-1]
mask = id.variables['mask_rho'][:-15,:-1]
id.close()
# Get differentials
dx, dy = cartesian_grid_2d(lon, lat)
# Calculate resolution: square root of the area, converted to km
res = sqrt(dx*dy)*1e-3
# Apply land mask
res = ma.masked_where(mask==0, res)
# Polar coordinates for plotting
x = -(lat+90)*cos(lon*deg2rad+pi/2)
y = (lat+90)*sin(lon*deg2rad+pi/2)
# Colour levels
lev = linspace(0, amax(res), num=50) #20, num=50)
# Plot
fig = figure(figsize=(16,12))
fig.add_subplot(1,1,1, aspect='equal')
contourf(x, y, res, lev, extend='both')
cbar = colorbar()
cbar.ax.tick_params(labelsize=20)
title('Grid resolution (km)', fontsize=30)
axis('off')
if save:
fig.savefig(fig_name)
else:
fig.show()
def calc_grid(file_path):
# Read grid variables
id = Dataset(file_path, 'r')
lon = id.variables['lon_rho'][:-15, 1:-1]
lat = id.variables['lat_rho'][:-15, 1:-1]
zice = id.variables['zice'][:-15, 1:-1]
id.close()
# Calculate dx and dy in another script
dx, dy = cartesian_grid_2d(lon, lat)
# Calculate dA and mask with zice
dA = ma.masked_where(zice == 0, dx * dy)
# Save dimensions
num_lat = size(lon, 0)
num_lon = size(lon, 1)
# Make longitude values go from -180 to 180, not 0 to 360
index = lon > 180
lon[index] = lon[index] - 360
return dA, lon, lat
def calc_grid (file_path):
# Read grid variables
id = Dataset(file_path, 'r')
lon = id.variables['lon_rho'][:-15,1:-1]
lat = id.variables['lat_rho'][:-15,1:-1]
zice = id.variables['zice'][:-15,1:-1]
id.close()
# Calculate dx and dy in another script
dx, dy = cartesian_grid_2d(lon, lat)
# Calculate dA and mask with zice
dA = ma.masked_where(zice==0, dx*dy)
# Save dimensions
num_lat = size(lon, 0)
num_lon = size(lon, 1)
# Make longitude values go from -180 to 180, not 0 to 360
index = lon > 180
lon[index] = lon[index] - 360
return dA, lon, lat
def mip_grid_res (roms_grid_file, fesom_mesh_low, fesom_mesh_high, save=False, fig_name=None):
# Spatial bounds on plot
lat_max = -63 + 90
# Bounds on colour scale (km)
limits = [0, 20]
# Degrees to radians conversion factor
deg2rad = pi/180
# FESOM plotting parameters
circumpolar = True
print 'Processing ROMS'
# Read ROMS grid
id = Dataset(roms_grid_file, 'r')
roms_lon = id.variables['lon_rho'][:,:]
roms_lat = id.variables['lat_rho'][:,:]
roms_mask = id.variables['mask_rho'][:,:]
id.close()
# Get differentials
roms_dx, roms_dy = cartesian_grid_2d(roms_lon, roms_lat)
# Calculate resolution: square root of the area, converted to km
roms_res = sqrt(roms_dx*roms_dy)*1e-3
# Apply land mask
roms_res = ma.masked_where(roms_mask==0, roms_res)
# Polar coordinates for plotting
roms_x = -(roms_lat+90)*cos(roms_lon*deg2rad+pi/2)
roms_y = (roms_lat+90)*sin(roms_lon*deg2rad+pi/2)
print 'Processing FESOM low-res'
# Build triangular patches for each element
elements_low, patches_low = make_patches(fesom_mesh_low, circumpolar)
# Calculate the resolution at each element
fesom_res_low = []
for elm in elements_low:
fesom_res_low.append(sqrt(elm.area())*1e-3)
print 'Processing FESOM high-res'
# Build triangular patches for each element
elements_high, patches_high = make_patches(fesom_mesh_high, circumpolar)
# Calculate the resolution at each element
fesom_res_high = []
for elm in elements_high:
fesom_res_high.append(sqrt(elm.area())*1e-3)
print 'Plotting'
fig = figure(figsize=(27,9))
# ROMS
ax1 = fig.add_subplot(1,3,1, aspect='equal')
pcolor(roms_x, roms_y, roms_res, vmin=limits[0], vmax=limits[1], cmap='jet')
xlim([-lat_max, lat_max])
ylim([-lat_max, lat_max])
ax1.set_xticks([])
ax1.set_yticks([])
title('a) MetROMS', fontsize=28)
# FESOM low-res
ax2 = fig.add_subplot(1,3,2, aspect='equal')
img_low = PatchCollection(patches_low, cmap='jet')
img_low.set_array(array(fesom_res_low))
img_low.set_clim(vmin=limits[0], vmax=limits[1])
img_low.set_edgecolor('face')
ax2.add_collection(img_low)
xlim([-lat_max, lat_max])
ylim([-lat_max, lat_max])
ax2.set_xticks([])
ax2.set_yticks([])
title('b) FESOM low-res', fontsize=28)
# FESOM high-res
ax3 = fig.add_subplot(1,3,3, aspect='equal')
img_high = PatchCollection(patches_high, cmap='jet')
img_high.set_array(array(fesom_res_high))
img_high.set_clim(vmin=limits[0], vmax=limits[1])
img_high.set_edgecolor('face')
ax3.add_collection(img_high)
xlim([-lat_max, lat_max])
ylim([-lat_max, lat_max])
ax3.set_xticks([])
ax3.set_yticks([])
title('c) FESOM high-res', fontsize=28)
cbaxes = fig.add_axes([0.92, 0.2, 0.01, 0.6])
cbar = colorbar(img_high, cax=cbaxes, extend='max', ticks=arange(limits[0], limits[1]+5, 5))
cbar.ax.tick_params(labelsize=24)
suptitle('Horizontal grid resolution (km)', fontsize=36)
subplots_adjust(wspace=0.05)
if save:
fig.savefig(fig_name)
else:
fig.show()
def timeseries_seaice (file_path, log_path):
time = []
total_area = []
total_volume = []
# Check if the log file exists
if exists(log_path):
print 'Reading previously calculated values'
f = open(log_path, 'r')
# Skip first line (header for time array)
f.readline()
for line in f:
try:
time.append(float(line))
except(ValueError):
# Reached the header for the next variable
break
for line in f:
try:
total_area.append(float(line))
except(ValueError):
break
for line in f:
total_volume.append(float(line))
f.close()
print 'Analysing grid'
id = Dataset(file_path, 'r')
lon = id.variables['TLON'][:-15,:]
lat = id.variables['TLAT'][:-15,:]
# Calculate area on the tracer grid
dx, dy = cartesian_grid_2d(lon, lat)
dA = dx*dy
# Read time values and convert from days to years
new_time = id.variables['time'][:]/365.25
# Concatenate with time values from log file
for t in range(size(new_time)):
time.append(new_time[t])
print 'Reading data'
# Read sea ice concentration and height
# Throw away overlapping periodic boundary and northern sponge layer
aice = id.variables['aice'][:,:-15,:]
hi = id.variables['hi'][:,:-15,:]
id.close()
print 'Setting up arrays'
# Remove masks and fill with zeros (was having weird masking issues here)
aice_nomask = aice.data
aice_nomask[aice.mask] = 0.0
hi_nomask = hi.data
hi_nomask[hi.mask] = 0.0
# Build timeseries
for t in range(size(new_time)):
# Integrate area and convert to million km^2
total_area.append(sum(aice_nomask[t,:,:]*dA)*1e-12)
# Integrate volume and convert to million km^3
total_volume.append(sum(aice_nomask[t,:,:]*hi_nomask[t,:,:]*dA)*1e-12)
print 'Plotting total sea ice area'
clf()
plot(time, total_area)
xlabel('Years')
ylabel(r'Total Sea Ice Area (million km$^2$)')
grid(True)
savefig('seaice_area.png')
print 'Plotting total sea ice volume'
clf()
plot(time, total_volume)
xlabel('Years')
ylabel(r'Total Sea Ice Volume (million km$^3$)')
grid(True)
savefig('seaice_volume.png')
print 'Saving results to log file'
f = open(log_path, 'w')
f.write('Time (years):\n')
for elm in time:
f.write(str(elm) + '\n')
f.write('Total Sea Ice Area (million km^2):\n')
for elm in total_area:
f.write(str(elm) + '\n')
f.write('Total Sea Ice Volume (million km^3):\n')
for elm in total_volume:
f.write(str(elm) + '\n')
f.close()
def seaice_budget_thermo(cice_file, roms_grid, save=False, fig_names=None):
# Read bathymetry values for ROMS grid
id = Dataset(roms_grid, 'r')
h = id.variables['h'][1:-1, 1:-1]
id.close()
# Read CICE grid
id = Dataset(cice_file, 'r')
lon = id.variables['TLON'][:, :]
lat = id.variables['TLAT'][:, :]
# Calculate elements of area
dx, dy = cartesian_grid_2d(lon, lat)
dA = dx * dy
# Read time values
time = id.variables['time'][:] / 365.25
# Read all the fields we need
aice = id.variables['aice'][:, :, :]
congel = id.variables['congel'][:, :, :]
frazil = id.variables['frazil'][:, :, :]
snoice = id.variables['snoice'][:, :, :]
meltt = -1 * id.variables['meltt'][:, :, :]
meltb = -1 * id.variables['meltb'][:, :, :]
meltl = -1 * id.variables['meltl'][:, :, :]
id.close()
# Create masks for shelf and offshore region
shelf = (lat < -60) * (h < 1500)
offshore = invert(shelf)
congel_shelf = []
frazil_shelf = []
snoice_shelf = []
meltt_shelf = []
meltb_shelf = []
meltl_shelf = []
congel_offshore = []
frazil_offshore = []
snoice_offshore = []
meltt_offshore = []
meltb_offshore = []
meltl_offshore = []
# Loop over timesteps
for t in range(size(time)):
# Only average over regions with at least 10% sea ice
aice_flag = aice[t, :, :] > 0.1
# Congelation averaged over the continental shelf
congel_shelf.append(
sum(congel[t, :, :] * dA * shelf * aice_flag) /
sum(dA * shelf * aice_flag))
# Frazil ice formation averaged over the continental shelf
frazil_shelf.append(
sum(frazil[t, :, :] * dA * shelf * aice_flag) /
sum(dA * shelf * aice_flag))
# Snow-to-ice flooding averaged over the continental shelf
snoice_shelf.append(
sum(snoice[t, :, :] * dA * shelf * aice_flag) /
sum(dA * shelf * aice_flag))
# Top melt averaged over the continental shelf
meltt_shelf.append(
sum(meltt[t, :, :] * dA * shelf * aice_flag) /
sum(dA * shelf * aice_flag))
# Basal melt averaged over the continental shelf
meltb_shelf.append(
sum(meltb[t, :, :] * dA * shelf * aice_flag) /
sum(dA * shelf * aice_flag))
# Lateral melt averaged over the continental shelf
meltl_shelf.append(
sum(meltl[t, :, :] * dA * shelf * aice_flag) /
sum(dA * shelf * aice_flag))
# Congelation averaged over the offshore region
congel_offshore.append(
sum(congel[t, :, :] * dA * offshore * aice_flag) /
sum(dA * offshore * aice_flag))
# Frazil ice formation averaged over the offshore region
frazil_offshore.append(
sum(frazil[t, :, :] * dA * offshore * aice_flag) /
sum(dA * offshore * aice_flag))
# Snow-to-ice flooding averaged over the offshore region
snoice_offshore.append(
sum(snoice[t, :, :] * dA * offshore * aice_flag) /
sum(dA * offshore * aice_flag))
# Top melt averaged over the offshore region
meltt_offshore.append(
sum(meltt[t, :, :] * dA * offshore * aice_flag) /
sum(dA * offshore * aice_flag))
# Basal melt averaged over the offshore region
meltb_offshore.append(
sum(meltb[t, :, :] * dA * offshore * aice_flag) /
sum(dA * offshore * aice_flag))
# Lateral melt averaged over the offshore region
meltl_offshore.append(
sum(meltl[t, :, :] * dA * offshore * aice_flag) /
sum(dA * offshore * aice_flag))
# Convert to arrays and sum to get total volume tendency for each region
congel_shelf = array(congel_shelf)
frazil_shelf = array(frazil_shelf)
snoice_shelf = array(snoice_shelf)
meltt_shelf = array(meltt_shelf)
meltb_shelf = array(meltb_shelf)
meltl_shelf = array(meltl_shelf)
total_shelf = congel_shelf + frazil_shelf + snoice_shelf + meltt_shelf + meltb_shelf + meltl_shelf
congel_offshore = array(congel_offshore)
frazil_offshore = array(frazil_offshore)
snoice_offshore = array(snoice_offshore)
meltt_offshore = array(meltt_offshore)
meltb_offshore = array(meltb_offshore)
meltl_offshore = array(meltl_offshore)
total_offshore = congel_offshore + frazil_offshore + snoice_offshore + meltt_offshore + meltb_offshore + meltl_offshore
# Legends need small font to fit
fontP = FontProperties()
fontP.set_size('small')
# Set up continental shelf plot
fig1, ax1 = subplots(figsize=(8, 6))
# Add one timeseries at a time
ax1.plot(time,
congel_shelf,
label='Congelation',
color='blue',
linewidth=2)
ax1.plot(time, frazil_shelf, label='Frazil', color='red', linewidth=2)
ax1.plot(time,
snoice_shelf,
label='Snow-to-ice',
color='cyan',
linewidth=2)
ax1.plot(time, meltt_shelf, label='Top melt', color='magenta', linewidth=2)
ax1.plot(time, meltb_shelf, label='Basal melt', color='green', linewidth=2)
ax1.plot(time,
meltl_shelf,
label='Lateral melt',
color='yellow',
linewidth=2)
ax1.plot(time, total_shelf, label='Total', color='black', linewidth=2)
# Configure plot
title('Volume tendency averaged over continental shelf')
xlabel('Time (years)')
ylabel('cm/day')
grid(True)
# Add a legend
ax1.legend(loc='upper left', prop=fontP)
if save:
fig1.savefig(fig_names[0])
else:
fig1.show()
# Same for offshore plot
fig2, ax2 = subplots(figsize=(8, 6))
ax2.plot(time,
congel_offshore,
label='Congelation',
color='blue',
linewidth=2)
ax2.plot(time, frazil_offshore, label='Frazil', color='red', linewidth=2)
ax2.plot(time,
snoice_offshore,
label='Snow-to-ice',
color='cyan',
linewidth=2)
ax2.plot(time,
meltt_offshore,
label='Top melt',
color='magenta',
linewidth=2)
ax2.plot(time,
meltb_offshore,
label='Basal melt',
color='green',
linewidth=2)
ax2.plot(time,
meltl_offshore,
label='Lateral melt',
color='yellow',
linewidth=2)
ax2.plot(time, total_offshore, label='Total', color='black', linewidth=2)
title('Volume tendency averaged over offshore region')
xlabel('Time (years)')
ylabel('cm/day')
grid(True)
ax2.legend(loc='lower right', prop=fontP)
if save:
fig2.savefig(fig_names[1])
else:
fig2.show()
# Get cumulative sums of each term
congel_shelf_cum = cumsum(congel_shelf) * 5
frazil_shelf_cum = cumsum(frazil_shelf) * 5
snoice_shelf_cum = cumsum(snoice_shelf) * 5
meltt_shelf_cum = cumsum(meltt_shelf) * 5
meltb_shelf_cum = cumsum(meltb_shelf) * 5
meltl_shelf_cum = cumsum(meltl_shelf) * 5
total_shelf_cum = cumsum(total_shelf) * 5
congel_offshore_cum = cumsum(congel_offshore) * 5
frazil_offshore_cum = cumsum(frazil_offshore) * 5
snoice_offshore_cum = cumsum(snoice_offshore) * 5
meltt_offshore_cum = cumsum(meltt_offshore) * 5
meltb_offshore_cum = cumsum(meltb_offshore) * 5
meltl_offshore_cum = cumsum(meltl_offshore) * 5
total_offshore_cum = cumsum(total_offshore) * 5
# Continental shelf cumulative plot
fig3, ax3 = subplots(figsize=(8, 6))
ax3.plot(time,
congel_shelf_cum,
label='Congelation',
color='blue',
linewidth=2)
ax3.plot(time, frazil_shelf_cum, label='Frazil', color='red', linewidth=2)
ax3.plot(time,
snoice_shelf_cum,
label='Snow-to-ice',
color='cyan',
linewidth=2)
ax3.plot(time,
meltt_shelf_cum,
label='Top melt',
color='magenta',
linewidth=2)
ax3.plot(time,
meltb_shelf_cum,
label='Basal melt',
color='green',
linewidth=2)
ax3.plot(time,
meltl_shelf_cum,
label='Lateral melt',
color='yellow',
linewidth=2)
ax3.plot(time, total_shelf_cum, label='Total', color='black', linewidth=2)
title('Cumulative volume tendency averaged over continental shelf')
xlabel('Time (years)')
ylabel('cm')
grid(True)
ax3.legend(loc='lower left', prop=fontP)
if save:
fig3.savefig(fig_names[2])
else:
fig3.show()
# Offshore cumulative plot
fig4, ax4 = subplots(figsize=(8, 6))
ax4.plot(time,
congel_offshore_cum,
label='Congelation',
color='blue',
linewidth=2)
ax4.plot(time,
frazil_offshore_cum,
label='Frazil',
color='red',
linewidth=2)
ax4.plot(time,
snoice_offshore_cum,
label='Snow-to-ice',
color='cyan',
linewidth=2)
ax4.plot(time,
meltt_offshore_cum,
label='Top melt',
color='magenta',
linewidth=2)
ax4.plot(time,
meltb_offshore_cum,
label='Basal melt',
color='green',
linewidth=2)
ax4.plot(time,
meltl_offshore_cum,
label='Lateral melt',
color='yellow',
linewidth=2)
ax4.plot(time,
total_offshore_cum,
label='Total',
color='black',
linewidth=2)
title('Cumulative volume tendency averaged over offshore region')
xlabel('Time (years)')
ylabel('cm/day')
grid(True)
ax4.legend(loc='lower left', prop=fontP)
if save:
fig4.savefig(fig_names[3])
else:
fig4.show()
def mip_seaice_tamura ():
# File paths
# ROMS grid (just for bathymetry)
roms_grid = '/short/m68/kaa561/metroms_iceshelf/apps/common/grid/circ30S_quarterdegree.nc'
# FESOM mesh paths
fesom_mesh_path_lr = '/short/y99/kaa561/FESOM/mesh/meshA/'
fesom_mesh_path_hr = '/short/y99/kaa561/FESOM/mesh/meshB/'
# CICE 1992-2013 mean ice production (precomputed in calc_ice_prod.py)
cice_file = '/short/m68/kaa561/metroms_iceshelf/tmproms/run/intercomparison/ice_prod_1992_2013.nc'
# FESOM 1992-2013 mean ice production (precomputed in calc_annual_ice_prod.py in fesomtools)
fesom_lr_file = '/short/y99/kaa561/FESOM/intercomparison_lowres/output/ice_prod_1992_2013.nc'
fesom_hr_file = '/short/y99/kaa561/FESOM/intercomparison_highres/output/ice_prod_1992_2013.nc'
# Tamura's 1992-2013 mean ice production (precomputed on desktop with Matlab)
tamura_file = '/short/m68/kaa561/tamura_1992_2013_monthly_climatology.nc'
# Output ASCII file
output_file = 'seaice_prod_bins.log'
# Size of longitude bin
dlon_bin = 1.0
# Definition of continental shelf: everywhere south of lat0 with
# bathymetry shallower than h0
lat0 = -60
h0 = 1500
# Radius of the Earth in metres
r = 6.371e6
# Degrees to radians conversion factor
deg2rad = pi/180.0
# Set up longitude bins
bin_edges = arange(-180, 180+dlon_bin, dlon_bin)
bin_centres = 0.5*(bin_edges[:-1] + bin_edges[1:])
num_bins = len(bin_centres)
print 'Processing MetROMS'
# Read CICE grid
id = Dataset(cice_file, 'r')
cice_lon = id.variables['TLON'][:,:]
cice_lat = id.variables['TLAT'][:,:]
# Read sea ice production
cice_data = id.variables['ice_prod'][:,:]
id.close()
# Get area integrands
dx, dy = cartesian_grid_2d(cice_lon, cice_lat)
dA = dx*dy
# Make sure longitude is in the range [-180, 180]
index = cice_lon > 180
cice_lon[index] = cice_lon[index] - 360
# Read bathymetry (ROMS grid file) and trim to CICE grid
id = Dataset(roms_grid, 'r')
cice_bathy = id.variables['h'][1:-1,1:-1]
id.close()
# Set up integral
cice_data_bins = zeros(num_bins)
# Loop over all cells
num_lon = size(cice_lon,1)
num_lat = size(cice_lat,0)
for j in range(num_lat):
for i in range(num_lon):
# Check for land mask or ice shelves
if cice_data[j,i] is ma.masked:
continue
# Check for continental shelf
if cice_lat[j,i] < lat0 and cice_bathy[j,i] < h0:
# Find the right bin
bin_index = nonzero(bin_edges > cice_lon[j,i])[0][0] - 1
# Integrate (m^3/y)
cice_data_bins[bin_index] += cice_data[j,i]*dA[j,i]
# Convert to 10^9 m^3/y
cice_data_bins *= 1e-9
print 'Processing low-res FESOM'
# Build mesh
elements_lr = fesom_grid(fesom_mesh_path_lr, circumpolar=True, cross_180=False)
# Read sea ice production
id = Dataset(fesom_lr_file, 'r')
fesom_data_lr = id.variables['ice_prod'][:]
id.close()
# Set up integral
fesom_data_bins_lr = zeros(num_bins)
# Loop over elements
for elm in elements_lr:
# Exclude ice shelf cavities
if not elm.cavity:
# Check for continental shelf in 2 steps
if all(elm.lat < lat0):
elm_bathy = mean([elm.nodes[0].find_bottom().depth, elm.nodes[1].find_bottom().depth, elm.nodes[2].find_bottom().depth])
if elm_bathy < h0:
# Get element-averaged sea ice production
elm_data = mean([fesom_data_lr[elm.nodes[0].id], fesom_data_lr[elm.nodes[1].id], fesom_data_lr[elm.nodes[2].id]])
# Find the right bin
elm_lon = mean(elm.lon)
if elm_lon < -180:
elm_lon += 360
elif elm_lon > 180:
elm_lon -= 360
bin_index = nonzero(bin_edges > elm_lon)[0][0] - 1
# Integrate (m^3/y)
fesom_data_bins_lr[bin_index] += elm_data*elm.area()
# Convert to 10^9 m^3/y
fesom_data_bins_lr *= 1e-9
print 'Processing high-res FESOM'
elements_hr = fesom_grid(fesom_mesh_path_hr, circumpolar=True, cross_180=False)
id = Dataset(fesom_hr_file, 'r')
fesom_data_hr = id.variables['ice_prod'][:]
id.close()
fesom_data_bins_hr = zeros(num_bins)
for elm in elements_hr:
if not elm.cavity:
if all(elm.lat < lat0):
elm_bathy = mean([elm.nodes[0].find_bottom().depth, elm.nodes[1].find_bottom().depth, elm.nodes[2].find_bottom().depth])
if elm_bathy < h0:
elm_data = mean([fesom_data_hr[elm.nodes[0].id], fesom_data_hr[elm.nodes[1].id], fesom_data_hr[elm.nodes[2].id]])
elm_lon = mean(elm.lon)
if elm_lon < -180:
elm_lon += 360
elif elm_lon > 180:
elm_lon -= 360
bin_index = nonzero(bin_edges > elm_lon)[0][0] - 1
fesom_data_bins_hr[bin_index] += elm_data*elm.area()
fesom_data_bins_hr *= 1e-9
print 'Processing Tamura obs'
id = Dataset(tamura_file, 'r')
# Read grid and data
tamura_lon = id.variables['longitude'][:,:]
tamura_lat = id.variables['latitude'][:,:]
# Read sea ice formation
tamura_data = id.variables['ice_prod'][:,:]
id.close()
# Interpolate to a regular grid so we can easily integrate over area
dlon_reg = 0.2
dlat_reg = 0.1
lon_reg_edges = arange(-180, 180+dlon_reg, dlon_reg)
lon_reg = 0.5*(lon_reg_edges[:-1] + lon_reg_edges[1:])
lat_reg_edges = arange(-80, -60+dlat_reg, dlat_reg)
lat_reg = 0.5*(lat_reg_edges[:-1] + lat_reg_edges[1:])
lon_reg_2d, lat_reg_2d = meshgrid(lon_reg, lat_reg)
dx_reg = r*cos(lat_reg_2d*deg2rad)*dlon_reg*deg2rad
dy_reg = r*dlat_reg*deg2rad
dA_reg = dx_reg*dy_reg
# Be careful with the periodic boundary here
num_pts = size(tamura_lon)
num_wrap1 = count_nonzero(tamura_lon < -179)
num_wrap2 = count_nonzero(tamura_lon > 179)
points = empty([num_pts+num_wrap1+num_wrap2,2])
values = empty(num_pts+num_wrap1+num_wrap2)
points[:num_pts,0] = ravel(tamura_lon)
points[:num_pts,1] = ravel(tamura_lat)
values[:num_pts] = ravel(tamura_data)
# Wrap the periodic boundary on both sides
index = tamura_lon < -179
points[num_pts:num_pts+num_wrap1,0] = tamura_lon[index] + 360
points[num_pts:num_pts+num_wrap1,1] = tamura_lat[index]
values[num_pts:num_pts+num_wrap1] = tamura_data[index]
index = tamura_lon > 179
points[num_pts+num_wrap1:,0] = tamura_lon[index] - 360
points[num_pts+num_wrap1:,1] = tamura_lat[index]
values[num_pts+num_wrap1:] = tamura_data[index]
values = ma.masked_where(isnan(values), values)
xi = empty([size(lon_reg_2d),2])
xi[:,0] = ravel(lon_reg_2d)
xi[:,1] = ravel(lat_reg_2d)
result = griddata(points, values, xi)
tamura_data_reg = reshape(result, shape(lon_reg_2d))
# Now, regrid the MetROMS bathymetry to this regular grid
num_pts = size(cice_lon)
num_wrap1 = count_nonzero(cice_lon < -179)
num_wrap2 = count_nonzero(cice_lon > 179)
points = empty([num_pts+num_wrap1+num_wrap2,2])
values = empty(num_pts+num_wrap1+num_wrap2)
points[:num_pts,0] = ravel(cice_lon)
points[:num_pts,1] = ravel(cice_lat)
values[:num_pts] = ravel(cice_bathy)
index = cice_lon < -179
points[num_pts:num_pts+num_wrap1,0] = cice_lon[index] + 360
points[num_pts:num_pts+num_wrap1,1] = cice_lat[index]
values[num_pts:num_pts+num_wrap1] = cice_bathy[index]
index = cice_lon > 179
points[num_pts+num_wrap1:,0] = cice_lon[index] - 360
points[num_pts+num_wrap1:,1] = cice_lat[index]
values[num_pts+num_wrap1:] = cice_bathy[index]
values = ma.masked_where(isnan(values), values)
xi = empty([size(lon_reg_2d),2])
xi[:,0] = ravel(lon_reg_2d)
xi[:,1] = ravel(lat_reg_2d)
result = griddata(points, values, xi)
bathy_reg = reshape(result, shape(lon_reg_2d))
# Mask everything but the continental shelf from dA_reg
dA_reg = ma.masked_where(lat_reg_2d > lat0, dA_reg)
dA_reg = ma.masked_where(bathy_reg > h0, dA_reg)
# Mask the land mask (and ice shelves) from tamura_data_reg
tamura_data_reg = ma.masked_where(isnan(tamura_data_reg), tamura_data_reg)
# Set up integral
tamura_data_bins = zeros(num_bins)
# Loop over longitude only
for i in range(len(lon_reg)):
# Find the right bin
bin_index = nonzero(bin_edges > lon_reg[i])[0][0] - 1
# Integrate (m^3/y)
tamura_data_bins[bin_index] += sum(tamura_data_reg[:,i]*dA_reg[:,i])
# Convert to 10^9 m^3/y
tamura_data_bins *= 1e-9
# Write data to ASCII file
print 'Writing to file'
f = open(output_file, 'w')
f.write('Longitude:\n')
for val in bin_centres:
f.write(str(val) + '\n')
f.write('MetROMS sea ice production (10^9 m^3/y):\n')
for val in cice_data_bins:
f.write(str(val) + '\n')
f.write('FESOM (low-res) sea ice production (10^9 m^3/y):\n')
for val in fesom_data_bins_lr:
f.write(str(val) + '\n')
f.write('FESOM (high-res) sea ice production (10^9 m^3/y):\n')
for val in fesom_data_bins_hr:
f.write(str(val) + '\n')
f.write('Tamura sea ice production (10^9 m^3/y):\n')
for val in tamura_data_bins:
f.write(str(val) + '\n')
f.close()
def timeseries_i2osalt(file_path, log_path):
# Density of freshwater
rho_fw = 1000.0
# Density of seawater
rho_sw = 1025.0
# Conversion from m/s to cm/day
mps_to_cmpday = 8.64e6
time = []
avg_ssflux = []
# Check if the log file exists
if exists(log_path):
print 'Reading previously calculated values'
f = open(log_path, 'r')
# Skip first line (header for time array)
f.readline()
for line in f:
try:
time.append(float(line))
except (ValueError):
# Reached the header for the next variable
break
for line in f:
avg_ssflux.append(float(line))
f.close()
print 'Analysing grid'
id = Dataset(file_path, 'r')
lon = id.variables['TLON'][:-15, :]
lat = id.variables['TLAT'][:-15, :]
# Calculate area on the tracer grid
dx, dy = cartesian_grid_2d(lon, lat)
dA = dx * dy
# Read time values and convert from days to years
new_time = id.variables['time'][:] / 365.25
# Concatenate with time values from log file
for t in range(size(new_time)):
time.append(new_time[t])
print 'Reading data'
# Read freshwater, salt, and rain fluxes (all scaled by aice) and
# sea surface salinity
# Throw away northern sponge layer
fresh_ai = id.variables['fresh_ai'][:, :-15, :]
sss = id.variables['sss'][:, :-15, :]
rain_ai = id.variables['rain_ai'][:, :-15, :]
fsalt_ai = id.variables['fsalt_ai'][:, :-15, :]
id.close()
# Build timeseries
for t in range(size(new_time)):
# Merge CICE's freshwater and salt fluxes as in set_vbc.F
# Subtract rain because we don't care about that
# Convert to kg/m^2/s
avg_ssflux.append(
sum(-1 / rho_fw *
((fresh_ai[t, :, :] - rain_ai[t, :, :]) * sss[t, :, :] *
rho_sw / mps_to_cmpday - fsalt_ai[t, :, :] * 1e3) * dA) /
sum(dA))
print 'Plotting'
clf()
plot(time, avg_ssflux)
xlabel('Years')
ylabel(r'Average sea ice to ocean salt flux (kg/m$^2$/s)')
grid(True)
savefig('avg_i2osalt.png')
print 'Saving results to log file'
f = open(log_path, 'w')
f.write('Time (years):\n')
for elm in time:
f.write(str(elm) + '\n')
f.write('Average sea ice to ocean salt flux (kg/m^2/s):\n')
for elm in avg_ssflux:
f.write(str(elm) + '\n')
f.close()
def holland_fig1(grid_path, file_path):
deg2rad = pi / 180.0
# Read grid
id = Dataset(grid_path, 'r')
lon = id.variables['lon_rho'][:-15, 1:]
lat = id.variables['lat_rho'][:-15, 1:]
h = id.variables['h'][:-15, 1:]
zice = id.variables['zice'][:-15, 1:]
angle = id.variables['angle'][:-15, :]
id.close()
# Set up figure
x = -(lat + 90) * cos(lon * deg2rad + pi / 2)
y = (lat + 90) * sin(lon * deg2rad + pi / 2)
fig = figure(figsize=(16, 12))
# Barotropic streamfunction
# First read bartropic velocity vector
id = Dataset(file_path, 'r')
ubar_xy = mean(id.variables['ubar'][:, :-15, :], axis=0)
vbar_xy = mean(id.variables['vbar'][:, :-15, :], axis=0)
id.close()
# Rotate to lon-lat space
ubar, vbar = rotate_vector_roms(ubar_xy, vbar_xy, angle)
# Throw away the overlapping periodic boundary
ubar = ubar[:, 1:]
# Mask ice shelves
ubar = ma.masked_where(zice != 0, ubar)
# Water column thickness
wct = h + zice
# Horizontal differentials
dx, dy = cartesian_grid_2d(lon, lat)
# Indefinite integral from south to north of u*dz*dy, convert to Sv
baro_strf = cumsum(ubar * wct * dy, axis=0) * 1e-6
# Colour levels
lev1 = arange(-50, 150 + 10, 10)
# Plot
ax1 = fig.add_subplot(2, 2, 1, aspect='equal')
img = contourf(x, y, baro_strf, lev1, extend='both')
# Contour 0 Sv in black
contour(x, y, baro_strf, levels=[0], colors=('black'))
title('Barotropic streamfunction (Sv)', fontsize=24)
xlim([-35, 39])
ylim([-35, 39])
axis('off')
cbaxes1 = fig.add_axes([0.07, 0.6, 0.02, 0.3])
cbar1 = colorbar(img, ticks=arange(-50, 150 + 50, 50), cax=cbaxes1)
cbar1.ax.tick_params(labelsize=16)
# JJA mixed layer depth
start_month = 6 # Start in June
end_month = 8 # End in August
start_day = 1 # First day in June
next_startday = 1 # First day in September
end_day = 31 # Last day in August
prev_endday = 31 # Last day in May
ndays_season = 92 # Number of days in June+July+August
id = Dataset(file_path, 'r')
# Read time axis and get dates
time_id = id.variables['ocean_time']
time = num2date(time_id[:],
units=time_id.units,
calendar=time_id.calendar.lower())
# Find the last timestep we care about
end_t = -1 # Missing value flag
for t in range(size(time) - 1, -1, -1):
if time[t].month == end_month and time[t].day in range(
end_day - 2, end_day + 1):
end_t = t
break
if time[t].month == end_month + 1 and time[t].day in range(
next_startday, next_startday + 2):
end_t = t
break
# Make sure we actually found it
if end_t == -1:
print 'Error: ' + file_path + ' does not contain a complete JJA'
return
# Find the first timestep we care about
start_t = -1 # Missing value flag
for t in range(end_t, -1, -1):
if time[t].month == start_month - 1 and time[t].day in range(
prev_endday - 1, prev_endday + 1):
start_t = t
break
if time[t].month == start_month and time[t].day in range(
start_day, start_day + 3):
start_t = t
break
# Make sure we found it
if start_t == -1:
print 'Error: ' + file_path + ' does not contain a complete JJA'
return
# Initialise time-averaged KPP boundary layer depth
hsbl = ma.empty(shape(lon))
hsbl[:, :] = 0.0
ndays = 0
# Figure out how many of the 5 days represented in start_t we care about
if time[start_t].month == start_month and time[
start_t].day == start_day + 2:
start_days = 5
elif time[start_t].month == start_month and time[
start_t].day == start_day + 1:
start_days = 4
elif time[start_t].month == start_month and time[start_t].day == start_day:
start_days = 3
elif time[start_t].month == start_month - 1 and time[
start_t].day == prev_endday:
start_days = 2
elif time[start_t].month == start_month - 1 and time[
start_t].day == prev_endday - 1:
start_days = 1
else:
print 'Error: starting index is month ' + str(
time[start_t].month) + ', day ' + str(time[start_t].day)
return
# Integrate Hsbl weighted by start_days
hsbl += id.variables['Hsbl'][start_t, :-15, 1:] * start_days
ndays += start_days
# Between start_t and end_t, we care about all the days
for t in range(start_t + 1, end_t):
hsbl += id.variables['Hsbl'][t, :-15, 1:] * 5
ndays += 5
# Figure out how many of the 5 days represented in end_t we care about
if time[end_t].month == end_month + 1 and time[
end_t].day == next_startday + 1:
end_days = 1
elif time[end_t].month == end_month + 1 and time[
end_t].day == next_startday:
end_days = 2
elif time[end_t].month == end_month and time[end_t].day == end_day:
end_days = 3
elif time[end_t].month == end_month and time[end_t].day == end_day - 1:
end_days = 4
elif time[end_t].month == end_month and time[end_t].day == end_day - 2:
end_days = 5
else:
print 'Error: ending index is month ' + str(
time[end_t].month) + ', day ' + str(time[end_t].day)
return
# Integrate weighted by end_days
hsbl += id.variables['Hsbl'][end_t, :-15, 1:] * end_days
ndays += end_days
if ndays != ndays_season:
print 'Error: found ' + str(ndays) + ' days instead of ' + str(
ndays_season)
return
id.close()
# Convert from integral to average
hsbl[:, :] = hsbl[:, :] / ndays
# Mask out ice shelves, change sign, and call it mixed layer depth
mld = ma.masked_where(zice != 0, -hsbl)
# Colour levels
lev2 = arange(0, 300 + 25, 25)
# Plot
ax2 = fig.add_subplot(2, 2, 2, aspect='equal')
img = contourf(x, y, mld, lev2, extend='both')
# Contour 100 m in black
contour(x, y, mld, levels=[100], colors=('black'))
title('Winter mixed layer depth (m)', fontsize=24)
xlim([-35, 39])
ylim([-35, 39])
axis('off')
cbaxes2 = fig.add_axes([0.9, 0.6, 0.02, 0.3])
cbar2 = colorbar(img, ticks=arange(0, 300 + 100, 100), cax=cbaxes2)
cbar2.ax.tick_params(labelsize=16)
# Bottom water temperature
id = Dataset(file_path, 'r')
bwtemp = mean(id.variables['temp'][:, 0, :-15, 1:], axis=0)
id.close()
# Mask ice shelves
bwtemp = ma.masked_where(zice != 0, bwtemp)
# Colour levels
lev3 = arange(-2, 2 + 0.2, 0.2)
# Plot
ax3 = fig.add_subplot(2, 2, 3, aspect='equal')
img = contourf(x, y, bwtemp, lev3, extend='both')
# Contour 0C in black
contour(x, y, bwtemp, levels=[0], colors=('black'))
title(r'Bottom temperature ($^{\circ}$C', fontsize=24)
xlim([-35, 39])
ylim([-35, 39])
axis('off')
cbaxes3 = fig.add_axes([0.07, 0.1, 0.02, 0.3])
cbar3 = colorbar(img, ticks=arange(-2, 2 + 1, 1), cax=cbaxes3)
cbar3.ax.tick_params(labelsize=16)
# Bottom water salinity
id = Dataset(file_path, 'r')
bwsalt = mean(id.variables['salt'][:, 0, :-15, 1:], axis=0)
bwsalt = ma.masked_where(zice != 0, bwsalt)
id.close()
lev4 = arange(34.5, 34.8 + 0.025, 0.025)
ax4 = fig.add_subplot(2, 2, 4, aspect='equal')
img = contourf(x, y, bwsalt, lev4, extend='both')
# Contour 34.65 psu in black
contour(x, y, bwsalt, levels=[34.65], colors=('black'))
title('Bottom salinity (psu)', fontsize=24)
xlim([-35, 39])
ylim([-35, 39])
axis('off')
cbaxes4 = fig.add_axes([0.9, 0.1, 0.02, 0.3])
cbar4 = colorbar(img, ticks=arange(34.5, 34.8 + 0.1, 0.1), cax=cbaxes4)
cbar4.ax.tick_params(labelsize=16)
fig.show()
def timeseries_sss (file_path, log_path):
time = []
avg_sss = []
avg_ssflux = []
avg_restore = []
# Check if the log file exists
if exists(log_path):
print 'Reading previously calculated values'
f = open(log_path, 'r')
# Skip first line (header for time array)
f.readline()
for line in f:
try:
time.append(float(line))
except(ValueError):
# Reached the header for the next variable
break
for line in f:
try:
avg_sss.append(float(line))
except(ValueError):
break
for line in f:
try:
avg_ssflux.append(float(line))
except(ValueError):
break
for line in f:
avg_restore.append(float(line))
f.close()
print 'Analysing grid'
id = Dataset(file_path, 'r')
lon = id.variables['lon_rho'][:-15,1:-1]
lat = id.variables['lat_rho'][:-15,1:-1]
zice = id.variables['zice'][:-15,1:-1]
# Calculate area on the tracer grid and mask ice shelves
dx, dy = cartesian_grid_2d(lon, lat)
dA = ma.masked_where(zice!=0, dx*dy)
# Read time values and convert from seconds to years
new_time = id.variables['ocean_time'][:]/(365.25*24*60*60)
# Concatenate with time values from log file
for t in range(size(new_time)):
time.append(new_time[t])
print 'Reading data'
# Read surface salinity, salt flux, and restoring flux
# Throw away overlapping periodic boundary and northern sponge layer
sss = id.variables['salt'][:,-1,:-15,1:-1]
ssflux = id.variables['ssflux'][:,:-15,1:-1]
ssflux_restoring = id.variables['ssflux_restoring'][:,:-15,1:-1]
id.close()
# Build timeseries
for t in range(size(new_time)):
avg_sss.append(sum(sss[t,:,:]*dA)/sum(dA))
avg_ssflux.append(sum(ssflux[t,:,:]*dA)/sum(dA))
avg_restore.append(sum(ssflux_restoring[t,:,:]*dA)/sum(dA))
print 'Plotting'
clf()
plot(time, avg_sss)
xlabel('Years')
ylabel('Average sea surface salinity (psu)')
grid(True)
savefig('avg_sss.png')
clf()
plot(time, avg_ssflux)
xlabel('Years')
ylabel(r'Average surface salt flux (kg/m$^2$/s)')
grid(True)
savefig('avg_ssflux.png')
clf()
plot(time, avg_restore)
xlabel('Years')
ylabel(r'Average surface salt flux from salinity restoring (kg/m$^2$/s)')
grid(True)
savefig('avg_restore.png')
print 'Saving results to log file'
f = open(log_path, 'w')
f.write('Time (years):\n')
for elm in time:
f.write(str(elm) + '\n')
f.write('Average sea surface salinity (psu):\n')
for elm in avg_sss:
f.write(str(elm) + '\n')
f.write('Average surface salt flux (kg/m^2/s):\n')
for elm in avg_ssflux:
f.write(str(elm) + '\n')
f.write('Average surface salt flux from salinity restoring (kg/m^2/s):\n')
for elm in avg_restore:
f.write(str(elm) + '\n')
f.close()
def timeseries_seaice_extent (file_path, log_path):
time = []
extent = []
# Check if the log file exists
if exists(log_path):
print 'Reading previously calculated values'
f = open(log_path, 'r')
# Skip first line (header for time array)
f.readline()
for line in f:
try:
time.append(float(line))
except(ValueError):
# Reached the header for the next variable
break
for line in f:
extent.append(float(line))
f.close()
print 'Analysing grid'
id = Dataset(file_path, 'r')
lon = id.variables['TLON'][:-15,:]
lat = id.variables['TLAT'][:-15,:]
# Calculate area on the tracer grid
dx, dy = cartesian_grid_2d(lon, lat)
dA = dx*dy
# Read time values and convert from days to years
new_time = id.variables['time'][:]/365.25
# Concatenate with time values from log file
for t in range(size(new_time)):
time.append(new_time[t])
print 'Reading data'
# Read sea ice concentration
# Throw away northern sponge layer
aice = id.variables['aice'][:,:-15,:]
id.close()
# Select cells with concentration >= 15%
flag = aice >= 0.15
print 'Building timeseries'
for t in range(size(new_time)):
# Integrate extent and convert to million km^2
extent.append(sum(flag[t,:,:]*dA)*1e-12)
print 'Plotting'
clf()
plot(time, extent)
xlabel('Years')
ylabel(r'Sea Ice Extent (million km$^2$)')
grid(True)
savefig('seaice_extent.png')
print 'Saving results to log file'
f = open(log_path, 'w')
f.write('Time (years):\n')
for elm in time:
f.write(str(elm) + '\n')
f.write('Sea Ice Extent (million km^2):\n')
for elm in extent:
f.write(str(elm) + '\n')
f.close()
def seaice_budget(cice_file, roms_grid, save=False, fig_names=None):
# Read bathymetry values for ROMS grid
id = Dataset(roms_grid, 'r')
h = id.variables['h'][1:-1, 1:-1]
id.close()
# Read CICE grid
id = Dataset(cice_file, 'r')
lon = id.variables['TLON'][:, :]
lat = id.variables['TLAT'][:, :]
# Calculate elements of area
dx, dy = cartesian_grid_2d(lon, lat)
dA = dx * dy
# Read time values
time = id.variables['time'][:] / 365.25
# Read data (concentration and thermodynamic/dynamic volume tendencies)
aice = id.variables['aice'][:, :, :]
dvidtt = id.variables['dvidtt'][:, :, :]
dvidtd = id.variables['dvidtd'][:, :, :]
id.close()
# Create masks for shelf and offshore region
shelf = (lat < -60) * (h < 1500)
offshore = invert(shelf)
dvidtt_shelf = []
dvidtd_shelf = []
dvidtt_offshore = []
dvidtd_offshore = []
# Loop over timesteps
for t in range(size(time)):
# Only average over regions with at least 10% sea ice
aice_flag = aice[t, :, :] > 0.1
# Thermodynamic volume tendency averaged over the continental shelf
dvidtt_shelf.append(
sum(dvidtt[t, :, :] * dA * shelf * aice_flag) /
sum(dA * shelf * aice_flag))
# Dynamic volume tendency averaged over the continental shelf
dvidtd_shelf.append(
sum(dvidtd[t, :, :] * dA * shelf * aice_flag) /
sum(dA * shelf * aice_flag))
# Thermodynamic volume tendency averaged over the offshore region
dvidtt_offshore.append(
sum(dvidtt[t, :, :] * dA * offshore * aice_flag) /
sum(dA * offshore * aice_flag))
# Dynamic volume tendency averaged over the offshore region
dvidtd_offshore.append(
sum(dvidtd[t, :, :] * dA * offshore * aice_flag) /
sum(dA * offshore * aice_flag))
# Convert to arrays and sum to get total volume tendencies for each region
dvidtt_shelf = array(dvidtt_shelf)
dvidtd_shelf = array(dvidtd_shelf)
dvi_shelf = dvidtt_shelf + dvidtd_shelf
dvidtt_offshore = array(dvidtt_offshore)
dvidtd_offshore = array(dvidtd_offshore)
dvi_offshore = dvidtt_offshore + dvidtd_offshore
# Set up continental shelf plot
fig1, ax1 = subplots(figsize=(8, 6))
# Add one timeseries at a time
ax1.plot(time,
dvidtt_shelf,
label='Thermodynamics',
color='blue',
linewidth=2)
ax1.plot(time, dvidtd_shelf, label='Dynamics', color='green', linewidth=2)
ax1.plot(time, dvi_shelf, label='Total', color='black', linewidth=2)
# Configure plot
title('Volume tendency averaged over continental shelf')
xlabel('Time (years)')
ylabel('cm/day')
grid(True)
# Add a legend
ax1.legend(loc='upper left')
if save:
fig1.savefig(fig_names[0])
else:
fig1.show()
# Same for offshore plot
fig2, ax2 = subplots(figsize=(8, 6))
ax2.plot(time,
dvidtt_offshore,
label='Thermodynamics',
color='blue',
linewidth=2)
ax2.plot(time,
dvidtd_offshore,
label='Dynamics',
color='green',
linewidth=2)
ax2.plot(time, dvi_offshore, label='Total', color='black', linewidth=2)
title('Volume tendency averaged over offshore region')
xlabel('Time (years)')
ylabel('cm/day')
grid(True)
ax2.legend(loc='lower right')
if save:
fig2.savefig(fig_names[1])
else:
fig2.show()
# Get cumulative sums of each term
dvidtt_shelf_cum = cumsum(dvidtt_shelf) * 5
dvidtd_shelf_cum = cumsum(dvidtd_shelf) * 5
dvi_shelf_cum = cumsum(dvi_shelf) * 5
dvidtt_offshore_cum = cumsum(dvidtt_offshore) * 5
dvidtd_offshore_cum = cumsum(dvidtd_offshore) * 5
dvi_offshore_cum = cumsum(dvi_offshore) * 5
# Continental shelf cumulative plot
fig3, ax3 = subplots(figsize=(8, 6))
ax3.plot(time,
dvidtt_shelf_cum,
label='Thermodynamics',
color='blue',
linewidth=2)
ax3.plot(time,
dvidtd_shelf_cum,
label='Dynamics',
color='green',
linewidth=2)
ax3.plot(time, dvi_shelf_cum, label='Total', color='black', linewidth=2)
title('Cumulative volume tendency averaged over continental shelf')
xlabel('Time (years)')
ylabel('cm')
grid(True)
ax3.legend(loc='upper left')
if save:
fig3.savefig(fig_names[2])
else:
fig3.show()
# Offshore cumulative plot
fig4, ax4 = subplots(figsize=(8, 6))
ax4.plot(time,
dvidtt_offshore_cum,
label='Thermodynamics',
color='blue',
linewidth=2)
ax4.plot(time,
dvidtd_offshore_cum,
label='Dynamics',
color='green',
linewidth=2)
ax4.plot(time, dvi_offshore_cum, label='Total', color='black', linewidth=2)
title('Cumulative volume tendency averaged over offshore region')
xlabel('Time (years)')
ylabel('cm')
grid(True)
ax4.legend(loc='upper right')
if save:
fig4.savefig(fig_names[3])
else:
fig4.show()
def timeseries_seaice(file_path, log_path, add_years=0):
time = []
total_area = []
total_volume = []
# Check if the log file exists
if exists(log_path):
print 'Reading previously calculated values'
f = open(log_path, 'r')
# Skip first line (header for time array)
f.readline()
for line in f:
try:
time.append(float(line))
except (ValueError):
# Reached the header for the next variable
break
for line in f:
try:
total_area.append(float(line))
except (ValueError):
break
for line in f:
total_volume.append(float(line))
f.close()
print 'Analysing grid'
id = Dataset(file_path, 'r')
lon = id.variables['TLON'][:-15, :]
lat = id.variables['TLAT'][:-15, :]
# Calculate area on the tracer grid
dx, dy = cartesian_grid_2d(lon, lat)
dA = dx * dy
# Read time values and convert from days to years
new_time = id.variables['time'][:] / 365.25 + add_years
# Concatenate with time values from log file
for t in range(size(new_time)):
time.append(new_time[t])
print 'Reading data'
# Read sea ice concentration and height
# Throw away northern sponge layer
aice = id.variables['aice'][:, :-15, :]
hi = id.variables['hi'][:, :-15, :]
id.close()
print 'Setting up arrays'
# Remove masks and fill with zeros (was having weird masking issues here)
aice_nomask = aice.data
aice_nomask[aice.mask] = 0.0
hi_nomask = hi.data
hi_nomask[hi.mask] = 0.0
# Build timeseries
for t in range(size(new_time)):
# Integrate area and convert to million km^2
total_area.append(sum(aice_nomask[t, :, :] * dA) * 1e-12)
# Integrate volume and convert to thousand km^3
total_volume.append(
sum(aice_nomask[t, :, :] * hi_nomask[t, :, :] * dA) * 1e-12)
print 'Plotting total sea ice area'
clf()
plot(time, total_area)
xlabel('Years')
ylabel(r'Total Sea Ice Area (million km$^2$)')
grid(True)
savefig('seaice_area.png')
print 'Plotting total sea ice volume'
clf()
plot(time, total_volume)
xlabel('Years')
ylabel(r'Total Sea Ice Volume (thousand km$^3$)')
grid(True)
savefig('seaice_volume.png')
print 'Saving results to log file'
f = open(log_path, 'w')
f.write('Time (years):\n')
for elm in time:
f.write(str(elm) + '\n')
f.write('Total Sea Ice Area (million km^2):\n')
for elm in total_area:
f.write(str(elm) + '\n')
f.write('Total Sea Ice Volume (thousand km^3):\n')
for elm in total_volume:
f.write(str(elm) + '\n')
f.close()
