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mip_circumpolar_drift.py
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from netCDF4 import Dataset
from numpy import *
from matplotlib.pyplot import *
from matplotlib.patches import Polygon
from matplotlib.collections import PatchCollection
from scipy.interpolate import RegularGridInterpolator
from cartesian_grid_3d import *
from circumpolar_plot import average_btw_depths
# Import FESOM scripts (have to modify path first)
import sys
sys.path.insert(0, '/short/y99/kaa561/fesomtools')
from fesom_grid import *
from unrotate_grid import *
# Needs python/2.7.6
def mip_circumpolar_drift ():
# File paths
# ECCO2 initial conditions file for temperature
ecco2_ini_file = '/short/m68/kaa561/metroms_iceshelf/data/originals/ECCO2/THETA.1440x720x50.199201.nc'
# ROMS grid file
roms_grid = '/short/m68/kaa561/metroms_iceshelf/apps/common/grid/circ30S_quarterdegree.nc'
# ROMS January 2016 mean temp
roms_end_file = '/short/m68/kaa561/metroms_iceshelf/tmproms/run/intercomparison/temp_salt_jan2016.nc'
# FESOM mesh paths
fesom_mesh_path_lr = '/short/y99/kaa561/FESOM/mesh/meshA/'
fesom_mesh_path_hr = '/short/y99/kaa561/FESOM/mesh/meshB/'
# FESOM January 2016 mean temp
fesom_end_file_lr = '/short/y99/kaa561/FESOM/intercomparison_lowres/output/temp_salt_jan2016.nc'
fesom_end_file_hr = '/short/y99/kaa561/FESOM/intercomparison_highres/output/temp_salt_jan2016.nc'
# Depth bounds to average between
shallow_bound = 300
deep_bound = 1000
# ROMS grid parameters
theta_s = 7.0
theta_b = 2.0
hc = 250
N = 31
deg2rad = pi/180
# Bound for colour scale
colour_bound = 3
# Northern boundary for plot
nbdry = -50+90
print 'Processing ECCO2'
id = Dataset(ecco2_ini_file, 'r')
ecco_lon_tmp = id.variables['LONGITUDE_T'][:]
ecco_lat = id.variables['LATITUDE_T'][:]
ecco_depth = id.variables['DEPTH_T'][:] # Depth is positive
ecco_temp_3d_tmp = id.variables['THETA'][0,:,:,:]
id.close()
# Wrap periodic boundary
ecco_lon = zeros(size(ecco_lon_tmp)+2)
ecco_lon[0] = ecco_lon_tmp[-1]-360
ecco_lon[1:-1] = ecco_lon_tmp
ecco_lon[-1] = ecco_lon_tmp[0]+360
ecco_temp_3d = ma.array(zeros((size(ecco_depth), size(ecco_lat), size(ecco_lon))))
ecco_temp_3d[:,:,0] = ecco_temp_3d_tmp[:,:,-1]
ecco_temp_3d[:,:,1:-1] = ecco_temp_3d_tmp
ecco_temp_3d[:,:,-1] = ecco_temp_3d_tmp[:,:,0]
# Calculate dz
ecco_depth_edges = zeros(size(ecco_depth)+1)
ecco_depth_edges[1:-1] = 0.5*(ecco_depth[:-1] + ecco_depth[1:])
# Surface is zero
# Extrapolate for bottom
ecco_depth_edges[-1] = 2*ecco_depth[-1] - ecco_depth_edges[-2]
ecco_dz = ecco_depth_edges[1:] - ecco_depth_edges[:-1]
# Average between bounds
# Find the first level below shallow_bound
k_start = nonzero(ecco_depth > shallow_bound)[0][0]
# Find the first level below deep_bound
# Don't worry about regions where this hits the seafloor, as they will
# get masked out in the final plot
k_end = nonzero(ecco_depth > deep_bound)[0][0]
# Integrate between
ecco_temp = sum(ecco_temp_3d[k_start:k_end,:,:]*ecco_dz[k_start:k_end,None,None], axis=0)/sum(ecco_dz[k_start:k_end])
# Fill land mask with zeros
index = ecco_temp.mask
ecco_temp = ecco_temp.data
ecco_temp[index] = 0.0
# Prepare interpolation function
interp_function = RegularGridInterpolator((ecco_lat, ecco_lon), ecco_temp)
print 'Processing MetROMS'
# Read grid
id = Dataset(roms_grid, 'r')
roms_h = id.variables['h'][:,:]
roms_zice = id.variables['zice'][:,:]
roms_mask = id.variables['mask_rho'][:,:]
roms_lon = id.variables['lon_rho'][:,:]
roms_lat = id.variables['lat_rho'][:,:]
num_lon = size(roms_lon,1)
num_lat = size(roms_lat,0)
id.close()
# Interpolate ECCO2 depth-averaged values to the ROMS grid
roms_temp_ini = interp_function((roms_lat, roms_lon))
# Apply ROMS land mask
roms_temp_ini = ma.masked_where(roms_mask==0, roms_temp_ini)
# Read Jan 2016 values
id = Dataset(roms_end_file, 'r')
roms_temp_3d_end = id.variables['temp'][0,:,:,:]
id.close()
# Get z and dz
roms_dx, roms_dy, roms_dz, roms_z = cartesian_grid_3d(roms_lon, roms_lat, roms_h, roms_zice, theta_s, theta_b, hc, N)
# Vertically average between given depths
roms_temp_end = average_btw_depths(roms_temp_3d_end, roms_z, roms_dz, [-1*shallow_bound, -1*deep_bound])
# Mask regions shallower than 1000 m
roms_temp_ini = ma.masked_where(roms_h < deep_bound, roms_temp_ini)
roms_temp_end = ma.masked_where(roms_h < deep_bound, roms_temp_end)
# Mask ice shelf cavities
roms_temp_ini = ma.masked_where(roms_zice < 0, roms_temp_ini)
roms_temp_end = ma.masked_where(roms_zice < 0, roms_temp_end)
# Get difference
roms_temp_drift = roms_temp_end - roms_temp_ini
# Convert to spherical coordinates
roms_x = -(roms_lat+90)*cos(roms_lon*deg2rad+pi/2)
roms_y = (roms_lat+90)*sin(roms_lon*deg2rad+pi/2)
print 'Processing low-res FESOM'
print '...Building mesh'
elements_lr = fesom_grid(fesom_mesh_path_lr, circumpolar=True)
# Read rotated lat and lon for each 2D node
f = open(fesom_mesh_path_lr + 'nod2d.out', 'r')
f.readline()
rlon_lr = []
rlat_lr = []
for line in f:
tmp = line.split()
lon_tmp = float(tmp[1])
if lon_tmp < -180:
lon_tmp += 360
elif lon_tmp > 180:
lon_tmp -= 360
rlon_lr.append(lon_tmp)
rlat_lr.append(float(tmp[2]))
f.close()
rlon_lr = array(rlon_lr)
rlat_lr = array(rlat_lr)
# Unrotate grid
fesom_lon_lr, fesom_lat_lr = unrotate_grid(rlon_lr, rlat_lr)
# Get longitude in the range (-180, 180) to match ECCO
index = fesom_lon_lr < 0
fesom_lon_lr[index] = fesom_lon_lr[index] + 360
print '...Interpolating ECCO2'
fesom_temp_nodes_ini_lr = interp_function((fesom_lat_lr, fesom_lon_lr))
# Read January 2016 temp
id = Dataset(fesom_end_file_lr, 'r')
fesom_temp_3d_nodes_end_lr = id.variables['temp'][0,:]
id.close()
print '...Looping over elements'
fesom_temp_ini_lr = []
fesom_temp_end_lr = []
patches_lr = []
for elm in elements_lr:
# Make sure we're not in an ice shelf cavity, or shallower than deep_bound
if not elm.cavity:
if all(array([elm.nodes[0].find_bottom().depth, elm.nodes[1].find_bottom().depth, elm.nodes[2].find_bottom().depth]) > deep_bound):
# Add a new patch
coord = transpose(vstack((elm.x, elm.y)))
patches_lr.append(Polygon(coord, True, linewidth=0.))
# Average initial temp over element
fesom_temp_ini_lr.append(mean([fesom_temp_nodes_ini_lr[elm.nodes[0].id], fesom_temp_nodes_ini_lr[elm.nodes[1].id], fesom_temp_nodes_ini_lr[elm.nodes[2].id]]))
# Vertically integrate final temp for this element
fesom_temp_end_lr.append(fesom_element_average_btw_depths(elm, shallow_bound, deep_bound, fesom_temp_3d_nodes_end_lr))
fesom_temp_ini_lr = array(fesom_temp_ini_lr)
fesom_temp_end_lr = array(fesom_temp_end_lr)
# Get difference
fesom_temp_drift_lr = fesom_temp_end_lr - fesom_temp_ini_lr
print 'Processing high-res FESOM'
print '...Building mesh'
elements_hr = fesom_grid(fesom_mesh_path_hr, circumpolar=True)
f = open(fesom_mesh_path_hr + 'nod2d.out', 'r')
f.readline()
rlon_hr = []
rlat_hr = []
for line in f:
tmp = line.split()
lon_tmp = float(tmp[1])
if lon_tmp < -180:
lon_tmp += 360
elif lon_tmp > 180:
lon_tmp -= 360
rlon_hr.append(lon_tmp)
rlat_hr.append(float(tmp[2]))
f.close()
rlon_hr = array(rlon_hr)
rlat_hr = array(rlat_hr)
fesom_lon_hr, fesom_lat_hr = unrotate_grid(rlon_hr, rlat_hr)
index = fesom_lon_hr < 0
fesom_lon_hr[index] = fesom_lon_hr[index] + 360
print '...Interpolating ECCO2'
fesom_temp_nodes_ini_hr = interp_function((fesom_lat_hr, fesom_lon_hr))
id = Dataset(fesom_end_file_hr, 'r')
fesom_temp_3d_nodes_end_hr = id.variables['temp'][0,:]
id.close()
print '...Looping over elements'
fesom_temp_ini_hr = []
fesom_temp_end_hr = []
patches_hr = []
for elm in elements_hr:
if not elm.cavity:
if all(array([elm.nodes[0].find_bottom().depth, elm.nodes[1].find_bottom().depth, elm.nodes[2].find_bottom().depth]) > deep_bound):
coord = transpose(vstack((elm.x, elm.y)))
patches_hr.append(Polygon(coord, True, linewidth=0.))
fesom_temp_ini_hr.append(mean([fesom_temp_nodes_ini_hr[elm.nodes[0].id], fesom_temp_nodes_ini_hr[elm.nodes[1].id], fesom_temp_nodes_ini_hr[elm.nodes[2].id]]))
fesom_temp_end_hr.append(fesom_element_average_btw_depths(elm, shallow_bound, deep_bound, fesom_temp_3d_nodes_end_hr))
fesom_temp_ini_hr = array(fesom_temp_ini_hr)
fesom_temp_end_hr = array(fesom_temp_end_hr)
fesom_temp_drift_hr = fesom_temp_end_hr - fesom_temp_ini_hr
print 'Plotting'
fig = figure(figsize=(19,8))
fig.patch.set_facecolor('white')
gs = GridSpec(1,3)
gs.update(left=0.05, right=0.95, bottom=0.1, top=0.85, wspace=0.05)
# ROMS
ax = subplot(gs[0,0], aspect='equal')
ax.pcolor(roms_x, roms_y, roms_temp_drift, vmin=-colour_bound, vmax=colour_bound, cmap='RdBu_r')
xlim([-nbdry, nbdry])
ylim([-nbdry, nbdry])
title('a) MetROMS', fontsize=28)
ax.set_xticks([])
ax.set_yticks([])
# FESOM (low-res)
ax = subplot(gs[0,1], aspect='equal')
img = PatchCollection(patches_lr, cmap='RdBu_r')
img.set_array(fesom_temp_drift_lr)
img.set_clim(vmin=-colour_bound, vmax=colour_bound)
img.set_edgecolor('face')
ax.add_collection(img)
xlim([-nbdry, nbdry])
ylim([-nbdry, nbdry])
title('b) FESOM (low-res)', fontsize=28)
ax.set_xticks([])
ax.set_yticks([])
# FESOM (high-res)
ax = subplot(gs[0,2], aspect='equal')
img = PatchCollection(patches_hr, cmap='RdBu_r')
img.set_array(fesom_temp_drift_hr)
img.set_clim(vmin=-colour_bound, vmax=colour_bound)
img.set_edgecolor('face')
ax.add_collection(img)
xlim([-nbdry, nbdry])
ylim([-nbdry, nbdry])
title('c) FESOM (high-res)', fontsize=28)
ax.set_xticks([])
ax.set_yticks([])
# Add a horizontal colourbar on the bottom
cbaxes = fig.add_axes([0.3, 0.05, 0.4, 0.04])
cbar = colorbar(img, orientation='horizontal', cax=cbaxes, ticks=arange(-colour_bound, colour_bound+1, 1), extend='both')
cbar.ax.tick_params(labelsize=20)
# Main title
suptitle(r'Change in temperature from initial conditions ($^{\circ}$C), '+str(shallow_bound)+'-'+str(deep_bound)+' m average', fontsize=34)
fig.show()
fig.savefig('circumpolar_temp_drift.png')
# Element is assumed to be not in an ice shelf cavity, with bathymetry deeper
# than deep_bound
def fesom_element_average_btw_depths (elm, shallow_bound, deep_bound, data):
area = elm.area()
nodes = [elm.nodes[0], elm.nodes[1], elm.nodes[2]]
# Find the first 3D element which is entirely below shallow_bound
while True:
if nodes[0].depth > shallow_bound and nodes[1].depth > shallow_bound and nodes[2].depth > shallow_bound:
# Save these nodes
first_nodes = copy(nodes)
break
else:
for i in range(3):
nodes[i] = nodes[i].below
# Integrate downward until one of the next nodes is deeper than deep_bound
integral = 0.0
volume = 0.0
while True:
if nodes[0].below.depth > deep_bound or nodes[1].below.depth > deep_bound or nodes[2].below.depth > deep_bound:
# Save these nodes
last_nodes = copy(nodes)
break
else:
# Calculate mean of data at six corners of this triangular prism,
# and mean depths at three edges
values_tmp = []
dz_tmp = []
for i in range(3):
values_tmp.append(data[nodes[i].id])
values_tmp.append(data[nodes[i].below.id])
dz_tmp.append(abs(nodes[i].depth - nodes[i].below.depth))
# Get ready for next iteration of while loop
nodes[i] = nodes[i].below
# Integrand is mean of data at corners * area of upper face * mean of depths at edges
integral += mean(array(values_tmp))*area*mean(array(dz_tmp))
volume += mean(array(dz_tmp))*area
# Now integrate from shallow_bound to first_nodes by linearly interpolating
# each node to shallow_bound
values_tmp = []
dz_tmp = []
for i in range(3):
values_tmp.append(data[first_nodes[i].id])
id1, id2, coeff1, coeff2 = elm.nodes[i].find_depth(shallow_bound)
values_tmp.append(coeff1*data[id1] + coeff2*data[id2])
dz_tmp.append(abs(first_nodes[i].depth - shallow_bound))
integral += mean(array(values_tmp))*area*mean(array(dz_tmp))
volume += mean(array(dz_tmp))*area
# Now integrate from last_nodes to deep_bound by linearly interpolating
# each node to deep_bound
values_tmp = []
dz_tmp = []
for i in range(3):
values_tmp.append(data[last_nodes[i].id])
id1, id2, coeff1, coeff2 = elm.nodes[i].find_depth(deep_bound)
values_tmp.append(coeff1*data[id1] + coeff2*data[id2])
dz_tmp.append(abs(deep_bound - last_nodes[i].depth))
integral += mean(array(values_tmp))*area*mean(array(dz_tmp))
volume += mean(array(dz_tmp))*area
# All done; divide integral by volume to get the average
return integral/volume