def yzplot(field, y=None, z=None, ylabel=None, yunits=None, zlabel=None, zunits=None,
splitscale=None, title='', suptitle='', clim=None, colormap=None, extend=None, centerlabels=False,
nbins=None, landcolor=[.5,.5,.5], show_stats=2, aspect=[16,9], resolution=576, axis=None,
ignore=None, debug=False, cbar=True, cbar_hor=False, cbar_label=''):
# Create coordinates if not provided
ylabel, yunits, zlabel, zunits = createYZlabels(y, z, ylabel, yunits, zlabel, zunits)
if debug: print('y,z label/units=',ylabel,yunits,zlabel,zunits)
if len(y)==z.shape[-1]: y = expand(y)
elif len(y)==z.shape[-1]+1: y = y
else: raise Exception('Length of y coordinate should be equal or 1 longer than horizontal length of z')
if ignore is not None: maskedField = np.ma.masked_array(field, mask=[field==ignore])
else: maskedField = field.copy()
yCoord, zCoord, field2 = section2quadmesh(y, z, maskedField)
# Diagnose statistics
sMin, sMax, sMean, sStd, sRMS = myStats(maskedField, yzWeight(y, z), debug=debug)
yLims = np.amin(yCoord), np.amax(yCoord)
zLims = boundaryStats(zCoord)
# Choose colormap
if nbins is None and (clim is None or len(clim)==2): nbins=35
if colormap is None: colormap = chooseColorMap(sMin, sMax)
cmap, norm, extend = chooseColorLevels(sMin, sMax, colormap, clim=clim, nbins=nbins, extend=extend)
if axis is None:
setFigureSize(aspect, resolution, debug=debug)
axis = plt.gca()
cs = axis.pcolormesh(yCoord, zCoord, field2, cmap=cmap, norm=norm)
if cbar:
if cbar_hor:
cb = plt.colorbar(cs,ax=axis, fraction=.08, pad=0.02, extend=extend, orientation='horizontal')
else:
cb = plt.colorbar(cs,ax=axis, fraction=.08, pad=0.02, extend=extend)
if len(cbar_label)>0:
cb.set_label(cbar_label)
if centerlabels and len(clim)>2: cb.set_ticks( 0.5*(clim[:-1]+clim[1:]) )
axis.set_facecolor(landcolor)
if splitscale is not None:
for zzz in splitscale[1:-1]: axis.axhline(zzz,color='k',linestyle='--')
axis.set_yscale('splitscale', zval=splitscale)
axis.set_xlim( yLims ); axis.set_ylim( zLims )
if show_stats > 0:
axis.annotate('max=%.5g\nmin=%.5g'%(sMax,sMin), xy=(0.0,1.01), xycoords='axes fraction', verticalalignment='bottom', fontsize=10)
if show_stats > 1:
if sMean is not None:
axis.annotate('mean=%.5g\nrms=%.5g'%(sMean,sRMS), xy=(1.0,1.01), xycoords='axes fraction', verticalalignment='bottom', horizontalalignment='right', fontsize=10)
axis.annotate(' sd=%.5g\n'%(sStd), xy=(1.0,1.01), xycoords='axes fraction', verticalalignment='bottom', horizontalalignment='left', fontsize=10)
if len(ylabel+yunits)>0: axis.set_xlabel(label(ylabel, yunits))
if len(zlabel+zunits)>0: axis.set_ylabel(label(zlabel, zunits))
if len(title)>0: axis.set_title(title)
if len(suptitle)>0: plt.suptitle(suptitle)
return cs
def main():
# Get options
args = options()
nw = args.number_of_workers
if not os.path.isdir('PNG/MOC'):
print('Creating a directory to place figures (PNG/MOC)... \n')
os.system('mkdir -p PNG/MOC')
if not os.path.isdir('ncfiles'):
print('Creating a directory to place figures (ncfiles)... \n')
os.system('mkdir ncfiles')
# Read in the yaml file
diag_config_yml = yaml.load(open(args.diag_config_yml_path, 'r'),
Loader=yaml.Loader)
# Create the case instance
dcase = DiagsCase(diag_config_yml['Case'])
args.case_name = dcase.casename
args.savefigs = True
args.outdir = 'PNG/MOC/'
RUNDIR = dcase.get_value('RUNDIR')
print('Run directory is:', RUNDIR)
print('Casename is:', dcase.casename)
print('Number of workers to be used:', nw)
# set avg dates
avg = diag_config_yml['Avg']
if not args.start_date: args.start_date = avg['start_date']
if not args.end_date: args.end_date = avg['end_date']
# read grid info
grd = MOM6grid(RUNDIR + '/' + dcase.casename + '.mom6.static.nc')
depth = grd.depth_ocean
# remote Nan's, otherwise genBasinMasks won't work
depth[np.isnan(depth)] = 0.0
basin_code = m6toolbox.genBasinMasks(grd.geolon, grd.geolat, depth)
parallel, cluster, client = m6toolbox.request_workers(nw)
print('Reading {} dataset...'.format(args.file_name))
startTime = datetime.now()
# load data
def preprocess(ds):
variables = ['vmo', 'vhml', 'vhGM']
for v in variables:
if v not in ds.variables:
ds[v] = xr.zeros_like(ds.vo)
return ds[variables]
if parallel:
ds = xr.open_mfdataset(
RUNDIR + '/' + dcase.casename + args.file_name,
parallel=True,
combine="nested", # concatenate in order of files
concat_dim="time", # concatenate along time
preprocess=preprocess,
).chunk({"time": 12})
else:
ds = xr.open_mfdataset(RUNDIR+'/'+dcase.casename+args.file_name, data_vars='minimal', \
coords='minimal', compat='override', preprocess=preprocess)
print('Time elasped: ', datetime.now() - startTime)
# compute yearly means first since this will be used in the time series
print('Computing yearly means...')
startTime = datetime.now()
ds_yr = ds.resample(time="1Y", closed='left').mean('time')
print('Time elasped: ', datetime.now() - startTime)
print('Selecting data between {} and {}...'.format(args.start_date,
args.end_date))
startTime = datetime.now()
ds_sel = ds_yr.sel(time=slice(args.start_date, args.end_date))
print('Time elasped: ', datetime.now() - startTime)
print('Computing time mean...')
startTime = datetime.now()
ds_mean = ds_sel.mean('time').compute()
print('Time elasped: ', datetime.now() - startTime)
# create a ndarray subclass
class C(np.ndarray):
pass
varName = 'vmo'
conversion_factor = 1.e-9
tmp = np.ma.masked_invalid(ds_mean[varName].values)
tmp = tmp[:].filled(0.)
VHmod = tmp.view(C)
VHmod.units = ds[varName].units
Zmod = m6toolbox.get_z(ds, depth, varName) # same here
if args.case_name != '': case_name = args.case_name
else: case_name = ''
# Global MOC
m6plot.setFigureSize([16, 9], 576, debug=False)
axis = plt.gca()
cmap = plt.get_cmap('dunnePM')
zg = Zmod.min(axis=-1)
psiPlot = MOCpsi(VHmod) * conversion_factor
psiPlot = 0.5 * (psiPlot[0:-1, :] + psiPlot[1::, :])
yyg = grd.geolat_c[:, :].max(axis=-1) + 0 * zg
ci = m6plot.pmCI(0., 40., 5.)
plotPsi(
yyg, zg, psiPlot, ci, 'Global MOC [Sv],' + 'averaged between ' +
args.start_date + ' and ' + args.end_date)
plt.xlabel(r'Latitude [$\degree$N]')
plt.suptitle(case_name)
plt.gca().invert_yaxis()
findExtrema(yyg, zg, psiPlot, max_lat=-30.)
findExtrema(yyg, zg, psiPlot, min_lat=25., min_depth=250.)
findExtrema(yyg, zg, psiPlot, min_depth=2000., mult=-1.)
objOut = args.outdir + str(case_name) + '_MOC_global.png'
plt.savefig(objOut)
if 'zl' in ds:
zl = ds.zl.values
elif 'z_l' in ds:
zl = ds.z_l.values
else:
raise ValueError("Dataset does not have vertical coordinate zl or z_l")
# create dataset to store results
moc = xr.Dataset(data_vars={
'moc': (('zl', 'yq'), psiPlot),
'amoc': (('zl', 'yq'), np.zeros((psiPlot.shape))),
'moc_FFM': (('zl', 'yq'), np.zeros((psiPlot.shape))),
'moc_GM': (('zl', 'yq'), np.zeros((psiPlot.shape))),
'amoc_45': (('time'), np.zeros((ds_yr.time.shape))),
'moc_GM_ACC': (('time'), np.zeros((ds_yr.time.shape))),
'amoc_26': (('time'), np.zeros((ds_yr.time.shape)))
},
coords={
'zl': zl,
'yq': ds.yq,
'time': ds_yr.time
})
attrs = {
'description': 'MOC time-mean sections and time-series',
'units': 'Sv',
'start_date': avg['start_date'],
'end_date': avg['end_date'],
'casename': dcase.casename
}
m6toolbox.add_global_attrs(moc, attrs)
# Atlantic MOC
m6plot.setFigureSize([16, 9], 576, debug=False)
cmap = plt.get_cmap('dunnePM')
m = 0 * basin_code
m[(basin_code == 2) | (basin_code == 4) | (basin_code == 6) |
(basin_code == 7) | (basin_code == 8)] = 1
ci = m6plot.pmCI(0., 22., 2.)
z = (m * Zmod).min(axis=-1)
psiPlot = MOCpsi(VHmod,
vmsk=m * np.roll(m, -1, axis=-2)) * conversion_factor
psiPlot = 0.5 * (psiPlot[0:-1, :] + psiPlot[1::, :])
yy = grd.geolat_c[:, :].max(axis=-1) + 0 * z
plotPsi(
yy, z, psiPlot, ci, 'Atlantic MOC [Sv],' + 'averaged between ' +
args.start_date + ' and ' + args.end_date)
plt.xlabel(r'Latitude [$\degree$N]')
plt.suptitle(case_name)
plt.gca().invert_yaxis()
findExtrema(yy, z, psiPlot, min_lat=26.5, max_lat=27.,
min_depth=250.) # RAPID
findExtrema(yy, z, psiPlot, max_lat=-33.)
findExtrema(yy, z, psiPlot)
findExtrema(yy, z, psiPlot, min_lat=5.)
objOut = args.outdir + str(case_name) + '_MOC_Atlantic.png'
plt.savefig(objOut, format='png')
moc['amoc'].data = psiPlot
print('Plotting AMOC profile at 26N...')
rapid_vertical = xr.open_dataset(
'/glade/work/gmarques/cesm/datasets/RAPID/moc_vertical.nc')
fig, ax = plt.subplots(nrows=1, ncols=1)
ax.plot(rapid_vertical.stream_function_mar.mean('time'),
rapid_vertical.depth,
'k',
label='RAPID')
ax.plot(moc['amoc'].sel(yq=26, method='nearest'), zl, label=case_name)
ax.legend()
plt.gca().invert_yaxis()
plt.grid()
ax.set_xlabel('AMOC @ 26N [Sv]')
ax.set_ylabel('Depth [m]')
objOut = args.outdir + str(case_name) + '_MOC_profile_26N.png'
plt.savefig(objOut, format='png')
print('Computing time series...')
startTime = datetime.now()
# time-series
dtime = ds_yr.time
amoc_26 = np.zeros(len(dtime))
amoc_45 = np.zeros(len(dtime))
moc_GM_ACC = np.zeros(len(dtime))
if args.debug: startTime = datetime.now()
# loop in time
for t in range(len(dtime)):
tmp = np.ma.masked_invalid(ds_yr[varName][t, :].values)
tmp = tmp[:].filled(0.)
# m is still Atlantic ocean
psi = MOCpsi(tmp, vmsk=m * np.roll(m, -1, axis=-2)) * conversion_factor
psi = 0.5 * (psi[0:-1, :] + psi[1::, :])
amoc_26[t] = findExtrema(yy,
z,
psi,
min_lat=26.,
max_lat=27.,
plot=False,
min_depth=250.)
amoc_45[t] = findExtrema(yy,
z,
psi,
min_lat=44.,
max_lat=46.,
plot=False,
min_depth=250.)
tmp_GM = np.ma.masked_invalid(ds_yr['vhGM'][t, :].values)
tmp_GM = tmp_GM[:].filled(0.)
psiGM = MOCpsi(tmp_GM) * conversion_factor
psiGM = 0.5 * (psiGM[0:-1, :] + psiGM[1::, :])
moc_GM_ACC[t] = findExtrema(yyg,
zg,
psiGM,
min_lat=-65.,
max_lat=-30,
mult=-1.,
plot=False)
print('Time elasped: ', datetime.now() - startTime)
# add dataarays to the moc dataset
moc['amoc_26'].data = amoc_26
moc['amoc_45'].data = amoc_45
moc['moc_GM_ACC'].data = moc_GM_ACC
if parallel:
print('Releasing workers ...')
client.close()
cluster.close()
print('Plotting...')
# load AMOC time series data (5th) cycle used in Danabasoglu et al., doi:10.1016/j.ocemod.2015.11.007
path = '/glade/p/cesm/omwg/amoc/COREII_AMOC_papers/papers/COREII.variability/data.original/'
amoc_core_26 = xr.open_dataset(path + 'AMOCts.cyc5.26p5.nc')
# load AMOC from POP JRA-55
amoc_pop_26 = xr.open_dataset(
'/glade/u/home/bryan/MOM6-modeloutputanalysis/'
'AMOC_series_26n.g210.GIAF_JRA.v13.gx1v7.01.nc')
# load RAPID time series
rapid = xr.open_dataset(
'/glade/work/gmarques/cesm/datasets/RAPID/moc_transports.nc').resample(
time="1Y", closed='left', keep_attrs=True).mean('time',
keep_attrs=True)
# plot
fig = plt.figure(figsize=(12, 6))
plt.plot(np.arange(len(moc.time)) + 1958.5,
moc['amoc_26'].values,
color='k',
label=case_name,
lw=2)
# core data
core_mean = amoc_core_26['MOC'].mean(axis=0).data
core_std = amoc_core_26['MOC'].std(axis=0).data
plt.plot(amoc_core_26.time,
core_mean,
'k',
label='CORE II (group mean)',
color='#1B2ACC',
lw=1)
plt.fill_between(amoc_core_26.time,
core_mean - core_std,
core_mean + core_std,
alpha=0.25,
edgecolor='#1B2ACC',
facecolor='#089FFF')
# pop data
plt.plot(np.arange(len(amoc_pop_26.time)) + 1958.5,
amoc_pop_26.AMOC_26n.values,
color='r',
label='POP',
lw=1)
# rapid
plt.plot(np.arange(len(rapid.time)) + 2004.5,
rapid.moc_mar_hc10.values,
color='green',
label='RAPID',
lw=1)
plt.title('AMOC @ 26 $^o$ N', fontsize=16)
plt.ylim(5, 20)
plt.xlim(1948, 1958.5 + len(moc.time))
plt.xlabel('Time [years]', fontsize=16)
plt.ylabel('Sv', fontsize=16)
plt.legend(fontsize=13, ncol=2)
objOut = args.outdir + str(case_name) + '_MOC_26N_time_series.png'
plt.savefig(objOut, format='png')
amoc_core_45 = xr.open_dataset(path + 'AMOCts.cyc5.45.nc')
amoc_pop_45 = xr.open_dataset(
'/glade/u/home/bryan/MOM6-modeloutputanalysis/'
'AMOC_series_45n.g210.GIAF_JRA.v13.gx1v7.01.nc')
# plot
fig = plt.figure(figsize=(12, 6))
plt.plot(np.arange(len(moc.time)) + 1958.5,
moc['amoc_45'],
color='k',
label=case_name,
lw=2)
# core data
core_mean = amoc_core_45['MOC'].mean(axis=0).data
core_std = amoc_core_45['MOC'].std(axis=0).data
plt.plot(amoc_core_45.time,
core_mean,
'k',
label='CORE II (group mean)',
color='#1B2ACC',
lw=2)
plt.fill_between(amoc_core_45.time,
core_mean - core_std,
core_mean + core_std,
alpha=0.25,
edgecolor='#1B2ACC',
facecolor='#089FFF')
# pop data
plt.plot(np.arange(len(amoc_pop_45.time)) + 1958.5,
amoc_pop_45.AMOC_45n.values,
color='r',
label='POP',
lw=1)
plt.title('AMOC @ 45 $^o$ N', fontsize=16)
plt.ylim(5, 20)
plt.xlim(1948, 1958 + len(moc.time))
plt.xlabel('Time [years]', fontsize=16)
plt.ylabel('Sv', fontsize=16)
plt.legend(fontsize=14)
objOut = args.outdir + str(case_name) + '_MOC_45N_time_series.png'
plt.savefig(objOut, format='png')
# Submesoscale-induced Global MOC
class C(np.ndarray):
pass
varName = 'vhml'
conversion_factor = 1.e-9
tmp = np.ma.masked_invalid(ds_mean[varName].values)
tmp = tmp[:].filled(0.)
VHml = tmp.view(C)
VHml.units = ds[varName].units
Zmod = m6toolbox.get_z(ds, depth, varName) # same here
m6plot.setFigureSize([16, 9], 576, debug=False)
axis = plt.gca()
cmap = plt.get_cmap('dunnePM')
z = Zmod.min(axis=-1)
psiPlot = MOCpsi(VHml) * conversion_factor
psiPlot = 0.5 * (psiPlot[0:-1, :] + psiPlot[1::, :])
yy = grd.geolat_c[:, :].max(axis=-1) + 0 * z
ci = m6plot.pmCI(0., 20., 2.)
plotPsi(yy,
z,
psiPlot,
ci,
'Global FFH MOC [Sv],' + 'averaged between ' + args.start_date +
' and ' + args.end_date,
zval=[0., -400., -1000.])
plt.xlabel(r'Latitude [$\degree$N]')
plt.suptitle(case_name)
plt.gca().invert_yaxis()
objOut = args.outdir + str(case_name) + '_FFH_MOC_global.png'
plt.savefig(objOut)
moc['moc_FFM'].data = psiPlot
# GM-induced Global MOC
class C(np.ndarray):
pass
varName = 'vhGM'
conversion_factor = 1.e-9
tmp = np.ma.masked_invalid(ds_mean[varName].values)
tmp = tmp[:].filled(0.)
VHGM = tmp.view(C)
VHGM.units = ds[varName].units
Zmod = m6toolbox.get_z(ds, depth, varName) # same here
m6plot.setFigureSize([16, 9], 576, debug=False)
axis = plt.gca()
cmap = plt.get_cmap('dunnePM')
z = Zmod.min(axis=-1)
psiPlot = MOCpsi(VHGM) * conversion_factor
psiPlot = 0.5 * (psiPlot[0:-1, :] + psiPlot[1::, :])
yy = grd.geolat_c[:, :].max(axis=-1) + 0 * z
ci = m6plot.pmCI(0., 20., 2.)
plotPsi(
yy, z, psiPlot, ci, 'Global GM MOC [Sv],' + 'averaged between ' +
args.start_date + ' and ' + args.end_date)
plt.xlabel(r'Latitude [$\degree$N]')
plt.suptitle(case_name)
plt.gca().invert_yaxis()
findExtrema(yy, z, psiPlot, min_lat=-65., max_lat=-30, mult=-1.)
objOut = args.outdir + str(case_name) + '_GM_MOC_global.png'
plt.savefig(objOut)
moc['moc_GM'].data = psiPlot
print('Saving netCDF files...')
moc.to_netcdf('ncfiles/' + str(case_name) + '_MOC.nc')
return
def main(stream=False):
# Get options
args = options()
# mom6 grid
grd = MOM6grid(args.infile + args.static)
depth = grd.depth_ocean
# remote Nan's, otherwise genBasinMasks won't work
depth[numpy.isnan(depth)] = 0.0
basin_code = m6toolbox.genBasinMasks(grd.geolon, grd.geolat, depth)
# load data
ds = xr.open_mfdataset(args.infile + args.monthly, decode_times=False)
# convert time in years
ds['time'] = ds.time / 365.
ti = args.year_start
tf = args.year_end
# check if data includes years between ti and tf
m6toolbox.check_time_interval(ti, tf, ds)
# create a ndarray subclass
class C(numpy.ndarray):
pass
if 'vmo' in ds.variables:
varName = 'vmo'
conversion_factor = 1.e-9
elif 'vh' in ds.variables:
varName = 'vh'
conversion_factor = 1.e-6
if 'zw' in ds.variables:
conversion_factor = 1.e-9 # Backwards compatible for when we had wrong units for 'vh'
else:
raise Exception('Could not find "vh" or "vmo" in file "%s"' %
(args.infile + args.static))
tmp = numpy.ma.masked_invalid(
ds[varName].sel(time=slice(ti, tf)).mean('time').data)
tmp = tmp[:].filled(0.)
VHmod = tmp.view(C)
VHmod.units = ds[varName].units
Zmod = m6toolbox.get_z(ds, depth, varName)
if args.case_name != '': case_name = args.case_name + ' ' + args.label
else: case_name = ds.title + ' ' + args.label
imgbufs = []
# Global MOC
m6plot.setFigureSize([16, 9], 576, debug=False)
axis = plt.gca()
cmap = plt.get_cmap('dunnePM')
z = Zmod.min(axis=-1)
psiPlot = MOCpsi(VHmod) * conversion_factor
#yy = y[1:,:].max(axis=-1)+0*z
yy = grd.geolat_c[:, :].max(axis=-1) + 0 * z
print(z.shape, yy.shape, psiPlot.shape)
ci = m6plot.pmCI(0., 40., 5.)
plotPsi(yy, z, psiPlot[1::, :], ci, 'Global MOC [Sv]')
plt.xlabel(r'Latitude [$\degree$N]')
plt.suptitle(case_name)
findExtrema(yy, z, psiPlot, max_lat=-30.)
findExtrema(yy, z, psiPlot, min_lat=25.)
findExtrema(yy, z, psiPlot, min_depth=2000., mult=-1.)
if stream is True: objOut = io.BytesIO()
else: objOut = args.outdir + '/MOC_global.png'
plt.savefig(objOut)
if stream is True: imgbufs.append(objOut)
# Atlantic MOC
m6plot.setFigureSize([16, 9], 576, debug=False)
cmap = plt.get_cmap('dunnePM')
m = 0 * basin_code
m[(basin_code == 2) | (basin_code == 4) | (basin_code == 6) |
(basin_code == 7) | (basin_code == 8)] = 1
ci = m6plot.pmCI(0., 22., 2.)
z = (m * Zmod).min(axis=-1)
psiPlot = MOCpsi(VHmod,
vmsk=m * numpy.roll(m, -1, axis=-2)) * conversion_factor
#yy = y[1:,:].max(axis=-1)+0*z
yy = grd.geolat_c[:, :].max(axis=-1) + 0 * z
plotPsi(yy, z, psiPlot[1::, :], ci, 'Atlantic MOC [Sv]')
plt.xlabel(r'Latitude [$\degree$N]')
plt.suptitle(case_name)
findExtrema(yy, z, psiPlot, min_lat=26.5, max_lat=27.) # RAPID
findExtrema(yy, z, psiPlot, max_lat=-33.)
findExtrema(yy, z, psiPlot)
findExtrema(yy, z, psiPlot, min_lat=5.)
if stream is True: objOut = io.BytesIO()
else: objOut = args.outdir + '/MOC_Atlantic.png'
plt.savefig(objOut, format='png')
if stream is True: imgbufs.append(objOut)
if stream is True:
return imgbufs
def main():
# Get options
args = options()
# mom6 grid
grd = MOM6grid(args.infile + args.case_name + '.mom6.static.nc')
depth = grd.depth_ocean
# remote Nan's, otherwise genBasinMasks won't work
depth[numpy.isnan(depth)] = 0.0
basin_code = m6toolbox.genBasinMasks(grd.geolon, grd.geolat, depth)
# load data
ds = xr.open_mfdataset(args.infile + args.case_name + '.mom6.hm_*.nc',
combine='by_coords')
ti = args.start_date
tf = args.end_date
# create a ndarray subclass
class C(numpy.ndarray):
pass
if 'vmo' in ds.variables:
varName = 'vmo'
conversion_factor = 1.e-9
elif 'vh' in ds.variables:
varName = 'vh'
conversion_factor = 1.e-6
if 'zw' in ds.variables:
conversion_factor = 1.e-9 # Backwards compatible for when we had wrong units for 'vh'
else:
raise Exception('Could not find "vh" or "vmo" in file "%s"' %
(args.infile + args.static))
# selected dates
ds_var = ds[varName].sel(time=slice(ti, tf))
# yearly means
ds_var_yr = ds_var.resample(time="1Y", closed='left',
keep_attrs=True).mean(dim='time',
keep_attrs=True).load()
tmp = numpy.ma.masked_invalid(ds_var_yr.mean('time').values)
tmp = tmp[:].filled(0.)
VHmod = tmp.view(C)
VHmod.units = ds[varName].units
Zmod = m6toolbox.get_z(ds, depth, varName)
if args.case_name != '': case_name = args.case_name + ' ' + args.label
else: case_name = ds.title + ' ' + args.label
imgbufs = []
# Global MOC
m6plot.setFigureSize([16, 9], 576, debug=False)
axis = plt.gca()
cmap = plt.get_cmap('dunnePM')
z = Zmod.min(axis=-1)
psiPlot = MOCpsi(VHmod) * conversion_factor
psiPlot = 0.5 * (psiPlot[0:-1, :] + psiPlot[1::, :])
yy = grd.geolat_c[:, :].max(axis=-1) + 0 * z
ci = m6plot.pmCI(0., 40., 5.)
plotPsi(yy, z, psiPlot, ci,
'Global MOC [Sv],' + 'averaged between ' + ti + 'and ' + tf)
plt.xlabel(r'Latitude [$\degree$N]')
plt.suptitle(case_name)
plt.gca().invert_yaxis()
findExtrema(yy, z, psiPlot, max_lat=-30.)
findExtrema(yy, z, psiPlot, min_lat=25.)
findExtrema(yy, z, psiPlot, min_depth=2000., mult=-1.)
objOut = str(case_name) + '_MOC_global.png'
plt.savefig(objOut)
# Atlantic MOC
m6plot.setFigureSize([16, 9], 576, debug=False)
cmap = plt.get_cmap('dunnePM')
m = 0 * basin_code
m[(basin_code == 2) | (basin_code == 4) | (basin_code == 6) |
(basin_code == 7) | (basin_code == 8)] = 1
ci = m6plot.pmCI(0., 22., 2.)
z = (m * Zmod).min(axis=-1)
psiPlot = MOCpsi(VHmod,
vmsk=m * numpy.roll(m, -1, axis=-2)) * conversion_factor
psiPlot = 0.5 * (psiPlot[0:-1, :] + psiPlot[1::, :])
yy = grd.geolat_c[:, :].max(axis=-1) + 0 * z
plotPsi(yy, z, psiPlot, ci,
'Atlantic MOC [Sv],' + 'averaged between ' + ti + 'and ' + tf)
plt.xlabel(r'Latitude [$\degree$N]')
plt.suptitle(case_name)
plt.gca().invert_yaxis()
findExtrema(yy, z, psiPlot, min_lat=26.5, max_lat=27.) # RAPID
findExtrema(yy, z, psiPlot, max_lat=-33.)
findExtrema(yy, z, psiPlot)
findExtrema(yy, z, psiPlot, min_lat=5.)
objOut = str(case_name) + '_MOC_Atlantic.png'
plt.savefig(objOut, format='png')
# time-series
dtime = ds_var_yr.time.values
amoc_26 = numpy.zeros(len(dtime))
amoc_45 = numpy.zeros(len(dtime))
# loop in time
for t in range(len(dtime)):
tmp = numpy.ma.masked_invalid(ds_var_yr.sel(time=dtime[t]).values)
tmp = tmp[:].filled(0.)
psi = MOCpsi(tmp,
vmsk=m * numpy.roll(m, -1, axis=-2)) * conversion_factor
psi = 0.5 * (psi[0:-1, :] + psi[1::, :])
amoc_26[t] = findExtrema(yy,
z,
psi,
min_lat=26.5,
max_lat=27.,
plot=False)
amoc_45[t] = findExtrema(yy,
z,
psi,
min_lat=44.,
max_lat=46.,
plot=False)
# create dataarays
amoc_26_da = xr.DataArray(amoc_26, dims=['time'], coords={'time': dtime})
amoc_45_da = xr.DataArray(amoc_45, dims=['time'], coords={'time': dtime})
# load AMOC time series data (5th) cycle used in Danabasoglu et al., doi:10.1016/j.ocemod.2015.11.007
path = '/glade/p/cesm/omwg/amoc/COREII_AMOC_papers/papers/COREII.variability/data.original/'
amoc_core_26 = xr.open_dataset(path + 'AMOCts.cyc5.26p5.nc')
# plot
fig = plt.figure(figsize=(12, 6))
plt.plot(numpy.arange(len(amoc_26_da.time)) + 1948.5,
amoc_26_da.values,
color='k',
label=case_name,
lw=2)
# core data
core_mean = amoc_core_26['MOC'].mean(axis=0).data
core_std = amoc_core_26['MOC'].std(axis=0).data
plt.plot(amoc_core_26.time,
core_mean,
'k',
label='CORE II (group mean)',
color='#1B2ACC',
lw=2)
plt.fill_between(amoc_core_26.time,
core_mean - core_std,
core_mean + core_std,
alpha=0.25,
edgecolor='#1B2ACC',
facecolor='#089FFF')
plt.title('AMOC @ 26 $^o$ N', fontsize=16)
plt.xlabel('Time [years]', fontsize=16)
plt.ylabel('Sv', fontsize=16)
plt.legend(fontsize=14)
objOut = str(case_name) + '_MOC_26N_time_series.png'
plt.savefig(objOut, format='png')
amoc_core_45 = xr.open_dataset(path + 'AMOCts.cyc5.45.nc')
# plot
fig = plt.figure(figsize=(12, 6))
plt.plot(numpy.arange(len(amoc_45_da.time)) + 1948.5,
amoc_45_da.values,
color='k',
label=case_name,
lw=2)
# core data
core_mean = amoc_core_45['MOC'].mean(axis=0).data
core_std = amoc_core_45['MOC'].std(axis=0).data
plt.plot(amoc_core_45.time,
core_mean,
'k',
label='CORE II (group mean)',
color='#1B2ACC',
lw=2)
plt.fill_between(amoc_core_45.time,
core_mean - core_std,
core_mean + core_std,
alpha=0.25,
edgecolor='#1B2ACC',
facecolor='#089FFF')
plt.title('AMOC @ 45 $^o$ N', fontsize=16)
plt.xlabel('Time [years]', fontsize=16)
plt.ylabel('Sv', fontsize=16)
plt.legend(fontsize=14)
objOut = str(case_name) + '_MOC_45N_time_series.png'
plt.savefig(objOut, format='png')
return