T & S biases

T & S biases#

%load_ext autoreload
%autoreload 2

%%capture 
# comment above line to see details about the run(s) displayed
from misc import *
import nc_time_axis
%matplotlib inline

Drifts#

# these are the regions for the drift plots
reg_mom = ['Global', 'PersianGulf', 'RedSea', 'BlackSea', 'MedSea', 'BalticSea',
           'HudsonBay', 'Arctic', 'PacificOcean', 'AtlanticOcean', 'IndianOcean',
           'SouthernOcean', 'LabSea', 'BaffinBay', 'EGreenlandIceland',
           'GulfOfMexico', 'Maritime', 'SouthernOcean60S']

Potential temperature#

%matplotlib inline
nr=1
nc=1
fs=(9,5.5)

matplotlib.rcParams.update({'font.size': 17})
vmax=1.5; vmin=-vmax
for m in reg_mom:
  fig, ax = plt.subplots(nrows=nr, ncols=nc, figsize=fs, sharex=False, sharey=True)

  plt.suptitle(str(m)+', Potential Temperature bias [C]', fontsize=14)
  for path, case, i in zip(ocn_path, casename, range(len(casename))):
    ds_mom = xr.open_dataset(path+case+'_thetao_drift.nc').sel(time=slice('0001-01-01', end_date))
    
    splitscale =  [0., -500., -4000]
    dummy_mom = np.ma.masked_invalid(ds_mom.sel(region=m).thetao_drift.values)
    ztplot(dummy_mom, ds_mom.time.values, ds_mom.z_l.values*-1, ignore=np.nan, splitscale=splitscale, 
           contour=True, axis=ax , title=label[i], extend='both', colormap='dunnePM', 
           autocenter=True, tunits='Year', show=False, clim=(vmin, vmax)) 
    plt.subplots_adjust(top = 0.8)
    plt.tight_layout()

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Salinity#

matplotlib.rcParams.update({'font.size': 17})
vmax=0.2; vmin=-vmax
for m in reg_mom:
  fig, ax = plt.subplots(nrows=nr, ncols=nc, figsize=fs, sharex=False, sharey=True)
  plt.suptitle(str(m)+', Salinity bias [psu]', fontsize=14)
  for path, case, i in zip(ocn_path, casename, range(len(casename))):
    ds_mom = xr.open_dataset(path+case+'_so_drift.nc').sel(time=slice('0001-01-01', end_date))
    
    splitscale =  [0., -500., -4000]
    dummy_mom = np.ma.masked_invalid(ds_mom.sel(region=m).so_drift.values)
    ztplot(dummy_mom, ds_mom.time.values, ds_mom.z_l.values*-1, ignore=np.nan, splitscale=splitscale, 
           contour=True, axis=ax , title=label[i], extend='both', colormap='dunnePM', 
           autocenter=True, tunits='Year', show=False, clim=(vmin, vmax)) 
    plt.subplots_adjust(top = 0.8)
    plt.tight_layout()

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RMSE#

Potential Temperature#

reg_mom = ['Global','SouthernOcean','PacificOcean','IndianOcean','AtlanticOcean','Arctic','LabSea']
matplotlib.rcParams.update({'font.size': 17})
vmax=3; vmin=0
for m in reg_mom:
  fig, ax = plt.subplots(nrows=nr, ncols=nc, figsize=fs, sharex=False, sharey=True)

  plt.suptitle(str(m)+', Potential Temperature [C] RMS (model - obs)', fontsize=14)
  for path, case, i in zip(ocn_path, casename, range(len(casename))):
    ds_mom = xr.open_dataset(path+case+'_thetao_rmse.nc').sel(time=slice('0001-01-01', end_date))
    
    splitscale =  [0., -500., -4000]
    dummy_mom = np.ma.masked_invalid(ds_mom.sel(region=m).thetao_rms.values)
    ztplot(dummy_mom, ds_mom.time.values, ds_mom.z_l.values*-1, ignore=np.nan, splitscale=splitscale, 
           contour=True, axis=ax , title=label[i], extend='both', colormap='hot_r', 
           autocenter=True, tunits='Year', show=False, clim=(vmin, vmax)) 
    plt.subplots_adjust(top = 0.8)
    plt.tight_layout()

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Salinity#

reg_mom = ['Global','SouthernOcean','PacificOcean','IndianOcean','AtlanticOcean','Arctic','LabSea']

vmax=0.5; vmin=0
for m in reg_mom:
  fig, ax = plt.subplots(nrows=nr, ncols=nc, figsize=fs, sharex=False, sharey=True)
  plt.suptitle(str(m)+', Salinity [psu] RMS (model - obs)', fontsize=14)
  for path, case, i in zip(ocn_path, casename, range(len(casename))):
    ds_mom = xr.open_dataset(path+case+'_so_rmse.nc').sel(time=slice('0001-01-01', end_date))
    
    splitscale =  [0., -500., -4000]
    dummy_mom = np.ma.masked_invalid(ds_mom.sel(region=m).so_rms.values)
    ztplot(dummy_mom, ds_mom.time.values, ds_mom.z_l.values*-1, ignore=np.nan, splitscale=splitscale, 
           contour=True, axis=ax , title=label[i], extend='both', colormap='hot_r', 
           autocenter=True, tunits='Year', show=False, clim=(vmin, vmax)) 
    plt.subplots_adjust(top = 0.8)
    plt.tight_layout()

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Biases at selected vertical levels#

Potential Temperature#

# change k indice to see bias at other depths
levels=[0,1,2,5,10,15,18,20,25,30,32]
tlim=[5,7.5,7.5,5,5,5,3,2,1,1,1]

for k,t in zip(levels,tlim):
  for path, case, i in zip(ocn_path, casename, range(len(casename))):
    ds_mom_t = xr.open_dataset(path+case+'_thetao_time_mean.nc')
    if 'area_t' in dir(grd[i]):
      area = grd[i].area_t
    else:
      area = grd[i].areacello
    
    fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(12,6.5))
    temp_mom = np.ma.masked_invalid(ds_mom_t.thetao_bias[k,:].values)
  
    plt.suptitle('Temperature bias [C] at depth = {} m (level = {})'.format(ds_mom_t.z_l[k].values, k))
    xyplot(temp_mom, grd[i].geolon, grd[i].geolat, area, title=str(label[i]+' - obs'), axis=ax, 
         clim=(-t,t), nbins=100, colormap=plt.cm.bwr, centerlabels=True)
    plt.subplots_adjust(top = 0.8)

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Salinity#

# change k indice to see bias at other depths
#levels=[0,5,10,15,20,25]
slim=[5,3,2,2,1.5,1,1,0.5,0.1,0.1,0.1]

for k,s in zip(levels,slim):
  for path, case, i in zip(ocn_path, casename, range(len(casename))):
    if 'area_t' in dir(grd[i]):
      area = grd[i].area_t
    else:
      area = grd[i].areacello
    
    ds_mom_s = xr.open_dataset(path+case+'_so_time_mean.nc')
    fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(12,6.5))
    salt_mom = np.ma.masked_invalid(ds_mom_s.so_bias[k,:].values)
    
    plt.suptitle('Salinity bias [psu] at depth = {} m (level = {})'.format(ds_mom_s.z_l[k].values, k))
    xyplot(salt_mom, grd[i].geolon, grd[i].geolat, area, title=str(label[i]+' - obs'), axis=ax, 
         clim=(-s,s), nbins=50, colormap=plt.cm.bwr, centerlabels=True)
    plt.subplots_adjust(top = 0.8)

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Zonal biases#

reg_mom = ['Global', 'PersianGulf', 'RedSea', 'BlackSea', 'MedSea', 'BalticSea',
           'HudsonBay', 'Arctic', 'PacificOcean', 'AtlanticOcean', 'IndianOcean',
           'SouthernOcean', 'LabSea', 'BaffinBay', 'EGreenlandIceland',
           'GulfOfMexico', 'Maritime', 'SouthernOcean60S']

Potential temperature#

reg = "AtlanticOcean"
var = 'thetao_bias'
units = r'$^o$C'
regions = ['Global','MedSea','Arctic','PacificOcean',
           'AtlanticOcean','IndianOcean','SouthernOcean',
          'LabSea']
ylim_min = [-90, 30, 62, -50, -50, -50, -90, 50]
ylim_max = [ 90, 47, 90,  65,  72,  30, -35, 68 ]

cmap = plt.get_cmap('bwr')  
cmap.set_bad(color='black')  
matplotlib.rcParams.update({'font.size': 16})

for reg, j in zip(regions, range(len(regions))):
  da  = []
  for path, case, i in zip(ocn_path, casename, range(len(casename))):
    ds_mom_t = xr.open_dataset(path+case+'_thetao_time_mean.nc')
    atl = basin_code[i].sel(region=reg)
    # Set zero values to NaN
    atl = atl.where(atl != 0, np.nan)
    da.append((ds_mom_t[var] * atl).weighted(grd_xr[i].areacello.fillna(0)).mean('xh').expand_dims(case=[label[i]]).sel(yh=slice(ylim_min[j], 
                                                                                                                                 ylim_max[j])))

  combined_da = xr.concat(da, dim="case")
  col = None
  if len(combined_da) > 1:
      col = "case"
  g = combined_da.plot(x="yh", y="z_l", col=col, 
                 yincrease=False, col_wrap=2,
                figsize=(12,4), cmap=cmap, robust=True,
                cbar_kwargs={"label": reg + ', ' + var + ' ({})'.format(units)})

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Salinity#

reg = "AtlanticOcean"
var = 'so_bias'
units = 'psu'
regions = ['Global','MedSea','Arctic','PacificOcean',
           'AtlanticOcean','IndianOcean','SouthernOcean',
          'LabSea']
cmap = plt.get_cmap('bwr')  
cmap.set_bad(color='black')  
matplotlib.rcParams.update({'font.size': 16})

for reg, j in zip(regions, range(len(regions))):
  #print(reg)
  da  = []
  for path, case, i in zip(ocn_path, casename, range(len(casename))):
    ds_mom_t = xr.open_dataset(path+case+'_so_time_mean.nc')
    atl = basin_code[i].sel(region=reg)
    # Set zero values to NaN
    atl = atl.where(atl != 0, np.nan)
    da.append((ds_mom_t[var] * atl).weighted(grd_xr[i].areacello.fillna(0)).mean('xh').expand_dims(case=[label[i]]).sel(yh=slice(ylim_min[j], ylim_max[j])))

  combined_da = xr.concat(da, dim="case")
  col = None
  if len(combined_da) > 1:
      col = "case"
  g = combined_da.plot(x="yh", y="z_l", col=col, 
                 yincrease=False, col_wrap=2,
                figsize=(12,4), cmap=cmap, robust=True,
                cbar_kwargs={"label": reg + ', ' + var + ' ({})'.format(units)})

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Meridional biases#

Potential temperature#

reg = "AtlanticOcean"
var = 'thetao_bias'
units = r'$^o$C'
regions = ['Global','MedSea','Arctic','PacificOcean',
           'AtlanticOcean','IndianOcean','SouthernOcean',
          'LabSea']
xlim_min = [-287, -10, -240, -255, -110, -287, -287, -62]
xlim_max = [ 73,   40,   40,  -68,  25,  73, 73, -42 ]
cmap = plt.get_cmap('bwr')  
cmap.set_bad(color='black')  
matplotlib.rcParams.update({'font.size': 16})

for reg, j in zip(regions, range(len(regions))):
  #print(reg)
  da  = []
  for path, case, i in zip(ocn_path, casename, range(len(casename))):
    ds_mom_t = xr.open_dataset(path+case+'_thetao_time_mean.nc')
    atl = basin_code[i].sel(region=reg)
    # Set zero values to NaN
    atl = atl.where(atl != 0, np.nan)
    da.append((ds_mom_t[var] * \
      atl).weighted(grd_xr[i].areacello.fillna(0)).mean('yh').expand_dims(case=[label[i]]).sel(xh=slice(xlim_min[j], 
                                                                                                xlim_max[j])))

  combined_da = xr.concat(da, dim="case")
  col = None
  if len(combined_da) > 1:
      col = "case"
  g = combined_da.plot(x="xh", y="z_l", col=col, 
                 yincrease=False, col_wrap=2,
                figsize=(12,4), cmap=cmap, robust=True,
                cbar_kwargs={"label": reg + ', ' + var + ' ({})'.format(units)})

_images/21b1c00b4245b6b2345ae2c09db1791b1fb88ec3618674c0f0bbed31cd76f374.png

_images/7b06778173ff6d024979c868df00bc449f540bab4f5055c05d6fd5a88f7cb1d2.png

_images/42cb776865d1ea1797d49ac95dce0f8e9f02e29f4d3c0274b1dc95d4e6d43020.png

_images/b0ae71e0f216dd99653407aab1f36467822c9ade85aad49af36e803e5dc84c59.png

_images/2bd97d1142ff05e55bb796b2c882b45aeac9c52474b33bcd52b43f09fd071f69.png

_images/66c244bc0449c60eb3795828463a5f1c7dc6051f6150476a346222aab94787d8.png

_images/24653fa9be51813286369852916a8435218bb10a21ba66f367d6c6b36131e0f2.png

_images/ed0b32e9537a7e8bc83553fcac2265e546c0c949e16f7dcc8f002d0507655211.png

Salinity#

reg = "AtlanticOcean"
var = 'so_bias'
units = 'psu'
regions = ['Global','MedSea','Arctic','PacificOcean',
           'AtlanticOcean','IndianOcean','SouthernOcean',
          'LabSea']
cmap = plt.get_cmap('bwr')  
cmap.set_bad(color='black')  
matplotlib.rcParams.update({'font.size': 16})

for reg, j in zip(regions, range(len(regions))):
  #print(reg)
  da  = []
  for path, case, i in zip(ocn_path, casename, range(len(casename))):
    ds_mom_t = xr.open_dataset(path+case+'_so_time_mean.nc')
    atl = basin_code[i].sel(region=reg)
    # Set zero values to NaN
    atl = atl.where(atl != 0, np.nan)
    da.append((ds_mom_t[var] * \
      atl).weighted(grd_xr[i].areacello.fillna(0)).mean('yh').expand_dims(case=[label[i]]).sel(xh=slice(xlim_min[j], 
                                                                                               xlim_max[j])))

  combined_da = xr.concat(da, dim="case")
  col = None
  if len(combined_da) > 1:
      col = "case"
  g = combined_da.plot(x="xh", y="z_l", col=col, 
                 yincrease=False, col_wrap=2,
                figsize=(12,4), cmap=cmap, robust=True,
                cbar_kwargs={"label": reg + ', ' + var + ' ({})'.format(units)})