Contents

Ocean Stats

Ocean Stats

%%capture 
# comment above line to see details about the run(s) displayed
from misc import *
print("Last update:", date.today())
%matplotlib inline

# Make the graphs a bit prettier, and bigger
#plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = (15, 5)
plt.rcParams.update({'font.size': 15})

Globally-integrated T & S

fig, ax = plt.subplots(nrows=1,ncols=1,figsize=(10,4))

for c, l, p in zip(casename,label, ocn_path):
  ds = xr.open_dataset(p+'{}_mon_ave_global_means.nc'.format(c)).sel(time=slice('0001-01-01',end_date))
  ds['opottempmint'].plot(ax=ax, label=l, lw=3)

#plt.suptitle(ds.opottempmint.attrs['long_name'])
ax.set_ylabel(ds.opottempmint.attrs['units'])
ax.set_xlabel('Year')
ax.grid()
ax.legend(ncol=3,loc=1);
_images/ocean_stats_4_0.png 
fig, ax = plt.subplots(nrows=1,ncols=1,figsize=(10,4))

for c, l, p in zip(casename,label, ocn_path):
  ds = xr.open_dataset(p+'{}_mon_ave_global_means.nc'.format(c)).sel(time=slice('0001-01-01',end_date))
  ds['somint'].plot(ax=ax, label=l, lw=3)

#plt.suptitle(ds.somint.attrs['long_name'])
ax.set_ylabel(ds.somint.attrs['units'])
ax.set_xlabel('Year')
ax.grid()
ax.legend(ncol=3,loc=1);
_images/ocean_stats_5_0.png

Globally-averaged T & S

fig, ax = plt.subplots(nrows=1,ncols=1,figsize=(10,4))

for c, l, p in zip(casename,label, ocn_path):
  ds = xr.open_dataset(p+'{}_mon_ave_global_means.nc'.format(c)).sel(time=slice('0001-01-01',end_date))
  ds['thetaoga'].plot(ax=ax, label=l, lw=3)

plt.suptitle(ds.thetaoga.attrs['long_name'])
ax.set_ylabel(ds.thetaoga.attrs['units'])
ax.set_xlabel('Year')
ax.grid()
ax.legend(ncol=3,loc=1);
_images/ocean_stats_7_0.png 
fig, ax = plt.subplots()
for c, l, p in zip(casename,label, ocn_path):
  ds = xr.open_dataset(p+'{}_mon_ave_global_means.nc'.format(c)).sel(time=slice('0001-01-01',end_date))
  ds['soga'].plot(ax=ax, label=l, lw=3)
    
ax.set_title(ds.soga.attrs['long_name'])
ax.set_ylabel(ds.soga.attrs['units'])
ax.set_xlabel('Year')
ax.grid()
ax.legend();
_images/ocean_stats_8_0.png 
ocean_stats = []
for c, l, p in zip(casename,label, ocn_path):
  ds = xr.open_dataset(p+'{}_ocean.stats.nc'.format(c), decode_times=False)#.sel(time=slice('0001-01-01',end_date))
  stats_monthly = ds#.resample(time="1M", 
                    #           closed='left',
                    #           keep_attrs='True').mean('time', keep_attrs=True)
  ocean_stats.append(stats_monthly)

Truncations

fig, ax = plt.subplots()
for i,l in zip(range(len(label)), label):
  ocean_stats[i].Truncs.plot(ax=ax,label=l,lw=3);
    
ax.legend()
ax.grid()
_images/ocean_stats_11_0.png

Maximum finite-volume CFL

fig, ax = plt.subplots()
for i,l in zip(range(len(label)), label):
  ocean_stats[i].max_CFL_trans.plot(ax=ax,label=l,lw=3);
    
ax.legend()
ax.grid();
_images/ocean_stats_13_0.png

Maximum finite-difference CFL

fig, ax = plt.subplots()
for i,l in zip(range(len(label)), label):
  ocean_stats[i].max_CFL_lin.plot(ax=ax,label=l,lw=3);
    
ax.legend()
ax.grid();
_images/ocean_stats_15_0.png

Maximum CFL

fig, ax = plt.subplots()
for i,l in zip(range(len(label)), label):
  ocean_stats[i].MaximumCFL.plot(ax=ax,label=l,lw=3);
    
ax.legend()
ax.grid();
_images/ocean_stats_17_0.png

Energy/Mass

fig, ax = plt.subplots()
for i,l in zip(range(len(label)), label):
  ocean_stats[i].EnergyMass.plot(ax=ax,label=l,lw=3);
    
ax.legend()
ax.grid();
_images/ocean_stats_19_0.png

Mean Sea Level

fig, ax = plt.subplots()
for i,l in zip(range(len(label)), label):
  ocean_stats[i].MeanSeaLevel.plot.line(ax=ax,label=l,lw=3);
    
ax.legend()
ax.grid();
_images/ocean_stats_21_0.png

Total Mass

fig, ax = plt.subplots()
for i,l in zip(range(len(label)), label):
  ocean_stats[i].TotalMass.plot(ax=ax,label=l,lw=3);
    
ax.legend()
ax.grid();
_images/ocean_stats_23_0.png

Mean Salinity

fig, ax = plt.subplots()
for i,l in zip(range(len(label)), label):
  ocean_stats[i].MeanSalin.plot(ax=ax,label=l,lw=3);
    
ax.legend()
ax.grid();
_images/ocean_stats_25_0.png

Mean Temperature

fig, ax = plt.subplots()
for i,l in zip(range(len(label)), label):
  ocean_stats[i].MeanTemp.plot(ax=ax,label=l,lw=3);
    
ax.legend()
ax.grid();
_images/ocean_stats_27_0.png

Total Energy

fig, ax = plt.subplots()
for i,l in zip(range(len(label)), label):
  ocean_stats[i].En.plot(ax=ax,label=l,lw=3);
    
ax.legend()
ax.grid();
_images/ocean_stats_29_0.png

Available Potential Energy

fig, ax = plt.subplots()
for i,l in zip(range(len(label)), label):
  ocean_stats[i].APE.sum(axis=1,
                      keep_attrs=True).plot(ax=ax,label=l,lw=3);
    
ax.legend()
ax.grid();
_images/ocean_stats_31_0.png

Total Salt

fig, ax = plt.subplots()
for i,l in zip(range(len(label)), label):
  ocean_stats[i].Salt.plot(ax=ax,label=l,lw=3);
    
ax.legend()
ax.grid();
_images/ocean_stats_33_0.png

Total Salt Change between Entries

fig, ax = plt.subplots()
for i,l in zip(range(len(label)), label):
  ocean_stats[i].Salt_chg.plot(ax=ax,label=l,lw=3);
    
ax.legend()
ax.grid();
_images/ocean_stats_35_0.png

Anomalous Total Salt Change

fig, ax = plt.subplots()
for i,l in zip(range(len(label)), label):
  ocean_stats[i].Salt_anom.plot(ax=ax,label=l,lw=3);
    
ax.legend()
ax.grid();
_images/ocean_stats_37_0.png

Total Heat

fig, ax = plt.subplots()
for i,l in zip(range(len(label)), label):
  ocean_stats[i].Heat.plot(ax=ax,label=l,lw=3);
    
ax.legend()
ax.grid();
_images/ocean_stats_39_0.png

Total Heat Change between Entries

fig, ax = plt.subplots()
for i,l in zip(range(len(label)), label):
  ocean_stats[i].Heat_chg.plot(ax=ax,label=l,lw=3);
    
ax.legend()
ax.grid();
_images/ocean_stats_41_0.png

Anomalous Total Heat Change

fig, ax = plt.subplots()
for i,l in zip(range(len(label)), label):
  ocean_stats[i].Heat_anom.plot(ax=ax,label=l,lw=3);
    
ax.legend()
ax.grid();
_images/ocean_stats_43_0.png

Age

fig, ax = plt.subplots()
for i,l in zip(range(len(label)), label):
  ocean_stats[i].age.plot(ax=ax,label=l,lw=3);
    
ax.legend()
ax.grid();
_images/ocean_stats_45_0.png

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Welcome to the OMWG diagnostics

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2D Surface Fields