jbusecke

jbusecke

ds_sigma_map = ds_sigma.sel(
  time='2000-01',
  sigma_0=26.6,
  method='nearest'
).sel(y=slice(-90,0)).squeeze()

import cartopy.crs as ccrs
import matplotlib.path as mpath
import cartopy.feature as cfeature

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ds_sigma_section = ds_sigma.sel(
  time='2010-01',
  y=slice(-90,10)
).interp(x=230).squeeze()

fig, axarr = plt.subplots(nrows=2, figsize=[10,8], sharex=True)
for ax, var, cmap in zip(
  axarr.flat,
  ['so', 'cfc11'],
  [cmo.haline, cmo.matter]

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# Lets do the same thing with cfcs and package them into a dataset
cfc_sigma = grid.transform(
  ds.cfc11,
  'Z',
  target_values,
  target_data=ds.sigma_0)
ds_sigma = xr.Dataset({'cfc11':cfc_sigma, 'so':so_sigma})
ds_sigma

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so_sigma = grid.transform(
  ds.so,
  'Z',
  target_values,
  target_data=ds.sigma_0
)
so_sigma

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target_values = levels=np.arange(24.2,28, 0.2)

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# create the grid object
from xgcm import Grid
grid = Grid(
  ds,
  periodic=False,
  coords={'Z':{'center':'lev', 'outer':'lev_outer'}})
grid

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fig, axarr = plt.subplots(nrows=3, figsize=[10,12], sharex=True)
for ax, var, cmap in zip(
    axarr.flat,
    ['thetao', 'so', 'cfc11'],
    [cmo.thermal, cmo.haline, cmo.matter]
):
    
    kwargs = dict(yincrease=False, x='lat', y='lev', ax=ax)
    
    # Plot a section along the dateline in 1980 in the southern ocean

jbusecke

ds_section = ds.sel(
  time='2010-01',
  y=slice(-90,10),
  lev=slice(0,1500)
).interp(x=230).squeeze()

jbusecke

from fastjmd95 import rho
ds['sigma_0'] = rho(ds.so, ds.thetao, 0) - 1000