import os
import shutil
import cmocean # noqa: F401
import numpy as np
import xarray as xr
from mpas_tools.cime.constants import constants
from mpas_tools.io import write_netcdf
from compass.ocean.iceshelf import compute_land_ice_pressure_and_draft
from compass.ocean.tests.isomip_plus.geom import interpolate_geom
from compass.ocean.tests.isomip_plus.viz.plot import MoviePlotter
from compass.ocean.vertical import init_vertical_coord
from compass.step import Step
[docs]
class InitialState(Step):
"""
A step for creating a mesh and initial condition for ISOMIP+ test cases
Attributes
----------
resolution : float
The horizontal resolution (km) of the test case
experiment : {'Ocean0', 'Ocean1', 'Ocean2'}
The ISOMIP+ experiment
vertical_coordinate : str
The type of vertical coordinate (``z-star``, ``z-level``, etc.)
time_varying_forcing : bool
Whether the run includes time-varying land-ice forcing
thin_film_present: bool
Whether the run includes a thin film below grounded ice
"""
[docs]
def __init__(self, test_case, resolution, experiment, vertical_coordinate,
time_varying_forcing, thin_film_present):
"""
Create the step
Parameters
----------
test_case : compass.TestCase
The test case this step belongs to
resolution : float
The horizontal resolution (km) of the test case
experiment : {'Ocean0', 'Ocean1', 'Ocean2'}
The ISOMIP+ experiment
vertical_coordinate : str
The type of vertical coordinate (``z-star``, ``z-level``, etc.)
time_varying_forcing : bool
Whether the run includes time-varying land-ice forcing
thin_film_present: bool
Whether the run includes a thin film below grounded ice
"""
super().__init__(test_case=test_case, name='initial_state')
self.resolution = resolution
self.experiment = experiment
self.vertical_coordinate = vertical_coordinate
self.time_varying_forcing = time_varying_forcing
self.thin_film_present = thin_film_present
self.add_input_file(
filename='input_geometry_processed.nc',
target='../process_geom/input_geometry_processed.nc')
self.add_input_file(
filename='culled_mesh.nc',
target='../cull_mesh/culled_mesh.nc')
for file in ['initial_state.nc', 'init_mode_forcing_data.nc']:
self.add_output_file(file)
[docs]
def run(self):
"""
Run this step of the test case
"""
ds, frac = self._compute_initial_condition()
self._compute_restoring(ds, frac)
self._plot(ds)
def _compute_initial_condition(self):
config = self.config
thin_film_present = self.thin_film_present
if self.vertical_coordinate == 'single_layer':
config.set('vertical_grid', 'vert_levels', '1',
comment='Number of vertical levels')
config.set('vertical_grid', 'coord_type', 'z-level')
section = config['isomip_plus']
min_land_ice_fraction = section.getfloat('min_land_ice_fraction')
min_ocean_fraction = section.getfloat('min_ocean_fraction')
ds_geom = xr.open_dataset('input_geometry_processed.nc')
ds_mesh = xr.open_dataset('culled_mesh.nc')
ds = interpolate_geom(ds_mesh, ds_geom, min_ocean_fraction,
thin_film_present)
ds['landIceFraction'] = \
ds['landIceFraction'].expand_dims(dim='Time', axis=0)
ds['landIceFloatingFraction'] = \
ds['landIceFloatingFraction'].expand_dims(dim='Time', axis=0)
ds['modifyLandIcePressureMask'] = \
(ds['landIceFraction'] > 0.01).astype(int)
# This inequality needs to be > rather than >= to ensure correctness
# when min_land_ice_fraction = 0
mask = ds.landIceFraction > min_land_ice_fraction
floating_mask = np.logical_and(
ds.landIceFloatingFraction > 0,
ds.landIceFraction > min_land_ice_fraction)
ds['landIceMask'] = mask.astype(int)
ds['landIceFloatingMask'] = floating_mask.astype(int)
ds['landIceFraction'] = xr.where(mask, ds.landIceFraction, 0.)
ref_density = constants['SHR_CONST_RHOSW']
landIcePressure, landIceDraft = compute_land_ice_pressure_and_draft(
ssh=ds.ssh, modify_mask=ds.ssh < 0., ref_density=ref_density)
ds['landIcePressure'] = landIcePressure
ds['landIceDraft'] = landIceDraft
if self.time_varying_forcing:
self._write_time_varying_forcing(ds_init=ds)
ds['bottomDepth'] = -ds.bottomDepthObserved
section = config['isomip_plus']
# Deepen the bottom depth to maintain the minimum water-column
# thickness
min_column_thickness = section.getfloat('min_column_thickness')
min_layer_thickness = section.getfloat('min_layer_thickness')
min_levels = section.getint('minimum_levels')
min_column_thickness = max(min_column_thickness,
min_levels * min_layer_thickness)
min_depth = -ds.ssh + min_column_thickness
ds['bottomDepth'] = np.maximum(ds.bottomDepth, min_depth)
print(f'Adjusted bottomDepth for '
f'{np.sum(ds.bottomDepth.values<min_depth.values)} cells '
f'to achieve minimum column thickness of {min_column_thickness}')
init_vertical_coord(config, ds)
max_bottom_depth = -config.getfloat('vertical_grid', 'bottom_depth')
frac = (0. - ds.zMid) / (0. - max_bottom_depth)
# compute T, S
init_top_temp = section.getfloat('init_top_temp')
init_bot_temp = section.getfloat('init_bot_temp')
init_top_sal = section.getfloat('init_top_sal')
init_bot_sal = section.getfloat('init_bot_sal')
thin_film_mask = np.logical_and(mask.values,
np.logical_not(floating_mask.values))
# These coefficients are hard-coded as the defaults in the namelist
# Note that using the land ice pressure rather than the pressure at
# floatation will mean that there is a small amount of cooling from
# grounding line retreat. However, the thin film should be thin enough
# that this effect isn't signicant.
freezing_temp = (6.22e-2 +
-5.63e-2 * init_bot_sal +
-7.43e-8 * landIcePressure +
-1.74e-10 * landIcePressure * init_bot_sal)
_, thin_film_temp = np.meshgrid(ds.refZMid, freezing_temp)
_, thin_film_mask = np.meshgrid(ds.refZMid, thin_film_mask)
thin_film_temp = np.expand_dims(thin_film_temp, axis=0)
if self.vertical_coordinate == 'single_layer':
ds['temperature'] = init_bot_temp * xr.ones_like(frac)
ds['salinity'] = init_bot_sal * xr.ones_like(frac)
else:
ds['temperature'] = \
(1.0 - frac) * init_top_temp + frac * init_bot_temp
ds['salinity'] = \
(1.0 - frac) * init_top_sal + frac * init_bot_sal
# for thin film cells, set temperature to freezing point
ds['temperature'] = xr.where(thin_film_mask, thin_film_temp,
ds.temperature)
# compute coriolis
coriolis_parameter = section.getfloat('coriolis_parameter')
ds['fCell'] = coriolis_parameter * xr.ones_like(ds.xCell)
ds['fEdge'] = coriolis_parameter * xr.ones_like(ds.xEdge)
ds['fVertex'] = coriolis_parameter * xr.ones_like(ds.xVertex)
normalVelocity = xr.zeros_like(ds.xEdge)
normalVelocity = normalVelocity.broadcast_like(ds.refBottomDepth)
normalVelocity = normalVelocity.transpose('nEdges', 'nVertLevels')
ds['normalVelocity'] = normalVelocity.expand_dims(dim='Time', axis=0)
write_netcdf(ds, 'initial_state.nc')
return ds, frac
def _plot(self, ds):
"""
Plot several fields from the initial condition
"""
config = self.config
min_column_thickness = config.getfloat('isomip_plus',
'min_column_thickness')
plot_folder = '{}/plots'.format(self.work_dir)
if os.path.exists(plot_folder):
shutil.rmtree(plot_folder)
# plot a few fields
section_y = config.getfloat('isomip_plus_viz', 'section_y')
# show progress only if we're not writing to a log file
show_progress = self.log_filename is None
plotter = MoviePlotter(inFolder=self.work_dir,
streamfunctionFolder=self.work_dir,
outFolder=plot_folder, expt=self.experiment,
sectionY=section_y, dsMesh=ds, ds=ds,
showProgress=show_progress)
ds['oceanFracObserved'] = \
ds['oceanFracObserved'].expand_dims(dim='Time', axis=0)
ds['landIcePressure'] = \
ds['landIcePressure'].expand_dims(dim='Time', axis=0)
ds['landIceGroundedFraction'] = \
ds['landIceGroundedFraction'].expand_dims(dim='Time', axis=0)
ds['bottomDepth'] = ds['bottomDepth'].expand_dims(dim='Time', axis=0)
ds['totalColThickness'] = ds['ssh']
ds['totalColThickness'].values = \
ds['layerThickness'].sum(dim='nVertLevels')
tol = 1e-10
plotter.plot_horiz_series(ds.landIceMask,
'landIceMask', 'landIceMask',
True)
plotter.plot_horiz_series(ds.landIceFloatingMask,
'landIceFloatingMask', 'landIceFloatingMask',
True)
plotter.plot_horiz_series(ds.landIcePressure,
'landIcePressure', 'landIcePressure',
True, vmin=1, vmax=1e6, cmap_scale='log')
plotter.plot_horiz_series(ds.ssh,
'ssh', 'ssh',
True, vmin=-700, vmax=0)
plotter.plot_horiz_series(ds.bottomDepth,
'bottomDepth', 'bottomDepth',
True, vmin=0, vmax=700)
plotter.plot_horiz_series(ds.ssh + ds.bottomDepth,
'H', 'H', True,
vmin=min_column_thickness + tol, vmax=700,
cmap_set_under='r', cmap_scale='log')
plotter.plot_horiz_series(ds.totalColThickness,
'totalColThickness', 'totalColThickness',
True, vmin=min_column_thickness + 1e-10,
vmax=700, cmap_set_under='r')
plotter.plot_horiz_series(ds.landIceFraction,
'landIceFraction', 'landIceFraction',
True, vmin=0 + tol, vmax=1 - tol,
cmap='cmo.balance',
cmap_set_under='k', cmap_set_over='r')
plotter.plot_horiz_series(ds.landIceFloatingFraction,
'landIceFloatingFraction',
'landIceFloatingFraction',
True, vmin=0 + tol, vmax=1 - tol,
cmap='cmo.balance',
cmap_set_under='k', cmap_set_over='r')
plotter.plot_horiz_series(ds.landIceGroundedFraction,
'landIceGroundedFraction',
'landIceGroundedFraction',
True, vmin=0 + tol, vmax=1 - tol,
cmap='cmo.balance',
cmap_set_under='k', cmap_set_over='r')
plotter.plot_horiz_series(ds.oceanFracObserved,
'oceanFracObserved', 'oceanFracObserved',
True, vmin=0 + tol, vmax=1 - tol,
cmap='cmo.balance',
cmap_set_under='k', cmap_set_over='r')
plotter.plot_layer_interfaces()
plotter.plot_3d_field_top_bot_section(
ds.layerThickness, nameInTitle='layerThickness',
prefix='h', units='m',
vmin=min_column_thickness + tol, vmax=50,
cmap='cmo.deep_r', cmap_set_under='r')
plotter.plot_3d_field_top_bot_section(
ds.zMid, nameInTitle='zMid', prefix='zmid', units='m',
vmin=-720., vmax=0., cmap='cmo.deep_r')
plotter.plot_3d_field_top_bot_section(
ds.temperature, nameInTitle='temperature', prefix='temp',
units='C', vmin=-2., vmax=1., cmap='cmo.thermal')
plotter.plot_3d_field_top_bot_section(
ds.salinity, nameInTitle='salinity', prefix='salin',
units='PSU', vmin=33.8, vmax=34.7, cmap='cmo.haline')
def _compute_restoring(self, ds, frac):
config = self.config
section = config['isomip_plus']
ref_density = constants['SHR_CONST_RHOSW']
ds_forcing = xr.Dataset()
restore_top_temp = section.getfloat('restore_top_temp')
restore_bot_temp = section.getfloat('restore_bot_temp')
restore_top_sal = section.getfloat('restore_top_sal')
restore_bot_sal = section.getfloat('restore_bot_sal')
ds_forcing['temperatureInteriorRestoringValue'] = \
(1.0 - frac) * restore_top_temp + frac * restore_bot_temp
ds_forcing['salinityInteriorRestoringValue'] = \
(1.0 - frac) * restore_top_sal + frac * restore_bot_sal
restore_rate = section.getfloat('restore_rate')
restore_xmin = section.getfloat('restore_xmin')
restore_xmax = section.getfloat('restore_xmax')
frac = np.maximum(
(ds.xIsomipCell - restore_xmin) / (restore_xmax - restore_xmin),
0.)
frac = frac.broadcast_like(
ds_forcing.temperatureInteriorRestoringValue)
# convert from 1/days to 1/s
ds_forcing['temperatureInteriorRestoringRate'] = \
frac * restore_rate / constants['SHR_CONST_CDAY']
ds_forcing['salinityInteriorRestoringRate'] = \
ds_forcing.temperatureInteriorRestoringRate
# compute "evaporation"
restore_evap_rate = section.getfloat('restore_evap_rate')
mask = np.logical_and(ds.xIsomipCell >= restore_xmin,
ds.xIsomipCell <= restore_xmax)
mask = mask.expand_dims(dim='Time', axis=0)
# convert to m/s, negative for evaporation rather than precipitation
evap_rate = -restore_evap_rate / (constants['SHR_CONST_CDAY'] * 365)
# PSU*m/s to kg/m^2/s
sflux_factor = 1.
# C*m/s to W/m^2
hflux_factor = 1. / (ref_density * constants['SHR_CONST_CPSW'])
ds_forcing['evaporationFlux'] = mask * ref_density * evap_rate
ds_forcing['seaIceSalinityFlux'] = \
mask * evap_rate * restore_top_sal / sflux_factor
ds_forcing['seaIceHeatFlux'] = \
mask * evap_rate * restore_top_temp / hflux_factor
if self.vertical_coordinate == 'single_layer':
x_max = np.max(ds.xIsomipCell.values)
ds_forcing['tidalInputMask'] = xr.where(
ds.xIsomipCell > (x_max - 0.6 * self.resolution * 1e3), 1.0,
0.0)
else:
ds_forcing['tidalInputMask'] = xr.zeros_like(frac)
write_netcdf(ds_forcing, 'init_mode_forcing_data.nc')
def _write_time_varying_forcing(self, ds_init):
"""
Write time-varying land-ice forcing and update the initial condition
"""
config = self.config
dates = config.get('isomip_plus_forcing', 'dates')
dates = [date.ljust(64) for date in dates.replace(',', ' ').split()]
scales = config.get('isomip_plus_forcing', 'scales')
scales = [float(scale) for scale in scales.replace(',', ' ').split()]
ds_out = xr.Dataset()
ds_out['xtime'] = ('Time', dates)
ds_out['xtime'] = ds_out.xtime.astype('S')
landIceDraft = list()
landIcePressure = list()
landIceFraction = list()
landIceFloatingFraction = list()
for scale in scales:
landIceDraft.append(scale * ds_init.landIceDraft)
landIcePressure.append(scale * ds_init.landIcePressure)
landIceFraction.append(ds_init.landIceFraction)
landIceFloatingFraction.append(ds_init.landIceFloatingFraction)
ds_out['landIceDraftForcing'] = xr.concat(landIceDraft, 'Time')
ds_out.landIceDraftForcing.attrs['units'] = 'm'
ds_out.landIceDraftForcing.attrs['long_name'] = \
'The approximate elevation of the land ice-ocean interface'
ds_out['landIcePressureForcing'] = \
xr.concat(landIcePressure, 'Time')
ds_out.landIcePressureForcing.attrs['units'] = 'm'
ds_out.landIcePressureForcing.attrs['long_name'] = \
'Pressure from the weight of land ice at the ice-ocean interface'
ds_out['landIceFractionForcing'] = \
xr.concat(landIceFraction, 'Time')
ds_out.landIceFractionForcing.attrs['long_name'] = \
'The fraction of each cell covered by land ice'
ds_out['landIceFloatingFractionForcing'] = \
xr.concat(landIceFloatingFraction, 'Time')
ds_out.landIceFloatingFractionForcing.attrs['long_name'] = \
'The fraction of each cell covered by floating land ice'
write_netcdf(ds_out, 'land_ice_forcing.nc')
ds_init['landIceDraft'] = scales[0] * ds_init.landIceDraft
ds_init['ssh'] = ds_init.landIceDraft
ds_init['landIcePressure'] = scales[0] * ds_init.landIcePressure