def __init__(self,
dt_seconds=1800,
nx=64,
ny=32,
nz=10,
state=None,
input_fields_to_store=input_vars,
output_fields_to_store=output_vars,
input_save_fn=None,
output_save_fn=None,
save_interval=6,
convection=None,
extra_components=[]):
"""
Initialize model. Uses SSTs from Andersen and Kuang 2012.
Creates initial state unless state is given.
"""
climt.set_constants_from_dict(
{'stellar_irradiance': {
'value': 200,
'units': 'W m^-2'
}})
self.model_time_step = timedelta(seconds=dt_seconds)
self.step_counter = 0
self.save_interval = save_interval
# Create components
if convection is None:
convection = climt.EmanuelConvection(
tendencies_in_diagnostics=True)
simple_physics = TimeDifferencingWrapper(
climt.SimplePhysics(tendencies_in_diagnostics=True))
radiation = climt.GrayLongwaveRadiation(tendencies_in_diagnostics=True)
components = [simple_physics, radiation, convection] + extra_components
self.dycore = climt.GFSDynamicalCore(components,
number_of_damped_levels=2)
grid = climt.get_grid(nx=nx, ny=ny, nz=nz)
if state is None:
self.create_initial_state(grid)
else:
self.state = state
if not input_save_fn is None:
self.input_netcdf_monitor = NetCDFMonitor(
input_save_fn,
write_on_store=True,
store_names=input_fields_to_store)
if not output_save_fn is None:
self.output_netcdf_monitor = NetCDFMonitor(
output_save_fn,
write_on_store=True,
store_names=output_fields_to_store)
def test_adding_constant():
set_constants_from_dict(sample_constants)
for constant in sample_constants.keys():
constant_value = get_constant(constant,
sample_constants[constant]['units'])
assert constant_value == sample_constants[constant]['value']
NUM_TILES = NUM_ROWS*NUM_COLS
TILE_WIDTH = 5
LOOP_MS = 150 # ms per frame
CURRENT_DRAW = 'alt'
# Set up the climate model
fields_to_store = ['air_temperature', 'air_pressure', 'eastward_wind',
'northward_wind', 'air_pressure_on_interface_levels',
'surface_pressure', 'upwelling_longwave_flux_in_air',
'specific_humidity', 'surface_temperature',
'latitude', 'longitude',
'convective_heating_rate']
climt.set_constants_from_dict({
'stellar_irradiance': {'value': 200, 'units': 'W m^-2'}})
model_time_step = timedelta(seconds=600)
# Create components
convection = climt.EmanuelConvection()
simple_physics = TimeDifferencingWrapper(climt.SimplePhysics())
constant_duration = 6
radiation_lw = UpdateFrequencyWrapper(
climt.RRTMGLongwave(), constant_duration*model_time_step)
radiation_sw = UpdateFrequencyWrapper(
climt.RRTMGShortwave(), constant_duration*model_time_step)
def init_radiative_state(self, atmosphere, surface):
climt.set_constants_from_dict({
"stellar_irradiance": {
"value": self.solar_constant,
"units": 'W m^-2'
}
})
if self._is_mcica:
overlap = 'maximum_random'
else:
overlap = 'random'
self._rad_lw = climt.RRTMGLongwave(
cloud_optical_properties=self._cloud_optical_properties,
cloud_ice_properties=self._cloud_ice_properties,
cloud_liquid_water_properties='radius_dependent_absorption',
cloud_overlap_method=overlap,
mcica=self._is_mcica)
self._rad_sw = climt.RRTMGShortwave(
ignore_day_of_year=True,
cloud_optical_properties=self._cloud_optical_properties,
cloud_ice_properties=self._cloud_ice_properties,
cloud_liquid_water_properties='radius_dependent_absorption',
cloud_overlap_method=overlap,
mcica=self._is_mcica)
state_lw = {}
state_sw = {}
plev = atmosphere['plev']
phlev = atmosphere['phlev']
numlevels = len(plev)
for state0 in state_lw, state_sw:
state0['mid_levels'] = DataArray(np.arange(0, numlevels),
dims=('mid_levels', ),
attrs={'label': 'mid_levels'})
state0['interface_levels'] = DataArray(
np.arange(0, numlevels + 1),
dims=('interface_levels', ),
attrs={'label': 'interface_levels'})
state0['air_pressure'] = DataArray(plev,
dims=('mid_levels', ),
attrs={'units': 'Pa'})
state0['air_pressure_on_interface_levels'] = DataArray(
phlev, dims=('interface_levels', ), attrs={'units': 'Pa'})
state0['time'] = datetime.datetime(2000, 1, 1)
### Aerosols ###
# TODO: Should all the aerosol values be zero?!
# Longwave specific
num_lw_bands = self._rad_lw.num_longwave_bands
state_lw['longwave_optical_thickness_due_to_aerosol'] = DataArray(
np.zeros((num_lw_bands, numlevels)),
dims=('num_longwave_bands', 'mid_levels'),
attrs={'units': 'dimensionless'})
state_lw['surface_longwave_emissivity'] = DataArray(
surface.longwave_emissivity * np.ones((num_lw_bands, )),
dims=('num_longwave_bands'),
attrs={'units': 'dimensionless'})
# Shortwave specific changes
num_sw_bands = self._rad_sw.num_shortwave_bands
num_aerosols = self._rad_sw.num_ecmwf_aerosols
state_sw['aerosol_optical_depth_at_55_micron'] = DataArray(
np.zeros((num_aerosols, numlevels)),
dims=('num_ecmwf_aerosols', 'mid_levels'),
attrs={'units': 'dimensionless'})
state_sw['solar_cycle_fraction'] = DataArray(
0, attrs={'units': 'dimensionless'})
state_sw['flux_adjustment_for_earth_sun_distance'] = DataArray(
1, attrs={'units': 'dimensionless'})
for surface_albedo in [
'surface_albedo_for_diffuse_near_infrared',
'surface_albedo_for_direct_near_infrared',
'surface_albedo_for_diffuse_shortwave',
'surface_albedo_for_direct_shortwave'
]:
state_sw[surface_albedo] = DataArray(
np.array(float(surface.albedo)),
attrs={'units': 'dimensionless'})
for quant in [
'shortwave_optical_thickness_due_to_aerosol',
'single_scattering_albedo_due_to_aerosol',
'aerosol_asymmetry_parameter'
]:
state_sw[quant] = DataArray(np.zeros((num_sw_bands, numlevels)),
dims=('num_shortwave_bands',
'mid_levels'),
attrs={'units': 'dimensionless'})
return state_lw, state_sw
def init_radiative_state(self, atmosphere, surface):
climt.set_constants_from_dict({
"stellar_irradiance": {
"value": self.solar_constant,
"units": "W m^-2"
}
})
if self._is_mcica:
overlap = "maximum_random"
else:
overlap = "random"
self._rad_lw = climt.RRTMGLongwave(
cloud_optical_properties=self._cloud_optical_properties,
cloud_ice_properties=self._cloud_ice_properties,
cloud_liquid_water_properties="radius_dependent_absorption",
cloud_overlap_method=overlap,
mcica=self._is_mcica,
)
self._rad_sw = climt.RRTMGShortwave(
ignore_day_of_year=True,
cloud_optical_properties=self._cloud_optical_properties,
cloud_ice_properties=self._cloud_ice_properties,
cloud_liquid_water_properties="radius_dependent_absorption",
cloud_overlap_method=overlap,
mcica=self._is_mcica,
)
state_lw = {}
state_sw = {}
plev = atmosphere["plev"]
phlev = atmosphere["phlev"]
numlevels = len(plev)
for state0 in state_lw, state_sw:
state0["mid_levels"] = DataArray(
np.arange(0, numlevels),
dims=("mid_levels", ),
attrs={"label": "mid_levels"},
)
state0["interface_levels"] = DataArray(
np.arange(0, numlevels + 1),
dims=("interface_levels", ),
attrs={"label": "interface_levels"},
)
state0["air_pressure"] = DataArray(plev,
dims=("mid_levels", ),
attrs={"units": "Pa"})
state0["air_pressure_on_interface_levels"] = DataArray(
phlev, dims=("interface_levels", ), attrs={"units": "Pa"})
state0["time"] = datetime.datetime(2000, 1, 1)
### Aerosols ###
# TODO: Should all the aerosol values be zero?!
# Longwave specific
num_lw_bands = self._rad_lw.num_longwave_bands
state_lw["longwave_optical_thickness_due_to_aerosol"] = DataArray(
np.zeros((num_lw_bands, numlevels)),
dims=("num_longwave_bands", "mid_levels"),
attrs={"units": "dimensionless"},
)
state_lw["surface_longwave_emissivity"] = DataArray(
surface.longwave_emissivity * np.ones((num_lw_bands, )),
dims=("num_longwave_bands"),
attrs={"units": "dimensionless"},
)
# Shortwave specific changes
num_sw_bands = self._rad_sw.num_shortwave_bands
num_aerosols = self._rad_sw.num_ecmwf_aerosols
state_sw["aerosol_optical_depth_at_55_micron"] = DataArray(
np.zeros((num_aerosols, numlevels)),
dims=("num_ecmwf_aerosols", "mid_levels"),
attrs={"units": "dimensionless"},
)
state_sw["solar_cycle_fraction"] = DataArray(
0, attrs={"units": "dimensionless"})
state_sw["flux_adjustment_for_earth_sun_distance"] = DataArray(
1, attrs={"units": "dimensionless"})
for surface_albedo in [
"surface_albedo_for_diffuse_near_infrared",
"surface_albedo_for_direct_near_infrared",
"surface_albedo_for_diffuse_shortwave",
"surface_albedo_for_direct_shortwave",
]:
state_sw[surface_albedo] = DataArray(
np.array(float(surface.albedo)),
attrs={"units": "dimensionless"})
for quant in [
"shortwave_optical_thickness_due_to_aerosol",
"single_scattering_albedo_due_to_aerosol",
"aerosol_asymmetry_parameter",
]:
state_sw[quant] = DataArray(
np.zeros((num_sw_bands, numlevels)),
dims=("num_shortwave_bands", "mid_levels"),
attrs={"units": "dimensionless"},
)
return state_lw, state_sw