def test_rcm_emanuel():
num_lev = 30
water_depth = 5.
# Temperatures in a single column
state = climlab.column_state(num_lev=num_lev, water_depth=water_depth)
# Initialize a nearly dry column (small background stratospheric humidity)
state['q'] = np.ones_like(state.Tatm) * 5.E-6
# ASYNCHRONOUS COUPLING -- the radiation uses a much longer timestep
short_timestep = climlab.constants.seconds_per_hour
# The top-level model
model = climlab.TimeDependentProcess(name='Radiative-Convective Model',
state=state,
timestep=short_timestep)
# Radiation coupled to water vapor
rad = climlab.radiation.RRTMG(name='Radiation',
state=state,
specific_humidity=state.q,
albedo=0.3,
timestep=24*short_timestep)
# Convection scheme -- water vapor is a state variable
conv = climlab.convection.EmanuelConvection(name='Convection',
state=state,
timestep=short_timestep)
# Surface heat flux processes
shf = climlab.surface.SensibleHeatFlux(name='SHF',
state=state, Cd=0.5E-3,
timestep=climlab.constants.seconds_per_hour)
lhf = climlab.surface.LatentHeatFlux(name='LHF',
state=state, Cd=0.5E-3,
timestep=short_timestep)
# Couple all the submodels together
for proc in [rad, conv, shf, lhf]:
model.add_subprocess(proc.name, proc)
model.step_forward()
to_xarray(model)
def test_radiative_forcing():
'''Run a single-column radiative-convective model with RRTMG radiation
out to equilibrium. Clone the model, double CO2 and measure the instantaneous
change in TOA flux. It should be positive net downward flux.'''
# State variables (Air and surface temperature)
state = climlab.column_state(num_lev=30, water_depth=1.)
# Parent model process
rcm = climlab.TimeDependentProcess(state=state)
# Fixed relative humidity
h2o = climlab.radiation.ManabeWaterVapor(state=state)
# Couple water vapor to radiation
# Set icld=0 for clear-sky only (no need to call cloud overlap routine)
rad = climlab.radiation.RRTMG(state=state, specific_humidity=h2o.q, icld=0)
# Convective adjustment
conv = climlab.convection.ConvectiveAdjustment(state=state,
adj_lapse_rate=6.5)
# Couple everything together
rcm.add_subprocess('Radiation', rad)
rcm.add_subprocess('WaterVapor', h2o)
rcm.add_subprocess('Convection', conv)
rcm.integrate_years(5.)
assert np.abs(rcm.ASR - rcm.OLR) < 0.1 # close to energy balance
rcm2 = climlab.process_like(rcm)
rcm2.subprocess['Radiation'].absorber_vmr['CO2'] *= 2.
rcm2.compute_diagnostics()
assert (rcm2.ASR - rcm2.OLR) > 1. # positive radiative forcing
# Test the xarray interface
to_xarray(rcm2)
def test_latitude():
'''
Run a radiative equilibrum model with RRTMG radiation out to equilibrium
with an annual mean insolation profile as a function of latitude.
'''
num_lat = 8
# State variables (Air and surface temperature)
state = climlab.column_state(num_lev=30, num_lat=num_lat, water_depth=1.)
# Parent model process
model = climlab.TimeDependentProcess(state=state)
# insolation
sol = climlab.radiation.AnnualMeanInsolation(domains=model.Ts.domain)
# radiation module with insolation as input
# Set icld=0 for clear-sky only (no need to call cloud overlap routine)
rad = climlab.radiation.RRTMG(state=state, icld=0,
S0=sol.S0,
insolation=sol.insolation,
coszen=sol.coszen)
# Couple everything together
model.add_subprocess('Radiation', rad)
model.add_subprocess('Insolation', sol)
# Run out to equilibrium
model.integrate_years(2.)
# Test for energy balance
assert np.all(np.abs(model.ASR - model.OLR) < 0.1)
# Test for reasonable surface temperature gradient
# reversal of gradient at equator
grad = np.diff(model.Ts, axis=0)
assert np.all(grad[0:(int(num_lat/2)-1)] > 0.)
assert np.all(grad[int(num_lat/2):] < 0.)
## climlab setup
# create surface and atmosperic domains
sfc, atm = climlab.domain.single_column(lev=g.p / 100.)
# create atmosheric state
state = AttrDict()
# assign surface temperature and vertical temperature profiles
Ts_ = climlab.Field(np.array([Ts]), domain=sfc)
state['Ts'] = Ts_
Tatm = climlab.Field(g.T, domain=atm)
state['Tatm'] = Tatm
# Parent model process
rcm = climlab.TimeDependentProcess(state=state)
rad = climlab.radiation.RRTMG(state=state,
specific_humidity=g.q,
albedo=0.3,
ozone_file=None)
rad.subprocess['LW'].absorber_vmr = {
'CO2': CO2 * 1.e-6,
'CH4': 0.,
'N2O': 0.,
'O2': 0.,
'CFC11': 0.,
'CFC12': 0.,
'CFC22': 0.,
'CCL4': 0.,
'O3': 0.
}
def init_ram_no_advection(
ds,
m,
CO2,
timestep,
surface_diffk=None,
albedo=.77 #<- calculated value vs. first estimate = .8
):
#create two domains: atm and surface
sfc, atm = climlab.domain.single_column(
lev=ds['Pressure'].sel(month=m).values, water_depth=1.)
#create an atmospheric state
state = AttrDict()
#set up a surface temperature profile
Ts_dict = {}
for month in ds['month'].values:
Ts_dict[month] = (ds['Temperature'].sel(month=month)[-1] - 52 *
(ds['Temperature'].sel(month=month)[-2] -
ds['Temperature'].sel(month=month)[-1]) /
(ds['Altitude'].sel(month=month)[-2] -
ds['Altitude'].sel(month=month)[-1])).values
T_s = climlab.Field(Ts_dict[m], domain=sfc)
state['Ts'] = T_s #K
#set up an atmospheric temperature profile
T_atm = climlab.Field(ds['Temperature'].sel(month=m).values, domain=atm)
state['Tatm'] = T_atm #K
#radiation model setup
rad = climlab.radiation.RRTMG(
name='Radiation(all gases)',
state=state,
specific_humidity=ds['spec_humidity'].sel(month=m).values,
albedo=albedo,
timestep=timestep,
ozone_file=None,
S0=1365.2,
insolation=ds['monthly_insolation'].sel(month=m).values,
isolvar=
1 #see https://climlab.readthedocs.io/en/latest/_modules/climlab/radiation/rrtm/rrtmg_sw.html#RRTMG_SW._compute_heating_rates
#1 = Solar variability (using NRLSSI2 solar
# model) with solar cycle contribution
# determined by fraction of solar cycle
# with facular and sunspot variations
# fixed to their mean variations over the
# average of Solar Cycles 13-24;
# two amplitude scale factors allow
# facular and sunspot adjustments from
# mean solar cycle as defined by indsolvar
)
rad.absorber_vmr['O3'] = ds['O3'].sel(month=m).values #kg/kg
rad.absorber_vmr['CO2'] = CO2 #kg/kg
#create ram
ram = climlab.TimeDependentProcess(state=state, timestep=timestep)
#add latitude axis
lat = climlab.domain.axis.Axis(axis_type='lat', points=-90.)
ram.domains['Ts'].axes['lat'] = lat
#add radiation
ram.add_subprocess('Radiation', rad)
#compute ram
ram.compute()
#turbulence model setup (coupled to rad model, ds, and month)
turb = Turbulence(surface_diffk=surface_diffk,
name='Turbulence',
state=state,
rad=rad,
m=m,
ds=ds,
timestep=timestep)
#add turbulence
ram.add_subprocess('Turbulence', turb) #add insolation subprocess
#compute ram
ram.compute()
return ram
def init_ram(
ds,
m,
CO2,
timestep,
turbulence_on,
advection_on,
advection=None,
surface_diffk=None,
albedo=.77 #<- calculated value vs. first estimate = .8
):
#create two domains: atm and surface
sfc, atm = climlab.domain.single_column(
lev=ds['Pressure'].sel(month=m).values, water_depth=1.)
#change the level bounds
atm.axes['lev'].bounds[0] = atm.axes['lev'].bounds[1] - 2 * (
atm.axes['lev'].bounds[1] - atm.axes['lev'].points[0])
atm.axes['lev'].bounds[-1] = atm.axes['lev'].bounds[-2] + 2 * (
atm.axes['lev'].points[-1] - atm.axes['lev'].bounds[-2])
#change the level delta
atm.axes['lev'].delta = atm.axes['lev'].bounds[1:] - atm.axes[
'lev'].bounds[0:-1]
#update heat capacity
atm.set_heat_capacity()
#create an atmospheric state
state = AttrDict()
#set up a surface temperature profile
Ts_dict = {}
for month in ds['month'].values:
Ts_dict[month] = (ds['Temperature'].sel(month=month)[-1] - 52 *
(ds['Temperature'].sel(month=month)[-2] -
ds['Temperature'].sel(month=month)[-1]) /
(ds['Altitude'].sel(month=month)[-2] -
ds['Altitude'].sel(month=month)[-1])).values
T_s = climlab.Field(Ts_dict[m], domain=sfc)
state['Ts'] = T_s #K
#set up an atmospheric temperature profile
T_atm = climlab.Field(ds['Temperature'].sel(month=m).values, domain=atm)
state['Tatm'] = T_atm #K
#radiation model setup
rad = climlab.radiation.RRTMG(
name='Radiation(all gases)',
state=state,
specific_humidity=ds['spec_humidity'].sel(month=m).values,
albedo=albedo,
timestep=timestep,
ozone_file=None,
S0=1365.2,
insolation=ds['monthly_insolation'].sel(month=m).values,
isolvar=
1 #see https://climlab.readthedocs.io/en/latest/_modules/climlab/radiation/rrtm/rrtmg_sw.html#RRTMG_SW._compute_heating_rates
#1 = Solar variability (using NRLSSI2 solar
# model) with solar cycle contribution
# determined by fraction of solar cycle
# with facular and sunspot variations
# fixed to their mean variations over the
# average of Solar Cycles 13-24;
# two amplitude scale factors allow
# facular and sunspot adjustments from
# mean solar cycle as defined by indsolvar
)
rad.absorber_vmr['O3'] = ds['O3'].sel(month=m).values #kg/kg
rad.absorber_vmr['CO2'] = CO2 #kg/kg
#create ram
ram = climlab.TimeDependentProcess(state=state, timestep=timestep)
#add latitude axis
lat = climlab.domain.axis.Axis(axis_type='lat', points=-90.)
ram.domains['Ts'].axes['lat'] = lat
#add radiation
ram.add_subprocess('Radiation', rad)
#compute ram
ram.compute()
if turbulence_on == True:
#turbulence model setup (coupled to rad model, ds, and month)
turb = Turbulence(surface_diffk=surface_diffk,
name='Turbulence',
state=state,
rad=rad,
m=m,
ds=ds,
timestep=timestep)
#add turbulence
ram.add_subprocess('Turbulence', turb) #add insolation subprocess
#compute ram
ram.compute()
if turbulence_on == False:
turb = None
if advection_on:
#advective model setup (coupled to rad model)
adv = climlab.process.external_forcing.ExternalForcing(state=state,
ram=ram,
turb=turb)
if turbulence_on:
normal_advection = -(
(ram.TdotSW_clr + ram.TdotLW_clr) /
climlab.constants.seconds_per_day + turb.turb_atm_hr
) #(K/day + K/day)/(sec/day) + K/sec
if advection == None:
adv.forcing_tendencies['Tatm'] = normal_advection
else:
adv.forcing_tendencies['Tatm'] = np.copy(advection[m])
#add advection
ram.add_subprocess('Advection', adv)
#compute ram
ram.compute()
return ram