climlab.surface.turbulent¶
Processes for surface turbulent heat and moisture fluxes
SensibleHeatFlux
and
LatentHeatFlux
implement standard bulk formulae
for the turbulent heat fluxes, assuming that the heating or moistening
occurs in the lowest atmospheric model level.
 Example
Here is an example of setting up a singlecolumn RadiativeConvective model with interactive water vapor and surface latent and sensible heat fluxes.
This example also demonstrates asynchronous coupling: the radiation uses a longer timestep than the other model components:
import numpy as np import climlab from climlab import constants as const # Temperatures in a single column full_state = climlab.column_state(num_lev=30, water_depth=2.5) temperature_state = {'Tatm':full_state.Tatm,'Ts':full_state.Ts} # Initialize a nearly dry column (small background stratospheric humidity) q = np.ones_like(full_state.Tatm) * 5.E6 # Add specific_humidity to the state dictionary full_state['q'] = q # ASYNCHRONOUS COUPLING  the radiation uses a much longer timestep # The toplevel model model = climlab.TimeDependentProcess(state=full_state, timestep=const.seconds_per_hour) # Radiation coupled to water vapor rad = climlab.radiation.RRTMG(state=temperature_state, specific_humidity=full_state.q, albedo=0.3, timestep=const.seconds_per_day ) # Convection scheme  water vapor is a state variable conv = climlab.convection.EmanuelConvection(state=full_state, timestep=const.seconds_per_hour) # Surface heat flux processes shf = climlab.surface.SensibleHeatFlux(state=temperature_state, Cd=0.5E3, timestep=const.seconds_per_hour) lhf = climlab.surface.LatentHeatFlux(state=full_state, Cd=0.5E3, timestep=const.seconds_per_hour) # Couple all the submodels together model.add_subprocess('Radiation', rad) model.add_subprocess('Convection', conv) model.add_subprocess('SHF', shf) model.add_subprocess('LHF', lhf) print(model) # Run the model model.integrate_years(1) # Check for energy balance print(model.ASR  model.OLR)

class
climlab.surface.turbulent.
LatentHeatFlux
(Cd=0.003, **kwargs)[source]¶ Bases:
climlab.surface.turbulent._SurfaceFlux
Surface turbulent latent heat flux implemented through a bulk aerodynamic formula.
The flux is computed from
\[LH = r ~ L ~\rho ~ C_D ~ U \left( q_s  q_a \right)\]where:
\(L\) and \(\rho\) are the latent heat of vaporization and density of air
\(C_D\) is a drag coefficient (stored as
self.Cd
, default value is 3E3)\(U\) is the nearsurface wind speed, stored as
self.U
, default value is 5 m/s\(r\) is an optional resistance parameter (stored as
self.resistance
, default value = 1)
The surface specific humidity \(q_s\) is computed as the saturation specific humidity at the surface temperature
self.state['Ts']
and surface pressureself.ps
, while the nearsurface specific humidity \(q_a\) is taken as the lowest model level in the fieldself.q
(which must be provided either as a state variable or as input).Two diagnostics are computed:
self.LHF
gives the sensible heat flux in W/m2.self.evaporation
gives the evaporation rate in kg/m2/s (or mm/s)
How the tendencies are computed depends on whether specific humidity
q
is a state variable (i.e. is present inself.state
):If
q
is inself.state
then the evaporation determines the specific humidity tendencyself.tendencies['q']
. The water vapor is added to the lowest model level only. Evaporation cools the surface through the surface tendencyself.tendencies['Ts']
. Air temperature tendencies are zero everywhere.If
q
is not inself.state
then we compute an equivalent air temperature tendency for the lowest model layer instead of a specific humidity tendency (i.e. the latent heat flux is applied in the same way as a sensible heat flux).
This process does not apply a tendency to the surface water amount. In the absence of other water processes this implies an infinite water source at the surface (slab ocean).
 Attributes
depth
Depth at grid centers (m)
depth_bounds
Depth at grid interfaces (m)
diagnostics
Dictionary access to all diagnostic variables
input
Dictionary access to all input variables
lat
Latitude of grid centers (degrees North)
lat_bounds
Latitude of grid interfaces (degrees North)
lev
Pressure levels at grid centers (hPa or mb)
lev_bounds
Pressure levels at grid interfaces (hPa or mb)
lon
Longitude of grid centers (degrees)
lon_bounds
Longitude of grid interfaces (degrees)
timestep
The amount of time over which
step_forward()
is integrating in unit seconds.
Methods
add_diagnostic
(self, name[, value])Create a new diagnostic variable called
name
for this process and initialize it with the givenvalue
.add_input
(self, name[, value])Create a new input variable called
name
for this process and initialize it with the givenvalue
.add_subprocess
(self, name, proc)Adds a single subprocess to this process.
add_subprocesses
(self, procdict)Adds a dictionary of subproceses to this process.
compute
(self)Computes the tendencies for all state variables given current state and specified input.
compute_diagnostics
(self[, num_iter])Compute all tendencies and diagnostics, but don’t update model state.
declare_diagnostics
(self, diaglist)Add the variable names in
inputlist
to the list of diagnostics.declare_input
(self, inputlist)Add the variable names in
inputlist
to the list of necessary inputs.integrate_converge
(self[, crit, verbose])Integrates the model until model states are converging.
integrate_days
(self[, days, verbose])Integrates the model forward for a specified number of days.
integrate_years
(self[, years, verbose])Integrates the model by a given number of years.
remove_diagnostic
(self, name)Removes a diagnostic from the
process.diagnostic
dictionary and also delete the associated process attribute.remove_subprocess
(self, name[, verbose])Removes a single subprocess from this process.
set_state
(self, name, value)Sets the variable
name
to a new statevalue
.set_timestep
(self[, timestep, …])Calculates the timestep in unit seconds and calls the setter function of
timestep()
step_forward
(self)Updates state variables with computed tendencies.
to_xarray
(self[, diagnostics])Convert process variables to
xarray.Dataset
format.

class
climlab.surface.turbulent.
SensibleHeatFlux
(Cd=0.003, **kwargs)[source]¶ Bases:
climlab.surface.turbulent._SurfaceFlux
Surface turbulent sensible heat flux implemented through a bulk aerodynamic formula.
The flux is computed from
\[SH = r ~ c_p ~\rho ~ C_D ~ U \left( T_s  T_a \right)\]where:
\(c_p\) and \(\rho\) are the specific heat and density of air
\(C_D\) is a drag coefficient (stored as
self.Cd
, default value is 3E3)\(U\) is the nearsurface wind speed, stored as
self.U
, default value is 5 m/s\(r\) is an optional resistance parameter (stored as
self.resistance
, default value = 1)
The surface temperature \(T_s\) is taken directly from
self.state['Ts']
, while the nearsurface air temperature \(T_a\) is taken as the lowest model level inself.state['Tatm']
Diagnostic quantity
self.SHF
gives the sensible heat flux in W/m2.Temperature tendencies associated with this flux are computed for
Ts
and for the lowest model level inTatm
. All other tendencies (including air temperature tendencies at other levels) are set to zero. Attributes
depth
Depth at grid centers (m)
depth_bounds
Depth at grid interfaces (m)
diagnostics
Dictionary access to all diagnostic variables
input
Dictionary access to all input variables
lat
Latitude of grid centers (degrees North)
lat_bounds
Latitude of grid interfaces (degrees North)
lev
Pressure levels at grid centers (hPa or mb)
lev_bounds
Pressure levels at grid interfaces (hPa or mb)
lon
Longitude of grid centers (degrees)
lon_bounds
Longitude of grid interfaces (degrees)
timestep
The amount of time over which
step_forward()
is integrating in unit seconds.
Methods
add_diagnostic
(self, name[, value])Create a new diagnostic variable called
name
for this process and initialize it with the givenvalue
.add_input
(self, name[, value])Create a new input variable called
name
for this process and initialize it with the givenvalue
.add_subprocess
(self, name, proc)Adds a single subprocess to this process.
add_subprocesses
(self, procdict)Adds a dictionary of subproceses to this process.
compute
(self)Computes the tendencies for all state variables given current state and specified input.
compute_diagnostics
(self[, num_iter])Compute all tendencies and diagnostics, but don’t update model state.
declare_diagnostics
(self, diaglist)Add the variable names in
inputlist
to the list of diagnostics.declare_input
(self, inputlist)Add the variable names in
inputlist
to the list of necessary inputs.integrate_converge
(self[, crit, verbose])Integrates the model until model states are converging.
integrate_days
(self[, days, verbose])Integrates the model forward for a specified number of days.
integrate_years
(self[, years, verbose])Integrates the model by a given number of years.
remove_diagnostic
(self, name)Removes a diagnostic from the
process.diagnostic
dictionary and also delete the associated process attribute.remove_subprocess
(self, name[, verbose])Removes a single subprocess from this process.
set_state
(self, name, value)Sets the variable
name
to a new statevalue
.set_timestep
(self[, timestep, …])Calculates the timestep in unit seconds and calls the setter function of
timestep()
step_forward
(self)Updates state variables with computed tendencies.
to_xarray
(self[, diagnostics])Convert process variables to
xarray.Dataset
format.