AplusBT

class climlab.radiation.aplusbt.AplusBT(A=200.0, B=2.0, **kwargs)[source]

Bases: EnergyBudget

The simplest linear longwave radiation module.

Calculates the Outgoing Longwave Radation (OLR) \(R\uparrow\) as

\[R\uparrow = A + B \cdot T\]

where \(T\) is the state variable.

Should be invoked with a single temperature state variable only.

Initialization parameters n

An instance of AplusBT is initialized with the following arguments:

Parameters:

A (float) – parameter for linear OLR parametrization - unit: \(\frac{\textrm{W}}{\textrm{m}^2}\) - default value: 200.0
B (float) – parameter for linear OLR parametrization - unit: \(\frac{\textrm{W}} {\textrm{m}^2 ^{\circ}\textrm{C}}\) - default value: 2.0

Object attributes n

Additional to the parent class EnergyBudget following object attributes are generated or modified during initialization:

Variables:

A (float) – calls the setter function of A()
B (float) – calls the setter function of B()
diagnostics (dict) – key 'OLR' initialized with value: Field of zeros in size of self.Ts
OLR (Field) – the subprocess attribute self.OLR is created with correct dimensions

Warning

This module currently works only for a single state variable!

Example:

Simple linear radiation module (stand alone):

>>> import climlab

>>> # create a column atmosphere and scalar surface
>>> sfc, atm = climlab.domain.single_column()

>>> # Create a state variable
>>> Ts = climlab.Field(15., domain=sfc)

>>> # Make a dictionary of state variables
>>> s = {'Ts': Ts}

>>> # create process
>>> olr = climlab.radiation.AplusBT(state=s)

>>> print olr
climlab Process of type <class 'climlab.radiation.AplusBT.AplusBT'>.
State variables and domain shapes:
  Ts: (1,)
The subprocess tree:
top: <class 'climlab.radiation.AplusBT.AplusBT'>

>>> # to compute tendencies and diagnostics
>>> olr.compute()

>>> #  or to actually update the temperature
>>> olr.step_forward()

>>> print olr.state
{'Ts': Field([ 5.69123176])}

Attributes:

A: Property of AplusBT parameter A.
B: Property of AplusBT parameter B.
current_time
depth: Depth at grid centers (m)
depth_bounds: Depth at grid interfaces (m)
diagnostics: Dictionary access to all diagnostic variables
elapsed_time
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.
timestep_in_seconds: Return a float value representing the timestep in units of seconds

Methods

`add_diagnostic`(name[, value])	Create a new diagnostic variable called `name` for this process and initialize it with the given `value`.
`add_input`(name[, value])	Create a new input variable called `name` for this process and initialize it with the given `value`.
`add_subprocess`(name, proc[, verbose])	Adds a single subprocess to this process.
`add_subprocesses`(procdict)	Adds a dictionary of subproceses to this process.
`compute`()	Computes the tendencies for all state variables given current state and specified input.
`compute_diagnostics`([num_iter])	Compute all tendencies and diagnostics, but don't update model state.
`declare_diagnostics`(diaglist)	Add the variable names in `inputlist` to the list of diagnostics.
`declare_input`(inputlist)	Add the variable names in `inputlist` to the list of necessary inputs.
`integrate_converge`([crit, verbose])	Integrates the model until model states are converging.
`integrate_days`([days, verbose])	Integrates the model forward for a specified number of days.
`integrate_years`([years, verbose])	Integrates the model by a given number of years.
`remove_diagnostic`(name)	Removes a diagnostic from the `process.diagnostic` dictionary and also delete the associated process attribute.
`remove_subprocess`(name[, verbose])	Removes a single subprocess from this process.
`set_state`(name, value)	Sets the variable `name` to a new state `value`.
`step_forward`()	Updates state variables with computed tendencies.
`to_xarray`([diagnostics, timeave])	Convert process variables to `xarray.Dataset` format.

property A

Property of AplusBT parameter A.

Getter:

Returns the parameter A which is stored in attribute self._A

Setter:

sets parameter A which is addressed as self._A to the new value
updates the parameter dictionary self.param['A']

Type:

float

Example:

>>> import climlab
>>> model = climlab.EBM()

>>> # getter
>>> model.subprocess['LW'].A
210.0
>>> # setter
>>> model.subprocess['LW'].A = 220
>>> # getter again
>>> model.subprocess['LW'].A
220

>>> # subprocess parameter dictionary
>>> model.subprocess['LW'].param['A']
220

property B

Property of AplusBT parameter B.

Getter:

Returns the parameter B which is stored in attribute self._B

Setter:

sets parameter B which is addressed as self._B to the new value
updates the parameter dictionary self.param['B']

Type:

float

class climlab.radiation.aplusbt.AplusBT_CO2(CO2=300.0, **kwargs)[source]

Bases: EnergyBudget

Linear longwave radiation module considering CO2 concentration.

This radiation subprocess is based in the idea to linearize the Outgoing Longwave Radiation (OLR) emitted to space according to the surface temperature (see AplusBT).

To consider a the change of the greenhouse effect through range of \(CO_2\) in the atmosphere, the parameters A and B are computed like the following:

\[\begin{split}A(c) = -326.4 + 9.161 c - 3.164 c^2 + 0.5468 c^3 \\ B(c) = 1.953 - 0.04866 c + 0.01309 c^2 - 0.002577 c^3\end{split}\]

where \(c=\log \frac{p}{300}\) and \(p\) represents the concentration of \(CO_2\) in the atmosphere.

For further reading see Caldeira and Kasting [1992].

Initialization parameters

An instance of AplusBT_CO2 is initialized with the following argument:

Parameters:: CO2 (float) – The concentration of \(CO_2\) in the atmosphere. Referred to as \(p\) in the above given formulas. - unit: \(\textrm{ppm}\) (parts per million) - default value: 300.0

Object attributes n

Additional to the parent class EnergyBudget following object attributes are generated or updated during initialization:

Variables:

CO2 (float) – calls the setter function of CO2()
diagnostics (dict) – the subprocess’s diagnostic dictionary self.diagnostic is initialized through calling self.add_diagnostic('OLR', 0. * self.Ts)
OLR (Field) – the subprocess attribute self.OLR is created with correct dimensions

Example:

Replacing an the regular AplusBT subprocess in an energy balance model:

>>> import climlab
>>> from climlab.radiation.AplusBT import AplusBT_CO2

>>> # creating EBM model
>>> model = climlab.EBM()

>>> print model

climlab Process of type <class 'climlab.model.ebm.EBM'>.
State variables and domain shapes:
  Ts: (90, 1)
The subprocess tree:
top: <class 'climlab.model.ebm.EBM'>
   diffusion: <class 'climlab.dynamics.diffusion.MeridionalDiffusion'>
   LW: <class 'climlab.radiation.AplusBT.AplusBT'>
   albedo: <class 'climlab.surface.albedo.StepFunctionAlbedo'>
      iceline: <class 'climlab.surface.albedo.Iceline'>
      cold_albedo: <class 'climlab.surface.albedo.ConstantAlbedo'>
      warm_albedo: <class 'climlab.surface.albedo.P2Albedo'>
   insolation: <class 'climlab.radiation.insolation.P2Insolation'>

>>> #  creating and adding albedo feedback subprocess
>>> LW_CO2 = AplusBT_CO2(CO2=400, state=model.state, **model.param)

>>> # overwriting old 'LW' subprocess with same name
>>> model.add_subprocess('LW', LW_CO2)

>>> print model

climlab Process of type <class 'climlab.model.ebm.EBM'>.
State variables and domain shapes:
  Ts: (90, 1)
The subprocess tree:
top: <class 'climlab.model.ebm.EBM'>
   diffusion: <class 'climlab.dynamics.diffusion.MeridionalDiffusion'>
   LW: <class 'climlab.radiation.AplusBT.AplusBT_CO2'>
   albedo: <class 'climlab.surface.albedo.StepFunctionAlbedo'>
      iceline: <class 'climlab.surface.albedo.Iceline'>
      cold_albedo: <class 'climlab.surface.albedo.ConstantAlbedo'>
      warm_albedo: <class 'climlab.surface.albedo.P2Albedo'>
   insolation: <class 'climlab.radiation.insolation.P2Insolation'>

Attributes:

CO2: Property of AplusBT_CO2 parameter CO2.
current_time
depth: Depth at grid centers (m)
depth_bounds: Depth at grid interfaces (m)
diagnostics: Dictionary access to all diagnostic variables
elapsed_time
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.
timestep_in_seconds: Return a float value representing the timestep in units of seconds

Methods

`add_diagnostic`(name[, value])	Create a new diagnostic variable called `name` for this process and initialize it with the given `value`.
`add_input`(name[, value])	Create a new input variable called `name` for this process and initialize it with the given `value`.
`add_subprocess`(name, proc[, verbose])	Adds a single subprocess to this process.
`add_subprocesses`(procdict)	Adds a dictionary of subproceses to this process.
`compute`()	Computes the tendencies for all state variables given current state and specified input.
`compute_diagnostics`([num_iter])	Compute all tendencies and diagnostics, but don't update model state.
`declare_diagnostics`(diaglist)	Add the variable names in `inputlist` to the list of diagnostics.
`declare_input`(inputlist)	Add the variable names in `inputlist` to the list of necessary inputs.
`integrate_converge`([crit, verbose])	Integrates the model until model states are converging.
`integrate_days`([days, verbose])	Integrates the model forward for a specified number of days.
`integrate_years`([years, verbose])	Integrates the model by a given number of years.
`remove_diagnostic`(name)	Removes a diagnostic from the `process.diagnostic` dictionary and also delete the associated process attribute.
`remove_subprocess`(name[, verbose])	Removes a single subprocess from this process.
`set_state`(name, value)	Sets the variable `name` to a new state `value`.
`step_forward`()	Updates state variables with computed tendencies.
`to_xarray`([diagnostics, timeave])	Convert process variables to `xarray.Dataset` format.

property CO2

Property of AplusBT_CO2 parameter CO2.

Getter:

Returns the CO2 concentration which is stored in attribute self._CO2

Setter:

sets the CO2 concentration which is addressed as self._CO2 to the new value
updates the parameter dictionary self.param['CO2']

Type:

float