albedo

Inheritance diagram of climlab.surface.albedo
class climlab.surface.albedo.ConstantAlbedo(albedo=0.33, **kwargs)[source]

Bases: climlab.process.diagnostic.DiagnosticProcess

A class for constant albedo values at all spatial points of the domain.

Initialization parameters

Parameters

albedo (float) – albedo values [default: 0.33]

Object attributes

Additional to the parent class DiagnosticProcess following object attributes are generated and updated during initialization:

Variables

albedo (Field) – attribute to store the albedo value. During initialization the albedo() setter is called.

Example

Creation of a constant albedo subprocess on basis of an EBM domain:

>>> import climlab
>>> from climlab.surface.albedo import ConstantAlbedo

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

>>> sfc = model.domains['Ts']

>>> # subprocess creation
>>> const_alb = ConstantAlbedo(albedo=0.3, domains=sfc, **model.param)
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(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)

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.

set_timestep([timestep, num_steps_per_year])

Calculates the timestep in unit seconds and calls the setter function of timestep()

step_forward()

Updates state variables with computed tendencies.

to_xarray([diagnostics])

Convert process variables to xarray.Dataset format.

Uniform prescribed albedo.

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(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)

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.

set_timestep([timestep, num_steps_per_year])

Calculates the timestep in unit seconds and calls the setter function of timestep()

step_forward()

Updates state variables with computed tendencies.

to_xarray([diagnostics])

Convert process variables to xarray.Dataset format.

class climlab.surface.albedo.Iceline(Tf=-10.0, **kwargs)[source]

Bases: climlab.process.diagnostic.DiagnosticProcess

A class for an Iceline subprocess.

Depending on a freezing temperature it calculates where on the domain the surface is covered with ice, where there is no ice and on which latitude the ice-edge is placed.

Initialization parameters

Parameters

Tf (float) –

freezing temperature where sea water freezes and surface is covered with ice

  • unit: \(^{\circ} \textrm{C}\)

  • default value: -10

Object attributes

Additional to the parent class DiagnosticProcess following object attributes are generated and updated during initialization:

Variables
  • param (dict) – The parameter dictionary is updated with the input argument 'Tf'.

  • diagnostics (dict) – keys 'icelat' and 'ice_area' initialized

  • icelat (array) – the subprocess attribute self.icelat is created

  • ice_area (float) – the subprocess attribute self.ice_area is created

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(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)

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.

find_icelines()

Finds iceline according to the surface temperature.

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.

set_timestep([timestep, num_steps_per_year])

Calculates the timestep in unit seconds and calls the setter function of timestep()

step_forward()

Updates state variables with computed tendencies.

to_xarray([diagnostics])

Convert process variables to xarray.Dataset format.

find_icelines()[source]

Finds iceline according to the surface temperature.

This method is called by the private function _compute() and updates following attributes according to the freezing temperature self.param['Tf'] and the surface temperature self.param['Ts']:

Object attributes

Variables
  • noice (Field) – a Field of booleans which are True where \(T_s \ge T_f\)

  • ice (Field) – a Field of booleans which are True where \(T_s < T_f\)

  • icelat (array) – an array with two elements indicating the ice-edge latitudes

  • ice_area (float) – fractional area covered by ice (0 - 1)

  • diagnostics (dict) – keys 'icelat' and 'ice_area' are updated

class climlab.surface.albedo.P2Albedo(a0=0.33, a2=0.25, **kwargs)[source]

Bases: climlab.process.diagnostic.DiagnosticProcess

A class for parabolic distributed albedo values across the domain on basis of the second order Legendre Polynomial.

Calculates the latitude dependent albedo values as

\[\alpha(\varphi) = a_0 + a_2 P_2(x)\]

where \(P_2(x) = \frac{1}{2} (3x^2 - 1)\) is the second order Legendre Polynomial and \(x=sin(\varphi)\).

Initialization parameters

Parameters
  • a0 (float) – basic parameter for albedo function [default: 0.33]

  • a2 (float) – factor for second legendre polynominal term in albedo function [default: 0.25]

Object attributes

Additional to the parent class DiagnosticProcess following object attributes are generated and updated during initialization:

Variables
  • a0 (float) – attribute to store the albedo parameter a0. During initialization the a0() setter is called.

  • a2 (float) – attribute to store the albedo parameter a2. During initialization the a2() setter is called.

  • diagnostics (dict) – key 'albedo' initialized

  • albedo (Field) – the subprocess attribute self.albedo is created with correct dimensions (according to self.lat)

Example

Creation of a parabolic albedo subprocess on basis of an EBM domain:

>>> import climlab
>>> from climlab.surface.albedo import P2Albedo

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

>>> # modify a0 and a2 values in model parameter dictionary
>>> model.param['a0']=0.35
>>> model.param['a2']= 0.10

>>> # subprocess creation
>>> p2_alb = P2Albedo(domains=model.domains['Ts'], **model.param)

>>> p2_alb.a0
0.33
>>> p2_alb.a2
0.1
Attributes
a0

Property of albedo parameter a0.

a2

Property of albedo parameter a2.

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(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)

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.

set_timestep([timestep, num_steps_per_year])

Calculates the timestep in unit seconds and calls the setter function of timestep()

step_forward()

Updates state variables with computed tendencies.

to_xarray([diagnostics])

Convert process variables to xarray.Dataset format.

a0

Property of albedo parameter a0.

Getter

Returns the albedo parameter value which is stored in attribute self._a0

Setter
  • sets albedo parameter which is addressed as self._a0 to the new value

  • updates the parameter dictionary self.param['a0']

  • calls method _compute_fixed()

Type

float

a2

Property of albedo parameter a2.

Getter

Returns the albedo parameter value which is stored in attribute self._a2

Setter
  • sets albedo parameter which is addressed as self._a2 to the new value

  • updates the parameter dictionary self.param['a2']

  • calls method _compute_fixed()

Type

float

class climlab.surface.albedo.StepFunctionAlbedo(Tf=-10.0, a0=0.3, a2=0.078, ai=0.62, **kwargs)[source]

Bases: climlab.process.diagnostic.DiagnosticProcess

A step function albedo suprocess.

This class itself defines three subprocesses that are created during initialization:

Initialization parameters

Parameters
  • Tf (float) –

    freezing temperature for Iceline subprocess

    • unit: \(^{\circ} \textrm{C}\)

    • default value: -10

  • a0 (float) – basic parameter for P2Albedo subprocess [default: 0.3]

  • a2 (float) – factor for second legendre polynominal term in P2Albedo subprocess [default: 0.078]

  • ai (float) – ice albedo value for ConstantAlbedo subprocess [default: 0.62]

Additional to the parent class DiagnosticProcess following object attributes are generated/updated during initialization:

Variables
  • param (dict) – The parameter dictionary is updated with a couple of the initatilzation input arguments, namely 'Tf', 'a0', 'a2' and 'ai'.

  • topdown (bool) – is set to False to call subprocess compute method first

  • diagnostics (dict) – key 'albedo' initialized

  • albedo (Field) – the subprocess attribute self.albedo is created

Example

Creation of a step albedo subprocess on basis of an EBM domain:

>>> import climlab
>>> from climlab.surface.albedo import StepFunctionAlbedo
>>>
>>> model = climlab.EBM(a0=0.29, a2=0.1, ai=0.65, Tf=-2)
>>>
>>> step_alb = StepFunctionAlbedo(state= model.state, **model.param)
>>>
>>> print step_alb
climlab Process of type <class 'climlab.surface.albedo.StepFunctionAlbedo'>.
State variables and domain shapes:
  Ts: (90, 1)
The subprocess tree:
top: <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'>
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(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)

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.

set_timestep([timestep, num_steps_per_year])

Calculates the timestep in unit seconds and calls the setter function of timestep()

step_forward()

Updates state variables with computed tendencies.

to_xarray([diagnostics])

Convert process variables to xarray.Dataset format.