insolation

Classes to provide insolation as input for other CLIMLAB processes.

Options include

climlab.radiation.P2Insolation (idealized 2nd Legendre polynomial form)
climlab.radiation.FixedInsolation (generic steady-state insolation)
climlab.radiation.AnnualMeanInsolation (steady-state annual-mean insolation computed from orbital parameters and latitude)
climlab.radiation.DailyInsolation (time-varying daily-mean insolation computed from orbital parameters, latitude and time of year)
climlab.radiation.InstantInsolation (time-varying instantaneous insolation computed from orbital parameters, latitude, longitude, and time of year)

All are subclasses of climlab.process.DiagnosticProcess and do not add any tendencies to any state variables.

At least three diagnostics are provided:

insolation, the incoming solar radiation in \(\frac{\textrm{W}}{\textrm{m}^2}\)
coszen, cosine of the solar zenith angle (dimensionless)
irradiance_factor, ratio of current total irradiance to its annual average, i.e. solar constant (dimensionless)

class climlab.radiation.insolation.AnnualMeanInsolation(S0=1365.2, orb={'ecc': 0.017236, 'long_peri': 281.37, 'obliquity': 23.446}, weighting='time', **kwargs)[source]

Bases: _Insolation

A class for latitudewise solar insolation averaged over a year.

This class computes the daily-mean solar insolation for each day of the year and latitude specified in the domain on the basis of orbital parameters and astronomical formulas.

Internally this process calls the method daily_insolation_factors() to compute solar zenith angle and adjustments to total irradiance. See there for details on how the solar distribution depends on orbital parameters.

The mean over the year is calculated from data returned by daily_insolation_factors() and stored in the diagnostics insolation, coszen, and irrandiance_factor.

Different daily averaging methods can be specified for the zenith angle via the weighting argument. See daily_insolation_factors() for more details.

Initialization parameters

Parameters:

S0 (float) –
solar constant
- unit: \(\frac{\textrm{W}}{\textrm{m}^2}\)
- default value: 1365.2
orb (dict) –
a dictionary with three orbital parameters (as provided by OrbitalTable):
- 'ecc' - eccentricity
  - unit: dimensionless
  - default value: 0.017236
- 'long_peri' - longitude of perihelion (precession angle)
  - unit: degrees
  - default value: 281.37
- 'obliquity' - obliquity angle
  - unit: degrees
  - default value: 23.446
weighting (str) –
flag to specify daily averaging method for solar zenith angle. Valid options are:
- 'time' (default): unweighted 24 hour daily average
- 'sunlit': time average over sunlit hours
- 'insolation': insolation-weighted average

Object attributes

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

Variables:

insolation (Field) – Current insolation in W/m2
coszen (Field) – Cosine of the current solar zenith angle
orb (dict) – initialized with given argument orb

Example:

Create regular EBM and replace standard insolation subprocess by AnnualMeanInsolation:

>>> import climlab
>>> from climlab.radiation import AnnualMeanInsolation

>>> # model creation
>>> 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'>

>>> # catch model domain for subprocess creation
>>> sfc = model.domains['Ts']

>>> # create AnnualMeanInsolation subprocess
>>> new_insol = AnnualMeanInsolation(domains=sfc, **model.param)

>>> # add it to the model
>>> model.add_subprocess('insolation',new_insol)

>>> 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.AnnualMeanInsolation'>

Attributes:

S0: Property of solar constant S0.
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)
orb: Property of dictionary for orbital parameters.
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[, 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`.
`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, timeave])	Convert process variables to `xarray.Dataset` format.

property orb

Property of dictionary for orbital parameters.

orb contains: (for more information see OrbitalTable)

'ecc' - eccentricity [unit: dimensionless]
'long_peri' - longitude of perihelion (precession angle) [unit: degrees]
'obliquity' - obliquity angle [unit: degrees]

Getter:

Returns the orbital dictionary which is stored in attribute self._orb.

Setter:

sets orb which is addressed as self._orb to the new value
updates the parameter dictionary self.param['orb'] and
calls method _compute_fixed()

Type:

dict

class climlab.radiation.insolation.DailyInsolation(S0=1365.2, orb={'ecc': 0.017236, 'long_peri': 281.37, 'obliquity': 23.446}, weighting='time', **kwargs)[source]

Bases: AnnualMeanInsolation

A class to compute latitudewise daily average solar insolation for specific days of the year.

This class computes the solar insolation on basis of orbital parameters and astronomical formulas.

Internally this process calls the method daily_insolation_factors(). to compute solar zenith angle and adjustments to total irradiance. See there for details on how the solar distribution depends on orbital parameters.

Different daily averaging methods can be specified for the zenith angle via the weighting argument. See daily_insolation_factors() for more details.

Initialization parameters

Parameters:

S0 (float) –
solar constant
- unit: \(\frac{\textrm{W}}{\textrm{m}^2}\)
- default value: 1365.2
orb (dict) –
a dictionary with orbital parameters:
- 'ecc' - eccentricity
  - unit: dimensionless
  - default value: 0.017236
- 'long_peri' - longitude of perihelion (precession angle)
  - unit: degrees
  - default value: 281.37
- 'obliquity' - obliquity angle
  - unit: degrees
  - default value: 23.446
weighting (str) –
flag to specify daily averaging method for solar zenith angle. Valid options are:
- 'time' (default): unweighted 24 hour daily average
- 'sunlit': time average over sunlit hours
- 'insolation': insolation-weighted average

Object attributes

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

Variables:

insolation (Field) – Current insolation in W/m2
coszen (Field) – Cosine of the current solar zenith angle
orb (dict) – initialized with given argument orb

Example:

Create regular EBM and replace standard insolation subprocess by DailyInsolation:

>>> import climlab
>>> from climlab.radiation import DailyInsolation

>>> # model creation
>>> 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'>

>>> # catch model domain for subprocess creation
>>> sfc = model.domains['Ts']

>>> # create DailyInsolation subprocess and add it to the model
>>> model.add_subprocess('insolation',DailyInsolation(domains=sfc, **model.param))

>>> 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.DailyInsolation'>

Attributes:

S0: Property of solar constant S0.
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)
orb: Property of dictionary for orbital parameters.
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[, 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`.
`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, timeave])	Convert process variables to `xarray.Dataset` format.

class climlab.radiation.insolation.FixedInsolation(S0=341.3, **kwargs)[source]

Bases: _Insolation

A class for fixed insolation at each point of latitude off the domain.

The solar distribution for the whole domain is constant and specified by a parameter.

Initialization parameters

Parameters:

S0 (float) –

solar constant

unit: \(\frac{\textrm{W}}{\textrm{m}^2}\)
default value: const.S0/4 = 341.2

Example:

>>> import climlab
>>> from climlab.radiation.insolation import FixedInsolation

>>> model = climlab.EBM()
>>> sfc = model.Ts.domain

>>> fixed_ins = FixedInsolation(S0=340.0, domains=sfc)

>>> print(fixed_ins)
climlab Process of type <class 'climlab.radiation.insolation.FixedInsolation'>.
State variables and domain shapes:
The subprocess tree:
top: <class 'climlab.radiation.insolation.FixedInsolation'>

Attributes:

S0: Property of solar constant S0.
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[, 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`.
`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, timeave])	Convert process variables to `xarray.Dataset` format.

class climlab.radiation.insolation.InstantInsolation(S0=1365.2, orb={'ecc': 0.017236, 'long_peri': 281.37, 'obliquity': 23.446}, weighting='time', **kwargs)[source]

Bases: AnnualMeanInsolation

A class to compute latitudewise instantaneous solar insolation for specific days of the year.

This class computes the solar insolation on basis of orbital parameters and astronomical formulas.

Therefore it uses the method instant_insolation(). For details how the solar distribution is dependend on orbital parameters see there.

Initialization parameters

Parameters:

S0 (float) –
solar constant
- unit: \(\frac{\textrm{W}}{\textrm{m}^2}\)
- default value: 1365.2
orb (dict) –
a dictionary with orbital parameters:
- 'ecc' - eccentricity
  - unit: dimensionless
  - default value: 0.017236
- 'long_peri' - longitude of perihelion (precession angle)
  - unit: degrees
  - default value: 281.37
- 'obliquity' - obliquity angle
  - unit: degrees
  - default value: 23.446

Object attributes

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

Variables:

insolation (Field) – Current insolation in W/m2
coszen (Field) – Cosine of the current solar zenith angle
orb (dict) – initialized with given argument orb

Example:

Create regular EBM and replace standard insolation subprocess by DailyInsolation:

>>> import climlab
>>> from climlab.radiation import InstantInsolation

>>> # model creation
>>> 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'>

>>> # catch model domain for subprocess creation
>>> sfc = model.domains['Ts']

>>> # create InstantInsolation subprocess and add it to the model
>>> model.add_subprocess('insolation',InstantInsolation(domains=sfc, **model.param))

>>> 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.InstantInsolation'>

Attributes:

S0: Property of solar constant S0.
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)
orb: Property of dictionary for orbital parameters.
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[, 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`.
`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, timeave])	Convert process variables to `xarray.Dataset` format.

class climlab.radiation.insolation.P2Insolation(S0=1365.2, s2=-0.48, **kwargs)[source]

Bases: _Insolation

A class for parabolic solar distribution over the domain’s latitude on the basis of the second order Legendre Polynomial.

Calculates the latitude dependent solar distribution as

\[S(\varphi) = \frac{S_0}{4} \left( 1 + s_2 P_2(x) \right)\]

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

Initialization parameters

Parameters:

S0 (float) –
solar constant
- unit: \(\frac{\textrm{W}}{\textrm{m}^2}\)
- default value: 1365.2
s2 (floar) –
factor for second legendre polynominal term
- default value: -0.48

Example:

>>> import climlab
>>> from climlab.radiation.insolation import P2Insolation

>>> model = climlab.EBM()
>>> sfc = model.Ts.domain

>>> p2_ins = P2Insolation(S0=340.0, s2=-0.5, domains=sfc)

>>> print(p2_ins)
climlab Process of type <class 'climlab.radiation.insolation.P2Insolation'>.
State variables and domain shapes:
The subprocess tree:
top: <class 'climlab.radiation.insolation.P2Insolation'>

Attributes:

S0: Property of solar constant S0.
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)
s2: Property of second legendre polynomial factor s2.
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[, 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`.
`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, timeave])	Convert process variables to `xarray.Dataset` format.

property s2

Property of second legendre polynomial factor s2.

s2 in following equation:

\[S(\varphi) = \frac{S_0}{4} \left( 1 + s_2 P_2(x) \right)\]

Getter:

Returns the s2 parameter which is stored in attribute self._s2.

Setter:

sets s2 which is addressed as self._S0 to the new value
updates the parameter dictionary self.param['s2'] and
calls method _compute_fixed()

Type:

float