energy_budget

class climlab.process.energy_budget.EnergyBudget(**kwargs)[source]

Bases: TimeDependentProcess

A parent class for explicit energy budget processes.

This class solves equations that include a heat capacitiy term like \(C \frac{dT}{dt} = \textrm{flux convergence}\)

In an Energy Balance Model with model state \(T\) this equation will look like this:

\[\begin{split}C \frac{dT}{dt} = R\downarrow - R\uparrow - H \\ \frac{dT}{dt} = \frac{R\downarrow}{C} - \frac{R\uparrow}{C} - \frac{H}{C}\end{split}\]

Every EnergyBudget object has a heating_rate dictionary with items corresponding to each state variable. The heating rate accounts the actual heating of a subprocess, namely the contribution to the energy budget of \(R\\downarrow, R\\uparrow\) and \(H\) in this case. The temperature tendencies for each subprocess are then calculated through dividing the heating rate by the heat capacitiy \(C\).

Initialization parameters n

An instance of EnergyBudget is initialized with the forwarded keyword arguments **kwargs of the corresponding children classes.

Object attributes n

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

Variables:

time_type (str) – is set to 'explicit'
heating_rate (dict) – energy share for given subprocess in unit \(\textrm{W}/ \textrm{m}^2\) stored in a dictionary sorted by model states

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[, 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.process.energy_budget.ExternalEnergySource(**kwargs)[source]

Bases: EnergyBudget

A fixed energy source or sink to be specified by the user.

Object attributes

Additional to the parent class EnergyBudget the following object attribute is modified during initialization:

Variables:: heating_rate (dict) – energy share dictionary for this subprocess is set to zero for every model state.

After initialization the user should modify the fields in the heating_rate dictionary, which contain heating rates in unit \(\textrm{W}/ \textrm{m}^2\) for all state variables.

Example:

Creating an Energy Balance Model with a uniform external energy source of \(10 \ \textrm{W}/ \textrm{m}^2\) for all latitudes:

>>> import climlab
>>> from climlab.process.energy_budget import ExternalEnergySource
>>> import numpy as np

>>> # create model & external energy subprocess
>>> model = climlab.EBM(num_lat=36)
>>> ext_en = ExternalEnergySource(state= model.state,**model.param)

>>> # modify external energy rate
>>> ext_en.heating_rate.keys()
['Ts']

>>> np.squeeze(ext_en.heating_rate['Ts'])
Field([-0., -0., -0., -0., -0., -0., -0., -0., -0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0., -0., -0., -0., -0., -0., -0., -0., -0., -0.])

>>> ext_en.heating_rate['Ts'][:]=10

>>> np.squeeze(ext_en.heating_rate['Ts'])
Field([ 10.,  10.,  10.,  10.,  10.,  10.,  10.,  10.,  10.,  10.,  10.,
        10.,  10.,  10.,  10.,  10.,  10.,  10.,  10.,  10.,  10.,  10.,
        10.,  10.,  10.,  10.,  10.,  10.,  10.,  10.,  10.,  10.,  10.,
        10.,  10.,  10.])

>>> # add subprocess to model
>>> model.add_subprocess('ext_energy',ext_en)

>>> print model
climlab Process of type <class 'climlab.model.ebm.EBM'>.
State variables and domain shapes:
  Ts: (36, 1)
The subprocess tree:
top: <class 'climlab.model.ebm.EBM'>
   diffusion: <class 'climlab.dynamics.diffusion.MeridionalDiffusion'>
   LW: <class 'climlab.radiation.AplusBT.AplusBT'>
   ext_energy: <class 'climlab.process.energy_budget.ExternalEnergySource'>
   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:

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.