A realistic model

As mentioned before, climt includes some components which returns the a new version of the model state, and some which return just tendencies.

Since tendencies by themselves are not useful for much other than plotting, we need to couple them with numerical integration components to march the model forward in time. Again, we will use the grey radiation scheme as an example.

The following script is used to obtain the temperature profile of the atmosphere if no physical process other than radiation (specifically, grey gas radiation in this example) are present. The temperature profile obtained is called the radiative equilibrium profile.

As before, we will create the radiation component and the model state:

import climt
import matplotlib.pyplot as plt
import numpy as np
# Two new imports
from sympl import AdamsBashforth
from datetime import timedelta

# Create some components
radiation = climt.GrayLongwaveRadiation()

# Get a state dictionary filled with required quantities
# for the components to run
state = climt.get_default_state([radiation])

We have two new imports, AdamsBashforth and timedelta. The former is a numerical integrator which will step the model forward in time, and the latter is a standard python module which will be used to represent the time step of the model.

Now, to create the integrator and the timestep:

model_time_step = timedelta(hours=1)
model = AdamsBashforth([radiation])

We now have a model ready to run! The integrator will return the new model state and any diagnostics that radiation has generated. We can then update the current model state with the new model state and continue to step the model forward in time:

for step in range(10):
    diagnostics, new_state = model(state, model_time_step)
    ''' Update state with diagnostics.
    This updated state can be saved if necessary '''
    state.update(diagnostics)
    '''Update state quantities'''
    state.update(new_state)
    '''Update model time'''
    state['time'] += model_time_step
    '''See if the maximum temperature is changing'''
    print(state['time'], ': ', state['air_temperature'].max().values)

And voila, we have a model that actually evolves over time! Many example scripts that illustrate standard model configurations used in climate modelling are available in the github repository. These scripts include examples which setup graphics to view the evolution of the model over time.

Note

A more user friendly API called Federation will be available in a later version of climt. However, setting up models is easy enough even without Federation once you get used to the workflow.

--- title: "A realistic model" --- As mentioned before, climt includes some components which returns the a new version of the model state, and some which return just tendencies. Since tendencies by themselves are not useful for much other than plotting, we need to couple them with numerical integration components to march the model forward in time. Again, we will use the grey radiation scheme as an example. The following script is used to obtain the temperature profile of the atmosphere if no physical process other than radiation (specifically, grey gas radiation in this example) are present. The temperature profile obtained is called the **radiative equilibrium** profile. As before, we will create the radiation component and the model state: ``` python import climt import matplotlib.pyplot as plt import numpy as np # Two new imports from sympl import AdamsBashforth from datetime import timedelta # Create some components radiation = climt.GrayLongwaveRadiation() # Get a state dictionary filled with required quantities # for the components to run state = climt.get_default_state([radiation]) ``` We have two new imports, `AdamsBashforth` and `timedelta`. The former is a numerical [integrator](https://en.wikipedia.org/wiki/Linear_multistep_method) which will step the model forward in time, and the latter is a standard python module which will be used to represent the time step of the model. Now, to create the integrator and the timestep: ``` python model_time_step = timedelta(hours=1) model = AdamsBashforth([radiation]) ``` We now have a model ready to run! The integrator will return the new model state and any diagnostics that `radiation` has generated. We can then update the current model state with the new model state and continue to step the model forward in time: ``` python for step in range(10): diagnostics, new_state = model(state, model_time_step) ''' Update state with diagnostics. This updated state can be saved if necessary ''' state.update(diagnostics) '''Update state quantities''' state.update(new_state) '''Update model time''' state['time'] += model_time_step '''See if the maximum temperature is changing''' print(state['time'], ': ', state['air_temperature'].max().values) ``` And voila, we have a model that actually evolves over time! Many example scripts that illustrate standard model configurations used in climate modelling are available in the github [repository](https://github.com/CliMT/climt/tree/master/examples). These scripts include examples which setup graphics to view the evolution of the model over time. ::: {.callout-note} A more user friendly API called `Federation` will be available in a later version of climt. However, setting up models is easy enough even without `Federation` once you get used to the workflow. :::