scmcallib 0.5.1

Emulation

Emulation is the process of finding a set of parameters which best fit output from another model. Once this best fit point in parameter space has been found, the SCM provides a computationally cheap method for exploring how the these larger models would respond under various scenarios.

TODO: Add documention about extracting

Before we can start emulating a model we must define the initial guess of the parameter distributions (i.e. the priors), for the parameters that are being constrained.

[TODO: decide whether to put this example in e.g. a notebook so it’s under CI]

from scmcallib import ParameterSet

best_guess_c1 = 0.631
best_guess_c2 = 0.429
best_guess_a1 = 0.2240

param_set = ParameterSet()
param_set.set_tune('c1', Bound(Normal(mu=best_guess_c1, sd=1.), lower=0.1))
param_set.set_tune('c2', Bound(Normal(mu=best_guess_c2, sd=0.1), lower=0.1))
param_set.set_tune('a1', Bound(Normal(mu=best_guess_a1, sd=0.1), lower=0.0, upper=0.4))

Once we have the data and parameters which describe how the model will be constrained, we can instantiate the PointEstimateFinder. In this example we are using the A5IR SCM [TODO: fill out AR5IR SCM so it actually is the full things], a basic, but very fast model to speed up to emulation process. The first step in emulation is finding the initial starting point for optimisation. This start point is then used by the optimiser to find the point in parameter space which minimise the differences between the SCM output and the target timeseries (typically taken from a more complex model).

from scmcallib import PointEstimateFinder
from scmcallib.scm import AR5IR_SCM
emulator = PointEstimateFinder(param_set, reference_period=(2000, 2010))
emulator.set_target(observed=observed_gmt)

with AR5IR_SCM() as scm:
    results = emulator.find_best_fit(scm, optimiser_name='bayesopt')

results.plot_summary()
results.plot_fit()

scmcallib provides a method for reading tuningcore files which are used by simcap to describe how to tune magicc.

Calibration

Run simple calibration example

• edit calibration.py and run_calibration.py to fit your personal settings

• then run python run_calibration.py

• visualise with notebooks/show_calibration.ipynb