Misc,
ABC for climate: dealing with expensive simulators
(2015)
Abstract
This paper is due to appear as a chapter of the forthcoming Handbook of
Approximate Bayesian Computation (ABC) by S. Sisson, L. Fan, and M. Beaumont.
We describe the challenge of calibrating climate simulators, and discuss the
differences in emphasis in climate science compared to many of the more
traditional ABC application areas. The primary difficulty is how to do
inference with a computationally expensive simulator which we can only afford
to run a small number of times, and we describe how Gaussian process emulators
are used as surrogate models in this case. We introduce the idea of history
matching, which is a non-probabilistic calibration method, which divides the
parameter space into (not im)plausible and implausible regions. History
matching can be shown to be a special case of ABC, but with a greater emphasis
on defining realistic simulator discrepancy bounds, and using these to define
tolerances and metrics. We describe a design approach for choosing parameter
values at which to run the simulator, and illustrate the approach on a toy
climate model, showing that with careful design we can find the plausible
region with a very small number of model evaluations. Finally, we describe how
calibrated GENIE-1 (an earth system model of intermediate complexity)
predictions have been used, and why it is important to accurately characterise
parametric uncertainty.
