ABC for climate: dealing with expensive simulators

TL;DR

This paper discusses methods for calibrating expensive climate simulators using Gaussian process emulators and history matching, emphasizing efficient parameter space exploration and uncertainty quantification.

Contribution

It introduces a history matching approach for climate model calibration, integrating surrogate modeling and design strategies to efficiently identify plausible parameter regions.

Findings

Effective identification of plausible regions with few model runs

Demonstration on a toy climate model showing efficient calibration

Application to GENIE-1 model highlighting importance of uncertainty characterization

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.