A comparison of statistical emulation methodologies for multi-wave calibration of environmental models.

Abstract

Expensive computer codes, particularly those used for simulating environmental or geological processes, such as climate models, require calibration (sometimes called tuning). When calibrating expensive simulators using uncertainty quantification methods, it is usually necessary to use a statistical model called an emulator in place of the computer code when running the calibration algorithm. Though emulators based on Gaussian processes are typically many orders of magnitude faster to evaluate than the simulator they mimic, many applications have sought to speed up the computations by using regression‐only emulators within the calculations instead, arguing that the extra sophistication brought using the Gaussian process is not worth the extra computational power. This was the case for the analysis that produced the UK climate projections in 2009. In this paper, we compare the effectiveness of both emulation approaches upon a multi‐wave calibration framework that is becoming popular in the climate modeling community called “history matching.” We find that Gaussian processes offer significant benefits to the reduction of parametric uncertainty over regression‐only approaches. We find that in a multi‐wave experiment, a combination of regression‐only emulators initially, followed by Gaussian process emulators for refocussing experiments can be nearly as effective as using Gaussian processes throughout for a fraction of the computational cost. We also discover a number of design and emulator‐dependent features of the multi‐wave history matching approach that can cause apparent, yet premature, convergence of our estimates of parametric uncertainty. We compare these approaches to calibration in idealized examples and apply it to a well‐known geological reservoir model.