Parametric uncertainty in complex environmental models: a cheap emulation approach for models with high-dimensional output

TL;DR

This paper introduces a cost-effective emulation method to quantify parametric uncertainty in complex environmental models with high-dimensional outputs, demonstrated on the atmospheric transport model NAME.

Contribution

The paper presents a novel approach for efficiently accounting for parametric uncertainty in complex environmental simulators without extensive training runs.

Findings

Two key parameters influence NAME output variability.

The turbulence parameter should be larger than current defaults.

Discrepancies relate to ground height inconsistencies.

Abstract

In order to understand underlying processes governing environmental and physical processes, and predict future outcomes, a complex computer model is frequently required to simulate these dynamics. However there is inevitably uncertainty related to the exact parametric form or the values of such parameters to be used when developing these simulators, with \emph{ranges} of plausible values prevalent in the literature. Systematic errors introduced by failing to account for these uncertainties have the potential to have a large effect on resulting estimates in unknown quantities of interest. Due to the complexity of these types of models, it is often unfeasible to run large numbers of training runs that are usually required for full statistical emulators of the environmental processes. We therefore present a method for accounting for uncertainties in complex environmental simulators without the need for very large numbers of training runs and illustrate the method through an application to the Met Office's atmospheric transport model NAME. We conclude that there are two principle parameters that are linked with variability in NAME outputs, namely the free tropospheric turbulence parameter and particle release height. Our results suggest the former should be significantly larger than is currently implemented as a default in NAME, whilst changes in the latter most likely stem from inconsistencies between the model specified ground height at the observation locations and the true height at this location. Estimated discrepancies from independent data are consistent with the discrepancy between modelled and true ground height.