Ensemble-Based Experimental Design for Targeted High-Resolution Simulations to Inform Climate Models

Oliver R. A. Dunbar, Michael F. Howland, Tapio Schneider, Andrew M. Stuart

Preprint · 13 January 2022 · Massachusetts Institute of Technology (MIT), California Institute of Technology (Caltech)

In one sentence. An ensemble-based experimental design algorithm, using calibrate-emulate-sample, chooses high-resolution simulation regions that most reduce uncertainty in GCM convection parameters, with 10^2 rather than 10^5 model runs.

Abstract

Targeted high-resolution simulations driven by a general circulation model (GCM) can be used to calibrate GCM parameterizations of processes that are globally unresolvable but can be resolved in limited-area simulations. This raises the question of where to place high-resolution simulations to be maximally informative about the uncertain parameterizations in the global model. Here we construct an ensemble-based parallel algorithm to locate regions that maximize the uncertainty reduction, or information gain, in the uncertainty quantification of GCM parameters with regional data. The algorithm is based on a Bayesian framework that exploits a quantified posterior distribution on GCM parameters as a measure of uncertainty. The algorithm is embedded in the recently developed calibrate-emulate-sample (CES) framework, which performs efficient model calibration and uncertainty quantification with only O(10^2) forward model evaluations, compared with O(10^5) forward model evaluations typically needed for traditional approaches to Bayesian calibration. We demonstrate the algorithm with an idealized GCM, with which we generate surrogates of high-resolution data. In this setting, we calibrate parameters and quantify uncertainties in a quasi-equilibrium convection scheme. We consider (i) localization in space for a statistically stationary problem, and (ii) localization in space and time for a seasonally varying problem. In these proof-of-concept applications, the calculated information gain reflects the reduction in parametric uncertainty obtained from Bayesian inference when harnessing a targeted sample of data. The largest information gain results from regions near the intertropical convergence zone (ITCZ) and indeed the algorithm automatically targets these regions for data collection.

Labels

Labels and summary are generated by Claude and reviewed weekly. Wrong? Edit this entry.