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