Ensemble‐Based Experimental Design for Targeting Data Acquisition to Inform Climate Models

Abstract

Data required to calibrate uncertain general circulation model (GCM) parameterizations are often only available in limited regions or time periods, for example, observational data from field campaigns, or data generated in local high‐resolution simulations. This raises the question of where and when to acquire additional data to be maximally informative about parameterizations in a GCM. Here we construct a new ensemble‐based parallel algorithm to automatically target data acquisition to regions and times that maximize the uncertainty reduction, or information gain, about GCM parameters. The algorithm uses a Bayesian framework that exploits a quantified distribution of GCM parameters as a measure of uncertainty. This distribution is informed by time‐averaged climate statistics restricted to local regions and times. The algorithm is embedded in the recently developed calibrate‐emulate‐sample framework, which performs efficient model calibration and uncertainty quantification with only scriptOfalse(102false) $\mathcal{O}({10}^{2})$ model evaluations, compared with scriptOfalse(105false) $\mathcal{O}({10}^{5})$ evaluations typically needed for traditional approaches to Bayesian calibration. We demonstrate the algorithm with an idealized GCM, with which we generate surrogates of local data. In this perfect‐model setting, we calibrate parameters and quantify uncertainties in a quasi‐equilibrium convection scheme in the GCM. We consider targeted data that are (a) localized in space for statistically stationary simulations, and (b) localized in space and time for seasonally varying simulations. 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 typically, but not always, results from regions near the intertropical convergence zone.

EnsembleKalmanProcesses.jl: Derivative-free ensemble-based model calibration

“…It does so by defining transformation maps under-the-hood from the constrained space to an unconstrained space where the optimization problem can be suitably defined. Constrained optimization using this framework has been successfully demonstrated in a variety of settings (Dunbar et al, 2022;Lopez-Gomez et al, 2022;Schneider, Dunbar, et al, 2022).…”

“…Within CES, the trained emulators are used to sample this probability distribution, and by design are most accurate where they need to be. CES has been successfully used to quantify parameter uncertainty within the moist convection scheme of a simplified climate model (Dunbar et al, 2021(Dunbar et al, , 2022, within a droplet collision-coalescence scheme for cloud microphyiscs (Bieli et al, 2022), and within boundary layer turbulence schemes for ocean modeling (Hillier, 2022).…”

Improving Stratocumulus Cloud Amounts in a 200‐m Resolution Multi‐Scale Modeling Framework Through Tuning of Its Interior Physics

“…Second, (Lopez‐Gomez et al., 2020) sidesteps the turbulence parameterization problem by using very highly resolved (Δ x = Δ y = 35 m and Δ z = 5 m) three dimensional LES, managing computational expense by using a sparse ensemble as a library from which to train eddy diffusivity/mass flux based parameterization schemes (Cohen et al., 2020). Advantages of the highly resolved LES choice include a luxuriously converged limit that sidesteps most need to parameterize beyond microphysics; disadvantages include imposing idealizations of lateral periodicity and a scale separation in their harness to a global host, as well as limited geographic sampling due to the expense of such LES; however, the latter is positioned to be managed with calibration schemes that may inform where such calculations can be strategically deployed to maximum global benefit (Dunbar et al., 2022). Advantages of the Eddy‐Diffusivity Mass‐Flux (EDMF) framework include its interpretability and generalizability; disadvantages include its potential inability to subsume some complicated organization feedbacks.…”

A Bayesian method for selecting data points for thermodynamic modeling of off-stoichiometric metal oxides

“…Active data selection techniques have been studied for several decades in a variety of scientic disciplines [32][33][34][35] such as medical disease analysis, 36,37 weather prediction, 38 and material design. [39][40][41] To date, these have not been applied or rened for thermodynamic characterization of M x O y reduction/reoxidation cycle materials.…”