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

Dunbar, Oliver R. A. and Howland, Michael F. and Schneider, Tapio and Stuart, Andrew M. (2022) Ensemble-Based Experimental Design for Targeting Data Acquisition to Inform Climate Models. Journal of Advances in Modeling Earth Systems, 14 (9). Art. No. e2022MS002997. ISSN 1942-2466. doi:10.1029/2022ms002997. https://resolver.caltech.edu/CaltechAUTHORS:20220926-576391900.2

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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 O(10²) model evaluations, compared with O(10⁵) 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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1029/2022MS002997DOIArticle
ORCID:
AuthorORCID
Dunbar, Oliver R. A.0000-0001-7374-0382
Howland, Michael F.0000-0002-2878-3874
Schneider, Tapio0000-0001-5687-2287
Additional Information:We gratefully acknowledge the generous support of Eric and Wendy Schmidt (by recommendation of Schmidt Futures) and the National Science Foundation (grant AGS-1835860). The simulations were performed on Caltech's High Performance Cluster, which is partially supported by a grant from the Gordon and Betty Moore Foundation. AMS is also supported by the Office of Naval Research (grant N00014-17-1-2079).
Funders:
Funding AgencyGrant Number
Schmidt Futures ProgramUNSPECIFIED
NSFAGS-1835860
Gordon and Betty Moore FoundationUNSPECIFIED
Office of Naval Research (ONR)N00014‐17‐1‐2079
Issue or Number:9
DOI:10.1029/2022ms002997
Record Number:CaltechAUTHORS:20220926-576391900.2
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220926-576391900.2
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:117135
Collection:CaltechAUTHORS
Deposited By: Melissa Ray
Deposited On:30 Sep 2022 17:18
Last Modified:30 Sep 2022 17:18

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