Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published January 17, 2022 | Submitted
Report Open

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


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

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). Data Availability. All computer code used in this paper is open source. The code for the idealized GCM, the Julia code for the optimal design algorithm, the plot tools, and the slurm/bash scripts to run both GCM and design algorithms are available at: https://doi.org/10.5281/zenodo.5835269.

Attached Files

Submitted - 2201.06998.pdf

Submitted - essoar.10510142.1.pdf


Files (3.0 MB)
Name Size Download all
1.5 MB Preview Download
1.5 MB Preview Download

Additional details

September 15, 2023
October 23, 2023