Calibration of climate model parameterizations using Bayesian experimental design

Parameter uncertainty contributes to a significant portion of the overall uncertainty in future climate projections due to the considerable computational cost in running modern climate models. Current approaches to estimating parameter uncertainty often rely on randomly sampling parameter configurations, e.g. by using Latin hypercube sampling (LHS). However, this can be wasteful of computational resources because this sampling process does not take into account any observational constraints. With the goal of using computational resources more efficiently, we formulate the problem of calibrating the parameters in atmospheric model parameterizations as an active learning problem . Specifically, we leverage ideas from the field of Bayesian experimental design (BED) to incorporate the information from observations directly into the model calibration process. We further utilize the links to the BED literature to describe how to compare the performance of these active learning strategies. Empirically, we test the utility of active learning approaches on data from the Single Column Atmospheric Model, a single column version of the atmospheric model used in the Community Earth System Model version 2, as well as on an emulator of the full Community Atmosphere Model Version 6 with a 45-dimensional input parameter space. Our findings suggest that active learning can lead to smaller parameter uncertainty compared to LHS, even in high-dimensional input spaces, but the improvements in efficiency are reliant on having sufficiently strong observational constraints for the model parameterization.