Earth System Models (ESMs) are the state of the art for projecting the effects of climate change. However, longstanding uncertainties in their ability to simulate regional and local precipitation extremes and related processes inhibit decision making. Existing state-of-the art approaches for uncertainty quantification use Bayesian methods to weight ESMs based on a balance of historical skills and future consensus. Here we propose an empirical Bayesian model that extends an existing skill and consensus based weighting framework and examine the hypothesis that nontrivial, physics-guided measures of ESM skill can help produce reliable probabilistic characterization of climate extremes. Specifically, the model leverages knowledge of physical relationships between temperature, atmospheric moisture capacity, and extreme precipitation intensity to iteratively weight and combine ESMs and estimate probability distributions of return levels. Out-of-sample validation suggests that the proposed Bayesian method, which incorporates physics-guidance, has the potential to derive reliable precipitation projections, although caveats remain and the gain is not uniform across all cases.
Bibliographical noteFunding Information:
Funding was provided by NSF Expeditions award number 1029711 (Kodra, Chatterjee, Ganguly), NSF SBIR award number 1621576 (Kodra), NSF Big Data award number 1447587 (Ganguly), NSF Cyber SEES award 1442728 (Ganguly), a grant from NASA AMES (Ganguly), and NSF Division Of Mathematical Sciences awards numbers 1622483 (Chatterjee) and 1737918 (Chatterjee). Kodra is a principal of risQ, Inc., a private for-profit company. Chatterjee and Ganguly are advisers and shareholders of risQ, Inc. Chen is an intern at risQ, Inc. The data used are detailed in references and tables in the supplementary information. Significant portion of the work was performed when Kodra and Bhatia were graduate students at Sustainability and Data Science Laboratory at Northeastern University.
© 2020, The Author(s).
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