Physics-guided probabilistic modeling of extreme precipitation under climate change.

ABSTRACT: 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 moist

SUBMITTER: Kodra E 

PROVIDER: S-EPMC7314860 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature