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Physics-guided Probabilistic Modeling of Extreme Precipitation Under Climate Change

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Journal Sci Rep
Specialty Science
Date 2020 Jun 26
PMID 32581227
Citations 1
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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 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.

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References
1.
OGorman P, Schneider T . The physical basis for increases in precipitation extremes in simulations of 21st-century climate change. Proc Natl Acad Sci U S A. 2009; 106(35):14773-7. PMC: 2736420. DOI: 10.1073/pnas.0907610106. View

2.
Sugiyama M, Shiogama H, Emori S . Precipitation extreme changes exceeding moisture content increases in MIROC and IPCC climate models. Proc Natl Acad Sci U S A. 2010; 107(2):571-5. PMC: 2818899. DOI: 10.1073/pnas.0903186107. View

3.
Fasullo J, Trenberth K . A less cloudy future: the role of subtropical subsidence in climate sensitivity. Science. 2012; 338(6108):792-4. DOI: 10.1126/science.1227465. View

4.
Pierce D, Barnett T, Santer B, Gleckler P . Selecting global climate models for regional climate change studies. Proc Natl Acad Sci U S A. 2009; 106(21):8441-6. PMC: 2689003. DOI: 10.1073/pnas.0900094106. View

5.
Santer B, Taylor K, Gleckler P, Bonfils C, Barnett T, Pierce D . Incorporating model quality information in climate change detection and attribution studies. Proc Natl Acad Sci U S A. 2009; 106(35):14778-83. PMC: 2727480. DOI: 10.1073/pnas.0901736106. View