A comparison of U.S. precipitation extremes under RCP8.5 and RCP4.5 with an application of pattern scaling
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Precipitation extremes are expected to increase in a warming climate, which may have serious societal impacts. This study uses two initial condition ensembles conducted with the Community Earth System Model (CESM) to investigate potential changes in extreme precipitation under two climate change scenarios over the contiguous United States. We fit non-stationary generalized extreme value (GEV) models to annual maximum daily precipitation simulated from a 30-member ensemble under the RCP8.5 scenario and a 15-member ensemble under the RCP4.5 scenario. We then compare impacts using the 1 % annual exceedance probability (AEP) level, which is the amount of daily rainfall with only a 1 % chance of being exceeded in a given year. Under RCP8.5 between 2005 and 2080, the 1 % AEP level is projected to increase by 17 % on average across the U.S., and up to 36 % for some grid cells. Compared to RCP8.5 in the year 2080, RCP4.5 is projected to reduce the 1 % AEP level by 7 % on average, with reductions as large as 18 % for some grid cells. We also investigate a pattern scaling approach in which we produce predictive GEV distributions of annual precipitation maxima under RCP4.5 given only global mean temperatures for this scenario. We compare results from this less computationally intensive method to those obtained from our GEV model fitted directly to the CESM RCP4.5 output and find that pattern scaling produces reasonable projections.
M. Fix was partially supported by a Center for Interdisciplinary Mathematics and Statistics student fellowship at Colorado State University. D. Cooley was partially supported by NSF grant DMS-1243102. S. Sain undertook this work as a Scientist in the Institute for Mathematics Applied to Geosciences at the National Center for Atmospheric Research. C. Tebaldi is supported by the Regional and Global Climate Modeling program of the US Department of Energy, Office of Science (BER), through Cooperative Agreement DE-FC02-97ER62402. We thank B. O’Neill and three anonymous reviewers for comments on an earlier draft of this paper. We would like to acknowledge high-performance computing support from Yellowstone (ark:/85065/d7wd3xhc) provided by the National Center for Atmospheric Research’s Computational and Information Systems Laboratory, sponsored by NSF. In addition, we would also like to acknowledge the modeling groups who created the CESM model and generated the initial condition ensembles used in this analysis.
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