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Activity Number: 395 - Statistical Models for High-Dimensional Computer Output
Type: Topic Contributed
Date/Time: Tuesday, July 31, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics and the Environment
Abstract #329218 Presentation
Title: Changes in Spatiotemporal Precipitation Patterns in Changing Climate Conditions
Author(s): Won Chang* and Michael Stein and Jiali Wang and V. Rao Kotamarthi and Elisabeth J. Moyer
Companies: University of Cincinnati and University of Chicago, Dept. of Statistics and Argonne National Laboratory and Argonne National Laboratory and University of Chicago
Keywords: Storm Tracking; Precipitation; Image Component Analysis; Large Spatial Data; Regional Climate Model; High Resolution Model

Climate models robustly imply that some significant change in precipitation patterns will occur. Models consistently project that the intensity of individual precipitation events increases by approximately 6-7%/K, following the increase in atmospheric water content, but that total precipitation increases by a lesser amount (2-3%/K in the global average). Some other aspect of precipitation events must then change to compensate for this difference. We develop here a new methodology for identifying individual rainstorms and studying their physical characteristics - including starting location, intensity, spatial extent, duration, and trajectory - that allows identifying that compensating mechanism. We apply this technique to precipitation over the contiguous U.S. from both radar-based data products and high-resolution model runs simulating 100 years of business-as-usual warming. In model studies, we find that the dominant compensating mechanism is a reduction of storm size.

Authors who are presenting talks have a * after their name.

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