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The Relationships Between the Trends of Mean and Extreme PrecipitationThis study provides a better understanding of the relationships between the trends of mean and extreme precipitation in two observed precipitation data sets: the Climate Prediction Center Unified daily precipitation data set and the Global Precipitation Climatology Program (GPCP) pentad data set. The study employs three kinds of definitions of extreme precipitation: (1) percentile, (2) standard deviation and (3) generalize extreme value (GEV) distribution analysis for extreme events based on local statistics. Relationship between trends in the mean and extreme precipitation is identified with a novel metric, i.e. area aggregated matching ratio (AAMR) computed on regional and global scales. Generally, more (less) extreme events are likely to occur in regions with a positive (negative) mean trend. The match between the mean and extreme trends deteriorates for increasingly heavy precipitation events. The AAMR is higher in regions with negative mean trends than in regions with positive mean trends, suggesting a higher likelihood of severe dry events, compared with heavy rain events in a warming climate. AAMR is found to be higher in tropics and oceans than in the extratropics and land regions, reflecting a higher degree of randomness and more important dynamical rather than thermodynamical contributions of extreme events in the latter regions.
Document ID
20170001643
Acquisition Source
Goddard Space Flight Center
Document Type
Reprint (Version printed in journal)
External Source(s)
Authors
Zhou, Yaping
(Morgan State Univ. Baltimore, MD, United States)
Lau, William K.-M.
(NASA Goddard Space Flight Center Greenbelt, MD, United States)
Date Acquired
February 17, 2017
Publication Date
January 10, 2017
Publication Information
Publication: International Journal of Climatology
Publisher: Wiley
Subject Category
Meteorology And Climatology
Report/Patent Number
GSFC-E-DAA-TN39216
Funding Number(s)
CONTRACT_GRANT: NNG11HP16A
Distribution Limits
Public
Copyright
Other
Keywords
global warming
climate variability
precipitation

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