Probabilistic tail dependence of intense precipitation on spatiotemporal scale in observations, reanalyses, and GCMs

Verfasser / Beitragende:
[Nicholas Cavanaugh, Alexander Gershunov]
Ort, Verlag, Jahr:
2015
Enthalten in:
Climate Dynamics, 45/11-12(2015-12-01), 2965-2975
Format:
Artikel (online)
ID: 605472378
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024 7 0 |a 10.1007/s00382-015-2517-1  |2 doi 
035 |a (NATIONALLICENCE)springer-10.1007/s00382-015-2517-1 
245 0 0 |a Probabilistic tail dependence of intense precipitation on spatiotemporal scale in observations, reanalyses, and GCMs  |h [Elektronische Daten]  |c [Nicholas Cavanaugh, Alexander Gershunov] 
520 3 |a Daily precipitation variability as observed from weather stations is heavy tailed at most locations around the world. It is thought that diversity in precipitation-causing weather events is fundamental in producing heavy-tailed distributions, and it arises from theory that at least one of the precipitation types contributing to a heavy-tailed climatological record must also be heavy-tailed. Precipitation is a multi-scale phenomenon with a rich spatial structure and short decorrelation length and timescales; the spatiotemporal scale at which precipitation is observed is thus an important factor when considering its statistics and extremes. In this study, we examine the spatiotemporal scaling behavior of intense precipitation from point-scale to large grid cells and from 1day to 4weeks over the entire globe. We go on to validate the current generation of historically-forced climate models and reanalyses against observational data at consistent spatial scales. Our results demonstrate that the prevalence and magnitude of heavy tails in observations decrease when moving to lower spatiotemporal resolutions, as is consistent with stochastic theory. Reanalyses and climate models generally reproduce large, synoptic scale distribution classifications, but struggle to reproduce the statistics in regions that are strongly affected by mesoscale phenomena. We discuss these results in relation to physically consistent atmospheric regimes. We conclude with a global view of precipitation distribution type at daily resolution as calculated from the best-performing reanalysis, the Climate Forecast System Reanalysis. 
540 |a Springer-Verlag (outside the USA), 2015 
690 7 |a Daily intense precipitation  |2 nationallicence 
690 7 |a Extremes  |2 nationallicence 
690 7 |a Heavy tails  |2 nationallicence 
690 7 |a Extreme value theory  |2 nationallicence 
690 7 |a Scale dependence  |2 nationallicence 
700 1 |a Cavanaugh  |D Nicholas  |u Earth Science Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Mail Stop 74R316C, 94720, Berkeley, CA, USA  |4 aut 
700 1 |a Gershunov  |D Alexander  |u Climate Atmospheric Science and Physical Oceanography Division, Scripps Institution of Oceanography, La Jolla, CA, USA  |4 aut 
773 0 |t Climate Dynamics  |d Springer Berlin Heidelberg  |g 45/11-12(2015-12-01), 2965-2975  |x 0930-7575  |q 45:11-12<2965  |1 2015  |2 45  |o 382 
856 4 0 |u https://doi.org/10.1007/s00382-015-2517-1  |q text/html  |z Onlinezugriff via DOI 
898 |a BK010053  |b XK010053  |c XK010000 
900 7 |a Metadata rights reserved  |b Springer special CC-BY-NC licence  |2 nationallicence 
908 |D 1  |a research-article  |2 jats 
949 |B NATIONALLICENCE  |F NATIONALLICENCE  |b NL-springer 
950 |B NATIONALLICENCE  |P 856  |E 40  |u https://doi.org/10.1007/s00382-015-2517-1  |q text/html  |z Onlinezugriff via DOI 
950 |B NATIONALLICENCE  |P 700  |E 1-  |a Cavanaugh  |D Nicholas  |u Earth Science Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Mail Stop 74R316C, 94720, Berkeley, CA, USA  |4 aut 
950 |B NATIONALLICENCE  |P 700  |E 1-  |a Gershunov  |D Alexander  |u Climate Atmospheric Science and Physical Oceanography Division, Scripps Institution of Oceanography, La Jolla, CA, USA  |4 aut 
950 |B NATIONALLICENCE  |P 773  |E 0-  |t Climate Dynamics  |d Springer Berlin Heidelberg  |g 45/11-12(2015-12-01), 2965-2975  |x 0930-7575  |q 45:11-12<2965  |1 2015  |2 45  |o 382