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

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Abstract

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 1 day to 4 weeks 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.

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Acknowledgments

Cavanaugh was supported in part by NSF grants OCE0960770 and OCE1419306. A portion of the computing was accomplished using Yellowstone resources available through NCAR/UCAR. We thank Mary Tyree for archiving CMIP5 data sets and making them available locally. This work contributes to research supported by DOI via the Southwest Climate Science Center, by NOAA via the RISA program through the California and Nevada Applications Center, and by the California Energy Commission PIER Program. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups who produced the models listed in Table 2 for making available their model output. For CMIP the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. Gridded observational data as well as individual results for reanalyses and climate models discussed in this paper can be made available by request. Finally, we thank two anonymous reviewers whose comments helped improve the quality of the manuscript.

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  1. Earth Science Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Mail Stop 74R316C, Berkeley, CA, 94720, USA

    Nicholas R. Cavanaugh

  2. Climate Atmospheric Science and Physical Oceanography Division, Scripps Institution of Oceanography, La Jolla, CA, USA

    Nicholas R. Cavanaugh & Alexander Gershunov

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  1. Nicholas R. Cavanaugh
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  2. Alexander Gershunov
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Correspondence to Nicholas R. Cavanaugh.

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Cavanaugh, N.R., Gershunov, A. Probabilistic tail dependence of intense precipitation on spatiotemporal scale in observations, reanalyses, and GCMs. Clim Dyn 45, 2965–2975 (2015). https://doi.org/10.1007/s00382-015-2517-1

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