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  • 1
    Publication Date: 2020-10-20
    Description: Atmospheric rivers (ARs) are long, narrow filamentary regions of enhanced vertically integrated water vapor transport (IVT) that play an important role in regional water supply and hydrometeorological extremes. Here, an AR detection algorithm is applied to global reanalysis from Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), to objectively and consistently characterize ARs regionally across the continental United States (CONUS). The characteristics of AR and associated precipitation are computed at the gridpoint scale and summarized over the seven U.S. National Climate Assessment regions. ARs are most frequent in the autumn and winter in the West, spring in the Great Plains, and autumn in the Midwest and Northeast. ARs show regional and seasonal variability in basic geometry and IVT. AR IVT composites reveal annually consistent northeastward-directed moisture transport from the Pacific Ocean in the West, whereas moisture transport patterns vary seasonally across the Southern Great Plains and Midwest. Linked AR precipitation characteristics suggest that a substantial proportion of extreme events, defined as the top 5% of 3-day precipitation totals, are associated with ARs over many parts of CONUS, including the East. Regional patterns of AR-associated precipitation highlight that seasonally varying moisture transport and lifting mechanisms differ between the East and the West where orographic lifting is key. Our study aims to contribute a comprehensive and consistent CONUS-wide, regional-scale analysis of ARs in support of ongoing NCA efforts. Given the CONUS-wide role ARs play in extreme precipitation, findings motivate continued study of associated climate change impacts.
    Print ISSN: 1525-755X
    Electronic ISSN: 1525-7541
    Topics: Geography , Geosciences , Physics
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  • 2
    Publication Date: 2019-05-30
    Description: An extreme precipitation categorization scheme, used to temporally and spatially visualize and track the multiscale variability of extreme precipitation climatology, is applied over the continental United States. The scheme groups 3-day precipitation totals exceeding 100 mm into one of five precipitation categories, or “P-Cats.” To demonstrate the categorization scheme and assess its observational uncertainty across a range of precipitation measurement approaches, we compare the climatology of P-Cats defined using in situ station data from the Global Historical Climatology Network-Daily (GHCN-D); satellite-derived data from the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA); gridded station data from the Parameter-Elevation Regression on Independent Slopes Model (PRISM); global reanalysis from the Modern-Era Retrospective Analysis for Research and Applications, version 2; and regional reanalysis from the North American Regional Reanalysis. While all datasets capture the principal spatial patterns of P-Cat climatology, results show considerable variability across the suite in frequency, spatial extent, and magnitude. Higher-resolution datasets, PRISM and TMPA, most closely resemble GHCN-D and capture a greater frequency of high-end P-Cats relative to the lower-resolution products. When all datasets are rescaled to a common coarser grid, differences persist with datasets originally constructed at a high resolution maintaining a higher frequency and magnitude of P-Cats. Results imply that dataset choice matters when applying the P-Cat scheme to track extreme precipitation over space and time. Potential future applications of the P-Cat scheme include providing a target for climate model evaluation and a basis for characterizing future change in extreme precipitation as projected by climate model simulations.
    Print ISSN: 1525-755X
    Electronic ISSN: 1525-7541
    Topics: Geography , Geosciences , Physics
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