The Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Quantifying Uncertainties in Atmospheric River Climatology

Journal Article · Sun Nov 24 00:00:00 EST 2019 · Journal of Geophysical Research: Atmospheres

DOI:https://doi.org/10.1029/2019jd030936· OSTI ID:1659743

Rutz, Jonathan J.  ^[1]; Shields, Christine A.  ^[2]; Lora, Juan M. ^[3]; Payne, Ashley E. ^[4]; Guan, Bin  ^[5]; Ullrich, Paul  ^[6]; O’Brien, Travis  ^[7]; Leung, L. Ruby  ^[8]; Ralph, F. Martin ^[9]; Wehner, Michael  ^[7]; Brands, Swen  ^[10]; Collow, Allison  ^[11]; Goldenson, Naomi  ^[12]; Gorodetskaya, Irina ^[13]; Griffith, Helen ^[14]; Kashinath, Karthik  ^[7]; Kawzenuk, Brian ^[9]; Krishnan, Harinarayan  ^[7]; Kurlin, Vitaliy ^[15]; Lavers, David  ^[16] ; Magnusdottir, Gudrun  ^[17]; Mahoney, Kelly ^[18]; McClenny, Elizabeth ^[6]; Muszynski, Grzegorz ^[19]; Nguyen, Phu Dinh ^[17]; Prabhat, Mr  ^[7]; Qian, Yun  ^[8]; Ramos, Alexandre M.  ^[20]; Sarangi, Chandan ^[8]; Sellars, Scott  ^[21]; Shulgina, T.  ^[9]; Tome, Ricardo  ^[20]; Waliser, Duane ^[5]; Walton, Daniel ^[12]; Wick, Gary ^[17]; Wilson, Anna M.  ^[9]; Viale, Maximiliano  ^[22]

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1. National Oceanic and Atmospheric Administration, Salt Lake City, UT (United States)
2. National Center for Atmospheric Research, Boulder, CO (United States)
3. Yale University, New Haven, CT (United States)
4. University of Michigan, Ann Arbor, MI (United States)
5. California Institute of Technology, Pasadena, CA (United States)
6. University of California, Davis, CA (United States)
7. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
8. Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
9. Scripps Institution of Oceanography, La Jolla, CA (United States)
10. MeteoGalicia–Xunta de Galicia, Santiago de Compostela (Spain)
11. Universities Space Research Association, Columbia, MD (United States)
12. University of California, Los Angeles, CA (United States)
13. University of Aveiro (Portugal)
14. University of Reading (United Kingdom)
15. University of Liverpool (United Kingdom)
16. European Centre for Medium‐Range Weather Forecasts, Reading (United Kingdom)
17. University of California, Irvine, CA (United States)
18. National Oceanic and Atmospheric Administration, Boulder, CO (United States)
19. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); University of Liverpool (United Kingdom)
20. Universidade de Lisboa, Lisbon (Portugal)
21. University of California, La Jolla, CA (United States)
22. Instituto Argentino de Nivologia, Mendoza (Argentina)

Atmospheric rivers (ARs) are now widely known for their association with high–impact weather events and long–term water supply in many regions. Researchers within the scientific community have developed numerous methods to identify and track of ARs—a necessary step for analyses on gridded data sets, and objective attribution of impacts to ARs. These different methods have been developed to answer specific research questions and hence use different criteria (e.g., geometry, threshold values of key variables, and time dependence). Furthermore, these methods are often employed using different reanalysis data sets, time periods, and regions of interest. The goal of the Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is to understand and quantify uncertainties in AR science that arise due to differences in these methods. This paper presents results for key AR–related metrics based on 20+ different AR identification and tracking methods applied to Modern–Era Retrospective Analysis for Research and Applications Version 2 reanalysis data from January 1980 through June 2017. We show that AR frequency, duration, and seasonality exhibit a wide range of results, while the meridional distribution of these metrics along selected coastal (but not interior) transects are quite similar across methods. Furthermore, methods are grouped into criteria–based clusters, within which the range of results is reduced. AR case studies and an evaluation of individual method deviation from an all–method mean highlight advantages/disadvantages of certain approaches. For example, methods with less (more) restrictive criteria identify more (less) ARs and AR–related impacts. Lastly, this paper concludes with a discussion and recommendations for those conducting AR–related research to consider.

• Research Organization: University Corporation for Atmospheric Research, Boulder, CO (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Biological and Environmental Research (BER)
• Grant/Contract Number: 89243018SSC000007
• OSTI ID: 1659743
• Journal Information: Journal of Geophysical Research: Atmospheres, Vol. 124, Issue 24; ISSN 2169-897X
• Publisher: American Geophysical Union
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES
atmospheric river
intercomparison
climate
weather
hydroclimate
impacts