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The Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Quantifying Uncertainties in Atmospheric River Climatology
dc.contributor.authorRutz, Jonathan J.
dc.contributor.authorShields, Christine A.
dc.contributor.authorLora, Juan M.
dc.contributor.authorPayne, Ashley E.
dc.contributor.authorGuan, Bin
dc.contributor.authorUllrich, Paul
dc.contributor.authorO’brien, Travis
dc.contributor.authorLeung, L. Ruby
dc.contributor.authorRalph, F. Martin
dc.contributor.authorWehner, Michael
dc.contributor.authorBrands, Swen
dc.contributor.authorCollow, Allison
dc.contributor.authorGoldenson, Naomi
dc.contributor.authorGorodetskaya, Irina
dc.contributor.authorGriffith, Helen
dc.contributor.authorKashinath, Karthik
dc.contributor.authorKawzenuk, Brian
dc.contributor.authorKrishnan, Harinarayan
dc.contributor.authorKurlin, Vitaliy
dc.contributor.authorLavers, David
dc.contributor.authorMagnusdottir, Gudrun
dc.contributor.authorMahoney, Kelly
dc.contributor.authorMcClenny, Elizabeth
dc.contributor.authorMuszynski, Grzegorz
dc.contributor.authorNguyen, Phu Dinh
dc.contributor.authorPrabhat, Mr.
dc.contributor.authorQian, Yun
dc.contributor.authorRamos, Alexandre M.
dc.contributor.authorSarangi, Chandan
dc.contributor.authorSellars, Scott
dc.contributor.authorShulgina, T.
dc.contributor.authorTome, Ricardo
dc.contributor.authorWaliser, Duane
dc.contributor.authorWalton, Daniel
dc.contributor.authorWick, Gary
dc.contributor.authorWilson, Anna M.
dc.contributor.authorViale, Maximiliano
dc.date.accessioned2020-02-05T15:06:00Z
dc.date.availableWITHHELD_11_MONTHS
dc.date.available2020-02-05T15:06:00Z
dc.date.issued2019-12-27
dc.identifier.citationRutz, Jonathan J.; Shields, Christine A.; Lora, Juan M.; Payne, Ashley E.; Guan, Bin; Ullrich, Paul; O’brien, Travis ; Leung, L. Ruby; Ralph, F. Martin; Wehner, Michael; Brands, Swen; Collow, Allison; Goldenson, Naomi; Gorodetskaya, Irina; Griffith, Helen; Kashinath, Karthik; Kawzenuk, Brian; Krishnan, Harinarayan; Kurlin, Vitaliy; Lavers, David; Magnusdottir, Gudrun; Mahoney, Kelly; McClenny, Elizabeth; Muszynski, Grzegorz; Nguyen, Phu Dinh; Prabhat, Mr.; Qian, Yun; Ramos, Alexandre M.; Sarangi, Chandan; Sellars, Scott; Shulgina, T.; Tome, Ricardo; Waliser, Duane; Walton, Daniel; Wick, Gary; Wilson, Anna M.; Viale, Maximiliano (2019). "The Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Quantifying Uncertainties in Atmospheric River Climatology." Journal of Geophysical Research: Atmospheres 124(24): 13777-13802.
dc.identifier.issn2169-897X
dc.identifier.issn2169-8996
dc.identifier.urihttps://hdl.handle.net/2027.42/153636
dc.description.abstractAtmospheric rivers (ARs) are now widely known for their association with highâ impact weather events and longâ term water supply in many regions. Researchers within the scientific community have developed numerous methods to identify and track of ARsâ 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 ARâ related metrics based on 20+ different AR identification and tracking methods applied to Modernâ 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 criteriaâ based clusters, within which the range of results is reduced. AR case studies and an evaluation of individual method deviation from an allâ method mean highlight advantages/disadvantages of certain approaches. For example, methods with less (more) restrictive criteria identify more (less) ARs and ARâ related impacts. Finally, this paper concludes with a discussion and recommendations for those conducting ARâ related research to consider.Key PointsThe large number of atmospheric river identification/tracking methods produces large uncertainty related to AR climatology and impactsUncertainty is quantified using the same data (MERRA v2), time period (1980â 2017), region (global where possible), and common metricsThis study presents recommendations regarding the advantages/disadvantages of certain approaches based on science application
dc.publisherWiley Periodicals, Inc.
dc.subject.otherimpacts
dc.subject.otheratmospheric river
dc.subject.otherhydroclimate
dc.subject.otherweather
dc.subject.otherclimate
dc.subject.otherintercomparison
dc.titleThe Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Quantifying Uncertainties in Atmospheric River Climatology
dc.typeArticle
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelAtmospheric and Oceanic Sciences
dc.subject.hlbtoplevelScience
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/153636/1/jgrd55920_am.pdf
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/153636/2/jgrd55920.pdf
dc.identifier.doi10.1029/2019JD030936
dc.identifier.sourceJournal of Geophysical Research: Atmospheres
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dc.owningcollnameInterdisciplinary and Peer-Reviewed


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