The Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Quantifying Uncertainties in Atmospheric River Climatology
Rutz, 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
2019-12-27
Citation
Rutz, 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.
Abstract
Atmospheric 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 applicationPublisher
Wiley Periodicals, Inc.
ISSN
2169-897X 2169-8996
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