TY - JOUR
T1 - The Atmospheric River Tracking Method Intercomparison Project (ARTMIP)
T2 - Quantifying Uncertainties in Atmospheric River Climatology
AU - Rutz, Jonathan J.
AU - Shields, Christine A.
AU - Lora, Juan M.
AU - Payne, Ashley E.
AU - Guan, Bin
AU - Ullrich, Paul
AU - O’Brien, Travis
AU - Leung, L. Ruby
AU - Ralph, F. Martin
AU - Wehner, Michael
AU - Brands, Swen
AU - Collow, Allison
AU - Goldenson, Naomi
AU - Gorodetskaya, Irina
AU - Griffith, Helen
AU - Kashinath, Karthik
AU - Kawzenuk, Brian
AU - Krishnan, Harinarayan
AU - Kurlin, Vitaliy
AU - Lavers, David
AU - Magnusdottir, Gudrun
AU - Mahoney, Kelly
AU - McClenny, Elizabeth
AU - Muszynski, Grzegorz
AU - Nguyen, Phu Dinh
AU - Prabhat, Mr
AU - Qian, Yun
AU - Ramos, Alexandre M.
AU - Sarangi, Chandan
AU - Sellars, Scott
AU - Shulgina, T.
AU - Tome, Ricardo
AU - Waliser, Duane
AU - Walton, Daniel
AU - Wick, Gary
AU - Wilson, Anna M.
AU - Viale, Maximiliano
N1 - Publisher Copyright:
© 2019. American Geophysical Union. All Rights Reserved. This article has been contributed to by US Government employees and their work is in the public domain in the USA.
PY - 2019/12/27
Y1 - 2019/12/27
N2 - 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. Finally, this paper concludes with a discussion and recommendations for those conducting AR-related research to consider.
AB - 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. Finally, this paper concludes with a discussion and recommendations for those conducting AR-related research to consider.
KW - atmospheric river
KW - climate
KW - hydroclimate
KW - impacts
KW - intercomparison
KW - weather
UR - https://www.scopus.com/pages/publications/85074556420
U2 - 10.1029/2019JD030936
DO - 10.1029/2019JD030936
M3 - Article
AN - SCOPUS:85074556420
SN - 2169-897X
VL - 124
SP - 13777
EP - 13802
JO - Journal of Geophysical Research: Atmospheres
JF - Journal of Geophysical Research: Atmospheres
IS - 24
ER -