The Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Quantifying Uncertainties in Atmospheric River Climatology
The Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Quantifying Uncertainties in Atmospheric River Climatology
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DOI：
10.1029/2019jd030936
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发表时间：
2019-12
期刊：
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通讯作者：
J. Rutz;C. Shields;J. Lora;A. Payne;B. Guan;P. Ullrich;T. O’Brien;L. Leung;F. Ralph;M. Wehner;S. Brands;A. Collow;N. Goldenson;I. Gorodetskaya;Helen V. Griffith;K. Kashinath;B. Kawzenuk;Harinarayan Krishnan;V. Kurlin;D. Lavers;G. Magnusdottir;K. Mahoney;E. McClenny;G. Muszynski;P. Nguyen;M. Prabhat;Y. Qian;A. Ramos;C. Sarangi;S. Sellars;T. Shulgina;R. Tomé;D. Waliser;D. Walton;G. Wick;Anna M. Wilson;M. Viale
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文献类型：
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作者：
J. Rutz;C. Shields;J. Lora;A. Payne;B. Guan;P. Ullrich;T. O’Brien;L. Leung;F. Ralph;M. Wehner;S. Brands;A. Collow;N. Goldenson;I. Gorodetskaya;Helen V. Griffith;K. Kashinath;B. Kawzenuk;Harinarayan Krishnan;V. Kurlin;D. Lavers;G. Magnusdottir;K. Mahoney;E. McClenny;G. Muszynski;P. Nguyen;M. Prabhat;Y. Qian;A. Ramos;C. Sarangi;S. Sellars;T. Shulgina;R. Tomé;D. Waliser;D. Walton;G. Wick;Anna M. Wilson;M. Viale
在许多地区，大气河流(AR)因其与高影响天气事件和长期供水有关而广为人知。科学界的研究人员已经开发了许多方法来识别和跟踪ARs--这是对网格化数据集进行分析以及客观地将影响归因于ARs的必要步骤。这些不同的方法是为了回答具体的研究问题而开发的，因此使用了不同的标准(例如，几何形状、关键变量的阈值和时间相关性)。此外，这些方法通常使用不同的再分析数据集、时间段和感兴趣的区域。大气河流跟踪方法比对项目(ARTMIP)的目标是理解和量化由于这些方法的差异而引起的AR科学中的不确定性。本文介绍了基于20多种不同的AR识别和跟踪方法的关键AR相关指标的结果，这些方法应用于从1980年1月到2017年6月的研究和应用版本2再分析数据的现代追溯分析。我们表明，AR频率、持续时间和季节性表现出广泛的结果，而这些指标在选定的沿海(但不是内陆)横断面上的子午线分布在不同方法之间非常相似。此外，方法被分组为基于标准的簇，其中结果的范围被缩小。AR案例研究和对个别方法偏离所有方法的评估意味着突出某些方法的优点/缺点。例如，具有较少(较多)限制性标准的方法识别更多(较少)AR和AR相关影响。最后，本文进行了讨论，并提出了建议，供从事AR相关研究的人员参考。
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.