Abstract
AbstractAtmospheric 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.
Highlights
Over the past several years, interest in atmospheric river (AR) science and applications has increased rapidly
The goal of the Atmospheric River Tracking Method Intercomparison Project (ARTMIP; Shields et al, 2018) is to quantify and understand the uncertainties in AR climatology, precipitation, and related impacts that arise from different AR identification and tracking methods, and how uncertainties in these AR‐related metrics may change in the future
This section describes climatological characteristics of ARs based on the ARTMIP methods used in Tier 1
Summary
Over the past several years, interest in atmospheric river (AR) science and applications has increased rapidly. Machine learning techniques have been developed to identify and track ARs (e.g., Mudigonda et al, 2017; Muszynski et al, 2019; Radić et al, 2015) These different methods produce differences in AR climatologies and, differences in the impacts attributable to ARs. These different methods produce differences in AR climatologies and, differences in the impacts attributable to ARs These differences produce uncertainty in operational weather research and forecasting, water management, and climate projections, which require a current baseline of AR climatology and impacts to assess future changes. The goal of the Atmospheric River Tracking Method Intercomparison Project (ARTMIP; Shields et al, 2018) is to quantify and understand the uncertainties in AR climatology (e.g., frequency, duration, and intensity), precipitation, and related impacts that arise from different AR identification and tracking methods, and how uncertainties in these AR‐related metrics may change in the future. An assessment of method‐related uncertainty affecting AR climatology under climate change scenarios will be the subject of another paper, discussed at the end of section 4
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