Abstract. The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is an international collaborative effort to understand and quantify the uncertainties in atmospheric river (AR) science based on detection algorithm alone. Currently, there are many AR identification and tracking algorithms in the literature with a wide range of techniques and conclusions. ARTMIP strives to provide the community with information on different methodologies and provide guidance on the most appropriate algorithm for a given science question or region of interest. All ARTMIP participants will implement their detection algorithms on a specified common dataset for a defined period of time. The project is divided into two phases: Tier 1 will utilize the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) reanalysis from January 1980 to June 2017 and will be used as a baseline for all subsequent comparisons. Participation in Tier 1 is required. Tier 2 will be optional and include sensitivity studies designed around specific science questions, such as reanalysis uncertainty and climate change. High-resolution reanalysis and/or model output will be used wherever possible. Proposed metrics include AR frequency, duration, intensity, and precipitation attributable to ARs. Here, we present the ARTMIP experimental design, timeline, project requirements, and a brief description of the variety of methodologies in the current literature. We also present results from our 1-month “proof-of-concept” trial run designed to illustrate the utility and feasibility of the ARTMIP project.

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.

Atmospheric rivers (ARs) are synoptic-scale features characterized by their striking geometry-extending thousands of kilometres in length and an order of magni tude less in width 1-and vertically coherent low-level moisture transport concentrated in the bottom 3 km of the atmosphere 2 (Fig. 1). In total, ARs are estimated to accomplish as much as 90% of poleward moisture transport 3,4 , which, in the North Pacific, averages 700 kg m −1 s −1 (Fig. 1b), more than twice the mean annual discharge found at the mouth of the Amazon River 5. ARs do not describe continuous moisture transport. Rather, they are continually evolving pathways that incorporate moisture from local convergence and evaporation along their track 6,7 or, in select cases, from distant source regions in the tropics or subtropics 8-12. Owing to the complexity of their evolution, our baseline knowledge of AR characteristics at the global scale is uncertain due to the dependency on identification algorithms (Box 1), with factors such as genesis, development and termination only recently being explored 13,14. However, ARs are known to operate as one part of a larger, synoptic-scale dynamical system driving the poleward transport of sensible and latent heat 4,15. They are generally found in the vicinity of extratropical cyclones. Over the North Pacific, for example, 85% of ARs are paired with extratropical cyclones 16 , consistent with their observed relationship with baroclinic instabilities and the mid-latitude storm track 3,6. However, this relationship is nuanced; only 45% of extratropical cyclones over the same region are associated with an AR 16. Similar non-linear relationships are observed in the North Atlantic, where the evolution and life cycle of a single AR can span that of several cyclones 9. While the phenomena are clearly related, their relationship is interactive, with potential implications on the inten sification of storms and the severity of precipitation impacts on land 17,18. Indeed, given their intense moisture transport and moist-neutrality, ARs exhibit conditions that are ideal for forced precipitation, either through interaction with topography or ascent along a warm conveyor belt or frontal boundary 19. Thus, when ARs make landfall, they can have a range of hydrological impacts, including precipitation extremes and related hazards,

A large-scale analysis of landfalling atmospheric rivers (ARs) along the west coast of North America and their association with the upper-tropospheric flow is performed for the extended winter (November-March) for the years 1979-2011 using Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalysis data. The climatology, relationship to the El Niño-Southern Oscillation and the Madden-Julian oscillation, and upper-level characteristics of approximately 750 landfalling ARs are presented based on the 85th percentile of peak daily moisture flux. AR occurrence along the West Coast is dominated by early season events. In composites of upper-level fields during AR occurrences, certain characteristics stand out irrespective of the tropical climate indices. This suggests that extratropical dynamical processes play a key role in AR dynamics.The influence of the large-scale circulation on AR intensity prior to landfall is examined by objectively selecting an extreme subset of 112 landfalling AR dates representing the 95th percentile of strongest cases. Each landfalling AR date that is identified is traced backward in time using a novel semiautomated tracking algorithm based on spatially and temporally connected organized features in integrated moisture transport. Composites of dynamical fields following the eastward progression of ARs show a close relationship of the location of the jet, Rossby wave propagation, and anticyclonic Rossby wave breaking in the upper troposphere of the eastern Pacific and moisture transport in the lower troposphere. Comparison between the strongest and the weakest ARs within the most extreme subset shows differences in both the intensity of moisture transport and the scale and development of anticyclonic Rossby wave breaking in the eastern Pacific.

10The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is an international collaborative effort to understand and quantify the uncertainties in atmospheric river (AR) science based on detection algorithm alone. Currently, there are many AR identification and tracking algorithms in the literature with a wide range of techniques and conclusions. ARTMIP strives to provide the community with information on different 15 methodologies and provide guidance on the most appropriate algorithm for a given science question or region of interest. All ARTMIP participants will implement their detection algorithms on a specified common dataset for a defined period of time. The project is divided into two phases: Tier 1 will utilize the MERRA-2 reanalysis from January 1980 to June of 2017 and will be used as a baseline for all subsequent comparisons. Participation in Tier 1 is 20 required. Tier 2 will be optional and include sensitivity studies designed around specific science questions, such as reanalysis uncertainty and climate change. High resolution reanalysis and/or model output will be used wherever possible. Proposed metrics include AR frequency, duration, intensity, and precipitation attributable to ARs. Here we present the ARTMIP experimental design, timeline, project requirements, and a brief description of the 25 variety of methodologies in the current literature. We also present results from our 1-month "proof of concept" trial run designed to illustrate the utility and feasibility of the ARTMIP project.Geosci. Model Dev. Discuss., https://doi