Atmospheric rivers (ARs) are a weather phenomenon associated with long, narrow bands of atmospheric moisture transport from the tropics to higher latitudes (Zhu and Newell 1998). ARs are broadly recognized for their global significance in mediating energy and water cycles and for their regional importance for providing water supply but also as a source of hazard. There is a large body of literature that explores ARs from both global and regional perspectives and from time scales spanning hourly to centennial. Despite this broad literature, the consensus definition of ARs remains essentially qualitative (Ralph et al. 2018), so diverse methods have been developed to track ARs, leading potentially to an important source of uncertainty in understanding ARs and their myriads of impacts. ARTMIP is a grassroots effort initiated by U.S. Department of Energy (DOE) and National Oceanic and Atmospheric Administration (NOAA) scientists to understand and quantify the implications of the diverse set of AR identification and tracking methods found in the literature. The second Atmospheric River Tracking Method Intercomparison Project (ARTMIP) workshop, sponsored by the U.S. Department of Energy, built upon the framework established by the first ARTMIP workshop (held in May 2017 in San Diego, California). The goal of ARTMIP is to understand and quantify uncertainties in atmospheric river science based on choice of identification and/or tracking methodology (i.e., AR algorithms) and communicate this to the AR research and stakeholder communities. More information on ARTMIP’s goals, framework, and experimental design is available in Shields et al. (2018). The climatological characteristics of ARs, such as AR frequency, duration, intensity, and seasonality, are all strongly dependent on the method used to identify ARs. Understanding the uncertainties and how the choice of detection algorithm impacts quantities such as precipitation is imperative for stakeholders such as water managers, city and transportation planners, agriculture, or any industry that depends on global and regional water cycle information for the near term and into the future. Understanding and quantifying AR algorithm uncertainty is also important for developing metrics and diagnostics for evaluating model fidelity in simulating ARs and their impacts. ARTMIP launched a multitiered intercomparison effort designed to fill this community need. The first tier of the project is aimed at understanding the impact of AR algorithm on quantitative baseline statistics and characteristics of ARs, and the second tier of the project includes sensitivity studies designed around specific science questions, such as reanalysis uncertainty and climate change. The second ARTMIP workshop provided a forum for the AR community to 1. discuss analyses of the tier 1 dataset, 2. synthesize the results and implications of the tier 1 analyses, 3. use this information to define the experimental designs for the various tier 2 experiments, 4. work toward developing a set of recommendations regarding the advantages and disadvantages of different AR algorithms for various scientific questions, and 5. discuss gaps and emerging opportunities for advancing the tracking and science of ARs.