ARs are synoptic scale weather features, longer than wide, that serve as water and energy transport vehicles and are highly relevant to any populated region around the globe that depends on precipitation for a source of water. Despite the attention to ARs and climate change in the literature, much remains uncertain. AR characteristics, such as size, shape, and intensity, are tightly bound to how we define them and firmly tied to the science question being asked. Atmospheric river detection tools (ARDTs) that identify ARs in datasets reflect diverse perspectives of what constitutes an AR. Given ARDTs are developed for unique and often complementary scientific questions or purposes, they are also widely varied in their methodology. The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) was designed to quantify the uncertainties in AR science that arise solely from detection/definition. Although there have been numerous applications and publications utilizing ARTMIP data, this study emphasizes AR characteristics and precipitation on a regional scale using high horizontal resolution model simulations. Compared to lower-resolution models, high-resolution models better represent local geography and topography, as well as extreme precipitation and AR characteristics.
It is necessary to understand and quantify the uncertainties in AR metrics, processes, and ultimately, impacts when characterizing ARs in future climates. To do this, a robust comparison across detection methodologies within a common framework is required. ARTMIP provides this framework and gives scientists and stakeholders critical information when interpreting AR research. Ultimately, relying on one ARDT, or ARDTs too similar to each other without understanding algorithmic sensitivities, does a disservice to quantifying AR impacts. This study illustrates the necessity of understanding ARDTs sensitivities when choosing methodologies suitable for the science question posed. If possible, a selection of ARDTs should be applied to climate change AR analysis that applies both restrictive and nonrestrictive groups. Type of AR and/or regional focus area matter. Minimally, an evaluation of the ARDT’s restrictiveness, and AR metrics compared to other published ARDTs, should be standard procedure.
Atmospheric rivers (ARs) are long, narrow synoptic scale weather features important for Earth’s hydrological cycle typically transporting water vapor poleward, delivering precipitation important for local climates. Understanding ARs in a warming climate is problematic because the AR response to climate change is tied to how the feature is defined. The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) provides insights into this problem by comparing 16 atmospheric river detection tools (ARDTs) to a common dataset consisting of high-resolution climate change simulations from a global atmospheric general circulation model. ARDTs mostly show increases in frequency and intensity, but the scale of the response is largely dependent on algorithmic criteria. Across ARDTs, bulk characteristics suggest intensity and spatial footprint are inversely correlated, and most regions experience increases in precipitation volume coming from extreme ARs. The spread of the AR precipitation response under climate change is large and dependent on ARDT selection.