Atmospheric River Tracking Method Intercomparison Project (ARTMIP): project goals and experimental design

Abstract

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.

Global Climate Model Ensemble Approaches for Future Projections of Atmospheric Rivers

“…Previous calls in the literature, such as in the recent U.S. National Climate Assessment's Climate Science Special Report (USGCRP, 2017), to better constrain future AR projections in climate models given their significant societal impacts, are supported here. Other works on cataloging AR events, on AR process studies, and on model evaluation and improvement are currently taking place (e.g., Wick et al, 2013;Hagos et al, 2015;Payne & Magnusdottir, 2015;Ralph et al, 2016;Shields et al, 2018) to Figure 9. The standard deviation in year-to-year estimates of AR frequency for ERA-Interim data .…”

The Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Quantifying Uncertainties in Atmospheric River Climatology

“…The dramatic jump in AR frequency near 45°N may be due to some combination of climatology (i.e., placement of the storm track; e.g., Lukens et al, 2018) and the greater number of coastal transect points at latitudes north of 45°N. It is worth noting that during the ARTMIP 1-month experiment described in Shields et al (2018;their Figure 3), the human-control analysis yielded a greater AR frequency than any automated method along both coastal transects. (The human-control analysis consisted of two graduate students counting "by eye" all ARs making landfall for the North American and European coastlines for the month of February 2017.…”

Recent Warming of Landfalling Atmospheric Rivers Along the West Coast of the United States

“…Atmospheric reanalysis products provide the capability to continuously measure ARs across the globe during the satellite era. Horizontal resolution plays a role in AR detection (Jackson et al, 2016;Shields et al, 2018), as algorithms may fail to detect ARs in low resolution products (Jackson et al, 2016). However, products with coarser spatial resolution have successfully been employed in AR studies (Guan & Waliser, 2015;Ralph et al, 2018).…”