Skip to main content
U.S. flag

An official website of the United States government

Uncertainty in Atmospheric River Detection and Atmospheric River Induced Precipitation due to Reanalysis Selection

Presentation Date
Friday, December 16, 2022 at 10:10am - Friday, December 16, 2022 at 10:20am
Location
McCormick Place - E353ab
Authors

Author

Abstract

Atmospheric rivers (ARs) have been documented as the primary source of poleward integrated water vapor transport (IVT) outside of the tropics and are responsible for the majority of extreme precipitation and flooding events along the coastlines of mid-latitude continents. Despite their recognized importance within the climate system, important uncertainty remains surrounding the fundamental processes of ARs and how this may change in the future. One source of this uncertainty, and of the associated differences found in the literature, is that studies have used methods to detect and track ARs that vary widely in their approaches.

Quantifying uncertainties associated with AR tracking methods is the primary goal of the Atmospheric River Tracking Method Intercomparison Project (ARTMIP), an international collaborative effort to understand the uncertainties associated with ARs. Reanalysis products including the Modern Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and the European Centre for Medium-Range Weather Forecasts’ fifth generation atmospheric reanalysis (ERA5), and Japanese 55-year Reanalysis (JRA-55) provide invaluable data that can be used to study ARs and the uncertainties associated with AR tracking methods.

This presentation will give an overview of ARTMIP’s Tier 2 Reanalysis Intercomparison. Results to be presented will feature global and regional AR detection in MERRA-2, ERA5, and JRA-55 as submitted by research groups representing eleven AR detection algorithms. A focus will be placed on how AR detection differs among the reanalyses and tracking methods using an ensemble and consensus approach, as well as how this translates into varying statistics of precipitation associated with ARs.

Funding Program Area(s)