PNNL

Abstract

Atmospheric rivers (ARs) are filaments of enhanced horizontal moisture transport in the atmosphere. Due to their prominent role in the meridional moisture transport and regional weather extremes, ARs have been studied extensively in recent years. Yet, how well reanalyses represent ARs and their associated precipitation remains largely unknown on a global scale. In this study, we developed an AR detection algorithm specifically for satellite observations using moisture and the geostrophic winds derived from 3D geopotential height field from the combined retrievals of the Atmospheric Infrared Sounder and the Advanced Microwave Sounding Unit on NASA Aqua satellite. This algorithm enables us to develop the first global AR catalog based solely on satellite observations. The satellite-based AR catalog is then combined with the satellite-based precipitation (Integrated Muti-SatellitE Retrievals for GPM) to evaluate the representations of ARs and AR-induced precipitation in reanalysis products. Our results show that the spreads in AR frequency and AR length distribution are generally small across datasets, while the spread in AR width is relatively larger. In terms of the AR-induced precipitation, both AR-induced mean and extreme precipitation are too weak nearly everywhere in reanalyses. However, all reanalyses tend to precipitate too often under AR conditions, especially over low latitude regions. This finding is consistent with the “drizzling” bias which has plagued generations of climate models. Overall, the findings of this study can help to improve the representations of ARs and associated precipitation in reanalyses and climate models