Are Multiseasonal Forecasts of Atmospheric Rivers Possible?. Issue 17 (4th September 2021) - Tseng, Kai‐Chih; Johnson, Nathaniel C.; Kapnick, Sarah B.; Delworth, Thomas L.; Lu, Feiyu; Cooke, William; Wittenberg, Andrew T.; Rosati, Anthony J.; Zhang, Liping; McHugh, Colleen; Yang, Xiaosong; Harrison, Matthew; et al.
Abstract: Atmospheric rivers (ARs) exert significant socioeconomic impacts in western North America, where 30 % of the annual precipitation is determined by ARs that occur in less than 15 % of wintertime. ARs are thus beneficial to water supply but can produce extreme precipitation hazards when making landfall. While most prevailing research has focused on the subseasonal ( ≤ 5 weeks) prediction of ARs, only limited efforts have been made for AR forecasts on multiseasonal timescales ( ≥ 3 months) that are crucial for water resource management and disaster preparedness. Through the analysis of reanalysis data and retrospective predictions from a new seasonal‐to‐decadal forecast system, this research shows the existing potential of multiseasonal AR frequency forecasts with predictive skills 9 months in advance. Additional analysis explores the dominant predictability sources and challenges for multiseasonal AR prediction. Plain Language Summary: Atmospheric rivers (ARs), narrow corridors of intense moisture transport and heavy precipitation, are an important water resource but also a cause of flooding‐related disasters for western North America. Consequently, predictions of AR frequency several seasons in advance potentially would be of great value, but such operational forecasts are currently lacking due to the challenges in simulating such intense, small‐scale weather phenomena and their predictability sources on seasonal timescales. In this study, we examine the forecast skill of AR frequency on seasonal‐to‐multiseasonal timescales ( ≥ 3 months) in a new generation seasonal‐to‐decadal prediction system developed at the Geophysical Fluid Dynamics Laboratory. We find that AR frequency can be skillfully forecast at least 9 months in advance over certain regions of the west coast of North America, such as California and Alaska, while the forecasts are only reliable for the first season in other regions. This regional variability can be further explained by the large‐scale climate variability pattern that is responsible for much of the skill, which is strongly modulated by slowly varying sea surface temperature (SST) variations. A prototype probabilistic seasonal AR forecast product is proposed. Key Points: Atmospheric river frequency can be skillfully forecast at least 9 months in advance The skills are significant over certain regions of western North America such as Alaska and California El Nino‐Southern Oscillation and Interdecadal Pacific Oscillation are important predictability sources