A Dynamical Analysis of Present and Future Atmospheric River Behavior over the North Pacific in MERRA Reanalysis and CMIP5 RCP 8.5 Projections

Abstract:

A large-scale analysis of landfalling atmospheric rivers (ARs) is performed for the extended winter (November through March) for the last thirty years using MERRA reanalysis. Over the North Pacific, the climatology and upper-level characteristics of approximately 750 ARs are presented based on the 85^th percentile of peak daily moisture flux and compared to an extreme subset of events selected based on the 95^thpercentile of peak daily moisture flux and precipitation total. In composites of upper level fields during AR occurrences, certain characteristics stand out irrespective of the tropical climate indices, suggesting the key role of extratropical dynamical processes in AR development.

Composites of dynamical fields following the eastward progression of ARs show a close relationship between the location of the jet, Rossby wave propagation and anticyclonic Rossby wave breaking in the upper troposphere of the east Pacific and moisture transport in the lower troposphere (see attached figure). Comparison between the strongest and the weakest ARs show differences in both the intensity of moisture transport and the scale and development of anticyclonic Rossby wave breaking in the east Pacific.

To characterize shifts in AR behavior within the context of projected changes at the end of the century, we extend this line of work using data from 15 different models that participated in CMIP5 and compare our climatology, based on MERRA reanalysis, to daily historical model data (1980 – 2000) and RCP 8.5 (2080 – 2100) projection data. We examine how well ARs are represented in the model output and investigate, within a dynamical context, the role of warming in modifying AR intensity, landfalling latitude and precipitation distribution. We show that while intensity increases are nearly uniform across the models, precipitation and landfalling latitude are quite variable. Here, we focus on the dynamical factors leading to this variability.