PNNL

Abstract

In the coastal mountain regions of western North America, most extreme precipitation is associated with atmospheric rivers, narrow bands of moisture originating in the tropics. Here we quantify how the interannual variability in atmospheric rivers influences snowpack in the western United States in observations and a model. We simulate the historical climate with the Model for Prediction Across Scales with physics from the Community Atmosphere Model Version 5 (MPAS-CAM5) using prescribed sea surface temperatures. In the global variable resolution domain, regional refinement (at 30 km) is applied to our region of interest over western North America and upwind over the northeast Pacific. To better characterize internal variability, we conduct simulations with three ensemble members over 30 years of the historical period. In the Cascade Mountains, with some exceptions, winters with more atmospheric river days are associated with less snowpack. In California’s Sierra Nevada the sign is the opposite. In observations, the results are consistent in winter, but statistically significant only for California. In spring, internal variance plays an important role in determining the sign of the relationship between number of atmospheric river days and snowpack. Cumulative atmospheric river days starting in winter through spring, on the other hand, yield a spring relationship with the same sign as the winter one for a given location. Thus, the impact of atmospheric rivers on winter snowpack has a greater influence on spring snowpack than spring atmospheric rivers, in the model for both regions and in California consistently in observations.