Drivers of Model Spread in Snowpack Changes across the American Mountain West

Abstract As the climate is rapidly changing, regions with tight water budgets such as the American Mountain West will experience changes in their water availability that can have substantial impacts on agriculture, industry, and drinking water. In the American Mountain West, the amount of water available throughout the year is largely dependent on the mass of the wintertime snowpack that melts throughout the spring and summer, filling the downstream rivers and reservoirs. We provide an overview on how snowpack in the ERA5-Land reanalysis record has changed during the past century of warming and compare these changes with the historical simulations from an ensemble of CMIP6 global climate models (GCMs) to determine how skillful they are at predicting snowpack in the Mountain West, which models are the most skillful, and how we can constrain future predictions by selecting only the models with the best simulation of historical snowpack. We perform analytical and Gaussian process emulation decompositions of the historical snow water equivalent (SWE) changes to determine which processes are responsible for the spread in GCMs’ simulation of historical SWE. Taken together, these analyses show that intermodel spread in SWE change is mostly driven by spread in GCMs’ simulation of large-scale changes in surface temperature, precipitation, and evaporation, which drives spread in the amount of fallen snow that remains in the snowpack in the spring.