Abstract

In a warming climate, the characteristics of snow cover and snow water equivalent (SWE) are expected to change over North America. In regions with seasonal snow cover, shifts in the timing and amount of annual maximum SWE and the timing of the onset and rate of snowmelt will have important implic... Show moreIn a warming climate, the characteristics of snow cover and snow water equivalent (SWE) are expected to change over North America. In regions with seasonal snow cover, shifts in the timing and amount of annual maximum SWE and the timing of the onset and rate of snowmelt will have important implications for water resource management and flood risk. Regional climate models (RCMs) are commonly used to study and quantify regional climate changes, but the ability of these models to accurately represent snow varies. This study examines and evaluates SWE and snowmelt processes over North America in a suite of dynamically downscaled regional climate simulations combined from the North America Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) and the Framework for Assessing Climate’s Energy-water-land nexus using Targeted Simulations (FACETS) ensembles. SWE in the RCMs is evaluated against an ensemble SWE data set that includes SWE from satellite products, snow-pack models, land-surface reanalysis products, and ground-based interpolated products derived from multiple snow station networks. In these simulations, biases in the timing and magnitude of SWE and snowmelt can be due to biases in precipitation, temperature, and the way snow processes are represented in the land-surface model used in each RCM. We explore the role of model resolution, choice of RCM (including the land surface model), and the choice of driving GCM in causing biases in SWE and snowmelt over North America. We also focus on the atmospheric and hydrologic processes that control the timing of snowmelt in each RCM. Show less