Investigating Catchment‐Scale Daily Snow Depths of CMIP6 in Canada

Abstract

AbstractAccurate modeling of snow depth (SD) processes is critical for understanding global energy balance changes, affecting climate change mitigation strategies. This study evaluates the Coupled Model Intercomparison Project Phase 6 (CMIP6) model performance in simulating daily SD across Canada. We assess CMIP6 outputs against observed data, focusing on daily SD averages, snow cover durations, and rates of accumulation and depletion, alongside annual SD peaks for 11 major Canadian catchments. Our findings reveal that CMIP6 simulations generally overestimate daily SD by 57.7% and extend snow cover duration by 30.5 days on average. While three models (CESM2, UKESM1‐0‐LL and MIROC6) notably align with observed annual SD peaks, simulation biases suggest the need for enhanced model parameterization to accurately capture snow physics, particularly in regions with permanent snow cover and complex terrains. This analysis underscores the necessity of refining CMIP6 simulations and incorporating detailed geographical data for better SD predictions.