Quantifying the Skill of CMIP5 Models in Simulating Seasonal Albedo and Snow Cover Evolution

Abstract:

The influence of snow on climate in general circulation models (GCMs) has proven challenging to effectively model because of imperfect knowledge and parameterization of arctic and sub-arctic climate processes, and a shortage of reliable observations for model assessment and development. This analysis uses several satellite-derived datasets to evaluate how well the current generation of climate models from the fifth Coupled Model Intercomparison Project (CMIP5) simulate the seasonality of climatological snow cover fraction (SCF) and surface albedo over the Northern Hemisphere extratropical snow season (September – June). Using a variety of metrics, the CMIP5 models are found to simulate SCF evolution better than that of albedo. The seasonal cycle of SCF is well reproduced despite substantial biases in simulated surface albedo of snow-covered land (α_{sfc_snow}), which affect both the magnitude and timing of the seasonal maximum in α_{sfc_snow} during the fall snow accumulation period, and the springtime snow ablation period. Insolation-weighting demonstrates that the biases in α_{sfc_snow} during spring are of greater importance for the surface energy balance. Albedo biases are greatest across the boreal forest, where the simulated seasonal cycle of albedo is biased high in 15/16 CMIP5 models. This bias is explained primarily by unrealistic treatment of vegetation masking and subsequent overestimation (more than 50% in some cases) of peak α_{sfc_snow}, rather than by biases in SCF. While seemingly straightforward corrections to peak α_{sfc_snow} could yield significant improvements to simulated snow albedo feedbacks, changes in α_{sfc_snow} could potentially introduce biases in other important model variables such as surface temperature.