Quantifying the skill of CMIP5 models in simulating seasonal albedo and snow cover evolution

Effectively modeling the influence of terrestrial snow on climate in general circulation models is limited by imperfect knowledge and parameterization of arctic and subarctic climate processes and a lack of reliable observations for model evaluation and improvement. This study uses a number of satellite-derived data sets to evaluate how well the current generation of climate models from the Fifth Coupled Model Intercomparison Project (CMIP5) simulate the seasonal cycle of climatological snow cover fraction (SCF) and surface albedo over the Northern Hemisphere 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 ((sfc_snow)), which affect both the magnitude and timing of the seasonal peak in (sfc_snow) during the fall snow accumulation period, and the springtime snow ablation period. Insolation weighting demonstrates that the biases in (sfc_snow) during spring are of greater importance for the surface energy budget. Albedo biases are largest 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 (sfc_snow) rather than by biases in SCF. While seemingly straightforward corrections to peak (sfc_snow) could yield significant improvements to simulated snow albedo feedback, changes in (sfc_snow) could potentially introduce biases in other important model variables such as surface temperature.