Evaluating biases in simulated land surface albedo from CMIP5 global climate models

Land surface albedo is a key parameter affecting energy balance and near-surface climate. In this study, we used satellite data to evaluate simulated surface albedo in 37 models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5). There was a systematic overestimation in the simulated seasonal cycle of albedo with the highest bias occurring during the Northern Hemisphere's winter months. The bias in surface albedo during the snow-covered season was classified into that in snow cover fraction (SCF) and albedo contrast (beta(1)). There was a general overestimation of beta(1) due to the simulated snow-covered albedo being brighter than the observed value; negative biases in SCF were not always related to negative albedo biases, highlighting the need for realistic representation of snow-covered albedo in models. In addition, models with a lower leaf area index (LAI) tend to produce a higher surface albedo over the boreal forests during the winter, which emphasizes the necessity of improving LAI simulations in CMIP5 models. Insolation weighting showed that spring albedo biases were of greater importance for climate. The removal of albedo biases is expected to improve temperature simulations particularly over high-elevation regions.