资源环境科技发展态势分析平台

Many Earth system models contain substantial biases in the magnitude and seasonal cycle of the albedo of snow-covered surfaces. Various structural and parametric deficiencies have been identified as potential causes of these albedo biases, related to vegetation distribution and abundance, snow albedo, and the representation of snow interception by forest canopies. There is, however, little understanding of how the albedo biases directly influence simulated climate because of difficulties in isolating them from other complex processes and feedbacks. In this study, we conduct a number of novel simulations using the National Center for Atmospheric Research Community Earth System Model (CESM), replacing the model's internal surface albedo calculation with values prescribed from observations or from other model simulations. Results show that while biases in surface albedo are largest in winter, those during spring have the greatest impact on surface climate because incoming solar radiation is much stronger. Correcting biases in the seasonal cycle of albedo in CESM reduces climatological temperature biases across the boreal region in spring and partially corrects Arctic sea level pressure biases, but due to compensating errors, overall climate biases are not always reduced. Additionally, we impose albedo patterns extracted from other climate models with large positive and negative albedo biases to illustrate the climate responses that can result. Prescribed surface albedo produces significant impacts on surface radiation, near-surface land temperatures, and, more rarely, atmospheric circulation. This is important because small changes to mean climate during spring can have major implications for the snow and surface radiation regimes.