Spatial and temporal patterns of snow cover extent (SCE) and snow water equivalent (SWE) over the terrestrial Arctic are analyzed based on multiple observational datasets and an ensemble of CMIP5 models during 1979–2005. For evaluation of historical simulations of the Coupled Model Intercomparison Project (CMIP5) ensemble, we used two reanalysis products, one satellite-observed product and an ensemble of different datasets. The CMIP5 models tend to significantly underestimate the observed SCE in spring but are in better agreement with observations in autumn; overall, the observed annual SCE cycle is well captured by the CMIP5 ensemble. In contrast, for SWE, the annual cycle is significantly biased, especially over North America, where some models retain snow even in summer, in disagreement with observations. The snow margin position (SMP) in the CMIP5 historical simulations is in better agreement with observations in spring than in autumn, when close agreement across the CMIP5 models is only found in central Siberia. Historical experiments from most CMIP5 models show negative pan-Arctic trends in SCE and SWE. These trends are, however, considerably weaker (and less statistically significant) than those reported from observations. Most CMIP5 models can more accurately capture the trend pattern of SCE than that of SWE, which shows quantitative and qualitative differences with the observed trends over Eurasia. Our results demonstrate the importance of using multiple data sources for the evaluation of snow characteristics in climate models. Further developments should focus on the improvement of both dataset quality and snow representation in climate models, especially ESM-SnowMIP.

基于多个观测数据集和1979-2005年CMIP5模型的集合，分析了北极陆地上的积雪程度（SCE）和雪水当量（SWE）的时空格局。为了评估耦合模型比较项目（CMIP5）集成的历史模拟，我们使用了两种重新分析产品，一种是卫星观测产品，另一种是不同数据集的集成。CMIP5模型倾向于大大低估春季观测到的SCE，但与秋季观测更好地吻合。总体而言，CMIP5集合很好地记录了观测到的年度SCE周期。相反，对于SWE来说，年周期存在明显偏差，尤其是在北美地区，甚至在夏季，有些模型仍会积雪，这与观测结果不一致。CMIP5历史模拟中的雪边缘位置（SMP）与春季的观测值比秋季的观测值更好，而整个CMIP5模型的观测值仅在西伯利亚中部地区有很好的一致性。大多数CMIP5模型的历史实验表明，SCE和SWE中的负面泛北极趋势。但是，这些趋势要比观察到的报告要弱得多（统计上的意义要小）。与SWE相比，大多数CMIP5模型可以更准确地捕获SCE的趋势模式，这表明与欧亚大陆观测到的趋势存在数量和质量上的差异。我们的结果证明了使用多个数据源评估气候模型中雪特征的重要性。