To assess the performances of state-of-the-art global climate models on simulating the Arctic clouds and surface radiation balance, the 2001–2014 Arctic Basin surface radiation budget, clouds, and the cloud radiative effects (CREs) in 22 coupled model intercomparison project 6 (CMIP6) models are evaluated against satellite observations. For the results from CMIP6 multi-model mean, cloud fraction (CF) peaks in autumn and is lowest in winter and spring, consistent with that from three satellite observation products (CloudSat-CALIPSO, CERES-MODIS, and APP-x). Simulated CF also shows consistent spatial patterns with those in observations. However, almost all models overestimate the CF amount throughout the year when compared to CERES-MODIS and APP-x. On average, clouds warm the surface of the Arctic Basin mainly via the longwave (LW) radiation cloud warming effect in winter. Simulated surface energy loss of LW is less than that in CERES-EBAF observation, while the net surface shortwave (SW) flux is underestimated. The biases may result from the stronger cloud LW warming effect and SW cooling effect from the overestimated CF by the models. These two biases compensate each other, yielding similar net surface radiation flux between model output (3.0 W/m²) and CERES-EBAF observation (6.1 W/m²). During 2001–2014, significant increasing trend of spring CF is found in the multi-model mean, consistent with previous studies based on surface and satellite observations. Although most of the 22 CMIP6 models show common seasonal cycles of CF and liquid water path/ice water path (LWP/IWP), large inter-model spreads exist in the amounts of CF and LWP/IWP throughout the year, indicating the influences of different cloud parameterization schemes used in different models. Cloud Feedback Model Intercomparison Project (CFMIP) observation simulator package (COSP) is a great tool to accurately assess the performance of climate models on simulating clouds. More intuitive and credible evaluation results can be obtained based on the COSP model output. In the future, with the release of more COSP output of CMIP6 models, it is expected that those inter-model spreads and the model-observation biases can be substantially reduced. Longer term active satellite observations are also necessary to evaluate models’ cloud simulations and to further explore the role of clouds in the rapid Arctic climate changes.

为了评估最新的全球气候模型在模拟北极云和地表辐射平衡方面的性能，在22个耦合模型比较中模拟了2001-2014年北极盆地的地表辐射预算，云和云辐射效应（CRE）。项目6（CMIP6）模型是根据卫星观测进行评估的。对于CMIP6多模型平均值的结果，云分数（CF）在秋季达到峰值，而在冬季和春季则最低，这与三个卫星观测产品（CloudSat-CALIPSO，CERES-MODIS和APP-x）的结果一致。模拟CF还显示出与观测值一致的空间格局。但是，与CERES-MODIS和APP-x相比，几乎所有模型都高估了全年的CF量。一般，冬季主要通过长波（LW）辐射云的变暖效应使北极盆地的表面变暖。LW的模拟表面能损失少于CERES-EBAF观测，而净表面短波（SW）通量被低估了。这些偏差可能是由于模型高估了CF造成的云LW变暖效应和SW冷却效应更强所致。这两个偏置相互补偿，从而在模型输出之间产生相似的净表面辐射通量（3.0 W / m²）和CERES-EBAF观测（6.1 W / m ²）。在2001–2014年期间，多模式均值发现春季CF的显着增加趋势，这与以前基于地表和卫星观测的研究一致。尽管22个CMIP6模型中的大多数都显示了CF和液态水路径/冰水路径（LWP / IWP）的共同季节性周期，但全年CF和LWP / IWP的数量存在较大的模型间差异，这表明不同模型中使用的不同云参数化方案。云反馈模型比对项目（CFMIP）观测模拟器程序包（COSP）是一个很好的工具，可以准确地评估模拟云时气候模型的性能。基于COSP模型输出，可以获得更直观，更可靠的评估结果。将来，随着更多CMIP6模型的COSP输出的发布，期望这些模型间的扩展和模型观察的偏差可以大大减少。为了评估模型的云模拟并进一步探索云在北极快速的气候变化中的作用，还需要进行长期的主动卫星观测。