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全球大气环流模式FAMIL水平分辨率提高对全球云辐射效应模拟的影响

Impact of Increased Horizontal Resolution in the Global Atmospheric Circulation Model FAMIL on the Simulation of Global Cloud Radiative Effects

  • 摘要: 云模拟的不确定性会影响气候模式中的关键物理过程。本文利用CloudSat/CALIPSO和CERES卫星资料及ERA5再分析资料,分析了LASG/IAP开发的气候系统模式FGOALS-f3中的大气分量FAMIL在100 km、25 km、12.5 km分辨率下对全球气候态云量和云辐射效应(CRE)模拟的分布特征;并基于Xu-Randall云量诊断方案,讨论了模式偏差的可能来源。结果表明,不同分辨率下,云量模拟结果均呈现低(高)纬地区均被低(高)估的特征,而随分辨率增加,模拟技巧呈非线性变化。在全球尺度上,25 km模式云量模拟偏差较100 km模式增大,12.5 km模式较25 km模式在西太平洋暖池等区域云量偏差改善了约4%。模式分辨率增加后,模拟的CRE随云量的减少而偏低。在Xu-Randall方案中,相对湿度(RH)是气候态云量形成的主导因子,因此分辨率的提升会通过影响温度和垂直速度等变量进一步改变RH,间接影响云量偏差。在低纬度对流层中高层,100 km模式垂直速度偏弱,间接导致云量被低估;25 km模式较100 km模式温度偏高,导致云量偏差增大;12.5 km模式较25 km模式温度偏低,致使云量偏差减小。

     

    Abstract: Uncertainties in cloud simulations can affect key physical processes in climate modelling. Using satellite data from CloudSat/CALIPSO and CERES, together with ERA5 reanalysis data, this study analyzes the distribution characteristics of global climatological cloud fraction and cloud radiative effects (CREs) simulated by the atmospheric component FAMIL in the climate system model FGOALS-f3 at resolutions of 100, 25, and 12.5 km. Moreover, the study investigates the potential sources of simulation errors based on the Xu–Randall cloud diagnostic scheme. The results show that at different resolutions, cloud fraction is underestimated in low-latitude areas and overestimated in high-latitude areas and that the simulation performance changes nonlinearly with increasing resolution. On the global scale, the deviation in the 25 km model increases compared with the 100 km model, and the cloud fraction deviation of the 12.5 km model is approximately 4% higher than that of the 25 km model in regions such as the Western Pacific Warm Pool. As model resolution increases, the simulated CRE decreases in association with a reduction in cloud fraction. In the Xu–Randall scheme, relative humidity (RH) is the dominant factor controlling climatological cloud fraction. Resolution enhancement further changes RH by affecting variables such as temperature and vertical velocity, thereby indirectly affecting cloud fraction bias. The weak vertical velocity in the 100 km model indirectly leads to underestimation of middle- and high-level cloud fractions in low-latitude areas. The higher temperature in the 25 km model compared with the 100 km model increases the cloud fraction bias. Furthermore, the lower temperature in the 12.5 km model compared with the 25 km model decreases the cloud fraction bias.

     

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