Compensating Errors in Cloud Radiative and Physical Properties over the Southern Ocean in the CMIP6 Climate Models
Compensating Errors in Cloud Radiative and Physical Properties over the Southern Ocean in the CMIP6 Climate Models
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DOI：
10.1007/s00376-022-2036-z
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发表时间：
2022-09
影响因子：
5.8
通讯作者：
Lijun Zhao;Yuan Wang;Chuanfeng Zhao;Xiquan Dong;Y. Yung
中科院分区：
文献类型：
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作者：
Lijun Zhao;Yuan Wang;Chuanfeng Zhao;Xiquan Dong;Y. Yung
南大洋被大量的云层覆盖，云层高度很高。然而，正如以前的气候模式相互比较项目所报告的那样，低估的云量和高估的太阳辐射吸收（ASR）在南大洋导致气候敏感性的重大偏差。本研究重新审视了这个长期存在的问题，并探讨了最新CMIP6模型中的不确定性来源。我们采用10年的卫星观测，以评估云辐射效应（CRE）和云的物理特性在五个CMIP6模式，提供云，辐射和气溶胶的综合输出。CMIP6模拟的大气顶部的长波、短波和净CRE与CERES卫星观测结果基本一致。CMIP 6对总云率（CF）的模拟也比较合理，但对液云率（LCF）的模拟在空间分布和季节变化上存在明显的偏差。CMIP6模式与MODIS卫星观测资料在其他云的宏观和微观物理特性，包括液态水路径（LWP）、云光学厚度（COD）、云有效半径以及气溶胶光学厚度（AOD）等方面的差异更大。然而，LWP和云有效半径的大幅低估（区域平均值分别为20%和11%）导致COD的偏差相对较小，COD和LCF偏差的影响也相互抵消，CMIP6中合理预测了CRE和ASR。采用误差估计框架，CF和LWP的灵敏度误差和偏差的不同符号证实了在建模的短波CRE中存在补偿误差的概念。地理空间格局的进一步相关性分析表明，CF是最相关的因素，在确定CRE的观测，而模拟的CRE是太敏感的LWP和COD。还分析了云有效半径、LWP和COD之间的关系，以探讨不同模式中可能的不确定性来源。我们的研究呼吁对云的详细物理特性进行更严格的校准，以用于未来的气候模型开发和气候预测。
The Southern Ocean is covered by a large amount of clouds with high cloud albedo. However, as reported by previous climate model intercomparison projects, underestimated cloudiness and overestimated absorption of solar radiation (ASR) over the Southern Ocean lead to substantial biases in climate sensitivity. The present study revisits this long-standing issue and explores the uncertainty sources in the latest CMIP6 models. We employ 10-year satellite observations to evaluate cloud radiative effect (CRE) and cloud physical properties in five CMIP6 models that provide comprehensive output of cloud, radiation, and aerosol. The simulated longwave, shortwave, and net CRE at the top of atmosphere in CMIP6 are comparable with the CERES satellite observations. Total cloud fraction (CF) is also reasonably simulated in CMIP6, but the comparison of liquid cloud fraction (LCF) reveals marked biases in spatial pattern and seasonal variations. The discrepancies between the CMIP6 models and the MODIS satellite observations become even larger in other cloud macro- and micro-physical properties, including liquid water path (LWP), cloud optical depth (COD), and cloud effective radius, as well as aerosol optical depth (AOD). However, the large underestimation of both LWP and cloud effective radius (regional means ∼20% and 11%, respectively) results in relatively smaller bias in COD, and the impacts of the biases in COD and LCF also cancel out with each other, leaving CRE and ASR reasonably predicted in CMIP6. An error estimation framework is employed, and the different signs of the sensitivity errors and biases from CF and LWP corroborate the notions that there are compensating errors in the modeled shortwave CRE. Further correlation analyses of the geospatial patterns reveal that CF is the most relevant factor in determining CRE in observations, while the modeled CRE is too sensitive to LWP and COD. The relationships between cloud effective radius, LWP, and COD are also analyzed to explore the possible uncertainty sources in different models. Our study calls for more rigorous calibration of detailed cloud physical properties for future climate model development and climate projection.