Insights of warm-cloud biases in Community Atmospheric Model 5 and 6 from the single-column modeling framework and Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) observations
Insights of warm-cloud biases in Community Atmospheric Model 5 and 6 from the single-column modeling framework and Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) observations
复制标题
DOI：
10.5194/acp-23-8591-2023
复制
发表时间：
2023-08
影响因子：
6.3
通讯作者：
Yuan Wang;Xiaojian Zheng;Xiquan Dong;B. Xi;Y. Yung
中科院分区：
文献类型：
--
作者：
Yuan Wang;Xiaojian Zheng;Xiquan Dong;B. Xi;Y. Yung
抽象。人们越来越担心，大多数气候模式预测的降水过于频繁，可能是由于缺乏可靠的次网格变率和低层暖云微物理过程的垂直变化。在这项研究中，NCAR社区大气模式版本6和版本5的单柱配置中的暖云物理参数化（SCAM 6和SCAM 5，使用2017年期间亚速尔群岛附近的能源部（DOE）大气辐射测量（ARM）北大西洋东部气溶胶和云实验（ACE-ENA）现场活动的地基和空中观测进行评估-2018. 8个月的单柱模式（SCM）的模拟结果表明，SCAM 6和SCAM 5都可以大致再现海洋边界层云的结构，主要的宏观物理特性，以及它们的过渡。通过与观测结果的比较，可以发现来自共同体大气模式5和6（CAM 5到CAM 6）物理学的暖云特性的改善。同时，两种物理方案都低估了云液态水含量、云滴大小和雨液态水含量，而高估了地面降水量。模拟的云凝结核（CCN）浓度与飞机观测的在夏季，但高估了2倍，在冬季，主要是由于人为气溶胶，如硫酸盐的长距离传输的偏见。我们还测试了新重新校准的自动转换和吸积参数化，占液滴大小的垂直变化。与观察结果相比，SCAM 5比SCAM 6有更显著的改善。这一结果很可能是解释了在CAM 6云微物理，这进一步抑制了该计划的敏感性，个别暖雨微物理参数的云特性的亚网格变化的引入。在CAM 6中预测的云对CCN扰动的敏感性在合理的范围内，表明自CAM 5以来取得了重大进展，这产生了太强的气溶胶间接效应。本研究强调了通过将SCM与原位观测相结合来理解云物理参数化中偏差的重要性。
Abstract. There has been a growing concern that most climate models predict precipitation that is too frequent, likely due to lack of reliable subgrid variability and vertical variations in microphysical processes in low-level warm clouds. In this study, the warm-cloud physics parameterizations in the singe-column configurations of NCAR Community Atmospheric Model version 6 and 5 (SCAM6 and SCAM5, respectively) are evaluated using ground-based and airborne observations from the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign near the Azores islands during 2017–2018. The 8-month single-column model (SCM) simulations show that both SCAM6 and SCAM5 can generally reproduce marine boundary layer cloud structure, major macrophysical properties, and their transition. The improvement in warm-cloud properties from the Community Atmospheric Model 5 and 6 (CAM5 to CAM6) physics can be found through comparison with the observations. Meanwhile, both physical schemes underestimate cloud liquid water content, cloud droplet size, and rain liquid water content but overestimate surface rainfall. Modeled cloud condensation nuclei (CCN) concentrations are comparable with aircraft-observed ones in the summer but are overestimated by a factor of 2 in winter, largely due to the biases in the long-range transport of anthropogenic aerosols like sulfate. We also test the newly recalibrated autoconversion and accretion parameterizations that account for vertical variations in droplet size. Compared to the observations, more significant improvement is found in SCAM5 than in SCAM6. This result is likely explained by the introduction of subgrid variations in cloud properties in CAM6 cloud microphysics, which further suppresses the scheme's sensitivity to individual warm-rain microphysical parameters. The predicted cloud susceptibilities to CCN perturbations in CAM6 are within a reasonable range, indicating significant progress since CAM5 which produces an aerosol indirect effect that is too strong. The present study emphasizes the importance of understanding biases in cloud physics parameterizations by combining SCM with in situ observations.