Partitioning deep convective cloud condensates into components that sediment and detrain, known to be a challenge for global climate models, is important for cloud vertical distribution and anvil cloud formation. In this study, we address this issue by improving the convective microphysics scheme in the National Center for Atmospheric Research Community Atmosphere Model version 5.3 (CAM5.3). The improvements include: (1) considering sedimentation for cloud ice crystals that do not fall in the original scheme, (2) applying a new terminal velocity parameterization that depends on the environmental conditions for convective snow, (3) adding a new hydrometeor category, “rimed ice,” to the original four‐class (cloud liquid, cloud ice, rain, and snow) scheme, and (4) allowing convective clouds to detrain snow particles into stratiform clouds. Results from the default and modified CAM5.3 models were evaluated against observations from the U.S. Department of Energy Tropical Warm Pool‐International Cloud Experiment (TWP‐ICE) field campaign. The default model overestimates ice amount, which is largely attributed to the underestimation of convective ice particle sedimentation. By considering cloud ice sedimentation and rimed ice particles and applying a new convective snow terminal velocity parameterization, the vertical distribution of ice amount is much improved in the midtroposphere and upper troposphere when compared to observations. The vertical distribution of ice condensate also agrees well with observational best estimates upon considering snow detrainment. Comparison with observed convective updrafts reveals that current bulk model fails to reproduce the observed updraft magnitude and occurrence frequency, suggesting spectral distributions be required to simulate the subgrid updraft heterogeneity.

中文翻译：

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NCAR CAM模型中对流冰微物理参数的改进

将深层对流云划分成沉积物和减盐成分，这对全球气候模型来说是一个挑战，对云垂直分布和砧云形成非常重要。在这项研究中，我们通过改善国家大气研究中心大气模型版本5.3（CAM5.3）中的对流微物理学方案来解决此问题。改进措施包括：（1）考虑不属于原始方案的云冰晶的沉积；（2）根据对流雪的环境条件应用新的最终速度参数化；（3）添加新的水凝物类别，将“冰面冰”定义为原始的四级（云层液体，云层冰，雨和雪）方案，以及（4）允许对流云将雪颗粒迁移成层状云。根据美国能源部热带暖池-国际云实验（TWP-ICE）野外活动的观察结果，评估了默认和修改后的CAM5.3模型的结果。默认模型高估了冰量，这在很大程度上归因于对流冰粒沉降的低估。通过考虑云冰沉积和边缘冰粒并应用新的对流积雪终端速度参数化，与观测值相比，对流层中层和对流层中冰量的垂直分布得到了很大改善。在考虑降雪的情况下，冰凝结物的垂直分布也与观测的最佳估计非常吻合。