Implementing Ice Cloud Microphysics and Radiation Schemes into the Community Atmospheric Model (CAM)

该基金支持旨在改进社区气候系统模式（CCSM）大气模式组件中的冰云微物理和辐射参数化的研究。虽然CAM目前假设温度依赖于冰颗粒大小分布（SD），但最近的研究表明，砧状卷云中SD的温度依赖性与非对流卷云有很大不同，并且早期对小冰晶（d100微米）浓度的估计似乎被低估了。CAM中的冰粒下落速度基于斯托克定律和线性插值方案，而不是基于我们目前对冰粒下落速度的了解。通过使用新开发的砧状云和非对流卷云的SD-Temperature参数化，利用现有的冰粒子下落速度知识，以及使用包含改进的异常衍射近似的辐射方案来处理冰晶-辐射相互作用，可以在很大程度上消除上述缺点。该项目将允许这些变化在两个为期三个月的NCAR访问期间在CAM中实施和测试。这项工作的更广泛影响包括CCSM性能的潜在改进。CCSM是学术界在研究气候系统的短期和长期模拟中使用最多的全球气候模式（GCM）。性能的任何改进都将使这个社区受益。由于在地球辐射收支中的作用，在GCM气候预报中，云和云性质的表示仍然是产生最大不确定性的领域。因此，这项研究有可能改善气候预测，这将为环境和经济规划提供重要的信息。

This grant supports research aimed at the improvement of the ice cloud microphysics and radiation parameterizations in the atmospheric model component of the Community Climate Systems Model (CCSM), referred to as the CAM. While the CAM currently assumes a temperature dependent ice particle size distribution (SD), recent studies show that the temperature dependence of the SD in anvil cirrus is much different than non-convective cirrus, and earlier estimates of the concentrations of small ice crystals (D 100 micrometers) appear underestimated. Ice particle fall speeds in the CAM are based on Stoke's law and a linear interpolation scheme, which is not based on our current knowledge of ice particle fall velocities. The above shortcoming may be largely removed by using newly developed SD-Temperature parameterizations for anvil and non-convective cirrus clouds, by utilizing current knowledge of ice particle fall speeds, and by using a radiation scheme incorporating the modified anomalous diffraction approximation to treat ice crystal-radiation interactions. This project will allow these changes to be implemented and tested in the CAM during two three-month visits to NCAR. The broader impacts of this work include a potential improvement in CCSM performance. The CCSM is used by the academic community more than any other Global Climate Model (GCM) to study the climate system in short- and long-term simulations. Any improvement in performance would benefit this community. The representation of clouds and cloud properties in GCMs continues to be the area producing the greatest uncertainty in GCM climate forecasts due to their role in the earth's radiation budget. Thus, this research has the potential to improve climate predictions, which would provide important information for environmental and economic planning.