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

该补助金支持旨在改善社区气候系统模型（CCSM）大气模型组件中冰云微观物理和辐射参数化的研究，简称CAM。虽然CAM目前假设温度依赖的冰颗粒尺寸分布（SD），最近的研究表明，在砧卷云SD的温度依赖性是非常不同的非对流卷云，和早期的估计小冰晶（D 100微米）的浓度似乎低估。CAM中的冰粒下落速度是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。上述缺点可能会在很大程度上被删除，通过使用新开发的SD温度参数化砧和非对流卷云，通过利用目前的知识冰粒子的下降速度，并通过使用辐射计划，将修改后的异常衍射近似治疗冰晶辐射相互作用。该项目将允许在对NCAR的两次为期三个月的访问期间在CAM中实施和测试这些变更。 这项工作的更广泛影响包括CCSM性能的潜在改进。CCSM比任何其他全球气候模式（GCM）都更被学术界用来研究短期和长期模拟的气候系统。业绩的任何改善都将使这个社区受益。云和云的性质在GCM中的代表性仍然是GCM气候预测中产生最大不确定性的领域，因为它们在地球辐射收支中的作用。因此，这项研究有可能改善气候预测，为环境和经济规划提供重要信息。

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.