Implementing Ice Cloud Microphysics and Radiation Schemes into the Community Atmospheric Model (CAM)
Implementing Ice Cloud Microphysics and Radiation Schemes into the Community Atmospheric Model (CAM)
批准号:
0413401
负责人:
David Mitchell
金额:
$0.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2004
资助国家:
美国
项目状态:
已结题
起止时间:
2004-06-01 至 2007-11-30
中文摘要
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英文摘要
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.
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