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A Grey Zone GCM
来源:LASG    访问次数:    报告时间:2018-10-17
【报告人】        Prof. In-Sik Kang
【报告人单位】Second Institute of Oceanography, SOA
【报告时间】    2018年10月17日(星期三)10:00
【报告地点】    新楼12层报告厅
【报告简介】    

报告人简介:
Prof. In-Sik Kang is currently a Director of Indian Ocean Research Center at the Second Institute of Oceanography, the State Oceanic Administration (SOA). He is also a Professor of Emeritus at Seoul National University, Korea. He received his Ph.D. at Oregon State University, USA in 1984. Since 1986, he has been a Professor of Seoul National University after his post-doc at GFDL/ Princeton University. For 2000-2009, he had been a Director of Center of Excellence for Climate Environment System Research Center at SNU. He has a long history of working with international climate society since TOGA as members of various panels of World Climate Research Program and is now a member of WCRP Joint Science Committee (JSC) and a Distinguished Science Advisory of International CLIVAR Program Office, Qingdao, China. He has published more than 170 papers in SCI Journals with about 8,500 citations.

报告人摘要:
The present seminar talks about a development strategy of a relatively high-resolution general circulation model of an order of 10 km with explicit moisture physics. It is demonstrated that a general circulation model (GCM) requires a full representation of cloud microphysics to simulate the precipitation statistics, particularly extreme precipitation frequency, and moisture related phenomena such as the Madden and Julian Oscillation (MJO), close to those of observation. In the present study, the cloud microphysics is modified to allow its implementation into a GCM with a horizontal resolution of 50 km. The GCM with cloud microphysics requires additional vertical mixing processes in the lower and upper troposphere. The newly developed GCM with a horizontal resolution of 50 km, which includes explicit cloud microphysics and a scale-adaptive convective parameterization, simulates precipitation statistics and the MJO more realistically than the conventional GCMs with convective parameterization does.

 
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