globalchange  > 影响、适应和脆弱性
DOI: 10.1002/2017MS001222
Scopus记录号: 2-s2.0-85045349970
论文题名:
Estimating Convection Parameters in the GFDL CM2.1 Model Using Ensemble Data Assimilation
作者: Li S; , Zhang S; , Liu Z; , Lu L; , Zhu J; , Zhang X; , Wu X; , Zhao M; , Vecchi G; A; , Zhang R; -H; , Lin X
刊名: Journal of Advances in Modeling Earth Systems
ISSN: 19422466
出版年: 2018
语种: 英语
英文关键词: Climate models ; Forecasting ; Heat convection ; Parameterization ; Climate simulation ; Convection parameterization ; Coupled climate model ; Data assimilation ; Data assimilation methods ; Ensemble data assimilation ; Imperfect modeling ; Parametric uncertainties ; Parameter estimation
英文摘要: Parametric uncertainty in convection parameterization is one major source of model errors that cause model climate drift. Convection parameter tuning has been widely studied in atmospheric models to help mitigate the problem. However, in a fully coupled general circulation model (CGCM), convection parameters which impact the ocean as well as the climate simulation may have different optimal values. This study explores the possibility of estimating convection parameters with an ensemble coupled data assimilation method in a CGCM. Impacts of the convection parameter estimation on climate analysis and forecast are analyzed. In a twin experiment framework, five convection parameters in the GFDL coupled model CM2.1 are estimated individually and simultaneously under both perfect and imperfect model regimes. Results show that the ensemble data assimilation method can help reduce the bias in convection parameters. With estimated convection parameters, the analyses and forecasts for both the atmosphere and the ocean are generally improved. It is also found that information in low latitudes is relatively more important for estimating convection parameters. This study further suggests that when important parameters in appropriate physical parameterizations are identified, incorporating their estimation into traditional ensemble data assimilation procedure could improve the final analysis and climate prediction. © 2018. The Authors.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/75670
Appears in Collections:影响、适应和脆弱性
气候变化与战略

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作者单位: Laboratory for Climate and Ocean-Atmosphere Studies (LaCOAS), Department of Atmospheric and Oceanic Sciences, School of PhysicsPeking UniversityBeijing China; ICCES, Institute of Atmospheric Sciences, Chinese Academy of SciencesBeijing China; Key Laboratory of Physical OceanographyMinistry of Education, China, Ocean University of ChinaQingdao China; Qingdao National Laboratory for Marine Science and TechnologyQingdao China; Atmospheric Science Program, Department of GeographyThe Ohio State UniversityColumbus, Ohio USA; The College of Atmosphere and OceanographyOcean University of ChinaQingdao China; National Marine Data and Information ServiceTianjin China; GFDL/NOAAPrinceton, New Jersey USA; Department of GeosciencesPrinceton UniversityPrinceton, New Jersey USA; Key Laboratory of Ocean Circulation and WavesInstitute of Oceanology, Chinese Academy of SciencesQingdao China

Recommended Citation:
Li S,, Zhang S,, Liu Z,et al. Estimating Convection Parameters in the GFDL CM2.1 Model Using Ensemble Data Assimilation[J]. Journal of Advances in Modeling Earth Systems,2018-01-01
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