| Title | Estimating Convection Parameters in the GFDL CM2.1 Model Using Ensemble Data Assimilation |
| Authors | Li, Shan Zhang, Shaoqing Liu, Zhengyu Lu, Lv Zhu, Jiang Zhang, Xuefeng Wu, Xinrong Zhao, Ming Vecchi, Gabriel A. Zhang, Rong-Hua Lin, Xiaopei |
| Affiliation | Peking Univ, Sch Phys, Dept Atmospher & Ocean Sci, Lab Climate & Ocean Atmosphere Studies LaCOAS, Beijing, Peoples R China. Chinese Acad Sci, Inst Atmospher Sci, ICCES, Beijing, Peoples R China. Ocean Univ China, Minist Educ, Key Lab Phys Oceanog, Qingdao, Peoples R China. Qingdao Natl Lab Marine Sci & Technol, Qingdao, Peoples R China. Ohio State Univ, Dept Geog, Atmospher Sci Program, Columbus, OH 43210 USA. Ocean Univ China, Coll Atmosphere & Oceanog, Qingdao, Peoples R China. Natl Marine Data & Informat Serv, Tianjin, Peoples R China. GFDL NOAA, Princeton, NJ USA. Princeton Univ, Dept Geosci, Princeton, NJ 08544 USA. Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao, Peoples R China. Peking Univ, Sch Phys, Dept Atmospher & Ocean Sci, Lab Climate & Ocean Atmosphere Studies LaCOAS, Beijing, Peoples R China. Li, S (reprint author), Chinese Acad Sci, Inst Atmospher Sci, ICCES, Beijing, Peoples R China. Zhang, SQ (reprint author), Ocean Univ China, Minist Educ, Key Lab Phys Oceanog, Qingdao, Peoples R China. Zhang, SQ (reprint author), Qingdao Natl Lab Marine Sci & Technol, Qingdao, Peoples R China. |
| Keywords | parameter estimation data assimilation coupled climate model convection COUPLED CLIMATE MODELS SCHUBERT CUMULUS PARAMETERIZATION GENERAL-CIRCULATION MODEL SEA-SURFACE TEMPERATURE LARGE-SCALE ENVIRONMENT SIMULATED RADAR DATA ROOT KALMAN FILTER ARAKAWA-SCHUBERT MICROPHYSICAL PARAMETERS CLOUD ENSEMBLE |
| Issue Date | 2018 |
| Publisher | JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS |
| Citation | JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS. 2018, 10(4), 989-1010. |
| Abstract | 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. |
| URI | http://hdl.handle.net/20.500.11897/524364 |
| ISSN | 1942-2466 |
| DOI | 10.1002/2017MS001222 |
| Indexed | SCI(E) EI |
| Appears in Collections: | 物理学院 |
