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- W2791783322 abstract "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." @default.
- W2791783322 created "2018-03-29" @default.
- W2791783322 creator A5007905274 @default.
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- W2791783322 creator A5071813875 @default.
- W2791783322 creator A5084552336 @default.
- W2791783322 date "2018-04-01" @default.
- W2791783322 modified "2023-09-29" @default.
- W2791783322 title "Estimating Convection Parameters in the GFDL CM2.1 Model Using Ensemble Data Assimilation" @default.
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