Estimating Convection Parameters in the GFDL CM2.1 Model Using Ensemble Data Assimilation

Shan Li, Shaoqing Zhang, Zhengyu Liu, Lv Lu, Jiang Zhu, Xuefeng Zhang, Xinrong Wu, Ming Zhao, Gabriel Andres Vecchi, Rong Hua Zhang, Xiaopei Lin

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)989-1010
Number of pages22
JournalJournal of Advances in Modeling Earth Systems
Issue number4
StatePublished - Apr 2018

All Science Journal Classification (ASJC) codes

  • Global and Planetary Change
  • Environmental Chemistry
  • General Earth and Planetary Sciences


  • convection
  • coupled climate model
  • data assimilation
  • parameter estimation


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