Advances in Atmospheric Sciences

, Volume 35, Issue 4, pp 410–422 | Cite as

Idealized Experiments for Optimizing Model Parameters Using a 4D-Variational Method in an Intermediate Coupled Model of ENSO

  • Chuan Gao
  • Rong-Hua ZhangEmail author
  • Xinrong Wu
  • Jichang Sun
Original Paper


Large biases exist in real-time ENSO prediction, which can be attributed to uncertainties in initial conditions and model parameters. Previously, a 4D variational (4D-Var) data assimilation system was developed for an intermediate coupled model (ICM) and used to improve ENSO modeling through optimized initial conditions. In this paper, this system is further applied to optimize model parameters. In the ICM used, one important process for ENSO is related to the anomalous temperature of subsurface water entrained into the mixed layer (Te), which is empirically and explicitly related to sea level (SL) variation. The strength of the thermocline effect on SST (referred to simply as “the thermocline effect”) is represented by an introduced parameter, αTe. A numerical procedure is developed to optimize this model parameter through the 4D-Var assimilation of SST data in a twin experiment context with an idealized setting. Experiments having their initial condition optimized only, and having their initial condition plus this additional model parameter optimized, are compared. It is shown that ENSO evolution can be more effectively recovered by including the additional optimization of this parameter in ENSO modeling. The demonstrated feasibility of optimizing model parameters and initial conditions together through the 4D-Var method provides a modeling platform for ENSO studies. Further applications of the 4D-Var data assimilation system implemented in the ICM are also discussed.

Key words

intermediate coupled model ENSO modeling 4D-Var data assimilation system optimization of model parameter and initial condition 


ENSO实时预报存在很大的偏差, 这主要是由于初条件和模式参数的不确定性所造成的. 目前, 我们建立了一个基于中间型海气耦合模式(ICM)的四维变分资料同化系统, 通过最优初始化来提高ENSO模拟, 本文将进一步应用该系统对模式参数进行优化. 本文所采用的ICM的一个重要特征是, 表征了次表层海水上卷到混合层的温度异常(Te)对ENSO的影响, Te与海表高度(SL)的变化之间存在经验的统计关系, 可表示为Te = αTeFTe (SL), 其中, 参数αTe表征了次表层对SST影响的强度, 简化为“次表层效应”. 通过建立的四维变分资料同化系统, 在理想的“孪生”试验框架下, 同化SST数据来优化这一模式参数. 设计两组试验, 一是只优化初条件, 二是对初条件和模式参数同时优化, 结果表明, 对初条件和模式参数同时优化可以更有效地模拟ENSO的发展演变. 本文所示的基于四维变分同化方法同时优化初条件和模式参数的可行性为ENSO研究提供了一个模式平台. 同时, 也讨论了关于四维变分同化系统的进一步应用.


中间型耦合模式 ENSO模拟 四维变分同化系统 初条件和模式参数优化 


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We would like to thank Drs. Mu MU and Shaoqing ZHANG for their comments. We also wish to thank the anonymous reviewers for their insightful comments and constructive suggestions. This research was supported by the National Natural Science Foundation of China (Grant Nos. 41705082, 41475101, 41690122(41690120)), a Chinese Academy of Sciences Strategic Priority Project—the Western Pacific Ocean System (Grant Nos. XDA11010105 and XDA11020306), the National Programme on Global Change and Air–Sea Interaction (Grant Nos. GASI-IPOVAI- 06 and GASI-IPOVAI-01-01), the China Postdoctoral Science Foundation, and a Qingdao Postdoctoral Application Research Project.


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Copyright information

© Institute of Atmospheric Physics/Chinese Academy of Sciences, and Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Chuan Gao
    • 1
    • 2
    • 3
    • 4
  • Rong-Hua Zhang
    • 1
    • 2
    • 4
    Email author
  • Xinrong Wu
    • 5
  • Jichang Sun
    • 3
  1. 1.Key Laboratory of Ocean Circulation and Waves, Institute of OceanologyChinese Academy of SciencesQingdaoChina
  2. 2.Qingdao National Laboratory for Marine Science and TechnologyQingdaoChina
  3. 3.Institute of Oceanographic InstrumentationShandong Academy of SciencesQingdaoChina
  4. 4.University of Chinese Academy of SciencesBeijingChina
  5. 5.Key Laboratory of Marine Environmental Information Technology, State Oceanic AdministrationNational Marine Data and Information ServiceTianjinChina

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