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Advances in Atmospheric Sciences

, Volume 36, Issue 12, pp 1381–1392 | Cite as

The Optimal Precursors for ENSO Events Depicted Using the Gradient-definition-based Method in an Intermediate Coupled Model

  • Bin Mu
  • Juhui Ren
  • Shijin YuanEmail author
  • Rong-Hua Zhang
  • Lei Chen
  • Chuan Gao
Original Paper
  • 3 Downloads

Abstract

The predictability of El Niño-Southern Oscillation (ENSO) has been an important area of study for years. Searching for the optimal precursor (OPR) of ENSO occurrence is an effective way to understand its predictability. The CNOP (conditional nonlinear optimal perturbation), one of the most effective ways to depict the predictability of ENSO, is adopted to study the optimal sea surface temperature (SST) precursors (SST-OPRs) of ENSO in the IOCAS ICM (intermediate coupled model developed at the Institute of Oceanology, Chinese Academy of Sciences). To seek the SST-OPRs of ENSO in the ICM, non-ENSO events simulated by the ICM are chosen as the basic state. Then, the gradient-definition-based method (GD method) is employed to solve the CNOP for different initial months of the basic years to obtain the SST-OPRs. The experimental results show that the obtained SST-OPRs present a positive anomaly signal in the western-central equatorial Pacific, and obvious differences exist in the patterns between the different seasonal SST-OPRs along the equatorial western-central Pacific, showing seasonal dependence to some extent. Furthermore, the non-El Niño events can eventually evolve into El Niño events when the SST-OPRs are superimposed on the corresponding seasons; the peaks of the Niño3.4 index occur at the ends of the years, which is consistent with the evolution of the real El Niño. These results show that the GD method is an effective way to obtain SST-OPRs for ENSO events in the ICM. Moreover, the OPRs for ENSO depicted using the GD method provide useful information for finding the early signal of ENSO in the ICM.

Key words

optimal precursor ENSO gradient-definition-based method conditional nonlinear optimal perturbation intermediate coupled model 

摘 要

厄尔尼诺 - 南方涛动 (ENSO) 事件的可预报性研究多年来一直都是一个重要的研究领域, 而寻找 ENSO 事件发生的最优前期征兆 (OPR) 是研究其可预报性的有效途径之一. 本文基于中国科学院海洋研究所开发的中间型海气耦合模型——IOCAS ICM, 采用 ENSO 可预报性研究的最有效的方法之一——条件非线性最优扰动 (CNOP) 方法, 研究了 ENSO 事件的海表温度最优前期征兆 (SST-OPRs). 为了在 ICM 中寻找 ENSO 的 SST-OPR, 本文将 ICM 模拟得到的非 ENSO 事件作为基本状态, 然后采用基于梯度定义的方法 (GD 方法) 来求解不同年份不同初始月份的 CNOP, 进而得到相应的 SST-OPR. 实验结果表明, 求解得到的 SST-OPRs 在赤道中西太平洋呈现出正异常信号, 但是不同季节的 SST-OPR 模态不尽相同, 并且呈现出一定程度的季节依赖性. 此外, 将得到的 SST-OPR 叠加在相应的季节时, 非厄尔尼诺事件最终可演变为厄尔尼诺事件, 而且 Niño3.4 指数演变的峰值出现在年末, 这与真实厄尔尼诺现象的演变是一致的. 上述结果表明, GD 方法是求解 ICM 模式 ENSO 事件 SST-OPRs 的有效方法. 此外, 使用 GD 方法所描述的 ENSO 事件的 OPR 为找到 ICM 模式 ENSO 事件的早期信号提供了有用的信息.

关键词

最优前期征兆 厄尔尼诺-南方涛动 基于梯度定义的方法 条件非线性最优扰动 中尺度耦合模式 

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Notes

Acknowledgments

This work is supported by the Fundamental Research Funds for the Central Universities (Grant No. 22120190 207), the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA19060102), the National Key Research and Development Program of China (Grant No. 2017YFC1404102(2017YFC1404100)), the National Programme on Global Change and Air-Sea Interaction (Grant No. GASI-IPOVAI-06), National Natural Science Foundation of China (Grant Nos. 41690122(41690120), 41490644(41490640), 414210 05), and the Taishan Scholarship. Thanks to the Institute of Oceanology, Chinese Academy of Sciences, for providing technical support for IOCAS ICM. The authors also acknowledge the National Supercomputer Center in Tianjin for providing the computer resources. No conflicts of interest exist in relation to this manuscript.

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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 2019

Authors and Affiliations

  • Bin Mu
    • 1
  • Juhui Ren
    • 1
  • Shijin Yuan
    • 1
    Email author
  • Rong-Hua Zhang
    • 2
    • 3
    • 4
  • Lei Chen
    • 5
  • Chuan Gao
    • 2
    • 3
    • 4
  1. 1.School of Software EngineeringTongji UniversityShanghaiChina
  2. 2.Chinese Academy of Sciences Key Laboratory of Ocean Circulation and Waves, Institute of OceanologyChinese Academy of SciencesQingdaoChina
  3. 3.Qingdao National Laboratory for Marine Science and TechnologyQingdaoChina
  4. 4.University of Chinese Academy of SciencesBeijingChina
  5. 5.Shanghai Central Meteorological ObservatoryShanghaiChina

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