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Model parameter-related optimal perturbations and their contributions to El Niño prediction errors

  • Ling-Jiang Tao
  • Chuan Gao
  • Rong-Hua Zhang
Article
  • 51 Downloads

Abstract

Errors in initial conditions and model parameters (MPs) are the main sources that limit the accuracy of ENSO predictions. In addition to exploring the initial error-induced prediction errors, model errors are equally important in determining prediction performance. In this paper, the MP-related optimal errors that can cause prominent error growth in ENSO predictions are investigated using an intermediate coupled model (ICM) and a conditional nonlinear optimal perturbation (CNOP) approach. Two MPs related to the Bjerknes feedback are considered in the CNOP analysis: one involves the SST-surface wind coupling (\({\alpha _\tau }\)), and the other involves the thermocline effect on the SST (\({\alpha _{Te}}\)). The MP-related optimal perturbations (denoted as CNOP-P) are found uniformly positive and restrained in a small region: the \({\alpha _\tau }\) component is mainly concentrated in the central equatorial Pacific, and the \({\alpha _{Te}}\) component is mainly located in the eastern cold tongue region. This kind of CNOP-P enhances the strength of the Bjerknes feedback and induces an El Niño- or La Niña-like error evolution, resulting in an El Niño-like systematic bias in this model. The CNOP-P is also found to play a role in the spring predictability barrier (SPB) for ENSO predictions. Evidently, such error growth is primarily attributed to MP errors in small areas based on the localized distribution of CNOP-P. Further sensitivity experiments firmly indicate that ENSO simulations are sensitive to the representation of SST-surface wind coupling in the central Pacific and to the thermocline effect in the eastern Pacific in the ICM. These results provide guidance and theoretical support for the future improvement in numerical models to reduce the systematic bias and SPB phenomenon in ENSO predictions.

Keywords

Intermediate coupled model CNOP approach Model parameters El Niño predictability 

Notes

Acknowledgements

We would like to thank Drs. Qiang Wang, Wansuo Duan, Mu Mu, Xinrong Wu, and Hui Xu for their comments and help in implementing the CNOP approach into the IOCAS ICM. This research was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant no. XDA19060102), the National Natural Science Foundation of China (Grant nos. 41475101 and 41690122 (41690120)), the CAS Strategic Priority Project, the Western Pacific Ocean System (Grant nos. XDA11010105, XDA11020306), the NSFC-Shandong Joint Fund for Marine Science Research Centers (U1406402), the National Programme on Global Change and Air–Sea Interaction (Grant nos. GASI-IPOVAI-06, GASI-IPOVAI-01-01), and the Taishan Scholarship.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Key Laboratory of Ocean Circulation and Waves, Institute of OceanologyChinese Academy of SciencesQingdaoChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.Qingdao National Laboratory for Marine Science and TechnologyQingdaoChina

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