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Ocean Dynamics

, Volume 68, Issue 10, pp 1273–1284 | Cite as

Can intra-seasonal wind stress forcing strongly affect spring predictability barrier for ENSO in Zebiak–Cane model?

  • Yue-hua Peng
  • Chong-wei Zheng
  • Tao Lian
  • Jie Xiang
Article

Abstract

The influence of the uncertainties of intra-seasonal wind stress forcing on Spring Predictability Barrier (SPB) in El Niño–Southern Oscillation (ENSO) prediction is studied with the Zebiak–Cane model and observational wind data which are analyzed with Continuous Wavelet Transform (CWT) and utilized to extract intra-seasonal wind stress signals as external forcing. The observational intra-seasonal wind stress forcing are joined into Zebiak–Cane model to get the Zebiak–Cane-add model and subsequently with the Conditional Nonlinear Optimal Perturbation (CNOP) method, the evolutions of the optimal initial errors (i.e., CNOPs), model errors caused by intra-seasonal wind stress uncertainties, and their joint errors based on ENSO events are calculated. By investigating their error growth rates and prediction errors of Niño-3 indices, the effect of observational intra-seasonal wind stress forcing on seasonal error growth of ENSO is explored and the impact of initial error and model error on ENSO predictability is compared quantitatively. The results show that the model errors led by observational intra-seasonal wind stress forcing could scarcely cause a significant SPB whereas the initial errors and their joint errors can do; hence, the initial errors are most likely the main error source of SPB. In fact, this result emphasizes the primary influence of initial errors on ENSO predictability and lays the basis of adaptive data assimilation for ENSO forecast.

Keywords

El Niño–Southern Oscillation (ENSO) Intra-seasonal wind stress Spring predictability barrier (SPB) Zebiak–Cane model Conditional nonlinear optimal perturbation (CNOP) 

Notes

Acknowledgements

We would like to acknowledge the financial support by the National Natural Science Foundation of China (no. 41405062). Thanks to two anonymous reviewers for their careful and responsible comments.

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Copyright information

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

Authors and Affiliations

  1. 1.Dalian Naval AcademyDalianChina
  2. 2.Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  3. 3.State Key Laboratory of Satellite Ocean Environment DynamicsSecond Institute of OceanographyHangzhou, 310012China
  4. 4.College of Meteorology and OceanographyNational University of Defence TechnologyNanjingChina

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