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

被引:0
|
作者
Yue-hua Peng
Chong-wei Zheng
Tao Lian
Jie Xiang
机构
[1] Dalian Naval Academy,Institute of Atmospheric Physics
[2] Chinese Academy of Sciences,State Key Laboratory of Satellite Ocean Environment Dynamics
[3] Second Institute of Oceanography,College of Meteorology and Oceanography
[4] National University of Defence Technology,undefined
来源
Ocean Dynamics | 2018年 / 68卷
关键词
El Niño–Southern Oscillation (ENSO); Intra-seasonal wind stress; Spring predictability barrier (SPB); Zebiak–Cane model; Conditional nonlinear optimal perturbation (CNOP);
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摘要
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.
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页码:1273 / 1284
页数:11
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