A new approach to studying ENSO predictability: Conditional nonlinear optimal perturbation

被引：107

作者：

Mu, M ^{[1
]}

Duan, WS ^{[1
]}

机构：

[1] Chinese Acad Sci, Inst Atmospher Phys, LASG, Beijing 100029, Peoples R China

来源：

CHINESE SCIENCE BULLETIN | 2003年 / 48卷 / 10期

基金：

中国国家自然科学基金;

关键词：

nonlinear; optimal perturbation; predictability; ENSO model;

D O I：

10.1360/02wd0389

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

A new approach, the conditional nonlinear optimal perturbation (CNOP) is introduced to study the predictability of El Nino-Southern Oscillation (ENSO) using a theoretical coupled ocean-atmosphere model. The differences between CNOP and linear singular vector (LSV) are demonstrated. The results suggest that the nonlinear model and CNOP are superior in determining error growth for studying predictability of the ENSO. In particular, the CNOP approach is used to explore the nature of the 'spring predictability barrier' in ENSO prediction.

引用

页码：1045 / 1047

页数：3

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