Introducing driving-force information increases the predictability of the North Atlantic Oscillation

被引：0

作者：

PAN Xinnong ^{[1
,2
]}

WANG Geli ^{[1
]}

YANG Peicai ^{[1
]}

机构：

[1] Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences

[2] College of Earth and Planetary Science, University of Chinese Academy of Sciences

来源：

Atmospheric and Oceanic Science Letters | 2019年 / 12卷 / 05期

基金：

中国国家自然科学基金; 国家重点研发计划;

关键词：

北大西洋涛动; 慢特征分析; 驱动力特征; 时间序列预测;

D O I：

暂无

中图分类号：

P732 [海洋气象学];

学科分类号：

0706 ; 070601 ;

摘要：

北大西洋涛动(NAO)是北半球最重要的天气-气候变率模态之一。以往研究大都基于NAO的观测资料和数值模拟结果,分析它的可预报性特征问题。本文首先利用慢特征分析法提取逐日NAO指数的驱动力信息,并将之引入状态空间重构的过程。在此基础上计算引入慢特征信息后的NAO最大Lyapunov指数以及建立NAO的预测模型。结果表明,当引入其慢特征信号以后,NAO指数的可预报性及预报精度可以得到显著提高。并且利用子波分析探测到NAO的驱动力因子与太阳活动、QBO准两年振荡以及年循环相关。

引用

页码：329 / 336

页数：8

共 21 条

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Yang Peicai
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