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
基金
国家重点研发计划; 中国国家自然科学基金;
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。
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页码:329 / 336
页数:8
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