利用排列熵检测近40年华北地区气温突变的研究

运用一种新的动力学突变检测方法——排列熵(permutation entropy，PE)算法，计算并分析了中国华北地区52个站点1960年—2000年逐日平均气温资料的排列熵演化情况，发现中国华北地区气温在20世纪70年代中期、80年代初均发生了较大突变；进一步用经验模态分解(empirical mode decomposition，EMD)方法对排列熵序列进行逐级平稳化处理，结果发现这一地区的气温突变与准10年这一年代际时间尺度的周期变率密切相关，其原因与太阳黑子活动有着密切联系. 关键词： 华北 突变 排列熵算法 经验模态分解

A technique for distinguishing dynamical species in the temperature time series of north China

A new method named “permutation entropy” for detecting dynamical changes in time series was used to analysis the day-to-day temperature time series of north China from 1960a to 2000a. We detected three abrupt climate changes in the mid-1970s and early 1980s. Further analysis using Empirical Mode Decomposition to decompose the permutation entropy shows that the approximate 10a cycle is most important. So we can draw the conclusion that north China suffered three abrupt climate changes in the mid-1970s and early 1980s, and moreover, the the approximate 10a cycle induced the climate changes directly.