基于随机重排去趋势波动分析的极端低温事件研究及其综合指标的建立

摘要

By combining the detrended fluctuation analysis（DFA） method with surrogate data method,and using the heuristic segmentation algorithm as well as Chi-Square statistics,we develop a new method to determine the threshold of extreme events,e.g.stochastically re-sorting detrended fluctuation analysis（S-DFA） method.By using the S-DFA method,we obtain the thresholds of extremely low temperature events from 1961 to 2006 in China and analyze their spatiao temporal characteristics of distribution.We also validate the effectiveness of the S-DFA method through extreme event detection using the temperature series.By defining the composite index of extremely low temperature events from the angle of predictability,this composite index is integrated with the information about the frequency and the strength of the extremely low temperature events,with considering the characteristic of regional climate system.Based on the composite index,we divide the extremely low temperature events during 1961—2006 in China into four different zones according to their own rank.The composite index of extremely low temperature tends to be katabatic on the whole,Before the the early 80s in the 20th century,the composite index changed according to two distinct 10-year quasi-periods,and after that the composite index was in a downward trend and was well below the average.Until after the mid-90s in the 20th century,it rose to about average value once again.%将去趋势波动分析法（detrended fluctuation analysis,DFA）和替代数据法相结合,同时引入启发式分割算法和卡方检验,提出了一种确定极端气候事件阈值的新方法,称为随机重排去趋势波动分析（stochastic re-sort detrended fluctuation analysis,S-DFA）方法.同百分位阈值方法相比,S-DFA方法明确指出了极端事件和非极端事件之间的临界值.基于中国气象局公布的中国165个国际交换站1961-2006年无缺测的逐日日平均气温资料,利用随机重排去趋势波动分析（S-DFA）方法计算并分析了中国极端低温事件阈值的空间分布特征,并对S-DFA方法在实际资料中的应用进行了检验.从可预报性的角度给出了极端低温事件综合指标的定义.这一综合指标将极端低温事件的发生频次和强度综合起来,且兼顾了不同地区各自特有的区域气候背景,进一步说明了综合指标定义的合理性.基于极端低温事件综合指标的空间分布规律,将中国1961-2006年间极端低温事件分为四个不同等级的地区.极端低温综合指标整体表现出下降趋势,在20世纪80年代初期之前综合指标的变化具有两个明显的准10年周期,而在这之后则一直处于下降趋势且大大低于平均值,直到90年代中期以后才再次上升至平均值附近.