Abrupt climate change detection based on heuristic segmentation algorithm

Abstract

Climate system is nonlinear,non-stationary and hierarchical,which makes even harder to detect and analyze abrupt climate changes.Based on Student's t-test,Berna ola Galvan recently proposed a heuristic segmentation algorithm to segment the t ime series into several subsets with different scales,which is more effective in detecting the abrupt changes of nonlinear time series.In this paper,we try to v erify the effectiveness of heuristic segmentation algorithm in dealing with nonl inear time series by an ideal time series.Through detecting and analyzing the in formation of abrupt climate changes contained in recent 2000a's tree annual grow th ring,we succeeded in distinguishing abrupt changes with different scales.The research based on the newly defined paramcter of abrupt change density shows tha t human activities might have lead to the recent 1000a's unbalanced distribution of serial and spares segments of abrupt climate changes,which may be one of the manifestations of global temperature change.