The Method To Detect As Well As Assess The Extreme Climate Events And The Spatial And Temporal Characteristics Of Changes Of Temperature And Precipitation Extremes Over China During The Second Half Of The 20^th Century

Due to the increasing social and economic loss caused by extreme climate events and itsderivative disaster, the study of mechanism of occurrence and development of extreme climateevents, and its evaluation and early warning system is the fastest growing area of interest amongglobal change science. This questions, which are the hotspots of extreme climate events research,are as following:(1) Can we detect the change of extreme climate events?(2) Is climate change abnormal?(3) How can we reduce the uncertainties of our understanding?(4) Can we afford evidence as to correlate extreme climate change with influence of humanactivity?This contribution is especially interested in two questions: "How can we reduce theuncertainties of our understanding?" and "Can we afford evidence as to correlate extreme climatechange with influence of human activity?".When it comes to the question "How can we reduce the uncertainties of our understanding?",the most fundamental uncertainty of understanding root in the definition of climate exremes. Themost common used definition of climate extremes nowadays is method of percentile, and itcontains three major defect, e.g. the disunity of definition, uncertainty of volume of sample, thenonuniqueness of conclusion. As to "Can we afford evidence as to correlate extreme climatechange with influence of human activity?", existing research work are mainly focused on theextreme events or its index's frequency or amplitude, however, the conclusion drawn fromunivriate time series analysis only contains restricted information and can not reflect theinformation of extreme events as a whole, and thus can not grasp the overall characteristics ofextreme events' variation.In this paper through the combination of Detrended Fluctuation Analysis (DFA) andSurrogate Data method, we develop a new method of threshold of extreme events detection, e.g.DFA-S method, which has a certain phsical background. The obtained critical value of extremeevents are definite and unique and the required length of time series is the volume of sample. As to the second question, we integrate the threshold with the mean, variance and total number ofextreme high temperatures, and give a definition of extreme events complex indices from theangle of predictability. This work illustrates a brand new method and way of thinking of extremeclimate events research. The results and conclusions can be summarized as follows:(1) Based on the theory of mutual information, determine the parameter of DFA method bycompute mutual information function using symbolic analysis. This algorithm entirely depends onthe data itself and thus immune to the change of sample volume and its stability is comparativelygood. The algorithm of mutual information function can be use to decide if the length of timeseries fulfill the requirement of DFA index computation and whether a time series of certain lengthis appropriate for DFA method.(2) Combine DFA method with Surrogate Data method, e.g. once the DFA index of time serieremains unchange with values exceed a critical value, we decide the critical value as the thresholdwe need, and this strategy of selection is called DFA-S algorithm. We also validate theeffectiveness of DFA-S method through extreme events detection using artificial series andobservational data from various angles.(3) Using threshold as velocity mode, the difference between threshold and average numberof extreme high temperatures as temperature mode, the number of extreme high temperatures astemperature gradient mode, and thus integrate the threshold with the mean, variance and totalnumber of extreme high temperatures, and give a definition of extreme events complex indicesfrom the angle of predictability.(4) Obtained the thresholds of extreme high-low temperature and extreme precipitationevents from 1961 to 2000 of China through DFA-S method and anlyzed its spatial-temporalcharacteristics of distribution. We evaluate the frequency and amplitude of extreme high-lowtemperatures, extreme precipitation events from 1961 to 2000, analyzed its climate backgroundand possible impact factor. Furthermore, we study the complex indices of amplitude of extremehigh-low temperatures, extreme precipitation events from 1961 to 2000, and making classificationbased on its value of complex indices, and give a certain instruction for detection and earlywarning.(5) Using normalized annual complex indices of extreme high-low temperatures and extremeprecipitation events of 165 stations from 1961 to 2000 in China as fieldvariable, analyzed the characteristics of spatio-temporal distribution of extreme high-low temperatures and extremeprecipitation events' complex indices through EOF analysis and especially focused on the fourpreceding mode of spatial distribution and its temporal variation. Furthermore we obtained thesummer and winter SST's sensitive area of extreme high-low temperatures and extremeprecipitation events' complex indices. Through SVD decomposition we study the influence ofabnormal of SST on complex indices extreme temperature and precipitation events in China.