| Extreme climate events are of small probability and were given definitely definition in AR4according to probability. So it has rather theoretical significance to define extreme climate events from the point of probability. Because Box-Cox transformation can improve data normality and the data by Box-Cox transformation are normal, the skewed probability function is derived, which provides theory basis to define extreme events based on probability. According to the skewed probability function, most probable temperature (MPT) is defined and it has definite physics meaning and can reflect the changing trends of background temperature in different regions over China. Extreme temperature events are defined in terms of skewed function and the temporal-spatial distribution characteristics and time evolution laws of frequencies and strength of them are found, which provide fact foundations to try to answer the question whether extreme events are becoming more and more frequent and extremity. Basing on a series of detected extremum events, the processes of extreme events can be detected by phase synchronization. A new method to detect process of extreme events objectively is raised and it is applied in drought events from2009over Southwest China trying to supply a new angle of view to know the drought events. The following are the major conclusions about above all research:(1) Compared to normal distribution, the skewed probability function can fit temperature distribution better. The values of Box-Cox transformation index λ reflect the skewed degree of distribution.λ=1is a standard of reference, more big of Box-Cox transformation index, more right skewed of the distribution; more small of index, more left skewed. The changes of index also reflect indirectly the temperature changing trends.(2) MPT defined by skewed probability function has a definite signification and represent the background temperature fields of some region. MPT interannual variations show that in summer MPT was relative low-temperature-major before the mid-1990s and from then was warming with fluctuation but since2005the trend slowed down and that in winter MPT was relative low-temperature-major1961-1986and from1987to the beginning of the21st century was warming obviously, but since2000the warming trend slowed down. The warming amplitude of winter was stronger than of summer and the time of beginning warming of winter was earlier than of summer about5-10years. Studying three climate state MPT changes of1961-1990、1971-2000.1981-2008found that in summer MPT got warmer obviously in the third climate state while in winter in the second state. MPT’ warming trend was slowing down in the third state. It’s worthy of note that in Sichuan, Guangdong and Guangxi MPT even began to decline in the last few years. Whether it means climatic transformation needs further research.(3) Extreme temperature events are defined according to the skewed function. The temporal-spatial distribution characteristics of frequencies and strength of extreme temperature events over China in different climate sates against the backdrop of MPT (Most Probable Temperature) are analyzed. Spatially, frequencies and strength of Extreme high temperature in summer decreased significantly in the Yangtze-Huaihe river valley and Yellow River and Huaihe River valley in State Ⅰ and increased significantly in arid-semiarid region and developed Yangtze River delta in State Ⅲ. Frequencies of Extreme low temperature in winter reduced remarkably in the north and Yangtze River delta in State Ⅱ and in The Qinghai-Tibet Plateau, the southeast of northeast China, Yangtze River delta in State Ⅲ. The strength of extreme low temperature in winter reduced wholly and provincial characteristics were not obvious. The frequencies and strength of extreme temperature events were agreed in spatial. Temporally, frequencies and strength of extreme high temperature in summer increased obviously both in State Ⅱ and State Ⅲ. Frequencies and strength of extreme low temperature in winter reduced obviously in State Ⅱ and the reducing trend was slowing down in State Ⅲ. Extreme high temperature in summer occurred frequently and extreme low temperature in winter remained stably. Extremity of extreme high temperature in summer was more stronger while in winter was stable relatively. The frequencies and strength of extreme temperature events were agreed in temporal.(4) During the common time period of State Ⅰ, Ⅱ,Ⅲ, the frequencies of extreme high temperature events in the last state were always fewer than the former while the low temperature events were more, which is agreed with the fact that the background temperature steps up with the states; the strength of extreme high temperature events in the latter state was less than the former while of extreme low temperature events was stronger. For the abrupt change of climate at the end of1970s and the beginning of1980s, the frequencies of extreme high temperature events decreased before the change and increased obviously after the change while the ones of extreme low temperature did not change significantly for the change; the strength of extreme high temperature events decreased slightly before the change and increased obviously after the change while the one of extreme low temperature did not change significantly for the change but the whole strength after the change was slightly lower than the one before the change.(5) The distribution probability of extreme high temperature in summer is mainly left skewed in most regions in China especially in southeast of Southwest while in south of the middle and lower reaches of Changjiang River and China’s South-East coastal areas are obviously right skewed. The skewed index and the most probable high extreme temperature are regarded as the key marks about the distribution. The most probable high extreme temperature is rather stable and can be defined as the threshold of extreme temperature.(6) Basing on a series of detected extremum events, the processes of extreme events are detected by phase synchronization. Numerical modeling of phase synchronization showed that clustering measure Peff and the differences△peff can give the criterion about the proper number of clusters and phases can distinguish different stats of the system. So phase clustering applied to detect processes of extreme climate events is doable.(7) Drought index MSPI and precipitation anomaly of Southwest drought events in China are used to detect processed by phase synchronization. Drought index from2009presenting different degrees of drought is detected several states, including drought-wetness, wetness-drought, persistent drought or wetness. Processes detection can tell not only persistent drought and wetness state but also the changing state, i.e., from wetness to drought or from drought to wetness, which is different from drought index that can only reflect drought degree according to the values. So process here means changing state as well as stable state. The period of changing state is usually extreme value of drought or wetness or changing period between wetness and drought.(8) As far as drought index is concerned, drought process began from January2009and persisted to winter and spring in2010. From then, drought began to relive. Considering previous generating process, southwest drought events began from January2009, which is earlier than the result from drought index values. This interprets the meaning about process-including generating process.The results of precipitation anomaly are not one-to-one correspondence, which was caused by time response lagging of drought to less precipitation and complexity of drought formation cause. This does not contradict the fact that precipitation plays an important role in the formation of drought. The processes detection of southwest drought events provide a new angle of view on the drought events and the processes give the stage of climatic background changing, which provides critical period of climatic background changing about drought events. |
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