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Multi-Fractal Analysis of Daily Air Temperature Time Series in Coastal Areas of China

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摘要 In this article,the Multi-Fractal Detrended Fluctuation Analysis(MF-DFA)method is adopted to study the temperature,i.e.,the maximum temperature(Tmax),mean temperature(Tavg)and minimum(Tmin)air temperature,multifractal characteristics and their formation mechanism,in the typical temperature zones in the coastal regions in Guangdong,Jiangsu and Liaoning Provinces.Following are some terms and concepts used in the present study.Multifractality is defined as a term that characterizes the complexity and self-similarity of objects,and fractal characteristics depict the distribution of probability over the whole set caused by different local conditions or different levels in the process of evolution.Fractality strength denotes the fluctuation range of the data set,and long-range correlation(LRC)measures the stability of the climate system and the trend of climate change in the future.In this research,it is found that the internal stability and feedback mechanism of climate systems in different regions show regional differences.Furthermore,the research also proves that the Tavg,Tmaxand Tminof the above three provinces are highly multifractal.The temperature series multifractality of each province decreases in the order of temperature series multifractality of Liaoning>temperature series multifractality of Guangdong>temperature series multifractality of Jiangsu,and the corresponding long-range correlations follow the same order.It reveals that the most stable temperature series is that of Liaoning,followed by the temperature series of Guangdong,and the most unstable one is that of Jiangsu.Liaoning has the most stable climate system,and it will thus be less responsive to the future climate warming.The stability of the climate system in Jiangsu is the weakest,and its temperature fluctuation will continue to increase in the future,which will probably result in the meteorological disasters of high temperature and heat wave there.Guangdong possesses the strongest degree of multifractal strength,which indicates that its internal temperature series fluctuation is the largest among the three regions.The Tmaxmultifractal strength of Jiangsu is stronger than that of Liaoning,while the Tavgand Tminmultifractal strength of Jiangsu is weaker than that of Liaoning,showing that Jiangsu has a larger internal Tmaxfluctuation than Liaoning does,while it has a smaller fluctuation of Tavgand Tminthan Liaoning does.Guangdong and Liaoning both show the strongest Tminmultifractal strength,followed by Tavgmultifractal strength,and the weakest Tmax multifractal strength.However,Jiangsu has the strongest Tmax,followed by Tavg,and the weakest Tmin.The research findings show that these phenomena are closely related to solar radiation,monsoon strength,topography and some other factors.In addition,the multifractality of the temperature time series results from the negative power-law distribution and long-range correlation,in which the long-range correlation influence of temperature series itself plays the dominant role.With the backdrop of global climate change,this research can provide a theoretical basis for the prediction of the spatial-temporal air temperature variation in the eastern coastal areas of China and help us understand its characteristics and causes,and thus the present study will be significant for the environmental protection of coastal areas.
作者 李忠良 何光鑫 沈渭寿 甄晓菊 韩静 LI Zhong-liang;HE Guang-xin;SHEN Wei-shou;ZHEN Xiao-ju;HAN Jing(College of Geographic Sciences,Nanjing University of Information Science and Technology,Nanjing 210044 China;Nanjing Institute of Environmental Science,Ministry of Environmental Protection,Nanjing 210044 China;Key Laboratory of Meteorological Disaster,Ministry of Education(KLME)/International Joint Research Laboratory on Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters,Nanjing University of Information Science and Technology,Nanjing 210044 China;Key Laboratory of Meteorology and Ecological Environment of Hebei Province,Shijiazhuang 050021 China;Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province,Haikou 570203 China)
出处 《Journal of Tropical Meteorology》 SCIE 2020年第3期348-362,共15页 热带气象学报(英文版)
基金 National Key R&D Program of China(2018YFC1506900,2018YFC1506904) National Natural Science Foundation of China(41875027,41911530242) Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province(SCSF201804,419QN330) Research Program of Key Laboratory of Meteorology and Ecological Environment of Hebei Province(Z201603Z)。
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