近140年中国、北半球和全球气温的标度律

运用非趋势波动分析方法,有效地消除数据中噪声和非平稳性质的影响,研究全球、北半球和中国近140年气温变化的长程幂律相关性。结果表明：全球气温、北半球气温和中国气温都有由交叉点划分的两个标度不变区域,可能对应两个不同的物理机制。其一,都可以表现出正长程相关的性质,而且全球气温的持续性最强,北半球次之,中国气温最弱;其二,全球气温和北半球气温还可以有几乎是1／f噪声性质的变化,中国气温则可表现出介于1／f和布朗噪声之间的行为。     

重庆市年平均气温变化多时间尺度特征的诊断研究

利用长年代气温资料讨论了重庆市年平均气温的多时间尺度特征，结果表明:重庆市近几十年气温变化有明显的阶段性，但总的趋势是下降的，这与同期全球、北半球及中国气温有显著上升趋势有差异。     

气温涨落中的层次相似律

尝试近年来发展的湍流层次结构模型应用于分析气温序列涨落，试图寻求一种研究气温变化中多尺度现场的新方法。通过分析北半球近百年月平均气温距平的Jones序列，证实了层次相似律的存在，得到层次相似系数β，并对其物理含义进行了讨论。     

前言

全球气候变暖问题的研究已经有相当长的历史了。近20年来科学家们对这个问题更加重视。我国学对于中国近代气候变化特别是地面气温变化规律进行了大量分析，获得了一系列成果。这些成果表明，中国的气候变化与全球变化有较好的一致性，但也存在明显的差别。近50～100年内，中国地表气温呈明显增暖趋势，但20世纪30-40年代的暖期似乎比全球平均明显得多；中国温度变化的季节和地区特征同北半球基本一致；从历史时期温度变化分析看，有研究认为中国20世纪的增温是过去至少l000年内没有的。     

北极涡活动对我国同期及后期气温的影响

利用NCEP／NCAR1950-2002年500hPa月平均再分析高度场资料计算出北半球及各分区的月平均极涡面积、强度，讨论了它们与我国气温的相关。结果表明：北半球500hPa年平均和4季的极涡面积大小与我国多数站点同期气温呈负相关，尤其在年平均及冬季状况下最显著，1-12月极涡面积与气温的负相关呈由减弱到增强的变化趋势；而极涡强度与我国同期气温的相关性相对较弱，在秋冬季不少地区出现正相关，1-12月极涡强度与气温负相关性的变化趋势是由增强到减弱。利用奇异值分解研究极涡对后期我国气温的影响后发现，极涡指数与后期我国气温呈负相关，但不同季节差异较大。当冬季Ⅳ区极涡面积显著缩小、北半球极涡总强度明显增强时，长江中下游以北及东北地区下一年春季气温通常上升；春季Ⅰ区极涡面积、强度异常偏大，则夏季华南沿海、西南及河套地区气温比常年偏低；若春季北半球极涡强度减弱、夏季亚洲区极涡面积收缩，则其后的秋季华北气温相对较高。     

自适应多分辨数据滤波器及其在我国气候诊断中的应用

本文以小波分析的多分辨分析理论为基础，建立了一个自适应的多分辨数据滤波器。该滤波器不仅具有传统方法所不具有的局部化、多层次、多分辨的优点，而且对于突变点的判断也象M-K方法一样的精确。此外，该滤波器还具有可以同时判别多个突变点而不改变原时间序列的大尺度结构的优点。本文的应用表明:中国近百年来的气温变化主要经历了三个持续近30年的冷暖交替，它们分别是1919年以前的偏冷期；1920年至1954年的偏暖期和1955年至1986年的偏冷期。对应于这种较大时间尺度的气候演变，中国的气温变化表现出了十分明显的突变特性，其冷暖交替的突变点分别发生在1920年、1955年和1987年。对应于较小时间尺度的气候演变来说，中国近百年来的气温变化则增加了更多冷暖交替的层次结构和突变点。云南近百年来的气温变化与中国的气温变化是基本一致的，但与北半球和全球的气温变化并不完全一致，其中最主要的差异是北半球和全球的气温变化在1955年至1978年是一个极弱的偏冷期，1979年发生明显增暖的突变，而云南和中国的气温变化在1955年至1986年则是一个极强的偏冷期，从1987年开始才出现明显增暖的突变。     

我国近百年气温的非趋势波动分析

为研究我国近百年气温的长程相关规律，通过对我国近百年月平均温度序列的非趋势波动分析。初步得出以下几个结论：我国近百年气温存在内在的长程相关性；标度指数a≌0．66；尽管大于18a尺度区间的α与小于18a尺度区间的α有所不同，但可能由于所分析的温度序列长度受到限制，没有足够充分的理由认为18a是个交叉点。     

中国、北半球和全球的气温突变分析及其趋势预测研究

本文采用均值差异假设检验研究了中国、北半球和全球气温历史序列的突变现象。分析表明，中国气温从本世纪以来，在40年代末50年代初曾出现一次由暖到冷的突变。北半球和全球均曾在19世纪末和本世纪20年代发生了突变现象。功率谱分析表明，气温的突变指数曲线具有明显的周期性。一系列比较研究证明，按照分析出的突变点将气温序列分段建模，无论数值误差还是变化趋势，效果均优于整段序列的模型。所以，对未来气温变化趋势作预测，应首先搞清楚未来会处在怎样的气候阶段中，会不会出现突变。研究表明，本文叙述的均生函数累加延拓的时序建模方案，对气温序列有很好的拟合和预测效果。     

中国、北半球和全球的气温突变分析及其趋势预测研究

本文采用均值差异假设检验研究了中国、北半球和全球气温历史序列的突变现象。分析表明，中国气温从本世纪以来，在４０年代末扣年代初曾出现一次由暖到冷的突变。北半球和全球均曾在１９世纪末和本世纪２０年代发生了突变现象。功率谱分析表明，气温的突变指数曲线具有明显的周期性。一系列比较研究证明，按照分析出的突变点将气温序列分段建模，无论数值误差还是变化趋势，效果均优于整段序列的模型。所以，对未来气温变化趋势作预测，应首先搞清楚未来会处在怎样的气候阶段中，会不会出现突变。研究表明，本文叙述的均生函数累加延拓的时序建模方案，对气温序列有很好的拟合和预测效果。     

近百年中国、北半球和南半球气温内在结构比较(Ⅱ)

气温序列的变化不仅包含月、季、年、百年等不同的时间尺度,而且具有一定的局域性,全球、南半球、北半球和中国的气温变化各不相同．文中利用子波变换极大值线表征的突变点来构造一维映射动力系统的基本原理,研究中国、北半球和南半球气温的异同,从而映证了气候变化的局域性     

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

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, Tmax and Tmin of 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 Tmax multifractal strength of Jiangsu is stronger than that of Liaoning, while the Tavg and Tmin multifractal strength of Jiangsu is weaker than that of Liaoning, showing that Jiangsu has a larger internal Tmax fluctuation than Liaoning does, while it has a smaller fluctuation of Tavg and Tmin than Liaoning does. Guangdong and Liaoning both show the strongest Tmin multifractal strength, followed by Tavg multifractal 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.     

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

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.     

Wavelet multiresolution based multifractal analysis of electric fields by lightning return strokes

Lightning can be seen as a large-scale cooperative phenomenon, which may evolve in a self-similar cascaded way. Using the electric field waveforms recorded by the slow antenna system, the mono- and multifractal behaviors of 115 first return strokes in negative cloud-to-ground discharges have been investigated with a wavelet multiresolution based multifractal method. The results show that the return stroke process, in term of its electric field waveform, has apparent fractality and strong degree of multifractality. The multifractal spectra obtained for the 115 cases are all well fitted to a modified version of the binomial cascade multifractal model. The width of the multifractal spectra, which measure the strength of multifractality, is 1.6 on average. The fractal dimension of the electric field waveforms ranges from 1.2 to 1.5 with an average of 1.3, a similar value to the fractal dimension of the lightning channel obtained by others. This suggests that the lightning-produced electric fields may have the same fractal dimension as its channel. The relationship between the peak current of a return stroke and the charge deposition in its channel is also discussed. The results suggest that the wavelet and scaling analysis may be a powerful tool in interpretation of the lightning-produced electric fields and therefore in understanding lightning.     

Multifractal analysis of the rainfall time distribution on the metropolitan area of Barcelona (Spain)

In most of the studies on scale properties in the rainfall process, multifractal behavior has been investigated without taking into account the different rain generation mechanisms involved. However, it is known that rain processes are related to certain scales, determined by climatological characteristics as well as regional and local meteorological features. One of the implications derived from these correspondences is the possibility that the multifractal parameters of the rainfall could depend on the dominant precipitation generation mechanism. Fractal analysis techniques have been applied in this work to rainfall data recorded in the metropolitan area of Barcelona in the period 1994–2001, as well as to a selection of synoptic rainfall events registered in the same city in the period 1927–1992. The multifractal parameters obtained have been significantly different in each case probably showing the influence of the rain generation mechanisms involved. This influence has been revealed also in the analysis of the effects of seasonality on the multifractal behavior of rainfall in Barcelona.     

Fractal properties of temperature fluctuations in the convective surface layer

Temperature fluctuations in a convective surface layer were investigated. Box counting analysis was performed to investigate fractal properties of surfaces of constant temperature and was performed on sets of points obtained by setting thresholds on detrended records. Results indicate that surfaces of constant temperature have fractal properties for thresholds far from the mean. Estimated fractal dimensions of one-dimensional cuts through these surfaces varied between 0.23 and 0.66, increasing with threshold value approaching the mean temperature. For thresholds close to the mean, no fractal behavior was found. Asymmetry in results for thresholds above and below the mean temperature was attributed to the asymmetry between updrafts and downdrafts in the convective surface layer.The temperature dissipation rate (TD) was also investigated. It was found to be strongly intermittent with large fluctuations of the intermittency exponent. Moments were analyzed in order to investigate multifractal properties of TD. Results indicate scaling in the range of 50–1000 (where is the Kolmogorov scale) and multifractal properties resembling those observed for passive scalar dissipation in laboratory flows.     

雪崩、分形与雷暴放电中强闪的自组织演化

强调了雪崩、分形与远离平衡系统自组织演化的联系。利用2003年夏季青藏高原中部地区雷暴放电的地面电场野外观测资料,基于近年来发展的分形及多重分形分析中的去势涨落分析(detrend-ed fluctuation analysis,DFA)方法,对强闪前电场信号的关联及多重分形特征进行了分析,发现强放电前过程具有明显的分形和多重分形特征,多重分形谱与推广的二项倍增串级模式符合。分析在一定程度上表明了雷暴强放电发生的可能的自组织演化性。     

Climatic trends in the Amazonian area of Ecuador: Classical and multifractal analyses

The climate evolution and change in the Amazonian area is very important at least at a continental scale involving Latin America where more than 550 million people live. The objective of the present study was to investigate, from an environmental perspective, the climatic trends in the Amazonian area of continental Ecuador. We performed both classical and multifractal analyses of these trends on four climatic variables (maximum and minimum temperature, evaporation and evaporation/precipitation ratio). Data were collected from Puyo meteorological station, Pastaza Province, Ecuador. Data sets covered 31 years (from January 1974 to September 2005). Each time series consisted of 380 months.Piecewise regression analyses with breaking point showed two regimes with a cutoff ranging from t = 80 months (maximum and minimum temperature) to t = 133 months for the evaporation pattern (determination coefficient ≥ 0.979) while the multifractal analyses showed an increasing complexity within each climatic variable. All the considered climatic variables showed an increase since 1974 to approximately 1985. After that some type of smoother increase was observed.     

Multifractal detrended fluctuation analysis of the δ 18 O record of NGRIP ice core

The multifractal properties and scaling behaviors of the long-term and recent 2000-year δ ¹⁸ O records of NGRIP ice core are investigated by the multifractal detrended fluctuation analysis method. The generalized Hurst exponents, multifractal scaling exponents, and singularity spectrums of two δ ¹⁸ O records are derived to verify the multifractiality of two records. And the multifractal behaviors of two records are obviously different, which may reflect the climate change of the recent 2000-year time is quite different from one of the long-term time. In addition, the probability distribution analysis of two δ ¹⁸ O records is presented to manifest the different multifractality between two δ ¹⁸ O records of NGRIP ice core. Our results will be helpful to research the climate change.     

Nonlinear dynamics of meteorological variables: multifractality and chaotic invariants in daily records from Pastaza, Ecuador

Weather represents the daily state of the atmosphere. It is usually considered as a chaotic nonlinear dynamical system. The objectives of the present study were (1) to investigate multifractal meteorological trends and rhythms at the Amazonian area of Ecuador and (2) to estimate some nonlinear invariants for describing the meteorological dynamics. Six meteorological variables were considered in the study. Datasets were collected on a daily basis from January 1st 2001 to January 1st 2005 (1,460 observations). Based on a new multifractal method, we found interesting fractal rhythms and trends of antipersistence patterns (Fractal Dimension >1.5). Nonlinear time series analyses rendered Lyapunov exponent spectra containing more than one positive Lyapunov exponent in some cases. This sort of hyperchaotic structures could explain, to some extent, larger fractal dimension values as the Kaplan–Yorke dimension was also in most cases larger than two. The maximum prediction time ranged from ξ?=?1.69 days (approximately 41 h) for E/P ratio to ξ?=?14.71 days for evaporation. Nonlinear dynamics analyses could be combined with multifractal studies for describing the time evolution of meteorological variables.     

Fractal aspects of integrated concentration fluctuations

Time series of vertically integrated concentrations (VIC) across neutrally buoyant plumes are used to study the fractal and multifractal characteristics of passive scalar fluctuations in turbulent flow fields. Here, the multifractal analysis is based on a novel definition of the singularity spectrum-F() of the time records. Approximations for quantities such as the fractal dimension and the spectral exponent are derived as functions ofF() and are compared with the experimental results. Among other things, we show that VIC records are characterized by two typical subdomains. One domain, which is related to integrated concentration fluctuations, is a subfractal process; whereas the second one, which is directly related to the concentration fluctuations, is a fractal process.