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Many studies have shown that the climate system has the long‐range correlation (LRC) characteristics, which provide a good test bed for evaluating the performance of climate models. In this study, the LRC characteristics of the observed surface daily temperature data in China is first analysed by detrended fluctuation analysis. It is found that the daily temperature records, including daily mean temperature (Tmean), daily maximum temperature (Tmax), and daily minimum temperature (Tmin), all exhibit LRC characteristics with scaling exponents more than 0.6 in most of China. Then, the performances of two different versions for the Beijing Climate Center Climate System Model (BCC‐CSM), namely, BCC‐CSM1.1m in CMIP5 and BCC‐CMS2‐MR in CMIP6, are quantitatively evaluated by comparing the differences of the LRC characteristics between the observations and the simulations. The results indicate that BCC‐CSM2‐MR performs better than BCC‐CSM1.1(m) in western Northwest China, most of south of the middle and lower reaches of Yangtze River, North China, central Inner Mongolia for the daily Tmean and Tmax. As far as the daily Tmin is concerned, BCC‐CSM1.1(m) performs better than BCC‐CSM2‐MR in western South China and southern Xinjiang, and the performance of BCC‐CSM2‐MR in CMIP6 has not been significant improved. Compared with BCC‐CSM1.1(m), BCC‐CSM2‐MR cannot significantly improve the simulations for the LRC characteristics of the three daily temperature variables in central and eastern Qinghai‐Tibet plateau, and the areas of between lower reaches of Yellow River and Yangtze River. [ABSTRACT FROM AUTHOR]

Based on the long‐range correlation (LRC) of daily air temperature records in China, the simulation performance of 11 Coupled Model Intercomparison Project Phase 6 (CMIP6) models is first evaluated in this study. And then, those CMIP6 models which can reproduce LRC of the well in observed records are selected for multi‐model ensemble mean. The results indicate that multi‐model ensemble mean can significantly reduce model uncertainty in reproducing LRC characteristics of daily air temperature records. Comparing the differences of the scaling exponents of the ensemble mean temperature data between the historical well‐mixed greenhouse gas, historical natural‐only, historical anthropogenic aerosols, and historical experiments, the LRC characteristics of different temperature variables show some difference. It means that the different forcings are critical for the model to reproduce the LRC characteristics of different temperature variables well. Generally, the influence of anthropogenic forcing enhances LRC of daily maximum air temperature in parts of northern China. Therefore, anthropogenic forcing is very important for CMIP6 models to accurately reproduce the strength of LRC for observed daily air maximum temperature in northern China. [ABSTRACT FROM AUTHOR]

This study empirically compares the development of China’s interest rate market in the past decade. The yield of Shibor market from 2006 to 2016 showed anti-persistence, and the later five-year trend was stronger than that in the previous half period, indicating a significantly growing trend of China’s interest rate market. We perform a comparative analysis of detrended fluctuation analysis (DFA), detrending moving average (DMA) algorithm, and the maximum overlap wavelet transform (DFA-MODWT) using grey and original sequences. The autoregressive integrated moving average model is adopted to generate set-point H sequences. Results illustrated that 1) for original sequences, both the DFA-MODWT and DMA ( θ = 0 . 5 ) shows outstanding performances when the set Hurst exponents are 0.6 and 0.7 and 0.8 and 0.9, respectively. 2) For grey sequences, G-DFA performs best among the three methods, followed by G-DMA and G-DFA-MODWT. 3) The long-range correlation of the original sequences is slightly lower than the set values. On the contrary, the long-range correlation of grey sequences is much higher than the set values. Thus, the grey sequence exerts a strong aggregation effect as it can accumulate the trend of the sequence. Our results show that the grey sequence can determine the regular pattern from the messy sequence. However, grey sequences tend to be overly analyzed and misleading when large volumes of data are involved. Empirical studies confirm that China’s interest rate market remains an inefficient market. [ABSTRACT FROM AUTHOR]

ABSTRACT In the present study, the performance of the National Centers for Environmental Prediction/Department of Energy (NCEP/DOE) Atmospheric Model Intercomparison Project (AMIP-II) reanalysis ( NCEP-2) and NCEP Climate Forecast System Reanalysis ( CFSR) seasonal temperature data for China was quantitatively evaluated by using detrended fluctuation analysis. The results indicate that the quality of the NCEP-2 temperature data is the highest in autumn, while the quality of CFSR data set is the highest in summer. Both the quality of NCEP-2 and that of CFSR temperature data is the lowest in winter. The quality is very low in eastern Tibetan Plateau and most of Xinjiang at a significance level of 0.05 for the NCEP-2 and CFSR reanalysis temperature data including three elements: the daily average temperature ( Tmean), the daily maximum temperature ( Tmax) and the daily minimum temperature ( Tmin). The performance of the NCEP-2 Tmean data is poorer than those of the NCEP-2 Tmax and Tmin data for all four seasons, while the performance of CFSR Tmean is better than that of CFSR Tmax and Tmin for all four seasons. In addition, the NCEP-2 Tmax, Tmin and CFSR Tmean, Tmax data perform well in most of central and eastern China for all four seasons. The quality of the CFSR Tmean is better than that of the NCEP-2 Tmean for all four seasons. However, the NCEP-2 Tmax and Tmin data have higher credibility than that of the CFSR Tmax and Tmin data for all four seasons. Therefore, it is important to consider the reliability of the NCEP-2 and CFSR daily temperature reanalysis data in different districts and seasons when drawing conclusions from the different reanalysis data. [ABSTRACT FROM AUTHOR]

The daily temperatures from observational data, NCEP-2 and CFSR reanalysis data all exhibit long-range correlation (LRC) characteristics, which provides a test bed for assessing the reliability of reanalysis data. In this study, the quality of the NCEP-2 and CFSR data in China are evaluated on the basis of the LRC characteristics of daily temperatures, including daily average temperature (DAT), daily maximum temperature (DMAT), daily minimum temperature (DMIT), and diurnal temperature range (DTR). Compared with the observations, the quality of NCEP-2 daily temperature is relatively good in central and eastern Northwest China, and most of central and eastern China, especially for NCEP-2 DMAT. However, the NCEP-2 reanalysis data as well as CFSR has a significant difference with the LRC of the observations in most of Sichuan, Qinghai-Tibet Plateau and some areas of southwestern Xinjiang at a significance level of Alpha = 0.05. In general, the LRC characteristics of NCEP-2 daily temperature perform better than that of CFSR data. As far as DAT is concerned, CFSR perform worse in central and eastern Northwest China, and better than NCEP-2 only in South China and eastern Jiangnan. The quality of the CFSR DMAT is worse than that of NCEP-2 in central and eastern Northwest China, western Inner Mongolia, and eastern China. The quality of NCEP-2 DMIT is better than CFSR in central and eastern Northwest China, most of Inner Mongolia, and is worse than it in most of South China and eastern Jiangnan. The reliability of the CFSR DTR is very low in most of China. [ABSTRACT FROM AUTHOR]

In order to explore long-term evolution rule and future trend of runoff time series, and exactly detect its tendency and long-range correlation characteristics, runoff data covering 1952-2012 from 3 stations across the upper Fenhe River basin were analyzed. The moving average method, Empirical Mode Decomposition (EMD) method and Mann-Kendall (M-K) trend test method were simultaneously applied to analyze the trend characteristics firstly. Then Rescaled Range analysis ( R/ S) and Detrended Fluctuation Analysis (DFA) methods were employed to research the long-range correlation characteristics and length of non-periodic cycle of hydrological time series, they can systematically detect and overcome non-stationarity at all time scales. Finally, predict the future trend by combining the trend characteristics with the long-range correlation characteristics and length of non-periodic cycle. The results illustrate the annual runoff series is non-linear, non-normal time series, and have 10 years non-periodic cycle length and noticeable descending trend. This descending trend will continue in a period time of future. [ABSTRACT FROM AUTHOR]

ABSTRACT In this study, the detrended fluctuation analysis ( DFA) method is used to analyse the drought and flood index ( DFI) of the past 531 years in 20 stations over east central China. It is found that the variation of the DFI exhibits long-range correlation. The long-range correlation gradually decreases from northwest to southeast over the studied region. Moreover, the recurrence times of droughts or floods also show long-range correlation. The long-range correlation in the DFI data will disappear if the DFI data are randomly shuffled, which was also found in the recurrence time of the randomly shuffled DFI series for each station. The results show that long-range correlation is an intrinsic property of drought or flood events, which could result in the long-range correlation of the corresponding recurrence times. At present, the prediction skill of droughts/floods in Asian-Australian monsoon region is relatively low, particularly in most of China. The long-range correlation of the DFI provides a theoretical basis for climate predictions of droughts/floods and may help improve model performance by amending the model configuration, in terms of addressing parameterization problems and improving atmosphere-ocean coupling. [ABSTRACT FROM AUTHOR]

Vegetation is an important part of terrestrial ecosystems. Although vegetation dynamics have explicit spatial and temporal dimensions, the study of the temporal process is in its infancy. Evaluation of temporal scaling behavior could provide a unique perspective for exploring the temporal nature of vegetation dynamics. In this study, the Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) was used to reflect vegetation dynamics, and the temporal scaling behavior of the NDVI in China was determined via detrended fluctuation analysis (DFA). Our main objectives were to reveal the temporal scaling behavior of NDVI time series and to understand variation among vegetation types. First, DFA revealed similar exponents, which ranged from 0.6 to 0.9, for all selected pixels, implying that a long-range correlation was generally present in the NDVI time series at the individual pixel scale. We then extended the analysis to all of China and found that 99.30% of the pixel exponents ranged from 0.5 to 1. These results suggest that the NDVI time series displays strong long-range correlation throughout most of China; however, the exponents exhibited regional variability. To explain these differences, we further analyzed the exponents for 12 vegetation types based on a vegetation map of China. All of the vegetation types exhibited well-defined long-range correlation, with exponents ranging from 0.7189 to 0.8436. For all vegetation types, the maximum and average value and standard deviation of the exponents decreased with increasing annual maximum NDVI values, suggesting that low vegetation density is much more sensitive to external factors. These findings may be useful for understanding vegetation dynamics as a complex, temporally varying phenomenon. [ABSTRACT FROM AUTHOR]

The article presents a study on fractal and linear heart rate dynamics during sleep and at high altitudes. It states that the study involves electrocardiogram (ECG) monitoring of subjects at high altitude levels in Mount Everest. Results show that cardiac response during sleep at high altitudes can hardly be explored through heart rate variability (HRV) estimation. It also suggests supporting results with more sophisticated fractal and nonlinear cardiac variability features.

In this paper, we analyze and compare long-range power-law correlations of returns, absolute returns, squared returns, cubed returns and square waved returns for sixteen individual stocks from the block of energy sources of Chinese stock market and five stock indices (Shanghai Composite Index, Shenzhen Component Index, Dow Jones Industrial Average index, Nasdaq Composite Index, the Standard and Poor's 500 Index) by using a detrended fluctuation analysis approach. The empirical evidence suggests that Shanghai Composite Index is very close to Shenzhen Component Index and Nasdaq, DJIA is very close to S&P 500 in all cases. And the exponent trends of the returns are close to that of square waved returns. Also, five indices deviate from other sixteen individual energy stocks in all cases except square waved returns. Further, there are long-range correlations and persistence in volatility series of absolute returns and squared returns. Moreover, we investigate the long-term memory of these returns by applying Lo's modified rescaled range statistic. We find that the China energy market exhibits fractal and persistence properties. [ABSTRACT FROM AUTHOR]

Abstract: Long-range correlations of five kinds of daily temperature records (i.e. daily average temperature records, daily maximum temperature records, daily minimum temperature records, diurnal temperature range and the sum of daily maximum and minimum temperature records) from 164 weather stations over China during 1951–2004 are analyzed by means of detrended fluctuation analysis (DFA). These five kinds of fluctuation series are found to be power-law correlated with scaling exponents larger than 0.5. Local changes of scaling exponents are examined and the spatial distributions of these different kinds of temperature records are similar except the diurnal temperature range (DTR for short) records. Furthermore, the differences of the scaling behavior among diurnal temperature records and other kinds of temperature records are discussed. [Copyright &y& Elsevier]

The water pH series from Dongting Lake Inlet and Outlet in China are analyzed by detrended fluctuation analysis (DFA), spectral analysis and multifractal methods. The results show that these pH series are characterized by long-term memory, 1/f noise and multifractal scaling, and these characteristics have obvious difference between the Lake Inlet and Outlet. The comparison results show that monofractal (DFA exponent) and multifractal (Δα, Δf, B) parameters can be quantitative dynamical indexes reflecting the capability of anti-acidification of Dongting Lake. Furthermore, we investigate the frequency-size distribution of pH series from Dongting Lake Inlet and Outlet. Our findings suggest that water pH is an example of a self-organized criticality (SOC) process. Based on concept of self-organized ctiticality, we analysis the cause that different scale-free power-law behavior between pH series from Dongting Lake Inlet and Outlet. This work can be helpful to improvement of modeling of lake water quality. [ABSTRACT FROM AUTHOR]

Detrended fluctuation analysis (DFA) and multifractal methods are applied to the time-scaling properties analysis of water pH series in Poyang Lake Inlet and Outlet in China. The results show that these pH series are characterised by long-term memory and multifractal scaling, and these characteristics have obvious differences between the Lake Inlet and Outlet. The comparison results suggest that monofractal and multifractal parameters can be quantitative dynamical indexes reflecting the capability of anti-acidification of Poyang Lake. Furthermore, we investigated the frequency-size distribution of pH series in Poyang Lake Inlet and Outlet. Our findings suggest that water pH is an example of a self-organised criticality (SOC) process. The results show that it is different SOC behaviours that result in the differences of power-law relations between pH series in Poyang Lake Inlet and Outlet. This work can be helpful to improvement of modelling of lake water quality. [ABSTRACT FROM AUTHOR]

It seems evident that the understanding of the complex temporal evolution dynamic characteristics of air pollution indexes (APIs) can contribute to developing advanced techniques for air pollution forecasting. In this work, monofractal and multifractal methods have been successfully used to characterize the temporal fluctuations of APIs in Shanghai. It shows that APIs in Shanghai are characterized by scale invariance, long range dependence and multifractal scaling. Introducing a kind of variable window sizes method, we analyze the temporal evolution of the monofractal and multifractal behaviors of APIs. Some information about the temporal evolution dynamics of APIs is revealed. Our study suggests that the variable window sizes method can be help the analysis of air pollution time series temporal evolution dynamical mechanisms. This work could play an important role in the research of air pollution. [ABSTRACT FROM AUTHOR]

Abstract: Long-range correlations of daily relative humidity anomaly records from 191 weather stations over China during 1951–2000 are analyzed by means of fluctuation analysis (FA) and detrended fluctuation analysis (DFA). The information about trends in the relative humidity records can be obtained by comparing the FA curve with DFA curves. The daily relative humidity fluctuations are found to be power-law correlated and their average scaling exponent is higher than that of the temperature fluctuations, indicating that the relative humidity fluctuations take different statistical behavior from other meteorological quantities and there exists a stronger persistence in the relative humidity fluctuations. Furthermore, it is also found that these power-law scaling properties vary from station to station and show both spatial and temporal diversities, which may be explained by a proposed mechanism. [Copyright &y& Elsevier]