Long-range Correlation Characteristics Research Articles

Simulation of Daily Precipitation from CMIP5 in the Qinghai-Tibet Plateau

As the earth's third pole, Qinghai-Tibet Plateau belongs to one of the most sensitive regions to climate change in the world. Based on the observed and the simulated daily precipitation from the Coupled Model Intercomparison Project Phase 5 (CMIP5), we evaluated the simulation performance of daily precipitation from selected CMIP5 models from 1975 to 2005 over the Qinghai-Tibet Plateau. We found that daily precipitation exhibited obvious long-range correlation characteristics using the detrended fluctuation analysis method. The scaling exponents of daily precipitation in summer and autumn are significantly larger than those in spring and winter. MIROC4H with the best performance can reproduce long-range correlation characteristic of daily precipitation series probably because of the higher resolution, which can capture small scale cloud convections. Besides there are seasonal differences in the simulation results among different regions of the Qinghai-Tibet Plateau, simulation effects of all climate models in summer and winter are better than those in spring and autumn. The performance of MIROC4H model works the best in spring. Overall, the scaling exponents of daily precipitation from BCC-CSM1-1-M, CMCC-CM and MIROC4H are close to the observations. CCSM4 and MIROC4H climate models could reproduce the internal dynamics characteristic of daily precipitation in autumn. But for winter, all climate models have exaggerated the scaling value in southeastern Qinghai-Tibet Plateau compared with the observed values.

The long-range correlation and evolution law of centennial-scale temperatures in Northeast China

This paper applies the detrended fluctuation analysis (DFA) method to investigate the long-range correlation of monthly mean temperatures from three typical measurement stations at Harbin, Changchun, and Shenyang in Northeast China from 1909 to 2014. The results reveal the memory characteristics of the climate system in this region. By comparing the temperatures from different time periods and investigating the variations of its scaling exponents at the three stations during these different time periods, we found that the monthly mean temperature has long-range correlation, which indicates that the temperature in Northeast China has long-term memory and good predictability. The monthly time series of temperatures over the past 106 years also shows good long-range correlation characteristics. These characteristics are also obviously observed in the annual mean temperature time series. Finally, we separated the centennial-length temperature time series into two time periods. These results reveal that the long-range correlations at the Harbin station over these two time periods have large variations, whereas no obvious variations are observed at the other two stations. This indicates that warming affects the regional climate system’s predictability differently at different time periods. The research results can provide a quantitative reference point for regional climate predictability assessment and future climate model evaluation.

Numerical statistical analysis on self-organizing behavior of microfracturing events in rock failure

Current experimental investigations on microfracturing (or acoustic emission) events mainly focus on their location and distribution. A new function in rock failure process analysis (RFPA2D) code was developed to capture the size and number of damage element groups in each loading step. The rock failure process evolving from the initiation, propagation, and nucleation of microcracks was visually simulated by RFPA2Din this research. Based on the newly developed function, the statistical quantitative analysis of microfracturing events in rock was effectively conducted. The results show that microfracturing (failed element) events in the whole failure process accord with negative power law distribution, showing fractal features. When approaching a self-organized criticality state, the power exponent does not vary drastically, which ranges around 1.5 approximately. The power exponent decreases correspondingly as the stress increases. Through the analysis of the frequency and size of damaged element groups by rescaled range analysis method, the time series of microfracturing events exhibits the self-similar scale-invariant properties. Through the analysis by the correlation function method, the absolute value of the self-correlation coefficient of microfracturing sequence demonstrates a subsequent precursory increase after a long time delay, exhibiting long-range correlation characteristics. These fractal configuration and long-range correlations are two fingerprints of self-organized criticality, which indicates the occurrence of self-organized criticality in rock failure. Compared with the limited in situ monitoring data, this simulation can supply more sufficient information for the prediction of unstable failure and good understanding of the failure mechanism.

Trend and long-range correlation characteristics analysis of runoff in upper Fenhe River basin

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.

Long-range correlations in vehicular traffic flow studied in the framework of Kerner’s three-phase theory based on rescaled range analysis

In this paper, a study on the characteristics of synchronized traffic flow in a two-lane case with partial reduced lane based on the Kerner–Klenov–Wolf (KKW) cellular automaton model in the framework of Kerner’ three-phase theory is reported by using the rescaled range analysis. The local fundamental diagram and the cross correlation near the entry of the reduced lane have been analyzed. The typical two-dimensional scatter diagram in the local fundamental diagram and the non-correlation of the cross correlation between the local density and the flow reveal the characteristics of synchronized traffic flow near the entry of the reduced lane. By using the rescaled range analysis (R/S) method to calculate the Hurst exponent, we can obtain the long-range correlated characteristics of the synchronized flow at a traffic bottleneck. We have found that the larger the length of bottleneck, the higher the Hurst’s exponent does as well as the stronger the correlations of the system. Applying our correlation analysis to traffic flow simulations with the NaSch model, which cannot reproduce synchronized flow of the three-phase theory, we have indeed found that the NaSch model does not exhibit long-range correlation characteristics found for synchronized flow.

Performance evaluation of Chinese air temperature simulated by Beijing Climate Center Climate System Model on the basis of the long-range correlation

The traditional evaluation of simulation perfomance for numerical model is based on the differences in average, trend, probability density distribution, extreme value, variance and self-correlation between observational data and the simulated data by numerical model. These evaluation methods mainly depend on the comparison of statistical difference between the simulation and observation, but ignore the comparison in the dynamical characteristics of climate system. In view of this, in the present paper we use detrended fluctuation analysis (DFA) method to analyze the scaling characteristics of daily temperature from observational data and the corresponding simulated data by climate model, and investigate whether the long-range correlation of meteorological elements can exist in the simulated data of climate model. If so, we can assess the performance of climate model based on the comparison of scaling index between the observational data and the simulated data. As an example, the performances of Beijing Climate Center Climate System Model (BCC_CSM1.1(m)) model, including simulated daily average temperature, daily maximum temperature and daily minimum temperature, are assessed by using DFA. The results indicate that the long-range correlation characteristics of temperature in China can be simulated by BCC_CSM1.1(m). But the simulation errors are relatively large in Qinghai-Tibet Plateau, most West China, North China, and Huanghuai district. Thereinto, and the performance of BCC_CSM1.1(m) in Qinghai-Tibet Plateau and western West China is worst in China.

Detrended Fluctuation Analysis of Mixed Traffic Flow Time Series with a Blockage

Finding the influence of traffic accident on the road is helpful to analyze the characteristics of traffic flow, and take reasonable and effective control measures. Here, the detrended fluctuation analysis method is applied to investigate the complexity of time series in mixed traffic flow with a blockage induced by an accident. As a parameter to depict the long-term evolutionary behavior of the time series in traffic flow, the scaling exponent is analyzed. According to the scaling exponent, it is shown that the traffic flow time series can display long-range correlation characteristics, short-range correlation characteristics, and non-power-law relation in the long-range correlation characteristics, which is strongly dependent on the entering probability of vehicle, the ratio of slow vehicle and the blockage duration time.

Detrended fluctuation analysis of time series in mixed traffic flow

The detrended fluctuation analysis method is applied to analyse the complexity of traffic flow time series and the scale index is obtained, which is a parameter to depict the long term evolutionary behaviour of the mixed traffic flow time series. According to the changes of the scale index, it is shown that the traffic flow time series has long-range correlation characteristics or short-range correlation characteristics. It is found that the traffic flow series shows long-range correlation when the density ρ12; and when ρ1 or ρ>ρ2, the time series displays short-range correlation. That is to say, the vehicle density plays an important role in the changes of the scale index. Additionally, we also found that the ratio of slow vehicles have influence on the changes of the scale index.

The effects of rate and sequence complexity on repetitive finger movements

We studied the effects of movement rate and sequence complexity on the execution of externally paced finger movements. Simple thumb-index opposition movements (SEQ1), oppositions of thumb to index, medium, ring and little fingers (SEQ2), and oppositions of thumb to index, ring, medium and little fingers (SEQ3) were paced by a metronome at rates that ranged from 0.5 to 5 Hz. At rates higher than 2.5 Hz touch duration, as well as spatial and timing accuracy changed, although with a different pattern, for the sequences. Delayed movements were mostly present at lowest rates in SEQ1; at 0.5 Hz and at frequencies higher than 3.5 Hz in SEQ2; at rates higher than 3.5 Hz in SEQ3. Syncopation occurred at rates higher than 3 Hz but only for SEQ2 and SEQ3 when movements are delayed. Power spectrum analysis of timing error series indicated that SEQ1 performance is influenced by memory-related processes at all movement rates. On the other hand, for the other two sequences, at rates higher than 4 Hz the timing error series exhibit a slight but significant reduction of its long-range correlation characteristics. These findings suggest that different strategies are used for sensorimotor processing when the movement rate and sequence complexity are increased.

Effect of poloidal sheared flow on the long-range correlation characters of edge plasma turbulent transport

The long-range correlation characteristics of the turbulence-induced particle transport fluxes measured in the plasma edge region of the Chinese Tokamak-6B (CT-6B) [X. Yang et al., Nucl. Fusion 36, 1669 (1996)] have been investigated. The Hurst exponent calculations and the power spectra exhibit the self-similarity and long-range correlation character of the particle transport fluxes. The rescaled probability distribution functions (PDF’s) of the local fluxes are found to be self-similar, with the self-similarity parameter well above 0.5 in the range of time scales between the turbulence decorrelation and confinement time. The decorrelation effect of the E×B flow shear on the long-range correlation character of the flux has been identified by the changes in the radial dependences of the Hurst parameter, spectral decay index, and the outward flux tail of the PDF near the flow shear layer. This suggests that the mechanism for these changes is the simple decorrelation of the large-scale transport events by the shear flow.