arXiv subjects

Nicola Scafetta

Publications and source records attributed to Nicola Scafetta.

At least 55 records · Page 3Linked to original sources

arXiv2009

Empirical analysis of the solar contribution to global mean air surface temperature change

The solar contribution to global mean air surface temperature change is analyzed by using an empirical bi-scale climate model characterized by both fast and slow characteristic time responses to solar forcing: $τ_1 =0.4 \pm 0.1$ yr, and $τ_2= 8 \pm 2$ yr or $τ_2=12 \pm 3$ yr. Since 1980 the solar contribution to climate change is uncertain because of the severe uncertainty of the total solar irradiance satellite composites. The sun may have caused from a slight cooling, if PMOD TSI composite is used, to a significant warming (up to 65% of the total observed warming) if ACRIM, or other TSI composites are used. The model is calibrated only on the empirical 11-year solar cycle signature on the instrumental global surface temperature since 1980. The model reconstructs the major temperature patterns covering 400 years of solar induced temperature changes, as shown in recent paleoclimate global temperature records.

physics.geo-ph↗

arXiv2009

Total solar irradiance satellite composites and their phenomenological effect on climate

Herein I discuss and propose updated satellite composites of the total solar irradiance covering the period 1978-2008. The composites are compiled from measurements made with the three ACRIM experiments. Measurements from the NIMBUS7/ERB and the ERBS/ERBE satellite experiments are used to fill the gap from June 1989 to October 1991 between ACRIM1 and ACRIM2 experiments. The climate implications of the alternative satellite composites are discussed by using a phenomenological climate model for reconstructing the total solar irradiance signature on climate during the last four centuries.

astro-ph.SR↗

arXiv2005

Multiscaling comparative analysis of time series and geophysical phenomena

Different methods are used to determine the scaling exponents associated with a time series describing a complex dynamical process, such as those observed in geophysical systems. Many of these methods are based on the numerical evaluation of the variance of a diffusion process whose step increments are generated by the data. An alternative method focuses on the direct evaluation of the scaling coefficient of the Shannon entropy of the same diffusion distribution. The combined use of these methods can efficiently distinguish between fractal Gaussian and Lévy-walk time series and help to discern between alternative underling complex dynamics.

physics.geo-ph↗

arXiv2005

Estimate solar contribution to the global surface warming using the ACRIM TSI satellite composite

We study, by using a wavelet decomposition methodology, the solar signature on global surface temperature data using the ACRIM total solar irradiance satellite composite by Willson and Mordvinov. These data present a +0.047%/decade trend between minima during solar cycles 21-23 (1980-2002). We estimate that the ACRIM upward trend might have minimally contributed $\sim$10-30% of the global surface temperature warming over the period 1980-2002.

physics.geo-ph↗

arXiv2005

Correlation regimes in fluctuations of fatigue crack growth

This paper investigates correlation properties of fluctuations in fatigue crack growth of polycrystalline materials, such as ductile alloys, that are commonly encountered in structures and machinery components of complex electromechanical systems. The model of crack damage measure indicates that the fluctuations of fatigue crack growth are characterized by strong correlation patterns within short time scales and are uncorrelated for larger time scales. The two correlation regimes suggest that the 7075-T6 aluminum alloy, analyzed in this paper, is characterized by a micro-structure which is responsible for an intermittent correlated dynamics of fatigue crack growth within a certain scale. The constitutive equations of the damage measure are built upon the physics of fracture mechanics and are substantiated by Karhunen-Loève decomposition of fatigue test data. Statistical orthogonality of the estimated damage measure and the resulting estimation error is demonstrated in a Hilbert space setting.

cond-mat.mtrl-sci↗

arXiv2005

A Multifractal Dynamical Model of Human Gait

Summary: Walking is regulated through the motorcontrol system (MCS). The MCS consists of a network of neurons from the central nervous system (CNS) and the intraspinal nervous system (INS), which is capable of producing a syncopated output. The coupling of the latter two systems produces a complex stride interval time series that is characterized by fractal and multifractal properties that depend upon several biological and stress constraints. It has been shown that: (i) the gait phenomenon is essentially a rhythmic cycle that obeys particular phase symmetries in the synchronized movement of the limbs; (ii) the fractal and multifractal nature of the stride interval fluctuations become slightly more pronounced under faster or slower paced frequencies relative to the normal paced frequency of a subject; (iii) the randomness of the fluctuations increases if subjects are asked to synchronize their gait with the frequency of a metronome or if the subjects are elderly or suffering from neurodegenerative disease. Here we present a new model, called the super central pattern generator, able to reproduce these known properties of walking and discuss the physiological and psychological interpretations of the model parameters.

cond-mat.dis-nn↗

arXiv2004

Complexity, multiresolution, non-stationarity and entropic scaling: Teen birth thermodynamics

This paper presents a statistical methodology for analyzing a complex phenomenon in which deterministic and scaling components are superimposed. Our approach is based on the wavelet multiresolution analysis combined with the scaling analysis of the entropy of a time series. The wavelet multiresolution analysis decomposes the signal in a {\it scale-by-scale} manner. The {\it scale-by-scale} decomposition generates smooth and detail curves which are evaluated and studied. A wavelet-based smoothing filtering is used to estimate the daily birth rate and conception rate during the year. The scaling analysis is based upon the Diffusion Entropy Analysis (DEA). The joint use of the DEA and the wavelet multiresolution analysis allows: 1) the separation of the deterministic and, therefore, non-scaling component from the scaling component of the signal; 2) the determination of the stochastic information characterizing the teen birth phenomenon at each time scale. The daily data cover the number of births to teens in Texas during the period from 1964 to 1999.

cond-mat.stat-mech↗

arXiv2004

Multi-Scaling Comparative Analysis of Time Series and a Discussion on "Earthquake Conversations" in California

Time series are characterized by complex memory and/or distribution patterns. In this letter we show that models obeying to different statistics may equally reproduce some pattern of a time series. In particular we discuss the difference between Lévy-walk and fractal Gaussian intermittent signals and show that the adoption of complementary scaling analysis techniques may be useful to distinguish the two cases. Finally, we apply this methodology to the earthquake occurrences in California and suggest the possibility that earthquake occurrences are described by a \textit{colored} (= `long-range correlated') Generalized Poisson model.

cond-mat.stat-mech↗

arXiv2004

An out-of-equilibrium model of the distributions of wealth

The distribution of wealth among the members of a society is herein assumed to result from two fundamental mechanisms, trade and investment. An empirical distribution of wealth shows an abrupt change between the low-medium range, that may be fitted by a non-monotonic function with an exponential-like tail such as a Gamma distribution, and the high wealth range, that is well fitted by a Pareto or inverse power-law function. We demonstrate that an appropriate trade-investment model, depending on three adjustable parameters associated with the total wealth of a society, a social differentiation among agents, and economic volatility referred to as investment can successfully reproduce the distribution of empirical wealth data in the low, medium and high ranges. Finally, we provide an economic interpretation of the mechanisms in the model and, in particular, we discuss the difference between Classical and Neoclassical theories regarding the concepts of {\it value} and {\it price}. We consider the importance that out-of-equilibrium trade transactions, where the prices differ from values, have in real economic societies.

cond-mat.stat-mech↗

arXiv2003

A Trade-Investment Model for Distribution of Wealth

Econophysics provides a strategy for understanding the potential mechanisms underlying the anomalous distribution of wealth found in real societies. We present a computational nonlinear stochastic model for the distribution of wealth that depends upon three parameters and two mechanisms: trade and investment. To avoid economic paradoxes, the trade mechanism is assumed to be related to the poorer trader's wealth and to statistically advantage the poorer of the two traders. The two mechanisms together are shown to generate a distribution that reproduces the full range of the empirical wealth distribution, and not only the inverse power-law tail that Pareto found in western societies at the end of the 19th century.

cond-mat.stat-mech↗

arXiv2003

An intensity-expansion method to treat non-stationary time series: an application to the distance between prime numbers

We study the fractal properties of the distances between consecutive primes. The distance sequence is found to be well described by a non-stationary exponential probability distribution. We propose an intensity-expansion method to treat this non-stationarity and we find that the statistics underlying the distance between consecutive primes is Gaussian and that, by transforming the distance sequence into a stationary one, the range of Gaussian randomness of the sequence increases.

cond-mat.stat-mech↗

arXiv2003

Multiresolution Diffusion Entropy Analysis of time series: an application to births to teenagers in Texas

The multiresolution diffusion entropy analysis is used to evaluate the stochastic information left in a time series after systematic removal of certain non-stationarities. This method allows us to establish whether the identified patterns are sufficient to capture all relevant information contained in a time series. If they do not, the method suggests the need for further interpretation to explain the residual memory in the signal. We apply the multiresolution diffusion entropy analysis to the daily count of births to teens in Texas from 1964 through 2000 because it is a typical example of a non-stationary time series, having an anomalous trend, an annual variation, as well as short time fluctuations. The analysis is repeated for the three main racial/ethnic groups in Texas (White, Hispanic and African American), as well as, to married and unmarried teens during the years from 1994 to 2000 and we study the differences that emerge among the groups.

cond-mat.stat-mech↗

arXiv2003

Influence of progressive central hypovolemia on multifractal dimension of cardiac interbeat intervals

We analyzed the heartbeat time series of 12 human subjects exposed to progressive central hypovolemia with lower body negative pressure. Two data processing techniques based on wavelet transforms were used to determine the change in the non-stationary nature of the time series with changing negative pressure. Our results suggest that autonomic neural mechanisms driving cardiac interbeat intervals during central hypovolemia go through various levels of multifractility.

cond-mat.stat-mech↗

arXiv2003

Solar Flare Intermittency and the Earth's Temperature Anomalies

We argue that earth's short-term temperature anomalies and the solar flare intermittency are linked. The analysis is based upon the study of the scaling of both the spreading and the entropy of the diffusion generated by the fluctuations of the temperature time series. The joint use of these two methods evidences the presence of a Lévy component in the temporal persistence of the temperature data sets that corresponds to the one that would be induced by the solar flare intermittency. The mean monthly temperature datasets cover the period from 1856 to 2002.

cond-mat.stat-mech↗

arXiv2002

A nonlinear dynamical model of human gait

We present a nonlinear stochastic model of the human gait control system in a variety of gait regimes. The stride interval time series in normal human gait is characterized by slightly multifractal fluctuations. The fractal nature of the fluctuations become more pronounced under both an increase and decrease in the average gait. Moreover, the long-range memory in these fluctuations is lost when the gait is keyed on a metronome. The human locomotion is controlled by a network of neurons capable of producing a correlated syncopated output. The central nervous system is coupled to the motocontrol system, and together they control the locomotion of the gait cycle itself. The metronomic gait is simulated by a forced nonlinear oscillator with a periodic external force associated with the conscious act of walking in a particular way.

cond-mat.stat-mech↗

arXiv2002

Pareto's law: a model of human sharing and creativity

A computational model for the distribution of wealth among the members of an ideal society is presented. It is determined that a realistic distribution of wealth depends upon two mechanisms: an asymmetric flux of wealth in trading transactions that advantages the poorer of the two traders and a non-stationary creation and destruction of individual wealth. The former mechanism redistributes wealth by reducing the gap between the rich and poor, leading to the emergence of a middle class. The latter mechanism, together with the former one, generates a distribution of wealth having a power-law tail that is compatible with Pareto's law.

cond-mat.stat-mech↗

arXiv2002

Statistical analysis of air and sea temperature anomalies

This paper presents a global air and sea temperature anomalies analysis based upon a combination of the wavelet multiresolution analysis and the scaling analysis methods of a time series. The wavelet multiresolution analysis decomposes the two temperature signals on a scale-by-scale basis. The scale-by-scale smooth and detail curves are compared and the correlation coefficients between each couple of correspondent sets of data evaluated. The scaling analysis is based upon the study of the spreading and the entropy of the diffusion generated by the temperature signals. Therefore, we jointly adopt two distinct methods: the Diffusion Entropy Analysis (DEA) and the Standard Deviation Analysis (SDA). The joint use of these two methods allows us to establish with more confidence the nature of the signals, as well as their scaling, and it yields the discovery of a slight Levy component in the two temperature data sets. Finally, the DEA and SDA are used to study the wavelet residuals of the two temperature anomalies. The temporal regions of persistence and antipersistence of the signals are determined and the non-stationary effect of the 10-11 year solar cycle upon the temperature is studied. The temperature monthly data cover the period from 1860 to 2000 A.D.E.

cond-mat.stat-mech↗

arXiv2002

Lévy scaling: the Diffusion Entropy Analysis applied to DNA sequences

We address the problem of the statistical analysis of a time series generated by complex dynamics with a new method: the Diffusion Entropy Analysis (DEA) (Fractals, {\bf 9}, 193 (2001)). This method is based on the evaluation of the Shannon entropy of the diffusion process generated by the time series imagined as a physical source of fluctuations, rather than on the measurement of the variance of this diffusion process, as done with the traditional methods. We compare the DEA to the traditional methods of scaling detection and we prove that the DEA is the only method that always yields the correct scaling value, if the scaling condition applies. Furthermore, DEA detects the real scaling of a time series without requiring any form of de-trending. We show that the joint use of DEA and variance method allows to assess whether a time series is characterized by Lévy or Gauss statistics. We apply the DEA to the study of DNA sequences, and we prove that their large-time scales are characterized by Lévy statistics, regardless of whether they are coding or non-coding sequences. We show that the DEA is a reliable technique and, at the same time, we use it to confirm the validity of the dynamic approach to the DNA sequences, proposed in earlier work.

cond-mat.stat-mech↗