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The Gulf of Eilat (Gulf of Aqaba) is a semi-enclosed basin situated at the northern end of the Red Sea, renowned for its exceptional marine ecosystem. To evaluate the response of the Gulf to climate variations, we analyzed various factors including temperature down to 700 m, surface air temperature, and heat fluxes. We find that the sea temperature is rising at all depths despite inconclusive trends in local atmospheric variables, including the surface air temperature. The Gulf's sea surface temperature warms at a rate of a few hundredths of a degree Celsius per year, which is comparable to the warming of the global sea surface temperature and the Mediterranean Sea. The increase in sea warming is linked to fewer winter deep mixing events that used to occur more frequently in the past. Based on the analysis of the ocean-atmosphere heat fluxes, we conclude that the lateral advection of heat from the southern part of the Gulf likely leads to an increase in water temperature in the northern part of the Gulf. Our findings suggest that local ocean warming is not necessarily associated with local processes, but rather with the warming of other remote locations.

Rainfall is an important component of the climate system and its statistical properties are vital for prediction purposes. In this study, we have developed a statistical method for constructing the distribution of annual precipitation. The method is based on the convolution of the measured monthly rainfall distributions and does not depend on any presumed annual rainfall distribution. Using a simple statistical model, we demonstrate that our approach allows for a better prediction of extremely dry or wet years with a recurrence time several times longer than the original time series. The method that has been proposed can be utilized for other climate variables as well., Comment: 26 pages, 18 figures

Mainshocks are often followed by increased earthquake activity (aftershocks). According to the Omori-Utsu law, the rate of aftershocks decays as a power law over time. While aftershocks typically occur in the vicinity of the mainshock, previous studies have suggested that mainshocks can also trigger earthquakes in remote locations. Here we examine the earthquake rate in the days following mega-earthquakes (magnitude >= 7.5) and find that the rate is significantly lower beyond a certain distance from the epicenter compared to surrogate data. However, the remote earthquake rate after the strongest earthquakes (magnitude >= 8) can also be significantly higher than that of the rate based on surrogate data. Comparing our findings to the global ETAS model, we find that the model does not capture the earthquake rate found in the data, hinting at a potential missing mechanism. We suggest that the diminished earthquake rate is due the release of global energy/tension subsequent to substantial mainshock events. This conjecture holds the potential to enhance our comprehension of the intricacies governing post-seismic activity.

Earthquakes are a major threat to nations worldwide. Earthquake detection is an important scientific challenge, not only for its social impacts, but also since it reflects the actual degree of understanding of the physical processes controlling seismic events. We propose an approach for evaluating and understanding the dynamics of seismic events. The approach is based on the phase between the waveform signals of many stations, enabling detecting the evolution of relatively small magnitudes, down to Mw 1.3. We constructed a time-evolving network in which the network nodes are the stations, while the links are the level of correspondence between the stations' signals. The links' weights are quantified using the following statistical methods: cross-correlation, synchronization, mutual information, and coherence. Each of these methods reflects a different aspect of the phase relations between the waveforms of different stations in a given time window. We then developed global measures to study the properties of the time-evolving network of seismic activity. The global measures include the leading eigenvalues of the network links, the number of links above a certain threshold, and k-means clustering. We show that the network and its corresponding global measures vary significantly during seismic events. The results are based on detailed waveform station data and detailed catalogs from Southern California; our analysis focused on 27 mainshocks, during which we examined one-day data prior to the occurrence of the mainshock, as well as one hour of data following it. Among all the measures we investigated, we found that the coherence measure using the k-means clustering procedure exhibits the best performance. This technique correctly identifies earthquake events with magnitudes larger than 2.5 and exhibits moderate performance for weaker earthquakes with magnitudes larger than 1.3.

According to the Omori-Utsu law, the rate of aftershocks after a mainshock decays as a power law with an exponent close to 1. This well-established law was intensively used in the past to study and model the statistical properties of earthquakes. Moreover, according to the so-called inverse Omori law, the rate of earthquakes should also increase prior to a mainshock -- this law has received much less attention due to its large uncertainty. Here, we mainly study the inverse Omori law based on a highly detailed Southern California earthquake catalog, which is complete for magnitudes larger than M>0.3. First, we develop a technique to identify mainshocks, foreshocks, and aftershocks. We then find, based on a statistical procedure we developed, that the rate of earthquakes is higher a few days prior to a mainshock. We find that this increase is much smaller for a catalog with a magnitude threshold of m over 2.5 and for the Epidemic-Type Aftershocks Sequence (ETAS) model catalogs, even when used with a small magnitude threshold. We also analyze the rate of aftershocks after mainshocks and find that the Omori-Utsu law does not hold for many individual mainshocks and that it may be valid only statistically when considering many mainshocks together. Yet, the analysis of the ETAS model based on the Omori-Utsu law exhibits similar behavior as that of the real catalogs, indicating the validity of this law., Comment: 19 pages, 7 figures

It has been suggested that the ice shell of Jupiter's moon Europa may drift non-synchronously due to tidal torques. Here we argue that torques applied by the underlying ocean are also important and can result in non-synchronous rotation (NSR). The resulting spin rate can be slightly slower than the synchronous angular rate that would have kept the same point of the ice shell facing Jupiter. We develop an ice shell rotation model, driven by ocean stress calculated using a high-resolution state-of-the-art ocean general circulation model, and take into account the viscoelastic deformation of the ice shell. We use the ice shell model results together with observed limits on the ice shell drift speed to constrain ice shell parameters such as effective viscosity, which is currently uncertain by at least four orders of magnitude. Our results suggest, at best, sluggish ice shell convection. Depending on the relaxation time scale of the ice shell and on the ocean currents, the ice shell may exhibit negligible drift, constant drift, or oscillatory drift superimposed on random fluctuations. The expected rotation rate exceeds $\sim$30~m/yr; future spacecraft observations can be used to test these predictions and yield insight into the properties of the ice shell and underlying ocean.

Winds play an essential role in the climate system. In this study, we analyze the global pattern of the diurnal cycle of surface (10 m) winds from the ERA5 reanalysis data. We find that over the land and especially over sand dune regions, the maximal wind speed and wind drift potential (DP) occur during the hours around midday. However, over the ocean, the wind also peaks at night. Using the sensible heat flux, we show that the weaker winds over land at night are due to a nocturnal cooling that decouples upper atmospheric levels and their associated stronger winds from the surface -- nocturnal cooling is much smaller over the ocean. We also analyze wind data from more than 400 meteorological stations in the USA and find a similar diurnal trend as in the reanalysis data. The timing (during the day) of the maximum wind speed has not varied much over the past 70 years. Yet, the wind speed, wind power, and wind drift potential exhibit significant increases with time over the ocean and, to a much lesser degree, over the land and sand dune regions. We compare the USA and Europe DP and wind speed of the ERA5 to that of meteorological stations and find that the ERA5 significantly underestimates real winds; however, the temporal patterns of the two are similar.

Here we focus on a basic statistical measure of earthquake catalogs that has not been studied before, the asymmetry of interevent time series (e.g., reflecting the tendency to have more aftershocks than spontaneous earthquakes). We define the asymmetry metric as the ratio between the number of positive interevent time increments minus negative increments and the total (positive plus negative) number of increments. Such asymmetry commonly exists in time series data for non-linear geophysical systems like river flow which decays slowly and increases rapidly. We find that earthquake interevent time series are significantly asymmetric, where the asymmetry function exhibits a significant crossover to weak asymmetry at large lag-index. We suggest that the Omori law can be associated with the large asymmetry at short time intervals below the crossover whereas overlapping aftershock sequences and the spontaneous events can be associated with a fast decay of asymmetry above the crossover. We show that the asymmetry is better reproduced by a recently modified ETAS model with two triggering processes in comparison to the standard ETAS model which only has one.

We propose a model to study the spatiotemporal dynamics of biocrust and vegetation cover on sand dunes. The model consists of two coupled partial nonlinear differential equations and includes diffusion and advection terms for modeling the dispersal of vegetation and biocrust and the effect of wind on them. In the absence of spatial variability, the model exhibits self-sustained relaxation oscillations and regimes of bistability--the first state is dominated by biocrust and the second by vegetation. We concentrate on the one-dimensional dynamics of the model and show that the front that connects these two states propagates mainly due to the wind advection. In the oscillatory regime, the front propagation is complex. For low wind DP (drift potential) values, a series of spatially oscillatory domains develops as the front advances downwind. These domains form due to the oscillations of the spatially homogeneous states away from the front. However, for higher DP values, the dynamics is much more complex, becoming very sensitive to the initial conditions and exhibiting an irregular spatial pattern as small domains are created and annihilated during the front advance. Such irregular dynamics can be associated with the temporal variations of dune cover. In addition, similar behavior can be generated by other models that exhibit temporal oscillations and bistability., Comment: 20 pages, 11 figures

Global climate change, extreme climate events, earthquakes and their accompanying natural disasters pose significant risks to humanity. Yet due to the nonlinear feedbacks, strategic interactions and complex structure of the Earth system, the understanding and in particular the predicting of such disruptive events represent formidable challenges for both scientific and policy communities. During the past years, the emergence and evolution of Earth system science has attracted much attention and produced new concepts and frameworks. Especially, novel statistical physics and complex networks-based techniques have been developed and implemented to substantially advance our knowledge for a better understanding of the Earth system, including climate extreme events, earthquakes and Earth geometric relief features, leading to substantially improved predictive performances. We present here a comprehensive review on the recent scientific progress in the development and application of how combined statistical physics and complex systems science approaches such as, critical phenomena, network theory, percolation, tipping points analysis, as well as entropy can be applied to complex Earth systems (climate, earthquakes, etc.). Notably, these integrating tools and approaches provide new insights and perspectives for understanding the dynamics of the Earth systems. The overall aim of this review is to offer readers the knowledge on how statistical physics approaches can be useful in the field of Earth system science.

The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, is currently spreading rapidly throughout the world, causing many deaths and severe economic damage. It is believed that hot and humid conditions do not favor the novel coronavirus, yet this is still under debate due to many uncertainties associated with the COVID-19 data. Here we propose surrogate data tests to examine the preference of this virus to spread under different climate conditions. We find that the disease is significantly (above the 95% confidence level) more common when the temperature is $\sim$10$^\circ$C, the specific humidity is $\sim$5 g/kg, and the ultraviolet (UV) radiation is $\sim$80 kJ/m$^2$. The significance of relative humidity is below the 95% confidence level and does not show a preferred value. The results are supported using global and regional data, spanning the time period from January to July 2020. The COVID-19 data includes the daily reported new cases and daily death cases; for both, the population size is either taken into account or ignored.

The deep (~100 km) ocean of Europa, Jupiter's moon, covered by a thick icy shell, is one of the most probable places in the solar system to find extraterrestrial life. Yet, its ocean dynamics and its interaction with the ice cover have received little attention. Previous studies suggested that Europa's ocean is turbulent using a global model and taking into account non-hydrostatic effects and the full Coriolis force. Here we add critical elements, including consistent top and bottom heating boundary conditions and the effects of icy shell melting and freezing on ocean salinity. We find weak stratification that is dominated by salinity variations. The ocean exhibits strong transient convection, eddies, and zonal jets. Transient motions organize in Taylor columns parallel to Europa's axis of rotation, are static outside of the tangent cylinder and propagate equatorward within the cylinder. The meridional oceanic heat transport is intense enough to result in a nearly uniform ice thickness, that is expected to be observable in future missions., Comment: There are two animation files associated with the paper: T-surface-movie-d-all.gif, convection-movie-d-all.gif. The paper is in press in Nature Communications

A prominent feature of earthquakes is their empirical laws including memory (clustering) in time and space. Several earthquake forecasting models, like the EpidemicType Aftershock Sequence (ETAS) model, were developed based on earthquake empirical laws. Yet, a recent study showed that the ETAS model fails in reproducing significant long-term memory characteristics found in real earthquake catalogs. Here we modify and generalize the ETAS model to include short- and long-term triggering mechanisms, to account for the short- and long-time memory (exponents) recently discovered in the data. Our generalized ETAS model reproduces accurately the short- and long-term/distance memory observed in the Italian and South California earthquake catalogs. The revised ETAS model is also found to significantly improve earthquake forecasting.

Over the past decades much effort has been devoted towards understanding and forecasting natural hazards. However, earthquake forecasting skill is still very limited and remains a great scientific challenge. The limited earthquake predictability is partly due to the erratic nature of earthquakes and partly to the lack of understanding the underlying mechanisms of earthquakes. To improve our understanding and potential forecasting, here we study the spatial and temporal long-term memory of interevent earthquakes above a certain magnitude using lagged conditional probabilities. We find, in real data, that the lagged conditional probabilities show long-term memory for both the interevent times and interevent distances and that the memory functions obey scaling and decay slowly with time, while, at a characteristic time, the decay crossesover to a fast decay. We also show that the ETAS model, which is often used to forecast earthquake events, yields scaling functions of the temporal and spatial interevent intervals which are not consistent with those of real data.

Air pollution is associated with human diseases and has been found to be related to premature mortality. In response, environmental policies have been adopted in many countries, to decrease anthropogenic air pollution for the improvement of long-term air quality, since most air pollutant sources are anthropogenic. However, air pollution fluctuations have been found to strongly depend on the weather dynamics. This raises a fundamental question: What are the significant atmospheric processes that affect the local daily variability of air pollution? For this purpose, we develop here a multi-layered network analysis to detect the interlinks between the geopotential height of the upper air (~5 km) and surface air pollution in both China and the USA. We find that Rossby waves significantly affect air pollution fluctuations through the development of cyclone and anticyclone systems, and further affect the local stability of the air and the winds. The significant impacts of Rossby waves on air pollution are found to underlie most of the daily fluctuations in air pollution. Thus, the impact of Rossby waves on human life is greater than previously assumed. The rapid warming of the Arctic could slow down Rossby waves, thus increasing human health risks. Our method can help to determine the risk assessment of such extreme events and can improve potential predictability.

Earthquakes are one of the most devastating natural disasters that plague society. A skilled, reliable earthquake forecasting remains the ultimate goal for seismologists. Using the detrended fluctuation analysis (DFA) and conditional probability (CP) methods we find that memory exists not only in inter-occurrence seismic records, but also in released energy as well as in the series of the number of events per unit time. Analysis of the conventional earthquake model (Epidemic Type Aftershock Sequences, ETAS) indicates that earthquake memory can be reproduced only for a narrow range of model's parameters. This finding, therefore provides additional accuracy on the model parameters through tight restrictions on their values in different worldwide regions and can serve as a testbed for existing earthquake forecasting models.

Global climate warming poses a significant challenge to humanity; it is associated with, e.g., rising sea level and declining Arctic sea ice. Increasing extreme events are also considered to be a result of climate warming~\cite{pachauri2014climate,ogorman_contrasting_2014} and they may have widespread and diverse effects on health, agriculture, economics and political conflicts \cite{hsiang2011civil,helbing_globally_2013,schleussner_armed-conflict_2016,carleton_social_2016}. Still, the detection and quantification of climate change, both in observations and climate models, constitute a main focus of the scientific community. Here we develop a new approach based on network and percolation frameworks \cite{cohen2010complex} to study the impacts of climate changes in the past decades using historical models and reanalysis records, and we analyze the expected upcoming impacts using various future global warming scenarios. More specifically, we classify the globe area into evolving percolation clusters and find a discontinuous phase transition, which indicates a consistent poleward expansion of the largest (tropical) cluster, as well as the weakening of the link's strength. This is found both in the reanalysis data and in the Coupled Model Intercomparison Project Phase 5 (CMIP5) twenty-first century climate change simulations~\cite{taylor2012overview}. The analysis is based on high resolution surface (2~m) air temperature field records. We discuss the underlying mechanism for the observed expansion of the tropical cluster and associate it with changes in atmospheric circulation represented by the weakening and expansion of the Hadley cell. Our framework can also be useful for forecasting the extent of the tropical cluster in order to detect its influence on different areas in response to global warming.

El Nino is probably the most influential climate phenomenon on interannual time scales. It affects the global climate system and is associated with natural disasters and serious consequences in many aspects of human life. However, the forecasting of the onset and in particular the magnitude of El Nino are still not accurate, at least more than half a year in advance. Here, we introduce a new forecasting index based on network links representing the similarity of low frequency temporal temperature anomaly variations between different sites in the El Nino 3.4 region. We find that significant upward trends and peaks in this index forecast with high accuracy both the onset and magnitude of El Nino approximately 1 year ahead. The forecasting procedure we developed improves in particular the prediction of the magnitude of El Nino and is validated based on several, up to more than a century long, datasets.

Complex networks have been used intensively to investigate the flow and dynamics of many natural systems including the climate system. Here, we develop a percolation based measure, the order parameter, to study and quantify climate networks. We find that abrupt transitions of the order parameter usually occur $\sim$1 year before El Ni\~{n}o ~ events, suggesting that they can be used as early warning precursors of El Ni\~{n}o. Using this method we analyze several reanalysis datasets and show the potential for good forecasting of El Ni\~{n}o. The percolation based order parameter exhibits discontinuous features, indicating possible relation to the first order phase transition mechanism.

There are currently more than 5500 sinkholes along the Dead Sea in Israel. These were formed due to the dissolution of subsurface salt layers as a result of the replacement of hypersaline groundwater by fresh brackish groundwater. This process has been associated with a sharp decline in the Dead Sea water level, currently more than one meter per year, resulting in a lower water table that has allowed the intrusion of fresher brackish water. We studied the distribution of the sinkhole sizes and found that it is scale-free with a power-law exponent close to 2. We constructed a stochastic cellular automata model to understand the observed scale-free behavior and the growth of the sinkhole area in time. The model consists of a lower salt layer and an upper soil layer in which cavities that develop in the lower layer lead to collapses in the upper layer. The model reproduces the observed power-law distribution without involving the threshold behavior commonly associated with criticality.

We construct directed and weighted climate networks based on near surface air temperature to investigate the global impacts of El Nino and La Nina. We find that regions which are characterized by higher positive or negative network in weighted links, are exhibiting stronger correlations with the El Nino basin and are warmer or cooler during El Nino or La Nina periods. These stronger in-weighted activities are found to be concentrated in localized areas, as compared to non-El Nino periods, whereas a large fraction of the globe is not influenced by the events. The regions of localized activity vary from one El Nino (La Nina) event to another; still some El Nino (La Nina) events are more similar to each other. We quantify this similarity using network community structure. The results and methodology reported here may be used to improve the understanding and prediction of El Nino or La Nina events and also may be applied in the investigation of other climate variables.

Previous estimates of the annual mean surface temperature of Jupiter's moon, Europa, neglected the effect of the eccentricity of Jupiter's orbit around the Sun, the effect of the emissivity and heat capacity of Europa's ice, the effect of the eclipse of Europa (i.e., the relative time that Europa is within the shadow of Jupiter), the effect of Jupiter's radiation, and the effect of Europa's internal heating. Other studies concentrated on the diurnal cycle but neglected some of the above factors. In addition, to our knowledge, the seasonal cycle of the surface temperature of Europa was not estimated. Here we systematically estimate the diurnal, seasonal and annual mean surface temperature of Europa, when Europa's obliquity, emissivity, heat capacity, and eclipse, as well as Jupiter's radiation, internal heating, and eccentricity, are all taken into account. For a typical internal heating rate of 0.05 W m$^{-2}$, the equator, pole, and the global and mean annual mean surface temperatures are 96K, 46K, and 90K, respectively. We found that the temperature at the high latitudes is significantly affected by the internal heating, especially during the winter solstice, suggesting that measurements of high latitude surface temperatures can be used to constrain the internal heating. We also estimate the incoming solar radiation to Enceladus, the moon of Saturn.

Oceanic Kelvin and Rossby waves play an important role in tropical climate and \en dynamics. Here we develop and apply a climate network approach to quantify the characteristics of \en related oceanic waves, based on sea surface height satellite data. We associate the majority of dominant long distance ($\geq 500$ km) links of the network with (i) equatorial Kelvin waves, (ii) off-equatorial Rossby waves, and (iii) tropical instability waves. Notably, we find that the location of the hubs of out-going ($\sim 180^{\circ}\rm{E}$) and in-coming ($\sim 140^{\circ}\rm{W}$) links of the climate network coincide with the locations of the Kelvin wave initiation and dissipation, respectively. We also find that this dissipation at $\sim 140^{\circ}\rm{W}$ is much weaker during \en times. Moreover, the hubs of the off-equatorial network coincide with the locations of westerly wind burst activity and high wind vorticity, two mechanisms that were associated with Rossby waves activity. The quantitative methodology and measures developed here can improve the understanding of \en dynamics and possibly its prediction., Comment: 4 pages, 4 figures

Psammophilous plants are special plants that flourish in sand moving environments. There are two main mechanisms by which the wind affects these plants: (i) sand drift exposes roots and covers branches--the exposed roots turn into new plants and the covered branches turn into new roots; both mechanisms result in an enhanced growth rate of the psammophilous plant cover of the dunes; (ii) strong winds, often associated with sand movement, tear branches and seed them in nearby locations, resulting in new plants and an enhanced growth rate of the psammophilous plant cover of the dunes. Despite their important role in dune dynamics, to our knowledge, psammophilous plants have never been incorporated into mathematical models of sand dunes. Here, we attempt to model the effects of these plants on sand dune dynamics. We construct a set of three ordinary differential equations for the fractions of surface cover of regular vegetation, biogenic soil crust and psammophilous plants. The latter reach their optimal growth under (i) specific sand drift or (ii) specific wind power. We show that psammophilous plants enrich the sand dune dynamics. Depending on the climatological conditions, it is possible to obtain one, two, or three steady dune states. The activity of the dunes can be associated with the surface cover--bare dunes are active, and dunes with significant cover of vegetation, biogenic soil crust, or psammophilous plants are fixed. Our model shows that under suitable precipitation rates and wind power, the dynamics of the different cover types is in accordance with the common view that dunes are initially stabilized by psammophilous plants that reduce sand activity, thus enhancing the growth of regular vegetation that eventually dominates the cover of the dunes and determines their activity.

The connectivity pattern of networks, which are based on a correlation between ground level temperature time series, shows a dominant dense stripe of links in the southern ocean. We show that statistical categorization of these links yields a clear association with the pattern of an atmospheric Rossby wave, one of the major mechanisms associated with the weather system and with planetary scale energy transport. It is shown that alternating densities of negative and positive links (correlations) are arranged in half Rossby wave distances around 3,500 km, 7,000 km and 10,000 km and are aligned with the expected direction of energy flow, distribution of time delays and the seasonality of these waves. It is also shown that long distance links (i.e., of distances larger than 2,000 km) that are associated with Rossby waves are the most dominant in the climate network. Climate networks may thus be used as an efficient new way to detect and analyze Rossby waves, based on reliable and available ground level measurements, in addition to the frequently used 300 hPa reanalysis meridional wind data., Comment: 5 pages, 5 figures

Sand dunes are often covered by vegetation and biogenic crusts. Despite their significant role in dune stabilization, biogenic crusts have rarely been considered in studies of dune dynamics. Using a simple model, we study the existence and stability ranges of different dune-cover states along gradients of rainfall and wind power. Two ranges of alternative stable states are identified: fixed crusted dunes and fixed vegetated dunes at low wind power, and fixed vegetated dunes and active dunes at high wind power. These results suggest a cross-over between two different forms of desertification.

We study the statistics of wind-driven open ocean currents. Using the Ekman layer model for the integrated currents, we investigate, analytically and numerically, the relation between the wind distribution and its temporal correlations and the statistics of the open ocean currents. We find that temporally long-range correlated wind results in currents whose statistics is proportional to the wind-stress statistics. On the other hand, short-range correlated wind leads to Gaussian distributions of the current components, regardless of the stationary distribution of the winds, and therefore, to a Rayleigh distribution of the current amplitude if the wind stress is isotropic. An interesting result is the existence of an optimum in the amplitude of the ocean currents as a function of the correlation time of the wind stress. The results were validated using an oceanic general circulation model.

Previous studies indicate that nonlinear properties of Gaussian time series with long-range correlations, $u_i$, can be detected and quantified by studying the correlations in the magnitude series $|u_i|$, i.e., the ``volatility''. However, the origin for this empirical observation still remains unclear, and the exact relation between the correlations in $u_i$ and the correlations in $|u_i|$ is still unknown. Here we find analytical relations between the scaling exponent of linear series $u_i$ and its magnitude series $|u_i|$. Moreover, we find that nonlinear time series exhibit stronger (or the same) correlations in the magnitude time series compared to linear time series with the same two-point correlations. Based on these results we propose a simple model that generates multifractal time series by explicitly inserting long range correlations in the magnitude series; the nonlinear multifractal time series is generated by multiplying a long-range correlated time series (that represents the magnitude series) with uncorrelated time series [that represents the sign series $sgn(u_i)$]. Our results of magnitude series correlations may help to identify linear and nonlinear processes in experimental records., Comment: 7 pages, 5 figures

We analyze daily prices of 29 commodities and 2449 stocks, each over a period of $\approx 15$ years. We find that the price fluctuations for commodities have a significantly broader multifractal spectrum than for stocks. We also propose that multifractal properties of both stocks and commodities can be attributed mainly to the broad probability distribution of price fluctuations and secondarily to their temporal organization. Furthermore, we propose that, for commodities, stronger higher order correlations in price fluctuations result in broader multifractal spectra., Comment: Published in Euro Physics Letters (14 pages, 5 figures)

We study the spectral properties of the magnitudes of river flux increments, the volatility. The volatility series exhibits (i) strong seasonal periodicity and (ii) strongly power-law correlations for time scales less than one year. We test the nonlinear properties of the river flux increment series by randomizing its Fourier phases and find that the surrogate volatility series (i) has almost no seasonal periodicity and (ii) is weakly correlated for time scales less than one year. We quantify the degree of nonlinearity by measuring (i) the amplitude of the power spectrum at the seasonal peak and (ii) the correlation power-law exponent of the volatility series., Comment: 5 revtex pages, 6 pages

Evidence of past climate variations are stored in ice and indicate glacial-interglacial cycles characterized by three dominant time periods of 20kyr, 40kyr, and 100kyr. We study the scaling properties of temperature proxy records of four ice cores from Antarctica and Greenland. These series are long-range correlated in the time scales of 1-100kyr. We show that these series are nonlinear as expressed by volatility correlations and a broad multifractal spectrum. We present a stochastic model that captures the scaling and the nonlinear properties observed in the data., Comment: 4 revtex pages, 4 figures, 1 table

We study the correlation properties of long-range correlated time series, $x_i$, with tunable correlation exponent and built-in multifractal properties. For the cases we investigate we find that the correlation exponent of the magnitude series, $|x_{i+1}-x_i|$, is a monotonically increasing function of the multifractal spectrum width of the original series., Comment: 4 revtex pages, 3 figures

We study the heartbeat activity of healthy individuals at rest and during exercise. We focus on correlation properties of the intervals formed by successive peaks in the pulse wave and find significant scaling differences between rest and exercise. For exercise the interval series is anticorrelated at short time scales and correlated at intermediate time scales, while for rest we observe the opposite crossover pattern -- from strong correlations in the short-time regime to weaker correlations at larger scales. We suggest a physiologically motivated stochastic scenario to explain the scaling differences between rest and exercise and the observed crossover patterns., Comment: 4 pages, 4 figures

We present a stochastic model of gait rhythm dynamics, based on transitions between different ``neural centers'', that reproduces distinctive statistical properties of normal human walking. By tuning one model parameter, the hopping range, the model can describe alterations in gait dynamics from childhood to adulthood --- including a decrease in the correlation and volatility exponents with maturation. The model also generates time series with multifractal spectra whose broadness depends only on this parameter., Comment: 4 revtex pages, 6 figures

We study correlation properties of the magnitude and the sign of the increments in the time intervals between successive heartbeats during light sleep, deep sleep, and REM sleep using the detrended fluctuation analysis method. We find short-range anticorrelations in the sign time series, which are strong during deep sleep, weaker during light sleep and even weaker during REM sleep. In contrast, we find long-range positive correlations in the magnitude time series, which are strong during REM sleep and weaker during light sleep. We observe uncorrelated behavior for the magnitude during deep sleep. Since the magnitude series relates to the nonlinear properties of the original time series, while the signs series relates to the linear properties, our findings suggest that the nonlinear properties of the heartbeat dynamics are more pronounced during REM sleep. Thus, the sign and the magnitude series provide information which is useful in distinguishing between the sleep stages., Comment: 7 pages, 4 figures, revtex

Patients at high risk for sudden death often exhibit complex heart rhythms in which abnormal heartbeats are interspersed with normal heartbeats. We analyze such a complex rhythm in a single patient over a 12-hour period and show that the rhythm can be described by a theoretical model consisting of two interacting oscillators with stochastic elements. By varying the magnitude of the noise, we show that for an intermediate level of noise, the model gives best agreement with key statistical features of the dynamics., Comment: 4 pages, 4 figures, RevTeX