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Yosef Ashkenazy

Publications and source records attributed to Yosef Ashkenazy.

At least 37 records · Page 2Linked to original sources

Possible Origin of Memory in Earthquakes: Real catalogs and ETAS model

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.

physics.geo-ph↗

Climate network percolation reveals the expansion and weakening of the tropical component under global warming

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.

physics.soc-ph↗

Forecasting the magnitude and onset of El Nino based on climate network

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.

physics.geo-ph↗

Scale-free distribution of Dead Sea sinkholes--observations and modeling

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.

physics.geo-ph↗

Percolation framework to describe El Niño conditions

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 Niño ~ events, suggesting that they can be used as early warning precursors of El Niño. Using this method we analyze several reanalysis datasets and show the potential for good forecasting of El Niño. The percolation based order parameter exhibits discontinuous features, indicating possible relation to the first order phase transition mechanism.

physics.ao-ph↗

Climate network suggests enhanced El Niño global impacts in localized areas

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.

physics.ao-ph↗

Oceanic El-Niño wave dynamics and climate networks

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.

physics.ao-ph↗

The effects of psammophilous plants on sand dune dynamics

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.

physics.bio-ph↗

The relation between the statistics of open ocean currents and the temporal correlations of the wind

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.

physics.ao-ph↗

The dominant imprint of Rossby waves in the climate network

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.

physics.ao-ph↗

Biogenic crust dynamics on sand dunes

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.

physics.bio-ph↗

Volatility of Linear and Nonlinear Time Series

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.

cond-mat.stat-mech↗

Multifractal Properties of Price Fluctuations of Stocks and Commodities

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.

cond-mat.stat-mech↗

Relation between Magnitude Series Correlations and Multifractal Spectrum Width

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.

cond-mat↗

Nonlinear Volatility of River Flux Fluctuations

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.

cond-mat.stat-mech↗

Characterization of Sleep Stages by Correlations of Heartbeat Increments

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.

cond-mat.stat-mech↗

Nonlinearity and Multifractality of Climate Change in the Past 420,000 Years

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.

cond-mat.stat-mech↗

Correlation Differences in Heartbeat Fluctuations During Rest and Exercise

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.

cond-mat.stat-mech↗