Articles de revista
http://hdl.handle.net/2117/3215
2017-05-26T15:13:36ZInferring directed climatic interactions with renormalized partial directed coherence and directed partial correlation
http://hdl.handle.net/2117/104930
Inferring directed climatic interactions with renormalized partial directed coherence and directed partial correlation
Tirabassi, Giulio; Sommerlade, Linda; Masoller Alonso, Cristina
Inferring interactions between processes promises deeper insight into mechanisms underlying network phenomena. Renormalised partial directed coherence is a frequency-domain representation of the concept of Granger causality, while directed partial correlation is an alternative approach for quantifying Granger causality in the time domain. Both methodologies have been successfully applied to neurophysiological signals for detecting directed relationships. This paper introduces their application to climatological time series. We first discuss the application to El Niño–Southern Oscillation—Monsoon interaction and then apply the methodologies to the more challenging air-sea interaction in the South Atlantic Convergence Zone (SACZ). In the first case, the results obtained are fully consistent with the present knowledge in climate modeling, while in the second case, the results are, as expected, less clear, and to fully elucidate the SACZ air-sea interaction, further investigations on the specificity and sensitivity of these methodologies are needed.
Copyright 2017 AIP Publishing. This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing
2017-05-26T13:07:13ZTirabassi, GiulioSommerlade, LindaMasoller Alonso, CristinaInferring interactions between processes promises deeper insight into mechanisms underlying network phenomena. Renormalised partial directed coherence is a frequency-domain representation of the concept of Granger causality, while directed partial correlation is an alternative approach for quantifying Granger causality in the time domain. Both methodologies have been successfully applied to neurophysiological signals for detecting directed relationships. This paper introduces their application to climatological time series. We first discuss the application to El Niño–Southern Oscillation—Monsoon interaction and then apply the methodologies to the more challenging air-sea interaction in the South Atlantic Convergence Zone (SACZ). In the first case, the results obtained are fully consistent with the present knowledge in climate modeling, while in the second case, the results are, as expected, less clear, and to fully elucidate the SACZ air-sea interaction, further investigations on the specificity and sensitivity of these methodologies are needed.Identifying large-scale patterns of unpredictability and response to insolation in atmospheric data
http://hdl.handle.net/2117/104370
Identifying large-scale patterns of unpredictability and response to insolation in atmospheric data
Arizmendi, Fernando; Barreiro, Marcelo; Masoller Alonso, Cristina
Understanding the complex dynamics of the atmosphere is of paramount interest due to its impact in the entire climate system and in human society. Here we focus on identifying, from data, the geographical regions which have similar atmospheric properties. We study surface air temperature (SAT) time series with monthly resolution, recorded at a regular grid covering the Earth surface. We consider two datasets: NCEP CDAS1 and ERA Interim reanalysis. We show that two surprisingly simple measures are able to extract meaningful information: i) the distance between the lagged SAT and the incoming solar radiation and ii) the Shannon entropy of SAT and SAT anomalies. The distance uncovers well-defined spatial patterns formed by regions with similar SAT response to solar forcing while the entropy uncovers regions with similar degree of SAT unpredictability. The entropy analysis also allows identifying regions in which SAT has extreme values. Importantly, we uncover differences between the two datasets which are due to the presence of extreme values in one dataset but not in the other. Our results indicate that the distance and entropy measures can be valuable tools for the study of other climatological variables, for anomaly detection and for performing model inter-comparisons.
2017-05-12T12:37:37ZArizmendi, FernandoBarreiro, MarceloMasoller Alonso, CristinaUnderstanding the complex dynamics of the atmosphere is of paramount interest due to its impact in the entire climate system and in human society. Here we focus on identifying, from data, the geographical regions which have similar atmospheric properties. We study surface air temperature (SAT) time series with monthly resolution, recorded at a regular grid covering the Earth surface. We consider two datasets: NCEP CDAS1 and ERA Interim reanalysis. We show that two surprisingly simple measures are able to extract meaningful information: i) the distance between the lagged SAT and the incoming solar radiation and ii) the Shannon entropy of SAT and SAT anomalies. The distance uncovers well-defined spatial patterns formed by regions with similar SAT response to solar forcing while the entropy uncovers regions with similar degree of SAT unpredictability. The entropy analysis also allows identifying regions in which SAT has extreme values. Importantly, we uncover differences between the two datasets which are due to the presence of extreme values in one dataset but not in the other. Our results indicate that the distance and entropy measures can be valuable tools for the study of other climatological variables, for anomaly detection and for performing model inter-comparisons.Consistency of heterogeneous synchronization patterns in complex weighted networks
http://hdl.handle.net/2117/103772
Consistency of heterogeneous synchronization patterns in complex weighted networks
Malagarriga Guasch, Daniel; Villa, Alessandro; García Ojalvo, Jordi; Pons Rivero, Antonio Javier
Synchronization within the dynamical nodes of a complex network is usually considered homogeneous through all the nodes. Here we show, in contrast, that subsets of interacting oscillators may synchronize in different ways within a single network. This diversity of synchronization patterns is promoted by increasing the heterogeneous distribution of coupling weights and/or asymmetries in small networks. We also analyze consistency, defined as the persistence of coexistent synchronization patterns regardless of the initial conditions. Our results show that complex weighted networks display richer consistency than regular networks, suggesting why certain functional network topologies are often constructed when experimental data are analyzed.
Dynamical systems may synchronize in several ways, at the same time, when they are coupled in a single complex network. Examples of this diversity of synchronization patterns may be found in research fields as diverse as neuroscience, climate networks, or ecosystems. Here we report the conditions required to obtain coexisting synchronizations in arrangements of interacting chaotic oscillators, and relate these conditions to the distribution of coupling weights and asymmetries in complex networks. We also analyze the conditions required for a high statistical occurrence of the same synchronization patterns, regardless of the oscillators' initial conditions. Our results show that these persistent synchronization patterns are statistically more frequent in complex weighted networks than in regular ones, explaining why certain functional network topologies are often retrieved from experimental data. Besides, our results suggest that considering both the different coexisting synchronizations and also their statistics may result in a richer understanding of the relations between functional and structural networks of oscillators.
All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
2017-04-27T08:54:18ZMalagarriga Guasch, DanielVilla, AlessandroGarcía Ojalvo, JordiPons Rivero, Antonio JavierSynchronization within the dynamical nodes of a complex network is usually considered homogeneous through all the nodes. Here we show, in contrast, that subsets of interacting oscillators may synchronize in different ways within a single network. This diversity of synchronization patterns is promoted by increasing the heterogeneous distribution of coupling weights and/or asymmetries in small networks. We also analyze consistency, defined as the persistence of coexistent synchronization patterns regardless of the initial conditions. Our results show that complex weighted networks display richer consistency than regular networks, suggesting why certain functional network topologies are often constructed when experimental data are analyzed.
Dynamical systems may synchronize in several ways, at the same time, when they are coupled in a single complex network. Examples of this diversity of synchronization patterns may be found in research fields as diverse as neuroscience, climate networks, or ecosystems. Here we report the conditions required to obtain coexisting synchronizations in arrangements of interacting chaotic oscillators, and relate these conditions to the distribution of coupling weights and asymmetries in complex networks. We also analyze the conditions required for a high statistical occurrence of the same synchronization patterns, regardless of the oscillators' initial conditions. Our results show that these persistent synchronization patterns are statistically more frequent in complex weighted networks than in regular ones, explaining why certain functional network topologies are often retrieved from experimental data. Besides, our results suggest that considering both the different coexisting synchronizations and also their statistics may result in a richer understanding of the relations between functional and structural networks of oscillators.Locally parity-time-symmetric and globally parity-symmetric systems
http://hdl.handle.net/2117/103403
Locally parity-time-symmetric and globally parity-symmetric systems
Ahmed Waseem, Waqas Waseem; Herrero Simon, Ramon; Botey Cumella, Muriel; Staliunas, Kestutis
We introduce a class of systems holding parity-time (PT) symmetry locally, whereas being globally P symmetric. The potential, U = U(vertical bar r vertical bar), fulfills PT symmetry with respect to periodically distributed points r(0) : U(vertical bar r(0) + r vertical bar) = U*(vertical bar r(0) - r vertical bar) being r(0) not equal 0. We show that such systems hold unusual properties arising from the merging of the two different symmetries, leading to a strong field localization and enhancement at the double-symmetry center, r = 0, when the coupling of outward to inward propagating waves is favored. We explore such general potentials in one and two dimensions, which could have actual realizations combining gain-loss and index modulations in nanophotonic structures. In particular, we show how to render a broad aperture vertical-cavity surface-emitting laser into a bright and narrow beam source, as a direct application.
2017-04-05T15:54:50ZAhmed Waseem, Waqas WaseemHerrero Simon, RamonBotey Cumella, MurielStaliunas, KestutisWe introduce a class of systems holding parity-time (PT) symmetry locally, whereas being globally P symmetric. The potential, U = U(vertical bar r vertical bar), fulfills PT symmetry with respect to periodically distributed points r(0) : U(vertical bar r(0) + r vertical bar) = U*(vertical bar r(0) - r vertical bar) being r(0) not equal 0. We show that such systems hold unusual properties arising from the merging of the two different symmetries, leading to a strong field localization and enhancement at the double-symmetry center, r = 0, when the coupling of outward to inward propagating waves is favored. We explore such general potentials in one and two dimensions, which could have actual realizations combining gain-loss and index modulations in nanophotonic structures. In particular, we show how to render a broad aperture vertical-cavity surface-emitting laser into a bright and narrow beam source, as a direct application.Linear and Nonlinear Bullets of the Bogoliubov-de Gennes Excitations
http://hdl.handle.net/2117/103389
Linear and Nonlinear Bullets of the Bogoliubov-de Gennes Excitations
Kumar, Shubham; Perego, A.M.; Staliunas, Kestutis
We report on the focalization of Bogoliubov–de Gennes excitations of the nonlinear Schrödinger equation in the defocusing regime (Gross-Pitaevskii equation for repulsive Bose-Einstein condensates) with a spatially modulated periodic potential. Exploiting the modification of the dispersion relation induced by the modulation, we demonstrate the existence of localized structures of the Bogoliubov–de Gennes excitations, in both the linear and nonlinear regimes (linear and nonlinear “bullets”). These traveling Bogoliubov–de Gennes bullets, localized both spatially and temporally in the comoving reference frame, are robust and propagate remaining stable, without spreading or filamentation. The phenomena reported in this Letter could be observed in atomic Bose-Einstein condensates in the presence of a spatially periodic potential induced by an optical lattice.
2017-04-05T14:01:12ZKumar, ShubhamPerego, A.M.Staliunas, KestutisWe report on the focalization of Bogoliubov–de Gennes excitations of the nonlinear Schrödinger equation in the defocusing regime (Gross-Pitaevskii equation for repulsive Bose-Einstein condensates) with a spatially modulated periodic potential. Exploiting the modification of the dispersion relation induced by the modulation, we demonstrate the existence of localized structures of the Bogoliubov–de Gennes excitations, in both the linear and nonlinear regimes (linear and nonlinear “bullets”). These traveling Bogoliubov–de Gennes bullets, localized both spatially and temporally in the comoving reference frame, are robust and propagate remaining stable, without spreading or filamentation. The phenomena reported in this Letter could be observed in atomic Bose-Einstein condensates in the presence of a spatially periodic potential induced by an optical lattice.Two-dimensional domain structures in Lithium Niobate via domain inversion with ultrafast light
http://hdl.handle.net/2117/103122
Two-dimensional domain structures in Lithium Niobate via domain inversion with ultrafast light
Chen, Xin; Karpinski, Pawel; Shvedov, Vladlen; Wang, Bingxia; Trull Silvestre, José Francisco; Cojocaru, Crina; Boes, A.; Mitchell, A.; Krolikowski, Wieslaw; Sheng, Yan
Periodic inversion of ferroelectric domains is realized in a lithium niobate crystal by focused femtosecond near-infrared laser beam. One and two-dimensional domain patterns are fabricated. Quasi-phase matched frequency doubling of 815nm light is demonstrated in a channel waveguide with an inscribed periodic domain pattern with conversion efficiency as high as 17.45%.
2017-03-30T16:32:04ZChen, XinKarpinski, PawelShvedov, VladlenWang, BingxiaTrull Silvestre, José FranciscoCojocaru, CrinaBoes, A.Mitchell, A.Krolikowski, WieslawSheng, YanPeriodic inversion of ferroelectric domains is realized in a lithium niobate crystal by focused femtosecond near-infrared laser beam. One and two-dimensional domain patterns are fabricated. Quasi-phase matched frequency doubling of 815nm light is demonstrated in a channel waveguide with an inscribed periodic domain pattern with conversion efficiency as high as 17.45%.Mode-locking via dissipative Faraday instability
http://hdl.handle.net/2117/102229
Mode-locking via dissipative Faraday instability
Tarasov, N.; Perego, A.M.; Churkin, D.V.; Staliunas, Kestutis; Turitsyn, S.K.
Emergence of coherent structures and patterns at the nonlinear stage of modulation
instability of a uniform state is an inherent feature of many biological, physical and engineering
systems. There are several well-studied classical modulation instabilities, such as
Benjamin–Feir, Turing and Faraday instability, which play a critical role in the self-organization
of energy and matter in non-equilibrium physical, chemical and biological systems. Here
we experimentally demonstrate the dissipative Faraday instability induced by spatially
periodic zig-zag modulation of a dissipative parameter of the system—spectrally dependent
losses—achieving generation of temporal patterns and high-harmonic mode-locking in a fibre
laser. We demonstrate features of this instability that distinguish it from both the Benjamin–
Feir and the purely dispersive Faraday instability. Our results open the possibilities for new
designs of mode-locked lasers and can be extended to other fields of physics and engineering.
2017-03-09T15:22:54ZTarasov, N.Perego, A.M.Churkin, D.V.Staliunas, KestutisTuritsyn, S.K.Emergence of coherent structures and patterns at the nonlinear stage of modulation
instability of a uniform state is an inherent feature of many biological, physical and engineering
systems. There are several well-studied classical modulation instabilities, such as
Benjamin–Feir, Turing and Faraday instability, which play a critical role in the self-organization
of energy and matter in non-equilibrium physical, chemical and biological systems. Here
we experimentally demonstrate the dissipative Faraday instability induced by spatially
periodic zig-zag modulation of a dissipative parameter of the system—spectrally dependent
losses—achieving generation of temporal patterns and high-harmonic mode-locking in a fibre
laser. We demonstrate features of this instability that distinguish it from both the Benjamin–
Feir and the purely dispersive Faraday instability. Our results open the possibilities for new
designs of mode-locked lasers and can be extended to other fields of physics and engineering.Emergence of spike correlations in periodically forced excitable systems
http://hdl.handle.net/2117/101786
Emergence of spike correlations in periodically forced excitable systems
Reinoso, Jose A.; Torrent Serra, Maria del Carmen; Masoller Alonso, Cristina
2017-03-01T12:11:41ZReinoso, Jose A.Torrent Serra, Maria del CarmenMasoller Alonso, CristinaGlobal atmospheric dynamics investigated by using Hilbert frequency analysis
http://hdl.handle.net/2117/101413
Global atmospheric dynamics investigated by using Hilbert frequency analysis
Zappala, Dario; Barreiro, Marcelo; Masoller Alonso, Cristina
The Hilbert transform is a well-known tool of time series analysis that has been widely used to investigate oscillatory signals that resemble a noisy periodic oscillation, because it allows instantaneous phase and frequency to be estimated, which in turn uncovers interesting properties of the underlying process that generates the signal. Here we use this tool to analyze atmospheric data: we consider daily-averaged Surface Air Temperature (SAT) time series recorded over a regular grid of locations covering the Earth’s surface. From each SAT time series, we calculate the instantaneous frequency time series by considering the Hilbert analytic signal. The properties of the obtained frequency data set are investigated by plotting the map of the average frequency and the map of the standard deviation of the frequency fluctuations. The average frequency map reveals well-defined large-scale structures: in the extra-tropics, the average frequency in general corresponds to the expected one-year period of solar forcing, while in the tropics, a different behaviour is found, with particular regions having a faster average frequency. In the standard deviation map, large-scale structures are also found, which tend to be located over regions of strong annual precipitation. Our results demonstrate that Hilbert analysis of SAT time-series uncovers meaningful information, and is therefore a promising tool for the study of other climatological variables.
2017-02-22T19:10:46ZZappala, DarioBarreiro, MarceloMasoller Alonso, CristinaThe Hilbert transform is a well-known tool of time series analysis that has been widely used to investigate oscillatory signals that resemble a noisy periodic oscillation, because it allows instantaneous phase and frequency to be estimated, which in turn uncovers interesting properties of the underlying process that generates the signal. Here we use this tool to analyze atmospheric data: we consider daily-averaged Surface Air Temperature (SAT) time series recorded over a regular grid of locations covering the Earth’s surface. From each SAT time series, we calculate the instantaneous frequency time series by considering the Hilbert analytic signal. The properties of the obtained frequency data set are investigated by plotting the map of the average frequency and the map of the standard deviation of the frequency fluctuations. The average frequency map reveals well-defined large-scale structures: in the extra-tropics, the average frequency in general corresponds to the expected one-year period of solar forcing, while in the tropics, a different behaviour is found, with particular regions having a faster average frequency. In the standard deviation map, large-scale structures are also found, which tend to be located over regions of strong annual precipitation. Our results demonstrate that Hilbert analysis of SAT time-series uncovers meaningful information, and is therefore a promising tool for the study of other climatological variables.Unravelling the community structure of the climate system by using lags and symbolic time-series analysis
http://hdl.handle.net/2117/101373
Unravelling the community structure of the climate system by using lags and symbolic time-series analysis
Tirabassi, Giulio; Masoller Alonso, Cristina
Many natural systems can be represented by complex networks of dynamical units with modular structure in the form of communities of densely interconnected nodes. Unraveling this community structure from observed data requires the development of appropriate tools, particularly when the nodes are embedded in a regular space grid and the datasets are short and noisy. Here we propose two methods to identify communities, and validate them with the analysis of climate datasets recorded at a regular grid of geographical locations covering the Earth surface. By identifying mutual lags among time-series recorded at different grid points, and by applying symbolic time-series analysis, we are able to extract meaningful regional communities, which can be interpreted in terms of large-scale climate phenomena. The methods proposed here are valuable tools for the study of other systems represented by networks of dynamical units, allowing the identification of communities, through time-series analysis of the observed output signals.
This work is licensed under a Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/
2017-02-22T12:06:34ZTirabassi, GiulioMasoller Alonso, CristinaMany natural systems can be represented by complex networks of dynamical units with modular structure in the form of communities of densely interconnected nodes. Unraveling this community structure from observed data requires the development of appropriate tools, particularly when the nodes are embedded in a regular space grid and the datasets are short and noisy. Here we propose two methods to identify communities, and validate them with the analysis of climate datasets recorded at a regular grid of geographical locations covering the Earth surface. By identifying mutual lags among time-series recorded at different grid points, and by applying symbolic time-series analysis, we are able to extract meaningful regional communities, which can be interpreted in terms of large-scale climate phenomena. The methods proposed here are valuable tools for the study of other systems represented by networks of dynamical units, allowing the identification of communities, through time-series analysis of the observed output signals.