Temporal organization of magnetospheric fluctuations unveiled by recurrence patterns in the Dst index

Reik V. Donner, Veronika Stolbova, Georgios Balasis, Jonathan F. Donges, Marina Georgiou, Stelios M. Potirakis, Jürgen Kurths

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Magnetic storms constitute the most remarkable large-scale phenomena of nonlinear magnetospheric dynamics. Studying the dynamical organization of macroscopic variability in terms of geomagnetic activity index data by means of complexity measures provides a promising approach for identifying the underlying processes and associated time-scales. Here, we apply a suite of characteristics from recurrence quantification analysis (RQA) and recurrence network analysis (RNA) in order to unveil some key nonlinear features of the hourly Disturbance storm-time (Dst) index during periods with magnetic storms and such of normal variability. Our results demonstrate that recurrence-based measures can serve as excellent tracers for changes in the dynamical complexity along non-stationary records of geomagnetic activity. In particular, trapping time (characterizing the typical length of "laminar phases" in the observed dynamics) and recurrence network transitivity (associated with the number of the system's effective dynamical degrees of freedom) allow for a very good discrimination between magnetic storm and quiescence phases. In general, some RQA and RNA characteristics distinguish between storm and non-storm times equally well or even better than other previously considered nonlinear characteristics like Hurst exponent or symbolic dynamics based entropy concepts. Our results point to future potentials of recurrence characteristics for unveiling temporal changes in the dynamical complexity of the magnetosphere.
Original languageEnglish
Article number085716
Number of pages23
JournalChaos
Volume28
Issue number8
Early online date28 Aug 2018
DOIs
Publication statusPublished - 31 Aug 2018

Fingerprint

Recurrence
magnetic storms
disturbances
Disturbance
Fluctuations
network analysis
Recurrence Quantification Analysis
Network Analysis
Electric network analysis
Magnetosphere
Hurst Exponent
magnetospheres
Complexity Measure
Symbolic Dynamics
tracers
Transitivity
discrimination
Trapping
degrees of freedom
trapping

Keywords

  • physics.ao-ph
  • nlin.CD

Cite this

Donner, R. V., Stolbova, V., Balasis, G., Donges, J. F., Georgiou, M., Potirakis, S. M., & Kurths, J. (2018). Temporal organization of magnetospheric fluctuations unveiled by recurrence patterns in the Dst index. Chaos, 28(8), [085716]. https://doi.org/10.1063/1.5024792

Temporal organization of magnetospheric fluctuations unveiled by recurrence patterns in the Dst index. / Donner, Reik V.; Stolbova, Veronika; Balasis, Georgios; Donges, Jonathan F.; Georgiou, Marina; Potirakis, Stelios M.; Kurths, Jürgen.

In: Chaos, Vol. 28, No. 8, 085716, 31.08.2018.

Research output: Contribution to journalArticle

Donner, RV, Stolbova, V, Balasis, G, Donges, JF, Georgiou, M, Potirakis, SM & Kurths, J 2018, 'Temporal organization of magnetospheric fluctuations unveiled by recurrence patterns in the Dst index', Chaos, vol. 28, no. 8, 085716. https://doi.org/10.1063/1.5024792
Donner RV, Stolbova V, Balasis G, Donges JF, Georgiou M, Potirakis SM et al. Temporal organization of magnetospheric fluctuations unveiled by recurrence patterns in the Dst index. Chaos. 2018 Aug 31;28(8). 085716. https://doi.org/10.1063/1.5024792
Donner, Reik V. ; Stolbova, Veronika ; Balasis, Georgios ; Donges, Jonathan F. ; Georgiou, Marina ; Potirakis, Stelios M. ; Kurths, Jürgen. / Temporal organization of magnetospheric fluctuations unveiled by recurrence patterns in the Dst index. In: Chaos. 2018 ; Vol. 28, No. 8.
@article{d824327aaccc47c8a462c4178979ee17,
title = "Temporal organization of magnetospheric fluctuations unveiled by recurrence patterns in the Dst index",
abstract = "Magnetic storms constitute the most remarkable large-scale phenomena of nonlinear magnetospheric dynamics. Studying the dynamical organization of macroscopic variability in terms of geomagnetic activity index data by means of complexity measures provides a promising approach for identifying the underlying processes and associated time-scales. Here, we apply a suite of characteristics from recurrence quantification analysis (RQA) and recurrence network analysis (RNA) in order to unveil some key nonlinear features of the hourly Disturbance storm-time (Dst) index during periods with magnetic storms and such of normal variability. Our results demonstrate that recurrence-based measures can serve as excellent tracers for changes in the dynamical complexity along non-stationary records of geomagnetic activity. In particular, trapping time (characterizing the typical length of {"}laminar phases{"} in the observed dynamics) and recurrence network transitivity (associated with the number of the system's effective dynamical degrees of freedom) allow for a very good discrimination between magnetic storm and quiescence phases. In general, some RQA and RNA characteristics distinguish between storm and non-storm times equally well or even better than other previously considered nonlinear characteristics like Hurst exponent or symbolic dynamics based entropy concepts. Our results point to future potentials of recurrence characteristics for unveiling temporal changes in the dynamical complexity of the magnetosphere.",
keywords = "physics.ao-ph, nlin.CD",
author = "Donner, {Reik V.} and Veronika Stolbova and Georgios Balasis and Donges, {Jonathan F.} and Marina Georgiou and Potirakis, {Stelios M.} and J{\"u}rgen Kurths",
note = "This work has been financially supported by the joint Greek-German project “Transdisciplinary assessment of dynamical complexity in magnetosphere and climate: A unified description of the nonlinear dynamics across extreme events” funded by IKY and DAAD. Individual financial support of the authors has been granted by the LINC (Learning about Interacting Networks in Climate) project (project no. 289447) funded by the Marie Curie Initial Training Network (ITN) program (FP7-PEOPLE2011-ITN), the German Federal Ministry for Science and Education (BMBF) via the Young Investigator’s Group CoSy-CC2 (grant no. 01LN1306A) and the project GLUES, the Stordalen Foundation (Planetary Boundary Research Network PB.net), and the International Research Training Group IRTG 1740/TRP 2014/50151-0, jointly funded by the German Research Foundation (DFG, Deutsche Forschungsgemeinschaft) and the S˜ao Paulo Research Foundation (FAPESP, Funda¸c˜ao de Amparo `a Pesquisa do Estado de S˜ao Paulo). Numerical codes used for estimating RQA and RNA properties can be found in the software package pyunicorn70, which is available at https://github.com/pik-copan/pyunicorn. The Dst data have been obtained from the World Data Center for Geomagnetism, Kyoto (http://wdc.kugi.kyoto-u.ac.jp/index.html). We are grateful to three reviewers of an earlier version of this manuscript for their detailed comments.",
year = "2018",
month = "8",
day = "31",
doi = "10.1063/1.5024792",
language = "English",
volume = "28",
journal = "Chaos",
issn = "1054-1500",
publisher = "American Institute of Physics",
number = "8",

}

TY - JOUR

T1 - Temporal organization of magnetospheric fluctuations unveiled by recurrence patterns in the Dst index

AU - Donner, Reik V.

AU - Stolbova, Veronika

AU - Balasis, Georgios

AU - Donges, Jonathan F.

AU - Georgiou, Marina

AU - Potirakis, Stelios M.

AU - Kurths, Jürgen

N1 - This work has been financially supported by the joint Greek-German project “Transdisciplinary assessment of dynamical complexity in magnetosphere and climate: A unified description of the nonlinear dynamics across extreme events” funded by IKY and DAAD. Individual financial support of the authors has been granted by the LINC (Learning about Interacting Networks in Climate) project (project no. 289447) funded by the Marie Curie Initial Training Network (ITN) program (FP7-PEOPLE2011-ITN), the German Federal Ministry for Science and Education (BMBF) via the Young Investigator’s Group CoSy-CC2 (grant no. 01LN1306A) and the project GLUES, the Stordalen Foundation (Planetary Boundary Research Network PB.net), and the International Research Training Group IRTG 1740/TRP 2014/50151-0, jointly funded by the German Research Foundation (DFG, Deutsche Forschungsgemeinschaft) and the S˜ao Paulo Research Foundation (FAPESP, Funda¸c˜ao de Amparo `a Pesquisa do Estado de S˜ao Paulo). Numerical codes used for estimating RQA and RNA properties can be found in the software package pyunicorn70, which is available at https://github.com/pik-copan/pyunicorn. The Dst data have been obtained from the World Data Center for Geomagnetism, Kyoto (http://wdc.kugi.kyoto-u.ac.jp/index.html). We are grateful to three reviewers of an earlier version of this manuscript for their detailed comments.

PY - 2018/8/31

Y1 - 2018/8/31

N2 - Magnetic storms constitute the most remarkable large-scale phenomena of nonlinear magnetospheric dynamics. Studying the dynamical organization of macroscopic variability in terms of geomagnetic activity index data by means of complexity measures provides a promising approach for identifying the underlying processes and associated time-scales. Here, we apply a suite of characteristics from recurrence quantification analysis (RQA) and recurrence network analysis (RNA) in order to unveil some key nonlinear features of the hourly Disturbance storm-time (Dst) index during periods with magnetic storms and such of normal variability. Our results demonstrate that recurrence-based measures can serve as excellent tracers for changes in the dynamical complexity along non-stationary records of geomagnetic activity. In particular, trapping time (characterizing the typical length of "laminar phases" in the observed dynamics) and recurrence network transitivity (associated with the number of the system's effective dynamical degrees of freedom) allow for a very good discrimination between magnetic storm and quiescence phases. In general, some RQA and RNA characteristics distinguish between storm and non-storm times equally well or even better than other previously considered nonlinear characteristics like Hurst exponent or symbolic dynamics based entropy concepts. Our results point to future potentials of recurrence characteristics for unveiling temporal changes in the dynamical complexity of the magnetosphere.

AB - Magnetic storms constitute the most remarkable large-scale phenomena of nonlinear magnetospheric dynamics. Studying the dynamical organization of macroscopic variability in terms of geomagnetic activity index data by means of complexity measures provides a promising approach for identifying the underlying processes and associated time-scales. Here, we apply a suite of characteristics from recurrence quantification analysis (RQA) and recurrence network analysis (RNA) in order to unveil some key nonlinear features of the hourly Disturbance storm-time (Dst) index during periods with magnetic storms and such of normal variability. Our results demonstrate that recurrence-based measures can serve as excellent tracers for changes in the dynamical complexity along non-stationary records of geomagnetic activity. In particular, trapping time (characterizing the typical length of "laminar phases" in the observed dynamics) and recurrence network transitivity (associated with the number of the system's effective dynamical degrees of freedom) allow for a very good discrimination between magnetic storm and quiescence phases. In general, some RQA and RNA characteristics distinguish between storm and non-storm times equally well or even better than other previously considered nonlinear characteristics like Hurst exponent or symbolic dynamics based entropy concepts. Our results point to future potentials of recurrence characteristics for unveiling temporal changes in the dynamical complexity of the magnetosphere.

KW - physics.ao-ph

KW - nlin.CD

U2 - 10.1063/1.5024792

DO - 10.1063/1.5024792

M3 - Article

VL - 28

JO - Chaos

JF - Chaos

SN - 1054-1500

IS - 8

M1 - 085716

ER -