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Complex Systems Methods Characterizing Nonlinear Processes in the Near-Earth Electromagnetic Environment: Recent Advances and Open Challenges

Urheber*innen

Balasis,  Georgios
External Organizations;

Balikhin,  Michael A.
External Organizations;

Chapman,  Sandra C.
External Organizations;

Consolini,  Giuseppe
External Organizations;

Daglis,  Ioannis A.
External Organizations;

/persons/resource/Reik.Donner

Donner,  Reik V.
Potsdam Institute for Climate Impact Research;

/persons/resource/Juergen.Kurths

Kurths,  Jürgen
Potsdam Institute for Climate Impact Research;

Paluš,  Milan
External Organizations;

Runge,  Jakob
External Organizations;

Tsurutani,  Bruce T.
External Organizations;

Vassiliadis,  Dimitris
External Organizations;

Wing,  Simon
External Organizations;

Gjerloev,  Jesper W.
External Organizations;

Johnson,  Jay
External Organizations;

Materassi,  Massimo
External Organizations;

Alberti,  Tommaso
External Organizations;

Papadimitriou,  Constantinos
External Organizations;

Manshour,  Pouya
External Organizations;

Boutsi,  Adamantia Zoe
External Organizations;

Stumpo,  Mirko
External Organizations;

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Zitation

Balasis, G., Balikhin, M. A., Chapman, S. C., Consolini, G., Daglis, I. A., Donner, R. V., Kurths, J., Paluš, M., Runge, J., Tsurutani, B. T., Vassiliadis, D., Wing, S., Gjerloev, J. W., Johnson, J., Materassi, M., Alberti, T., Papadimitriou, C., Manshour, P., Boutsi, A. Z., Stumpo, M. (2023): Complex Systems Methods Characterizing Nonlinear Processes in the Near-Earth Electromagnetic Environment: Recent Advances and Open Challenges. - Space Science Reviews, 219, 38.
https://doi.org/10.1007/s11214-023-00979-7


Zitierlink: https://publications.pik-potsdam.de/pubman/item/item_28939
Zusammenfassung
Learning from successful applications of methods originating in statistical mechanics, complex systems science, or information theory in one scientific field (e.g., atmospheric physics or climatology) can provide important insights or conceptual ideas for other areas (e.g., space sciences) or even stimulate new research questions and approaches. For instance, quantification and attribution of dynamical complexity in output time series of nonlinear dynamical systems is a key challenge across scientific disciplines. Especially in the field of space physics, an early and accurate detection of characteristic dissimilarity between normal and abnormal states (e.g., pre-storm activity vs. magnetic storms) has the potential to vastly improve space weather diagnosis and, consequently, the mitigation of space weather hazards. This review provides a systematic overview on existing nonlinear dynamical systems-based methodologies along with key results of their previous applications in a space physics context, which particularly illustrates how complementary modern complex systems approaches have recently shaped our understanding of nonlinear magnetospheric variability. The rising number of corresponding studies demonstrates that the multiplicity of nonlinear time series analysis methods developed during the last decades offers great potentials for uncovering relevant yet complex processes interlinking different geospace subsystems, variables and spatiotemporal scales.