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

TL;DR

This study uses recurrence analysis techniques to identify and distinguish the nonlinear dynamical features of the Earth's magnetosphere during magnetic storms versus normal conditions, revealing key complexity measures.

Contribution

It introduces the application of recurrence quantification and network analysis to geomagnetic data, demonstrating their effectiveness in detecting storm-related dynamical changes.

Findings

Recurrence measures effectively distinguish storm from quiescent periods.

Trapping time and recurrence network transitivity are key discriminators.

Recurrence-based measures outperform some traditional nonlinear metrics.

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.