  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.
