  Magnetic storms are the most prominent global manifestations of
out-of-equilibrium magnetospheric dynamics. Investigating the dynamical
complexity exhibited by geomagnetic observables can provide valuable insights
into relevant physical processes as well as temporal scales associated with
this phenomenon. In this work, we utilize several innovative data analysis
techniques enabling a quantitative nonlinear analysis of the nonstationary
behavior of the disturbance storm time (Dst) index together with some of the
main drivers of its temporal variability, the $VB_{South}$ electric field
component, the vertical component of the interplanetary magnetic field, $B_z$,
and the dynamic pressure of the solar wind, $P_{dyn}$. Using recurrence
quantification analysis (RQA) and recurrence network analysis (RNA), we obtain
several complementary complexity measures that serve as markers of different
physical processes underlying quiet and storm time magnetospheric dynamics.
Specifically, our approach discriminates the magnetospheric activity in
response to external (solar wind) forcing from primarily internal variability
and provides a physically meaningful classification of magnetic storm periods
based on observations made at the Earth's surface. In this regard, the proposed
methodology could provide a relevant step towards future improved space weather
and magnetic storm forecasts.
