Abstract: The solar wind is a plasma that continuously flows outwards from the solar atmosphere. It is a natural laboratory for plasma turbulence, with a magnetic Reynolds number of the order 105 and multiple decades of high time resolution in-situ observations. However, it is also highly structured, exhibiting large-scale features of solar origin in addition to the small-scale fluctuations driven by in-situ turbulence. As it flows past Earth, the solar wind deposits energy into the magnetosphere, producing potentially hazardous space weather. Extreme space weather can severely damage communications satellites, disrupt the navigation capabilities of aircraft, and induce in power grids currents large enough to cause blackouts. Long-term trends in solar wind variability and space weather, known as “space climate”, can now also be explored using the substantial set of satellite measurements of the solar wind.
We use the data quantile-quantile (QQ) plot [Gilchrist, 2000] to study how the statistical distributions of solar wind variables evolve as the activity level of the Sun varies. Despite its prevalence in other fields, we present the first application of this method to space plasma physics. Using the QQ plot we can distinguish the turbulence-dominated component of the solar wind magnetic field distribution from the separate extremal component [Tindale and Chapman 2017]. We then track how the moments of these components separately evolve, leading to insights into the impact of varying solar activity on the solar wind. Subsequently, we extract bursts from the time series of solar wind magnetic energy density, where a burst is defined as a period where the time series continuously exceeds a given threshold. As solar activity fluctuates, the distributions of burst parameters (e.g. burst duration and size) also vary, however their power law scaling S ~ t^a remains constant. We discuss the implications of this result both for the occurrence of damaging space weather events and the possible dynamic processes underlying this system.
Gilchrist, W. G. (2000), Statistical modelling with quantile functions, Chapman and Hall, Florida.
Tindale, E., and S.C. Chapman (2017), J. Geophys. Res., 122(10), doi: 10.1002/2017JA024412.