The activity of the Sun varies over an 11 year cycle, however this cycle is itself variable; for example, the maximum of the most recent cycle was less active than previously observed solar maxima. A high speed stream of plasma, known as the solar wind, flows outwards from the Sun’s atmosphere, depositing energy into the magnetosphere as it reaches Earth. Because of its solar origin, the solar wind also exhibits the 11 year variability, which causes long term trends in space weather (e.g. auroral displays) observed on Earth.

Since the start of the space age, satellites positioned between the Earth and Sun have been measuring the magnetic field and velocity of the solar wind, including a 20 year continuous data set from NASA’s Wind spacecraft. From these measurements, we can estimate the amount of energy transferred from the solar wind into the Earth’s magnetosphere. There are many methods proposed for calculating the energy; we have studied the four most commonly used parameters, including the Poynting flux and a scaled version of it, the ε parameter. Extremely energetic events can cause damage to terrestrial power and communication networks, so it is important to understand the likely occurrence rate of such large events.

In our recent paper (Tindale & Chapman, 2016), we apply a new statistical technique, the quantile-quantile plot, to the Wind data in order to quantify how the likelihood of measuring a given energy in the solar wind changes between different solar cycles. We’ve found that the type of distribution doesn’t change; instead, the change in its shape is captured by a simple translation on the quantile-quantile plot. This transformation is unique to the cycle phases you’re comparing, and also depends on how you calculate the energy from the magnetic field and velocity measurements. These results provide a benchmark for models of the energy distribution, and may potentially aid prediction of the likelihood of extreme events.

• Caption to figure: Figure 1: Quantile-quantile plots of two measures of the solar wind energy (the Poynting flux (left) and ε parameter (right)) comparing their statistical distributions between successive solar cycle maxima. For each parameter, two distributions are calculated using data collected during the two solar cycle maxima. Each point on the graph then exceeds the same proportion of data in both distributions, for example, the q=0.9800 point is the value in each data set which is higher than 98% of the data in that set. Straight lines denote ranges where the type of distribution doesn’t change; the slope and intercept of the line tell you how the distribution shape changes between the two solar maxima. The Poynting flux plot splits into three linear regions, whereas one is sufficient for the ε parameter, indicating ε is less sensitive to changes in the distribution of its extreme values.
• Publication: Tindale, E., and S.C. Chapman (2016), Solar cycle variation of the statistical distribution of the solar wind ε parameter and its constituent variables, Geophys. Res. Lett., 43
• DOI: 10.1002/2016GL068920