Abstract
AbstractSolar wind variability spans a wide range of amplitudes and timescales, from turbulent fluctuations to the 11 year solar cycle. We apply the data quantile‐quantile (DQQ) method to NASA/Wind observations spanning solar cycles 23 and 24, to study how the uniqueness of each cycle maximum and minimum manifests in the changing statistical distribution of plasma parameters in fast and slow solar wind. The DQQ method allows us to discriminate between two distinct components of the distribution: the core region simply tracks the solar cycle in its moments but shows little sensitivity to solar wind state or the specific activity of each cycle. This would be consistent with an underlying in situ process such as turbulence driving the evolution of fluctuations up to an outer scale. In contrast, the tail component of the distribution is sensitive both to the differences between the maxima and minima of cycles 23 and 24, and the fast or slow state of the solar wind. The tail component varies over the solar cycle in such a way as to maintain a constant functional form, with only its moments varying with solar activity. Finally, after isolating the core region of the distribution, we test its lognormality over the solar cycle in each solar wind state and find the lognormal provides a more robust description of the statistics in slow wind than fast; however, in both states the goodness of fit is significantly reduced at solar maximum.
Highlights
The solar wind in the ecliptic exhibits two states: fast wind, which originates from the edges of long-lived, extended polar coronal holes (Hassler et al, 1999), and slow wind, thought to be generated in low-latitude, variable streamer belts and active regions (Schwenn, 2006)
The data quantile-quantile (DQQ) method allows us to discriminate between two distinct components of the distribution: the core region tracks the solar cycle in its moments but shows little sensitivity to solar wind state or the specific activity of each cycle
After isolating the core region of the distribution, we test its lognormality over the solar cycle in each solar wind state and find the lognormal provides a more robust description of the statistics in slow wind than fast; in both states the goodness of fit is significantly reduced at solar maximum
Summary
The solar wind in the ecliptic exhibits two states: fast wind, which originates from the edges of long-lived, extended polar coronal holes (Hassler et al, 1999), and slow wind, thought to be generated in low-latitude, variable streamer belts and active regions (Schwenn, 2006). The data quantile-quantile (DQQ) plot can be used as a model-independent method for tracking changes in the statistical distribution of a variable across its full range We applied it (Tindale & Chapman, 2016) to space weather relevant parameters in observations spanning solar cycles 23 and 24 seen by NASA’s Wind spacecraft. The more extreme values (the “tails”) of the likelihood distributions of IMF magnitude and component are composed of two parts whose functional forms do not change, but whose moments are sensitive both to solar cycle phase and intensity at solar maximum and minimum; this multicomponent behavior is not fully resolvable in slow wind, though the tail of the distribution is still distinct from the turbulent core.
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