Abstract
Solar activity affects the whole heliosphere and near-Earth space environment. It has been reported in the literature that the mechanism responsible for the solar activity modulation behaves like a low-dimensional chaotic system. Studying these kind of physical systems and, in particular, their temporal evolution requires non-linear analysis methods. To this regard, in this work we apply the recurrence quantification analysis (RQA) to the study of two of the most commonly used solar cycle indicators; i.e. the series of the sunspot number (SSN), and the radio flux 10.7 cm, with the aim of identifying possible dynamical transitions in the system; a task which is particularly suited to the RQA. The outcome of this analysis reveals the presence of large fluctuations of two RQA measures: namely the determinism and the laminarity. In addition, large differences are also seen between the evolution of the RQA measures of the SSN and the radio flux. That suggests the presence of transitions in the dynamics underlying the solar activity. Besides it also shows and quantifies the different nature of these two solar indices. Furthermore, in order to check whether our results are affected by dataartefacts, we have also applied the RQA to both the recently recalibrated SSN series and the previous one, unveiling the main differences between the two data sets. The results are discussed in light of the recent literature on the subject.
Highlights
The impact of solar magnetism and its activity cycle on the heliosphere and near-Earth space is nowadays well recognized
In this work we have shown the results of the application of the recurrence quantification analysis (RQA) on two indices of the solar cycle, namely the sunspot number (SSN) and the F10.7
The RQA is nowadays a widely used technique to investigate non-linear dynamical systems and their transitions, yet not fully exploited to investigate the solar activity cycle
Summary
The impact of solar magnetism and its activity cycle on the heliosphere and near-Earth space is nowadays well recognized. Linear data analysis techniques, such as Fast Fourier Transform (FFT) or wavelet, applied to solar indices time series, can fail to give a complete description of the process represented by the investigated data. This is because in such techniques, non-linearities are not preserved, meaning that a fundamental property of the system (i.e. its non-linear behavior) cannot be studied at all. For a complete description and analysis of the shortcomings of applying linear techniques to non-linear systems, we refer the reader to Huang et al (1998)
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