Wavelet entropy as a measure of solar cycle complexity

TL;DR

This paper introduces wavelet entropy as a novel measure to quantify the complexity of solar activity over time, revealing increased disorder in the current solar cycle and highlighting limitations in prediction methods.

Contribution

It demonstrates the application of wavelet entropy to analyze solar cycle complexity, providing a new tool that captures non-stationary dynamical features of solar activity.

Findings

Wavelet entropy increased during the current solar cycle.

Wavelet entropy correlates with solar dipole phases.

Current prediction methods show low accuracy for solar cycle forecasting.

Abstract

Using wavelet analysis approach, we can derive a measure of the disorder content of solar activity, following the temporal evolution of the so-called wavelet entropy. The interesting feature of this parameter is its ability to extract a dynamical complexity information, in terms of frequency distribution of the energy content, avoiding restrictions, common in the nonlinear dynamics theory, such as stationarity. The analysis is performed on the monthly time series of sunspot numbers. From the time behaviour of the wavelet entropy we found a clear increase in the disorder content of solar activity for the current 23th solar cycle. This result suggests general low accuracies for current solar cycleprediction methods. Moreover, we pointed out a possible connection between wavelet entropy behaviour and solar excursion phases of solar dipole.