Spectral characteristic of mid-term quasi-periodicities in sunspots data

TL;DR

This study investigates the spectral characteristics of mid-term quasi-periodicities in sunspot data, revealing no statistically significant dominant frequency in the 1 month to 11 years range and suggesting that observed patterns can be explained by smoothing small-scale spatial features.

Contribution

It provides a wavelet-based analysis showing the absence of a single dominant mid-term periodicity in solar activity data and links spectral features to spatial smoothing in solar dynamo models.

Findings

No statistically significant single periodicity in the 1 month to 11 years range.

Spectral patterns can be explained by smoothing small spatial scales.

Solar activity's global nature is reflected despite small-scale monitoring.

Abstract

Numerous analyses suggest the existence of various quasi-periodicities in solar activity. The power spectrum of solar activity recorded in sunspot data is dominated by the $\sim$11-year quasi-periodicity, known as the Schwabe cycle. In the mid-term range (1 month -- 11 years) a pronounced variability known as a quasi-biennial oscillation (QBO) is widely discussed. In the shorter time scale a pronounced peak, corresponding to the synodic solar rotation period ($\sim$ 27 days) is observed. Here we revisited the mid-term solar variability in terms of statistical dynamic of fully turbulent systems, where solid arguments are required to accept an isolated dominant frequency in a continuous (smooth) spectrum. For that, we first undertook an unbiased analysis of the standard solar data, sunspot numbers and the F10.7 solar radioflux index, by applying a wavelet tool, which allows one to perform a frequency-time analysis of the signal. Considering the spectral dynamics of solar activity cycle by cycle, we showed that no single periodicity can be separated, in a statistically significant manner, in the specified range of periods. We examine whether a model of solar dynamo can reproduce the mid-term oscillation pattern observed in solar data. We found that a realistically observed spectrum can be explained if small spatial (but not temporal) scales are effectively smoothed. This result is important because solar activity is a it global feature, although monitored via small-scale tracers like sunspots.