A Bayesian Analysis of the Correlations Among Sunspot Cycles

TL;DR

This paper uses Bayesian statistical methods to analyze sunspot cycle data, reaffirm known correlations, and develop a predictive framework for future solar activity, with a specific prediction for Cycle 24's maximum sunspot number.

Contribution

It introduces a Bayesian approach to quantify uncertainties and assess the significance of sunspot cycle features, improving predictive modeling of solar activity.

Findings

Correlations among sunspot cycle features are reaffirmed.

Predictive power does not persist beyond one cycle.

Cycle 24's maximum sunspot number is estimated at 97 ± 15 in early 2014.

Abstract

Sunspot numbers form a comprehensive, long-duration proxy of solar activity and have been used numerous times to empirically investigate the properties of the solar cycle. A number of correlations have been discovered over the 24 cycles for which observational records are available. Here we carry out a sophisticated statistical analysis of the sunspot record that reaffirms these correlations, and sets up an empirical predictive framework for future cycles. An advantage of our approach is that it allows for rigorous assessment of both the statistical significance of various cycle features and the uncertainty associated with predictions. We summarize the data into three sequential relations that estimate the amplitude, duration, and time of rise to maximum for any cycle, given the values from the previous cycle. We find that there is no indication of a persistence in predictive power beyond one cycle, and conclude that the dynamo does not retain memory beyond one cycle. Based on sunspot records up to October 2011, we obtain, for Cycle 24, an estimated maximum smoothed monthly sunspot number of 97 +- 15, to occur in January--February 2014 +- 6 months.