Predicting a Multi-Peak Solar Cycle using a Multi-Stage Analysis

Oregon Convention Center 

We develop a data-driven approach to describe the multi-peak behavior of the solar activity cycle. The method builds upon a multilevel Bayesian model for a single-peaked solar cycle. While the latter uses only monthly mean sunspot numbers as a proxy for solar activity, our approach incorporates additional physical data and uses Gaussian process regression to capture complex features of the solar cycle that are missed by the single-peak, single-proxy model. We demonstrate the capabilities of our methodology using hindcasts of previous cycle morphologies, and we make a prediction for the timing and characteristics of the upcoming solar cycle maximum.