Does Machine Learning reconstruct missing sunspots and forecast a new solar minimum?

Abstract The retrodiction and prediction of solar activity are two closely-related problems in dynamo theory. We applied Machine Learning (ML) algorithms and analyses to the World Data Center’s newly constructed annual sunspot time series (1700-2019; Version 2.0). This provides a unique model that gives insights into the various patterns of the Sun’s magnetic dynamo that drives solar activity maxima and minima. We found that the variability in the ∼ 11 -year Sunspot Cycle is closely connected with 120-year oscillatory magnetic activity variations. We also identified a previously under-eported 5.5 year periodicity in the sunspot record. This 5.5-year pattern is co-modulated by the 120-year oscillation and appears to influence the shape and energy/power content of individual 11-year cycles. Our ML algorithm was trained to recognize such underlying patterns and provides a convincing hindcast of the full sunspot record from 1700 to 2019. It also suggests the possibility of missing sunspots during Sunspot Cycles -1, 0, and 1 (ca. 1730s-1760s). In addition, our ML model forecasts a new phase of extended solar minima that began prior to Sunspot Cycle 24 (ca. 2008-2019) and will persist until Sunspot Cycle 27 (ca. 2050 or so). Our ML Bayesian model forecasts a peak annual sunspot number (SSN) of 95 with a probable range of 80 to 115 for Cycle 25 between 2023 and 2025.