EGU24-4181, updated on 08 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Prediction of sunspot number using Gaussian processes

Everton Frigo and Italo Gonçalves
Everton Frigo and Italo Gonçalves
  • Federal University of Pampa, Caçapava do Sul, Brazil (

The solar activity has various direct and indirect impacts on human activities. During periods of high solar activity, the harmful effects triggered by solar variability are maximized. On a decadal to multidecadal time scale, solar variability exhibits a main cycle of around 11 years known as the Schwabe solar cycle, leading to a solar maximum approximately every 11 years. The most commonly used variable for measuring solar activity is the sunspot number. Over the last few decades, numerous techniques have been employed to predict the time evolution of the solar cycle for subsequent years. Recently, there has been a growing number of studies utilizing machine learning methods to predict solar cycles. One such method is the Gaussian process, which is well-suited for working with small amounts of data and can also provide an uncertainty measure for predictions. In this study, the Gaussian process technique is employed to predict the sunspot number between 2024 and 2050. The dataset used to train and validate the model comprises monthly averages of sunspots relative to the period 1700-2023. According to the results, the current solar cycle, currently at its maximum, is anticipated to last until 2030. The subsequent solar maximum is projected to occur around the end of 2033, with an estimated maximum sunspot number of approximately 150. If this prediction holds true, the next solar cycle's maximum will resemble that observed in the current one.

How to cite: Frigo, E. and Gonçalves, I.: Prediction of sunspot number using Gaussian processes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4181,, 2024.