Comparison of physics-based prediction models of solar cycle 25

TL;DR

This paper reviews and compares physics-based models for predicting solar cycle 25, emphasizing the importance of initial magnetograms and sunspot emergence data in improving prediction accuracy.

Contribution

It provides a comprehensive comparison of seven physics-based solar cycle prediction models and highlights the impact of data assimilation on prediction outcomes.

Findings

Magnetogram assimilation significantly affects prediction accuracy.

Uncertainties in initial data influence model predictions.

Different models offer insights into solar cycle dynamics.

Abstract

Physics-based solar cycle predictions provide an effective way to verify our understanding of the solar cycle. Before the start of cycle 25, several physics-based solar cycle predictions were developed. These predictions use flux transport dynamo (FTD) models, surface flux transport (SFT) models, or a combination of the two kinds of models. The common physics behind these predictions is that the surface poloidal fields around cycle minimum dominate the subsequent cycle strength. In the review, we first give short introductions to SFT and FTD models. Then we compare 7 physics-based prediction models from 4 aspects, which are what the predictor is, how to get the predictor, how to use the predictor, and what to predict. Finally, we demonstrate the large effect of assimilated magnetograms on predictions by two SFT numerical tests. We suggest that uncertainties in both initial magnetograms and sunspot emergence should be included in such physics-based predictions because of their large effects on the results. In addition, in the review we put emphasis on what we can learn from different predictions, rather than an assessment of prediction results.