Prediction of solar magnetic cycles by a data assimilation method

We consider solar magnetic activity in the context of sunspot number variations, as a result of a non-linear oscillatory dynamo process. The apparent chaotic behavior of the 11-year sunspot cycles and undefined errors of observations create uncertainties for predicting the strength and duration of the cycles. Uncertainties in dynamo model parameters create additional difficulties for the forecasting. Modern data assimilation methods allow us to assimilate the observational data into the models for possible efficient and accurate estimations of the physical properties, which cannot be observed directly, such as the internal magnetic fields and helicity. We apply the Ensemble Kalman Filter method to a low-order non-linear dynamo model, which takes into account variations of the turbulent magnetic helicity and reproduces basic characteristics of the solar cycles. We investigate the predictive capabilities of this approach, and present test results for prediction of the previous cycles and a forecast of the next solar cycle 24.