A coupled 2$\times$2D Babcock-Leighton solar dynamo model. I. Surface magnetic flux evolution

TL;DR

This paper develops a solar dynamo model focusing on surface magnetic flux evolution, fitting it to real data, and analyzing the impact of magnetic region emergence variability on cycle predictions.

Contribution

It introduces a data-driven surface flux transport model fitted with a genetic algorithm and assesses the influence of emergence stochasticity on solar cycle predictions.

Findings

Surface flow profiles agree with Doppler measurements.

Emergence stochasticity significantly affects cycle strength and timing.

The model can reproduce observed magnetic flux evolution.

Abstract

The need for reliable predictions of the solar activity cycle motivates the development of dynamo models incorporating a representation of surface processes sufficiently detailed to allow assimilation of magnetographic data. In this series of papers we present one such dynamo model, and document its behavior and properties. This first paper focuses on one of the model's key components, namely surface magnetic flux evolution. Using a genetic algorithm, we obtain best-fit parameters of the transport model by least-squares minimization of the differences between the associated synthetic synoptic magnetogram and real magnetographic data for activity cycle 21. Our fitting procedure also returns Monte Carlo-like error estimates. We show that the range of acceptable surface meridional flow profiles is in good agreement with Doppler measurements, even though the latter are not used in the fitting process. Using a synthetic database of bipolar magnetic region (BMR) emergences reproducing the statistical properties of observed emergences, we also ascertain the sensitivity of global cycle properties, such as the strength of the dipole moment and timing of polarity reversal, to distinct realizations of BMR emergence, and on this basis argue that this stochasticity represents a primary source of uncertainty for predicting solar cycle characteristics.