Variational estimation of the large scale time dependent meridional circulation in the Sun: proofs of concept with a solar mean field dynamo model

TL;DR

This paper develops a variational data assimilation method using a solar mean field dynamo model and magnetic surface data to estimate the Sun's deep meridional circulation, demonstrating its effectiveness with synthetic data.

Contribution

It introduces a novel variational assimilation technique to constrain the Sun's deep meridional flow based on magnetic observations, a first in this context.

Findings

Successfully reconstructs time-varying meridional circulation from synthetic data.

Robustly estimates flow fluctuations up to 30% of the average.

Predictive horizon of about one solar cycle length.

Abstract

We present in this work the development of a solar data assimilation method based on an axisymmetric mean field dynamo model and magnetic surface data, our mid-term goal is to predict the solar quasi cyclic activity. Here we focus on the ability of our algorithm to constrain the deep meridional circulation of the Sun based on solar magnetic observations. To that end, we develop a variational data assimilation technique. Within a given assimilation window, the assimilation procedure minimizes the differences between data and the forecast from the model, by finding an optimal meridional circulation in the convection zone, and an optimal initial magnetic field, via a quasi-Newton algorithm. We demonstrate the capability of the technique to estimate the meridional flow by a closed-loop experiment involving 40 years of synthetic, solar-like data. By assimilating the synthetic magnetic proxies annually, we are able to reconstruct a (stochastic) time-varying meridional circulation which is also slightly equatorially asymmetric. We show that the method is robust in estimating a flow whose level of fluctuation can reach 30% about the average, and that the horizon of predictive capability of the method is of the order of 1 cycle length.