The effect of model errors in ensemble sequential assimilation of geomagnetic field

Earth’s magnetic field is generated in the fluid outer core through the dynamo process. Over the last decade, data assimilation has been used to retrieve the core dynamics and predict the evolution of the geomagnetic field. The presence of model errors in the geomagnetic data assimilation is inevitable because current numerical geodynamo models are still far from realistic core dynamics. In this paper, we investigate the effect of model errors in geomagnetic data assimilation based on ensemble Kalman filter (EnKF). We construct two dynamo models with different control parameters but exhibiting similar force balance and magnetic morphology at the core surface. We then use one dynamo model to generate synthetic observations and the other as the forward model in EnKF. Our test experiments show that the EnKF approach with the pre-setting model errors can nevertheless recover large-scale core surface flow and make a rough short-term (5-year) prediction. However, the data assimilation in the presence of model errors cannot keep improving the core state even though new observations are available. Motivated by the planned Macau Science Satellite-1, which is expected to provide improved internal geomagnetic field model, we also perform a test experiment using synthetic observations up to spherical harmonic degree\ell=18. Our results indicate that high-resolution observations are crucial in reconstructing small scale flow.