Modeling and Predicting the Short‐Term Evolution of the Geomagnetic Field

Abstract

We propose a reduced dynamical system describing the coupled evolution of fluid flow and magnetic field at the top of the Earth's core between the years 1900 and 2014. The flow evolution is modeled with a first‐order autoregressive process, while the magnetic field obeys the classical frozen flux equation. An ensemble Kalman filter algorithm serves to constrain the dynamics with the geomagnetic field and its secular variation given by the COV‐OBS.x1 model. Using a large ensemble with 40,000 members provides meaningful statistics including reliable error estimates. The model highlights two distinct flow scales. Slowly varying large‐scale elements include the already documented eccentric gyre. Localized short‐lived structures include distinctly ageostophic features like the high‐latitude polar jet on the Northern Hemisphere. Comparisons with independent observations of the length‐of‐day variations not only validate the flow estimates but also suggest an acceleration of the geostrophic flows over the last century. Hindcasting tests show that our model outperforms simpler predictions bases (linear extrapolation and stationary flow). The predictability limit, of about 2,000 years for the magnetic dipole component, is mostly determined by the random fast varying dynamics of the flow and much less by the geomagnetic data quality or lack of small‐scale information.

pygeodyn 1.1.0: a Python package for geomagnetic data assimilation

“…For more details on the above steps, we refer to Barrois et al (2017Barrois et al ( , 2018Barrois et al ( , 2019 and .…”

“…The intention is to have interchangeable algorithms and be able to easily expand the existing algorithms. In the version described in this article, we provide a subpackage augkf that implements algorithms based on an augmented-state Kalman filter (AugKF) initiated by Barrois et al (2017). The algorithm is based on the radial induction equation at the core surface that we write in the spherical harmonic spectral domain aṡ…”

“…The drift matrices being diagonal, the process is hereafter referred to as diagonal AR-1. Barrois et al (2017Barrois et al ( , 2019 used such diagonal AR-1 processes based on the coupled-Earth dynamo simulation.…”

“…It is made to ease the updating of data and the integration of new numerical models, for instance to test them against geophysical data. This way, it can be compared with other existing DA algorithms (e.g., Bärenzung et al, 2018;Sanchez et al, 2019).…”

“…Sect. 3 is a technical description of version 1.1.0 of the package (Huder et al, 2019a) that also gives the basic necessary scientific background (for details, see Barrois et al, 2017Barrois et al, , 2018. In Sect.…”