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Modeling and Predicting the Short‐Term Evolution of the Geomagnetic Field
Author(s) -
Bärenzung Julien,
Holschneider Matthias,
Wicht Johannes,
Sanchez Sabrina,
Lesur Vincent
Publication year - 2018
Publication title -
journal of geophysical research: solid earth
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.983
H-Index - 232
eISSN - 2169-9356
pISSN - 2169-9313
DOI - 10.1029/2017jb015115
Subject(s) - earth's magnetic field , statistical physics , extrapolation , physics , secular variation , geomagnetic secular variation , flow (mathematics) , dynamo theory , geology , magnetic field , geophysics , mathematics , dynamo , statistics , mechanics , geomagnetic storm , quantum mechanics
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.