Forecasting yearly geomagnetic variation through sequential estimation of core flow and magnetic diffusion

Maurits C. Metman,Philip W. Livermore,Jonathan E. Mound,Ciarán D. Beggan

doi:10.1186/s40623-020-01193-3

Maurits C. Metman, Philip W. Livermore + Show 2 more

Open Access

https://doi.org/10.1186/s40623-020-01193-3

Copy DOI

Abstract

Earth’s internal magnetic field is generated through motion of the electrically conductive iron-alloy fluid comprising its outer core. Temporal variability of this magnetic field, termed secular variation (SV), results from two processes: one is the interaction between core fluid motion and the magnetic field, the other is magnetic diffusion. As diffusion is widely thought to take place over relatively long, millennial time scales, it is common to disregard it when considering yearly to decadal field changes; in this frozen-flux approximation, core fluid motion may be inferred on the core–mantle boundary (CMB) using observations of SV at Earth’s surface. Such flow models have been used to forecast variation in the magnetic field. However, recent work suggests that diffusion may also contribute significantly to SV on short time scales provided that the radial length scale of the magnetic field structure within the core is sufficiently short. In this work, we introduce a hybrid method to forecast field evolution that considers a model based on both a steady flow and diffusion, in which we adopt a two-step process: first fitting the SV to a steady flow, and then fitting the residual by magnetic diffusion. We assess this approach by hindcasting the evolution for 2010–2015, based on fitting the models to CHAOS-6 using time windows prior to 2010. We find that including diffusion yields a reduction of up to 25% in the global hindcast error at Earth’s surface; at the CMB this error reduction can be in excess of 77%. We show that fitting the model over the shortest window that we consider, 2009–2010, yields the lowest hindcast error. Based on our hindcast tests, we present a candidate model for the SV over 2020–2025 for IGRF-13, fit over the time window 2018.3–2019.3. Our forecasts indicate that over the next decade the axial dipole will continue to decay, reversed-flux patches will increase in both area and intensity, and the north magnetic (dip) pole will continue to migrate towards Siberia.

Highlights

Earth’s time-dependent internal magnetic field, commonly referred to as the core or main field, is generated through turbulent motion of the fluid and electrically conducting iron alloy comprising its outer core
While magnetic diffusion is often considered negligible on short time intervals, we model it alongside core fluid motion, motivated by the fact that diffusion can explain field evolution over several decades (Metman et al 2019), and that a description of both physical processes governing secular variation (SV) could produce forecasts of increased accuracy
By hindcasting for the period 2010–2015, we assess whether the addition of diffusion improves the ability of frozen-flux core flow models to forecast beyond 2010 by comparing with the actual evolution of the field according to CHAOS-6-x9

Summary

Introduction

Earth’s time-dependent internal magnetic field, commonly referred to as the core or main field, is generated through turbulent motion of the fluid and electrically conducting iron alloy comprising its outer core. In reality, the flow described in all such models remains poorly resolved due to the effects of limited resolution and measurement errors (e.g., Rau et al 2000; Bärenzung et al 2016) Such flow models have been used to predict field evolution on yearly to decadal time scales, for example by calculating the field change that is expected when these fluid motions persist in time (Beggan and Whaler 2009; Whaler and Beggan 2015). Various authors have implemented the use of (ensemble) Kalman filtering for geomagnetic forecasting (Beggan and Whaler 2009; Fournier et al 2015; Barrois et al 2017, 2018; Bärenzung et al 2018; Beggan and Whaler 2018), an approach that enables a prediction of the uncertainties associated with the core field, and for the forecast to be updated rather when new data become available

Objectives

Results

Conclusion