Physics-based secular variation candidate models for the IGRF

Abstract

Each International Geomagnetic Reference Field (IGRF) model released under the auspices of the International Association of Geomagnetism and Aeronomy comprises a secular variation component that describes the evolution of the main magnetic field anticipated for the 5 years to come. Every Gauss coefficient, up to spherical harmonic degree and order 8, is assumed to undergo its own independent linear evolution. With a mathematical model of the core magnetic field and its time rate of change constructed from geomagnetic observations at hand, a standard prediction of the secular variation (SV) consists of taking the time rate of change of each Gauss coefficient at the final time of analysis as the predicted rate of change. The last three generations of the IGRF have additionally witnessed a growing number of candidate SV models relying upon physics-based forecasts. This surge is motivated by satellite data that now span more than two decades and by the concurrent progress in the numerical modelling of Earth’s core dynamics. Satellite data reveal rapid (interannual) geomagnetic features whose imprint can be detrimental to the quality of the IGRF prediction. This calls for forecasting frameworks able to incorporate at least part of the processes responsible for short-term geomagnetic variations. In this letter, we perform a retrospective analysis of the performance of past IGRF SV models and candidates over the past 35 years; we emphasize that over the satellite era, the quality of the 5-year forecasts worsens at times of rapid geomagnetic changes. After the definition of the time scales that are relevant for the IGRF prediction exercise, we cover the strategies followed by past physics-based candidates, which we categorize into a “‘core–surface flow” family and a “dynamo” family, noting that both strategies resort to “input” models of the main field and its secular variation constructed from observations. We next review practical lessons learned from our previous attempts. Finally, we discuss possible improvements on the current state of affairs in two directions: the feasibility of incorporating rapid physical processes into the analysis on the one hand, and the accuracy and quantification of the uncertainty impacting input models on the other hand.

Mid-latitude and equatorial core surface flow variations derived from observatory and satellite magnetic data

“…This has apparently no effect on the quality of the results. We observe nonetheless a regular decrease of the posterior stds over the first four years, possibly linked with the robustness of the secular variation that increases over the years as more data are assimilated (Fournier et al 2021).…”

Transient core surface dynamics from ground and satellite geomagnetic data

“…Fournier et al 2015;Sanchez et al 2020). For a review in the context of IGRF models, see Fournier et al (2021). Most of these approaches are based on Kalman filter or ensemble Kalman filter (EnKF) algorithms (Evensen 2003), which enable one to consider nonlinear dynamics.…”

“…The G-LASSO method for reliably estimating off-diagonal cross-covariance elements allows us to work with ensembles of moderate sizes (thus reducing the computational cost), while producing stable and realistic model state uncertainties. It may also be of interest for alternative assimilation tools based either on reduced stochastic models (Baerenzung et al 2018(Baerenzung et al , 2020 or on geodynamo models (Sanchez et al 2019;Fournier et al 2021).…”

The EU Center of Excellence for Exascale in Solid Earth (Cheese): Implementation, Results, and Roadmap for the Second Phase

“…PD11 [69] focussed on modelling the Earth's geomagnetic field, including both the simulation of past geomagnetic reversals with an unprecedented level of accuracy (using xSHELLS) and ensemble-based geomagnetic forecasts for the next few decades (using PARODY_PDAF), an aspect of particular interest when planning future space missions. In both cases, workflow tasks were orchestrated with WMS-light using archeomagnetic, volcanic and historical data from the HISTMAG database [70].…”