The Kalmag model as a candidate for IGRF-13

Baerenzung, Julien; Holschneider, Matthias; Lesur, Vincent; Sanchez, Sabrina

doi:10.1186/s40623-020-01295-y

Cited by 41 publications

(48 citation statements)

References 35 publications

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“…In this study, we used a geomagnetic sequential data assimilation scheme based on a full 3D-numerical dynamo model for forecasting the Earth's main magnetic field. The scheme adopts an EnKF approach that assimilates the main magnetic field from COV-OBS.x1 (Gillet et al 2015) and Kalmag (Baerenzung et al 2020) models from 1840 to 2020. A new spectral covariance localization method, extending the study by S2019, stabilized the assimilation scheme for moderate and small ensemble sizes.…”

Section: Resultsmentioning

confidence: 99%

“…We assimilate the main field component of a previous version of the Kalmag field model (Baerenzung et al 2020) spanning the years 2001 to 2020. This version of Kalmag relies on a Kalman filter and a subsequent Kalman smoother for the assimilation of CHAMP and (13)…”

Section: Observationsmentioning

confidence: 99%

“…In this study, we show that the ensemble size can be further reduced by an additional covariance localization which exploits the dominant equatorial symmetries observed in dynamo simulations. The new scheme is used for the EnKF assimilation of geomagnetic field models covering the past two centuries, namely the COV-OBS.x1 geomagnetic field model (Gillet et al 2015) from 1840 to 2000 and the Kalmag model (Baerenzung et al 2020) from 2001 to 2020. We propose the resulting SV in 2020 as a candidate model for the IGRF-13 (Alken et al 2020b) and also attempt to predict the evolution of the main field until 2070.…”

Section: Introductionmentioning

confidence: 99%

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Predictions of the geomagnetic secular variation based on the ensemble sequential assimilation of geomagnetic field models by dynamo simulations

Sanchez

Bärenzung

2020

Earth Planets Space

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The IGRF offers an important incentive for testing algorithms predicting the Earth’s magnetic field changes, known as secular variation (SV), in a 5-year range. Here, we present a SV candidate model for the 13th IGRF that stems from a sequential ensemble data assimilation approach (EnKF). The ensemble consists of a number of parallel-running 3D-dynamo simulations. The assimilated data are geomagnetic field snapshots covering the years 1840 to 2000 from the COV-OBS.x1 model and for 2001 to 2020 from the Kalmag model. A spectral covariance localization method, considering the couplings between spherical harmonics of the same equatorial symmetry and same azimuthal wave number, allows decreasing the ensemble size to about a 100 while maintaining the stability of the assimilation. The quality of 5-year predictions is tested for the past two decades. These tests show that the assimilation scheme is able to reconstruct the overall SV evolution. They also suggest that a better 5-year forecast is obtained keeping the SV constant compared to the dynamically evolving SV. However, the quality of the dynamical forecast steadily improves over the full assimilation window (180 years). We therefore propose the instantaneous SV estimate for 2020 from our assimilation as a candidate model for the IGRF-13. The ensemble approach provides uncertainty estimates, which closely match the residual differences with respect to the IGRF-13. Longer term predictions for the evolution of the main magnetic field features over a 50-year range are also presented. We observe the further decrease of the axial dipole at a mean rate of 8 nT/year as well as a deepening and broadening of the South Atlantic Anomaly. The magnetic dip poles are seen to approach an eccentric dipole configuration.

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Section: Resultsmentioning

confidence: 99%

Section: Observationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predictions of the geomagnetic secular variation based on the ensemble sequential assimilation of geomagnetic field models by dynamo simulations

Sanchez

Bärenzung

2020

Earth Planets Space

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“…9 to produce a forecast, by resorting instead to a "calibrated" stochastic equation to advance B in time, in the form of a collection of auto-regressive processes whose characteristic time scales are precisely the time scales discussed here. (In the IGRF context, see the candidates proposed by Gillet et al 2015;Baerenzung et al 2020;Huder et al 2020)…”

Section: Time Scales Of the Secular Variation And Secular Accelerationmentioning

confidence: 99%

“…Alternative approaches for modelling the core field do not use temporal smoothing to the cost of having large SV estimated variances (e.g. Baerenzung et al 2020;Ropp et al 2020). In principle, this approach provides valid estimates of these variances, yet they are likely to be also underestimated if all sources contributing to the magnetic signal are not described and co-estimated.…”

Section: Can We Decrease the Error Of Our Input Sv Models And Improve Its Estimates?mentioning

confidence: 99%

Physics-based secular variation candidate models for the IGRF

Fournier

Aubert

Lesur

et al. 2021

Earth Planets Space

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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.

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ArchKalMag14k: A Kalman-filter based global geomagnetic model for the Holocene

Schanner

Korte

Holschneider

2021

Preprint

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Global field reconstructions of the past are a key tool for understanding the dynamics of the Earth's magnetic field and the underlying processes in the Earth's core (e.g., Constable & Korte, 2015). This includes studying the evolution of field features, such as dipole decay, the South Atlantic Anomaly (SAA) and flux patches (Hartmann & Pacca, 2009;Jackson & Finlay, 2015). In the past, several techniques for constructing global field models have been developed and employed. Truncated spherical harmonics (SH) in the spatial domain combined with spline interpolation in time are widely used (Jackson et al., 2000;Korte et al., 2009). In the eighties, Constable and Parker (1988) first proposed using Gaussian processes to model the field dynamics, but until recently, the technique had not been applied to global field modeling. Only in the last years, statistical methods implementing this approach have been suggested (Hellio & Gillet, 2018;Nilsson & Suttie, 2021).Even though Bloxham and Jackson (1992) already discussed the accurate assessment of uncertainties, most following studies did not pursue the suggested analytical approaches, and either did not report uncertainties at

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The Kalmag model as a candidate for IGRF-13

Cited by 41 publications

References 35 publications

Predictions of the geomagnetic secular variation based on the ensemble sequential assimilation of geomagnetic field models by dynamo simulations

Predictions of the geomagnetic secular variation based on the ensemble sequential assimilation of geomagnetic field models by dynamo simulations

Physics-based secular variation candidate models for the IGRF

ArchKalMag14k: A Kalman-filter based global geomagnetic model for the Holocene

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