Frequency spectrum of the geomagnetic field harmonic coefficients from dynamo simulations

Bouligand, C.; Gillet, Nicolas; Jault, D.; Schaeffer, Nathanaël; Fournier, Alexandre; Aubert, Julien

doi:10.1093/gji/ggw326

Cited by 46 publications

(53 citation statements)

References 58 publications

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“…1). Departures at high frequency can be improved by allowing for the influence of correlated noise (Buffett and Matsui, 2015;Bouligand et al, 2016). The spectrum for ðtÞ with correlated noise (denoted by S c ðf Þ) reduces the power at high frequencies, but it does not change the behavior at low frequencies.…”

Section: Stochastic Description Of Dipole Fluctuationsmentioning

confidence: 99%

“…The origin of correlation at shorter time steps might reflect the lifetime of convective eddies in the core or possibly the time required to sweep normal and reversed patches of magnetic flux across the surface of the core (Metman et al, 2017). While we could account for correlated noise in the stochastic model (Buffett and Matsui, 2015;Bouligand et al, 2016), we avoid this complication in the realizations by choosing a sufficiently large time step.…”

Section: A Composite Paleomagnetic Power Spectrummentioning

confidence: 99%

“…There is also good reason to think that stochastic models can represent the relevant processes in the core. Stochastic models have been constructed from geodynamo simulations with only a few model parameters, yet these models are capable of reproducing most of the variability in these simulations (Kuipers et al, 2009;Buffett et al, 2014;Bouligand et al, 2016). Synthetic studies using geodynamo simulations are an ideal test of the general approach because the simulations have relatively low numerical error and we can control the temporal resolution of the output.…”

Section: Introductionmentioning

confidence: 99%

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Constructing stochastic models for dipole fluctuations from paleomagnetic observations

Buffett

Puranam

2017

Physics of the Earth and Planetary Interiors

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a b s t r a c tRecords of relative paleointensity are subject to several sources of error. Temporal averaging due to gradual acquisition of magnetization removes high-frequency fluctuations, whereas random errors introduce fluctuations at high frequency. Both sources of error limit our ability to construct stochastic models from paleomagnetic observations. We partially circumvent these difficulties by recognizing that the largest affects occur at high frequency. To illustrate we construct a stochastic model from two recent inversions of paleomagnetic observations for the axial dipole moment. An estimate of the noise term in the stochastic model is recovered from a high-resolution inversion (CALS10k.2), while the drift term is estimated from the low-frequency part of the power spectrum for a long, but lower-resolution inversion (PADM2M). Realizations of the resulting stochastic model yield a composite, broadband power spectrum that agrees well with the spectra from both PADM2M and CALS10k.2. A simple generalization of the stochastic model permits predictions for the mean rate of magnetic reversals. We show that the reversal rate depends on the time-averaged dipole moment, the variance of the dipole moment and a slow timescale that characterizes the adjustment of the dipole toward the time-averaged value. Predictions of the stochastic model give a mean rate of 4.2 Myr À1, which is in good agreement with observations from marine magnetic anomalies.

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Section: Stochastic Description Of Dipole Fluctuationsmentioning

confidence: 99%

Section: A Composite Paleomagnetic Power Spectrummentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Constructing stochastic models for dipole fluctuations from paleomagnetic observations

Buffett

Puranam

2017

Physics of the Earth and Planetary Interiors

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“…It has become customary (e.g. Bouligand et al, 2016) to assume that the secular variation time, τ sv , is about three times the overturn time τ u = L/V rms . In this case the adjusted rotation rate gives τ u = 188 yr and τ sv = 564 yr, which is not too far from the value τ sv = 415 yr, inferred from magnetic-field observations by Lhuillier et al (2011).…”

Section: Time Dependence Of Sourcesmentioning

confidence: 99%

“…This specific form is chosen to give constant power at low frequency and a f −4 decrease at high frequency, similar to the frequency spectrum of Gauss coefficients for the non-dipole field (Bouligand et al 2016). Taking the inverse Fourier transform gives the autocovariance function,…”

Section: Power Spectra Of Sourcesmentioning

confidence: 99%

Stochastic generation of MAC waves and implications for convection in Earth’s core

Buffett

Knezek

2017

Geophysical Journal International

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The internal structure and dynamics of Jupiter unveiled by a high resolution magnetic field and secular variation model

Sharan

Langlais

Amit

et al. 2022

Preprint

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The interior of the giant planets of our Solar System can be described in simple terms as consisting of a core of unknown composition surrounded by fluid envelopes (Guillot, 2005). For Jupiter, the core could be small and dense, but also large and dilute (Wahl et al., 2017). The overlying envelopes consist of an inner layer of metallic hydrogen and an outer layer of molecular hydrogen. Recent experimental results describe a transition H-He demixing layer, suggesting Helium rain between depths 0.68 and 0.84 R J (Jupiter's equatorial radius, 1 R J = 71,492 km) (Brygoo et al., 2021). The high temperature and pressure inside the planet renders it electrically conducting. Convection in the electrically conductive metallic hydrogen generates the strong Jovian magnetic field (Jones, 2011(Jones, , 2014. In contrast to rocky bodies, Jupiter does not have an abrupt change between its metallic hydrogen (magnetic source) and molecular hydrogen (source free) regions. The change is expected to be gradual. The electrical conductivity profile of the different hydrogen layers at different depths from an ab-initio simulation (French et al., 2012) does not indicate a clear value of the dynamo region radius. Previous attempts to constrain this radius using the magnetic energy spectrum place it somewhere between 0.80 and 0.90

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Frequency spectrum of the geomagnetic field harmonic coefficients from dynamo simulations

Cited by 46 publications

References 58 publications

Constructing stochastic models for dipole fluctuations from paleomagnetic observations

Constructing stochastic models for dipole fluctuations from paleomagnetic observations

Stochastic generation of MAC waves and implications for convection in Earth’s core

The internal structure and dynamics of Jupiter unveiled by a high resolution magnetic field and secular variation model

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