A simple stochastic model for dipole moment fluctuations in numerical dynamo simulations
Earth's axial dipole field changes in a complex fashion on many differenttime scales ranging from less than a year to tens of million years.Documenting, analysing, and replicating this intricate signalis a challenge for data acquisition, theoretical interpretation,and dynamo modelling alike. He...
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doaj-f4574a661eed42d2aceb875892b163a22020-11-25T00:31:19ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632016-04-01410.3389/feart.2016.00038182840A simple stochastic model for dipole moment fluctuations in numerical dynamo simulationsDomenico G. eMeduri0Johannes eWicht1Max Planck Institute for Solar System ResearchMax Planck Institute for Solar System ResearchEarth's axial dipole field changes in a complex fashion on many differenttime scales ranging from less than a year to tens of million years.Documenting, analysing, and replicating this intricate signalis a challenge for data acquisition, theoretical interpretation,and dynamo modelling alike. Here we explore whether axial dipole variationscan be described by the superposition of a slow deterministic driftand fast stochastic fluctuations, i.e. by a Langevin-type system.The drift term describes the time averaged behaviour of the axial dipole variations,whereas the stochastic part mimics complex flow interactions over convective time scales.The statistical behaviour of the system is described by a Fokker-Planck equation whichallows useful predictions, including the average rates of dipole reversals and excursions.We analyse several numerical dynamo simulations, most of which havebeen integrated particularly long in time, and also the palaeomagneticmodel PADM2M which covers the past 2 Myr.The results show that the Langevin description provides a viable statistical modelof the axial dipole variations on time scales longer than about 1 kyr.For example, the axial dipole probability distribution and the average reversalrate are successfully predicted.The exception is PADM2M where the stochastic model reversal rate seems too low.The dependence of the drift on the axial dipolemoment reveals the nonlinear interactions that establish thedynamo balance. A separate analysis of inductive and diffusive magnetic effectsin three dynamo simulations suggests that the classical quadraticquenching of induction predicted by mean-field theory seems at work.http://journal.frontiersin.org/Journal/10.3389/feart.2016.00038/fullnumerical simulationstatistical analysisStochastic Modelgeomagnetic fieldGeodynamoField reversals |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Domenico G. eMeduri Johannes eWicht |
spellingShingle |
Domenico G. eMeduri Johannes eWicht A simple stochastic model for dipole moment fluctuations in numerical dynamo simulations Frontiers in Earth Science numerical simulation statistical analysis Stochastic Model geomagnetic field Geodynamo Field reversals |
author_facet |
Domenico G. eMeduri Johannes eWicht |
author_sort |
Domenico G. eMeduri |
title |
A simple stochastic model for dipole moment fluctuations in numerical dynamo simulations |
title_short |
A simple stochastic model for dipole moment fluctuations in numerical dynamo simulations |
title_full |
A simple stochastic model for dipole moment fluctuations in numerical dynamo simulations |
title_fullStr |
A simple stochastic model for dipole moment fluctuations in numerical dynamo simulations |
title_full_unstemmed |
A simple stochastic model for dipole moment fluctuations in numerical dynamo simulations |
title_sort |
simple stochastic model for dipole moment fluctuations in numerical dynamo simulations |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Earth Science |
issn |
2296-6463 |
publishDate |
2016-04-01 |
description |
Earth's axial dipole field changes in a complex fashion on many differenttime scales ranging from less than a year to tens of million years.Documenting, analysing, and replicating this intricate signalis a challenge for data acquisition, theoretical interpretation,and dynamo modelling alike. Here we explore whether axial dipole variationscan be described by the superposition of a slow deterministic driftand fast stochastic fluctuations, i.e. by a Langevin-type system.The drift term describes the time averaged behaviour of the axial dipole variations,whereas the stochastic part mimics complex flow interactions over convective time scales.The statistical behaviour of the system is described by a Fokker-Planck equation whichallows useful predictions, including the average rates of dipole reversals and excursions.We analyse several numerical dynamo simulations, most of which havebeen integrated particularly long in time, and also the palaeomagneticmodel PADM2M which covers the past 2 Myr.The results show that the Langevin description provides a viable statistical modelof the axial dipole variations on time scales longer than about 1 kyr.For example, the axial dipole probability distribution and the average reversalrate are successfully predicted.The exception is PADM2M where the stochastic model reversal rate seems too low.The dependence of the drift on the axial dipolemoment reveals the nonlinear interactions that establish thedynamo balance. A separate analysis of inductive and diffusive magnetic effectsin three dynamo simulations suggests that the classical quadraticquenching of induction predicted by mean-field theory seems at work. |
topic |
numerical simulation statistical analysis Stochastic Model geomagnetic field Geodynamo Field reversals |
url |
http://journal.frontiersin.org/Journal/10.3389/feart.2016.00038/full |
work_keys_str_mv |
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