Skip to main content Accessibility help
×
Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-23T07:22:03.276Z Has data issue: false hasContentIssue false

19 - Long- and Short-Term Geomagnetic Prediction

from Part V - Magnetic Fields beyond the Earth and beyond Today

Published online by Cambridge University Press:  25 October 2019

Mioara Mandea
Affiliation:
Centre National d'études Spatiales, France
Monika Korte
Affiliation:
GeoforschungsZentrum, Helmholtz-Zentrum, Potsdam
Andrew Yau
Affiliation:
University of Calgary
Eduard Petrovsky
Affiliation:
Academy of Sciences of the Czech Republic, Prague
Get access

Summary

Prediction of geomagnetic variability depends on the accuracy of geomagnetic field modeling, dynamical modeling of source regions that contribute to geomagnetic signals, and advanced assimilation algorithms that combine effectively the results of geomagnetic field and dynamic models to make accurate estimates of the dynamic states of the sources and, therefore, accurate forecast of geomagnetic variations. Here, an overview of recent research efforts in these three research areas is provided, focusing primarily on geomagnetic variations from the dynamic outer core and from solar and lunar tidal effects, but also including a review of relevant research results and developments. Prediction of weak but periodic tidal phenomena, and of strong but chaotic secular variation showcases two very important new developments which will lead to new opportunities in geomagnetic research and application.

Type
Chapter
Information
Geomagnetism, Aeronomy and Space Weather
A Journey from the Earth's Core to the Sun
, pp. 312 - 326
Publisher: Cambridge University Press
Print publication year: 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aubert, J. 2014. Earth’s core internal dynamics 18402010 imaged by inverse geodynamo modelling. Geophys. J. Int., 197, 1321–34.Google Scholar
Aubert, J. and Fournier, A. 2011. Inferring internal properties of Earth’s core dynamics and their evolution from surface observations and a numerical geodynamo model. Nonlinear Process. Geophys., 18, 657‒74.Google Scholar
Aubert, J., Gastine, T. and Fournier, A. 2017. Spherical convective dynamos in the rapidly rotating asymptotic regime. J. Fluid. Mech., 813, 558–93.Google Scholar
Barrois, O., Gillet, N. and Aubert, J. 2017. Contributions to the geomagnetic secular variation from a reanalysis of core surface dynamics. Geophys. J. Int., 211, 5068.CrossRefGoogle Scholar
Beggan, C. D. and Whaler, K. A. 2009. Forecasting change of the magnetic field using core surface flows and ensemble Kalman filtering. Geophys. Res. Lett., doi: 10.1029/2009GL039927.CrossRefGoogle Scholar
Bouligand, C., Gillet, N., Jault, D., Schaeffer, N. Fournier, A. and Aubert, J. 2016. Frequency spectrum of the geomagnetic field harmonic coefficients from dynamo simulations. Geophys. J. Int., 207, 1142‒57.Google Scholar
Canet, E., Fournier, A. and Jault, D. 2009. Forward and adjoint quasi-geostrophic models of the geomagnetic secular variation. J. Geophys. Res., doi: 10.1029/2008JB006189.Google Scholar
Christensen, U. R., Aubert, J., Cardin, P., Dormy, E., Gibbons, S., Glatzmaier, G. A., Grote, E., Honkura, Y., Jones, C., Kono, M., Matsushima, M., Sakuraba, A., Takahashi, F., Tilgner, A., Wicht, J. and Zhang, K. 2001. A numerical dynamo benchmark. Phys. Earth Planet. Inter., 128, 2534.CrossRefGoogle Scholar
Christensen, U. R., Aubert, J. and Hulot, G. 2010. Conditions for Earth-like geodynamo models. Earth Planet. Sci. Lett., 296, 487‒96.CrossRefGoogle Scholar
Colosi, J. A. and Munk, W. 2006. Tales of the Venerable Honolulu Tide Gauge. J. Phys. Oceanogr., 36, 967‒96.CrossRefGoogle Scholar
Donadini, F., Korte, M. and Constable, C. G. 2009. Geomagnetic field for 0–3 ka: 1. New data sets for global modeling. Geochem. Geophys. Geosys., 10, Q06007, doi: 10.1029/2008GC002295.Google Scholar
Egbert, G. P. and Ray, R. D. 2017. Tidal prediction. J. Mar. Res., 189237, 149.Google Scholar
Finlay, C. C., Lesur, V., Thébault, E., Vervelidou, F., Morschhauser, A. and Shore, R. 2017. Challenges handling magnetospheric and ionospheric signals in internal geomagnetic field modeling. Space Sci. Rev., 206, 157–89, doi: 10.10007/s11214-016-0285-9.Google Scholar
Finlay, C. C., Olsen, N., Kotsiaros, S., Gillet, N. and Toffner-Clausen, L. 2016. Recent geomagnetic secular variation from Swarm and ground observatories as estimated in the CHAOS-6 geomagnetic field model. Earth Planets Space, 68, doi: 10.1186/s40623-016-0486-1.Google Scholar
Fournier, A., Eymin, C. and Alboussiere, T. 2007. A case for variational geomagnetic data assimilation: insights from a one-dimensional, nonlinear, and sparsely observed MHD system. Nonlin. Process. Geophys., 14, 163‒80.CrossRefGoogle Scholar
Fournier, A., Hulot, G., Jault, D., Kuang, W., Tangborn, A., Gillet, N., Canet, E., Aubert, J. and Lhuillier, F. 2010. An introduction to data assimilation and predictability in geomagnetism. Space Sci. Rev., 155, 247‒91.Google Scholar
Fournier, A., Aubert, J. and Thébault, E. 2011. Inference on core surface flow from observations and 3-D dynamo modelling. Geophys. J. Int., 186, 118‒36.Google Scholar
Fournier, A., Nerger, L. and Aubert, J. 2013. An ensemble Kalman filter for the time-dependent analysis of the geomagnetic field. Geochem. Geophys. Geosyst., doi: 10.1002/ggge.20252.CrossRefGoogle Scholar
Fournier, A., Aubert, J. and Thébaut, E. 2015. A candidate secular variation model for IGRF-12 based on Swarm data and inverse geodynamo modeling. Earth Planets Space, 67, doi: 10.1186/s40623-015-0245-8.CrossRefGoogle Scholar
Gillet, N., Jault, D., Canet, E. and Fournier, A. 2010. Fast torsional waves and strong magnetic field within the Earth’s core. Nature, doi: 10.1038/nature09010.CrossRefGoogle Scholar
Gillet, N., Jault, D., Finlay, C. C. and Olsen, N. 2013. Stochastic modeling of the Earth’s magnetic field: Inversion for covariances over the observatory era. Geochem. Geophys. Geosyst., doi: 10.1002/ggge.20041.Google Scholar
Gillet, N., Barrois, O. and Finlay, C. C. 2015. Stochastic forecasting of the geomagnetic field from the COV-Obs.x1 geomagnetic field model, and candidate models for IGRF-12. Earth Planets Space, doi: 10.1186/s40623-015-0225-z.Google Scholar
Grayver, A. V., Schnepf, N. R., Kuvshinov, A. V., Sabaka, T. J., Manoj, C. and Olsen, N. 2016 Satellite tidal magnetic signals constrain oceanic lithosphere-asthenosphere boundary. Sci. Adv., doi: 10.1126/sciadv.1600798.Google Scholar
Grayver, A. V., Munch, F. D., Kuvshinov, A. V., Khan, A., Sabaka, T. J. and Tøffner-Clausen, L. 2017. Joint inversion of satellite-detected tidal and magnetospheric signals constrains electrical conductivity and water content of the upper mantle and transition zone. Geophys. Res. Lett., doi: 10.1002/2017GL073446.Google Scholar
Hammill, T. M., Snyder, C. 2000. A Hybrid Ensemble Kalman Filter–3D Variational Analysis Scheme. Mon. Weather Rev., 128, 2905–15.Google Scholar
Holme, R. and Whaler, K. A. 2001. Steady core flow in an azimuthally drifting reference frame. Geophys. J. Int., 145, 560‒69.CrossRefGoogle Scholar
Hulot, G., Lhuillier, F. and Aubert, J. 2010. Earth’s dynamo limit of predictability. Geophys. Res. Lett., 37, doi: 10.1029/2009GL041869.Google Scholar
Jackson, A., Jonkers, A. R. T. and Walker, M. R. 2000. Four centuries of geomagnetic secular variation from historical records. Philos. Trans. R. Soc. London, A358, 957‒90.Google Scholar
Jault, D., Gire, C. and LeMouël, J.-L. 1988. Westward drift, core motions and exchanges of angular momentum between core and mantle. Nature, 333, 353‒6.Google Scholar
Jault, D. 2008. Axial invariance of rapidly varying diffusionless motions in the Earth?s core interior. Phys. Earth Planet. Inter., 166, 6776.Google Scholar
Jiang, W. and Kuang, W. 2008. An MPI-based MoSST core dynamics model. Phys. Earth Planet. Inter., 170, 4651.Google Scholar
Jones, C. A., Boronski, P., Brun, A. S. Glatzmaier, G. A., Gastine, T., Miesch, M. S. and Wicht, J. 2011. Anelastic convection-driven dynamo benchmarks. Icarus, 216, 120‒35.Google Scholar
Kalnay, E. 2011. Atmospheric Modeling, Data Assimilation and Predictability. Cambridge University Press, Cambridge.Google Scholar
Korte, M. and Constable, C. G. 2011. Improving geomagnetic field reconstructions for 0‒3 ka. Phys. Earth Planet. Inter., 188, 247‒59.Google Scholar
Kuang, W. and Bloxham, J. 1997. An Earth like numerical dynamo model. Nature, 389, 371‒4.Google Scholar
Kuang, W. and Bloxham, J. 1999. Numerical modeling of magnetohydrodynamic convection in a rapidly rotating spherical shell: Weak and strong dynamo action. J. Comput. Phys., 153, 5181.Google Scholar
Kuang, W. and Chao, B. F. 2003. Geodynamo modeling and core-mantle interactions, in Earth’s Core: Dynamics, Structure, Rotation, Geodynamics Series 31, ed. Dehant, V., Kreager, K. C., Karato, S. and Zatman, S. American Geophysical Union, Washington, DC.Google Scholar
Kuang, W. and Tangborn, A. 2015. Dynamic responses of the Earth’s outer core to assimilation of observed geomagnetic secular variation. Prog. Earth Planet. Sci., doi: 10.1186/s40645-015-0071-4.Google Scholar
Kuang, W., Tangborn, A., Jiang, W., Liu, D., Sun, Z., Bloxham, J. and Wei, Z. 2008. MoSSTDAS: The first generation geomagnetic data assimilation framework. Commun. Comput. Phys., 3, 85108.Google Scholar
Kuang, W., Wei, Z., Holme, R. and Tangborn, A. 2010. Constraining a numerical geodynamo model with 100 years of surface observations. Geophys. J. Int., doi: 10.1111/j.1365-246X.2009.04376.x.CrossRefGoogle Scholar
Kuang, W., Tangborn, A., Wei, Z. and Sabaka, T. 2009. Prediction of geomagnetic field with data assimilation: a candidate secular variation model for IGRF-11. Earth Planets Space, 62, 775‒85.Google Scholar
Kuang, W., Chao, B. F. and Chen, J. 2017. Decadal polar motion of the Earth excited by the convective outer core from geodynamo simulations. J. Geophys. Res., in press.CrossRefGoogle Scholar
Langel, R. A. 1987. The main geomagnetic field, in Geomagnetism, vol. 1, ed. Jacobs, J. A., Academic Press, London.Google Scholar
Lesur, V., Wardinski, I., Rother, M. and Mandea, M. 2008. GRIMM: The GFZ Reference Internal Magnetic Model based on vector satellite and observatory data. Geophys. J. Int., 173, 382‒94.Google Scholar
Lesur, V., Rother, M., Wardinski, I., Schachtschneider, R., Hamoudi, M. and Chambodut, A. 2015a. Parent magnetic field models for the IGRF-12: GFZ-candidates. Earth Planets Space, doi: 10.1186/s40623-015-0239-6.Google Scholar
Lesur, V., Whaler, K. and Wardinski, I. 2015b. Are geomagnetic data consistent with stably stratified flow at the core–mantle boundary? Geophys. J. Int., DOI: 10.1093/gji/ggv031.Google Scholar
Li, K., Jackson, A. and Livermore, P. W. 2011. Variational data assimilation for the initial-value dynamo problem. Phys. Rev. E, 84, doi: 10.1103/PhysRevE.84.056321.Google Scholar
Li, K., Jackson, A. and Livermore, P. W. 2014. Variational data assimilation for a forced, inertia-free magnetohydrodynamic dynamo model. Geophys. J. Int., 199, 1662–76.Google Scholar
Licht, A., Hulot, G., Gallet, Y. and Thébault, E. 2013. Ensembles of low degree archeomagnetic field models for the past three millennia. Phys. Earth Planet. Inter., 224, 3867.Google Scholar
Liu, D., Tangborn, A. and Kuang, W. 2007. Observing system simulation experiments in geomagnetic data assimilation. J. Geophys. Res., 112, doi: 10.1029/2006JB004691.CrossRefGoogle Scholar
Love, J. J. and Rigler, E. J. 2014. The magnetic tides of Honolulu. Geophys. J. Int., 197, 1335‒53.CrossRefGoogle Scholar
Matsui, H., Heien, E., Aubert, J., Aurnou, J. M., Avery, M., Brown, B., Buffett, B. A., Busse, F., Christensen, U. R., Davies, C. J., Featherstone, N., Gastine, T., Glatzmaier, G. A., Gubbins, D., Guermond, J.-L., Hayashi, Y., Hollerbach, R., Hwang, L. J., Jackson, A., Jones, C. A., Jiang, W., Kellogg, L. H., Kuang, W., Landeau, M., Marti, P., Olson, P., Ribeiro, A., Sasaki, Y., Schaeffer, N., Simitev, R. D., Sheyko, A., Silva, L., Stanley, S., Takahashi, F., Takehiro, S., Wicht, J. and Willis, A. P. 2016. Performance benchmarks for a next generation numerical dynamo model. Geochem. Geophys. Geosyst., doi: 10.1002/2015GC006159Google Scholar
Maus, S., Macmillan, S., Lowes, F. and Bondar, T. 2005. Evaluation of candidate geomagnetic field models for the 10th generation of IGRF. Earth Planets Space, 57, 1173‒81.Google Scholar
Maus, S., Rother, M., Stolle, C., Mai, W., Choi, S., Lühr, H., Cooke, D. and Roth, C. 2006. Third generation of the Potsdam Magnetic Model of the Earth (POMME). Geochem. Geophy. Geosyst., doi: 10.1029/2006GC001269.Google Scholar
Maus, S., Silva, L. and Hulot, G. 2008. Can core-surface flow models be used to improve the forecast of the Earth’s main magnetic field? J. Geophys. Res., doi: 10.1029/2007JB005199.Google Scholar
Morzfeld, M., Fournier, A. and Hulot, G. 2017. Coarse predictions of dipole reversals by low-dimensional modeling and data assimilation. Phys. Earth Planet. Inter., 262, 827.Google Scholar
Nilsson, A., Holme, R., Korte, M., Suttie, N. and Hill, M. 2014. Reconstructing Holocene geomagnetic field variation: new methods, models and implications. Geophys. J. Int., doi: 10.1093/gji/ggu120.CrossRefGoogle Scholar
Olsen, N. and Stolle, C. 2017. Magnetic signatures of ionospheric and magnetospheric current systems during geomagnetic quiet conditions ‒ an overview. Space Sci. Rev., 206, 525, doi: 10.1007/s11214-016-0279-7.CrossRefGoogle Scholar
Olsen, N., Lühr, H., Finlay, C. C., Sabaka, T. J., Michaelis, I., Rauberg, J. and Tøffner-Clausen, L. 2014. The CHAOS-4 geomagnetic field model. Geophys. J. Int., 197, 815‒27.Google Scholar
Sabaka, T. J. and Olsen, N. 2006. Enhancing comprehensive inversions using the Swarm constellation. Earth Planets Space, 58, 371‒95.Google Scholar
Sabaka, T. J., Olsen, N. and Langel, R. A. 2002. A comprehensive model of the quiet-time, near-Earth magnetic field: phase 3. Geophys. J. Int., 151, 3268.Google Scholar
Sabaka, T. J., Olsen, N. and Purucker, M. E. 2004. Extending comprehensive models of the Earth’s magnetic field with Ørsted and CHAMP data. Geophys. J. Int., 159, 521‒47.CrossRefGoogle Scholar
Sabaka, T. J., Tøffner-Clausen, L. and Olsen, N. 2013. Use of the comprehensive inversion method for Swarm satellite data analysis. Earth Planets Space, 65, 1201‒22.Google Scholar
Sabaka, T. J., Olsen, N., Tyler, R. H. and Kushinov, A. 2015. CM5, a pre-Swarm comprehensive geomagnetic field model derived from over 12 yr of CHAMP, Ørsted, SAC-C and observatory data. Geophys. J. Int., 200, 15961626.Google Scholar
Sabaka, T. J., Tyler, R. H. and Olsen, N. 2016. Extracting ocean-generated tidal magnetic signals from Swarm data through satellite gradiometry. Geophys. Res. Lett., doi: 10.1002/2016GL068180.Google Scholar
Sakuraba, A. and Roberts, P. H. 2009. Generation of a strong magnetic field using uniform heat flux at the surface of the core. Nat. Geosci., doi: 10.1038/NGEO643.Google Scholar
Sanchez, S., Fournier, A., Aubert, J. and Gallrt, Y. 2016. Modelling the archaeomagnetic field under spatial constraints from dynamo simulations: A resolution analysis. Geophy. J. Int., 207, 9831002.Google Scholar
Schnepf, N. R., Kuvshinov, A. and Sabaka, T. J. 2015. Can we probe the conductivity of the lithosphere and upper mantle using satellite tidal magnetic signals? Geophys. Res. Lett., doi: 10.1002/2015GL063540.Google Scholar
Sun, Z., Tangborn, A. and Kuang, W. 2007. Data assimilation in a sparsely observed one-dimensional modeled MHD system. Nonlinear Process. Geophys., 14, 181‒92.Google Scholar
Sun, Z. and Kuang, W. 2015. An ensemble algorithm based component for geomagnetic data assimilation. Terr. Atmos. Ocean. Sci., 26, 5361.Google Scholar
Tangborn, A. and Kuang, W. 2015. Geodynamo model and error parameter estimation using geomagnetic data assimilation. Geophys. J. Int., 200, 664‒75.Google Scholar
Tangborn, A. and Kuang, W. 2018. Impact of archeomagnetic field model data on modern era geomagnetic forecasts. Phys. Earth Planet. Inter., 276, 29.CrossRefGoogle Scholar
Tyler, R. H. 2013. Magnetic remote sensing of ocean flow variability, presented at IAGA 12th Scientific Assembly, Living on a Magnetic Planet, Merida, 26–31 August.Google Scholar
Tyler, R. H., Maus, S. and Lühr, H. 2003. Satellite observations of magnetic fields due to ocean tidal flow, Science, 299, 239‒41.Google Scholar
Tyler, R. H., Boyer, T. P., Minami, T., Zweng, M. M. and Reagan, J. R. 2017. Electrical conductivity of the global ocean. Earth Planets Space, doi: 10.1186/s40623-017-0739-7Google Scholar
Whaler, K. A. and Beggan, C. D. 2015. Derivation and use of core surface flows for forecasting secular variation. J. Geophys. Res., doi: 10.1002/2014JB011697.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×