Impact of archeomagnetic field model data on modern era geomagnetic forecasts

Tangborn, Andrew; Kuang, Weijia

doi:10.1016/j.pepi.2017.11.002

Cited by 11 publications

(6 citation statements)

References 33 publications

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“…Improved global archaeomagnetic field models may contribute to answering open questions about, e.g., the maximum possible rate of geomagnetic field change and the influences of lowermost mantle structure on the geodynamo (which is reflected in magnetic field morphology). They will also likely contribute to improved predictions of future geomagnetic field evolution by assimilation of data-based models into numerical simulations (e.g., Fournier et al, 2010;Tangborn and Kuang, 2018).…”

Section: Global Geomagnetic Field Modellingmentioning

confidence: 99%

Global archaeomagnetic data: The state of the art and future challenges

Brown

Hervé

Korte

et al. 2021

Physics of the Earth and Planetary Interiors

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Section: Global Geomagnetic Field Modellingmentioning

confidence: 99%

Global archaeomagnetic data: The state of the art and future challenges

Brown

Hervé

Korte

et al. 2021

Physics of the Earth and Planetary Interiors

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“…Although methods to forecast the geomagnetic field are in development (18,19), they cannot yet predict the field on long time scales. An alternative approach is to investigate the behavior of past reversals and excursions and infer whether the field structures we see today resemble those approaching reversals and excursions.…”

mentioning

confidence: 99%

Earth’s magnetic field is probably not reversing

Brown

Korte

Holme

et al. 2018

Proc. Natl. Acad. Sci. U.S.A.

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The geomagnetic field has been decaying at a rate of ∼5% per century from at least 1840, with indirect observations suggesting a decay since 1600 or even earlier. This has led to the assertion that the geomagnetic field may be undergoing a reversal or an excursion. We have derived a model of the geomagnetic field spanning 30-50 ka, constructed to study the behavior of the two most recent excursions: the Laschamp and Mono Lake, centered at 41 and 34 ka, respectively. Here, we show that neither excursion demonstrates field evolution similar to current changes in the geomagnetic field. At earlier times, centered at 49 and 46 ka, the field is comparable to today's field, with an intensity structure similar to today's South Atlantic Anomaly (SAA); however, neither of these SAA-like fields develop into an excursion or reversal. This suggests that the current weakened field will also recover without an extreme event such as an excursion or reversal. The SAA-like field structure at 46 ka appears to be coeval with published increases in geomagnetically modulated beryllium and chlorine nuclide production, despite the global dipole field not weakening significantly in our model during this time. This agreement suggests a greater complexity in the relationship between cosmogenic nuclide production and the geomagnetic field than is commonly assumed.

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“…The values of L obs for each model here decrease further back in time, so as to avoid introducing errors from the smoothing or regularization in the earlier models. These values may in fact not be optimal, but our previous experimentation (Tangborn and Kuang 2018 ) has shown that reducing them for the earlier field models can lead to more accurate forecasts in the modern era.…”

Section: Geodynamo Model and Data Assimilation Algorithmmentioning

confidence: 99%

Geomagnetic secular variation forecast using the NASA GEMS ensemble Kalman filter: A candidate SV model for IGRF-13

Tangborn

Kuang

Sabaka

et al. 2021

Earth Planets Space

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We have produced a 5-year mean secular variation (SV) of the geomagnetic field for the period 2020–2025. We use the NASA Geomagnetic Ensemble Modeling System (GEMS), which consists of the NASA Goddard geodynamo model and ensemble Kalman filter (EnKF) with 400 ensemble members. Geomagnetic field models are used as observations for the assimilation, including gufm1 (1590–1960), CM4 (1961–2000) and CM6 (2001–2019). The forecast involves a bias correction scheme that assumes that the model bias changes on timescales much longer than the forecast period, so that they can be removed by successive forecast series. The algorithm was validated on the time period 2010-2015 by comparing with CM6 before being applied to the 2020–2025 time period. This forecast has been submitted as a candidate predictive model of IGRF-13 for the period 2020–2025. Graphical abstract

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Impact of archeomagnetic field model data on modern era geomagnetic forecasts

Cited by 11 publications

References 33 publications

Global archaeomagnetic data: The state of the art and future challenges

Global archaeomagnetic data: The state of the art and future challenges

Earth’s magnetic field is probably not reversing

Geomagnetic secular variation forecast using the NASA GEMS ensemble Kalman filter: A candidate SV model for IGRF-13

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