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Characterization of oceanic signatures in the Earth's magnetic field in view of their applicability as ocean model constraints

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Irrgang,  Christopher
1.3 Earth System Modelling, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

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Zitation

Irrgang, C. (2017): Characterization of oceanic signatures in the Earth's magnetic field in view of their applicability as ocean model constraints, PhD Thesis, Berlin : Freie Univ., xxvi, 72 p.
URN: http://nbn-resolving.de/urn/resolver.pl?urn=urn:nbn:de:kobv:188-fudissthesis000000104991-1


Zitierlink: https://gfzpublic.gfz-potsdam.de/pubman/item/item_2576909
Zusammenfassung
The general circulation of the world ocean generates characteristic magnetic signals by interacting with the ambient geomagnetic field. These ocean-induced magnetic signals can principally be measured by satellites and could serve as indirect observations of the ocean. Since the so-called motionally induced magnetic field is to first order proportional to conductivity-weighted and depth-integrated ocean velocities, global oceanic magnetic field observations could provide new constraints on oceanic transports of water, heat, and salinity. However, many aspects of electromagnetic induction in the ocean are either not well understood or unknown. This ranges from the basic characterization of motional induction in the ocean to possible applications and benefits for ocean modelling. This cumulative thesis encompasses the characterization of electromagnetic induction in the ocean, both in terms of physical properties and model uncertainties. One new application of electromagnetic induction in the ocean is investigated, namely the possibility to constrain an ocean general circulation model with satellite observations of the ocean-induced magnetic field. An electromagnetic induction model is implemented into an ocean general circulation model. This model combination allows the investigation of specific influences of seawater properties on motional induction. In previous studies, the electric conductivity of seawater was often treated in a simplified way by assuming it to be uniformly distributed in the ocean and temporally constant. In the first application of the combined numerical models, it is shown that this assumption is insufficient for capturing the temporal variability of motional induction accurately. Considering a realistic three-dimensional seawater conductivity distribution based on ocean temperature and salinity increases the temporal variability of ocean-induced magnetic signals by up to 45 %. These changes are found to predominantly originate from large vertical gradients of seawater conductivity in the upper ocean. The modelling of the general ocean circulation and of motional induction is affected by various uncertainties and errors, which are introduced by forcing input data and by the numerical models themselves. For potential applications of motional induction, e.g., a reliable comparison of model results with observational data, or data assimilation experiments, a realistic estimation of model uncertainties is essential. Ensemble simulations based on different error scenarios are performed to estimate the aggregated uncertainty of the modelled ocean-induced magnetic field. It is shown that the uncertainty of the modelled ocean-induced magnetic field reaches up to 30 % of the signal strength and is subject to large spatial and seasonal variations. The wind stress forcing of the ocean model is a major source of uncertainty. However, specific spatially and temporally robust regions are identified in the ocean-induced magnetic field that retain a small uncertainty in all error scenarios. Based on the previous findings, data assimilation experiments with artificial satellite observations of the ocean-induced magnetic field are designed and conducted for the first time. In a model-based twin study, artificial satellite observations of the oceanic magnetic field are generated and sequentially assimilated into an ocean general circulation model with a localized ensemble Kalman filter. The impact of the data assimilation on the induced magnetic field, ocean velocities, temperature, and salinity is measured. Compared to a reference simulation without data assimilation, the ocean-induced magnetic field is improved by up to 17 % globally and up to 54 % locally. Improvements of the underlying depth-integrated ocean velocities show values up to 7 % globally, and up to 50 % locally. These improvements result from a consistently better recovery of ocean velocities from the sea surface down to the bottom of the ocean. However, the Kalman filter fails to improve ocean temperature and salinity globally. Kalman filter adjustments of the wind stress forcing of the ocean model are found to be essential for a successful data assimilation.