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  • 2020-2023  (3)
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  • 1
    Publication Date: 2022-09-20
    Description: North Atlantic Deep Water (NADW) is a crucial component of the Atlantic Meridional Overturning Circulation and, therefore, is an important factor of the climate system. In order to estimate the mean relative contributions, sources and pathways of the three different deep water mass components (namely Labrador Sea Water, Northeast Atlantic Deep Water and Denmark Strait Overflow Water) at the southern exit of the Labrador Sea, Lagrangian particle experiments were performed. The particles were seeded according to the strength of the velocity field along the 53° N section and computed 40 years backward in time in the three-dimensional velocity and hydrography field. Water masses were defined within the model output in the central Labrador Sea and the subpolar North Atlantic. The resulting transport pathways, their sources and corresponding transit time scales were inferred. Our experiments show that the majority of NADW passing 53° N is associated with diapycnal mass flux, accounting for 14.3 Sv (48 %), where 6.2 Sv originate from the Labrador Sea, compared to 4.7 Sv from the Irminger Sea. The second largest contribution originates from the mixed layer with 7.2 Sv (24 %), where the Labrador Sea contribution (5.9 Sv) dominates over the Irminger Sea contribution (1.0 Sv). Another 5.7 Sv (19 %) of NADW cross the Greenland–Scotland Ridge within the NADW density class, where about 2/3 pass Denmark Strait, while 1/3 cross the Iceland Scotland Ridge. The NADW exported at 53° N is hence dominated by entrainment through diapycnal mass flux and the mixed layer origin in the Labrador Sea.
    Type: Article , NonPeerReviewed
    Format: text
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  • 2
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    Elsevier
    In:  In: Climate Change. Elsevier, Amsterdam, The Netherlands, pp. 219-249. 3.ed. ISBN 978-0-12-821575-3
    Publication Date: 2022-01-06
    Description: The oceans' role in climate and climate change is manifold. The Ocean circulation transports large amounts of heat and freshwater on hemispheric space scales which have significant impacts on regional climate in the ocean itself but also noticeable consequences via atmospheric teleconnections on land. Due to the high heat capacity of seawater and the relatively slow ocean circulation, the oceans provide a significant “memory” for the climate system. Bodies of water that descend from the sea surface may reside in the ocean interior for decades and centuries, while preserving their temperature and salinity signature, before they surface again to interact with the overlying atmosphere. The residence time of water in the atmosphere is about ten days and the persistence of dynamical states of the atmospheric circulation may last up to a few weeks. Thus, on long time scales ocean dynamics becomes important for climate, which implies that climate variations and climate change can only partially be understood without consideration of ocean dynamics and the intricate ocean-atmosphere interaction. Since 1960 the heat uptake of the oceans has been 20 times larger than that of the atmosphere. Thus the oceans have been able to reduce the otherwise much more pronounced temperature rise in the atmospheric climate. Also, over the last 200 years the oceans have absorbed about half of the CO2 release into the atmosphere by human activities (fossil fuel combustion, de-forestation, cement production), thereby reducing the direct effect of greenhouse gases on atmospheric temperatures.This chapter aims to describe and explain fundamental principles of the ocean dynamics and gathers information about past, present and future states the world’s ocean and its role in climate change.
    Type: Book chapter , NonPeerReviewed
    Format: text
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  • 3
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    In:  [Paper] In: 17. International Symposium on Spatial and Temporal Databases, SSTD 2021, 23.-25.08.2021, Online ; pp. 126-129 .
    Publication Date: 2022-01-25
    Description: The distribution of passively drifting particles within highly turbulent flows is a classic problem in marine sciences. The use of trajectory clustering on huge amounts of simulated marine trajectory data to identify main pathways of drifting particles has not been widely investigated from a data science perspective yet. In this paper, we propose a fast and computationally light method to efficiently identify main pathways in large amounts of trajectory data. It aims at overcoming some of the issues of probabilistic maps and existing trajectory clustering approaches. Our approach is evaluated against simulated larvae dispersion data based on a real-world model that have been produced as part of work in the marine science domain.
    Type: Conference or Workshop Item , NonPeerReviewed
    Format: text
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