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  • 11
    Publication Date: 2024-02-27
    Keywords: 0; 1; 10; 100; 101; 102; 103; 104; 105; 106; 107; 108; 109; 11; 110; 111; 112; 113; 114; 115; 116; 117; 118; 119; 12; 120; 121; 122; 123; 124; 125; 126; 127; 128; 13; 14; 15; 16; 17; 18; 19; 2; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 3; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 4; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 5; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 6; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 7; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 8; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 9; 90; 91; 92; 93; 94; 95; 96; 97; 98; 99; Calculated; CTD, Sea-Bird SBE 911plus; CTD/Rosette; CTD-RO; Date/Time of event; Density, sigma-theta (0); DEPTH, water; Elevation of event; Event label; Latitude of event; Longitude of event; Maria S. Merian; MSM38; MSM38_343; MSM38_344; MSM38_345; MSM38_347; MSM38_348; MSM38_349; MSM38_350; MSM38_354; MSM38_355; MSM38_358; MSM38_359; MSM38_360; MSM38_361; MSM38_363; MSM38_364; MSM38_365; MSM38_366; MSM38_367; MSM38_368; MSM38_369; MSM38_370; MSM38_372; MSM38_373; MSM38_374; MSM38_375; MSM38_376; MSM38_377; MSM38_378; MSM38_379; MSM38_380; MSM38_381; MSM38_382; MSM38_383; MSM38_384; MSM38_385; MSM38_386; MSM38_387; MSM38_388; MSM38_389; MSM38_390; MSM38_391; MSM38_392; MSM38_393; MSM38_394; MSM38_395; MSM38_396; MSM38_397; MSM38_398; MSM38_399; MSM38_400; MSM38_401; MSM38_402; MSM38_403; MSM38_404; MSM38_405; MSM38_406; MSM38_407; MSM38_408; MSM38_409; MSM38_410; MSM38_411; MSM38_412; MSM38_413; MSM38_414; MSM38_415; MSM38_417; MSM38_418; MSM38_419; MSM38_420; MSM38_421; MSM38_422; MSM38_423; MSM38_424; MSM38_425; MSM38_426; MSM38_427; MSM38_428; MSM38_429; MSM38_430; MSM38_431; MSM38_432; MSM38_433; MSM38_434; MSM38_435; MSM38_436; MSM38_437; MSM38_438; MSM38_439; MSM38_440; MSM38_441; MSM38_442; MSM38_443; MSM38_444; MSM38_445; MSM38_446; MSM38_447; MSM38_448; MSM38_449; MSM38_450; MSM38_451; MSM38_452; MSM38_453; MSM38_454; MSM38_455; MSM38_456; MSM38_457; MSM38_458; MSM38_459; MSM38_460; MSM38_461; MSM38_462; MSM38_463; MSM38_464; MSM38_465; MSM38_466; MSM38_467; MSM38_468; MSM38_469; MSM38_470; MSM38_471; MSM38_472; MSM38_473; MSM38_474; MSM38_475; MSM38_476; MSM38_477; MSM38_478; MSM38_479; MSM38_480; Oxygen; Oxygen sensor, SBE 43; Pressure, water; Salinity; Temperature, water; Temperature, water, potential
    Type: Dataset
    Format: text/tab-separated-values, 2181198 data points
    Location Call Number Limitation Availability
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  • 12
    Publication Date: 2024-02-02
    Keywords: Campaign of event; CTD/Rosette; CTD-RO; Date/Time of event; Depth, bottom/max; DEPTH, water; Event label; Latitude of event; Longitude of event; Maria S. Merian; MSM21/4; MSM21/4_546-2; MSM21/4_550-1; MSM21/4_551-1; MSM21/4_552-1; MSM21/4_553-1; MSM21/4_554-1; MSM21/4_555-1; MSM21/4_556-1; MSM21/4_557-1; MSM21/4_558-1; MSM21/4_559-1; MSM21/4_580-1; MSM21/4_581-1; MSM21/4_582-1; MSM21/4_583-1; MSM21/4_584-1; MSM21/4_613-1; MSM21/4_633-1; MSM21/4_634-1; MSM21/4_635-1; MSM21/4_636-1; MSM21/4_637-1; MSM21/4_638-1; MSM21/4_639-1; MSM21/4_640-1; MSM21/4_641-1; MSM21/4_642-1; MSM21/4_654-1; MSM21/4_655-1; North Greenland Sea; Salinity; Sample code/label; Temperature, water; Type
    Type: Dataset
    Format: text/tab-separated-values, 55415 data points
    Location Call Number Limitation Availability
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  • 13
    Publication Date: 2024-02-02
    Keywords: 3H-CH4 incubation; Bacteria, methane oxidizing, abundance; Bottle number; Campaign of event; Cell density; CTD/Rosette; CTD-RO; Date/Time of event; Depth, bottom/max; DEPTH, water; Event label; Latitude of event; Longitude of event; Maria S. Merian; Methane; Methane oxidation rate; Methane oxidation rate, standard deviation; MSM21/4; MSM21/4_546-2; MSM21/4_550-1; MSM21/4_551-1; MSM21/4_552-1; MSM21/4_553-1; MSM21/4_554-1; MSM21/4_555-1; MSM21/4_556-1; MSM21/4_557-1; MSM21/4_558-1; MSM21/4_559-1; MSM21/4_580-1; MSM21/4_581-1; MSM21/4_582-1; MSM21/4_583-1; MSM21/4_584-1; MSM21/4_613-1; MSM21/4_633-1; MSM21/4_634-1; MSM21/4_635-1; MSM21/4_636-1; MSM21/4_637-1; MSM21/4_638-1; MSM21/4_639-1; MSM21/4_640-1; MSM21/4_641-1; MSM21/4_642-1; MSM21/4_654-1; MSM21/4_655-1; North Greenland Sea; Sample code/label; Turnover rate, methane oxidation; Turnover rate, standard deviation; Type
    Type: Dataset
    Format: text/tab-separated-values, 4829 data points
    Location Call Number Limitation Availability
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  • 14
    Publication Date: 2024-04-18
    Keywords: 3H-CH4 incubation; Campaign of event; CTD/Rosette; CTD-RO; Date/Time of event; Depth, bottom/max; DEPTH, water; Event label; Latitude of event; Longitude of event; Methane; Methane oxidation rate; Methane oxidation rate, standard deviation; Norway, Norwegian Basin; POS419; POS419_599-2; POS419_615-9; POS419_654-33; POS419_671-36; Poseidon; Sample code/label; Turnover rate, methane oxidation; Turnover rate, standard deviation; Type
    Type: Dataset
    Format: text/tab-separated-values, 229 data points
    Location Call Number Limitation Availability
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  • 15
    Publication Date: 2024-04-18
    Keywords: Campaign of event; CTD/Rosette; CTD-RO; Date/Time of event; Depth, bottom/max; DEPTH, water; Event label; Latitude of event; Longitude of event; Norway, Norwegian Basin; POS419; POS419_599-2; POS419_615-9; POS419_654-33; POS419_671-36; Poseidon; Salinity; Sample code/label; Temperature, water; Type
    Type: Dataset
    Format: text/tab-separated-values, 180 data points
    Location Call Number Limitation Availability
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  • 16
    Publication Date: 2022-09-20
    Description: Marine scientists investigate the movement of oceanic water particles with floating measurement devices released in the real ocean, as well as with virtual particles released in numerical model simulations. The detection, visualization, and evolution of clustered particles is key for gaining a comprehensive understanding of the underlying processes in the oceans. Thereby, vast amounts of mobility data (3D coordinates of these particles over time) need to be analyzed using mobility data science methods. In this paper, we describe the application of data science techniques to detect particle clusters and, more importantly, to track the evolution of these clusters over time in order to support the analysis of oceanic flows. In particular, we apply a well-known concept for tracking the cluster evolution from the data mining community that relies on pair-counting and, thus, is rather inefficient. In order to be applicable to large amounts of particles, we further elaborate two heuristic solutions to compute the cluster transitions based on spatial approximations. Experiments on real world data show a considerable speed-up while sacrificing marginal accuracy drops. Our prototype is used by domain experts for the analysis of the large-scale ocean by virtual particle release experiments in ocean simulations.
    Type: Conference or Workshop Item , NonPeerReviewed
    Format: text
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  • 17
    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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  • 18
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    AGU (American Geophysical Union) | Wiley
    In:  Journal of Geophysical Research: Oceans, 122 (4). 2830-2846 .
    Publication Date: 2020-02-06
    Description: The upstream sources and pathways of the Denmark Strait Overflow Water and their variability have been investigated using a high-resolution model hindcast. This global simulation covers the period from 1948 to 2009 and uses a fine model mesh (1/20°) to resolve mesoscale features and the complex current structure north of Iceland explicitly. The three sources of the Denmark Strait Overflow, the shelfbreak East Greenland Current (EGC), the separated EGC, and the North Icelandic Jet, have been analyzed using Eulerian and Lagrangian diagnostics. The shelfbreak EGC contributes the largest fraction in terms of volume and freshwater transport to the Denmark Strait Overflow and is the main driver of the overflow variability. The North Icelandic Jet contributes the densest water to the Denmark Strait Overflow and shows only small temporal transport variations. During summer, the net volume and freshwater transports to the south are reduced. On interannual time scales, these transports are highly correlated with the large-scale wind stress curl around Iceland and, to some extent, influenced by the North Atlantic Oscillation, with enhanced southward transports during positive phases. The Lagrangian trajectories support the existence of a hypothesized overturning loop along the shelfbreak north of Iceland, where water carried by the North Icelandic Irminger Current is transformed and feeds the North Icelandic Jet. Monitoring these two currents and the region north of the Iceland shelfbreak could provide the potential to track long-term changes in the Denmark Strait Overflow and thus also the AMOC.
    Type: Article , PeerReviewed , info:eu-repo/semantics/article
    Format: text
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  • 19
    Publication Date: 2021-02-08
    Description: Highlights: • Lagrangian ocean analysis is a powerful way to analyse the output of ocean circulation models • We present a review of the Kinematic framework, available tools, and applications of Lagrangian ocean analysis • While there are unresolved questions, the framework is robust enough to be used widely in ocean modelling Abstract: Lagrangian analysis is a powerful way to analyse the output of ocean circulation models and other ocean velocity data such as from altimetry. In the Lagrangian approach, large sets of virtual particles are integrated within the three-dimensional, time-evolving velocity fields. Over several decades, a variety of tools and methods for this purpose have emerged. Here, we review the state of the art in the field of Lagrangian analysis of ocean velocity data, starting from a fundamental kinematic framework and with a focus on large-scale open ocean applications. Beyond the use of explicit velocity fields, we consider the influence of unresolved physics and dynamics on particle trajectories. We comprehensively list and discuss the tools currently available for tracking virtual particles. We then showcase some of the innovative applications of trajectory data, and conclude with some open questions and an outlook. The overall goal of this review paper is to reconcile some of the different techniques and methods in Lagrangian ocean analysis, while recognising the rich diversity of codes that have and continue to emerge, and the challenges of the coming age of petascale computing.
    Type: Article , PeerReviewed
    Format: text
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  • 20
    Publication Date: 2021-02-08
    Description: Decadal variabilities in Indian Ocean subsurface ocean heat content (OHC; 50–300 m) since the 1950s are examined using ocean reanalyses. This study elaborates on how Pacific variability modulates the Indian Ocean on decadal time scales through both oceanic and atmospheric pathways. High correlations between OHC and thermocline depth variations across the entire Indian Ocean Basin suggest that OHC variability is primarily driven by thermocline fluctuations. The spatial pattern of the leading mode of decadal Indian Ocean OHC variability closely matches the regression pattern of OHC on the interdecadal Pacific oscillation (IPO), emphasizing the role of the Pacific Ocean in determining Indian Ocean OHC decadal variability. Further analyses identify different mechanisms by which the Pacific influences the eastern and western Indian Ocean. IPO-related anomalies from the Pacific propagate mainly through oceanic pathways in the Maritime Continent to impact the eastern Indian Ocean. By contrast, in the western Indian Ocean, the IPO induces wind-driven Ekman pumping in the central Indian Ocean via the atmospheric bridge, which in turn modifies conditions in the southwestern Indian Ocean via westward-propagating Rossby waves. To confirm this, a linear Rossby wave model is forced with wind stresses and eastern boundary conditions based on reanalyses. This linear model skillfully reproduces observed sea surface height anomalies and highlights both the oceanic connection in the eastern Indian Ocean and the role of wind-driven Ekman pumping in the west. These findings are also reproduced by OGCM hindcast experiments forced by interannual atmospheric boundary conditions applied only over the Pacific and Indian Oceans, respectively.
    Type: Article , PeerReviewed
    Format: text
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