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  • 2015-2019  (106)
  • 1995-1999  (8)
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  • 11
    facet.materialart.
    Unknown
    PANGAEA
    In:  Supplement to: Scott, Rebecca; Biastoch, Arne; Agamboue, Pierre D; Bayer, Till; Boussamba, Francois L; Formia, Angela; Godley, Brendan J; Mabert, Brice D K; Manfoumbi, Jean C; Schwarzkopf, Franziska; Sounguet, Guy-Philippe; Wagner, Patrick; Witt, Matthew J (2017): Spatio-temporal variation in ocean current-driven hatchling dispersion: Implications for the world's largest leatherback sea turtle nesting region. Diversity and Distributions, https://doi.org/10.1111/ddi.12554
    Publication Date: 2023-10-28
    Description: This data set describes the location of virtual floats representing turtle hatchlings throughout 60 modeled years. Floats were constrained to remain within depths of 0-6 m due to the positive buoyancy of hatchlings. Floats were first assigned to one of 20,000 random release locations within a large release area 125-400 km offshore from nesting beaches throughout the Republic/Democratic Republic of the Congo, Gabon and Equatorial Guinea spanning latitudes of c. 6°S to 3.5°N. For each month over the 4-month long hatching season (January-April), each of the 20,000 floats was assigned a random release day and drift simulations ran every year during the period 1960-2007 resulting in drift trajectories of approx. 4 million virtual floats. See Scott et al., 2017, Spatio-temporal variation in ocean current-driven hatchling dispersion: Implications for the world's largest leatherback sea turtle nesting region. Diversity Distrib, http://dx.doi.org/10.1111%2Fddi.12554 for details as to the model parameters. Each data set consists of data on the float ID (number 1,2,3 etc..) and its trajectory attributes (latitude/longitude) at each time step. Data are also provided on the temperature, salinity and density of the float at its respective position/time step. Data sets are sorted by float release date, and contain one data file for each year. Each data file has 11 columns, which contain the following data: float id, longitude, latitude, depth, time step, temperature, salinity, density, no time steps since start, distance to start point, bearing from start point
    Keywords: Climate - Biogeochemistry Interactions in the Tropical Ocean; File content; File format; File name; File size; Model; Sea-turtle_model; SFB754; Uniform resource locator/link to model result file
    Type: Dataset
    Format: text/tab-separated-values, 60 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: 2024-02-27
    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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  • 15
    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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  • 16
    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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  • 17
    facet.materialart.
    Unknown
    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
    Format: text
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  • 18
    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
    Location Call Number Limitation Availability
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  • 19
    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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  • 20
    Publication Date: 2020-06-18
    Description: High primary productivity in the equatorial Atlantic and Pacific oceans is one of the key features of tropical ocean biogeochemistry and fuels a substantial flux of particulate matter towards the abyssal ocean. How biological processes and equatorial current dynamics shape the particle size distribution and flux, however, is poorly understood. Here we use high-resolution size-resolved particle imaging and Acoustic Doppler Current Profiler data to assess these influences in equatorial oceans. We find an increase in particle abundance and flux at depths of 300 to 600 m at the Atlantic and Pacific equator, a depth range to which zooplankton and nekton migrate vertically in a daily cycle. We attribute this particle maximum to faecal pellet production by these organisms. At depths of 1,000 to 4,000 m, we find that the particulate organic carbon flux is up to three times greater in the equatorial belt (1° S–1° N) than in off-equatorial regions. At 3,000 m, the flux is dominated by small particles less than 0.53 mm in diameter. The dominance of small particles seems to be caused by enhanced active and passive particle export in this region, as well as by the focusing of particles by deep eastward jets found at 2° N and 2° S. We thus suggest that zooplankton movements and ocean currents modulate the transfer of particulate carbon from the surface to the deep ocean.
    Type: Article , PeerReviewed
    Format: text
    Format: text
    Format: video
    Format: video
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