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  • 2015-2019  (106)
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  • 11
    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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  • 12
    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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  • 13
    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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  • 14
    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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  • 15
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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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  • 16
    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
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  • 17
    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
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  • 18
    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
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  • 19
    Publication Date: 2021-02-08
    Description: Highlights: • Comparison of global NEMO and FESOM configurations with emphasis on the Agulhas system. • Both models simulate a reasonable and comparable large-scale circulation. • Both models have individual strengths and weaknesses to match the observations of the WBC system. • The numerical cost of FESOM is twice the one of NEMO. Abstract: Many questions in ocean and climate modelling require the combined use of high resolution, global coverage and multi-decadal integration length. For this combination, even modern resources limit the use of traditional structured-mesh grids. Here we compare two approaches: A high-resolution grid nested into a global model at coarser resolution (NEMO with AGRIF) and an unstructured-mesh grid (FESOM) which allows to variably enhance resolution where desired. The Agulhas system around South Africa is used as a testcase, providing an energetic interplay of a strong western boundary current and mesoscale dynamics. Its open setting into the horizontal and global overturning circulations also requires global coverage. Both model configurations simulate a reasonable large-scale circulation. Distribution and temporal variability of the wind-driven circulation are quite comparable due to the same atmospheric forcing. However, the overturning circulation differs, owing each model's ability to represent formation and spreading of deep water masses. In terms of regional, high-resolution dynamics, all elements of the Agulhas system are well represented. Owing to the strong nonlinearity in the system, Agulhas Current transports of both configurations and in comparison with observations differ in strength and temporal variability. Similar decadal trends in Agulhas Current transport and Agulhas leakage are linked to the trends in wind forcing. Although the number of 3D wet grid points used in FESOM is similar to that in the nested NEMO, FESOM uses about two times the number of CPUs to obtain the same model throughput (in terms of simulated model years per day). This is feasible due to the high scalability of the FESOM code.
    Type: Article , PeerReviewed
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  • 20
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    AGU (American Geophysical Union) | Wiley
    In:  Journal of Geophysical Research: Oceans, 123 (2). pp. 1471-1484.
    Publication Date: 2021-02-08
    Description: The variability of the Atlantic Meridional Overturning Circulation (AMOC) may play a role in sea surface temperature predictions on seasonal to decadal time scales. Therefore, AMOC seasonal cycles are a potential baseline for interpreting predictions. Here we present estimates for the seasonal cycle of transports of volume, temperature, and freshwater associated with the upper limb of the AMOC in the eastern subpolar North Atlantic on the Extended Ellett Line hydrographic section between Scotland and Iceland. Due to weather, ship‐based observations are primarily in summer. Recent glider observations during other seasons present an opportunity to investigate the seasonal variability in the upper layer of the AMOC. First, we document a new method to quality control and merge ship, float, and glider hydrographic observations. This method accounts for the different spatial sampling rates of the three platforms. The merged observations are used to compute seasonal cycles of volume, temperature, and freshwater transports in the Rockall Trough. These estimates are similar to the seasonal cycles in two eddy‐resolving ocean models. Volume transport appears to be the primary factor modulating other Rockall Trough transports. Finally, we show that the weakest transports occur in summer, consistent with seasonal changes in the regional‐scale wind stress curl. Although the seasonal cycle is weak compared to other variability in this region, the amplitude of the seasonal cycle in the Rockall Trough, roughly 0.5–1 Sv about a mean of 3.4 Sv, may account for up to 7–14% of the heat flux between Scotland and Greenland.
    Type: Article , PeerReviewed , info:eu-repo/semantics/article
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