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  • Elsevier  (9)
  • PANGAEA  (6)
  • 2015-2019  (15)
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  • 1
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    PANGAEA
    In:  Supplement to: Steinle, Lea; Graves, Carolyn; Treude, Tina; Ferre, Benedicte; Biastoch, Arne; Bussmann, Ingeborg; Berndt, Christian; Krastel, Sebastian; James, Rachael H; Behrens, Erik; Böning, Claus W; Greinert, Jens; Sapart, Célia-Julia; Scheinert, Markus; Sommer, Stefan; Lehmann, Moritz F; Niemann, Helge (2015): Water column methanotrophy controlled by a rapid oceanographic switch. Nature Geoscience, 8(5), 378–382, https://doi.org/10.1038/ngeo2420
    Publication Date: 2023-03-03
    Description: Large amounts of the greenhouse gas methane are released from the seabed to the water column where it may be consumed by aerobic methanotrophic bacteria. This microbial filter is consequently the last marine sink for methane before its liberation to the atmosphere. The size and activity of methanotrophic communities, which determine the capacity of the water column methane filter, are thought to be mainly controlled by nutrient and redox dynamics, but little is known about the effects of ocean currents. Here, we report measurements of methanotrophic activity and biomass (CARD-FISH) at methane seeps west of Svalbard, and related them to physical water mass properties (CTD) and modelled current dynamics. We show that cold bottom water containing a large number of aerobic methanotrophs was rapidly displaced by warmer water with a considerably smaller methanotrophic community. This water mass exchange, caused by short-term variations of the West Spitsbergen Current, constitutes a rapid oceanographic switch severely reducing methanotrophic activity in the water column. Strong and fluctuating currents are widespread oceanographic features common at many methane seep systems and are thus likely to globally affect methane oxidation in the ocean water column.
    Type: Dataset
    Format: application/zip, 4 datasets
    Location Call Number Limitation Availability
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  • 2
    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
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  • 3
    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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  • 4
    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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  • 5
    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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  • 6
    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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  • 7
    Publication Date: 2021-02-08
    Description: We present a new surface-atmospheric dataset for driving ocean–sea-ice models based on Japanese 55-year atmospheric reanalysis (JRA-55), referred to here as JRA55-do. The JRA55-do dataset aims to replace the CORE interannual forcing version 2 (hereafter called the CORE dataset), which is currently used in the framework of the Coordinated Ocean-ice Reference Experiments (COREs) and the Ocean Model Intercomparison Project (OMIP). A major improvement in JRA55-do is the refined horizontal grid spacing (∼ 55 km) and temporal interval (3 hr). The data production method for JRA55-do essentially follows that of the CORE dataset, whereby the surface fields from an atmospheric reanalysis are adjusted relative to reference datasets. To improve the adjustment method, we use high-quality products derived from satellites and from several other atmospheric reanalysis projects, as well as feedback on the CORE dataset from the ocean modelling community. Notably, the surface air temperature and specific humidity are adjusted using multi-reanalysis ensemble means. In JRA55-do, the downwelling radiative fluxes and precipitation, which are affected by an ambiguous cloud parameterisation employed in the atmospheric model used for the reanalysis, are based on the reanalysis products. This approach represents a notable change from the CORE dataset, which imported independent observational products. Consequently, the JRA55-do dataset is more self-contained than the CORE dataset, and thus can be continually updated in near real-time. The JRA55-do dataset extends from 1958 to the present, with updates expected at least annually. This paper details the adjustments to the original JRA-55 fields, the scientific rationale for these adjustments, and the evaluation of JRA55-do. The adjustments successfully corrected the biases in the original JRA-55 fields. The globally averaged features are similar between the JRA55-do and CORE datasets, implying that JRA55-do can suitably replace the CORE dataset for use in driving global ocean–sea-ice models.
    Type: Article , PeerReviewed
    Format: text
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  • 8
    Publication Date: 2019-09-23
    Description: Highlights: • We focus on ACC and Southern Ocean MOC during 1958–2007 in 17 CORE-II forced models. • Most CORE-II simulations are close to eddy saturation. • Most CORE-II simulations are far from showing signs of eddy compensation. • Constant in time or space k results in poor representation of mesoscale eddy effects. • MOC has larger sensitivity than ACC transport even in eddy saturated state. Abstract: In the framework of the second phase of the Coordinated Ocean-ice Reference Experiments (CORE-II), we present an analysis of the representation of the Antarctic Circumpolar Current (ACC) and Southern Ocean meridional overturning circulation (MOC) in a suite of seventeen global ocean–sea ice models. We focus on the mean, variability and trends of both the ACC and MOC over the 1958–2007 period, and discuss their relationship with the surface forcing. We aim to quantify the degree of eddy saturation and eddy compensation in the models participating in CORE-II, and compare our results with available observations, previous fine-resolution numerical studies and theoretical constraints. Most models show weak ACC transport sensitivity to changes in forcing during the past five decades, and they can be considered to be in an eddy saturated regime. Larger contrasts arise when considering MOC trends, with a majority of models exhibiting significant strengthening of the MOC during the late 20th and early 21st century. Only a few models show a relatively small sensitivity to forcing changes, responding with an intensified eddy-induced circulation that provides some degree of eddy compensation, while still showing considerable decadal trends. Both ACC and MOC interannual variabilities are largely controlled by the Southern Annular Mode (SAM). Based on these results, models are clustered into two groups. Models with constant or two-dimensional (horizontal) specification of the eddy-induced advection coefficient κ show larger ocean interior decadal trends, larger ACC transport decadal trends and no eddy compensation in the MOC. Eddy-permitting models or models with a three-dimensional time varying κ show smaller changes in isopycnal slopes and associated ACC trends, and partial eddy compensation. As previously argued, a constant in time or space κ is responsible for a poor representation of mesoscale eddy effects and cannot properly simulate the sensitivity of the ACC and MOC to changing surface forcing. Evidence is given for a larger sensitivity of the MOC as compared to the ACC transport, even when approaching eddy saturation. Future process studies designed for disentangling the role of momentum and buoyancy forcing in driving the ACC and MOC are proposed.
    Type: Article , PeerReviewed
    Format: text
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  • 9
    Publication Date: 2019-06-28
    Description: Highlights: • We compare the simulated Arctic Ocean in 15 global ocean–sea ice models. • There is a large spread in temperature bias in the Arctic Ocean between the models. • Warm bias models have a strong temperature anomaly of inflow of Atlantic Water. • Dense outflows formed on Arctic shelves are not captured accurately in the models. In this paper we compare the simulated Arctic Ocean in 15 global ocean-sea ice models in the framework of the Coordinated Ocean-ice Reference Experiments, phase II (CORE-II). Most of these models are the ocean and sea-ice components of the coupled climate models used in the Coupled Model Intercomparison Project Phase 5 (CMIP5) experiments. We mainly focus on the hydrography of the Arctic interior, the state of Atlantic Water layer and heat and volume transports at the gateways of the Davis Strait, the Bering Strait, the Fram Strait and the Barents Sea Opening. We found that there is a large spread in temperature in the Arctic Ocean between the models, and generally large differences compared to the observed temperature at intermediate depths. Warm bias models have a strong temperature anomaly of inflow of the Atlantic Water entering the Arctic Ocean through the Fram Strait. Another process that is not represented accurately in the CORE-II models is the formation of cold and dense water, originating on the eastern shelves. In the cold bias models, excessive cold water forms in the Barents Sea and spreads into the Arctic Ocean through the St. Anna Through. There is a large spread in the simulated mean heat and volume transports through the Fram Strait and the Barents Sea Opening. The models agree more on the decadal variability, to a large degree dictated by the common atmospheric forcing. We conclude that the CORE-II model study helps us to understand the crucial biases in the Arctic Ocean. The current coarse resolution state-of-the-art ocean models need to be improved in accurate representation of the Atlantic Water inflow into the Arctic and density currents coming from the shelves.
    Type: Article , PeerReviewed , info:eu-repo/semantics/article
    Format: text
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  • 10
    Publication Date: 2017-04-13
    Description: Highlights: • A joint analysis of deep current meter records in the western North Atlantic. • Intra-seasonal variability dominates the deep boundary current. • Topographic waves near 10d periods trapped over steep topography. • Basin centers are showing longer periods (50d) caused by the eddy field. • Observed variability characteristics compared to high resolution model simulation. Abstract The Deep Western Boundary Current (DWBC) along the western margin of the subpolar North Atlantic is an important component of the deep limb of the Meridional Overturning near its northern origins. A network of moored arrays from Denmark Strait to the tail of the Grand Banks has been installed for almost two decades to observe the boundary currents and transports of North Atlantic Deep Water as part of an internationally coordinated observatory for the Atlantic Meridional Overturning Circulation. The dominant variability in all of the moored velocity time series is in the week-to-month period range. While the temporal characteristics of this variability change only gradually between Denmark Strait and Flemish Cap, a broad band of longer term variability is present farther along the path of the DWBC at the Grand Banks and in the interior basins (Labrador and Irminger Seas). The vigorous intra-seasonal variability may well mask possible interannual to decadal variability that is typically an order of magnitude smaller than the high-frequency fluctuations. Here, the intra-seasonal variability is quantified at key positions along the DWBC path using both, observations and high resolution model data. The results are used to evaluate the model circulation, and in turn the model is used to relate the discrete measurements to the overall pattern of the subpolar circulation. Topographic waves are found to be trapped by the steep topography all around the western basins, the Labrador and Irminger Seas. In the Labrador Sea, the high intra-seasonal variability of the boundary current regime is separated by a region of extremely low variability in narrow recirculation cells from the basin interior. There, the variability is also on intra-seasonal timescales, but at much longer periods around 50 days.
    Type: Article , PeerReviewed , info:eu-repo/semantics/article
    Format: text
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