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  • 1
    Publication Date: 2024-05-14
    Description: The thermokarst lakes of permafrost regions play a major role in the global carbon cycle. These lakes are sources of methane to the atmosphere although the methane flux is restricted by an ice cover for most of the year. How methane concentrations and fluxes in these waters are affected by the presence of an ice cover is poorly understood. To relate water body morphology, ice formation and methane to each other, we studied the ice of three different water bodies in locations typical of the transition of permafrost from land to ocean in a continuous permafrost coastal region in Siberia. In total, 11 ice cores were analyzed as records of the freezing process and methane composition during the winter season. The three water bodies differed in terms of connectivity to the sea, which affected fall freezing. The first was a bay underlain by submarine permafrost (Tiksi Bay, BY), the second a shallow thermokarst lagoon cut off from the sea in winter (Polar Fox Lagoon, LG) and the third a land-locked freshwater thermokarst lake (Goltsovoye Lake, LK). Ice on all water bodies was mostly methane-supersaturated with respect to atmospheric equilibrium concentration, except for three cores from the isolated lake. In the isolated thermokarst lake, ebullition from actively thawing basin slopes resulted in the localized integration of methane into winter ice. Stable δ13C-CH4 isotope signatures indicated that methane in the lagoon ice was oxidized to concentrations close to or below the calculated atmospheric equilibrium concentration. Increasing salinity during winter freezing led to a micro-environment on the lower ice surface where methane oxidation occurred and the lagoon ice functioned as a methane sink. In contrast, the ice of the coastal marine environment was slightly supersaturated with methane, consistent with the brackish water below. Our interdisciplinary process study shows how water body morphology affects ice formation which mitigates methane fluxes to the atmosphere.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
    Format: application/pdf
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  • 2
    Publication Date: 2024-05-14
    Description: Here we provide particle size and biovolume distribution data from an Underwater Vision Profiler 6, mounted on a BGC Argo Float with the WMO number 6903095. The float was deployed in a cyclonic eddy off Cape Columbine, South Africa on the 13 April 2021 close to the eddy center at 33.07 degree South, 13.89 degree East. Parking depth was set at 300 m and profiling depth initially to 600 m and later increased to 1000 m depth to maintain the float in the eddy. Profiling frequency was every three days. It stayed within this eddy for about five months and then operated East and Southeast of South Africa until it was deliberately picked up on the 17 September 2022 at 34.43 degrees South and 10.21 degrees East.
    Keywords: 0000a_WMO6903095; 0000p_WMO6903095; 0001a_WMO6903095; 0001p_WMO6903095; 0002a_WMO6903095; 0002p_WMO6903095; 0003a_WMO6903095; 0003p_WMO6903095; 0004a_WMO6903095; 0004p_WMO6903095; 0005a_WMO6903095; 0005p_WMO6903095; 0006a_WMO6903095; 0006p_WMO6903095; 0007a_WMO6903095; 0007p_WMO6903095; 0008a_WMO6903095; 0008p_WMO6903095; 0009a_WMO6903095; 0009p_WMO6903095; 0010a_WMO6903095; 0010p_WMO6903095; 0011a_WMO6903095; 0011p_WMO6903095; 0012a_WMO6903095; 0012p_WMO6903095; 0013a_WMO6903095; 0013p_WMO6903095; 0014a_WMO6903095; 0014p_WMO6903095; 0015a_WMO6903095; 0015p_WMO6903095; 0016a_WMO6903095; 0016p_WMO6903095; 0017a_WMO6903095; 0017p_WMO6903095; 0018a_WMO6903095; 0018p_WMO6903095; 0019a_WMO6903095; 0019p_WMO6903095; 0020a_WMO6903095; 0020p_WMO6903095; 0021a_WMO6903095; 0021p_WMO6903095; 0022a_WMO6903095; 0022p_WMO6903095; 0023a_WMO6903095; 0023p_WMO6903095; 0024a_WMO6903095; 0024p_WMO6903095; 0025a_WMO6903095; 0025p_WMO6903095; 0026a_WMO6903095; 0026p_WMO6903095; 0027a_WMO6903095; 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0083p_WMO6903095; 0084a_WMO6903095; 0084p_WMO6903095; 0085a_WMO6903095; 0085p_WMO6903095; 0086a_WMO6903095; 0086p_WMO6903095; 0087a_WMO6903095; 0087p_WMO6903095; 0088a_WMO6903095; 0088p_WMO6903095; 0089a_WMO6903095; 0089p_WMO6903095; 0090a_WMO6903095; 0090p_WMO6903095; 0091a_WMO6903095; 0091p_WMO6903095; 0092a_WMO6903095; 0092p_WMO6903095; 0093a_WMO6903095; 0093p_WMO6903095; 0094a_WMO6903095; 0094p_WMO6903095; 0095a_WMO6903095; 0095p_WMO6903095; 0096a_WMO6903095; 0096p_WMO6903095; 0097a_WMO6903095; 0097p_WMO6903095; 0098a_WMO6903095; 0098p_WMO6903095; 0099a_WMO6903095; 0099p_WMO6903095; 0100a_WMO6903095; 0100p_WMO6903095; 0101a_WMO6903095; 0101p_WMO6903095; 0102a_WMO6903095; 0102p_WMO6903095; 0103a_WMO6903095; 0103p_WMO6903095; 0104a_WMO6903095; 0104p_WMO6903095; 0105a_WMO6903095; 0105p_WMO6903095; 0106a_WMO6903095; 0106p_WMO6903095; 0107a_WMO6903095; 0107p_WMO6903095; 0108a_WMO6903095; 0108p_WMO6903095; 0109a_WMO6903095; 0109p_WMO6903095; 0110a_WMO6903095; 0110p_WMO6903095; 0111a_WMO6903095; 0111p_WMO6903095; 0112a_WMO6903095; 0112p_WMO6903095; 0113a_WMO6903095; 0113p_WMO6903095; 0114a_WMO6903095; 0114p_WMO6903095; 0115a_WMO6903095; 0115p_WMO6903095; 0116a_WMO6903095; 0116p_WMO6903095; 0117a_WMO6903095; 0117p_WMO6903095; 0118a_WMO6903095; 0118p_WMO6903095; 0119a_WMO6903095; 0119p_WMO6903095; 0120a_WMO6903095; 0120p_WMO6903095; 0121a_WMO6903095; 0121p_WMO6903095; 0122a_WMO6903095; 0122p_WMO6903095; 0123a_WMO6903095; 0123p_WMO6903095; 0124a_WMO6903095; 0124p_WMO6903095; 0125a_WMO6903095; 0125p_WMO6903095; 0126a_WMO6903095; 0126p_WMO6903095; 0127a_WMO6903095; 0127p_WMO6903095; 0128a_WMO6903095; 0128p_WMO6903095; 0129a_WMO6903095; 0129p_WMO6903095; 0130a_WMO6903095; 0130p_WMO6903095; 0131a_WMO6903095; 0131p_WMO6903095; 0132a_WMO6903095; 0132p_WMO6903095; 0133a_WMO6903095; 0133p_WMO6903095; 0134a_WMO6903095; 0134p_WMO6903095; 0135a_WMO6903095; 0135p_WMO6903095; 0136a_WMO6903095; 0136p_WMO6903095; 0137a_WMO6903095; 0137p_WMO6903095; 0138a_WMO6903095; 0138p_WMO6903095; 0139a_WMO6903095; 0139p_WMO6903095; 0140a_WMO6903095; 0140p_WMO6903095; 0141a_WMO6903095; 0141p_WMO6903095; 0142a_WMO6903095; 0142p_WMO6903095; 0143a_WMO6903095; 0143p_WMO6903095; 0144a_WMO6903095; 0144p_WMO6903095; 0145a_WMO6903095; 0145p_WMO6903095; 0146a_WMO6903095; 0146p_WMO6903095; 0147a_WMO6903095; 0147p_WMO6903095; 0148a_WMO6903095; 0148p_WMO6903095; 0149a_WMO6903095; 0149p_WMO6903095; 0150a_WMO6903095; 0150p_WMO6903095; 0151a_WMO6903095; 0151p_WMO6903095; 0152a_WMO6903095; 0152p_WMO6903095; 0153a_WMO6903095; 0153p_WMO6903095; 0154a_WMO6903095; 0154p_WMO6903095; 0155a_WMO6903095; 0155p_WMO6903095; 0156a_WMO6903095; 0156p_WMO6903095; 0157a_WMO6903095; 0157p_WMO6903095; 0158a_WMO6903095; 0158p_WMO6903095; 0159a_WMO6903095; 0159p_WMO6903095; 0160a_WMO6903095; 0160p_WMO6903095; 0161a_WMO6903095; 0161p_WMO6903095; 0162a_WMO6903095; 0162p_WMO6903095; 0163a_WMO6903095; 0163p_WMO6903095; 0164a_WMO6903095; 0164p_WMO6903095; 0165a_WMO6903095; 0165p_WMO6903095; 0166a_WMO6903095; 0166p_WMO6903095; 0167a_WMO6903095; 0167p_WMO6903095; 0168a_WMO6903095; 0168p_WMO6903095; 0169a_WMO6903095; 0169p_WMO6903095; 0170a_WMO6903095; 0170p_WMO6903095; 0171a_WMO6903095; 0171p_WMO6903095; 0172a_WMO6903095; 0172p_WMO6903095; 0173a_WMO6903095; 0173p_WMO6903095; 0174a_WMO6903095; 0174p_WMO6903095; 0175a_WMO6903095; 0175p_WMO6903095; 0176a_WMO6903095; 0176p_WMO6903095; 0177a_WMO6903095; 0177p_WMO6903095; 0178a_WMO6903095; 0178p_WMO6903095; 0179a_WMO6903095; 0179p_WMO6903095; 0180a_WMO6903095; 0180p_WMO6903095; 0181a_WMO6903095; 0181p_WMO6903095; 0182a_WMO6903095; 0182p_WMO6903095; 0183a_WMO6903095; 0183p_WMO6903095; ARGOFL; Argo float; Biovolume; DATE/TIME; Event label; in situ imaging; LATITUDE; LONGITUDE; MOPGA-TAD; Particle concentration, fractionated; particle distribution; Pressure, water; Sample code/label; TRIATLAS; Tropical and South Atlantic climate-based marine ecosystem predictions for sustainable management; Tropical Atlantic Deoxygenation: gateway dynamics, feedback mechanisms and ecosystem impacts; Volume
    Type: Dataset
    Format: text/tab-separated-values, 2518238 data points
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  • 3
    Publication Date: 2024-05-13
    Keywords: Course of study: MSc Biological Oceanography
    Type: Thesis , NonPeerReviewed
    Format: text
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  • 4
  • 5
    Publication Date: 2024-05-13
    Description: This dataset, resulting from Task 4.5 quantifies the potential of ocean-based negative emission technologies (NETs) using Earth System Models (ESMs). The dataset consists of simulations of ocean liming and direct CO2 removal from seawater. The ocean liming scenarios utilize excess CaO and cement production capacities from the EU, China, and the US, exploring their application for ocean alkalinization and gauging termination effects. Simulations ran from 2015-2100 using NorESM2-LM, EC-Earth3-CC, and AWI-CM models. This comprehensive dataset informs on the efficacy of ocean-based NETs and provides insights for future climate mitigation strategies, aligning with the Paris Agreement goals. It facilitates further analysis and supports ongoing research in global carbon cycle feedbacks of ocean-based NETs.
    Type: Report , NonPeerReviewed , info:eu-repo/semantics/book
    Format: text
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  • 6
    Publication Date: 2024-05-13
    Description: Wissenschaftliche Untersuchungen haben gezeigt, dass einige invasive Arten bzw. Populationen gegenüber Umweltstress robuster sind als nicht invasive Arten bzw. Populationen derselben Art. Es wird vermutet, dass die Toleranz gegenüber abiotischen Stressoren wahrscheinlich eine Schlüsseleigenschaft von invasiven Arten darstellt. Während des Transports von Individuen in einen neuen Lebensraum oder durch Stressereignisse könnten widrige abiotische Bedingungen eine Selektion stresstoleranter Individuen hervorrufen, was die erhöhte Stresstoleranz von invasiven Arten bzw. Populationen erklären könnte. Um dies zu testen, habe ich in einem Laborexperiment ein Transport- bzw. Stressereignis simuliert. In zwei unabhängigen Experimenten habe ich in einer Gruppe von Individuen der Weißbeingarnele Penaeus vannamei durch erhöhte Temperaturen mindestens 50 % Mortalität erzeugt. Hierbei wurden die Individuen in unabhängigen Replikaten für 13 Tage 37°C (Langzeit-Stressexperiment) bzw. für 2 h 41°C (Kurzzeit-Stressexperiment) Wassertemperatur ausgesetzt. Nach dieser ersten Stressphase wurde ein Teil der überlebenden gleichzeitig mit einer gleichen Anzahl an bis dahin nicht vorgestressten Individuen sofort wieder denselben erhöhten Temperaturen ausgesetzt und die Überlebenskurven in beiden Gruppen verglichen. Den übrigen überlebenden der ersten Stressphase wurde vor der zweiten Stressphase eine 13 Tage (Langzeit-Stressexperiment) bzw. 6 Tage (Kurzzeit-Stressexperiment) lange Erholungsphase gewährt, damit etwaige physiologische Anpassungen an Stressbedingungen abklingen konnten. Der Vergleich der Überlebenskurven einer vorgestressten Gruppe, die nach der ersten Stressphase direkt wieder Stressbedingungen ausgesetzt wurde und einer vorgestressten Gruppe, der eine Erholungsphase gewährt wurde, sollte Aufschluss darüber geben, ob eine mögliche erhöhte Stresstoleranz der vorgestressten Gruppen auf eine Selektion stresstoleranter Individuen oder physiologischer Anpassungen an Stressbedingungen zurückzuführen war. In beiden Experimenten zeigten die vorgestressten Gruppen, unabhängig davon, ob ihnen eine Erholungsphase gewährt wurde oder nicht, stets höhere Überlebensraten als die Gruppen, die nicht vorgestresst waren. Meine Ergebnisse legen einerseits nahe, dass es durch Temperaturstress zu einer physiologischen Stressabhärtung und dadurch zu erhöhten Überlebensraten der vorgestressten Individuen kam. Da Literaturdaten darauf hinweisen, dass die Erholungsphasen wahrscheinlich lang genug waren, um die physiologische Stressabhärtung in den vorgestressten Gruppen, denen eine Erholungsphase gewährt wurden, abklingen zu lassen, kann man andererseits annehmen, dass es durch den Temperaturstress bei P. vannamei zu einer Selektion stresstoleranterer Individuen gekommen ist. Dies konnte im Rahmen meiner Diplomarbeit jedoch nicht direkt bewiesen werden.
    Type: Thesis , NonPeerReviewed
    Format: text
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  • 7
    Publication Date: 2024-05-13
    Type: Thesis , NonPeerReviewed
    Format: text
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  • 8
    Publication Date: 2024-05-13
    Description: The distributed, cross-institutional User Support Network (USN) for NFDI4Earth is based on the ex- isting and well embedded user support structures of the participating institutions. The USN serves as a single point of contact for user requests that could not be handled via OneStop4All and require individual consulting. By combining the distributed RDM knowledge of experts in the USN in con- junction with the Knowledge Hub, the NFDI4Earth team will convey the notion (knowledge) of a best practice for dealing with data and how data can be made FAIRer and open. We will present the current status and the conncetion to D.A.M and DataHUB.
    Type: Conference or Workshop Item , NonPeerReviewed
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  • 9
    Publication Date: 2024-05-13
    Description: We explore a parameterization for mesoscale turbulence, closely related to that of Gent and McWilliams, in which forcing terms proportional to the isopycnal flux of potential vorticity appear in the averaged momentum equations. We show that in the presence of variable bottom topography, the parameterization predicts alongslope mean flow and a corresponding upslope bolus (eddy) flux of tracer that is associated with an alongslope-directed bottom eddy stress. The upslope bolus flux is in qualitative agreement with observations of a cold dome over seamounts. The predicted alongslope flow corresponds to flow fields found in geostrophic turbulence experiments and has some similarity (although conceptually very different, as discussed in the text) to Holloway's prediction based on statistical mechanics. By considering continuous stratification as a limiting case of a multilayer model, we show how to treat the surface and bottom boundaries. Practical application of the parameterization is illustrated using a three-dimensional -coordinate ocean circulation model that is very similar to the Bryan–Cox–Semtner model. The model-computed flow is consistent with observations of anticyclonic flow around a seamount. We show that the bottom eddy stress associated with the parameterization can be large, even compared to the annual mean surface wind stress, and hence could have important implications for the biology and water mass distribution of the coastal ocean as well as for the large-scale ocean circulation. From the climate modelling perspective, the approach adopted here provides a single formalism that combines the advantages of the Gent and McWilliams parameterization with alongslope mean flow similar to that suggested by Holloway.
    Type: Article , PeerReviewed
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  • 10
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    GEOMAR Helmholtz-Zentrum für Ozeanforschung
    Publication Date: 2024-05-13
    Type: Report , NonPeerReviewed
    Format: text
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