Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
10
datasets available to search
ShareScore release 0.9.0
Dataset results
10 results for “continental shelves”
Ensemble of ice shelf basal melt rates and ocean properties for tipped-over continental shelves
<p><strong>Summary</strong><strong>:</strong></p> <p>This dataset contains the reference and tipped states from several model configurations developed at the <a href="https://www.awi.de/en/">Alfred Wegener Institute (AWI)</a> and the <a href="https://www.ige-grenoble.fr/?lang=en">Institut des Géosciences de l’Environnement (IGE)</a>. They were gathered here in the context of the <a href="https://www.tipaccs.eu">TiPACCs European project</a> and constitute a useful ensemble of reference and tipped ocean–ice-shelf simulations that <strong>can be used to feed ice-sheet simulations or to train melt parameterizations</strong>.</p> <p>The simulations produced by AWI are based on the <a href="https://fesom.de">FESOM</a> global ocean–sea-ice model using either Z- or Sigma- coordinates and all show a cold-to-warm tipping point for Filchner-Ronne Ice Shelf. The two sets of simulations produced by IGE are based on the <a href="https://www.nemo-ocean.eu">NEMO</a> ocean–sea-ice model. They include a global configuration showing a cold-to-warm tipping point for Ross Ice Shelf, and regional Amundsen Sea configuration showing a warm-to-warmer transition (likely not a proper tipping point). </p> <p>The files include 3-dimensional and sea-floor ocean temperatures and salinities, ice-shelf melt rates, as well as topographic and grid data. All variables are interpolated onto the common 8km stereographic grid that was used to provide ocean forcing in ISMIP6 (<a href="https://doi.org/10.5194/tc-14-2331-2020">Nowicki et al. 2020</a>).</p> <p>We provide the reference state and the anomaly, so that the tipped state is:</p> <ul> <li><em>Tipped = Reference + Anomaly</em></li> </ul> <p>To have an overview of the reference and tipped states, have a look at these figures:</p> <ul> <li><em>figure_ref_and_anomalies_1.pdf</em></li> <li> <p><em>figure_ref_and_anomalies_2.pdf</em></p> </li> <li> <p><em>figure_seafloor_temp_zooms.pdf</em></p> </li> </ul> <p> </p> <p>_______________________________________________</p> <p><strong>Detailed Data Description</strong><strong>:</strong></p> <p> </p> <ul> <li><strong>reference_high_FESOM_sigma_AWI_TiPACCs.nc</strong> <ul> <li>contact: Ralph Timmermann <a href="mailto:ralph.timmermann@awi.de">ralph.timmermann@awi.de</a>, Verena Haid <a href="mailto:verena.haid@awi.de">verena.haid@awi.de</a></li> <li>model: FESOM1.4, sigma-coordinates (global with refined grid around Antarctica)</li> <li>atmospheric forcing: HadCM3 20C</li> <li>provided average: 1990-1999 (10-year mean)</li> <li>more: <a href="https://doi.org/10.1007/s10236-013-0642-0">Timmermann and Hellmer (2013)</a></li> </ul> </li> </ul> <p> </p> <ul> <li><strong>reference_low_FESOM_sigma_AWI_TiPACCs.nc</strong> <ul> <li>contact: Ralph Timmermann <a href="mailto:ralph.timmermann@awi.de">ralph.timmermann@awi.de</a>, Verena Haid <a href="mailto:verena.haid@awi.de">verena.haid@awi.de</a></li> <li>model: FESOM1.4, sigma-coordinates (global with refined grid around Antarctica)</li> <li>atmospheric forcing: HadCM3 20C</li> <li>provided average: 1990-1999 (10-year mean)</li> <li>more: <a href="https://doi.org/10.5194/os-13-765-2017">Timmermann and Goeller (2017)</a></li> </ul> </li> </ul> <p> </p> <ul> <li><strong>reference_FESOM_z_AWI_TiPACCs.nc</strong> <ul> <li>contact: Verena Haid <a href="mailto:verena.haid@awi.de">verena.haid@awi.de</a></li> <li>model: FESOM1.4, Z-coordinates (global with refined grid around Antarctica)</li> <li>atmospheric forcing: ERA Interim</li> <li>provided average: 2008-2017 (10-year mean), i.e. model year 30-39</li> <li>more: same mesh as <a href="https://doi.org/10.5194/tc-13-2317-2019">Gürses et al. (2019)</a></li> </ul> </li> </ul> <p> </p> <ul> <li><strong>reference_NEMO4_eORCA025.L121_IGE_TiPACCs.nc</strong> <ul> <li>contact: Pierre Mathiot <a href="mailto:pierre.mathiot@univ-grenoble-alpes.fr">pierre.mathiot@univ-grenoble-alpes.fr</a></li> <li>model: NEMO-4.0, eORCA025.L121 (Global, 1/4°, 121 vertical levels)</li> <li>atmospheric forcing: JRA55do</li> <li>provided average: 2<sup>nd</sup> cycle of 1989-1998 (10-year mean); we first run 1979-2018, and we redo 1979-1998 starting from the 2018 state.</li> <li>more: <a href="https://pmathiot.github.io/NEMOCFG/docs/build/html/simu_eORCA025_OPM021.html">https://pmathiot.github.io/NEMOCFG/docs/build/html/simu_eORCA025_OPM021.html</a></li> </ul> </li> </ul> <p> </p> <ul> <li><strong>reference_NEMO3_AMUXL12.L75_IGE_TiPACCs.nc</strong> <ul> <li>contact: Nicolas Jourdain <a href="mailto:nicolas.jourdain@univ-grenoble-alpes.fr">nicolas.jourdain@univ-grenoble-alpes.fr</a></li> <li>model: NEMO-3.6, AMUXL12.L75 (Amundsen, 1/12°, 75 vertical levels)</li> <li>atmospheric forcing: MAR (<a href="https://doi.org/10.5194/tc-14-229-2020">Donat-Magnin et al. 2020</a>)</li> <li>provided average: 1989-2009 (21-year mean)</li> <li>more: similar model set-up as <a href="https://doi.org/10.1016/j.ocemod.2018.11.001">Jourdain et al. (2019)</a>.</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>anomaly_high_FESOM_sigma_AWI_TiPACCs.nc</strong> <ul> <li>continuation of reference_high_FESOM_sigma_AWI_TiPACCs.nc</li> <li>forced with HadCM3 A1B</li> <li>provided average: 2190-2199 (10-year mean)</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>anomaly_low_FESOM_sigma_AWI_TiPACCs.nc</strong> <ul> <li>continuation of reference_low_FESOM_sigma_AWI_TiPACCs.nc</li> <li>forced with HadCM3 A1B</li> <li>provided average: 2190-2199 (10-year mean)</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>anomaly_high_FESOM_z_AWI_TiPACCs.nc</strong> <ul> <li>same model set-up as reference_FESOM_z_AWI_TiPACCs.nc</li> <li>atmospheric forcing south of 60°S HadCM3 A1B starting 2050, otherwise ERA Interim starting 1979</li> <li>provided average: model year 69-78 (10-year mean), i.e. 2008-2017 of 2<sup>nd</sup> 39yr-cycle</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>anomaly_medium_FESOM_z_AWI_TiPACCs.nc</strong> <ul> <li>same model set-up as reference_FESOM_z_AWI_TiPACCs.nc</li> <li>atmospheric forcing: ERA Interim modified with a strong imprint of the seasonal cycle of HadCM3 A1B 2070-2089</li> <li>provided average: model year 69-78 (10-year mean), i.e. 2008-2017 of 2<sup>nd</sup> 39yr-cycle</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>anomaly_low_FESOM_z_AWI_TiPACCs.nc</strong> <ul> <li>same model set-up as reference_FESOM_z_AWI_TiPACCs.nc</li> <li>atmospheric forcing: manipulated ERA Interim with prolongued summer and shorter, milder winter south of 50°S, additional modification of winds in Weddell Sea region</li> <li>provided average: model year 108-117 (10-year mean), i.e. 2008-2017 of 3<sup>rd</sup> 39yr-cycle</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>anomaly_NEMO4_eORCA025.L121_IGE_TiPACCs.nc</strong> <ul> <li>similar to reference_NEMO4_eORCA025.L121_IGE_TiPACCs.nc</li> <li>perturbation of the model parameters: Different iceberg distribution and different sea-ice–ocean drag and snow conductivity on sea-ice, leading to less sea-ice production in the eastern Ross Sea.</li> <li>More: <a href="https://pmathiot.github.io/NEMOCFG/docs/build/html/simu_eORCA025_OPM020.html">https://pmathiot.github.io/NEMOCFG/docs/build/html/simu_eORCA025_OPM020.html</a></li> </ul> </li> </ul> <p> </p> <ul> <li><strong>anomaly_NEMO3_AMUXL12.L75_IGE_TiPACCs.nc</strong> <ul> <li>similar to reference_NEMO3_AMUXL12.L75_IGE_TiPACCs.nc</li> <li>perturbation of atmospheric forcing: MAR forced by the CMIP5 multi-model anomaly under the RCP8.5 scenario (<a href="https://doi.org/10.5194/tc-15-571-2021">Donat-Magnin et al. 2021</a>).</li> <li>provided average: 2080-2100 (21-year average)</li> </ul> </li> </ul> <p> </p>
FESOM-REcoM model data: A regime shift on Weddell Sea continental shelves with local and remote physical-biogeochemical implications is avoidable in a 2°C scenario
<p>This data set includes the minimal data necessary to reproduce the findings of Nissen et al. (2023). Output of model simulations with the global ocean biogeochemical model FESOM1.4-REcoM2 is provided. In particular, besides information on the model grid, the data set includes annual mean water mass properties (temperature, salinity, density, oxygen, pH) and freshwater fluxes from sea ice and ice shelves and decadal averages of air-sea CO2 fluxes and deep-ocean carbon accumulation rates. Model results are provided from 1980-2100 for the four emission scenarios SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 (sorted from low emission to high emission).</p> <p>Please see README for more information on the individual files. </p> <p>Data set belongs to: </p> <p>Nissen, C., R. Timmermann, M. Hoppema, and J. Hauck, 2023: A regime shift on Weddell Sea continental shelves with local and remote physical-biogeochemical implications is avoidable in a 2°C scenario. <em>J. Climate</em>, <a href="https://doi.org/10.1175/JCLI-D-22-0926.1">https://doi.org/10.1175/JCLI-D-22-0926.1</a>, in press.</p>
Fig. 6 in Trilobite faunal dynamics on the Devonian continental shelves of the Ardenne Massif and Boulonnais (France, Belgium)
Fig. 6. Distribution of trilobite associations in the southern border of Dinant synclinorium, Ardenne Massif (Belgium, France) during the Middle and Late Devonian (see Fig. 2 for abbreviations). A. Eifelian ramp. B. Eifelian reef. C. Givetian carbonated platform. D. Givetian ramp. E. Frasnian platform. F. Frasnian deep facies of Namur synclinorium.
Fig. 5 in Trilobite faunal dynamics on the Devonian continental shelves of the Ardenne Massif and Boulonnais (France, Belgium)
Fig. 5. Diversity (Shannon-Weaver index) of the 67 trilobites samples from the Middle and Upper Devonian of the Ardenne Massif and Boulonnais (North France, Belgium) for four delineated associations (see Figs. 2, 3 for abbreviations).
Fig. 2 in Trilobite faunal dynamics on the Devonian continental shelves of the Ardenne Massif and Boulonnais (France, Belgium)
Fig. 2. Generalized lithostatigraphic section of Middle and Upper Devonian of the Ardenne Massif (France-Belgium) and Boulonnais (France) A. Correlation of the Ardenne Massif and Boulonnais formations. E1, Eau Noire Formation; E2, Couvin Formation; E3, Jemelle Formation; E4, X Formation; G1, Hanonet Formation; G2, Trois-Fontaines Formation; G3, Terres d'Haurs Formation; G4, Fromelennes Formation; G5, Griset Member; G6, Couderousse Member; F1, Arche Member; F2, Ermitage Member; F3, Bieumont Member; F4, Lion Member; F5, Boussu-en-Fagne Member and Neuville Formation (lateral facies); F6, Neuville Formation, Petit Mont Member; F7, Bovesse Formation; F8, Noces Member; F9, Pâture Member; F10, Ferques Formation (after Bultynck and Dejonghe 2001 and Hubert 2008). B. Reconstruction of the Frasnian platform of the Ardenne Massif (after Da Silva and Boulvain 2012).
Fig. 1 in Trilobite faunal dynamics on the Devonian continental shelves of the Ardenne Massif and Boulonnais (France, Belgium)
Fig. 1. Geographic location of the studied area (A) and geological map of the Ardenne and Boulonnais areas (B) with studied fossiliferous sections (modified after Crônier and van Viersen 2007).
Fig. 7 in Trilobite faunal dynamics on the Devonian continental shelves of the Ardenne Massif and Boulonnais (France, Belgium)
Fig. 7. Distribution of trilobite associations in the Boulonnais (North of France) during the Middle and Upper Devonian (see Fig. 2 for abbreviations). A. Givetian carbonated platform. B. Frasnian platform. C. Frasnian shaly facies.
Data from: How long does a brachiopod shell last on a seafloor? Modern mid-bathyal environments as taphonomic analogues of continental shelves prior to the Mesozoic Marine Revolution
<p class="MsoNormal">Carbonate skeletal remains are altered and disintegrate at yearly to decadal scales in present-day shallow-marine environments with intense bioerosion and dissolution. Present-day brachiopod death assemblages are invariably characterized by poor preservation on continental shelves, and abundant articulated shells of brachiopods with well-preserved brachidia are thus not expected to be preserved if not rapidly buried. However, such preservation is paradoxically observed in shallow-water Paleozoic and Mesozoic brachiopod assemblages. Here, we show that a bathyal death assemblage time-averaged to several millennia (Adriatic Sea) consists of sediment-filled articulated shells of <em>Gryphus</em> <em>vitreus</em> with complete brachidia. Postmortem age distributions indicate that disintegration half-lives exceed several centuries (~500-1,700 years). The high frequency of articulated but centuries-old shells (>50%) and the fitting of taphonomic models to postmortem ages indicate that disarticulation half-life is unusually long (~200 years). Rapid sediment filling of shells (1) inhibited disarticulation, loop fragmentation and colonization by coelobites and (2) induced precipitation of ferromanganese oxides at redox fronts within shells. Sediment-filled articulated shells, however, still resided at the sediment-water interface as indicated by encrusters and sponges that infested them after death. Sediment-filled shells disintegrated through bioerosion and wear when residence time in the taphonomically active zone exceeded ~2,000 years. We suggest that the articulation paradox is driven by the Mesozoic Marine Revolution (MMR) that escalated predation, bioturbation and organic matter recycling, all intensifying shell disintegration. A scenario with slow disarticulation in bathyal environments can be an analogue of conditions leading to preservation of articulated shells in shallow-water assemblages prior to the MMR.</p>
Data from: How long does a brachiopod shell last on a seafloor? Modern mid-bathyal environments as taphonomic analogues of continental shelves prior to the Mesozoic Marine Revolution
Open the record for dataset details and reuse information.
Evolution of sediment temperature, pressure, phases distribution, carbon pools and seabed methane flux at the Arctic continental Shelves since the Last Glacial Maximum
<p>This dataset are produced by a manuscript (<strong>Biodegradation of Ancient Organic Carbon Fuels Seabed Methane Emission at the Arctic Continental Shelves</strong>) to be submitted to the Journal of Geophysical Research - Global Biogeochemical Cycles. I</p> <p>The file "MethaneEmission_Permafrost" contains the predicted temperature, pressure, pore water salinity, ice stable zone, methane hydrate stable zone, ice saturation, methane hydrate saturation, free methane gas saturation, labile organic carbon content, stable organic carbon content, and methanogenesis rate from seafloor to 1200 m depth from 18,000 years before present to 2,000 years after present for 8 different simulation scenarios. </p> <p>The file "Seabed_Methane_Flux" contains the predicted seabed methane emission rate from 18,000 years before present to 2,000 years after present for 8 different simulation scenarios. </p> <p>Detailed information about the model could be found in the paper <strong>Biodegradation of Ancient Organic Carbon Fuels Seabed Methane Emission at the Arctic Continental Shelves. </strong></p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.