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.
31
datasets available to search
ShareScore release 0.9.0
Dataset results
31 results for “Sea level projections”
Using the history of the Antarctic Ice Sheet to reduce uncertainties in projections of global sea level rise
<p>Ice sheet models are the most descriptive tools available to simulate the future evolution of the Antarctic Ice Sheet (AIS), including its contribution towards changes in global sea level. However, our knowledge of the dynamics of the coupled ice-ocean-lithosphere system is inevitably limited, in part due to a lack of observations. Furthermore, to build computationally efficient models that can be run for multiple millennia, it is necessary to use simplified descriptions of ice dynamics. Ice sheet modelling is therefore a poorly constrained exercise. The past evolution of the AIS provides an opportunity to improve the description of physical processes within ice sheet models and, therefore, to constrain our understanding of the role of the AIS in driving changes in global sea level.</p> <p>We use the Parallel Ice Sheet Model (PISM) to demonstrate how past changes can be used to improve our ability to predict the future evolution of the AIS. A large perturbed-physics ensemble is generated, spanning uncertainty in the parameterisations of key physical processes within the model. A Latin hypercube approach is used to optimally sample the range of uncertainty in parameter values. This perturbed-physics ensemble is used to simulate the evolution of the AIS from the Last Glacial Maximum (21,000 years ago) until 5,000 years into the future. Records of past ice sheet thickness and extent are then used to determine which ensemble members are the most realistic. This allows us to use the known history of the AIS to constrain our understanding of its past contribution towards changes in global sea level. Critically, it also allows us to determine which ensemble members are most likely to generate realistic projections of the future evolution of the AIS. This enables us to use past changes in the AIS to reduce uncertainty in projections of future sea level rise.</p>
Reducing uncertainties in projections of global sea level rise
<p>Ice sheet models are the most descriptive tools available to simulate the future evolution of the Antarctic Ice Sheet (AIS), including its contribution towards changes in global sea level. However, our knowledge of the dynamics of the coupled ice-ocean- lithosphere system is inevitably limited, in part due to a lack of observations. Furthermore, to build computationally efficient models that can be run for multiple millennia, it is necessary to use simplified descriptions of ice dynamics. Ice sheet modelling is therefore a poorly constrained exercise. The past evolution of the AIS provides an opportunity to improve the description of physical processes within ice sheet models and, therefore, to constrain our understanding of the role of the AIS in driving changes in global sea level.<br> <br> We use the Parallel Ice Sheet Model (PISM) to demonstrate how past changes can be used to improve our ability to predict the future evolution of the AIS. A large perturbed-physics ensemble is generated, spanning uncertainty in the parameterisations of key physical processes within the model. A Latin hypercube approach is used to optimally sample the range of uncertainty in parameter values. This perturbed-physics ensemble is used to simulate the evolution of the AIS from the Last Glacial Maximum (21,000 years ago) until 5,000 years into the future. Records of past ice sheet thickness and extent are then used to determine which ensemble members are the most realistic. This allows us to use the known history of the AIS to constrain our understanding of its past contribution towards changes in global sea level. Critically, it also allows us to determine which ensemble members are most likely to generate realistic projections of the future evolution of the AIS. This enables us to use past changes in the AIS to reduce uncertainty in projections of future sea level rise.</p>
Data and code for publication "The role of history and strength of the oceanic forcing in sea level projections from Antarctica with the Parallel Ice Sheet Model"
<p>Data and code underlying the publication <a href="https://tc.copernicus.org/preprints/tc-2019-330/">"The role of history and strength of the oceanic forcing in sea level projections from Antarctica with the Parallel Ice Sheet Model"</a>.</p> <p>Journal: The Cryosphere</p> <p>Authors: <em>Ronja Reese<sup>1*</sup></em><em>, Anders Levermann</em><sup><em>1,2,3</em></sup><em>, Torsten Albrecht</em><sup><em>1</em></sup><em>, Hélène Seroussi<sup>4</sup></em><em>, Ricarda Winkelmann<sup>1,2 </sup></em></p> <p>(1) Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, D-14412 Potsdam, Germany</p> <p>(2) Institute of Physics and Astronomy, University of Potsdam, Karl-Liebknecht-Str. 24-25, 14476 Potsdam, Germany</p> <p>(3) LDEO, Columbia University, New York, USA</p> <p>(4) Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA</p> <p>(*) email ronja.reese@pik-potsdam.de</p> <p>Abstract:<br> Mass loss from the Antarctic Ice Sheet constitutes the largest uncertainty in projections of future sea level rise. Ocean-driven melting underneath the floating ice shelves and subsequent acceleration of the inland ice streams is the major reason for currently observed mass loss from Antarctica and is expected to become more important in the future. Here we show that for projections of future mass loss from the Antarctic Ice Sheet, it is essential (1) to better constrain the sensitivity of sub-shelf melt rates to ocean warming and (2) to include the historic trajectory of the ice sheet. In particular, we find that while the ice sheet response in simulations using the Parallel Ice Sheet Model is comparable to the median response of models in three Antarctic Ice Sheet Intercomparison projects – initMIP, LARMIP-2 and ISMIP6 – conducted with a range of ice sheet models, the projected 21st century sea level contribution differs significantly depending on these two factors. For the highest emission scenario RCP8.5, this leads to projected ice loss ranging from 1.4 to 4.0 cm of sea level equivalent in the ISMIP6 simulations where the sub-shelf melt sensitivity is comparably low, opposed to a likely range of 9.2 to 35.9 cm using the exact same initial setup, but emulated from the LARMIP-2 experiments with a higher melt sensitivity based on oceanographic studies. Furthermore, using two initial states, one with and one without a previous historic simulation from 1850 to 2014, we show that while differences between the ice sheet configurations in 2015 are marginal, the historic simulation increases the susceptibility of the ice sheet to ocean warming, thereby increasing mass loss from 2015 to 2100 by about 50 %. Our results emphasize that the uncertainty that arises from the forcing is of the same order of magnitude as the ice dynamic response for future sea level projections.</p> <p>Large zip files contain data, small zip file python notebooks for data analysis and PISM code. Please contact ronja.reese@pik-potsdam.de if you have any further questions.</p>
Relative sea-level projections for Norway based on IPCC AR6
<p>This dataset contains IPCC AR6 relative sea-level projections tailored to Norway and published in the Norwegian Centre for Climate Services report by Simpson et al. (2024).</p> <p>The projections has been produced by taking the IPCC AR6 relative sea-level projections without the background vertical land motion (VLM) component (Kopp, 2021), which have then been combined with the semi-empirical NKG2016LU VLM model (Vestøl et al., 2019). See Simpson et al. (2024) for more details.</p> <p><strong>References</strong></p> <p>Kopp, R. E. (2021). IPCC AR6 Relative Sea Level Projections without Background Component (Version 20210809) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5967269</p> <p>Vestøl, O., Ågren, J., Steffen, H., Kierulf, H., & Tarasov, L. (2019). NKG2016LU: A new land uplift model for Fennoscandia and the Baltic Region. <em>Journal of Geodesy</em>, <em>93</em>(9), 1759–1779. <a href="https://doi.org/10.1007/s00190-019-01280-8">https://doi.org/10.1007/s00190-019-01280-8</a></p> <p>Simpson, M.J.R., Bonaduce, A., Borck, H.S., Breili, K., Breivik, Ø., Ravndal, O.R., Richter, K., 2024. Sea-Level Rise and Extremes in Norway: Observations and Projections Based on IPCC AR6. Norwegian Centre for Climate Services report 1/2024, ISSN 2704-1018, Oslo, Norway.</p>
Elevation projections for the Mekong delta (Vietnam) under sedimentation strategies, subsidence, compaction, and sea-level rise
<p>Modelled elevation projections for the Mekong delta (Vietnam) to 2050 under scenarios of fluvial sedimentation, organic accumulation, anthropogenic-accelerated subsidence, natural compaction, and global sea-level rise. Data is in meters above sea level and georeferenced (WGS84 UTM zone 48N).<br> For methodology and detailed description of the data, please see Dunn and Minderhoud (2021) DOI to follow, in which part of the dataset is presented in Figure 6.</p>
Global mean sea-level projections from MimiBRICK forced by uncertain emissions trajectories
<p>Model output for Darnell et al (2025), . Output contains csv files for components of GMSLR from 1850--2300, as well as parameter values for the simulations. There are two files: emissions_parameters.zip, which contains the parameter values used to generate the emissions trajectories, and ensemble_output.zip, which contains the model outputs. Rows correspond to ensemble members and columns correspond to years.</p>
Hector-BRICK sampled projections for climate sensitivity - sea level paper
<p>Projections for an upcoming paper exploring the effect of climate sensitivity on sea-level projections. These are created using the Hector-BRICK model, using parameter values from a calibration published in https://doi.org/10.5281/zenodo.3236413 in file: TTEGICGISAIS.csv.</p> <p>Here, the RData file includes a full set of 10k sampled projections. The .tsv files include 95% ranges for projections with high climate sensitivity (>= 5 K) and moderate climate sensitivity (< 5 K).</p> <p>More information is available in README.txt.</p>
Global Mean Sea-Level Rise Projections to 2150 and 2300 (PROTECT)
<p>Full-track projections of global mean sea-level rise from PROTECT. Please check the deliverable text for details regarding these data.</p>
Regional Sea-Level Change Projections to 2150 and 2300 (PROTECT)
<p>Full-track projections of regional sea-level change from PROTECT. Due to file size constraints, only quantiles are shared here instead of complete distributions. Please check the deliverable text for details regarding these data.</p>
PROTECT global mean sea level projections D7.6 (PRELIMINARY)
Open the record for dataset details and reuse information.
Fast-track regional sea level projections up to 2500 (PROTECT)
<p>This NetCDF4 file contains regional sea-level change projections from 2020 to 2500 based on IPCC AR6 projections and a combination of linear extrapolations and scaling methods. Data are provided for scenarios SS1-2.6 and SSP2-4.5. </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.