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.
49
datasets available to search
ShareScore release 0.9.0
Dataset results
49 results for “Antarctic Ice Sheet”
Southern Ocean and Antarctic floating ice sheets self-consistent spatial discretization mesh
<p>Southern Ocean and Antarctic floating ice sheets<br> ================================================</p> <p>An unstructured mesh spatial discretisation of the Southern Ocean and floating ice sheets of Antarctica.</p> <p>This is stored in two unstructured VTU files defined by the visualisation toolkit VTK [2].</p> <p>Five state PVSM file for Paraview [3] are also provided to reproduce visualisations shown in [1]. Note that Paraview requires absolute pathnames, so it may be necessary to edit file references to the VTU files in this state file.</p> <p>Files<br> -----</p> <p>- AntarcticaSouthernOcean.vtu<br> - AntarcticaSouthernOcean_ice.vtu<br> - AntarcticaSouthernOcean.pvsm<br> - AntarcticaSouthernOcean_below.pvsm<br> - AntarcticaSouthernOcean_Ross_FilchnerRonne_cutaway.pvsm<br> - AntarcticaSouthernOcean_Ross_FilchnerRonne_cross_section.pvsm<br> - AntarcticaSouthernOcean_depth_below.pvsm</p> <p>Author<br> ------</p> <p>- Dr Adam S. Candy <a.s.candy@tudelft.nl>, <candy@cantab.net><br> - Technische Universiteit Delft<br> - Imperial College London</p> <p>References<br> ----------</p> <p>[1] Candy, A.S., 2016. A consistent approach to unstructured mesh generation for geophysical models. In review. Preprint available at https://arxiv.org/abs/1703.08491.</p> <p>[2] The Visualization Toolkit (VTK), version 5.10.1. URL: http://www.vtk.org.</p> <p>[3] Paraview, version 4.3.1. https://www.paraview.org.</p>
NASA GSFC Firn Densification Model version 1.2.1 (GSFC-FDMv1.2.1) for the Greenland and Antarctic Ice Sheets: Jan 1980 - Jul 2024
<p><strong>Overview</strong></p> <p>The NASA GSFC-FDM v1.2.1 provides the evolution of firn air content (FAC), surface mass balance (SMB) (and its individual components), and total firn height change over the Greenland and Antarctic Ice Sheets from January 1, 1980 to July 30, 2024 at 5-day temporal resolution. The model uses atmospheric forcing from NASA GMAO's Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) global atmospheric reanalysis, combined with a higher resolution replay (see Medley et al., 2022) as input into the Community Firn Model (CFMv1.1.6) to simulate the evolution of firn properties across the ice sheets. The GSFC-FDMv1.2.1 is provided on a 12.5 km x 12.5 km North/South Polar Stereographic Grid, depending on the ice sheet.</p> <p>For a thorough description of how the GSFC-FDMv1.2.1 was generated see Medley et al. (2022) in <em>The Cryosphere</em>. Release 2 contains model output up through June 30, 2022, whereas the initial release only extended through September 30, 2021. Release 3 contains model output up through July 31, 2024 and uses CFMv2.3.1. The model set up is identical between releases.</p> <h3>*** The spatial grids are incorrect in this version, so we have restricted access to these files. Please use Version 4. ***</h3>
Circum-Antarctic data used in "Tipping point behaviour of ice-sheet grounding-zone melting due to ocean water intrusion" by Bradley and Hewitt
<p>The file 'Antarctica-data.mat' contains the following fields:</p><p>'x' [units: m] x position of grid points</p><p>'y' [units m] y position of grid points</p><p>'tf_max' [units: C] maximum thermal forcing from Adusumilli et al. 2020 (doi: https://doi.org/10.1038/s41561-020-0616-z)</p><p>'H' [units: m] ice thickness from Bedmachine V3</p><p>'B' [units: m] bed elevation from Bedmachine V3</p><p>'mask' [units: n/a] Bedmachine V3 mask</p><p>'isedge' [units: n/a] Logical array with 1 corresponding to edges of ice shelves and 0 otherwise</p><p>'isgl' [units: n/a] Logical array with 1 corresponding to grounding line points and 0 otherwise</p><p>'isfront' [units: n/a] Logical array with 1 corresponding to ice fronts and 0 otherwise</p><p>'vx' [units: m/a] Ice velocity in the x-direction from ITS_LIVE 240m mosaic</p><p>'vy' [units: m/a] Ice velocity in the y-direction from ITS_LIVE 240m mosaic</p>
Data For: A Framework for Automated Supraglacial Lake Detection and Depth Retrieval in ICESat-2 Photon Data Across the Greenland and Antarctic Ice Sheets
<p>HDF5 data files for 1249 supraglacial lakes detected in ICESat-2 ATL03 data over Central West Greenland (melt seasons 2019 and 2020) and the Amery Ice Shelf Catchment (melt seasons 2018-19 and 2020-21). Each HDF5 data file is associated with a .jpg "quicklook" file of the same name, showing ATL03 photon elevations with the estimated along-track fits to the lake surface and lakebed and the resulting maximum lake depth, along with the corresponding ICESat-2 ground track over cloud-free concurrent satellite imagery.</p> <p>The data files are structured as following: </p> <div> <div> <div> <div> <div> <pre>group: depth_data/ - dataset: bathymetry_confidence - dataset: lakebed_fit_elevation_meters - dataset: lat - dataset: lon - dataset: surface_fit_elevation_meters - dataset: water_depth_meters - dataset: x_along_track_meters group: fluid_bathymetry_peaks/ - dataset: elevation_meters - dataset: peak_prominence - dataset: x_along_track_meters group: mframe_data/ - dataset: delta_time - dataset: density_ratio_1 - dataset: density_ratio_2 - dataset: density_ratio_3 - dataset: density_ratio_4 - dataset: major_frame_id - dataset: passes_bathymetry_check - dataset: passes_flatness_check - dataset: photon_density_peak_elevation - dataset: q_1_number_peaks - dataset: q_2_prominece - dataset: q_3_elev_spread - dataset: q_4_alignment - dataset: q_s - dataset: x_along_track_meters_end - dataset: x_along_track_meters_start group: photon_data/ - dataset: afterpulse_probability - dataset: fluid_signal_confidence - dataset: geoid_elevation_meters - dataset: lat - dataset: lon - dataset: photon_elevation_above_geoid_meters - dataset: pulse_saturation_level - dataset: x_along_track_meters group: properties/ - dataset: beam_number - dataset: beam_strength - dataset: cycle_number - dataset: granule_id - dataset: gtx - dataset: ice_sheet - dataset: lake_quality - dataset: lat - dataset: lon - dataset: melt_season - dataset: rgt - dataset: sc_orient - dataset: surface_elevation - dataset: time_utc </pre> </div> </div> </div> </div> </div>
Improving surface melt estimation over the Antarctic Ice Sheet using deep learning: a proof of concept over the Larsen Ice Shelf
<p>Hu, Z., Kuipers Munneke, P., Lhermitte, S., Izeboud, M., and van den Broeke, M.: Improving Surface Melt Estimation over Antarctica Using Deep Learning: A Proof-of-Concept over the Larsen Ice Shelf, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-102, in review, 2021.</p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>(1) MLP_model_surface_melt_corr.h5</strong> is the developed MLP model used for correcting RACMO2 surface melt.</p> <p><strong>(2) RACMO2_surface_melt_corr_MLP_AWS14.xlsx </strong>corrected surface melt [mm w.e. per day] from RACMO2 at AWS 14 during austral summers 2001 - 2016. The model inputs are (1) the simulated albedo, (2) the albedo difference between the observed and simulated albedo, (3) air temperature at 2m, (4) incoming shortwave radiation, (5) downwelling longwave radiation, (6) simulated surface melt, (7) Boolean melt flag, (8) surface melt difference to the previous day, and (9) record date as day of the year.</p> <p><strong>(3) RACMO2_surface_melt_corr_MLP_AWS17.xlsx </strong>The same as point 2 but for AWS 17</p> <p><strong>(4) RACMO2_surface_melt_corr_MLP_AWS18.xlsx </strong>The same as point 2 but for AWS 18</p> <p>Note: Data 2-4 are corrected RACMO2 simulations of surface melt at the pixels in RACMO2 27 km grid corresponding to AWS 14, 17, and 18 locations. They are not AWS observations.</p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>Related data set:</strong></p> <p>MODIS/Terra Surface Reflectance Daily L2G Global 1 km and 500 m SIN Grid product is available via the Land Processes Distributed Active Archive Center (LP DAAC) (https://doi.org/10.5067/MODIS/MOD09GA.006, last access: 3 December 2021). MODIS/Terra+Aqua Albedo Daily L3 Global 500 m SIN Grid product is also available via LP DAAC (https://doi.org/10.5067/MODIS/MCD43A3.006, last access: 3 December 2021). Sentinel-1 images are provided by the European Space Agency (ESA) (https://sentinel.esa.int/web/sentinel/sentinel-data-access, last access: 3 December 2021). Automatic weather station observations from AWS 14, 17, and 18 are available via https://doi.pangaea.de/10.1594/PANGAEA.910473 (last access: 3 December 2021). RACMO2 simulations (https://www.projects.science.uu.nl/iceclimate/models/antarctica.php#2-1, last access: 3 December 2021) are provided by van Wessem et al. (2018) which are available on request to the original authors.</p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>You should also refer to and cite the following paper:</strong></p> <p>Hu, Z., Kuipers Munneke, P., Lhermitte, S., Izeboud, M., and van den Broeke, M.: Improving Surface Melt Estimation over Antarctica Using Deep Learning: A Proof-of-Concept over the Larsen Ice Shelf, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-102, in review, 2021.</p>
Ocean-forced instability of the West Antarctic Ice Sheet since the mid-Pleistocene
<p>This data file contains the IRD abundance, clay mineralogy, water content in Excel file 'Wang et al.xlsx (sheet: sedimentology)', and Sr-Nd isotopes in Excel file 'Wang et al.xlsx (sheet: Sr-Nd isotope)' in gravity core ANT34/A2-10 (LATITUDE: -67.036111 and LONGITUDE: -125.591944) from the Amundsen abyssal plain since 770 ka.<br>Supplement to Jiakai Wang et al., Ocean-forced instability of the West Antarctic Ice Sheet since the mid-Pleistocene. Geochemistry Geophysics Geosystems (in review).<br> </p>
The Antarctic ice sheet iron source : a sensitivity study with a global ocean model
<p>Contains model data and freshwater fluxes from icebergs and ice shelves (used as forcing file to represent the Fe supply from the Antarctic ice sheet) of the study "Sensitivity of ocean biogeochemistry to the iron supply from the Antarctic ice sheet explored with a biogeochemical model", submitted to Biogeosciences (EGU)</p>
Supplementary Tables for "A 3.3-Million-Year Record of Antarctic Iceberg Rafted Debris and Ice Sheet Evolution Quantified by Machine Learning"
<p>Supplementary Tables for "A 3.3-Million-Year Record of Antarctic Iceberg Rafted Debris and Ice Sheet Evolution Quantified by Machine Learning"</p> <p> </p> <p><strong>Table Captions:</strong></p> <p><strong>Table S1.</strong> Site U1537 Age Model Tie Points from Weber et al. (2022) and Reilly et al. (2021)</p> <p><strong>Table S2. </strong>Site U1537 Age Model used in this study, applying both the age tie points from Weber et al. (2022) and Reilly et al. (2021)</p> <p><strong>Table S3. </strong>Hole U1538A correlation to the Dove Basin Stack from Bailey et al. (2022), and the addition of the U1538 splice CCSF-A depth to the Dove Basin CCSF-A</p> <p><strong>Table S4. </strong>Site U1538 splice table used in this study, note the continuation down Hole A after Core 14H</p> <p><strong>Table S5. </strong>New top core section offsets for Site U1536 cores added to the Reilly et al. (2021) extended splice table</p> <p><strong>Table S6. </strong>New top core section offsets for Site U1537 cores added to Reilly et al. (2021) extended splice table</p> <p><strong>Table S7. </strong>Comparison of Convolutional Neural Network IRD counts to shipboard eye counts of IRD at Site U1536</p> <p><strong>Table S8. </strong>Site U1537 CNN IRD Counts per 50 cm bins</p> <p><strong>Table S9. </strong>Site U1536 IRD Fluxes Per 5 kyr Quantified by a Convolutional Neural Network (0-3.3 Ma)</p> <p><strong>Table S10. </strong>Site U1537 IRD Fluxes Per 5 kyr Quantified by a Convolutional Neural Network (0-3.3 Ma)</p> <p><strong>Table S11. </strong>Site U1536 IRD Fluxes Per 1 kyr Quantified by a Convolutional Neural Network (0-1.2 Ma)</p> <p><strong>Table S12. </strong>Site U1537 IRD Fluxes Per 1 kyr Quantified by a Convolutional Neural Network (0-1.2 Ma)</p> <p><strong>Table S13. </strong>Site U1538 IRD Fluxes Per 1 kyr Quantified by a Convolutional Neural Network (0-1.2 Ma)</p>
A fast and simplified subglacial hydrological model for the Antarctic Ice Sheet and outlet glaciers
<p>KazmierczakGregov24_data.zip contains the Matlab scripts and data necessary to reproduce the results and figures of the article "A fast and simplified subglacial hydrological model for the Antarctic Ice Sheet and outlet glaciers" by Kazmierczak, Gregov, Coulon, and Pattyn. For more details, please, open the README.txt file or contact elise (dot) kazmierczak (at) ulb (dot) be or thomas (dot) gregov (at) uliege (dot) be.</p>
Spatially and temporally continuous reconstruction of Antarctic Amundsen Sea sector ice sheet surface velocities: 1996-2018
<p>Spatially and temporally continuous reconstruction of ice sheet surface velocities for the Amundson Sea Sector of the Antarctica. The reconstruction is derived from the synthesis of annual published InSAR (R14: Rignot et al. 2014) and optical (G18: Gardner et al., 2018 & Gardner et al., 2022) surface velocities. Data are posted on a uniform 240 m by 240 m grid in Antarctic Polar Stereographic (EPSG:3031) coordinates. The temporal posting is every 2.4 months or 1/5 of a year.</p> <p>R14 and G18 annual velocity data have large errors and data gaps in both space and time that make the data challenging to work with. For this reason, a Spatially and temporally continuous reconstruction was made. These are the preprocessing steps that were applied to create the reconstruction:</p> <ol> <li>R14 component velocities [vx/vy] are mapped to the same 240-m grid as G18 for the Amundson Sea sector.</li> <li>Velocities falling outside of mapped ice extents (see Paolo et al., 2022) are set to no data values.</li> <li>A reference velocity is defined as the 1996 velocity field or the earliest valid measurement thereafter. The average of both velocities is taken if multiple observations exist for the first year of data.</li> <li>For areas moving faster than 200 m/yr., the percentage anomalies are calculated for all years relative the reference velocity. This was done for both G18 and R14 velocities separately.</li> <li>Annual velocity anomalies are then filter with a 5-km windowed moving median.</li> <li>G18 and R14 filtered anomalies are merge by taking the mean of each year. Years with less than 30% coverage for fast moving ice (>= 200 m/yr.) were discarded.</li> <li>If missing annual values were within 25 km of a valid datapoint they are filled using natural neighbor interpolation, otherwise anomalies were set to zero.</li> <li>Outside of fast-moving areas, annual anomalies are tapered to zero using a 10-km cosine taper.</li> <li>Merged and filled annual anomalies are then smoothed one last time using a 5-km windowed moving mean.</li> <li>To create a continuous record of velocity, annual anomalies are interpolated in time to every 1/5 of a year for every 240 m pixel using a spline interpolant and multiplied by the reference velocity.</li> </ol> <p>All x and y component velocities [vx/vy] and velocity magnitudes [v] are stored as individual geotiff files and are contained in the .zip included file. A visualization of the velocity magnitudes is included as an animated gif. </p> <p> </p> <p>References:</p> <p>Gardner, A., M. Fahnestock, and T. Scambos. (2022). MEaSUREs ITS_LIVE Regional Glacier and Ice Sheet Surface Velocities, Version 1 [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/6II6VW8LLWJ7. Date Accessed 04-07-2019.<br> <br> Gardner, A. S., Moholdt, G., Scambos, T., Fahnstock, M., Ligtenberg, S., van den Broeke, M., & Nilsson, J. (2018). Increased West Antarctic and unchanged East Antarctic ice discharge over the last 7 years. <em>The Cryosphere</em>, <em>12</em>(2), 521–547. https://doi.org/10.5194/tc-12-521-2018</p> <p>Paolo, F., Gardner, A., Greene, C., Nilsson, J., Schodlok, M., Schlegel, N., & Fricker, H. (2022). Widespread slowdown in thinning rates of West Antarctic Ice Shelves. <em>EGUsphere</em>, <em>2022</em>, 1–45. https://doi.org/10.5194/egusphere-2022-1128</p> <p>Rignot, E., J. Mouginot, and B. Scheuchl. (2014). MEaSUREs InSAR-Based Ice Velocity of the Amundsen Sea Embayment, Antarctica, Version 1 [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/MEASURES/CRYOSPHERE/nsidc-0545.001. Date Accessed 04-07-2019.</p>
Data from: Using Antarctic subglacial relic landscapes to inform past ice sheet retreat in the warm Pliocene
Open the record for dataset details and reuse information.
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>
Data and code for manuscript ``Insights on the vulnerability of Antarctic glaciers from the ISMIP6 ice sheet model ensemble and associated uncertainty''
<p>Supporting data and code for manuscript:</p><p>Seroussi, H., Verjans, V., Nowicki, S., Payne, A. J., Goelzer, H., Lipscomb, W. H., Abe-Ouchi, A., Agosta, C., Albrecht, T., Asay-Davis, X., Barthel, A., Calov, R., Cullather, R., Dumas, C., Galton-Fenzi, B. K., Gladstone, R., Golledge, N. R., Gregory, J. M., Greve, R., Hattermann, T., Hoffman, M. J., Humbert, A., Huybrechts, P., Jourdain, N. C., Kleiner, T., Larour, E., Leguy, G. R., Lowry, D. P., Little, C. M., Morlighem, M., Pattyn, F., Pelle, T., Price, S. F., Quiquet, A., Reese, R., Schlegel, N.-J., Shepherd, A., Simon, E., Smith, R. S., Straneo, F., Sun, S., Trusel, L. D., Van Breedam, J., Van Katwyk, P., van de Wal, R. S. W., Winkelmann, R., Zhao, C., Zhang, T., and Zwinger, T.: Insights into the vulnerability of Antarctic glaciers from the ISMIP6 ice sheet model ensemble and associated uncertainty, The Cryosphere, 17, 5197–5217, https://doi.org/10.5194/tc-17-5197-2023, 2023.</p><p> </p><p>It contains the code to prepare the datasets, to create the figures and the data for the analysis, and the scalar values computed for the 198 Antarctic glaciers stored by ice flow models.</p><p>The files Glacier_XX contain the data to emulate the results for individual glaciers.</p><p>The files Antarctica and AntarcticaWithCtrl contain the data to emulate the results for the Antarctic runs without and with the ctrl_proj experiment.</p><p>The files GROUP_ICEFLOW contain the ice flow model data for all the experiments recomputed for the 198 glaciers in Antarctica.</p>
Seafloor roughness reduces melting of the East Antarctic ice sheets
<p>MITgcm model setup and<span> </span>MATLAB script and data that support figures for "<strong>Seafloor roughness reduces melting of East Antarctic ice shelves</strong>" by Y. Liu, M. Nikurashin, and B. Pena-Molino</p> <p><strong>MITgcm model setup: </strong></p> <p>We provide the two MITgcm model configurations of the Denman regional model, using BedMachine and SRTM15+ bathymetry datasets, described in the main text of the paper. Both simulations can be run from a pickup file corresponding to 5 years from the beginning of the simulation, when the model is well equilibrated. Complete model outputs used for the analysis in the paper can be produced by running the simulations for additional 5 years.</p> <p>(Contents)</p> <ul> <li><strong><em>denman_0025_RYF_SHI_tides_bedmachine.zip</em></strong> (code, parameter files, and initial and boundary conditions to run the simulation with BedMachine bathymetry)</li> <li><strong><em>denman_0025_RYF_SHI_tides_srtm15.zip</em></strong> (code, parameter files, and initial and boundary conditions to run the simulation with SRTM15+ bathymetry)</li> <li><strong><em>denman_external_forcing_files.zip</em></strong> (3-hourly atmospheric forcing files that are used for both simulations take nearly 100Gb of disk space. Due to Zenodo size limit of 50GB, we provide the original JRA-55 forcing files and a Matlab script that interpolates them onto the regional model grid.)</li> </ul> <p>(How to build and run)</p> <p>The reader is referred to MITgcm documentation for instructions on how to download, compile and run the model: https://mitgcm.readthedocs.io/en/latest/getting_started/getting_started.html</p> <p><strong>MATLAB script and data:</strong></p> <ul> <li><strong><em>data & script.zip</em></strong> includes the raw data saved in Matlab data format and the script to create the figures in the paper. Please download all files into a folder and run the script.m under MATLAB.</li> </ul>
Improving surface melt estimation over the Antarctic Ice Sheet using deep learning: a proof of concept over the Larsen Ice Shelf
<p>Hu, Z., Kuipers Munneke, P., Lhermitte, S., Izeboud, M., and van den Broeke, M.: Improving Surface Melt Estimation over Antarctica Using Deep Learning: A Proof-of-Concept over the Larsen Ice Shelf, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-102, in review, 2021.</p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>(1) MLP_model_surface_melt_corr.h5</strong> is the developed MLP model used for correcting RACMO2 surface melt.</p> <p><strong>(2) RACMO2_surface_melt_corr_MLP_AWS14.xlsx </strong>corrected surface melt [mm w.e. per day] from RACMO2 at AWS 14 during austral summers 2001 - 2016. The model inputs are (1) the simulated albedo, (2) the albedo difference between the observed and simulated albedo, (3) air temperature at 2m, (4) incoming shortwave radiation, (5) downwelling longwave radiation, (6) simulated surface melt, (7) Boolean melt flag, (8) surface melt difference to the previous day, and (9) record date as day of the year.</p> <p><strong>(3) RACMO2_surface_melt_corr_MLP_AWS17.xlsx </strong>The same as point 2 but for AWS 17</p> <p><strong>(4) RACMO2_surface_melt_corr_MLP_AWS18.xlsx </strong>The same as point 2 but for AWS 18</p> <p>Note: Data 2-4 are corrected RACMO2 simulations of surface melt at the pixels in RACMO2 27 km grid corresponding to AWS 14, 17, and 18 locations. They are not AWS observations.</p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>Related data set:</strong></p> <p>MODIS/Terra Surface Reflectance Daily L2G Global 1 km and 500 m SIN Grid product is available via the Land Processes Distributed Active Archive Center (LP DAAC) (https://doi.org/10.5067/MODIS/MOD09GA.006, last access: 3 December 2021). MODIS/Terra+Aqua Albedo Daily L3 Global 500 m SIN Grid product is also available via LP DAAC (https://doi.org/10.5067/MODIS/MCD43A3.006, last access: 3 December 2021). Sentinel-1 images are provided by the European Space Agency (ESA) (https://sentinel.esa.int/web/sentinel/sentinel-data-access, last access: 3 December 2021). Automatic weather station observations from AWS 14, 17, and 18 are available via https://doi.pangaea.de/10.1594/PANGAEA.910473 (last access: 3 December 2021). RACMO2 simulations (https://www.projects.science.uu.nl/iceclimate/models/antarctica.php#2-1, last access: 3 December 2021) are provided by van Wessem et al. (2018) which are available on request to the original authors.</p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>You should also refer to and cite the following paper:</strong></p> <p>Hu, Z., Kuipers Munneke, P., Lhermitte, S., Izeboud, M., and van den Broeke, M.: Improving Surface Melt Estimation over Antarctica Using Deep Learning: A Proof-of-Concept over the Larsen Ice Shelf, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-102, in review, 2021.</p>
STREAMICE code and inputs for "The West Antarctic Ice Sheet may not be vulnerable to Marine Ice Cliff Instability during the 21st Century"
<p>This repository contains all inputs and code to carry out the STREAMICE calving experiments run for the manuscript "The West Antarctic Ice Sheet may not be vulnerable to Marine Ice Cliff Instability during the 21st Century" using the modelling framework MITgcm.</p> <p>MITgcm-front_retreat/ contains a branch of the MITgcm code that enables calving front advance and retreat in the STREAMICE model, and is a branch of checkpoint 68d. Please see https://github.com/MITgcm/MITgcm/blob/master/LICENSE.txt for the MITgcm open source license detail.</p> <p>code/ contains experiment-specific code for the runs detailed in the manuscript</p> <p>input_fwd/ contains all binary and parameter input files for the runs detailed in the manuscript</p> <p>archer_scripts/ contains shell scripts written for the ARCHER2 UK supercomputer to demonstrate how the model is compiled</p> <p> </p> <p> </p>
Model data and figure code for results and figures in the manuscript submitted to Geophysical Research Letters "Hysteresis of the Antarctic ice sheet with a coupled ice sheet climate model"
<p>This folder contains the model data and figure code for results and figures in the manuscript submitted to Geophysical Research Letters "Hysteresis of the Antarctic ice sheet with a coupled ice sheet climate model"</p> <p>The code for plotting the figures is the notebook Plot_figures.ipynb</p> <p>Fig1/simulation_output/ : Model output necessary for plotting the first figure </p> <p>The last timestep of each simulation is provided. There is one file for 1D variables (ice volume, ice volume above flotation), and one file for 2D variables (ice sheet thickness for instance).</p> <ul> <li><span>melt_insoPI_output/ : melt branch, pre-industrial insolation. Results for different CO2 levels</span></li> <li><span>growth_insoPI_output/ : growth branch, pre-industrial insolation. Results for different CO2 levels</span></li> <li><span>melt_insoMAX_output/ : melt branch, maximum insolation. Results for different CO2 levels</span></li> <li><span>growth_insoMIN_output/ : growth branch, minimum insolation. Results for different CO2 levels</span></li> </ul> <p><span>compute_SLR_equivalent.py : code to compute the ice sheet volume in SLRe based on model output</span></p> <p><span>Fig1/SLR_files/ : contains the equilibrium ice sheet volume of the different simulations according to the CO2 level</span></p> <p> </p> <p>Fig2/simulation_output/ : Model output necessary for plotting the second figure </p> <p>The last timestep of each simulation is provided. </p> <ul> <li><span>melt_insoPI_enhancedmelt_albfb/ : melt branch, pre-industrial insolation, enhanced melt and albedo feedback. Results for different CO2 levels</span></li> <li><span>growth_insoPI_enhancedmelt_albfb/ : growth branch, pre-industrial insolation, enhanced melt and albedo feedback. Results for different CO2 levels</span></li> <li><span>melt_insoPI_enhancedmelt_fixedalb/ : melt branch, pre-industrial insolation, enhanced melt, no albedo feedback. Results for different CO2 levels</span></li> <li><span>growth_insoPI_enhancedmelt_fixedalb/: growth branch, pre-industrial insolation, enhanced melt, no albedo feedback. Results for different CO2 levels</span></li> </ul> <p><span>compute_SLR_equivalent.py : code to compute the ice sheet volume in SLRe based on model output</span></p> <p><span>Fig2/SLR_files/ : contains the equilibrium ice sheet volume of the different simulations according to the CO2 level</span></p> <p> </p> <p><span>Fig3/simulation_output/ : Model output necessary for plotting the third figure </span></p> <ul> <li><span>1xCO2_nocoupling/ : simulation with pre-industrial CO2 levels and insolation and no coupling to the ice sheet model</span></li> <li><span>8xCO2_nocoupling/ : simulation with 8xpiCO2 (pre-industrial CO2) levels, pre-industrial insolation and no coupling to the ice sheet model</span></li> <li><span>8xCO2_transient_albfb/ : quasi transient simulation, 8xpiCO2 levels, pre-industrial insolation, coupling with the ice sheet model </span></li> <li><span>8xCO2_transient_fixedalb/ : quasi transient simulation, 8xpiCO2 levels, pre-industrial insolation, coupling with the ice sheet model excluding the albedo-melt feedback</span></li> </ul> <p> </p>
Response of water isotopes in precipitation to a collapse of the West Antarctic Ice Sheet in high-resolution simulations with the Weather Research and Forecasting Model
<p>This archive includes data and ipython notebooks to create the figures for the manuscript "Response of water isotopes in precipitation to a collapse of the West Antarctic Ice Sheet in high-resolution simulations with the Weather Research and Forecasting Model" submitted to Journal of Climate in August 2022.</p> <p>Model output from WRFwiso and iCAM is in data.zip (saved as monthly means)</p> <p>Notebooks and python modules are in scripts.zip</p> <p>Required python packages (all included in environment.yml):</p> <ul> <li>numpy</li> <li>matplotlib</li> <li>netcdf4</li> <li>basemap</li> <li>scipy</li> <li>wrf-python</li> <li>windspharm</li> <li>metpy</li> <li>intergrid</li> <li>cmocean</li> </ul> <p>Version 1 is the original upload from the first submission.</p> <p>Version 2 includes small updates of the data and scripts from the revision.</p>
Chapter 3 - A highly-dynamic East Antarctic Ice Sheet during the Miocene: A multi-proxy sedimentary provenance approach using in-situ 87Rb/87Sr dating of detrital K-feldspar in ODP Site 1165, Prydz Bay
Open the record for dataset details and reuse information.
Data for "Antarctic ice-sheet meltwater reduces transient warming and climate sensitivity through the sea-surface temperature pattern effect"
<p>Data of the Historical Hosing simulations presented in "Antarctic ice-sheet meltwater reduces transient warming and climate sensitivity through the sea-surface temperature pattern effect" submitted to Geophysical Research Letters</p> <p>Authors: Yue Dong, Andrew G. Pauling, Shaina Sadai, Kyle C. Armour </p> <p>Abstract:</p> <p>Coupled global climate models (GCMs) generally fail to reproduce the observed sea-surface temperature (SST) trend pattern since the 1980s. The model-observation discrepancies may arise in part from the lack of realistic Antarctic ice-sheet meltwater imbalance in GCMs. Here we employ two sets of CESM1-CAM5 simulations forced by anomalous Antarctic meltwater fluxes over 1980--2013 and into the 21st century. Both show a reduced global warming rate and an SST trend pattern that better resembles observations. The meltwater drives surface cooling in the Southern Ocean and the tropical southeast Pacific, in turn increasing low-cloud cover and driving radiative feedbacks to become more stabilizing (corresponding to a lower effective climate sensitivity). These feedback changes contribute more than ocean heat uptake efficiency changes in reducing the global warming rate. Accurately projecting historical and future warming thus requires improved representation of Antarctic meltwater and its impacts in models. </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.