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.
186
datasets available to search
ShareScore release 0.9.0
Dataset results
186 results for “Ice Sheet”
Dataset for "Climate and ice sheet evolutions from the last glacial maximum to the pre-industrial period with an ice sheet -- climate coupled model"
<p>This archive contains the source data of the figures presented in the manuscript "Climate and ice sheet evolutions from the last glacial maximum to the pre-industrial period with an ice sheet -- climate coupled model".</p> <p>Contact: aurelien.quiquet@lsce.ipsl.fr</p>
Atmospheric river contributions to ice sheet hydroclimate at the Last Glacial Maximum
<p>This dataset contains the atmospheric river catalogues and the associated precipitation and temperature data for the Preindustrial and Last Glacial Maximum CESM2 simulations presented in the GRL manuscript: Atmospheric river contributions to ice sheet hydro climate at the Last Glacial Maximum. The atmospheric river catalogue files (zipped) are in netcdf format and organized by year. There are 100 years of data for both simulations. The Preindustrial simulation catalogue begins in model year 41 and ends in model year 140. The LGM simulation catalogue begins in model year 1 and ends in year 100. Each yearly file has a temporal resolution of 6 hours (1460 time steps each file) and a spatial resolution of 0.9° x 1.25° (the native resolution of the CESM simulation). A variable in the file called "ar_binary_tag" indicates whether an atmospheric river is present at each grid cell and each tilmestep: 1 indicates an atmospheric river is present; 0 indicates an atmospheric river is not present. The precipitation and temperature files are 100-year annual or 100-year seasonal averages of atmospheric river precipitation/temperature. See the Methods section of the article for more details on the atmospheric river detection algorithm and precipitation/temperature calculations.</p> <p>Associated article abstract:</p> <p>Atmospheric rivers (ARs) are an important driver of surface mass balance over today’s Greenland and Antarctic ice sheets. Using paleoclimate simulations with the Community Earth System Model, we find ARs also had a key influence on the extensive ice sheets of the Last Glacial Maximum (LGM). ARs provide up to 53% of total precipitation along the margins of the eastern Laurentide ice sheet and up to 22-27% of precipitation along the margins of the Patagonian, western Cordilleran, and western Fennoscandian ice sheets. Despite overall cold conditions at the LGM, surface temperatures during AR events are often above freezing, resulting in more rain than snow along ice sheet margins and conditions that promote surface melt. The results suggest ARs may have had an important role in ice sheet growth and melt during previous glacial periods and may have accelerated ice sheet retreat following the LGM.</p>
Idealized simulations of marine ice sheet instability
<p>Ensemble of idealized simulations of the unstable retreat of an outlet glacier with the Parallel Ice Sheet Model (PISM). Varied parameters are the width, length and depth of the glacier, the softness of the ice and the basal friction.</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>
Deglacial climate changes as forced by different ice sheet reconstructions - model ouputs
<p>This dataset contains the model output corresponding to the paper entitled "Deglacial climate changes as forced by different ice sheet reconstructions" submitted to Climate of the Past. For the description of the model and simulations we refer to this article.</p> <p> </p> <p><strong>Simulations:</strong><br> degla_P_bathy_500yr_is_SH_nobathy = with ICE_6G_C, fixed bathymetry<br> degla_P_bathy_500yr_is_SH = with ICE_6G_C, evolving bathymetry<br> degla_P_bathy_500yr_is_SH_bis = with ICE_6G_C, evolving bathymetry, mask modified<br> degla_T_bathyT_100yr_is_SH_nobathy = with GLAC-1D, fixed bathymetry<br> degla_T_bathyT_100yr_is_SH = with GLAC-1D, evolving bathymetry<br> degla_T_bathyT_100yr_is_SH_FWF = with GLAC-1D, evolving bathymetry, fresh water flux<br> degla_T_bathyT_100yr_is_SH_FWFtest3 = with GLAC-1D, evolving bathymetry, fresh water flux with intensity divided by 3<br> degla_T_bathyT_100yr_is_SH_FWFtest4 = with GLAC-1D, evolving bathymetry, fresh water flux with intensity divided by 4</p> <p> </p> <p><strong>Variables and corresponding files:</strong><br> <em>Evolution of ocean volume (m3):</em><br> volume_ocean_degla_P_bathy_500yr_is_SH.txt<br> volume_ocean_degla_T_bathyT_100yr_is_SH.txt</p> <p><em>Evolution of ocean surface area (1e6 km2):</em><br> surface_area_degla_P_bathy_500yr_is_SH.txt<br> surface_area_degla_T_bathyT_100yr_is_SH.txt</p> <p><em>Sea land masks for time slices:</em><br> tmask_bathy_P_0yr_SH_CC_PI.nc<br> tmask_degla_P_bathy_500yr_is_SH_21ka.nc<br> tmask_degla_T_bathyT_100yr_is_SH_21ka.nc<br> tmask_degla_P_bathy_500yr_is_SH_12ka.nc<br> tmask_degla_T_bathyT_100yr_is_SH_12ka.nc<br> tmask_degla_P_bathy_500yr_is_SH_9ka.nc<br> tmask_degla_T_bathyT_100yr_is_SH_9ka.nc</p> <p><em>Evolution of global mean temperature (degree C):</em><br> Temperature_evolution_degla_P_bathy_500yr_is_SH_nobathy.txt<br> Temperature_evolution_degla_T_bathyT_100yr_is_SH_nobathy.txt<br> Temperature_evolution_degla_P_bathy_500yr_is_SH.txt<br> Temperature_evolution_degla_P_bathy_500yr_is_SH_bis.txt<br> Temperature_evolution_degla_T_bathyT_100yr_is_SH_nobathy.txt<br> Temperature_evolution_degla_T_bathyT_500yr_is_SH.txt<br> Temperature_evolution_degla_T_bathyT_100yr_is_SH.txt<br> Temperature_evolution_degla_T_bathyT_100yr_is_SH_FWF.txt<br> Temperature_evolution_degla_T_bathyT_100yr_is_SH_FWFtest3.txt<br> Temperature_evolution_degla_T_bathyT_100yr_is_SH_FWFtest4.txt</p> <p><em>Temperature maps for time slices:</em><br> temp_degla_P_bathy_500yr_is_SH_nobathy_21ka.nc<br> temp_degla_T_bathyT_100yr_is_SH_nobathy_21ka.nc<br> temp_degla_P_bathy_500yr_is_SH_nobathy_10ka.nc<br> temp_degla_T_bathyT_100yr_is_SH_nobathy_10ka.nc</p> <p><em>Evolution of salinity:</em><br> iLOVECLIM_salinity_ICE-6G_C.nc<br> iLOVECLIM_salinity_GLAC-1D.nc</p> <p><em>Temperature evolution at NGRIP location:</em><br> iLOVECLIM_t2m_NGRIP_degla_P_bathy_500yr_is_SH_nobathy.nc<br> iLOVECLIM_t2m_NGRIP_degla_P_bathy_500yr_is_SH.nc<br> iLOVECLIM_t2m_NGRIP_degla_P_bathy_500yr_is_SH_bis.nc<br> iLOVECLIM_t2m_NGRIP_degla_T_bathyT_100yr_is_SH_nobathy.nc<br> iLOVECLIM_t2m_NGRIP_degla_T_bathyT_100yr_is_SH.nc<br> iLOVECLIM_t2m_NGRIP_degla_T_bathyT_100yr_is_SH_FWF.nc<br> iLOVECLIM_t2m_NGRIP_degla_T_bathyT_100yr_is_SH_FWFtest3.nc<br> iLOVECLIM_t2m_NGRIP_degla_T_bathyT_100yr_is_SH_FWFtest4.nc</p> <p><em>Temperature evolution at EDC location:</em><br> iLOVECLIM_t2m_EDC_degla_T_bathyT_100yr_is_SH.nc<br> iLOVECLIM_t2m_EDC_degla_T_bathyT_100yr_is_SH_nobathy.nc<br> iLOVECLIM_t2m_EDC_degla_P_bathy_500yr_is_SH_nobathy.nc<br> iLOVECLIM_t2m_EDC_degla_P_bathy_500yr_is_SH.nc<br> iLOVECLIM_t2m_EDC_degla_P_bathy_500yr_is_SH_bis.nc<br> iLOVECLIM_t2m_EDC_degla_T_bathyT_100yr_is_SH_FWF.nc<br> iLOVECLIM_t2m_EDC_degla_T_bathyT_100yr_is_SH_FWFtest3.nc<br> iLOVECLIM_t2m_EDC_degla_T_bathyT_100yr_is_SH_FWFtest4.nc</p> <p><em>Evolution of surface albedo (all globe):</em><br> iLOVECLIM_alb_all_degla_P_bathy_500yr_is_SH_nobathy.nc<br> iLOVECLIM_alb_all_degla_P_bathy_500yr_is_SH.nc<br> iLOVECLIM_alb_all_degla_P_bathy_500yr_is_SH_bis.nc<br> iLOVECLIM_alb_all_degla_T_bathyT_100yr_is_SH_nobathy.nc<br> iLOVECLIM_alb_all_degla_T_bathyT_100yr_is_SH.nc</p> <p><em>Evolution of surface albedo (Northern Hemisphere):</em><br> iLOVECLIM_alb_NH_degla_T_bathyT_100yr_is_SH_nobathy.nc<br> iLOVECLIM_alb_NH_degla_T_bathyT_100yr_is_SH.nc<br> iLOVECLIM_alb_NH_degla_P_bathy_500yr_is_SH_nobathy.nc<br> iLOVECLIM_alb_NH_degla_P_bathy_500yr_is_SH.nc<br> iLOVECLIM_alb_NH_degla_P_bathy_500yr_is_SH_bis.nc</p> <p><em>Evolution of surface albedo (Southern Hemisphere):</em><br> iLOVECLIM_alb_SH_degla_P_bathy_500yr_is_SH_nobathy.nc<br> iLOVECLIM_alb_SH_degla_P_bathy_500yr_is_SH.nc<br> iLOVECLIM_alb_SH_degla_P_bathy_500yr_is_SH_bis.nc<br> iLOVECLIM_alb_SH_degla_T_bathyT_100yr_is_SH_nobathy.nc<br> iLOVECLIM_alb_SH_degla_T_bathyT_100yr_is_SH.nc</p> <p><em>Evolution of sea ice area in the Northern Hemisphere (1e12 km2):</em><br> iLOVECLIM_sea_ice_NH_degla_P_bathy_500yr_is_SH_nobathy.nc<br> iLOVECLIM_sea_ice_NH_degla_P_bathy_500yr_is_SH.nc<br> iLOVECLIM_sea_ice_NH_degla_T_bathyT_100yr_is_SH_nobathy.nc<br> iLOVECLIM_sea_ice_NH_degla_T_bathyT_100yr_is_SH.nc</p> <p><em>Evolution of sea ice area in the Southern Hemisphere (1e12 km2):</em><br> iLOVECLIM_sea_ice_SH_degla_P_bathy_500yr_is_SH_nobathy.nc<br> iLOVECLIM_sea_ice_SH_degla_P_bathy_500yr_is_SH.nc<br> iLOVECLIM_sea_ice_SH_degla_T_bathyT_100yr_is_SH_nobathy.nc<br> iLOVECLIM_sea_ice_SH_degla_T_bathyT_100yr_is_SH.nc</p> <p><em>Winter sea ice fraction and mixed layer depth (m) at time slices:</em><br> iLOVECLIM_sea_ice_mld_bathy_P_21000yr_SH_21ka.nc<br> iLOVECLIM_sea_ice_mld_bathy_T_21000yr_SH_21ka.nc<br> iLOVECLIM_sea_ice_mld_degla_P_bathy_500yr_is_SH_nobathy_10ka.nc<br> iLOVECLIM_sea_ice_mld_degla_T_bathyT_100yr_is_SH_nobathy_10ka.nc<br> iLOVECLIM_sea_ice_mld_degla_P_bathy_500yr_is_SH_10ka.nc<br> iLOVECLIM_sea_ice_mld_degla_T_bathyT_100yr_is_SH_10ka.nc</p> <p><em>Evolution of the maximum strength of AMOC:</em><br> iLOVECLIM_AMOC_degla_P_bathy_500yr_is_SH_nobathy.nc<br> iLOVECLIM_AMOC_degla_P_bathy_500yr_is_SH.nc<br> iLOVECLIM_AMOC_degla_T_bathyT_100yr_is_SH_nobathy.nc<br> iLOVECLIM_AMOC_degla_T_bathyT_100yr_is_SH.nc<br> iLOVECLIM_AMOC_degla_T_bathyT_100yr_is_SH_FWF.nc<br> iLOVECLIM_AMOC_degla_T_bathyT_100yr_is_SH_FWFtest3.nc<br> iLOVECLIM_AMOC_degla_T_bathyT_100yr_is_SH_FWFtest4.nc</p> <p><em>Meridional overtunring circulation at time slices:</em><br> MOC_degla_P_bathy_500yr_is_SH_21ka.nc<br> MOC_degla_P_bathy_500yr_is_SH_10ka.nc<br> MOC_degla_P_bathy_500yr_is_SH_nobathy_10ka.nc<br> MOC_degla_T_bathyT_100yr_is_SH_21ka.nc<br> MOC_degla_T_bathyT_100yr_is_SH_10ka.nc</p>
Ice-Flow Perturbation Analysis: A method to estimate ice-sheet bed topography and conditions from surface datasets (data)
<p>This dataset accompanies the paper 'Ice-Flow Perturbation Analysis: A method to estimate ice-sheet bed topography and conditions from surface datasets' in Journal of Glaciology, and can be used alongside the provided code to reproduce the figures,</p>
GRACE High-Resolution Trend Mascons - Greenland Ice Sheet (2007-2015)
<p>High-resolution mascon trend solution, computed for the Greenland Ice Sheet over the time period from January 2007 and January 2015, where each mascon regression model (including the trend) has been directly estimated from the Gravity Recovery and Climate Experiment (GRACE) Level 1B data. The GAD product has not been restored, meaning the ocean mascons are consistent with the Level 2 GSM product information.</p><p>Description of columns in the dataset:</p><ol><li>Latitude center (deg)</li><li>Longitude center (deg)</li><li>Mass change trend (cm w.e. / yr)</li><li>Mass change trend uncertainty (cm w.e. / yr)</li><li>Latitude minimum (deg)</li><li>Latitude maximum (deg)</li><li>Longitude minimum (deg)</li><li>Longitude maximum (deg)</li><li>Area of mascon (sq. km)</li><li>Label of mascon</li></ol><p>When citing this dataset, please also include this citation:</p><p>Loomis, B. D., D. Felikson, T. J. Sabaka, and B. Medley (2021). High‐spatial‐resolution mass rates from GRACE and GRACE‐FO: Global and ice sheet analyses. <i> Journal of Geophysical Research: Solid Earth, </i><a href="https://doi.org/10.1029/2021JB023024">https://doi.org/10.1029/2021JB023024</a></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 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>
An iterative process for efficient optimisation of parameters in geoscientific models: a demonstration using the Parallel Ice Sheet Model (PISM) version 0.7.3
<p>Physical processes within geoscientific models are sometimes described by simplified schemes known as parameterisations. The values of the parameters within these schemes can be poorly constrained by theory or observation. Uncertainty in the parameter values translates into uncertainty in the outputs of the models. Proper quantification of the uncertainty in model predictions therefore requires a systematic approach for sampling parameter space. In this study, we develop a simple and efficient approach to identify regions of multi-dimensional parameter space that are consistent with observations. Using the Parallel Ice Sheet Model to simulate the present-day state of the Antarctic Ice Sheet, we find that co-dependencies between parameters preclude the identification of a single optimal set of parameter values. Approaches such as large ensemble modelling are therefore required in order to generate model predictions that incorporate proper quantification of the uncertainty arising from the parameterisation of physical processes.</p>
Greenland ice sheet surface melt projections using artifical neural networks
<p>Surface melt projections for the Greenland ice sheet using artificial neural networks.</p> <p>Organized as follows:</p> <p><scenario>/<variable>/<model>/<ensemble_number>.nc</p>
Data from: Dynamic ice loss from the Greenland Ice Sheet driven by sustained glacier retreat
<p>Here we share remotely-sensed Greenland outlet glacier data for 234 individual glaciers. Data include observations of mena monthly discharge (ice volume flux exported by the glacier), front position changes, velocity, and ice thickness for the 1985-2018 period. Data will be periodically updated as needed.</p> <p> </p> <p>The Greenland Ice Sheet is losing mass at accelerated rates in the 21st century, making it the largest single contributor to rising sea levels. Faster flow of outlet glaciers has substantially contributed to this loss, with the cause of speedup, and potential for future change, uncertain. Here we combine more than three decades of remotely sensed observational products of outlet glacier velocity, elevation, and front position changes over the full ice sheet. We compare decadal variability in discharge and calving front position and find that increased glacier discharge was due almost entirely to the retreat of glacier fronts, rather than inland ice sheet processes, with a remarkably consistent speedup of 4-5% per km of retreat across the ice sheet. We show that widespread retreat between 2000 and 2005 resulted in a step-increase in discharge and a switch to a new dynamic state of sustained mass loss that would persist even under a decline in surface melt.</p>
Dataset associated with "Emulating subglacial hydrology in ice sheet models with deep learning methods" by Verjans and Robel.
<p>See Readme file for descriptions.</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>
Laurentide Ice Sheet evolution towards the Last Glacial Maximum using AWIESM model with interactive ice sheets
<p>This archive contains model data and figures associated with the study titiled "Rapid Laurentide Ice Sheet growth preceding the Last Glacial Maximum due to summer snowfall" (Niu et al., 2024). The comprehensive Earth system model AWI-ESM with interactive ice sheets is used for the model simulations. The notation of the individual files in the archive corresponds to the respective figure numbers in the paper. The respective file content is described by the corresponding figure caption in the paper.</p> <p>Niu, L., Knorr, G., Krebs-Kanzow, U. et al. Rapid Laurentide Ice Sheet growth preceding the Last Glacial Maximum due to summer snowfall. Nat. Geosci. (2024). https://doi.org/10.1038/s41561-024-01419-z</p> <p> </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>
Dataset for "History and dynamics of Fennoscandian Ice Sheet retreat, contemporary ice-dammed lake evolution, and faulting in the Torneträsk area, northwestern Sweden"
<p>This dataset contains the GIS shapefiles and the high resolution map for the manuscript "Ice-dammed lakes and contemporary faulting in the Torneträsk region of northern Sweden are key to understanding regional Fennoscandian Ice Sheet deglaciation patterns and dynamics" in preparation for <em>The Cryosphere</em>. </p>
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>
Data from: Temporal variability in snow accumulation and density at Summit Camp, Greenland ice sheet
<p>A 3-year record of weekly snow water equivalent (SWE) accumulation at Summit Camp, central Greenland ice sheet, obtained by direct sampling, is presented. While the overall SWE accumulation of 24.2 cm w.e. a<sup>−1</sup> matches long-term ice core estimates, variability increases at shorter timescales. Half of the annual SWE accumulation occurs during a few large events, with the average accumulation rate decreasing 35% between the first and second halves of the record coinciding with exceptional anticyclonic conditions in the spring and summer of 2019. No seasonality in accumulation is detected. Rather, local accumulation rates appear to be significantly impacted by wind redistribution that obscures temporal patterns in snowfall. Surface snow density is consistent, on average, with previously measured values but does not correlate with near surface temperature or wind speed. Two surface mass balance reanalysis models significantly underestimate accumulation rates at Summit Camp. This is concerning because such models are often used to estimate ice-sheet mass loss.</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.