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Dataset for "The future sea-level contribution of the Greenland ice sheet: a multi-model ensemble study of ISMIP6"
<p>This data set provides processed model output of ISMIP6 Greenland projections as documented and analysed in the following publication:</p> <p>Heiko Goelzer, Sophie Nowicki, Anthony Payne, Eric Larour, Helene Seroussi, William H. Lipscomb, Jonathan Gregory, Ayako Abe-Ouchi, Andy Shepherd, Erika Simon, Cecile Agosta, Patrick Alexander, Andy Aschwanden, Alice Barthel, Reinhard Calov, Christopher Chambers, Youngmin Choi, Joshua Cuzzone, Christophe Dumas, Tamsin Edwards, Denis Felikson, Xavier Fettweis, Nicholas R. Golledge, Ralf Greve, Angelika Humbert, Philippe Huybrechts, Sebastien Le clec'h, Victoria Lee, Gunter Leguy, Chris Little, Daniel P. Lowry, Mathieu Morlighem, Isabel Nias, Aurelien Quiquet, Martin Rückamp, Nicole-Jeanne Schlegel, Donald Slater, Robin Smith, Fiamma Straneo, Lev Tarasov, Roderik van de Wal, and Michiel van den Broeke: The future sea-level contribution of the Greenland ice sheet: a multi-model ensemble study of ISMIP6 , The Cryosphere, 2020. doi:10.5194/tc-2019-319</p> <p>About the data:<br> - The results are based on model output regridded conservatively to a 5x5 km regular ISMIP6 grid unless this is already the native grid. <br> - The results are calculated over the ice-covered area of Greenland, map projection error corrected, ice sheet model specific densities taken into account.<br> - The contribution of peripheral glaciers and ice caps has been removed, by considering their area-coverage in each grid cell.<br> - The results for the projections 'exp*' are all calculated as differences to the control experiment ctrl_proj (suffix cr in filename for control removed).<br> - Results for ctrl_proj and historical are un-corrected (no suffix cr in filename).</p> <p><br> Directory structure:<br> versionid<br> groupname1<br> modelname1<br> expid<br> scalars_mm_cr_GIS_groupname1_modelname1_expid.nc<br> scalars_rm_cr_GIS_groupname1_modelname1_expid.nc<br> scalars_zm_cr_GIS_groupname1_modelname1_expid.nc<br> ...</p> <p>Variables per file:</p> <p>scalars_mm_cr_GIS ----------------- Greenland wide numbers </p> <p>oarea - assumed ocean area [m2]<br> rhof - model specific freshwater density [kg m-3]<br> rhoi - model specific ice density [kg m-3]<br> rhow - model specific ocean water density [kg m-3]</p> <p>time - time, typically in days since X<br> iarea - Fraction of grid cell covered by land ice [1]<br> iareagr - Fraction of grid cell covered by grounded ice sheet<br> iareafl - Fraction of grid cell covered by ice sheet flowing over seawater</p> <p>ivol - ice volume [m3]<br> ivolgr - grounded ice volume [m3]<br> ivolfl - floating ice volume [m3]<br> ivaf - ice volume above flotation [m3]</p> <p>lim - ice mass [kg]<br> limgr - grounded ice mass [kg]<br> limfl - floating ice mass [kg]<br> limaf - ice mass above flotation [kg]</p> <p>sle - sea-level equivalent mass [m] !! decreases with mass loss !! <br> smb - spatially integrated surface mass balance anomaly [kg s-1]</p> <p><br> scalars_rm_cr_GIS ----------------- IMBIE2-Rignot basins xx=[no,ne,se,sw,cw,nw]</p> <p>oarea - assumed ocean area [m2]<br> rhof - model specific freshwater density [kg m-3]<br> rhoi - model specific ice density [kg m-3]<br> rhow - model specific ocean water density [kg m-3]</p> <p>time - time, typically in days since X<br> ivaf_xx - ice volume above flotation [m3]<br> smb_xx - spatially integrated surface mass balance anomaly [kg s-1]<br> limaf_xx - ice mass above flotation [kg]<br> sle_xx - sea-level equivalent mass [m] !! decreases with mass loss !! </p> <p><br> scalars_zm_cr_GIS ----------------- IMBIE2-Zwally basins xx=[z11,z12,z13,z14,z21,z22,z31,z32,z33,z41,z42,z43,z50,z61,z62,z71,z72,z81,z82]</p> <p>oarea - assumed ocean area [m2]<br> rhof - model specific freshwater density [kg m-3]<br> rhoi - model specific ice density [kg m-3]<br> rhow - model specific ocean water density [kg m-3]</p> <p>time - time, typically in days since X<br> ivaf_xx - ice volume above flotation [m3]<br> smb_xx - spatially integrated surface mass balance anomaly [kg s-1]<br> limaf_xx - ice mass above flotation [kg]<br> sle_xx - sea-level equivalent mass [m] !! decreases with mass loss !! </p> <p> </p> <p>Data usage notice:<br> If you use any of these results, please acknowledge the work of the people involved in producing them. Acknowledgements should have language similar to the below.</p> <p>“We thank the Climate and Cryosphere (CliC) effort, which provided support for ISMIP6 through sponsoring of workshops, hosting the ISMIP6 website and wiki, and promoted ISMIP6. We acknowledge the World Climate Research Programme, which, through it's Working Group on Coupled Modelling, coordinated and promoted CMIP5 and CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the CMIP data and providing access, the University at Buffalo for ISMIP6 data distribution and upload, and the multiple funding agencies who support CMIP5 and CMIP6 and ESGF. We thank the ISMIP6 steering committee, the ISMIP6 model selection group and ISMIP6 dataset preparation group for their continuous engagement in defining ISMIP6."</p> <p>You should also refer to and cite the following papers:</p> <p>Heiko Goelzer, Sophie Nowicki, Anthony Payne, Eric Larour, Helene Seroussi, William H. Lipscomb, Jonathan Gregory, Ayako Abe-Ouchi, Andy Shepherd, Erika Simon, Cecile Agosta, Patrick Alexander, Andy Aschwanden, Alice Barthel, Reinhard Calov, Christopher Chambers, Youngmin Choi, Joshua Cuzzone, Christophe Dumas, Tamsin Edwards, Denis Felikson, Xavier Fettweis, Nicholas R. Golledge, Ralf Greve, Angelika Humbert, Philippe Huybrechts, Sebastien Le clec'h, Victoria Lee, Gunter Leguy, Chris Little, Daniel P. Lowry, Mathieu Morlighem, Isabel Nias, Aurelien Quiquet, Martin Rückamp, Nicole-Jeanne Schlegel, Donald Slater, Robin Smith, Fiamma Straneo, Lev Tarasov, Roderik van de Wal, and Michiel van den Broeke: The future sea-level contribution of the Greenland ice sheet: a multi-model ensemble study of ISMIP6 , The Cryosphere, 2020. doi:10.5194/tc-2019-319</p> <p>Sophie Nowicki, Antony Payne, Heiko Goelzer, Helene Seroussi, William Lipscomb, Ayako Abe-Ouchi, Cecile Agosta, Patrick Alexander, Xylar Asay-Davis, Alice Barthel, Thomas Bracegirdle, Richard Cullather, Denis Felikson, Xavier Fettweis, Jonathan Gregory, Tore Hatterman, Nicolas Jourdain, Peter Kuipers Munneke, Eric Larour, Christopher Little, Mathieu Morlinghem, Isabel Nias, Andrew Shepherd, Erika Simon, Donald Slater, Robin Smith, Fiammetta Straneo, Luke Trusel, Michiel van den Broeke, and Roderik van de Wal: Experimental protocol for sea level projections from ISMIP6 standalone ice sheet models, The Cryosphere, doi:10.5194/tc-2019-322, 2020.</p>
Data set: "A 21st Century Warming Threshold for Sustained Greenland Ice Sheet Mass Loss"
<p>This data set includes the materials required to reproduce the figures and tables presented in the study: "A 21<sup>st</sup> Century Warming Threshold for Sustained Greenland Ice Sheet Mass Loss". The data consist of:</p> <ul> <li>Maps of annual historical and projected surface mass balance (SMB) of the Greenland ice sheet (GrIS) from RACMO2.3p2 at 1 km spatial resolution in NetCDF format.</li> </ul> <ol> <li><strong>smb_rec.1950-2014.BN_RACMO2.3p2-CESM2-Historical.1km.YY.nc</strong>: annual cumulative GrIS SMB (kg m<sup>-2</sup> or mm w.e. per year) from RACMO2.3p2 forced by CESM2 for the historical period 1950-2014, further statistically downscaled to 1 km spatial resolution.</li> <li><strong>smb_rec.2015-2099.BN_RACMO2.3p2-CESM2-SSP5-85.1km.YY.nc</strong>: annual cumulative GrIS SMB (kg m<sup>-2</sup> or mm w.e. per year) from RACMO2.3p2 forced by CESM2 under a high-end warming scenario SSP5-8.5 for the period 2015-2099, further statistically downscaled to 1 km spatial resolution.</li> <li><strong>Icemask_Topography_lon_lat_average_1km_GrIS.nc</strong>: mask file including an ice mask (Promicemask) separating Greenland's peripheral glaciers and ice caps (values 1 and 2) from the main ice sheet (value 3), surface topography derived from the GIMP DEM, and longitude/latitude coordinates on the 1 km grid. </li> </ol> <p>NB: the NetCDF files above use a Polar Stereographic North (EPSG:3413) projection with a horizontal resolution of 1 km x 1 km. The reference point is located at 45ºW longitude and 70ºN latitude.</p> <ul> <li>Time series of historical and projected annual GrIS-integrated SMB (Gigatons or Gt per year) and annual mean GrIS temperature (TGrIS; K) from RACMO2.3p2 at 1 km spatial resolution in ASCII format.</li> </ul> <ol> <li><strong>SMB_TGrIS_RACMO2.3p2-CESM2_Historical_1950-2014.dat</strong>: time series of annual GrIS-integrated SMB (Gt yr<sup>-1</sup>) and mean TGrIS (K) from the CESM2-forced RACMO2.3p2 simulation for the historical period 1950-2014.</li> <li><strong>SMB_TGrIS_RACMO2.3p2-CESM2_SSP5-8.5_2015-2099.dat</strong>: time series of annual GrIS-integrated SMB (Gt yr<sup>-1</sup>) and mean TGrIS (K) from the CESM2-forced RACMO2.3p2 projection under a high-end warming scenario SSP5-8.5 (2015-2099).</li> </ol> <ul> <li>Time series of historical and projected reconstruction of annual GrIS-integrated SMB (Gt yr<sup>-1</sup>) and mean TGrIS (K) from 12 CESM2 historical members and 10 CESM2 projections under various warming scenarios, namely SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5.</li> </ul> <ol> <li><strong>Reconstructed_SMB_CESM2_Historical_1950-2014.dat</strong>: time series of reconstructed annual GrIS-integrated SMB (Gt yr<sup>-1</sup>) from the parent historical CESM2 simulation (HIST-parent) used to force RACMO2.3p2 and 11 additional historical CESM2 members (HIST-X) for 1950-2014. </li> <li><strong>Reconstructed_SMB_CESM2_Projections_2015-2099.dat</strong>: time series of reconstructed annual GrIS-integrated SMB (Gt yr<sup>-1</sup>) from the parent CESM2 projection (SSP5-8.5-parent) used to force RACMO2.3p2 and 9 additional CESM2 projection members (SSP1-2.6-X to SSP5-8.5-X ) for 2015-2099. </li> <li><strong>TGrIS_CESM2_Historical_1950-2014.dat</strong>: time series of annual mean TGrIS (K) from the parent historical CESM2 simulation (HIST-parent) used to force RACMO2.3p2 and 11 additional historical CESM2 members (HIST-X) for 1950-2014.</li> <li><strong>TGrIS_CESM2_Projections_2015-2099.dat</strong>: time series of annual mean TGrIS (K) from the parent CESM2 projection (SSP5-8.5-parent) used to force RACMO2.3p2 and 9 additional CESM2 projection members (SSP1-2.6-X to SSP5-8.5-X ) for 2015-2099. </li> </ol> <p>NB: the file <strong>Crossref_sim_names.txt </strong>cross-references the simulation abbreviations in the above .dat files to official simulation names from the National Center for Atmospheric Research (NCAR).</p> <p>The daily downscaled SMB data set from the CESM2-forced RACMO2.3p2 historical simulation and SSP5-8.5 projection are freely available from the authors upon request and without conditions (contact: <strong>b.p.y.noel@uu.nl</strong>). Besides SMB, the data set includes daily total precipitation (snow and rain), snowfall, total melt (snow and ice), meltwater runoff, retention and refreezing, total sublimation (surface and drifting snow), snow drift erosion, as well as 2 m air temperature at 1 km horizontal resolution. </p> <p>Abstract: "Under anticipated future warming, the Greenland ice sheet (GrIS) will pass a threshold when meltwater runoff exceeds the accumulation of snow, resulting in a negative surface mass balance (SMB < 0) and sustained mass loss. In spite of several recent warm summers with high melt rates, SMB < 0 has not been reached since at least the year 1958. Here we dynamically and statistically downscale the outputs of an Earth system model to 1 km resolution to infer that a Greenland near-surface atmospheric warming of 4.5 ± 0.3 °C—relative to pre-industrial—is required for GrIS SMB to become persistently negative. Climate models from CMIP5 and CMIP6 translate this regional temperature change to a global warming threshold of 2.7 ± 0.2 °C. Under a high-end warming scenario, this threshold may be reached around 2055, while for a strong mitigation scenario it will likely not be passed."</p>
Antarctic Ice Sheet grounding line discharge from 1996 to 2024
<p>This dataset provides estimates of grounding line discharge from the Antarctic Ice Sheet and all of it's drainage basins, as described in the following pre-print (under review):</p> <p>Davison, B. J., Hogg, A. E., Slater, T., Rigby, R., and Hansen, N.: Antarctic Ice Sheet grounding line discharge from 1996–2024, Earth Syst. Sci. Data, 17, 3259–3281, https://doi.org/10.5194/essd-17-3259-2025, 2025.</p> <p>Check the README/UserGuide for summaries of what each .zip file includes</p> <p> </p> <p><strong>Update notes</strong></p> <ul> <li>Updated through November 2024</li> <li>Added BedMap3 </li> </ul> <p> </p> <p>Any questions, comments or suggestions, send them to: b.j.davison@sheffield.ac.uk</p> <h3> </h3> <p> </p>
Data set: "Higher Antarctic ice sheet accumulation and surface melt rates revealed at 2 km resolution"
<p>This data set includes the materials required to reproduce the figures and tables presented in the study: "Higher Antarctic ice sheet accumulation and surface melt rates revealed at 2 km resolution". The data consist of:</p><p> </p><p><strong>ASCII files:</strong></p><ol><li><strong>SMB-ANT-Sectors-1979-2021-RACMO2.3p2-ERA5-2km.txt</strong>: time series of Antarctic sector-integrated annual SMB<strong> (Gt per year)</strong> from ERA5-forced RACMO2.3p2 at 27 km, statistically downscaled to 2 km resolution (1979-2021).</li><li><strong>Melt-ANT-Sectors-1979-2021-RACMO2.3p2-ERA5-2km.txt:</strong> time series of Antarctic sector-integrated annual surface melt (Gt per year) from ERA5-forced RACMO2.3p2 at 27 km, statistically downscaled to 2 km resolution (1979-2021).</li><li><strong>Melt-ANT-Sectors-1950-2014-RACMO2.3p2-CESM2-HIST-2km.txt:</strong> time series of Antarctic sector-integrated annual surface melt from CESM2-forced RACMO2.3p2 historical reconstruction (HIST), statistically downscaled to 2 km resolution (1950-2014).</li><li><strong>Melt-ANT-Sectors-2015-2099-RACMO2.3p2-CESM2-SSP126-2km.txt:</strong> time series of Antarctic sector-integrated annual surface melt from CESM2-forced RACMO2.3p2 SSP1-2.6 projection (SSP126), statistically downscaled to 2 km resolution (2015-2099).</li><li><strong>Melt-ANT-Sectors-2015-2099-RACMO2.3p2-CESM2-SSP245-2km.txt:</strong> time series of Antarctic sector-integrated annual surface melt from CESM2-forced RACMO2.3p2 SSP2-4.5 projection (SSP245), statistically downscaled to 2 km resolution (2015-2099).</li><li><strong>Melt-ANT-Sectors-2015-2099-RACMO2.3p2-CESM2-SSP585-2km.txt:</strong> time series of Antarctic sector-integrated annual surface melt from CESM2-forced RACMO2.3p2 SSP5-8.5 projection (SSP585), statistically downscaled to 2 km resolution (2015-2099).</li></ol><p>Antarctic sectors include the Antarctic Peninsula (APIS), the West Antarctic ice sheet (WAIS), the East Antarctic ice sheet (EAIS), the grounded Antarctic ice sheet (AIS), the floating ice shelves (Ice shelves), and the whole of Antarctica (ANT) including both the AIS and Ice shelves. The APIS, WAIS, EAIS and AIS sectors include land ice from neighbouring Antarctic islands.</p><p> </p><p><strong>Netcdf files:</strong></p><ol><li><strong>smb_rec.1979-2021.BN_RACMO2.3p2_ANT27_ERA5-3h.AIS.2km.YY.nc: </strong>map of annual SMB (kg per m² or mm w.e. per year) from ERA5-forced RACMO2.3p2 at 27 km, statistically downscaled to 2 km resolution, covering the whole of Antarctica (1979-2021).</li><li><strong>snowmelt.1979-2021.BN_RACMO2.3p2_ANT27_ERA5-3h.AIS.2km.YY.nc: </strong>map of annual surface melt (kg per m² or mm w.e. per year) from ERA5-forced RACMO2.3p2 at 27 km, statistically downscaled to 2 km resolution, covering the whole of Antarctica (1979-2021).</li><li><strong>snowmelt.1950-2014.BN_RACMO2.3p2_ANT27_CESM2_HIST.AIS.2km.YY.nc: </strong>map of annual surface melt (kg per m² or mm w.e. per year) from CESM2-forced RACMO2.3p2 historical reconstruction (HIST), statistically downscaled to 2 km resolution, covering the whole of Antarctica (1950-2014).</li><li><strong>snowmelt.2015-2099.BN_RACMO2.3p2_ANT27_CESM2_SSP126.AIS.2km.YY.nc: </strong>map of annual surface melt (kg per m² or mm w.e. per year) from CESM2-forced RACMO2.3p2 SSP1-2.6 projection (SSP126), statistically downscaled to 2 km resolution, covering the whole of Antarctica (2015-2099).</li><li><strong>snowmelt.2015-2099.BN_RACMO2.3p2_ANT27_CESM2_SSP245.AIS.2km.YY.nc: </strong>map of annual surface melt (kg per m² or mm w.e. per year) from CESM2-forced RACMO2.3p2 SSP2-4.5 projection (SSP245), statistically downscaled to 2 km resolution, covering the whole of Antarctica (2015-2099).</li><li><strong>snowmelt.2015-2099.BN_RACMO2.3p2_ANT27_CESM2_SSP585.AIS.2km.YY.nc: </strong>map of annual surface melt (kg per m² or mm w.e. per year) from CESM2-forced RACMO2.3p2 SSP5-8.5 projection (SSP585), statistically downscaled to 2 km resolution, covering the whole of Antarctica (2015-2099).</li><li><strong>ANT_masks.2km.nc</strong>: file including the grounded AIS mask (AIS), Antarctic sectors mask (Sectors), floating ice shelves mask (Shelves), surface elevation down-sampled from REMA (Topography), latitude and longitude on the 2 km grid<strong>. </strong>The sector mask includes: 0 – Ocean, 1 – APIS, 2 – WAIS, 3 – EAIS, 4 – APIS islands, 5 – WAIS islands, 6 – EAIS islands, and 7 – ice shelves.<strong> </strong></li></ol><p>The projection used for statistical downscaling is Polar Stereographic South (EPSG:3031) with a spatial resolution of 2 km x 2 km. </p><p> </p><p><strong>Additional data: </strong>The gridded, daily downscaled SMB data sets from the ERA-forced RACMO2.3p2 simulation, and the CESM2-forced RACMO2.3p2 projections under a low-end SSP1-2.6, moderate SSP2-4.5 and high-end SSP5-8.5 warming scenario are freely available from the authors upon request and without conditions (contact: bnoel@uliege.be). Besides SMB, the data sets include total precipitation (snow and rain), snowfall, total melt (snow and ice), runoff, refreezing and retention, drifting snow erosion, and total sublimation (surface and drifting snow) at 2 km horizontal resolution. </p><p> </p><p><strong>Abstract:</strong> Antarctic ice sheet (AIS) mass loss is predominantly driven by increased solid ice discharge, but its variability is governed by surface processes. Snowfall fluctuations control the surface mass balance (SMB) of the grounded AIS, while meltwater ponding can trigger ice shelf collapse potentially accelerating discharge. Surface processes are essential to quantify AIS mass change, but remain poorly represented in climate models typically running at 25-100 km resolution. Here we present SMB and surface melt products statistically downscaled to 2 km resolution for the contemporary climate (1979-2021) and low, moderate and high-end warming scenarios until 2100. We show that statistical downscaling modestly enhances contemporary SMB (3%), which is sufficient to reconcile modelled and satellite mass change. Furthermore, melt strongly increases (46%), notably near the grounding line, in better agreement with in-situ and satellite records. The melt increase persists by 2100 in all warming scenarios, revealing higher surface melt rates than previously estimated.</p>
Disentangling the drivers of future Antarctic ice loss with a historically-calibrated ice-sheet model
<p>=========================================================================<br>Disentangling the drivers of future Antarctic ice loss with a historically-calibrated ice-sheet model<br>=========================================================================</p><p>-----------------------<br>INTRODUCTION<br>-----------------------</p><p>This dataset contains the data and scripts required to reproduce the figures and tables presented in the study:<br>"Disentangling the drivers of future Antarctic ice loss with a historically-calibrated ice-sheet model" in <i>The Cryosphere</i>.</p><p>We perform an ensemble of simulations of the Antarctic ice sheet between 1950 and 3014, forced by a panel of CMIP6 climate models, starting from present-day geometry with the Kori-ULB ice-sheet model v0.9. We calibrate our ensemble in a Bayesian framework to produce observationally-calibrated Antarctic projections used to investigate the future trajectory of the Antarctic ice sheet related to uncertainties in the future balance between sub-shelf melting and ice discharge on the one hand, and the surface mass balance on the other. All simulations are performed at a spatial resolution of 16 km.</p><p>Hindcasts of the behaviour of the AIS over the period 1950-2014 CE are reproduced using changes in oceanic and atmospheric boundary conditions derived from the CMIP5 climate model NorESM1-M. As of the year 2015 CE, climate projections derived from a subset of CMIP6 climate models (MRI-ESM2-0, IPSL-CM6A-LR, CESM2-WACCM and UKESM1-0-LL) are used as forcing until the year 2300 CE. Afterwards, no climate trend is applied. The forcing applied is derived from both the Shared Socioeconomic Pathways (SSP) 5-8.5 and 1-2.6 scenarios. </p><p>------------------------------<br>PROVIDED SCRIPTS: <br>------------------------------</p><p> - 'KoriModelAll.m' and 'KoriInputParams.m': Kori-ULB ice flow model (more info at https://github.com/FrankPat/Kori-ULB)<br> - 'Compute_Bayesian_Weight.m': calculation of the ensemble likelihood weights used in the Bayesian calibration.<br> - 'Plot_parameter_space_distributions.m': calculation and plots of prior and posterior parameter probability distributions.<br> - 'Plot_sea_level_distributions.m': calculation and plots of prior and posterior sea-level distributions.<br> - 'Plot_mass_balance_components_distributions.m': calculation and plots of mass balance components distributions.<br> - 'Plot_mean_thickness_change.m': calculation and plots of calibrated mean thickness change.<br> - 'Plot_ungrounded_probability.m': calculation and plots of the marginal probability of being ungrounded.<br> - 'Plot_SMB_sensitivity.m': Calculation and plots of surface mass balance sensitivity.<br> - 'run_MISMIPplus.m' and 'MISMIPplus.m': run and compare MISMIP+ experiment</p><p>-------------------------<br>PROVIDED DATA: <br>-------------------------</p><ul><li>'LHSensemble.mat': 100x9 matrices containing the values of the 100-member ensemble sampled (using maximin Latin Hypercube) within the parameter space in Table 1.<ul><li>1rst column ((:,1)) contains values of atmospheric present-day climatology (CLIMatm): MARv3.11 (1) - RACMOv2.3p2 (2)</li><li>2nd column ((:,2)) contains values of oceanic present-day climatology (CLIMocn): Jourdain2020 (1) - Schmidtko2014 (2)</li><li>3rd column ((:,3)) contains values of the atmospheric lapse rate (°C/km)</li><li>4th column ((:,4)) contains values of the thickness of the thermally-active layer influencing surface refreezing (m)</li><li>5th column ((:,5)) contains values of the contains values of the Degree day factor for the melting of ice (mm/PDD)</li><li>6th column ((:,6)) contains values of the contains values of the Degree day factor for the melting of snow (mm/PDD)</li><li>7th column ((:,7)) contains values of the applied Sub-shelf melt parameterisation: Quadratic-local Antarctic slope parameterisation (1) - PICO model (2) - Plume model (3) - ISMIP6 Nonlocal quadratic parameterisation (4) - ISMIP6 Nonlocal quadratic parameterisation including dependency on local slope (5)</li><li>8th column ((:,8)) contains values of the effective ice-ocean heat flux: [0.1 x 10^-5 - 10 x 10^-5] m/s for gammaT* in PICO - [1 x 10^-4 - 10 x 10^-4] for Cd^1/2Gamma_TS in Plume - [1 x 10^-4 - 10 x 10^-4] for K in Quadratic-local Antarctic slope parameterisation - [1 x 10^4 - 4 x 10^4] m/yr for gamma0 in ISMIP6 Nonlocal quadratic parameterisation - [1 x 10^6 - 4 x 10^6] m/yr for gamma0 in ISMIP6 Nonlocal quadratic parameterisation with slope dependency</li><li>9th column ((:,9)) contains values of the CMIP6 climate model applied for climate forcing: MRI-ESM2-0 (1) - UKESM1-0-LL (2) - CESM2-WACCM (3) - IPSL-CM6A-LR (4)<br><br>'LHval' and 'LHS' contain the absolute values and the values of the parameters scaled linearly between 0 and 1 (0: minimum value, 1:maximum value) of the nine parameters, respectively.<br> </li></ul></li><li>'HIST_ENSEMBLE_DATA.mat' contains the following variables describing the evolution of the 100-member ensemble of simulations of the Antarctic ice sheet over the historical period (1950-2014).<ul><li>H_ensemble: 4D matrix of dimension [X, Y, snap_time, ensemble member] with ice thickness field (in meters) for the 100 ensemble members at different years (snap_time). X and Y represent spatial coordinates on a grid.</li><li>MASK_ensemble: 4D matrix of dimension [X, Y, snap_time, ensemble member] with grounded mask field (in meters) for the 100 ensemble members at different years (snap_time). X and Y represent spatial coordinates on a grid. <br>It distinguishes grounded ice (1: grounded) from ocean or floating ice (0: ocean/floating).</li><li>mbcomp_ensemble: 3D matrix of dimension [time, mbcomp, ensemble member] with timeseries (yearly values at years time) of various mass balance components for the 100 ensemble members (in gigatons per year, Gt/yr). <br>The components mbcomp include the following ice-sheet aggregated and grounded ice sheet components:<br> (1) Ice-sheet aggregated surface mass balance<br> (2) Ice-sheet aggregated accumulation<br> (3) Ice-sheet aggregated surface melt<br> (4) Ice-sheet aggregated runoff<br> (5) Ice-sheet aggregated rain<br> (6) sub-shelf melt<br> (7) dynamic ice loss (calving)<br> (8) surface mass balance over the grounded ice sheet<br> (9) accumulation over the grounded ice sheet<br> (10) surface melt over the grounded ice sheet<br> (11) runoff over the grounded ice sheet<br> (12) rain over the grounded ice sheet <br> (13) Net mass balance (rate of HAF change)</li><li>SLC_ensemble: 2D matrix of dimension [ensemble member, time] with timeseries (yearly values at years time) of the ice-sheet sea-level contribution (in m) <br> </li></ul></li><li>'HIST_ENSEMBLE_DATA_NO_ELEVATION_FEEDBACK.mat': same as 'HIST_ENSEMBLE_DATA.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the historical period (1950-2014) when neglecting the melt-elevation feedback.<br> </li><li>'HIST_ENSEMBLE_DATA_HYDROFRAC.mat': same as 'HIST_ENSEMBLE_DATA.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the historical period (1950-2014) when including surface melt-driven hydrofracturing of the ice shelves (estimated following Pollard et al., 2015).<br> </li><li>'CONTROL_ENSEMBLE_DATA.mat': contains the variables H_ensemble, MASK_ensemble, mbcomp_ensemble and SLC_ensemble (as in 'HIST_ENSEMBLE_DATA') describing the evolution of the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 when considering constant present-day conditions as of the year 2015.<br> </li><li>'SSP126_ENSEMBLE_DATA.mat': contains the variables H_ensemble, MASK_ensemble, mbcomp_ensemble and SLC_ensemble (as in 'HIST_ENSEMBLE_DATA') describing the evolution of the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under a SSP1-2.6 scenario.<br> </li><li>'SSP585_ENSEMBLE_DATA.mat': contains the variables H_ensemble, MASK_ensemble, mbcomp_ensemble and SLC_ensemble (as in 'HIST_ENSEMBLE_DATA') describing the evolution of the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under a SSP5-8.5 scenario. It also contains the variable Runoff_ensemble, a 4D matrix of dimension [X, Y, snap_time, ensemble member] with surface runoff field (in m/yr i.e.) for the 100 ensemble members at different years (snap_time). X and Y represent spatial coordinates on a grid, as used in Fig. 7.<br> </li><li>'SSP585_ENSEMBLE_DATA_NO_ELEVATION_FEEDBACK.mat': same as 'SSP585_ENSEMBLE_DATA.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario when neglecting the melt-elevation feedback.<br> </li><li>'SSP585_ENSEMBLE_DATA_HYDROFRAC.mat': same as 'SSP585_ENSEMBLE_DATA.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario when including surface melt-driven hydrofracturing of the ice shelves (estimated following Pollard et al., 2015).<br> </li><li>'SSP585_ENSEMBLE_DATA_ATM_ONLY.mat': same as 'SSP585_ENSEMBLE_DATA.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario when considering constant oceanic present-day conditions as of the year 2015.<br> </li><li>'SSP585_ENSEMBLE_DATA_NO_ELEVATION_FEEDBACK_ATM_ONLY.mat': same as 'SSP585_ENSEMBLE_DATA.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario when neglecting the melt-elevation feedback and considering constant oceanic present-day conditions as of the year 2015.<br> </li><li>'SSP585_ENSEMBLE_DATA_OCEAN_ONLY.mat': same as 'SSP585_ENSEMBLE_DATA.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario considering constant atmospheric present-day conditions as of the year 2015.<br> </li><li>'HIST_ENSEMBLE_DATA_BASIN.mat' contains the following variables describing the evolution of the 100-member ensemble of simulations of the Antarctic ice sheet over the historical period (1950-2014) integrated over 27 drainage basins (http://imbie.org/imbie-2016/drainage-basins/).<ul><li>SLC_ensemble_basin: 3D matrix of dimension [basin, ensemble member, time] with timeseries (yearly values at years time) of the ice-sheet sea-level contribution (in m) by basin</li><li>mbcomp_ensemble_basin: 4D matrix of dimension [basin, time, mbcomp, ensemble member] with timeseries (yearly values at years time) of various mass balance components for the 100 ensemble members (in gigatons per year, Gt/yr) by basin. The components mbcomp include the same ice-sheet aggregated and grounded ice-sheet components as in 'HIST_ENSEMBLE_DATA.mat'.<br> </li></ul></li><li>'HIST_ENSEMBLE_DATA_BASIN_NO_ELEVATION_DATA.mat': same as 'HIST_ENSEMBLE_DATA_BASIN.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the historical period (1950-2014) when neglecting the melt-elevation feedback.<br> </li><li>'HIST_ENSEMBLE_DATA_BASIN_HYDROFRAC.mat': same as 'HIST_ENSEMBLE_DATA_BASIN.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the historical period (1950-2014) when including surface melt-driven hydrofracturing of the ice shelves (estimated following Pollard et al., 2015).<br> </li><li>'SSP126_ENSEMBLE_DATA_BASIN.mat': contains the variables SLC_ensemble_basin and mbcomp_ensemble_basin (as in 'HIST_ENSEMBLE_DATA°BASIN') describing the evolution of the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under a SSP1-2.6 scenario.<br> </li><li>'SSP585_ENSEMBLE_DATA_BASIN.mat': contains the variables SLC_ensemble_basin and mbcomp_ensemble_basin (as in 'HIST_ENSEMBLE_DATA') describing the evolution of the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under a SSP5-8.5 scenario.<br> </li><li>'SSP585_ENSEMBLE_DATA_BASIN_NO_ELEVATION_FEEDBACK.mat': same as 'SSP585_ENSEMBLE_DATA_BASIN.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under a SSP5-8.5 scenario when neglecting the melt-elevation feedback.<br> </li><li>'SSP585_ENSEMBLE_DATA_BASIN_HYDROFRAC.mat': same as 'SSP585_ENSEMBLE_DATA_BASIN.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario when including surface melt-driven hydrofracturing of the ice shelves (estimated following Pollard et al., 2015).<br> </li><li>'SSP585_ENSEMBLE_DATA_BASIN_ATM_ONLY.mat': same as 'SSP585_ENSEMBLE_DATA_BASIN.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario when considering constant oceanic present-day conditions as of the year 2015.<br> </li><li>'SSP585_ENSEMBLE_DATA_BASIN_NO_ELEVATION_FEEDBACK_ATM_ONLY.mat': same as 'SSP585_ENSEMBLE_DATA_BASIN.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario when neglecting the melt-elevation feedback and considering constant oceanic present-day conditions as of the year 2015.<br> </li><li>'SSP585_ENSEMBLE_DATA_BASIN_OCEAN_ONLY.mat': same as 'SSP585_ENSEMBLE_DATA_BASIN.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario considering constant atmospheric present-day conditions as of the year 2015.<br> </li><li>'GCM_SSPXXX_mean_aTs.mat': Timeseries of the regionally-averaged (between 90–60°S) annual near-surface (2-m) air temperature anomaly (°C) projected by the climate model 'GCM' from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) between 2015 and 2300 under the SSPXXX emission scenario, compared to the 1995-2014 reference period. SSPXXX may be 'SSP126' and 'SSP585' and GCM may be 'MRI-ESM2-0', 'CESM2-WACCM', 'IPSL-CM6A-LR', or 'UKESM1-0-LL'.<br> </li><li>'CALIBRATION DATA.mat': values ('val'), uncertainty ('sigma'), beginning ('year1') and end ('year2') of the average time period of the 12 regionally and temporally aggregated IMBIE data used in the Bayesian calibration (Table 2 in this study, coming from Table 2 from Otosaka et al., 2023)<br> </li><li>'INIT_MAR_aNorESM1-M_1950.mat' and 'INIT_RACMO_aNorESM1-M_1950.mat': Ice-sheet initial states at year 1950 obtained with the 1995-2014 atmospheric climatology from MARv3.11(Kittel eta l.,2021) or RACMOv2.3p2 (van Wessem et al., 2018), respectively, adjusted with a 1945-1955 anomaly from NorESM1-M. H is the ice thickness (in meters), B is the bedrock topography (in meters), and u is the surface velocity (in m/yr). These files were provided as input files to Kori-ULB to produce the projections. More info on the input files and their variables can be found here: https://github.com/FrankPat/Kori-ULB.</li></ul><p>----------------------------------------------------------<br>MATLAB FUNCTIONS USED IN SCRIPTS: <br>----------------------------------------------------------</p><p>- imagescn: imagesc with transparent NaNs, by Chad Greene (2023), downloaded from MATLAB Central File Exchange (https://www.mathworks.com/matlabcentral/fileexchange/61293-imagescn), <br>- brewermap: provides all ColorBrewer colorschemes for MATLAB, by Stephen23. Downloaded from https://github.com/DrosteEffect/BrewerMap.<br>- crameri: returns perceptually-uniform scientific colormaps created by Fabio Crameri (requires CrameriColourMaps8.0.mat)</p><p>----------------------------------------------------------------------------------<br>EXTERNAL DATA NOT CONTAINED IN THIS REPOSITORY:<br>----------------------------------------------------------------------------------</p><p>- BedMachine data used for the present-day grounding lines in Figures 2 and 7: It is BedMachine v2 (Morlighem et al., 2020) and can be found here: https://nsidc.org/data/nsidc-0756/versions/2.<br>- The delineation of the 27 Zwally Basins used to identify and separate the West and East Antarctic ice sheets and the Antarctic Peninsula can be found at http://imbie.org/imbie-2016/drainage-basins/<br>- Outputs from MAR(CNRM-CM6-1) and MAR(CESM2) used in Figures 7 and S10. The data can be downloaded at 10.5281/zenodo.4529004 and 10.5281/zenodo.4529002, respectively. It was then interpolated to the 16-km grid used by Kori-ULB.<br>- CESM2-WACCM outputs used in Figure 7 were downloaded from the CMIP6 search interface (https://esgf-node.llnl.gov/search/cmip6/) and interpolated to the 16-km grid used by Kori-ULB.<br>- The CMIP6 forcing data used in this study (and plotted in Figures S6 and S7) are accessible through the CMIP6 search interface (https://esgf-node.llnl.gov/search/cmip6/). They have been interpolated to the interpolated to the 16-km grid used by Kori-ULB.</p><p>---------------------<br>REFERENCES: <br>---------------------</p><p>Kittel, C., Amory, C., Agosta, C., Jourdain, N. C., Hofer, S., Delhasse, A., Doutreloup, S., Huot, P.-V., Lang, C., Fichefet, T., and Fettweis, X.: Diverging future surface mass balance between the Antarctic ice shelves and grounded ice sheet, The Cryosphere, 15, 1215–1236, https://doi.org/10.5194/tc-15-1215-2021, 2021.</p><p>Morlighem, M., Rignot, E., Binder, T. et al. Deep glacial troughs and stabilizing ridges unveiled beneath the margins of the Antarctic ice sheet. Nat. Geosci. 13, 132–137 (2020). https://doi.org/10.1038/s41561-019-0510-8</p><p>Otosaka, I. N., Shepherd, A., Ivins, E. R., Schlegel, N.-J., Amory, C., van den Broeke, M. R., Horwath, M., Joughin, I., King, M. D., Krinner, G., Nowicki, S., Payne, A. J., Rignot, E., Scambos, T., Simon, K. M., Smith, B. E., Sørensen, L. S., Velicogna, I., Whitehouse, P. L., A, G., Agosta, C., Ahlstrøm, A. P., Blazquez, A., Colgan, W., Engdahl, M. E., Fettweis, X., Forsberg, R., Gallée, H., Gardner, A., Gilbert, L., Gourmelen, N., Groh, A., Gunter, B. C., Harig, C., Helm, V., Khan, S. A., Kittel, C., Konrad, H., Langen, P. L., Lecavalier, B. S., Liang, C.-C., Loomis, B. D., McMillan, M., Melini, D., Mernild, S. H., Mottram, R., Mouginot, J., Nilsson, J., Noël, B., Pattle, M. E., Peltier, W. R., Pie, N., Roca, M., Sasgen, I., Save, H. V., Seo, K.-W., Scheuchl, B., Schrama, E. J. O., Schröder, L., Simonsen, S. B., Slater, T., Spada, G., Sutterley, T. C., Vishwakarma, B. D., van Wessem, J. M., Wiese, D., van der Wal, W., and Wouters, B.: Mass balance of the Greenland and Antarctic ice sheets from 1992 to 2020, Earth Syst. Sci. Data, 15, 1597–1616, https://doi.org/10.5194/essd-15-1597-2023, 2023.</p><p>Pollard, D., DeConto, R. M., and Alley, R. B.: Potential Antarctic Ice Sheet retreat driven by hydrofracturing and ice cliff failure, Earth and Planetary Science Letters, 412, 112–121, https://doi.org/10.1016/j.epsl.2014.12.035, 2015.<br> <br>van Wessem, J. M., van de Berg, W. J., Noël, B. P. Y., van Meijgaard, E., Amory, C., Birnbaum, G., Jakobs, C. L., Krüger, K., Lenaerts, J. T. M., Lhermitte, S., Ligtenberg, S. R. M., Medley, B., Reijmer, C. H., van Tricht, K., Trusel, L. D., van Ulft, L. H., Wouters, B., Wuite, J., and van den Broeke, M. R.: Modelling the climate and surface mass balance of polar ice sheets using RACMO2 – Part 2: Antarctica (1979–2016), The Cryosphere, 12, 1479–1498, https://doi.org/10.5194/tc-12-1479-2018, 2018.</p>
Future freshwater fluxes from the Antarctic ice sheet
<p>===============================================================<br>Future freshwater fluxes from the Antarctic ice sheet (dataset)<br>===============================================================</p> <p>-----------------------<br>INTRODUCTION<br>-----------------------</p> <p>This dataset contains historically-calibrated projections of Antarctic freshwater fluxes (ice-shelf melting, iceberg calving, and surface meltwater runoff) under low and very-high emission scenarios for 27 drainage basins and 5 ocean sectors until 2300.</p> <p>We perform, with the Kori-ULB ice-sheet model v0.91, an ensemble of historically-calibrated simulations of the Antarctic ice sheet between 1990 and 2300 forced by atmospheric and oceanic projections inferred from a subset of models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) under low- and very high-emission scenarios. </p> <p>We refer to the associated manuscript for more information on the applied methodology.</p> <p>-------------------------<br>PROVIDED DATA<br>-------------------------</p> <ul> <li>'<em>SSP126_FWF_1990_2300_ZwallyBasins.nc</em>' and '<em>SSP585_FWF_1990_2300_ZwallyBasins.nc</em>' each contain yearly timeseries of the [5 25 50 75 95] percentiles for the calibrated probabilistic projections of Antarctic net mass balance, surface mass balance, sub-shelf melt, and calving fluxes (in Gt/yr) for each of the 27 Zwally drainage basins (see http://imbie.org/imbie-2016/drainage-basins/) under a SSP1-2.6 and SSP5-8.5 scenario, respectively.<br><br></li> <li>'<em>SSP126_FWF_1990_2300_OceanSectors.nc</em>' and '<em>SSP585_FWF_1990_2300_OceanSectors.nc</em>' each contain yearly timeseries of the [5 25 50 75 95] percentiles for the calibrated probabilistic projections of Antarctic net mass balance, surface mass balance, sub-shelf melt, and calving fluxes (in Gt/yr) for each of the 5 ocean sectors (Weddell Sea, Indian Ocean, western Pacific Ocean, Ross Sea, Amundsen & Bellingshausen Sea) under a SSP1-2.6 and SSP5-8.5 scenario, respectively.<br><br></li> <li>'<em>SSP126_FWF_1990_2300_AIS.nc</em>' and '<em>SSP585_FWF_1990_2300_AIS.nc</em>' each contain yearly timeseries of the [5 25 50 75 95] percentiles for the calibrated probabilistic projections of Antarctic net mass balance, surface meltwater runoff, sub-shelf melt, and calving fluxes (in Gt/yr) for the Antarctic Ice Sheet under a SSP1-2.6 and SSP5-8.5 scenario, respectively.</li> </ul>
Data: Constraining Ice Slab Thickness at the Onset of Visible Surface Runoff from the Greenland Ice Sheet
<h1>Data repository associated with the manuscript 'Constraining Ice Slab Thickness at the Onset of Visible Surface Runoff from the Greenland Ice Sheet', Nicolas Jullien, Andrew J. Tedstone, Horst Machguth, (under review in the Journal of Glaciology)</h1> <h2> </h2> <h2>Introduction:</h2> <p>We provide a short description of each file present in this data repository, and flag to the corresponding reference when applicable. Please cite the appropriate references when using these data.</p> <h2> </h2> <h2>Data:</h2> <h3>In this repository:</h3> <ul> <li>'Ice_Layer_Output_Thicknesses_Likelihood_2010_2018_jullienetal2021_modified.csv'. Modified 2010-2018 ice slabs thickness retrievals from Jullien et al., (2023) where ice slabs thickness > 16 m thick and < 1 m thick are retained, and flight-lines not holding ice slab were set to hold an ice content of 0 m thick.</li> <li>'master_maps.zip'. Raster files. Surface hydrology connectivity map over the Greeland Ice sheet, first presented in Tedstone and Machguth (2022). The easiest way to handle this dataset is to use the 'master_map_GrIS_mean.vrt' file.</li> <li>'MARv.3.14_MoA_2000_2012.nc'. Melt over accumulation from 2000 to 2012 extracted from MARv3.14. See file '<a title="melt_over_accumulation_calculations.py" href="https://github.com/jullienn/IceSlabs_SurfaceRunoff/blob/main/melt_over_accumulation_calculations.py">melt_over_accumulation_calculations.py</a>' in the code repository for post processing analysis.</li> <li>'RunoffLimits.zip'. '.csv' files. Maximum visible runoff limits in 2012 and 2019, sorted for each boxes generated by Tedstone and Machguth (2022). Each '.csv' file stores the data points coordinates (Geographical Reference System: WGS 84 / NSIDC Sea Ice Polar Stereographic North (EPSG:3413)) of the maximum visible runoff limit retrievals after filtering out the outliers. The maximum visible runoff limits where first presented in Tedstone and Machguth (2022).</li> </ul> <h3>Used in this study but from other datasets:</h3> <ul> <li>The ice slabs extent and ice slabs thickness were first presented in Jullien et al., (2023), and are accessible at: https://zenodo.org/records/7505426</li> <li>The radargrams displayed in Fig. 5c-f were first presented in Jullien et al., (2023), and are accessible at: https://zenodo.org/records/7505426. The following files were used: <ul> <li>'L1_may12_03_1_aggregated.pickle'</li> <li>'L1_may12_03_2_aggregated.pickle'</li> <li>'20100508_01_114_115_Depth_CORRECTED.pickle'</li> <li>'20140424_01_002_004_Depth_CORRECTED.pickle'</li> <li>'20180427_01_170_172_Depth_CORRECTED.pickle'</li> </ul> </li> <li>The surface topography present in Fig. 5g are 10 m resolution mosaics from the ArcticDEMv3 (Porter et al., 2018), and accessible at: https://data.pgc.umn.edu/elev/dem/setsm/ArcticDEM/mosaic/v3.0/</li> <li>The winter time strain rates map displayed in Fig. 5h were first presented in Poinar and Andrews (2021), and are accessible at: https://ubir.buffalo.edu/xmlui/handle/10477/82127</li> </ul> <p> </p> <h2>References:</h2> <p>Jullien, N., Tedstone, A. J., Machguth, H., Karlsson, N. B., & Helm, V. (2023). Greenland Ice Sheet Ice Slab Expansion and Thickening. <em>Geophysical Research Letters</em>, <em>50</em>(10), e2022GL100911. https://doi.org/10.1029/2022GL100911</p> <p>Poinar, K., & Andrews, L. C. (2021). Challenges in predicting Greenland supraglacial lake drainages at the regional scale. <em>The Cryosphere</em>, <em>15</em>(3), 1455–1483. https://doi.org/10.5194/tc-15-1455-2021</p> <p>Porter, C., Morin, P., Howat, I., Noh, M.-J., Bates, B., Peterman, K., Keesey, S., Schlenk, M., Gardiner, J., Tomko, K., Willis, M., Kelleher, C., Cloutier, M., Husby, E., Foga, S., Nakamura, H., Platson, M., Wethington, M., Jr., Williamson, C., … Bojesen, M. (2018). <em>ArcticDEM, Version 3</em> (Version V1) [dataset]. Harvard Dataverse. https://doi.org/10.7910/DVN/OHHUKH</p> <p>Tedstone, A. J., & Machguth, H. (2022). Increasing surface runoff from Greenland’s firn areas. <em>Nature Climate Change</em>. https://doi.org/10.1038/s41558-022-01371-z</p>
Ice sheet weathering crust evolution code (1D, saturated)
<p>This repository contains the code used to produce the results in the article "Modelling the evolution of an ice sheet's weathering crust" by Tilly Woods and Ian J. Hewitt, 2024, IMA Journal of Applied Mathematics, https://doi.org/10.1093/imamat/hxae031. The code finds time-dependent solutions for the porosity and temperature profiles in and below an ice sheet's weathering crust. The model is based on mass conservation, energy conservation, internal shortwave radiation and a surface energy balance. It is solved using an ethalpy method with finite volumes and semi-implicit timestepping. The code was written in MATLAB R2024a.</p>
A 21st century high-resolution glacier and ice sheet fractional area dataset: Code and Data
<p>This resource contains code and input data to develop a high-resolution (0.1°) gridded global glacier and ice sheet fractional area dataset, which is also available in this resource. The dataset is developed from Randolph Glacier Inventory v6.0 shapefiles and supplementary shapefiles for the Antarctic and Greenland ice sheets. The approach is adapted from Li et al., (2021; <a href="https://doi.org/10.1017/jog.2021.28">https://doi.org/10.1017/jog.2021.28</a>). The dataset provides estimates of the fraction (0 to 1) of land cover that is glaciated in each 0.1° x 0.1° grid cell. The dataset was designed for use in the SPEAR model (<a href="https://www.gfdl.noaa.gov/spear/">https://www.gfdl.noaa.gov/spear/</a>) from the NOAA Geophysical Fluid Dynamics Laboratory (GFDL), but may be useful for other applications as well.</p> <p>This work is documented in a NOAA Technical Memorandum (citation information to follow).</p>
Elmer/Ice repository for 3D Greenland Ice-Sheet initial states
<p>initial states of the Greenland Ice-Sheet produced with the <a href="http://elmerice.elmerfem.org/">Elmer/Ice model</a>.</p> <p>This initial states are obtained using a control inverse method that optimise the basal friction field to minimise the mismatch between model and observed velocities.</p> <p> </p> <p>Results used in N. Maier, F. Gimbert and F. Gillet-Chaulet, Threshold response to surface melt drives large-scale bed weakening in Greenland, submitted to Nature</p>
Evolution of ocean circulation in the North Atlantic Ocean during the Miocene: impact of the Greenland Ice Sheet and the Eastern Tethys Seaway
<p>This dataset contains atmosphere and ocean outputs (NetCDF files) from modeling experiments with realistic early Miocene paleogeography as well as sensitivity to Greenland Ice Sheet and Eastern Tethys Seaway. The set of simulation targets the evolution of the North Atlantic Deep Water during the Miocene. The simulations have been run using the IPSL-CM5A2 General Circulation Model (Sepulchre et al. 2020 - IPSL-CM5A2 – an Earth system model designed for multi-millennial climate simulations, GMD). It includes 3 ocean-atmosphere simulations. Data are monthly averages over the last 100 years of the simulations. </p>
Economic impacts of melting of the Antarctic Ice Sheet
<p>Dataset supporting figures and tables in Dietz, Simon and Koninx, Felix (forthcoming), "Economic impacts of melting of the Antarctic Ice Sheet", Nature Communications</p>
Subglacial hydrology modulates basal sliding response of the Antarctic ice sheet to climate forcing
<p><strong><em>Kazmierczak22_data.zip</em></strong><strong> contains the </strong><strong>dataset for the publication </strong><strong>« </strong>Subglacial hydrology modulates basal sliding response of the Antarctic ice sheet to climate forcing » <strong>and </strong><strong>the <em>MATLAB</em> codes used to create the figures appearing in the paper. For more details, please, open the <em>Read me.txt</em> file. </strong></p>
DATASET: In situ measurements of meltwater flow through snow and firn in the accumulation zone of the SW Greenland Ice Sheet
<p>This repository contains all the data and code used to analyse these data related to the paper "In situ measurements of meltwater flow through snow and firn in the accumulation zone of the SW Greenland Ice Sheet" by Clerx et al. (2022), published in "The Cryosphere".</p>
The impact of ice sheet geometry on meltwater ingress and reactive solute transport in sedimentary basins
<p> We investigate the effect of ice sheet geometry on groundwater flow patterns and meltwater ingress in a hypothetical sedimentary basin. The simulation results indicate that meltwater ingress is much greater in 3D domains with a relatively narrow ice sheet extent compared to a wide ice sheet, or a simplified 2D model. In high permeability units (HPUs), the simulated meltwater penetration depth can reach up to 750 m in 3D domains compared to 400 m in a comparable 2D domain. In low permeability units (LPUs), very limited meltwater penetration occurs, indicating that ice sheet geometry and model dimensionality do not substantially affect water flow in these units. A conservative tracer, with a source located at a depth of 500 meters in both HPUs and LPUs, illustrates that solutes can be transported to greater depths in HPUs for a narrow ice lobe scenario, in comparison to a wide ice sheet or the 2D approach. Tracer transport in LPUs is unaffected by ice sheet geometry. Similarly, simulation results indicate that the ingress of dissolved oxygen (O<sub>2</sub>) into HPUs is most substantial below a narrow ice lobe, while O<sub>2</sub> ingress into LPUs is not affected by ice sheet geometry. The numerical experiments indicate that 3D analysis will give more comprehensive results for flow patterns and reactive solute transport subjected to glaciation/deglaciation cycles in the case of a narrow ice lobe, but also suggest that a 2D approach might provide an adequate representation for the case of a relatively wide ice sheet.</p>
Ice-sheet model simulation ensembles (produced Fall 2018)
<p>Ice-sheet model simulation ensembles over the last interglacial and a future high emissions scenario (RCP8.5), and varied over two model parameters (CREVLIQ and CLIFVMAX). The data was pickled as a pandas dataframe with python 3, and can be retrieved by loading using pickle with the same version.</p>
Data for "ISMIP6-based Antarctic Projections to 2100: simulations with the BISICLES ice sheet model"
<p>Data to accompany:</p> <p>O’Neill, J.F., Edwards, T.L., Martin, D.F., Shafer, C., Cornford, S.L., Seroussi, H.L., Nowicki, S., Adhikari, M., Gregoire, L.J.. (2024). "ISMIP6-based Antarctic Projections to 2100: simulations with the BISICLES ice sheet model in the Cryosphere". <em>The Cryosphere</em>. DOI: 10.5194/egusphere-2024-441 (preprint)</p> <p>Zipped directories called ismip6_<em>expname</em>_8km containing NetCDFs of output data from each experiment, on an 8 km EPSG3031 polar stereographic common grid for ISMIP6. Variable names are the same as those used for ISMIP6 i.e: land ice mass (lim), land ice mass above floatation (limnsw), floating area (iareaf), grounded area (iareag), thickness (lithk), x component of mean velocity (xvelmean), y component of mean velocity (yvelmean), basal mass flux (libmassbffl), acabf (surface mass balance), sftflf (floating ice mask), sftgrf (grounded ice mask), sftgif (ice mask), dlithkdt (ice thickness imbalance), base (elevation at base of ice sheet) and orog (surface elevation of ice sheet). These latter two are only included for the experiments plotted in Figure 11 in "ISMIP6-based Antarctic Projections to 2100: simulations with the BISICLES ice sheet model in the Cryosphere".</p> <p> </p> <p>Also included are csv data for summary variables, masked regionally, and by sectors detailed in the main paper. Please contact J ONeill with any questions or requests. </p>
Datasets used in van den Akker et al (2024) 'Present day mass loss rates are a precursor precursor for West Antarctic Ice Sheet Collapse
<p>This repository contains the default initialization and the continuation runs shown in the paper. </p>
Main output data used in "Exploring the Greenland Ice Sheet's response to future atmospheric warming-threshold scenarios over 200 years" (Delhasse et al., 2025)
<p>Outputs used in:</p> <p>Delhasse, A., Kittel, C. and Beckmann, J.: Exploring the Greenland Ice Sheet's response to future atmospheric warming-threshold scenarios over 200 years, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-709, 2025.</p> <p>Each MAR-PISM coupling experiment (1991-2200) is related to the Greenland warming over a 10-year period compared to our reference period (1961-1990) at which climate is stabilized until 2200. The last experiment is the Reverse one, where the climate is year by year reversed after 2100 to go back to 2000-climate as forcing in 2200, the last year of the simulation. Please refer to Delhasse et al. (2024) for the coupling description.</p> <div> <table> <tbody> <tr> <th> <p>Experiment </p> </th> <th> <p>Exact Greenland warming at 600hPa (°C)</p> </th> <th> <p>10-years period</p> </th> </tr> </tbody> <tbody> <tr> <td> <p>CTRL</p> </td> <td> <p>+0.00</p> </td> <td> <p>1961-1990</p> </td> </tr> <tr> <td> <p>+1</p> </td> <td> <p>+1.04</p> </td> <td> <p>1995-2004</p> </td> </tr> <tr> <td> <p>+1.5</p> </td> <td> <p>+1.51</p> </td> <td> <p>2010-2019</p> </td> </tr> <tr> <td> <p>+2</p> </td> <td> <p>+2.04</p> </td> <td> <p>2021-2030</p> </td> </tr> <tr> <td> <p>+3</p> </td> <td> <p>+2.98</p> </td> <td> <p>2040-2049</p> </td> </tr> <tr> <td> <p>+4</p> </td> <td> <p>+4.04</p> </td> <td> <p>2058-2067</p> </td> </tr> <tr> <td> <p>+5</p> </td> <td> <p>+5.00</p> </td> <td> <p>2074-2083</p> </td> </tr> <tr> <td> <p>+6</p> </td> <td> <p>+5.96</p> </td> <td> <p>2083-2092</p> </td> </tr> <tr> <td> <p>+7</p> </td> <td> <p>+6.85</p> </td> <td> <p>2091-2100</p> </td> </tr> </tbody> </table> </div> <p><strong>Table 1. Greenland warmings at 600hPa since 1961-1990 used to define our experiments and the corresponding 10-years periods over which warmings are determined. </strong></p> <p>For each experiment, 3 types of output are available (where <em>EXP</em> corresponds to the name of the experiment as referenced in Table 1) : </p> <ul> <li> <p>EXP-PISM-thk-msk-1991-2200.nc: contain yearly ice thickness (THK) and ice mask (MASK) as simulated by PISM (PISM grid, 4.5 km);</p> </li> <li> <p>EXP-SMB-ME-RU-MAPI-CESM2-1991-2200.nc: contain yearly SMB (surface mass balance), ME (melt), and RU (runoff) on the MAR grid (25 km);</p> </li> <li> <p>EXP-ts-MB-D-SMB-1991-2200.nc: contain time series of the total MB (mass balance), D (discharge), and SMB (surface mass balance) integrated over the all ice sheet mask from PISM.</p> </li> </ul> <p>The MAR code used in this dataset is tagged as v3.11.3 on https://gitlab.com/Mar-Group/MARv3/-/tree/v3.11.3 (last access: 24 October 2024) (MARTeam, 2024). The PISM code used is tagged as PISMv1.2.2 on <a href="https://github.com/pism/pism/releases/tag/v1.2.2">https://github.com/pism/pism/releases/tag/v1.2.2</a> (last access: 24 October 2024).</p> <p>If you need other variables from MAR or PISM, send us an email (alison.delhasse@uliege.be) and we will be glad to help you. We will also be happy to share the scripts we have developed to analyze the outputs and make the figures in this paper if needed. Please cite the paper if you use these MAR-PISM outputs.<br><br><strong><em>Data usage notice:</em></strong></p> <p>If you use any of these results, please acknowledge the work of the people involved in producing them. Acknowledgments should be similar to the one below that contains information related to MAR and PISM. To document MAR scientific impact and enable ongoing support of the model, users are likely encouraged to contact me to add their works to the list of MAR-related publications. </p> <p>"We thank A. Delhasse, C. Kittel, and J. Beckmann, as well as the MAR and PISM teams which make available the model outputs. We also thank agencies (F.R.S - FNRS, CÉCI, and the Walloon Region) that provided computational resources for MAR-PISM simulations. "</p> <p>You should also refer to and cite the following paper in its latest version:</p> <p>Delhasse, A., Kittel, C. and Beckmann, J.: Exploring the Greenland Ice Sheet’s response to future warming-threshold scenarios over 200 years, [JOURNAL UNDER REVIEW], 2024.</p> <p><strong><em>References</em></strong></p> <p>Delhasse, A., Beckmann, J., Kittel, C., and Fettweis, X.: Coupling MAR (Modèle Atmosphérique Régional) with PISM (Parallel Ice Sheet Model) mitigates the positive melt–elevation feedback, The Cryosphere, 18, 633–651, https://doi.org/10.5194/tc-18-633-2024, 2024.</p> <p>MARTeam: MARv3.11, GitLab [data set], <a href="https://gitlab.com/Mar-Group/MARv3">https://gitlab.com/Mar-Group/MARv3#</a> (last access: 24 October 2024), 2024.</p> <p> </p>
A new sampling capability for uncertainty quantification in the Ice-sheet and Sea-level System Model v4.19 using Gaussian Markov random fields -- Datasets and results
<p>Data archives for test experiments (Section 3) and Pine Island Glacier application (Section 4) from the manuscript "Kevin Bulthuis and Eric Larour, A new sampling capability for uncertainty quantification in the Ice-sheet and Sea-Level System Model v4.19 using Gaussian Markov random fields"</p> <p>Source code is available at https://doi.org/10.5281/zenodo.5532775.</p>
ScienceDex guides
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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.