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 "Competing climate feedbacks of ice sheet freshwater discharge in a warming world", Part I
<p>This is Part I of the output dataset from coupled ice sheet-climate model simulations that investigate the interactions between ice sheet freshwater flux and the warming climate. Description of models, coupling scheme, and design of these simulations is provided in a paper titled "Competing climate feedbacks of ice sheet freshwater discharge in a warming world", which is currently under peer review. More information will be updated when available.</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>
Datasets and models for "No general stability conditions for marine ice-sheet grounding lines in the presence of feedbacks"
<p>This repository contains datasets shown in figures (figs.tar.gz) of the manuscript "No general stability conditions for marine ice-sheet grounding lines in the presence of feedbacks" (doi: 10.1038/s41467-022-29892-3) and COMSOL<sup>TM</sup> models (model.tar.gz) used in the study. A folder “figures” contains data displayed on the corresponding figures. The data sets in folders “Fig1a’” and “Fig1b” are from Kittel et al. (2021) for Antarctica and Fettweis et al. (2017) for Greenland. All other data are outputs of numerical simulations with COMSOL models contained in a folder “model”. The models have been created with COMSOL Multiphysics version 5.6.0.401 and Optimization Module.</p> <p> </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.<br> Model output was downloaded from https://zenodo.org/record/4459259</p> <p>Fettweis, X., Box, J. E., Agosta, C., Amory, C., Kittel, C., Lang, C., van As, D., Machguth, H., and Gallée, H.: Reconstructions of the 1900–2015 Greenland ice sheet surface mass balance using the regional climate MAR model, The Cryosphere, 11, 1015–1033, https://doi.org/10.5194/tc-11-1015-2017, 2017.<br> Model output was downloaded from ftp://ftp.climato.be/fettweis/MARv3.5/Greenland/</p>
A Semi-Empirical Framework for Ice Sheet Response Analysis under Oceanic Forcing in Antarctica and Greenland
<p>Supplementary material for</p> <p>Luo, X. & Lin, T. (2022). “A Semi-Empirical Framework for Ice Sheet Response Analysis under Oceanic Forcing in Antarctica and Greenland.” Climate Dynamics, in press. <a href="https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.1007%2Fs00382-022-06317-x&data=05%7C01%7CXiao.Luo%40ttu.edu%7C2a6949c147dd4aa9880f08da345e55ae%7C178a51bf8b2049ffb65556245d5c173c%7C0%7C0%7C637879877007681605%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=2G3mD6n0ZOogcEFQcuCCElFmgKE0%2B%2F5KPTtzBeq8kM4%3D&reserved=0">https://doi.org/10.1007/s00382-022-06317-x</a></p> <p>This repository contains data and scripts that are used to facilitate the analysis of Greenland and Antarctica ice sheet melting induced by ocean warming.</p> <p>The GCM directory contains ocean potential temperature time series for both Antarctica ice shelves near open sea retrieved from FGOALS-s2 under RCP 6.0 and Greenland store glacier retrieved from MIROC5 under RPC8.5 and HadGEM3-GC31-MM projections under update of RCP8.5 based on SSP5. The raw data are accessed from CMIP5 (https://esgf-node.llnl.gov/search/cmip5/) and CMIP6 (https://esgf-node.llnl.gov/search/cmip6/) projects respectively and the processed data deposited here are stored in Excel format.</p> <p>The WOD directory contains the NetCDF files of Present-Day ocean conditions including temperature, salinity, depth, and other variables retrieved from World Ocean Database (WOD). The data are deposited as NetCDF format.</p> <p>The Analysis directory contains 3 subdirectories that illustrates the analysis workflow for both Greenland and Antarctica.</p> <p> The Antarctica subdirectory contains the analysis codes to reproduce Antarctica ice shelf melting response and future projection. Antarctica_analysis.m is the main routine for computation and plotting, and the other files are input files including PISM/PICO simulations outputs and ocean temperature time series retrieved from CMIP5 project that can be found on the previous GCM directory.</p> <p> The Greenland subdirectory contains the main routine store_analysis.m for Greenland Store glacier computation and plotting, the response time series (store.mat), perturbed response time series (store_pert.mat), ocean temperature projection (store_temp.mat) generated by MIROC5, and subglacial runoff time series (store_runoff.mat) estimated from annual basin surface runoff using MAR data forced by MIROC5 under RCP8.5.</p> <p> The Historical subdirectory contains the data and scripts used for the historical analyses in the main text. AIS_melt_historical.m is the main script of the analysis, along with two other plotting functional scripts including plot_melt_rate.m and plot_ocean_temp.m. The input files include the PISM/PICO simulations outputs, which are the same files in Antarctica subdirectory, and other 2 input files that contain the temperature time series from 6 CMIP5 GCMs.</p>
Ocean-forced instability of the West Antarctic Ice Sheet since the mid-Pleistocene
<p>This data file contains the IRD abundance, clay mineralogy, water content in Excel file 'Wang et al.xlsx (sheet: sedimentology)', and Sr-Nd isotopes in Excel file 'Wang et al.xlsx (sheet: Sr-Nd isotope)' in gravity core ANT34/A2-10 (LATITUDE: -67.036111 and LONGITUDE: -125.591944) from the Amundsen abyssal plain since 770 ka.<br>Supplement to Jiakai Wang et al., Ocean-forced instability of the West Antarctic Ice Sheet since the mid-Pleistocene. Geochemistry Geophysics Geosystems (in review).<br> </p>
Datasets for "A Detection of the Sea Level Fingerprint of Greenland Ice Sheet Melt" Coulson et al., 2022, Science
<p>Sea surface height (SSH) change altimetry-derived datasets and model predictions for:<br> "A Detection of the Sea Level Fingerprint of Greenland Ice Sheet Melt", Submitted to Science January 2022. Sophie Coulson, Sönke Dangendorf, Jerry X. Mitrovica, Mark E. Tamisiea, Linda Pan, David T. Sandwell.<br> Email: slcoulson@lanl.gov, sdangendorf1@tulane.edu</p> <p>See README for file details.</p>
Glacial ice sheet extent effects on tidal mixing and the global overturning circulation - Model Output
<p>This dataset contains the output from the tide model and climate model simulations from the publication Wilmes et al. (2018) "Glacial ice sheet extent effects on tidal mixing and the global overturning circulation" submitted to Paleoceanography. The user is referred to the paper for details on the methodology.</p> <p>Dissipation files:</p> <p>Files beginning with "diss" contain tidal dissipation files calculated from the OTIS tide model output at 1/8th deg using the direct method. Files with the M2 constituent only are in .mat format and extend from 86deg S to 89deg N whereas the files containing all constituents (M2, S2, K1 and O1) are in netcdf format and extend from 90deg S to 90deg N. These files regridded and are used as the climate model tidal forcing.</p> <p>Dissipation file list:</p> <p>diss_dir_ze_1_8_rtp_21kyrBP_i6g_-I1.5_-t_8299008.nc Dissipation for LGM ICE-6G ZE ITdrag 1/8th deg<br> diss_dir_ze_1_8_rtp_21kyrBP_i5g_-I1.5_-t_8299031.nc Dissipation for LGM ICE-5G ZE ITdrag 1/8th deg<br> diss_dir_ze_1_8_rtp_00kyrBP_-I1.5_pdsal_8299034.nc Dissipation for PD ZE ITdrag 1/8th deg</p> <p>diss_dir_js_1_8_rtop_21kyrBP_i6g_-t_-I6.0_7673000.nc Dissipation for LGM ICE-6G JS ITdrag 1/8th deg<br> diss_dir_js_1_8_rtop_21kyrBP_i5g_-t_-I6.0_7672999.nc Dissipation for LGM ICE-5G JS ITdrag 1/8th deg<br> diss_dir_js_1_8_rtop_00kyrBP_-I6.0_7672998.nc Dissipation for PD JS ITdrag 1/8th deg</p> <p>diss_dir_ze_m2_1_8_rtp_21kyrBP_i5g_blk5_NH_lmsk_-I1.5_8299652.mat M2 dissipation for LGM ICE-5G blk1 + NH ICE-6G land mask ZE ITdrag 1/8th deg<br> diss_dir_ze_m2_1_8_rtp_21kyrBP_i5g_blk5_-I1.5_8299534.mat M2 dissipation for LGM ICE-5G blk5 ZE ITdrag 1/8th deg<br> diss_dir_ze_m2_1_8_rtp_21kyrBP_i5g_blk4_-I1.5_8299533.mat M2 dissipation for LGM ICE-5G blk4 ZE ITdrag 1/8th deg<br> diss_dir_ze_m2_1_8_rtp_21kyrBP_i5g_blk3_-I1.5_8299531.mat M2 dissipation for LGM ICE-5G blk3 ZE ITdrag 1/8th deg<br> diss_dir_ze_m2_1_8_rtp_21kyrBP_i5g_blk2_-I1.5_8299530.mat M2 dissipation for LGM ICE-5G blk2 ZE ITdrag 1/8th deg<br> diss_dir_ze_m2_1_8_rtp_21kyrBP_i5g_blk1_-I1.5_8299529.mat M2 dissipation for LGM ICE-5G blk1 ZE ITdrag 1/8th deg<br> diss_dir_ze_m2_1_8_rtp_21kyrBP_140mSLD_i6g_lmsk_-I1.5_8299543.mat M2 dissipation for PD 140mSLD ICE-6G land mask ZE ITdrag 1/8th deg<br> diss_dir_ze_m2_1_8_rtp_21kyrBP_140mSLD_i5g_lmsk_-I1.5_8299542.mat M2 dissipation for PD 140mSLD ICE-5G land mask ZE ITdrag 1/8th deg<br> diss_dir_ze_m2_1_8_rtp_21kyrBP_130mSLD_i6g_lmsk_-I1.5_8299544.mat M2 dissipation for PD 130mSLD ICE-6G land mask ZE ITdrag 1/8th deg<br> diss_dir_ze_m2_1_8_rtp_21kyrBP_130mSLD_i5g_lmsk_-I1.5_8299541.mat M2 dissipation for PD 130mSLD ICE-5G land mask ZE ITdrag 1/8th deg<br> diss_dir_ze_m2_1_8_rtp_21kyrBP_120mSLD_i6g_lmsk_-I1.5_8299545.mat M2 dissipation for PD 120mSLD ICE-6G land mask ZE ITdrag 1/8th deg<br> diss_dir_ze_m2_1_8_rtp_21kyrBP_120mSLD_i5g_lmsk_-I1.5_8299540.mat M2 dissipation for PD 120mSLD ICE-5G land mask ZE ITdrag 1/8th deg<br> diss_dir_ze_m2_1_8_rtp_21kyrBP_110mSLD_i6g_lmsk_-I1.5_8299546.mat M2 dissipation for PD 110mSLD ICE-6G land mask ZE ITdrag 1/8th deg<br> diss_dir_ze_m2_1_8_rtp_21kyrBP_110mSLD_i5g_lmsk_-I1.5_8299539.mat M2 dissipation for PD 110mSLD ICE-5G land mask ZE ITdrag 1/8th deg<br> diss_dir_ze_m2_1_8_rtp_21kyrBP_100mSLD_i6g_lmsk_-I1.5_8299547.mat M2 dissipation for PD 100mSLD ICE-6G land mask ZE ITdrag 1/8th deg<br> diss_dir_ze_m2_1_8_rtp_21kyrBP_100mSLD_i5g_lmsk_-I1.5_8299538.mat M2 dissipation for PD 100mSLD ICE-5G land mask ZE ITdrag 1/8th deg<br> diss_dir_ze_m2_1_8_rtp_21kyrBP_120mSLD_-I1.5_8299537.mat M2 dissipation for PD 120mSLD JS ITdrag 1/8th deg</p> <p> </p> <p>Climate model output:</p> <p>UVic climate model output for all simulations in the paper has been compressed using tar and zip. Each folder contains the output yearly averages (tavg.xxx.nc) which have been used in the results section of the paper. The model input files are located in /data. The tidal input file is in /data/O_tideenrg_green.nc. Furthermore included are restart files (rest.xxx.nc), model code in /code, and the model exectuables.</p> <p>Climate mode output list:</p> <p>preind_tidal_ze_00kyr_rtop_-1.5_8299034_dir.tgz Output from PIC<br> lgm_tidal_ze_21kyr_i6g_rtop_-1.5_8299008_dir_tau_lgm.tgz Output from LGM_i6gT_lgmW<br> lgm_tidal_ze_21kyr_i6g_rtop_-1.5_8299008_dir.tgz Output from LGM_i6gT_pdW<br> lgm_tidal_ze_21kyr_i5g_rtop_-1.5_8299031_dir_tau_lgm.tgz Output from LGM_i5gT_lgmW<br> lgm_tidal_ze_21kyr_i5g_rtop_-1.5_8299031_dir.tgz Output from LGM_i5gT_pdW<br> lgm_tidal_ze_00kyr_rtop_-1.5_8299034_dir_tau_lgm.tgz Output from LGM_pdT_lgmW<br> lgm_tidal_ze_00kyr_rtop_-1.5_8299034_dir.tgz Output from LGM_pdT_pdW</p> <p>preind_tidal_js_1_2_rtp_00kyrBP_-I1.0_7881173.tgz Output from PIC_1_2_rtp82<br> preind_js_1_2_SandS8.2_00kyrBP_82SNcb_-I1.0_8317333_dir.tgz Output from PIC_1_2_SS82<br> lgm_tidal_js_1_2_SandS8.2_00kyrBP_120mSLD_82SNcb_-t_-I1.0_8317331_dir.tgz Output from LGM_1_2_SS82_sldT<br> lgm_tidal_js_1_2_SandS8.2_00kyrBP_82SNcb_-I1.0_8317333_dir.tgz Output from LGM_1_2_SS82_pdT<br> lgm_tidal_js_1_2_rtop_00kyrBP_120mSLD_82SN_-t_-I1.0_8315693.tgz Output from LGM_1_2_rtp82_sldT<br> lgm_tidal_js_1_2_rtop_00kyrBP_82SN_pdsal_-I1.0_8315702.tgz Output from LGM_1_2_rtp82_pdT<br> <br> </p> <p> </p> <p> </p>
Exploring the Vulnerability of the Greenland Ice Sheet
<p>Uncover the challenges of understanding Greenland’s massive ice sheet and its potential impact on sea-level rise. With unique features like surface melting and a sponge-like firn layer, the Greenland Ice Sheet holds global and local significance, contributing about 1/3rd of the total land-ice contribution to sea-level rise. The video stresses the importance of collaborative research for a better understanding of our planet’s future.</p>
Climate and ice sheet dynamics in Patagonia throughout Marine Isotope Stages 3 and 2
<p>This dataset contains the modelled ice thickness over Patagonia during the global Last Glacial Maximum at 4 km resolution by using SICOPOLIS forced by the results from PMIP models. It also contains the modelled output of the transient simulations performed at 8 km resolution forced by MPI-ESM1-2-LR and different cores.</p>
Investigating similarities and differences of the penultimate and last glacial terminations with a coupled ice sheet - climate model
<p>This archive provides the iLOVECLIM-GRISLI outputs as part of the manuscript "Investigating similarities and differences of the penultimate and last glacial terminations with a coupled ice sheet - climate model". Contact: aurelien.quiquet@lsce.ipsl.fr</p>
Greenland Ice Sheet precipitation and surface temperature from CloudSat and ECMWF
<p>This dataset contains code, data, and instructions for recreating the figures and analysis in Thompson-Munson et al. (submitted), "An Observational Constraint for Future Greenland Rainfall in a Warmer Atmosphere". Please see the readme for instructions and descriptions of the data and code.</p>
Northern Hemisphere ice sheets and ocean interactions during the last glacial period in a coupled ice sheet-climate model
<p>This archive provides the GRISLI ice sheet model and iLOVECLIM model outputs as part of the manuscript "Northern Hemisphere ice sheets and ocean interactions during the last glacial period in a coupled ice sheet-climate model".<br><br></p> <p>Contact: louise.abot@locean.ipsl.fr</p>
The Antarctic ice sheet iron source : a sensitivity study with a global ocean model
<p>Contains model data and freshwater fluxes from icebergs and ice shelves (used as forcing file to represent the Fe supply from the Antarctic ice sheet) of the study "Sensitivity of ocean biogeochemistry to the iron supply from the Antarctic ice sheet explored with a biogeochemical model", submitted to Biogeosciences (EGU)</p>
Results of the initMIP-Antarctica experiments: an ice sheet initialization intercomparison of ISMIP6
<p>This archive provides the forcing data and ice sheet model output produced as part of the publication " initMIP-Antarctica: an ice sheet model initialization experiment of ISMIP6", published in The Cryosphere, <a href="https://www.the-cryosphere.net/13/1441/2019/">https://www.the-cryosphere.net/13/1441/2019/</a></p> <p>Seroussi, H., Nowicki, S., Simon, E., Abe-Ouchi, A., Albrecht, T., Brondex, J., Cornford, S., Dumas, C., Gillet-Chaulet, F., Goelzer, H., Golledge, N. R., Gregory, J. M., Greve, R., Hoffman, M. J., Humbert, A., Huybrechts, P., Kleiner, T., Larour, E., Leguy, G., Lipscomb, W. H., Lowry, D., Mengel, M., Morlighem, M., Pattyn, F., Payne, A. J., Pollard, D., Price, S. F., Quiquet, A., Reerink, T. J., Reese, R., Rodehacke, C. B., Schlegel, N.-J., Shepherd, A., Sun, S., Sutter, J., Van Breedam, J., van de Wal, R. S. W., Winkelmann, R., and Zhang, T.: initMIP-Antarctica: an ice sheet model initialization experiment of ISMIP6, The Cryosphere, 13, 1441-1471, https://doi.org/10.5194/tc-13-1441-2019, 2019.</p> <p>Contact: Helene Seroussi, Helene.seroussi@jpl.nasa.gov</p> <p>Further information on ISMIP6 and initMIP-Antarctica can be found here:<br> http://www.climate-cryosphere.org/activities/targeted/ismip6<br> http://www.climate-cryosphere.org/wiki/index.php?title=InitMIP-Antarctica</p> <p>Users should cite the original publication when using all or part of the data. <br> In order to document CMIP6’s scientific impact and enable ongoing support of CMIP, users are also obligated to acknowledge CMIP6, ISMIP6 and the participating modeling groups.</p> <p>Archive overview<br> ----------------<br> README.txt - this information</p> <p>dSMB.zip - Surface mass balance anomaly forcing data and description<br> dSMB/<br> dSMB_AIS_01km.nc<br> dSMB_AIS_02km.nc<br> dSMB_AIS_04km.nc<br> dSMB_AIS_08km.nc<br> dSMB_AIS_16km.nc<br> dSMB_AIS_32km.nc<br> README_dSMB_AIS.txt</p> <p>dBasalMelt.zip – Ice shelf Basal Melt anomaly forcing data and description<br> dBasalMelt/<br> dBasalMelt_AIS_01km.nc<br> dBasalMelt_AIS_02km.nc<br> dBasalMelt_AIS_04km.nc<br> dBasalMelt_AIS_08km.nc<br> dBasalMelt_AIS_16km.nc<br> dBasalMelt_AIS_32km.nc<br> README_dBasalMelt_AIS.txt</p> <p><group>_<model>_<experiment>.zip - The model output per group, model and experiment (init, ctrl, asmb, abmb)<br> <group1>_<model1>_init/<br> acabf_AIS_<group1>_<model1>_init.nc<br> ...<br> <group1>_<model1>_ctrl/<br> acabf_AIS_<group1>_<model1>_ctrl.nc<br> ...<br> <group1>_<model1>_asmb/<br> acabf_AIS_<group1>_<model1>_asmb.nc<br> ...<br> <group1>_<model1>_abmb/<br> acabf_AIS_<group1>_<model1>_abmb.nc<br> ...</p> <p><group1>_<model2>_init/<br> ...<br> <group1>_<model2>_ctrl/<br> ...<br> <group1>_<model2>_asmb/<br> ...<br> <group1>_<model2>_abmb/<br> ...</p> <p><group2>_<model1>_init/<br> ...<br> <group2>_<model1>_ctrl/<br> ...<br> <group2>_<model1>_asmb/<br> ...<br> <group2>_<model1>_asmb/<br> ...</p> <p>The following script may be used to download the content of the archive.<br> #!/bin/bash<br> wget https://zenodo.org/record/2651652/files/README.txt<br> wget https://zenodo.org/record/2651652/files/dSMB.zip<br> wget https://zenodo.org/record/2651652/files/dBasalMelt.zip</p> <p>for amodel in ARC_PISM1 ARC_PISM2 ARC_PISM3 ARC_PISM4 AWI_PISM1Eq AWI_PISM1Pal CPOM_BISICLES_A_500m CPOM_BISICLES_B_500m DMI_PISM0 DMI_PISM1 DOE_MALI IGE_ELMER ILTS_SICOPOLIS1 ILTS_SICOPOLIS2 IMAU_IMAUICE JPL1_ISSM LSCE_GRISLI NCAR_CISM PIK_PISM3PAL PIK_PISM4EQUI PSU_EQNOMEC PSU_GLNOMEC UCIJPL_ISSM ULB_FETISH1 VUB_AISMPALEO; do<br> wget https://zenodo.org/record/2651652/files/${amodel}_init.zip<br> wget https://zenodo.org/record/2651652/files/${amodel}_ctrl.zip<br> wget https://zenodo.org/record/2651652/files/${amodel}_asmb.zip<br> wget https://zenodo.org/record/2651652/files/${amodel}_abmb.zip</p> <p>done</p>
Supplementary Tables for "A 3.3-Million-Year Record of Antarctic Iceberg Rafted Debris and Ice Sheet Evolution Quantified by Machine Learning"
<p>Supplementary Tables for "A 3.3-Million-Year Record of Antarctic Iceberg Rafted Debris and Ice Sheet Evolution Quantified by Machine Learning"</p> <p> </p> <p><strong>Table Captions:</strong></p> <p><strong>Table S1.</strong> Site U1537 Age Model Tie Points from Weber et al. (2022) and Reilly et al. (2021)</p> <p><strong>Table S2. </strong>Site U1537 Age Model used in this study, applying both the age tie points from Weber et al. (2022) and Reilly et al. (2021)</p> <p><strong>Table S3. </strong>Hole U1538A correlation to the Dove Basin Stack from Bailey et al. (2022), and the addition of the U1538 splice CCSF-A depth to the Dove Basin CCSF-A</p> <p><strong>Table S4. </strong>Site U1538 splice table used in this study, note the continuation down Hole A after Core 14H</p> <p><strong>Table S5. </strong>New top core section offsets for Site U1536 cores added to the Reilly et al. (2021) extended splice table</p> <p><strong>Table S6. </strong>New top core section offsets for Site U1537 cores added to Reilly et al. (2021) extended splice table</p> <p><strong>Table S7. </strong>Comparison of Convolutional Neural Network IRD counts to shipboard eye counts of IRD at Site U1536</p> <p><strong>Table S8. </strong>Site U1537 CNN IRD Counts per 50 cm bins</p> <p><strong>Table S9. </strong>Site U1536 IRD Fluxes Per 5 kyr Quantified by a Convolutional Neural Network (0-3.3 Ma)</p> <p><strong>Table S10. </strong>Site U1537 IRD Fluxes Per 5 kyr Quantified by a Convolutional Neural Network (0-3.3 Ma)</p> <p><strong>Table S11. </strong>Site U1536 IRD Fluxes Per 1 kyr Quantified by a Convolutional Neural Network (0-1.2 Ma)</p> <p><strong>Table S12. </strong>Site U1537 IRD Fluxes Per 1 kyr Quantified by a Convolutional Neural Network (0-1.2 Ma)</p> <p><strong>Table S13. </strong>Site U1538 IRD Fluxes Per 1 kyr Quantified by a Convolutional Neural Network (0-1.2 Ma)</p>
Contribution of surface and cloud radiative feedbacks to Greenland Ice Sheet meltwater production during 2002-2023
<p>These datasets accompany the paper titled "Contribution of surface and cloud radiative feedbacks to Greenland Ice Sheet meltwater production during 2002-2023". </p>
Projections of Precipitation and Temperatures in Greenland and the Impact of Spatially Uniform Anomalies on the Evolution of the Ice Sheet
<p>Supplementary data used in the article <em>Projections of Precipitation and Temperatures in Greenland and the Impact of Spatially Uniform Anomalies on the Evolution of the Ice Sheet</em> submitted to The Cryosphere.</p> <p>Please cite the corresponding paper if you use this data. </p> <p>We supply</p> <ol> <li>the regridded CMIP6 precipitation (pr) and temperature (tas) model output for Greenland.</li> <li>local precipitation-near surface temperature sensitivities for each model in the folder <em>local_slopes_precipitation</em></li> <li>The monthly anomalies for near-surface temperature and precipitation as well as the local change in precipitation (compare to the climatology, in %) with respect to the climatology (year 1980-200) in the folder <em>2100_anomalies</em></li> <li>The CMIP6 monthly climatology from the years 1980-2000 (precipitation & near-surface temperature) </li> <li>The monthly precipitation and near-surface temperature anomalies from 2015-2100 in the folder <em>2100_85years</em></li> </ol> <p> </p> <p>License of CMIP6 model output: </p> <p>CMIP6 model data produced is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0; https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file). The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</p>
A fast and simplified subglacial hydrological model for the Antarctic Ice Sheet and outlet glaciers
<p>KazmierczakGregov24_data.zip contains the Matlab scripts and data necessary to reproduce the results and figures of the article "A fast and simplified subglacial hydrological model for the Antarctic Ice Sheet and outlet glaciers" by Kazmierczak, Gregov, Coulon, and Pattyn. For more details, please, open the README.txt file or contact elise (dot) kazmierczak (at) ulb (dot) be or thomas (dot) gregov (at) uliege (dot) be.</p>
A Factor Two Difference in 21st-Century Greenland Ice Sheet Surface Mass Balance Projections from Three Regional Climate Models for a Strong Warming Scenario (SSP5-8.5)
<p>1km regridded Greenland Ice Sheet SMB / Runoff / Melt projection until 2100. Projections from MAR, RACMO, HIRHAM forced by CESM2 (SSP5-8.5).</p>
Fettweis et al., 2021, TC, Greenland ice sheet and geoengineering: MAR outputs
<p>Monthly MARv3.11.3 outputs used in:</p> <p>Fettweis, X., Hofer, S., Séférian, R., Amory, C., Delhasse, A., Doutreloup, S., Kittel, C., Lang, C., Van Bever, J., Veillon, F., and Irvine, P.: Brief Communication: Reduction of the future Greenland ice sheet surface melt with the help of solar geoengineering, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2020-347, accepted, 2020.</p> <p>The available variables are:</p> <pre><code> name title I J K L LON Longitude 1:73 1:135 ... ... LAT Latitude 1:73 1:135 ... ... SH Surface Height 1:73 1:135 ... ... MSK Ice Sheet Area 1:73 1:135 ... ... SOL Soil Type 1:73 1:135 ... ... FRV Vegetation Class Coverage 1:73 1:135 1:2 ... VEG Vegetation Type Index 1:73 1:135 1:2 ... SMB SMB (mmWE/month) 1:73 1:135 ... 1:12 RU Runoff (mmWE/month) 1:73 1:135 ... 1:12 ME Melt (mmWE/month) 1:73 1:135 ... 1:12 SF Snowfall (mmWE/month) 1:73 1:135 ... 1:12 RF Rainfall (mmWE/month) 1:73 1:135 ... 1:12 SU Sublimation/evaporation (mmWE/m 1:73 1:135 ... 1:12 TT Temperature (degC) 1:73 1:135 ... 1:12 SWD Shortwave downward (w/m^2) 1:73 1:135 ... 1:12 LWD Longwave downward (w/m^2) 1:73 1:135 ... 1:12 SWA Absorbed shortwave radiation (w 1:73 1:135 ... 1:12 AL Albedo 1:73 1:135 ... 1:12 </code></pre> <p><br> </p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.