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85 results for “Greenland Ice Sheet”
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>
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>
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>
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>
Retrieval and Validation of Total Seasonal Liquid Water Amounts in the Percolation Zone of Greenland Ice Sheet Using L-band Radiometry
<p>This repository contains the dataset associated with the analyses presented in the following study:</p> <p>Hossan, A., Colliander, A., Vandecrux, B., Schlegel, N.-J., Harper, J., Marshall, S., and Miller, J. Z.: <em>Retrieval and validation of total seasonal liquid water amounts in the percolation zone of the Greenland Ice Sheet using L-band radiometry</em>, <strong>The Cryosphere</strong>, 19, 4237–4258, <a href="https://doi.org/10.5194/tc-19-4237-2025" target="_new">https://doi.org/10.5194/tc-19-4237-2025</a>, 2025.</p> <p>In this study, we demonstrated the capability of NASA's Soil Moisture Active Passive (SMAP) L-band radiometer to estimate surface and subsurface liquid water amounts (LWA) in the percolation zone of the Greenland Ice Sheet. The article presents our initial retrieval algorithm, validation results, and highlights the potential for developing a Greenland-wide LWA data product.</p> <p><strong>Contents of this Repository</strong></p> <p>This repository includes:</p> <ul> <li><strong>SMAP-retrieved daily, vertically integrated LWA gridded initial data products</strong> (2015–2023), derived from enhanced-resolution SMAP TB observations. These data include spatial coordinates, acquisition dates, and a melt flag indicator.</li> <ul> <li>SMAP_LWA_time_series_AWS contains daily time series at a AWS location (point observation)</li> <li>Samimi_EBM_LWA_time_series_AWS contains corresponding time series of LWA estimated by Samimi model forced by PROMICE AWS.</li> <li>GEMB_LWA_time_series_AWS contains corresponding time series of LWA estimated by GEMB model forced by PROMICE AWS</li> <li>The locations and name ID of the AWS are given in AWS.txt/xls file</li> <li>L_band_LWA_yyyy.nc files contain daily LWA and TB data over the entire percolation zone</li> </ul> <li><strong>Corresponding vertically polarized brightness temperature (TBV) data</strong>, including their winter mean and standard deviation.</li> <li><strong>Model-based LWA estimates used for validation</strong>, including outputs from:</li> <ul> <li>The locally calibrated <strong>Energy and Mass Balance (EMB)</strong> model.</li> <li>The <strong>Glacier Energy and Mass Balance (GEMB)</strong> model within NASA’s <strong>Ice-sheet and Sea-level System Model (ISSM)</strong>.</li> </ul> </ul> <p><strong>Retrieval and Validation Codebase</strong></p> <p>The MATLAB scripts and tools used for the microwave retrieval algorithm, radiative transfer modeling, inversion process, and comparative validation with in situ AWS-driven model outputs are available at the following GitHub repository:</p> <p>🔗 <a href="https://github.com/HossanAlamgir/SMAP_MWA_Retrieval_and_Validation_GrIS" target="_new">https://github.com/HossanAlamgir/SMAP_MWA_Retrieval_and_Validation_GrIS</a><br><em>(Last accessed: 17 September 2025)</em></p> <p>The codebase includes:</p> <ul> <li>Preprocessing routines for SMAP TB data.</li> <li>Implementation of the radiative transfer forward model.</li> <li>Inversion and threshold-based detection algorithms.</li> <li>Validation scripts for comparison against AWS-forced EMB and GEMB model outputs.</li> </ul> <p><strong>Relevance</strong></p> <p>These data and methods support ongoing efforts to improve surface mass balance (SMB) estimates and enhance projections of Greenland’s contribution to global sea level rise.</p> <p> </p>
Automated mapping and classification of valleys beneath the Greenland Ice Sheet: Datasets
<p><strong>Valley morphology and classification: training dataset and full dataset for Greenland</strong></p> <p>This dataset contains:</p> <ul> <li>TrainingDataValleys.csv: A .csv file of the training dataset used to classify valleys in Greenland as 'fluvial' or 'glacial'. These valleys were extracted from the Copernicus 90 m global digital elevation model.</li> <li>GreenlandValleys.csv: A .csv file of the locations, morphometrics, and class of Greenland valleys identified in Operation IceBridge and CReSIS radio-echo sounding data. These valleys have been classified using a random forest model trained using the above training dataset.</li> </ul>
Smoothed monthly Greenland ice sheet elevation changes during 2003-2023
Open the record for dataset details and reuse information.
Results of "Sensitivity of Greenland ice sheet projections to spatial resolution in higher-order simulations: the AWI contribution to ISMIP6-Greenland using ISSM"
<p>This archive provides ice sheet model output produced as part of the publication:</p> <p>Rückamp, M., Goelzer, H., and Humbert, A.: Sensitivity of Greenland ice sheet projections to spatial resolution in higher-order simulations: the AWI contribution to ISMIP6-Greenland using ISSM, The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-329, in review, 2020.</p> <p>About the data:<br> The archive contains 4 tar-files for the 4 different resolutions. Each tar-file contains Matlab files for the dfferent scenarios. An examplary file structure looks as follows:</p> <p>results =</p> <p> struct with fields</p> <p>mesh_description: {'x=x_EPSG3413(m)' 'y=y_EPSG3413(m)'}<br> inputs_description: {'friction coefficient k^2 (s/m)' 'bedrock (m)'}<br> data_description: {1x13 cell}<br> mesh: [1x1 struct]<br> inputs: [1x1 struct]<br> data: [1x87 struct]<br> model_description: {'output from ISMIP6 AWI-ISSM, simulation: G4000 ctrl_proj, correspondence Martin Rueckamp (AWI): marin.rueckamp@awi.de'}<br> data_citation: {'Rückamp, M., Goelzer, H., and Humbert, A.: Results of "Sensitivity of Greenland ice sheet projections to spatial resolution in higher-order simulations: the AWI contribution to ISMIP6-Greenland using ISSM", Zenodo, https://doi.org/10.5281/zenodo.3992605, 2020.'}<br> model_citation: {'Rückamp, M., Goelzer, H., and Humbert, A.: Sensitivity of Greenland ice sheet projections to spatial resolution in higher-order simulations: the AWI contribution to ISMIP6-Greenland using ISSM, The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-329, in review, 2020.'}</p> <p>The fields results.[mesh,input,data]_description contain header information. The field results.mesh contains [x,y]-coordinates of the grid. The field results.inputs contains time-independent quantities such as bedrock. The field results.data contains results of the transient simulations, such as ice thickness or surface velocity. The fields results.[model,data]_citation contain information about data citation.</p>
Results of ISMIP6 CMIP6 forced simulations: a multi-model ensemble of the Greenland and Antarctic ice sheet evolution over the 21st century
<p>This archive provides the ice sheet model outputs produced as part of the publication "Payne et al. 2021 Future sea level change under CMIP5 and CMIP6 scenarios from the Greenland and Antarctic ice sheets", published in GRL</p> <p>Contact: Tony Payne a.j.payne@bristol.ac.uk, Sophie Nowicki sophien@buffalo.edu, ismip6@gmail.com </p> <p><br> Further information on ISMIP6 can be found here:<br> http://www.climate-cryosphere.org/activities/targeted/ismip6<br> http://www.climate-cryosphere.org/wiki/index.php?title=ISMIP6-Projections-Antarctica<br> http://www.climate-cryosphere.org/wiki/index.php?title=ISMIP6-Projections-Greenland</p> <p>Data usage notice:<br> If you use any of these results, please acknowledge the work of the people involved in the process producing this data set. Acknowledgements should have language similar to the below (if you only use CMIP5 forcing, remove CMIP6 and vice versa).</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>For Greenland datasets </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>Slater, D. A., Felikson, D., Straneo, F., Goelzer, H., Little, C. M., Morlighem, M., Fettweis, X., and Nowicki, S.: Twenty-first century ocean forcing of the Greenland ice sheet for modelling of sea level contribution , The Cryosphere, 14, 985–1008, https://doi.org/10.5194/tc-14-985-2020, 2020.</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: <br> Experimental protocol for sea level projections from ISMIP6 standalone ice sheet models, The Cryosphere, doi:10.5194/tc-2019-322, 2020.</p> <p>For Antarctica datasets</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>Jourdain, N. C., Asay-Davis, X., Hattermann, T., Straneo, F., Seroussi, H., Little, C. M., and Nowicki, S.: A protocol for calculating basal melt rates in the ISMIP6 Antarctic ice sheet projections, The Cryosphere, 14, 3111–3134, https://doi.org/10.5194/tc-14-3111-2020, 2020.</p> <p><br> 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> <p> </p>
Greenland ice sheet self-consistent spatial discretization mesh
<p>Greenland ice sheet<br> ===================</p> <p>Two unstructured mesh spatial discretisations of the Greenland ice sheet, with minimum element sizes of 5km and 1km.</p> <p>These are stored in unstructured VTU files defined by the visualisation toolkit VTK [2].</p> <p>Two associated state PVSM files for Paraview [3] are also provided to reproduce visualisations shown in [1]. Note that Paraview requires absolute pathnames, so it may be necessary to edit file references to the VTU files in these state files.</p> <p>Files<br> -----</p> <p>- GreenlandIcesheet1km.vtu<br> - GreenlandIcesheet5km.vtu<br> - GreenlandIcesheet1km.pvsm<br> - GreenlandIcesheet5km.pvsm</p> <p>Author<br> ------</p> <p>- Dr Adam S. Candy <a.s.candy@tudelft.nl>, <candy@cantab.net><br> - Technische Universiteit Delft<br> - Imperial College London</p> <p>References<br> ----------</p> <p>[1] Candy, A.S., 2016. A consistent approach to unstructured mesh generation for geophysical models. In review. Preprint available at https://arxiv.org/abs/1703.08491.</p> <p>[2] The Visualization Toolkit (VTK), version 5.10.1. URL: http://www.vtk.org.</p> <p>[3] Paraview, version 4.3.1. https://www.paraview.org.</p>
Data Release for Retreat and Regrowth of the Greenland Ice Sheet During the Last Interglacial as Simulated by the CESM2-CISM2 Coupled Climate–Ice Sheet Model
<p>CESM2 and CISM2 data files for figures in "Retreat and Regrowth of the Greenland Ice Sheet During the Last Interglacial as Simulated by the CESM2-CISM2 Coupled Climate–Ice Sheet Model" (Sommers et al., 2021, Paleoceanography and Paleoclimatology)</p>
NASA GSFC Firn Densification Model version 1.2.1 (GSFC-FDMv1.2.1) for the Greenland and Antarctic Ice Sheets: Jan 1980 - Jul 2024
<p><strong>Overview</strong></p> <p>The NASA GSFC-FDM v1.2.1 provides the evolution of firn air content (FAC), surface mass balance (SMB) (and its individual components), and total firn height change over the Greenland and Antarctic Ice Sheets from January 1, 1980 to July 30, 2024 at 5-day temporal resolution. The model uses atmospheric forcing from NASA GMAO's Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) global atmospheric reanalysis, combined with a higher resolution replay (see Medley et al., 2022) as input into the Community Firn Model (CFMv1.1.6) to simulate the evolution of firn properties across the ice sheets. The GSFC-FDMv1.2.1 is provided on a 12.5 km x 12.5 km North/South Polar Stereographic Grid, depending on the ice sheet.</p> <p>For a thorough description of how the GSFC-FDMv1.2.1 was generated see Medley et al. (2022) in <em>The Cryosphere</em>. Release 2 contains model output up through June 30, 2022, whereas the initial release only extended through September 30, 2021. Release 3 contains model output up through July 31, 2024 and uses CFMv2.3.1. The model set up is identical between releases.</p> <h3>*** The spatial grids are incorrect in this version, so we have restricted access to these files. Please use Version 4. ***</h3>
Data For: A Framework for Automated Supraglacial Lake Detection and Depth Retrieval in ICESat-2 Photon Data Across the Greenland and Antarctic Ice Sheets
<p>HDF5 data files for 1249 supraglacial lakes detected in ICESat-2 ATL03 data over Central West Greenland (melt seasons 2019 and 2020) and the Amery Ice Shelf Catchment (melt seasons 2018-19 and 2020-21). Each HDF5 data file is associated with a .jpg "quicklook" file of the same name, showing ATL03 photon elevations with the estimated along-track fits to the lake surface and lakebed and the resulting maximum lake depth, along with the corresponding ICESat-2 ground track over cloud-free concurrent satellite imagery.</p> <p>The data files are structured as following: </p> <div> <div> <div> <div> <div> <pre>group: depth_data/ - dataset: bathymetry_confidence - dataset: lakebed_fit_elevation_meters - dataset: lat - dataset: lon - dataset: surface_fit_elevation_meters - dataset: water_depth_meters - dataset: x_along_track_meters group: fluid_bathymetry_peaks/ - dataset: elevation_meters - dataset: peak_prominence - dataset: x_along_track_meters group: mframe_data/ - dataset: delta_time - dataset: density_ratio_1 - dataset: density_ratio_2 - dataset: density_ratio_3 - dataset: density_ratio_4 - dataset: major_frame_id - dataset: passes_bathymetry_check - dataset: passes_flatness_check - dataset: photon_density_peak_elevation - dataset: q_1_number_peaks - dataset: q_2_prominece - dataset: q_3_elev_spread - dataset: q_4_alignment - dataset: q_s - dataset: x_along_track_meters_end - dataset: x_along_track_meters_start group: photon_data/ - dataset: afterpulse_probability - dataset: fluid_signal_confidence - dataset: geoid_elevation_meters - dataset: lat - dataset: lon - dataset: photon_elevation_above_geoid_meters - dataset: pulse_saturation_level - dataset: x_along_track_meters group: properties/ - dataset: beam_number - dataset: beam_strength - dataset: cycle_number - dataset: granule_id - dataset: gtx - dataset: ice_sheet - dataset: lake_quality - dataset: lat - dataset: lon - dataset: melt_season - dataset: rgt - dataset: sc_orient - dataset: surface_elevation - dataset: time_utc </pre> </div> </div> </div> </div> </div>
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>
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>
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>
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>
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>
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
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DANDI Archive for NWB datasets
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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.