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85 results for “Greenland Ice Sheet”
Ice sheet surface elevation change from ablation stake measurements on bare ice in the western Greenland ablation zone during July 2016
<p>Measurements of ice surface elevation change from a network of twelve bamboo ablation stakes installed in the western Greenland ice sheet ablation zone (67.0496o N, 49.0201o W, 1215 m a.s.l.). Stakes were installed by drilling 3 m deep holes into the ice, inserting the bamboo stakes, and allowing them to freeze into the ice for 24 hours. Following the 24 hour freeze-in period, measurements of the distance from the top of the stake to its base were recorded at nominal 3 hour intervals continuously from 12:00 local time (UTC-2) on 6 July 2016 to 23:00 local time on 12 July 2016. Prior to each measurement, a 24×24 cm square wooden ablation board was placed at the base of the stake and oriented to true north. This board operated as a datum from which the stake height above the ice surface was measured.</p>
Dataset for "Remapping of Greenland ice sheet surface mass balance anomalies for large ensemble sea-level change projections"
<p>This dataset is used to reproduce the results presented in the following publication:</p> <p>Goelzer, H., Noel, B. P. Y., Edwards, T. L., Fettweis, X., Gregory, J. M., Lipscomb, W. H., van de Wal, R. S. W., and van den Broeke, M. R.: Remapping of Greenland ice sheet surface mass balance anomalies for large ensemble sea-level change projections, The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-188, in review, 2019.</p> <p> </p>
Supporting Data - Sentinel-1 Detection of Ice Slabs on the Greenland Ice Sheet
<p>This dataset contains supporting data accompanying Culberg, R., Michaelides, R. J., and Miller, J. Z.: Sentinel-1 Detection of Ice Slabs on the Greenland Ice Sheet, EGUsphere [preprint], <a href="https://doi.org/10.5194/egusphere-2023-2652">https://doi.org/10.5194/egusphere-2023-2652</a>, 2023. The final accepted manuscript will be linked via the same preprint server at the time of publication. The dataset contains the following files:</p> <ul> <li>Sentinel-1 HV and HV/HH backscatter mosaics of the Greenland Ice Sheet formed using data from 1 Oct 2016 - 30 April 2017.</li> <li>Estimated average annual summer melt extent between 1 Nov 2014 and 31 Aug 2020, detected using seasonal variations in Sentinel-1 HH backscatter.</li> <li>The firn aquifer extent over Greenland derived from Sentinel-1 in Brangers et al. (2020), reprojected to EPSG:3413.</li> <li>The ice mask used in the study, derived from the BedMachine Greenland ice mask.</li> <li>The training and validation datasets derived from the Jullien et al. (2023) ice slabs detections from ice penetrating radar data that were used to optimize ice slab detection thresholds for the Sentinel-1 backscatter mosaics. </li> </ul>
Greenland Ice Sheet crevasse map from ArcticDEM
<p><em>This dataset is produced using the ArcticDEM v3 mosaic. Since release, the new ArcticDEM v4.1 mosaic has been released, and I have developed newer and open-source methods for extracting crevasse geometries from ArcticDEM and REMA strips. I am happy to help direct you towards the solution or dataset most appropriate for your needs - please contact me at thomas.r.chudley@durham.ac.uk.</em></p> <p><strong>Data Description</strong></p> <p><em>Title</em>: Greenland Ice Sheet crevasse map from ArcticDEM<br><em>Version</em>: 1.00<br><em>Format</em>: GeoTiff<br><em>Projection</em>: WGS84 / NSIDC Sea Ice Polar Stereographic North (EPSG: 3413)<br><em>Resolution</em>: 2 m (binary) and 200 m (fraction)<br><em>Size</em>: 3.5 GB (total binary) and 22 MB (total fraction)<br><em>Citation</em>: Chudley et al. (2021) <a href="https://doi.org/10.1029/2021JF006287" target="_blank" rel="noopener">https://doi.org/10.1029/2021JF006287</a><br><em>Contact</em>: Tom Chudley<br><em>Email</em>: thomas.r.chudley@durham.ac.uk</p> <p><strong>Products</strong></p> <p>This dataset contains crevasse locations identified from the ArcticDEM v3 mosaic. There are two primary products: a 2 m binary crevasse map, and a 200 m crevasse fraction map. It is divided into the six IMBIE 'Rignot' drainage basins: central west (CW), southwest (SW), southeast (SE), northeast (NE), north (NO), northwest (NW).</p> <p><em>This dataset is for scientific purposes only. The method is not able to detect metre-scale and snow-covered crevasses, and should not be used for field safety purposes.</em></p> <p><em><strong>2 m crevasse binary</strong></em></p> <p>Byte GeoTiff product indicating derived crevasses at 2 m resolution. File naming convention is <em>crevasse_binary_XX_2m.tif</em>, where XX is the IMBIE basin code.</p> <p>Values have the following meaning: </p> <ul> <li>0: No data</li> <li>1: No crevasses identified</li> <li>2: Crevasses identified</li> </ul> <p><em><strong>200 m crevasse fraction</strong></em></p> <p>Float32 GeoTiff product indicating fraction of 200 m grid cell identified as crevasses in the 200 m product. File naming convention is <em>crevasse_fraction_XX_2m.tif</em>, where XX is the IMBIE basin code.</p> <p>Values have the following meaning: </p> <ul> <li>0 - 1: Fraction of grid cell identified as crevasse in 2 m dataset</li> <li>-9999: No data</li> </ul> <p><strong>Method</strong></p> <p>The full processing chain for data derivation is described in Chudley et al. (2021). A binary crevasse mask of the Greenland Ice Sheet is generated using ArcticDEM v3 mosaic data at 2 m resolution (Porter et al., 2018), with data processed in Google Earth Engine (Gorelick et al., 2017). The ArcticDEM is cropped to the GIMP ice mask (Howat et al., 2014), before a smoothed elevation model is generated by performing an image convolution with a circular kernel of 50 m radius. Residuals greater than 1 m between the smoothed and raw elevation values were identified as crevasses. To compare with public velocity datasets (and derived strain rates, stress, etc.), the 2 m dataset was aggregated (using GDAL) into grid cells to match the resolution (200 m) of the Making Earth System Data Records for Use in Research Environments (MEaSUREs) ice sheet surface velocity grid (Joughin, 2010; 2021). Aggregated values represent the fraction of grid cell area classified as crevasses.</p> <p><strong>Caveats</strong></p> <ul> <li>The method, including kernel size was tuned manually based on the region of interest of the original Chudley et al. (2021) paper. As such, it may not be optimal for other regions of interest on the ice sheet, in particular in the east, where medial moraines and marginal valleys are more prevalent (see caveat #4).</li> <li>The ArcticDEM is derived from optical MAXAR imagery. As such, snow-filled crevasses will not be identified here, which will be problematic above the ablation zone.</li> <li>Following comparison with Uncrewed Aerial Vehicle (UAV) data in Chudley et al. (2021), the approximate lower bound of crevasse width identified is ~10 m. These are large crevasses, far greater than are commonly encountered in safe fieldwork environments.</li> <li>This method is relatively crude: at its core, it is effectively a high-pass filter applied to the ArcticDEM mosaic. As such, there are false positives that occur around other supraglacial features (rivers, moraines, etc.) as well as marginal features (proglacial geomorphology, fjord sikkusak, etc.) that are captured in regions where the GIMP ice mask does not accurately capture the terrestrial ice extent at the time of ArcticDEM data capture. Users are encouraged to critically evaluate data in their areas of interest using the 2 m binary map and external data, even when intending to use only the 200 m fraction dataset.</li> </ul> <p><strong>Citation</strong></p> <p>When using this data, please cite the method as being from:</p> <p>Chudley, T. R., Christoffersen, P., Doyle, S. H., Dowling, T. P. F., Law, R., Schoonman, C. M., Bougamont, M., & Hubbard, B. (2021). Controls on water storage and drainage in crevasses on the Greenland Ice Sheet. Journal of Geophysical Research: Earth Surface, 126, e2021JF006287. <a href="https://doi.org/10.1029/2021JF006287">https://doi.org/10.1029/2021JF006287</a>.</p> <p><strong>Acknowledgements</strong></p> <p>Initial processing chain created whilst Chudley was supported by a Natural Environment Research Council Doctoral Training Partnership Studentship (Grant No. NE/L002507/1).</p> <p>ArcticDEM v3 mosaic is provided by the Polar Geospatial Center under NSF-OPP awards 1043681, 1559691, and 1542736.</p>
Supraglacial lakes derived from Sentinel-1 SAR imagery over the Watson basin on the Greenland Ice Sheet.
<p>An experimental dataset produced for the 4D-Greenland project, one of the Polar+ projects funded by the European Space Agency. The dataset provides a classification of supraglacial lake extent, derived using Sentinel-1 SAR imagery, over the Watson case study site. The dataset is produced using a dynamic thresholding approach (Miles et al 2018). </p> <p>The dataset is produced for the period May 2017- Sept 2019. The temporal resolution of the dataset is approximately fortnightly (subject to methodological limitations) and is delivered as rasters in GeoTIFF format (epsg:3413). Raster pixels are denoted as: 0 where no surface water was detected; 1 where either HH or HV polarisation detected a backscatter signature representative of surface water; 2 where both HH and HV polarisations detected a backscatter signature representative of surface water; or 999 where the signal has been saturated and the output cannot distinguish if the signal is due to melt or other surface characteristics with the same backscattered signature. </p> <p>The naming convention indicates the original SAR tile used in the analysis and is identified by the sequence of fields described here:</p> <p><product_type>_<mission>_<mode>_<product>_<polarisation>_<starttime>_<endtime>_<orbitnumber>_<dataID>_<image>.fileextension</p> <p>For example:</p> <p>extent_S1B_EW_GRDH_1SDH_20180811T202931_20180811T203031_012220_016839_916F.tif</p>
Dataset for: A revised and expanded deep radiostratigraphy of the Greenland Ice Sheet from airborne radar sounding surveys between 1993–2019
<p>Version 5 changes the variable names in the NetCDF file so that are more readable by xarray.</p> <p>The NetCDF v4 file (Greenland_radiostratigraphy_v2.nc) contains both the gridded depths of synthetic isochrones (i.e., at ages of interest that were not necessarily directly observed) and the age at regular normalized depths (10–80%) in the ice sheet.</p> <p>For each campaign that was traced, there is an HDF5-compliant .mat MATLAB file (Greenland_radiostratigraphy_v2_X_Y, where X is the year the campaign was flown and Y is the aircraft that was used) that contains the original traced and dated reflections for each traced segment in that campaign. If using Python instead of MATLAB, the mat73 package and its loadmat function can be used to load these files. An example Jupyter notebook is included that illustrates how to access the full contents of .mat file using Python and this package. There is also a zipped (.zip) archive for each campaign that contains GeoPackage (.gpkg) files for each traced segment of that campaign. Because of format restrictions, these GeoPackages only include the traced reflections' depths, with the ages in the metadata. </p> <p> </p>
MEaSUREs ITS_LIVE Sentinel-1 Image-Pair Glacier and Ice Sheet Surface Velocities: Version 2 (Greenland Sample Products)
<p>We provide 21 sample products of MEaSUREs ITS_LIVE Sentinel-1 Image-Pair Glacier and Ice Sheet Surface Velocities: Version 2 in three test regions of Greenland Ice Sheet. The full archive of version 2 ITS_LIVE products (including image pair maps, data cubes and mosaics) from Sentinel-1 as well as other optical sensors (Landsat-4/5/6/7/8 and Sentinel-2) can be found at the ITS_LIVE project website: <a href="https://its-live.jpl.nasa.gov/">https://its-live.jpl.nasa.gov</a>.</p> <p><strong>Sensor</strong>: Sentinel-1A/B</p> <p><strong>Processor</strong>: <a href="https://github.com/isce-framework/isce2">ISCE</a>v2.4.1 (topsApp -> <a href="https://github.com/leiyangleon/Geogrid">Geogrid</a>v1.4.0 -> <a href="https://github.com/nasa-jpl/autoRIFT">autoRIFT</a>v1.4.0)</p> <p><strong>Project</strong>: NASA MEaSUREs project <a href="https://its-live.jpl.nasa.gov">ITS_LIVE</a></p> <p><strong>Region 1</strong> (69.13N, 50.88W; Jakobshavn Isbræ Glacier): 7 ascending image pairs</p> <p><strong>Region 2</strong> (77.61N, 42.79W; central north of interior Greenland): 3 ascending image pairs</p> <p><strong>Region 3</strong> (72.48N, 35.87W; central south of interior Greenland): 10 descending image pairs and 1 ascending image pair</p> <p>This serves as a supplementary dataset for the companion journal article submitted to Earth System Science Data (to appear).</p> <p> </p> <p><strong>Acknowledgement</strong>: This effort was funded by the NASA MEaSUREs program in contribution to the Inter-mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) project (<a href="https://its-live.jpl.nasa.gov/">https://its-live.jpl.nasa.gov/</a>) and through Alex Gardner’s participation in the NASA NISAR Science Team.</p>
Dataset for "Mass loss of the Greenland ice sheet until the year 3000 under a sustained late-21st-century climate"
<p>Dataset for the paper "Mass loss of the Greenland ice sheet until the year 3000 under a sustained late-21st-century climate" (Journal of Glaciology, <a href="https://doi.org/10.1017/jog.2022.9">https://doi.org/10.1017/jog.2022.9</a>).</p> <p>Please see the README for details.</p> <p>V1.2: Now providing compressed netCDF files with reduced size.<br>V1.1: Scalar flux variable 'dlimdt' (total ice mass change) added. README updated.<br>V1: Initial upload.</p> <p>* * * * * * *</p> <p>Users should cite the original publication when using all or parts of these data.</p>
Datasets for "Large subglacial source of mercury from the southwestern margin of the Greenland Ice Sheet"
<p>Geochemical measurements and hydrochemical datasets linked to the publication "Large subglacial source of mercury from the southwestern margin of the Greenland Ice Sheet" in Nature Geoscience. Presented are (1) data for mercury concentrations in glacial meltwater outflows from the Greenland Ice Sheet taken in 2012, 2015 and 2018, (2) data for mercury concentrations in fjord waters from Nuup Kangerlua, Ameralik Fjord and Søndre Strømfjord, and (3) all associated hydrochemical data presented in the manuscript. For additional details (analytical techniques, precision, accuracy and limits of detection) please refer to the methodology in the publication.</p> <p>This third version has additional riverine data added the the 2012 dataset. </p>
joemacgregor/GBaTSv2: Greenland Ice Sheet Likely Basal Thermal State version 2 [dataset+code] FINAL
<p>Greenland Ice Sheet Likely Basal Thermal State version 2, FINAL dataset+code to be published in The Cryosphere (tc-2022-40)</p>
Code and data: Slush limits of the western flank of the Greenland Ice Sheet, mapped from MODIS, 2000 - 2021
<p>Code and output of the slush limit detection for the western flank of the Greenland Ice Sheet, as described in Machguth, H., A. Tedstone and E. Mattea (2022), <strong>Daily Variations in Western Greenland Slush Limits, 2000 to 2021, mapped from MODIS</strong>, <em>Journal of Glaciology, </em>https://doi.org/10.1017/jog.2022.65<em>.</em></p>
Along flow acceleration of the Greenland ice sheet
<p>The acceleration of Greenland ice flow derived from ITS_LIVE annual velocity data spanning 1985-2018.</p> <p>There are two variations of using either weighted and unweighted least squares in the estimation. The data files follow the format:</p> <ul> <li>weighted--ax.tif | acceleration in the x direction:</li> <li>weighted--ay.tif | acceleration in the y-direction.</li> <li>weighted--a.tif | acceleration in the dominant flow direction</li> <li>weighted--asigma.tif | standard error estimate of the "weighted--a" data.</li> <li>weighted--N.tif | Number of years with data for each grid point.</li> </ul> <p>All data are using a polar stereographic projection (EPSG:3413). </p> <p> </p> <p>This dataset was created as part of the study: Grinsted et al. 2022, Accelerating ice flow at the onset of the Northeast Greenland ice stream. Processing choices is detailed there. </p>
Results of the ice sheet model initialisation experiments initMIP-Greenland: an ISMIP6 intercomparison
<p>This archive provides the forcing data and ice sheet model output produced as part of the publication "Design and results of the ice sheet model initialisation experiments initMIP-Greenland: an ISMIP6 intercomparison", published in The Cryosphere, https://www.the-cryosphere.net/12/1433/2018/</p> <p>Goelzer, H., Nowicki, S., Edwards, T., Beckley, M., Abe-Ouchi, A., Aschwanden, A., Calov, R., Gagliardini, O., Gillet-Chaulet, F., Golledge, N. R., Gregory, J., Greve, R., Humbert, A., Huybrechts, P., Kennedy, J. H., Larour, E., Lipscomb, W. H., Le clec´h, S., Lee, V., Morlighem, M., Pattyn, F., Payne, A. J., Rodehacke, C., Rückamp, M., Saito, F., Schlegel, N., Seroussi, H., Shepherd, A., Sun, S., van de Wal, R., and Ziemen, F. A.: Design and results of the ice sheet model initialisation experiments initMIP-Greenland: an ISMIP6 intercomparison, The Cryosphere, 12, 1433-1460, 2018, doi:10.5194/tc-12-1433-2018.</p> <p>Contact: Heiko Goelzer, h.goelzer@uu.nl</p> <p>Further information on ISMIP6 and initMIP-Greenland can be found here:<br> http://www.climate-cryosphere.org/activities/targeted/ismip6<br> http://www.climate-cryosphere.org/wiki/index.php?title=InitMIP-Greenland</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 modelling groups.</p> <p><br> *** Important note ***<br> For consistency with future ISMIP6 intercomparison exercises and some observational data sets, we have re-gridded all output to a diagnostic grid following the EPSG:3413 specifications, which differs from the grid originally used to distribute the forcing data. We also provide the forcing data conservatively interpolated to the new grid. </p> <p><br> Archive overview<br> ----------------<br> README.txt - this information</p> <p>dSMB.zip - The original surface mass balance anomaly forcing data and description<br> dSMB/<br> dsmb_01B13_ISMIP6_v2.nc<br> dsmb_05B13_ISMIP6_v2.nc<br> dsmb_10B13_ISMIP6_v2.nc<br> dsmb_20B13_ISMIP6_v2.nc<br> README_dSMB_v2.txt</p> <p>dSMB_epsg3413.zip - The surface mass balance anomaly forcing data and description, interpolated to the new grid on EPSG:3413<br> dSMB_epsg3413/<br> dsmb_01e3413_ISMIP6_v2.nc<br> dsmb_05e3413_ISMIP6_v2.nc<br> dsmb_10e3413_ISMIP6_v2.nc<br> dsmb_20e3413_ISMIP6_v2.nc<br> README_dSMB_v2_epsg3413.txt</p> <p><group>_<model>_<experiment>.zip - The model output per group, model and experiment (init, ctrl, asmb)<br> <group1>_<model1>_init/<br> acabf_GIS_<group1>_<model1>_init.nc<br> ...<br> <group1>_<model1>_ctrl/<br> acabf_GIS_<group1>_<model1>_ctrl.nc<br> ...<br> <group1>_<model1>_asmb/<br> acabf_GIS_<group1>_<model1>_asmb.nc<br> ...</p> <p><group1>_<model2>_init/<br> ...<br> <group1>_<model2>_ctrl/<br> ...<br> <group1>_<model2>_asmb/<br> ...</p> <p><group2>_<model1>_init/<br> ...<br> <group2>_<model1>_ctrl/<br> ... <br> <group2>_<model1>_asmb/</p> <p>...</p> <p> </p> <p>The following script may be used to download the content of the archive.</p> <p>#!/bin/bash<br> wget https://zenodo.org/record/1173088/files/README.txt<br> wget https://zenodo.org/record/1173088/files/dSMB_epsg3413.zip<br> wget https://zenodo.org/record/1173088/files/dSMB.zip<br> <br> for amodel in ARC_PISM AWI_ISSM1 AWI_ISSM2 BGC_BISICLES1 BGC_BISICLES2 BGC_BISICLES3 DMI_PISM1 DMI_PISM2 DMI_PISM3 DMI_PISM4 DMI_PISM5 IGE_ELMER1 IGE_ELMER2 ILTS_SICOPOLIS ILTSPIK_SICOPOLIS IMAU_IMAUICE1 IMAU_IMAUICE2 IMAU_IMAUICE3 JPL_ISSM LANL_CISM LSCE_GRISLI MIROC_ICIES1 MIROC_ICIES2 MPIM_PISM UAF_PISM1 UAF_PISM2 UAF_PISM3 UAF_PISM4 UAF_PISM5 UAF_PISM6 UCIJPL_ISSM ULB_FETISH1 ULB_FETISH2 VUB_GISM1 VUB_GISM2; do</p> <p>wget https://zenodo.org/record/1173088/files/${amodel}_init.zip<br> wget https://zenodo.org/record/1173088/files/${amodel}_ctrl.zip<br> wget https://zenodo.org/record/1173088/files/${amodel}_asmb.zip</p> <p>done</p> <p> </p>
Using variable-resolution grids to model precipitation from atmospheric rivers around the Greenland ice sheet
<p>This dataset can be used to reproduce the figures created in Waling et al. 2024, "Using variable-resolution grids to model precipitation from atmospheric rivers around the Greenland ice sheet." Each figure has its own script which can be executed.<br><br></p>
Data: Greenland Ice Sheet ice slab expansion and thickening
<p>Dataset from the manuscript 'Greenland Ice Sheet ice slab expansion and thickening' (2023), published by Nicolas Jullien, Andrew J. Tedstone, Horst Machguth, Nanna B. Karlsson, Veit Helm.</p> <p>If you use any of these file please cite the paper associated with the dataset.</p>
Greenland Ice Sheet modeled firn properties from SNOWPACK and the Community Firn Model (1980-2020)
<p>This dataset contains model output from the physics-based SNOWPACK firn model and the semi-empirical Community Firn Model (CFM) over the Greenland Ice Sheet from 1980 through 2020. Included are individual density profiles for locations with firn density observations as well as firn air content (FAC) calculated over different depth intervals. Data for both models are supplied. These data are used in a manuscript to be submitted to The Cryosphere journal (see Thompson-Munson et al., in review).</p>
Data: Contrasting current and future surface melt rates on the ice sheets of Greenland and Antarctica: lessons from in situ observations and climate models
<p>These data accompany the publication "Contrasting current and future surface melt rates on the ice sheets of Greenland and Antarctica: lessons from in situ observations and climate models". The data are organized as follows:</p> <p>- two files with time series (csv) of hourly near-surface climate and surface energy balance values for Neumayer station (ice shelf, East Antarctic ice sheet) and automatic weather station S5 (southwest Greenland ice sheet)</p> <p>- two files (nc) with monthly melt fields from the regional climate model RACMO2.3p2 forced by ERA5 over Greenland (0.05-degree resolution) and Antarctica (0.25-degree resolution)</p> <p>- two files (nc) with annual melt fields from the regional climate model RACMO2.3p2 forced by CESM2 over Greenland (0.1-degree resolution) and Antarctica (0.25-degree resolution) or the historical period (1950-2014)</p> <p>- two files (nc) with annual melt fields from the regional climate model RACMO2.3p2 forced by CESM2 over Greenland (0.1-degree resolution) and Antarctica (0.25-degree resolution) for the future emission scenario SSP5-8.5 (2015-2099)</p>
Overshooting the critical threshold for the Greenland ice sheet
<p>Model output of PISM-dEBM-simple and Yelmo-REMBO used in the paper <a href="https://doi.org/10.1038/s41586-023-06503-9"><strong>Overshooting the critical threshold for the Greenland ice sheet</strong></a>. Code for analysis/recreating the main figures of the paper is provided as well as an example script of how to run PISM-dEBM-simple.</p><p>The models, methods and the used parameters are described in the paper.</p><p>Contact: nils.bochow@uit.no</p>
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>
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