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186 results for “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>
Data from PISM-LakeCC: Implementing an adaptive proglacial lake boundary in an ice sheet model
<p>In our study, we describe the implementation of an adaptive proglacial lake boundary in the Parallel Ice Sheet Model (PISM). The model was tested by applying it to the glacial retreat of the North American ice sheets after the LGM.</p> <p>This dataset contains selected timeslices and variables of the model output for our three main experiments (LAKE, CTRL and DEF). More details about the experiments can be found in our study:</p> <blockquote> <p>Hinck, S., Gowan, E. J., Zhang, X., and Lohmann, G.: PISM-LakeCC: Implementing an adaptive proglacial lake boundary in an ice sheet model, The Cryosphere, 16, 941–965, https://doi.org/10.5194/tc-16-941-2022, 2022.</p> </blockquote>
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>
Data and code used in manuscript: Basal freeze-on generates complex ice-sheet stratigraphy
<p>Mapped plumes location obtained from ice-sheet radio echo sounding data of North Greenland (https://data.cresis.ku.edu/data/rds/ for 2010-2014_Greenland files) and map of calculated freeze-on index are found in 'FreezeOnIndex_MappedPlume_Data.nc'. Model code of the three models used to obtain the findings shown in the manuscript 'Basal freeze-on generates complex ice-sheet stratigraphy'. As well as code to calculate the freeze-on index.</p>
Indicative distribution map for Ecosystem Functional Group T6.1 Ice sheets, glaciers and perennial snowfields
<p>This archive contains indicative distribution maps and profiles for <strong>T6.1 Ice sheets, glaciers and perennial snowfields</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</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>
Simulations of Miocene Antarctic ice-sheet variability under increased precipitation and sub-shelf melt, using the ice-sheet model IMAU-ICE
<p>To demonstrate the viability of a precipitation regime change leading to a fundamentally different volume-to-area ratio of the Antarctic ice sheet, we deploy the 3D thermodynamical ice sheet/shelf model IMAU-ICE v1.1.1. In the standard set-up (<a href="https://doi.org/10.5194/cp-2023-12">Stap et al., 2021a</a>, <a href="https://doi.pangaea.de/10.1594/PANGAEA.939114">2021b</a>), climate forcing follows from pre-run warm and cold snapshot climate simulations. The applied climate forcing is transiently calculated based on the prescribed CO<sub>2</sub> concentration and the modelled ice sheet size, through a matrix interpolation method. Equilibrium experiments are performed at various CO<sub>2</sub> levels between preindustrial and 3x preindustrial CO<sub>2</sub> values, with insolation at present-day levels and initiated from an ice-free Miocene Antarctic topography (dataset <a href="https://doi.pangaea.de/10.1594/PANGAEA.923109">Hochmuth et al., 2020</a>). Here, we perform additional sensitivity experiments, in which we apply a fixed precipitation increase and extreme sub-shelf melt rates. The precipitation anomaly is calculated as 25% of the warm snapshot precipitation fields, sub-shelf melt rates are set to 400 m/yr.</p> <p> </p>
Constraints on mantle viscosity and Laurentide ice sheet evolution from pluvial paleolake shorelines in the western United States: Datasets
<p>*********** Please view the README.txt file for detailed documentation of data. ***********</p> <p><strong>Title:</strong> Constraints on mantle viscosity and Laurentide ice sheet evolution from pluvial paleolake shorelines in the western United States: Datasets</p> <p><strong>Version: </strong>1.0</p> <p><strong>Date of Release: </strong>2019/12/16</p> <p><strong>Identifier: </strong>10.5281/zenodo.3576251</p> <p><strong>Associated publication:</strong> Austermann, J., Chen, C.Y., Lau, H.C.P., Maloof, A.C., and Latychev, K. (2019) Constraints on mantle viscosity and Laurentide ice sheet evolution from pluvial paleolake shorelines in the western United States. <em>Earth and Planetary Science Letters</em>. doi: 10.1016/j.epsl.2019.116006</p> <p><strong>Link to publication: </strong><a href="https://doi.org/10.1016/j.epsl.2019.116006">https://doi.org/10.1016/j.epsl.2019.116006</a></p> <p><strong>Suggested citation: </strong>Please reference the associated publication above when using any datasets or materials described in the README file.</p> <p><strong>Contact information:</strong> Jacky Austermann (jackya@ldeo.columbia.edu) and Christine Y. Chen (cychen.earth@gmail.com)</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>This directory contains the following datasets:</p> <p>SHORELINE FEATURE ELEVATION DATA</p> <ul> <li><strong>Bonneville_Provo_Sehoo_shoreline_feature_elev_Austermann2019_EPSL.xlsx</strong>: shoreline feature elevation measurements of the Bonneville, Provo, and Sehoo lake stages of Lake Bonneville and Lake Lahontan; original measurements were made by Adams et al. (1999), Chen and Maloof (2017), and Currey (1982)</li> </ul> <p>MODELED RECONSTRUCTIONS OF LAKE VOLUME AND PALEOTOPOGRAPHY</p> <ul> <li><strong>LakeBonneville_NAICE_l20.ump02p25.lmp5VM5.mat:</strong> model output for Lake Bonneville, including reconstructions of lake volume and paleotopography</li> <li><strong>LakeLahontan_NAICE_l20.ump02p25.lmp5VM5.mat</strong>: model output for Lake Lahontan, including reconstructions of lake volume and paleotopography</li> </ul>
Alpine ice sheet glacial cycle simulations aggregated variables
<p>These data contain time-integrated and otherwise time-reduced glacier model output variables.</p> <p><strong>Reference:</strong></p> <ul> <li>Seguinot, J., Ivy-Ochs, S., Jouvet, G., Huss, M., Funk, M., and Preusser, F.: Modelling last glacial cycle ice dynamics in the Alps, <em>The Cryosphere</em>, 12, 3265-3285, doi:<a href="https://doi.org/10.5194/tc-12-3265-2018">10.5194/tc-12-3265-2018</a>, 2018.</li> </ul> <p><strong>File names:</strong></p> <pre><code>alpcyc.{1km|2km}.{epic|grip|md01}.{cp|pp}.agg.nc</code></pre> <ul> <li>Horizontal resolution: <ul> <li><em>1km</em>: 1 km horizontal resolution</li> <li><em>2km</em>: 2 km horizontal resolution</li> </ul> </li> <li>Temperature forcing: <ul> <li><em>epic</em>: EPICA ice core temperature forcing</li> <li><em>grip</em>: GRIP ice core temperature forcing</li> <li><em>md01</em>: MD01-2444 core temperature forcing</li> </ul> </li> <li>Precipitation forcing: <ul> <li><em>cp</em>: constant precipitation</li> <li><em>pp</em>: palaeo-precipitation reduction</li> </ul> </li> </ul> <p><strong>Data format:</strong></p> <p>The data use compressed netCDF format. For quick inspection I recommend ncview. Conversion to GeoTIFF (and other GIS formats) can be achieved with e.g. GDAL::</p> <pre><code>gdal_translate NETCDF:filename.nc:variable filename.variable.tif</code></pre> <p>The list of variables (subdatasets) can be obtained from ncdump or gdalinfo. To convert all variables to separate files use:</p> <pre><code>gdalinfo $filename | grep NETCDF | cut -d '=' -f 2 | egrep -v '(lat|lon|time_bounds)' | while read sub do gdal_translate $sub ${filename%.nc}.${sub##*:}.tif done</code></pre> <p>Variable long names, units, PISM configuration parametres and additional information are contained within the netCDF metadata. Also see <a href="https://doi.org/10.5281/zenodo.1423175">continuous</a> variables.</p> <p><strong>Changelog:</strong></p> <ul> <li>Version 2: <ul> <li>Add age coordinate in kiloyears (ka) before present.</li> <li>Use ka units for covertime, deglacage and maxthkage.</li> </ul> </li> <li>Version 1: <ul> <li>Initial version</li> </ul> </li> </ul>
The commitment to global sea level rise over the next 500 years: exploring the threat of the Antarctic Ice Sheet to coastal infrastructure
<p>Within Australia alone, more than A$226 billion of coastal infrastructure is vulnerable to the anticipated rise in sea level by the end of the century. The IPCC Fifth Assessment Report concludes that the likely increase in global mean sea level during the 21st century ranges from 26-55 centimetres (under the low-end RCP2.6 climate scenario) to 45-82 centimetres (under the high-end RCP8.5 climate scenario). However, these projections do not take into account the potential for collapse of the marine-based sectors of the Antarctic Ice Sheet.</p> <p>Recent evidence has indicated that the IPCC projections may be under-estimates, with sea level increases of up to 2.5 metres possible by the end of the 21st century. Modelling studies have also demonstrated the potential for the Antarctic Ice Sheet to undergo irreversible collapse during the coming centuries, leading to dramatic increases in global sea level on time scales relevant to critical coastal infrastructure such as refineries and airports. The most extreme prediction is that Antarctica could contribute 15.65±2.00 metres to global sea level by the year 2500.</p> <p>Here, we combine climate modelling and ice sheet modelling to explore the evolution of the Antarctic Ice Sheet over the next 500 years under a range of climate scenarios. We run the models many times to take into account gaps in our understanding of ice sheet dynamics. This allows us to generate robust projections of the Antarctic contribution to global sea level from the present to the year 2500, complete with quantified confidence intervals. We conclude that the sea level contribution during the 21st century will be modest, consistent with the IPCC Fifth Assessment Report, but that melting of the Antarctic Ice Sheet will accelerate thereafter. By the year 2500, we predict that the Antarctic contribution to global sea level will be at least 5 metres.</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>
Additional steady-state simulations of Miocene Antarctic ice-sheet variability using 3D thermodynamical ice-sheet model IMAU-ICE
<div> </div> <div> <div> <div>We supplement our previous dataset (<a href="https://doi.pangaea.de/10.1594/PANGAEA.939114">doi:10.1594/PANGAEA.939114</a>), with six additional steady-state simulations of the Miocene Antarctic ice sheet using the reference Miocene settings.</div> <div> </div> <div>IMAU-ICE was run using a 40x40km grid covering the Antarctic continent. Initial conditions were obtained from reconstructions of the Antarctic bathymetry and bedrock topography pertaining to 23 to 24 million years (Myr) ago (dataset <a href="https://doi.pangaea.de/10.1594/PANGAEA.923109" target="_self">doi:10.1594/PANGAEA.923109</a>). The simulations were forced by climate input data obtained from GENESIS simulations with varying CO2 levels (280 to 840 ppm) and Antarctic ice sheet cover (no ice to a large East-Antarctic ice sheet), and with present-day insolation. We utilized a matrix interpolation method to construct the time-varying climate forcing, based on the prescribed CO2 levels and ice cover simulated by IMAU-ICE.</div> <div> </div> <div>For each simulation, we provide the run script, 1D output variables including CO2 level and the sea level contribution of the Antarctic ice sheet, and 3D output variables including ice thickness, bedrock and surface height, surface mass balance, basal mass balance, ice velocities, and ice temperatures. For more information, please contact L.B. Stap at l.b.stap@uu.nl.</div> </div> </div>
Cordilleran ice sheet improved bedrock simulations continuous variables
<p>These data contain a subset of time-dependent glacier model output variables. The <em>ghf70</em> data files are an update on the reference below, fixing significant problems affecting the computation of the bedrock deformation in response to ice load (PISM Github issues <a href="https://github.com/pism/pism/issues/370">#370</a> and <a href="https://github.com/pism/pism/issues/377">#377</a>) and the computation of ice temperature (PISM Github issue <a href="https://github.com/pism/pism/issues/371">#371</a>). The other data files additionally include spatially-variable geothermal heat flux (<em>dav13</em>, <em>gou11comb</em>, <em>gou11simi</em>, <em>sha04</em>), different lithospheric rigidity (<em>eet30km</em>) or mantle viscosity (<em>num1e21</em>), and higher horizontal resolution (<em>3km</em>).</p> <p><strong>Reference:</strong></p> <ul> <li>Seguinot, J., Rogozhina, I., Stroeven, A. P., Margold, M. and Kleman, J.: Numerical simulations of the Cordilleran ice sheet through the last glacial cycle, <em>The Cryosphere</em>, 10(2), 639–664, doi:<a href="https://doi.org/10.5194/tc-10-639-2016">10.5194/tc-10-639-2016</a>, 2016.</li> </ul> <p><strong>File names:</strong></p> <p><code>cisbed.{res}.{forcing}.{ex.100a|ts.10a}.{ghf}.{props}.nc</code></p> <ul> <li>Horizontal resolution: <ul> <li><em> 10km</em>: 10 km horizontal resolution</li> <li><em>5km</em>: 5 km horizontal resolution</li> <li><em>3km</em>: 3 km horizontal resolution</li> </ul> </li> <li>Temperature forcing: <ul> <li><em>epica</em>: EPICA ice core temperature forcing</li> <li><em>grip</em>: GRIP ice core temperature forcing</li> </ul> </li> <li>Variable types: <ul> <li><em>ex.100a</em>: spatial diagnostics every hundred years</li> <li><em>ts.10a</em>: scalar time-series every ten years</li> </ul> </li> <li>Geothermal heat flow: <ul> <li><em>ghf70</em>: constant 70 mW m-2 heat flow</li> <li><em>dav13</em>: Davies (2013) geothermal heat flow map</li> <li><em>gou11comb</em>: Goutorbe et al. (2011) best combination method</li> <li><em>gou11simi</em>: Goutorbe et al. (2011) similarity method</li> <li><em>sha04</em>: Shapiro and Ritzwoller (2004) heat flow map</li> </ul> </li> <li>Bedrock properties <ul> <li><em>eet30km</em>: lithosphere elastic thickness of 30 km</li> <li><em>num1e21</em>: astenosphere viscosity of 1e21 Pa s</li> </ul> </li> </ul> <p><strong>Data format:</strong></p> <p>The data use compressed netCDF format. For quick inspection I recommend ncview. Spatial diagnostics (<em>*.ex.100a.nc</em>) can be converted to GeoTIFF (and other GIS formats) e.g. using GDAL:</p> <p><code>gdal_translate NETCDF:filename.nc:variable -b band filename.variable.band.tif</code></p> <p>The list of variables (subdatasets) can be obtained from ncdump or gdalinfo. Band information can be displayed with:</p> <p><code>gdalinfo NETCDF:filename.nc:variable</code></p> <p>Variable long names, units, PISM configuration parametres and additional information are contained within the netCDF metadata.</p> <p><strong>Funding:</strong></p> <p>Swiss National Supercomputing Centre (CSCS) grants s573 and sm13 to J. Seguinot, Swiss National Science Foundation (SNSF) grants no.~200020-169558 and 200021-153179/1 to M. Funk, and Research Foundation – Flanders (FWO) Odysseus Type II project G0DCA23N 'GlaciersMD' to H. Zekollari.</p> <p><strong>Changelog:</strong></p> <ul> <li>Version 1: <ul> <li>Initial version.</li> </ul> </li> </ul>
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>
Dataset for "Mass loss of the Antarctic ice sheet until the year 3000 under a sustained late-21st-century climate"
<p>Dataset for the paper "Mass loss of the Antarctic ice sheet until the year 3000 under a sustained late-21st-century climate" (Journal of Glaciology, <a href="https://doi.org/10.1017/jog.2021.124">https://doi.org/10.1017/jog.2021.124</a>).</p> <p>Please see the README for details.</p> <p>V2: New version with ISMIP6-type variables and compressed netCDF files.<br> V1: Initial upload.</p> <p>* * * * * * *</p> <p>The following script may be used to download the entire content of the archive on a Unix/Linux system:</p> <p>#!/bin/bash<br> # --- download_all.sh ---<br> wget https://zenodo.org/record/6215117/files/_README.pdf<br> wget https://zenodo.org/record/6215117/files/run_specs_headers.zip<br> for aexp in hist ctrl_proj_long \<br> exp05_long exp06_long exp07_long exp08_long exp09_long exp10_long \<br> exp12_long exp13_long \<br> expA5_long expA6_long expA7_long expA8_long \<br> expB6_long expB7_long expB8_long expB9_long expB10_long; do<br> wget https://zenodo.org/record/6215117/files/${aexp}.zip<br> done<br> for aexp in abuc_long abuciso_long \<br> abuk_long abukiso_long \<br> abum_long abumiso_long; do<br> wget https://zenodo.org/record/6215117/files/${aexp}.zip<br> done</p> <p>* * * * * * *</p> <p>Users should cite the original publication when using all or parts of these data.<br> </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>
IODP Expedition 382: Supplementary Tables for "Episodes of early Pleistocene West Antarctic Ice Sheet retreat recorded by Iceberg Alley sediments"
<p>IODP Expedition 382: Supplementary Tables for "Episodes of early Pleistocene West Antarctic Ice Sheet retreat recorded by Iceberg Alley sediments"</p> <p>Includes SEM QEMSCAN® and <sup>40</sup>Ar/<sup>39</sup>Ar data for International Ocean Discovery Program (IODP) Expedition 382 Site U1538. Also includes a movie of a 3D-volume realization of an iceberg-rafted sedimentary layer from this site based on non-destructive X-ray microtomography imaging.</p> <p> </p> <p><strong>Data Set Captions:</strong></p> <p> </p> <p><strong>Data Set S1. </strong>Modal mineralogy data based on QEMSCAN® analyses, which infer minerals from chemistry. The mineral name assignations for each chemistry-based category stated in this table are aided by visual (microscope-based) inspection of the raw sieved samples.</p> <p><strong>Data Set S2. </strong>Mineral association data based on QEMSCAN® analyses. Please read data in columns, mineral against mineral (down then across left). These data define what touches what in the sample and is displayed as a percentage. Association refers to adjacency. Two minerals are “associated” if a pixel of one of the minerals occurs adjacent to a pixel of the other mineral. iExplorer software used scans the measured particles horizontally, from left to right, counting the associations that occur in the images (so the more pixels/closer the x-ray spacing the more accurate the data). Each column is independent. That is, it is split into a percentage of what touches what, so it is not expected that any two minerals’ data are reciprocal. The background category primarily reflects the free boundaries of ‘grains’ rather than liberated grains/particles. While it may provide an indicator of liberation, it does not represent liberation since it does not describe ‘particles’ which are made up of mineral grains. Inclusions and composite particles are therefore not described. Please consider the modal mineralogy (Tab. S1) when examining these mineral association data.</p> <p><strong>Data Set S3. </strong>Lithotyping data based on QEMSCAN® analyses. Particles have been digitally filtered using a set of lithotype rules (also displayed in this data set). These rules are based on the mineral grains in the particles themselves and use their area percent within each particle and their size in microns. The lithotype names stated here are largely assigned based on the dominant mineral grain in each category.</p> <p><strong>Data Set S4. </strong>40Ar/39Ar ages of individual sand-sized hornblende and mica. See main text for method used to generate these ages.</p> <p><strong>Data Set S5.</strong> Ties to place Hole U1538A NGR data on Dove Basin Stack (Reilly et al., 2021) depths.</p> <p><strong>Movie S1. </strong>3D-volume realization based on non-destructive X-ray microtomography imaging of a centimeter-scale iceberg-rafted debris-rich layer in Hole U1538A-36X-3W. 3D images were generated using a helical scanning trajectory that allows for long scan sequences and fast acquisition time. Based on the sample geometry, a voxel (pixel) resolution of ~14-μm was achieved. The 7000+ projection images were reconstructed to produce a 3D volume of image intensities (where higher values indicate greater x-ray attenuation). Avizo software was used for 3D segmentation and volume rendering to visualize gravel and sand to create this animation. The different colors assigned to each clast were chosen arbitrary.</p>
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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