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85 results for “Greenland Ice Sheet”
A Factor Two Difference in 21st-Century Greenland Ice Sheet Surface Mass Balance Projections from Three Regional Climate Models for a Strong Warming Scenario (SSP5-8.5)
<p>1km regridded Greenland Ice Sheet SMB / Runoff / Melt projection until 2100. Projections from MAR, RACMO, HIRHAM forced by CESM2 (SSP5-8.5).</p>
Fettweis et al., 2021, TC, Greenland ice sheet and geoengineering: MAR outputs
<p>Monthly MARv3.11.3 outputs used in:</p> <p>Fettweis, X., Hofer, S., Séférian, R., Amory, C., Delhasse, A., Doutreloup, S., Kittel, C., Lang, C., Van Bever, J., Veillon, F., and Irvine, P.: Brief Communication: Reduction of the future Greenland ice sheet surface melt with the help of solar geoengineering, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2020-347, accepted, 2020.</p> <p>The available variables are:</p> <pre><code> name title I J K L LON Longitude 1:73 1:135 ... ... LAT Latitude 1:73 1:135 ... ... SH Surface Height 1:73 1:135 ... ... MSK Ice Sheet Area 1:73 1:135 ... ... SOL Soil Type 1:73 1:135 ... ... FRV Vegetation Class Coverage 1:73 1:135 1:2 ... VEG Vegetation Type Index 1:73 1:135 1:2 ... SMB SMB (mmWE/month) 1:73 1:135 ... 1:12 RU Runoff (mmWE/month) 1:73 1:135 ... 1:12 ME Melt (mmWE/month) 1:73 1:135 ... 1:12 SF Snowfall (mmWE/month) 1:73 1:135 ... 1:12 RF Rainfall (mmWE/month) 1:73 1:135 ... 1:12 SU Sublimation/evaporation (mmWE/m 1:73 1:135 ... 1:12 TT Temperature (degC) 1:73 1:135 ... 1:12 SWD Shortwave downward (w/m^2) 1:73 1:135 ... 1:12 LWD Longwave downward (w/m^2) 1:73 1:135 ... 1:12 SWA Absorbed shortwave radiation (w 1:73 1:135 ... 1:12 AL Albedo 1:73 1:135 ... 1:12 </code></pre> <p><br> </p> <p> </p>
GRACE High-Resolution Trend Mascons - Greenland Ice Sheet (2007-2015)
<p>High-resolution mascon trend solution, computed for the Greenland Ice Sheet over the time period from January 2007 and January 2015, where each mascon regression model (including the trend) has been directly estimated from the Gravity Recovery and Climate Experiment (GRACE) Level 1B data. The GAD product has not been restored, meaning the ocean mascons are consistent with the Level 2 GSM product information.</p><p>Description of columns in the dataset:</p><ol><li>Latitude center (deg)</li><li>Longitude center (deg)</li><li>Mass change trend (cm w.e. / yr)</li><li>Mass change trend uncertainty (cm w.e. / yr)</li><li>Latitude minimum (deg)</li><li>Latitude maximum (deg)</li><li>Longitude minimum (deg)</li><li>Longitude maximum (deg)</li><li>Area of mascon (sq. km)</li><li>Label of mascon</li></ol><p>When citing this dataset, please also include this citation:</p><p>Loomis, B. D., D. Felikson, T. J. Sabaka, and B. Medley (2021). High‐spatial‐resolution mass rates from GRACE and GRACE‐FO: Global and ice sheet analyses. <i> Journal of Geophysical Research: Solid Earth, </i><a href="https://doi.org/10.1029/2021JB023024">https://doi.org/10.1029/2021JB023024</a></p>
Greenland ice sheet surface melt projections using artifical neural networks
<p>Surface melt projections for the Greenland ice sheet using artificial neural networks.</p> <p>Organized as follows:</p> <p><scenario>/<variable>/<model>/<ensemble_number>.nc</p>
Data from: Dynamic ice loss from the Greenland Ice Sheet driven by sustained glacier retreat
<p>Here we share remotely-sensed Greenland outlet glacier data for 234 individual glaciers. Data include observations of mena monthly discharge (ice volume flux exported by the glacier), front position changes, velocity, and ice thickness for the 1985-2018 period. Data will be periodically updated as needed.</p> <p> </p> <p>The Greenland Ice Sheet is losing mass at accelerated rates in the 21st century, making it the largest single contributor to rising sea levels. Faster flow of outlet glaciers has substantially contributed to this loss, with the cause of speedup, and potential for future change, uncertain. Here we combine more than three decades of remotely sensed observational products of outlet glacier velocity, elevation, and front position changes over the full ice sheet. We compare decadal variability in discharge and calving front position and find that increased glacier discharge was due almost entirely to the retreat of glacier fronts, rather than inland ice sheet processes, with a remarkably consistent speedup of 4-5% per km of retreat across the ice sheet. We show that widespread retreat between 2000 and 2005 resulted in a step-increase in discharge and a switch to a new dynamic state of sustained mass loss that would persist even under a decline in surface melt.</p>
Data from: Temporal variability in snow accumulation and density at Summit Camp, Greenland ice sheet
<p>A 3-year record of weekly snow water equivalent (SWE) accumulation at Summit Camp, central Greenland ice sheet, obtained by direct sampling, is presented. While the overall SWE accumulation of 24.2 cm w.e. a<sup>−1</sup> matches long-term ice core estimates, variability increases at shorter timescales. Half of the annual SWE accumulation occurs during a few large events, with the average accumulation rate decreasing 35% between the first and second halves of the record coinciding with exceptional anticyclonic conditions in the spring and summer of 2019. No seasonality in accumulation is detected. Rather, local accumulation rates appear to be significantly impacted by wind redistribution that obscures temporal patterns in snowfall. Surface snow density is consistent, on average, with previously measured values but does not correlate with near surface temperature or wind speed. Two surface mass balance reanalysis models significantly underestimate accumulation rates at Summit Camp. This is concerning because such models are often used to estimate ice-sheet mass loss.</p>
Clumped isotope measurements reveal aerobic oxidation of methane below the Greenland ice sheet
<p><strong><span>Archive Data</span></strong></p> <p><strong><span>Author(s)</span></strong></p> <p><span>Getachew Agmuas Adnew, Thomas </span><span>R</span><span>o</span><span>¨</span><span>c</span><span>kmann</span><span>,</span><span> </span><span>Thomas Blunier, Christian<span> </span>Juncher<span> </span>Jørgensen,<span> </span>Sarah<span> </span>Elise<span> </span>Sapper,<span> </span>Carina<span> </span>van<span> </span>der<span> </span>Veen,</span></p> <p><span>Malavika<span> </span>Sivan,<span> </span>Maria<span> </span>Elena<span> </span>Popa,<span> </span>Jesper<span> </span>Riis<span> </span><span>Christiansen</span></span><strong><span> </span></strong></p> <p><strong><span>Title</span></strong></p> <p><span>Clumped isotope measurements reveal aerobic oxidation of methane below the Greenland ice sheet</span></p> <p><strong><span>Description</span></strong></p> <p><span>Data collection related to the manuscript/paper " Clumped isotope measurements reveal aerobic oxidation of methane below the Greenland ice sheet".</span></p> <p><span> </span></p> <p><span>The data are the basis for figure 4, 5, 6,7 and 9, table 1 and supplementary figure 2 in manuscript/paper. The data is provided as an excel file as described below. </span></p> <p><em><span>Sheet 1</span></em><span>: Mass scan for clumped methane measurement cup configuration. This data set is used to produce figure 4. </span></p> <p><em><span>Sheet 2</span></em><span>: <span> </span>contains all data for the concentration and isotope composition of methane <span> </span>(bulk and clumped isotope) and the </span><span>𝛅</span><span>13C of carbon dioixed . These data is provided in Table 1 and used to produce Figure 5 <span> </span>to 7 and supplemetary figure 2</span></p> <p><em><span>Sheet 3</span></em><span>: Water isotope data of the subglacial melt water</span></p> <p><em><span>Sheet 4</span></em><span>: Data for end pont members used to produce Figure 9</span></p> <p><span> </span></p> <p><span>2.1 Site description</span></p> <p><span>The study site is located at an elevation of 450 m above sea level at a lateral subglacial meltwater outlet on the southern flank at the terminus of the Isunnguata Sermia Glacier at the western margin of the GrIS (67°09’16.40’’N 50°04’08.48’’W). </span></p> <p><span>The area in front of the meltwater outlet consists of abraded granodioritic gneiss bedrock with large boulders and patches of gravel, sand and silt deposited by meltwater. The glacier front contained highly irregular cracks and air-filled cavities, which changed over the season as the ice melted and deformed. </span></p> <p><span> </span></p> <p><span>2.2. Collection of air samples </span></p> <p><span>Air samples were collected in june 2022 from the ice cave using a small compressor that compressed ambient air into pre-evacuated 6 and 15 L volume canisters to a final pressure of 3 bar (Entech Instruments,USA) (Figure 2 and 3). The mole fraction of CH4 and CO2 in the air sampled was monitored with a microportable Greenhouse Gas Analyzer (MGGA) (GLA131-GGA, ABB-Los Gatos Research, San Jose, CA, USA). Background air was collected at the top of the glacier in 2 L volume stainless steel canisters.</span></p> <p><span> </span></p> <p><span>2.2. Collection of water samples </span></p> <p><span>We collected water samples from the subglacial meltwater at the margin of ISG for water isotope analysis in June 2022 between 12:00 and 15:00 (local time). The water samples were filtered using 0.45 µm pore size syringe filters and stored in an amber glass with a small headspace in a refrigerator prior to analysis. </span></p> <p><span> </span></p> <p><span>2.7 Isotopic analyses of gas and water samples</span></p> <p><span> </span></p> <p><span>The bulk isotope composition (δ13C(CH4) and δD(CH4)) and clumped isotopes (∆13CH3D and ∆12CH2D2) of CH4 were measured using a high-resolution gas source isotope ratio mass spectrometer (HR-IRMS) (Thermo Scientific, Germany) in dual inlet mode at Utrecht University. CH4 from air samples was extracted using cryogenic separation (see Figure 3). The extraction process employed two traps to remove CO2 and other condensable gases, followed by a charcoal trap to collect CH4, all operated at a temperature of liquid nitrogen (-196°C). Further purification of CH4 was achieved using a custom-made gas chromatography setup with two columns: a stainless steel column packed with molecular sieve to separate gases like H2, Ar, O2, and N2 and a second column packed with HayeSep-D porous polymer to separate CH4 from higher-order hydrocarbons such as C2H6 and C3H8.</span></p> <p><span> </span></p> <p><span>The isotopic composition of CO2 (δ13C) was analyzed with the CF-IRMS system at Utrecht University. In short, the CO2 is cryogenically separated from the air, further purified chromatographically, and then injected into the IRMS via an open split inlet. The results are related to the VPDB and VSMOW scales via a reference air cylinder with known isotopic composition. </span></p> <p><span> </span></p> <p><span>Water isotopes were measured at Physics of Ice and Climate (PIC), Niels Bohr Institute, University of Copenhagen, Denmark with a cavity ring-down spectroscopy (Picarro L2130, PICARRO, USA).</span></p>
First application of artificial neural networks to estimate 21st century Greenland ice sheet surface melt: scripts and models
<p>In this repository you will find the models and the scripts used to generate the journal article: "First application of artificial neural networks to estimate 21st century Greenland ice sheet surface melt."</p> <p>The model.tar contains the script for making a model, in addition to the models used in the journal artcile.</p> <p>The proc.tar contains the scripts used for processing of the CMIP6 data.</p> <p>The plots.tar contains scripts for generating the plots in the journal article, as well as the supplementary information.</p>
Dataset for "Increased variability in Greenland Ice Sheet runoff from satellite observations"
<p>##################################</p> <p>This archive contains source data for Slater et al. 2021 - Increased variability in Greenland Ice Sheet runoff from satellite observations, published in Nature Communications:</p> <p><a href="https://doi.org/10.1038/s41467-021-26229-4">https://doi.org/10.1038/s41467-021-26229-4</a></p> <p><br> Centre for Polar Observation and Modelling, School of Earth and Environment, University of Leeds<br> Corresponding author: t.slater1@leeds.ac.uk</p> <p>##################################</p> <p><br> The interannual elevation change maps, seasonal elevation change and runoff data are contained in the following data files:</p> <p><br> --------------------------------------------------------------<br> gris_ablation_zone_dh_cryosat.csv<br> --------------------------------------------------------------<br> Surface height change time series for the Greenland Ice Sheet ablation zone (in m) derived from CryoSat-2 satellite radar altimetry, and its estimated 1 sigma uncertainty between 2011 and 2020<br> --------------------------------------------------------------<br> gris_ablation_zone_dh_may_aug_cryosat.csv<br> --------------------------------------------------------------<br> Seasonal height changes between May and August (in m) for the Greenland Ice Sheet ablation zone within the 8 principal Zwally drainage basins derived from CryoSat-2 satellite radar altimetry between 2011 and 2020</p> <p>--------------------------------------------------------------<br> gris_ablation_zone_dh_may_aug_cryosat.csv<br> --------------------------------------------------------------<br> Seasonal height changes between September and April (in m) for the Greenland Ice Sheet ablation zone within the 8 principal Zwally drainage basins derived from CryoSat-2 satellite radar altimetry between 2011 and 2020</p> <p>--------------------------------------------------------------<br> gris_interannual_seasonal_dhdt_cryosat.nc<br> --------------------------------------------------------------<br> Interannual and seasonal elevation trends derived from CryoSat-2 satellite radar altimeter data acquired between 2011 and 2020. Posted on a 5 km grid in polar stereo graphic projection using EPSG:3413 - WGS 84 / NSIDC Sea Ice Polar Stereographic North </p> <p>--------------------------------------------------------------<br> gris_runoff_cryosat.csv<br> --------------------------------------------------------------<br> Annual Greenland Ice Sheet runoff estimates and their estimated uncertainty derived (in Gt/yr) derived from CryoSat-2 satellite radar altimetry between 2011 and 2020</p> <p>--------------------------------------------------------------<br> Acknowledgements:</p> <p>This work was supported by NERC through National Capability funding, undertaken by a partnership between the Centre for Polar Observation Modelling and the British Antarctic Survey, and by the European Space Agency’s Polar+ Earth Observation for Mass Balance study (4000132154/20/I-EF). M.M was supported by the Lancaster University-UKCEH Centre of Excellence in Environmental Data Science. A.L was supported by the NERC Meltwater Ice-sheet Interactions and the changing climate of Greenland research grant (MII Greenland NE/S011390/1). B.N was funded by NWO VENI grant VI.Veni.192.019. M.v.d.B and P.K.M acknowledge support from the Netherlands Earth System Science Centre (NESSC). Computational resources used to perform MAR simulations have been provided by the Consortium des Équipements de Calcul Intensif (CÉCI), funded by the F.R.S.FNRS under grant 2.5020.11 and the Tier-1 supercomputer (Zenobe) of the Fédération Wallonie Bruxelles infrastructure funded by the Walloon Region under grant agreement 1117545.</p> <p>Projects:</p> <p>European Space Agency’s Polar+ Earth Observation for Mass Balance study (4000132154/20/I-EF)</p> <p><a href="https://smb.eo4cryo.dk/">https://smb.eo4cryo.dk/</a></p>
Southwest Greenland Ice Sheet Yearly Ice Velocities dataset from 1984 to 2020
<p>The present dataset is published after the work of Paul Halas, Jérémie Mouginot, Basile de Fleurian and Petra Langebroek on the Southwest of the Greenland Ice Sheet. It provides ice velocity products derived using the processing chain developped by Jérémie Mouginot and collaborators, following the steps described in Romain Millan’s paper ”Mapping Surface Flow Velocity of Glaciers at Regional Scale Using a Multiple Sensors Approach” (https://doi.org/10.3390/rs11212498). In order to derive the velocity fields, we used all available imagery from Landsat 5, Landsat 7 and Landsat 8, from 1984 up to 2021, with less than 40% cloud coverage. Unfortunately, no data was collected for 1984, 1993, 1996, 1997 and 1998. Please also note that the spatial coverage is really limited before 1999. From 2016, satellite imagery from Sentinel-2 is also used, improving the spatial coverage of our velocity maps.<br> In this archive, we provide:<br> • Complete dataset of all velocity fields derived from every image pair;<br> • Yearly median results run through all data for every single pixel;<br> • Yearly GeoTIFF spatial aggregate of all previously computed medians;<br> • The shapefile ”cube grid.shp” describing the grid used for our area.</p> <p>For any question, please contact Paul Halas (<a href="mailto:paul.halas@uib.no">paul.halas@uib.no</a>).</p>
Supplementary video of Petrini et al. 2023, submitted to TC. "Topographically-controlled tipping point for complete Greenland Ice Sheet melt"
<p>Animations showing the evolution of the Greenland Ice Sheet in simulations with different SMB and global mean temperature levels. </p>
Supraglacial hydrology extent and depth estimates for Greenland ice sheet from 2013 through 2022.
<p>We present a first decadal scale study of supraglacial extent and volume across the entire Greenland Ice Sheet. We adapted and validated a random forest algorithm (RF) to classify superglacial hydrology (SGH) using the optical sensors Sentinel-2 and Landsat-8. We used a radiative transfer model to quantify the depth of SGH features to estimate the volume of surface water which exists on the Greenland Ice Sheet on a monthly and yearly scale from 2013 through 2022. We find strong seasonal trends in SGH behaviour, with melt beginning in May, peaking in July/August, and refreezing occurring in September in each year. We find significant variability in the extent and volume of SGH features between melt seasons, and although the inter-annular trend suggests SGH is becoming more widespread with a greater volume of water, a study of longer duration is needed to fully determine<br> inter-annular trends of SGH on the ice sheet.</p>
Data from: Temporal variability in snow accumulation and density at Summit Camp, Greenland ice sheet
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Data from: Dynamic ice loss from the Greenland Ice Sheet driven by sustained glacier retreat
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Annual Ice Velocity of the Greenland Ice Sheet (2001-2010)
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Annual Ice Velocity of the Greenland Ice Sheet (1991-2000)
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Annual Ice Velocity of the Greenland Ice Sheet (1972-1990)
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Inland limits to diffusion of thinning along Greenland Ice Sheet outlet glaciers
<p>This dataset contains limits to the inland diffusion of terminus-initiated thinning along 141 Greenland Ice Sheet outlet glaciers. The data is separated in 187 files and each file contains data for an individual outlet glacier or a branch of an outlet glacier. Shapefiles and NetCDF files are provided with data for 6 primary flowlines, and up to 18 iterated flowlines, spanning the width of each glacier. The iterated flowlines are used to find the furthest inland thinning limits (see the associated publication for more details). Files are named <em>glacierXXXX </em>where <em>XXXX </em>is the identifier for an individual glacier or branch. Identifiers that begin with a letter ('a', 'b', 'c', or 'd') represent a separate branch of an outlet glacier. Shapefiles filenames have an additional <em>_iterNN </em>suffix, where <em>NN</em> identifies the iteration number of that set of flowlines.</p> <p>The shapefiles contain multiple features, each of which is one flowline. Each flowline has a "flowline" attribute, a two-digit identifier that corresponds to the same identifier in the NetCDF files. The NetCDF files contains glacier geometry, dynamic thinning, Peclet number, and identified knickpoints, extracted and calculated along the primary and iterated flowlines. The flowlines in the NetCDF files have a regular 50-meter spacing between nodes, whereas the flowlines in the shapefiles have a coarser and irregular spacing.</p> <p>More information on how the flowlines were derived and how data was extracted and calculated is in the associated publication (Felikson et al., 2020), with a DOI to be provided. Example code that can be used to read and plot the data can be obtained at http://doi.org/10.5281/zenodo.4284715.</p>
Mass Balance and Velocity Data for Greenland Ice Sheet at High Elevations
<p>This is an estimation of mass balance of the Greenland Ice Sheet at higher elevations, computed as the difference between the estimated annual total snow accumulation and ice discharge. Measurements are taken at 161 stations located 30 km apart, at 2000 m elevation that circumnavigates Greenland. Ice velocities are determined from GPS data, and ice thickness from ice-penetrating radar. Results are presented for single gates between adjacent stations, and gate combinations representing larger areas.</p> <p><strong>Interactive Map</strong><br><a href="http://rsl.geology.buffalo.edu/data/Pages/Greenland_MB_AllGates.html" target="_blank" rel="nofollow noreferrer noopener noreferrer noopener noreferrer">http://rsl.geology.buffalo.edu/data/Pages/Greenland_MB_AllGates.html</a></p> <p><strong>NSIDC page</strong><br><a href="https://nsidc.org/data/NSIDC-0618" target="_blank" rel="nofollow noreferrer noopener noreferrer noopener noreferrer">https://nsidc.org/data/NSIDC-0618</a></p> <p><strong>References</strong></p> <ul> <li>Thomas, R., T. Akins, B. Csatho, M. Fahnestock, P. Gogineni, C. Kim, and J. Sonntag. 2000. Mass balance of the Greenland Ice Sheet at high elevations. Science 289: 426-427.</li> <li>Thomas, R., B. Csatho, C. Davis, C. Kim, W. Krabill, S. Manizade, J. McConnell and J. Sonntag. 2001. Mass balance of higher-elevation parts of the Greenland Ice Sheet. Journal of Geophysical Research - Atmosphere 106 (D24) (December): 33707-33716.</li> <li>Thomas, R., B. Csatho, S. Gogineni, K. Jezek, and K. Kuivinen. 1998. Thickening of the western part of the Greenland Ice Sheet. Journal of Glaciology 44: 653-658.</li> </ul>
Retreat of the Greenland Ice Sheet leads to divergent patterns of reconfiguration at its freshwater and tidewater margins
<p>Datasets to accomany paper.</p>
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