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186 results for “Ice Sheet”
The role of ocean and atmospheric dynamics in the marine-based collapse of the last Eurasian Ice Sheet
<p><strong>EIS_reconstruction.zip: </strong> .shp files of Eurasian Ice Sheet reconstruction 20-14 ka (1 ka time step)</p> <p><strong>EIS_thickness.zip</strong>: .shp files of Eurasian Ice Sheet thickness for 19 ka, 18 ka, 16 ka and 15 ka.</p> <p><strong>Supplementary Data 1:</strong> An Excel spreadsheet containing radiocarbon dates from the North Sea</p> <p><strong>Supplementary Data 2</strong>: An Excel spreadsheet containing radiocarbon dates from the Mid Norwegian margin</p> <p><strong>Supplementary Data</strong> <strong>3: </strong>An Excel spreadsheet containing radiocarbon dates from the Svalbard-Kara Sea-Barents Sea</p> <p><strong>Supplementary Data 4: </strong> An Excel spreadsheet containing model output GIA adjusted</p> <p><strong>Supplementary Data 5: </strong> An Excel spreadsheet containing Model output GIA adjusted -20%</p> <p><strong>Supplementary Data 6:</strong> An Excel spreadsheet containing Model output GIA adjusted +20%</p>
geography/topography and ice sheet models
<p>geography/topography: topo.txt</p> <p>ice sheet: ice thickness distribution for every 25 m change in ESL (e.g. 0500.txt for 500 m change in ESL)</p> <p>5 minute grid</p>
STREAMICE code and inputs for "The West Antarctic Ice Sheet may not be vulnerable to Marine Ice Cliff Instability during the 21st Century"
<p>This repository contains all inputs and code to carry out the STREAMICE calving experiments run for the manuscript "The West Antarctic Ice Sheet may not be vulnerable to Marine Ice Cliff Instability during the 21st Century" using the modelling framework MITgcm.</p> <p>MITgcm-front_retreat/ contains a branch of the MITgcm code that enables calving front advance and retreat in the STREAMICE model, and is a branch of checkpoint 68d. Please see https://github.com/MITgcm/MITgcm/blob/master/LICENSE.txt for the MITgcm open source license detail.</p> <p>code/ contains experiment-specific code for the runs detailed in the manuscript</p> <p>input_fwd/ contains all binary and parameter input files for the runs detailed in the manuscript</p> <p>archer_scripts/ contains shell scripts written for the ARCHER2 UK supercomputer to demonstrate how the model is compiled</p> <p> </p> <p> </p>
Model data and figure code for results and figures in the manuscript submitted to Geophysical Research Letters "Hysteresis of the Antarctic ice sheet with a coupled ice sheet climate model"
<p>This folder contains the model data and figure code for results and figures in the manuscript submitted to Geophysical Research Letters "Hysteresis of the Antarctic ice sheet with a coupled ice sheet climate model"</p> <p>The code for plotting the figures is the notebook Plot_figures.ipynb</p> <p>Fig1/simulation_output/ : Model output necessary for plotting the first figure </p> <p>The last timestep of each simulation is provided. There is one file for 1D variables (ice volume, ice volume above flotation), and one file for 2D variables (ice sheet thickness for instance).</p> <ul> <li><span>melt_insoPI_output/ : melt branch, pre-industrial insolation. Results for different CO2 levels</span></li> <li><span>growth_insoPI_output/ : growth branch, pre-industrial insolation. Results for different CO2 levels</span></li> <li><span>melt_insoMAX_output/ : melt branch, maximum insolation. Results for different CO2 levels</span></li> <li><span>growth_insoMIN_output/ : growth branch, minimum insolation. Results for different CO2 levels</span></li> </ul> <p><span>compute_SLR_equivalent.py : code to compute the ice sheet volume in SLRe based on model output</span></p> <p><span>Fig1/SLR_files/ : contains the equilibrium ice sheet volume of the different simulations according to the CO2 level</span></p> <p> </p> <p>Fig2/simulation_output/ : Model output necessary for plotting the second figure </p> <p>The last timestep of each simulation is provided. </p> <ul> <li><span>melt_insoPI_enhancedmelt_albfb/ : melt branch, pre-industrial insolation, enhanced melt and albedo feedback. Results for different CO2 levels</span></li> <li><span>growth_insoPI_enhancedmelt_albfb/ : growth branch, pre-industrial insolation, enhanced melt and albedo feedback. Results for different CO2 levels</span></li> <li><span>melt_insoPI_enhancedmelt_fixedalb/ : melt branch, pre-industrial insolation, enhanced melt, no albedo feedback. Results for different CO2 levels</span></li> <li><span>growth_insoPI_enhancedmelt_fixedalb/: growth branch, pre-industrial insolation, enhanced melt, no albedo feedback. Results for different CO2 levels</span></li> </ul> <p><span>compute_SLR_equivalent.py : code to compute the ice sheet volume in SLRe based on model output</span></p> <p><span>Fig2/SLR_files/ : contains the equilibrium ice sheet volume of the different simulations according to the CO2 level</span></p> <p> </p> <p><span>Fig3/simulation_output/ : Model output necessary for plotting the third figure </span></p> <ul> <li><span>1xCO2_nocoupling/ : simulation with pre-industrial CO2 levels and insolation and no coupling to the ice sheet model</span></li> <li><span>8xCO2_nocoupling/ : simulation with 8xpiCO2 (pre-industrial CO2) levels, pre-industrial insolation and no coupling to the ice sheet model</span></li> <li><span>8xCO2_transient_albfb/ : quasi transient simulation, 8xpiCO2 levels, pre-industrial insolation, coupling with the ice sheet model </span></li> <li><span>8xCO2_transient_fixedalb/ : quasi transient simulation, 8xpiCO2 levels, pre-industrial insolation, coupling with the ice sheet model excluding the albedo-melt feedback</span></li> </ul> <p> </p>
Scherrenberg et al. (2024) supplement (Climate of the past): Ice-sheet model code, and output of Northern Hemisphere ice-sheet evolution of the past 800 kyr
<p>Supplement to Scherrenberg et al. (2024), article in Climate of the Past.</p> <p>This data-set contains ice-sheet model (IMAU-ICE) code (see IMAU_ICE_Code.zip; see https://github.com/IMAU-paleo/IMAU-ICE/tree/main for the most recent version of the model), the model output and configuration files (see Data_output.zip), and scripts to create figures (see Scripts_and_Figures.zip).</p> <p>Please note that additional input fields are required to run IMAU-ICE and to produce the figures. See Scherrenberg et al., (2024) for more information or contact the corresponding author.</p> <p>Citation: M.D.W. Scherrenberg, C.J. Berends, R.S.W. van de Wal: Late Pleistocene glacial terminations accelerated by proglacial lakes, climate of the past, special issue "icy landscapes of the past", 2024</p>
Groundwater dynamics beneath a marine ice sheet – supplementary animations
<p>Supplementary animations for the article "Groundwater dynamics beneath a marine ice sheet" by Gabriel J. Cairns, Graham P. Benham and Ian J. Hewitt. </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>
A Gaussian process emulator for simulating ice sheet-climate interactions on a multi-million year timescale: CLISEMv1.0 (Video supplement)
<p>This video illustrates the ice sheet evolution during a 3 Myr period for three different emulators as described in the manuscript "A Gaussian process emulator for simulating ice sheet - climate interactions on a multi-million year timescale", submitted to Geoscientific Model Development. The ice sheet is forced by declining carbon dioxide concentrations from 980 to 720 ppmv and orbital parameter variations during the late Eocene (between 38 Ma and 35 Ma).</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>
Last glacial cycle simulations forced by PMIP3 climate with a matrix and index method using a 3D thermodynamical ice-sheet model IMAU-ICE
<p>IMAU-ICE 2.0 model output of the ice evolution during the last glacial cycle at a 10 ka temporal resolution, as described in Scherrenberg at al., 2023.</p>
Response of water isotopes in precipitation to a collapse of the West Antarctic Ice Sheet in high-resolution simulations with the Weather Research and Forecasting Model
<p>This archive includes data and ipython notebooks to create the figures for the manuscript "Response of water isotopes in precipitation to a collapse of the West Antarctic Ice Sheet in high-resolution simulations with the Weather Research and Forecasting Model" submitted to Journal of Climate in August 2022.</p> <p>Model output from WRFwiso and iCAM is in data.zip (saved as monthly means)</p> <p>Notebooks and python modules are in scripts.zip</p> <p>Required python packages (all included in environment.yml):</p> <ul> <li>numpy</li> <li>matplotlib</li> <li>netcdf4</li> <li>basemap</li> <li>scipy</li> <li>wrf-python</li> <li>windspharm</li> <li>metpy</li> <li>intergrid</li> <li>cmocean</li> </ul> <p>Version 1 is the original upload from the first submission.</p> <p>Version 2 includes small updates of the data and scripts from the revision.</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>
Dataset from: Relative Sea-Level Sensitivity in the Eurasian Region to Earth and Ice-Sheet Model Uncertainty During the Last Interglacial
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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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