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186 results for “Ice sheet”
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>
Cuzzone2024: Ice sheet model simulations reveal polythermal ice conditions existed across the NE USA during the Last Glacial Maximum
<p>Here you will find model output associated with Cuzzone et al. (2024) for simulations conducted to reconstruct the Last Glacial Maximum conditions across the Northeast United States. Model output is available as: 1) Simulated Ensemble Mean LGM Ice Thickness, 2.) Simulated Ensemble Mean LGM Velocity 3.) Simulated Ensemble Mean LGM Velocity in X and Y direction, and 4.) The simulated LGM thermal state, shown as the Model ensemble agreement for warm and cold-based ice.</p> <p>These outputs are given for 3 model domains: 1) The Northeast USA (NE Domain), 2) The Adirondack Mountains (ADK), 3) The White Mountains (White), and 4) Mount Katahdin (Kat).</p> <p>Model output is given in .tif format, and the Map Projection is ESPG: 4326 , WGS 84</p> <p>Units for model output is:</p> <p>1) Ice Thickness: meters</p> <p>2) Velocity (vel, vx, vy): meters/yr</p> <p>3) Model Thermal Agreement: -5 to 5</p> <p>-5: All ensemble members agree cold-based ice</p> <p>-4: 4/5 ensemble members agree cold-based ice</p> <p>-3: 3/5 ensemble members agree cold-based ice</p> <p>-2: 2/5 ensemble members agree cold-based ice</p> <p>-1: 1/5 ensemble members agree cold-based ice</p> <p>0: 50% ensemble members either cold or warm-based</p> <p>1: 1/5 ensemble members agree cold-based ice</p> <p>2: 2/5 ensemble members agree cold-based ice</p> <p>3: 3/5 ensemble members agree cold-based ice</p> <p>4: 4/5 ensemble members agree cold-based ice</p> <p>5: 5/5 ensemble members agree cold-based ice</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>
Alpine ice sheet erosion potential aggregated variables
<p>These data contain domain-integrated and time-integrated model output variables presented in the reference below or otherwise relevant to last glacial cycle glacier erosion in the Alps.</p> <p><strong>Reference:</strong></p> <ul> <li>J. Seguinot and I. Delanay. Last glacial cycle glacier erosion potential in the Alps, <em>submitted to Earth Surface Dynamics Discussions</em>, 2021.</li> </ul> <p><strong>File names:</strong></p> <pre><code>alpero.{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>Variables:</strong></p> <ul> <li>Coordinate variables: <ul> <li><em>x</em>: X-coordinate in Cartesian system</li> <li><em>y</em>: Y-coordinate in Cartesian system</li> <li><em>lon</em>: longitude</li> <li><em>lat</em>: latitude</li> <li><em>time</em>: time</li> <li><em>age</em>: model age</li> <li><em>z</em>: elevation band midpoints</li> <li><em>d</em>: distance along transect</li> </ul> </li> <li>Glacier erosion variables: <ul> <li><em>coo2020_cumu</em>: Cook et al. (2020) cumulative glacial erosion potential</li> <li><em>coo2020_rate</em>: Cook et al. (2020) domain total volumic erosion rate</li> <li><em>coo2020_hyps</em>: Cook et al. (2020) erosion rate geometric mean</li> <li><em>coo2020_rhin</em>: Cook et al. (2020) rhine transect erosion rate</li> <li><em>her2015_cumu</em>: Herman et al. (2015) cumulative glacial erosion potential</li> <li><em>her2015_rate</em>: Herman et al. (2015) domain total volumic erosion rate</li> <li><em>her2015_hyps</em>: Herman et al. (2015) erosion rate geometric mean</li> <li><em>her2015_rhin</em>: Herman et al. (2015) rhine transect erosion rate</li> <li><em>hum1994_cumu</em>: Humphrey and Raymond (1994) cumulative glacial erosion potential</li> <li><em>hum1994_rate</em>: Humphrey and Raymond (1994) domain total volumic erosion rate</li> <li><em>hum1994_hyps</em>: Humphrey and Raymond (1994) erosion rate geometric mean</li> <li><em>hum1994_rhin</em>: Humphrey and Raymond (1994) rhine transect erosion rate</li> <li><em>kop2015_cumu</em>: Koppes et al. (2015) cumulative glacial erosion potential</li> <li><em>kop2015_rate</em>: Koppes et al. (2015) domain total volumic erosion rate</li> <li><em>kop2015_hyps</em>: Koppes et al. (2015) erosion rate geometric mean</li> <li><em>kop2015_rhin</em>: Koppes et al. (2015) rhine transect erosion rate</li> </ul> </li> <li>Other variables: <ul> <li><em>cumu_sliding</em>: cumulative basal motion</li> <li><em>glacier_time</em>: total ice cover duration</li> <li><em>warmbed_time</em>: temperate-based ice cover duration</li> <li><em>glacier_area</em>: glacierized area</li> <li><em>volumic_lift</em>: volumic bedrock uplift</li> <li><em>warmbed_area</em>: temperate-based ice cover area</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 glacial cycle <a href="https://doi.org/10.5281/zenodo.1423160">aggregated</a> and <a href="https://doi.org/10.5281/zenodo.1423175">continuous</a> variables.</p> <p><strong>Changes:</strong></p> <ul> <li>Version 2: <ul> <li>Add variable for glacierized area within 100-m elevation band.</li> <li>Use 100-m instead of 10-m elevation bands for erosion rate.</li> </ul> </li> <li>Version 1: <ul> <li>Initial version.</li> </ul> </li> </ul>
Model Output and Figure Scripts for: "Uncertainty in reconstructing paleo-elevation of the Antarctic Ice Sheet from temperature-sensitive ice core records"
<p>New climate model output and figure scripts for the paper "Uncertainty in reconstructing paleo-elevation of the Antarctic Ice Sheet from temperature-sensitive ice core records".</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>
Cordilleran ice sheet glacial cycle simulations continuous variables
<p>These data contain a subset of time-dependent glacier model output variables:</p> <p><strong>Reference:</strong></p> <ul> <li>J. Seguinot, I. Rogozhina, A. P. Stroeven, M. Margold, and J. Kleman. Numerical simulations of the Cordilleran ice sheet through the last glacial cycle, <em>The Cryosphere</em>, 10(2):639–664, <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> <pre><code>ciscyc4.{10km|5km}.{forcing}.{ex.100a|ex.1ka|ts.10a}.nc</code></pre> <ul> <li>Horizontal resolution: <ul> <li><em>10km</em>: 10 km horizontal resolution</li> <li><em>5km</em>: 5 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> <li><em>ngrip</em>: NGRIP ice core temperature forcing</li> <li><em>odp1012</em>: ODP 1012 ocean core temperature forcing</li> <li><em>odp1020</em>: ODP 1020 ocean core temperature forcing</li> <li><em>vostok</em>: Vostok ice core temperature forcing</li> </ul> </li> <li>Variable types: <ul> <li><em>ex.100a</em>: spatial diagnostics every hundred years</li> <li><em>ex.1ka</em>: spatial diagnostics every thousand years</li> <li><em>ts.10a</em>: scalar time-series every ten years</li> </ul> </li> </ul> <p> </p> <p><strong>Data format:</strong></p> <p>The data use compressed netCDF format. For quick inspection I recommend ncview. Spatial diagnostics (<em>*.ex.*.nc</em>) can be converted to GeoTIFF (and other GIS formats) e.g. using GDAL:</p> <pre><code>gdal_translate NETCDF:filename.nc:variable -b band filename.variable.band.tif</code></pre> <p>The list of variables (subdatasets) can be obtained from ncdump or gdalinfo. The <em>band</em> number equals 120 minus the age in ka. Band information can be displayed with:</p> <pre><code>gdalinfo NETCDF:filename.nc:variable</code></pre> <p>Variable long names, units, PISM configuration parametres and additional information are contained within the netCDF metadata.</p> <p><strong>Changelog:</strong></p> <ul> <li>Version 2: <ul> <li>Add spatial diagnostics every hundred years (<em>*.ex.100a.nc</em>)</li> </ul> </li> <li>Version 1: <ul> <li>Initial version.</li> </ul> </li> </ul>
Alpine ice sheet glacial cycle simulations continuous variables
<p>These data contain a subset of time-dependent 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}.{ex.100a|ex.1ka|ts.10a}.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> <li>Variable types: <ul> <li><em>ex.100a:</em> spatial diagnostics every hundred years</li> <li><em>ex.1ka:</em> spatial diagnostics every thousand years</li> <li><em>ts.10a:</em> scalar time-series every ten years</li> </ul> </li> </ul> <p>Data format: The data use compressed netCDF format. For quick inspection I recommend ncview. Spatial diagnostics (<em>*.ex.*.nc</em>) can be converted to GeoTIFF (and other GIS formats) e.g. using GDAL:</p> <pre><code>gdal_translate NETCDF:filename.nc:variable -b band filename.variable.band.tif</code></pre> <p>The list of variables (subdatasets) can be obtained from ncdump or gdalinfo. The <em>band</em> number equals 120 minus the age in ka. Band information can be displayed with:</p> <pre><code>gdalinfo NETCDF:filename.nc:variable</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.1423159">aggregated</a> variables.</p> <p><strong>Changelog:</strong></p> <ul> <li>Version 3: <ul> <li>Add spatial diagnostics every hundred years (<em>*.ex.100a.nc</em>)</li> </ul> </li> <li>Version 2: <ul> <li>Add age coordinate in kiloyears (ka) before present.</li> <li>Replace NCO by Xarray workflow (no effect on the results).</li> </ul> </li> <li>Version 1: <ul> <li>Initial version.</li> </ul> </li> </ul>
Antarctic ice sheet daily surface melt detection from ASCAT (2007-2022)
<p>Antarctic ice sheet-wide surface melt detection using enhanced resolution ASCAT C-band radar scatterometer data. Data are daily temporal resolution spanning 2007-2022 and gridded at 4.45 km. Melt detection approach follows Trusel et al., (2012) with updates to threshold and masking procedures. These data were used as a binary melt presence/absence estimate and a predictor in a machine learning-based estimation of Antarctic Peninsula surface meltwater production (https://zenodo.org/record/7995543). </p> <p>Please reach out to Luke Trusel with any questions!</p>
Dataset for "Future projections for the Antarctic ice sheet until the year 2300 with a climate-index method"
<p>Dataset for the paper "Future projections for the Antarctic ice sheet until the year 2300 with a climate-index method" (Journal of Glaciology, <a href="https://doi.org/10.1017/jog.2023.41">doi: 10.1017/jog.2023.41</a>).</p> <p>Please see the README for details.</p> <p>V1.1: Run-specs header files for SICOPOLIS 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>
BISICLES ice-sheet model for the Amundsen Sea Embayment, Antarctica : ensemble simulations to 2050
<p>BISICLES ice-sheet model simulations for the Amundsen Sea Embayment. Full details of the model set-up and ensemble design are described in the attached manuscript which has been accepted for publication in Journal of Glaciology.<br> In brief, a 213-member ensemble of simulations was created by varying four different model parameters. The parameters are the u0 value in a regularised Coulomb friction law, the rate of imposed thinning of floating ice (∂h/∂t(Ωf)), and scaling factors for sliding and viscosity coefficients (<em>C</em> and ϕ) between 0.9 and 1.1. We attach a summary text file of results, as well as NetCDF files of simulated variables land ice thickness and u and v components of velocity.<br> <strong>ASE2050_bisicles.csv </strong>contains annual (2007 to 2050, columns 5 to 48) sea level equivalent (mm) mass losses of ice from the Pine Island and Thwaites Glacier catchment basins. The parameters, given in columns 1 to 4, respectively, are the u0 (m/a), the rate of imposed thinning of floating ice (m/a), and the scaling factors for sliding and viscosity coefficients.<br> The NetCDF files in <strong>ASE_BISICLES.tar.gz</strong> contain annual (2007 to 2050) simulated output variables for the Amundsen Sea region at a spatial resolution of 1 km, with one file per ensemble member. The variables follow the ISMIP6 naming protocol:<br> (https://www.climate-cryosphere.org/wiki/index.php?title=ISMIP6-Projections-Antarctica#A2.3_Model_output_variables_and_README_file).<br> We include state variables lithk, uvelmean, and vvelmean. Each file is named according to the variable, the simulation parameters, and the resultant 2050 SLE value of ice loss (mm). For example, <strong>lithk_ASE_BISICLES.uj_20.dhfdt_5.C_0.90.phi_0.90.slr_43.06.nc </strong>is the land ice thickness data for simulation u0=20 m/a, ∂h/∂t(Ωf) = 5 m/a, C scaled by 0.9, ϕ scaled by 0.9, and a final SLE of 43.06 mm.</p> <p> </p>
The coupled ice sheet-Earth system model Bern3D v3.0: Model output
<p>This dataset contains model output of climate and ice sheet variables for the simulations performed in the study:</p> <p>Pöppelmeier, F., Joos, F., Stocker, T. F. (2023). The coupled ice sheet-Earth system model Bern3D v3.0. Journal of Climate.</p> <p>2D and 3D output variables are available for the preindustrial (PI) and Last Glacial Maximum (LGM) control simulations. Timeseries output is provided for CO<sub>2</sub> experiments for which CO<sub>2</sub> concentrations were increased to 2 and 4 times PI concentrations with rates of 0.5, 1, and 2% per year. Timeseries output is also provided for the simulation of the entire last glacial cycle in the standard setup and with logarithmically scaled dust for the aerosol radiative forcing. More details are provided in the above mentioned manuscript.</p>
PROTECT-SLR BISICLES ice-sheet model simulations for Amundsen Sea Embayment to 2050
<p>BISICLES ice-sheet model results. The NetCDF files in ASE_BISICLES.tar.gz contain simulated output variables for the Amundsen Sea Embayment sector of the West Antarctic Ice Sheet at a spatial resolution of 1 km. The model start date is 2007 and the outputs are yearly to 2052. Each of the 30 simulations is a result of a different combination of model parameters. The parameters are the u<sub>0</sub> value in a regularized Coulomb friction law, the rate of imposed thinning of floating ice, and scaling factors for sliding and viscosity coefficients between 0.9 and 1.1. The final part of each dataset name gives the sea-level equivalent (SLE) of loss of ice above floatation within Pine Island and Thwaites Glacier catchment basins. Each output was randomly selected from a 2 cm 2050 SLE band of a histogram of a large ensemble of simulations.</p> <p>See the pdf report included for further details.</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 "Brief communication: On calculating the sea-level contribution in marine ice-sheet models"
<p>This archive provides the data in Figures 3 and S1 of the following publication:</p> <p>Goelzer, H., Coulon, V., Pattyn, F., de Boer, B., and van de Wal, R.: Brief communication: On calculating the sea-level contribution in marine ice-sheet models , The Cryosphere, 14, 833–840, https://doi.org/10.5194/tc-14-833-2020, 2020.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.