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774 results for “glacier”
Raw sequencing data for studying the colonization of soil communities after glacier retreat
<p>Glaciers show a pattern of retreat at the global scale. Deglaciated areas are exposed and colonized by multiple organisms, but lack of global studies hampers a complete understanding of the future of these ecosystems. Until now, the complete reconstruction of soil communities was hampered by the complex identification of organisms, thus analyses at broad geographical and taxonomic scale have been so far impossible. The dataset used for this study represents the assemblages of Bacteria, Mycota, Eukaryota, Collembola (springtails), Oligochaeta (Earth worms), Insecta, Arthropoda and Vascular Plants obtained using environmental DNA (eDNA) metabarcoding. eDNA was extracted from soil samples collected from multiple glacier forelands representative of some of the main mountain chains of Europe, Asia, the Americas and Oceania. We investigated chronosequences of glacier retreat (i.e., the chronological sequence of specific geomorphological features along deglaciated areas for which the date of glacier retreat is known) ranging from recent years to the Little Ice Age (~1850). We used this newly assembled global DNA metabarcoding dataset to obtain a complete reconstruction of community changes in novel ecosystems after glacier retreat. Information on assemblages can be then combined with analyses of soil, landscape and climate to identify the drivers of community changes.</p>
Totten Glacier Thermal Structure
<p>This is the modelled basal melt rates and modelled basal temperature relative to the pressure melting point in ten experiments using different geothermal heat flux (GHF) maps as forcing for an offline coupling between a forward thermal model and an inverse mechanical model. The forward model consists of a thermomechanical steady state model using an improved shallow ice approximation in equilibrium with the subglacial hydrological system. The inverse model is solved using 3D full Stokes model.</p><p>Eight GHF maps are from Martos et al. (2017), Purucker (2012), Shen et al. (2020), An et al. (2015), Shapiro and Ritzwoller (2004), Stål et al. (2021), Lösing et al. (2021) and Haeger et al. (2022). The other two maps are mean GHF and constant GHF. Mean GHF is the ensemble mean of the 8 GHF datasets above interpolated into 2.0 km resolution. The constant GHF is the mean of the ensemble mean GHF, 59 mW m-2.</p><p>For the modelled results using GHF map from the 8 datasets, the filename includes the family name of the first author of the GHF map that is used. For instance, basal_meltrate_an.nc provides the modelled basal melt rates using An et al. (2015) GHF. For the modelled results using the ensemble mean GHF, the filename includes _mean. For instance, basal_meltrate_mean.nc provides the modelled basal melt rates using the ensemble mean of 8 GHF maps. For the modelled results using constant GHF, the filename includes _constant. For instance, basal_meltrate_constant.nc provides the modelled basal melt rates using constant GHF.</p><p>Basal melt rate and temperature files are provided in netcdf format. The netcdf files have 2D grids only. The unit of modelled basal melt rate is mm/yr. Positive values represent melting and negative values represent freezing. The unit of modelled basal temperature relative to the pressure melting point is degrees Celsius. The nodata value outside our modelled domain is given as -9999. </p><p>The specularity content is derived from radar data collected by ICECAP (Dow et al., 2020) and interpolated to 10 km by 10 km grids. Specularity content data for Totten glacier is provided as a txt file called: specularity_content(5)_10km.txt with easting (m), northing (m) and the upper fifth percentile of the specularity content.<br> </p><p>For more information, please see the following paper that is currently accepted by The Cryosphere:</p><p>Y. Huang et al.: Using specularity content to evaluate eight geothermal heat flow maps of Totten Glacier, The Cryosphere, 2023. https://doi.org/10.5194/tc-2023-58</p>
What can radar-based measures of subglacial hydrology tell us about basal shear stress? A case study at Thwaites Glacier, West Antarctica (Interpolated Data)
<p>This dataset accompanies the paper 'What can radar-based measures of subglacial hydrology tell us about basal shear stress? A case study at Thwaites Glacier, West Antarctica' in Journal of Glaciology, and can be used alongside the code found on Github (https://github.com/rohaizharis/inversion_radar2022) to reproduce the figures. The dataset consists of ice-penetrating radar data (specularity and relative reflectivity) and basal shear stress inversions that have been linearly interpolated onto radar flight tracks.</p>
Data for 'Mapping and characterization of avalanches on mountain glaciers with Sentinel-1 satellite imagery'
<div>This dataset contains avalanche deposit outlines (as shapefiles) derived for the study 'Mapping and characterization of avalanches on mountain glaciers with Sentinel-1 satellite imagery'</div> <div> </div> <div>They were outlined at three different sites (Mt Blanc, Everest and Hispar regions) for the periods 11/2016-10/2021 (Mt Blanc) and 11/2017-10/2022 (Everest and Hispar). The time period is indicated in the file name.</div> <div> </div> <div>For each dataset we give the raw outlines (Automated_outlines_dates), the manually updated (Automated_outlines_dates_ManualUpd) and the manually updated after accounting for surface elevation change (Automated_outlines_dates_ManualUpd_shifted). </div> <div> </div> <div>In order to know which scenes were used for the mapping (if no avalanche was detected, we did not provide a shapefile, but this doesn't been that there is a gap in the Sentinel-1 time series), we provide a Sentinel1_date file that shows all the Sentinel-1 RGB pairs that we used to detect the avalanches.</div> <div> </div> <div>We also provide as geotiffs the temporally aggregated outlines (Automated_outlines_dates_ManualUpd_shifted_aggregated; over one specific year yn - from 01/11/yn-1 to 01/11/yn - or the full study period):</div> <div>- as heatmaps (where the value of each pixel corresponds to the number of avalanches that occured) </div> <div>- as binary maps of deposits (where 1 is when an avalanche occured over the time period and 0 is where none were detected).</div> <div> </div> <div> </div> <div>Finally we provide a csv file for each region with metrics per glacier:</div> <div> </div> <div>RGI ID</div> <div>Glacier size (in m^2)</div> <div>Catchment size (in m^2)</div> <div>Area of slopes steeper than 30° (in m^2)</div> <div>The area of total deposits detected (by summing all the pixels of the deposit binary maps) in the ascending obits (in m^2)</div> <div>The area of total deposits detected (by summing all the pixels of the deposit binary maps) in the descending obits (in m^2)</div> <div>The avalanche activity detected (by summing all pixels of the heat maps) in the ascending orbits (in m^2)</div> <div>The avalanche activity detected (by summing all pixels of the heat maps) in the descending orbits (in m^2)</div> <div>The area of the glacier visible in the ascending orbits (in m^2)</div> <div>The area of the glacier visible in the descending orbits (in m^2)</div> <div> </div> <div> </div> <div>The main Google Earth Engine and Matlab scripts used to pre-process the Sentinel-1 GRD images and to map the avalanches are available on GitHub: https://github.com/MarinKneib/S1_avalanches</div> <div> </div>
GloGEM CMIP6 global glacier projections
<p>The files contain the glacier evolution as modelled with the Global Glacier Evolution Model (GloGEM; Huss and Hock, 2015) under the CMIP6 (Coupled Model Intercomparison Project Phase 6) climate models. For details about the simulations, refer to Zekollari et al. (2024), where the model setup and the results are described.</p> <p>The files are provided at the regional scale, for each of the 19 glacier regions as defined in the Randolph Glacier Inventory v6.0 (RGI Consortium, 2017), for two variables (glacier volume and glacier area).</p> <p>The folder structure is as follows: ‘Variable/RGIXX’, with:</p> <ul> <li>‘Variable’: ‘Area’ or ‘Volume'</li> <li>‘RGIXX’: the region, where XX refers to the RGI v6.0 region number</li> </ul> <p>Every file contains the evolution (of Volume [km<sup>3</sup>] or Area [km<sup>2</sup>]) for a given Shared Socioeconomic Pathways (SSP), with ssp119.csv corresponding to SSP1-1.9, ssp126 corresponding to SSP1-2.6,…etc.</p> <p>If you use these data, please cite the dataset as following:</p> <ul> <li>Cite ZENODO</li> <li>Cite the corresponding publication (Zekollari et al., 2024)</li> </ul> <p> </p> <p><strong>Somes notes/remarks:</strong></p> <ul> <li>In these GloGEM simulations, every glacier is calibrated to match the glacier-specific observed mass changes by Hugonnet et al. (2021).</li> <li>Until 2020, the glacier evolution forcing is from ERA5, while from 2020 onwards, the forcing comes from the respective climate models. Very slight differences can exist in the modelled glacier evolution prior to 2020 for the different climate models, because of randomly generated sub-monthly air temperature variability used to estimate monthly positive degree days more accurately.</li> <li>In the manuscript that describes the glacier evolution (Zekollari et al., 2024), the results are shown for SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 (for 12 climate models). In the data provided here on ZENODO, the evolution as modelled under SSP1-1.9 is also included, but these simulations are not described in the manuscript (owing to limited climate model ensemble size, n=3).</li> <li>For SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, the considered climate models are the same as in Rounce et al. (2023).</li> <li>The glacier evolution was modelled with the same climate model forcing as OGGM v1.6.1. The OGGM v1.6.1 data is available from Schuster et al. (2023) and these projections are also described in Zekollari et al. (2024).</li> </ul> <p><strong>References</strong></p> <p>Hugonnet, R., McNabb, R., Berthier, E., Menounos, B., Nuth, C., Girod, L., Farinotti, D., Huss, M., Dussaillant, I., Brun, F., and Kääb, A.: Accelerated global glacier mass loss in the early twenty-first century, Nature, 592, 726–731, https://doi.org/10.1038/s41586-021-03436-z, 2021.</p> <p>Huss, M. and Hock, R.: A new model for global glacier change and sea-level rise, Frontiers in Earth Science, 3, 54, https://doi.org/10.3389/feart.2015.00054, 2015.</p> <p>RGI Consortium: Randolph Glacier Inventory – A Dataset of Global Glacier Outlines: Version 6.0: Technical Report, Global Land Ice Measurements from Space, Colorado, USA. Digital Media, , https://doi.org/10.7265/N5-RGI-60, 2017.</p> <p>Rounce, D. R., Hock, R., Maussion, F., Hugonnet, R., Kochtitzky, W., Huss, M., Berthier, E., Brinkerhoff, D., Compagno, L., Copland, L., Farinotti, D., Menounos, B., and McNabb, R. W.: Global glacier change in the 21st century: Every increase in temperature matters, Science, 379, 78–83, https://doi.org/10.1126/science.abo1324, 2023.</p> <p>Schuster, L., Schmitt, P., Vlug, A., and Maussion, F.: OGGM/oggm-standard-projections-csv-files: v1.0 (v1.0), https://doi.org/10.5281/zenodo.8286065, 2023.</p> <p>Zekollari, H., Huss, M., Schuster, L., Maussion, F., Rounce, D. R., Aguayo, R., Champollion, N., Compagno, L., Hugonnet, R., Marzeion, B., Mojtabavi, S., and Farinotti, D.: Twenty-first century global glacier evolution under CMIP6 scenarios and the role of glacier-specific observations, The Cryosphere, 18, 5045-5066, https://doi.org/10.5194/tc-18-5045-2024, 2024.</p> <p> </p>
Vanishing_Glaciers_KEGGdata
<p>KEGG count data and alpha diversity measures for Vanishing Glacier project's glacier-fed streams (GFS) including KEGG counts from other cryospheric, non-GFS samples used in https://www.nature.com/articles/s41467-022-30816-4. </p>
RTK-GPS measurements on 6 rock glaciers in the La Sal and Uinta Mountains, Utah between 2021 and 2023
<p>RTK-GPS surveying of points marked on the surfaces of 6 rock glaciers in the La Sal and Uinta Mountains, Utah</p> <p>All<span> </span>measurements made with a pair of Emlid Reach RS2 RTK-GPS receivers connected in FIX mode</p> <p>Comparison of the x/y coordinates for points in subsequent surveys reveals planimetric motion of the rock glacier</p> <p>Error on measurements in the z direction (vertical) is large enough that up/down changes in the rock glaicer surface cannot be quantified from these data alone</p>
SUPPLEMENTARY DATA TO: Using a citizen science approach to assess nanoplastics pollution in remote high-altitude glaciers
<p>This is the repository of the supplementary data, and it contains the following files: </p> <p>Raw data files as the original output of TD-PTR-ToF-MS for all the samples, all the blanks, all the spikes and all the calibration runs (.h5 files in three zip arcives)</p> <p>Polymer library files (a zip archive including csv files.</p> <p>A data analysis file including raw data, blank subtraction and LOD correction of all measurements (xlsx file).</p> <p>A fingerprinting result file for each plastic type (xlsx file)</p> <p>A data analysis file after plastic fingerprinting (xlsx file). </p> <p> </p> <p> </p>
Multi-temporal elevation changes of Fedchenko Glacier (Tajikistan) from 1928 to 2021
<p>This dataset contains rasters of elevation changes on and around Fedchenko Glacier. All the elevation change maps are provided relatively to a Pléiades DEM acquired on 2021-09-20. Rasters are georeferenced in UTM43, and are provided in the form of *.tif files.</p> <p>For methodological details, please refer to the final publication of the article "Multi-temporal elevation changes of Fedchenko Glacier (Tajikistan) from 1928 to 2021" by Brun and others, or refer to the pre-print available at <a href="https://doi.org/10.31223/X5CX1H">https://doi.org/10.31223/X5CX1H</a></p>
Unmanned aerial system data of Lirung Glacier and Langtang Glacier for 2013–2018
<p>This dataset contains the raw data as well as produced image mosaics, digital elevation models (DEMs) and data derivatives of optical (RGB) unmanned aerial vehicle surveys of the debris-covered Lirung Glacier (9 surveys, 2013–2018) and Langtang Glacier (7 surveys, 2014–2018) in the <a href="https://www.google.com/maps/@28.2525566,85.6197635,32922m/data=!3m1!1e3">Langtang Catchment</a>, Nepalese Himalaya.</p> <p>All data in this dataset are stored in tape archive (<code>tar</code>) or gzip-compressed tape archive (<code>tar.gz</code>) formats and require extraction before use.</p> <p>The projected coordinate system used in this dataset is <em>WGS 1984 UTM Zone 45N (EPSG:32645)</em>. Survey dates are always provided as <em>yyyymmdd</em>.</p> <p> </p> <p><strong>File descriptions</strong></p> <ul> <li><strong><code>dems_<glacier>.tar</code></strong><br>20 cm resolution DEMs that were derived from the raw UAV images. DEMs of all survey dates are included in the tar archives. File format is GeoTIFF.<br> </li> <li><strong><code>orthomosaics_<glacier>.tar</code></strong><br>10 cm resolution image mosaics of orthorectified source imagery (orthomosaics) that were derived from the raw UAV images. Orthomosaics of all survey dates are included in the tar archives. File format is GeoTIFF.<br> </li> <li><strong><code>point-clouds_<glacier>.tar.gz</code></strong><br>Raw dense point clouds that were derived from the raw UAV images. Point clouds of all survey dates are included in the tar archive. File format is ASPRS LAS. Note that additional gzip-compression has been applied to the archives.<br> </li> <li><strong><code>raw-data_<glacier>_<datestamp>.tar</code></strong><br>Raw data of each of the surveys that were performed over 2013–2018. The filename includes the glacier name and the survey date. Each tar archive contains directories for each UAV flight associated to that specific survey, indicated by <em>f1, f2, ..., fn</em>. The flight directories have the following contents: <ul> <li><code>img</code><br>Subdirectory that contains the individual images in JPEG format captured by the UAV camera.</li> <li><code>*flight_path.kml</code> (not present for all surveys)<br>Keyhole Markup Language file that contains the flight path of the UAV recorded by the UAV's internal GPS+GLONASS sensor.</li> <li><code>*image_geoinfo.txt</code><br>Table with coordinates (<em>x,y,z</em>) and UAV orientation (<em>roll, tilt, yaw</em>) for every image in <code>img</code>, which were recorded by the UAV's internal GPS+GLONASS sensor and gyroscope, respectively.</li> <li><code>*drone_log.bbx</code> or <code>*drone_log.bb3</code><br>Binary flight log file from the UAV containing detailed flight information. Can be read by the proprietary eMotion software by UAV manufacturer <a href="https://www.sensefly.com/">senseFly</a>.<br> </li> </ul> </li> <li><strong><code>supplementary-animation_<glacier>.gif</code></strong><br>Animations of Langtang Glacier (2014–2018) and Lirung Glacier (2013–2017) supplementary to Kraaijenbrink and Immerzeel (2025). The high resolution time lapse animations are constructed from composites of the orthomosaic and hillshaded DEM. Since the animations are in GIF format, they are best viewed in a web browser in which they can be zoomed and panned.</li> <li><strong><code>supplementary-animation_lirung_terminus_retreat.mp4<br></code></strong>Three-dimensional fly-by video animation of the terminus retreat of Lirung Glacier (2013–2017), supplementary to Kraaijenbrink and Immerzeel (2025).<br> </li> <li><strong><code>supplementary-data-to-article.tar</code></strong><br>Data derivatives as presented in Kraaijenbrink & Immerzeel (2024). The tar archive contains a README file with additional information for each of the datasets present in the archive. The archive contains: <ul> <li>Error measurements of the UAV product</li> <li>Vector outlines of the area of interests of both glaciers</li> <li>Point cloud extracts of supraglacial ice cliff cross profiles</li> <li>Flow and gradient corrected DEMs (1 m resolution)</li> <li>Pixel-wise regression of the uncorrected and flow-corrected DEMs (1 m resolution)</li> <li>Surface velocity between survey pairs (8 m resolution)</li> </ul> </li> </ul> <p> </p> <p><strong>Reference</strong></p> <p>For further information, e.g. about the UAV systems and cameras used, as well as detailed descriptions of the data and the applied data processing please refer to the accompanying journal article.</p> <p>Kraaijenbrink, P. D. A., & Immerzeel, W. W. (2025). Spatial and temporal variability of the surface mass balance of debris‐covered glacier tongues. Journal of Geophysical Research: Earth Surface, 130, e2024JF007935. <a href="https://doi.org/10.1029/2024JF007935" target="_blank" rel="noopener">https://doi.org/10.1029/2024JF007935</a></p> <p> </p> <p><strong>License</strong></p> <p>This dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).<br>(<a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a>)</p> <p> </p> <p><strong>Correspondence</strong></p> <p>Dr Philip Kraaijenbrink (<a href="mailto:p.d.a.kraaijenbrink@uu.nl">p.d.a.kraaijenbrink@uu.nl</a>)<br>Prof Dr Walter Immerzeel (<a href="mailto:w.w.immerzeel@uu.nl">w.w.immerzeel@uu.nl</a>)</p> <p> </p> <p> </p>
Seismic source location with a match field processing approach during the RESOLVE dense seismic array experiment on the Glacier d'Argentiere
<p>This deposit contains the data set we used in our paper ‘<em>Dynamic imaging of glacier structures at high-resolution using source localization with a dense seismic array</em>’. The paper is in review for GRL and a preprint can be found here: <a href="http://dx.doi.org/10.1002/essoar.10507953.1">10.1002/essoar.10507953.1</a>.</p> <p>The dataset present here contains 34 files named ‘<strong>beam_15423_jd***.h5</strong>’. These files correspond to the output of the matched field processing for each day. They are in .h5 format and we provide a matlab code (<strong>read_MFP_data.m</strong>) to read these files. These files can be read with any other language since they are in . h5.</p> <p>In linux you can use <strong>h5dump –A filename.h5</strong> and you can see the content of each files.</p> <p> </p> <p>More information on how the MFP process is conducted can be found in on the <a href="https://lecoinal.gricad-pages.univ-grenoble-alpes.fr/resolve/">website </a>dedicated to this aspect or on our paper. The whole procedure and associated codes is provided on the <a href="http://lecoinal.gricad-pages.univ-grenoble-alpes.fr/resolve/">lecoinal.gricad-pages.univ-grenoble-alpes.fr/resolve/</a>.</p> <p>We also deliver with this deposit one day of seismic data <strong><a href="https://zenodo.org/api/files/873ccbe8-814d-4202-90ae-e115aab1942d/ZO_2018_121.h5?versionId=c59d2014-a6e1-43c5-96a6-91ee9a6b89ce">ZO_2018_121.h5 </a></strong>that can be used to test our MFP process. The data corresponds to the signal measured for 24 hours at each of the 98 sensors with a sampling rate of 500 Hz. More information on these seimsic signals can be found on our <a href="https://lecoinal.gricad-pages.univ-grenoble-alpes.fr/resolve/">website </a>and the whole seimsic dataset can be found here <a href="https://seismology.resif.fr/networks/#/ZO__2018">https://seismology.resif.fr/networks/#/ZO__2018</a>. Detailed for downloading the dataset should be search on our website.</p> <p> </p> <p>Other dataset linked to this project are:</p> <ul> <li>Nanni, Ugo, Gimbert, Florent, Roux, Phillipe, & Lecointre, Albanne. (2020). DATA of "Resolving the 2D temporal evolution of subglacial water flow with dense seismic array observations." [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.4024660">https://doi.org/10.5281/zenodo.4024660 </a></li> <li>Nanni, Gimbert, Roux, Helmstetter, Garambois, Lecointre, Walpersdorf, Jourdain, Langlais, Laarman, Lindner, Sergenat, Vincent, & Walter. (2020). DATA of the RESOLVE Project (https://resolve.osug.fr/) [Data set]. In Seismological Research Letters (Version v0). Zenodo. <a href="https://doi.org/10.5281/zenodo.3971815">https://doi.org/10.5281/zenodo.3971815 </a></li> </ul> <p>This dataset is also linked to two other study:</p> <p><em>Observing the subglacial hydrology network and its dynamics with a dense seismic array: </em></p> <p><a href="https://doi.org/10.1073/pnas.2023757118">https://doi.org/10.1073/pnas.2023757118</a></p> <p><em>A Multi‐Physics Experiment with a Temporary Dense Seismic Array on the Argentière Glacier, French Alps: The RESOLVE Project</em></p> <p><a href="https://doi.org/10.1785/0220200280">https://doi.org/10.1785/0220200280</a></p> <p> </p> <p> </p> <p> </p> <p> </p> <p>Do not hesitate to contact us if you would like to try this approach an another dataset.</p>
Central and Eastern Himalaya glacier velocities 2017-2019 (Sentinel 2)
<p> </p> <p>This dataset contains the median glacier surface velocity for the Central and Eastern Himalaya glacier velocities 2017-2019 (Sentinel 2). The velocities have been obtained by feature-tracking of November Sentinel 2 images spaced 1 year apart.</p> <p>The folder contains the following fields at 80 m resolution in GeoTiff format:</p> <ul> <li>the velocity magnitude 'vel' (meters per year)</li> <li>the x/y velocity components x_vel/y_vel (meters per year)</li> <li>the associated errors err, x_err, y_err (meters per year)</li> <li>the median absolute deviation of all the merged velocities 'MAD' (meters per year)</li> <li>the number of image pairs that have been merged in the median</li> </ul> <p>I recommend filtering data with error larger than 5 m/yr.</p> <p> </p> <table> <caption>Metadata Properties</caption> <tbody> <tr> <td>CRS</td> <td>EPSG:32645 - WGS 84 / UTM zone 45N - Projected</td> </tr> <tr> <td>Extent</td> <td>9425.6070999999992637,3045085.1858000000938773 : 883803.9936000000452623,3368628.0986000001430511</td> </tr> <tr> <td>Unit</td> <td>meters</td> </tr> <tr> <td>Width</td> <td>11192</td> </tr> <tr> <td>Height</td> <td>3981</td> </tr> <tr> <td>Data type</td> <td>Float32 - Thirty two bit floating point</td> </tr> </tbody> </table> <p> </p>
Supplementary data for: "Historical glacier change on Svalbard predicts doubling of mass loss by 2100"
<p>Supplementary datasets for:</p> <p>Geyman, E.C., van Pelt, W.J.J., Maloof, A.C., Faste Aas, H., and Kohler, J., 2022. "Historical glacier change on Svalbard predicts doubling of mass loss by 2100." Nature.</p> <p>Abstract:</p> <p>The melting of glaciers and ice caps accounts for about one-third of current sea-level rise, exceeding the mass loss from the more voluminous Greenland or Antarctic Ice Sheets. The Arctic archipelago of Svalbard, which hosts spatial climate gradients that are larger than the expected temporal climate shifts over the next century, is a natural laboratory to constrain the climate sensitivity of glaciers and predict their response to future warming. Here we link historical and modern glacier observations to predict that twenty-first century glacier thinning rates will more than double those from 1936 to 2010. Making use of an archive of historical aerial imagery from 1936 and 1938, we use structure-from-motion photogrammetry to reconstruct the three-dimensional geometry of 1,594 glaciers across Svalbard. We compare these reconstructions to modern ice elevation data to derive the spatial pattern of mass balance over a more than 70-year timespan, enabling us to see through the noise of annual and decadal variability to quantify how variables such as temperature and precipitation control ice loss. We find a robust temperature dependence of melt rates, whereby a 1°C rise in mean summer temperature corresponds to a decrease in area-normalized mass balance of -0.28 m yr<sup>-1</sup> of water equivalent. Finally, we design a space-for-time substitution8 to combine our historical glacier observations with climate projections and make first-order predictions of twenty-first century glacier change across Svalbard.</p> <p> </p> <p>Dataset description: </p> <p><br> This dataset contains the digital elevation models (DEMs), elevation change maps, point clouds, orthophotos, and vector outlines of glacier extents based on the Norwegian Polar Institute's collection of 5,507 high-oblique aerial images captured over Svalbard in 1936/1938. The photographs were analyzed through structure-from-motion (SfM) photogrammetry to generate 3D models. We also provide an .xlsx spreadsheet containing glacier-by-glacier statistics of ice loss and climate fields. Note that all of the raster and point cloud files listed below have been georeferenced in Metashape using the ground control points (GCPs) illustrated in Main Text, Fig. 2e, but have not undergone the co-registration and bias-correction following the methods of Nuth & Kaab (2011), which was done on a glacier-by-glacier basis. However, the glacier change budgets in the .xlsx file [#5 below] do reflect the values from the glacier-by-glacier co-registered and bias-corrected DEMs. See below for descriptions of each dataset (each number below corresponds to a different zipped folder).</p> <p>------------------------------------------------------------------------------------- </p> <p><strong>Svalbard-wide datasets [all georeferenced Svalbard-wide datasets are in the coordinate system UTM 33N]: </strong></p> <p><br> 1. Svalbard-wide 1936 DEM (20 m and 50 m resolution) [georeferenced .tif file] </p> <p>2. Svalbard-wide 1936 orthophotomosaic (20 m resolution) [georeferenced .tif file] </p> <p>3. Svalbard-wide dh (1936-2010) (20 m and 50 m resolution) [georeferenced .tif file] </p> <p>4. Shapefile of 1936 glacier extents [ESRI .shp file] </p> <p>5. Glacier-by-glacier statistics [.xlsx file] </p> <p>------------------------------------------------------------------------------------- </p> <p><strong>Regional-datasets: </strong></p> <p><em>Due to file size limitations, the high-resolution (5 m) datasets are split into the 8 regions illustrated in Main Text, Fig. 2d: </em></p> <p><em>Zone 1 - South Spitsbergen</em></p> <p><em>Zone 2 - Barentsoya-Edgeoya</em></p> <p><em>Zone 3 - Austfonna</em></p> <p><em>Zone 4 - Vestfonna</em></p> <p><em>Zone 5 - Northeast Spitsbergen</em></p> <p><em>Zone 6 - Central Spitsbergen</em></p> <p><em>Zone 7 - Northwest Spitsbergen</em></p> <p><em>Zone 8 - North Spitsbergen</em></p> <p><br> 6. Regional 1936 DEMs (5 m resolution) [georeferenced .tif files] </p> <p>7. Regional dh (1936-2010) (5 m resolution) [georeferenced .tif files] </p> <p>8. Local 1936 orthomosaics (5 m resolution) [georeferenced .tif files] </p> <p>9. Unprocessed point clouds [.laz files]. These files represent the raw 3D point clouds (x,y,z) generated in Agisoft Metashape for each of the 17 local models described in Extended Data Figure 3.</p> <p>10. Thumbnail-sized copies of the 5,507 historical aerial images (1936 and 1938) analyzed in this study, along with a .csv file labeling the approximate location of each photograph.</p>
Data set: "North Atlantic cooling is slowing down mass loss of Icelandic glaciers"
<p>This data set includes the materials required to reproduce the figures and tables presented in the study: "North Atlantic cooling is slowing down mass loss of Icelandic glaciers". The data consist of:</p> <p>1. Maps of annual surface mass balance (SMB) of Icelandic glaciers and ice caps (ICL) from RACMO2.3 at 500 m spatial resolution in NetCDF format.</p> <ul> <li><strong>smb_rec.1958-2019.RACMO2.3-ERA.ICL-0.5km.YY.nc</strong>: annual cumulative SMB of Icelandic glaciers and ice caps (kg m<sup>-2</sup> or mm w.e. per year) from RACMO2.3 forced by ERA reanalyses for the period 1958-2019, and further statistically downscaled to 500 m spatial resolution. Forcing includes ERA-40 (1958-1978), ERA-Interim (1979-2018) and ERA5 (2019) reanalyses.</li> <li><strong>smb_rec.1958-2099.RACMO2.3-CESM2-SSP85.ICL-0.5km.YY.nc</strong>: annual cumulative SMB of Icelandic glaciers and ice caps (kg m<sup>-2</sup> or mm w.e. per year) from RACMO2.3 forced by CESM2 for the historical period 1958-2014 and by CESM2 under a high-end warming scenario SSP5-8.5 for the period 2015-2099, further statistically downscaled to 500 m spatial resolution.</li> <li><strong>Topo_icemask_lsm_lon_lat_ICL-0.5km.nc</strong>: mask file including an ice mask, land/sea mask and surface topography derived from the ArcticDEM, and longitude/latitude coordinates on the 500 m grid.</li> </ul> <p><strong>NB</strong>: the NetCDF files above use a Polar Stereographic North (EPSG:3413) projection with a horizontal resolution of 500 m x 500 m. The reference point is located at 45ºW longitude and 70ºN latitude.</p> <p>2. Time series of annual ICL-integrated SMB components (Gigatons or Gt per year), annual mean 2 m air temperature above Icelandic glaciers and ice caps (T2m; K), annual mean sea surface temperature (SST) in the Northern Blue Blob. These time series are available in ASCII format for the RACMO2.3 simulation forced by ERA reanalyses (1958-2019) and the RACMO2.3 projection forced by CESM2 under a high-end warming scenario SSP5-8.5 (1958-2099).</p> <p><strong>RACMO2.3-ERA</strong></p> <ul> <li><strong>SMB-components-RACMO2.3-ERA-1958-2019.txt</strong>: time series of annual integrated SMB, snowfall, rainfall, runoff, total melt, refreezing and retention (Gt per year) from the ERA-forced RACMO2.3 simulation (1958-2019).</li> <li><strong>T2m-glacier-RACMO2.3-ERA-1958-2019.txt</strong>: time series of annual mean glacier T2m and anomalies relative to the period 1958-1994 (K) from the ERA-forced RACMO2.3 simulation (1958-2019).</li> <li><strong>SST-Northern-Blue-Blob-RACMO2.3-ERA-1958-2019.txt</strong>: time series of annual mean Northern Blue Blob SST and anomalies relative to the period 1958-1994 (K) derived from the ERA reanalyses (1958-2019).The reanalyses include ERA-40 (1958-1978), ERA-Interim (1979-2018) and ERA5 (2019).</li> </ul> <p><strong>RACMO2.3-CESM2</strong></p> <ul> <li><strong>SMB-components-RACMO2.3-CESM2-SSP85-1958-2099.txt</strong>: time series of annual integrated SMB, snowfall, rainfall, runoff, total melt, refreezing and retention (Gt per year) from the CESM2-forced RACMO2.3 projection under a SSP5-8.5 scenario (1958-2099).</li> <li><strong>T2m-glacier-RACMO2.3-CESM2-SSP85-1958-2099.txt</strong>: time series of annual mean glacier T2m and anomalies relative to the period 1958-1994 (K) from the CESM2-forced RACMO2.3 projection under a SSP5-8.5 scenario (1958-2099).</li> <li><strong>SST-Northern-Blue-Blob-RACMO2.3-CESM2-SSP85-1958-2099.txt</strong>: time series of annual mean Northern Blue Blob SST and anomalies relative to the period 1958-1994 (K) derived from the CESM2 projection under a SSP5-8.5 scenario (1958-2099).</li> </ul> <p>3. Time series of monthly ICL-integrated SMB (Gt per month) for the period 1958-2099. </p> <ul> <li><strong>SMB-monthly-RACMO2.3-1958-2099.txt</strong>: time series of monthly integrated SMB (Gt per month) combining RACMO2.3-ERA (January 1958 - December 2019) with RACMO2.3-CESM2 under a SSP5-8.5 scenario (January 2020 - December 2099) at 500 m horizontal resolution.</li> </ul> <p>The daily downscaled SMB data sets from the ERA-forced RACMO2.3 simulation and the CESM2-forced RACMO2.3 projection under a SSP5-8.5 scenario are freely available from the authors upon request and without conditions (contact: b.p.y.noel@uu.nl). Besides SMB, the data sets include daily total precipitation (snow and rain), snowfall, total melt (snow and ice), runoff, refreezing and retention, total sublimation (surface and drifting snow), snow drift erosion, as well as 2 m air temperature at 500 m horizontal resolution. </p> <p><strong>Abstract</strong>: Icelandic glaciers have been losing mass since the Little Ice Age in the mid-to-late 1800s, with higher mass loss rates in the early 21<sup>st </sup>century, followed by a slowdown since 2011. As of yet, it remains unclear whether this mass loss slowdown will persist in the future. By reconstructing the contemporary (1958-2019) surface mass balance of Icelandic glaciers, we show that the post-2011 mass loss slowdown coincides with the development of the Blue Blob, an area of regional cooling in the North Atlantic Ocean to the south of Greenland. This regional cooling signal mitigates atmospheric warming in Iceland since 2011, in turn decreasing glacier mass loss through reduced meltwater runoff. In a future high-end warming scenario, North Atlantic cooling is projected to mitigate mass loss of Icelandic glaciers until the mid-2050s. High mass loss rates resume thereafter as the regional cooling signal weakens. </p>
Stable Isotopes of Water from the Glacier de Martinets, 14-September-2021
<p>Thirty samples from the Martinets Glacier in Vaud, Switzerland collected on single day in September 2021. Data set includes ice, water flowing on glacier, and old snow. All ice was sampled at a maximum of 10 cm under the surface. Some duplicate samples are included. Dataset includes deuterium, O-17 and O-18. All analyses were completed with a Picarro CRDS at the Geography Institute of the University of Bern. Metadata includes latitude, longitude, elevation, date, time, and notes. </p>
Photon time-of-flight histograms measured with a photon-counting diffuse LiDAR on Crook Glacier and Collier Glacier, Oregon
<p>This data set contains photon time-of-flight histograms measured in September 2021 on Crook Glacier, Oregon, and two sites on Collier Glacier, Oregon (USA). Each data file is associated with a single measurement using a photon-counting diffuse LiDAR. The files contain a header with geo-location (WGS84) and instrument settings as well as the raw count numbers and integration time for each temporal bin. The given arrival times represent the center of each temporal bin. The color naming scheme of the file names represents the used laser wavelength (blue=405nm, green=520nm, red=640nm), the last number in each filename represents the distance between laser and detector (i.e. 1.8m at 520nm for file "green5_1.8.txt").</p> <p>The data is organized in folders for each site plus an additional folder containing Matlab-code needed for data evaluation. The code uses this folder structure for relative path referencing. Data is evaluated using ExampleDataEvalV2.m, which employs the other three files as helper functions. The helper function ReadTofHisto.m reads the raw data from the measurement files and provides a named structure with the header information.</p> <p>If you wish to use this data set please contact Markus Allgaier at markusa@uoregon.edu with a description of the work and any questions so that we may offer guidance in regards to the best usage of our dataset. When using the data set within a publication, please cite:</p> <p>Markus ALLGAIER, Matthew G. COOPER, Anders E. CARLSON, Sarah W. COOLEY, Jonathan C. RYAN, Brian J. SMITH, "Direct measurement of optical properties of glacier ice using a photon-counting diffuse LiDAR", in preparation (2022)</p>
MPAS-Albany Land Ice model simulations of Humboldt Glacier, North Greenland, from 2007–2100
<p>This dataset contains model input and output in netCDF format, model code, and analysis scripts for simulations of Humboldt Glacier, North Greenland, through the 21st century (Hillebrand et al., 2022) using the MPAS-Albany Land Ice model (Hoffman et al., 2018). We calibrate parameters controlling basal traction, iceberg calving, and submarine melt against observations from 2007–2017. We then explore the glacier’s sensitivity to climate forcing, iceberg calving, and basal conditions in an ensemble of 24 simulations from 2007–2100. We further explore its sensitivity to uncertainties in ice-shelf melt, bed topography, and calving rate limits in targeted sensitivity experiments. Input files include surface mass balance, ocean thermal forcing, and subglacial runoff forcings provided by ISMIP6 (Nowicki et al., 2020; Slater et al., 2020). Output includes basal traction optimization solutions for the year 2007; annual 2D ice speed, basal shear and driving stresses, and geometry; annual 3D temperature; and grounded, floating, and global mass budgets at every timestep.</p> <p>References:</p> <p>Hillebrand, T. R., Hoffman, M. J., Perego, M., Price, S. F., and Howat, I. M. (2022): The contribution of Humboldt Glacier, northern Greenland, to sea-level rise through 2100 constrained by recent observations of speedup and retreat, The Cryosphere, 16, 4679–4700, <a href="https://doi.org/10.5194/tc-16-4679-2022">https://doi.org/10.5194/tc-16-4679-2022</a>.</p> <p>Hoffman, M. J., Perego, M., Price, S. F., Lipscomb, W. H., Zhang, T., Jacobsen, D., et al. (2018). MPAS-Albany Land Ice (MALI): a variable-resolution ice sheet model for Earth system modeling using Voronoi grids. <em>Geoscientific Model Development</em>, <em>11</em>(9), 3747–3780.<a href="https://doi.org/10.5194/gmd-11-3747-2018"> https://doi.org/10.5194/gmd-11-3747-2018</a></p> <p>Nowicki, S., Goelzer, H., Seroussi, H., Payne, A. J., Lipscomb, W. H., Abe-Ouchi, A., et al. (2020). Experimental protocol for sea level projections from ISMIP6 stand-alone ice sheet models. <em>The Cryosphere</em>, <em>14</em>(7), 2331–2368.<a href="https://doi.org/10.5194/tc-14-2331-2020"> https://doi.org/10.5194/tc-14-2331-2020</a></p> <p>Slater, D. A., Felikson, D., Straneo, F., Goelzer, H., Little, C. M., Morlighem, M., et al. (2020). Twenty-first century ocean forcing of the Greenland ice sheet for modelling of sea level contribution. <em>The Cryosphere</em>, <em>14</em>(3), 985–1008.<a href="https://doi.org/10.5194/tc-14-985-2020"> https://doi.org/10.5194/tc-14-985-2020</a></p>
Glacier shrinkage will accelerate downstream decomposition of organic matter and alters microbiome structure and function
<p>Two datasets supporting the publication, "Glacier shrinkage will accelerate downstream decomposition of organic matter and alters microbiome structure and function" in Global Change Biology.</p> <p><strong>DATA S1 </strong>Detailed metadata for sampled glacier-fed streams, including sample date and time, GPS coordinates, elevation, physical streamwater measurements, glacier characteristics, nutrient chemistry, and a column indicating samples used in metagenomics analyses.</p> <p><strong>DATA S2 </strong>Full patch-level dataset of extracellular enzyme activities (nmol h<sup>-1</sup> g<sup>-1</sup> DM sediment) and chlorophyll <em>a </em>(µg chlorophyll <em>a</em> g<sup>-1</sup> DM).</p>
A 21st century high-resolution glacier and ice sheet fractional area dataset: Code and Data
<p>This resource contains code and input data to develop a high-resolution (0.1°) gridded global glacier and ice sheet fractional area dataset, which is also available in this resource. The dataset is developed from Randolph Glacier Inventory v6.0 shapefiles and supplementary shapefiles for the Antarctic and Greenland ice sheets. The approach is adapted from Li et al., (2021; <a href="https://doi.org/10.1017/jog.2021.28">https://doi.org/10.1017/jog.2021.28</a>). The dataset provides estimates of the fraction (0 to 1) of land cover that is glaciated in each 0.1° x 0.1° grid cell. The dataset was designed for use in the SPEAR model (<a href="https://www.gfdl.noaa.gov/spear/">https://www.gfdl.noaa.gov/spear/</a>) from the NOAA Geophysical Fluid Dynamics Laboratory (GFDL), but may be useful for other applications as well.</p> <p>This work is documented in a NOAA Technical Memorandum (citation information to follow).</p>
On-Glacier Air Temperatures for Miage Debris-Covered Glacier, 2014
<p>Miage_Glacier_Air_Temperature_Data_2014.xlsx<br> %------------------------------------------%<br> Data Generated on 13th May 2022</p> <p>Data Curator: Dr. Thomas Shaw (Swiss Federal Institute, WSL, Switzerland)</p> <p>Data Provider(s): Dr. Thomas Shaw (Swiss Federal Institute, WSL, Switzerland) thomas.shaw@wsl.ch<br> Prof. Benjamin Brock (Northumbria University, Newcastle, UK) benjamin.brock@northumbria.ac.uk</p> <p>Data period: 1st July - 27th September, 2014</p> <p><br> Details:<br> Hourly data are generated for air temperature stations ('T-Loggers') distributed across the debris-covered Miage Glacier, Italy (45.8129°N, 6.8458°E).<br> Air temperatures (°C) were measured using Tinytag thermistors (accuracy +/- 0.2-0.35°C) housed in naturally ventilated Campbell MET20 / MET21 radiation shields.</p> <p>Off-Glacier air temperatures (measured as above) are provided for comparison with on-glacier air temperatures. </p> <p>For additional details can be found in the article: <br> Shaw, T. E., Brock, B. W., Fyffe, C. L., Pellicciotti, F., Rutter, N., & Diotri, F. (2016).<br> Air temperature distribution and energy-balance modelling of a debris-covered glacier. Journal of Glaciology, 62(231), 1–14. <br> https://doi.org/10.1017//jog.2016.31</p> <p>Please cite the above article for any usage of the dataset.</p> <p>Data are shared and compiled as part of a wider project to estimate on-glacier air temperatures from off-glacier data<br> For more details on the 'TEMPEST' project, visit: https://tempestglacier.com/</p>
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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.