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774 results for “glacier”
500-yr Projections of Thwaites Glacier, Antarctica, with MALI, including glacial isostatic adjustment: no elastic displacement
<p>This archive contains a subset of results reproducing the material presented in the manuscript "Stabilizing effect of bedrock uplift on retreat of Thwaites Glacier, Antarctica, at centennial timescales" by Cameron Book, et al. This archive only includes results for the simulations with elastic displacement in the GIA model disabled. These results are briefly discussed in the first paragraph of section 'Factors controlling feedbacks between glacier retreat and bedrock uplift'. The main results from the manuscript are in a separate archive. Questions should be directed to Matt Hoffman (mhoffman@lanl.gov).</p> <p>This archive contains the following directories:</p> <p>|-- N1_no_elastic<br> |-- N2_no_elastic<br> |-- N3_no_elastic<br> `-- N4_no_elastic</p> <p>The individual runs have the following corresponence to the manuscript, but<br> with the elastic displacement disabled:<br> N1=TYP<br> N2=BEST2<br> N3=VLV-THIN<br> N4=VLV</p> <p>Within each run directory, are the following model output files:<br> globalStats.nc: MALI global, scalar time-series<br> output_*.nc: MALI spatial output fields, separated by century</p>
Glacier types of the Mont Blanc massif
<p>This is a new glacier inventory for the Mont Blanc massif digitized on high-resolution images. The inventory divides glaciers into different classes commonly known to glaciologists and geomorphologists. </p>
Dataset for: Evaluation of low-cost Raspberry Pi sensors for photogrammetry of glacier calving fronts
<p>Points clouds of Fjallsjökull calving front, as derived by a Raspberry Pi and a Unoccupied Aerial Vehicle. For each sensor, eight sub-sections are analysed. Point clouds are provided in .las format. Each sub-section is generated within its own spatial reference (matching that of the other sensor to allow for comparison). </p>
Accelerating ice loss from peripheral glaciers in North Greenland
<p class="MsoNormal"><span>In recent decades, Greenland's peripheral glaciers have experienced large-scale mass loss</span><span>, resulting in a substantia</span><span>l contribution to sea-level rise. Only 4% of Greenland's ice cover are small peripheral glaciers that are distinct from the ice sheet proper. Despite comprising this relatively small area, these small peripheral glaciers are responsible for 11% of the ice loss associated with Greenland's recent sea-level rise contribution. Using the satellite laser platforms ICESat and ICESat-2, we estimate that ice loss from these Greenland glaciers increased from 27±6 Gt/yr (2003–2009) to 42±6 Gt/yr (2018–2021).</span></p> <p class="MsoNormal"><span>Here, we provide the laser altimetry assessment of </span><span>changing</span> <span>ice loss rates from Greenland peripheral glaciers that bridges both the ICESat and ICESat-2 periods of Feb 2003 – Dec 2021. We provide peripheral glaciers elevation changes including correction for firn compaction, elastic uplift rates from present-day mass changes, and long-term past ice changes (Glacial Isostatic Adjustment- GIA).</span></p> <p class="MsoNormal"><span>We provide:</span></p> <p class="MsoNormal"><span>(1) Mean elevation change rates of the Greenland peripheral glaciers during, Feb 2003 - Oct 2009, Oct 2008 - Apr 2019, and Oct 2018 – Dec 2021 obtained from ICESat and ICESat-2 data. The grid resolution is 500x500 meters.</span></p> <p class="MsoNormal"><span>(2) Mean elastic uplift rates of the bedrock (in mm/yr) due to ice loss during, Feb 2003 - Oct 2009, Oct 2008 - Apr 2019, and Oct 2018 – Dec 2021.</span></p> <p class="MsoNormal"><span>(3) Mean firn compaction rates in m/yr during, Feb 2003 - Oct 2009, Oct 2008 - Apr 2019, and Oct 2018 – Dec 2021.</span></p> <p class="MsoNormal"><span>(4) Glacial Isostatic Adjustment- GIA rates in mm/yr from the GNET-GIA empirical model of Khan et al. (2016).</span></p> <p class="MsoNormal"><span>(5) Time series of mean surface air temperature in degrees Celcius during May-September in north, northwest, southeast, southwest, and northwest Greenland from RACMO2.3p2.</span></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><span> </span></p>
On following pages: 48. Mutable Shrew (Sorex mutabilis); 49. Verapaz Shrew (Sorex veraepacis); 50. Rock Shrew (Sorex Mexican Long-tailed Shrew (Sorex oreopolus); 55. Orizaba Long-tailed Shrew (Sorex orizabae); 56. Chestnut-bellied Shrew (Sorex rohweri): 60. South-eastern Shrew (Sorex longirostris); 61. Masked Shrew (Sorex cinereus); 62. Maryland Shrew Mountain Shrew (Sorex milleri): 66. Preble's Shrew (Sorex preblei); 67. Prairie Shrew (Sorex hayden); 68. Pribilof Island Saint Lawrence Island Shrew (Sorex jackson); 72. Kamchatka Shrew (Sorex camtschaticus); 73. Paramushir Shrew (Sorex (Sorex ornatus); 77. American Water Shrew (Sorex palustris); 78. Western Water Shrew (Sorex navigaton; 79. Glacier Bay Baird's Shrew (Sorex bairdi); 83. Pacific Shrew (Sorex pacificus); 84. Fog Shrew (Sorex sonomae); 85. New Mexico Shrew dispan; 51. Smoky Shrew (Sorex fumeus); 52. Inyo Shrew (Sorex tenellus); 53. Dwarf Shrew (Sorex nanus); 54. (Sorex ventralis); 57. Veracruz Shrew (Sorex veraecrucis); 58. Ixtlan Shrew (Sorex ixtlanensis); 59. Olympic Shrew (Sorex fontinalis); 63. Mount Lyell Shrew (Sorex lyell); 64. Zacatecas Shrew (Sorex emarginatus), 65. Carmen Shrew (Sorex pribilofensis); 69. Barren Ground Shrew (Sorex ugyunak); 70. Portenko's Shrew (Sorex portenkol); 71. leucogasten; 74. American Pygmy Shrew (Sorex hoyi); 75. Vagrant Shrew (Sorex vagrans); 76. Ornate Shrew Water Shrew (Sorex alaskanus); 80. Eastern Water Shrew (Sorex albibarbis); 81. Marsh Shrew (Sorex bendiril); 82. (Sorex neomexicanus); 86. Montane Shrew (Sorex monticolus). in Soricidae
On following pages: 48. Mutable Shrew (Sorex mutabilis); 49. Verapaz Shrew (Sorex veraepacis); 50. Rock Shrew (Sorex Mexican Long-tailed Shrew (Sorex oreopolus); 55. Orizaba Long-tailed Shrew (Sorex orizabae); 56. Chestnut-bellied Shrew (Sorex rohweri): 60. South-eastern Shrew (Sorex longirostris); 61. Masked Shrew (Sorex cinereus); 62. Maryland Shrew Mountain Shrew (Sorex milleri): 66. Preble's Shrew (Sorex preblei); 67. Prairie Shrew (Sorex hayden); 68. Pribilof Island Saint Lawrence Island Shrew (Sorex jackson); 72. Kamchatka Shrew (Sorex camtschaticus); 73. Paramushir Shrew (Sorex (Sorex ornatus); 77. American Water Shrew (Sorex palustris); 78. Western Water Shrew (Sorex navigaton; 79. Glacier Bay Baird's Shrew (Sorex bairdi); 83. Pacific Shrew (Sorex pacificus); 84. Fog Shrew (Sorex sonomae); 85. New Mexico Shrew dispan; 51. Smoky Shrew (Sorex fumeus); 52. Inyo Shrew (Sorex tenellus); 53. Dwarf Shrew (Sorex nanus); 54. (Sorex ventralis); 57. Veracruz Shrew (Sorex veraecrucis); 58. Ixtlan Shrew (Sorex ixtlanensis); 59. Olympic Shrew (Sorex fontinalis); 63. Mount Lyell Shrew (Sorex lyell); 64. Zacatecas Shrew (Sorex emarginatus), 65. Carmen Shrew (Sorex pribilofensis); 69. Barren Ground Shrew (Sorex ugyunak); 70. Portenko's Shrew (Sorex portenkol); 71. leucogasten; 74. American Pygmy Shrew (Sorex hoyi); 75. Vagrant Shrew (Sorex vagrans); 76. Ornate Shrew Water Shrew (Sorex alaskanus); 80. Eastern Water Shrew (Sorex albibarbis); 81. Marsh Shrew (Sorex bendiril); 82. (Sorex neomexicanus); 86. Montane Shrew (Sorex monticolus).
Simulation outputs associated to the study: "Brief communication: Everest South Col Glacier did not thin during the last three decades" by Brun et al.
<p>This folder contains the outputs of the South Col Glacier mass balance simulations used in the study: "Brief communication: Everest South Col Glacier did not thin during the last three decades" by Brun et al., submitted to The Cryophere Journal in August 2022. The simulation outputs are obtained with two different models: COSIPY (Sauter et al., 2010) and Crocus (Vionnet et al., 2012). Note that forcing and initialization information are provided to reproduce Crocus simulations.</p>
Multisensor monitoring data of Hochebenkar Rock Glacier
<p>HRG_DSM_stack.tif: Area-wide monitoring time series of Hochebenkar Rock Glacier (Oetz valley, Tyrol, Austria) based on multitemporal digital surface models (DSMs) derived from</p> <ul> <li>photogrammetry using historical arerial imagery (1953, 1971, 1977, 1990, 1997)</li> <li>airborne laser scanning (2006, 2009, 2010, 2011, 2017)</li> <li>and unmanned aerial vehicle-based laser scanning (2018, 2019, 2020, 2021).</li> </ul> <p>The airborne laser scanning data from 2006 and 2017 were provided by the Federal Government of Tyrol and are licenced under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/deed.de) and must be used according to the following conditions of use: https://www.tirol.gv.at/data/nutzungsbedingungen/</p> <p>HRG_HS_stack.tif: Shaded reliefs computed from the multitemporal DSMs, which were used for applying the image correlation technique to derive displacement vectors.</p> <p>HRG_DDSM_stack.tif: Differential digital surface models computed by subtracting the subsequent DSMs. The uncertainty of the individual DDSMs are masked.</p> <p>Shapefiles in HRG_velocity_vectors.zip: Mean velocity vectors (m/yr) computed from the displacement vectors by dividing the vector length by the time period between the acquisition campaigns.</p> <p>All datasets are provided in the Austrian GK West projection (EPSG 31254). The spatial resolution of the raster datasets is 1 metre.</p>
Dataset: Quantification of post-glacier bedrock surface erosion in the European Alps using 10Be and optically stimulated luminescence exposure dating
<p>This contains the dataset associated with the publication titled "Quantification of post-glacier bedrock surface erosion in the European Alps using 10Be and optically stimulated luminescence exposure dating" published in Earth Surface Dynamics (2022).</p>
Data and MATLAB code: Glacier contributions to river discharge during the current Chilean megadrought
<p>Data and MATLAB code to support the article <em>Glacier contributions to river discharge during the current Chilean megadrought </em>in <em>Earth's Future</em> by McCarthy and others</p>
Monthly changes in glacier surface elevation on HMA
<p><a href="https://zenodo.org/api/files/5c8e256d-a2db-43aa-9f1f-d421b1881b58/Time_Series_Glacier_Surface_Elevation.rar">Time_Series_Glacier_Surface_Elevation.rar</a> is Monthly changes in glacier surface elevation on HMA by ICESat-2 from Oct. 2018 to Nov. 2020.</p> <p><a href="https://zenodo.org/api/files/5c8e256d-a2db-43aa-9f1f-d421b1881b58/F477_c.rar">F477_c.rar</a> is the software used to calculate the undulation between the WGS84 ellipsoid (GPS height) and the EGM96 geoid (mean sea level) in C code (namely, f477.f).</p>
Model output for: Rate of mass loss across the instability threshold for Thwaites Glacier determines rate of mass loss for entire basin
<p><strong>Results from “Rate of mass loss across the instability threshold for Thwaites Glacier determines rate of mass loss for entire basin.”</strong></p> <p>The tar files herein contain multi-resolution grounding line position data and 4 km resolution output of modeled fields for all model runs. The region of interest represented is the Thwaites catchment in West Antarctica. The files for modeled fields have been coarsened or “flattened” to 4 km from their original adaptive mesh refinement (AMR) structure.</p> <p>Metadata contents</p> <p><em>1. Grounding line position data – text files</em></p> <p><em>2. Modeled Fields – HDF5 files</em></p> <p><em>3. BISICLES grid and coordinate system</em></p> <p> </p> <p><em>1. Grounding line position data</em></p> <p>Tar Files with "GLposition" in the title contain the annual grounding line positions, at cell faces, over the discretized Thwaites catchment for the specified model run. Each tar file contains a series of text files for a particular model run that used a specific background melt rate. The background marine melt rate is specified in the third field of the tar file name as delimited by the underscore character (“_”). Additionally, the last year of anomalous forcing before it was turned off leaving only the background marine melting is listed in the third field.</p> <p>nonuniformMM indicates the non-uniform background marine melting.</p> <p>uniformMM indicates the uniform background marine melting.</p> <p>260 and 270 are the last model years where anomalous marine melting were applied.</p> <p>After untarring a file, the naming convention for the individual text files is seen to be similar to the name of the respective tar file. The first field as delimeted by the underscore character contains either “glnonMM” or “gluniMM” followed by the last year of anomalous forcing used; e.g “gluniMM260”. The second field indicates the model year. Note that the last year forced is included for all runs.</p> <p>The text files contain three columns of data: an indicator of model resolution followed by <em>x- </em>and<em> y-</em>coordinates, respectively. Location coordinate units are meters and are BISICLES physical coordinates (see 3. BISICLES grid and coordinate system).</p> <p>For the first column:</p> <p>1 is 2 km resolution</p> <p>2 is 1 km resolution</p> <p>3 is 500 m resolution</p> <p>4 is 250 m resolution</p> <p>Zero (0) would be the base resolution of 4 km, however, all grounded ice was tagged to refine to level 1 so it does not appear in these files. Additionally, if a region was refined at a high resolution, then the grounding line positions for this region are not reported at any lower resolutions below this.</p> <p> </p> <p><em>2. Modeled fields</em></p> <p>Modeled fields are 4 km resolution in Chombo HDF5 file format. Each tar file contains the annual data as individual HDF5 files for the specified model run. The second field of the tar file as delimited by the underscore character specifies the background melt rate used and the last year of anomalous marine forcing (ramp).</p> <p>NonUniformMM260 indicates the non-uniform background marine melt rate with ramp shutoff after year 260.</p> <p>NonUniformMM270 same as above but ramp shutoff at year 270</p> <p>UniformMM260 indicates the spatially uniform background marine melt rate with ramp shutoff after year 270</p> <p>UniformMM270 same as above but ramp shutoff at year 270</p> <p>HDF5 files: The third field as delimited by the period (“.”) character shows the background melt rate used in individual HDF5 files and the fifth field indicates the model year.</p> <p>Contents of HDF5 files (field name: variable)</p> <p>xVel: velocity in the direction of the x-axis (m/a)</p> <p>yVel: velocity in the direction of the y-axis (m/a)</p> <p>Z_surface: upper ice surface elevation (masl)</p> <p>Z_bottom: underside surface ice elevation (masl)</p> <p>Z_base: bed elevation (masl)</p> <p>basal_friction: Basal friction coefficients</p> <p>div_uh: mass divergence</p> <p>mask: differentiates physical setting of cells (Note that coarsening introduces averages of numbers below at interfaces)</p> <p> grounded ice = 1</p> <p> floating ice = 2</p> <p> ocean = 4</p> <p> rock = 8</p> <p>basalThicknessSource: melt rate (m/a)</p> <p>surfaceThicknessSource: accumulation rate (m/a)</p> <p>surfaceThicknessBalance: sum of melt rate and accumulation rate (m/a)</p> <p> </p> <p><em>3. BISICLES grid and coordinate system</em></p> <p>The BISICLES model uses cell-centered grids with each cell represented by (i, j) pairs that begin numbering at (0,0) typically in the lower left hand corner of a domain. This project maintained the number ordering for the continental dataset such that (i = 366, j = 561) is the lower left cell for the included 4 km resolution HDF5 files and (i = 504, j=732) is the upper right cell. As the resolution is 4 km, this is noted as dx = 4000 in the HDF5 files.</p> <p>Since the data is located at cell centers, the physical coordinates relative to the BISICLES grid for a variable at (i, j) in meters is:</p> <p>(dx*(i + 0.5), dx*(j + 0.5)) = (BISICLES_X, BISICLES_Y)</p> <p>where dx is the cell resolution</p> <p>The translation from BISICLES physical coordinates (m) to polar stereographic projection in meters (standard parallel at -71 degrees) is as follows:</p> <p>(BISICLES_X – 3071500, BISICLES_Y – 3072500)</p> <p><br> </p> <p><br> </p> <p><br> </p> <p><br> </p> <p> </p>
Simulations and scripts for "Glacier surges controlled by the close interplay between subglacial friction and drainage".
<p>This repository contains the model and scripts to reproduce the results presented in "Glacier surges controlled by the close interplay between subglacial friction and drainage" and submitted to the Journal of Geophysical Research - Earth Surface. It provides the running model files associated with each result figure of the manuscript as well as the Python script to generate them from the simulation output. The model is also described and updated at: https://github.com/kjetilthogersen/pyGlacier.</p>
Model output for: "Freshwater input from glacier melt outside Greenland alters modeled northern high-latitude ocean circulation"
<p>NEMO-ANHA4 and OGGM model output files used in the article: "Freshwater input from glacier melt outside Greenland alters modeled northern high-latitude ocean circulation".</p> <p> </p> <p>halfsolid.zip and noOGGM.zip contain output of the runs called <em>halfsolid</em> and <em>noOGGM</em> in the article. output_data_OGGM.zip contains the output files of OGGM runs.</p>
Ground Freezing Index (GFI) and the relative change of the minimum seasonal velocity compared to the mean velocity between 2005 and 2017 at Becs-de-Bosson rock glacier in the Swiss Alps
<p>This dataset contains the Ground Freezing Index (GFI) and the relative change of the minimum seasonal velocity compared to the mean velocity between 2005 and 2017 at Becs-de-Bosson rock glacier in the Swiss Alps. Velocity data is measured by GNSS surveys. GFI is the sum of the daily negative GSTs during the entire freezing season, indicating the coldness of the winter temperature.</p>
Debris cover polygons of Pyrenean glaciers from 2000 to 2022
<p>We present the debris cover polygons of the last remaining Pyrenean glaciers for the years 2000, 2011, 2020 and 2022. The mapping was performed with QGIS (v3.28.1-Firenze) based on high-resolution orthophotos (available on the IGN website for 2000 and 2011) and very high-resolution orthomosaics (derived from UAV flights in 2020 and 2022).</p>
Arctic Glaciers Mass Balance from satellite gravimetry and DEM differencing
<p>This document describes the LEGOS-Magellium mass balance dataset for Arctic glacier regions based on satellite gravimetry and DEM (Digital Elevation Model) differencing. The total mass balance is evaluated for five regions of the 6th version of the Randolph Glacier Inventory namely the Arctic Canada North, Arctic Canada South, Iceland, Svalbard, and the Russian Arctic. The total mass balance of Arctic glaciers is evaluated mainly based on satellite gravimetry measurements. An ensemble approach updated by Blazquez et al. (2018) is adopted to assess uncertainties associated with the processing and post-processing of GRACE (Gravity Recovery And Climate Experiment) and GRACE-FO (GRACE-Follow On) data. A priori information from DEM differencing (Hugonnet et al., 2021) is used to reduce leakage errors associated with the mislocation of signal sources. Total mass changes expressed in Gt are estimated from April 2002 to September 2022 for five RGI regions. The uncertainty on total mass changes is provided with a confidence interval of 95%. The dataset is provided in the GlaMBIE CSV file format.</p> <p>References:</p> <ul> <li>Blazquez, A., Meyssignac, B., Lemoine, J., Berthier, E., Ribes, A., & Cazenave, A. (2018). Exploring the uncertainty in GRACE estimates of the mass redistributions at the Earth surface : Implications for the global water and sea level budgets. Geophysical Journal International, 215(1), 415‑430. https://doi.org/10.1093/gji/ggy293</li> <li>Hugonnet, R., McNabb, R., Berthier, E., Menounos, B., Nuth, C., Girod, L., Farinotti, D., Huss, M., Dussaillant, I., Brun, F., & Kääb, A. (2021). Accelerated global glacier mass loss in the early twenty-first century. Nature, 592(7856), Article 7856. https://doi.org/10.1038/s41586-021-03436-z</li> </ul> <p>The data product has been developed in collaboration between LEGOS and Magellium within the scope of the hybridization<br>challenge funded by the CNES (R&T Hybrid Spatial Gravimetry 2022/2023). </p>
FIGURE 1 in Tardigrada and Rotifera from moss microhabitats on a disappearing Ugandan glacier, with the description of a new species of water bear
FIGURE 1. Study area: A—map of Uganda with the location of Rwenzori Mountains, inserted map of Africa with Uganda marked, B—terminus of glacier with glacier moss aggregation.
FIGURE 5 in Tardigrada and Rotifera from moss microhabitats on a disappearing Ugandan glacier, with the description of a new species of water bear
FIGURE 5. Hypsibius sp.: A—habitus, dorso-ventral projection (PCM), B, C—dorso-caudal cuticle with distinct sculpturing formed by folds and thickenings, D—buccal apparatus (PCM), E—claws III, arrowheads indicate poorly visible pseudolunulae (PCM). All scale bars in micrometres.
FIGURE 3 in Tardigrada and Rotifera from moss microhabitats on a disappearing Ugandan glacier, with the description of a new species of water bear
FIGURE 3. Adropion afroglacialis sp. nov.: A—bucco-pharyngeal apparatus, holotype (PCM), B—bucco-pharyngeal apparatus, holotype (DIC), C—pharyngeal structures, holotype (PCM), D—annulation of the pharyngeal tube, paratype (SEM). All scale bars in micrometres.
FIGURE 4 in Tardigrada and Rotifera from moss microhabitats on a disappearing Ugandan glacier, with the description of a new species of water bear
FIGURE 4. Adropion afroglacialis sp. nov.: A—claws I, paratype (PCM), B—claws II, paratype (SEM), C—claws IV, paratype (PCM), D—claws IV, paratype (SEM), E—claws IV and cloaca, paratype (SEM). Arrowheads indicate poorly visible pseudolunulae. All scale bars in micrometres.
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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.