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32 results for “glacier melt”
FESOM2.1 model data used in the paper 'Atmospheric blocking slows ocean-driven melting of Greenland's largest glacier tongue'
<p>This data set includes the data necessary to reproduce the findings of McPherson et al., in revision, and recreate the figures in the manuscript.</p> <p>The output of model simulations with the global ocean sea ice model FESOM2.1 is provided. </p> <p>In particular, the data set includes:</p> <ol> <li>long term means of potential temperature, salinity, velocity and basal melt of the 79N Glacier averaged over 1970-2021 (<a target="_blank" rel="noopener noreferrer">fesom.mean.1970_2021.t.s.u.v.mat</a>)</li> <li>air-sea heat flux anomaly and anomalous wind field at 10m, taking mean winter (DJF) conditions between 2014 - 2016 from the mean of the years 2017 - 2020 (fesom.anom.fh.wind.mat)</li> <li>Atlantic Water temperature and velocity anomalies, taking mean winter (DJF) conditions between 2014 - 2016 from the mean of the years 2017 - 2020 (fesom.anom.t.u.v.mat)</li> </ol> <p>Each file includes information on the model grid (longitude and latitude of nodes, depths of the vertical layers, elements, nodal areas) needed for plotting the data.</p>
Dataset: Meltwater discharge, suspended sediment and bedload flux, air temperature and precipitation at the Otemma Glacier terminus (2022 melt season)
<p>This dataset extends existing timeseries from the Otemma Glacier terminus on meltwater discharge (Müller and Miesen, 2022), suspended sediment and bedload flux (Mancini et al., 2023a), and air temperature and precipitation (Müller, 2022) into the 2022 melt season. The methodologies used for data collection were consistent with those used in previous years (see included technical report). Water and sediment flux measurements were performed at gauging station GS1 (aka Station 1) located in the main proglacial river, approx. 0.35 downstream of the glacier terminus (Müller and Miesen, 2022; Mancini et al., 2023a). Air temperature and precipitation data were collected at the "Glacier snout station" adjacent to GS1 (Müller, 2022). Data collection specifics for the 2022 melt season are provided in the <code>report2022.pdf</code> file.</p> <p><strong>DATA:</strong><br><code>river.csv</code> - water and sediment flux timeseries from GS1 with columns:<br>year: yyyy<br>frac_day: fractional day of the year<br>dt: measurement interval [mins]<br>Qw: meltwater discharge [m3/s]<br>Qss: suspended sediment flux [kg/s]<br>Qb: bedload flux [kg/s]<br>Q<em>xx</em>_lo or Q<em>xx</em>_hi: the lower and upper uncertainty bounds for Qw, Qss, Qb [kg/s]<br><em>Extended data for 2020 and 2021 melt seasons also included*</em></p> <p><code>weather.csv</code> - air temperature and precipitation timeseries spanning 18 Nov 2021 to 16 Aug 2022 from 'Glacier snout station' with columns:<br>datetime: local time (UTC+01 with DST) [yyyy-mm-dd hh:mm:ss] <br>temperature: air temperature [°C]<br>precipitation: liquid or solid precipitation [mm w.eq.]</p> <p><em>*Extended flux datasets are also provided for 2020 and 2021 given the difference in scope between Mancini et al. (2023b) and Jenkin et al. (2024, submitted for review). The datasets in Mancini et al. (2023b) have been post-processed for comparative analysis between two different gauging stations (upstream and downstream), while in this work, the most temporally complete timeseries of subglacial sediment export at the upstream gauging station was required. Therefore, the data provided here for 2020 and 2021 include: (i) a short extension of the timeseries, (ii) the removal of a suspended sediment clipping threshold, and (iii) filling of gaps in the 2020 suspended sediment timeseries with real measured data.</em></p>
Supplementary data for the publication: 'Impact of ice topography, basal channels and subglacial discharge on basal melting under the floating ice tongue of 79N Glacier, northeast Greenland'
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Data from: Effect of subshelf melt variability on sea level rise contribution from Thwaites Glacier, Antarctica
<p>This archive includes output from the ice sheet model MALI used in the paper:<br> Effect of subshelf melt variability on sea level rise contribution from<br> Thwaites Glacier, Antarctica by Matthew Hoffman, et al.<br> submitted to Journal of Geophysical Research-Earth Surface in May 2019.</p> <p>The contents of this archive are:</p> <p>* control_run: annual output file for the control run</p> <p>* main_ensembles: global statistics files for the 72 runs making up the main<br> ensembles described in the paper and shown in Figures 4-6.</p> <p>* scaled_melt_ensemble: global statistics files for the scaled-melt ensemble<br> shown in Figures 9 and 10.</p> <p>* linear_perturbation_runs: global statistics files for each of the linear<br> perturbation runs used in Figure 11.</p> <p>The model code used to to generate the output and the scripts used to process<br> the data are available here:<br> https://github.com/matthewhoffman/MPAS-Model<br> or<br> https://github.com/MPAS-Dev/MPAS-Model<br> in the commit 93359c339b6d06726f8e3fbbfadc19337c20c0c7</p> <p>The processing scripts are in the directory:<br> testing_and_setup/compass/landice/Thwaites_variability</p>
Data: Spatially consistent microbial biomass and future cellular carbon release from melting Northern Hemisphere glacier surfaces
<p>Hydrology, flow cytometry and upscaling data for the manuscript "Spatially consistent microbial biomass and future cellular carbon release from melting Northern Hemisphere glacier surfaces". Each .zip file includes an individual 'readme' document.</p>
Data from: The last 50 years of climate-induced melting of the Maliy Aktru glacier (Altai Mountains, Russia) revealed in a primary ecological succession
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Iceberg melting substantially modifies oceanic heat flux towards a major Greenlandic tidewater glacier - data and code
<p>This repository contains code and data required to reproduce the analysis presented in the manuscript:</p> <p>Davison, B. J., Cowton, T. R., Cottier, F. R., and Sole, A. J. 2020. Iceberg melting substantially<br>modifies oceanic heat flux towards a major Greenlandic tidewater glacier". Nature Communications. <a href="https://www.nature.com/articles/s41467-020-19805-7">Iceberg melting substantially modifies oceanic heat flux towards a major Greenlandic tidewater glacier | Nature Communications</a></p> <p> </p> <p>NOTE: it has recently come to my attention that the ICEBERG package for MITgcm does not provide a physical blocking effect to the ocean (with thanks to Paul Summers for alerting me to this issue). </p>
The Glaciers of the Dolomites: last 40 years of melting
<p><strong>This dataset refers to: </strong></p> <p>Securo, A., Del Gobbo, C., Baccolo, G., Barbante, C., Citterio, M., De Blasi, F., Marcer, M., Valt, M., and Colucci, R. R.: The Glaciers of the Dolomites: last 40 years of melting, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-1357, 2024.</p> <p>Read more here: <a href="https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1357/">https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1357/</a></p> <p>The study presents a multi-decadal (1980s-2023) estimation of surface elevation change and geodetic mass balance of the current mountain glaciers present in the area. Calculations are based on geodetic data: high resolution and accuracy is obtained with unmanned aerial vehicle (UAV) Structure from Motion (SfM) and airborne Light Detection and Ranging (LiDAR), from 2010 to 2023. SfM on historical aerial imagery is used for previous decades.</p> <p><strong>The dataset contains:</strong></p> <ul> <li>1-band raster files (.tif) with the M3C2 (Multi Scale Model to Model Cloud Comparison) distance calculations from all mountain glaciers of the Dolomites, computed on common glacier area between the two periods. File names are as following: "AreaID_glaciername_year1_year2.tif"<br>SR<em> Monte Mario / Italy zone 2 - EPSG:3004</em></li> <li>Geopackage (.gpkg) files with glaciers area during different periods. Polygon have the following attributes: Area ID, Glacier name, CGI-ID (Comitato Glaciologico Italiano), RGI-ID (Randoph Glacier Inventory), Area, Year.<br>SR <em>WGS84 - EPSG: 4326</em></li> </ul> <p><strong>Additional 3D models of the Dolomites Glaciers in 2023 can be found here: <a title="Sketchfab Collection" href="https://skfb.ly/oRtOW" target="_blank" rel="noopener">Sketchfab</a></strong></p> <p><strong>Full file list:</strong></p> <p>"1_Popera_2010_2014.tif"<br>"1_Popera_Alto_1992_2010.tif"<br>"1_Popera_Alto_2010_2023.tif"<br>"1_Popera_Alto_2014_2023.tif"<br>"1_Popera_Pensile_1992_2010.tif"<br>"1_Popera_Pensile_2010_2023.tif"<br>"1_Popera_Pensile_2014_2023.tif"<br>"2_Cristallo_1992_2010.tif"<br>"2_Cristallo_2010_2014.tif"<br>"2_Cristallo_2010_2023.tif"<br>"2_Cristallo_2014_2023.tif"<br>"3_Sorapiss_Occidentale_1980_2010.tif"<br>"3_Sorapiss_Occidentale_2010_2014.tif"<br>"3_Sorapiss_Occidentale_2010_2023.tif"<br>"3_Sorapiss_Occidentale_2014_2023.tif"<br>"4_Antelao_2010_2014.tif"<br>"4_Antelao_Inferiore_1982_2010.tif"<br>"4_Antelao_Inferiore_2010_2023.tif"<br>"4_Antelao_Inferiore_2014_2023.tif"<br>"4_Antelao_Superiore_1982_2010.tif"<br>"4_Antelao_Superiore_2010_2023.tif"<br>"4_Antelao_Superiore_2014_2023.tif"<br>"5_Marmolada_1982_2010.tif"<br>"5_Marmolada_2010_2014.tif"<br>"5_Marmolada_2010_2023.tif"<br>"5_Marmolada_2014_2023.tif"<br>"6_Fradusta_1982_2010.tif"<br>"6_Fradusta_2010_2014.tif"<br>"6_Fradusta_2010_2023.tif"<br>"6_Fradusta_2014_2023.tif"<br>"6_Travignolo_1982_2010.tif"<br>"6_Travignolo_2010_2014.tif"<br>"6_Travignolo_2010_2023.tif"<br>"6_Travignolo_2014_2023.tif"</p> <p>"area_dolomites_glaciers.gpkg"<br>"area_dolomites_glaciers_1980s_1990s.gpkg"<br>"area_dolomites_glaciers_2010.gpkg"<br>"area_dolomites_glaciers_2023.gpkg"</p>
Model setup for simulations of melting beneath Thwaites Glacier
<p>Model parameter and forcing files needed to simulate the ocean beneath Thwaites Glacier during the period 2011-2022 as described in the paper "Strong ocean melting feedback during the recent retreat of Thwaites Glacier" in Geophysical Research Letters by the same authors. This is version 2 of the model setup. Version 1 had the seabed bathymetry and ice shelf topography files incorrectly oriented.</p>
The role of snowmelt, glacier melt and rainfall in streamflow dynamics on James Ross Island, Antarctic Peninsula
<p>Description: The file in this record represents a supplementary data for the journal paper <br>Ondřej Nedělčev, Michael Matějka, Kamil Láska, Zbyněk Engel, Jan Kavan, and Michal Jenicek (2024):<br>The role of snowmelt, glacier melt and rainfall in streamflow dynamics on James Ross Island, Antarctic Peninsula, DOY: 10.5281/zenodo.11001370 </p> <p>The file contains simulations of the HBV-light rainfall-runoff model for Triangular catchment on James Ross Island.<br>The model simulated different water balance components, such as runoff, snow water equivalent, galcier water equivalent, <br>evapotranspiration and groundwater storage for the study period 2010/11–2020/21.</p>
Global Glacier Debris Thickness Estimates and Sub-Debris Melt Factors V001
This data set includes spatially distributed estimates of the debris thickness and sub-debris melt enhancement factors for every debris-covered glacier in the Randolph Glacier Inventory Version 6, excluding the ice sheets and Antarctic Periphery. The debris thickness estimates are derived using a novel approach that uses a combination of sub-debris melt inversion and surface temperature inversion methods. The sub-debris melt enhancement factors are estimated from the debris thickness using debris thickness-melt curves normalized by estimates of the clean-ice melt.
High Mountain Asia Annual 90m Glacier Surface Melt/Freeze Phenology from SAR Imagery V001
This data set contains annual surface melt onset and freeze onset dates across all glaciers in the Hindu Kush Himalayas (HKH) retrieved from time series synthetic aperture radar (SAR) imagery. The data set was based on analysis of C-band Sentinel-1 A/B SAR time series, comprising 32,741 Sentinel-1 A/B SAR images. The duration of annual glacier surface melt was determined for 105,432 mapped glaciers (83,102 km2 glacierized area) during the calendar years 2017-2020.
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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