Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
291
datasets available to search
ShareScore release 0.9.0
Dataset results
291 results for “mass balance”
Seasonal Ice Mass-balance Buoy (SIMB) measurements from sites along the Beaufort Sea Coast, Alaska, 2018-ongoing
Measurements of the thickness of sea ice and the depth of its snow cover allow us to calculate how their mass changes in response to the varying fluxes of heat between the ocean and atmosphere over the course of a season. Repeated drill measurements are not ideal for this purpose since each drill hole disturbs the ice and its insulating snow cover. Also, spatial variability in ice thickness can mask temporal changes if holes are not drilled in the same place each time. Hence, methods that do not require re-drilling are preferred. Automated systems such as the Seasonal Ice Mass-balance Buoy (SIMB; Planck et al, 2019) provide high temporal resolution for capturing sub-daily variations and typically include sensor strings to measure the vertical temperature profile from the air to the ocean, which can be used to infer other properties of the ice cover such as strength and porosity. Under the Beaufort Lagoon Ecosystems LTER (BLE LTER) research program, several SIMBs are deployed at sites along the Beaufort Sea coast and record a suite of parameters including but not limited to snow depth, ice thickness, position of ice surface and bottom, water/air temperature, and vertical profiles of temperature. Planck, C. J., J. Whitlock, C. Polashenski, and D. Perovich (2019), The evolution of the seasonal ice mass balance buoy, Cold Regions Science and Technology, 165, 102792, doi: https://doi.org/10.1016/j.coldregions.2019.102792.
Dataset for "Remapping of Greenland ice sheet surface mass balance anomalies for large ensemble sea-level change projections"
<p>This dataset is used to reproduce the results presented in the following publication:</p> <p>Goelzer, H., Noel, B. P. Y., Edwards, T. L., Fettweis, X., Gregory, J. M., Lipscomb, W. H., van de Wal, R. S. W., and van den Broeke, M. R.: Remapping of Greenland ice sheet surface mass balance anomalies for large ensemble sea-level change projections, The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-188, in review, 2019.</p> <p> </p>
Summarized glacier mass balance measurements, McMurdo Dry Valleys, Antarctica (1993-2023, ongoing)
As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, a systematic sampling program has been undertaken to monitor glacial mass balance and meltwater flow. This data package contains total mass balance changes at each stake measured on six glaciers (Canada, Commonwealth, Hughes, Suess, Howard, and Taylor) in Taylor Valley and one glacier (Adams) in Miers Valley, all of which are located in the McMurdo Dry Valleys region of Antarctica. These values are the result of an analysis of the raw data presented in other data files (glacier stake heights, snow depths, and snow densities). Included here for each stake is the total water equivalent mass change. The standard deviation or the range for each total is given. Most measurements began during the 93-94 field season. Adams measurements were established during the 14-15 field season. Measurements are ongoing except at Hughes and Suess Glaciers where monitoring ceased following the 08-09 field season. Monitoring the changes in these measurements over time provides a record of mass balance, and aids in determining the role of glaciers in the polar hydrologic cycle.
Snow, ice, and total glacier mass balance measurements, McMurdo Dry Valleys, Antarctica (1993-2023, ongoing)
As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, a systematic sampling program has been undertaken to monitor glacial mass balance and meltwater flow. This data package includes mass balance changes at each stake on six glaciers (Canada, Commonwealth, Hughes, Suess, Howard, and Taylor) in Taylor Valley and one glacier (Adams) in Miers Valley, all of which are located in the McMurdo Dry Valleys region of Antarctica. The values are the result of an analysis of the raw data presented in other data files (glacier stake heights, snow depths, and glacier snow densities). Included here for each stake are the change in ice and snow water equivalent (mass) values, and the total mass change. The standard deviation or the range for each total is also given. Most measurements began during the 93-94 field season. Adams measurements were established during the 14-15 field season. Measurements are ongoing except at Hughes and Suess Glaciers where monitoring ceased following the 08-09 field season. Monitoring the changes in these measurements over time provides a record of mass balance, and aids in determining the role of glaciers in the polar hydrologic cycle.
Output from the Glacier Energy and Mass Balance (GEMB v1.0) forced with 3-hourly ERA5 fields and gridded to 10km, Greenland and Antarctica 1979-2024
<p>These model output of firn air content (FAC) and surface mass balance (SMB) are from version 1.0 of the open-source Glacier Energy and Mass Balance model. GEMB is a column model of ice sheet and glacier surface-atmospheric energy and mass exchange as well as firn state. GEMB has been integrated into the open-source Ice-Sheet and Sea-level System Model which can be downloaded at https://issm.jpl.nasa.gov/. Here, GEMB is forced with 3-hourly ERA5 output from 1979 through end of 2024. For Greenland and its periphery, the ERA5 surface temperature and downwelling longwave radiation forcing are spatially bias-corrected for each month. All values are adjusted by the difference between the RACMO2.3 and the ERA5 1980-2015 monthly means. The GEMB output is bilinearly interpolated onto a 10km grid, from the native ISSM grid, and the output is given as 5-day output or as monthly.</p>
Understanding monsoon controls on the energy and mass balance of glaciers in the Central and Eastern Himalaya (Data Sets and Codes)
<p>This repository contains AWS datasets for the modelling periods considered in the analysis presented in the research paper, together with ablation measurements, pre-processed forcing data, T&C model codes, outputs and scripts for analysing outputs. When previously published elsewhere, references and links to the full, original datasets are provided under References.</p> <p>Matlab scripts for executing the T&C model are provided and should work stand-alone on any machine with a Matlab version 2019b or later installed.</p>
Arctic glaciers mass balance from satellite gravimetry only
<p>This document describes the LEGOS-Magellium mass balance dataset for Arctic glacier regions based on satellite gravimetry only. A similar dataset using an a priori based on DEM differencing has been submitted to GLAMBIE (Pfeffer et al., 2024). previously. The dataset submitted here does not use an a priori, but uses only satellite gravimetry data. </p> <p>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, Russian Arctic. The total mass balance of Arctic glaciers is evaluated mainly based on satellite gravimetry measurements. An ensemble approach updated from Blazquez et al. (2018) is adopted to evaluate uncertainties associated with the processing and post-processing of GRACE (Gravity Recovery And Climate Experiment) and GRACE-FO (GRACE-Follow On) data. The effect of land hydrology is estimated for each region, but not corrected in the total mass balance dataset, because of the small water mass balance values and large errors inherent to hydrological models. 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>The data product has been developed in collaboration between LEGOS and Magellium within the scope of the hybridation<br>challenge funded by the CNES (R&T Hybrid Spatial Gravimetry 2022/2023).</p> <p><strong>Reference</strong>:</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>Pfeffer, J., Coupry, B., Berthier, E., Blazquez, A., & Barnoud, A. (2024). Arctic Glaciers Mass Balance from satellite gravimetry and DEM differencing [Jeu de données]. Zenodo. https://doi.org/10.5281/ZENODO.13134559</li> </ul> <p> </p>
MARv3.10 outputs: What is the Surface Mass Balance of Antarctica? An Intercomparison of Regional Climate Model Estimates
<p>MARv3.10 outputs used in:</p> <p><em>Mottram, R., Hansen, N., Kittel, C., van Wessem, M., Agosta, C., Amory, C., Boberg, F., van de Berg, W. J., Fettweis, X., Gossart, A., van Lipzig, N. P. M., van Meijgaard, E., Orr, A., Phillips, T., Webster, S., Simonsen, S. B., and Souverijns, N.: What is the Surface Mass Balance of Antarctica? An Intercomparison of Regional Climate Model Estimates, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2019-333, accepted, 2020.</em></p> <ul> <li>MARv3.10 forced by ERA-Interim outputs with monthly values of SMB and components (kg m<sup>-2</sup> month<sup>-1</sup>), and (near-) surface temperature (°C) over the Antarctic ice sheet (1981--2018)</li> <li>Grid file used in MAR simulation</li> </ul> <p>Be carreful that the unit metadata in the netcdf files from SMB and its components are uncorrect. <strong>Values are in kg m<sup>-2</sup> month<sup>-1</sup></strong> instead of kg m<sup>-2</sup> day<sup>-1</sup>.<br> <br> If you need other variables or output frequencies from MAR, write me (c2kittel@gmail.com) and I will be glad to help you. I will also be happy to share the scripts I have developed to analyse the outputs and make the figures in this paper if needed. Please cite the paper if you use these MAR outputs. However, note that these outputs are now considered as deprecated since new outputs using a more recent model version (MARv3.11) and forcing (ERA5) have been published (see Kittel et al., 2021: https://tc.copernicus.org/articles/15/1215/2021/).<br> <br> Data usage notice:</p> <p>If you use any of these results, please acknowledge the work of the people involved in producing them. Acknowledgements should have language similar to the below that contained informations related to MAR. In order to document MAR scientific impact and enable ongoing support of the model, users are likely encouraged to contact C. Kittel and C. Agosta to add their works in the list of MAR-related publications. </p> <p>"We thank the MAR team which make available the model outputs, as well agencies (F.R.S - FNRS, CÉCI, and the Walloon Region) that provided computational resources for MAR simulations."</p> <p>You should also refer to and cite the following paper:</p> <p><em>Mottram, R., Hansen, N., Kittel, C., van Wessem, M., Agosta, C., Amory, C., Boberg, F., van de Berg, W. J., Fettweis, X., Gossart, A., van Lipzig, N. P. M., van Meijgaard, E., Orr, A., Phillips, T., Webster, S., Simonsen, S. B., and Souverijns, N.: What is the Surface Mass Balance of Antarctica? An Intercomparison of Regional Climate Model Estimates, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2019-333, accepted, 2020.</em></p>
Data accompanying the article "Arctic sea ice mass balance in a new coupled ice-ocean model using a brittle rheology framework"
<p><em>Amonthly_files.tar.gz</em> contains the gridded monthly averaged quantities used in the manuscript Arctic sea ice mass balance in a new coupled ice-ocean model using a brittle rheology framework" for each year between 2000 and 2018.</p> <p>Files containing "simba" in their name contain quantities related to the sea ice mass balance (volume of melt/growth...)</p> <p>Files containing "icemod" in their name contain other quantities related to sea ice properties (thickness, concentration...)</p> <p>In case information is missing, do not hesitate to contact guillaume.boutin@nersc.no , heather.regan@nersc.no or einar.olason@nersc.no</p> <p>This research has been funded by the Norwegian Research Council (Nansen Legacy: grant no. 27673, FRASIL: grant no. 263044, and ARIA: grant no. 302934), JPI Climate and JPI Oceans (MEDLEY project, under agreement with the Norwegian Research Council, grant no 316730), and by Copernicus Marine Environment Monitoring Service (CMEMS) WIzARd project. CMEMS is implemented by Mercator Ocean in the framework of a delegation agreement with the European Union<br> Copernicus Marine Environment Monitoring Services (contract no.<br> 69), and the European Space Agency through the Cryosphere Virtual Laboratory (CVL, grant no. 4000128808/19/I-NS).</p>
Downscaled surface mass balance in Antarctica: impacts of subsurface processes and large-scale atmospheric circulation
<p>Here is the surface mass balance calculated from a offline subsurface model, that is used in the paper Downscaled surface mass balance in Antarctica: impacts of subsurface processes and large-scale atmospheric circulation.<br> More data are available by contacting nichsen@space.dtu.dk</p>
Climatic mass balance of the Cordillera Darwin Icefield (2000-2023), Tierra del Fuego, Chile
<p>This dataset contains the annual averages of the climatological input and the modelled climatic mass balance of the Cordillera Darwin Icefield, Tierra del Fuego. The climatological input is calculated by statistically downscaling ERA5 reanalysis data to weather stations across the Cordillera Darwin via Quantile Mapping. The precipitation is simulated with an orographic precipitation model. Global radiation is simulated with a radiation model. The applied climatic mass balance model is the coupled snowpack and ice surface energy and mass balance model (COSIPY).</p>
Glaciological data (point mass balance, SWE, snow depth, bulk snow density, modelled runoff) from Werenskioldbreen (Svabard) 2009-2020
<p>This repository contains supporting data associated to the manuscript to <em>Earth System Science Data: </em></p> <p><strong>Ignatiuk D., Błaszczyk M., Budzik T., Grabiec M., Jania J., Kondracka M., Laska M., Małarzewski Ł., Stachnik Ł. A decade of glaciological and meteorological observations in the High Arctic (Werenskioldbreen, Svalbard)</strong></p> <p>In 2009-2020, 9 ablation stakes were installed on the Werenskioldbreen.<strong> </strong>Based on the data collected, the following glaciological variables are available for Werenskioldbreen: annual and seasonal point ablation and accumulation, snow cover depth, bulk snow density and SWE (snow water equivalent) at the measuring points and modelled total runoff from the surface ablation. </p>
Monthly accumulated sublimation and yearly accumulated surface mass balance (SMB) components RACMO model simulations for Antarctica on 27km grid for 2000-2012
<p>Monthly accumulated (denoted monthlyS) sublimation components and yearly accumulated (denoted yearlyS) surface mass balance (SMB) components for Antarctica (ANT) on 27 km horizontal grid produced by RACMO model are presented in this dataset for the year 2000-2012. The dataset consists of data from three simulations named, NODRIFT, Rp3, and RpNew. NODRIFT represents the run with no blowing snow sublimation, Rp3 corresponds to version of the blowing snow model with simplifications, RpNew corresponds to the advanced version with new updates to the blowing snow model in RACMO. Details of the simulations can be found in the associated paper : <a title="Contribution of blowing snow sublimation to the surface mass balance of Antarctica" href="https://doi.org/10.5194/egusphere-2024-116" target="_blank" rel="noopener">https://doi.org/10.5194/egusphere-2024-116</a>. The data includes yealy accumulated SMB components including SMB, snow melt, refreezing, precipiation, runoff, blowing snow erosion, surface sublimation, and blowing snow sublimation, the data also includes yearly averaged (denoted yearlyA) . Furthermore, the data includes monthly accumulated sublimation components of surface sublimation (subl), and blowing snow sublimation (suds). </p>
Antarctic surface mass balance with the regional climate model MAR (1979–2015)
<p>Outputs of the regional climate model MAR v3.6.41 for Antarctica, resolution 35km + source code</p> <p>===========================================</p> <p>Cécile Agosta, 23 Jan 2019 </p> <p>cecile.agosta@gmail.com</p> <p>===========================================</p> <p>Grid specifications are given in MAR-ant35km-grid.nc (projection : EPSG 3031).</p> <p>* State variables are averages of daily means:</p> <p> TT > temperature (°C)</p> <p> ZZ > height above sea level (m)</p> <p> UU, VV > x-wind and y-wind in the stereographic grid (m s-1)</p> <p> UV > wind speed (m s-1)</p> <p>State variables ending with z (e.g. UUz) are interpolated on fixed altitude levels above the ground.</p> <p>State variables ending with p (e.g. UUp) are interpolated on fixed pressure levels.</p> <p>* SMB components are summed: kg m-2 month-1 for montly files, kg m-2 year-1 for annual files, kg m-2 year-1 for clim files</p> <p> snf > snowfall</p> <p> rnf > rainfall</p> <p> rof > run-off</p> <p> sbl > sublimation/condensation</p> <p> smb = snf + rnf - sbl - rof</p> <p> mlt > snowmelt</p> <p> rfz > refreezing</p> <p>If you use this data, please cite the final accepted version of this article:</p> <p>Agosta C., Amory C., Kittel C., Orsi A., Favier V., Gallée H., van den Broeke M.R., Lenaerts J.T., van Wessem J.M., & Fettweis X. (in review, 2018). Estimation of the Antarctic surface mass balance using MAR (1979-2015) and identification of dominant processes. <em>The Cryosphere Discussions</em>, 1–22, <a href="https://doi.org/10.5194/tc-2018-76">doi:10.5194/tc-2018-76</a>.</p> <p>Please contact me if you need other outputs (variables/daily or hourly time steps)</p>
Greenland monthly surface mass balance 1840-2012
<p>This surface mass balance (SMB) product is based on Box (2013), with refinements described by Schlegel et al. (2016), Appendix. It includes monthly estimates of accumulation and melt, as well as estimates for SMB error for the historical period (1960-2012).</p> <p>It is the results of a calibration of observational data to regional climate model (RCM) output, in this case RACMO2.3 (Noël et al., 2016). The calibration for temperature (T) and SMB components is based on a 53-year overlap period (1960-2012). Note that the overlap period for the calibration of snow accumulation rate is shorter, since ice core data availability drops after 1999. Calibration is made using linear regression coefficients for 5 km grid cells that match the average of the reconstruction to RACMO2.3. The RACMO2.3 output are resampled and reprojected from the native 0.1 deg (~10 km) grid to a 5 km grid better resolving areas where sharp gradients occur, especially near the ice margin where mass fluxes are largest. </p>
Projected surface mass balance of the Juneau Icefield, Southeast Alaska (2030 to 2060, RCP8.5)
<p>This dataset contains the modelled projected surface mass balance of the Juneau Icefield in Southeast Alaska for two global climate models (GFDL-CM3 and NCAR-CCSM4) for the RCP8.5 emissions scenario from 2030 to 2060. The simulated surface mass balance was produced using COSIPY - a coupled snowpack and ice surface energy and mass balance model. The input climate data was originally dynamically downscaled in Lader et al., (2020, <em>J. Appl. Meteor. Climatol.)</em></p> <p>This data supplements the manuscript "<em>Surface mass balance modelling of the Juneau Icefield highlights the potential for rapid ice loss by the mid-21st century</em>" submitted to the Journal of Glaciology. </p>
Past surface mass balance of the Juneau Icefield, Southeast Alaska (1980 to 2019)
<p>This dataset contains the modelled past surface mass balance of the Juneau Icefield in Southeast Alaska for three models. A reanalysis model, CFSR from 1980 to 2019. And two global climate models (GFDL-CM3 and NCAR-CCSM4) from 1980 to 2010. The simulated surface mass balance was produced using COSIPY - a coupled snowpack and ice surface energy and mass balance model. The input climate data was originally dynamically downscaled in Lader et al., (2020, <em>J. Appl. Meteor. Climatol.)</em></p> <p>This data supplements the manuscript "<em>Surface mass balance modelling of the Juneau Icefield highlights the potential for rapid ice loss by the mid-21st century</em>" submitted to the Journal of Glaciology. </p>
Wood Buffalo Environmental Association (WBEA) Historical Monitoring Data used in "Passive Tracer Modelling at Super-Resolution with WRF-ARW to Assess Mass-Balance Schemes"
<p>Wood Buffalo Environmental Association (WBEA) Historical Monitoring Data from two monitoring stations Bertha Ganter – Fort McKay and Barge Landing for 20 August 2013 to 2 September 2013. This data was used in "Passive Tracer Modelling at Super-Resolution with WRF-ARW to Assess Mass-Balance Schemes" (Fathi et al., 2022 - egusphere-2022-1125) for model output and observational data comparisons. The same data can be accessed and downloaded from "<a href="https://wbea.org/historical-monitoring-data/">https://wbea.org/historical-monitoring-data/</a>".</p>
Surface mass balance of the Monte Sarmiento Massif (2000-2022), Tierra del Fuego, Chile.
<p>This dataset contains the climatological input and the modelled surface mass balance of the Monte Sarmiento Massif, Tierra del Fuego, calculated with four different surface mass balance models. The climatological input is calculated by statistically downscaling ERA5 reanalysis data to a weather station at Schiaparelli Glacier via Quantile Mapping. The precipitation is simulated with an orographic precipitation model. The four applied surface mass balance models are a) a positive degree day model (PDD), b) a simplified energy balance model with potential radiation (SEBGpot) and c) with actual radiation including cloud cover and shading (SEBG), and d) a coupled snowpack and ice surface energy and mass balance model (COSIPY).</p>
Dataset - "Ice core evidence for a 20th century increase in surface mass balance in coastal Dronning Maud Land, East Antarctica"
<p>We provide in this dataset the isotopic and ionic records from a 120 m-long ice core drilled at the summit of Derwael ice rise (70°14'44.88'' S, 26°20'5.64'' E) situated in coastal Dronning Maud Land, East Antarctica. Ions concentrations (Na<sup>+</sup>, MSA, Cl<sup>-</sup>, SO<sub>4</sub><sup>2-</sup> and NO<sub>3</sub><sup>-</sup>) and water stable isotopes (δ<sup>18</sup>O, δD and d-excess) are presented for the top 103 meters (corresponding to 1815 and the Tambora eruption) with a continuous record for the water stable isotopes and discontinuous sections for ions concentrations. A complementary database “Annual layer thicknesses and age-depth (oldest estimate) of Derwael Ice Rise (IC12), Dronning Maud Land, East Antarctica” with age model and annual layer thickness is available at https://doi.org/10.1594/PANGAEA.857574 and the companion paper is Philippe et al., 2016 (“Ice core evidence for a 20th century increase in surface mass balance in coastal Dronning Maud Land, East Antarctica”, https://doi.org/10.5194/tc-10-2501-2016).</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.