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291 results for “mass balance”
Global continental discharge estimates from ocean mass balance
<p>These files include a time series of global continental discharge estimated from ocean mass balance following Chandanpurkar et al., 2017. </p> <p>The ocean mass balance is obtained from these components:</p> <p>dM/dt (change in ocean mass): From altimetry and from GRACE/FO. When derived from altimetry, steric level change is subtracted from the GMSL using EN4.2.2 temperature and salinity data. </p> <p>E-P: Here, two methods are used:</p> <p>1. Directly, using estimates of ocean E and P, using OAFlux for E and GPCP and CMAP separately for P</p> <p>2. Indirectly, using atmospheric moisture balance using vertically integrated horizontal moisture flux divergence, and change in the total column water vapor. These are obtained using ERA5 and MERRA-2 reanalyses products.</p> <p>The eight discharge estimates are combinations of the above, and the exact combination is mentioned in the filename. </p>
Fifty years of instrumental surface mass balance observations at Vostok Station, central Antarctica
<p>The database of snow buildup, density and accumulation rate values as observed at the accumulation-stake farms in the vicinity of Vostok station (central East Antarctica) since January 1970.</p> <p>The reference for the data: Ekaykin A.A., Lipenkov V.Ya., Tebenkova N.A. Fifty years of instrumental surface mass balance observations at Vostok Station, central Antarctica. - J. of Glaciology, 2023, 1–13. https://doi.org/10.1017/jog.2023.53.</p> <p> </p>
Figure Data for "Continuous Estimates of Glacier Mass Balance in High Mountain Asia Based on ICESat-1,2 and GRACE/GRACE Follow-On"
<p>Figure Data for "Continuous Estimates of Glacier Mass Balance in High Mountain Asia Based on ICESat-1,2 and GRACE/GRACE Follow-On" Data" </p>
COSIPY distributed simulations of Mera Glacier mass and energy balance (20161101-20201101)
<p>The four netCDF files contain outputs from COSIPY model (Sauter et al., 2020) for Mera Glacier for the period 20161101 to 20201101. The model is run on a 0.003°*0.003° grid, and forced with meteological variables collected locally and distributed with constant gradients. The "constants.py" is the python file that contains the specific model settings.</p>
Foam fractionation for removal of per- and polyfluoroalkyl substances: Towards closing the mass balance
<p>This data is associated with the publication <em><span>Foam fractionation for removal of per- and polyfluoroalkyl substances: Towards closing the mass balance</span></em><span><em>:</em> </span><span><a>https://doi.org/10.1016/j.scitotenv.2023.162050. </a></span></p>
Nitrate δ15N values and surface mass balance reconstructions from East Antarctica
<p>Geographic information, surface mass balance (SMB) data, and sub-photic zone (>0.3 m) nitrate concentration and nitrogen isotopic composition (δ15NNO3) for 135 sites across East Antarctica. This database was used to examine and define the relationship between δ15NNO3 and SMB in Antarctica as part of the SCADI (Snow Core Accumulation from Delta-15N Isotopes) and EAIIST (East Antarctic International Ice Sheet Traverse) projects. Of these 135 sites, 92 are newly reported here while the other site data were previously published and are cited accordingly. Snow bearing nitrate was sampled from snow pits and firn/ice cores at different dates depending on the original scientific campaign, but predominately between 2010 and 2020, with the earliest sampling occurring in 2004. Nitrate was later extracted from the snow, concentrated, and analyzed for δ15NNO3. Surface mass balance data comes from a combination of previous ground-based observations (e.g., stakes, ice core data) and the output from Modèle Atmosphérique Régional version 3.6.4 with European Centre for Medium-Range Weather Forecasts “Interim” re-analysis data (ERA-interim) data, adjusted for observed model SMB biases. Elevation data were extracted from the Reference Elevation Model of Antarctica (REMA, <a href="https://doi.org/10.5194/tc-13-665-2019">https://doi.org/10.5194/tc-13-665-2019</a>).</p> <p>Also contains nitrate concentration and isotopic (δ15NNO3) data, ice density, and surface mass balance estimates from the ABN1314-103 ice core. This 103 m long core was drilled beginning on 07 January 2014 as one of three ice cores at Aurora Basin North, Antarctica (-71.17, 111.37, 2679 m.a.s.l), in the 2013-2014 field season. The age-depth model for ABN1314-103 was matched through ion profiles from an annually-resolved model (ALC01112018) originally developed for one of the other ABN cores through seasonal ion and water isotope cycles and constrained by volcanic horizons. Each 1 m segment of the core was weighed and measured for ice density calculations, and then sampled for nitrate at 0.33 m resolution. Nitrate concentrations were taken on melted ice aliquots with ion chromatography, while isotopic analysis was achieved through bacterial denitrification and MAT 253 mass spectrometry after concentrating with anionic resin. Using the density data and the age-depth model’s dates for the top and bottom of each 1 m core segment, we reconstructed a history of surface mass balance changes as recorded in ABN1314-103. Additionally, we also estimated the effect of upstream topographic changes on the ice core’s surface mass balance record through a ground penetrating radar transect that extended 11.5 km against the direction of glacial ice flow. The modern SMB changes along this upstream transect were linked to ABN1314-103 core depths by through the local horizontal ice flow rate (16.2 m a-1) and the core’s age-depth model, and included here for comparative analysis. </p>
Antarctic surface climate and surface mass balance in the Community Earth System Model version 2 (1850-2100) - AWS data
<p>This Antarctica AWS temperature and wind speed dataset was compiled by Alexandra Gossart and Niels Souverijns (<a href="https://doi.org/10.1175/JCLI-D-19-0030.1">https://doi.org/10.1175/JCLI-D-19-0030.1</a>).</p>
Reconstructed, Long-Term Meteorological Forcing and Mass Balance over Glaciers in High Mountain Asia
<p>%%%% Reconstructed_WRF_Forcing_HMA.mat %%%%%%%%<br> A reconstructed, long-term data series of meteorological data for three glaciers in distinct regions of High Mountain Asia. The meteorological forcing consists of hourly Weather Research and Forecasting (WRF) data bias-corrected by high elevation, off-glacier automatic weather station (AWS) data. The variables of the forcing data consist of 2m air temperature ('T' °C), relative humidity ('RH' %), air pressure ('PRESS' hPa), incoming shortwave ('SWIN' Wm-2) and longwave ('LWIN' Wm-2) radiation, precipitation ('PP', mm hr) and wind speed ('FF' m s-1). The period of the timeseries ranges from January 1981 to December 2019. <br> <br> Data are available for the following glaciers (bias-corrected to the given coordinates / elevation of the off-glacier AWS)<br> Yala Glacier, Nepal (28.237°N 85.619°E, 5090 m a.s.l.) - 'AWS Yala Basecamp' (AWS Data available on the ICIMOD RDS)<br> Parlung Glacier Number 4 (29.245°N 96.928°E, 4600 m a.s.l.) - 'AWS4600' (Contact author Wei Yang for AWS data requests)</p> <p>Mugagangqiong Glacier (32.234°N, 87.485°E, 5850 m a.s.l.) - 'AWS5850' (Contact author Wei Yang for AWS data requests)</p> <p> </p> <p>Data are stored as matlab tables in .mat files. Data were created using Matlab version 2020b. </p> <p> </p> <p>%%%% Reconstructed_Mass_Balance_HMA.mat %%%%%%%%<br> <br> Reconstructed cumulative mass balances for the aforementioned glaciers (values in m w.e.)</p> <p> </p> <p> </p> <p> </p> <p> </p>
Inconsistent mass balance relationships in Central Asia
<p>These are the files to reproduce the results of the manuscript "Inconsistencies in Central Asia's spatio-temporal glacier response: On issues about meteorological and mass balance estimates, and regional simplifications" submitted to The Cryosphere Discussions.</p>
Geodetic mass balance of Mýrdalsjökull ice cap, 1999−2021: DEM processing and climate analysis
<p>This repository gathers the data I used and produced during my master thesis at the University of Iceland from April to September 2022 with the financial support of the Landsvirkjun.</p> <p>The geodetic mass of Mýrdalsjökull, the fourth largest Icelandic ice cap, was investigated over the period 1999-2021. The untapped <strong>SPOT5 </strong>archive (2002−2015), the <strong>lidar </strong>data, the <strong>Pléiades </strong>imagery (2011−present), <strong>aerial photographs</strong> from 1999 [1] and the <strong>ArcticDEM </strong>dataset (2010−2019) [2] were used to create Digital Elevation Models (DEMs) of the ice cap. A pre-processing of the DEMs was first performed: co-registration, filtering and interpolation. Then, applying a <strong>Gaussian Process regression</strong> (GP) [3], a state-of-the-art method in DEM processing, a spatially and temporally continuous DEM dataset was created, in 15 x 15 m resolution and 1-month interval from 1999 to 2021. <strong>Volume and mass changes</strong> based on the synthetic GP-generated DEMs were computed and analyzed in 5-year and annual intervals between 1999 and 2019. A local analysis of three glacierized catchments of Mýrdalsjökull (southern catchment, northern catchment and Kötlujökull outlet) was also performed. Errors were estimated using the method from [4]. Tools from the following repositories were used:</p> <ul> <li> <p><em>demcoreg </em>(<a href="https://doi.org/10.5281/zenodo.5733347">https://doi.org/10.5281/zenodo.5733347</a>): DEM co-registration</p> </li> <li> <p><em>xdem </em>(<a href="https://doi.org/10.5281/zenodo.4809698">https://doi.org/10.5281/zenodo.4809698</a>): uncertainties computation and DEM manipulation </p> </li> <li> <p><em>pyddem </em>(<a href="https://pypi.org/project/pyddem/">https://pypi.org/project/pyddem/</a>): Gaussian Process regression</p> </li> </ul> <p>The complete master thesis can be accessed at :</p> <p> </p> <p>The repository contains the following data:</p> <p><strong>1</strong> – <strong>DEM_coregistered</strong></p> <p>All DEMs have been coregistered considering the Islandsdem v1.0 as a reference (atlas.lmi.is/dem)</p> <p>The DEM naming works as follow: </p> <p><em>Glaciername_DEM_date_sensor_resolution_zmae_projection_otherinformation.tif</em></p> <p>The files ending with <strong>*_filtered.tif</strong> (SPOT5, AerialPhotographs) have been filtered using the filtering combination described in 3.1.3.</p> <p>The files ending with<strong> *_mosaic.tif</strong> (SPOT5, Pléiades) are the result of the mosaicking of several DEMs.</p> <p> </p> <p><strong>2</strong> – <strong>Shapefiles</strong></p> <p>Outlines of Mýrdalsjökull in 1999 [1], 2003, 2010 and 2019 [6]</p> <p>Outlines of the three catchments (South, North and Kötlujökull) in 1999.</p> <p>Reference buffer around Mýrdalsjökull used to crop all DEMs to the same extent. </p> <p>Equilibrium Line Altitude (ELA) from 2004-10-05.</p> <p> </p> <p><strong>3</strong> – <strong>Gaussian_Process_regression</strong></p> <p>2 netcdf files: the stack of DEMs (<strong>*_DEMstack_*</strong>) and the result of the Gaussian Process regression (<strong>*_GPregression_*</strong>).</p> <p>Both files were obtained thanks to <em>pyddem </em>tools.</p> <p>The Gaussian Process regression was run at a spatial resolution of 15 x 15m and a temporal resolution of 1 month, starting in January 1999 and ending in December 2022.</p> <p> </p> <p><strong>4</strong> – <strong>Mass_balance_results</strong></p> <p>1 csv file containing mass balance results:</p> <ul> <li> <p>Annual mass balance for the ice cap & the 3 catchments</p> </li> <li> <p>4-year mass balance for the ice cap & the 3 catchments</p> </li> <li> <p>Results from the comparison with survey dates mass balance (Fig 11(a))</p> </li> <li> <p>Results from the comparison with [3] (Fig 11(b))</p> </li> <li> <p>Mass balance overview (Fig 11(c))</p> </li> </ul> <p> </p> <p><strong>References</strong></p> <p>[1] Belart, J., Magnússon, E., Berthier, E., Gunnlaugsson, Á. Þ., Pálsson, F., Aðalgeirsdóttir, G., Jóhannesson, T., Thorsteinsson, T., and Björnsson, H. (2020). Mass balance of 14 Icelandic glaciers, 19452017: spatial variations and links with climate. Frontiers in Earth Science, page 163.</p> <p>[2] Porter, C., Morin, P., Howat, I., Noh, M., Bates, B., Peterman, K., Keesey, S., Schlenk, M., Gardiner, J., et al. (2018). ArcticDEM. Harvard Dataverse, 1.</p> <p>[3] Hugonnet, R., McNabb, R., Berthier, E., Menounos, B., Nuth, C., Girod, L., Farinotti, D., Huss, M., Dussaillant, I., Brun, F., et al. (2021). Accelerated global glacier mass loss in the early twenty-first century. Nature, 592(7856):726731.</p> <p>[4] Hugonnet, R., Brun, F., Berthier, E., Dehecq, A., Mannerfelt, E. S., Eckert, N., and Farinotti, D. (2022). Uncertainty analysis of digital elevation models by spatial inference from stable terrain. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.</p> <p>[5] Hannesdóttir, H., Sigurðsson, O., Þrastarson, R. H., Guðmundsson, S., Belart, J. M., Pálsson, F., Magnússon, E., Víkingsson, S., Kaldal, I., and Jóhannesson, T. (2020). A national glacier inventory and variations in glacier extent in Iceland from the Little Ice Age maximum to 2019. Jökull 2020: 1, 34.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>Pléiades images were acquired at research price thanks to the CNES ISIS programme (http://www.isiscnes.fr). This study uses the lidar mapping of the glaciers in Iceland, funded by the Icelandic Research Fund, the Landsvirkjun research fund, the Icelandic Road Administration, the Reykjavík Energy Environmental and Energy Research Fund, the Klima- og Luftgruppen research fund of the Nordic Council of Ministers, the Vatnajökull National Park, the organization Friends of Vatnajökull, LMÍ, IMO, and the UI research fund.</p> <p> </p> <p><strong>Dataset Attribution</strong> </p> <p>This dataset is licensed under a <a href="https://creativecommons.org/licenses/by-nc/4.0/">Creative Commons CC BY-NC 4.0 International License</a> (Attribution-NonCommercial).</p>
Ice thickness and surface velocities for the manuscritpt Mass balance and stability of ice tongues in the Western Ross Sea
<p>The data set contains:</p> <p>ICESat-2 derived point ice thickness for 9 ice tongues in csv format</p> <p>Surface average velocities derived from the ASF Vertex platform on demand HYP3 autoRIFT velocity product for 9 ice tongues.</p>
Paleohydrology on Mars constrained by mass balance and mineralogy of pre‐Amazonian sodium chloride lakes
<p>Included are the raw results obtained by running the numerical models and analyzing the remote sensing maps. Also included are the input data and output data for all CHIM-XPT and SOLVEQ-XPT geochemical models, so the user may reproduce the results from the paper. Programs CHIM-XPT and SOLVEQ-XPT ((c) Mark H. Reed, University of Oregon) are required to run the geochemical models. Please refer to the paper for details on methodology, and get in touch if clarification is required ("Paleohydrology on Mars constrained by mass balance and mineralogy of pre-Amazonian sodium chloride lakes", by M. Melwani Daswani and E. S. Kite, published in the Journal of Geophysical Research: Planets Volume 122, Issue 9 (2017), https://doi.org/10.1002/2017JE005319).</p> <p>Please get in touch if you notice any mistakes too. All data are backed up by M. Melwani Daswani. A lot of data were generated for this project, and it is possible something may have been missed while uploading.</p> <p>Mohit Melwani Daswani, 20 June 2018<br> melwani.mohit@gmail.com</p> <p>EOM</p>
A Factor Two Difference in 21st-Century Greenland Ice Sheet Surface Mass Balance Projections from Three Regional Climate Models for a Strong Warming Scenario (SSP5-8.5)
<p>1km regridded Greenland Ice Sheet SMB / Runoff / Melt projection until 2100. Projections from MAR, RACMO, HIRHAM forced by CESM2 (SSP5-8.5).</p>
North American Regional Reanalysis (NARR) data used in "Passive Tracer Modelling at Super-Resolution with WRF-ARW to Assess Mass-Balance Schemes"
<p>North American Regional Reanalysis (NARR) data from National Oceanic and Atmospheric Administration (NOAA) - 20 August 2013, 26 August 2013, 2 September 2013 - used as input information (initial and boundary condition) for WRF simulations described in "Passive Tracer Modelling at Super-Resolution with WRF-ARW to Assess Mass-Balance Schemes" (Fathi et al., 2022 - egusphere-2022-1125). NARR data can be accessed and downloaded at the following web address "https://www.ncei.noaa.gov/products/weather-climate-models/north-american-regional/". </p>
Raw data from thermistor string ice mass balance buoy 2021M31
<p>Raw data from thermistor string sea ice mass balance buoy. Raw data, no quality control. Data files (csv) are in zip file. Deployment card (pdf) is also attached to the data set.</p>
MAR-ERA5 reanalysis of the Arctic land ice surface mass balance between 1950 and 2020
<p>This archive provides monthly outputs of the surface mass balance variables over the Arctic land ice, as modeled by MAR forced by ERA5.</p><p>These outputs were produced as part of the publication "Maure, D., Kittel,C., Lambin, C., Delhasse, A. and Fettweis, X.: "Spatially heterogeneous effect of climate warming<br>on the Arctic land ice", The Cryosphere, accepted. (2023). The data comes from the 6km domains presented in Fig.1 of the study, reinterpolated to a single Pan-Arctic 6km grid.</p><p> </p><p>Contact: Damien Maure </p><p>damien.maure@uliege.be</p><p> </p><p>The MAR code is available at https://gitlab.com/Mar-Group/MARv3. The version used to generate this dataset is tagged as v3.11.5.</p><p>About the dataset:<br>It contains one file per year</p><p>MAR_arctic_ERA5_v1_<strong>*year*</strong>.nc</p><p>MAR311</p><p>SMB: surface mass balance<br>SF: snowfall<br>RF: rainfall<br>RU: runoff<br>ME: melt<br>SU: sublimation - deposition (positive values indicates mass losses through sublimation)<br>(units: mm we day)</p><p> </p><p> </p>
Mass-balance Study of [14C]-APD421 in Healthy Volunteers
ClinicalTrials.gov study NCT02881840. IPD Sharing: NO. Countries: 1. Publications: 1.
Oral Bioavailability and Mass Balance Trial With Pimasertib
ClinicalTrials.gov study NCT01713036. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Berzosertib Human Mass Balance Study (DDRiver Solid Tumors 208)
ClinicalTrials.gov study NCT05246111. IPD Sharing: NO. Countries: 1. Publications: 0.
A Study to Assess Absolute Bioavailability (ABA) of Mobocertinib (TAK-788) and to Characterize Mass Balance, Pharmacokinetics (PK), Metabolism, and Excretion of Carbon-14 ([14C])-Mobocertinib in Male
ClinicalTrials.gov study NCT03811834. IPD Sharing: NO. Countries: 1. Publications: 1.
ScienceDex guides
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