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106 results for “Mass loss”
Mass and Nutrient Loss in Decomposing Hardwood Boles on Watershed 1 at the Hubbard Brook Experimental Forest, 1990 - present
In 1990-1991 segments of boles from felled sugar maple (Acer saccharum), yellow birch (Betula alleghaniensis) and American beech (Fagus grandifolia) trees were placed in the field to study the rate of decomposition and nutrient loss (or gain) over time. The segments incubated in the field, ranging from 0.5-1.3 meters in length, were paired with fresh segments from the same trees. The fresh segments were taken to the lab shortly after felling, dried, weighed and subsampled. Fresh samples of wood and bark were collected separately. Incubated bole segments were collected in 1993 (T1), 1997 (T2), 2001 (T3), 2007 (T4) and 2015/2016 (T5). The whole bole segments were transported to the lab, measured, dried and weighed to determine mass loss. Subsamples of the bole wood and bark were collected for chemical analysis, including C, N, H, Ca, Mg, K, Si, Al, Pb, Zn, Mn and Fe. Chemical analyses were conducted concurrently on the fresh (T0) and incubated samples. This data set includes the masses of the fresh and incubated boles along with the concentrations of the chemical analytes. Element pools in the boles can be calculated by multiplying the concentrations by the mass values. This data set includes chemical data for samples collected in 1993, 2001, and 2007 and their paired fresh samples. Samples from 1997 were measured for mass, but inadvertently discarded prior to chemical analysis. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Consequences of non-random tree species loss on litter mass loss, nutrient dynamics, carbon cycling, and decomposer communities across a terrestrial-aquatic interface at Coweeta Hydrologic Lab, Otto, NC
Although litter decomposition is a fundamental ecological process, most of our understanding comes from studies of single-species decay. Recently, litter-mixing studies have tested whether monoculture data can be applied to mixed-litter systems. These studies have mainly attempted to detect non-additive effects of litter mixing, which address potential consequences of random species loss. The focus is not on which species are lost, but the decline in diversity per se. Under global change, species loss is likely to be non-random, with some species more vulnerable to extinction than others. Under such scenarios, the effects of individual species (additivity) as well as of species interactions (non-additivity) on decomposition rates are of interest. To examine potential impacts of non-random species loss on ecosystems, we studied additive and non-additive effects of litter mixing on decomposition. A full-factorial litterbag experiment was conducted using four deciduous leaf species, from which mass loss and nitrogen content were measured. Data were analysed using a statistical approach that first looks for additive identity effects based on the presence or absence of species and then significant species interactions occurring beyond those. It partitions non-additive effects into those caused by richness and or composition.
Aggregate mesquite litter mass-loss following soil-mixing and decomposition in a semi-arid grassland at the Jornada Basin LTER, 2010-2012
This package contains litter mass loss data from a litter decomposition experiment at the Jornada Basin LTER. To assess the role of soil-litter mixing (SLM) in aridland litter decomposition, litterbags were deployed in the Chihuahuan Desert and interrelationships between vegetation structure, SLM, and rates of decomposition were quantified. To assess the role of vegetation structure, litterbags were deployed in contrasting vegetation microsites, including grass, shrub, and bare ground microsites. This dataset contains the mass-loss data (including ash-corrections) from the experiment. This study is complete.
Supporting Data for "Impacts of Antarctic ice mass loss on New Zealand climate"
<p>Contains the model output necessary to reproduce the results of "Impacts of Antarctic ice mass loss on New Zealand climate" by Andrew G. Pauling, Inga J. Smith, Jeff K. Ridley, T. Martin, M. Thomas and D. P. Stevens. Submitted for publication to Geophysical Research Letters.</p> <p>Please use the "getdata.sh" script in the Github repository here: LINK to download and extract the data into the correct location for the notebooks to reproduce the results of the paper.</p>
Dataset for "Mass loss of the Greenland ice sheet until the year 3000 under a sustained late-21st-century climate"
<p>Dataset for the paper "Mass loss of the Greenland ice sheet until the year 3000 under a sustained late-21st-century climate" (Journal of Glaciology, <a href="https://doi.org/10.1017/jog.2022.9">https://doi.org/10.1017/jog.2022.9</a>).</p> <p>Please see the README for details.</p> <p>V1.2: Now providing compressed netCDF files with reduced size.<br>V1.1: Scalar flux variable 'dlimdt' (total ice mass change) added. README updated.<br>V1: Initial upload.</p> <p>* * * * * * *</p> <p>Users should cite the original publication when using all or parts of these data.</p>
Dataset for "Mass loss of the Antarctic ice sheet until the year 3000 under a sustained late-21st-century climate"
<p>Dataset for the paper "Mass loss of the Antarctic ice sheet until the year 3000 under a sustained late-21st-century climate" (Journal of Glaciology, <a href="https://doi.org/10.1017/jog.2021.124">https://doi.org/10.1017/jog.2021.124</a>).</p> <p>Please see the README for details.</p> <p>V2: New version with ISMIP6-type variables and compressed netCDF files.<br> V1: Initial upload.</p> <p>* * * * * * *</p> <p>The following script may be used to download the entire content of the archive on a Unix/Linux system:</p> <p>#!/bin/bash<br> # --- download_all.sh ---<br> wget https://zenodo.org/record/6215117/files/_README.pdf<br> wget https://zenodo.org/record/6215117/files/run_specs_headers.zip<br> for aexp in hist ctrl_proj_long \<br> exp05_long exp06_long exp07_long exp08_long exp09_long exp10_long \<br> exp12_long exp13_long \<br> expA5_long expA6_long expA7_long expA8_long \<br> expB6_long expB7_long expB8_long expB9_long expB10_long; do<br> wget https://zenodo.org/record/6215117/files/${aexp}.zip<br> done<br> for aexp in abuc_long abuciso_long \<br> abuk_long abukiso_long \<br> abum_long abumiso_long; do<br> wget https://zenodo.org/record/6215117/files/${aexp}.zip<br> done</p> <p>* * * * * * *</p> <p>Users should cite the original publication when using all or parts of these data.<br> </p>
Termite foraging data from bait mass loss at eleven locations at the Jornada Basin LTER site, 1988-2000
This data package contains data on yearly mass loss of termite baits at the Jornada Basin LTER site in southern New Mexico, USA. Termites are important to litter decomposition and nutrient cycling in desert grasslands. This study measured annual feeding activity on paper baits by subterranean termites in desert shrubland and black-grama (Bouteloua eriopoda) grassland ecosystems over twelve years. Eleven sites, known as the "consumer plots" were included in the study. Toilet paper roll termite baits were placed on grids on each consumer plot. Data include initial bait weights before deployment, and bait weights after they were retrieved from the field each year. Mass loss of the baits was calculated as a measure of termite foraging activity. This study is complete.
Canopy opening increased leaf shredding arthropods and nutrient mineralization but not mass loss in a wet tropical forest
Canopy opening increased leaf shredding arthropods and nutrient mineralization but not mass loss in a wet tropical forest Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Data set: "A 21st Century Warming Threshold for Sustained Greenland Ice Sheet Mass Loss"
<p>This data set includes the materials required to reproduce the figures and tables presented in the study: "A 21<sup>st</sup> Century Warming Threshold for Sustained Greenland Ice Sheet Mass Loss". The data consist of:</p> <ul> <li>Maps of annual historical and projected surface mass balance (SMB) of the Greenland ice sheet (GrIS) from RACMO2.3p2 at 1 km spatial resolution in NetCDF format.</li> </ul> <ol> <li><strong>smb_rec.1950-2014.BN_RACMO2.3p2-CESM2-Historical.1km.YY.nc</strong>: annual cumulative GrIS SMB (kg m<sup>-2</sup> or mm w.e. per year) from RACMO2.3p2 forced by CESM2 for the historical period 1950-2014, further statistically downscaled to 1 km spatial resolution.</li> <li><strong>smb_rec.2015-2099.BN_RACMO2.3p2-CESM2-SSP5-85.1km.YY.nc</strong>: annual cumulative GrIS SMB (kg m<sup>-2</sup> or mm w.e. per year) from RACMO2.3p2 forced by CESM2 under a high-end warming scenario SSP5-8.5 for the period 2015-2099, further statistically downscaled to 1 km spatial resolution.</li> <li><strong>Icemask_Topography_lon_lat_average_1km_GrIS.nc</strong>: mask file including an ice mask (Promicemask) separating Greenland's peripheral glaciers and ice caps (values 1 and 2) from the main ice sheet (value 3), surface topography derived from the GIMP DEM, and longitude/latitude coordinates on the 1 km grid. </li> </ol> <p>NB: the NetCDF files above use a Polar Stereographic North (EPSG:3413) projection with a horizontal resolution of 1 km x 1 km. The reference point is located at 45ºW longitude and 70ºN latitude.</p> <ul> <li>Time series of historical and projected annual GrIS-integrated SMB (Gigatons or Gt per year) and annual mean GrIS temperature (TGrIS; K) from RACMO2.3p2 at 1 km spatial resolution in ASCII format.</li> </ul> <ol> <li><strong>SMB_TGrIS_RACMO2.3p2-CESM2_Historical_1950-2014.dat</strong>: time series of annual GrIS-integrated SMB (Gt yr<sup>-1</sup>) and mean TGrIS (K) from the CESM2-forced RACMO2.3p2 simulation for the historical period 1950-2014.</li> <li><strong>SMB_TGrIS_RACMO2.3p2-CESM2_SSP5-8.5_2015-2099.dat</strong>: time series of annual GrIS-integrated SMB (Gt yr<sup>-1</sup>) and mean TGrIS (K) from the CESM2-forced RACMO2.3p2 projection under a high-end warming scenario SSP5-8.5 (2015-2099).</li> </ol> <ul> <li>Time series of historical and projected reconstruction of annual GrIS-integrated SMB (Gt yr<sup>-1</sup>) and mean TGrIS (K) from 12 CESM2 historical members and 10 CESM2 projections under various warming scenarios, namely SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5.</li> </ul> <ol> <li><strong>Reconstructed_SMB_CESM2_Historical_1950-2014.dat</strong>: time series of reconstructed annual GrIS-integrated SMB (Gt yr<sup>-1</sup>) from the parent historical CESM2 simulation (HIST-parent) used to force RACMO2.3p2 and 11 additional historical CESM2 members (HIST-X) for 1950-2014. </li> <li><strong>Reconstructed_SMB_CESM2_Projections_2015-2099.dat</strong>: time series of reconstructed annual GrIS-integrated SMB (Gt yr<sup>-1</sup>) from the parent CESM2 projection (SSP5-8.5-parent) used to force RACMO2.3p2 and 9 additional CESM2 projection members (SSP1-2.6-X to SSP5-8.5-X ) for 2015-2099. </li> <li><strong>TGrIS_CESM2_Historical_1950-2014.dat</strong>: time series of annual mean TGrIS (K) from the parent historical CESM2 simulation (HIST-parent) used to force RACMO2.3p2 and 11 additional historical CESM2 members (HIST-X) for 1950-2014.</li> <li><strong>TGrIS_CESM2_Projections_2015-2099.dat</strong>: time series of annual mean TGrIS (K) from the parent CESM2 projection (SSP5-8.5-parent) used to force RACMO2.3p2 and 9 additional CESM2 projection members (SSP1-2.6-X to SSP5-8.5-X ) for 2015-2099. </li> </ol> <p>NB: the file <strong>Crossref_sim_names.txt </strong>cross-references the simulation abbreviations in the above .dat files to official simulation names from the National Center for Atmospheric Research (NCAR).</p> <p>The daily downscaled SMB data set from the CESM2-forced RACMO2.3p2 historical simulation and SSP5-8.5 projection are freely available from the authors upon request and without conditions (contact: <strong>b.p.y.noel@uu.nl</strong>). Besides SMB, the data set includes daily total precipitation (snow and rain), snowfall, total melt (snow and ice), meltwater runoff, retention and refreezing, total sublimation (surface and drifting snow), snow drift erosion, as well as 2 m air temperature at 1 km horizontal resolution. </p> <p>Abstract: "Under anticipated future warming, the Greenland ice sheet (GrIS) will pass a threshold when meltwater runoff exceeds the accumulation of snow, resulting in a negative surface mass balance (SMB < 0) and sustained mass loss. In spite of several recent warm summers with high melt rates, SMB < 0 has not been reached since at least the year 1958. Here we dynamically and statistically downscale the outputs of an Earth system model to 1 km resolution to infer that a Greenland near-surface atmospheric warming of 4.5 ± 0.3 °C—relative to pre-industrial—is required for GrIS SMB to become persistently negative. Climate models from CMIP5 and CMIP6 translate this regional temperature change to a global warming threshold of 2.7 ± 0.2 °C. Under a high-end warming scenario, this threshold may be reached around 2055, while for a strong mitigation scenario it will likely not be passed."</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>
Dataset - Warming-induced monsoon precipitation phase change intensifies glacier mass loss in the southeastern Tibetan Plateau
<p>Materials and data results needed to reproduce the findings of the study published in (PNAS) Proceedings of the National Academy of Sciences of the United States of America:</p> <p>"<em>Warming-induced monsoon precipitation phase change intensifies glacier mass loss in the southeastern Tibetan Plateau"</em>. A. Jouberton, T. E. Shaw, E. Miles, M. McCarthy, S. Fugger, S. Ren, A. Dehecq, W. Yang and F. Pellicciotti</p> <p>It includes the meteorological forcing time-series, an exhaustive list of the model parameters, the outputs of TOPKAPI-ETH, and Matlab scripts allowing to reproduce the figures and compute the numbers given in the main manuscript as well as in the Supplementary Information.</p> <p>---------------</p> <p><strong>Contents</strong> :</p> <p>Folder : "Matlab_scripts"<br> '<strong>Climate_import.m</strong>' : Organizes meteorological forcing and generates Figure S9<br> <strong> 'TOPKAPI_result_import.m' :</strong> Imports TOPKAPI's reference run outputs and prepares them for analysis<br> <strong> 'Experiment_analysis.m' : </strong>Analyses the results of the forcing experiments, generates Figure 4 and Figure S25<br> <strong> 'Main_text_results.m' : </strong>Analyses the results of TOPKAPI's reference runs, generates Figure 1D, FIgure 2 and Figure 3<br> <strong> 'TOPKAPI_validation.m' : </strong>Compares TOPKAPI's reference run results with several validation datasets, generates the figures and performance metrics of the model calibration and validation procedure.<br> <strong> 'Parlung_albedo_regional_analysis.m'</strong>: Computes the mean glacier albedo per elevation band for each glacier within the Southeastern Tibean Plateau and compares it to the albedo of Parlung No.4 glacier.<br> <strong> 'Parlung_GMB_regional_analysis.m'</strong>: Computes the mean glacier mass balance per elevation band for each glacier within the Southeastern Tibean Plateau and compares it to the glacier mass balance of Parlung No.4 glacier.<br> <strong> 'Precipitation_phase_sensitivity_analysis.m'</strong>: Performs a sensitivity analysis on the simulated monsoon snowfall ratio per elevation band and on the attribution of glacier mass loss to precipitation<br> phase change using Monte Carlo simulations.<br> <strong> 'TOPKAPI_MODIS_validation.m'</strong>: Compares the snow cover at Parlung No.4 catchment simulated by TOPKAPI-ETH and observed by MODIS, generates Figure S19.</p> <p> </p> <p>Folder : "Remote_sensing" :</p> <p> Sub-Folder: 'Hugonnet' = Glacier mass balance averaged over 2000-2020 covering the Southeastern Tibetan Plateau, 100m resolution, derived from Hugonnet et al. 2021<br> Sub-Folder: 'MODIS' = contains the snow cover at Parlung No.4 derived from the daily product MOD10A1 version 61, for the period 2000-2018<br> Sub-Folder: 'Regional_glacier_albedo' = contains the annual glacier surface albedo from 2000 to 2020, covering the Southeastern Tibetan Plateau, 500m resolution.<br> Sub-Folder: 'Shapefiles' = contains the Parlung No.4 glacier outlines in 1974 and from the RGI 6.0<br> <strong>'ASTER_Nyainqentanglha_15m_utm.tif'</strong> = ASTER Digital elevation model at 15m resolution covering the Southeastern Tibetan Plateau<br> <strong> 'parlung_mask_1974.mat' </strong>= Parlung No.4 glacier mask as a matlab file<br> <strong> 'dh_ASTER_SRTM_30m.tif' </strong>= Mean elevation change rate from 2000 to 2016 at Parlung No.4 catchment.<br> <strong> 'Geodetic_map.mat'</strong> = Elevation change maps for the periods 1974-2000 and 1974-2014, as a matlab file<br> <strong> 'GMB_geodetic.mat' </strong>= Geodetic mass balance (glacier-wide mean and profile per elevation band) used in Figure S13<br> <strong> 'parlung_30m_catchment_mask.tif'</strong> = Parlung No.4 catchment mask<br> <strong>'parlung_1974_30m_dem.tif' </strong>= DEM of Parlung No.4 catchment, 30 m resolution<br> <strong> 'parlung_1974_30m_gla.tif'</strong> = Parlung No. 4 glacier mask, 30m resolution<br> <strong>'parlung_1974_30m_glah.tif' </strong>= Reconstructed ice thickness of 1975 for Parlung No.4 glacier<br> <strong>'parlung_1974_2000_diff_24m.tif' </strong>= Elevation change from DEM differencing at Parlung No.4 catchment for 1974-2000<br> <strong> 'parlung_1974_2014_diff_24m.tif' </strong>= Elevation change from DEM differencing at Parlung No.4 catchment for 1974-2014<br> <strong> 'Parlung_1974_bedrock_dem_30m.tif' </strong>= Bedrock surface digital elevation model of the catchment, 30m spatial resolution<br> <strong>'RGI_KangriKarpo_100m_utm_id.tif' </strong>= Glacier mask covering the Kangri Karpo mountain region, 100m resolution, with glacier IDs in the attribute table<br> <strong> 'RGI_Nyainqentanglha_100m_utm_id.tif' </strong>= = Glacier mask covering the Southeastern Tibetan Plateau, 100m resolution, with glacier IDs in the attribute table</p> <p> </p> <p>Folder : "TOPKAPI_forcing" :<br> <strong> CCT_AWS4600_extended.csv : </strong>Hourly cloud cover transmissivity from 1975 to 2018 reconstructed at AWSoff location <br> <strong> Climate.mat : </strong>Organizes meteorological forcings, output from the matlab script '<strong>Climate_import.m</strong>'<br> <strong> LR_AWS4600_extended.csv :</strong> Hourly temperature lapse-rates from 1975 to 2018 reconstructed at AWSoff location <br> <strong> Precipitation_AWS4600_extended.csv : </strong>Hourly precipitation from 1975 to 2018 reconstructed at AWSoff location <br> <strong> Ta_AWS4600_extended.csv :</strong> Hourly air temperature from 1975 to 2018 reconstructed at AWSoff location<br> Sub-Folder: 'National_meteorological_stations' = Contains the daily air temperature and precipitation measured at the national meteorological stations of Bomi, Zayu, Zuogong and Basu<br> Sub-Folder: 'Reference_run_inputs' = Contains the input files necessary to run TOPKAPI-ETH to obtain the outputs from which the results of this study are based on.</p> <p> </p> <p>Folder : "TOPKAPI_output":<br> <strong> </strong> Sub-Folder : "Forcing experiment" = organized TOPKAPI outputs from the forcing experiment<br> Sub-Folder :" Reference_run_outputs" = raw TOPKAPI outputs from the reference run (catchment average, spatial and grid cells)<br> Sub-Folder : "Reference_run_results" = organized TOPKAPI outputs from the reference run<br> Sub-Folder : "Snow_ice_cover" = contains TOPKAPI-ETH derived snow cover maps (daily map outputs)<br> Sub-Folder : "Regional_analysis" =<br> 'Alb'= Table containing the mean glacier albedo (2000-2020) per normalized elevation band, for each glacier in the SETP (RGI 6.0)<br> 'GMB'= Table containing the mean glacier mass balance (2000-2020) per normalized elevation band, for each glacier in the SETP (RGI 6.0)<br> 'Hypso_xxm' = Table containing the percentage of glacier area per normalized elevation band, for each glacier in the SETP (RGI 6.0), resolution of 100/500m<br> 'NormEl_100m' = Table containing the elevation per normalized elevation band, for each glacier in the SETP (RGI 6.0), resolution of 100/500m<br> Sub-Folder : "Semi_distributed_outputs" = Precipitation phase and amounts resulting from TOPKAPI-ETH simulation per elevation band, for the reference run and for the Monte Carlo sensitivity analysis</p> <p> </p> <p>Folder : "Validation_data"<br> <strong> 'topkapi.out_reference_discharge2016' </strong>= <strong> </strong>raw TOPKAPI outputs run in 2016 with AWSoff air temperature<br> <strong> </strong><strong> 'master_file_parlung.mat' </strong>=<strong> </strong>matlab structure containing AWS measurements, necessary for running <strong>'TOPKAPI_validation.m'</strong><br> <strong> 'Qdigit.mat' </strong>= Discharge measured at the Parlung No.4 glacier outlet, from Li et al., (2016)<br> <strong> 'Parlung_Q_1970.mat'</strong> = 'Discharge time-series used to run TOPKAPI-ETH (goes back to 1975, but filled with 0 when no measurements are available)</p> <p> </p> <p>In order to run the Matlab scripts, it is recommended to download all folders and gather them into the same folder. Any request about data or questions on how to run the Matlab scripts can be asked to the author of the paper (at achille.jouberton@wsl.ch).</p>
Time to run: Late rather than early exercise training in mice promotes fat mass loss and reduces atherosclerosis development
<p>This dataset contains forward and reverse reads of 16S sequencing of the V3-V4 region of caecal bacteria. As indicated in the metadata file, mice are divided into three groups - SED (sedentary mice, E-SED for early sedentary and L-SED for late sedentary), E-RUN (early runners) and L-RUN (late runners). For more information about the groups, sequencing etc., consult the manuscript.</p>
Datasets used in van den Akker et al (2024) 'Present day mass loss rates are a precursor precursor for West Antarctic Ice Sheet Collapse
<p>This repository contains the default initialization and the continuation runs shown in the paper. </p>
Phenotypic plasticity in mass loss during chick-rearing in the European starling (Sturnus vulgaris)
Open the record for dataset details and reuse information.
Plasticizer Content by TGA datasets - Mass loss curves of PVB grades and derivative TGA curves
<p>Datasets obtained for the Plasticizer Content by TGA analysis, from the paper "An Integrated Characterization Strategy on Board for Recycling of poly(vinyl butyral) (PVB) from Laminated Glass Wastes"</p>
Pulsation-driven mass loss observed in action from ground and space
<p>It is believed that pulsation plays a substantial role in the mass loss mechanism operating in Be stars, acting on top of rotation to lift material off the photosphere to feed the circumstellar disks. The causal relation between the combination of different non-radial pulsation modes and mass ejection is already well established. The next step forward will be given when the ejecta, which are the building blocks of the disks, are studied to determine their mass and angular content. To address this question, one must combine observations that probe the photospheric changes (photometry and spectroscopy of photospheric lines) with observations that track the ejecta proper (spectroscopy of emission lines). We are analyzing TESS data for what is by far the largest sample of Be stars observed from space to study their pulsational properties and circumstellar variability. This effort is aided by years of ground-based photometry, archival spectroscopy, and, most importantly, a campaign that is acquiring time-series Echelle spectra simultaneous with TESS observations. With the contemporaneous spectra, we are measuring changes in the photosphere and the evolution of the circumstellar environment at the same time, and establishing links between stellar and circumstellar variability. We find dramatic changes in the spectral features that probe the very inner regions of the disk on timescales of hours and days, which are correlated with signals seen by TESS when stars are actively ejecting material. Studying the star-disk interface in this way is of fundamental importance in understanding the elusive mass ejection mechanism of Be stars.</p>
Reproduction package for the paper "Constraining planetary mass-loss rates by simulating Parker wind profiles with Cloudy"
<p>This is a basic reproduction package for the paper "Constraining planetary mass-loss rates by simulating Parker wind profiles with Cloudy" by Linssen et al. (2022). It provides the data products necessary to reproduce the figures of the paper.</p>
Dust formation and mass loss around intermediate-mass AGB stars with initial metallicity Zini ≤ 10-4 in the early Universe - I. Effect of surface opacity on stellar evolution and the dust-driven wind
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2017MNRAS.466.1709T">Dust formation and mass loss around intermediate-mass AGB stars with initial metallicity Zini ≤ 10-4 in the early Universe - I. Effect of surface opacity on stellar evolution and the dust-driven wind</a></p>
Mass loss in main-sequence B stars
<p>MESA run_star_extras associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2014A&A...564A..70K">Krtička (2014)</a>. MESA version 7624.</p> <p>Publication DOI: <a href="https://doi.org/10.1051/0004-6361/201321980">10.1051/0004-6361/201321980</a></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.