Find research datasets worth reusing

opencc-by-4.0Nov 2020View details →

Large-eddy simulation investigating the role of double-diffusive convection in basal melting of Antarctic ice shelves: model output

Model output used in the publication: M. G. Rosevear, B. Gayen, B. K. Galton-Fenzi, The role of double-diffusive convection in the basal melting of Antarctic ice shelves. Proc. Natl. Acad. Sci. (2021) https://doi.org/10.1073/pnas.207541118 See README.md for a description of the data.

opencc-by-4.0Oct 2022View details →

Ensemble of NEMO present-day (1989-2009) and future (2080-2100 under RCP8.5) ocean properties and ice shelf melt rates in the Amundsen Sea

Model outputs used in <a href="https://www.essoar.org/doi/10.1002/essoar.10511482.3">Jourdain et al. (GRL, 2022)</a> The output files consist of monthly climatologies over either 1989-2009 or 2080-2100. The file names have the form: climato_monthly_AMUXL12-GNJ002_<simu>_<group>_1989_2009.nc, where : <ul> <li><simu> is either : <ul> <li>"BM02MAR" (ensemble member A, present-day),</li> <li>"BM03MAR" (ensemble member B, present-day),</li> <li>"BM04MAR" (ensemble member C, present-day),</li> <li>"BM02MARrcp85" (ensemble member A, future for both surface and lateral boundaries),</li> <li>"BM03MARrcp85" (ensemble member B, future for surface BUT NOT for lateral boundaries),</li> <li>"BM03MARrcBDY" (ensemble member B, future for both surface and lateral boundaries),</li> <li>"BM04MARrcp85" (ensemble member C, future for both surface and lateral boundaries),</li> </ul> </li> <li><group> is either : <ul> <li>"SBC" (surface boundary conditions),</li> <li>"icemod" (sea ice variables),</li> <li>"gridT" (temperature, salinity),</li> <li>"gridU" (zonal velocities),</li> <li>"gridV" (meridional velocities).</li> </ul> </li> </ul> Grid information in: <ul> <li>mesh_mask_AMUXL12_BedMachineAntarctica-2019-05-24.nc (ensemble member A),</li> <li>mesh_mask_AMUXL12_BedMachineAntarctica-2020-07-15_v02_ICB380.nc (ensemble members B & C).</li> </ul> where: <ul> <li>glamt : longitude</li> <li>gphit: latitude</li> <li>e1t, e2t, e3t_0 : mesh size (in meters) along x, y, z</li> <li>tmask = 1 for ocean mesh, = 0 otherwise (land, continental ice).</li> </ul>   Acknowledgments: This work was granted access to the HPC resources of CINES (occigen) under the allocation A0100106035 attributed by GENCI.

opencc-by-4.0Jul 2021View details →

Ensemble of ice shelf basal melt rates and ocean properties for tipped-over continental shelves

Summary: This dataset contains the reference and tipped states from several model configurations developed at the <a href="https://www.awi.de/en/">Alfred Wegener Institute (AWI)</a> and the <a href="https://www.ige-grenoble.fr/?lang=en">Institut des Géosciences de l’Environnement (IGE)</a>. They were gathered here in the context of the <a href="https://www.tipaccs.eu">TiPACCs European project</a> and constitute a useful ensemble of reference and tipped ocean–ice-shelf simulations that can be used to feed ice-sheet simulations or to train melt parameterizations. The simulations produced by AWI are based on the <a href="https://fesom.de">FESOM</a> global ocean–sea-ice model using either Z- or Sigma- coordinates and all show a cold-to-warm tipping point for Filchner-Ronne Ice Shelf. The two sets of simulations produced by IGE are based on the <a href="https://www.nemo-ocean.eu">NEMO</a> ocean–sea-ice model. They include a global configuration showing a cold-to-warm tipping point for Ross Ice Shelf, and regional Amundsen Sea configuration showing a warm-to-warmer transition (likely not a proper tipping point).  The files include 3-dimensional and sea-floor ocean temperatures and salinities, ice-shelf melt rates, as well as topographic and grid data. All variables are interpolated onto the common 8km stereographic grid that was used to provide ocean forcing in ISMIP6 (<a href="https://doi.org/10.5194/tc-14-2331-2020">Nowicki et al. 2020</a>). We provide the reference state and the anomaly, so that the tipped state is: <ul> <li>Tipped = Reference + Anomaly</li> </ul> To have an overview of the reference and tipped states, have a look at these figures: <ul> <li>figure_ref_and_anomalies_1.pdf</li> <li> figure_ref_and_anomalies_2.pdf </li> <li> figure_seafloor_temp_zooms.pdf </li> </ul>   _______________________________________________ Detailed Data Description:   <ul> <li>reference_high_FESOM_sigma_AWI_TiPACCs.nc <ul> <li>contact: Ralph Timmermann <a href="mailto:ralph.timmermann@awi.de">ralph.timmermann@awi.de</a>, Verena Haid <a href="mailto:verena.haid@awi.de">verena.haid@awi.de</a></li> <li>model: FESOM1.4, sigma-coordinates (global with refined grid around Antarctica)</li> <li>atmospheric forcing: HadCM3 20C</li> <li>provided average: 1990-1999 (10-year mean)</li> <li>more: <a href="https://doi.org/10.1007/s10236-013-0642-0">Timmermann and Hellmer (2013)</a></li> </ul> </li> </ul>   <ul> <li>reference_low_FESOM_sigma_AWI_TiPACCs.nc <ul> <li>contact: Ralph Timmermann <a href="mailto:ralph.timmermann@awi.de">ralph.timmermann@awi.de</a>, Verena Haid <a href="mailto:verena.haid@awi.de">verena.haid@awi.de</a></li> <li>model: FESOM1.4, sigma-coordinates (global with refined grid around Antarctica)</li> <li>atmospheric forcing: HadCM3 20C</li> <li>provided average: 1990-1999 (10-year mean)</li> <li>more: <a href="https://doi.org/10.5194/os-13-765-2017">Timmermann and Goeller (2017)</a></li> </ul> </li> </ul>   <ul> <li>reference_FESOM_z_AWI_TiPACCs.nc <ul> <li>contact: Verena Haid <a href="mailto:verena.haid@awi.de">verena.haid@awi.de</a></li> <li>model: FESOM1.4, Z-coordinates (global with refined grid around Antarctica)</li> <li>atmospheric forcing: ERA Interim</li> <li>provided average: 2008-2017 (10-year mean), i.e. model year 30-39</li> <li>more: same mesh as <a href="https://doi.org/10.5194/tc-13-2317-2019">Gürses et al. (2019)</a></li> </ul> </li> </ul>   <ul> <li>reference_NEMO4_eORCA025.L121_IGE_TiPACCs.nc <ul> <li>contact: Pierre Mathiot <a href="mailto:pierre.mathiot@univ-grenoble-alpes.fr">pierre.mathiot@univ-grenoble-alpes.fr</a></li> <li>model: NEMO-4.0, eORCA025.L121 (Global, 1/4°, 121 vertical levels)</li> <li>atmospheric forcing: JRA55do</li> <li>provided average: 2nd cycle of 1989-1998 (10-year mean); we first run 1979-2018, and we redo 1979-1998 starting from the 2018 state.</li> <li>more: <a href="https://pmathiot.github.io/NEMOCFG/docs/build/html/simu_eORCA025_OPM021.html">https://pmathiot.github.io/NEMOCFG/docs/build/html/simu_eORCA025_OPM021.html</a></li> </ul> </li> </ul>   <ul> <li>reference_NEMO3_AMUXL12.L75_IGE_TiPACCs.nc <ul> <li>contact: Nicolas Jourdain <a href="mailto:nicolas.jourdain@univ-grenoble-alpes.fr">nicolas.jourdain@univ-grenoble-alpes.fr</a></li> <li>model: NEMO-3.6, AMUXL12.L75 (Amundsen, 1/12°, 75 vertical levels)</li> <li>atmospheric forcing: MAR (<a href="https://doi.org/10.5194/tc-14-229-2020">Donat-Magnin et al. 2020</a>)</li> <li>provided average: 1989-2009 (21-year mean)</li> <li>more: similar model set-up as <a href="https://doi.org/10.1016/j.ocemod.2018.11.001">Jourdain et al. (2019)</a>.</li> </ul> </li> </ul>   <ul> <li>anomaly_high_FESOM_sigma_AWI_TiPACCs.nc <ul> <li>continuation of reference_high_FESOM_sigma_AWI_TiPACCs.nc</li> <li>forced with HadCM3 A1B</li> <li>provided average: 2190-2199 (10-year mean)</li> </ul> </li> </ul>   <ul> <li>anomaly_low_FESOM_sigma_AWI_TiPACCs.nc <ul> <li>continuation of reference_low_FESOM_sigma_AWI_TiPACCs.nc</li> <li>forced with HadCM3 A1B</li> <li>provided average: 2190-2199 (10-year mean)</li> </ul> </li> </ul>   <ul> <li>anomaly_high_FESOM_z_AWI_TiPACCs.nc <ul> <li>same model set-up as reference_FESOM_z_AWI_TiPACCs.nc</li> <li>atmospheric forcing south of 60°S HadCM3 A1B starting 2050, otherwise ERA Interim starting 1979</li> <li>provided average: model year 69-78 (10-year mean), i.e. 2008-2017 of 2nd 39yr-cycle</li> </ul> </li> </ul>   <ul> <li>anomaly_medium_FESOM_z_AWI_TiPACCs.nc <ul> <li>same model set-up as reference_FESOM_z_AWI_TiPACCs.nc</li> <li>atmospheric forcing: ERA Interim modified with a strong imprint of the seasonal cycle of HadCM3 A1B 2070-2089</li> <li>provided average: model year 69-78 (10-year mean), i.e. 2008-2017 of 2nd 39yr-cycle</li> </ul> </li> </ul>   <ul> <li>anomaly_low_FESOM_z_AWI_TiPACCs.nc <ul> <li>same model set-up as reference_FESOM_z_AWI_TiPACCs.nc</li> <li>atmospheric forcing: manipulated ERA Interim with prolongued summer and shorter, milder winter south of 50°S, additional modification of winds in Weddell Sea region</li> <li>provided average: model year 108-117 (10-year mean), i.e. 2008-2017 of 3rd 39yr-cycle</li> </ul> </li> </ul>   <ul> <li>anomaly_NEMO4_eORCA025.L121_IGE_TiPACCs.nc <ul> <li>similar to reference_NEMO4_eORCA025.L121_IGE_TiPACCs.nc</li> <li>perturbation of the model parameters: Different iceberg distribution and different sea-ice–ocean drag and snow conductivity on sea-ice, leading to less sea-ice production in the eastern Ross Sea.</li> <li>More: <a href="https://pmathiot.github.io/NEMOCFG/docs/build/html/simu_eORCA025_OPM020.html">https://pmathiot.github.io/NEMOCFG/docs/build/html/simu_eORCA025_OPM020.html</a></li> </ul> </li> </ul>   <ul> <li>anomaly_NEMO3_AMUXL12.L75_IGE_TiPACCs.nc <ul> <li>similar to reference_NEMO3_AMUXL12.L75_IGE_TiPACCs.nc</li> <li>perturbation of atmospheric forcing: MAR forced by the CMIP5 multi-model anomaly under the RCP8.5 scenario (<a href="https://doi.org/10.5194/tc-15-571-2021">Donat-Magnin et al. 2021</a>).</li> <li>provided average: 2080-2100 (21-year average)</li> </ul> </li> </ul>

opencc-by-4.0Nov 2022View details →

Performance Data of an Ice-Melting Probe from Field Tests in two Different Ice Environments

This dataset was acquired at field tests of the steerable ice-melting probe "EnEx-IceMole" (Dachwald et al., 2014). A field test in summer 2014 was used to test the melting probe's system, before the probe was shipped to Antarctica, where, in international cooperation with the MIDGE project, the objective of a sampling mission in the southern hemisphere summer 2014/2015 was to return a clean englacial sample from the subglacial brine reservoir supplying the Blood Falls at Taylor Glacier (Badgeley et al., 2017, German et al., 2021). The standardized log-files generated by the IceMole during melting operation include more than 100 operational parameters, housekeeping information, and error states, which are reported to the base station in intervals of 4 s. Occasional packet loss in data transmission resulted in a sparse number of increased sampling intervals, which where compensated for by linear interpolation during post processing. The presented dataset is based on a subset of this data: The penetration distance is calculated based on the ice screw drive encoder signal, providing the rate of rotation, and the screw's thread pitch. The melting speed is calculated from the same data, assuming the rate of rotation to be constant over one sampling interval. The contact force is calculated from the longitudinal screw force, which es measured by strain gauges. The used heating power is calculated from binary states of all heating elements, which can only be either switched on or off. Temperatures are measured at each heating element and averaged for three zones (melting head, side-wall heaters and back-plate heaters).

opencc-by-4.0Oct 2023View details →

Simulations of Miocene Antarctic ice-sheet variability under increased precipitation and sub-shelf melt, using the ice-sheet model IMAU-ICE

To demonstrate the viability of a precipitation regime change leading to a fundamentally different volume-to-area ratio of the Antarctic ice sheet, we deploy the 3D thermodynamical ice sheet/shelf model IMAU-ICE v1.1.1. In the standard set-up (<a href="https://doi.org/10.5194/cp-2023-12">Stap et al., 2021a</a>, <a href="https://doi.pangaea.de/10.1594/PANGAEA.939114">2021b</a>), climate forcing follows from pre-run warm and cold snapshot climate simulations. The applied climate forcing is transiently calculated based on the prescribed CO2 concentration and the modelled ice sheet size, through a matrix interpolation method. Equilibrium experiments are performed at various CO2 levels between preindustrial and 3x preindustrial CO2 values, with insolation at present-day levels and initiated from an ice-free Miocene Antarctic topography (dataset <a href="https://doi.pangaea.de/10.1594/PANGAEA.923109">Hochmuth et al., 2020</a>). Here, we perform additional sensitivity experiments, in which we apply a fixed precipitation increase and extreme sub-shelf melt rates. The precipitation anomaly is calculated as 25% of the warm snapshot precipitation fields, sub-shelf melt rates are set to 400 m/yr.

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Contribution of CO2 and CH4 emissions at ice-melt to annual emissions from 450 and 270 lakes, respectively, 1986 to 2014

The ice-covered period on lakes in the northern hemisphere can be extensive, lasting up to 7 months of the year. During this time, C cycling in lakes is altered affecting CO2 and CH4 dynamics below ice. Lake ice impedes atmospheric exchange, trapping CO2 and CH4 in the lake over winter. As lake ice-melts, CO2 and CH4 that has accumulated over winter is emitted from the into the atmosphere. To investigate the importance of CO2 and CH4 emissions during the ice-melt period, we conducted a literature search for studies that had CO2 and CH4 emission estimates for both the ice-melt and open water period. From these literature values, we could calculate the percent contribution of the ice-melt period to annual CO2 and CH4 emissions. We obtained data for 271 (n= 258) and 447 (n= 689) individual lakes, for CH4 and CO2, respectively.

openCC0Mar 2018View details →

opencc-by-4.0Dec 2023View details →

FESOM2.1 model data used in the paper "Atlantic Water warming increases melt below Northeast Greenland's last floating ice tongue"

This data set includes the minimal data necessary to reproduce the findings of Wekerle et al., in revision. Output of model simulations with the global ocean sea ice model FESOM2.1 is provided. In particular, the data set includes: a) long term means of potential temperature, salinity, velocity and basal melt of the 79N Glacier averaged over 1970-2021 (Wekerle2024_FESOM2_ltm_REF.nc) b) annual means of maximum potential temperature and basal melt rate of the 79N Glacier from the reference experiment REF for the years 1970-2021 (Wekerle2024_FESOM2_annual_avg_REF.nc) c) annual means of maximum potential temperature and basal melt rate of the 79N Glacier from experiment CLIM for the years 2000-2021 (Wekerle2024_FESOM2_annual_avg_CLIM.nc) d) daily mean basal melt rates of experiments with varying subglacial discharge averaged over the years 2010-2014 (Wekerle2024_FESOM2_daily_avg_EXP_subglacial_discharge.nc) e) daily mean basal melt rates of experiments with varying drag coefficients for the year 2000 (Wekerle2024_FESOM2_daily_EXP_basal_drag.nc) Each netcdf file includes information on the model grid (longitude and latitude of nodes, depths of the vertical layers, elements, nodal areas).

opencc-by-4.0Jan 2022View details →

Lateral_melting_TC_2022: Data for sea ice sensitivity to lateral melting, CESM2

CESM2 model data for Smith, M. et al, Arctic sea ice sensitivity to lateral melting representation in a coupled climate model, In The Cryosphere, 2022

opencc-by-4.0Feb 2023View details →

Data: Contrasting current and future surface melt rates on the ice sheets of Greenland and Antarctica: lessons from in situ observations and climate models

These data accompany the publication "Contrasting current and future surface melt rates on the ice sheets of Greenland and Antarctica: lessons from in situ observations and climate models". The data are organized as follows: - two files with time series (csv) of hourly near-surface climate and surface energy balance values for Neumayer station (ice shelf, East Antarctic ice sheet) and automatic weather station S5 (southwest Greenland ice sheet) - two files (nc) with monthly melt fields from the regional climate model RACMO2.3p2 forced by ERA5 over Greenland (0.05-degree resolution) and Antarctica (0.25-degree resolution) - two files (nc) with annual melt fields from the regional climate model RACMO2.3p2 forced by CESM2 over Greenland (0.1-degree resolution) and Antarctica (0.25-degree resolution) or the historical period (1950-2014) - two files (nc) with annual melt fields from the regional climate model RACMO2.3p2 forced by CESM2 over Greenland (0.1-degree resolution) and Antarctica (0.25-degree resolution) for the future emission scenario SSP5-8.5 (2015-2099)

opencc-by-4.0May 2023View details →

Data and Software for "Gullies on Mars could have formed by melting of water ice during periods of high obliquity"

Code, movies and climate model outputs for "Gullies on Mars could have formed by melting of water ice during periods of high obliquity" by Dickson et al. Science, 2023.

opencc-by-4.0May 2023View details →

Antarctic ice sheet daily surface melt detection from ASCAT (2007-2022)

Antarctic ice sheet-wide surface melt detection using enhanced resolution ASCAT C-band radar scatterometer data. Data are daily temporal resolution spanning 2007-2022 and gridded at 4.45 km. Melt detection approach follows Trusel et al., (2012) with updates to threshold and masking procedures. These data were used as a binary melt presence/absence estimate and a predictor in a machine learning-based estimation of Antarctic Peninsula surface meltwater production (https://zenodo.org/record/7995543).  Please reach out to Luke Trusel with any questions!

opencc-by-4.0Oct 2023View details →

Data set: "Higher Antarctic ice sheet accumulation and surface melt rates revealed at 2 km resolution"

This data set includes the materials required to reproduce the figures and tables presented in the study: "Higher Antarctic ice sheet accumulation and surface melt rates revealed at 2 km resolution". The data consist of: ASCII files:<ol><li>SMB-ANT-Sectors-1979-2021-RACMO2.3p2-ERA5-2km.txt: time series of Antarctic sector-integrated annual SMB (Gt per year) from ERA5-forced RACMO2.3p2 at 27 km, statistically downscaled to 2 km resolution (1979-2021).</li><li>Melt-ANT-Sectors-1979-2021-RACMO2.3p2-ERA5-2km.txt: time series of Antarctic sector-integrated annual surface melt (Gt per year) from ERA5-forced RACMO2.3p2 at 27 km, statistically downscaled to 2 km resolution (1979-2021).</li><li>Melt-ANT-Sectors-1950-2014-RACMO2.3p2-CESM2-HIST-2km.txt: time series of Antarctic sector-integrated annual surface melt from CESM2-forced RACMO2.3p2 historical reconstruction (HIST), statistically downscaled to 2 km resolution (1950-2014).</li><li>Melt-ANT-Sectors-2015-2099-RACMO2.3p2-CESM2-SSP126-2km.txt: time series of Antarctic sector-integrated annual surface melt from CESM2-forced RACMO2.3p2 SSP1-2.6 projection (SSP126), statistically downscaled to 2 km resolution (2015-2099).</li><li>Melt-ANT-Sectors-2015-2099-RACMO2.3p2-CESM2-SSP245-2km.txt: time series of Antarctic sector-integrated annual surface melt from CESM2-forced RACMO2.3p2 SSP2-4.5 projection (SSP245), statistically downscaled to 2 km resolution (2015-2099).</li><li>Melt-ANT-Sectors-2015-2099-RACMO2.3p2-CESM2-SSP585-2km.txt: time series of Antarctic sector-integrated annual surface melt from CESM2-forced RACMO2.3p2 SSP5-8.5 projection (SSP585), statistically downscaled to 2 km resolution (2015-2099).</li></ol>Antarctic sectors include the Antarctic Peninsula (APIS), the West Antarctic ice sheet (WAIS), the East Antarctic ice sheet (EAIS), the grounded Antarctic ice sheet (AIS), the floating ice shelves (Ice shelves), and the whole of Antarctica (ANT) including both the AIS and Ice shelves. The APIS, WAIS, EAIS and AIS sectors include land ice from neighbouring Antarctic islands. Netcdf files:<ol><li>smb_rec.1979-2021.BN_RACMO2.3p2_ANT27_ERA5-3h.AIS.2km.YY.nc: map of annual SMB (kg per m² or mm w.e. per year) from ERA5-forced RACMO2.3p2 at 27 km, statistically downscaled to 2 km resolution, covering the whole of Antarctica (1979-2021).</li><li>snowmelt.1979-2021.BN_RACMO2.3p2_ANT27_ERA5-3h.AIS.2km.YY.nc: map of annual surface melt (kg per m² or mm w.e. per year) from ERA5-forced RACMO2.3p2 at 27 km, statistically downscaled to 2 km resolution, covering the whole of Antarctica (1979-2021).</li><li>snowmelt.1950-2014.BN_RACMO2.3p2_ANT27_CESM2_HIST.AIS.2km.YY.nc: map of annual surface melt (kg per m² or mm w.e. per year) from CESM2-forced RACMO2.3p2 historical reconstruction (HIST), statistically downscaled to 2 km resolution, covering the whole of Antarctica (1950-2014).</li><li>snowmelt.2015-2099.BN_RACMO2.3p2_ANT27_CESM2_SSP126.AIS.2km.YY.nc: map of annual surface melt (kg per m² or mm w.e. per year) from CESM2-forced RACMO2.3p2 SSP1-2.6 projection (SSP126), statistically downscaled to 2 km resolution, covering the whole of Antarctica (2015-2099).</li><li>snowmelt.2015-2099.BN_RACMO2.3p2_ANT27_CESM2_SSP245.AIS.2km.YY.nc: map of annual surface melt (kg per m² or mm w.e. per year) from CESM2-forced RACMO2.3p2 SSP2-4.5 projection (SSP245), statistically downscaled to 2 km resolution, covering the whole of Antarctica (2015-2099).</li><li>snowmelt.2015-2099.BN_RACMO2.3p2_ANT27_CESM2_SSP585.AIS.2km.YY.nc: map of annual surface melt (kg per m² or mm w.e. per year) from CESM2-forced RACMO2.3p2 SSP5-8.5 projection (SSP585), statistically downscaled to 2 km resolution, covering the whole of Antarctica (2015-2099).</li><li>ANT_masks.2km.nc: file including the grounded AIS mask (AIS), Antarctic sectors mask (Sectors), floating ice shelves mask (Shelves), surface elevation down-sampled from REMA (Topography), latitude and longitude on the 2 km grid. The sector mask includes: 0 – Ocean, 1 – APIS, 2 – WAIS, 3 – EAIS, 4 – APIS islands, 5 – WAIS islands, 6 – EAIS islands, and 7 – ice shelves.  </li></ol>The projection used for statistical downscaling is Polar Stereographic South (EPSG:3031) with a spatial resolution of 2 km x 2 km.  Additional data: The gridded, daily downscaled SMB data sets from the ERA-forced RACMO2.3p2 simulation, and the CESM2-forced RACMO2.3p2 projections under a low-end SSP1-2.6, moderate SSP2-4.5 and high-end SSP5-8.5 warming scenario are freely available from the authors upon request and without conditions (contact: bnoel@uliege.be). Besides SMB, the data sets include total precipitation (snow and rain), snowfall, total melt (snow and ice), runoff, refreezing and retention, drifting snow erosion, and total sublimation (surface and drifting snow) at 2 km horizontal resolution.  Abstract: Antarctic ice sheet (AIS) mass loss is predominantly driven by increased solid ice discharge, but its variability is governed by surface processes. Snowfall fluctuations control the surface mass balance (SMB) of the grounded AIS, while meltwater ponding can trigger ice shelf collapse potentially accelerating discharge. Surface processes are essential to quantify AIS mass change, but remain poorly represented in climate models typically running at 25-100 km resolution. Here we present SMB and surface melt products statistically downscaled to 2 km resolution for the contemporary climate (1979-2021) and low, moderate and high-end warming scenarios until 2100. We show that statistical downscaling modestly enhances contemporary SMB (3%), which is sufficient to reconcile modelled and satellite mass change. Furthermore, melt strongly increases (46%), notably near the grounding line, in better agreement with in-situ and satellite records. The melt increase persists by 2100 in all warming scenarios, revealing higher surface melt rates than previously estimated.

opencc-by-4.0Mar 2024View details →

Model output for "Geometric amplification and suppression of ice-shelf basal melt in West Antarctica"

<h2>Content description </h2> This data repository contains Úa-MITgcm model output in support of the manuscript 'Geometric amplification and suppression of ice-shelf basal melt in West Antarctica' (De Rydt and Naughten, 2024). In the paper, results from 4 experiments with the coupled ice-ocean model Úa-MITgcm are presented. The experiments are ref_melt, hi_melt, av_melt and var_melt (see Table 1 in De Rydt and Naughten, 2024). Original MITgcm and Úa output files at the start, middle and end of each experiment are available in the .zip-files with corresponding experiment name. Data for other timestamps are available upon request from the authors. The structure of each file is as follows: <table> <tbody> <tr> <td>exp_name.zip/</td> <td>yyyymm/</td> <td>  MITgcm/</td> <td> bathymetry.shice</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>data.diagnostics</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>output.nc</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>pload.mdjwf</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>shelfice_topo.bin</td> </tr> <tr> <td> </td> <td> </td> <td>Ua/</td> <td>DataForMIT.mat</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>NewMeltrate.mat</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>UaDefaultRun_yyyymm_dd.mat</td> </tr> </tbody> </table> <code>yyyymm</code> refers to the start month of the coupling timestep. Note that while the initial state of the ice sheet has been optimized to represent its present-day  configuration, the future evolution of the ocean and ice sheet is driven by idealized forcings. The results should not be treated as projections, and the timestamps correspond to model time (in years), not real time. MITgcm diagnostics (temperature, salinity, velocities and ice-shelf freshwater flux) are stored in <code>output.nc</code> and provided as an average value over the 1-month coupling time interval. The <code>bathymetry.shice</code>, <code>data</code>, <code>data.diagnostics</code>, <code>pload.mdjwf </code>and <code>shelfice_topo.bin</code> files are MITgcm input files for ocean bathymetry, general model parameters, diagnostic model parameters, intial pressure loading under the ice shelf and ice-shelf draft for the corresponding coupling timestep, respectively. Úa data is provided in <code>UaDefaultRun_yyyymm_dd.mat</code> as a snapshot at the end of the coupling interval (<code>yyyymm_dd</code>). The other files, <code>DataForMIT.mat </code>and <code>NewMeltrate.mat</code> are ice-shelf geometry and MITgcm melt rates at the start of the coupling interval, respectively. <h2>Other resources</h2> The Úa-MITgcm source code and custom Úa-MITgcm configuration files for the experiments are available <a href="https://github.com/knaughten/UaMITgcm/tree/archer2">here</a> and <a href="https://github.com/knaughten/UaMITgcm/tree/archer2/example/ASE_999">here.</a> Scripts to analyse and plot the data are available <a href="https://github.com/janderydt/uamitgcm_ASE">here</a>. <h2>References</h2> De Rydt, J. and Naughten, K.: Geometric amplification and suppression of ice-shelf basal melt in West Antarctica, The Cryosphere, 18, 1863–1888, https://doi.org/10.5194/tc-18-1863-2024, 2024.

opencc-by-4.0Sep 2022View details →

Data and scripts for 'Sub-seasonal variability of supraglacial ice cliff melt rates and associated processes from time-lapse photogrammetry'

This repository contains three zipped elements used in the study Sub-seasonal variability of supraglacial ice cliff melt rates and associated processes from time-lapse photogrammetry (https://doi.org/10.5194/tc-2022-81): 1. The time-lapse DEMs (original and flow-corrected), orthomosaics (not flow-corrected) and cliff outlines (original and flow-corrected) of the 24K and Langtang survey areas used in this study. The spatial resolution is the same as used in the analysis. These zipped files also contain a .csv file (time_selection.csv) indicating for each index (indicated in the file name) the serial date number in days (date origin January 0, 0000). 2. The R and Python scripts (Scripts_final.zip) used to process the DEMs and orthomosaics from the time-lapse images as well as the script to calculate the slope-perpendicular melt. These scripts come with .csv and .txt files that serve as template for the required input data format.

opencc-by-4.0Sep 2022View details →

Economic impacts of melting of the Antarctic Ice Sheet

Dataset supporting figures and tables in Dietz, Simon and Koninx, Felix (forthcoming), "Economic impacts of melting of the Antarctic Ice Sheet", Nature Communications

opencc-by-4.0Jul 2023View details →

High-end projections of Southern Ocean warming and Antarctic ice shelf melting in conditions typical of the end of the 23rd century

<h2>High-end projections of Southern Ocean warming and Antarctic ice shelf melting in conditions typical of the end of the 23rd century</h2> To evaluate the response of the Southern Ocean and Antarctic ice shelf cavities to an abrupt change to high-end atmospheric conditions typical of the late 23rd century under the SSP5-8.5 scenario, in Mathiot and Jourdain (2023, submitted soon), we conducted 2 experiments. Our reference experiment (called REF) is driven by present day atmospheric condition. In the 23rd century simulation (called PERT), the present day atmospheric forcing is perturbed by the anomaly (2260-2299 minus 1975-2014) extracted from monthly outputs of the IPSL-CM6A-LR projections under the SSP5-8.5 emission scenario. REF is run over the latest 40 years and PERT is run for 100y starting from PERT at year 1999. This data set contains: <ul> <li>The atmospheric forcing anomalies used to perturbed our reference atmospheric forcing in the PERT simulation;</li> <li>30y monthly climatologies of multiple variables (ocean temperature, salinity, ssh, velocities, barotropic stream function, iceberg melt, ice shelf melt, sea ice concentration, thickness, velocities and snow thickness) for PERT and REF.</li> </ul> All the details on each dataset have been added in separated README in ATMO_ANOMALIES and OCEAN_CLIMATOLOGIES directory. As stated in each README, all the detailed on the simulations and atmospheric perturbation are available in Mathiot and Jourdain (2023). Changes with respect to v1.0.0: iceberg melt and ice shelf melt climatologies for REF and PERT has been added to the dataset (*flxT.nc files). Reference paper: Mathiot, P. and Jourdain, N. C.: Southern Ocean warming and Antarctic ice shelf melting in conditions plausible by late 23rd century in a high-end scenario, Ocean Sci., 19, 1595–1615, https://doi.org/10.5194/os-19-1595-2023, 2023. <div> <div>Acknowledgements: This study was funded by the European Union's Horizon 2020 research and innovation programme under grant agreement no. 820575 (TiPACCs) and by the French National Research Agency under grant no. ANR-19-CE01-0015 (EIS). N. Jourdain was also supported by EU-H2020 grant nos. 101003536 (ESM2025) and 869304 (PROTECT). This work was granted access to the high-performance computing (HPC) resources of CINES and TGCC under allocations A0100106035 and A0120106035 attributed by GENCI.</div> </div>

opencc-by-4.0Feb 2017View details →

Additional radiative forcing (warming) under the different scenarios for ice melt

This data set contains estimates of additional radiative forcing for 3 different sea-ice–albedo feedback (SIAF) scenarios, in addition to the baseline (no additional radiative forcing) scenario. The three scenarios are identical up to 2050, but vary significantly after this date.  Full details of methods used to create the dataset are provided within the ReadMe file.

opengpl-2.0Jun 2018View details →

Simulation data for convection in radiatively heated melt ponds on sea ice

This dataset includes data and processing code from simulations of radiatively heated convection in melt ponds on sea ice.    Simulation results produced using code written by Andrew Wells and Tom Langton for use with and exploiting examples from the code Dedalus: http://dedalus-project.org/index.html   Contact andrew.wells@physics.ox.ac.uk for further details.  The data included in this version correspond to figures 3 and S5 , movies S2 and S3, and code described in supporting information in the study: Salinity control of thermal evolution of late summer melt ponds on Arctic sea ice  Joo-Hong Kim1, Woosok Moon2,3, Andrew J. Wells4, Jeremy P. Wilkinson5, Tom Langton4, Byongjun Hwang6,7, Mats A. Granskog8 and David Rees Jones9 1Korea Polar Research Institute, Incheon, South Korea 2Nordic Institute for Theoretical Physics, Stockholm, Sweden 3Department of Mathematics, Stockholm University, Stockholm, Sweden 4Atmospheric, Oceanic and Planetary Physics, University of Oxford, Oxford, UK 5British Antarctic Survey, Cambridge, UK. 6Scottish Association for Marine Science, Oban, UK 7University of Huddersfield, Huddersfield, UK 8Norwegian Polar Institute, Fram Centre, Tromsø, Norway 9Dept. of Earth Sciences, University of Oxford, Oxford, UK   Citation: Kim, J.-H., Moon, W., Wells, A. J., Wilkinson, J. P., Langton, T., Hwang, B., Granskog, M. A., & Rees Jones, D. W. (2018). Salinity control of thermal evolution of late summer melt ponds on Arctic sea ice. Geophysical Research Letters, 45. https://doi.org/10.1029/2018GL078077 Alternative weblink: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2018GL078077   v1: submitted during review of the manuscript. v2: updated with details of accepted publication.

opencc-by-4.0Aug 2021View details →

Sea ice, snow and melt pond example data from MOSAiC transect observations

This data set contains in-situ observation of sea ice, snow and melt pond properties from two days in winter (January 23, 2020) and summer (July 7, 2020) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. It originates from two sensors: <ol> <li>Broad-band electromagnetic induction sensor (Geophex GEM-2) measuring the combined thickness of the sea ice and snow layers</li> <li>A GPS snow depth probe (Snow-Hydro MagnaProbe) measuring the thickness of the snow layer and melt ponds depth during summer</li> </ol> Both sensors were operated coincidently along transect loops while different loops were used in both days. This data set is a subset of similar weekly activities between October 2019 and September 2020. This publication intends to provide a preview of the data properties and approximate changes between the winter and summer periods. It also has to be noted, that the final data of the EM induction sensor might differ from this release, which is based on a quick-look processing directly after data acquisition. GEM-2 data files The file format of GEM-2 data is a text file with comma-separated values. Notable parameters are: <ul> <li>‘time’: UTC time</li> <li>‘longitude’: Longitude in degrees east (fill value: 0.0)</li> <li>‘latitude’: Latitude in degrees north (fill value: 0.0)</li> <li>‘`f{frequency}Hz_hcp_{i:Inphase|q:Quadrature}`: total (ice + snow) thickness of the sea ice and snow layers in meter for different channels*</li> </ul> *The channels correspond to the real (Inphase) or imaginary (Quadrature) part of the EM signal at a given frequency. The variable name in the csv file is to be read as `f{frequency}Hz_hcp_{i:Inphase|q:Quadrature}`. It is recommended to use the Inphase component of the 18.325 kHz frequency (variable `f18325Hz_hcp_i`) for analysis. MagnaProbe data files The file format of MagnaProbe data is a text file with comma-separated values. Notable parameters are: <ul> <li>`timestamp`: Timestamp</li> <li>`longitude_a`: longitude degree in degrees east</li> <li>`longitude_b`: longitude minute</li> <li>`latitude_a`: latitude degree in degrees north</li> <li>`latitude_b`: latitude minute</li> <li>`depthCm`: Snow thickness or melt ponds depth in cm</li> <li>`flag`: flag value indicating the type or measurement*</li> </ul> Flag values are: <ul> <li>-1 : melt pond</li> <li>1 : snow or surface scattering layer depth,</li> <li>2 : mixed surface type when pond water pools at the base of a melting</li> </ul> The filenames follow the naming convention of <sensor>-mosaic-transect-<date>-<device-operations-id>.csv with the device operation id as a unique identifier of the sensor raw data within the MOSAiC project.