Find research datasets worth reusing

opencc-by-4.0Apr 2022View details →

Time-averaged borehole temperatures at AM01–AM06 on the Amery Ice Shelf

These are supplementary materials for the paper: Wang, Y., Zhao, C., Gladstone, R., Galton-Fenzi, B., and Warner, R.: Thermal structure of the Amery Ice Shelf from borehole observations and simulations, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-248, in review, 2021. Full description is given in the paper.

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-4.0Sep 2021View details →

Bathymetry beneath the Amery ice shelf, East Antarctica, revealed by airborne gravity

We estimated the seafloor topography beneath the Amery Ice Shelf, East Antarctica, from airborne gravity anomaly through a nonlinear inversion method called simulated annealing. The estimation results provide a view of the seafloor beneath the Amery Ice Shelf, where direct bathymetric observations are rare. The model, 'gravity_estimated_seafloor_topography_beneath_the_Amery_Ice_Shelf.nc', is in NetCDF format which can be read through MATLAB commands "ncdisp" and "ncread". Contents of the model can be found in "contents.txt". The MATLAB program "nc2mat.m" reads the NetCDF ".nc" format model and saves the variables in the model to a MATLAB ".mat" format file.

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.

openmit-licenseNov 2020View details →

ICESat-2 Water Depth Retrieval Comparisons for Four Supraglacial on Amery Ice Shelf, East Antarctica

This archive contains the code used for analysis and producing figures for the following paper: Fricker, H.A., Arndt, P.S., Brunt, K.M., Datta, R.T., Fair, Z., Jasinski, M.F., Kingslake, J., Magruder, L.A., Moussavi, M., Pope, A. and Spergel, J.J., 2021. “ICESat-2 meltwater depth estimates: application to surface melt on Amery Ice Shelf, East Antarctica.” Geophysical Research Letters, 48(8), DOI: 10.1029/2020GL090550. URL: <a href="https://doi.org/10.1029/2020GL090550">https://doi.org/10.1029/2020GL090550</a> These materials are also on GitHub: <a href="github.com/fliphilipp/ameryMeltLakesICESat2">https://github.com/fliphilipp/ameryMeltLakesICESat2</a>    The code for generating manually annotated baseline depth estimates from ICESat-2 ATL03 photon data is available here: <a href="https://github.com/fliphilipp/pondpicking">https://github.com/fliphilipp/pondpicking</a>

restrictedcc-by-4.0Aug 2024View details →

Dataset for the paper "Ephemeral grounding on the Pine Island Ice Shelf, West Antarctica, from 2014 to 2023"

This code and related datasets are used for generating the figures for the paper "Ephemeral grounding on the Pine Island Ice Shelf, West Antarctica, from 2014 to 2023". Includes corrected REMA DSM stripes at the central ice shelf region of Pine Island Ice Shelf, the double differential vertical displacement results from 2014 to 2023 which cazlculated from the offset tracking results output from GAMMA software. Other dataset for other analysis are also included in the ZIP file. Each MATLAB codes in MATLAB_function.zip includes the discriptions that guide the user how to used it and how to find the dataset that used for processing. Some sample files are provided in data_and_results.zip that can let user test the code easily.  These data can be accessed after the paper is accepted.

opencc-by-4.0May 2021View details →

Nansen Ice Shelf data (2016-2019)

Data collected at Nansen Ice Shelf between 2016 and 2019 including:1. Ice-penetrating radar data with dGPS surface elevations. These are divided into three sites with site 1 closest to the grounding line. Each file contains: Easting(m), Northing (m), ice thickness (m), ice surface elevation (m asl) and ice draft (m asl).2. Airborne radar transect with columns as follows: Easting(m), Northing (m), ice draft (m asl).3. Ocean glider data from adjacent to, and underneath the Nansen Ice Shelf with columns as follows: Latitude, Longitude, depth below ocean surface (m)Easting and northing data are Antarctic Polar Stereographic coordinates

opencc-by-4.0Nov 2021View details →

Bathymetry beneath the Amery ice shelf, East Antarctica, revealed by airborne gravity

opencc-by-4.0Jul 2024View details →

Data used in the manuscript entitled "Turbulent heat flux dynamics along the Dotson and Getz ice-shelf fronts (Amundsen Sea, Antarctica)"

Data files used in the analysis in the manuscript entitled "Turbulent heat flux dynamics along the Dotson and Getz ice-shelf fronts (Amundsen Sea, Antarctica)". Data were collected during the RV NB Palmer NBP2202 cruise, during the 2022 TARSAN campagine in the Amundsen Sea. Underway data provides daily files from the underway and meteorology sensors in JGOFS format. CTD data collected from the cruise. Information about sensors and data formats is included in the data report. Glider data was processed through the UEA Seaglider Toolbox (https://bitbucket.org/bastienqueste/uea-seaglider-toolbox/src/toolbox/) and is provided in Matlab format.   Manuscript abstract: In coastal polynyas, where sea–ice formation occurs, it is crucial to have accurate estimates of heat fluxes in order to predict future rates of sea–ice formation. The Amundsen Sea Polynya is the fourth largest coastal polynya around Antarctica, yet remains poorly observed because of its remoteness. Consequently, we rely on models and reanalysis that are unvalidated to study the effect of atmospheric forcing on polynya dynamics. We use summer ship-board data from the NBP22/02 cruise to understand the turbulent heat flux dynamics in the Amundsen Sea Polynya and evaluate our ability to represent these dynamics in ERA5. We show that cold and dry air outbreaks from Antarctica enhance air–sea temperature and humidity gradients, triggering episodic heat loss events. The heat loss is larger along the ice shelves, and it is also where the ERA5 turbulent heat flux exhibits the largest biases, underestimating the flux by up to 141~W~m$^{-2}$ due to its coarse resolution and misrepresentation of ice-shelf location. By reconstructing a turbulent heat flux product from ERA5 variables using a nearest neighbour approach to obtain sea surface temperature, we decrease the bias to 107 W m$^{-2}$. Using a 1D-model, we show that the mean co-located ERA5 heat loss underestimation of -28~W~m$^{-2}$ led to an overestimation of the summer evolution of sea surface temperature (heat content) by +0.76~°C (+8.2e+07~J) over 35-days. By obtaining the reconstructed flux, the reduced heat loss bias (12 W~m$^{-2}$) reduced the seasonal bias in sea surface temperature (heat content) to -0.17~°C (-3.30e+07~J) over the 35-days. This study shows that caution should be applied when retrieving ERA5 turbulent flux along the ice shelves, and that a reconstructed flux using ERA5 variables shows better accuracy.

opencc-by-4.0Oct 2024View details →

VMP and SADCP at Dotson Ice Shelf outflow (2022)

Matlab files of vertical microstructure profiler (VMP) 2000 and shipboard acoustic doppler current profiler (SADCP) transects at the Dotson Ice Shelf (DIS) outflow measured during the NBP2202 expedition between 22 Jan 2022 and 06 Feb 2022, used in the manuscript Dotto et al. (in prep).   Please, read the ReadMe for an explanation of the variables and dataset.   These data were collected onboard Nathaniel B Palmer, and are part of the the Thwaites-Amundsen Regional Survey and Network Integrating Atmosphere-Ice-Ocean Processes (TARSAN) project, a component of the International Thwaites Glacier Collaboration (ITGC; https://thwaitesglacier.org/).   Citing paper: Tiago S. Dotto, Peter M. F. Sheehan, Yixi Zheng, Rob A. Hall, Gillian M. Damerell and Karen J. Heywood (in prep.) Heterogeneous Mixing Processes Observed in the Dotson Ice Shelf Outflow, Antarctica

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.

openogl-uk-3.0Oct 2022View details →

Output from model simulations of a turbulent ice shelf-ocean boundary current

This dataset contains output from 2 LES and 19 MITgcm simulations of an idealised configuration of the ice shelf-ocean boundary current. Core fields are provided such as velocity and density and these are given as ice-plane averaged. The output was generated to make an inter-model comparison of the representation of dynamical processes at the ice shelf-ocean boundary. The two LES configurations differ in their sub-grid-scale parameterisation. The MITgcm simulations investigate the sensitivity to various parameter changes including: resolution, diffusivity coefficients, advection scheme and melt-parameterisation. All configurations are outlined in Patmore et al. (2022).

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-4.0Jul 2024View details →

A compilation of beryllium-isotope, element, and grainsize data from sediments sampled from Prydz Bay and beneath Amery Ice Shelf, East Antarctica

All tables are included in a single .xlsx file across three sheets. Each sheet includes sample information data: expedition and sample location information, reference to corresponding method section in text, and a reference to the source of the method employed for different procedures, or the reference to source data. Footnotes are used where necessary to explain a component of a table. Supplementary Table 1: All beryllium data used for Sequential, Grainsize, Partial, and Total experiments described in text. 10Be concentration and corresponding 1-sigma (10^8 at/g), 9Be concentration and corresponding 1-sigma (10^15 at/g), and the 10Be/9Be ratio and corresponding 1-sigma (10^-8 at/at). Supplementary Table 2: Element concentrations (µg/g) from samples across open marine and sub-ice shelf environments and their resultant enrichment factors (EF). Enrichment factors calculated using in text Equation 1. Estimated crustal abundance and ratio displayed below the data table. Supplementary Table 3:  Grainsize of samples used in this study.    This research was supported by the Australian Research Council Special Research Initiative, Australian Centre for Excellence in Antarctic Science (Project Number SR200100008).

opencc-by-4.0Sep 2024View details →

Interannual Salinity Variability on the Ross Sea Continental Shelf in a Regional Ocean-Sea Ice-Ice Shelf Model

This data is only used for paper submitted to the JPO  entitled 'Interannual Salinity Variability on the Ross Sea Continental Shelf in a Regional Ocean-Sea Ice-Ice Shelf Model'.

opencc-by-4.0Aug 2021View details →

MITgcm model setup and output for "Impact of subglacial freshwater discharge on Pine Island Ice Shelf"

Here, it contains the results of the regional PIG simulation. Model grid is lat-lon similar to Nakayama et al., 2019 roughly 200 m in the region. See Nakayama et al., submitted to GRL for detail.  For complete model output (10*Qsg and 50*Qsg cases), please access NASA data (Registration is required). https://ecco.jpl.nasa.gov/drive/files/ECCO2/High_res_PIG/PIG_only_200m  Contents can be downloaded easily using wget (see link below).  https://ecco-group.org/docs/wget_download_multiple_files_and_directories.pdf (Contents) code.zip (code to run this simulation) input.zip (input file required for this simulation) latlon_run23.zip (CTRL) latlon_run24.zip (Qsg case) latlon_run25.zip (2*Qsg case) (How to build and run) mkdir build ./../../tools/genmake2 -of ../../../tools/build_options/linux_amd64_ifort+mpi_ice_nas -mpi -mods ../code/ make depend make -j 16 cd .. mkdir test cd test ln -sf ../input/* . ln -sf /nobackup/hzhang1/forcing/era_xx_it50/ . cp ../build/mitgcm_uv . qsub latlon_run23.sh

opencc-by-4.0Nov 2022View details →

Supplementary data for 'Melting and refreezing in an ice shelf basal channel at the grounding line of the Kamb Ice Stream, West Antarctica' Whiteford et al 2022

These data are described in detail by 'Melting and refreezing in an ice shelf basal channel at the grounding line of the Kamb Ice Stream, West Antarctica' Whiteford et al 2022. 'ApRES dataset.zip' contains raw ApRES data and processed results from a spatial survey of basal mass balance, detailed in Sections 2.2.4 and 3.2.2 of the above paper. README.md describes the file contents. 'radar_dataset.tar.gz' contains raw data from a low frequency radar survey profiling ice thickness, detailed in Section 2.2.1 of the above paper.   'channel_base_surface_map' contains six files. *_Ice_thickness is raster data of an estimation of ice thickness in the area. This is produced through processing radar data 'radar_dataset.tar.gz' and interpolation, described in Sections 2.2.2 and 3.1.2. *_REMA_surf is raster data of the ice surface, sampled a REMA strip from 9 November 2016 (Howat 2019).  Ice_base is raster data  calculated by subtracting the ice thickness from this surface. *_x_grid and *_y_grid are the x and y UTM coordinates accompanying the raster data, in Antarctic Polar Stereographic projection. *_extent is the x and y extent of the area covered by raster data. Reference: Howat, I. M., Porter, C., Smith, B. E., Noh, M.-J., & Morin, P. (2019). The Reference Elevation Model of Antarctica. Cryosphere, 13 (2)

opencc-by-4.0Apr 2023View details →

Change in Antarctic Ice Shelf Area from 2009 to 2019

Antarctic Ice Shelves provide buttressing support to the ice sheet, stabilising the flow of grounded ice and its contribution to global sea levels. Over the past 50-years satellite observations have shown ice shelves collapse, thin and retreat, however, there are few measurements of the Antarctic wide change in ice shelf area. Here, we use MODIS satellite data to measure the change in ice shelf calving front position and area on 34 ice shelves in Antarctica, from 2009 to 2019. Over the last decade, a reduction in area on the Antarctic Peninsula (6,692.5 km2) and West Antarctica (5,563.1 km2), has been outweighed by area growth in East Antarctica (3,532.1 km2) and the large Ross and Ronne-Filchner Ice Shelves (14,027.9 km2). The largest retreat was observed on Larsen-C Ice Shelf where 5,916.6 km2 of ice was lost during an individual calving event in 2017, and the largest area increase was observed on Ronne Ice Shelf in East Antarctica, where gradual advance over the past decade (535.3 km2/yr) led to a 5,888.6 km2 area gain from 2009–2019. Overall, the Antarctic Ice Shelf area has grown by 5,304.5 km2 since 2009, with 18 ice shelves retreating and 16 larger shelves growing in area. Our observations show that Antarctic Ice Shelves gained 660.6 Gt of ice mass over the decade whereas the steady state approach would estimate substantial ice loss over the same period, demonstrating the importance of using time-variable calving flux observations to measure change.