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61 results for “Ice Shelf”

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zenodo36/100

Pine Island Glacier ice shelf ocean cavity self-consistent spatial discretization mesh

Pine Island Glacier ice shelf ocean cavity ========================================== An unstructured mesh spatial discretisation of the Pine Island Glacier ice shelf ocean cavity. This is stored in an unstructured VTU file defined by the visualisation toolkit VTK [2]. A state PVSM file for Paraview [3] is also provided to reproduce visualisations shown in [1]. Note that Paraview requires absolute pathnames, so it may be necessary to edit file references to the VTU file in this state file. Files ----- - PineIslandGlacierIceShelfOceanCavity.vtu - PineIslandGlacierIceShelfOceanCavity_grid_quality_analysis.pvsm Author ------ - Dr Adam S. Candy <a.s.candy@tudelft.nl>, <candy@cantab.net> - Technische Universiteit Delft - Imperial College London References ---------- [1] Candy, A.S., 2016. A consistent approach to unstructured mesh generation for geophysical models. In review. Preprint available at https://arxiv.org/abs/1703.08491. [2] The Visualization Toolkit (VTK), version 5.10.1. URL: http://www.vtk.org. [3] Paraview, version 4.3.1. https://www.paraview.org.

opencc-by-4.0Jun 2013View details →

zenodo36/100

Filchner-Ronne ice shelf ocean cavity self-consistent spatial discretization mesh

Filchner-Ronne ice shelf ocean cavity ===================================== An unstructured mesh spatial discretisation of the Filchner-Ronne ice shelf ocean cavity and the ice sheet floating above. This is stored in two unstructured VTU files defined by the visualisation toolkit VTK [2]. A state PVSM file for Paraview [3] is also provided to reproduce visualisations shown in [1]. Note that Paraview requires absolute pathnames, so it may be necessary to edit file references to the VTU files in this state file. Files ----- - FilchnerRonneIceShelfOceanCavity.vtu - FilchnerRonneIceShelfOceanCavity_ice.vtu - FilchnerRonneIceShelfOceanCavity.pvsm Author ------ - Dr Adam S. Candy <a.s.candy@tudelft.nl>, <candy@cantab.net> - Technische Universiteit Delft - Imperial College London References ---------- [1] Candy, A.S., 2016. A consistent approach to unstructured mesh generation for geophysical models. In review. Preprint available at https://arxiv.org/abs/1703.08491. [2] The Visualization Toolkit (VTK), version 5.10.1. URL: http://www.vtk.org. [3] Paraview, version 4.3.1. https://www.paraview.org.

opencc-by-4.0Sep 2012View details →

zenodo36/100

Improving surface melt estimation over the Antarctic Ice Sheet using deep learning: a proof of concept over the Larsen Ice Shelf

Hu, Z., Kuipers Munneke, P., Lhermitte, S., Izeboud, M., and van den Broeke, M.: Improving Surface Melt Estimation over Antarctica Using Deep Learning: A Proof-of-Concept over the Larsen Ice Shelf, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-102, in review, 2021. ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- (1) MLP_model_surface_melt_corr.h5 is the developed MLP model used for correcting RACMO2 surface melt. (2) RACMO2_surface_melt_corr_MLP_AWS14.xlsx corrected surface melt [mm w.e. per day] from RACMO2 at AWS 14 during austral summers 2001 - 2016. The model inputs are (1) the simulated albedo, (2) the albedo difference between the observed and simulated albedo, (3) air temperature at 2m, (4) incoming shortwave radiation, (5) downwelling longwave radiation, (6) simulated surface melt, (7) Boolean melt flag, (8) surface melt difference to the previous day, and (9) record date as day of the year. (3) RACMO2_surface_melt_corr_MLP_AWS17.xlsx The same as point 2 but for AWS 17 (4) RACMO2_surface_melt_corr_MLP_AWS18.xlsx The same as point 2 but for AWS 18 Note: Data 2-4 are corrected RACMO2 simulations of surface melt at the pixels in RACMO2 27 km grid corresponding to AWS 14, 17, and 18 locations. They are not AWS observations. ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Related data set: MODIS/Terra Surface Reflectance Daily L2G Global 1 km and 500 m SIN Grid product is available via the Land Processes Distributed Active Archive Center (LP DAAC) (https://doi.org/10.5067/MODIS/MOD09GA.006, last access: 3 December 2021). MODIS/Terra+Aqua Albedo Daily L3 Global 500 m SIN Grid product is also available via LP DAAC (https://doi.org/10.5067/MODIS/MCD43A3.006, last access: 3 December 2021). Sentinel-1 images are provided by the European Space Agency (ESA) (https://sentinel.esa.int/web/sentinel/sentinel-data-access, last access: 3 December 2021). Automatic weather station observations from AWS 14, 17, and 18 are available via https://doi.pangaea.de/10.1594/PANGAEA.910473 (last access: 3 December 2021). RACMO2 simulations (https://www.projects.science.uu.nl/iceclimate/models/antarctica.php#2-1, last access: 3 December 2021) are provided by van Wessem et al. (2018) which are available on request to the original authors. ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- You should also refer to and cite the following paper: Hu, Z., Kuipers Munneke, P., Lhermitte, S., Izeboud, M., and van den Broeke, M.: Improving Surface Melt Estimation over Antarctica Using Deep Learning: A Proof-of-Concept over the Larsen Ice Shelf, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-102, in review, 2021.

opencc-by-4.0Dec 2021View details →

zenodo36/100

Data set used for the article "Atmospheric triggers of the Brunt Ice Shelf calving in February 2021"

Data set used for the manuscript "Atmospheric triggers of the Brunt Ice Shelf calving in February 2021". Which is under review in "Geophysical Research Letters" Journal.

opencc-by-4.0Dec 2021View details →

zenodo36/100

Dataset: Large-eddy simulation of the ice shelf-ocean boundary layer model output

This repository contains large-eddy simulation output from the CFD model Diablo. The simulations are of the boundary layer beneath a melting ice shelf. This model output underpins the submitted manuscript Regimes and transitions in the basal melting of Antarctic ice shelves submitted to the Journal of Physical Oceanography (December 2021).

openJan 2022View details →

dryad36/100

Grounding line remote operated vehicle (GROV) exploration of the ice shelf cavity of Petermann Glacier, Greenland

The melting of ice by ocean waters along the periphery of ice sheets is a major physical process driving their evolution in a warming climate. Using the fiber-optic-tethered Grounding line Remote Operated Vehicle (GROV), we explored the ice shelf cavity of Petermann Glacier, in Northwestern Greenland, in May 2023, using a novel interferometric multibeam sonar operating at 117 KHz with 360° viewing capability. The seafloor depth is uniform at 820 m and 200 m deeper than anticipated. At the ice shelf base, we find a succession of terraces interrupted by 20-40 m ice cliffs that have no signature at the surface, but are consistent with double-diffusive convection. The central melt channel deviates by ± 80 m from flotation, is smoother than indicated by the surface, and reveals asymmetric melt. The results demonstrate the fundamental importance of surveying the geometry of ice shelf cavities to document ice-ocean interaction.

opencc-zeroMay 2024View details →

zenodo36/100

Model data for probabilistic projections of the Amery Ice Shelf catchment, Antarctica, under high ice-shelf basal melt conditions

Model data for probabilistic projections of the Amery Ice Shelf catchment,  Antarctica, under high ice-shelf basal melt conditions ====================================================================== This archive contains ice-sheet model output and statistical models  used in the manuscript: "Probabilistic projections of the Amery Ice Shelf catchment, Antarctica, under high ice-shelf basal melt conditions" by: Sanket Jantre, Matthew Hoffman, Nathan M. Urban, Trevor Hillebrand, Mauro Perego, Stephen Price, John D. Jakeman OVERVIEW -------- Statistical models and corresponding datasets are archived in the file Amery_UQ_Study_Statistical_Models.zip.  That file contains a README that describes its contents. Contact for statistical models: Sanket Jantre, Brookhaven National Laboratory, sjantre@bnl.gov The remaining files in this archive contain output from ice-sheet model  simulations using the MPAS-Albany Land Ice (MALI) model  (Hoffman et al. 2018, https://github.com/MALI-Dev/E3SM) applied to a regional domain of the Amery Ice Shelf catchment of Antarctica with mesh resolution varying from 4 to 20 km. The contents of these files are described below. Contact for MALI simulations: Matthew Hoffman, Los Alamos National Laboratory, mhoffman@lanl.gov ENSEMBLES --------- The ensembles consist of all or a subset of 200 model runs with values for 6 parameters sampled from a Sobol' sequence over a specified range. Parameters and Sampled Ranges: 1. Ice stiffness scaling factor, Cφ: (0.8, 1.2) 2. Basal friction scaling factor, Cμ: (0.8, 1.2) 3. Basal slip exponent, q: (0.1, 0.333) 4. Calving yield stress, σmax: (80, 180) kPa 5. Ice-shelf melt coefficient, γ0: (9620, 471000) m yr−1 6. Ice-shelf basal melt rate, m: (12, 58) Gt yr−1 SCENARIOS --------- The archive contains output from 4 scenarios described in the manuscript: Historical relaxation (RELX): For each ensemble member, we conducted a 50 year relaxation from the initial condition using historical climate forcing to integrate out fast transient behavior. For surface mass balance, we applied a 1995–2017 climatological average from RACMO2.3p1 (Van Wessem et al., 2014; van den Broeke, 2019). The ocean thermal forcing was the observation-based climatology compiled for ISMIP6-Antarctica, which uses data from 1995–2018 (Jourdain et al., 2020; Nowicki et al., 2020). The 50 year relaxation duration was chosen as the most rapid adjustments occur in the first few decades of integration, while the long term adjustment to a fully steady state takes thousands of years. Relaxation to full steady state would require substantially more computing resources than our entire set of ensembles and also leads to the complication of different runs having potentially very different initial states. Future improvements to model initialization that account for surface elevation change (Perego et al., 2014) may reduce model drift and adjust this requirement. The final model state in each run at the end of RELX was given the nominal date of January 1, 2015, and all three projection ensembles were branched from these states. Control projection (CTRL): The CTRL projection ensemble were an extension of the RELX configurations, continuing the same surface mass balance and ocean thermal forcing from January 1, 2015, to January 1, 2300. This ensemble was used to assess model drift relative to the forced response of the climate scenarios.  SSP1-2.6 projection (SSP1): Our SSP1 projection used annual surface mass balance and ocean thermal forcing derived from a UKESM SSP1-2.6 climate scenario (expAE10 from Seroussi et al., 2024). Surface mass balance and ocean thermal forcing were applied as anomalies relative to the climatological mean forcings in RELX/CTRL to avoid issues related to climate model bias and abrupt changes in forcing. This ensemble was also run from January 1, 2015, to January 1, 2300. SSP5-8.5 projection (SSP5) Our SSP5 projection used the UKESM SSP5-8.5 projection forcings (expAE05 from Seroussi et al., 2024), again applied as anomalies and from 2015 to 2300. OUTPUT ------ Each ensemble directory contains at the base level: * branch_ensemble.cfg - configuration file for the ensemble_generator test   case (https://mpas-dev.github.io/compass/latest/users_guide/landice/test_groups/ensemble_generator.html)    of COMPASS (Configuration Of Model for Prediction Across Scales Setups, https://github.com/MPAS-Dev/compass)   used to set up the ensemble. * mesh_vars.nc - netCDF file containing the mesh variables.  This file is the   same for all ensembles.  These fields need to be appended to an output file to   visualize in, e.g., Paraview.  This can be done with the command:   ncks -A mesh_vars.nc output.nc Each ensemble contains subdirectories for each run numbered 000-199.  Each run directory contains the files: * globalStats.nc - scalar metrics at every time step * output.nc - spatial fields at 10 year intervals * run_info.cfg - summary of parameter values Notes: * The SSP1 and SSP5 ensembles contain a subset of the full 200 runs   because some runs were filtered out as being unnecessary during model   calibration (see manuscript). * The RELX ensemble is run for 50 years with an arbitrary start year of 2000.   The CTRL, SSP1, and SSP5 ensembles are started in 2015 from the final state   of the corresponding RELX run (nominally 2050).  Because spatial data has   been saved in this archive at 10 year intervals, the first output for the   three projection ensembles is 2020.  To get the initial condition for the   projection ensembles, use the 2050 state from RELX for the corresponding   model run. * Spatial field output at 1 year intervals, as well as run input, log, and   restart files are available from the corresponding contact.  The complete   run data is about 2 TB. CITATION -------- If you find these data useful, please cite the DOI for this  archive (10.5281/zenodo.11166628), as well as our manuscript submitted to The Cryosphere. This archive is approved by Los Alamos National Laboratory for public release under LA-UR-24-24969; distribution is unlimited.   REFERENCES ---------- Hoffman, M. J., Perego, M., Price, S. F., Lipscomb, W. H., Zhang, T., Jacobsen, D., Tezaur, I., Salinger, A. G., Tuminaro, R., and Bertagna, L.: MPAS-Albany Land Ice (MALI): a variable-resolution ice sheet model for Earth system modeling using Voronoi grids, Geoscientific Model Development, 11, 3747–3780, 2018. Jourdain, N. C., Asay-Davis, X., Hattermann, T., Straneo, F., Seroussi, H., Little, C. M., and Nowicki, S.: A protocol for calculating basal melt rates in the ISMIP6 Antarctic ice sheet projections, The Cryosphere, 14, 3111–3134, https://doi.org/10.5194/tc-14-3111-2020, 2020. Nowicki, S., Goelzer, H., Seroussi, H., Payne, A. J., Lipscomb, W. H., Abe-Ouchi, A., Agosta, C., Alexander, P., Asay-Davis, X. S., Barthel, A., Bracegirdle, T. J., Cullather, R., Felikson, D., Fettweis, X., Gregory, J. M., Hattermann, T., Jourdain, N. C., Kuipers Munneke, P., Larour, E., Little, C. M., Morlighem, M., Nias, I., Shepherd, A., Simon, E., Slater, D., Smith, R. S., Straneo, F., Trusel, L. D., van den Broeke, M. R., and van de Wal, R.: Experimental protocol for sea level projections from ISMIP6 stand-alone ice sheet models, The Cryosphere, 14, 2331–2368, https://doi.org/10.5194/tc-14-2331-2020, 2020. Perego, M., Price, S., and Stadler, G.: Optimal Initial Conditions for Coupling Ice Sheet Models to Earth System Models, Journal of Geophysical Research: Earth Surface, 119, 1894–1917, https://doi.org/10.1002/2014JF003181, 2014. Seroussi, H., et al. 2024. ISMIP6 Projections 2300 Antarctica Protocol. https://theghub.org/groups/ismip6/wiki/ISMIP6-Projections2300-Antarctica.  van den Broeke, M.: RACMO2.3p1 annual surface mass balance Antarctica (1979-2014), PANGAEA - Data Publisher for Earth & Environmental Science, https://doi.org/10.1594/PANGAEA.896940, 2019. van Wessem, J., Reijmer, C., Morlighem, M., Mouginot, J., Rignot, E., Medley, B., Joughin, I., Wouters, B., Depoorter, M., Bamber, J., Lenaerts, J., Van De Berg, W., Van Den Broeke, M., and Van Meijgaard, E.: Improved Representation of East Antarctic Surface Mass Balance in a Regional Atmospheric Climate Model, Journal of Glaciology, 60, 761–770, https://doi.org/10.3189/2014JoG14J051, 2014.

opencc-by-4.0May 2024View details →

zenodo36/100

Ice front blocking in a laboratory model of the Antarctic ice shelf

Mass loss from the Antarctic Ice Sheet to the ocean has increased in recent decades, largely because the thinning of its floating ice shelves has allowed the outflow of grounded ice to accelerate. Enhanced basal melting of the ice shelves is thought to be the ultimate driver of change, motivating a recent focus on the processes that control ocean heat transport onto and across the seabed of the Antarctic continental shelf towards the ice. However, the shoreward heat flux typically far exceeds that required to match observed melt rates, suggesting other critical controls. By laboratory experiments on the Coriolis rotating platform, we show that the depth-independent component of the flow towards an ice shelf is blocked by the dramatic step shape of the ice front, and that only the depth-varying component, typically much smaller, can enter the sub-ice cavity. These results are consistent with direct observations of the Getz Ice Shelf system, as shown by Wahlin et al. (Nature 2019, in press). The selected data are velocity fields from a selection of 6 experiments, described in Wahlin et al. (2019), supplementary material, fig 4 and 9. <ul> <li>  EXP26,30,34 correspond to Fig. 4 a,b,c respectively (barotropic case)</li> <li>  EXP44,50,51 correspond to Fig. 9 a,b,c respectively (baroclinic case)</li> </ul> The velocity fields are measured by PIV from short image series (bursts) obtained by laser sheet illumination in several quasi-horizontal planes (for Fig 4, N=12 planes vertically separated by 6.2 cm, with 25 images per level, for Fig 9, N=7 planes vertically separated by 5.8 cm, with 19 images per level). The quasi-horizontal planes are parallel to the channel, slanted downward toward the iceshelf with an angle of 1.15 degree. For each experiment, the whole series of velocity fields is provided in /PCO1.png.sback.civ-PCO2.png.sback.civ.mproj (those are obtained by merging velocity fields from the images of two cameras denoted PCO1 and PCO2) velocity fields averaged inside each burst are provided in PCO1.png.sback.civ-PCO2.png.sback.civ.mproj.stat. Each velocity field is in a single netcdf file labeled by two indices denoting respectively the time and the index in the burst. Planes are scanned in a cyclic way, so that the same position is reached again after each increment of N in the first index. The average of these averaged velocity fields over 4 volumes when the current is fully established are provided in PCO1.png.sback.civ-PCO2.png.sback.civ.mproj.stat.stat. Details of the set-up and experimental conditions are provided in <a href="http://servforge.legi.grenoble-inp.fr/projects/pj-coriolis-17iceshelf">http://servforge.legi.grenoble-inp.fr/projects/pj-coriolis-17iceshelf</a>   <div> </div>

opencc-by-4.0Nov 2019View details →

zenodo36/100

The Seasonality of the Heat Budget on the Ross Sea Continental Shelf in a Coupled Regional Ocean-Sea Ice-Ice Shelf Model

This dataset includes the model data used in our paper entitled 'The Seasonality of the Heat Budget on the Ross Sea Continental Shelf in a Coupled Regional Ocean-Sea Ice-Ice Shelf Model'

opencc-by-4.0Sep 2024View details →

zenodo36/100

Monthly average profiles of Distributed Temperature Sensing at Thwaites Eastern Ice Shelf

Monthly average profiles of Distributed Temperature Sensing (DTS) for Thwaites Eastern Ice Shelf used in Dotto et al. (under review). The profiles are from April 2020, September 2020 and February 2021 and cover the ice-shelf-ocean interface depths. DTS utilizes a fibre-optic cable installed within the ice and through the ocean, and it uses Raman backscattered photons to estimate the in situ temperature of the fiber (Hausner et al 2011). The DTS interrogators (Silixa XT-DTS, Silixa LLC. Elstree, UK) used a spatial sampling of 25 cm. Each DTS profile uses a 1-minute integration time to reduce signal to noise. Independent temperature measurements from the MicroCATs are employed to calibrate the backscatter signal (Tyler et al., 2013; Hausner et al 2011).   Hausner, M. B., Suárez, F., Glander, K. E., van de Giesen, N., Selker, J. S. & Tyler, S. W. Calibrating single-ended fiber-optic Raman spectra distributed temperature sensing data. Sensors (Basel), 11(11), 10,859–10,879 (2011). Tyler, S. W., Holland, D. M., Zagorodnov, V., Stern, A. A., Sladek, C., Kobs, S., White, S., Suárez, F. & Bryenton, J. Using distributed temperature sensors to monitor an Antarctic ice shelf and sub-ice-shelf cavity. Journal of Glaciology, 59(215), 583–591 (2013). Dotto, T. S., Heywood, K., Hall, R. et al. Ocean variability beneath Thwaites Eastern Ice Shelf driven by the Pine Island Bay Gyre strength, 11 October 2022, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-1466534/v1]

opencc-by-4.0Nov 2022View details →

zenodo36/100

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.

opencc-by-4.0Apr 2023View details →

zenodo36/100

Replication data for: Future response of Antarctic continental shelf temperatures to ice shelf basal melting and calving

Replication data for: Future response of Antarctic continental shelf temperatures to ice shelf basal melting and calving This manuscript is in press at Geophysical Research Letters. <a href="https://zenodo.org/api/files/e04b47c6-d136-4833-b8c6-a79f5b05eb65/SSP585FW_ensemble.tar.gz?versionId=6f33274b-a436-4850-99a8-9dac047b812b">SSP585FW_ensemble.tar.gz</a> contains data needed to replicate the figures and analysis in the manuscript.

opencc-by-4.0Aug 2023View details →

dryad36/100