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{"Abstract":["This classified_bed data product represents the radar bed classification shown in <a href="https://doi.org/10.1098/rsta.2014.0297">Young et al., 2016</a>. Values of 0 represent specularity content below 20%; values of 3.3 represent specularity content above 20% and energy 1 microsecond below the bed 15 dB lower than the bed echo, and values of 6.7 represent specularity content above 20% and energy 1 microsecond below the bed 15 dB within than the bed echo. Grids for specularity content and post bed echo are also available. Data is available as COARDS-compliant netCDF-4/HDF5 grids (.grd) and GeoTiffs (.tiff), both in EPSG 3031 (Antarctic Polar Stereographic) projection.\n<p>\n<p>\nData were gridded using <a href="https://docs.generic-mapping-tools.org/6.1/gmt.html"> GMT6.1</a> and the <a href="https://github.com/sakov/nn-c">nnbathy</a> natural neighbor interpolator. Cell size was 1 km, gaussian filter distance was 5 km, and mask radius was 2 km.\n<p>\nBrowse images, with Bedmap3 (Pritchard et al., 2025) surface elevation contours and MEASURES phase derived surface velocities (Mouginot et al. 2019) are available for each dataset.\n\n<p>\n<p>\nAn interpretation of the values in the classified_bed product is that low values are rough bed, intermediate values are isotropic wet bed, and high values are anisotropic wet bed.\n\nVersion 1 includes data from the 2016 paper, including AGASEA over Thwaites Glacier (Holt et al., 2006), ATRS over West Antarctica (Peters et al., 2005), GIMBLE over Marie Byrd Land (Young et al, 2013) and parts of ICECAP over Wilkes Subglacial Basin, Dome C, Highland B and Totten Glacier. (Young et al, 2011, Young et al., 2016). We expect updates to the coverage as part of work funded by the Arête Glaciers Initiative.\n\n<p>\n<b>References</b>\n<br>\nHolt, J. W., Blankenship, D. D., Morse, D. L., Young, D. A., Peters, M. E., Kempf, S. D., Richter, T. G., Vaughan, D. G., and Corr, H., New boundary conditions for the West Antarctic ice sheet: subglacial topography of the Thwaites and Smith Glacier catchments, 2006, Geophysical Research Letters, 33 (L09502), pp., https://doi.org/10.1029/2005GL025561\n<br>\nMouginot, J., Rignot, E., and Scheuchl, B., Continent-wide, interferometric SAR phase, mapping of Antarctic ice velocity, 2019, Geophysical Research Letters, 46(16), pp.9710-9718, https://doi.org/10.1029/2019GL083826\n<br>\nPeters, M. E., Blankenship, D. D., and Morse, D. L., Analysis techniques for coherent airborne radar sounding: Application to West Antarctic ice streams, 2005 ,Journal of Geophysical Research, 110(B06303), pp.,https://doi.org/10.1029/2004JB003222\n<br>\nPritchard, H. D., and others.,Bedmap3 updated ice bed, surface and thickness gridded datasets for Antarctica,2025,Scientific Data,12(1), pp.414,https://doi.org/10.1038/s41597-025-04672-y\n<br>\nYoung, D. A., D. D. Blankenship, J. S. Greenbaum, E. Quartini, G. L. Muldoon, F. Habbal, L. E. Lindzey, C. A. Greene, E. M. Powell, G. C. Ng, T. G. Richter, G. Echeverry, and S. Kempf, 2024, Geophysical Investigations of Marie Byrd Land Lithospheric Evolution (GIMBLE) Airborne VHF Radar Transects: 2012/2013 and 2014/2015, https://doi.org/10.18738/T8/BMXUHX, Texas Data Repository\n<br>\nYoung, D. A., Wright, A. P., Roberts, J. L., Warner, R. C., Young, N. W., Greenbaum, J. S., Schroeder, D. M., Holt, J. W., Sugden, D. E., Blankenship, D. D., van Ommen, T. D., and Siegert, M. J.,A dynamic early East Antarctic Ice Sheet suggested by ice covered fjord landscapes, 2011, Nature, 474, pp.72-75, https://doi.org/10.1038/nature10114\n<br>\nYoung, D. A., Schroeder, D. M., Blankenship, D. D., Kempf, S. D., and Quartini, E.,The distribution of basal water between Antarctic subglacial lakes from radar sounding,2016,Philosophical Transactions of the Royal Society A, 374 (20140297), pp.1-21, https://doi.org/10.1098/rsta.2014.0297\n\n<p>\n<b>Change Log</b>\n<br>\nChanges from V1: changes to gridding parameters to more closely match the figures from Young 2016; updated metadata gridding description"]}
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Modèle Atmosphérique Régional (MAR) version 3.11 regional climate model output, 1979-2019, Greenland domain, 10 kilometer (km) horizontal resolution
Modèle Atmosphérique Régional (MAR) is a regional climate model that is fully coupled to a one-dimensional surface-atmosphere energy and mass transfer scheme, SISVAT (Soil Ice Snow Vegetation Atmosphere Transfer) (Fettweis et al., 2005, 2020; Lefebre et al., 2005). SISVAT employs a multilayered snowpack model, CROCUS, that simulates meltwater production, percolation, and refreeze (Brun et al., 1989), while also accounting for changes in albedo due to snow metamorphism (Brun et al., 1992). MAR has been extensively verified over the Greenland Ice Sheet and is therefore particularly well suited for analyses of Greenland ice sheet surface mass balance (Fettweis et al., 2011; Fettweis et al., 2020; Lefebre et al. 2005; Mattingly et al. 2020). Brun, E., Martin, E., Simon, V., Gendre, C., and Coléou, C. (1989). An energy and mass model of snow cover suitable for operational avalanche forecasting. Journal of Glaciology, 35, 333. https://doi.org/10.1017/S0022143000009254 Brun, E., David, P., Sudul, M., and Brunot, G. (1992). A numerical model to simulate snow-cover stratigraphy for operational avalanche forecasting. Journal of Glaciology, 38(128), 13–22. https://doi.org/10.3189/S0022143000009552 Fettweis, X., Gallée, H., Lefebre, F., and van Ypersele, J.-P. (2005). Greenland surface mass balance simulated by a regional climate model and comparison with satellite-derived data in 1990–1991. Climate Dynamics, 24(6), 623–640. https://doi.org/10.1007/s00382-005-0010-y Fettweis, X., Tedesco, M., van den Broeke, M., and Ettema, J. (2011). Melting trends over the Greenland ice sheet (1958–2009) from spaceborne microwave data and regional climate models. The Cryosphere, 5(2), 359–375. https://doi.org/10.5194/tc-5-359-2011 Fettweis, X., Hofer, S., Krebs-Kanzow, U., Amory, C., Aoki, T., Berends, C. J., et al. (2020). GrSMBMIP: intercomparison of the modelled 1980–2012 surface mass balance over the Greenland Ice Sheet. The Cryosphere, 14(11), 3935–3958. https://doi.org/10.5194/tc-14-3935-2020 Lefebre, F., Fettweis, X., Gallée, H., Van Ypersele, J.-P., Marbaix, P., Greuell, W., and Calanca, P. (2005). Evaluation of a high-resolution regional climate simulation over Greenland. Climate Dynamics, 25(1), 99–116. https://doi.org/10.1007/s00382-005-0005-8 Mattingly, K. S., Mote, T. L., Fettweis, X., van As, D., Van Tricht, K., Lhermitte, S., et al. (2020). Strong summer atmospheric rivers trigger Greenland ice sheet melt through spatially varying surface energy balance and cloud regimes. Journal of Climate, 33(16), 6809–6832. https://doi.org/10.1175/JCLI-D-19-0835.1
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- Award ID(s):
- 1900324
- PAR ID:
- 10488581
- Publisher / Repository:
- NSF Arctic Data Center
- Date Published:
- Subject(s) / Keyword(s):
- Greenland surface mass balance surface energy balance regional climate model albedo ice sheets
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract The rate of growth or retreat of the Greenland and Antarctic ice sheets remains a highly uncertain component of future sea level change. Here we examine the simulation of Greenland ice sheet surface mass balance (GrIS SMB) in a development branch of the ModelE2 version of the NASA Goddard Institute for Space Studies (GISS) general circulation model (GCM). GCMs are often limited in their ability to represent SMB compared with polar region regional climate models. We compare ModelE2‐simulated GrIS SMB for present‐day (1996–2005) simulations with fixed ocean conditions, at a spatial resolution of 2° latitude by 2.5° longitude (~200 km), with SMB simulated by the Modèle Atmosphérique Régionale (MAR) regional climate model (1996–2005 at a 25‐km resolution). ModelE2 SMB agrees well with MAR SMB on the whole, but there are distinct spatial patterns of differences and large differences in some SMB components. The impacts of changes to the ModelE2 surface are tested, including a subgrid‐scale representation of SMB with surface elevation classes. This has a minimal effect on ice sheet‐wide SMB but corrects local biases. Replacing fixed surface albedo with satellite‐derived values and an age‐dependent scheme has a larger impact, increasing simulated melt by 60%–100%. We also find that lower surface albedo can enhance the effects of elevation classes. Reducing ModelE2 surface roughness length to values closer to MAR reduces sublimation by ~50%. Further work is required to account for meltwater refreezing in ModelE2 and to understand how differences in atmospheric processes and model resolution influence simulated SMB.more » « less
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E., Blankenship, D. D., Carter, S. P., Young, D. A., Kempf, S. D., and Holt, J. W., 2007, Along-track Focusing of Airborne Radar Sounding Data From West Antarctica for Improving Basal Reflection Analysis and Layer Detection, IEEE Transactions On Geoscience And Remote Sensing, 45, 9, 2725-2736, 10.1109/TGRS.2007.897416Rutishauser, A., Blankenship, D. D., Young, D. A., Wolfenbarger, N. S., Beem, L. H., Skidmore, M. L., Dubnick, A., and Criscitiello, A. S., 2022, Radar sounding survey over Devon Ice Cap indicates the potential for a diverse hypersaline subglacial hydrological environment, The Cryosphere, 16, 379-395, https://doi.org/10.5194/tc-16-379-2022 Schroeder, D. M., Blankenship, D. D., Raney, R. K., and Grima, C., 2015, Estimating subglacial water geometry using radar bed echo specularity: application to Thwaites Glacier, West Antarctica, IEEE Geoscience And Remote Sensing Letters, 12, 3, 443-447, 10.1109/LGRS.2014.2337878 Young, D. A., Schroeder, D. M., Blankenship, D. 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