Articles | Volume 17, issue 7
https://doi.org/10.5194/tc-17-2793-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-17-2793-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Jul 2023
Research article |
| 13 Jul 2023
| 13 Jul 2023
Polar firn properties in Greenland and Antarctica and related effects on microwave brightness temperatures
Haokui Xu
CORRESPONDING AUTHOR
Radiation Laboratory, Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48105, USA
Brooke Medley
Cryospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Leung Tsang
Radiation Laboratory, Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48105, USA
Joel T. Johnson
ElectroScience Laboratory, The Ohio State University, Columbus, OH 43212, USA
Kenneth C. Jezek
Byrd Polar and Climate Research Center, School of Earth Sciences, The Ohio State University, Columbus, OH 43210, USA
Marco Brogioni
Institute of Applied Physics “Nello Carrara”, CNR, Florence, 50019, Italy
Lars Kaleschke
Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung, 27570 Bremerhaven, Germany
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Cited articles
Andrews, M., Li, H., Johnson, J. T., Jezek, K. C., Bringer, A., Yardim, C., Chen, C. C., Belgiovane, D., Leuski, V., Durand, M., Duan, Y., Macelloni, G., Brogioni, M., Tan, S., and Tsang, L.: The Ultra-Wideband Software Defined Microwave Radiometer (UWBRAD) for Ice sheet subsurface temperature sensing: Calibration and campaign results, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA, 23–28 July 2017, IEEE, 237–240, https://doi.org/10.1109/IGARSS.2017.8126938, 2017.
Baker, I.: NEEM Firn Core 2009S2 Density and Permeability: https://arcticdata.io/catalog/view/doi:10.18739/A2Q88G (last access: July 2012), 2012.
Bringer, A., Miller, J., Johnson, J. T., and Jezek, K. C.: Radiative transfer modelking of the brightness temperature signatures of firn aquifers, American Geophysical Union Fall Meeting, New Orleans, LA, USA, https://agu.confex.com/agu/fm17/meetingapp.cgi/Paper/283159 (last access: July 2023), 11–15 December 2017.
Brogioni, M., Macelloni, G., Montomoli, F., and Jezek, K. C.: Simulating Multifrequency Ground-Based Radiometric Measurements at Dome C—Antarctica, IEEE J. Sel. Top. Appl., 8, 4405–4417, https://doi.org/10.1109/JSTARS.2015.2427512, 2015.
Brucker, L., Picard, G., and Fily, M.: Snow grain-size profiles deduced from microwave snow emissivities in Antarctica, J. Glaciol., 56, 514–526, https://doi.org/10.3189/002214310792447806, 2010.
Champollion, N., Picard, G., Arnaud, L., Lefebvre, E., and Fily, M.: Hoar crystal development and disappearance at Dome C, Antarctica: observation by near-infrared photography and passive microwave satellite, The Cryosphere, 7, 1247–1262, https://doi.org/10.5194/tc-7-1247-2013, 2013.
CReSIS: Snow radar Data, Lawrence, Kansas, USA, Digital Media, University of Kansas, http://data.cresis.ku.edu/ (last access: July 2022), 2021.
Dattler, M. E., Lenaerts, J. T., and Medley, B.: Significant Spatial Variability in Radar-Derived West Antarctic Accumulation Linked to Surface Winds and Topography, Geophys. Res. Lett., 46, 13126–13134, https://doi.org/10.1029/2019GL085363, 2019.
Fausto, R. S., Box, J. E., Vandecrux, B., Van As, D., Steffen, K., MacFerrin, M. J., Machguth, H., Colgan, W., Koenig, L. S., McGrath, D. Charalampidis, C., and Braithwaite, R. J.: A snow density dataset for improving surface boundary conditions in Greenland ice sheet firn modeling, Front. Earth Sci., 6, 51, https://doi.org/10.3389/feart.2018.00051, 2018.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Koster, R., Luccheis, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schuber, S. D., Sienkiewicz, M., and Zhao, B.: The modern-era retrospective analysis for research and applications, version 2 (MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
Jezek, K. C., Gogineni, P., and Shanableh, M.: Radar Measurements of Melt Zones on the Greenland Ice Sheet, Geophys. Res. Lett., 21, 33–36, https://doi.org/10.1029/93GL03377, 1994.
Jezek, K. C., Johnson, J. T., Drinkwater, M. R., Macelloni, G., Tsang, L., Aksoy, M., and Durand, M.: Radiometric Approach for Estimating Relative Changes in Intraglacier Average Temperature, IEEE T. Geosci. Remote, 53, 134–143, https://doi.org/10.1109/TGRS.2014.2319265, 2015.
Jezek, K. C., Johnson, J. T., Tan, S., Tsang, L., Andrews, M. J., Brogioni, M., Macelloni, G., Durand, M., Chen, C. C., Belgiovane, D. J., Duan, Y., Yardim, C., Li, H., Bringer, A., and Aksoy, M.: 500–2000-MHz Brightness Temperature Spectra of the Northwestern Greenland Ice Sheet, IEEE T. Geosci. Remote, 56, 1485–1496, https://doi.org/10.1109/TGRS.2017.2764381, 2018.
Johnson, J. T.: Ultra-Wideband Software Defined Microwave Radiometer (UWBRAD) Data over Greenland 2017, http://www2.ece.ohio-state.edu/~johnson/uwbrad.html (last access: July 2023), 2017.
Koenig, L. S., Steig, E. J., Winebrenner, D. P., and Shuman, C. A.: A link between microwave extinction length, firn thermal diffusivity, and accumulation rate in West Antarctica, J. Geophys. Res., 112, F03018, https://doi.org/10.1029/2006JF000716, 2007.
Koenig, L. S., Ivanoff, A., Alexander, P. M., MacGregor, J. A., Fettweis, X., Panzer, B., Paden, J. D., Forster, R. R., Das, I., McConnell, J. R., Tedesco, M., Leuschen, C., and Gogineni, P.: Annual Greenland accumulation rates (2009–2012) from airborne snow radar, The Cryosphere, 10, 1739–1752, https://doi.org/10.5194/tc-10-1739-2016, 2016.
Kuipers Munneke, P., Ligtenberg, S. R. M., Noël, B. P. Y., Howat, I. M., Box, J. E., Mosley-Thompson, E., McConnell, J. R., Steffen, K., Harper, J. T., Das, S. B., and van den Broeke, M. R.: Elevation change of the Greenland Ice Sheet due to surface mass balance and firn processes, 1960–2014, The Cryosphere, 9, 2009–2025, https://doi.org/10.5194/tc-9-2009-2015, 2015.
Leduc-Leballeur, M. Picard, G., Mialon, A., Arnaud, L., Lefebvre, E., Possenti, P., and Kerr, Y.: Modeling L-Band Brightness Temperature at Dome C in Antarctica and Comparison With SMOS Observations, IEEE T. Geosci. Remote, 53, 4022–4032, https://doi.org/10.1109/TGRS.2015.2388790, 2015.
Lewis, C. and Gognineni, P.: Airborne UHF radar for fine resolution mapping of near surface accumulation layers in Greenland and West Antarctica, M. S. thesis, Dept. Elect. Eng. Comput. Sci., Univ. Kansas, Lawrence, KS, USA, 158 pp., http://hdl.handle.net/1808/7008 (last access: July 2022), 2010.
Li, J. and Zwally, H. J.: Modeling of firn compaction for estimating ice-sheet mass change from observed ice-sheet elevation change, Ann. Glaciol., 52, 1–7, https://doi.org/10.3189/172756411799096321, 2011.
Ligtenberg, S. R. M., Helsen, M. M., and van den Broeke, M. R.: An improved semi-empirical model for the densification of Antarctic firn, The Cryosphere, 5, 809–819, https://doi.org/10.5194/tc-5-809-2011, 2011.
Matzler, C.: Microwave permittivity of dry snow, IEEE T. Geosci. Remote, 34, 573–581, https://doi.org/10.1109/36.485133, 1996.
Medley, B., Joughin, I., Das, S. B., Steig, E. J., Conway, H., Gogineni, S., Criscitiello, A. S., McConnel, R., Smith, B. E., van den Broeke, M. R., Lenaerts, J. T., Bromwich, D. H., and Nicolas, J. P.: Airborne-radar and ice-core observations of annual snow accumulation over Thwaites Glacier, West Antarctica confirm the spatiotemporal variability of global and regional atmospheric models, Geophys. Res. Lett., 40, 3649–3654, https://doi.org/10.1002/grl.50706, 2013.
Medley, B., Joughin, I., Smith, B. E., Das, S. B., Steig, E. J., Conway, H., Gogineni, S., Lewis, C., Criscitiello, A. S., McConnell, J. R., van den Broeke, M. R., Lenaerts, J. T. M., Bromwich, D. H., Nicolas, J. P., and Leuschen, C.: Constraining the recent mass balance of Pine Island and Thwaites glaciers, West Antarctica, with airborne observations of snow accumulation, The Cryosphere, 8, 1375–1392, https://doi.org/10.5194/tc-8-1375-2014, 2014.
Medley, B., Ligtenberg, S. R. M., Joughin, I., Van den Broeke, M. R., Gogineni, S., and Nowicki, S.: Antarctic firn compaction rates from repeat-track airborne radar data: I. Methods, Ann. Glaciol., 56, 155–166, https://doi.org/10.3189/2015AoG70A203, 2015.
Medley, B., Neumann, T. A., Zwally, H. J., Smith, B. E., and Stevens, C. M.: Simulations of firn processes over the Greenland and Antarctic ice sheets: 1980–2021, The Cryosphere, 16, 3971–4011, https://doi.org/10.5194/tc-16-3971-2022, 2022.
Miller, J. Z., Long, D. G., Jezek, K. C., Johnson, J. T., Brodzik, M. J., Shuman, C. A., Koenig, L. S., and Scambos, T. A.: Brief communication: Mapping Greenland's perennial firn aquifers using enhanced-resolution L-band brightness temperature image time series, The Cryosphere, 14, 2809–2817, https://doi.org/10.5194/tc-14-2809-2020, 2020.
Miller, J. Z., Culberg, R., Long, D. G., Shuman, C. A., Schroeder, D. M., and Brodzik, M. J.: An empirical algorithm to map perennial firn aquifers and ice slabs within the Greenland Ice Sheet using satellite L-band microwave radiometry, The Cryosphere, 16, 103–125, https://doi.org/10.5194/tc-16-103-2022, 2022.
Montgomery, L., Koenig, L., and Alexander, P.: The SUMup dataset: compiled measurements of surface mass balance components over ice sheets and sea ice with analysis over Greenland, Earth Syst. Sci. Data, 10, 1959–1985, https://doi.org/10.5194/essd-10-1959-2018, 2018.
Morris, E. M. and Wingham, D. J.: The effect of fluctuations in surface density, accumulation and compaction on elevation change rates along the EGIG line, central Greenland, J. Glaciol., 57, 416–430, https://doi.org/10.3189/002214311796905613, 2011.
Panzer, B., Leuschen, C., Patel, A., Markus, T., and Gogineni, S.: Ultra-wideband radar measurements of snow thickness over sea ice, 2010 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA, 25–30 July 2010, 3130–3133, https://doi.org/10.1109/IGARSS.2010.5654342, 2010.
Schaller, C. F., Freitag, J., Kipfstuhl, S., Laepple, T., Steen-Larsen, H. C., and Eisen, O.: A representative density profile of the North Greenland snowpack, The Cryosphere, 10, 1991–2002, https://doi.org/10.5194/tc-10-1991-2016, 2016.
Shepherd, A., Gilbert, L., Muir, A. S., Konrad, H., McMillan, M., Slater, T., Briggs, K. H., Sundal, A. V., Hogg, A. E., and Engdahl, M. E.: Trends in Antarctic Ice Sheet elevation and mass, Geophys. Res. Lett., 46, 8174–8183, https://doi.org/10.1029/2019GL082182, 2019.
Smith, B., Fricker, H. A., Gardner, A. S., Medley, B., Nilsson, J., Paolo, F. S., Holschuh, N., Adusumilli, S., Brunt, K., Csatho, B., Harbeck, K., Markus, T., Neumann, T., Siegfried, M. R., and Zwally, H. J.: Pervasive ice sheet mass loss reflects competing ocean and atmosphere processes, Science, 368, 1239–1242, https://doi.org/10.1126/science.aaz5845, 2020.
Stevens, C. M., Verjans, V., Lundin, J. M. D., Kahle, E. C., Horlings, A. N., Horlings, B. I., and Waddington, E. D.: The Community Firn Model (CFM) v1.0, Geosci. Model Dev., 13, 4355–4377, https://doi.org/10.5194/gmd-13-4355-2020, 2020.
Stevens, M., Vo, H., emmakahle, and Jboat: UWGlaciology/CommunityFirnModel: Version 1.1.6, Zenodo [code], https://doi.org/10.5281/zenodo.5719748, 2021.
Tan, S., Aksoy, M., Brogioni, M., Macelloni, G., Durand, M., Jezek, K. C., Wang, T.-L, Tsang, L., Johnson, J. T., Drinkwater, M. R., and Brucker, L.: Physical Models of Layered Polar Firn Brightness Temperatures From 0.5 to 2 GHz, IEEE J. Sel. Top. Appl. 8, 3681–3691, https://doi.org/10.1109/JSTARS.2015.2403286, 2015.
Tan, S., Tsang, L., Xu, H., Johnson, J. T., Jezek, K. C., Yardim, C., Durand, M., and Duan, Y.: A Partially Coherent Approach for Modeling Polar Ice Sheet 0.5–2-GHz Thermal Emission, IEEE T. Geosci. Remote, 59, 8062–8072, https://doi.org/10.1109/TGRS.2020.3039057, 2021.
Tiuri, M., Sihvola, A., Nyfors, E., and Hallikaiken, M.: The complex dielectric constant of snow at microwave frequencies, IEEE J. Oceanic Eng., 9, 377–382, https://doi.org/10.1109/JOE.1984.1145645, 1984.
Tsang, L., Kong, J. A., and Ding, K. H.: Scattering of Electromagnetic Waves, 1: Theory and Applications, Wiley Interscience, 426 pp., ISBN 0-471-38799-7, 2000.
Vallelonga, P.: Northeast Greenland Ice Stream (NEGIS) 2012 Ice 110 Core Chemistry and Density, NOAA, https://www1.ncdc.noaa.gov/pub/data/paleo/icecore/greenland/negis2012dens.txt (last access: July 2022), 2016.
Vallelonga, P., Christianson, K., Alley, R. B., Anandakrishnan, S., Christian, J. E. M., Dahl-Jensen, D., Gkinis, V., Holme, C., Jacobel, R. W., Karlsson, N. B., Keisling, B. A., Kipfstuhl, S., Kjær, H. A., Kristensen, M. E. L., Muto, A., Peters, L. E., Popp, T., Riverman, K. L., Svensson, A. M., Tibuleac, C., Vinther, B. M., Weng, Y., and Winstrup, M.: Initial results from geophysical surveys and shallow coring of the Northeast Greenland Ice Stream (NEGIS), The Cryosphere, 8, 1275–1287, https://doi.org/10.5194/tc-8-1275-2014, 2014.
Yardim, C., Johnson, J. T., Jezk, K. C., Andrews, M. J., Durand, M., Duan, Y., Tan, S., Tsang, L., Brogioni, M., Macelloni, G., and Bringer, A.: Greenland Ice Sheet Subsurface Temperature Estimation Using Ultrawideband Microwave Radiometry, IEEE T. Geosci. Remote, 60, 1–12, 4300312, https://doi.org/10.1109/TGRS.2020.3043954, 2022.
Zabel, I. H. H., Jezek, K. C., Baggeroer, P. A., and Gogineni, S. P.: Radar Observations of Snow Stratigraphy and Melt Processes on the Greenland Ice Sheet, Ann. Glaciol., 21, 40–44, https://doi.org/10.3189/S0260305500015573, 1995.
Short summary
The density profile of polar ice sheets is a major unknown in estimating the mass loss using lidar tomography methods. In this paper, we show that combing the active radar data and passive radiometer data can provide an estimation of density properties using the new model we implemented in this paper. The new model includes the short and long timescale variations in the firn and also the refrozen layers which are not included in the previous modeling work.
The density profile of polar ice sheets is a major unknown in estimating the mass loss using...
