Articles | Volume 18, issue 9
https://doi.org/10.5194/tc-18-4399-2024
© Author(s) 2024. 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-18-4399-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Sep 2024
Research article |
| 24 Sep 2024
| 24 Sep 2024
Quantifying the influence of snow over sea ice morphology on L-band passive microwave satellite observations in the Southern Ocean
Institute for Marine and Atmospheric Research Utrecht, Utrecht University, Utrecht, the Netherlands
Department of Earth Sciences, University of Gothenburg, Gothenburg, Sweden
Julienne Stroeve
Centre for Earth Observation Science (CEOS), University of Manitoba, Winnipeg, Canada
Centre for Polar Observation Modelling (CPOM), University College London, London, United Kingdom
National Snow and Ice Data Center (NSIDC), Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado, Boulder, Colorado, USA
Vishnu Nandan
Department of Electronics & Communication Engineering, Amrita School of Engineering, Amrita University, Bengaluru, India
Department of Geography, University of Calgary, Alberta, Canada
Rosemary Willatt
Centre for Polar Observation Modelling (CPOM), University College London, London, United Kingdom
Department of Geography and Environmental Sciences, Centre for Polar Observation and Modelling, Northumbria University, Newcastle, United Kingdom
Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Tsinghua University, Beijing, China
University Corporation for Polar Research, Beijing, China
Weixin Zhu
Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Tsinghua University, Beijing, China
Sahra Kacimi
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
Stefanie Arndt
Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung, Bremerhaven, Germany
Institute of Oceanography, University of Hamburg, Hamburg, Germany
Zifan Yang
School of Ecology and Nature Conservation, Beijing Forestry University, Beijing, China
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Long-term ecological observations are key to assess, understand and predict impacts of environmental change on biotas. We present a multidisciplinary framework for such largely lacking investigations in the East Antarctic Southern Ocean, combined with case studies, experimental and modelling work. As climate change is still minor here but is projected to start soon, the timely implementation of this framework provides the unique opportunity to document its ecological impacts from the very onset.
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David N. Wagner, Matthew D. Shupe, Christopher Cox, Ola G. Persson, Taneil Uttal, Markus M. Frey, Amélie Kirchgaessner, Martin Schneebeli, Matthias Jaggi, Amy R. Macfarlane, Polona Itkin, Stefanie Arndt, Stefan Hendricks, Daniela Krampe, Marcel Nicolaus, Robert Ricker, Julia Regnery, Nikolai Kolabutin, Egor Shimanshuck, Marc Oggier, Ian Raphael, Julienne Stroeve, and Michael Lehning
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Based on measurements of the snow cover over sea ice and atmospheric measurements, we estimate snowfall and snow accumulation for the MOSAiC ice floe, between November 2019 and May 2020. For this period, we estimate 98–114 mm of precipitation. We suggest that about 34 mm of snow water equivalent accumulated until the end of April 2020 and that at least about 50 % of the precipitated snow was eroded or sublimated. Further, we suggest explanations for potential snowfall overestimation.
William Gregory, Julienne Stroeve, and Michel Tsamados
The Cryosphere, 16, 1653–1673, https://doi.org/10.5194/tc-16-1653-2022, https://doi.org/10.5194/tc-16-1653-2022, 2022
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Isolde A. Glissenaar, Jack C. Landy, Alek A. Petty, Nathan T. Kurtz, and Julienne C. Stroeve
The Cryosphere, 15, 4909–4927, https://doi.org/10.5194/tc-15-4909-2021, https://doi.org/10.5194/tc-15-4909-2021, 2021
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Scientists can estimate sea ice thickness using satellites that measure surface height. To determine the sea ice thickness, we also need to know the snow depth and density. This paper shows that the chosen snow depth product has a considerable impact on the findings of sea ice thickness state and trends in Baffin Bay, showing mean thinning with some snow depth products and mean thickening with others. This shows that it is important to better understand and monitor snow depth on sea ice.
Stefanie Arndt, Christian Haas, Hanno Meyer, Ilka Peeken, and Thomas Krumpen
The Cryosphere, 15, 4165–4178, https://doi.org/10.5194/tc-15-4165-2021, https://doi.org/10.5194/tc-15-4165-2021, 2021
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Céline Heuzé, Lu Zhou, Martin Mohrmann, and Adriano Lemos
The Cryosphere, 15, 3401–3421, https://doi.org/10.5194/tc-15-3401-2021, https://doi.org/10.5194/tc-15-3401-2021, 2021
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For navigation or science planning, knowing when sea ice will open in advance is a prerequisite. Yet, to date, routine spaceborne microwave observations of sea ice are unable to do so. We present the first method based on spaceborne infrared that can forecast an opening several days ahead. We develop it specifically for the Weddell Polynya, a large hole in the Antarctic winter ice cover that unexpectedly re-opened for the first time in 40 years in 2016, and determine why the polynya opened.
Marcel Kleinherenbrink, Anton Korosov, Thomas Newman, Andreas Theodosiou, Alexander S. Komarov, Yuanhao Li, Gert Mulder, Pierre Rampal, Julienne Stroeve, and Paco Lopez-Dekker
The Cryosphere, 15, 3101–3118, https://doi.org/10.5194/tc-15-3101-2021, https://doi.org/10.5194/tc-15-3101-2021, 2021
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Harmony is one of the Earth Explorer 10 candidates that has the chance of being selected for launch in 2028. The mission consists of two satellites that fly in formation with Sentinel-1D, which carries a side-looking radar system. By receiving Sentinel-1's signals reflected from the surface, Harmony is able to observe instantaneous elevation and two-dimensional velocity at the surface. As such, Harmony's data allow the retrieval of sea-ice drift and wave spectra in sea-ice-covered regions.
Robbie D. C. Mallett, Julienne C. Stroeve, Michel Tsamados, Jack C. Landy, Rosemary Willatt, Vishnu Nandan, and Glen E. Liston
The Cryosphere, 15, 2429–2450, https://doi.org/10.5194/tc-15-2429-2021, https://doi.org/10.5194/tc-15-2429-2021, 2021
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We re-estimate pan-Arctic sea ice thickness (SIT) values by combining data from the Envisat and CryoSat-2 missions with data from a new, reanalysis-driven snow model. Because a decreasing amount of ice is being hidden below the waterline by the weight of overlying snow, we argue that SIT may be declining faster than previously calculated in some regions. Because the snow product varies from year to year, our new SIT calculations also display much more year-to-year variability.
Rasmus T. Tonboe, Vishnu Nandan, John Yackel, Stefan Kern, Leif Toudal Pedersen, and Julienne Stroeve
The Cryosphere, 15, 1811–1822, https://doi.org/10.5194/tc-15-1811-2021, https://doi.org/10.5194/tc-15-1811-2021, 2021
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A relationship between the Ku-band radar scattering horizon and snow depth is found using a radar scattering model. This relationship has implications for (1) the use of snow climatology in the conversion of satellite radar freeboard into sea ice thickness and (2) the impact of variability in measured snow depth on the derived ice thickness. For both 1 and 2, the impact of using a snow climatology versus the actual snow depth is relatively small.
Ron Kwok, Alek A. Petty, Marco Bagnardi, Nathan T. Kurtz, Glenn F. Cunningham, Alvaro Ivanoff, and Sahra Kacimi
The Cryosphere, 15, 821–833, https://doi.org/10.5194/tc-15-821-2021, https://doi.org/10.5194/tc-15-821-2021, 2021
Shiming Xu, Jialiang Ma, Lu Zhou, Yan Zhang, Jiping Liu, and Bin Wang
Geosci. Model Dev., 14, 603–628, https://doi.org/10.5194/gmd-14-603-2021, https://doi.org/10.5194/gmd-14-603-2021, 2021
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A multi-resolution tripolar grid hierarchy is constructed and integrated in CESM (version 1.2.1). The resolution range includes 0.45, 0.15, and 0.05°. Based on atmospherically forced sea ice experiments, the model simulates reasonable sea ice kinematics and scaling properties. Landfast ice thickness can also be systematically shifted due to non-convergent solutions to an
elastic–viscous–plastic (EVP) model. This work is a framework for multi-scale modeling of the ocean and sea ice with CESM.
Lu Zhou, Julienne Stroeve, Shiming Xu, Alek Petty, Rachel Tilling, Mai Winstrup, Philip Rostosky, Isobel R. Lawrence, Glen E. Liston, Andy Ridout, Michel Tsamados, and Vishnu Nandan
The Cryosphere, 15, 345–367, https://doi.org/10.5194/tc-15-345-2021, https://doi.org/10.5194/tc-15-345-2021, 2021
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Snow on sea ice plays an important role in the Arctic climate system. Large spatial and temporal discrepancies among the eight snow depth products are analyzed together with their seasonal variability and long-term trends. These snow products are further compared against various ground-truth observations. More analyses on representation error of sea ice parameters are needed for systematic comparison and fusion of airborne, in situ and remote sensing observations.
Masa Kageyama, Louise C. Sime, Marie Sicard, Maria-Vittoria Guarino, Anne de Vernal, Ruediger Stein, David Schroeder, Irene Malmierca-Vallet, Ayako Abe-Ouchi, Cecilia Bitz, Pascale Braconnot, Esther C. Brady, Jian Cao, Matthew A. Chamberlain, Danny Feltham, Chuncheng Guo, Allegra N. LeGrande, Gerrit Lohmann, Katrin J. Meissner, Laurie Menviel, Polina Morozova, Kerim H. Nisancioglu, Bette L. Otto-Bliesner, Ryouta O'ishi, Silvana Ramos Buarque, David Salas y Melia, Sam Sherriff-Tadano, Julienne Stroeve, Xiaoxu Shi, Bo Sun, Robert A. Tomas, Evgeny Volodin, Nicholas K. H. Yeung, Qiong Zhang, Zhongshi Zhang, Weipeng Zheng, and Tilo Ziehn
Clim. Past, 17, 37–62, https://doi.org/10.5194/cp-17-37-2021, https://doi.org/10.5194/cp-17-37-2021, 2021
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The Last interglacial (ca. 127 000 years ago) is a period with increased summer insolation at high northern latitudes, resulting in a strong reduction in Arctic sea ice. The latest PMIP4-CMIP6 models all simulate this decrease, consistent with reconstructions. However, neither the models nor the reconstructions agree on the possibility of a seasonally ice-free Arctic. Work to clarify the reasons for this model divergence and the conflicting interpretations of the records will thus be needed.
Sahra Kacimi and Ron Kwok
The Cryosphere, 14, 4453–4474, https://doi.org/10.5194/tc-14-4453-2020, https://doi.org/10.5194/tc-14-4453-2020, 2020
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Our current understanding of Antarctic ice cover is largely informed by ice extent measurements from passive microwave sensors. These records, while useful, provide a limited picture of how the ice is responding to climate change. In this paper, we combine measurements from ICESat-2 and CryoSat-2 missions to assess snow depth and ice thickness of the Antarctic ice cover over an 8-month period (April through November 2019). The potential impact of salinity in the snow layer is discussed.
Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Rasmus Tonboe, Stefan Hendricks, Robert Ricker, James Mead, Robbie Mallett, Marcus Huntemann, Polona Itkin, Martin Schneebeli, Daniela Krampe, Gunnar Spreen, Jeremy Wilkinson, Ilkka Matero, Mario Hoppmann, and Michel Tsamados
The Cryosphere, 14, 4405–4426, https://doi.org/10.5194/tc-14-4405-2020, https://doi.org/10.5194/tc-14-4405-2020, 2020
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This study provides a first look at the data collected by a new dual-frequency Ka- and Ku-band in situ radar over winter sea ice in the Arctic Ocean. The instrument shows potential for using both bands to retrieve snow depth over sea ice, as well as sensitivity of the measurements to changing snow and atmospheric conditions.
Cited articles
Ackley, S., Perovich, D., Maksym, T., Weissling, B., and Xie, H.: Surface flooding of Antarctic summer sea ice, Ann. Glaciol., 61, 117–126, https://doi.org/10.1017/aog.2020.22, 2020. a
Alaska Satellite Facility: ALOS Phased Array type L-band Synthetic Aperture Radar, Alaska Satellite Facility [data set], https://doi.org/10.5067/NXY378J3DFZQ, 2010. a
Al Bitar, A., Mialon, A., Kerr, Y. H., Cabot, F., Richaume, P., Jacquette, E., Quesney, A., Mahmoodi, A., Tarot, S., Parrens, M., Al-Yaari, A., Pellarin, T., Rodriguez-Fernandez, N., and Wigneron, J.-P.: The global SMOS Level 3 daily soil moisture and brightness temperature maps, Earth Syst. Sci. Data, 9, 293–315, https://doi.org/10.5194/essd-9-293-2017, 2017. a
Arndt, S.: Sensitivity of Sea Ice Growth to Snow Properties in Opposing Regions of the Weddell Sea in Late Summer, Geophys. Res. Lett., 49, e2022GL099653, https://doi.org/10.1029/2022GL099653, 2022. a
Arndt, S. and Paul, S.: Variability of winter snow properties on different spatial scales in the Weddell Sea, J. Geophys. Res.-Oceans, 123, 8862–8876, https://doi.org/10.1029/2018JC014447, 2018. a, b
Arndt, S., Willmes, S., Dierking, W., and Nicolaus, M.: Timing and regional patterns of snowmelt on Antarctic sea ice from passive microwave satellite observations, J. Geophys. Res.-Oceans, 121, 5916–5930, https://doi.org/10.1002/2015JC011504, 2016. a
Arndt, S., Meiners, K. M., Ricker, R., Krumpen, T., Katlein, C., and Nicolaus, M.: Influence of snow depth and surface flooding on light transmission through Antarctic pack ice, J. Geophys. Res.-Oceans, 122, 2108–2119, https://doi.org/10.1002/2016JC012325, 2017. a
Brodzik, M., Long, D., and Hardman, M.: SMAP Radiometer Twice-Daily rSIR-Enhanced EASE-Grid 2.0 Brightness Temperatures, Version 2, Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/YAMX52BXFL10, 2021. a, b
Burke, W., Schmugge, T., and Paris, J.: Comparison of 2.8- and 21-cm microwave radiometer observations over soils with emission model calculations, J. Geophys. Res.-Oceans, 84, 287–294, https://doi.org/10.1029/JC084iC01p00287, 1979. a
Calonne, N., Flin, F., Morin, S., Lesaffre, B., du Roscoat, S. R., and Geindreau, C.: Numerical and experimental investigations of the effective thermal conductivity of snow, Geophys. Res. Lett., 38, L23501, https://doi.org/10.1029/2011GL049234, 2011. a
Cavalieri, D. J., Markus, T., and Comiso, J. C.: AMSR-E/Aqua Daily L3 12.5 km Brightness Temperature, Sea Ice Concentration, & Snow Depth Polar Grids, Version 3, Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/AMSR-E/AE_SI12.003, 2014. a
Cox, G. F. and Weeks, W. F.: Equations for determining the gas and brine volumes in sea-ice samples, J. Glaciol., 29, 306–316, https://doi.org/10.3189/S0022143000008364, 1983. a
Cox, G. F. N. and Weeks, W. F.: Brine Drainage and Initial Salt Entrapment in Sodium Chloride Ice, U.S. Army Cold Regions Research and Engineering Laboratory. Research Report 345, http://hdl.handle.net/11681/5820 (last access: 19 September 2024), 1975. a
Deming, J., Ewert, M., Bowman, J., Colangelo-Lillis, J., and Carpenter, S.: Brine-Wetted Snow on the Surface of Sea Ice: A Potentially Vast and Overlooked Microbial Habitat, in: AGU Fall Meeting Abstracts, vol. 2010, pp. C43D–0575, https://ui.adsabs.harvard.edu/abs/2010AGUFM.C43D0575D/abstract (last access: 19 September 2024), 2010. a
Drinkwater, M. R. and Crocker, G.: Modelling Changes in Scattering Properties of the Dielectric and Young Snow-Covered Sea Ice at GHz Requencies, J. Glaciol., 34, 274–282, https://doi.org/10.3189/S0022143000007012, 1988. a
Fuller, M. C., Isleifson, D., Barber, D., and Yackel, J.: A framework for coupling thermodynamic and backscatter models toward the estimation of Arctic sea ice, snow on sea ice, and snow brine volume, in: 2021 IEEE 19th International Symposium on Antenna Technology and Applied Electromagnetics (ANTEM), pp. 1–2, IEEE, https://doi.org/10.1109/ANTEM51107.2021.9518905, 2021. a
Geldsetzer, T., Langlois, A., and Yackel, J.: Dielectric properties of brine-wetted snow on first-year sea ice, Cold Reg. Sci. Technol., 58, 47–56, https://doi.org/10.1016/j.coldregions.2009.03.009, 2009. a, b, c
Giles, K. A., Laxon, S. W., and Worby, A. P.: Antarctic sea ice elevation from satellite radar altimetry, Geophys. Res. Lett., 35, L03503, https://doi.org/10.1029/2007GL031572, 2008. a
Gusmeroli, A. and Grosse, G.: Ground penetrating radar detection of subsnow slush on ice-covered lakes in interior Alaska, The Cryosphere, 6, 1435–1443, https://doi.org/10.5194/tc-6-1435-2012, 2012. a
Heil, P., Massom, R., Stevens, R., Steer, A., and Hutchings, J.: Ice-physics transect data obtained during the SIPEX II voyage of the Aurora Australis, 2012, Ver. 1, Australian Antarctic Data Centre [data set], https://doi.org/10.4225/15/5a8f94c228afb, 2018. a, b
Huntemann, M., Patilea, C., and Heygster, G.: Thickness of thin sea ice retrieved from SMOS and SMAP, in: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 5248–5251, IEEE, https://doi.org/10.1109/IGARSS.2016.7730367, 2016. a
Jackson, K., Wilkinson, J., Maksym, T., Meldrum, D., Beckers, J., Haas, C., and Mackenzie, D.: A novel and low-cost sea ice mass balance buoy, J. Atmos. Ocean. Tech., 30, 2676–2688, https://doi.org/10.1175/JTECH-D-13-00058.1, 2013. a
Ji, Q., Pang, X., Zhao, X., and Lei, R.: Snow features on sea ice in the western Arctic Ocean during summer 2016, Int. J. Digit. Earth, 14, 1397–1410, https://doi.org/10.1080/17538947.2021.1966524, 2021. a
Jiménez, C., Tenerelli, J., Prigent, C., Kilic, L., Lavergne, T., Skarpalezos, S., Hoeyer, J. L., Reul, N., and Donlon, C.: Ocean and Sea Ice Retrievals From an End-To-End Simulation of the Copernicus Imaging Microwave Radiometer (CIMR) 1.4–36.5 GHz Measurements, J. Geophys. Res.-Oceans, 126, e2021JC017610, https://doi.org/10.1029/2021JC017610, 2021. a
Jutila, A., King, J., Paden, J., Ricker, R., Hendricks, S., Polashenski, C., Helm, V., Binder, T., and Haas, C.: High-resolution snow depth on arctic sea ice from low-altitude airborne microwave radar data, IEEE T. Geosci. Remote, 60, 1–16, https://doi.org/10.1109/TGRS.2021.3063756, 2021. a
Jutras, M., Vancoppenolle, M., Lourenço, A., Vivier, F., Carnat, G., Madec, G., Rousset, C., and Tison, J.-L.: Thermodynamics of slush and snow–ice formation in the Antarctic sea-ice zone, Deep-Sea Res. Pt. II, 131, 75–83, https://doi.org/10.1016/j.dsr2.2016.03.008, 2016. a, b
Kacimi, S. and Kwok, R.: Arctic snow depth, ice thickness and volume from ICESat-2 and CryoSat-2: 2018-2021, Geophys. Res. Lett., 49, e2021GL097448, https://doi.org/10.1029/2021GL097448, 2022. a
Kaleschke, L., Maaß, N., Haas, C., Hendricks, S., Heygster, G., and Tonboe, R. T.: A sea-ice thickness retrieval model for 1.4 GHz radiometry and application to airborne measurements over low salinity sea-ice, The Cryosphere, 4, 583–592, https://doi.org/10.5194/tc-4-583-2010, 2010. a
Kaleschke, L., Tian-Kunze, X., Maaß, N., Mäkynen, M., and Drusch, M.: Sea ice thickness retrieval from SMOS brightness temperatures during the Arctic freeze-up period, Geophys. Res. Lett., 39, L05501, https://doi.org/10.1029/2012GL050916, 2012. a
Kaleschke, L., Tian-Kunze, X., Maaß, N., Beitsch, A., Wernecke, A., Miernecki, M., Müller, G., Fock, B. H., Gierisch, A. M., Schlünzen, K. H., Pohlmann, T., Dobrynin, M., Hendricks, S., Asseng, J., Gerdes, R., Jochmann, P., Reimer, N., Holfort, J., Melsheimer, C., Heygster, G., Spreen, G., Gerland, S., King, J., Skou, N., Schmidl Søbjærg, S., Haas, C., Richter, F., and Casal, T.: SMOS sea ice product: Operational application and validation in the Barents Sea marginal ice zone, Remote Sens. Environ., 180, 264–273, https://doi.org/10.1016/j.rse.2016.03.009, 2016. a
Kern, S.: ESA-CCI_Phase2_Standardized_Manual_Visual_Ship-Based_SeaIceObservations_v01, World Data Center for Climate [data set], https://doi.org/10.26050/WDCC/ESACCIPSMVSBSIO, 2019. a
Kern, S.: ESA-CCI_Phase2_Standardized_Manual_Visual_Ship-Based_SeaIceObservations_v02, World Data Center for Climate (WDCC) at DKRZ [data set], https://doi.org/10.26050/WDCC/ESACCIPSMVSBSIOV2, 2020. a
Kerr, Y. H., Waldteufel, P., Wigneron, J.-P., Delwart, S., Cabot, F., Boutin, J., Escorihuela, M.-J., Font, J., Reul, N., Gruhier, C., Juglea, S. E., Drinkwater, M. R., Hahne, A., Martín-Neira, M., and Mecklenburg, S.: The SMOS mission: New tool for monitoring key elements ofthe global water cycle, Proc. IEEE, 98, 666–687, https://doi.org/10.1109/JPROC.2010.2043032, 2010. a
Kilic, L., Tonboe, R. T., Prigent, C., and Heygster, G.: Estimating the snow depth, the snow–ice interface temperature, and the effective temperature of Arctic sea ice using Advanced Microwave Scanning Radiometer 2 and ice mass balance buoy data, The Cryosphere, 13, 1283–1296, https://doi.org/10.5194/tc-13-1283-2019, 2019. a
Kilic, L., Prigent, C., Aires, F., Heygster, G., Pellet, V., and Jimenez, C.: Ice concentration retrieval from the analysis of microwaves: A new methodology designed for the copernicus imaging microwave radiometer, Remote Sens., 12, 1060, https://doi.org/10.3390/rs12071060, 2020. a
King, J., Brady, M., and Newman, T.: kingjml/pySnowRadar: Updated IEEE TGRS Submission, Zenodo [code], https://doi.org/10.5281/zenodo.4071947, 2020a. a
King, J., Howell, S., Brady, M., Toose, P., Derksen, C., Haas, C., and Beckers, J.: Local-scale variability of snow density on Arctic sea ice, The Cryosphere, 14, 4323–4339, https://doi.org/10.5194/tc-14-4323-2020, 2020b. a
Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi, K., Kamahori, H., Kobayashi, C., Endo, H., Miyaoka, K., and Takahashi, K.: The JRA-55 reanalysis: General specifications and basic characteristics, J. Meteorol. Soc. Jpn. Ser. II, 93, 5–48, https://doi.org/10.2151/jmsj.2015-001, 2015. a
Kurtz, N. T., Farrell, S. L., Studinger, M., Galin, N., Harbeck, J. P., Lindsay, R., Onana, V. D., Panzer, B., and Sonntag, J. G.: Sea ice thickness, freeboard, and snow depth products from Operation IceBridge airborne data, The Cryosphere, 7, 1035–1056, https://doi.org/10.5194/tc-7-1035-2013, 2013. a
Kurtz, N., Studinger, M., Harbeck, J., Onana, V., and Yi., D.: IceBridge L4 Sea Ice Freeboard, Snow Depth, and Thickness, Version 1, Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/G519SHCKWQV6, 2015. a, b
Kwok, R. and Kacimi, S.: Three years of sea ice freeboard, snow depth, and ice thickness of the Weddell Sea from Operation IceBridge and CryoSat-2, The Cryosphere, 12, 2789–2801, https://doi.org/10.5194/tc-12-2789-2018, 2018. a, b
Kwok, R., Cunningham, G. F., Manizade, S., and Krabill, W.: Arctic sea ice freeboard from IceBridge acquisitions in 2009: Estimates and comparisons with ICESat, J. Geophys. Res.-Oceans, 117, C02018, https://doi.org/10.1029/2011JC007654, 2012. a
Kwok, R., Kurtz, N. T., Brucker, L., Ivanoff, A., Newman, T., Farrell, S. L., King, J., Howell, S., Webster, M. A., Paden, J., Leuschen, C., MacGregor, J. A., Richter-Menge, J., Harbeck, J., and Tschudi, M.: Intercomparison of snow depth retrievals over Arctic sea ice from radar data acquired by Operation IceBridge, The Cryosphere, 11, 2571–2593, https://doi.org/10.5194/tc-11-2571-2017, 2017. a
Lavergne, T. and Down, E.: A climate data record of year-round global sea-ice drift from the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF), Earth Syst. Sci. Data, 15, 5807–5834, https://doi.org/10.5194/essd-15-5807-2023, 2023. a
Laxon, S. W., Giles, K. A., Ridout, A. L., Wingham, D. J., Willatt, R., Cullen, R., Kwok, R., Schweiger, A., Zhang, J., Haas, C., Hendricks, S., Krishfield, R., Kurtz, N., Farrell, S., and Davidson, M.: CryoSat-2 estimates of Arctic sea ice thickness and volume, Geophys. Res. Lett., 40, 732–737, https://doi.org/10.1002/grl.50193, 2013. a
Lecomte, O., Fichefet, T., Vancoppenolle, M., Dominé, F., Massonnet, F., Mathiot, P., Morin, S., and Barriat, P.-Y.: On the formulation of snow thermal conductivity in large-scale sea ice models, J. Adv. Model. Earth Sy., 5, 542–557, https://doi.org/10.1002/jame.20039, 2013. a
Lemke, P.: The expedition of the research vessel Polarstern to the Antarctic in 2013 (ANT-XXIX/6), TIB [data set], https://doi.org/10.2312/BzPM_0679_2014, 2014. a, b
Leppäranta, M. and Manninen, T.: The brine and gas content of sea ice with attention to low salinities and high temperatures, http://hdl.handle.net/1834/23905 (last access: 19 September 2024), 1988. a
Lewis, M., Tison, J.-L., Weissling, B., Delille, B., Ackley, S., Brabant, F., and Xie, H.: Sea ice and snow cover characteristics during the winter–spring transition in the Bellingshausen Sea: An overview of SIMBA 2007, Deep-Sea Res. Pt. II, 58, 1019–1038, https://doi.org/10.1109/TGRS.2006.883134, 2011. a
Li, N., Lei, R., and Li, B.: Temperature and mass balance measurements from sea ice mass balance buoy ZS2009, deployed on landfast ice of east Antarctica, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.950178, 2022a. a
Li, N., Lei, R., and Li, B.: Temperature and mass balance measurements from sea ice mass balance buoy ZS2010, deployed on landfast ice of east Antarctica, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.950181, 2022b. a
Li, N., Lei, R., and Li, B.: Temperature and heating induced temperature difference measurements from SIMBA-type sea ice mass balance buoy ZS2013a, deployed on landfast ice in Prydz Bay, East Antarctica, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.950095, 2022c. a
Li, N., Lei, R., and Li, B.: Temperature and heating induced temperature difference measurements from SIMBA-type sea ice mass balance buoy ZS2013b, deployed on landfast ice in Prydz Bay, East Antarctica, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.950126, 2022d. a
Li, N., Lei, R., and Li, B.: Temperature and heating induced temperature difference measurements from SIMBA-type sea ice mass balance buoy ZS2014, deployed on landfast ice in Prydz Bay, East Antarctica, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.950151, 2022e. a
Li, N., Lei, R., and Li, B.: Temperature and heating induced temperature difference measurements from SIMBA-type sea ice mass balance buoy ZS2015, deployed on landfast ice in Prydz Bay, East Antarctica, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.950068, 2022f. a
Li, N., Lei, R., and Li, B.: Temperature and heating induced temperature difference measurements from SIMBA-type sea ice mass balance buoy DS2014, deployed on landfast ice in Prydz Bay, East Antarctica, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.950086, 2022g. a
Li, N., Lei, R., and Li, B.: Temperature and heating induced temperature difference measurements from SIMBA-type sea ice mass balance buoy DS2015, deployed on landfast ice in Prydz Bay, East Antarctica, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.950131, 2022h. a
Li, N., Lei, R., and Li, B.: Temperature and heating induced temperature difference measurements from SIMBA-type sea ice mass balance buoy DS2016, deployed on landfast ice in Prydz Bay, East Antarctica, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.950044, 2022i. a
Li, N., Lei, R., and Li, B.: Temperature and heating induced temperature difference measurements from SIMBA-type sea ice mass balance buoy DS2018a, deployed on landfast ice in Prydz Bay, East Antarctica, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.950141, 2022j. a
Li, N., Lei, R., and Li, B.: Temperature and heating induced temperature difference measurements from SIMBA-type sea ice mass balance buoy DS2018b, deployed on landfast ice in Prydz Bay, East Antarctica, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.950121, 2022k. a
Li, N., Lei, R., Heil, P., Cheng, B., Ding, M., Tian, Z., and Li, B.: Seasonal and interannual variability of the landfast ice mass balance between 2009 and 2018 in Prydz Bay, East Antarctica, The Cryosphere, 17, 917–937, https://doi.org/10.5194/tc-17-917-2023, 2023. a
Ludwig, V., Spreen, G., Haas, C., Istomina, L., Kauker, F., and Murashkin, D.: The 2018 North Greenland polynya observed by a newly introduced merged optical and passive microwave sea-ice concentration dataset, The Cryosphere, 13, 2051–2073, https://doi.org/10.5194/tc-13-2051-2019, 2019. a
Lytle, V. and Ackley, S.: Snow-ice growth: a fresh-water flux inhibiting deep convection in the Weddell Sea, Antarctica, Ann. Glaciol., 33, 45–50, https://doi.org/10.3189/172756401781818752, 2001. a
Mahmud, M. S., Geldsetzer, T., Howell, S. E., Yackel, J. J., Nandan, V., and Scharien, R. K.: Incidence angle dependence of HH-polarized C-and L-band wintertime backscatter over Arctic sea ice, IEEE T. Geosci. Remote, 56, 6686–6698, https://doi.org/10.1109/TGRS.2018.2841343, 2018. a
Mahmud, M. S., Nandan, V., Howell, S. E., Geldsetzer, T., and Yackel, J.: Seasonal evolution of L-band SAR backscatter over landfast Arctic sea ice, Remote Sens. Environ., 251, 112049, https://doi.org/10.1016/j.rse.2020.112049, 2020. a
Maksym, T. and Jeffries, M. O.: A one-dimensional percolation model of flooding and snow ice formation on Antarctic sea ice, J. Geophys. Res.-Oceans, 105, 26313–26331, https://doi.org/10.1029/2000JC900130, 2000. a, b
Maksym, T. and Markus, T.: Antarctic sea ice thickness and snow-to-ice conversion from atmospheric reanalysis and passive microwave snow depth, J. Geophys. Res.-Oceans, 113, C02S12, https://doi.org/10.1029/2006JC004085, 2008. a
Mallett, R. D. C., Stroeve, J. C., Tsamados, M., Landy, J. C., Willatt, R., Nandan, V., and Liston, G. E.: Faster decline and higher variability in the sea ice thickness of the marginal Arctic seas when accounting for dynamic snow cover, The Cryosphere, 15, 2429–2450, https://doi.org/10.5194/tc-15-2429-2021, 2021. a
Markus, T. and Cavalieri, D. J.: Snow depth distribution over sea ice in the Southern Ocean from satellite passive microwave data, Antarctic sea ice: physical processes, interactions and variability, 74, 19–39, https://doi.org/10.1029/AR074p0019, 1998. a
Markus, T. and Cavalieri, D. J.: An enhancement of the NASA Team sea ice algorithm, IEEE T. Geosci. Remote, 38, 1387–1398, https://doi.org/10.1109/36.843033, 2000. a, b
Maaß, N.: Remote sensing of sea ice thickness using SMOS data, Ph.D. thesis, University of Hamburg Hamburg, https://pure.mpg.de/rest/items/item_1737721/component/file_1737720/content (last access: 19 September 2024), 2013. a
Maaß, N., Kaleschke, L., Tian-Kunze, X., and Drusch, M.: Snow thickness retrieval over thick Arctic sea ice using SMOS satellite data, The Cryosphere, 7, 1971–1989, https://doi.org/10.5194/tc-7-1971-2013, 2013. a, b, c
Massom, R., Lytle, V., Worby, A., and Allison, I.: Winter snow cover variability on East Antarctic sea ice, J. Geophys. Res.-Oceans, 103, 24837–24855, https://doi.org/10.1029/98JC01617, 1998. a, b
Massom, R. A., Eicken, H., Hass, C., Jeffries, M. O., Drinkwater, M. R., Sturm, M., Worby, A. P., Wu, X., Lytle, V. I., Ushio, S., Morris, K., Reid, P. A., Warren, S. G., and Allison, I.: Snow on Antarctic sea ice, Rev. Geophys., 39, 413–445, https://doi.org/10.1029/2000RG000085, 2001. a, b, c, d, e
Massonnet, F., Barthélemy, A., Worou, K., Fichefet, T., Vancoppenolle, M., Rousset, C., and Moreno-Chamarro, E.: On the discretization of the ice thickness distribution in the NEMO3.6-LIM3 global ocean–sea ice model, Geosci. Model Dev., 12, 3745–3758, https://doi.org/10.5194/gmd-12-3745-2019, 2019. a
Mätzler, C.: Microwave permittivity of dry snow, IEEE T. Geosci. Remote, 34, 573–581, https://doi.org/10.1109/36.485133, 1996. a
Mätzler, C.: Microwave properties of ice and snow, in: Solar System Ices: Based on Reviews Presented at the International Symposium “Solar System Ices”, 27–30 March 1995, Toulouse, France, pp. 241–257, Springer, https://doi.org/10.1007/978-94-011-5252-5_10, 1998. a
Mätzler, C. (Ed.): Thermal microwave radiation: applications for remote sensing, in: Electromagnetic Waves, Institution of Engineering and Technology, https://doi.org/10.1049/PBEW052E, 2006. a
Mätzler, C. and Wiesmann, A.: Extension of the microwave emission model of layered snowpacks to coarse-grained snow, Remote Sens. Environ., 70, 317–325, https://doi.org/10.1016/S0034-4257(99)00047-4, 1999. a
Matzler, C., Schanda, E., and Good, W.: Towards the definition of optimum sensor specifications for microwave remote sensing of snow, IEEE T. Geosci. Remote, GE-20, 57–66, https://doi.org/10.1109/TGRS.1982.4307521, 1982. a
Melsheimer, C., Spreen, G., Ye, Y., and Shokr, M.: First results of Antarctic sea ice type retrieval from active and passive microwave remote sensing data, The Cryosphere, 17, 105–126, https://doi.org/10.5194/tc-17-105-2023, 2023. a
Merkouriadi, I., Cheng, B., Graham, R. M., Rösel, A., and Granskog, M. A.: Critical role of snow on sea ice growth in the Atlantic sector of the Arctic Ocean, Geophys. Res. Lett., 44, 10–479, https://doi.org/10.1002/2017GL075494, 2017. a
Morey, R. M., Kovacs, A., and Cox, G. F.: Electromagnetic properties of sea ice, Cold Reg. Sci. Technol., 9, 53–75, https://doi.org/10.1016/0165-232X(84)90048-X, 1984. a
Nandan, V., Geldsetzer, T., Yackel, J., Mahmud, M., Scharien, R., Howell, S., King, J., Ricker, R., and Else, B.: Effect of snow salinity on CryoSat-2 Arctic first-year sea ice freeboard measurements, Geophys. Res. Lett., 44, 10–419, https://doi.org/10.1002/2017GL074506, 2017. a, b
Nandan, V., Scharien, R. K., Geldsetzer, T., Kwok, R., Yackel, J. J., Mahmud, M. S., Rösel, A., Tonboe, R., Granskog, M., Willatt, R., Stroeve, J., Nomura, D., and Frey, M.: Snow Property Controls on Modeled Ku-Band Altimeter Estimates of First-Year Sea Ice Thickness: Case Studies From the Canadian and Norwegian Arctic, IEEE J. Sel. Top. Appl. Earth Obs., 13, 1082–1096, https://doi.org/10.1109/JSTARS.2020.2966432, 2020. a, b, c
Newman, T., Farrell, S. L., Richter-Menge, J., Connor, L. N., Kurtz, N. T., Elder, B. C., and McAdoo, D.: Assessment of radar-derived snow depth over Arctic sea ice, J. Geophys. Res.-Oceans, 119, 8578–8602, https://doi.org/10.1002/2014JC010284, 2014. a
Nicolaus, M., Haas, C., and Willmes, S.: Evolution of first-year and second-year snow properties on sea ice in the Weddell Sea during spring-summer transition, J. Geophys. Res.-Atmos., 114, D17109, https://doi.org/10.1029/2008JD011227, 2009. a
Nicolaus, M., Hoppmann, M., Arndt, S., Hendricks, S., Katlein, C., Nicolaus, A., Rossmann, L., Schiller, M., and Schwegmann, S.: Snow depth and air temperature seasonality on sea ice derived from snow buoy measurements, Front. Marine Sci., 8, 655446, https://doi.org/10.3389/fmars.2021.655446, 2021. a
Nomura, D., Aoki, S., Simizu, D., and Iida, T.: Influence of sea ice crack formation on the spatial distribution of nutrients and microalgae in flooded Antarctic multiyear ice, J. Geophys. Res.-Oceans, 123, 939–951, https://doi.org/10.1002/2017JC012941, 2018. a
Paul, S., Arndt, S., and Stoll, N.: Snow density measurements at ice stations during POLARSTERN cruise PS81 (ANT-XXIX/6, AWECS), PANGAEA [data set], https://doi.org/10.1594/PANGAEA.881717, 2017a. a, b, c
Paul, S., Arndt, S., and Stoll, N.: Snow salinity measurements at ice stations during POLARSTERN cruise PS81 (ANT-XXIX/6, AWECS), PANGAEA [data set], https://doi.org/10.1594/PANGAEA.881714, 2017b. a, b, c
Paul, S., Arndt, S., and Stoll, N.: Snow grain size and type measurements at ice stations during POLARSTERN cruise PS81 (ANT-XXIX/6, AWECS), PANGAEA [data set], https://doi.org/10.1594/PANGAEA.881713, 2017c. a
Picard, G. and Fily, M.: Surface melting observations in Antarctica by microwave radiometers: Correcting 26-year time series from changes in acquisition hours, Remote Sens. Environ., 104, 325–336, https://doi.org/10.1016/j.rse.2006.05.010, 2006. a
Picard, G., Sandells, M., and Löwe, H.: SMRT: an active–passive microwave radiative transfer model for snow with multiple microstructure and scattering formulations (v1.0), Geosci. Model Dev., 11, 2763–2788, https://doi.org/10.5194/gmd-11-2763-2018, 2018. a, b
Piepmeier, J. R., Focardi, P., Horgan, K. A., Knuble, J., Ehsan, N., Lucey, J., Brambora, C., Brown, P. R., Hoffman, P. J., French, R. T., Mikhaylov, R. L., Kwack, E.-Y., Slimko, E. M., Dawson, D. E., Hudson, D., Peng, J., Mohammed, P. N., De Amici, G., Freedman, A. P., Medeiros, J., Sacks, F., Estep, R., Spencer, M. W., Chen, C. W., Wheeler, K. B., Edelstein, W. N., O'Neill, P. E., and Njoku, E. G.: SMAP L-band microwave radiometer: Instrument design and first year on orbit, IEEE T. Geosci. Remote, 55, 1954–1966, https://doi.org/10.1109/TGRS.2016.2631978, 2017. a
Poe, G., Stogryn, A., and Edgerton, A.: A Study of the Microwave Emission Characteristics of the Sea Ice: Final Technical Report, Report (Aerojet-General Corporation. Aerojet ElectroSystems Company), Aerojet ElectroSystems Company, Aerojet-General Corporation, https://books.google.nl/books?id=Ej5hzwEACAAJ (last access: 19 September 2024), 1972. a
Pouder, E.: CHAPTER 7 – The Thermal and Electrical Properties of Ice, in: The Physics of Ice, edited by: Pouder, E., pp. 116–132, Pergamon, ISBN 978-1-4832-1353-8, https://doi.org/10.1016/B978-1-4832-1353-8.50010-6, 1965. a
Raphael, M. N. and Handcock, M. S.: A new record minimum for Antarctic sea ice, Nat. Rev. Earth Environ., 3, 215–216, https://doi.org/10.1038/s43017-022-00281-0, 2022. a
Rösel, A., Farrell, S. L., Nandan, V., Richter-Menge, J., Spreen, G., Divine, D. V., Steer, A., Gallet, J.-C., and Gerland, S.: Implications of surface flooding on airborne estimates of snow depth on sea ice, The Cryosphere, 15, 2819–2833, https://doi.org/10.5194/tc-15-2819-2021, 2021. a
Rostosky, P., Spreen, G., Farrell, S., Heygster, G., Frost, T., and Melsheimer, C.: Snow depth on Arctic sea ice retrieval from passive microwave radiometers – Improvements and extension to lower frequencies, J. Geophys. Res.-Oceans, 123, 7120–7138, https://doi.org/10.1029/2018JC014028, 2018. a
Saloranta, T. M.: Modeling the evolution of snow, snow ice and ice in the Baltic Sea, Tellus A, 52, 93–108, https://doi.org/10.3402/tellusa.v52i1.12255, 2000. a, b
Scarlat, R. C., Spreen, G., Heygster, G., Huntemann, M., Paţilea, C., Pedersen, L. T., and Saldo, R.: Sea ice and atmospheric parameter retrieval from satellite microwave radiometers: Synergy of AMSR2 and SMOS compared with the CIMR candidate mission, J. Geophys. Res.-Oceans, 125, e2019JC015749, https://doi.org/10.1029/2019JC015749, 2020. a
Schmidt, K. and Wauer, J.: Application of the dense medium radiative transfer theory for calculating microwave emissivities of different sea ice types, Int. J. Remote Sens., 20, 3165–3182, https://doi.org/10.1080/014311699211688, 1999. a
Segal, R. A., Scharien, R. K., Cafarella, S., and Tedstone, A.: Characterizing winter landfast sea-ice surface roughness in the Canadian Arctic Archipelago using Sentinel-1 synthetic aperture radar and the Multi-angle Imaging SpectroRadiometer, Ann. Glaciol., 61, 284–298, https://doi.org/10.1017/aog.2020.48, 2020. a
Sihvola, A. H.: Electromagnetic mixing formulas and applications, Iet, 47, https://doi.org/10.1049/PBEW047E, 1999. a
Spreen, G., Kaleschke, L., and Heygster, G.: Sea ice remote sensing using AMSR-E 89-GHz channels, J. Geophys. Res.-Oceans, 113, C02S03, https://doi.org/10.1029/2005JC003384, 2008. a
Stogryn, A. and Desargant, G.: The dielectric properties of brine in sea ice at microwave frequencies, IEEE Transactions on Antennas and Propagation, 33, 523–532, https://doi.org/10.1109/TAP.1985.1143610, 1985. a, b
Studinger, M., Smith, B. E., Kurtz, N., Petty, A., Sutterley, T., and Tilling, R.: Estimating differential penetration of green (532 nm) laser light over sea ice with NASA's Airborne Topographic Mapper: observations and models, The Cryosphere, 18, 2625–2652, https://doi.org/10.5194/tc-18-2625-2024, 2024. a
Sturm, M. and Benson, C. S.: Vapor transport, grain growth and depth-hoar development in the subarctic snow, J. Glaciol., 43, 42–59, https://doi.org/10.3189/S0022143000002793, 1997. a, b, c
Sturm, M. and Massom, R. A.: Snow in the sea ice system: friend or foe?, chap. 3, pp. 65–109, John Wiley & Sons, Ltd, ISBN 9781118778371, https://doi.org/10.1002/9781118778371.ch3, 2017. a, b, c
Sturm, M., Morris, K., and Massom, R.: The Winter Snow Cover of the West Antarctic Pack Ice: Its Spatial and Temporal Variability, https://figshare.utas.edu.au/articles/chapter/The_Winter_Snow_Cover_of_the_West_Antarctic_Pack_Ice_Its_Spatial_and_Temporal_Variability/23122187 (last access: 19 September 2024), 1998. a
Takizawa, T.: Salination of snow on sea ice and formation of snow ice, Ann. Glaciol., 6, 309–310, https://doi.org/10.3189/1985AoG6-1-309-310, 1985. a
Tian-Kunze, X., Kaleschke, L., and Maass, N.: SMOS Daily Polar Gridded Brightness Temperatures, 2010–2019, Digital Media, ICDC, University of Hamburg, Hamburg, Germany [data set], https://www.cen.uni-hamburg.de/en/icdc/data/cryosphere/l3b-smos-tb.html, 2012. a
Tonboe, R., Andersen, S., Toudal, L., and Heygster, G.: Sea ice emission modelling, IET Digital Library, https://doi.org/10.1049/PBEW052E_ch4, 2006. a
Toyota, T., Massom, R., Tateyama, K., Tamura, T., and Fraser, A.: Properties of snow overlying the sea ice off East Antarctica in late winter, 2007, Deep-Sea Res. Pt. II, 58, 1137–1148, https://doi.org/10.1016/j.dsr2.2010.12.002, 2011. a
Toyota, T., Massom, R., Lecomte, O., Nomura, D., Heil, P., Tamura, T., and Fraser, A. D.: On the extraordinary snow on the sea ice off East Antarctica in late winter, 2012, Deep-Sea Res. Pt. II, 131, 53–67, https://doi.org/10.1016/j.dsr2.2016.02.003, 2016. a
Toyota, T., Lecomte, O., Massom, R., Giles, B., and Heil, P.: Ice and snow pit measurements observed during the SIPEX II voyage of the Aurora Australis, 2012, Ver. 1, Australian Antarctic Data Centre [data set], https://doi.org/10.4225/15/59b0c7fd5c76f, 2017. a, b
Tsang, L., Kong, J. A., and Ding, K.-H.: Scattering of electromagnetic waves: theories and applications, John Wiley & Sons, Ltd, ISBN 9780471224280, https://doi.org/10.1002/0471224286, 2000. a
Tucker III, W. B., Perovich, D. K., Gow, A. J., Weeks, W. F., and Drinkwater, M. R.: Physical Properties of Sea Ice Relevant to Remote Sensing, Chap. 2, pp. 9–28, American Geophysical Union (AGU), ISBN 9781118663950, https://doi.org/10.1029/GM068p0009, 1992. a
Ulaby, F., Fung, A., and Moore, R.: Microwave Remote Sensing: Active and Passive. 1 : Microwave remote sensing fundamentals and radiometry, ISBN 0201107600, 9780201107609, 1981. a
Ulaby, F., Long, D., and of Michigan. Press, U.: Microwave Radar and Radiometric Remote Sensing, University of Michigan Press, ISBN 9780472119356, 2014. a
Vancoppenolle, M., Fichefet, T., and Goosse, H.: Simulating the mass balance and salinity of Arctic and Antarctic sea ice. 2. Importance of sea ice salinity variations, Ocean Model., 27, 54–69, https://doi.org/10.1016/j.ocemod.2008.11.003, 2009. a
Webster, M., Gerland, S., Holland, M., Hunke, E., Kwok, R., Lecomte, O., Massom, R., Perovich, D., and Sturm, M.: Snow in the changing sea-ice systems, Nat. Clim. Change, 8, 946–953, https://doi.org/10.1038/s41558-018-0286-7, 2018. a, b
Weissling, B., Ackley, S., Wagner, P., and Xie, H.: EISCAM – Digital image acquisition and processing for sea ice parameters from ships, Cold Reg. Sci. Technol., 57, 49–60, https://doi.org/10.1016/j.coldregions.2009.01.001, 2009. a
Wever, N.: One-dimensional and spatially distributed simulations of the effect of snow on mass balance and flooding of Antarctic sea ice, Zenodo [code], https://doi.org/10.5281/zenodo.4717809, 2021. a
Wever, N., Maksym, T., White, S., and Leonard, K. C.: Automatic weather station buoy data PS81/506-1 from Weddell Sea, Antarctica, 2013–2014, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.933415, 2021b. a
Wever, N., Maksym, T., White, S., and Leonard, K. C.: Ice mass balance data PS81/506-1 from Weddell Sea, Antarctica, 2013–2014, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.933417, 2021c. a
Wever, N., Maksym, T., White, S., and Leonard, K. C.: Automatic weather station buoy data PS81/517 from Weddell Sea, Antarctica, 2013, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.933425, 2021d. a
Wever, N., Maksym, T., White, S., and Leonard, K. C.: Ice mass balance data PS81/517 from Weddell Sea, Antarctica, 2013, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.933424, 2021e. a
Willatt, R. C., Giles, K. A., Laxon, S. W., Stone-Drake, L., and Worby, A. P.: Field Investigations of Ku-Band Radar Penetration Into Snow Cover on Antarctic Sea Ice, IEEE T. Geosci. Remote, 48, 365–372, https://doi.org/10.1109/TGRS.2009.2028237, 2010. a
Willmes, S., Nicolaus, M., and Haas, C.: The microwave emissivity variability of snow covered first-year sea ice from late winter to early summer: a model study, The Cryosphere, 8, 891–904, https://doi.org/10.5194/tc-8-891-2014, 2014. a
Worby, A. P. and Ackley, S. F.: Antarctic research yields circumpolar sea ice thickness data, Eos, Transactions American Geophysical Union, 81, 181–185, https://doi.org/10.1029/00EO00124, 2000. a
Worby, A. P., Jeffries, M. O., Weeks, W. F., Morris, K., and Jaña, R.: The thickness distribution of sea ice and snow cover during late winter in the Bellingshausen and Amundsen Seas, Antarctica, J. Geophys. Res.-Oceans, 101, 28441–28455, https://doi.org/10.1029/96JC02737, 1996. a
Worby, A. P., Geiger, C. A., Paget, M. J., Van Woert, M. L., Ackley, S. F., and DeLiberty, T. L.: Thickness distribution of Antarctic sea ice, J. Geophys. Res.-Oceans, 113, C05S92, https://doi.org/10.1029/2007JC004254, 2008. a, b
Xu, S., Zhou, L., Liu, J., Lu, H., and Wang, B.: Data Synergy between Altimetry and L-Band Passive Microwave Remote Sensing for the Retrieval of Sea Ice Parameters—A Theoretical Study of Methodology, Remote Sens., 9, 1079, https://doi.org/10.3390/rs9101079, 2017. a
Zhaka, V., Bridges, R., Riska, K., Hagermann, A., and Cwirzen, A.: Initial snow-ice formation on a laboratory scale, Ann. Glaciol., 64, 77–94, https://doi.org/10.1017/aog.2023.58, 2023. a, b
Zhou, L. and Xu, S.: RAdiative transfer model Developed for Ice and Snow in the L-band (RADIS-L) v1.0., Zenodo [code], https://doi.org/10.5281/zenodo.10003441, 2023. a
Zhou, L., Xu, S., Liu, J., and Wang, B.: On the retrieval of sea ice thickness and snow depth using concurrent laser altimetry and L-band remote sensing data, The Cryosphere, 12, 993–1012, https://doi.org/10.5194/tc-12-993-2018, 2018. a
Short summary
Snow over Antarctic sea ice, influenced by highly variable meteorological conditions and heavy snowfall, has a complex stratigraphy and profound impact on the microwave signature. We employ advanced radiation transfer models to analyse the effects of complex snow properties on brightness temperatures over the sea ice in the Southern Ocean. Great potential lies in the understanding of snow processes and the application to satellite retrievals.
Snow over Antarctic sea ice, influenced by highly variable meteorological conditions and heavy...
