Articles | Volume 16, issue 10
https://doi.org/10.5194/tc-16-4473-2022
© Author(s) 2022. 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-16-4473-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Oct 2022
Research article |
| 21 Oct 2022
| 21 Oct 2022
A comparison between Envisat and ICESat sea ice thickness in the Southern Ocean
Jinfei Wang
School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China
School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China
Robert Ricker
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine
Research, Bremerhaven 27570, Germany
School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China
Bo Han
School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China
Stefan Hendricks
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine
Research, Bremerhaven 27570, Germany
Renhao Wu
School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China
Qinghua Yang
School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China
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This paper presents a new satellite-derived gridded dataset, including 10 years of sea surface height and geostrophic velocity at monthly resolution, over the Arctic ice-covered and ice-free regions, up to 88° N. We assess the dataset by comparison to independent satellite and mooring data. Results correlate well with independent satellite data at monthly timescales, and the geostrophic velocity fields can resolve seasonal to interannual variability of boundary currents wider than about 50 km.
Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Ruzica Dadic, Philip Rostosky, Michael Gallagher, Robbie Mallett, Andrew Barrett, Stefan Hendricks, Rasmus Tonboe, Michelle McCrystall, Mark Serreze, Linda Thielke, Gunnar Spreen, Thomas Newman, John Yackel, Robert Ricker, Michel Tsamados, Amy Macfarlane, Henna-Reetta Hannula, and Martin Schneebeli
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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.
Fengguan Gu, Qinghua Yang, Frank Kauker, Changwei Liu, Guanghua Hao, Chao-Yuan Yang, Jiping Liu, Petra Heil, Xuewei Li, and Bo Han
The Cryosphere, 16, 1873–1887, https://doi.org/10.5194/tc-16-1873-2022, https://doi.org/10.5194/tc-16-1873-2022, 2022
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The sea ice thickness was simulated by a single-column model and compared with in situ observations obtained off Zhongshan Station in the Antarctic. It is shown that the unrealistic precipitation in the atmospheric forcing data leads to the largest bias in sea ice thickness and snow depth modeling. In addition, the increasing snow depth gradually inhibits the growth of sea ice associated with thermal blanketing by the snow.
Sutao Liao, Hao Luo, Jinfei Wang, Qian Shi, Jinlun Zhang, and Qinghua Yang
The Cryosphere, 16, 1807–1819, https://doi.org/10.5194/tc-16-1807-2022, https://doi.org/10.5194/tc-16-1807-2022, 2022
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The Global Ice-Ocean Modeling and Assimilation System (GIOMAS) can basically reproduce the observed variability in Antarctic sea-ice volume and its changes in the trend before and after 2013, and it underestimates Antarctic sea-ice thickness (SIT) especially in deformed ice zones. Assimilating additional sea-ice observations with advanced assimilation methods may result in a more accurate estimation of Antarctic SIT.
Klaus Dethloff, Wieslaw Maslowski, Stefan Hendricks, Younjoo J. Lee, Helge F. Goessling, Thomas Krumpen, Christian Haas, Dörthe Handorf, Robert Ricker, Vladimir Bessonov, John J. Cassano, Jaclyn Clement Kinney, Robert Osinski, Markus Rex, Annette Rinke, Julia Sokolova, and Anja Sommerfeld
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Sea ice thickness anomalies during the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) winter in January, February and March 2020 were simulated with the coupled Regional Arctic climate System Model (RASM) and compared with CryoSat-2/SMOS satellite data. Hindcast and ensemble simulations indicate that the sea ice anomalies are driven by nonlinear interactions between ice growth processes and wind-driven sea-ice transports, with dynamics playing a dominant role.
Arttu Jutila, Stefan Hendricks, Robert Ricker, Luisa von Albedyll, Thomas Krumpen, and Christian Haas
The Cryosphere, 16, 259–275, https://doi.org/10.5194/tc-16-259-2022, https://doi.org/10.5194/tc-16-259-2022, 2022
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Sea-ice thickness retrieval from satellite altimeters relies on assumed sea-ice density values because density cannot be measured from space. We derived bulk densities for different ice types using airborne laser, radar, and electromagnetic induction sounding measurements. Compared to previous studies, we found high bulk density values due to ice deformation and younger ice cover. Using sea-ice freeboard, we derived a sea-ice bulk density parameterisation that can be applied to satellite data.
Thomas Krumpen, Luisa von Albedyll, Helge F. Goessling, Stefan Hendricks, Bennet Juhls, Gunnar Spreen, Sascha Willmes, H. Jakob Belter, Klaus Dethloff, Christian Haas, Lars Kaleschke, Christian Katlein, Xiangshan Tian-Kunze, Robert Ricker, Philip Rostosky, Janna Rückert, Suman Singha, and Julia Sokolova
The Cryosphere, 15, 3897–3920, https://doi.org/10.5194/tc-15-3897-2021, https://doi.org/10.5194/tc-15-3897-2021, 2021
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We use satellite data records collected along the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) drift to categorize ice conditions that shaped and characterized the floe and surroundings during the expedition. A comparison with previous years is made whenever possible. The aim of this analysis is to provide a basis and reference for subsequent research in the six main research areas of atmosphere, ocean, sea ice, biogeochemistry, remote sensing and ecology.
H. Jakob Belter, Thomas Krumpen, Luisa von Albedyll, Tatiana A. Alekseeva, Gerit Birnbaum, Sergei V. Frolov, Stefan Hendricks, Andreas Herber, Igor Polyakov, Ian Raphael, Robert Ricker, Sergei S. Serovetnikov, Melinda Webster, and Christian Haas
The Cryosphere, 15, 2575–2591, https://doi.org/10.5194/tc-15-2575-2021, https://doi.org/10.5194/tc-15-2575-2021, 2021
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Summer sea ice thickness observations based on electromagnetic induction measurements north of Fram Strait show a 20 % reduction in mean and modal ice thickness from 2001–2020. The observed variability is caused by changes in drift speeds and consequential variations in sea ice age and number of freezing-degree days. Increased ocean heat fluxes measured upstream in the source regions of Arctic ice seem to precondition ice thickness, which is potentially still measurable more than a year later.
Francesca Doglioni, Robert Ricker, Benjamin Rabe, and Torsten Kanzow
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-170, https://doi.org/10.5194/essd-2021-170, 2021
Manuscript not accepted for further review
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This paper presents a new satellite-derived gridded dataset of sea surface height and geostrophic velocity, over the Arctic ice-covered and ice-free regions up to 88° N. The dataset includes velocities north of 82° N, which were not available before. We assess the dataset by comparison to one independent satellite dataset and to independent mooring data. Results show that the geostrophic velocity fields can resolve seasonal to interannual variability of boundary currents wider than about 50 km.
Xuewei Li, Qinghua Yang, Lejiang Yu, Paul R. Holland, Chao Min, Longjiang Mu, and Dake Chen
The Cryosphere Discuss., https://doi.org/10.5194/tc-2020-359, https://doi.org/10.5194/tc-2020-359, 2021
Preprint withdrawn
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The Arctic sea ice thickness record minimum is confirmed occurring in autumn 2011. The dynamic and thermodynamic processes leading to the minimum thickness is analyzed based on a daily sea ice thickness reanalysis data covering the melting season. The results demonstrate that the dynamic transport of multiyear ice and the subsequent surface energy budget response is a critical mechanism actively contributing to the evolution of Arctic sea ice thickness in 2011.
Chao Min, Qinghua Yang, Longjiang Mu, Frank Kauker, and Robert Ricker
The Cryosphere, 15, 169–181, https://doi.org/10.5194/tc-15-169-2021, https://doi.org/10.5194/tc-15-169-2021, 2021
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An ensemble of four estimates of the sea-ice volume (SIV) variations in Baffin Bay from 2011 to 2016 is generated from the locally merged satellite observations, three modeled sea ice thickness sources (CMST, NAOSIM, and PIOMAS) and NSIDC ice drift data (V4). Results show that the net increase of the ensemble mean SIV occurs from October to April with the largest SIV increase in December, and the reduction occurs from May to September with the largest SIV decline in July.
Qian Shi, Qinghua Yang, Longjiang Mu, Jinfei Wang, François Massonnet, and Matthew R. Mazloff
The Cryosphere, 15, 31–47, https://doi.org/10.5194/tc-15-31-2021, https://doi.org/10.5194/tc-15-31-2021, 2021
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The ice thickness from four state-of-the-art reanalyses (GECCO2, SOSE, NEMO-EnKF and GIOMAS) are evaluated against that from remote sensing and in situ observations in the Weddell Sea, Antarctica. Most of the reanalyses can reproduce ice thickness in the central and eastern Weddell Sea but failed to capture the thick and deformed ice in the western Weddell Sea. These results demonstrate the possibilities and limitations of using current sea-ice reanalysis in Antarctic climate research.
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
Barber, D. G., Reddan, S. P., and LeDrew, E. F.: Statistical
characterization of the geophysical and electrical properties of snow on
Landfast first-year sea ice, J. Geophys. Res., 100, 2673–2686,
https://doi.org/10.1029/94JC02200, 1995.
Beaven, S. G., Lockhart, G. L., Gogineni, S. P., Hossetnmostafa, A. R.,
Jezek, K., Gow, A. J., Perovich, D. K., Fung, A. K., and Tjuatja, S.:
Laboratory measurements of radar backscatter from bare and snow-covered
saline ice sheets, Int. J. Remote Sens., 16, 851–876,
https://doi.org/10.1080/01431169508954448, 1995.
Behrendt, A.: The Sea Ice Thickness in the Atlantic Sector of the Southern
Ocean, PhD thesis, University of Bremen, Germany, 239 pp., https://epic.awi.de/id/eprint/33453/1/BzPM_0667_2013.pdf (last access: 13 December 2021), 2013.
Behrendt, A., Dierking, W., Fahrbach, E., and Witte, H.: Sea ice draft
measured by upward looking sonars in the Weddell Sea (Antarctica), PANGAEA [data set],
https://doi.org/10.1594/PANGAEA.785565, 2013a.
Behrendt, A., Dierking, W., Fahrbach, E., and Witte, H.: Sea ice draft in the Weddell Sea, measured by upward looking sonars, Earth Syst. Sci. Data, 5, 209–226, https://doi.org/10.5194/essd-5-209-2013, 2013b.
Bunzel, F., Notz, D., and Pedersen, L. T.: Retrievals of Arctic Sea-Ice
Volume and Its Trend Significantly Affected by Interannual Snow Variability,
Geophys. Res. Lett., 45, 11751–11759,
https://doi.org/10.1029/2018GL078867, 2018.
Comiso, J. C., Cavalieri, D. J., and Markus, T.: Sea ice concentration, ice
temperature, and snow depth using AMSR-E data, IEEE T. Geosci. Remote, 41, 243–252, https://doi.org/10.1109/TGRS.2002.808317, 2003.
Comiso, J. C., Kwok, R., Martin, S., and Gordon, A. L.: Variability and
trends in sea ice extent and ice production in the Ross Sea, J. Geophys.
Res., 116, C04021, https://doi.org/10.1029/2010JC006391, 2011.
Comiso, J. C., Gersten, R. A., Stock, L. V., Turner, J., Perez, G. J., and
Cho, K.: Positive Trend in the Antarctic Sea Ice Cover and Associated
Changes in Surface Temperature, J. Climate, 30, 2251–2267,
https://doi.org/10.1175/JCLI-D-16-0408.1, 2017.
Connor, L. N., Laxon, S. W., Ridout, A. L., Krabill, W. B., and McAdoo, D.
C.: Comparison of Envisat radar and airborne laser altimeter measurements
over Arctic sea ice, Remote Sens. Environ., 113, 563–570,
https://doi.org/10.1016/j.rse.2008.10.015, 2009.
Drucker, R., Martin, S., and Kwok, R.: Sea ice production and export from
coastal polynyas in the Weddell and Ross Seas, Geophys. Res. Lett., 38,
L17502, https://doi.org/10.1029/2011GL048668, 2011.
El Naggar, S. E. D., Dieckmann, G., Haas, C., Schröder, M., and
Spindler, M.: The Expeditions ANTARKTIS-XXII/1 and XII/2 of the Research
Vessel “Polarstern” in 2004/2005, Reports on Polar and Marine Research, 551,
268 pp., https://doi.org/10.2312/BzPM_0551_2007, 2007.
Fons, S. W. and Kurtz, N. T.: Retrieval of snow freeboard of Antarctic sea ice using waveform fitting of CryoSat-2 returns, The Cryosphere, 13, 861–878, https://doi.org/10.5194/tc-13-861-2019, 2019.
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.
Goosse, H. and Zunz, V.: Decadal trends in the Antarctic sea ice extent ultimately controlled by ice–ocean feedback, The Cryosphere, 8, 453–470, https://doi.org/10.5194/tc-8-453-2014, 2014.
Haas, C., Thomas, D., and Bareiss, J.: Surface properties and processes of
perennial Antarctic sea ice in summer, J. Glaciol., 47,
613–625, https://doi.org/10.3189/172756501781831864, 2001.
Harms, S., Fahrbach, E., and Strass, V. H.: Sea ice transports in the
Weddell Sea, J. Geophys. Res., 106, 9057–9073,
https://doi.org/10.1029/1999JC000027, 2001.
Hendricks, S., Stenseng, L., Helm, V., and Haas, C.: Effects of surface
roughness on sea ice freeboard retrieval with an Airborne Ku-Band SAR radar
altimeter, 2010 IEEE International Geoscience and Remote Sensing Symposium,
Honolulu, HI, USA, 25–30 July 2010, 3126–3129, https://doi.org/10.1109/IGARSS.2010.5654350, 2010.
Hendricks, S., Paul, S., and Rinne, E.: ESA Sea Ice Climate Change Initiative
(Sea_Ice_cci): Southern hemisphere sea ice
thickness from the CryoSat-2 satellite on a monthly grid (L3C) v2.0, Centre
for Environmental Data Analysis [data set],
https://doi.org/10.5285/48fc3d1e8ada405c8486ada522dae9e8, 2018a.
Hendricks, S., Paul, S., and Rinne, E.: ESA Sea Ice Climate Change Initiative
(Sea_Ice_cci): Southern hemisphere sea ice
thickness from the Envisat satellite on a monthly grid (L3C) v2.0, Centre
for Environmental Data Analysis [data set],
https://doi.org/10.5285/b1f1ac03077b4aa784c5a413a2210bf5, 2018b.
Kacimi, S. and Kwok, R.: The Antarctic sea ice cover from ICESat-2 and CryoSat-2: freeboard, snow depth, and ice thickness, The Cryosphere, 14, 4453–4474, https://doi.org/10.5194/tc-14-4453-2020, 2020.
Kaleschke, L., Lüpkes, C., Vihma, T., Haarpaintner, J., Bochert, A.,
Hartmann, J., and Heygster, G.: SSM/I sea ice remote sensing for meoscale
ocean-atmosphere interaction analysis., Can. J. Remote Sens., 27, 526–537,
https://doi.org/10.1080/07038992.2001.10854892, 2001.
Kern, S. and Ozsoy-Çiçek, B.: Satellite Remote Sensing of Snow Depth on
Antarctic Sea Ice: An Inter-Comparison of Two Empirical Approaches, Remote
Sens., 8, 450, https://doi.org/10.3390/rs8060450, 2016.
Kern, S. and Spreen, G.: Uncertainties in Antarctic sea-ice thickness
retrieval from ICESat, Ann. Glaciol., 56, 107–119,
https://doi.org/10.3189/2015AoG69A736, 2015.
Kern, S., Frost, T., and Heygster, G.: D1.3 Product User Guide (PUG) for
Antarctic AMSR-E snow depth product SD v1.1, https://fiona.uni-hamburg.de/bdc0b40b/sicci-ant-sit-option-pug-d1-3-issue-2-1-final.pdf (last access: 23 May 2020), 2015.
Kern, S., Ozsoy-Çiçek, B., and Worby, A. P.: Antarctic sea-ice
thickness retrieval from ICESat: Inter-comparison of different approaches,
Remote Sens., 8, 538, https://doi.org/10.3390/rs8070538, 2016 (data available at: https://www.cen.uni-hamburg.de/en/icdc/data/restricted-access/esa-cci-antarctic-sea-ice-thickness.html, last access: 25 September 2020).
Kern, S., Khvorostovsky K., and Skourup, H.: D4.1 Product Validation &
Intercomparison Report (PVIR-SIT), https://fiona.uni-hamburg.de/bdc0b40b/sicci-p2-pvir-sit-d4-1-issue-1-1.pdf (last access: 22 May 2020), 2018.
Koenig, L., Martin, S., Studinger, M., and Sonntag, J.: Polar Airborne
Observations Fill Gap in Satellite Data, Eos Trans. Amer. Geophys. Union,
91, 333–334, https://doi.org/10.1029/2010EO380002, 2010.
Kurtz, N. T. and Markus, T.: Satellite observations of Antarctic sea ice
thickness and volume, J. Geophys. Res., 117, C08025,
https://doi.org/10.1029/2012JC008141, 2012.
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.
Kwok, R. and Maksym, T.: Snow depth of the Weddell and Bellingshausen sea
ice covers from IceBridge surveys in 2010 and 2011: An examination, J.
Geophys. Res., 119, 4141–4167, https://doi.org/10.1002/2014JC009943, 2014.
Kwok, R., Zwally, H. J., and Yi, D.: ICESat observations of Arctic sea ice:
A first look, Geophys. Res. Lett., 31, L16401,
https://doi.org/10.1029/2004GL020309, 2004.
Kwok, R., Cunningham, G., Markus, T., Hancock, D., Morison, J. H., Palm, S. P., Farrell, S. L., Ivanoff, A., Wimert, J., and the ICESat-2 Science Team.: ATLAS/ICESat-2 L3A Sea Ice Height, Version 1,
NSIDC: National Snow and Ice Data Center, Boulder, Colorado USA [data set],
https://doi.org/10.5067/ATLAS/ATL07.001, 2019.
Landy, J. C., Petty, A. A., Tsamados, M., and Stroeve, J. C.: Sea Ice
Roughness Overlooked as a Key Source of Uncertainty in CryoSat-2 Ice
Freeboard Retrievals, J. Geophys. Res., 125, e2019JC015820,
https://doi.org/10.1029/2019JC015820, 2020.
Laxon, S., Peacock, N., and Smith, D.: High interannual variability of sea
ice thickness in the Arctic region, Nature, 425, 947–950,
https://doi.org/10.1038/nature02050, 2003.
Lemke, P.: The expedition of the research vessel “Polarstern” to the
Antarctic in 2006 (ANT-XXIII/7), Reports on Polar and Marine Research, 586,
147 pp., https://doi.org/10.2312/BzPM_0586_2009, 2009.
Lemke, P.: The Expedition of the Research Vessel Polarstern to the Antarctic
in 2013 (ANT-XXIX/6), Reports on Polar and Marine Research, 679, 1–154, https://doi.org/10.2312/BzPM_0679_2014,
2014.
Li, H., Xie, H., Kern, S., Wan, W., Ozsoy, B., Ackley, S., and Hong, Y.:
Spatio-temporal variability of Antarctic sea-ice thickness and volume
obtained from ICESat data using an innovative algorithm, Remote Sens.
Environ., 219, 44–61,
https://doi.org/10.1016/j.rse.2018.09.031, 2018.
Maksym, T.: Arctic and Antarctic Sea Ice Change: Contrasts, Commonalities,
and Causes, Annu. Rev. Mar. Sci., 11, 187–213,
https://doi.org/10.1146/annurev-marine-010816-060610, 2019.
Mallett, R. D. C., Lawrence, I. R., Stroeve, J. C., Landy, J. C., and Tsamados, M.: Brief communication: Conventional assumptions involving the speed of radar waves in snow introduce systematic underestimates to sea ice thickness and seasonal growth rate estimates, The Cryosphere, 14, 251–260, https://doi.org/10.5194/tc-14-251-2020, 2020.
Markus, T. and Cavalieri, D. J.: Snow Depth Distribution Over Sea Ice in
the Southern Ocean from Satellite Passive Microwave Data, in Antarctic Sea
Ice: Physical Processes, Interactions and Variability, edited by:
Jeffries, M. O., AGU, Washington, D. C., 19–39,
https://doi.org/10.1029/AR074p0019, 1998 (data available at: https://www.cen.uni-hamburg.de/en/icdc/data/restricted-access/esa-cci-antarctic-snow-depth.html, last access: 25 October 2021).
Markus, T., Massom, R., Worby, A., Lytle, V., Kurtz, N., and Maksym, T.:
Freeboard, snow depth and sea-ice roughness in East Antarctica from in situ
and multiple satellite data, Ann. Glaciol., 52, 242–248,
https://doi.org/10.3189/172756411795931570, 2011.
Massom, R. A., Scambos, T. A., Bennetts, L. G., Reid, P., Squire, V. A., and
Stammerjohn, S. E.: Antarctic ice shelf disintegration triggered by sea ice
loss and ocean swell, Nature, 558, 383–389,
https://doi.org/10.1038/s41586-018-0212-1, 2018.
Massonnet, F., Mathiot, P., Fichefet, T., Goosse, H., König Beatty, C.,
Vancoppenolle, M., and Lavergne, T.: A model reconstruction of the Antarctic
sea ice thickness and volume changes over 1980–2008 using data
assimilation, Ocean Model., 64, 67–75,
https://doi.org/10.1016/j.ocemod.2013.01.003, 2013.
McLaren, A. J., Banks, H. T., Durman, C. F., Gregory, J. M., Johns, T. C.,
Keen, A. B., Ridley, J. K., Roberts, M. J., Lipscomb, W. H., Connolley, W.
M., and Laxon, S. W.: Evaluation of the sea ice simulation in a new coupled
atmosphere-ocean climate model (HadGEM1), J. Geophys. Res., 111, C12014,
https://doi.org/10.1029/2005JC003033, 2006.
Meiners, K. M., Vancoppenolle, M., Thanassekos, S., Dieckmann, G. S.,
Thomas, D. N., Tison, J. L., Arrigo, K. R., Garrison, D. L., McMinn, A.,
Lannuzel, D., van der Merwe, P., Swadling, K. M., Smith Jr., W. O., Melnikov,
I., and Raymond, B.: Chlorophyll a in Antarctic sea ice from historical ice
core data, Geophys. Res. Lett., 39, L21602, https://doi.org/10.1029/2012GL053478,
2012.
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,
10419–10426, https://doi.org/10.1002/2017GL074506, 2017.
Nandan, V., Scharien, R. K., Geldsetzer, T., Kwok, R., Yackel, J. J.,
Mahmud, M. S., Rosel, A., Tonboe, R., Granskog, M., Willatt, R., Stroeve,
J., Nomura, D., and Frey, M.: Snow Property Controls on Modeled Ku-Band
Altimeter Estimates of FirstYear Sea Ice Thickness: Case Studies from the
Canadian and Norwegian Arctic, IEEE J. Sel. Top. Appl., 13, 1082–1096,
https://doi.org/10.1109/jstars.2020.2966432, 2020.
Nihashi, S. and Ohshima, K. I.: Circumpolar Mapping of Antarctic Coastal
Polynyas and Landfast Sea Ice: Relationship and Variability, J. Climate, 28,
3650–3670, https://doi.org/10.1175/JCLI-D-14-00369.1, 2015.
Ozsoy-Çiçek, B., Kern, S., Ackley, S. F., Xie, H., and Tekeli, A. E.:
Intercomparisons of Antarctic sea ice types from visual ship, RADARSAT-1
SAR, Envisat ASAR, QuikSCAT, and AMSR-E satellite observations in the
Bellingshausen Sea, Deep-Sea Res. II, 58, 1092–1111,
https://doi.org/10.1016/j.dsr2.2010.10.031, 2011.
Parkinson, C. L. and Cavalieri, D. J.: Antarctic sea ice variability and trends, 1979–2010, The Cryosphere, 6, 871–880, https://doi.org/10.5194/tc-6-871-2012, 2012.
Parkinson, C. L. and DiGirolamo, N. E.: Sea ice extents continue to set new
records: Arctic, Antarctic, and global results, Remote Sens.
Environ., 267, 112753, https://doi.org/10.1016/j.rse.2021.112753, 2021.
Paul, S., Hendricks, S., and Rinne, E.: Sea Ice Thickness Algorithm
Theoretical Basis Document (ATBD), v1.0, ESA Climate Change Initiative on
Sea Ice (SICCI), https://admin.climate.esa.int/media/documents/Sea_Ice_Thickness_Algorithm_Theoretical_Basis_Document_1.0.pdf (last access: 20 May 2020), 2017.
Paul, S., Hendricks, S., Ricker, R., Kern, S., and Rinne, E.: Empirical parametrization of Envisat freeboard retrieval of Arctic and Antarctic sea ice based on CryoSat-2: progress in the ESA Climate Change Initiative, The Cryosphere, 12, 2437–2460, https://doi.org/10.5194/tc-12-2437-2018, 2018.
Peacock, N. R. and Laxon, S. W.: Sea surface height determination in the
Arctic Ocean from ERS altimetry, J. Geophys. Res., 109, C07001,
https://doi.org/10.1029/2001JC001026, 2004.
Ricker, R., Hendricks, S., Helm, V., Skourup, H., and Davidson, M.: Sensitivity of CryoSat-2 Arctic sea-ice freeboard and thickness on radar-waveform interpretation, The Cryosphere, 8, 1607–1622, https://doi.org/10.5194/tc-8-1607-2014, 2014.
Schwegmann, S., Rinne, E., Ricker, R., Hendricks, S., and Helm, V.: About the consistency between Envisat and CryoSat-2 radar freeboard retrieval over Antarctic sea ice, The Cryosphere, 10, 1415–1425, https://doi.org/10.5194/tc-10-1415-2016, 2016.
Tian, L., Xie, H., Ackley, S., Tang, J., Mestas-Nuñez, A., and Wang, X.:
Sea-ice freeboard and thickness in the Ross Sea from airborne (IceBridge
2013) and satellite (ICESat 2003–2008) observations, Ann. Glaciol., 61,
24–39, https://doi.org/10.1017/aog.2019.49, 2020.
Tilling, R., Ridout, A., and Shepherd, A.: Assessing the Impact of Lead and
Floe Sampling on Arctic Sea Ice Thickness Estimates from Envisat and
CryoSat-2, J. Geophys. Res., 124, 7473–7485,
https://doi.org/10.1029/2019JC015232, 2019.
Tschudi, M., Meier W. N., Stewart J. S., Fowler C., and Maslanik J.: Polar
Pathfinder Daily 25 km EASE-Grid Sea Ice Motion Vectors, Version 4, NASA National Snow and Ice Data Center Distributed Active
Archive Center, Boulder,
Colorado USA [data set], https://doi.org/10.5067/INAWUWO7QH7B, 2019.
Turner, J. and Comiso, J.: Solve Antarctica's sea-ice puzzle, Nature, 547,
275–277, https://doi.org/10.1038/547275a, 2017.
Turner, J., Holmes, C., Caton Harrison, T., Phillips, T., Jena, B.,
Reeves-Francois T., Fogt, R., Thomas, E. R., and Bajish, C. C.: Record low
Antarctic sea ice cover in February 2022, Geophys. Res. Lett., 49,
e2022GL098904, https://doi.org/10.1029/2022GL098904, 2022.
Wang, X., Jiang, W., Xie, H., Ackley, S., and Li, H.: Decadal variations
of sea ice thickness in the Amundsen-Bellingshausen and Weddell seas
retrieved from ICESat and IceBridge laser altimetry, 2003–2017, J. Geophys.
Res., 125, e2020JC016077, https://doi.org/10.1029/2020JC016077, 2020.
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.
Williams, G., Maksym, T., Wilkinson, J., Kunz, C., Murphy, C., Kimball, P.,
and Singh, H.: Thick and deformed Antarctic sea ice mapped with autonomous
underwater vehicles, Nat. Geosci., 8, 61–67,
https://doi.org/10.1038/ngeo2299, 2015.
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., 113, C05S92, https://doi.org/10.1029/2007JC004254, 2008a.
Worby, A. P., Markus, T., Steer, A. D., Lytle, V. I., and Massom, R. A.:
Evaluation of AMSR-E snow depth product over East Antarctic sea ice using in
situ measurements and aerial photography, J. Geophys. Res., 113, C05S94,
https://doi.org/10.1029/2007JC004181, 2008b.
Xie, H., Tekeli, A. E., Ackley, S. F., Yi, D., and Zwally, H. J.: Sea ice
thickness estimations from ICESat Altimetry over the Bellingshausen and
Amundsen Seas, 2003–2009, J. Geophys. Res., 118, 2438–2453,
https://doi.org/10.1002/jgrc.20179, 2013.
Xu, Y., Li, H., Liu, B., Xie, H., and Ozsoy-Cicek, B.: Deriving Antarctic sea-ice thickness from satellite altimetry and estimating consistency for NASA's ICESat/ICESat-2 missions, Geophys. Res. Lett., 48, e2021GL093425, https://doi.org/10.1029/2021GL093425, 2021.
Yi, D., Zwally, H. J., and Robbins, J. W.: ICESat observations of seasonal
and interannual variations of sea-ice freeboard and estimated thickness in
the Weddell Sea, Antarctica (2003–2009), Ann. Glaciol., 52, 43–51,
https://doi.org/10.3189/172756411795931480, 2011.
Zelli, C. and Aerospazio, A.: ENVISAT RA-2 advanced radar altimeter:
Instrument design and pre-launch performance assessment review, Acta
Astronaut., 44, 323–333,
https://doi.org/10.1016/S0094-5765(99)00063-6, 1999.
Zhang, J.: Increasing Antarctic Sea Ice under Warming Atmospheric and
Oceanic Conditions, J. Climate, 20, 2515–2529,
https://doi.org/10.1175/JCLI4136.1, 2007.
Short summary
The differences between Envisat and ICESat sea ice thickness (SIT) reveal significant temporal and spatial variations. Our findings suggest that both overestimation of Envisat sea ice freeboard, potentially caused by radar backscatter originating from inside the snow layer, and the AMSR-E snow depth biases and sea ice density uncertainties can possibly account for the differences between Envisat and ICESat SIT.
The differences between Envisat and ICESat sea ice thickness (SIT) reveal significant temporal...
