Articles | Volume 19, issue 3
https://doi.org/10.5194/tc-19-1241-2025
© Author(s) 2025. 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-19-1241-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Mar 2025
Research article |
| 17 Mar 2025
| 17 Mar 2025
Inferring the seasonality of sea ice floes in the Weddell Sea using ICESat-2
Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, the Netherlands
Environmental Science and Engineering, California Institute of Technology, Pasadena, CA, USA
Heather Regan
Nansen Environmental and Remote Sensing Center, Bergen, Norway
Bjerknes Centre for Climate Research, Bergen, Norway
Younghyun Koo
Department of Earth and Planetary Sciences, University of Texas at San Antonio, San Antonio, TX, USA
Sean Minhui Tashi Chua
Nansen Environmental and Remote Sensing Center, Bergen, Norway
Australian Antarctic Division, Kingston, Australia
Australian Antarctic Program Partnership, University of Tasmania, Hobart, Australia
Department of Earth and Environmental Science, University of Pennsylvania, Philadelphia, PA, USA
Petra Heil
Australian Antarctic Division, Kingston, Australia
Australian Antarctic Program Partnership, University of Tasmania, Hobart, Australia
WSL Institute for Snow and Avalanche Research, Davos, Switzerland
Related authors
Rigoberto Moncada, Mukund Gupta, Jacinto Ulloa, Andrew F. Thompson, and Jose E. Andrade
EGUsphere, https://doi.org/10.5194/egusphere-2025-3940, https://doi.org/10.5194/egusphere-2025-3940, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
We studied how ocean currents break up fast sea ice at their edges using discrete element simulations of bonded floes. We found that swirling eddies can crack ice into realistic patterns and fragment size distributions. Larger eddies penetrate deeper and break more ice than smaller scale eddies. However, larger eddies require faster speeds to induce breakage compared to smaller eddies. This research uses computer models to better understand and predict how sea ice breaks due to ocean movements.
Rigoberto Moncada, Mukund Gupta, Jacinto Ulloa, Andrew F. Thompson, and Jose E. Andrade
EGUsphere, https://doi.org/10.5194/egusphere-2025-3940, https://doi.org/10.5194/egusphere-2025-3940, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
We studied how ocean currents break up fast sea ice at their edges using discrete element simulations of bonded floes. We found that swirling eddies can crack ice into realistic patterns and fragment size distributions. Larger eddies penetrate deeper and break more ice than smaller scale eddies. However, larger eddies require faster speeds to induce breakage compared to smaller eddies. This research uses computer models to better understand and predict how sea ice breaks due to ocean movements.
Younghyun Koo, Gong Cheng, Mathieu Morlighem, and Maryam Rahnemoonfar
The Cryosphere, 19, 2583–2599, https://doi.org/10.5194/tc-19-2583-2025, https://doi.org/10.5194/tc-19-2583-2025, 2025
Short summary
Short summary
Calving, the breaking of ice bodies from the terminus of a glacier, plays an important role in the mass losses of Greenland ice sheets. However, calving parameters have been poorly understood because of the intensive computational demands of traditional numerical models. To address this issue and find the optimal calving parameter that best represents real observations, we develop deep-learning emulators based on graph neural network architectures.
Jennifer Veitch, Enrique Alvarez-Fanjul, Arthur Capet, Stefania Ciliberti, Mauro Cirano, Emanuela Clementi, Fraser Davidson, Ghada el Serafy, Guilherme Franz, Patrick Hogan, Sudheer Joseph, Svitlana Liubartseva, Yasumasa Miyazawa, Heather Regan, and Katerina Spanoudaki
State Planet, 5-opsr, 6, https://doi.org/10.5194/sp-5-opsr-6-2025, https://doi.org/10.5194/sp-5-opsr-6-2025, 2025
Short summary
Short summary
Ocean forecast systems provide information about a future state of the ocean. This information is provided in the form of decision support tools, or downstream applications, that can be accessed by various stakeholders to support livelihoods, coastal resilience and the good governance of the marine environment. This paper provides an overview of the various downstream applications of ocean forecast systems that are utilized around the world.
Mauro Cirano, Enrique Alvarez-Fanjul, Arthur Capet, Stefania Ciliberti, Emanuela Clementi, Boris Dewitte, Matias Dinápoli, Ghada El Serafy, Patrick Hogan, Sudheer Joseph, Yasumasa Miyazawa, Ivonne Montes, Diego A. Narvaez, Heather Regan, Claudia G. Simionato, Gregory C. Smith, Joanna Staneva, Clemente A. S. Tanajura, Pramod Thupaki, Claudia Urbano-Latorre, Jennifer Veitch, and Jorge Zavala Hidalgo
State Planet, 5-opsr, 5, https://doi.org/10.5194/sp-5-opsr-5-2025, https://doi.org/10.5194/sp-5-opsr-5-2025, 2025
Short summary
Short summary
Operational ocean forecasting systems (OOFSs) are crucial for human activities, environmental monitoring, and policymaking. An assessment across eight key regions highlights strengths and gaps, particularly in coastal and biogeochemical forecasting. AI offers improvements, but collaboration, knowledge sharing, and initiatives like the OceanPrediction Decade Collaborative Centre (DCC) are key to enhancing accuracy, accessibility, and global forecasting capabilities.
Einar Ólason, Guillaume Boutin, Timothy Williams, Anton Korosov, Heather Regan, Jonathan Rheinlænder, Pierre Rampal, Daniela Flocco, Abdoulaye Samaké, Richard Davy, Timothy Spain, and Sean Chua
EGUsphere, https://doi.org/10.5194/egusphere-2024-3521, https://doi.org/10.5194/egusphere-2024-3521, 2025
Short summary
Short summary
This paper introduces a new version of the neXtSIM sea-ice model. NeXtSIM is unique among sea-ice models in how it represents sea-ice dynamics, focusing on features such as cracks and ridges and how these impact interactions between the atmosphere and ocean where sea ice is present. The new version introduces some physical parameterisations and model options detailed and explained in the paper. Following the paper's publication, the neXtSIM code will be released publicly for the first time.
Diana Francis, Ricardo Fonseca, Narendra Nelli, Petra Heil, Jonathan Wille, Irina Gorodetskaya, and Robert Massom
EGUsphere, https://doi.org/10.5194/egusphere-2024-3535, https://doi.org/10.5194/egusphere-2024-3535, 2025
Short summary
Short summary
This study investigates the impact of atmospheric rivers and associated atmospheric dynamics on sea-ice thickness and snow depth at a coastal site in East Antarctica during July–November 2022 using in-situ measurements and numerical modelling. The passage of an atmospheric river induced a reduction of up to 0.06 m in both fields. Precipitation occurred from the convergence of katabatic winds with advected low-latitude moist air.
Kai Liu, Hongyan Zhang, Yong Bo, Dehui Li, Long Li, Hang Li, Shudong Wang, and Xueke Li
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-129, https://doi.org/10.5194/hess-2024-129, 2024
Manuscript not accepted for further review
Short summary
Short summary
Our framework brings together remote sensing, machine learning, and numerical modeling to enhance soil moisture records. We merge outputs from various machine learning algorithms to ensure the model reliability. The ability of our approach in capturing drought dynamics is noticeable, making it invaluable in arid and semi-arid regions globally, such as northern China and the northern-central United States, where drought susceptibility is high.
Stefania A. Ciliberti, Enrique Alvarez Fanjul, Jay Pearlman, Kirsten Wilmer-Becker, Pierre Bahurel, Fabrice Ardhuin, Alain Arnaud, Mike Bell, Segolene Berthou, Laurent Bertino, Arthur Capet, Eric Chassignet, Stefano Ciavatta, Mauro Cirano, Emanuela Clementi, Gianpiero Cossarini, Gianpaolo Coro, Stuart Corney, Fraser Davidson, Marie Drevillon, Yann Drillet, Renaud Dussurget, Ghada El Serafy, Katja Fennel, Marcos Garcia Sotillo, Patrick Heimbach, Fabrice Hernandez, Patrick Hogan, Ibrahim Hoteit, Sudheer Joseph, Simon Josey, Pierre-Yves Le Traon, Simone Libralato, Marco Mancini, Pascal Matte, Angelique Melet, Yasumasa Miyazawa, Andrew M. Moore, Antonio Novellino, Andrew Porter, Heather Regan, Laia Romero, Andreas Schiller, John Siddorn, Joanna Staneva, Cecile Thomas-Courcoux, Marina Tonani, Jose Maria Garcia-Valdecasas, Jennifer Veitch, Karina von Schuckmann, Liying Wan, John Wilkin, and Romane Zufic
State Planet, 1-osr7, 2, https://doi.org/10.5194/sp-1-osr7-2-2023, https://doi.org/10.5194/sp-1-osr7-2-2023, 2023
Haihan Hu, Jiechen Zhao, Petra Heil, Zhiliang Qin, Jingkai Ma, Fengming Hui, and Xiao Cheng
The Cryosphere, 17, 2231–2244, https://doi.org/10.5194/tc-17-2231-2023, https://doi.org/10.5194/tc-17-2231-2023, 2023
Short summary
Short summary
The oceanic characteristics beneath sea ice significantly affect ice growth and melting. The high-frequency and long-term observations of oceanic variables allow us to deeply investigate their diurnal and seasonal variation and evaluate their influences on sea ice evolution. The large-scale sea ice distribution and ocean circulation contributed to the seasonal variation of ocean variables, revealing the important relationship between large-scale and local phenomena.
Heather Regan, Pierre Rampal, Einar Ólason, Guillaume Boutin, and Anton Korosov
The Cryosphere, 17, 1873–1893, https://doi.org/10.5194/tc-17-1873-2023, https://doi.org/10.5194/tc-17-1873-2023, 2023
Short summary
Short summary
Multiyear ice (MYI), sea ice that survives the summer, is more resistant to changes than younger ice in the Arctic, so it is a good indicator of sea ice resilience. We use a model with a new way of tracking MYI to assess the contribution of different processes affecting MYI. We find two important years for MYI decline: 2007, when dynamics are important, and 2012, when melt is important. These affect MYI volume and area in different ways, which is important for the interpretation of observations.
Na Li, Ruibo Lei, Petra Heil, Bin Cheng, Minghu Ding, Zhongxiang Tian, and Bingrui Li
The Cryosphere, 17, 917–937, https://doi.org/10.5194/tc-17-917-2023, https://doi.org/10.5194/tc-17-917-2023, 2023
Short summary
Short summary
The observed annual maximum landfast ice (LFI) thickness off Zhongshan (Davis) was 1.59±0.17 m (1.64±0.08 m). Larger interannual and local spatial variabilities for the seasonality of LFI were identified at Zhongshan, with the dominant influencing factors of air temperature anomaly, snow atop, local topography and wind regime, and oceanic heat flux. The variability of LFI properties across the study domain prevailed at interannual timescales, over any trend during the recent decades.
Guillaume Boutin, Einar Ólason, Pierre Rampal, Heather Regan, Camille Lique, Claude Talandier, Laurent Brodeau, and Robert Ricker
The Cryosphere, 17, 617–638, https://doi.org/10.5194/tc-17-617-2023, https://doi.org/10.5194/tc-17-617-2023, 2023
Short summary
Short summary
Sea ice cover in the Arctic is full of cracks, which we call leads. We suspect that these leads play a role for atmosphere–ocean interactions in polar regions, but their importance remains challenging to estimate. We use a new ocean–sea ice model with an original way of representing sea ice dynamics to estimate their impact on winter sea ice production. This model successfully represents sea ice evolution from 2000 to 2018, and we find that about 30 % of ice production takes place in leads.
Kai Liu, Xueke Li, Shudong Wang, and Hongyan Zhang
Hydrol. Earth Syst. Sci., 27, 577–598, https://doi.org/10.5194/hess-27-577-2023, https://doi.org/10.5194/hess-27-577-2023, 2023
Short summary
Short summary
Remote sensing has opened opportunities for mapping spatiotemporally continuous soil moisture, but it is hampered by data gaps. We propose a robust gap-filling approach to reconstruct daily satellite soil moisture. The merit of our approach is to integrate satellite observations, model-driven knowledge, and spatiotemporal machine learning. We also apply the developed approach to long-term datasets. Our study provides a potential avenue for hydrological applications.
Yetang Wang, Xueying Zhang, Wentao Ning, Matthew A. Lazzara, Minghu Ding, Carleen H. Reijmer, Paul C. J. P. Smeets, Paolo Grigioni, Petra Heil, Elizabeth R. Thomas, David Mikolajczyk, Lee J. Welhouse, Linda M. Keller, Zhaosheng Zhai, Yuqi Sun, and Shugui Hou
Earth Syst. Sci. Data, 15, 411–429, https://doi.org/10.5194/essd-15-411-2023, https://doi.org/10.5194/essd-15-411-2023, 2023
Short summary
Short summary
Here we construct a new database of Antarctic automatic weather station (AWS) meteorological records, which is quality-controlled by restrictive criteria. This dataset compiled all available Antarctic AWS observations, and its resolutions are 3-hourly, daily and monthly, which is very useful for quantifying spatiotemporal variability in weather conditions. Furthermore, this compilation will be used to estimate the performance of the regional climate models or meteorological reanalysis products.
Minghu Ding, Xiaowei Zou, Qizhen Sun, Diyi Yang, Wenqian Zhang, Lingen Bian, Changgui Lu, Ian Allison, Petra Heil, and Cunde Xiao
Earth Syst. Sci. Data, 14, 5019–5035, https://doi.org/10.5194/essd-14-5019-2022, https://doi.org/10.5194/essd-14-5019-2022, 2022
Short summary
Short summary
The PANDA automatic weather station (AWS) network consists of 11 stations deployed along a transect from the coast (Zhongshan Station) to the summit of the East Antarctic Ice Sheet (Dome A). It covers the different climatic and topographic units of East Antarctica. All stations record hourly air temperature, relative humidity, air pressure, wind speed and direction at two or three heights. The PANDA AWS dataset commences from 1989 and is planned to be publicly available into the future.
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
Short summary
Short summary
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.
Tian R. Tian, Alexander D. Fraser, Noriaki Kimura, Chen Zhao, and Petra Heil
The Cryosphere, 16, 1299–1314, https://doi.org/10.5194/tc-16-1299-2022, https://doi.org/10.5194/tc-16-1299-2022, 2022
Short summary
Short summary
This study presents a comprehensive validation of a satellite observational sea ice motion product in Antarctica by using drifting buoys. Two problems existing in this sea ice motion product have been noticed. After rectifying problems, we use it to investigate the impacts of satellite observational configuration and timescale on Antarctic sea ice kinematics and suggest the future improvement of satellite missions specifically designed for retrieval of sea ice motion.
Joey J. Voermans, Qingxiang Liu, Aleksey Marchenko, Jean Rabault, Kirill Filchuk, Ivan Ryzhov, Petra Heil, Takuji Waseda, Takehiko Nose, Tsubasa Kodaira, Jingkai Li, and Alexander V. Babanin
The Cryosphere, 15, 5557–5575, https://doi.org/10.5194/tc-15-5557-2021, https://doi.org/10.5194/tc-15-5557-2021, 2021
Short summary
Short summary
We have shown through field experiments that the amount of wave energy dissipated in landfast ice, sea ice attached to land, is much larger than in broken ice. By comparing our measurements against predictions of contemporary wave–ice interaction models, we determined which models can explain our observations and which cannot. Our results will improve our understanding of how waves and ice interact and how we can model such interactions to better forecast waves and ice in the polar regions.
YoungHyun Koo, Hongjie Xie, Stephen F. Ackley, Alberto M. Mestas-Nuñez, Grant J. Macdonald, and Chang-Uk Hyun
The Cryosphere, 15, 4727–4744, https://doi.org/10.5194/tc-15-4727-2021, https://doi.org/10.5194/tc-15-4727-2021, 2021
Short summary
Short summary
This study demonstrates for the first time the potential of Google Earth Engine (GEE) cloud-computing platform and Sentinel-1 synthetic aperture radar (SAR) images for semi-automated tracking of area changes and movements of iceberg B43. Our novel GEE-based iceberg tracking can be used to construct a large iceberg database for a better understanding of the behavior of icebergs and their interactions with surrounding environments.
Diana Francis, Kyle S. Mattingly, Stef Lhermitte, Marouane Temimi, and Petra Heil
The Cryosphere, 15, 2147–2165, https://doi.org/10.5194/tc-15-2147-2021, https://doi.org/10.5194/tc-15-2147-2021, 2021
Short summary
Short summary
The unexpected September 2019 calving event from the Amery Ice Shelf, the largest since 1963 and which occurred almost a decade earlier than expected, was triggered by atmospheric extremes. Explosive twin polar cyclones provided a deterministic role in this event by creating oceanward sea surface slope triggering the calving. The observed record-anomalous atmospheric conditions were promoted by blocking ridges and Antarctic-wide anomalous poleward transport of heat and moisture.
Joey J. Voermans, Jean Rabault, Kirill Filchuk, Ivan Ryzhov, Petra Heil, Aleksey Marchenko, Clarence O. Collins III, Mohammed Dabboor, Graig Sutherland, and Alexander V. Babanin
The Cryosphere, 14, 4265–4278, https://doi.org/10.5194/tc-14-4265-2020, https://doi.org/10.5194/tc-14-4265-2020, 2020
Short summary
Short summary
In this work we demonstrate the existence of an observational threshold which identifies when waves are most likely to break sea ice. This threshold is based on information from two recent field campaigns, supplemented with existing observations of sea ice break-up. We show that both field and laboratory observations tend to converge to a single quantitative threshold at which the wave-induced sea ice break-up takes place, which opens a promising avenue for operational forecasting models.
Cited articles
Ackley, S. F., Perovich, D. K., 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, b
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
Boutin, G., Lique, C., Ardhuin, F., Rousset, C., Talandier, C., Accensi, M., and Girard-Ardhuin, F.: Towards a coupled model to investigate wave–sea ice interactions in the Arctic marginal ice zone, The Cryosphere, 14, 709–735, https://doi.org/10.5194/tc-14-709-2020, 2020. a, b
Brenner, S., Horvat, C., Hall, P., Lo Piccolo, A., Fox-Kemper, B., Labbé, S., and Dansereau, V.: Scale-dependent air-sea exchange in the polar oceans: floe-floe and floe-flow coupling in the generation of ice-ocean boundary layer turbulence, Geophys. Res. Lett., 50, e2023GL105703, https://doi.org/10.1029/2023GL105703, 2023. a
Bushuk, M., Winton, M., Haumann, F. A., Delworth, T., Lu, F., Zhang, Y., Jia, L., Zhang, L., Cooke, W., Harrison, M., Hurlin, B., Johnson, N. C., Kapnick, S. B., McHugh, C., Murakami, H., Rosati, A., Tseng, K.-C., Wittenberg, A. T., Yang, X., and Zeng, F.: Seasonal prediction and predictability of regional Antarctic sea ice, J. Climate, 34, 6207–6233, https://doi.org/10.1175/JCLI-D-20-0965.1, 2021. a
Denton, A. A. and Timmermans, M.-L.: Characterizing the sea-ice floe size distribution in the Canada Basin from high-resolution optical satellite imagery, The Cryosphere, 16, 1563–1578, https://doi.org/10.5194/tc-16-1563-2022, 2022. a
Eicken, H.: The role of sea ice in structuring Antarctic ecosystems, Polar Biol., 12, 3–13, https://doi.org/10.1007/BF00239960, 1992. a
England, M. R., Polvani, L. M., Sun, L., and Deser, C.: Tropical climate responses to projected Arctic and Antarctic sea-ice loss, Nat. Geosci., 13, 275–281, https://doi.org/10.1038/s41561-020-0546-9, 2020. a
Farrell, S. L., Duncan, K., Buckley, E. M., Richter-Menge, J., and Li, R.: Mapping sea ice surface topography in high fidelity with ICESat-2, Geophys. Res. Lett., 47, e2020GL090708, https://doi.org/10.1029/2020GL090708, 2020. a, b, c, d
Fiedler, E. K., Martin, M. J., Blockley, E., Mignac, D., Fournier, N., Ridout, A., Shepherd, A., and Tilling, R.: Assimilation of sea ice thickness derived from CryoSat-2 along-track freeboard measurements into the Met Office's Forecast Ocean Assimilation Model (FOAM), The Cryosphere, 16, 61–85, https://doi.org/10.5194/tc-16-61-2022, 2022. a
Fretwell, P. T., Boutet, A., and Ratcliffe, N.: Record low 2022 Antarctic sea ice led to catastrophic breeding failure of emperor penguins, Nature Communications Earth and Environment, 4, 273, https://doi.org/10.1038/s43247-023-00927-x, 2023. a
Geise, G. R., Barton, C. C., and Tebbens, S. F.: Power scaling and seasonal changes of floe areas in the Arctic East Siberian Sea, Pure Appl. Geophys., 174, 387–396, https://doi.org/10.1007/s00024-016-1364-2, 2017. a
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
Gill, A.: Circulation and bottom water production in the Weddell Sea, Deep Sea Research and Oceanographic Abstracts, 20, 111–140, https://doi.org/10.1016/0011-7471(73)90048-X, 1973. a
Goosse, H., Allende Contador, S., Bitz, C. M., Blanchard-Wrigglesworth, E., Eayrs, C., Fichefet, T., Himmich, K., Huot, P.-V., Klein, F., Marchi, S., Massonnet, F., Mezzina, B., Pelletier, C., Roach, L., Vancoppenolle, M., and van Lipzig, N. P. M.: Modulation of the seasonal cycle of the Antarctic sea ice extent by sea ice processes and feedbacks with the ocean and the atmosphere, The Cryosphere, 17, 407–425, https://doi.org/10.5194/tc-17-407-2023, 2023. a
Gupta, M. and Thompson, A. F.: Regimes of sea-ice floe melt: ice-ocean coupling at the submesoscales, J. Geophys. Res.-Oceans, 127, e2022JC018894, https://doi.org/10.1029/2022JC018894, 2022. a, b
Gupta, M., Follows, M. J., and Lauderdale, J. M.: The effect of Antarctic sea ice on southern ocean carbon outgassing: Capping Versus Light Attenuation, Global Biogeochem. Cy., 34, e2019GB006489, https://doi.org/10.1029/2019GB006489, 2020. a
Haas, C., Nicolaus, M., Willmes, S., Worby, A., and Flinspach, D.: Sea ice and snow thickness and physical properties of an ice floe in the western Weddell Sea and their changes during spring warming, Deep-Sea Res. Pt. II, 55, 963–974, https://doi.org/10.1016/j.dsr2.2007.12.020, 2008. a, b
Herman, A.: Influence of ice concentration and floe-size distribution on cluster formation in sea-ice floes, Open Phys., 10, 715–722, https://doi.org/10.2478/s11534-012-0071-6, 2012. a
Herman, A.: Discrete-Element bonded-particle Sea Ice model DESIgn, version 1.3a – model description and implementation, Geosci. Model Dev., 9, 1219–1241, https://doi.org/10.5194/gmd-9-1219-2016, 2016. a
Herman, A., Wenta, M., and Cheng, S.: Sizes and shapes of sea ice floes broken by waves–a case study from the East Antarctic coast, Front. Earth Sci., 9, 655977, https://doi.org/10.3389/feart.2021.655977, 2021. a, b
Hopkins, M. A.: Four stages of pressure ridging, J. Geophys. Res.-Oceans, 103, 21883–21891, https://doi.org/10.1029/98JC01257, 1998. a
Horvat, C., Tziperman, E., and Campin, J.-M.: Interaction of sea ice floe size, ocean eddies, and sea ice melting, Geophys. Res. Lett., 43, 8083–8090, https://doi.org/10.1002/2016GL069742, 2016. a, b
Horvat, C., Roach, L. A., Tilling, R., Bitz, C. M., Fox-Kemper, B., Guider, C., Hill, K., Ridout, A., and Shepherd, A.: Estimating the sea ice floe size distribution using satellite altimetry: theory, climatology, and model comparison, The Cryosphere, 13, 2869–2885, https://doi.org/10.5194/tc-13-2869-2019, 2019. a, b, c
Hutchings, J. K., Heil, P., Steer, A., and Hibler III, W. D.: Subsynoptic scale spatial variability of sea ice deformation in the western Weddell Sea during early summer, J. Geophys. Res.-Oceans, 117, C01002, https://doi.org/10.1029/2011JC006961, 2012. a, b
Hwang, B. and Wang, Y.: Multi-scale satellite observations of Arctic sea ice: new insight into the life cycle of the floe size distribution, Philos. T. Roy. Soc. A, 380, 20210259, https://doi.org/10.1098/rsta.2021.0259, 2022. a
Hwang, B., Wilkinson, J., Maksym, T., Graber, H. C., Schweiger, A., Horvat, C., Perovich, D. K., Arntsen, A. E., Stanton, T. P., Ren, J., and Wadhams, P.: Winter-to-summer transition of Arctic sea ice breakup and floe size distribution in the Beaufort Sea, Elementa: Science of the Anthropocene, 5, 40, https://doi.org/10.1525/elementa.232, 2017. a
Kohlbach, D., Graeve, M., Lange, B. A., David, C., Schaafsma, F. L., van Franeker, J. A., Vortkamp, M., Brandt, A., and Flores, H.: Dependency of Antarctic zooplankton species on ice algae-produced carbon suggests a sea ice-driven pelagic ecosystem during winter, Glob. Change Biol., 24, 4667–4681, https://doi.org/10.1111/gcb.14392, 2018. a
Koo, Y., Xie, H., Kurtz, N. T., Ackley, S. F., and Wang, W.: Sea ice surface type classification of ICESat-2 ATL07 data by using data-driven machine learning model: Ross Sea, Antarctic as an example, Remote Sens. Environ., 296, 113726, https://doi.org/10.1016/j.rse.2023.113726, 2023. a, b, c, d
Kurtz, N. T. and Markus, T.: Satellite observations of Antarctic sea ice thickness and volume, J. Geophys. Res.-Oceans, 117, C08025, https://doi.org/10.1029/2012JC008141, 2012. a, b
Kwok, R., Cunningham, G. F., Wensnahan, M., Rigor, I., Zwally, H. J., and Yi, D.: Thinning and volume loss of the Arctic Ocean sea ice cover: 2003–2008, J. Geophys. Res.-Oceans, 114, C07005, https://doi.org/10.1029/2009JC005312, 2009. a, b
Kwok, R., Markus, T., Kurtz, N. T., Petty, A. A., Neumann, T. A., Farrell, S. L., Cunningham, G. F., Hancock, D. W., Ivanoff, A., and Wimert, J. T.: Surface height and sea ice freeboard of the Arctic Ocean from ICESat-2: characteristics and early results, J. Geophys. Res.-Oceans, 124, 6942–6959, https://doi.org/10.1029/2019JC015486, 2019. a, b
Kwok, R., Petty, A. A., Bagnardi, M., Kurtz, N. T., Cunningham, G. F., Ivanoff, A., and Kacimi, S.: Refining the sea surface identification approach for determining freeboards in the ICESat-2 sea ice products, The Cryosphere, 15, 821–833, https://doi.org/10.5194/tc-15-821-2021, 2021. a
Kwok, R., Petty, A., Bagnardi, M., Wimert, J. T., Cunningham, G. F., Hancock, D. W., Ivanoff, A., and Kurtz, N.: Ice, Cloud, and Land Elevation Satellite (ICESat-2) Project Algorithm Theoretical Basis Document (ATBD) for Sea Ice Products, Version 6, National Snow and Ice Data Center, https://doi.org/10.5067/9VT7NJWOTV3I, 2022. a, b
Kwok, R., Petty, A. A., Cunningham, G., Markus, T., Hancock, D., Ivanoff, A., Wimert, J., Bagnardi, M., Kurtz, N., and the ICESat-2 Science Team: ATLAS/ICESat-2 L3A Sea Ice Height, Version 6, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/ATLAS/ATL07.006, 2023. a, b
Lange, M. A. and Eicken, H.: The sea ice thickness distribution in the northwestern Weddell Sea, J. Geophys. Res.-Oceans, 96, 4821–4837, https://doi.org/10.1029/90JC02441, 1991. 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
Leonard, K. C. and Maksym, T.: The importance of wind-blown snow redistribution to snow accumulation on Bellingshausen Sea ice, Ann. Glaciol., 52, 271–278, https://doi.org/10.3189/172756411795931651, 2011. a, b
Mallett, R. D. C., Stroeve, J. C., Cornish, S. B., Crawford, A. D., Lukovich, J. V., Serreze, M. C., Barrett, A. P., Meier, W. N., Heorton, H. D. B. S., and Tsamados, M.: Record winter winds in 2020/21 drove exceptional Arctic sea ice transport, Communications Earth and Environment, 2, 149, https://doi.org/10.1038/s43247-021-00221-8, 2021. a
Manucharyan, G. E. and Montemuro, B. P.: SubZero: a sea ice model with an explicit representation of the floe life cycle, J. Adv. Model. Earth Sy., 14, e2022MS003247, https://doi.org/10.1029/2022MS003247, 2022. a
Markus, T., Neumann, T., Martino, A., Abdalati, W., Brunt, K., Csatho, B., Farrell, S., Fricker, H., Gardner, A., Harding, D., Jasinski, M., Kwok, R., Magruder, L., Lubin, D., Luthcke, S., Morison, J., Nelson, R., Neuenschwander, A., Palm, S., Popescu, S., Shum, C., Schutz, B. E., Smith, B., Yang, Y., and Zwally, J.: The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2): science requirements, concept, and implementation, Remote Sens. Environ., 190, 260–273, https://doi.org/10.1016/j.rse.2016.12.029, 2017. a
Maykut, G. A. and Perovich, D. K.: The role of shortwave radiation in the summer decay of a sea ice cover, J. Geophys. Res.-Oceans, 92, 7032–7044, https://doi.org/10.1029/jc092ic07p07032, 1987. a
Melsheimer, C., Spreen, G., Ye, Y., and Shokr, M.: Multiyear Ice Concentration, Antarctic, 12.5 km grid, cold seasons 2013–2018 (from satellite), PANGAEA [data set], https://doi.org/10.1594/PANGAEA.909054, 2019. a
Moncada, R., Gupta, M., Thompson, A., and Andrade, J. E.: Level set discrete element method for modeling sea ice floes, Comput. Method. Appl. M., 406, 115891, https://doi.org/10.1016/j.cma.2023.115891, 2023. a
Montiel, F. and Mokus, N.: Theoretical framework for the emergent floe size distribution in the marginal ice zone: the case for log-normality, Philos. T. Roy. Soc. A, 380, 20210257, https://doi.org/10.1098/rsta.2021.0257, 2022. a, b
Moon, W., Nandan, V., Scharien, R. K., Wilkinson, J., Yackel, J. J., Barrett, A., Lawrence, I., Segal, R. A., Stroeve, J., Mahmud, M., Duke, P. J., and Else, B.: Physical length scales of wind-blown snow redistribution and accumulation on relatively smooth Arctic first-year sea ice, Environ. Res. Lett., 14, 104003, https://doi.org/10.1088/1748-9326/ab3b8d, 2019. a, b
Muchow, M., Schmitt, A. U., and Kaleschke, L.: A lead-width distribution for Antarctic sea ice: a case study for the Weddell Sea with high-resolution Sentinel-2 images, The Cryosphere, 15, 4527–4537, https://doi.org/10.5194/tc-15-4527-2021, 2021. a
Neumann, T. A., Martino, A. J., Markus, T., Bae, S., Bock, M. R., Brenner, A. C., Brunt, K. M., Cavanaugh, J., Fernandes, S. T., Hancock, D. W., Harbeck, K., Lee, J., Kurtz, N. T., Luers, P. J., Luthcke, S. B., Magruder, L., Pennington, T. A., Ramos-Izquierdo, L., Rebold, T., Skoog, J., and Thomas, T. C.: The Ice, Cloud, and Land Elevation Satellite – 2 mission: a global geolocated photon product derived from the Advanced Topographic Laser Altimeter System, Remote Sens. Environ., 233, 111325, https://doi.org/10.1016/j.rse.2019.111325, 2019. a
Nicholls, K. W., Østerhus, S., Makinson, K., Gammelsrød, T., and Fahrbach, E.: Ice-ocean processes over the continental shelf of the southern Weddell Sea, Antarctica: a review, Rev. Geophys., 47, RG3003, https://doi.org/10.1029/2007RG000250, 2009. a
Notz, D. and Stroeve, J.: Observed Arctic sea-ice loss directly follows anthropogenic CO2 emission, Science, 354, 747–750, https://doi.org/10.1126/science.aag2345, 2016. a
Parkinson, C. L.: A 40-y record reveals gradual Antarctic sea ice increases followed by decreases at rates far exceeding the rates seen in the Arctic, P. Natl. Acad. Sci. USA, 116, 14414–14423, https://doi.org/10.1073/pnas.1906556116, 2019. a
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. a
Petty, A. A., Kwok, R., Bagnardi, M., Ivanoff, A., Kurtz, N., Lee, J., Wimert, J., and Hancock, D.: ATLAS/ICESat-2 L3B Daily and Monthly Gridded Sea Ice Freeboard, Version 1, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/ATLAS/ATL20.001, 2020. a
Petty, A. A., Bagnardi, M., Kurtz, N. T., Tilling, R., Fons, S., Armitage, T., Horvat, C., and Kwok, R.: Assessment of ICESat-2 sea ice surface classification with Sentinel-2 imagery: implications for freeboard and new estimates of lead and floe geometry, Earth and Space Science, 8, e2020EA001491, https://doi.org/10.1029/2020EA001491, 2021. a, b, c, d, e
Purich, A. and Doddridge, E. W.: Record low Antarctic sea ice coverage indicates a new sea ice state, Communications Earth and Environment, 4, 314, https://doi.org/10.1038/s43247-023-00961-9, 2023. a, b
Rampal, P., Weiss, J., and Marsan, D.: Positive trend in the mean speed and deformation rate of Arctic sea ice, 1979–2007, J. Geophys. Res.-Oceans, 114, C05013, https://doi.org/10.1029/2008JC005066, 2009. a, b
Roach, L. A., Horvat, C., Dean, S. M., and Bitz, C. M.: An emergent sea ice floe size distribution in a global coupled ocean-sea ice model, J. Geophys. Res.-Oceans, 123, 4322–4337, https://doi.org/10.1029/2017JC013692, 2018. a, b
Roach, L. A., Eisenman, I., Wagner, T. J. W., Blanchard-Wrigglesworth, E., and Bitz, C. M.: Asymmetry in the seasonal cycle of Antarctic sea ice driven by insolation, Nat. Geosci., 15, 277–281, https://doi.org/10.1038/s41561-022-00913-6, 2022. a
Rothrock, D. A. and Thorndike, A. S.: Measuring the sea ice floe size distribution, J. Geophys. Res.-Oceans, 89, 6477–6486, https://doi.org/10.1029/JC089iC04p06477, 1984. a
Shokr, M., Lambe, A., and Agnew, T.: A new algorithm (ECICE) to estimate ice concentration from remote sensing observations: an application to 85-GHz passive microwave data, IEEE T. Geosci. Remote, 46, 4104–4121, https://doi.org/10.1109/TGRS.2008.2000624, 2008. a, b
Sievers, I., Rasmussen, T. A. S., and Stenseng, L.: Assimilating CryoSat-2 freeboard to improve Arctic sea ice thickness estimates, The Cryosphere, 17, 3721–3738, https://doi.org/10.5194/tc-17-3721-2023, 2023. a
Steele, M.: Sea ice melting and floe geometry in a simple ice-ocean model, J. Geophys. Res.-Oceans, 97, 17729–17738, https://doi.org/10.1029/92JC01755, 1992. a
Steer, A., Worby, A., and Heil, P.: Observed changes in sea-ice floe size distribution during early summer in the western Weddell Sea, Deep-Sea Res. Pt. II, 55, 933–942, https://doi.org/10.1016/j.dsr2.2007.12.016, 2008. a
Stephens, B. B. and Keeling, R. F.: The influence of Antarctic sea ice on glacial–interglacial CO2 variations, Nature, 404, 171–174, https://doi.org/10.1038/35004556, 2000. a
Stern, H. L., Schweiger, A. J., Zhang, J., and Steele, M.: On reconciling disparate studies of the sea-ice floe size distribution, Elementa: Science of the Anthropocene, 6, 49, https://doi.org/10.1525/elementa.304, 2018. a
Tilling, R. L., Ridout, A., and Shepherd, A.: Estimating Arctic sea ice thickness and volume using CryoSat-2 radar altimeter data, Adv. Space Res., 62, 1203–1225, https://doi.org/10.1016/j.asr.2017.10.051 2018. a
Timco, G. and Burden, R.: An analysis of the shapes of sea ice ridges, Cold Reg. Sci. Technol., 25, 65–77, https://doi.org/10.1016/S0165-232X(96)00017-1, 1997. a
Toyota, T., Takatsuji, S., and Nakayama, M.: Characteristics of sea ice floe size distribution in the seasonal ice zone, Geophys. Res. Lett., 33, L02616, https://doi.org/10.1029/2005GL024556, 2006. a
Trathan, P. N., Wienecke, B., Barbraud, C., Jenouvrier, S., Kooyman, G., Le Bohec, C., Ainley, D. G., Ancel, A., Zitterbart, D. P., Chown, S. L., LaRue, M., Cristofari, R., Younger, J., Clucas, G., Bost, C.-A., Brown, J. A., Gillett, H. J., and Fretwell, P. T.: The emperor penguin – vulnerable to projected rates of warming and sea ice loss, Biol. Conserv., 241, 108216, https://doi.org/10.1016/j.biocon.2019.108216, 2020. a
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 [data set], https://doi.org/10.5067/INAWUWO7QH7B, 2019. a, b
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. a, b
Vella, D. and Wettlaufer, J. S.: Finger rafting: a generic instability of floating elastic sheets, Phys. Rev. Lett., 98, 088303, https://doi.org/10.1103/PhysRevLett.98.088303, 2007. a
Vernet, M., Geibert, W., Hoppema, M., Brown, P. J., Haas, C., Hellmer, H. H., Jokat, W., Jullion, L., Mazloff, M., Bakker, D. C. E., Brearley, J. A., Croot, P., Hattermann, T., Hauck, J., Hillenbrand, C.-D., Hoppe, C. J. M., Huhn, O., Koch, B. P., Lechtenfeld, O. J., Meredith, M. P., Naveira Garabato, A. C., Nöthig, E.-M., Peeken, I., Rutgers van der Loeff, M. M., Schmidtko, S., Schröder, M., Strass, V. H., Torres-Valdés, S., and Verdy, A.: The Weddell Gyre, Southern Ocean: present knowledge and future challenges, Rev. Geophys., 57, 623–708, https://doi.org/10.1029/2018RG000604, 2019. a
Virkar, Y. and Clauset, A.: Power-law distributions in binned empirical data, Ann. Appl. Stat., 8, 89–119, https://doi.org/10.1214/13-AOAS710, 2014. a
Williams, N., Byrne, N., Feltham, D., Van Leeuwen, P. J., Bannister, R., Schroeder, D., Ridout, A., and Nerger, L.: The effects of assimilating a sub-grid-scale sea ice thickness distribution in a new Arctic sea ice data assimilation system, The Cryosphere, 17, 2509–2532, https://doi.org/10.5194/tc-17-2509-2023, 2023. 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., 113, C05S92, https://doi.org/10.1029/2007JC004254, 2008. a
Xie, H., Ackley, S., Yi, D., Zwally, H., Wagner, P., Weissling, B., Lewis, M., and Ye, K.: Sea-ice thickness distribution of the Bellingshausen Sea from surface measurements and ICESat altimetry, Deep-Sea Res. Pt. II, 58, 1039–1051, https://doi.org/10.1016/j.dsr2.2010.10.038, antarctic Sea Ice Research during the International Polar Year 2007–2009, 2011. a
Ye, Y., Heygster, G., and Shokr, M.: Improving multiyear ice concentration estimates with reanalysis air temperatures, IEEE T. Geosci. Remote, 54, 2602–2614, https://doi.org/10.1109/TGRS.2015.2503884, 2016a. a
Ye, Y., Shokr, M., Heygster, G., and Spreen, G.: Improving multiyear sea ice concentration estimates with sea ice drift, Remote Sens.-Basel, 8, 397, https://doi.org/10.3390/rs8050397, 2016b. a
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. a, b
Zhu, Z., Liu, J., Song, M., Wang, S., and Hu, Y.: Impacts of Antarctic Sea ice, AMV and IPO on extratropical southern hemisphere climate: a modeling study, Front. Earth Sci., 9, 757475, https://doi.org/10.3389/feart.2021.757475, 2021. a
Short summary
The sea ice cover is composed of floes, whose shapes set the material properties of the pack. Here, we use a satellite product (ICESat-2) to investigate these floe shapes within the Weddell Sea in Antarctica. We find that floes tend to become smaller during the melt season, while their thickness distribution exhibits different behavior between the western and southern regions of the pack. These metrics will help calibrate models and improve our understanding of sea ice physics across scales.
The sea ice cover is composed of floes, whose shapes set the material properties of the pack....
