Articles | Volume 18, issue 5
https://doi.org/10.5194/tc-18-2207-2024
© 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-18-2207-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 May 2024
Research article |
| 03 May 2024
| 03 May 2024
SAR deep learning sea ice retrieval trained with airborne laser scanner measurements from the MOSAiC expedition
Remote Sensing Technology Institute, German Aerospace Center (DLR), Bremen, Germany
Institute of Environmental Physics, University of Bremen, Bremen, Germany
Suman Singha
National Center for Climate Research, Danish Meteorological Institute (DMI), Copenhagen, Denmark
Department of Geography, University of Calgary, Calgary, Canada
Gunnar Spreen
Institute of Environmental Physics, University of Bremen, Bremen, Germany
Nils Hutter
Sea Ice Physics, Alfred-Wegener-Institut (AWI), Bremerhaven, Germany
Arttu Jutila
Finnish Meteorological Institute (FMI), Helsinki, Finland
Sea Ice Physics, Alfred-Wegener-Institut (AWI), Bremerhaven, Germany
Christian Haas
Sea Ice Physics, Alfred-Wegener-Institut (AWI), Bremerhaven, Germany
Related authors
Karl Kortum, Suman Singha, and Gunnar Spreen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3351, https://doi.org/10.5194/egusphere-2024-3351, 2024
Short summary
Short summary
Improved sea ice observations are essential to understanding the processes that lead to the strong warming effect currently being observed in the Arctic. In this work, we combine complementary satellite measurement techniques and find remarkable correlations between the two observations. This allows us to expand the coverage of ice topography measurements to a scope and resolution that could not previously be observed.
Steven Franke, Mara Neudert, Veit Helm, Arttu Jutila, Océane Hames, Niklas Neckel, Stefanie Arndt, and Christian Haas
EGUsphere, https://doi.org/10.5194/egusphere-2025-2657, https://doi.org/10.5194/egusphere-2025-2657, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Our research explored how icebergs affect the distribution of snow and flooding on Antarctic coastal sea ice. Using aircraft-based radar and laser scanning, we found that icebergs create thick snow drifts on their wind-facing sides and leave snow-free zones in their lee. The weight of these snow drifts often causes the ice below to flood, forming slush. These patterns, driven by wind and iceberg placement, are crucial for understanding sea ice changes and improving climate models.
Yi-Jie Yang, Suman Singha, Ron Goldman, and Florian Schütte
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-208, https://doi.org/10.5194/essd-2025-208, 2025
Preprint under review for ESSD
Short summary
Short summary
This data descriptor presents a dataset containing oil slicks, look-alikes, and other remarkable ocean phenomena in synthetic aperture radar (SAR) data, which can be used for training oil spill detection methods. It explains the formation of various oceanic phenomena, supported by examples and supporting materials. These insights can help researchers from diverse backgrounds, such as remote sensing, oceanography, and machine learning, to better understand the sources of the signatures.
Lena G. Buth, Thomas Krumpen, Niklas Neckel, Melinda A. Webster, Gerit Birnbaum, Niels Fuchs, Philipp Heuser, Ole Johannsen, and Christian Haas
EGUsphere, https://doi.org/10.5194/egusphere-2025-1103, https://doi.org/10.5194/egusphere-2025-1103, 2025
Short summary
Short summary
Arctic sea ice is becoming smoother, raising the question of how these changes affect melt pond coverage and thereby surface albedo. Using airborne imagery and laser altimeter data, we investigated how pressure ridges influence melt ponds. The presence of ridges does not directly control pond fraction, but it does influence pond size distribution and pond geometry. Small ponds have a more complex shape on rough ice than on smooth ice, while the opposite is true for large ponds.
Evgenii Salganik, Odile Crabeck, Niels Fuchs, Nils Hutter, Philipp Anhaus, and Jack Christopher Landy
The Cryosphere, 19, 1259–1278, https://doi.org/10.5194/tc-19-1259-2025, https://doi.org/10.5194/tc-19-1259-2025, 2025
Short summary
Short summary
To measure Arctic ice thickness, we often check how much ice sticks out of the water. This method depends on knowing the ice's density, which drops significantly in summer. Our study, validated with sonar and laser data, shows that these seasonal changes in density can complicate melt measurements. We stress the importance of considering these density changes for more accurate ice thickness readings.
Jean-Francois Lemieux, Mathieu Plante, Nils Hutter, Damien Ringeisen, Bruno Tremblay, Francois Roy, and Philippe Blain
EGUsphere, https://doi.org/10.5194/egusphere-2024-3831, https://doi.org/10.5194/egusphere-2024-3831, 2025
Short summary
Short summary
Sea ice models simulate angles between cracks that are too wide compared to observations. Ringeisen et al. argue that this is due to the flow rule which defines the fracture deformations. We implemented a non-normal flow rule. This flow rule also leads to angles that are too wide. This is a consequence of deformations that tend to align with the grid. Nevertheless, this flow rule could be used to optimize deformations while other parameters could be used to modify landfast ice and ice drift.
Larysa Istomina, Hannah Niehaus, and Gunnar Spreen
The Cryosphere, 19, 83–105, https://doi.org/10.5194/tc-19-83-2025, https://doi.org/10.5194/tc-19-83-2025, 2025
Short summary
Short summary
Melt water puddles, or melt ponds on top of the Arctic sea ice, are a good measure of the Arctic climate state. In the context of recent climate warming, the Arctic has warmed about 4 times faster than the rest of the world, and a long-term dataset of the melt pond fraction is needed to be able to model the future development of the Arctic climate. We present such a dataset, produce 2002–2023 trends and highlight a potential melt regime shift with drastic regional trends of + 20 % per decade.
Karl Kortum, Suman Singha, and Gunnar Spreen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3351, https://doi.org/10.5194/egusphere-2024-3351, 2024
Short summary
Short summary
Improved sea ice observations are essential to understanding the processes that lead to the strong warming effect currently being observed in the Arctic. In this work, we combine complementary satellite measurement techniques and find remarkable correlations between the two observations. This allows us to expand the coverage of ice topography measurements to a scope and resolution that could not previously be observed.
Rui Xu, Chaofang Zhao, Stefanie Arndt, and Christian Haas
The Cryosphere, 18, 5769–5788, https://doi.org/10.5194/tc-18-5769-2024, https://doi.org/10.5194/tc-18-5769-2024, 2024
Short summary
Short summary
The onset of snowmelt on Antarctic sea ice is an important indicator of sea ice change. In this study, we used two radar scatterometers to detect the onset of snowmelt on perennial Antarctic sea ice. Results show that since 2007, snowmelt onset has demonstrated strong interannual and regional variabilities. We also found that the difference in snowmelt onsets between the two scatterometers is closely related to snow metamorphism.
Tore Wulf, Jørgen Buus-Hinkler, Suman Singha, Hoyeon Shi, and Matilde Brandt Kreiner
The Cryosphere, 18, 5277–5300, https://doi.org/10.5194/tc-18-5277-2024, https://doi.org/10.5194/tc-18-5277-2024, 2024
Short summary
Short summary
Here, we present ASIP: a new and comprehensive deep-learning-based methodology to retrieve high-resolution sea ice concentration with accompanying well-calibrated uncertainties from satellite-based active and passive microwave observations at a pan-Arctic scale for all seasons. In a comparative study against pan-Arctic ice charts and well-established passive-microwave-based sea ice products, we show that ASIP generalizes well to the pan-Arctic region.
Rémy Lapere, Louis Marelle, Pierre Rampal, Laurent Brodeau, Christian Melsheimer, Gunnar Spreen, and Jennie L. Thomas
Atmos. Chem. Phys., 24, 12107–12132, https://doi.org/10.5194/acp-24-12107-2024, https://doi.org/10.5194/acp-24-12107-2024, 2024
Short summary
Short summary
Elongated open-water areas in sea ice, called leads, can release marine aerosols into the atmosphere. In the Arctic, this source of atmospheric particles could play an important role for climate. However, the amount, seasonality and spatial distribution of such emissions are all mostly unknown. Here, we propose a first parameterization for sea spray aerosols emitted through leads in sea ice and quantify their impact on aerosol populations in the high Arctic.
Hannah Niehaus, Gunnar Spreen, Larysa Istomina, and Marcel Nicolaus
EGUsphere, https://doi.org/10.5194/egusphere-2024-3127, https://doi.org/10.5194/egusphere-2024-3127, 2024
Short summary
Short summary
Melt ponds on Arctic sea ice affect how much solar energy is absorbed, influencing ice melt and climate change. This study used satellite data from 2017–2023 to examine how these ponds vary across regions and seasons. The results show that the surface fraction of melt ponds is more stable in the Central Arctic, with air temperature and ice surface roughness playing key roles in their formation. Understanding these patterns can help to improve climate models and predictions for Arctic warming.
Yi Zhou, Xianwei Wang, Ruibo Lei, Arttu Jutila, Donald K. Perovich, Luisa von Albedyll, Dmitry V. Divine, Yu Zhang, and Christian Haas
EGUsphere, https://doi.org/10.5194/egusphere-2024-2821, https://doi.org/10.5194/egusphere-2024-2821, 2024
Preprint archived
Short summary
Short summary
This study examines how the bulk density of Arctic sea ice varies seasonally, a factor often overlooked in satellite measurements of sea ice thickness. From October to April, we found significant seasonal variations in sea ice bulk density at different spatial scales using direct observations as well as airborne and satellite data. New models were then developed to indirectly predict sea ice bulk density. This advance can improve our ability to monitor changes in Arctic sea ice.
Renée M. Fredensborg Hansen, Henriette Skourup, Eero Rinne, Arttu Jutila, Isobel R. Lawrence, Andrew Shepherd, Knut V. Høyland, Jilu Li, Fernando Rodriguez-Morales, Sebastian B. Simonsen, Jeremy Wilkinson, Gaelle Veyssiere, Donghui Yi, René Forsberg, and Taniâ G. D. Casal
EGUsphere, https://doi.org/10.5194/egusphere-2024-2854, https://doi.org/10.5194/egusphere-2024-2854, 2024
Short summary
Short summary
In December 2022, an airborne campaign collected unprecedented coincident multi-frequency radar and lidar data over sea ice along a CryoSat-2 and ICESat-2 (CRYO2ICE) orbit in the Weddell Sea useful for evaluating microwave penetration. We found limited snow penetration at Ka- and Ku-bands, with significant contributions from the air-snow interface, contradicting traditional assumptions. These findings challenge current methods for comparing air- and spaceborne altimeter estimates of sea ice.
Nils Risse, Mario Mech, Catherine Prigent, Gunnar Spreen, and Susanne Crewell
The Cryosphere, 18, 4137–4163, https://doi.org/10.5194/tc-18-4137-2024, https://doi.org/10.5194/tc-18-4137-2024, 2024
Short summary
Short summary
Passive microwave observations from satellites are crucial for monitoring Arctic sea ice and atmosphere. To do this effectively, it is important to understand how sea ice emits microwaves. Through unique Arctic sea ice observations, we improved our understanding, identified four distinct emission types, and expanded current knowledge to include higher frequencies. These findings will enhance our ability to monitor the Arctic climate and provide valuable information for new satellite missions.
Manfred Wendisch, Susanne Crewell, André Ehrlich, Andreas Herber, Benjamin Kirbus, Christof Lüpkes, Mario Mech, Steven J. Abel, Elisa F. Akansu, Felix Ament, Clémantyne Aubry, Sebastian Becker, Stephan Borrmann, Heiko Bozem, Marlen Brückner, Hans-Christian Clemen, Sandro Dahlke, Georgios Dekoutsidis, Julien Delanoë, Elena De La Torre Castro, Henning Dorff, Regis Dupuy, Oliver Eppers, Florian Ewald, Geet George, Irina V. Gorodetskaya, Sarah Grawe, Silke Groß, Jörg Hartmann, Silvia Henning, Lutz Hirsch, Evelyn Jäkel, Philipp Joppe, Olivier Jourdan, Zsofia Jurányi, Michail Karalis, Mona Kellermann, Marcus Klingebiel, Michael Lonardi, Johannes Lucke, Anna E. Luebke, Maximilian Maahn, Nina Maherndl, Marion Maturilli, Bernhard Mayer, Johanna Mayer, Stephan Mertes, Janosch Michaelis, Michel Michalkov, Guillaume Mioche, Manuel Moser, Hanno Müller, Roel Neggers, Davide Ori, Daria Paul, Fiona M. Paulus, Christian Pilz, Felix Pithan, Mira Pöhlker, Veronika Pörtge, Maximilian Ringel, Nils Risse, Gregory C. Roberts, Sophie Rosenburg, Johannes Röttenbacher, Janna Rückert, Michael Schäfer, Jonas Schaefer, Vera Schemann, Imke Schirmacher, Jörg Schmidt, Sebastian Schmidt, Johannes Schneider, Sabrina Schnitt, Anja Schwarz, Holger Siebert, Harald Sodemann, Tim Sperzel, Gunnar Spreen, Bjorn Stevens, Frank Stratmann, Gunilla Svensson, Christian Tatzelt, Thomas Tuch, Timo Vihma, Christiane Voigt, Lea Volkmer, Andreas Walbröl, Anna Weber, Birgit Wehner, Bruno Wetzel, Martin Wirth, and Tobias Zinner
Atmos. Chem. Phys., 24, 8865–8892, https://doi.org/10.5194/acp-24-8865-2024, https://doi.org/10.5194/acp-24-8865-2024, 2024
Short summary
Short summary
The Arctic is warming faster than the rest of the globe. Warm-air intrusions (WAIs) into the Arctic may play an important role in explaining this phenomenon. Cold-air outbreaks (CAOs) out of the Arctic may link the Arctic climate changes to mid-latitude weather. In our article, we describe how to observe air mass transformations during CAOs and WAIs using three research aircraft instrumented with state-of-the-art remote-sensing and in situ measurement devices.
Gemma M. Brett, Greg H. Leonard, Wolfgang Rack, Christian Haas, Patricia J. Langhorne, Natalie J. Robinson, and Anne Irvin
The Cryosphere, 18, 3049–3066, https://doi.org/10.5194/tc-18-3049-2024, https://doi.org/10.5194/tc-18-3049-2024, 2024
Short summary
Short summary
Glacial meltwater with ice crystals flows from beneath ice shelves, causing thicker sea ice with sub-ice platelet layers (SIPLs) beneath. Thicker sea ice and SIPL reveal where and how much meltwater is outflowing. We collected continuous measurements of sea ice and SIPL. In winter, we observed rapid SIPL growth with strong winds. In spring, SIPLs grew when tides caused offshore circulation. Wind-driven and tidal circulation influence glacial meltwater outflow from ice shelf cavities.
Niels Fuchs, Luisa von Albedyll, Gerit Birnbaum, Felix Linhardt, Natascha Oppelt, and Christian Haas
The Cryosphere, 18, 2991–3015, https://doi.org/10.5194/tc-18-2991-2024, https://doi.org/10.5194/tc-18-2991-2024, 2024
Short summary
Short summary
Melt ponds are key components of the Arctic sea ice system, yet methods to derive comprehensive pond depth data are missing. We present a shallow-water bathymetry retrieval to derive this elementary pond property at high spatial resolution from aerial images. The retrieval method is presented in a user-friendly way to facilitate replication. Furthermore, we provide pond properties on the MOSAiC expedition floe, giving insights into the three-dimensional pond evolution before and after drainage.
Yi Zhou, Xianwei Wang, Ruibo Lei, Luisa von Albedyll, Donald K. Perovich, Yu Zhang, and Christian Haas
EGUsphere, https://doi.org/10.5194/egusphere-2024-1240, https://doi.org/10.5194/egusphere-2024-1240, 2024
Preprint archived
Short summary
Short summary
This study examines how the density of Arctic sea ice varies seasonally, a factor often overlooked in satellite measurements of sea ice thickness. From October to April, using direct observations and satellite data, we found that sea ice density decreases significantly until mid-January due to increased porosity as the ice ages, and then stabilizes until April. We then developed new models to estimate sea ice density. This advance can improve our ability to monitor changes in Arctic sea ice.
Luisa von Albedyll, Stefan Hendricks, Nils Hutter, Dmitrii Murashkin, Lars Kaleschke, Sascha Willmes, Linda Thielke, Xiangshan Tian-Kunze, Gunnar Spreen, and Christian Haas
The Cryosphere, 18, 1259–1285, https://doi.org/10.5194/tc-18-1259-2024, https://doi.org/10.5194/tc-18-1259-2024, 2024
Short summary
Short summary
Leads (openings in sea ice cover) are created by sea ice dynamics. Because they are important for many processes in the Arctic winter climate, we aim to detect them with satellites. We present two new techniques to detect lead widths of a few hundred meters at high spatial resolution (700 m) and independent of clouds or sun illumination. We use the MOSAiC drift 2019–2020 in the Arctic for our case study and compare our new products to other existing lead products.
Evelyn Jäkel, Sebastian Becker, Tim R. Sperzel, Hannah Niehaus, Gunnar Spreen, Ran Tao, Marcel Nicolaus, Wolfgang Dorn, Annette Rinke, Jörg Brauchle, and Manfred Wendisch
The Cryosphere, 18, 1185–1205, https://doi.org/10.5194/tc-18-1185-2024, https://doi.org/10.5194/tc-18-1185-2024, 2024
Short summary
Short summary
The results of the surface albedo scheme of a coupled regional climate model were evaluated against airborne and ground-based measurements conducted in the European Arctic in different seasons between 2017 and 2022. We found a seasonally dependent bias between measured and modeled surface albedo for cloudless and cloudy situations. The strongest effects of the albedo model bias on the net irradiance were most apparent in the presence of optically thin clouds.
Hannah Niehaus, Larysa Istomina, Marcel Nicolaus, Ran Tao, Aleksey Malinka, Eleonora Zege, and Gunnar Spreen
The Cryosphere, 18, 933–956, https://doi.org/10.5194/tc-18-933-2024, https://doi.org/10.5194/tc-18-933-2024, 2024
Short summary
Short summary
Melt ponds are puddles of meltwater which form on Arctic sea ice in the summer period. They are darker than the ice cover and lead to increased absorption of solar energy. Global climate models need information about the Earth's energy budget. Thus satellite observations are used to monitor the surface fractions of melt ponds, ocean, and sea ice in the entire Arctic. We present a new physically based algorithm that can separate these three surface types with uncertainty below 10 %.
Pablo Saavedra Garfias, Heike Kalesse-Los, Luisa von Albedyll, Hannes Griesche, and Gunnar Spreen
Atmos. Chem. Phys., 23, 14521–14546, https://doi.org/10.5194/acp-23-14521-2023, https://doi.org/10.5194/acp-23-14521-2023, 2023
Short summary
Short summary
An important Arctic climate process is the release of heat fluxes from sea ice openings to the atmosphere that influence the clouds. The characterization of this process is the objective of this study. Using synergistic observations from the MOSAiC expedition, we found that single-layer cloud properties show significant differences when clouds are coupled or decoupled to the water vapour transport which is used as physical link between the upwind sea ice openings and the cloud under observation.
Alexander Mchedlishvili, Christof Lüpkes, Alek Petty, Michel Tsamados, and Gunnar Spreen
The Cryosphere, 17, 4103–4131, https://doi.org/10.5194/tc-17-4103-2023, https://doi.org/10.5194/tc-17-4103-2023, 2023
Short summary
Short summary
In this study we looked at sea ice–atmosphere drag coefficients, quantities that help with characterizing the friction between the atmosphere and sea ice, and vice versa. Using ICESat-2, a laser altimeter that measures elevation differences by timing how long it takes for photons it sends out to return to itself, we could map the roughness, i.e., how uneven the surface is. From roughness we then estimate drag force, the frictional force between sea ice and the atmosphere, across the Arctic.
Olivia Linke, Johannes Quaas, Finja Baumer, Sebastian Becker, Jan Chylik, Sandro Dahlke, André Ehrlich, Dörthe Handorf, Christoph Jacobi, Heike Kalesse-Los, Luca Lelli, Sina Mehrdad, Roel A. J. Neggers, Johannes Riebold, Pablo Saavedra Garfias, Niklas Schnierstein, Matthew D. Shupe, Chris Smith, Gunnar Spreen, Baptiste Verneuil, Kameswara S. Vinjamuri, Marco Vountas, and Manfred Wendisch
Atmos. Chem. Phys., 23, 9963–9992, https://doi.org/10.5194/acp-23-9963-2023, https://doi.org/10.5194/acp-23-9963-2023, 2023
Short summary
Short summary
Lapse rate feedback (LRF) is a major driver of the Arctic amplification (AA) of climate change. It arises because the warming is stronger at the surface than aloft. Several processes can affect the LRF in the Arctic, such as the omnipresent temperature inversion. Here, we compare multimodel climate simulations to Arctic-based observations from a large research consortium to broaden our understanding of these processes, find synergy among them, and constrain the Arctic LRF and AA.
Philip Rostosky and Gunnar Spreen
The Cryosphere, 17, 3867–3881, https://doi.org/10.5194/tc-17-3867-2023, https://doi.org/10.5194/tc-17-3867-2023, 2023
Short summary
Short summary
During winter, storms entering the Arctic region can bring warm air into the cold environment. Strong increases in air temperature modify the characteristics of the Arctic snow and ice cover. The Arctic sea ice cover can be monitored by satellites observing the natural emission of the Earth's surface. In this study, we show that during warm air intrusions the change in the snow characteristics influences the satellite-derived sea ice cover, leading to a false reduction of the estimated ice area.
Vishnu Nandan, Rosemary Willatt, Robbie Mallett, Julienne Stroeve, Torsten Geldsetzer, Randall Scharien, Rasmus Tonboe, John Yackel, Jack Landy, David Clemens-Sewall, Arttu Jutila, David N. Wagner, Daniela Krampe, Marcus Huntemann, Mallik Mahmud, David Jensen, Thomas Newman, Stefan Hendricks, Gunnar Spreen, Amy Macfarlane, Martin Schneebeli, James Mead, Robert Ricker, Michael Gallagher, Claude Duguay, Ian Raphael, Chris Polashenski, Michel Tsamados, Ilkka Matero, and Mario Hoppmann
The Cryosphere, 17, 2211–2229, https://doi.org/10.5194/tc-17-2211-2023, https://doi.org/10.5194/tc-17-2211-2023, 2023
Short summary
Short summary
We show that wind redistributes snow on Arctic sea ice, and Ka- and Ku-band radar measurements detect both newly deposited snow and buried snow layers that can affect the accuracy of snow depth estimates on sea ice. Radar, laser, meteorological, and snow data were collected during the MOSAiC expedition. With frequent occurrence of storms in the Arctic, our results show that
wind-redistributed snow needs to be accounted for to improve snow depth estimates on sea ice from satellite radars.
Robert Ricker, Steven Fons, Arttu Jutila, Nils Hutter, Kyle Duncan, Sinead L. Farrell, Nathan T. Kurtz, and Renée Mie Fredensborg Hansen
The Cryosphere, 17, 1411–1429, https://doi.org/10.5194/tc-17-1411-2023, https://doi.org/10.5194/tc-17-1411-2023, 2023
Short summary
Short summary
Information on sea ice surface topography is important for studies of sea ice as well as for ship navigation through ice. The ICESat-2 satellite senses the sea ice surface with six laser beams. To examine the accuracy of these measurements, we carried out a temporally coincident helicopter flight along the same ground track as the satellite and measured the sea ice surface topography with a laser scanner. This showed that ICESat-2 can see even bumps of only few meters in the sea ice cover.
Wenkai Guo, Polona Itkin, Suman Singha, Anthony P. Doulgeris, Malin Johansson, and Gunnar Spreen
The Cryosphere, 17, 1279–1297, https://doi.org/10.5194/tc-17-1279-2023, https://doi.org/10.5194/tc-17-1279-2023, 2023
Short summary
Short summary
Sea ice maps are produced to cover the MOSAiC Arctic expedition (2019–2020) and divide sea ice into scientifically meaningful classes. We use a high-resolution X-band synthetic aperture radar dataset and show how image brightness and texture systematically vary across the images. We use an algorithm that reliably corrects this effect and achieve good results, as evaluated by comparisons to ground observations and other studies. The sea ice maps are useful as a basis for future MOSAiC studies.
Christian Melsheimer, Gunnar Spreen, Yufang Ye, and Mohammed Shokr
The Cryosphere, 17, 105–126, https://doi.org/10.5194/tc-17-105-2023, https://doi.org/10.5194/tc-17-105-2023, 2023
Short summary
Short summary
It is necessary to know the type of Antarctic sea ice present – first-year ice (grown in one season) or multiyear ice (survived one summer melt) – to understand and model its evolution, as the ice types behave and react differently. We have adapted and extended an existing method (originally for the Arctic), and now, for the first time, daily maps of Antarctic sea ice types can be derived from microwave satellite data. This will allow a new data set from 2002 well into the future to be built.
Julian Gutt, Stefanie Arndt, David Keith Alan Barnes, Horst Bornemann, Thomas Brey, Olaf Eisen, Hauke Flores, Huw Griffiths, Christian Haas, Stefan Hain, Tore Hattermann, Christoph Held, Mario Hoppema, Enrique Isla, Markus Janout, Céline Le Bohec, Heike Link, Felix Christopher Mark, Sebastien Moreau, Scarlett Trimborn, Ilse van Opzeeland, Hans-Otto Pörtner, Fokje Schaafsma, Katharina Teschke, Sandra Tippenhauer, Anton Van de Putte, Mia Wege, Daniel Zitterbart, and Dieter Piepenburg
Biogeosciences, 19, 5313–5342, https://doi.org/10.5194/bg-19-5313-2022, https://doi.org/10.5194/bg-19-5313-2022, 2022
Short summary
Short summary
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.
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
The Cryosphere, 16, 4223–4250, https://doi.org/10.5194/tc-16-4223-2022, https://doi.org/10.5194/tc-16-4223-2022, 2022
Short summary
Short summary
Impacts of rain on snow (ROS) on satellite-retrieved sea ice variables remain to be fully understood. This study evaluates the impacts of ROS over sea ice on active and passive microwave data collected during the 2019–20 MOSAiC expedition. Rainfall and subsequent refreezing of the snowpack significantly altered emitted and backscattered radar energy, laying important groundwork for understanding their impacts on operational satellite retrievals of various sea ice geophysical variables.
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
The Cryosphere, 16, 981–1005, https://doi.org/10.5194/tc-16-981-2022, https://doi.org/10.5194/tc-16-981-2022, 2022
Short summary
Short summary
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.
Alexander Mchedlishvili, Gunnar Spreen, Christian Melsheimer, and Marcus Huntemann
The Cryosphere, 16, 471–487, https://doi.org/10.5194/tc-16-471-2022, https://doi.org/10.5194/tc-16-471-2022, 2022
Short summary
Short summary
In this paper we show that the activity leading to the open-ocean polynyas near the Maud Rise seamount that have occurred repeatedly from 1974–1976 as well as 2016–2017 does not simply stop for polynya-free years. Using apparent sea ice thickness retrieval, we have identified anomalies where there is thinning of sea ice on a scale that is comparable to that of the polynya events of 2016–2017. These anomalies took place in 2010, 2013, 2014 and 2018.
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
Short summary
Short summary
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.
Nele Lamping, Juliane Müller, Jens Hefter, Gesine Mollenhauer, Christian Haas, Xiaoxu Shi, Maria-Elena Vorrath, Gerrit Lohmann, and Claus-Dieter Hillenbrand
Clim. Past, 17, 2305–2326, https://doi.org/10.5194/cp-17-2305-2021, https://doi.org/10.5194/cp-17-2305-2021, 2021
Short summary
Short summary
We analysed biomarker concentrations on surface sediment samples from the Antarctic continental margin. Highly branched isoprenoids and GDGTs are used for reconstructing recent sea-ice distribution patterns and ocean temperatures respectively. We compared our biomarker-based results with data obtained from satellite observations and estimated from a numerical model and find reasonable agreements. Further, we address caveats and provide recommendations for future investigations.
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
Short summary
Short summary
We present here snow and ice core data from the northwestern Weddell Sea in late austral summer 2019, which allow insights into possible reasons for the recent low summer sea ice extent in the Weddell Sea. We suggest that the fraction of superimposed ice and snow ice can be used here as a sensitive indicator. However, snow and ice properties were not exceptional, suggesting that the summer surface energy balance and related seasonal transition of snow properties have changed little in the past.
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
Short summary
Short summary
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.
Susanne Crewell, Kerstin Ebell, Patrick Konjari, Mario Mech, Tatiana Nomokonova, Ana Radovan, David Strack, Arantxa M. Triana-Gómez, Stefan Noël, Raul Scarlat, Gunnar Spreen, Marion Maturilli, Annette Rinke, Irina Gorodetskaya, Carolina Viceto, Thomas August, and Marc Schröder
Atmos. Meas. Tech., 14, 4829–4856, https://doi.org/10.5194/amt-14-4829-2021, https://doi.org/10.5194/amt-14-4829-2021, 2021
Short summary
Short summary
Water vapor (WV) is an important variable in the climate system. Satellite measurements are thus crucial to characterize the spatial and temporal variability in WV and how it changed over time. In particular with respect to the observed strong Arctic warming, the role of WV still needs to be better understood. However, as shown in this paper, a detailed understanding is still hampered by large uncertainties in the various satellite WV products, showing the need for improved methods to derive WV.
Anja Rösel, Sinead Louise Farrell, Vishnu Nandan, Jaqueline Richter-Menge, Gunnar Spreen, Dmitry V. Divine, Adam Steer, Jean-Charles Gallet, and Sebastian Gerland
The Cryosphere, 15, 2819–2833, https://doi.org/10.5194/tc-15-2819-2021, https://doi.org/10.5194/tc-15-2819-2021, 2021
Short summary
Short summary
Recent observations in the Arctic suggest a significant shift towards a snow–ice regime caused by deep snow on thin sea ice which may result in a flooding of the snowpack. These conditions cause the brine wicking and saturation of the basal snow layers which lead to a subsequent underestimation of snow depth from snow radar mesurements. As a consequence the calculated sea ice thickness will be biased towards higher values.
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
Short summary
Short summary
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.
Gemma M. Brett, Gregory H. Leonard, Wolfgang Rack, Christian Haas, Patricia J. Langhorne, and Anne Irvin
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-61, https://doi.org/10.5194/tc-2021-61, 2021
Manuscript not accepted for further review
Short summary
Short summary
Using a geophysical technique, we observe temporal variability in the influence of ice shelf meltwater on coastal sea ice which forms platelet ice crystals which contribute to the thickness of the sea ice and accumulate into a thick mass called a sub-ice platelet layer (SIPL). The variability observed in the SIPL indicated that circulation of ice shelf meltwater out from the cavity in McMurdo Sound is influenced by tides and strong offshore winds which affect surface ocean circulation.
Luisa von Albedyll, Christian Haas, and Wolfgang Dierking
The Cryosphere, 15, 2167–2186, https://doi.org/10.5194/tc-15-2167-2021, https://doi.org/10.5194/tc-15-2167-2021, 2021
Short summary
Short summary
Convergent sea ice motion produces a thick ice cover through ridging. We studied sea ice deformation derived from high-resolution satellite imagery and related it to the corresponding thickness change. We found that deformation explains the observed dynamic thickness change. We show that deformation can be used to model realistic ice thickness distributions. Our results revealed new relationships between thickness redistribution and deformation that could improve sea ice models.
Yu Zhang, Tingting Zhu, Gunnar Spreen, Christian Melsheimer, Marcus Huntemann, Nick Hughes, Shengkai Zhang, and Fei Li
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-85, https://doi.org/10.5194/tc-2021-85, 2021
Revised manuscript not accepted
Short summary
Short summary
We developed an algorithm for ice-water classification using Sentinel-1 data during melting seasons in the Fram Strait. The proposed algorithm has the OA of nearly 90 % with STD less than 10 %. The comparison of sea ice concentration demonstrate that it can provide detailed information of sea ice with the spatial resolution of 1km. The time series shows the average June to September sea ice area does not change so much in 2015–2017 and 2019–2020, but it has a significant decrease in 2018.
Christian Haas, Patricia J. Langhorne, Wolfgang Rack, Greg H. Leonard, Gemma M. Brett, Daniel Price, Justin F. Beckers, and Alex J. Gough
The Cryosphere, 15, 247–264, https://doi.org/10.5194/tc-15-247-2021, https://doi.org/10.5194/tc-15-247-2021, 2021
Short summary
Short summary
We developed a method to remotely detect proxy signals of Antarctic ice shelf melt under adjacent sea ice. It is based on aircraft surveys with electromagnetic induction sounding. We found year-to-year variability of the ice shelf melt proxy in McMurdo Sound and spatial fine structure that support assumptions about the melt of the McMurdo Ice Shelf. With this method it will be possible to map and detect locations of intense ice shelf melt along the coast of Antarctica.
Maria-Elena Vorrath, Juliane Müller, Lorena Rebolledo, Paola Cárdenas, Xiaoxu Shi, Oliver Esper, Thomas Opel, Walter Geibert, Práxedes Muñoz, Christian Haas, Gerhard Kuhn, Carina B. Lange, Gerrit Lohmann, and Gesine Mollenhauer
Clim. Past, 16, 2459–2483, https://doi.org/10.5194/cp-16-2459-2020, https://doi.org/10.5194/cp-16-2459-2020, 2020
Short summary
Short summary
We tested the applicability of the organic biomarker IPSO25 for sea ice reconstructions in the industrial era at the western Antarctic Peninsula. We successfully evaluated our data with satellite sea ice observations. The comparison with marine and ice core records revealed that sea ice interpretations must consider climatic and sea ice dynamics. Sea ice biomarker production is mainly influenced by the Southern Annular Mode, while the El Niño–Southern Oscillation seems to have a minor impact.
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
Short summary
Short summary
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.
Larysa Istomina, Henrik Marks, Marcus Huntemann, Georg Heygster, and Gunnar Spreen
Atmos. Meas. Tech., 13, 6459–6472, https://doi.org/10.5194/amt-13-6459-2020, https://doi.org/10.5194/amt-13-6459-2020, 2020
Joshua King, Stephen Howell, Mike Brady, Peter Toose, Chris Derksen, Christian Haas, and Justin Beckers
The Cryosphere, 14, 4323–4339, https://doi.org/10.5194/tc-14-4323-2020, https://doi.org/10.5194/tc-14-4323-2020, 2020
Short summary
Short summary
Physical measurements of snow on sea ice are sparse, making it difficulty to evaluate satellite estimates or model representations. Here, we introduce new measurements of snow properties on sea ice to better understand variability at distances less than 200 m. Our work shows that similarities in the snow structure are found at longer distances on younger ice than older ice.
Arantxa M. Triana-Gómez, Georg Heygster, Christian Melsheimer, Gunnar Spreen, Monia Negusini, and Boyan H. Petkov
Atmos. Meas. Tech., 13, 3697–3715, https://doi.org/10.5194/amt-13-3697-2020, https://doi.org/10.5194/amt-13-3697-2020, 2020
Short summary
Short summary
In the Arctic, in situ measurements are sparse and standard remote sensing retrieval methods have problems. We present advances in a retrieval algorithm for vertically integrated water vapour tuned for polar regions. In addition to the initial sensor used (AMSU-B), we can now also use data from the successor instrument (MHS). Additionally, certain artefacts are now filtered out. Comparison with radiosondes shows the overall good performance of the updated algorithm.
H. Jakob Belter, Thomas Krumpen, Stefan Hendricks, Jens Hoelemann, Markus A. Janout, Robert Ricker, and Christian Haas
The Cryosphere, 14, 2189–2203, https://doi.org/10.5194/tc-14-2189-2020, https://doi.org/10.5194/tc-14-2189-2020, 2020
Short summary
Short summary
The validation of satellite sea ice thickness (SIT) climate data records with newly acquired moored sonar SIT data shows that satellite products provide modal rather than mean SIT in the Laptev Sea region. This tendency of satellite-based SIT products to underestimate mean SIT needs to be considered for investigations of sea ice volume transports. Validation of satellite SIT in the first-year-ice-dominated Laptev Sea will support algorithm development for more reliable SIT records in the Arctic.
Thomas Krumpen, Florent Birrien, Frank Kauker, Thomas Rackow, Luisa von Albedyll, Michael Angelopoulos, H. Jakob Belter, Vladimir Bessonov, Ellen Damm, Klaus Dethloff, Jari Haapala, Christian Haas, Carolynn Harris, Stefan Hendricks, Jens Hoelemann, Mario Hoppmann, Lars Kaleschke, Michael Karcher, Nikolai Kolabutin, Ruibo Lei, Josefine Lenz, Anne Morgenstern, Marcel Nicolaus, Uwe Nixdorf, Tomash Petrovsky, Benjamin Rabe, Lasse Rabenstein, Markus Rex, Robert Ricker, Jan Rohde, Egor Shimanchuk, Suman Singha, Vasily Smolyanitsky, Vladimir Sokolov, Tim Stanton, Anna Timofeeva, Michel Tsamados, and Daniel Watkins
The Cryosphere, 14, 2173–2187, https://doi.org/10.5194/tc-14-2173-2020, https://doi.org/10.5194/tc-14-2173-2020, 2020
Short summary
Short summary
In October 2019 the research vessel Polarstern was moored to an ice floe in order to travel with it on the 1-year-long MOSAiC journey through the Arctic. Here we provide historical context of the floe's evolution and initial state for upcoming studies. We show that the ice encountered on site was exceptionally thin and was formed on the shallow Siberian shelf. The analyses presented provide the initial state for the analysis and interpretation of upcoming biogeochemical and ecological studies.
Xiaoyong Yu, Annette Rinke, Wolfgang Dorn, Gunnar Spreen, Christof Lüpkes, Hiroshi Sumata, and Vladimir M. Gryanik
The Cryosphere, 14, 1727–1746, https://doi.org/10.5194/tc-14-1727-2020, https://doi.org/10.5194/tc-14-1727-2020, 2020
Short summary
Short summary
This study presents an evaluation of Arctic sea ice drift speed for the period 2003–2014 in a state-of-the-art coupled regional model for the Arctic, called HIRHAM–NAOSIM. In particular, the dependency of the drift speed on the near-surface wind speed and sea ice conditions is presented. Effects of sea ice form drag included by an improved parameterization of the transfer coefficients for momentum and heat over sea ice are discussed.
Christine Pohl, Larysa Istomina, Steffen Tietsche, Evelyn Jäkel, Johannes Stapf, Gunnar Spreen, and Georg Heygster
The Cryosphere, 14, 165–182, https://doi.org/10.5194/tc-14-165-2020, https://doi.org/10.5194/tc-14-165-2020, 2020
Short summary
Short summary
A spectral to broadband conversion is developed empirically that can be used in combination with the Melt Pond Detector algorithm to derive broadband albedo (300–3000 nm) of Arctic sea ice from MERIS data. It is validated and shows better performance compared to existing conversion methods. A comparison of MERIS broadband albedo with respective values from ERA5 reanalysis suggests a revision of the albedo values used in ERA5. MERIS albedo might be useful for improving albedo representation.
Nils Hutter and Martin Losch
The Cryosphere, 14, 93–113, https://doi.org/10.5194/tc-14-93-2020, https://doi.org/10.5194/tc-14-93-2020, 2020
Short summary
Short summary
Sea ice is composed of a multitude of floes that constantly deform due to wind and ocean currents and thereby form leads and pressure ridges. These features are visible in the ice as stripes of open-ocean or high-piled ice. High-resolution sea ice models start to resolve these deformation features. In this paper we present two simulations that agree with satellite data according to a new evaluation metric that detects deformation features and compares their spatial and temporal characteristics.
Maria-Elena Vorrath, Juliane Müller, Oliver Esper, Gesine Mollenhauer, Christian Haas, Enno Schefuß, and Kirsten Fahl
Biogeosciences, 16, 2961–2981, https://doi.org/10.5194/bg-16-2961-2019, https://doi.org/10.5194/bg-16-2961-2019, 2019
Short summary
Short summary
The study highlights new approaches in the investigation of past sea ice in Antarctica to reconstruct the climate conditions in earth's history and reveal its future development under global warming. We examined the distribution of organic remains from different algae at the Western Antarctic Peninsula and compared it to fossil and satellite records. We evaluated IPSO25 – the sea ice proxy for the Southern Ocean with 25 carbon atoms – as a useful tool for sea ice reconstructions in this region.
Valentin Ludwig, Gunnar Spreen, Christian Haas, Larysa Istomina, Frank Kauker, and Dmitrii Murashkin
The Cryosphere, 13, 2051–2073, https://doi.org/10.5194/tc-13-2051-2019, https://doi.org/10.5194/tc-13-2051-2019, 2019
Short summary
Short summary
Sea-ice concentration, the fraction of an area covered by sea ice, can be observed from satellites with different methods. We combine two methods to obtain a product which is better than either of the input measurements alone. The benefit of our product is demonstrated by observing the formation of an open water area which can now be observed with more detail. Additionally, we find that the open water area formed because the sea ice drifted in the opposite direction and faster than usual.
Stefanie Arndt and Christian Haas
The Cryosphere, 13, 1943–1958, https://doi.org/10.5194/tc-13-1943-2019, https://doi.org/10.5194/tc-13-1943-2019, 2019
Damien Ringeisen, Martin Losch, L. Bruno Tremblay, and Nils Hutter
The Cryosphere, 13, 1167–1186, https://doi.org/10.5194/tc-13-1167-2019, https://doi.org/10.5194/tc-13-1167-2019, 2019
Short summary
Short summary
We study the creation of fracture in sea ice plastic models. To do this, we compress an ideal piece of ice of 8 km by 25 km. We use two different mathematical expressions defining the resistance of ice. We find that the most common one is unable to model the fracture correctly, while the other gives better results but brings instabilities. The results are often in opposition with ice granular nature (e.g., sand) and call for changes in ice modeling.
Cătălin Paţilea, Georg Heygster, Marcus Huntemann, and Gunnar Spreen
The Cryosphere, 13, 675–691, https://doi.org/10.5194/tc-13-675-2019, https://doi.org/10.5194/tc-13-675-2019, 2019
Short summary
Short summary
Sea ice thickness is important for representing atmosphere–ocean interactions in climate models. A validated satellite sea ice thickness measurement algorithm is transferred to a new sensor. The results offer a better temporal and spatial coverage of satellite measurements in the polar regions. Here we describe the calibration procedure between the two sensors, taking into account their technical differences. In addition a new filter for interference from artificial radio sources is implemented.
Nils Hutter, Lorenzo Zampieri, and Martin Losch
The Cryosphere, 13, 627–645, https://doi.org/10.5194/tc-13-627-2019, https://doi.org/10.5194/tc-13-627-2019, 2019
Short summary
Short summary
Arctic sea ice is an aggregate of ice floes with various sizes. The different sizes result from constant deformation of the ice pack. If a floe breaks, open ocean is exposed in a lead. Collision of floes forms pressure ridges. Here, we present algorithms that detect and track these deformation features in satellite observations and model output. The tracked features are used to provide a comprehensive description of localized deformation of sea ice and help to understand its material properties.
Iina Ronkainen, Jonni Lehtiranta, Mikko Lensu, Eero Rinne, Jari Haapala, and Christian Haas
The Cryosphere, 12, 3459–3476, https://doi.org/10.5194/tc-12-3459-2018, https://doi.org/10.5194/tc-12-3459-2018, 2018
Short summary
Short summary
We quantify the sea ice thickness variability in the Bay of Bothnia using various observational data sets. For the first time we use helicopter and shipborne electromagnetic soundings to study changes in drift ice of the Bay of Bothnia. Our results show that the interannual variability of ice thickness is larger in the drift ice zone than in the fast ice zone. Furthermore, the mean thickness of heavily ridged ice near the coast can be several times larger than that of fast ice.
Aleksey Malinka, Eleonora Zege, Larysa Istomina, Georg Heygster, Gunnar Spreen, Donald Perovich, and Chris Polashenski
The Cryosphere, 12, 1921–1937, https://doi.org/10.5194/tc-12-1921-2018, https://doi.org/10.5194/tc-12-1921-2018, 2018
Short summary
Short summary
Melt ponds occupy a large part of the Arctic sea ice in summer and strongly affect the radiative budget of the atmosphere–ice–ocean system. The melt pond reflectance is modeled in the framework of the radiative transfer theory and validated with field observations. It improves understanding of melting sea ice and enables better parameterization of the surface in Arctic atmospheric remote sensing (clouds, aerosols, trace gases) and re-evaluating Arctic climatic feedbacks at a new accuracy level.
Paul J. Kushner, Lawrence R. Mudryk, William Merryfield, Jaison T. Ambadan, Aaron Berg, Adéline Bichet, Ross Brown, Chris Derksen, Stephen J. Déry, Arlan Dirkson, Greg Flato, Christopher G. Fletcher, John C. Fyfe, Nathan Gillett, Christian Haas, Stephen Howell, Frédéric Laliberté, Kelly McCusker, Michael Sigmond, Reinel Sospedra-Alfonso, Neil F. Tandon, Chad Thackeray, Bruno Tremblay, and Francis W. Zwiers
The Cryosphere, 12, 1137–1156, https://doi.org/10.5194/tc-12-1137-2018, https://doi.org/10.5194/tc-12-1137-2018, 2018
Short summary
Short summary
Here, the Canadian research network CanSISE uses state-of-the-art observations of snow and sea ice to assess how Canada's climate model and climate prediction systems capture variability in snow, sea ice, and related climate parameters. We find that the system performs well, accounting for observational uncertainty (especially for snow), model uncertainty, and chaotic climate variability. Even for variables like sea ice, where improvement is needed, useful prediction tools can be developed.
Robert Ricker, Stefan Hendricks, Lars Kaleschke, Xiangshan Tian-Kunze, Jennifer King, and Christian Haas
The Cryosphere, 11, 1607–1623, https://doi.org/10.5194/tc-11-1607-2017, https://doi.org/10.5194/tc-11-1607-2017, 2017
Short summary
Short summary
We developed the first merging of CryoSat-2 and SMOS sea-ice thickness retrievals. ESA’s Earth Explorer SMOS satellite can detect thin sea ice, whereas its companion CryoSat-2, designed to observe thicker perennial sea ice, lacks sensitivity. Using these satellite missions together completes the picture of the changing Arctic sea ice and provides a more accurate and comprehensive view on the actual state of Arctic sea-ice thickness.
Gunnar Spreen, Ron Kwok, Dimitris Menemenlis, and An T. Nguyen
The Cryosphere, 11, 1553–1573, https://doi.org/10.5194/tc-11-1553-2017, https://doi.org/10.5194/tc-11-1553-2017, 2017
Paul Vallelonga, Niccolo Maffezzoli, Andrew D. Moy, Mark A. J. Curran, Tessa R. Vance, Ross Edwards, Gwyn Hughes, Emily Barker, Gunnar Spreen, Alfonso Saiz-Lopez, J. Pablo Corella, Carlos A. Cuevas, and Andrea Spolaor
Clim. Past, 13, 171–184, https://doi.org/10.5194/cp-13-171-2017, https://doi.org/10.5194/cp-13-171-2017, 2017
Short summary
Short summary
We present a study of bromine, iodine and sodium in an ice core from Law Dome, in coastal East Antarctica. We find that bromine and iodine variability at Law Dome is correlated to changes in the area of sea ice along the Law Dome coast as observed by satellite since the early 1970s. These findings are in agreement with a previous study based on MSA and confirm a long-term trend of sea ice decrease for this sector of Antarctica over the 20th century.
T. Krumpen, R. Gerdes, C. Haas, S. Hendricks, A. Herber, V. Selyuzhenok, L. Smedsrud, and G. Spreen
The Cryosphere, 10, 523–534, https://doi.org/10.5194/tc-10-523-2016, https://doi.org/10.5194/tc-10-523-2016, 2016
Short summary
Short summary
We present an extensive data set of ground-based and airborne electromagnetic ice thickness measurements covering Fram Strait in summer between 2001 and 2012. An investigation of back trajectories of surveyed sea ice using satellite-based sea ice motion data allows us to examine the connection between thickness variability, ice age and source area. In addition, we determine across and along strait gradients in ice thickness and associated volume fluxes.
A. Spolaor, T. Opel, J. R. McConnell, O. J. Maselli, G. Spreen, C. Varin, T. Kirchgeorg, D. Fritzsche, A. Saiz-Lopez, and P. Vallelonga
The Cryosphere, 10, 245–256, https://doi.org/10.5194/tc-10-245-2016, https://doi.org/10.5194/tc-10-245-2016, 2016
Short summary
Short summary
The role of sea ice in the Earth climate system is still under debate, although it is known to influence albedo, ocean circulation, and atmosphere-ocean heat and gas exchange. Here we present a reconstruction of 1950 to 1998 AD sea ice in the Laptev Sea based on the Akademii Nauk ice core (Severnaya Zemlya, Russian Arctic) and halogen measurements. The results suggest a connection between bromine and sea ice, as well as a connection between iodine concentration in snow and summer sea ice.
D. Price, W. Rack, P. J. Langhorne, C. Haas, G. Leonard, and K. Barnsdale
The Cryosphere, 8, 1031–1039, https://doi.org/10.5194/tc-8-1031-2014, https://doi.org/10.5194/tc-8-1031-2014, 2014
S. Willmes, M. Nicolaus, and C. Haas
The Cryosphere, 8, 891–904, https://doi.org/10.5194/tc-8-891-2014, https://doi.org/10.5194/tc-8-891-2014, 2014
L. Rabenstein, T. Krumpen, S. Hendricks, C. Koeberle, C. Haas, and J. A. Hoelemann
The Cryosphere, 7, 947–959, https://doi.org/10.5194/tc-7-947-2013, https://doi.org/10.5194/tc-7-947-2013, 2013
Related subject area
Discipline: Sea ice | Subject: Remote Sensing
Grounded ridge detection and characterization along the Alaska Arctic coastline using ICESat-2 surface height retrievals
Novel methods to study sea ice deformation, linear kinematic features and coherent dynamic clusters from imaging remote sensing data
Drift-aware sea ice thickness maps from satellite remote sensing
Snow depth estimation on leadless landfast ice using Cryo2Ice satellite observations
Sea Ice Concentration Estimates from ICESat-2 Linear Ice Fraction. Part 2: Gridded Data Comparison and Bias Estimation
Updated Arctic melt pond fraction dataset and trends 2002–2023 using ENVISAT and Sentinel-3 remote sensing data
Impact assessment of snow thickness, sea ice density and water density in CryoSat-2-derived sea ice thickness
Sea Ice Concentration Estimates from ICESat-2 Linear Ice Fraction. Part 1: Multi-sensor Comparison of Sea Ice Concentration Products
Pan-Arctic sea ice concentration from SAR and passive microwave
Anticipating CRISTAL: An exploration of multi-frequency satellite altimeter snow depth estimates over Arctic sea ice, 2018–2023
Exploring microwave penetration into snow on Antarctic summer sea ice along CryoSat-2 and ICESat-2 (CRYO2ICE) orbit from multi-frequency air- and spaceborne altimetry
Assessing sea ice microwave emissivity up to submillimeter waves from airborne and satellite observations
The AutoICE Challenge
A study of sea ice topography in the Weddell and Ross seas using dual-polarimetric TanDEM-X imagery
Estimating differential penetration of green (532 nm) laser light over sea ice with NASA's Airborne Topographic Mapper: observations and models
Estimating the uncertainty of sea-ice area and sea-ice extent from satellite retrievals
Sea ice transport and replenishment across and within the Canadian Arctic Archipelago, 2016–2022
MMSeaIce: a collection of techniques for improving sea ice mapping with a multi-task model
Lead fractions from SAR-derived sea ice divergence during MOSAiC
Ice floe segmentation and floe size distribution in airborne and high-resolution optical satellite images: towards an automated labelling deep learning approach
New estimates of pan-Arctic sea ice–atmosphere neutral drag coefficients from ICESat-2 elevation data
Relevance of warm air intrusions for Arctic satellite sea ice concentration time series
Observing the evolution of summer melt on multiyear sea ice with ICESat-2 and Sentinel-2
Spaceborne thermal infrared observations of Arctic sea ice leads at 30 m resolution
Wind redistribution of snow impacts the Ka- and Ku-band radar signatures of Arctic sea ice
First observations of sea ice flexural–gravity waves with ground-based radar interferometry in Utqiaġvik, Alaska
Feasibility of retrieving Arctic sea ice thickness from the Chinese HY-2B Ku-band radar altimeter
Sea ice classification of TerraSAR-X ScanSAR images for the MOSAiC expedition incorporating per-class incidence angle dependency of image texture
Aerial observations of sea ice breakup by ship waves
Monitoring Arctic thin ice: a comparison between CryoSat-2 SAR altimetry data and MODIS thermal-infrared imagery
The effects of surface roughness on the calculated, spectral, conical–conical reflectance factor as an alternative to the bidirectional reflectance distribution function of bare sea ice
Inter-comparison and evaluation of Arctic sea ice type products
A simple model for daily basin-wide thermodynamic sea ice thickness growth retrieval
Ice ridge density signatures in high-resolution SAR images
Rain on snow (ROS) understudied in sea ice remote sensing: a multi-sensor analysis of ROS during MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate)
Quantifying the effects of background concentrations of crude oil pollution on sea ice albedo
Characterizing the sea-ice floe size distribution in the Canada Basin from high-resolution optical satellite imagery
Generating large-scale sea ice motion from Sentinel-1 and the RADARSAT Constellation Mission using the Environment and Climate Change Canada automated sea ice tracking system
Rotational drift in Antarctic sea ice: pronounced cyclonic features and differences between data products
Satellite passive microwave sea-ice concentration data set intercomparison using Landsat data
Cross-platform classification of level and deformed sea ice considering per-class incident angle dependency of backscatter intensity
Advances in altimetric snow depth estimates using bi-frequency SARAL and CryoSat-2 Ka–Ku measurements
Antarctic snow-covered sea ice topography derivation from TanDEM-X using polarimetric SAR interferometry
Impacts of snow data and processing methods on the interpretation of long-term changes in Baffin Bay early spring sea ice thickness
A lead-width distribution for Antarctic sea ice: a case study for the Weddell Sea with high-resolution Sentinel-2 images
Satellite altimetry detection of ice-shelf-influenced fast ice
MOSAiC drift expedition from October 2019 to July 2020: sea ice conditions from space and comparison with previous years
Towards a swath-to-swath sea-ice drift product for the Copernicus Imaging Microwave Radiometer mission
Spaceborne infrared imagery for early detection of Weddell Polynya opening
Estimating instantaneous sea-ice dynamics from space using the bi-static radar measurements of Earth Explorer 10 candidate Harmony
Kennedy A. Lange, Alice C. Bradley, Kyle Duncan, and Sinéad L. Farrell
The Cryosphere, 19, 2045–2065, https://doi.org/10.5194/tc-19-2045-2025, https://doi.org/10.5194/tc-19-2045-2025, 2025
Short summary
Short summary
Grounded sea ice ridges stabilize nearshore sea ice by anchoring it in the seafloor. In this study, we develop a method to identify grounded ridges in satellite data and measure the height, depth, distance from shore, and width of a thousand ridges across the Alaska Arctic, finding regional differences in these metrics across the coastline. This method lays the groundwork for a better understanding of nearshore ice stability, holding importance for Arctic community food security and safety.
Polona Itkin
The Cryosphere, 19, 1135–1151, https://doi.org/10.5194/tc-19-1135-2025, https://doi.org/10.5194/tc-19-1135-2025, 2025
Short summary
Short summary
Radar satellite images of sea ice were analyzed to understand how sea ice moves and deforms. These data are noisy, especially when looking at small details. A method was developed to filter out the noise. The filtered data were used to monitor how ice plates stretch and compress over time, revealing slow healing of ice fractures. Cohesive clusters of ice plates that move together were studied too. These methods provide climate-relevant insights into the dynamic nature of winter sea ice cover.
Robert Ricker, Thomas Lavergne, Stefan Hendricks, Stephan Paul, Emily Down, Mari Anne Killie, and Marion Bocquet
EGUsphere, https://doi.org/10.5194/egusphere-2025-359, https://doi.org/10.5194/egusphere-2025-359, 2025
Short summary
Short summary
We developed a new method to map Arctic sea ice thickness daily using satellite measurements. We address a problem similar to motion blur in photography. Traditional methods collect satellite data over one month to get a full picture of Arctic sea ice thickness. But like in photos of moving objects, long exposure leads to motion blur, making it difficult to identify certain features in the sea ice maps. Our method corrects for this motion blur, providing a sharper view of the evolving sea ice.
Monojit Saha, Julienne Stroeve, Dustin Isleifson, John Yackel, Vishnu Nandan, Jack Christopher Landy, and Hoi Ming Lam
The Cryosphere, 19, 325–346, https://doi.org/10.5194/tc-19-325-2025, https://doi.org/10.5194/tc-19-325-2025, 2025
Short summary
Short summary
Snow on sea ice is vital for near-shore sea ice geophysical and biological processes. Past studies have measured snow depths using the satellite altimeters Cryosat-2 and ICESat-2 (Cryo2Ice), but estimating sea surface height from leadless landfast sea ice remains challenging. Snow depths from Cryo2Ice are compared to in situ data after adjusting for tides. Realistic snow depths are retrieved, but differences in roughness, satellite footprints, and snow geophysical properties are identified.
Christopher Horvat, Ellen M. Buckley, and Madelyn Stewart
EGUsphere, https://doi.org/10.5194/egusphere-2024-3864, https://doi.org/10.5194/egusphere-2024-3864, 2025
Short summary
Short summary
Since the late 1970s, standard methods for observing sea ice area from satellite contrast its passive microwave emissions to that of the ocean. Since 2018, a new satellite, ICESat-2, may offer a unique and independent way to sample sea ice area at high skill and resolution, using laser altimetry. We develop a new product of sea ice area for the Arctic using ICESat-2 and constrain the biases associated with the use of altimetry instead of passive microwave emissions.
Larysa Istomina, Hannah Niehaus, and Gunnar Spreen
The Cryosphere, 19, 83–105, https://doi.org/10.5194/tc-19-83-2025, https://doi.org/10.5194/tc-19-83-2025, 2025
Short summary
Short summary
Melt water puddles, or melt ponds on top of the Arctic sea ice, are a good measure of the Arctic climate state. In the context of recent climate warming, the Arctic has warmed about 4 times faster than the rest of the world, and a long-term dataset of the melt pond fraction is needed to be able to model the future development of the Arctic climate. We present such a dataset, produce 2002–2023 trends and highlight a potential melt regime shift with drastic regional trends of + 20 % per decade.
Imke Sievers, Henriette Skourup, and Till A. S. Rasmussen
The Cryosphere, 18, 5985–6004, https://doi.org/10.5194/tc-18-5985-2024, https://doi.org/10.5194/tc-18-5985-2024, 2024
Short summary
Short summary
To derive sea ice thickness (SIT) from satellite freeboard (FB) observations, assumptions about snow thickness, snow density, sea ice density and water density are needed. These parameters are impossible to observe alongside FB, so many existing products use empirical values. In this study, modeled values are used instead. The modeled values and otherwise commonly used empirical values are evaluated against in situ observations. In a further analysis, the influence on SIT is quantified.
Ellen M. Buckley, Christopher Horvat, and Pittayuth Yoosiri
EGUsphere, https://doi.org/10.5194/egusphere-2024-3861, https://doi.org/10.5194/egusphere-2024-3861, 2024
Short summary
Short summary
Sea ice coverage is a key indicator of changes in polar and global climate. There is a long (40+ year) record of sea ice concentration and area from passive microwave measurements. In this work we show the biases in these data based on high resolution imagery. We also suggest the use of ICESat-2, a high resolution satellite laser, that can supplement the passive microwave estimates.
Tore Wulf, Jørgen Buus-Hinkler, Suman Singha, Hoyeon Shi, and Matilde Brandt Kreiner
The Cryosphere, 18, 5277–5300, https://doi.org/10.5194/tc-18-5277-2024, https://doi.org/10.5194/tc-18-5277-2024, 2024
Short summary
Short summary
Here, we present ASIP: a new and comprehensive deep-learning-based methodology to retrieve high-resolution sea ice concentration with accompanying well-calibrated uncertainties from satellite-based active and passive microwave observations at a pan-Arctic scale for all seasons. In a comparative study against pan-Arctic ice charts and well-established passive-microwave-based sea ice products, we show that ASIP generalizes well to the pan-Arctic region.
Jack C. Landy, Claude de Rijke-Thomas, Carmen Nab, Isobel Lawrence, Isolde A. Glissenaar, Robbie D. C. Mallett, Renée M. Fredensborg Hansen, Alek Petty, Michel Tsamados, Amy R. Macfarlane, and Anne Braakmann-Folgmann
EGUsphere, https://doi.org/10.5194/egusphere-2024-2904, https://doi.org/10.5194/egusphere-2024-2904, 2024
Short summary
Short summary
In this study we use three satellites to test the planned remote sensing approach of the upcoming mission CRISTAL over sea ice: that its dual radars will accurately measure the heights of the top and base of snow sitting atop floating sea ice floes. Our results suggest that CRISTAL's dual radars won’t necessarily measure the snow top and base under all conditions. We find that accurate height measurements depend much more on surface roughness than on snow properties, as is commonly assumed.
Renée M. Fredensborg Hansen, Henriette Skourup, Eero Rinne, Arttu Jutila, Isobel R. Lawrence, Andrew Shepherd, Knut V. Høyland, Jilu Li, Fernando Rodriguez-Morales, Sebastian B. Simonsen, Jeremy Wilkinson, Gaelle Veyssiere, Donghui Yi, René Forsberg, and Taniâ G. D. Casal
EGUsphere, https://doi.org/10.5194/egusphere-2024-2854, https://doi.org/10.5194/egusphere-2024-2854, 2024
Short summary
Short summary
In December 2022, an airborne campaign collected unprecedented coincident multi-frequency radar and lidar data over sea ice along a CryoSat-2 and ICESat-2 (CRYO2ICE) orbit in the Weddell Sea useful for evaluating microwave penetration. We found limited snow penetration at Ka- and Ku-bands, with significant contributions from the air-snow interface, contradicting traditional assumptions. These findings challenge current methods for comparing air- and spaceborne altimeter estimates of sea ice.
Nils Risse, Mario Mech, Catherine Prigent, Gunnar Spreen, and Susanne Crewell
The Cryosphere, 18, 4137–4163, https://doi.org/10.5194/tc-18-4137-2024, https://doi.org/10.5194/tc-18-4137-2024, 2024
Short summary
Short summary
Passive microwave observations from satellites are crucial for monitoring Arctic sea ice and atmosphere. To do this effectively, it is important to understand how sea ice emits microwaves. Through unique Arctic sea ice observations, we improved our understanding, identified four distinct emission types, and expanded current knowledge to include higher frequencies. These findings will enhance our ability to monitor the Arctic climate and provide valuable information for new satellite missions.
Andreas Stokholm, Jørgen Buus-Hinkler, Tore Wulf, Anton Korosov, Roberto Saldo, Leif Toudal Pedersen, David Arthurs, Ionut Dragan, Iacopo Modica, Juan Pedro, Annekatrien Debien, Xinwei Chen, Muhammed Patel, Fernando Jose Pena Cantu, Javier Noa Turnes, Jinman Park, Linlin Xu, Katharine Andrea Scott, David Anthony Clausi, Yuan Fang, Mingzhe Jiang, Saeid Taleghanidoozdoozan, Neil Curtis Brubacher, Armina Soleymani, Zacharie Gousseau, Michał Smaczny, Patryk Kowalski, Jacek Komorowski, David Rijlaarsdam, Jan Nicolaas van Rijn, Jens Jakobsen, Martin Samuel James Rogers, Nick Hughes, Tom Zagon, Rune Solberg, Nicolas Longépé, and Matilde Brandt Kreiner
The Cryosphere, 18, 3471–3494, https://doi.org/10.5194/tc-18-3471-2024, https://doi.org/10.5194/tc-18-3471-2024, 2024
Short summary
Short summary
The AutoICE challenge encouraged the development of deep learning models to map multiple aspects of sea ice – the amount of sea ice in an area and the age and ice floe size – using multiple sources of satellite and weather data across the Canadian and Greenlandic Arctic. Professionally drawn operational sea ice charts were used as a reference. A total of 179 students and sea ice and AI specialists participated and produced maps in broad agreement with the sea ice charts.
Lanqing Huang and Irena Hajnsek
The Cryosphere, 18, 3117–3140, https://doi.org/10.5194/tc-18-3117-2024, https://doi.org/10.5194/tc-18-3117-2024, 2024
Short summary
Short summary
Interferometric synthetic aperture radar can measure the total freeboard of sea ice but can be biased when radar signals penetrate snow and ice. We develop a new method to retrieve the total freeboard and analyze the regional variation of total freeboard and roughness in the Weddell and Ross seas. We also investigate the statistical behavior of the total freeboard for diverse ice types. The findings enhance the understanding of Antarctic sea ice topography and its dynamics in a changing climate.
Michael Studinger, Benjamin E. Smith, Nathan Kurtz, Alek Petty, Tyler Sutterley, and Rachel Tilling
The Cryosphere, 18, 2625–2652, https://doi.org/10.5194/tc-18-2625-2024, https://doi.org/10.5194/tc-18-2625-2024, 2024
Short summary
Short summary
We use green lidar data and natural-color imagery over sea ice to quantify elevation biases potentially impacting estimates of change in ice thickness of the polar regions. We complement our analysis using a model of scattering of light in snow and ice that predicts the shape of lidar waveforms reflecting from snow and ice surfaces based on the shape of the transmitted pulse. We find that biased elevations exist in airborne and spaceborne data products from green lidars.
Andreas Wernecke, Dirk Notz, Stefan Kern, and Thomas Lavergne
The Cryosphere, 18, 2473–2486, https://doi.org/10.5194/tc-18-2473-2024, https://doi.org/10.5194/tc-18-2473-2024, 2024
Short summary
Short summary
The total Arctic sea-ice area (SIA), which is an important climate indicator, is routinely monitored with the help of satellite measurements. Uncertainties in observations of sea-ice concentration (SIC) partly cancel out when summed up to the total SIA, but the degree to which this is happening has been unclear. Here we find that the uncertainty daily SIA estimates, based on uncertainties in SIC, are about 300 000 km2. The 2002 to 2017 September decline in SIA is approx. 105 000 ± 9000 km2 a−1.
Stephen E. L. Howell, David G. Babb, Jack C. Landy, Isolde A. Glissenaar, Kaitlin McNeil, Benoit Montpetit, and Mike Brady
The Cryosphere, 18, 2321–2333, https://doi.org/10.5194/tc-18-2321-2024, https://doi.org/10.5194/tc-18-2321-2024, 2024
Short summary
Short summary
The CAA serves as both a source and a sink for sea ice from the Arctic Ocean, while also exporting sea ice into Baffin Bay. It is also an important region with respect to navigating the Northwest Passage. Here, we quantify sea ice transport and replenishment across and within the CAA from 2016 to 2022. We also provide the first estimates of the ice area and volume flux within the CAA from the Queen Elizabeth Islands to Parry Channel, which spans the central region of the Northwest Passage.
Xinwei Chen, Muhammed Patel, Fernando J. Pena Cantu, Jinman Park, Javier Noa Turnes, Linlin Xu, K. Andrea Scott, and David A. Clausi
The Cryosphere, 18, 1621–1632, https://doi.org/10.5194/tc-18-1621-2024, https://doi.org/10.5194/tc-18-1621-2024, 2024
Short summary
Short summary
This paper introduces an automated sea ice mapping pipeline utilizing a multi-task U-Net architecture. It attained the top score of 86.3 % in the AutoICE challenge. Ablation studies revealed that incorporating brightness temperature data and spatial–temporal information significantly enhanced model accuracy. Accurate sea ice mapping is vital for comprehending the Arctic environment and its global climate effects, underscoring the potential of deep learning.
Luisa von Albedyll, Stefan Hendricks, Nils Hutter, Dmitrii Murashkin, Lars Kaleschke, Sascha Willmes, Linda Thielke, Xiangshan Tian-Kunze, Gunnar Spreen, and Christian Haas
The Cryosphere, 18, 1259–1285, https://doi.org/10.5194/tc-18-1259-2024, https://doi.org/10.5194/tc-18-1259-2024, 2024
Short summary
Short summary
Leads (openings in sea ice cover) are created by sea ice dynamics. Because they are important for many processes in the Arctic winter climate, we aim to detect them with satellites. We present two new techniques to detect lead widths of a few hundred meters at high spatial resolution (700 m) and independent of clouds or sun illumination. We use the MOSAiC drift 2019–2020 in the Arctic for our case study and compare our new products to other existing lead products.
Qin Zhang and Nick Hughes
The Cryosphere, 17, 5519–5537, https://doi.org/10.5194/tc-17-5519-2023, https://doi.org/10.5194/tc-17-5519-2023, 2023
Short summary
Short summary
To alleviate tedious manual image annotations for training deep learning (DL) models in floe instance segmentation, we employ a classical image processing technique to automatically label floes in images. We then apply a DL semantic method for fast and adaptive floe instance segmentation from high-resolution airborne and satellite images. A post-processing algorithm is also proposed to refine the segmentation and further to derive acceptable floe size distributions at local and global scales.
Alexander Mchedlishvili, Christof Lüpkes, Alek Petty, Michel Tsamados, and Gunnar Spreen
The Cryosphere, 17, 4103–4131, https://doi.org/10.5194/tc-17-4103-2023, https://doi.org/10.5194/tc-17-4103-2023, 2023
Short summary
Short summary
In this study we looked at sea ice–atmosphere drag coefficients, quantities that help with characterizing the friction between the atmosphere and sea ice, and vice versa. Using ICESat-2, a laser altimeter that measures elevation differences by timing how long it takes for photons it sends out to return to itself, we could map the roughness, i.e., how uneven the surface is. From roughness we then estimate drag force, the frictional force between sea ice and the atmosphere, across the Arctic.
Philip Rostosky and Gunnar Spreen
The Cryosphere, 17, 3867–3881, https://doi.org/10.5194/tc-17-3867-2023, https://doi.org/10.5194/tc-17-3867-2023, 2023
Short summary
Short summary
During winter, storms entering the Arctic region can bring warm air into the cold environment. Strong increases in air temperature modify the characteristics of the Arctic snow and ice cover. The Arctic sea ice cover can be monitored by satellites observing the natural emission of the Earth's surface. In this study, we show that during warm air intrusions the change in the snow characteristics influences the satellite-derived sea ice cover, leading to a false reduction of the estimated ice area.
Ellen M. Buckley, Sinéad L. Farrell, Ute C. Herzfeld, Melinda A. Webster, Thomas Trantow, Oliwia N. Baney, Kyle A. Duncan, Huilin Han, and Matthew Lawson
The Cryosphere, 17, 3695–3719, https://doi.org/10.5194/tc-17-3695-2023, https://doi.org/10.5194/tc-17-3695-2023, 2023
Short summary
Short summary
In this study, we use satellite observations to investigate the evolution of melt ponds on the Arctic sea ice surface. We derive melt pond depth from ICESat-2 measurements of the pond surface and bathymetry and melt pond fraction (MPF) from the classification of Sentinel-2 imagery. MPF increases to a peak of 16 % in late June and then decreases, while depth increases steadily. This work demonstrates the ability to track evolving melt conditions in three dimensions throughout the summer.
Yujia Qiu, Xiao-Ming Li, and Huadong Guo
The Cryosphere, 17, 2829–2849, https://doi.org/10.5194/tc-17-2829-2023, https://doi.org/10.5194/tc-17-2829-2023, 2023
Short summary
Short summary
Spaceborne thermal infrared sensors with kilometer-scale resolution cannot support adequate parameterization of Arctic leads. For the first time, we applied the 30 m resolution data from the Thermal Infrared Spectrometer (TIS) on the emerging SDGSAT-1 to detect Arctic leads. Validation with Sentinel-2 data shows high accuracy for the three TIS bands. Compared to MODIS, the TIS presents more narrow leads, demonstrating its great potential for observing previously unresolvable Arctic leads.
Vishnu Nandan, Rosemary Willatt, Robbie Mallett, Julienne Stroeve, Torsten Geldsetzer, Randall Scharien, Rasmus Tonboe, John Yackel, Jack Landy, David Clemens-Sewall, Arttu Jutila, David N. Wagner, Daniela Krampe, Marcus Huntemann, Mallik Mahmud, David Jensen, Thomas Newman, Stefan Hendricks, Gunnar Spreen, Amy Macfarlane, Martin Schneebeli, James Mead, Robert Ricker, Michael Gallagher, Claude Duguay, Ian Raphael, Chris Polashenski, Michel Tsamados, Ilkka Matero, and Mario Hoppmann
The Cryosphere, 17, 2211–2229, https://doi.org/10.5194/tc-17-2211-2023, https://doi.org/10.5194/tc-17-2211-2023, 2023
Short summary
Short summary
We show that wind redistributes snow on Arctic sea ice, and Ka- and Ku-band radar measurements detect both newly deposited snow and buried snow layers that can affect the accuracy of snow depth estimates on sea ice. Radar, laser, meteorological, and snow data were collected during the MOSAiC expedition. With frequent occurrence of storms in the Arctic, our results show that
wind-redistributed snow needs to be accounted for to improve snow depth estimates on sea ice from satellite radars.
Dyre Oliver Dammann, Mark A. Johnson, Andrew R. Mahoney, and Emily R. Fedders
The Cryosphere, 17, 1609–1622, https://doi.org/10.5194/tc-17-1609-2023, https://doi.org/10.5194/tc-17-1609-2023, 2023
Short summary
Short summary
We investigate the GAMMA Portable Radar Interferometer (GPRI) as a tool for evaluating flexural–gravity waves in sea ice in near real time. With a GPRI mounted on grounded ice near Utqiaġvik, Alaska, we identify 20–50 s infragravity waves in landfast ice with ~1 mm amplitude during 23–24 April 2021. Observed wave speed and periods compare well with modeled wave propagation and on-ice accelerometers, confirming the ability to track propagation and properties of waves over hundreds of meters.
Zhaoqing Dong, Lijian Shi, Mingsen Lin, Yongjun Jia, Tao Zeng, and Suhui Wu
The Cryosphere, 17, 1389–1410, https://doi.org/10.5194/tc-17-1389-2023, https://doi.org/10.5194/tc-17-1389-2023, 2023
Short summary
Short summary
We try to explore the application of SGDR data in polar sea ice thickness. Through this study, we find that it seems difficult to obtain reasonable results by using conventional methods. So we use the 15 lowest points per 25 km to estimate SSHA to retrieve more reasonable Arctic radar freeboard and thickness. This study also provides reference for reprocessing L1 data. We will release products that are more reasonable and suitable for polar sea ice thickness retrieval to better evaluate HY-2B.
Wenkai Guo, Polona Itkin, Suman Singha, Anthony P. Doulgeris, Malin Johansson, and Gunnar Spreen
The Cryosphere, 17, 1279–1297, https://doi.org/10.5194/tc-17-1279-2023, https://doi.org/10.5194/tc-17-1279-2023, 2023
Short summary
Short summary
Sea ice maps are produced to cover the MOSAiC Arctic expedition (2019–2020) and divide sea ice into scientifically meaningful classes. We use a high-resolution X-band synthetic aperture radar dataset and show how image brightness and texture systematically vary across the images. We use an algorithm that reliably corrects this effect and achieve good results, as evaluated by comparisons to ground observations and other studies. The sea ice maps are useful as a basis for future MOSAiC studies.
Elie Dumas-Lefebvre and Dany Dumont
The Cryosphere, 17, 827–842, https://doi.org/10.5194/tc-17-827-2023, https://doi.org/10.5194/tc-17-827-2023, 2023
Short summary
Short summary
By changing the shape of ice floes, wave-induced sea ice breakup dramatically affects the large-scale dynamics of sea ice. As this process is also the trigger of multiple others, it was deemed relevant to study how breakup itself affects the ice floe size distribution. To do so, a ship sailed close to ice floes, and the breakup that it generated was recorded with a drone. The obtained data shed light on the underlying physics of wave-induced sea ice breakup.
Felix L. Müller, Stephan Paul, Stefan Hendricks, and Denise Dettmering
The Cryosphere, 17, 809–825, https://doi.org/10.5194/tc-17-809-2023, https://doi.org/10.5194/tc-17-809-2023, 2023
Short summary
Short summary
Thinning sea ice has significant impacts on the energy exchange between the atmosphere and the ocean. In this study we present visual and quantitative comparisons of thin-ice detections obtained from classified Cryosat-2 radar reflections and thin-ice-thickness estimates derived from MODIS thermal-infrared imagery. In addition to good comparability, the results of the study indicate the potential for a deeper understanding of sea ice in the polar seas and improved processing of altimeter data.
Maxim L. Lamare, John D. Hedley, and Martin D. King
The Cryosphere, 17, 737–751, https://doi.org/10.5194/tc-17-737-2023, https://doi.org/10.5194/tc-17-737-2023, 2023
Short summary
Short summary
The reflectivity of sea ice is crucial for modern climate change and for monitoring sea ice from satellites. The reflectivity depends on the angle at which the ice is viewed and the angle illuminated. The directional reflectivity is calculated as a function of viewing angle, illuminating angle, thickness, wavelength and surface roughness. Roughness cannot be considered independent of thickness, illumination angle and the wavelength. Remote sensors will use the data to image sea ice from space.
Yufang Ye, Yanbing Luo, Yan Sun, Mohammed Shokr, Signe Aaboe, Fanny Girard-Ardhuin, Fengming Hui, Xiao Cheng, and Zhuoqi Chen
The Cryosphere, 17, 279–308, https://doi.org/10.5194/tc-17-279-2023, https://doi.org/10.5194/tc-17-279-2023, 2023
Short summary
Short summary
Arctic sea ice type (SITY) variation is a sensitive indicator of climate change. This study gives a systematic inter-comparison and evaluation of eight SITY products. Main results include differences in SITY products being significant, with average Arctic multiyear ice extent up to 1.8×106 km2; Ku-band scatterometer SITY products generally performing better; and factors such as satellite inputs, classification methods, training datasets and post-processing highly impacting their performance.
James Anheuser, Yinghui Liu, and Jeffrey R. Key
The Cryosphere, 16, 4403–4421, https://doi.org/10.5194/tc-16-4403-2022, https://doi.org/10.5194/tc-16-4403-2022, 2022
Short summary
Short summary
A prominent part of the polar climate system is sea ice, a better understanding of which would lead to better understanding Earth's climate. Newly published methods for observing the temperature of sea ice have made possible a new method for estimating daily sea ice thickness growth from space using an energy balance. The method compares well with existing sea ice thickness observations.
Mikko Lensu and Markku Similä
The Cryosphere, 16, 4363–4377, https://doi.org/10.5194/tc-16-4363-2022, https://doi.org/10.5194/tc-16-4363-2022, 2022
Short summary
Short summary
Ice ridges form a compressing ice cover. From above they appear as walls of up to few metres in height and extend even kilometres across the ice. Below they may reach tens of metres under the sea surface. Ridges need to be observed for the purposes of ice forecasting and ice information production. This relies mostly on ridging signatures discernible in radar satellite (SAR) images. New methods to quantify ridging from SAR have been developed and are shown to agree with field observations.
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
The Cryosphere, 16, 4223–4250, https://doi.org/10.5194/tc-16-4223-2022, https://doi.org/10.5194/tc-16-4223-2022, 2022
Short summary
Short summary
Impacts of rain on snow (ROS) on satellite-retrieved sea ice variables remain to be fully understood. This study evaluates the impacts of ROS over sea ice on active and passive microwave data collected during the 2019–20 MOSAiC expedition. Rainfall and subsequent refreezing of the snowpack significantly altered emitted and backscattered radar energy, laying important groundwork for understanding their impacts on operational satellite retrievals of various sea ice geophysical variables.
Benjamin Heikki Redmond Roche and Martin D. King
The Cryosphere, 16, 3949–3970, https://doi.org/10.5194/tc-16-3949-2022, https://doi.org/10.5194/tc-16-3949-2022, 2022
Short summary
Short summary
Sea ice is bright, playing an important role in reflecting incoming solar radiation. The reflectivity of sea ice is affected by the presence of pollutants, such as crude oil, even at low concentrations. Modelling how the brightness of three types of sea ice is affected by increasing concentrations of crude oils shows that the type of oil, the type of ice, the thickness of the ice, and the size of the oil droplets are important factors. This shows that sea ice is vulnerable to oil pollution.
Alexis Anne Denton and Mary-Louise Timmermans
The Cryosphere, 16, 1563–1578, https://doi.org/10.5194/tc-16-1563-2022, https://doi.org/10.5194/tc-16-1563-2022, 2022
Short summary
Short summary
Arctic sea ice has a distribution of ice sizes that provides insight into the physics of the ice. We examine this distribution from satellite imagery from 1999 to 2014 in the Canada Basin. We find that it appears as a power law whose power becomes less negative with increasing ice concentrations and has a seasonality tied to that of ice concentration. Results suggest ice concentration be considered in models of this distribution and are important for understanding sea ice in a warming Arctic.
Stephen E. L. Howell, Mike Brady, and Alexander S. Komarov
The Cryosphere, 16, 1125–1139, https://doi.org/10.5194/tc-16-1125-2022, https://doi.org/10.5194/tc-16-1125-2022, 2022
Short summary
Short summary
We describe, apply, and validate the Environment and Climate Change Canada automated sea ice tracking system (ECCC-ASITS) that routinely generates large-scale sea ice motion (SIM) over the pan-Arctic domain using synthetic aperture radar (SAR) images. The ECCC-ASITS was applied to the incoming image streams of Sentinel-1AB and the RADARSAT Constellation Mission from March 2020 to October 2021 using a total of 135 471 SAR images and generated new SIM datasets (i.e., 7 d 25 km and 3 d 6.25 km).
Wayne de Jager and Marcello Vichi
The Cryosphere, 16, 925–940, https://doi.org/10.5194/tc-16-925-2022, https://doi.org/10.5194/tc-16-925-2022, 2022
Short summary
Short summary
Ice motion can be used to better understand how weather and climate change affect the ice. Antarctic sea ice extent has shown large variability over the observed period, and dynamical features may also have changed. Our method allows for the quantification of rotational motion caused by wind and how this may have changed with time. Cyclonic motion dominates the Atlantic sector, particularly from 2015 onwards, while anticyclonic motion has remained comparatively small and unchanged.
Stefan Kern, Thomas Lavergne, Leif Toudal Pedersen, Rasmus Tage Tonboe, Louisa Bell, Maybritt Meyer, and Luise Zeigermann
The Cryosphere, 16, 349–378, https://doi.org/10.5194/tc-16-349-2022, https://doi.org/10.5194/tc-16-349-2022, 2022
Short summary
Short summary
High-resolution clear-sky optical satellite imagery has rarely been used to evaluate satellite passive microwave sea-ice concentration products beyond case-study level. By comparing 10 such products with sea-ice concentration estimated from > 350 such optical images in both hemispheres, we expand results of earlier evaluation studies for these products. Results stress the need to look beyond precision and accuracy and to discuss the evaluation data’s quality and filters applied in the products.
Wenkai Guo, Polona Itkin, Johannes Lohse, Malin Johansson, and Anthony Paul Doulgeris
The Cryosphere, 16, 237–257, https://doi.org/10.5194/tc-16-237-2022, https://doi.org/10.5194/tc-16-237-2022, 2022
Short summary
Short summary
This study uses radar satellite data categorized into different sea ice types to detect ice deformation, which is significant for climate science and ship navigation. For this, we examine radar signal differences of sea ice between two similar satellite sensors and show an optimal way to apply categorization methods across sensors, so more data can be used for this purpose. This study provides a basis for future reliable and constant detection of ice deformation remotely through satellite data.
Florent Garnier, Sara Fleury, Gilles Garric, Jérôme Bouffard, Michel Tsamados, Antoine Laforge, Marion Bocquet, Renée Mie Fredensborg Hansen, and Frédérique Remy
The Cryosphere, 15, 5483–5512, https://doi.org/10.5194/tc-15-5483-2021, https://doi.org/10.5194/tc-15-5483-2021, 2021
Short summary
Short summary
Snow depth data are essential to monitor the impacts of climate change on sea ice volume variations and their impacts on the climate system. For that purpose, we present and assess the altimetric snow depth product, computed in both hemispheres from CryoSat-2 and SARAL satellite data. The use of these data instead of the common climatology reduces the sea ice thickness by about 30 cm over the 2013–2019 period. These data are also crucial to argue for the launch of the CRISTAL satellite mission.
Lanqing Huang, Georg Fischer, and Irena Hajnsek
The Cryosphere, 15, 5323–5344, https://doi.org/10.5194/tc-15-5323-2021, https://doi.org/10.5194/tc-15-5323-2021, 2021
Short summary
Short summary
This study shows an elevation difference between the radar interferometric measurements and the optical measurements from a coordinated campaign over the snow-covered deformed sea ice in the western Weddell Sea, Antarctica. The objective is to correct the penetration bias of microwaves and to generate a precise sea ice topographic map, including the snow depth on top. Excellent performance for sea ice topographic retrieval is achieved with the proposed model and the developed retrieval scheme.
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
Short summary
Short summary
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.
Marek Muchow, Amelie U. Schmitt, and Lars Kaleschke
The Cryosphere, 15, 4527–4537, https://doi.org/10.5194/tc-15-4527-2021, https://doi.org/10.5194/tc-15-4527-2021, 2021
Short summary
Short summary
Linear-like openings in sea ice, also called leads, occur with widths from meters to kilometers. We use satellite images from Sentinel-2 with a resolution of 10 m to identify leads and measure their widths. With that we investigate the frequency of lead widths using two different statistical methods, since other studies have shown a dependency of heat exchange on the lead width. We are the first to address the sea-ice lead-width distribution in the Weddell Sea, Antarctica.
Gemma M. Brett, Daniel Price, Wolfgang Rack, and Patricia J. Langhorne
The Cryosphere, 15, 4099–4115, https://doi.org/10.5194/tc-15-4099-2021, https://doi.org/10.5194/tc-15-4099-2021, 2021
Short summary
Short summary
Ice shelf meltwater in the surface ocean affects sea ice formation, causing it to be thicker and, in particular conditions, to have a loose mass of platelet ice crystals called a sub‐ice platelet layer beneath. This causes the sea ice freeboard to stand higher above sea level. In this study, we demonstrate for the first time that the signature of ice shelf meltwater in the surface ocean manifesting as higher sea ice freeboard in McMurdo Sound is detectable from space using satellite technology.
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
Short summary
Short summary
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.
Thomas Lavergne, Montserrat Piñol Solé, Emily Down, and Craig Donlon
The Cryosphere, 15, 3681–3698, https://doi.org/10.5194/tc-15-3681-2021, https://doi.org/10.5194/tc-15-3681-2021, 2021
Short summary
Short summary
Pushed by winds and ocean currents, polar sea ice is on the move. We use passive microwave satellites to observe this motion. The images from their orbits are often put together into daily images before motion is measured. In our study, we measure motion from the individual orbits directly and not from the daily images. We obtain many more motion vectors, and they are more accurate. This can be used for current and future satellites, e.g. the Copernicus Imaging Microwave Radiometer (CIMR).
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
Short summary
Short summary
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
Short summary
Short summary
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.
Cited articles
Boulze, H., Korosov, A., and Brajard, J.: Classification of Sea Ice Types in Sentinel-1 SAR Data Using Convolutional Neural Networks, Remote Sens., 12, 2165, https://doi.org/10.3390/rs12132165, 2020. a, b
Doulgeris, A. P.: An Automatic 𝒰-Distribution and Markov Random Field Segmentation Algorithm for PalSAR Images, IEEE T. Geosci. Remote, 53, 1819–1827, https://doi.org/10.1109/TGRS.2014.2349575, 2015. a
Fily, M. and Rothrock, D. A.: Extracting Sea Ice Data from Satellite SAR Imagery, IEEE T. Geosci. Remote, GE-24, 849–854, https://doi.org/10.1109/TGRS.1986.289699, 1986. a
Fritz, T., Mittermayer, J., Schaettler, B., Buckreuss, S., Werninghaus, R., and Balzer, W.: Level 1b Product Format Specification, DLR: TerraSAR-X Ground Segment, https://www.intelligence-airbusds.com/files/pmedia/public/r460_9_030201_level-1b-product-format-specification_1.3.pdf (last access: November 2022), 2007. a
Geldsetzer, T. and Yackel, J. J.: Sea ice type and open water discrimination using dual co-polarized C-band SAR, Can. J. Remote Sens., 35, 73–84, https://doi.org/10.5589/m08-075, 2009. a
Guo, W., Itkin, P., Singha, S., Doulgeris, A. P., Johansson, M., and Spreen, G.: Sea ice classification of TerraSAR-X ScanSAR images for the MOSAiC expedition incorporating per-class incidence angle dependency of image texture, The Cryosphere, 17, 1279–1297, https://doi.org/10.5194/tc-17-1279-2023, 2023. a
Han, Y., Zhao, Y., Zhang, Y., Wang, J., Yang, S., Hong, Z., and Cao, S.: A Cooperative Framework Based on Active and Semi-supervised Learning for Sea Ice Classification using EO-1 Hyperion Data, T. Jpn. Soc. Aeronaut. S., 62, 318–330, https://doi.org/10.2322/tjsass.62.318, 2019. a
Hara, Y., Atkins, R., Shin, R., Kong, J. A., Yueh, S., and Kwok, R.: Application of neural networks for sea ice classification in polarimetric SAR images, IEEE T. Geosci. Remote, 33, 740–748, https://doi.org/10.1109/36.387589, 1995. a
He, K., Zhang, X., Ren, S., and Sun, J.: Deep Residual Learning for Image Recognition, arXiv [preprint], https://doi.org/10.48550/ARXIV.1512.03385, 2015. a
Hendricks, S.: Ice Drift – Transformation of GPS positions into a translating and rotating coordinate reference system, https://gitlab.awi.de/floenavi-crs/icedrift (last access: October 2021), 2019. a
Hutter, N., Hendricks, S., Jutila, A., Birnbaum, G., von Albedyll, L., Ricker, R., and Haas, C.: Merged Grids of Sea-Ice or snow freeboard from helicopter-borne laser scanner during the MOSAiC Expedition, version 1, PANGEA [data set], https://doi.org/10.1594/PANGAEA.950896, 2022. a, b, c, d
Hutter, N., Hendricks, S., Jutila, A., Birnbaum, G., von Albedyll, L., Ricker, R., and Haas, C.: Digital elevation models of the sea-ice surface from airborne laser scanning during MOSAiC, Sci. Data, 10, 729, https://doi.org/10.1038/s41597-023-02565-6, 2023. a, b
Imber, J.: Generative Network For Semi-supervised Sea Ice Classification, TechRxiv [preprint], https://doi.org/10.36227/techrxiv.21081136.v1, 2022. a
Itkin, P., Webster, M., Hendricks, S., Oggier, M., Jaggi, M., Ricker, R., Arndt, S., Divine, D. V., von Albedyll, L., Raphael, I., Rohde, J., and Liston, G. E.: Magnaprobe snow and melt pond depth measurements from the 2019–2020 MOSAiC expedition, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.937781, 2021. a
JCOMM: Sea-Ice Information Services in the World, World Meteorological Organization, https://doi.org/10.25607/OBP-1325, 2017. a
Johansson, A. M., Malnes, E., Gerland, S., Cristea, A., Doulgeris, A., Divine, D., Pavlova, O., and Lauknes, T. R.: Consistent ice and open water classification combining historical synthetic aperture radar satellite images from ERS-1/2, Envisat ASAR, RADARSAT-2 and Sentinel-1A/B, Ann. Glaciol., 61, 1–11, https://doi.org/10.1017/aog.2019.52, 2020. a
Karvonen, J.: Baltic Sea ice SAR segmentation and classification using modified pulse-coupled neural networks, IEEE T. Geosci. Remote, 42, 1566–1574, https://doi.org/10.1109/TGRS.2004.828179, 2004. a
Khaleghian, S., Ullah, H., Kræmer, T., Eltoft, T., and Marinoni, A.: Deep Semisupervised Teacher–Student Model Based on Label Propagation for Sea Ice Classification, IEEE J. Sel. Top. Appl., 14, 10761–10772, https://doi.org/10.1109/JSTARS.2021.3119485, 2021a. a
Khaleghian, S., Ullah, H., Kræmer, T., Hughes, N., Eltoft, T., and Marinoni, A.: Sea Ice Classification of SAR Imagery Based on Convolution Neural Networks, Remote Sens., 13, 1734, https://doi.org/10.3390/rs13091734, 2021b. a
Kingma, D. P. and Ba, J.: Adam: A Method for Stochastic Optimization, arXiv [preprint], https://doi.org/10.48550/arXiv.1412.6980, 2017. a
Kortum, K., Singha, S., Spreen, G., and Hendricks, S.: Automating Sea Ice Characterisation from X-Band SAR with Co-Located Airborne Laser Scanner Data Obtained During The MOSAiC Expedition, in: Geoscience and Remote Sensing Symposium, 2021 IEEE International, https://doi.org/10.1109/IGARSS47720.2021.9553340, 2021. a, b
Kwok, R., Rignot, E., Holt, B., and Onstott, R.: Identification of sea ice types in spaceborne synthetic aperture radar data, J. Geophys. Res.-Oceans, 97, 2391–2402, https://doi.org/10.1029/91JC02652, 1992. a
Li, F., Clausi, D. A., Wang, L., and Xu, L.: A semi-supervised approach for ice-water classification using dual-polarization SAR satellite imagery, in: 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 28–35, https://doi.org/10.1109/CVPRW.2015.7301380, 2015. a
Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., and Xie, S.: A ConvNet for the 2020s, arXiv [preprint], https://doi.org/10.48550/ARXIV.2201.03545, 2022. a, b, c
Lohse, J., Doulgeris, A., and Dierking, W.: Incident Angle Dependence of Sentinel-1 Texture Features for Sea Ice Classification, Remote Sens., 13, 552, https://doi.org/10.3390/rs13040552, 2021. a
Murashkin, D. and Frost, A.: Arctic Sea ICE Mapping Using Sentinel-1 SAR Scenes with a Convolutional Neural Network, in: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 5660–5663, https://doi.org/10.1109/IGARSS47720.2021.9553206, 2021. a
Murashkin, D., Spreen, G., Huntemann, M., and Dierking, W.: Method for detection of leads from Sentinel-1 SAR images, Ann. Glaciol., 59, 1–13, https://doi.org/10.1017/aog.2018.6, 2018. a, b
Nagi, A. S., Kumar, D., Sola, D., and Scott, K. A.: RUF: Effective Sea Ice Floe Segmentation Using End-to-End RES-UNET-CRF with Dual Loss, Remote Sens., 13, 2460, https://doi.org/10.3390/rs13132460, 2021. a
Nicolaus, M., Perovich, D. K., Spreen, G., Granskog, M. A., von Albedyll, L., Angelopoulos, M., Anhaus, P., Arndt, S., Belter, H. J., Bessonov, V., Birnbaum, G., Brauchle, J., Calmer, R., Cardellach, E., Cheng, B., Clemens-Sewall, D., Dadic, R., Damm, E., de Boer, G., Demir, O., Dethloff, K., Divine, D. V., Fong, A. A., Fons, S., Frey, M. M., Fuchs, N., Gabarró, C., Gerland, S., Goessling, H. F., Gradinger, R., Haapala, J., Haas, C., Hamilton, J., Hannula, H.-R., Hendricks, S., Herber, A., Heuzé, C., Hoppmann, M., Høyland, K. V., Huntemann, M., Hutchings, J. K., Hwang, B., Itkin, P., Jacobi, H.-W., Jaggi, M., Jutila, A., Kaleschke, L., Katlein, C., Kolabutin, N., Krampe, D., Kristensen, S. S., Krumpen, T., Kurtz, N., Lampert, A., Lange, B. A., Lei, R., Light, B., Linhardt, F., Liston, G. E., Loose, B., Macfarlane, A. R., Mahmud, M., Matero, I. O., Maus, S., Morgenstern, A., Naderpour, R., Nandan, V., Niubom, A., Oggier, M., Oppelt, N., Pätzold, F., Perron, C., Petrovsky, T., Pirazzini, R., Polashenski, C., Rabe, B., Raphael, I. A., Regnery, J., Rex, M., Ricker, R., Riemann-Campe, K., Rinke, A., Rohde, J., Salganik, E., Scharien, R. K., Schiller, M., Schneebeli, M., Semmling, M., Shimanchuk, E., Shupe, M. D., Smith, M. M., Smolyanitsky, V., Sokolov, V., Stanton, T., Stroeve, J., Thielke, L., Timofeeva, A., Tonboe, R. T., Tavri, A., Tsamados, M., Wagner, D. N., Watkins, D., Webster, M., and Wendisch, M.: Overview of the MOSAiC expedition: Snow and sea ice, Elementa, 10, 000046, https://doi.org/10.1525/elementa.2021.000046, 2022. a
Nixdorf, U., Dethloff, K., Rex, M., Shupe, M., Sommerfeld, A., Perovich, D. K., Nicolaus, M., Heuzé, C., Rabe, B., Loose, B., Damm, E., Gradinger, R., Fong, A., Maslowski, W., Rinke, A., Kwok, R., Spreen, G., Wendisch, M., Herber, A., Hirsekorn, M., Mohaupt, V., Frickenhaus, S., Immerz, A., Weiss-Tuider, K., König, B., Mengedoht, D., Regnery, J., Gerchow, P., Ransby, D., Krumpen, T., Morgenstern, A., Haas, C., Kanzow, T., Rack, F. R., Saitzev, V., Sokolov, V., Makarov, A., Schwarze, S., Wunderlich, T., Wurr, K., and Boetius, A.: MOSAiC Extended Acknowledgement, Zenodo, https://doi.org/10.5281/zenodo.5541624, 2021. a
Radhakrishnan, K., Scott, K. A., and Clausi, D. A.: Sea Ice Concentration Estimation: Using Passive Microwave and SAR Data With a U-Net and Curriculum Learning, IEEE J. Sel. Top. Appl., 14, 5339–5351, https://doi.org/10.1109/JSTARS.2021.3076109, 2021. a
Ren, Y., Li, X., Yang, X., and Xu, H.: Development of a Dual-Attention U-Net Model for Sea Ice and Open Water Classification on SAR Images, IEEE Geosci. Remote Sens. Lett., 19, 1–5, https://doi.org/10.1109/LGRS.2021.3058049, 2022. a, b
Ressel, R., Frost, A., and Lehner, S.: A Neural Network-Based Classification for Sea Ice Types on X-Band SAR Images, IEEE J. Sel. Top. Appl., 8, 1–9, https://doi.org/10.1109/JSTARS.2015.2436993, 2015. a
Ressel, R., Singha, S., Lehner, S., Rösel, A., and Spreen, G.: Investigation into Different Polarimetric Features for Sea Ice Classification Using X-Band Synthetic Aperture Radar, IEEE J. Sel. Top. Appl., 9, 3131–3143, https://doi.org/10.1109/JSTARS.2016.2539501, 2016. a, b
Ronneberger, O., Fischer, P., and Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation, arXiv [preprint], https://doi.org/10.48550/ARXIV.1505.04597, 2015. a, b, c
Simonyan, K. and Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition, arXiv [preprint], https://doi.org/10.48550/arXiv.1409.1556, 2015. a, b, c
Singha, S., Johansson, M., Hughes, N., Hvidegaard, S. M., and Skourup, H.: Arctic Sea Ice Characterization Using Spaceborne Fully Polarimetric L-, C-, and X-Band SAR With Validation by Airborne Measurements, IEEE T. Geosci. Remote, 56, 3715–3734, https://doi.org/10.1109/TGRS.2018.2809504, 2018. a, b
Soh, L.-K. and Tsatsoulis, C.: Texture Analysis of SAR Sea Ice Imagery using Gray Level Co-occurrence Matrices, IEEE T. Geosci. Remote, 37, 780–795, https://doi.org/10.1109/36.752194, 1999. a
Song, W., Li, M., Gao, W., Huang, D., Ma, Z., Liotta, A., and Perra, C.: Automatic Sea-Ice Classification of SAR Images Based on Spatial and Temporal Features Learning, IEEE T. Geosci. Remote, 59, 9887–9901, https://doi.org/10.1109/TGRS.2020.3049031, 2021. a
Ullah, H., Khaleghian, S., Kromer, T., Eltoft, T., and Marinoni, A.: A Noise-Aware Deep Learning Model for Sea Ice Classification Based on Sentinel-1 Sar Imagery, in: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 816–819, https://doi.org/10.1109/IGARSS47720.2021.9553971, 2021. a, b
Wang, Y.-R. and Li, X.-M.: Arctic sea ice cover data from spaceborne synthetic aperture radar by deep learning, Earth Syst. Sci. Data, 13, 2723–2742, https://doi.org/10.5194/essd-13-2723-2021, 2021. a, b, c
Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., and Liang, J.: Unet++: A Nested U-Net Architecture for Medical Image Segmentation, in: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, arXiv [preprint], https://doi.org/10.48550/arXiv.1807.10165, 2018. a, b, c
Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., and Liang, J.: UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation, IEEE T. Med. Imaging, 39, 1856–1867, https://doi.org/10.1109/TMI.2019.2959609, 2020. a, b, c
Short summary
A dataset of 20 radar satellite acquisitions and near-simultaneous helicopter-based surveys of the ice topography during the MOSAiC expedition is constructed and used to train a variety of deep learning algorithms. The results give realistic insights into the accuracy of retrieval of measured ice classes using modern deep learning models. The models able to learn from the spatial distribution of the measured sea ice classes are shown to have a clear advantage over those that cannot.
A dataset of 20 radar satellite acquisitions and near-simultaneous helicopter-based surveys of...
