Articles | Volume 7, issue 4
https://doi.org/10.5194/tc-7-1315-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/tc-7-1315-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
19 Aug 2013
Research article |
| 19 Aug 2013
Waveform classification of airborne synthetic aperture radar altimeter over Arctic sea ice
M. Zygmuntowska
Nansen Environmental and Remote Sensing Center, Bergen, Norway
K. Khvorostovsky
Nansen Environmental and Remote Sensing Center, Bergen, Norway
Alfred Wegener Institute, Bremerhaven, Germany
S. Sandven
Nansen Environmental and Remote Sensing Center, Bergen, Norway
Related authors
M. Zygmuntowska, P. Rampal, N. Ivanova, and L. H. Smedsrud
The Cryosphere, 8, 705–720, https://doi.org/10.5194/tc-8-705-2014, https://doi.org/10.5194/tc-8-705-2014, 2014
Shfaqat A. Khan, Helene Seroussi, Mathieu Morlighem, William Colgan, Veit Helm, Gong Cheng, Danjal Berg, Valentina R. Barletta, Nicolaj K. Larsen, William Kochtitzky, Michiel van den Broeke, Kurt H. Kjær, Andy Aschwanden, Brice Noël, Jason E. Box, Joseph A. MacGregor, Robert S. Fausto, Kenneth D. Mankoff, Ian M. Howat, Kuba Oniszk, Dominik Fahrner, Anja Løkkegaard, Eigil Y. H. Lippert, Alicia Bråtner, and Javed Hassan
Earth Syst. Sci. Data, 17, 3047–3071, https://doi.org/10.5194/essd-17-3047-2025, https://doi.org/10.5194/essd-17-3047-2025, 2025
Short summary
Short summary
The surface elevation of the Greenland Ice Sheet is changing due to surface mass balance processes and ice dynamics, each exhibiting distinct spatiotemporal patterns. Here, we employ satellite and airborne altimetry data with fine spatial (1 km) and temporal (monthly) resolutions to document this spatiotemporal evolution from 2003 to 2023. This dataset of fine-resolution altimetry data in both space and time will support studies of ice mass loss and be useful for GIS ice sheet modeling.
Matthias O. Willen, Bert Wouters, Taco Broerse, Eric Buchta, and Veit Helm
The Cryosphere, 19, 2213–2227, https://doi.org/10.5194/tc-19-2213-2025, https://doi.org/10.5194/tc-19-2213-2025, 2025
Short summary
Short summary
Collapse of the West Antarctic Ice Sheet in the Amundsen Sea Embayment is likely in the near future. Vertical uplift of bedrock due to glacial isostatic adjustment stabilizes the ice sheet and may delay its collapse. So far, only spatially and temporally sparse GPS measurements have been able to observe this bedrock motion. We have combined satellite data and quantified a region-wide bedrock motion that independently matches GPS measurements. This can improve ice sheet predictions.
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.
Charlotte M. Carter, Steven Franke, Daniela Jansen, Chris R. Stokes, Veit Helm, John Paden, and Olaf Eisen
EGUsphere, https://doi.org/10.5194/egusphere-2025-1743, https://doi.org/10.5194/egusphere-2025-1743, 2025
Short summary
Short summary
The landscapes beneath actively fast-flowing ice in Greenland have not been explored in detail, as digital elevation models do not have a high enough resolution to see these subglacial features. We use swath radar imaging to visualise these landforms at a high resolution, revealing a landscape that would usually be assumed to be indicative of faster ice flow than the current velocities. Interpretation of the landscape also gives an indication of the properties of the bed beneath the ice stream.
Steven Franke, Daniel Steinhage, Veit Helm, Alexandra M. Zuhr, Julien A. Bodart, Olaf Eisen, and Paul Bons
The Cryosphere, 19, 1153–1180, https://doi.org/10.5194/tc-19-1153-2025, https://doi.org/10.5194/tc-19-1153-2025, 2025
Short summary
Short summary
The study presents internal reflection horizons (IRHs) over an area of 450 000 km² from western Dronning Maud Land, Antarctica, spanning 4.8–91 ka. Using radar and ice core data, nine IRHs were dated and correlated with volcanic events. The data enhance our understanding of the ice sheet's age–depth architecture, accumulation, and dynamics. The findings inform ice flow models and contribute to Antarctic-wide comparisons of IRHs, supporting efforts toward a 3D age–depth ice sheet model.
Christian T. Wild, Reinhard Drews, Niklas Neckel, Joohan Lee, Sihyung Kim, Hyangsun Han, Won Sang Lee, Veit Helm, Sebastian Harry Reid Rosier, Oliver J. Marsh, and Wolfgang Rack
EGUsphere, https://doi.org/10.5194/egusphere-2024-3593, https://doi.org/10.5194/egusphere-2024-3593, 2024
Short summary
Short summary
The stability of the Antarctic Ice Sheet depends on how resistance along the sides of large glaciers slows down the flow of ice into the ocean. We present a method to map ice strength using the effect of ocean tides on floating ice shelves. Incorporating weaker ice in shear zones improves the accuracy of model predictions compared to satellite observations. This demonstrates the untapped potential of radar satellites to map ice stiffness in the most critical areas for ice sheet stability.
Veit Helm, Alireza Dehghanpour, Ronny Hänsch, Erik Loebel, Martin Horwath, and Angelika Humbert
The Cryosphere, 18, 3933–3970, https://doi.org/10.5194/tc-18-3933-2024, https://doi.org/10.5194/tc-18-3933-2024, 2024
Short summary
Short summary
We present a new approach (AWI-ICENet1), based on a deep convolutional neural network, for analysing satellite radar altimeter measurements to accurately determine the surface height of ice sheets. Surface height estimates obtained with AWI-ICENet1 (along with related products, such as ice sheet height change and volume change) show improved and unbiased results compared to other products. This is important for the long-term monitoring of ice sheet mass loss and its impact on sea level rise.
Angelika Humbert, Veit Helm, Ole Zeising, Niklas Neckel, Matthias H. Braun, Shfaqat Abbas Khan, Martin Rückamp, Holger Steeb, Julia Sohn, Matthias Bohnen, and Ralf Müller
EGUsphere, https://doi.org/10.5194/egusphere-2024-1151, https://doi.org/10.5194/egusphere-2024-1151, 2024
Short summary
Short summary
We study the evolution of a massive lake on the Greenland Ice Sheet using satellite and airborne data and some modelling. The lake is emptying rapidly. The water flows to the base of the glacier through cracks and gullies that remain visible over years. Some of them become reactive. We find features inside the glacier that stem from the drainage events with even 1 km width. These features are persistent over the years, although they are changing in shape.
Ole Zeising, Niklas Neckel, Nils Dörr, Veit Helm, Daniel Steinhage, Ralph Timmermann, and Angelika Humbert
The Cryosphere, 18, 1333–1357, https://doi.org/10.5194/tc-18-1333-2024, https://doi.org/10.5194/tc-18-1333-2024, 2024
Short summary
Short summary
The 79° North Glacier in Greenland has experienced significant changes over the last decades. Due to extreme melt rates, the ice has thinned significantly in the vicinity of the grounding line, where a large subglacial channel has formed since 2010. We attribute these changes to warm ocean currents and increased subglacial discharge from surface melt. However, basal melting has decreased since 2018, indicating colder water inflow into the cavity below the glacier.
Matthias O. Willen, Martin Horwath, Eric Buchta, Mirko Scheinert, Veit Helm, Bernd Uebbing, and Jürgen Kusche
The Cryosphere, 18, 775–790, https://doi.org/10.5194/tc-18-775-2024, https://doi.org/10.5194/tc-18-775-2024, 2024
Short summary
Short summary
Shrinkage of the Antarctic ice sheet (AIS) leads to sea level rise. Satellite gravimetry measures AIS mass changes. We apply a new method that overcomes two limitations: low spatial resolution and large uncertainties due to the Earth's interior mass changes. To do so, we additionally include data from satellite altimetry and climate and firn modelling, which are evaluated in a globally consistent way with thoroughly characterized errors. The results are in better agreement with independent data.
Ailsa Chung, Frédéric Parrenin, Daniel Steinhage, Robert Mulvaney, Carlos Martín, Marie G. P. Cavitte, David A. Lilien, Veit Helm, Drew Taylor, Prasad Gogineni, Catherine Ritz, Massimo Frezzotti, Charles O'Neill, Heinrich Miller, Dorthe Dahl-Jensen, and Olaf Eisen
The Cryosphere, 17, 3461–3483, https://doi.org/10.5194/tc-17-3461-2023, https://doi.org/10.5194/tc-17-3461-2023, 2023
Short summary
Short summary
We combined a numerical model with radar measurements in order to determine the age of ice in the Dome C region of Antarctica. Our results show that at the current ice core drilling sites on Little Dome C, the maximum age of the ice is almost 1.5 Ma. We also highlight a new potential drill site called North Patch with ice up to 2 Ma. Finally, we explore the nature of a stagnant ice layer at the base of the ice sheet which has been independently observed and modelled but is not well understood.
Angelika Humbert, Veit Helm, Niklas Neckel, Ole Zeising, Martin Rückamp, Shfaqat Abbas Khan, Erik Loebel, Jörg Brauchle, Karsten Stebner, Dietmar Gross, Rabea Sondershaus, and Ralf Müller
The Cryosphere, 17, 2851–2870, https://doi.org/10.5194/tc-17-2851-2023, https://doi.org/10.5194/tc-17-2851-2023, 2023
Short summary
Short summary
The largest floating glacier mass in Greenland, the 79° N Glacier, is showing signs of instability. We investigate how crack formation at the glacier's calving front has changed over the last decades by using satellite imagery and airborne data. The calving front is about to lose contact to stabilizing ice islands. Simulations show that the glacier will accelerate as a result of this, leading to an increase in ice discharge of more than 5.1 % if its calving front retreats by 46 %.
Michael J. Bentley, James A. Smith, Stewart S. R. Jamieson, Margaret R. Lindeman, Brice R. Rea, Angelika Humbert, Timothy P. Lane, Christopher M. Darvill, Jeremy M. Lloyd, Fiamma Straneo, Veit Helm, and David H. Roberts
The Cryosphere, 17, 1821–1837, https://doi.org/10.5194/tc-17-1821-2023, https://doi.org/10.5194/tc-17-1821-2023, 2023
Short summary
Short summary
The Northeast Greenland Ice Stream is a major outlet of the Greenland Ice Sheet. Some of its outlet glaciers and ice shelves have been breaking up and retreating, with inflows of warm ocean water identified as the likely reason. Here we report direct measurements of warm ocean water in an unusual lake that is connected to the ocean beneath the ice shelf in front of the 79° N Glacier. This glacier has not yet shown much retreat, but the presence of warm water makes future retreat more likely.
Inès N. Otosaka, Andrew Shepherd, Erik R. Ivins, Nicole-Jeanne Schlegel, Charles Amory, Michiel R. van den Broeke, Martin Horwath, Ian Joughin, Michalea D. King, Gerhard Krinner, Sophie Nowicki, Anthony J. Payne, Eric Rignot, Ted Scambos, Karen M. Simon, Benjamin E. Smith, Louise S. Sørensen, Isabella Velicogna, Pippa L. Whitehouse, Geruo A, Cécile Agosta, Andreas P. Ahlstrøm, Alejandro Blazquez, William Colgan, Marcus E. Engdahl, Xavier Fettweis, Rene Forsberg, Hubert Gallée, Alex Gardner, Lin Gilbert, Noel Gourmelen, Andreas Groh, Brian C. Gunter, Christopher Harig, Veit Helm, Shfaqat Abbas Khan, Christoph Kittel, Hannes Konrad, Peter L. Langen, Benoit S. Lecavalier, Chia-Chun Liang, Bryant D. Loomis, Malcolm McMillan, Daniele Melini, Sebastian H. Mernild, Ruth Mottram, Jeremie Mouginot, Johan Nilsson, Brice Noël, Mark E. Pattle, William R. Peltier, Nadege Pie, Mònica Roca, Ingo Sasgen, Himanshu V. Save, Ki-Weon Seo, Bernd Scheuchl, Ernst J. O. Schrama, Ludwig Schröder, Sebastian B. Simonsen, Thomas Slater, Giorgio Spada, Tyler C. Sutterley, Bramha Dutt Vishwakarma, Jan Melchior van Wessem, David Wiese, Wouter van der Wal, and Bert Wouters
Earth Syst. Sci. Data, 15, 1597–1616, https://doi.org/10.5194/essd-15-1597-2023, https://doi.org/10.5194/essd-15-1597-2023, 2023
Short summary
Short summary
By measuring changes in the volume, gravitational attraction, and ice flow of Greenland and Antarctica from space, we can monitor their mass gain and loss over time. Here, we present a new record of the Earth’s polar ice sheet mass balance produced by aggregating 50 satellite-based estimates of ice sheet mass change. This new assessment shows that the ice sheets have lost (7.5 x 1012) t of ice between 1992 and 2020, contributing 21 mm to sea level rise.
Angelika Humbert, Julia Christmann, Hugh F. J. Corr, Veit Helm, Lea-Sophie Höyns, Coen Hofstede, Ralf Müller, Niklas Neckel, Keith W. Nicholls, Timm Schultz, Daniel Steinhage, Michael Wolovick, and Ole Zeising
The Cryosphere, 16, 4107–4139, https://doi.org/10.5194/tc-16-4107-2022, https://doi.org/10.5194/tc-16-4107-2022, 2022
Short summary
Short summary
Ice shelves are normally flat structures that fringe the Antarctic continent. At some locations they have channels incised into their underside. On Filchner Ice Shelf, such a channel is more than 50 km long and up to 330 m high. We conducted field measurements of basal melt rates and found a maximum of 2 m yr−1. Simulations represent the geometry evolution of the channel reasonably well. There is no reason to assume that this type of melt channel is destabilizing ice shelves.
Steven Franke, Daniela Jansen, Tobias Binder, John D. Paden, Nils Dörr, Tamara A. Gerber, Heinrich Miller, Dorthe Dahl-Jensen, Veit Helm, Daniel Steinhage, Ilka Weikusat, Frank Wilhelms, and Olaf Eisen
Earth Syst. Sci. Data, 14, 763–779, https://doi.org/10.5194/essd-14-763-2022, https://doi.org/10.5194/essd-14-763-2022, 2022
Short summary
Short summary
The Northeast Greenland Ice Stream (NEGIS) is the largest ice stream in Greenland. In order to better understand the past and future dynamics of the NEGIS, we present a high-resolution airborne radar data set (EGRIP-NOR-2018) for the onset region of the NEGIS. The survey area is centered at the location of the drill site of the East Greenland Ice-Core Project (EastGRIP), and radar profiles cover both shear margins and are aligned parallel to several flow lines.
Coen Hofstede, Sebastian Beyer, Hugh Corr, Olaf Eisen, Tore Hattermann, Veit Helm, Niklas Neckel, Emma C. Smith, Daniel Steinhage, Ole Zeising, and Angelika Humbert
The Cryosphere, 15, 1517–1535, https://doi.org/10.5194/tc-15-1517-2021, https://doi.org/10.5194/tc-15-1517-2021, 2021
Short summary
Short summary
Support Force Glacier rapidly flows into Filcher Ice Shelf of Antarctica. As we know little about this glacier and its subglacial drainage, we used seismic energy to map the transition area from grounded to floating ice where a drainage channel enters the ocean cavity. Soft sediments close to the grounding line are probably transported by this drainage channel. The constant ice thickness over the steeply dipping seabed of the ocean cavity suggests a stable transition and little basal melting.
Stefan Kowalewski, Veit Helm, Elizabeth Mary Morris, and Olaf Eisen
The Cryosphere, 15, 1285–1305, https://doi.org/10.5194/tc-15-1285-2021, https://doi.org/10.5194/tc-15-1285-2021, 2021
Short summary
Short summary
This study presents estimates of total mass input for the Pine Island Glacier (PIG) over the period 2005–2014 from airborne radar measurements. Our analysis reveals a total mass input similar to an earlier estimate for the period 1985–2009 and same area. This suggests a stationary total mass input contrary to the accelerated mass loss of PIG over the past decades. However, we also find that its uncertainty is highly sensitive to the geostatistical assumptions required for its calculation.
Evelyn Jäkel, Tim Carlsen, André Ehrlich, Manfred Wendisch, Michael Schäfer, Sophie Rosenburg, Konstantina Nakoudi, Marco Zanatta, Gerit Birnbaum, Veit Helm, Andreas Herber, Larysa Istomina, Linlu Mei, and Anika Rohde
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-14, https://doi.org/10.5194/tc-2021-14, 2021
Preprint withdrawn
Short summary
Short summary
Different approaches to retrieve the optical-equivalent snow grain size using satellite, airborne, and ground-based observations were evaluated and compared to modeled data. The study is focused on low Sun and partly rough surface conditions encountered North of Greenland in March/April 2018. We proposed an adjusted airborne retrieval method to reduce the retrieval uncertainty.
Tim Carlsen, Gerit Birnbaum, André Ehrlich, Veit Helm, Evelyn Jäkel, Michael Schäfer, and Manfred Wendisch
The Cryosphere, 14, 3959–3978, https://doi.org/10.5194/tc-14-3959-2020, https://doi.org/10.5194/tc-14-3959-2020, 2020
Short summary
Short summary
The angular reflection of solar radiation by snow surfaces is particularly anisotropic and highly variable. We measured the angular reflection from an aircraft using a digital camera in Antarctica in 2013/14 and studied its variability: the anisotropy increases with a lower Sun but decreases for rougher surfaces and larger snow grains. The applied methodology allows for a direct comparison with satellite observations, which generally underestimated the anisotropy measured within this study.
Marco Meloni, Jerome Bouffard, Tommaso Parrinello, Geoffrey Dawson, Florent Garnier, Veit Helm, Alessandro Di Bella, Stefan Hendricks, Robert Ricker, Erica Webb, Ben Wright, Karina Nielsen, Sanggyun Lee, Marcello Passaro, Michele Scagliola, Sebastian Bjerregaard Simonsen, Louise Sandberg Sørensen, David Brockley, Steven Baker, Sara Fleury, Jonathan Bamber, Luca Maestri, Henriette Skourup, René Forsberg, and Loretta Mizzi
The Cryosphere, 14, 1889–1907, https://doi.org/10.5194/tc-14-1889-2020, https://doi.org/10.5194/tc-14-1889-2020, 2020
Short summary
Short summary
This manuscript aims to describe the evolutions which have been implemented in the new CryoSat Ice processing chain Baseline-D and the validation activities carried out in different domains such as sea ice, land ice and hydrology.
This new CryoSat processing Baseline-D will maximise the uptake and use of CryoSat data by scientific users since it offers improved capability for monitoring the complex and multiscale changes over the cryosphere.
Timo Vihma, Petteri Uotila, Stein Sandven, Dmitry Pozdnyakov, Alexander Makshtas, Alexander Pelyasov, Roberta Pirazzini, Finn Danielsen, Sergey Chalov, Hanna K. Lappalainen, Vladimir Ivanov, Ivan Frolov, Anna Albin, Bin Cheng, Sergey Dobrolyubov, Viktor Arkhipkin, Stanislav Myslenkov, Tuukka Petäjä, and Markku Kulmala
Atmos. Chem. Phys., 19, 1941–1970, https://doi.org/10.5194/acp-19-1941-2019, https://doi.org/10.5194/acp-19-1941-2019, 2019
Short summary
Short summary
The Arctic marine climate system, ecosystems, and socio-economic systems are changing rapidly. This calls for the establishment of a marine Arctic component of the Pan-Eurasian Experiment (MA-PEEX), for which we present a plan. The program will promote international collaboration; sustainable marine meteorological, sea ice, and oceanographic observations; advanced data management; and multidisciplinary research on the marine Arctic and its interaction with the Eurasian continent.
Ludwig Schröder, Martin Horwath, Reinhard Dietrich, Veit Helm, Michiel R. van den Broeke, and Stefan R. M. Ligtenberg
The Cryosphere, 13, 427–449, https://doi.org/10.5194/tc-13-427-2019, https://doi.org/10.5194/tc-13-427-2019, 2019
Short summary
Short summary
We developed an approach to combine measurements of seven satellite altimetry missions over the Antarctic Ice Sheet. Our resulting monthly grids of elevation changes between 1978 and 2017 provide unprecedented details of the long-term and interannual variation. Derived mass changes agree well with contemporaneous data of surface mass balance and satellite gravimetry and show which regions were responsible for the significant accelerations of mass loss in recent years.
Thomas Lavergne, Atle Macdonald Sørensen, Stefan Kern, Rasmus Tonboe, Dirk Notz, Signe Aaboe, Louisa Bell, Gorm Dybkjær, Steinar Eastwood, Carolina Gabarro, Georg Heygster, Mari Anne Killie, Matilde Brandt Kreiner, John Lavelle, Roberto Saldo, Stein Sandven, and Leif Toudal Pedersen
The Cryosphere, 13, 49–78, https://doi.org/10.5194/tc-13-49-2019, https://doi.org/10.5194/tc-13-49-2019, 2019
Short summary
Short summary
The loss of polar sea ice is an iconic indicator of Earth’s climate change. Many satellite-based algorithms and resulting data exist but they differ widely in specific sea-ice conditions. This spread hinders a robust estimate of the future evolution of sea-ice cover.
In this study, we document three new climate data records of sea-ice concentration generated using satellite data available over the last 40 years. We introduce the novel algorithms, the data records, and their uncertainties.
Nanna B. Karlsson, Tobias Binder, Graeme Eagles, Veit Helm, Frank Pattyn, Brice Van Liefferinge, and Olaf Eisen
The Cryosphere, 12, 2413–2424, https://doi.org/10.5194/tc-12-2413-2018, https://doi.org/10.5194/tc-12-2413-2018, 2018
Short summary
Short summary
In this study, we investigate the probability that the Dome Fuji region in East Antarctica contains ice more than 1.5 Ma old. The retrieval of a continuous ice-core record extending beyond 1 Ma is imperative to understand why the frequency of ice ages changed from 40 to 100 ka approximately 1 Ma ago.
We use a new radar dataset to improve the ice thickness maps, and apply a thermokinematic model to predict basal temperature and age of the ice. Our results indicate several areas of interest.
Elena V. Shalina and Stein Sandven
The Cryosphere, 12, 1867–1886, https://doi.org/10.5194/tc-12-1867-2018, https://doi.org/10.5194/tc-12-1867-2018, 2018
Short summary
Short summary
In this paper we analyze snow data from Soviet airborne expeditions, Sever, which operated in late winter 1959-1986, in the Arctic and made snow measurements on the ice around plane landing sites. The snow measurements were made on the multiyear ice in the central Arctic and on the first-year ice in the Eurasian seas in the areas for which snow characteristics are poorly described in the literature. The main goal of this study is to produce an improved data set of snow depth on the sea ice.
Sophie Berger, Reinhard Drews, Veit Helm, Sainan Sun, and Frank Pattyn
The Cryosphere, 11, 2675–2690, https://doi.org/10.5194/tc-11-2675-2017, https://doi.org/10.5194/tc-11-2675-2017, 2017
Short summary
Short summary
Floating ice shelves act as a plug for the Antarctic ice sheet. The efficiency of this ice plug depends on how and how much the ocean melts the ice from below. This study relies on satellite imagery and a Lagrangian approach to map in detail the basal mass balance of an Antarctic ice shelf. Although the large-scale melting pattern of the ice shelf agrees with previous studies, our technique successfully detects local variability (< 1 km) in the basal melting of the ice shelf.
Stefan Muckenhuber and Stein Sandven
The Cryosphere, 11, 1835–1850, https://doi.org/10.5194/tc-11-1835-2017, https://doi.org/10.5194/tc-11-1835-2017, 2017
Short summary
Short summary
Sea ice drift has a strong impact on sea ice distribution on different temporal and spatial scales. An open-source sea ice drift algorithm for Sentinel-1 satellite imagery is introduced based on the combination of feature tracking and pattern matching. The algorithm is designed to utilise the respective advantages of the two approaches and allows drift calculation at user-defined locations.
Christopher J. Merchant, Frank Paul, Thomas Popp, Michael Ablain, Sophie Bontemps, Pierre Defourny, Rainer Hollmann, Thomas Lavergne, Alexandra Laeng, Gerrit de Leeuw, Jonathan Mittaz, Caroline Poulsen, Adam C. Povey, Max Reuter, Shubha Sathyendranath, Stein Sandven, Viktoria F. Sofieva, and Wolfgang Wagner
Earth Syst. Sci. Data, 9, 511–527, https://doi.org/10.5194/essd-9-511-2017, https://doi.org/10.5194/essd-9-511-2017, 2017
Short summary
Short summary
Climate data records (CDRs) contain data describing Earth's climate and should address uncertainty in the data to communicate what is known about climate variability or change and what range of doubt exists. This paper discusses good practice for including uncertainty information in CDRs for the essential climate variables (ECVs) derived from satellite data. Recommendations emerge from the shared experience of diverse ECV projects within the European Space Agency Climate Change Initiative.
Kirill Khvorostovsky and Pierre Rampal
The Cryosphere, 10, 2329–2346, https://doi.org/10.5194/tc-10-2329-2016, https://doi.org/10.5194/tc-10-2329-2016, 2016
Short summary
Short summary
We analyse two methods of freeboard retrieval from ICESat satellite data that were used to derive the two widely used Arctic sea ice thickness products. We show that although different factors result in significant local differences between freeboards, they roughly compensate each other with respect to overall freeboard estimation. Thus the difference found between the sea ice thickness datasets should be attributed to different parameters used in the freeboard-to-thickness conversion.
Thomas B. Overly, Robert L. Hawley, Veit Helm, Elizabeth M. Morris, and Rohan N. Chaudhary
The Cryosphere, 10, 1679–1694, https://doi.org/10.5194/tc-10-1679-2016, https://doi.org/10.5194/tc-10-1679-2016, 2016
Short summary
Short summary
We demonstrate that snow accumulation rates across the Greenland Ice Sheet, determined from RADAR layers and modeled snow density profiles, are identical to ground-based measurements of snow accumulation. Three regional climate models underestimate snow accumulation compared to RADAR layer estimates. Using RADAR increases spatial coverage and improves accuracy of snow accumulation estimates. Incorporating our results into climate models may reduce uncertainty of sea-level rise estimates.
Sandra Schwegmann, Eero Rinne, Robert Ricker, Stefan Hendricks, and Veit Helm
The Cryosphere, 10, 1415–1425, https://doi.org/10.5194/tc-10-1415-2016, https://doi.org/10.5194/tc-10-1415-2016, 2016
Short summary
Short summary
Our study aimed to investigate whether CS-2 and Envisat radar freeboard can be merged without intermission biases in order to obtain a 20-year data set. The comparison revealed a reasonable regional agreement between radar freeboards derived from both sensors. Differences are mostly below 0.1 m for modal freeboard and even less for mean freeboard over winter months (May–October). The highest differences occur in regions with multi-year sea ice and along the coasts.
S. Goeller, V. Helm, M. Thoma, and K. Grosfeld
The Cryosphere Discuss., https://doi.org/10.5194/tcd-9-3995-2015, https://doi.org/10.5194/tcd-9-3995-2015, 2015
Revised manuscript has not been submitted
Short summary
Short summary
The Ross Ice Streams in West Antarctica showed a high variability in the past. We model basal water pathways and catchment areas for present and future ice sheet geometries (gained by applying satellite-derived elevation change rates) in this sector. Thus, we can explain the current ice stream configuration and estimate implications for the next two centuries, where we find that a major basal hydraulic tributary of the Kamb and Whillans IS could be redirected underneath the Bindschadler IS.
S. Kern, K. Khvorostovsky, H. Skourup, E. Rinne, Z. S. Parsakhoo, V. Djepa, P. Wadhams, and S. Sandven
The Cryosphere, 9, 37–52, https://doi.org/10.5194/tc-9-37-2015, https://doi.org/10.5194/tc-9-37-2015, 2015
Short summary
Short summary
Snow depth and ice density are equally important parameters for sea ice thickness retrieval from radar altimetry of Arctic sea ice. Development of a new snow depth data set is mandatory as the Warren snow depth climatology does not represent the actual snow depth distribution. An optimal choice of ice density can be realized by including ice type and degree of deformation. Retrieval and validation enhancement requires more contemporary ice freeboard, thickness, and density and snow depth data.
R. T. W. L. Hurkmans, J. L. Bamber, C. H. Davis, I. R. Joughin, K. S. Khvorostovsky, B. S. Smith, and N. Schoen
The Cryosphere, 8, 1725–1740, https://doi.org/10.5194/tc-8-1725-2014, https://doi.org/10.5194/tc-8-1725-2014, 2014
R. Ricker, S. Hendricks, V. Helm, H. Skourup, and M. Davidson
The Cryosphere, 8, 1607–1622, https://doi.org/10.5194/tc-8-1607-2014, https://doi.org/10.5194/tc-8-1607-2014, 2014
V. Helm, A. Humbert, and H. Miller
The Cryosphere, 8, 1539–1559, https://doi.org/10.5194/tc-8-1539-2014, https://doi.org/10.5194/tc-8-1539-2014, 2014
M. Zygmuntowska, P. Rampal, N. Ivanova, and L. H. Smedsrud
The Cryosphere, 8, 705–720, https://doi.org/10.5194/tc-8-705-2014, https://doi.org/10.5194/tc-8-705-2014, 2014
L. Gray, D. Burgess, L. Copland, R. Cullen, N. Galin, R. Hawley, and V. Helm
The Cryosphere, 7, 1857–1867, https://doi.org/10.5194/tc-7-1857-2013, https://doi.org/10.5194/tc-7-1857-2013, 2013
J. F. Levinsen, K. Khvorostovsky, F. Ticconi, A. Shepherd, R. Forsberg, L. S. Sørensen, A. Muir, N. Pie, D. Felikson, T. Flament, R. Hurkmans, G. Moholdt, B. Gunter, R. C. Lindenbergh, and M. Kleinherenbrink
The Cryosphere Discuss., https://doi.org/10.5194/tcd-7-5433-2013, https://doi.org/10.5194/tcd-7-5433-2013, 2013
Revised manuscript not accepted
Related subject area
Sea Ice
Spring 2021 sea ice transport in the southern Beaufort Sea occurred during coastal-lead opening events
National Weather Service Alaska Sea Ice Program: gridded ice concentration maps for the Alaskan Arctic
Improving Seasonal Arctic Sea Ice Predictions with the Combination of Machine Learning and Earth System Model
Estimation of duration and its changes in Lagrangian observations relying on ice floes in the Arctic Ocean utilizing sea ice motion product
Seasonal evolution of the sea ice floe size distribution in the Beaufort Sea from 2 decades of MODIS data
Suitability of the CICE sea ice model for seasonal prediction and positive impact of CryoSat-2 ice thickness initialization
Seasonality and scenario dependence of rapid Arctic sea ice loss events in CMIP6 simulations
A large-scale high-resolution numerical model for sea-ice fragmentation dynamics
Experimental modelling of the growth of tubular ice brinicles from brine flows under sea ice
Why is summertime Arctic sea ice drift speed projected to decrease?
Developing a deep learning forecasting system for short-term and high-resolution prediction of sea ice concentration
Impact of atmospheric rivers on Arctic sea ice variations
The impacts of anomalies in atmospheric circulations on Arctic sea ice outflow and sea ice conditions in the Barents and Greenland seas: case study in 2020
Atmospheric highs drive asymmetric sea ice drift during lead opening from Point Barrow
Spatial characteristics of frazil streaks in the Terra Nova Bay Polynya from high-resolution visible satellite imagery
Modelling the evolution of Arctic multiyear sea ice over 2000–2018
A quasi-objective single-buoy approach for understanding Lagrangian coherent structures and sea ice dynamics
Linking scales of sea ice surface topography: evaluation of ICESat-2 measurements with coincident helicopter laser scanning during MOSAiC
Analysis of microseismicity in sea ice with deep learning and Bayesian inference: application to high-resolution thickness monitoring
A collection of wet beam models for wave–ice interaction
First results of Antarctic sea ice type retrieval from active and passive microwave remote sensing data
Probabilistic spatiotemporal seasonal sea ice presence forecasting using sequence-to-sequence learning and ERA5 data in the Hudson Bay region
Predictability of Arctic sea ice drift in coupled climate models
Recovering and monitoring the thickness, density, and elastic properties of sea ice from seismic noise recorded in Svalbard
Influences of changing sea ice and snow thicknesses on simulated Arctic winter heat fluxes
Reassessing seasonal sea ice predictability of the Pacific-Arctic sector using a Markov model
A new state-dependent parameterization for the free drift of sea ice
Arctic sea ice sensitivity to lateral melting representation in a coupled climate model
Retrieval and parameterisation of sea-ice bulk density from airborne multi-sensor measurements
A generalized stress correction scheme for the Maxwell elasto-brittle rheology: impact on the fracture angles and deformations
Wave dispersion and dissipation in landfast ice: comparison of observations against models
The influence of snow on sea ice as assessed from simulations of CESM2
Meltwater sources and sinks for multiyear Arctic sea ice in summer
An X-ray micro-tomographic study of the pore space, permeability and percolation threshold of young sea ice
Calibration of sea ice drift forecasts using random forest algorithms
Multiscale variations in Arctic sea ice motion and links to atmospheric and oceanic conditions
The flexural strength of bonded ice
Interannual variability in Transpolar Drift summer sea ice thickness and potential impact of Atlantification
An inter-comparison of the mass budget of the Arctic sea ice in CMIP6 models
Refining the sea surface identification approach for determining freeboards in the ICESat-2 sea ice products
Surface-based Ku- and Ka-band polarimetric radar for sea ice studies
Statistical predictability of the Arctic sea ice volume anomaly: identifying predictors and optimal sampling locations
Satellite-based sea ice thickness changes in the Laptev Sea from 2002 to 2017: comparison to mooring observations
Modeling the annual cycle of daily Antarctic sea ice extent
Changes of the Arctic marginal ice zone during the satellite era
An enhancement to sea ice motion and age products at the National Snow and Ice Data Center (NSIDC)
Accuracy and inter-analyst agreement of visually estimated sea ice concentrations in Canadian Ice Service ice charts using single-polarization RADARSAT-2
Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks
Variability scaling and consistency in airborne and satellite altimetry measurements of Arctic sea ice
Sea ice volume variability and water temperature in the Greenland Sea
MacKenzie E. Jewell, Jennifer K. Hutchings, and Angela C. Bliss
The Cryosphere, 19, 1413–1430, https://doi.org/10.5194/tc-19-1413-2025, https://doi.org/10.5194/tc-19-1413-2025, 2025
Short summary
Short summary
We quantify the contribution of sea ice drift during coastal-lead events to seasonal ice transport in the Beaufort Sea. Data from GPS tracker buoys deployed as part of the Sea Ice Dynamic Experiment (SIDEx) in spring 2021 show that sea ice in the southern Beaufort Sea became more responsive to wind forcing during fracturing events detected from satellite observations. Nearly all the spring 2021 sea ice transport occurred during these events, highlighting their importance at seasonal timescales.
Astrid Pacini, Michael Steele, and Mary-Beth Schreck
The Cryosphere, 19, 1391–1411, https://doi.org/10.5194/tc-19-1391-2025, https://doi.org/10.5194/tc-19-1391-2025, 2025
Short summary
Short summary
While sea ice concentration data are critically important for climate research, obtaining high-resolution data remains a challenge. Here we present and validate the US National Weather Service Alaska Sea Ice Program (ASIP) ice maps. These maps are shown to be highly accurate when compared to in situ observations and to outperform a passive-microwave-based product, especially at low concentrations. Therefore, ASIP data provide an exciting new tool to study ice conditions in the Pacific Arctic.
Zikang He, Yiguo Wang, Julien Brajard, Xidong Wang, and Zheqi Shen
EGUsphere, https://doi.org/10.5194/egusphere-2024-4092, https://doi.org/10.5194/egusphere-2024-4092, 2025
Short summary
Short summary
Declining Arctic sea ice presents both risks and opportunities for ecosystems, communities, and economic activities. To address prediction errors in dynamical models, we apply machine learning for error correction during prediction (online) or post-processing (offline). Our results show both methods enhance sea ice predictions, particularly from September to January, with offline corrections outperforming online corrections.
Fanyi Zhang, Ruibo Lei, Meng Qu, Na Li, Ying Chen, and Xiaoping Pang
EGUsphere, https://doi.org/10.5194/egusphere-2024-2723, https://doi.org/10.5194/egusphere-2024-2723, 2024
Short summary
Short summary
We reconstructed sea ice drift trajectories and identified optimal deployment areas for Lagrangian observations. It revealed a preference for ice advection towards the Transpolar Drift region over the Beaufort Gyre, with endpoints influenced by large-scale atmospheric circulation patterns. This study could help the future ice camp/buoy deployment strategies, ensuring the sustainability of crucial Arctic observations in the face of changing environmental conditions.
Ellen M. Buckley, Leela Cañuelas, Mary-Louise Timmermans, and Monica M. Wilhelmus
The Cryosphere, 18, 5031–5043, https://doi.org/10.5194/tc-18-5031-2024, https://doi.org/10.5194/tc-18-5031-2024, 2024
Short summary
Short summary
Arctic sea ice cover evolves seasonally from large plates separated by long, linear leads in the winter to a mosaic of smaller sea ice floes in the summer. Here, we present a new image segmentation algorithm applied to thousands of images and identify over 9 million individual pieces of ice. We observe the characteristics of the floes and how they evolve throughout the summer as the ice breaks up.
Shan Sun and Amy Solomon
The Cryosphere, 18, 3033–3048, https://doi.org/10.5194/tc-18-3033-2024, https://doi.org/10.5194/tc-18-3033-2024, 2024
Short summary
Short summary
The study brings to light the suitability of CICE for seasonal prediction being contingent on several factors, such as initial conditions like sea ice coverage and thickness, as well as atmospheric and oceanic conditions including oceanic currents and sea surface temperature. We show there is potential to improve seasonal forecasting by using a more reliable sea ice thickness initialization. Thus, data assimilation of sea ice thickness is highly relevant for advancing seasonal prediction skills.
Annelies Sticker, François Massonnet, Thierry Fichefet, Patricia DeRepentigny, Alexandra Jahn, David Docquier, Christopher Wyburn-Powell, Daphne Quint, Erica Shivers, and Makayla Ortiz
EGUsphere, https://doi.org/10.5194/egusphere-2024-1873, https://doi.org/10.5194/egusphere-2024-1873, 2024
Short summary
Short summary
Our study analyses rapid Arctic sea ice loss events (RILEs), which are significant reductions in sea ice extent. RILEs are expected throughout the year, varying in frequency and duration with the seasons. Our research gives a year-round analysis of their characteristics in climate models and suggests that summer RILEs could begin before the mid-century. Understanding these events is crucial as they can have profound impacts on the Arctic environment.
Jan Åström, Fredrik Robertsen, Jari Haapala, Arttu Polojärvi, Rivo Uiboupin, and Ilja Maljutenko
The Cryosphere, 18, 2429–2442, https://doi.org/10.5194/tc-18-2429-2024, https://doi.org/10.5194/tc-18-2429-2024, 2024
Short summary
Short summary
The HiDEM code has been developed for analyzing the fracture and fragmentation of brittle materials and has been extensively applied to glacier calving. Here, we report on the adaptation of the code to sea-ice dynamics and breakup. The code demonstrates the capability to simulate sea-ice dynamics on a 100 km scale with an unprecedented resolution. We argue that codes of this type may become useful for improving forecasts of sea-ice dynamics.
Sergio Testón-Martínez, Laura M. Barge, Jan Eichler, C. Ignacio Sainz-Díaz, and Julyan H. E. Cartwright
The Cryosphere, 18, 2195–2205, https://doi.org/10.5194/tc-18-2195-2024, https://doi.org/10.5194/tc-18-2195-2024, 2024
Short summary
Short summary
Brinicles are tubular ice structures that grow under the sea ice in cold regions. This happens because the salty water going downwards from the sea ice is colder than the seawater. We have successfully recreated an analogue of these structures in our laboratory. Three methods were used, producing different results. In this paper, we explain how to use these methods and study the behaviour of the brinicles created when changing the flow of water and study the importance for natural brinicles.
Jamie L. Ward and Neil F. Tandon
The Cryosphere, 18, 995–1012, https://doi.org/10.5194/tc-18-995-2024, https://doi.org/10.5194/tc-18-995-2024, 2024
Short summary
Short summary
Over the long term, the speed at which sea ice in the Arctic moves has been increasing during all seasons. However, nearly all climate models project that sea ice motion will decrease during summer. This study aims to understand the mechanisms responsible for these projected decreases in summertime sea ice motion. We find that models produce changes in winds and ocean surface tilt which cause the sea ice to slow down, and it is realistic to expect such changes to also occur in the real world.
Are Frode Kvanum, Cyril Palerme, Malte Müller, Jean Rabault, and Nick Hughes
EGUsphere, https://doi.org/10.5194/egusphere-2023-3107, https://doi.org/10.5194/egusphere-2023-3107, 2024
Short summary
Short summary
Recent studies have shown that machine learning models are effective at predicting sea ice concentration, yet few have explored the development of such models in an operational context. In this study, we present the development of a machine learning forecasting system which can predict sea ice concentration at 1 km resolution, up to 3 days ahead using real time operational data. The developed forecasts predict the sea ice edge position with a better accuracy than physical and baseline forecasts.
Linghan Li, Forest Cannon, Matthew R. Mazloff, Aneesh C. Subramanian, Anna M. Wilson, and Fred Martin Ralph
The Cryosphere, 18, 121–137, https://doi.org/10.5194/tc-18-121-2024, https://doi.org/10.5194/tc-18-121-2024, 2024
Short summary
Short summary
We investigate how the moisture transport through atmospheric rivers influences Arctic sea ice variations using hourly atmospheric ERA5 for 1981–2020 at 0.25° × 0.25° resolution. We show that individual atmospheric rivers initiate rapid sea ice decrease through surface heat flux and winds. We find that the rate of change in sea ice concentration has significant anticorrelation with moisture, northward wind and turbulent heat flux on weather timescales almost everywhere in the Arctic Ocean.
Fanyi Zhang, Ruibo Lei, Mengxi Zhai, Xiaoping Pang, and Na Li
The Cryosphere, 17, 4609–4628, https://doi.org/10.5194/tc-17-4609-2023, https://doi.org/10.5194/tc-17-4609-2023, 2023
Short summary
Short summary
Atmospheric circulation anomalies lead to high Arctic sea ice outflow in winter 2020, causing heavy ice conditions in the Barents–Greenland seas, subsequently impeding the sea surface temperature warming. This suggests that the winter–spring Arctic sea ice outflow can be considered a predictor of changes in sea ice and other marine environmental conditions in the Barents–Greenland seas, which could help to improve our understanding of the physical connections between them.
MacKenzie E. Jewell, Jennifer K. Hutchings, and Cathleen A. Geiger
The Cryosphere, 17, 3229–3250, https://doi.org/10.5194/tc-17-3229-2023, https://doi.org/10.5194/tc-17-3229-2023, 2023
Short summary
Short summary
Sea ice repeatedly fractures near a prominent Alaskan headland as winds move ice along the coast, challenging predictions of sea ice drift. We find winds from high-pressure systems drive these fracturing events, and the Alaskan coastal boundary modifies the resultant ice drift. This observational study shows how wind patterns influence sea ice motion near coasts in winter. Identified relations between winds, ice drift, and fracturing provide effective test cases for dynamic sea ice models.
Katarzyna Bradtke and Agnieszka Herman
The Cryosphere, 17, 2073–2094, https://doi.org/10.5194/tc-17-2073-2023, https://doi.org/10.5194/tc-17-2073-2023, 2023
Short summary
Short summary
The frazil streaks are one of the visible signs of complex interactions between the mixed-layer dynamics and the forming sea ice. Using high-resolution visible satellite imagery we characterize their spatial properties, relationship with the meteorological forcing, and role in modifying wind-wave growth in the Terra Nova Bay Polynya. We provide a simple statistical tool for estimating the extent and ice coverage of the region of high ice production under given wind speed and air temperature.
Heather Regan, Pierre Rampal, Einar Ólason, Guillaume Boutin, and Anton Korosov
The Cryosphere, 17, 1873–1893, https://doi.org/10.5194/tc-17-1873-2023, https://doi.org/10.5194/tc-17-1873-2023, 2023
Short summary
Short summary
Multiyear ice (MYI), sea ice that survives the summer, is more resistant to changes than younger ice in the Arctic, so it is a good indicator of sea ice resilience. We use a model with a new way of tracking MYI to assess the contribution of different processes affecting MYI. We find two important years for MYI decline: 2007, when dynamics are important, and 2012, when melt is important. These affect MYI volume and area in different ways, which is important for the interpretation of observations.
Nikolas O. Aksamit, Randall K. Scharien, Jennifer K. Hutchings, and Jennifer V. Lukovich
The Cryosphere, 17, 1545–1566, https://doi.org/10.5194/tc-17-1545-2023, https://doi.org/10.5194/tc-17-1545-2023, 2023
Short summary
Short summary
Coherent flow patterns in sea ice have a significant influence on sea ice fracture and refreezing. We can better understand the state of sea ice, and its influence on the atmosphere and ocean, if we understand these structures. By adapting recent developments in chaotic dynamical systems, we are able to approximate ice stretching surrounding individual ice buoys. This illuminates the state of sea ice at much higher resolution and allows us to see previously invisible ice deformation patterns.
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.
Ludovic Moreau, Léonard Seydoux, Jérôme Weiss, and Michel Campillo
The Cryosphere, 17, 1327–1341, https://doi.org/10.5194/tc-17-1327-2023, https://doi.org/10.5194/tc-17-1327-2023, 2023
Short summary
Short summary
In the perspective of an upcoming seasonally ice-free Arctic, understanding the dynamics of sea ice in the changing climate is a major challenge in oceanography and climatology. It is therefore essential to monitor sea ice properties with fine temporal and spatial resolution. In this paper, we show that icequakes recorded on sea ice can be processed with artificial intelligence to produce accurate maps of sea ice thickness with high temporal and spatial resolutions.
Sasan Tavakoli and Alexander V. Babanin
The Cryosphere, 17, 939–958, https://doi.org/10.5194/tc-17-939-2023, https://doi.org/10.5194/tc-17-939-2023, 2023
Short summary
Short summary
We have tried to develop some new wave–ice interaction models by considering two different types of forces, one of which emerges in the ice and the other of which emerges in the water. We have checked the ability of the models in the reconstruction of wave–ice interaction in a step-wise manner. The accuracy level of the models is acceptable, and it will be interesting to check whether they can be used in wave climate models or not.
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.
Nazanin Asadi, Philippe Lamontagne, Matthew King, Martin Richard, and K. Andrea Scott
The Cryosphere, 16, 3753–3773, https://doi.org/10.5194/tc-16-3753-2022, https://doi.org/10.5194/tc-16-3753-2022, 2022
Short summary
Short summary
Machine learning approaches are deployed to provide accurate daily spatial maps of sea ice presence probability based on ERA5 data as input. Predictions are capable of predicting freeze-up/breakup dates within a 7 d period at specific locations of interest to shipping operators and communities. Forecasts of the proposed method during the breakup season have skills comparing to Climate Normal and sea ice concentration forecasts from a leading subseasonal-to-seasonal forecasting system.
Simon Felix Reifenberg and Helge Friedrich Goessling
The Cryosphere, 16, 2927–2946, https://doi.org/10.5194/tc-16-2927-2022, https://doi.org/10.5194/tc-16-2927-2022, 2022
Short summary
Short summary
Using model simulations, we analyze the impact of chaotic error growth on Arctic sea ice drift predictions. Regarding forecast uncertainty, our results suggest that it matters in which season and where ice drift forecasts are initialized and that both factors vary with the model in use. We find ice velocities to be slightly more predictable than near-surface wind, a main driver of ice drift. This is relevant for future developments of ice drift forecasting systems.
Agathe Serripierri, Ludovic Moreau, Pierre Boue, Jérôme Weiss, and Philippe Roux
The Cryosphere, 16, 2527–2543, https://doi.org/10.5194/tc-16-2527-2022, https://doi.org/10.5194/tc-16-2527-2022, 2022
Short summary
Short summary
As a result of global warming, the sea ice is disappearing at a much faster rate than predicted by climate models. To better understand and predict its ongoing decline, we deployed 247 geophones on the fast ice in Van Mijen Fjord in Svalbard, Norway, in March 2019. The analysis of these data provided a precise daily evolution of the sea-ice parameters at this location with high spatial and temporal resolution and accuracy. The results obtained are consistent with the observations made in situ.
Laura L. Landrum and Marika M. Holland
The Cryosphere, 16, 1483–1495, https://doi.org/10.5194/tc-16-1483-2022, https://doi.org/10.5194/tc-16-1483-2022, 2022
Short summary
Short summary
High-latitude Arctic wintertime sea ice and snow insulate the relatively warmer ocean from the colder atmosphere. As the climate warms, wintertime Arctic conductive heat fluxes increase even when the sea ice concentrations remain high. Simulations from the Community Earth System Model Large Ensemble (CESM1-LE) show how sea ice and snow thicknesses, as well as the distribution of these thicknesses, significantly impact large-scale calculations of wintertime surface heat budgets in the Arctic.
Yunhe Wang, Xiaojun Yuan, Haibo Bi, Mitchell Bushuk, Yu Liang, Cuihua Li, and Haijun Huang
The Cryosphere, 16, 1141–1156, https://doi.org/10.5194/tc-16-1141-2022, https://doi.org/10.5194/tc-16-1141-2022, 2022
Short summary
Short summary
We develop a regional linear Markov model consisting of four modules with seasonally dependent variables in the Pacific sector. The model retains skill for detrended sea ice extent predictions for up to 7-month lead times in the Bering Sea and the Sea of Okhotsk. The prediction skill, as measured by the percentage of grid points with significant correlations (PGS), increased by 75 % in the Bering Sea and 16 % in the Sea of Okhotsk relative to the earlier pan-Arctic model.
Charles Brunette, L. Bruno Tremblay, and Robert Newton
The Cryosphere, 16, 533–557, https://doi.org/10.5194/tc-16-533-2022, https://doi.org/10.5194/tc-16-533-2022, 2022
Short summary
Short summary
Sea ice motion is a versatile parameter for monitoring the Arctic climate system. In this contribution, we use data from drifting buoys, winds, and ice thickness to parameterize the motion of sea ice in a free drift regime – i.e., flowing freely in response to the forcing from the winds and ocean currents. We show that including a dependence on sea ice thickness and taking into account a climatology of the surface ocean circulation significantly improves the accuracy of sea ice motion estimates.
Madison M. Smith, Marika Holland, and Bonnie Light
The Cryosphere, 16, 419–434, https://doi.org/10.5194/tc-16-419-2022, https://doi.org/10.5194/tc-16-419-2022, 2022
Short summary
Short summary
Climate models represent the atmosphere, ocean, sea ice, and land with equations of varying complexity and are important tools for understanding changes in global climate. Here, we explore how realistic variations in the equations describing how sea ice melt occurs at the edges (called lateral melting) impact ice and climate. We find that these changes impact the progression of the sea-ice–albedo feedback in the Arctic and so make significant changes to the predicted Arctic sea ice.
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.
Mathieu Plante and L. Bruno Tremblay
The Cryosphere, 15, 5623–5638, https://doi.org/10.5194/tc-15-5623-2021, https://doi.org/10.5194/tc-15-5623-2021, 2021
Short summary
Short summary
We propose a generalized form for the damage parameterization such that super-critical stresses can return to the yield with different final sub-critical stress states. In uniaxial compression simulations, the generalization improves the orientation of sea ice fractures and reduces the growth of numerical errors. Shear and convergence deformations however remain predominant along the fractures, contrary to observations, and this calls for modification of the post-fracture viscosity formulation.
Joey J. Voermans, Qingxiang Liu, Aleksey Marchenko, Jean Rabault, Kirill Filchuk, Ivan Ryzhov, Petra Heil, Takuji Waseda, Takehiko Nose, Tsubasa Kodaira, Jingkai Li, and Alexander V. Babanin
The Cryosphere, 15, 5557–5575, https://doi.org/10.5194/tc-15-5557-2021, https://doi.org/10.5194/tc-15-5557-2021, 2021
Short summary
Short summary
We have shown through field experiments that the amount of wave energy dissipated in landfast ice, sea ice attached to land, is much larger than in broken ice. By comparing our measurements against predictions of contemporary wave–ice interaction models, we determined which models can explain our observations and which cannot. Our results will improve our understanding of how waves and ice interact and how we can model such interactions to better forecast waves and ice in the polar regions.
Marika M. Holland, David Clemens-Sewall, Laura Landrum, Bonnie Light, Donald Perovich, Chris Polashenski, Madison Smith, and Melinda Webster
The Cryosphere, 15, 4981–4998, https://doi.org/10.5194/tc-15-4981-2021, https://doi.org/10.5194/tc-15-4981-2021, 2021
Short summary
Short summary
As the most reflective and most insulative natural material, snow has important climate effects. For snow on sea ice, its high reflectivity reduces ice melt. However, its high insulating capacity limits ice growth. These counteracting effects make its net influence on sea ice uncertain. We find that with increasing snow, sea ice in both hemispheres is thicker and more extensive. However, the drivers of this response are different in the two hemispheres due to different climate conditions.
Don Perovich, Madison Smith, Bonnie Light, and Melinda Webster
The Cryosphere, 15, 4517–4525, https://doi.org/10.5194/tc-15-4517-2021, https://doi.org/10.5194/tc-15-4517-2021, 2021
Short summary
Short summary
During summer, Arctic sea ice melts on its surface and bottom and lateral edges. Some of this fresh meltwater is stored on the ice surface in features called melt ponds. The rest flows into the ocean. The meltwater flowing into the upper ocean affects ice growth and melt, upper ocean properties, and ocean ecosystems. Using field measurements, we found that the summer meltwater was equal to an 80 cm thick layer; 85 % of this meltwater flowed into the ocean and 15 % was stored in melt ponds.
Sönke Maus, Martin Schneebeli, and Andreas Wiegmann
The Cryosphere, 15, 4047–4072, https://doi.org/10.5194/tc-15-4047-2021, https://doi.org/10.5194/tc-15-4047-2021, 2021
Short summary
Short summary
As the hydraulic permeability of sea ice is difficult to measure, observations are sparse. The present work presents numerical simulations of the permeability of young sea ice based on a large set of 3D X-ray tomographic images. It extends the relationship between permeability and porosity available so far down to brine porosities near the percolation threshold of a few per cent. Evaluation of pore scales and 3D connectivity provides novel insight into the percolation behaviour of sea ice.
Cyril Palerme and Malte Müller
The Cryosphere, 15, 3989–4004, https://doi.org/10.5194/tc-15-3989-2021, https://doi.org/10.5194/tc-15-3989-2021, 2021
Short summary
Short summary
Methods have been developed for calibrating sea ice drift forecasts from an operational prediction system using machine learning algorithms. These algorithms use predictors from sea ice concentration observations during the initialization of the forecasts, sea ice and wind forecasts, and some geographical information. Depending on the calibration method, the mean absolute error is reduced between 3.3 % and 8.0 % for the direction and between 2.5 % and 7.1 % for the speed of sea ice drift.
Dongyang Fu, Bei Liu, Yali Qi, Guo Yu, Haoen Huang, and Lilian Qu
The Cryosphere, 15, 3797–3811, https://doi.org/10.5194/tc-15-3797-2021, https://doi.org/10.5194/tc-15-3797-2021, 2021
Short summary
Short summary
Our results show three main sea ice drift patterns have different multiscale variation characteristics. The oscillation period of the third sea ice transport pattern is longer than the other two, and the ocean environment has a more significant influence on it due to the different regulatory effects of the atmosphere and ocean environment on sea ice drift patterns on various scales. Our research can provide a basis for the study of Arctic sea ice dynamics parameterization in numerical models.
Andrii Murdza, Arttu Polojärvi, Erland M. Schulson, and Carl E. Renshaw
The Cryosphere, 15, 2957–2967, https://doi.org/10.5194/tc-15-2957-2021, https://doi.org/10.5194/tc-15-2957-2021, 2021
Short summary
Short summary
The strength of refrozen floes or piles of ice rubble is an important factor in assessing ice-structure interactions, as well as the integrity of an ice cover itself. The results of this paper provide unique data on the tensile strength of freeze bonds and are the first measurements to be reported. The provided information can lead to a better understanding of the behavior of refrozen ice floes and better estimates of the strength of an ice rubble pile.
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.
Ann Keen, Ed Blockley, David A. Bailey, Jens Boldingh Debernard, Mitchell Bushuk, Steve Delhaye, David Docquier, Daniel Feltham, François Massonnet, Siobhan O'Farrell, Leandro Ponsoni, José M. Rodriguez, David Schroeder, Neil Swart, Takahiro Toyoda, Hiroyuki Tsujino, Martin Vancoppenolle, and Klaus Wyser
The Cryosphere, 15, 951–982, https://doi.org/10.5194/tc-15-951-2021, https://doi.org/10.5194/tc-15-951-2021, 2021
Short summary
Short summary
We compare the mass budget of the Arctic sea ice in a number of the latest climate models. New output has been defined that allows us to compare the processes of sea ice growth and loss in a more detailed way than has previously been possible. We find that that the models are strikingly similar in terms of the major processes causing the annual growth and loss of Arctic sea ice and that the budget terms respond in a broadly consistent way as the climate warms during the 21st century.
Ron Kwok, Alek A. Petty, Marco Bagnardi, Nathan T. Kurtz, Glenn F. Cunningham, Alvaro Ivanoff, and Sahra Kacimi
The Cryosphere, 15, 821–833, https://doi.org/10.5194/tc-15-821-2021, https://doi.org/10.5194/tc-15-821-2021, 2021
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.
Leandro Ponsoni, François Massonnet, David Docquier, Guillian Van Achter, and Thierry Fichefet
The Cryosphere, 14, 2409–2428, https://doi.org/10.5194/tc-14-2409-2020, https://doi.org/10.5194/tc-14-2409-2020, 2020
Short summary
Short summary
The continuous melting of the Arctic sea ice observed in the last decades has a significant impact at global and regional scales. To understand the amplitude and consequences of this impact, the monitoring of the total sea ice volume is crucial. However, in situ monitoring in such a harsh environment is hard to perform and far too expensive. This study shows that four well-placed sampling locations are sufficient to explain about 70 % of the inter-annual changes in the pan-Arctic sea ice volume.
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.
Mark S. Handcock and Marilyn N. Raphael
The Cryosphere, 14, 2159–2172, https://doi.org/10.5194/tc-14-2159-2020, https://doi.org/10.5194/tc-14-2159-2020, 2020
Short summary
Short summary
Traditional methods of calculating the annual cycle of sea ice extent disguise the variation of amplitude and timing (phase) of the advance and retreat of the ice. We present a multiscale model that explicitly allows them to vary, resulting in a much improved representation of the cycle. We show that phase is the dominant contributor to the variability in the cycle and that the anomalous decay of Antarctic sea ice in 2016 was due largely to a change of phase.
Rebecca J. Rolph, Daniel L. Feltham, and David Schröder
The Cryosphere, 14, 1971–1984, https://doi.org/10.5194/tc-14-1971-2020, https://doi.org/10.5194/tc-14-1971-2020, 2020
Short summary
Short summary
It is well known that the Arctic sea ice extent is declining, and it is often assumed that the marginal ice zone (MIZ), the area of partial sea ice cover, is consequently increasing. However, we find no trend in the MIZ extent during the last 40 years from observations that is consistent with a widening of the MIZ as it moves northward. Differences of MIZ extent between different satellite retrievals are too large to provide a robust basis to verify model simulations of MIZ extent.
Mark A. Tschudi, Walter N. Meier, and J. Scott Stewart
The Cryosphere, 14, 1519–1536, https://doi.org/10.5194/tc-14-1519-2020, https://doi.org/10.5194/tc-14-1519-2020, 2020
Short summary
Short summary
A new version of a set of data products that contain the velocity of sea ice and the age of this ice has been developed. We provide a history of the product development and discuss the improvements to the algorithms that create these products. We find that changes in sea ice motion and age show a significant shift in the Arctic ice cover, from a pack with a high concentration of older ice to a sea ice cover dominated by younger ice, which is more susceptible to summer melt.
Angela Cheng, Barbara Casati, Adrienne Tivy, Tom Zagon, Jean-François Lemieux, and L. Bruno Tremblay
The Cryosphere, 14, 1289–1310, https://doi.org/10.5194/tc-14-1289-2020, https://doi.org/10.5194/tc-14-1289-2020, 2020
Short summary
Short summary
Sea ice charts by the Canadian Ice Service (CIS) contain visually estimated ice concentration produced by analysts. The accuracy of manually derived ice concentrations is not well understood. The subsequent uncertainty of ice charts results in downstream uncertainties for ice charts users, such as models and climatology studies, and when used as a verification source for automated sea ice classifiers. This study quantifies the level of accuracy and inter-analyst agreement for ice charts by CIS.
Young Jun Kim, Hyun-Cheol Kim, Daehyeon Han, Sanggyun Lee, and Jungho Im
The Cryosphere, 14, 1083–1104, https://doi.org/10.5194/tc-14-1083-2020, https://doi.org/10.5194/tc-14-1083-2020, 2020
Short summary
Short summary
In this study, we proposed a novel 1-month sea ice concentration (SIC) prediction model with eight predictors using a deep-learning approach, convolutional neural networks (CNNs). The proposed CNN model was evaluated and compared with the two baseline approaches, random-forest and simple-regression models, resulting in better performance. This study also examined SIC predictions for two extreme cases in 2007 and 2012 in detail and the influencing factors through a sensitivity analysis.
Shiming Xu, Lu Zhou, and Bin Wang
The Cryosphere, 14, 751–767, https://doi.org/10.5194/tc-14-751-2020, https://doi.org/10.5194/tc-14-751-2020, 2020
Short summary
Short summary
Sea ice thickness parameters are key to polar climate change studies and forecasts. Airborne and satellite measurements provide complementary observational capabilities. The study analyzes the variability in freeboard and snow depth measurements and its changes with scale in Operation IceBridge, CryoVEx, CryoSat-2 and ICESat. Consistency between airborne and satellite data is checked. Analysis calls for process-oriented attribution of variability and covariability features of these parameters.
Valeria Selyuzhenok, Igor Bashmachnikov, Robert Ricker, Anna Vesman, and Leonid Bobylev
The Cryosphere, 14, 477–495, https://doi.org/10.5194/tc-14-477-2020, https://doi.org/10.5194/tc-14-477-2020, 2020
Short summary
Short summary
This study explores a link between the long-term variations in the integral sea ice volume in the Greenland Sea and oceanic processes. We link the changes in the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) regional sea ice volume with the mixed layer, depth and upper-ocean heat content derived using the ARMOR dataset.
Cited articles
Alexandrov, V., Sandven, S., Wahlin, J., and Johannessen, O. M.: The relation between sea ice thickness and freeboard in the Arctic, The Cryosphere, 4, 373–380, https://doi.org/10.5194/tc-4-373-2010, 2010.
Andersen, S., Breivik, L., Eastwood, S., Godøy, Ø., Lind, M., Porcires, M., and Schyberg, H.: OSI SAF Sea Ice Product Manual–v3. 5, EUMETSAT OSI SAF–Ocean and Sea Ice Sattelite Application Facility, Tech. Rep. SAF/OSI/met. no/TEC/MA/125, 2007.
Beaven, S., Lockhart, G., Gogineni, S., Hossetnmostafa, A., Jezek, K., Gow, A., Perovich, D., Fung, A., and Tjuatja, S.: Laboratory measurements of radar backscatter from bare and snow-covered saline ice sheets, Int. J. Remote Sens., 16, 851–876, 1995.
Brown, G.: The average impulse response of a rough surface and its applications, IEEE T. Antenn. Propag., 25, 67–74, 1977.
Chase, J. and Holyer, R.: Estimation of sea ice type and concentration by linear unmixing of Geosat altimeter waveforms, J. Geophys. Res., 95, 18015–18018, 1990.
Comiso, J. C., Parkinson, C. L., Gersten, R., and Stock, L.: Accelerated decline in the Arctic sea ice cover, Geophys. Res. Lett., 34, L01703, https://doi.org/10.1029/2007GL031972, 2007.
Drinkwater, M.: Ku band airborne radar altimeter observations of marginal sea ice during the 1984 marginal ice zone experiment, J. Geophys. Res., 96, 4555–4572, 1991.
Drinkwater, M., Francis, R., Ratier, G., and Wingham, D.: The European Space Agency's earth explorer mission CryoSat: measuring variability in the cryosphere, Ann. Glaciol., 39, 313–320, 2004.
Dwyer, R. and Godin, R.: Determining sea-ice boundaries and ice roughness using GEOS-3 altimeter data, 1980.
Fedor, L., Hayne, G., and Walsh, E.: Airborne pulse-limited radar altimeter return waveform characteristics over ice in the Beaufort Sea, in: OCEANS'88: a Partnership of Marine Interests, Proceedings, 1704–1710, IEEE, 1988.
Fedor, L., Hayne, G., and Walsh, E.: Ice-type Classifications From Airborne Pulse-limited Radar Altimeter Return Waveform Characteristics, in: Geoscience and Remote Sensing Symposium, 1989, IGARSS'89, 12th Canadian Symposium on Remote Sensing, 1989 International, vol. 3, 1949–1952, IEEE, 1989.
Fetterer, F.: Sea ice altimetry, Tech. rep., DTIC Document, 1992.
Fetterer, F., Gineris, D., and Kwok, R.: Sea ice type maps from Alaska synthetic aperture radar facility imagery: an assessment, J. Geophys. Res., 99, 22–22, 1994.
Forström, S., Gerland, S., and Pedersen, C.: Thickness and density of snow-covered sea ice and hydrostatic equilibrium assumption from in situ measurements in Fram Strait, the Barents Sea and the Svalbard coast, Ann. Glaciol., 52, 261–271, 2011.
Fowler, C., Emery, W., and Maslanik, J.: Satellite-derived evolution of Arctic sea ice age: October 1978 to March 2003, IEEE Geosci. Remote S., 1, 71–74, 2004.
Garrity, C.: Characterization of snow on floating ice and case studies of brightness temperature changes during the onset of melt, Geoph. Monog. Ser., 68, 313–328, 1992.
Giles, K., Laxon, S., Wingham, D., Wallis, D., Krabill, W., Leuschen, C., McAdoo, D., Manizade, S., and Raney, R.: Combined airborne laser and radar altimeter measurements over the Fram Strait in May 2002, Remote Sens. Environ., 111, 182–194, 2007.
Giles, K., Laxon, S., and Ridout, A.: Circumpolar thinning of Arctic sea ice following the 2007 record ice extent minimum, Geophys. Res. Lett., 35, L22502, https://doi.org/10.1029/2008GL035710, 2008.
Haas, C., Gerland, S., Eicken, H., and Miller, H.: Comparison of sea-ice thickness measurements under summer and winter conditions in the Arctic using a small electromagnetic induction device, Geophysics, 62, 749–757, 1997.
Haas, C., Hendricks, S., Eicken, H., and Herber, A.: Synoptic airborne thickness surveys reveal state of Arctic sea ice cover, Geophys. Res. Lett., 37, L09501, https://doi.org/10.1029/2010GL042652, 2010.
Haas, C., Goff, H., Audrain, S., Perovich, D., and Haapala, J.: Comparison of seasonal sea-ice thickness change in the Transpolar Drift observed by local ice mass-balance observations and floe-scale EM surveys, Ann. Glaciol., 52, 97–102, 2011.
Hanson, R., Stutz, J. and Cheeseman, P.: Bayesian classification theory, NASA Ames Research Center, Artificial Intelligence Research Branch, 1991.
Helm, V. and Steinhage, D.: CryoVEx 2007-Data Acquisition and Final Processing Report, Tech. rep., 2008.
Hendricks, S., Gerland, S., Smedsrud, L., Haas, C., Pfaffhuber, A., and Nilsen, F.: Sea-ice thickness variability in Storfjorden, Svalbard, Ann. Glaciol., 52, 61–68, 2011.
Howell, S., Yackel, J., De Abreu, R., Geldsetzer, T., and Breneman, C.: On the utility of SeaWinds/QuikSCAT data for the estimation of the thermodynamic state of first-year sea ice, IEEE T. Geosci. Remote, 43, 1338–1350, 2005.
Hvidegaard, S. M., Forsberg, R., Helm, V., Hendricks, S., Skourup, H., Stenseng, L., Hanson, S., and Haas, C.: CryoVEx 2008 Final Report, Tech. rep., Dansh National Space Institute, DTU Space, 2009.
Kurtz, N. and Farrell, S.: Large-scale surveys of snow depth on Arctic sea ice from operation IceBridge, Geophys. Res. Lett., 38, L20505, https://doi.org/10.1029/2011GL049216, 2011.
Kwok, R.: The RADARSAT geophysical processor system, 1996.
Kwok, R.: Sea ice concentration estimates from satellite passive microwave radiometry and openings from SAR ice motion, Geophys. Res. Let., 29, 25-1–25-4, 2002.
Kwok, R. and Rothrock, D.: Decline in Arctic sea ice thickness from submarine and ICESat records: 1958–2008, Geophys. Res. Lett., 36, L15501, https://doi.org/10.1029/2009GL039035, 2009.
Kwok, R. and Untersteiner, N.: The thinning of Arctic sea ice, Phys. Today, 64, 36–41, 2011.
Kwok, R., Rignot, E., Holt, B., and Onstott, R.: Identification of sea ice types in spaceborne synthetic aperture radar data, J. Geophys. Res., 97, 2391–2402, 1992.
Kwok, R., Zwally, H., and Yi, D.: ICESat observations of Arctic sea ice: a first look, Geophys. Res. Lett, 31, L16401, https://doi.org/10.1029/2004GL020309, 2004.
Laxon, S.: Sea ice altimeter processing scheme at the EODC, Int. J. Remote Sens., 15, 915–924, 1994a.
Laxon, S.: Sea ice extent mapping using the ERS-1 radar altimeter, EARSeL Adv. Remote Sens., 3, 112–116, 1994b.
Laxon, S., Peacock, N., and Smith, D.: High interannual variability of sea ice thickness in the Arctic region, Nature, 425, 947–950, 2003.
Laxon, S. W., Giles, K. A., Ridout, A. L., Wingham, D. J., Willatt, R., Cullen, R., Kwok, R., Schweiger, A., Zhang, J., Haas, C., Hendricks, S., Krishfield, R., Kurtz, N., Farrell, S., and Davidson, M.: CryoSat-2 estimates of Arctic sea ice thickness and volume, Geophys. Res. Lett, 40, 732–737, https://doi.org/10.1002/grl.50193, 2013.
Legresy, B., Papa, F., Remy, F., Vinay, G., van den Bosch, M., and Zanife, O.: ENVISAT radar altimeter measurements over continental surfaces and ice caps using the ICE-2 retracking algorithm, Remote Sens. Environ., 95, 150–163, 2005.
Makynen, M. and Hallikainen, M.: Simulation of ASIRAS altimeter echoes for snow-covered first-year sea ice, IEEE Geosci. Remote S., 6, 486–490, 2009.
Maslanik, J., Fowler, C., Stroeve, J., Drobot, S., Zwally, J., Yi, D., and Emery, W.: A younger, thinner Arctic ice cover: Increased potential for rapid, extensive sea-ice loss, Geophys. Res. Lett., 34, L24501, https://doi.org/10.1029/2007GL032043, 2007.
Maslanik, J., Stroeve, J., Fowler, C., and Emery, W.: Distribution and trends in Arctic sea ice age through spring 2011, Geophys. Res. Lett., 38, L13502, https://doi.org/10.1029/2011GL047735, 2011.
Onstott, R., Grenfell, T., Matzler, C., Luther, C., and Svendsen, E.: Evolution of microwave sea ice signatures during early summer and midsummer in the marginal ice zone, J. Geophys. Res., 92, 6825–6835, 1987.
Peacock, N. and Laxon, S.: Sea surface height determination in the Arctic Ocean from ERS altimetry, J. Geophys. Res, 109, C07001, https://doi.org/10.1029/2001JC001026, 2004.
Polyakov, I., Kwok, R., and Walsh, J.: Recent changes of arctic multiyear sea-ice coverage and the likely causes, B. Am. Meteorol. Soc., 92, 145–151, https://doi.org/10.1175/BAMS-D-11-00070.1, 2011.
Raney, R.: The delay/Doppler radar altimeter, IEEE T. Geosci. Remote, 36, 1578–1588, 1998.
Ridout, A., Ivanova, N., Tonboe, R., Laxon, S., Timms, G., and Kern, S. Kloster, K.: Algorithm Theoretical Basis Document SICCI-ATBDv0-07-12 Version 1.1./03Sept 2012, Tech. rep., ESA, 2012.
Röhrs, J. and Kaleschke, L.: An algorithm to detect sea ice leads by using AMSR-E passive microwave imagery, The Cryosphere, 6, 343–352, https://doi.org/10.5194/tc-6-343-2012, 2012.
Rothrock, D., Yu, Y., and Maykut, G.: Thinning of the Arctic sea-ice cover, Geophys. Res. Lett., 26, 3469–3472, 1999.
Rothrock, D., Percival, D., and Wensnahan, M.: The decline in arctic sea-ice thickness: Separating the spatial, annual, and interannual variability in a quarter century of submarine data, J. Geophys. Res., 113, C05003, https://doi.org/10.1029/2007JC004252, 2008.
Stroeve, J., Serreze, M., Holland, M., Kay, J., Malanik, J., and Barrett, A.: The Arctic's rapidly shrinking sea ice cover: a research synthesis, Climatic Change, 110, 1005–1027, 2012.
Ulander, L.: Interpretation of Seasat radar-altimeter data over sea ice using near-simultaneous SAR imagery, Int. J. Remote Sens., 8, 1679–1686, 1987.
Warren, S., Rigor, I., Untersteiner, N., Radionov, V., Bryazgin, N., Aleksandrov, Y., and Colony, R.: Snow depth on Arctic sea ice, J. Climate, 12, 1814–1829, 1999.
Willat, R., Laxon, S., Giles, K.and Cullen, R., Haas, C., and Helm, V.: Ku-band radar penetration into snow cover on Arctic sea ice using airborne data, Ann. Glaciol., 52, 197–205, 2011.
Wingham, D.; Francis, C.; Baker, S.; Bouzinac, C.; Brockley, D.; Cullen, R.; de Chateau-Thierry, P.; Laxon, S.; Mallow, U.; Mavrocordatos, C. and others:CryoSat: A mission to determine the fluctuations in Earth's land and marine ice fields, Adv. Space Res., Elsevier, 37, 841–871, 2006.
Zakhvatkina, N.: Classification of Sea Ice Types in ENVISAT Synthetic Aperture Radar Images, IEEE ASSP Magazine, 2012.
Special issue