Articles | Volume 15, issue 12
https://doi.org/10.5194/tc-15-5483-2021
© Author(s) 2021. 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-15-5483-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Dec 2021
Research article |
| 10 Dec 2021
| 10 Dec 2021
Advances in altimetric snow depth estimates using bi-frequency SARAL and CryoSat-2 Ka–Ku measurements
Florent Garnier
CORRESPONDING AUTHOR
Laboratoire d'Etudes en Géophysique et Océanographie Spatiales (LEGOS), CNRS/UMR5566, Université Paul Sabbatier, 31400 Toulouse, France
Sara Fleury
Laboratoire d'Etudes en Géophysique et Océanographie Spatiales (LEGOS), CNRS/UMR5566, Université Paul Sabbatier, 31400 Toulouse, France
Gilles Garric
Mercator Ocean, 31520 Ramonville Saint Agne, France
Jérôme Bouffard
Earth Observation Directorate, ESA (European Space Agency), Via Galileo Galilei, 2-00044 Frascati, Italy
Michel Tsamados
Centre for Polar Observation and Modelling, Department of Earth Sciences, University College London, London, WC1E 6BT, UK
Antoine Laforge
Serco c/o ESA, Earth Observation Directorate, Via Galileo Galilei, 2-00044 Frascati, Italy
Marion Bocquet
Laboratoire d'Etudes en Géophysique et Océanographie Spatiales (LEGOS), CNRS/UMR5566, Université Paul Sabbatier, 31400 Toulouse, France
Renée Mie Fredensborg Hansen
Earth Observation Directorate, ESA (European Space Agency), Via Galileo Galilei, 2-00044 Frascati, Italy
Frédérique Remy
Laboratoire d'Etudes en Géophysique et Océanographie Spatiales (LEGOS), CNRS/UMR5566, Université Paul Sabbatier, 31400 Toulouse, France
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Alexei V. Kouraev, Elena A. Zakharova, Andrey G. Kostianoy, Mikhail N. Shimaraev, Lev V. Desinov, Evgeny A. Petrov, Nicholas M. J. Hall, Frédérique Rémy, and Andrey Ya. Suknev
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William Gregory, Isobel R. Lawrence, and Michel Tsamados
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Robbie D. C. Mallett, Julienne C. Stroeve, Michel Tsamados, Jack C. Landy, Rosemary Willatt, Vishnu Nandan, and Glen E. Liston
The Cryosphere, 15, 2429–2450, https://doi.org/10.5194/tc-15-2429-2021, https://doi.org/10.5194/tc-15-2429-2021, 2021
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Renée Mie Fredensborg Hansen, Eero Rinne, Sinéad Louise Farrell, and Henriette Skourup
The Cryosphere, 15, 2511–2529, https://doi.org/10.5194/tc-15-2511-2021, https://doi.org/10.5194/tc-15-2511-2021, 2021
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Lu Zhou, Julienne Stroeve, Shiming Xu, Alek Petty, Rachel Tilling, Mai Winstrup, Philip Rostosky, Isobel R. Lawrence, Glen E. Liston, Andy Ridout, Michel Tsamados, and Vishnu Nandan
The Cryosphere, 15, 345–367, https://doi.org/10.5194/tc-15-345-2021, https://doi.org/10.5194/tc-15-345-2021, 2021
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Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Rasmus Tonboe, Stefan Hendricks, Robert Ricker, James Mead, Robbie Mallett, Marcus Huntemann, Polona Itkin, Martin Schneebeli, Daniela Krampe, Gunnar Spreen, Jeremy Wilkinson, Ilkka Matero, Mario Hoppmann, and Michel Tsamados
The Cryosphere, 14, 4405–4426, https://doi.org/10.5194/tc-14-4405-2020, https://doi.org/10.5194/tc-14-4405-2020, 2020
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Yeray Santana-Falcón, Pierre Brasseur, Jean Michel Brankart, and Florent Garnier
Ocean Sci., 16, 1297–1315, https://doi.org/10.5194/os-16-1297-2020, https://doi.org/10.5194/os-16-1297-2020, 2020
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Michael Kern, Robert Cullen, Bruno Berruti, Jerome Bouffard, Tania Casal, Mark R. Drinkwater, Antonio Gabriele, Arnaud Lecuyot, Michael Ludwig, Rolv Midthassel, Ignacio Navas Traver, Tommaso Parrinello, Gerhard Ressler, Erik Andersson, Cristina Martin-Puig, Ole Andersen, Annett Bartsch, Sinead Farrell, Sara Fleury, Simon Gascoin, Amandine Guillot, Angelika Humbert, Eero Rinne, Andrew Shepherd, Michiel R. van den Broeke, and John Yackel
The Cryosphere, 14, 2235–2251, https://doi.org/10.5194/tc-14-2235-2020, https://doi.org/10.5194/tc-14-2235-2020, 2020
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The Copernicus Polar Ice and Snow Topography Altimeter will provide high-resolution sea ice thickness and land ice elevation measurements and the capability to determine the properties of snow cover on ice to serve operational products and services of direct relevance to the polar regions. This paper describes the mission objectives, identifies the key contributions the CRISTAL mission will make, and presents a concept – as far as it is already defined – for the mission payload.
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
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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.
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
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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.
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Robbie D. C. Mallett, Isobel R. Lawrence, Julienne C. Stroeve, Jack C. Landy, and Michel Tsamados
The Cryosphere, 14, 251–260, https://doi.org/10.5194/tc-14-251-2020, https://doi.org/10.5194/tc-14-251-2020, 2020
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Soils store large carbon and are important for global warming. We do not know what factors are important for soil carbon storage in the alpine Andes and how they work. We studied how rainfall affects soil carbon storage related to soil structure. We found soil structure is not important, but soil carbon storage and stability controlled by rainfall are dependent on rocks under the soils. The results indicate that we should pay attention to the rocks when studying soil carbon storage in the Andes.
Florent Garnier, Pierre Brasseur, Jean-Michel Brankart, Yeray Santana-Falcon, and Emmanuel Cosme
Ocean Sci. Discuss., https://doi.org/10.5194/os-2018-153, https://doi.org/10.5194/os-2018-153, 2019
Publication in OS not foreseen
David Schröder, Danny L. Feltham, Michel Tsamados, Andy Ridout, and Rachel Tilling
The Cryosphere, 13, 125–139, https://doi.org/10.5194/tc-13-125-2019, https://doi.org/10.5194/tc-13-125-2019, 2019
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This paper uses sea ice thickness data (CryoSat-2) to identify and correct shortcomings in simulating winter ice growth in the widely used sea ice model CICE. Adding a model of snow drift and using a different scheme for calculating the ice conductivity improve model results. Sensitivity studies demonstrate that atmospheric winter conditions have little impact on winter ice growth, and the fate of Arctic summer sea ice is largely controlled by atmospheric conditions during the melting season.
Antonio Bonaduce, Mounir Benkiran, Elisabeth Remy, Pierre Yves Le Traon, and Gilles Garric
Ocean Sci., 14, 1405–1421, https://doi.org/10.5194/os-14-1405-2018, https://doi.org/10.5194/os-14-1405-2018, 2018
Isobel R. Lawrence, Michel C. Tsamados, Julienne C. Stroeve, Thomas W. K. Armitage, and Andy L. Ridout
The Cryosphere, 12, 3551–3564, https://doi.org/10.5194/tc-12-3551-2018, https://doi.org/10.5194/tc-12-3551-2018, 2018
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In this paper we estimate the thickness of snow cover on Arctic sea ice from space. We use data from two radar altimeter satellites, AltiKa and CryoSat-2, that have been operating synchronously since 2013. We produce maps of monthly average snow depth for the four growth seasons (October to April): 2012–2013, 2013–2014, 2014–2015, and 2015–2016. Snow depth estimates are essential for the accurate retrieval of sea ice thickness from satellite altimetry.
Jean-Michel Lellouche, Eric Greiner, Olivier Le Galloudec, Gilles Garric, Charly Regnier, Marie Drevillon, Mounir Benkiran, Charles-Emmanuel Testut, Romain Bourdalle-Badie, Florent Gasparin, Olga Hernandez, Bruno Levier, Yann Drillet, Elisabeth Remy, and Pierre-Yves Le Traon
Ocean Sci., 14, 1093–1126, https://doi.org/10.5194/os-14-1093-2018, https://doi.org/10.5194/os-14-1093-2018, 2018
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In the coming decades, a strong growth of the ocean economy is expected. Scientific advances in operational oceanography will play a crucial role in addressing many environmental challenges and in the development of ocean-related economic activities. In this context, remarkable improvements have been achieved with the current Mercator Ocean system. 3-D water masses, sea level, sea ice and currents have been improved, and thus major oceanic variables are hard to distinguish from the data.
Graham D. Quartly, Eero Rinne, Marcello Passaro, Ole B. Andersen, Salvatore Dinardo, Sara Fleury, Kevin Guerreiro, Amandine Guillot, Stefan Hendricks, Andrey A. Kurekin, Felix L. Müller, Robert Ricker, Henriette Skourup, and Michel Tsamados
The Cryosphere Discuss., https://doi.org/10.5194/tc-2018-148, https://doi.org/10.5194/tc-2018-148, 2018
Revised manuscript not accepted
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Radar altimetry is a high-precision technique for measuring sea level and sea ice thickness from space, which are important for monitoring ocean circulation, sea level rise and changes in the Arctic ice cover. This paper reviews the processing techniques needed to best extract the information from complicated radar echoes, and considers the likely developments in the coming decade.
Nicolas Bouhier, Jean Tournadre, Frédérique Rémy, and Rozenn Gourves-Cousin
The Cryosphere, 12, 2267–2285, https://doi.org/10.5194/tc-12-2267-2018, https://doi.org/10.5194/tc-12-2267-2018, 2018
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The evolution of two large Southern Ocean icebergs, in terms of area and thickness, are used to study the melting and fragmentation laws of icebergs. The area and thickness are estimated by the mean of satellite images and radar altimeter data. Two classical formulations of melting are tested and a fragmentation law depending on the sea temperature and iceberg velocity is proposed and tested. The size distribution of the pieces generated by fragmentation is also estimated.
Julienne C. Stroeve, David Schroder, Michel Tsamados, and Daniel Feltham
The Cryosphere, 12, 1791–1809, https://doi.org/10.5194/tc-12-1791-2018, https://doi.org/10.5194/tc-12-1791-2018, 2018
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This paper looks at the impact of the warm winter and anomalously low number of total freezing degree days during winter 2016/2017 on thermodynamic ice growth and overall thickness anomalies. The approach relies on evaluation of satellite data (CryoSat-2) and model output. While there is a negative feedback between rapid ice growth for thin ice, with thermodynamic ice growth increasing over time, since 2012 that relationship is changing, in part because the freeze-up is happening later.
Fifi Ibrahime Adodo, Frédérique Remy, and Ghislain Picard
The Cryosphere, 12, 1767–1778, https://doi.org/10.5194/tc-12-1767-2018, https://doi.org/10.5194/tc-12-1767-2018, 2018
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In Antarctica, the seasonal cycle of the backscatter behaves differently at high and low frequencies, peaking in winter and in summer, respectively. At the intermediate frequency, some areas behave analogously to low frequency in terms of the seasonal cycle, but other areas behave analogously to high frequency. This calls into question the empirical relationships often used to correct elevation changes from radar penetration into the snowpack using backscatter.
Kevin Guerreiro, Sara Fleury, Elena Zakharova, Alexei Kouraev, Frédérique Rémy, and Philippe Maisongrande
The Cryosphere, 11, 2059–2073, https://doi.org/10.5194/tc-11-2059-2017, https://doi.org/10.5194/tc-11-2059-2017, 2017
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We analyse CryoSat-2 and Envisat freeboard height discrepancy over Arctic sea ice and we study the potential role of ice roughness.
Based on our results, we build a CryoSat-2-like version of Envisat freeboard height. The improved Envisat freeboard is converted to sea ice draught and compared to in situ mooring observations to demonstrate the potential of our methodology to produce accurate ice thickness estimates over the 2002–2012 period.
Thomas W. K. Armitage, Sheldon Bacon, Andy L. Ridout, Alek A. Petty, Steven Wolbach, and Michel Tsamados
The Cryosphere, 11, 1767–1780, https://doi.org/10.5194/tc-11-1767-2017, https://doi.org/10.5194/tc-11-1767-2017, 2017
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We present a new 12-year record of geostrophic currents at monthly resolution in the ice-covered and ice-free Arctic Ocean and characterise their seasonal to decadal variability. We also present seasonal climatologies of eddy kinetic energy, and examine the changing location of the Beaufort Gyre. Geostrophic current variability highlights the complex interplay between seasonally varying forcing and sea ice conditions, changing ice–ocean coupling and increasing ocean surface stress in the 2000s.
Alek A. Petty, Michel C. Tsamados, Nathan T. Kurtz, Sinead L. Farrell, Thomas Newman, Jeremy P. Harbeck, Daniel L. Feltham, and Jackie A. Richter-Menge
The Cryosphere, 10, 1161–1179, https://doi.org/10.5194/tc-10-1161-2016, https://doi.org/10.5194/tc-10-1161-2016, 2016
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This study presents an analysis of Arctic sea ice topography using high-resolution, three-dimensional surface elevation data from the Airborne Topographic Mapper (ATM) laser altimeter, flown as part of NASA's Operation IceBridge mission. We describe and implement a newly developed sea ice surface feature-picking algorithm and derive novel information regarding the height, volume and geometry of surface features over the western Arctic sea ice cover.
Daniela Flocco, Daniel L. Feltham, David Schroeder, and Michel Tsamados
The Cryosphere Discuss., https://doi.org/10.5194/tc-2016-118, https://doi.org/10.5194/tc-2016-118, 2016
Preprint withdrawn
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Melt ponds form over the sea ice cover in the Arctic and impact the surface albedo inducing a positive feedback leading to further melting.
While they refreeze, ponds delay basal sea ice growth in Autumn impacting the internal sea ice temperature and therefore its basal growth rate. By using a numerical model we estimate an inhibited basal growth of up to 228 km3, which represents 25 % of the basal sea ice growth estimated by PIOMAS during the months of September and October.
F. Dupont, S. Higginson, R. Bourdallé-Badie, Y. Lu, F. Roy, G. C. Smith, J.-F. Lemieux, G. Garric, and F. Davidson
Geosci. Model Dev., 8, 1577–1594, https://doi.org/10.5194/gmd-8-1577-2015, https://doi.org/10.5194/gmd-8-1577-2015, 2015
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1/12th degree resolution runs of Arctic--Atlantic were compared for the period 2003-2009. We found good representation of sea surface height and of its statistics; model temperature and salinity in general agreement with in situ measurements, but upper ocean properties in Beaufort Sea are challenging; distribution of concentration and volume of sea ice is improved when slowing down the ice and further improvements require better initial conditions and modifications to mixing.
Related subject area
Discipline: Sea ice | Subject: Remote Sensing
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
SAR deep learning sea ice retrieval trained with airborne laser scanner measurements from the MOSAiC expedition
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
Pan-Arctic Sea Ice Concentration from SAR and Passive Microwave
Ice floe segmentation and floe size distribution in airborne and high-resolution optical satellite images: towards an automated labelling deep learning approach
Snow Depth Estimation on Lead-less Landfast ice using Cryo2Ice satellite observations
Updated Arctic melt pond fraction dataset and trends 2002–2023 using ENVISAT and Sentinel-3 remote sensing data
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
The Variability of CryoSat-2 derived Sea Ice Thickness introduced by modelled vs. empirical snow thickness, sea ice density and water density
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
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
Estimating subpixel turbulent heat flux over leads from MODIS thermal infrared imagery with deep learning
An improved sea ice detection algorithm using MODIS: application as a new European sea ice extent indicator
Faster decline and higher variability in the sea ice thickness of the marginal Arctic seas when accounting for dynamic snow cover
Estimation of degree of sea ice ridging in the Bay of Bothnia based on geolocated photon heights from ICESat-2
Linking sea ice deformation to ice thickness redistribution using high-resolution satellite and airborne observations
Simulated Ka- and Ku-band radar altimeter height and freeboard estimation on snow-covered Arctic sea ice
Improved machine-learning-based open-water–sea-ice–cloud discrimination over wintertime Antarctic sea ice using MODIS thermal-infrared imagery
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
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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
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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
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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
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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
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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
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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.
Karl Kortum, Suman Singha, Gunnar Spreen, Nils Hutter, Arttu Jutila, and Christian Haas
The Cryosphere, 18, 2207–2222, https://doi.org/10.5194/tc-18-2207-2024, https://doi.org/10.5194/tc-18-2207-2024, 2024
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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.
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
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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
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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.
Tore Wulf, Jørgen Buus-Hinkler, Suman Singha, Hoyeon Shi, and Matilde Brandt Kreiner
EGUsphere, https://doi.org/10.5194/egusphere-2024-178, https://doi.org/10.5194/egusphere-2024-178, 2024
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Here, we present ASIP (Automated Sea Ice Products): a new and comprehensive deep learning-based methodology to retrieve high-resolution sea ice concentration with accompanying well-calibrated uncertainties from Sentinel-1 SAR and AMSR2 passive microwave observations at a pan-Arctic scale for all seasons. In a comparative study against pan-Arctic ice charts and passive microwave-based sea ice products, we show that ASIP generalizes well to the pan-Arctic region.
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
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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.
Monojit Saha, Julienne Stroeve, Dustin Isleifson, John Yackel, Vishnu Nandan, Jack Christopher Landy, and Hoi Ming Lam
EGUsphere, https://doi.org/10.5194/egusphere-2023-2509, https://doi.org/10.5194/egusphere-2023-2509, 2023
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Snow on sea ice is vital for near-shore sea ice geophysical and biological processes. Past studies have measured snow depths using satellite altimeters Cryosat-2 and ICESat-2 (Cryo2Ice) but estimating sea surface height from lead-less land-fast sea ice remains challenging. Snow depths from Cryo2Ice are compared to in-situ after adjusting for tides. Realistic snow depths are retrieved but difference in roughness, satellite footprints and snow geophysical properties are identified as challenges.
Larysa Istomina, Hannah Niehaus, and Gunnar Spreen
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-142, https://doi.org/10.5194/tc-2023-142, 2023
Revised manuscript accepted for TC
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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 the 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.
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
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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
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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
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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.
Imke Sievers, Henriette Skourup, and Till A. S. Rasmussen
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-122, https://doi.org/10.5194/tc-2023-122, 2023
Revised manuscript accepted for TC
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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 the SIT is quantified.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
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
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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
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Scientists can estimate sea ice thickness using satellites that measure surface height. To determine the sea ice thickness, we also need to know the snow depth and density. This paper shows that the chosen snow depth product has a considerable impact on the findings of sea ice thickness state and trends in Baffin Bay, showing mean thinning with some snow depth products and mean thickening with others. This shows that it is important to better understand and monitor snow depth on sea ice.
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
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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
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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
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We use satellite data records collected along the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) drift to categorize ice conditions that shaped and characterized the floe and surroundings during the expedition. A comparison with previous years is made whenever possible. The aim of this analysis is to provide a basis and reference for subsequent research in the six main research areas of atmosphere, ocean, sea ice, biogeochemistry, remote sensing and ecology.
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
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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
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For navigation or science planning, knowing when sea ice will open in advance is a prerequisite. Yet, to date, routine spaceborne microwave observations of sea ice are unable to do so. We present the first method based on spaceborne infrared that can forecast an opening several days ahead. We develop it specifically for the Weddell Polynya, a large hole in the Antarctic winter ice cover that unexpectedly re-opened for the first time in 40 years in 2016, and determine why the polynya opened.
Marcel Kleinherenbrink, Anton Korosov, Thomas Newman, Andreas Theodosiou, Alexander S. Komarov, Yuanhao Li, Gert Mulder, Pierre Rampal, Julienne Stroeve, and Paco Lopez-Dekker
The Cryosphere, 15, 3101–3118, https://doi.org/10.5194/tc-15-3101-2021, https://doi.org/10.5194/tc-15-3101-2021, 2021
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Harmony is one of the Earth Explorer 10 candidates that has the chance of being selected for launch in 2028. The mission consists of two satellites that fly in formation with Sentinel-1D, which carries a side-looking radar system. By receiving Sentinel-1's signals reflected from the surface, Harmony is able to observe instantaneous elevation and two-dimensional velocity at the surface. As such, Harmony's data allow the retrieval of sea-ice drift and wave spectra in sea-ice-covered regions.
Zhixiang Yin, Xiaodong Li, Yong Ge, Cheng Shang, Xinyan Li, Yun Du, and Feng Ling
The Cryosphere, 15, 2835–2856, https://doi.org/10.5194/tc-15-2835-2021, https://doi.org/10.5194/tc-15-2835-2021, 2021
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MODIS thermal infrared (TIR) imagery provides promising data to study the rapid variations in the Arctic turbulent heat flux (THF). The accuracy of estimated THF, however, is low (especially for small leads) due to the coarse resolution of the MODIS TIR data. We train a deep neural network to enhance the spatial resolution of estimated THF over leads from MODIS TIR imagery. The method is found to be effective and can generate a result which is close to that derived from Landsat-8 TIR imagery.
Joan Antoni Parera-Portell, Raquel Ubach, and Charles Gignac
The Cryosphere, 15, 2803–2818, https://doi.org/10.5194/tc-15-2803-2021, https://doi.org/10.5194/tc-15-2803-2021, 2021
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We describe a new method to map sea ice and water at 500 m resolution using data acquired by the MODIS sensors. The strength of this method is that it achieves high-accuracy results and is capable of attenuating unwanted resolution-breaking effects caused by cloud masking. Our resulting March and September monthly aggregates reflect the loss of sea ice in the European Arctic during the 2000–2019 period and show the algorithm's usefulness as a sea ice monitoring tool.
Robbie D. C. Mallett, Julienne C. Stroeve, Michel Tsamados, Jack C. Landy, Rosemary Willatt, Vishnu Nandan, and Glen E. Liston
The Cryosphere, 15, 2429–2450, https://doi.org/10.5194/tc-15-2429-2021, https://doi.org/10.5194/tc-15-2429-2021, 2021
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We re-estimate pan-Arctic sea ice thickness (SIT) values by combining data from the Envisat and CryoSat-2 missions with data from a new, reanalysis-driven snow model. Because a decreasing amount of ice is being hidden below the waterline by the weight of overlying snow, we argue that SIT may be declining faster than previously calculated in some regions. Because the snow product varies from year to year, our new SIT calculations also display much more year-to-year variability.
Renée Mie Fredensborg Hansen, Eero Rinne, Sinéad Louise Farrell, and Henriette Skourup
The Cryosphere, 15, 2511–2529, https://doi.org/10.5194/tc-15-2511-2021, https://doi.org/10.5194/tc-15-2511-2021, 2021
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Ice navigators rely on timely information about ice conditions to ensure safe passage through ice-covered waters, and one parameter, the degree of ice ridging (DIR), is particularly useful. We have investigated the possibility of estimating DIR from the geolocated photons of ICESat-2 (IS2) in the Bay of Bothnia, show that IS2 retrievals from different DIR areas differ significantly, and present some of the first steps in creating sea ice applications beyond e.g. thickness retrieval.
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
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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.
Rasmus T. Tonboe, Vishnu Nandan, John Yackel, Stefan Kern, Leif Toudal Pedersen, and Julienne Stroeve
The Cryosphere, 15, 1811–1822, https://doi.org/10.5194/tc-15-1811-2021, https://doi.org/10.5194/tc-15-1811-2021, 2021
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A relationship between the Ku-band radar scattering horizon and snow depth is found using a radar scattering model. This relationship has implications for (1) the use of snow climatology in the conversion of satellite radar freeboard into sea ice thickness and (2) the impact of variability in measured snow depth on the derived ice thickness. For both 1 and 2, the impact of using a snow climatology versus the actual snow depth is relatively small.
Stephan Paul and Marcus Huntemann
The Cryosphere, 15, 1551–1565, https://doi.org/10.5194/tc-15-1551-2021, https://doi.org/10.5194/tc-15-1551-2021, 2021
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Cloud cover in the polar regions is difficult to identify at night when using only thermal-infrared data. This is due to occurrences of warm clouds over cold sea ice and cold clouds over warm sea ice. Especially the standard MODIS cloud mask frequently tends towards classifying open water and/or thin ice as cloud cover. Using a neural network, we present an improved discrimination between sea-ice, open-water and/or thin-ice, and cloud pixels in nighttime MODIS thermal-infrared satellite data.
Cited articles
Adodo, F. I., Remy, F., and Picard, G.: Seasonal variations of the backscattering coefficient measured by radar altimeters over the Antarctic Ice Sheet, The Cryosphere, 12, 1767–1778, https://doi.org/10.5194/tc-12-1767-2018, 2018. a
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. a
Andersen, O. B. and Knudsen, P.: The DTU15 Mean Sea Surface and Mean Dynamic
Topography, in: Oral Presentation in the 2015 OSTST Meeeting, Reston,
USA, 2015. a
Andersen, O. B., Nilsen, K., Sørensen, L. S., Skourup, H., Andersen, N. H.,
Nagler, T., Wuite, J., Kouraev, A., Zakharova, E., and Fernandez, D.: Arctic
freshwater fluxes from earth observation data, in: Fiducial Reference
Measurements for Altimetry, 97–103, Springer, Cham, 2019. a
Andreas, E. L., Jordan, R. E., and Makshtas, A. P.: Parameterizing turbulent
exchange over sea ice: The Ice Station Weddell results,
Bound.-Lay. Meteorol., 114, 439–460, 2005. a
Armitage, T. W. K. and Ridout, A. L.: Arctic sea ice freeboard from AltiKa and
comparison with CryoSat2 and Operation IceBridge, Geophys. Res. Lett., 42, 6724–6731, https://doi.org/10.1002/2015GL064823, 2015. a, b
Arndt, S. and Nicolaus, M.: Seasonal cycle and long-term trend of solar energy fluxes through Arctic sea ice, The Cryosphere, 8, 2219–2233, https://doi.org/10.5194/tc-8-2219-2014, 2014. a
Arndt, S., Meiners, K. M., Ricker, R., Krumpen, T., Katlein, C., and Nicolaus,
M.: Influence of snow depth and surface flooding on light transmission
through A ntarctic pack ice, J. Geophys. Res.-Oceans, 122,
2108–2119, 2017. a
Bin, C., Vihma, T., Zhanhai, Z., Zhijun, L., and Huiding, W.: Snow and sea ice
thermodynamics in the Arctic: Model validation and sensitivity study against
SHEBA data, Advances in Polar Science, 19, 108–122, 2008. a
Blanchard-Wrigglesworth, E., Farrell, S., Newman, T., and Bitz, C.: Snow cover
on Arctic sea ice in observations and an Earth System Model,
Geophys. Res. Lett., 42, 10–342, 2015. a
Blazey, B. A., Holland, M. M., and Hunke, E. C.: Arctic Ocean sea ice snow depth evaluation and bias sensitivity in CCSM, The Cryosphere, 7, 1887–1900, https://doi.org/10.5194/tc-7-1887-2013, 2013. a
Boisvert, L. N., Webster, M. A., Petty, A. A., Markus, T., Bromwich, D. H., and
Cullather, R. I.: Intercomparison of Precipitation Estimates over the Arctic
Ocean and Its Peripheral Seas from Reanalyses, J. Climate, 31,
8441–8462, https://doi.org/10.1175/JCLI-D-18-0125.1, 2018. a
Bouffard, J., Naeije, M., Banks, C. J., Calafat, F. M., Cipollini, P., Snaith,
H. M., Webb, E., Hall, A., Mannan, R., Féménias, P., and Parrinello, T.:
CryoSat ocean product quality status and future evolution, Adv. Space Res., 62, 1549–1563, https://doi.org/10.1016/j.asr.2017.11.043, 2018a. a
Bouffard, J., Webb, E., Scagliola, M., Garcia-Mondéjar, A., Baker, S.,
Brockley, D., Gaudelli, J., Muir, A., Hall, A., Mannan, R., Roca, M.,
Fornari, M., Féménias, P., and Parrinello, T.: CryoSat instrument
performance and ice product quality status, Adv. Space Res., 62,
1526–1548, https://doi.org/10.1016/j.asr.2017.11.024,
2018b. a
Braakmann-Folgmann, A. and Donlon, C.: Estimating snow depth on Arctic sea ice using satellite microwave radiometry and a neural network, The Cryosphere, 13, 2421–2438, https://doi.org/10.5194/tc-13-2421-2019, 2019. a
Brucker, L. and Markus, T.: Arctic-scale assessment of satellite passive
microwave-derived snow depth on sea ice using Operation IceBridge airborne
data, J. Geophys. Res.-Oceans, 118, 2892–2905, 2013. a
Bunzel, F., Notz, D., Baehr, J., Müller, W. A., and Fröhlich, K.: Seasonal
climate forecasts significantly affected by observational uncertainty of
Arctic sea ice concentration, Geophys. Res. Lett., 43, 852–859,
https://doi.org/10.1002/2015GL066928, 2016. a
Chang, A., Foster, J., and Hall, D. K.: Nimbus-7 SMMR derived global snow cover
parameters, Ann. Glaciol., 9, 39–44, 1987. a
Chevallier, M., Smith, G. C., Dupont, F., Lemieux, J.-F., Forget, G., Fujii, Y., Hernandez, F., Msadek, R., Peterson, K. A., Storto, A., Toyoda, T., Valdivieso, M., Vernieres, G., Zuo, H., Balmaseda, M., Chang, Y.-S., Ferry, N., Garric, G., Haines, K., Keeley, S., Kovach, R. M., Kuragano, T., Masina, S., Tang, Y., Tsujino H., and Wang, X.:
Intercomparison of the Arctic sea ice cover in global ocean–sea ice
reanalyses from the ORA-IP project, Clim. Dynam., 49, 1107–1136, 2017. a
Comiso, J. C., Cavalieri, D. J., and Markus, T.: Sea ice concentration, ice
temperature, and snow depth using AMSR-E data,
IEEE T. Geosci. Remote Sens., 41, 243–252, 2003. a
CTOH: ASD data, available at: http://ctoh.legos.obs-mip.fr/data/sea-ice-products, last access: November 2021. a
Déry, S. J. and Tremblay, L.: Modeling the effects of wind redistribution
on the snow mass budget of polar sea ice, J. Phys. Oceanogr.,
34, 258–271, 2004. a
Dong, C., Gao, X., Zhang, Y., Yang, J., Zhang, H., and Chao, Y.: Multiple-scale
variations of sea ice and ocean circulation in the Bering Sea using remote
sensing observations and numerical modeling, Remote Sens., 11, 1484, 2019. a
Eicken, H., Lange, M. A., and Wadhams, P.: Characteristics and distribution patterns of snow and meteoric ice in the Weddell Sea and their contribution to the mass balance of sea ice, Ann. Geophys., 12, 80–93, https://doi.org/10.1007/s00585-994-0080-x, 1994. a
Eicken, H., Fischer, H., and Lemke, P.: Effects of the snow cover on Antarctic
sea ice and potential modulation of its response to climate change, Ann. Glaciol., 21, 369–376, 1995. a
Farrell, S. L., Kurtz, N., Connor, L. N., Elder, B. C., Leuschen, C., Markus,
T., McAdoo, D. C., Panzer, B., Richter-Menge, J., and Sonntag, J. G.: A first
assessment of IceBridge snow and ice thickness data over Arctic sea ice, IEEE T. Geosci. Remote Sens., 50, 2098–2111, 2011. a
Fichefet, T. and Maqueda, M. M.: Sensitivity of a global sea ice model to the
treatment of ice thermodynamics and dynamics, J. Geophys. Res.-Oceans, 102, 12609–12646, 1997. a
Fiedler, E. K., Martin, M., Blockley, E., Mignac, D., Fournier, N., Ridout, A., Shepherd, A., and Tilling, R.: Assimilation of sea ice thickness derived from CryoSat-2 along-track freeboard measurements into the Met Office’s Forecast Ocean Assimilation Model (FOAM), The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-127, in review, 2021. a
Fons, S., Kurtz, N. T., Bagnardi, M., Petty, A. A., and Tilling, R.: Assessing
CryoSat-2 Antarctic snow freeboard retrievals using data from ICESat-2, Earth
and Space Science Open Archive, 23, https://doi.org/10.1002/essoar.10506473.1, 2021. a
Fons, S. W. and Kurtz, N. T.: Retrieval of snow freeboard of Antarctic sea ice using waveform fitting of CryoSat-2 returns, The Cryosphere, 13, 861–878, https://doi.org/10.5194/tc-13-861-2019, 2019. a
Forsström, S., Gerland, S., and Pedersen, C. A.: 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, 261–270, 2011. a
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. a
Giles, K. A., Laxon, S. W., Ridout, A. L., Wingham, D. J., and Bacon, S.:
Western Arctic Ocean freshwater storage increased by wind-driven spin-up of
the Beaufort Gyre, Nat. Geosci., 5, 194–197, 2012. a
Granskog, M. A., Assmy, P., Gerland, S., Spreen, G., Steen, H., and Smedsrud,
L. H.: Arctic research on thin ice: Consequences of Arctic sea ice loss, Eos
Trans. AGU, 97, 22–26, 2016. a
Grenfell, T. C. and Maykut, G. A.: The optical properties of ice and snow in
the Arctic Basin, J. Glaciol., 18, 445–463, 1977. a
Grody, N. C.: Classification of snow cover and precipitation using the Special
Sensor Microwave Imager, J. Geophys. Res.-Atmos., 96,
7423–7435, 1991. a
Grosfeld, K., Treffeisen, R., Asseng, J., Bartsch, A., Bräuer, B., Fritzsch, B., Gerdes, R., Hendricks, S., Hiller, W., Heygster, G., Krumpen, T., Lemke, P., Melsheimer, C., Nicolaus, M., Ricker, R., and Weigelt, M.:
Online sea-ice knowledge and data platform, available at: https://www.meereisportal.de/ (last access: March 2021),
Polarforschung, 85, 143–155, 2016. a
Guerreiro, K., Fleury, S., Zakharova, E., Kouraev, A., Rémy, F., and Maisongrande, P.: Comparison of CryoSat-2 and ENVISAT radar freeboard over Arctic sea ice: toward an improved Envisat freeboard retrieval, The Cryosphere, 11, 2059–2073, https://doi.org/10.5194/tc-11-2059-2017, 2017. a, b, c
Haas, C., Haapala, J., Hanson, S., Rabenstein, L., Rinne, E., and Wilkinson,
J.: CryoVEx 2006: field report, ESA/ESTEC contract 18677/04/NL/GS, CCN 2, Bremerhaven, Alfred Wegener Institute for Polar and Marine Research, 2006. a
Haas, C., Beckers, J., King, J., Silis, A., Stroeve, J., Wilkinson, J.,
Notenboom, B., Schweiger, A., and Hendricks, S.: Ice and snow thickness
variability and change in the high Arctic Ocean observed by in situ
measurements, Geophys. Res. Lett., 44, 10–462, 2017. a
Helm, V., Hendricks, S., Göbell, S., Rack, W., Haas, C., Nixdorf, U., and
Boebel, T.: CryoVex 2004 and 2005 (BoB) data acquisition and final report,
Alfred Wegener Institute, Bremerhaven, Germany, 2006. a
Helm, V., Humbert, A., and Miller, H.: Elevation and elevation change of Greenland and Antarctica derived from CryoSat-2, The Cryosphere, 8, 1539–1559, https://doi.org/10.5194/tc-8-1539-2014, 2014. a
Hendricks, S., Stenseng, L., Helm, V., and Haas, C.: Effects of surface
roughness on sea ice freeboard retrieval with an Airborne Ku-Band SAR radar
altimeter, in: 2010 IEEE International Geoscience and Remote Sensing
Symposium, 3126–3129, IEEE, Honolulu, 2010. a
Hendricks, S., Paul, S., and Rinne, E.: Southern hemisphere sea ice thickness
from the CryoSat-2 satellite on a monthly grid (L3C), v2.0e thickness and
volume, Centre for Environmental Data Analysis [data set], https://doi.org/10.5285/48fc3d1e8ada405c8486ada522dae9e8, 2018. a
Holland, D. M., Mysak, L. A., Manak, D. K., and Oberhuber, J. M.: Sensitivity
study of a dynamic thermodynamic sea ice model, J. Geophys. Res.-Oceans, 98, 2561–2586, 1993. a
Hvidegaard, S. M., Forsberg, R., and Skourup, H.: Sea ice thickness estimates
from airborne laser scanning, Sea Ice Thickness: Past, Present and Future,
193–206, 2006. a
Ingram, W., Wilson, C., and Mitchell, J.: Modeling climate change: An
assessment of sea ice and surface albedo feedbacks, J. Geophys. Res.-Atmos., 94, 8609–8622, 1989. a
Janjić, T., Bormann, N., Bocquet, M., Carton, J. A., Cohn, S. E., Dance,
S. L., Losa, S. N., Nichols, N. K., Potthast, R., Waller, J. A., and Weston,
P.: On the representation error in data assimilation, Q. J. Roy. Meteor. Soc., 144, 1257–1278,
https://doi.org/10.1002/qj.3130, 2018. a
Kacimi, S. and Kwok, R.: The Antarctic sea ice cover from ICESat-2 and CryoSat-2: freeboard, snow depth, and ice thickness, The Cryosphere, 14, 4453–4474, https://doi.org/10.5194/tc-14-4453-2020, 2020. a, b
Kaminski, T., Kauker, F., Toudal Pedersen, L., Voßbeck, M., Haak, H., Niederdrenk, L., Hendricks, S., Ricker, R., Karcher, M., Eicken, H., and Gråbak, O.: Arctic Mission Benefit Analysis: impact of sea ice thickness, freeboard, and snow depth products on sea ice forecast performance, The Cryosphere, 12, 2569–2594, https://doi.org/10.5194/tc-12-2569-2018, 2018. a
Kelly, R.: The AMSR-E snow depth algorithm: Description and initial results,
Journal of the Remote Sensing Society of Japan, 29, 307–317, 2009. a
Kern, M., Cullen, R., Berruti, B., Bouffard, J., Casal, T., Drinkwater, M. R., Gabriele, A., Lecuyot, A., Ludwig, M., Midthassel, R., Navas Traver, I., Parrinello, T., Ressler, G., Andersson, E., Martin-Puig, C., Andersen, O., Bartsch, A., Farrell, S., Fleury, S., Gascoin, S., Guillot, A., Humbert, A., Rinne, E., Shepherd, A., van den Broeke, M. R., and Yackel, J.: The Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL) high-priority candidate mission, The Cryosphere, 14, 2235–2251, https://doi.org/10.5194/tc-14-2235-2020, 2020. a
Kern, S.:
ESA-CCI_Phase2_Standardized_Manual_Visual_Ship-Based_SeaIceObservations_v02,
https://doi.org/10.26050/WDCC/ESACCIPSMVSBSIOV2, WDC climate, DKRZ German
climate computing center [data set], 2020. a
Kern, S., Khvorostovsky, K., Skourup, H., Rinne, E., Parsakhoo, Z. S., Djepa, V., Wadhams, P., and Sandven, S.: The impact of snow depth, snow density and ice density on sea ice thickness retrieval from satellite radar altimetry: results from the ESA-CCI Sea Ice ECV Project Round Robin Exercise, The Cryosphere, 9, 37–52, https://doi.org/10.5194/tc-9-37-2015, 2015. a
King, J., Howell, S., Derksen, C., Rutter, N., Toose, P., Beckers, J. F., Haas,
C., Kurtz, N., and Richter-Menge, J.: Evaluation of Operation IceBridge
quick-look snow depth estimates on sea ice, Geophys. Res. Lett., 42,
9302–9310, https://doi.org/10.1002/2015GL066389, 2015. a
Koenig, L., Martin, S., Studinger, M., and Sonntag, J.: Polar Airborne
Observations Fill Gap in Satellite Data, Eos, Transactions American
Geophysical Union, 91, 333–334, https://doi.org/10.1029/2010EO380002, 2010. a
Kurtz, N., Studinger, M., Harbeck, J., Onana, V., and Farrell, S.: IceBridge
sea ice freeboard, snow depth, and thickness, Digital media, NASA Distributed
Active Archive Center at the National Snow and Ice Data Center, Boulder,
Colorado, USA, available at: http://nsidc. org/data/idcsi2 (last access: March 2021), 2012. a
Kurtz, N. T., Farrell, S. L., Studinger, M., Galin, N., Harbeck, J. P., Lindsay, R., Onana, V. D., Panzer, B., and Sonntag, J. G.: Sea ice thickness, freeboard, and snow depth products from Operation IceBridge airborne data, The Cryosphere, 7, 1035–1056, https://doi.org/10.5194/tc-7-1035-2013, 2013. a, b
Kurtz, N. T. and Farrell, S. L.: Large-scale surveys of snow depth on Arctic
sea ice from Operation IceBridge, Geophys. Res. Lett., 38,
https://doi.org/10.1029/2011GL049216, 2011. a, b
Kwok, R. and Cunningham, G.: Variability of Arctic sea ice thickness and volume
from CryoSat-2, Phil. Trans. R. Soc. A, 373, 20140157, https://doi.org/10.1098/rsta.2014.0157, 2015. a, b, c
Kwok, R., Panzer, B., Leuschen, C., Pang, S., Markus, T., Holt, B., and
Gogineni, S.: Airborne surveys of snow depth over Arctic sea ice, J. Geophys. Res.-Oceans, 116, https://doi.org/10.1029/2011JC007371, 2011. a
Kwok, R., Kurtz, N. T., Brucker, L., Ivanoff, A., Newman, T., Farrell, S. L., King, J., Howell, S., Webster, M. A., Paden, J., Leuschen, C., MacGregor, J. A., Richter-Menge, J., Harbeck, J., and Tschudi, M.: Intercomparison of snow depth retrievals over Arctic sea ice from radar data acquired by Operation IceBridge, The Cryosphere, 11, 2571–2593, https://doi.org/10.5194/tc-11-2571-2017, 2017. a, b
Kwok, R., Kacimi, S., Webster, M., Kurtz, N., and Petty, A.: Arctic Snow Depth
and Sea Ice Thickness From ICESat-2 and CryoSat-2 Freeboards: A First
Examination, J. Geophys. Res.-Oceans, 125, e2019JC016008,
2020. a
Landy, J. C., Ehn, J. K., Babb, D. G., Thériault, N., and Barber, D. G.:
Sea ice thickness in the Eastern Canadian Arctic: Hudson Bay Complex &
Baffin Bay, Remote Sens. Environ., 200, 281–294, 2017. a
Landy, J. C., Tsamados, M., and Scharien, R. K.: A facet-based numerical model
for simulating SAR altimeter echoes from heterogeneous sea ice surfaces, IEEE T. Geosci. Remote Sens., 57, 4164–4180, 2019. a
Lawrence, I. R., Tsamados, M. C., Stroeve, J. C., Armitage, T. W. K., and Ridout, A. L.: Estimating snow depth over Arctic sea ice from calibrated dual-frequency radar freeboards, The Cryosphere, 12, 3551–3564, https://doi.org/10.5194/tc-12-3551-2018, 2018. a, b, c
Laxon, S., Peacock, N., and Smith, D.: High interannual variability of sea ice
thickness in the Arctic region, Nature, 425, 947–950, 2003. a
Laxon, S. W., Giles, K. A., Ridout, A. L., Wingham, D. J., Willatt, R., Cullen,
R., Kwok, R., Schweiger, A., Zhang, J., Haas, C., Hendricks, S., Krishfield, R., Kurtz, N., Farrell, S., and Davidson, M.: CryoSat-2 estimates
of Arctic sea ice thickness and volume, Geophys. Res. Lett., 40,
732–737, 2013. a, b, c
Lecomte, O., Fichefet, T., Vancoppenolle, M., and Nicolaus, M.: A new snow
thermodynamic scheme for large-scale sea-ice models, Ann. Glaciol.,
52, 337–346, 2011. a
Ledley, T. S.: Snow on sea ice: Competing effects in shaping climate, J. Geophys. Res.-Atmos., 96, 17195–17208, 1991. a
Lee, Y.-K., Kongoli, C., and Key, J.: An in-depth evaluation of heritage
algorithms for snow cover and snow depth using AMSR-E and AMSR2 measurements,
J. Atmos. Ocean. Technol., 32, 2319–2336, 2015. a
Lellouche, J.-M., Greiner, E., Le Galloudec, O., Garric, G., Regnier, C., Drevillon, M., Benkiran, M., Testut, C.-E., Bourdalle-Badie, R., Gasparin, F., Hernandez, O., Levier, B., Drillet, Y., Remy, E., and Le Traon, P.-Y.: Recent updates to the Copernicus Marine Service global ocean monitoring and forecasting real-time 1/12∘ high-resolution system, Ocean Sci., 14, 1093–1126, https://doi.org/10.5194/os-14-1093-2018, 2018. a, b
Leonard, K. C. and Maksym, T.: The importance of wind-blown snow redistribution
to snow accumulation on Bellingshausen Sea ice, Ann. Glaciol., 52,
271–278, 2011. a
Liston, G. E., Itkin, P., Stroeve, J., Tschudi, M., Stewart, J. S., Pedersen,
S. H., Reinking, A. K., and Elder, K.: A Lagrangian Snow-Evolution System for
Sea-Ice Applications (SnowModel-LG): Part I – Model Description, J. Geophys. Res.-Oceans, 125, e2019JC015913,
https://doi.org/10.1029/2019JC015913, 2020. a
Lundberg, A., Richardson-Näslund, C., and Andersson, C.: Snow density
variations: consequences for ground-penetrating radar, Hydrol.
Process., 20, 1483–1495, https://doi.org/10.1002/hyp.5944, 2006. a
Maaß, N., Kaleschke, L., Tian-Kunze, X., and Drusch, M.: Snow thickness retrieval over thick Arctic sea ice using SMOS satellite data, The Cryosphere, 7, 1971–1989, https://doi.org/10.5194/tc-7-1971-2013, 2013. a
Madec, G., Gurvan, M., Bourdallé-Badie, R., Bouttier, P.-A., Bricaud, C., Bruciaferri, D., Calvert, D., Chanut, J., Océan, M., Clementi, E., Coward, A., Delrosso, D., Ethé, C., Flavoni, S., Graham, T., Harle, J., Iovino, D., Lea, D., Lévy, C., Lovato, T., Martin, N., Masson, S., Mocavero, S., Paul, J., Rousset, C., Storkey, D., Storto, A., and Vancoppenolle, M.: NEMO ocean engine, Notes du Pôle de modélisation de l'Institut Pierre-Simon Laplace (IPSL): 27, ISSN 1288-1619, https://doi.org/10.5281/zenodo.3248739, 2015. a
Maksym, T. and Markus, T.: Antarctic sea ice thickness and snow-to-ice
conversion from atmospheric reanalysis and passive microwave snow depth,
J. Geophys. Res.-Oceans, 113,
https://doi.org/10.1029/2006JC004085, 2008. a
Mäkynen, M., Haapala, J., Aulicino, G., Balan-Sarojini, B., Balmaseda, M.,
Gegiuc, A., Girard-Ardhuin, F., Hendricks, S., Heygster, G., Istomina, L., Kaleschke, L., Karvonen, J., Krumpen, T.,
Lensu, M., Mayer, M., Parmiggiani, F., Ricker, R., Rinne, E., Schmitt,
A., Similä, M., Tietsche, S., Tonboe, R., Wadhams, P., Winstrup,
M., and Zuo, H.: Satellite Observations for Detecting and Forecasting Sea-Ice
Conditions: A Summary of Advances Made in the SPICES Project by the EU’s
Horizon 2020 Programme, Remote Sens., 12, 1214, https://doi.org/10.3390/rs12071214, 2020. a
Mallett, R. D. C., Lawrence, I. R., Stroeve, J. C., Landy, J. C., and Tsamados, M.: Brief communication: Conventional assumptions involving the speed of radar waves in snow introduce systematic underestimates to sea ice thickness and seasonal growth rate estimates, The Cryosphere, 14, 251–260, https://doi.org/10.5194/tc-14-251-2020, 2020. a, b
Markus, T. and Cavalieri, D. J.: Snow depth distribution over sea ice in the
Southern Ocean from satellite passive microwave data, Antarctic sea ice:
physical processes, interactions and variability, 19–39, https://doi.org/10.1029/AR074p0019, 1998. a
Massom, R. A., Drinkwater, M. R., and Haas, C.: Winter snow cover on sea ice in
the Weddell Sea, J. Geophys. Res.-Oceans, 102, 1101–1117,
https://doi.org/10.1029/96JC02992, 1997. a, b
Massom, R. A., Worby, A., Lytle, V., Markus, T., Allison, I., Scambos, T.,
Enomoto, H., Tamura, T., Tateyama, K., Haran, T., Comiso, J. C., Pfaffling, A., Muto, A., Kanagaratnam, P., Giles, B., Young, N., Hyland, G., and Key, E.: ARISE (Antarctic
Remote Ice Sensing Experiment) in the East 2003: Validation of
satellite-derived sea-ice data products, Ann. Glaciol., 44, 288–296,
2006. a
Massonnet, F., Fichefet, T., and Goosse, H.: Prospects for improved seasonal
Arctic sea ice predictions from multivariate data assimilation, Ocean
Modell., 88, 16–25, 2015. a
Meier, W., Markus, T., and Comiso, J.: AMSR-E/AMSR2 unified L3 Daily 12.5 km
Brightness Temperatures, Sea Ice Concentration, Motion and Snow Depth Polar
Grids, Version 1., NASA National Snow and Ice Data Center Distributed archive
Center [data set], https://doi.org/10.5067/RA1MIJOYPK3P, 2018. a, b
Merkouriadi, I., Cheng, B., Graham, R. M., Rösel, A., and Granskog, M. A.:
Critical Role of Snow on Sea Ice Growth in the Atlantic Sector of the Arctic
Ocean, Geophys. Res. Lett., 44, 10,479–10,485,
https://doi.org/10.1002/2017GL075494, 2017. a
Munoz-Martin, J. F., Perez, A., Camps, A., Ribó, S., Cardellach, E.,
Stroeve, J., Nandan, V., Itkin, P., Tonboe, R., Hendricks, S., Huntemann, M., Spreen, G., and Pastena, M.: Snow
and Ice Thickness Retrievals Using GNSS-R: Preliminary Results of the MOSAiC
Experiment, Remote Sens., 12, 4038, 2020. a
Nandan, V., Scharien, R. K., Geldsetzer, T., Kwok, R., Yackel, J. J.,
Mahmud, M. S., Rösel, A., Tonboe, R., Granskog, M., Willatt, R.,
Stroeve, J., Nomura, D., and Frey, M.: Snow Property Controls on
Modeled Ku-Band Altimeter Estimates of First-Year Sea Ice Thickness: Case
Studies From the Canadian and Norwegian Arctic, IEEE J. Sel. Top. Appl., 13, 1082–1096,
https://doi.org/10.1109/JSTARS.2020.2966432, 2020. a
Newman, T., Farrell, S. L., Richter-Menge, J., Connor, L. N., Kurtz, N. T.,
Elder, B. C., and McAdoo, D.: Assessment of radar-derived snow depth over A
rctic sea ice, J. Geophys. Res.-Oceans, 119, 8578–8602,
2014. a
Nghiem, S. V., Clemente-Colón, P., Douglas, T., Moore, C., Obrist, D.,
Perovich, D. K., Pratt, K. A., Rigor, I. G., Simpson, W., Shepson, P. B., Steffen, A., and Woods, J.: Studying bromine, ozone, and mercury chemistry in the Arctic, Eos,
Transactions American Geophysical Union, 94, 289–291, 2013. a
Notz, D.: Challenges in simulating sea ice in Earth System Models, WIRES Clim. Change, 3, 509–526, 2012. a
Overland, J., Dunlea, E., Box, J. E., Corell, R., Forsius, M., Kattsov, V.,
Olsen, M. S., Pawlak, J., Reiersen, L.-O., and Wang, M.: The urgency of
Arctic change, Polar Sci., 21, 6–13, 2019. a
Parrinello, T., Shepherd, A., Bouffard, J., Badessi, S., Casal, T., Davidson,
M., Fornari, M., Maestroni, E., and Scagliola, M.: CryoSat: ESA’s ice
mission – Eight years in space, Adv. Space Res., 62, 1178–1190,
https://doi.org/10.1016/j.asr.2018.04.014, 2018. a
Paul, S., Hendricks, S., Ricker, R., Kern, S., and Rinne, E.: Empirical parametrization of Envisat freeboard retrieval of Arctic and Antarctic sea ice based on CryoSat-2: progress in the ESA Climate Change Initiative, The Cryosphere, 12, 2437–2460, https://doi.org/10.5194/tc-12-2437-2018, 2018. a
Perovich, D., Richter-Menge, J., and Polashenski, C.: Observing and
understanding climate change: Monitoring the mass balance, motion, and
thickness of Arctic sea ice, available at: http://imb-crrel-dartmouth.org (last access: March 2021),
2021. a
Perovich, D. K.: Light reflection and transmission by a temperate snow cover,
J. Glaciol., 53, 201–210, 2007. a
Perovich, D.-K. and Richter-Menge: Regional variability in sea ice melt in a
changing Arctic, Mathematical, physical and engineering sciences, 373, 20140165, https://doi.org/10.1098/rsta.2014.0165, 2015. a
Perovich, D. K. and Richter-Menge, J. A.: Surface characteristics of lead ice,
J. Geophys. Res.-Oceans, 99, 16341–16350, 1994. a
Perovich, D. K., Andreas, E. L., Curry, J. A., Eiken, H., Fairall, C. W.,
Grenfell, T. C., Guest, P., Intrieri, J., Kadko, D., Lindsay, R. W., McPhee,
M. G., Morison, J., Moritz, R. E., Paulson, C. A., Pegau, W. S., Persson, P.,
Pinkel, R., Richter-Menge, J. A., Stanton, T., Stern, H., Sturm, M.,
Tucker III, W., and Uttal, T.: Year on ice gives climate insights, Eos,
Transactions American Geophysical Union, 80, 481–486,
https://doi.org/10.1029/EO080i041p00481-01, 1999. a
Perovich, D. K., Grenfell, T. C., Richter-Menge, J. A., Light, B., Tucker III,
W. B., and Eicken, H.: Thin and thinner: Sea ice mass balance measurements
during SHEBA, J. Geophys. Res.-Oceans, 108,
https://doi.org/10.1029/2001JC001079, 2003. a
Petty, A. A., Webster, M., Boisvert, L., and Markus, T.: The NASA Eulerian Snow on Sea Ice Model (NESOSIM) v1.0: initial model development and analysis, Geosci. Model Dev., 11, 4577–4602, https://doi.org/10.5194/gmd-11-4577-2018, 2018. a, b, c
Powell, D. C., Markus, T., and Stössel, A.: Effects of snow depth forcing
on Southern Ocean sea ice simulations, J. Geophys. Res.-Oceans, 110, https://doi.org/10.1029/2003JC002212, 2005. a
Schweiger, A., Lindsay, R., Zhang, J., Steele, M., Stern, H., and Kwok, R.:
Uncertainty in modeled Arctic sea ice volume, J. Geophys. Res.-Oceans, 116, https://doi.org/10.1029/2011JC007084, 2011. a, b
Semenov, A., Zhang, X., Rinke, A., Dorn, W., and Dethloff, K.: Arctic intense
summer storms and their impacts on sea ice – A regional climate modeling
study, Atmosphere, 10, 218, https://doi.org/10.3390/atmos10040218, 2019. a
Serreze, M., Walsh, J., Chapin, F. S., Osterkamp, T., Dyurgerov, M.,
Romanovsky, V., Oechel, W., Morison, J., Zhang, T., and Barry, R.:
Observational evidence of recent change in the northern high-latitude
environment, Climatic Change, 46, 159–207, 2000. a
Shalina, E. V. and Sandven, S.: Snow depth on Arctic sea ice from historical in situ data, The Cryosphere, 12, 1867–1886, https://doi.org/10.5194/tc-12-1867-2018, 2018. a
Shupe, M., Rex, M., Dethloff, K., Damm, E., Fong, A., Gradinger, R., Heuze, C.,
Loose, B., Makarov, A., Maslowski, W., Nicolaus, M., Perovich, D., Rabe, B., Rinke, A., Sokolov, V., and Sommerfeld, A: The MOSAiC Expedition: A Year
Drifting with the Arctic Sea Ice, Arctic report card, NFS Public access repository, https://doi.org/10.25923/9g3v-xh92, 2020. a
Singarayer, J. S., Bamber, J. L., and Valdes, P. J.: Twenty-first-century
climate impacts from a declining Arctic sea ice cover, J. Climate,
19, 1109–1125, 2006. a
Skourup, H., Hvidegaard, S. M., Forsberg, R., Einarsson, I., Olesen, A. V.,
Sornsen, L. S., Stenseng, L., Hendricks, S., Helm, V., and Davidson, M.:
CryoVEx 2011-12 Airborne Campaigns for CryoSat Validation, 20 Years of
Progress in Radar Altimatry, edited by: Ouwehand, L., 710, 98, 24–29 September 2012, Venice, Italy, ISBN 978-92-9221-274-2, 2013. a
Stewart, L. M., Dance, S. L., and Nichols, N. K.: Correlated observation errors
in data assimilation, Int. J. Numer. Meth. Fl.,
56, 1521–1527, https://doi.org/10.1002/fld.1636, 2008. a
Stroeve, J., Liston, G. E., Buzzard, S., Zhou, L., Mallett, R., Barrett, A.,
Tschudi, M., Tsamados, M., Itkin, P., and Stewart, J. S.: A Lagrangian Snow
Evolution System for Sea Ice Applications (SnowModel-LG): Part II – Analyses,
J. Geophys. Res.-Oceans, 125, e2019JC015900,
https://doi.org/10.1029/2019JC015900, 2020. a
Sturm, M. and Massom, R.: Snow in the sea ice system: Friend or foe?, 65–109, Wiley publisher, in: Sea ice, https://doi.org/10.1002/9781118778371.ch3, 2016. a
Sturm, M. and Massom, R. A.: Snow and sea ice, Sea ice, 2, 153–204, 2009. a
Sturm, M., Holmgren, J., König, M., and Morris, K.: The thermal
conductivity of seasonal snow, J. Glaciol., 43, 26–41, 1997. a
Sturm, M., Holmgren, J., and Perovich, D. K.: Winter snow cover on the sea ice
of the Arctic Ocean at the Surface Heat Budget of the Arctic Ocean (SHEBA):
Temporal evolution and spatial variability, J. Geophys. Res.-Oceans, 107, SHE 23–1–SHE 23–17, https://doi.org/10.1029/2000JC000400, 2002. a
Sturm, M., Maslanik, J. A., Perovich, D., Stroeve, J. C., Richter-Menge, J.,
Markus, T., Holmgren, J., Heinrichs, J. F., and Tape, K.: Snow depth and ice
thickness measurements from the Beaufort and Chukchi Seas collected during
the AMSR-Ice03 campaign, IEEE T. Geosci. Remote,
44, 3009–3020, 2006. a
Ulaby, F., Moore, R. K., and Fung, A. K.: Microwave remote sensing: Active and
passive. Volume 3 – From theory to applications, Addison-Wesley, Theory to Applications, 997 pp., 1986. a
Uotila, P., Goosse, H., Haines, K., Chevallier, M., Barthélemy, A.,
Bricaud, C., Carton, J., Fučkar, N., Garric, G., Iovino, D., Kauker, F., Korhonen, M., Lien, V. S., Marnela, M., Massonnet, F., Mignac, D., Peterson, K. A., Sadikni, R., Shi, L., Tietsche, S., Toyoda,
T., Xie, J., and Zhang, Z.: An
assessment of ten ocean reanalyses in the polar regions, Clim. Dynam.,
52, 1613–1650, 2019. a
Van Leeuwe, M. A., Tedesco, L., Arrigo, K. R., Assmy, P., Campbell, K.,
Meiners, K. M., Rintala, J.-M., Selz, V., Thomas, D. N., and Stefels, J.:
Microalgal community structure and primary production in Arctic and Antarctic
sea ice: A synthesis, Elementa: Science of the Anthropocene, 6, https://doi.org/10.1525/elementa.267, 2018. a
Vancoppenolle, M., Bouillon, S., Fichefet, T., Goosse, H., Lecomte, O.,
Morales Maqueda, M., and Madec, G.: The Louvain-la-Neuve sea ice model, Notes
du pole de modélisation, Institut Pierre-Simon Laplace (IPSL), Paris,
France, 2012. a
Warren, S. G., Rigor, I. G., Untersteiner, N., Radionov, V. F., Bryazgin,
N. N., Aleksandrov, Y. I., and Colony, R.: Snow Depth on Arctic Sea Ice,
J. Climate, 12, 1814–1829,
https://doi.org/10.1175/1520-0442(1999)012<1814:SDOASI>2.0.CO;2, 1999. a
Webster, M. A., Rigor, I. G., Nghiem, S. V., Kurtz, N. T., Farrell, S. L.,
Perovich, D. K., and Sturm, M.: Interdecadal changes in snow depth on Arctic
sea ice, J. Geophys. Res.-Oceans, 119, 5395–5406,
https://doi.org/10.1002/2014JC009985, 2014. a
Wingham, D., Francis, C., Baker, S., Bouzinac, C., Brockley, D., Cullen, R.,
de Chateau-Thierry, P., Laxon, S., Mallow, U., Mavrocordatos, C., Phalippou, L., Ratier, G., Rey, L., Rostand, F., Viau, P., and Wallis, D. W.:
CryoSat: A mission to determine the fluctuations in Earth’s land and marine
ice fields, Adv. Space Res., 37, 841–871, 2006. a
Worby, A. P., Geiger, C. A., Paget, M. J., Van Woert, M. L., Ackley, S. F., and
DeLiberty, T. L.: Thickness distribution of Antarctic sea ice, J. Geophys. Res.-Oceans, 113, 2008a. a
Worby, A. P., Markus, T., Steer, A. D., Lytle, V. I., and Massom, R. A.:
Evaluation of AMSR-E snow depth product over East Antarctic sea ice using in
situ measurements and aerial photography, J. Geophys. Res.-Oceans, 113, https://doi.org/10.1029/2007JC004181, 2008b. a, b
Worby, A. P., Steer, A., Lieser, J. L., Heil, P., Yi, D., Markus, T., Allison,
I., Massom, R. A., Galin, N., and Zwally, J.: Regional-scale sea-ice and snow
thickness distributions from in situ and satellite measurements over East
Antarctica during SIPEX 2007, Deep-Sea. Res. Pt. II, 58, 1125–1136, 2011. a
Zhang, J. and Rothrock, D. A.: Modeling Global Sea Ice with a Thickness and
Enthalpy Distribution Model in Generalized Curvilinear Coordinates, Mon.
Weather Rev., 131, 845–861,
https://doi.org/10.1175/1520-0493(2003)131<0845:MGSIWA>2.0.CO;2, 2003. a
Zhou, L., Xu, S., Liu, J., and Wang, B.: On the retrieval of sea ice thickness and snow depth using concurrent laser altimetry and L-band remote sensing data, The Cryosphere, 12, 993–1012, https://doi.org/10.5194/tc-12-993-2018, 2018. a
Zhou, L., Stroeve, J., Xu, S., Petty, A., Tilling, R., Winstrup, M., Rostosky, P., Lawrence, I. R., Liston, G. E., Ridout, A., Tsamados, M., and Nandan, V.: Inter-comparison of snow depth over Arctic sea ice from reanalysis reconstructions and satellite retrieval, The Cryosphere, 15, 345–367, https://doi.org/10.5194/tc-15-345-2021, 2021. a, b
Zygmuntowska, M., Rampal, P., Ivanova, N., and Smedsrud, L. H.: Uncertainties in Arctic sea ice thickness and volume: new estimates and implications for trends, The Cryosphere, 8, 705–720, https://doi.org/10.5194/tc-8-705-2014, 2014. a
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.
Snow depth data are essential to monitor the impacts of climate change on sea ice volume...
