Articles | Volume 15, issue 12
The Cryosphere, 15, 5483–5512, 2021
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.
The Cryosphere, 15, 5483–5512, 2021
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
Advances in altimetric snow depth estimates using bi-frequency SARAL and CryoSat-2 Ka–Ku measurements
Florent Garnier et al.
Related authors
Marion Bocquet, Sara Fleury, Fanny Piras, Eero Rinne, Heidi Sallila, Florent Garnier, and Frédérique Rémy
EGUsphere, https://doi.org/10.5194/egusphere-2022-214, https://doi.org/10.5194/egusphere-2022-214, 2022
Short summary
Short summary
Sea ice has a large interannual variability, studying its evolution requires long time series of observation. In this paper, we propose the first method to extend Arctic sea ice thickness measurements time series from ERS-2 altimeter. The developed method is based on a neural network to calibrate past missions on the current one by taking advantage of their differences during the mission-overlap periods. Data are available as monthly maps for each winter between 1995 and 2021.
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
Short summary
Short summary
Data assimilation is the most comprehensive strategy to estimate the biogeochemical state of the ocean. Here, surface Chl a data are daily assimilated into a 24-member NEMO–PISCES ensemble configuration to implement a complete 4D assimilation system. Results show the assimilation increases the skills of the ensemble, though a regional diagnosis suggests that the description of model and observation uncertainties needs to be refined according to the biogeochemical characteristics of each region.
Marco Meloni, Jerome Bouffard, Tommaso Parrinello, Geoffrey Dawson, Florent Garnier, Veit Helm, Alessandro Di Bella, Stefan Hendricks, Robert Ricker, Erica Webb, Ben Wright, Karina Nielsen, Sanggyun Lee, Marcello Passaro, Michele Scagliola, Sebastian Bjerregaard Simonsen, Louise Sandberg Sørensen, David Brockley, Steven Baker, Sara Fleury, Jonathan Bamber, Luca Maestri, Henriette Skourup, René Forsberg, and Loretta Mizzi
The Cryosphere, 14, 1889–1907, https://doi.org/10.5194/tc-14-1889-2020, https://doi.org/10.5194/tc-14-1889-2020, 2020
Short summary
Short summary
This manuscript aims to describe the evolutions which have been implemented in the new CryoSat Ice processing chain Baseline-D and the validation activities carried out in different domains such as sea ice, land ice and hydrology.
This new CryoSat processing Baseline-D will maximise the uptake and use of CryoSat data by scientific users since it offers improved capability for monitoring the complex and multiscale changes over the cryosphere.
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
Vishnu Nandan, Rosemary Willatt, Robbie Mallett, Julienne Stroeve, Torsten Geldsetzer, Randall Scharien, Rasmus Tonboe, Jack Landy, David Clemens-Sewall, Arttu Jutila, David N. Wagner, Daniela Krampe, Marcus Huntemann, John Yackel, 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 Hoppman
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-116, https://doi.org/10.5194/tc-2022-116, 2022
Preprint under review for TC
Short summary
Short summary
We show that, wind blows and redistributes snow on sea ice, and Ka- and Ku-band radar signatures detect both newly deposited and buried snow layers that can critically affect snow depth measurements on ice. Radar measurements, meteorological and snow physical data were collected during the MOSAiC Expedition. With frequent occurrence of storms in the Arctic, our results provide baseline information that are vitally important for accurately calculating snow depth on sea ice from satellite radars.
Marion Bocquet, Sara Fleury, Fanny Piras, Eero Rinne, Heidi Sallila, Florent Garnier, and Frédérique Rémy
EGUsphere, https://doi.org/10.5194/egusphere-2022-214, https://doi.org/10.5194/egusphere-2022-214, 2022
Short summary
Short summary
Sea ice has a large interannual variability, studying its evolution requires long time series of observation. In this paper, we propose the first method to extend Arctic sea ice thickness measurements time series from ERS-2 altimeter. The developed method is based on a neural network to calibrate past missions on the current one by taking advantage of their differences during the mission-overlap periods. Data are available as monthly maps for each winter between 1995 and 2021.
William Gregory, Julienne Stroeve, and Michel Tsamados
The Cryosphere, 16, 1653–1673, https://doi.org/10.5194/tc-16-1653-2022, https://doi.org/10.5194/tc-16-1653-2022, 2022
Short summary
Short summary
This research was conducted to better understand how coupled climate models simulate one of the large-scale interactions between the atmosphere and Arctic sea ice that we see in observational data, the accurate representation of which is important for producing reliable forecasts of Arctic sea ice on seasonal to inter-annual timescales. With network theory, this work shows that models do not reflect this interaction well on average, which is likely due to regional biases in sea ice thickness.
Joris Pianezze, Jonathan Beuvier, Cindy Lebeaupin Brossier, Guillaume Samson, Ghislain Faure, and Gilles Garric
Nat. Hazards Earth Syst. Sci., 22, 1301–1324, https://doi.org/10.5194/nhess-22-1301-2022, https://doi.org/10.5194/nhess-22-1301-2022, 2022
Short summary
Short summary
Most numerical weather and oceanic prediction systems do not consider ocean–atmosphere feedback during forecast, and this can lead to significant forecast errors, notably in cases of severe situations. A new high-resolution coupled ocean–atmosphere system is presented in this paper. This forecast-oriented system, based on current regional operational systems and evaluated using satellite and in situ observations, shows that the coupling improves both atmospheric and oceanic forecasts.
Filomena Catapano, Stephan Buchert, Enkelejda Qamili, Thomas Nilsson, Jerome Bouffard, Christian Siemes, Igino Coco, Raffaella D'Amicis, Lars Tøffner-Clausen, Lorenzo Trenchi, Poul Erik Holmdahl Olsen, and Anja Stromme
Geosci. Instrum. Method. Data Syst., 11, 149–162, https://doi.org/10.5194/gi-11-149-2022, https://doi.org/10.5194/gi-11-149-2022, 2022
Short summary
Short summary
The quality control and validation activities performed by the Swarm data quality team reveal the good-quality LPs. The analysis demonstrated that the current baseline plasma data products are improved with respect to previous baseline. The LPs have captured the ionospheric plasma variability over more than half of a solar cycle, revealing the data quality dependence on the solar activity. The quality of the LP data will further improve promotion of their application to a broad range of studies.
Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Ruzica Dadic, Philip Rotosky, Michael Gallagher, Robbie Mallett, Andrew Barrett, Stefan Hendricks, Rasmus Tonboe, Mark Serreze, Linda Thielke, Gunnar Spreen, Thomas Newman, John Yackel, Robert Ricker, Michel Tsamados, Amy Macfarlane, Henna-Reetta Hannula, and Martin Schneebeli
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-383, https://doi.org/10.5194/tc-2021-383, 2022
Preprint under review for TC
Short summary
Short summary
Impacts of rain-on-snow (ROS) on satellite-retrieved sea ice variables remains 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.
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
The Cryosphere, 15, 4501–4516, https://doi.org/10.5194/tc-15-4501-2021, https://doi.org/10.5194/tc-15-4501-2021, 2021
Short summary
Short summary
Giant ice rings are a beautiful and puzzling natural phenomenon. Our data show that ice rings are generated by lens-like warm eddies below the ice. We use multi-satellite data to analyse lake ice cover in the presence of eddies in April 2020 in southern Baikal. Unusual changes in ice colour may be explained by the competing influences of atmosphere above and the warm eddy below the ice. Tracking ice floes also helps to estimate eddy currents and their influence on the upper water layer.
William Gregory, Isobel R. Lawrence, and Michel Tsamados
The Cryosphere, 15, 2857–2871, https://doi.org/10.5194/tc-15-2857-2021, https://doi.org/10.5194/tc-15-2857-2021, 2021
Short summary
Short summary
Satellite measurements of radar freeboard allow us to compute the thickness of sea ice from space; however attaining measurements across the entire Arctic basin typically takes up to 30 d. Here we present a statistical method which allows us to combine observations from three separate satellites to generate daily estimates of radar freeboard across the Arctic Basin. This helps us understand how sea ice thickness is changing on shorter timescales and what may be causing these changes.
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
Snow on sea ice plays an important role in the Arctic climate system. Large spatial and temporal discrepancies among the eight snow depth products are analyzed together with their seasonal variability and long-term trends. These snow products are further compared against various ground-truth observations. More analyses on representation error of sea ice parameters are needed for systematic comparison and fusion of airborne, in situ and remote sensing observations.
Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Rasmus Tonboe, Stefan Hendricks, Robert Ricker, James Mead, Robbie Mallett, Marcus Huntemann, Polona Itkin, Martin Schneebeli, Daniela Krampe, Gunnar Spreen, Jeremy Wilkinson, Ilkka Matero, Mario Hoppmann, and Michel Tsamados
The Cryosphere, 14, 4405–4426, https://doi.org/10.5194/tc-14-4405-2020, https://doi.org/10.5194/tc-14-4405-2020, 2020
Short summary
Short summary
This study provides a first look at the data collected by a new dual-frequency Ka- and Ku-band in situ radar over winter sea ice in the Arctic Ocean. The instrument shows potential for using both bands to retrieve snow depth over sea ice, as well as sensitivity of the measurements to changing snow and atmospheric conditions.
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
Short summary
Short summary
Data assimilation is the most comprehensive strategy to estimate the biogeochemical state of the ocean. Here, surface Chl a data are daily assimilated into a 24-member NEMO–PISCES ensemble configuration to implement a complete 4D assimilation system. Results show the assimilation increases the skills of the ensemble, though a regional diagnosis suggests that the description of model and observation uncertainties needs to be refined according to the biogeochemical characteristics of each region.
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
Short summary
Short summary
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
Short summary
Short summary
In October 2019 the research vessel Polarstern was moored to an ice floe in order to travel with it on the 1-year-long MOSAiC journey through the Arctic. Here we provide historical context of the floe's evolution and initial state for upcoming studies. We show that the ice encountered on site was exceptionally thin and was formed on the shallow Siberian shelf. The analyses presented provide the initial state for the analysis and interpretation of upcoming biogeochemical and ecological studies.
Marco Meloni, Jerome Bouffard, Tommaso Parrinello, Geoffrey Dawson, Florent Garnier, Veit Helm, Alessandro Di Bella, Stefan Hendricks, Robert Ricker, Erica Webb, Ben Wright, Karina Nielsen, Sanggyun Lee, Marcello Passaro, Michele Scagliola, Sebastian Bjerregaard Simonsen, Louise Sandberg Sørensen, David Brockley, Steven Baker, Sara Fleury, Jonathan Bamber, Luca Maestri, Henriette Skourup, René Forsberg, and Loretta Mizzi
The Cryosphere, 14, 1889–1907, https://doi.org/10.5194/tc-14-1889-2020, https://doi.org/10.5194/tc-14-1889-2020, 2020
Short summary
Short summary
This manuscript aims to describe the evolutions which have been implemented in the new CryoSat Ice processing chain Baseline-D and the validation activities carried out in different domains such as sea ice, land ice and hydrology.
This new CryoSat processing Baseline-D will maximise the uptake and use of CryoSat data by scientific users since it offers improved capability for monitoring the complex and multiscale changes over the cryosphere.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
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
Rain-on-Snow (ROS) Understudied in Sea Ice Remote Sensing: A Multi-Sensor Analysis of ROS during MOSAiC
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
Spring melt pond fraction in the Canadian Arctic Archipelago predicted from RADARSAT-2
Simultaneous estimation of wintertime sea ice thickness and snow depth from space-borne freeboard measurements
Observations of sea ice melt from Operation IceBridge imagery
Estimating statistical errors in retrievals of ice velocity and deformation parameters from satellite images and buoy arrays
Brief Communication: Mesoscale and submesoscale dynamics in the marginal ice zone from sequential synthetic aperture radar observations
Classification of sea ice types in Sentinel-1 synthetic aperture radar images
A linear model to derive melt pond depth on Arctic sea ice from hyperspectral data
Satellite passive microwave sea-ice concentration data set inter-comparison for Arctic summer conditions
Opportunistic evaluation of modelled sea ice drift using passively drifting telemetry collars in Hudson Bay, Canada
Combining TerraSAR-X and time-lapse photography for seasonal sea ice monitoring: the case of Deception Bay, Nunavik
Satellite observations of unprecedented phytoplankton blooms in the Maud Rise polynya, Southern Ocean
Effects of decimetre-scale surface roughness on L-band brightness temperature of sea ice
Brief communication: Conventional assumptions involving the speed of radar waves in snow introduce systematic underestimates to sea ice thickness and seasonal growth rate estimates
Broadband albedo of Arctic sea ice from MERIS optical data
Satellite passive microwave sea-ice concentration data set intercomparison: closed ice and ship-based observations
Estimating the sea ice floe size distribution using satellite altimetry: theory, climatology, and model comparison
The 2018 North Greenland polynya observed by a newly introduced merged optical and passive microwave sea-ice concentration dataset
Estimation of turbulent heat flux over leads using satellite thermal images
Snow-driven uncertainty in CryoSat-2-derived Antarctic sea ice thickness – insights from McMurdo Sound
Instantaneous sea ice drift speed from TanDEM-X interferometry
Estimating the snow depth, the snow–ice interface temperature, and the effective temperature of Arctic sea ice using Advanced Microwave Scanning Radiometer 2 and ice mass balance buoy data
Assessment of contemporary satellite sea ice thickness products for Arctic sea ice
Baffin Bay sea ice inflow and outflow: 1978–1979 to 2016–2017
Combined SMAP–SMOS thin sea ice thickness retrieval
Leads and ridges in Arctic sea ice from RGPS data and a new tracking algorithm
Mapping pan-Arctic landfast sea ice stability using Sentinel-1 interferometry
Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records
Satellite-derived sea ice export and its impact on Arctic ice mass balance
A scatterometer record of sea ice extents and backscatter: 1992–2016
Alexis Anne Denton and Mary-Louise Timmermans
The Cryosphere, 16, 1563–1578, https://doi.org/10.5194/tc-16-1563-2022, https://doi.org/10.5194/tc-16-1563-2022, 2022
Short summary
Short summary
Arctic sea ice has a distribution of ice sizes that provides insight into the physics of the ice. We examine this distribution from satellite imagery from 1999 to 2014 in the Canada Basin. We find that it appears as a power law whose power becomes less negative with increasing ice concentrations and has a seasonality tied to that of ice concentration. Results suggest ice concentration be considered in models of this distribution and are important for understanding sea ice in a warming Arctic.
Stephen E. L. Howell, Mike Brady, and Alexander S. Komarov
The Cryosphere, 16, 1125–1139, https://doi.org/10.5194/tc-16-1125-2022, https://doi.org/10.5194/tc-16-1125-2022, 2022
Short summary
Short summary
We describe, apply, and validate the Environment and Climate Change Canada automated sea ice tracking system (ECCC-ASITS) that routinely generates large-scale sea ice motion (SIM) over the pan-Arctic domain using synthetic aperture radar (SAR) images. The ECCC-ASITS was applied to the incoming image streams of Sentinel-1AB and the RADARSAT Constellation Mission from March 2020 to October 2021 using a total of 135 471 SAR images and generated new SIM datasets (i.e., 7 d 25 km and 3 d 6.25 km).
Wayne de Jager and Marcello Vichi
The Cryosphere, 16, 925–940, https://doi.org/10.5194/tc-16-925-2022, https://doi.org/10.5194/tc-16-925-2022, 2022
Short summary
Short summary
Ice motion can be used to better understand how weather and climate change affect the ice. Antarctic sea ice extent has shown large variability over the observed period, and dynamical features may also have changed. Our method allows for the quantification of rotational motion caused by wind and how this may have changed with time. Cyclonic motion dominates the Atlantic sector, particularly from 2015 onwards, while anticyclonic motion has remained comparatively small and unchanged.
Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Ruzica Dadic, Philip Rotosky, Michael Gallagher, Robbie Mallett, Andrew Barrett, Stefan Hendricks, Rasmus Tonboe, Mark Serreze, Linda Thielke, Gunnar Spreen, Thomas Newman, John Yackel, Robert Ricker, Michel Tsamados, Amy Macfarlane, Henna-Reetta Hannula, and Martin Schneebeli
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-383, https://doi.org/10.5194/tc-2021-383, 2022
Preprint under review for TC
Short summary
Short summary
Impacts of rain-on-snow (ROS) on satellite-retrieved sea ice variables remains 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.
Stefan Kern, Thomas Lavergne, Leif Toudal Pedersen, Rasmus Tage Tonboe, Louisa Bell, Maybritt Meyer, and Luise Zeigermann
The Cryosphere, 16, 349–378, https://doi.org/10.5194/tc-16-349-2022, https://doi.org/10.5194/tc-16-349-2022, 2022
Short summary
Short summary
High-resolution clear-sky optical satellite imagery has rarely been used to evaluate satellite passive microwave sea-ice concentration products beyond case-study level. By comparing 10 such products with sea-ice concentration estimated from > 350 such optical images in both hemispheres, we expand results of earlier evaluation studies for these products. Results stress the need to look beyond precision and accuracy and to discuss the evaluation data’s quality and filters applied in the products.
Wenkai Guo, Polona Itkin, Johannes Lohse, Malin Johansson, and Anthony Paul Doulgeris
The Cryosphere, 16, 237–257, https://doi.org/10.5194/tc-16-237-2022, https://doi.org/10.5194/tc-16-237-2022, 2022
Short summary
Short summary
This study uses radar satellite data categorized into different sea ice types to detect ice deformation, which is significant for climate science and ship navigation. For this, we examine radar signal differences of sea ice between two similar satellite sensors and show an optimal way to apply categorization methods across sensors, so more data can be used for this purpose. This study provides a basis for future reliable and constant detection of ice deformation remotely through satellite data.
Lanqing Huang, Georg Fischer, and Irena Hajnsek
The Cryosphere, 15, 5323–5344, https://doi.org/10.5194/tc-15-5323-2021, https://doi.org/10.5194/tc-15-5323-2021, 2021
Short summary
Short summary
This study shows an elevation difference between the radar interferometric measurements and the optical measurements from a coordinated campaign over the snow-covered deformed sea ice in the western Weddell Sea, Antarctica. The objective is to correct the penetration bias of microwaves and to generate a precise sea ice topographic map, including the snow depth on top. Excellent performance for sea ice topographic retrieval is achieved with the proposed model and the developed retrieval scheme.
Isolde A. Glissenaar, Jack C. Landy, Alek A. Petty, Nathan T. Kurtz, and Julienne C. Stroeve
The Cryosphere, 15, 4909–4927, https://doi.org/10.5194/tc-15-4909-2021, https://doi.org/10.5194/tc-15-4909-2021, 2021
Short summary
Short summary
Scientists can estimate sea ice thickness using satellites that measure surface height. To determine the sea ice thickness, we also need to know the snow depth and density. This paper shows that the chosen snow depth product has a considerable impact on the findings of sea ice thickness state and trends in Baffin Bay, showing mean thinning with some snow depth products and mean thickening with others. This shows that it is important to better understand and monitor snow depth on sea ice.
Marek Muchow, Amelie U. Schmitt, and Lars Kaleschke
The Cryosphere, 15, 4527–4537, https://doi.org/10.5194/tc-15-4527-2021, https://doi.org/10.5194/tc-15-4527-2021, 2021
Short summary
Short summary
Linear-like openings in sea ice, also called leads, occur with widths from meters to kilometers. We use satellite images from Sentinel-2 with a resolution of 10 m to identify leads and measure their widths. With that we investigate the frequency of lead widths using two different statistical methods, since other studies have shown a dependency of heat exchange on the lead width. We are the first to address the sea-ice lead-width distribution in the Weddell Sea, Antarctica.
Gemma M. Brett, Daniel Price, Wolfgang Rack, and Patricia J. Langhorne
The Cryosphere, 15, 4099–4115, https://doi.org/10.5194/tc-15-4099-2021, https://doi.org/10.5194/tc-15-4099-2021, 2021
Short summary
Short summary
Ice shelf meltwater in the surface ocean affects sea ice formation, causing it to be thicker and, in particular conditions, to have a loose mass of platelet ice crystals called a sub‐ice platelet layer beneath. This causes the sea ice freeboard to stand higher above sea level. In this study, we demonstrate for the first time that the signature of ice shelf meltwater in the surface ocean manifesting as higher sea ice freeboard in McMurdo Sound is detectable from space using satellite technology.
Thomas Krumpen, Luisa von Albedyll, Helge F. Goessling, Stefan Hendricks, Bennet Juhls, Gunnar Spreen, Sascha Willmes, H. Jakob Belter, Klaus Dethloff, Christian Haas, Lars Kaleschke, Christian Katlein, Xiangshan Tian-Kunze, Robert Ricker, Philip Rostosky, Janna Rückert, Suman Singha, and Julia Sokolova
The Cryosphere, 15, 3897–3920, https://doi.org/10.5194/tc-15-3897-2021, https://doi.org/10.5194/tc-15-3897-2021, 2021
Short summary
Short summary
We use satellite data records collected along the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) drift to categorize ice conditions that shaped and characterized the floe and surroundings during the expedition. A comparison with previous years is made whenever possible. The aim of this analysis is to provide a basis and reference for subsequent research in the six main research areas of atmosphere, ocean, sea ice, biogeochemistry, remote sensing and ecology.
Thomas Lavergne, Montserrat Piñol Solé, Emily Down, and Craig Donlon
The Cryosphere, 15, 3681–3698, https://doi.org/10.5194/tc-15-3681-2021, https://doi.org/10.5194/tc-15-3681-2021, 2021
Short summary
Short summary
Pushed by winds and ocean currents, polar sea ice is on the move. We use passive microwave satellites to observe this motion. The images from their orbits are often put together into daily images before motion is measured. In our study, we measure motion from the individual orbits directly and not from the daily images. We obtain many more motion vectors, and they are more accurate. This can be used for current and future satellites, e.g. the Copernicus Imaging Microwave Radiometer (CIMR).
Céline Heuzé, Lu Zhou, Martin Mohrmann, and Adriano Lemos
The Cryosphere, 15, 3401–3421, https://doi.org/10.5194/tc-15-3401-2021, https://doi.org/10.5194/tc-15-3401-2021, 2021
Short summary
Short summary
For navigation or science planning, knowing when sea ice will open in advance is a prerequisite. Yet, to date, routine spaceborne microwave observations of sea ice are unable to do so. We present the first method based on spaceborne infrared that can forecast an opening several days ahead. We develop it specifically for the Weddell Polynya, a large hole in the Antarctic winter ice cover that unexpectedly re-opened for the first time in 40 years in 2016, and determine why the polynya opened.
Marcel Kleinherenbrink, Anton Korosov, Thomas Newman, Andreas Theodosiou, Alexander S. Komarov, Yuanhao Li, Gert Mulder, Pierre Rampal, Julienne Stroeve, and Paco Lopez-Dekker
The Cryosphere, 15, 3101–3118, https://doi.org/10.5194/tc-15-3101-2021, https://doi.org/10.5194/tc-15-3101-2021, 2021
Short summary
Short summary
Harmony is one of the Earth Explorer 10 candidates that has the chance of being selected for launch in 2028. The mission consists of two satellites that fly in formation with Sentinel-1D, which carries a side-looking radar system. By receiving Sentinel-1's signals reflected from the surface, Harmony is able to observe instantaneous elevation and two-dimensional velocity at the surface. As such, Harmony's data allow the retrieval of sea-ice drift and wave spectra in sea-ice-covered regions.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
Convergent sea ice motion produces a thick ice cover through ridging. We studied sea ice deformation derived from high-resolution satellite imagery and related it to the corresponding thickness change. We found that deformation explains the observed dynamic thickness change. We show that deformation can be used to model realistic ice thickness distributions. Our results revealed new relationships between thickness redistribution and deformation that could improve sea ice models.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Stephen E. L. Howell, Randall K. Scharien, Jack Landy, and Mike Brady
The Cryosphere, 14, 4675–4686, https://doi.org/10.5194/tc-14-4675-2020, https://doi.org/10.5194/tc-14-4675-2020, 2020
Short summary
Short summary
Melt ponds form on the surface of Arctic sea ice during spring and have been shown to exert a strong influence on summer sea ice area. Here, we use RADARSAT-2 satellite imagery to estimate the predicted peak spring melt pond fraction in the Canadian Arctic Archipelago from 2009–2018. Our results show that RADARSAT-2 estimates of peak melt pond fraction can be used to provide predictive information about summer sea ice area within certain regions of the Canadian Arctic Archipelago.
Hoyeon Shi, Byung-Ju Sohn, Gorm Dybkjær, Rasmus Tage Tonboe, and Sang-Moo Lee
The Cryosphere, 14, 3761–3783, https://doi.org/10.5194/tc-14-3761-2020, https://doi.org/10.5194/tc-14-3761-2020, 2020
Short summary
Short summary
To estimate sea ice thickness from satellite freeboard measurements, snow depth information has been required; however, the snow depth estimate has been considered largely uncertain. We propose a new method to estimate sea ice thickness and snow depth simultaneously from freeboards by imposing a thermodynamic constraint. Obtained ice thicknesses and snow depths were consistent with airborne measurements, suggesting that uncertainty of ice thickness caused by uncertain snow depth can be reduced.
Nicholas C. Wright, Chris M. Polashenski, Scott T. McMichael, and Ross A. Beyer
The Cryosphere, 14, 3523–3536, https://doi.org/10.5194/tc-14-3523-2020, https://doi.org/10.5194/tc-14-3523-2020, 2020
Short summary
Short summary
This work presents a new dataset of sea ice surface fractions along NASA Operation IceBridge flight tracks created by processing hundreds of thousands of aerial images. These results are then analyzed to investigate the behavior of meltwater on first-year ice in comparison to multiyear ice. We find preliminary evidence that first-year ice frequently has a lower melt pond fraction than adjacent multiyear ice, contrary to established knowledge in the sea ice community.
Wolfgang Dierking, Harry L. Stern, and Jennifer K. Hutchings
The Cryosphere, 14, 2999–3016, https://doi.org/10.5194/tc-14-2999-2020, https://doi.org/10.5194/tc-14-2999-2020, 2020
Short summary
Short summary
Monitoring deformation of sea ice is useful for studying effects of ice compression and divergent motion on the ice mass balance and ocean–ice–atmosphere interactions. In calculations of deformation parameters not only the measurement uncertainty of drift vectors has to be considered. The size of the area and the time interval used in the calculations have to be chosen within certain limits to make sure that the uncertainties of deformation parameters are smaller than their real magnitudes.
Igor E. Kozlov, Evgeny V. Plotnikov, and Georgy E. Manucharyan
The Cryosphere, 14, 2941–2947, https://doi.org/10.5194/tc-14-2941-2020, https://doi.org/10.5194/tc-14-2941-2020, 2020
Short summary
Short summary
Here we demonstrate a recently emerged opportunity to retrieve high-resolution surface current velocities from sequential spaceborne radar images taken over low-concentration ice regions of polar oceans. Such regularly available data uniquely resolve complex surface ocean dynamics even at small scales and can be used in operational applications to assess and predict transport and distribution of biogeochemical substances and pollutants in ice-covered waters.
Jeong-Won Park, Anton Andreevich Korosov, Mohamed Babiker, Joong-Sun Won, Morten Wergeland Hansen, and Hyun-Cheol Kim
The Cryosphere, 14, 2629–2645, https://doi.org/10.5194/tc-14-2629-2020, https://doi.org/10.5194/tc-14-2629-2020, 2020
Short summary
Short summary
A new Sentinel-1 radar-based sea ice classification algorithm is proposed. We show that the readily available ice charts from operational ice services can reduce the amount of manual work in preparation of large amounts of training/testing data and feed highly reliable data to the trainer in an efficient way. Test results showed that the classifier is capable of retrieving three generalized cover types with overall accuracy of 87 % and 67 % in the winter and summer seasons, respectively.
Marcel König and Natascha Oppelt
The Cryosphere, 14, 2567–2579, https://doi.org/10.5194/tc-14-2567-2020, https://doi.org/10.5194/tc-14-2567-2020, 2020
Short summary
Short summary
We used data that we collected on RV Polarstern cruise PS106 in summer 2017 to develop a model for the derivation of melt pond depth on Arctic sea ice from reflectance measurements. We simulated reflectances of melt ponds of varying color and water depth and used the sun zenith angle and the slope of the log-scaled reflectance at 710 nm to derive pond depth. We validated the model on the in situ melt pond data and found it to derive pond depth very accurately.
Stefan Kern, Thomas Lavergne, Dirk Notz, Leif Toudal Pedersen, and Rasmus Tonboe
The Cryosphere, 14, 2469–2493, https://doi.org/10.5194/tc-14-2469-2020, https://doi.org/10.5194/tc-14-2469-2020, 2020
Short summary
Short summary
Arctic sea-ice concentration (SIC) estimates based on satellite passive microwave observations are highly inaccurate during summer melt. We compare 10 different SIC products with independent satellite data of true SIC and melt pond fraction (MPF). All products disagree with the true SIC. Regional and inter-product differences can be large and depend on the MPF. An inadequate treatment of melting snow and melt ponds in the products’ algorithms appears to be the main explanation for our findings.
Ron R. Togunov, Natasha J. Klappstein, Nicholas J. Lunn, Andrew E. Derocher, and Marie Auger-Méthé
The Cryosphere, 14, 1937–1950, https://doi.org/10.5194/tc-14-1937-2020, https://doi.org/10.5194/tc-14-1937-2020, 2020
Short summary
Short summary
Sea ice drift affects important geophysical and biological processes in the Arctic. Using the motion of dropped polar bear GPS collars, our study evaluated the accuracy of a popular satellite-based ice drift model in Hudson Bay. We observed that velocity was underestimated, particularly at higher speeds. Direction was unbiased, but it was less precise at lower speeds. These biases should be accounted for in climate and ecological research relying on accurate/absolute drift velocities.
Sophie Dufour-Beauséjour, Anna Wendleder, Yves Gauthier, Monique Bernier, Jimmy Poulin, Véronique Gilbert, Juupi Tuniq, Amélie Rouleau, and Achim Roth
The Cryosphere, 14, 1595–1609, https://doi.org/10.5194/tc-14-1595-2020, https://doi.org/10.5194/tc-14-1595-2020, 2020
Short summary
Short summary
Inuit have reported greater variability in seasonal sea ice conditions. For Deception Bay (Nunavik), an area prized for seal and caribou hunting, an increase in snow precipitation and a shorter snow cover period is expected in the near future. In this context, and considering ice-breaking transport in the fjord by mining companies, we combined satellite images and time-lapse photography to monitor sea ice in the area between 2015 and 2018.
Babula Jena and Anilkumar N. Pillai
The Cryosphere, 14, 1385–1398, https://doi.org/10.5194/tc-14-1385-2020, https://doi.org/10.5194/tc-14-1385-2020, 2020
Short summary
Short summary
Records of multiple ocean color satellite data indicated unprecedented phytoplankton blooms on the Maud Rise with a backdrop of anomalous upper ocean warming and sea ice loss in 2017. The bloom appearance may indicate it as a potential sink of atmospheric CO2 through biological pumping, and it can be a major source of carbon and energy for the regional food web. The reoccurrence of the bloom is important considering the high-nutrient low-chlorophyll conditions of the Southern Ocean.
Maciej Miernecki, Lars Kaleschke, Nina Maaß, Stefan Hendricks, and Sten Schmidl Søbjærg
The Cryosphere, 14, 461–476, https://doi.org/10.5194/tc-14-461-2020, https://doi.org/10.5194/tc-14-461-2020, 2020
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
Short summary
Short summary
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.
Christine Pohl, Larysa Istomina, Steffen Tietsche, Evelyn Jäkel, Johannes Stapf, Gunnar Spreen, and Georg Heygster
The Cryosphere, 14, 165–182, https://doi.org/10.5194/tc-14-165-2020, https://doi.org/10.5194/tc-14-165-2020, 2020
Short summary
Short summary
A spectral to broadband conversion is developed empirically that can be used in combination with the Melt Pond Detector algorithm to derive broadband albedo (300–3000 nm) of Arctic sea ice from MERIS data. It is validated and shows better performance compared to existing conversion methods. A comparison of MERIS broadband albedo with respective values from ERA5 reanalysis suggests a revision of the albedo values used in ERA5. MERIS albedo might be useful for improving albedo representation.
Stefan Kern, Thomas Lavergne, Dirk Notz, Leif Toudal Pedersen, Rasmus Tage Tonboe, Roberto Saldo, and Atle MacDonald Sørensen
The Cryosphere, 13, 3261–3307, https://doi.org/10.5194/tc-13-3261-2019, https://doi.org/10.5194/tc-13-3261-2019, 2019
Short summary
Short summary
A systematic evaluation of 10 global satellite data products of the polar sea-ice area is performed. Inter-product differences in evaluation results call for careful consideration of data product limitations when performing sea-ice area trend analyses and for further mitigation of the effects of sensor changes. We open a discussion about evaluation strategies for such data products near-0 % and near-100 % sea-ice concentration, e.g. with the aim to improve high-concentration evaluation accuracy.
Christopher Horvat, Lettie A. Roach, Rachel Tilling, Cecilia M. Bitz, Baylor Fox-Kemper, Colin Guider, Kaitlin Hill, Andy Ridout, and Andrew Shepherd
The Cryosphere, 13, 2869–2885, https://doi.org/10.5194/tc-13-2869-2019, https://doi.org/10.5194/tc-13-2869-2019, 2019
Short summary
Short summary
Changes in the floe size distribution (FSD) are important for sea ice evolution but to date largely unobserved and unknown. Climate models, forecast centres, ship captains, and logistic specialists cannot currently obtain statistical information about sea ice floe size on demand. We develop a new method to observe the FSD at global scales and high temporal and spatial resolution. With refinement, this method can provide crucial information for polar ship routing and real-time forecasting.
Valentin Ludwig, Gunnar Spreen, Christian Haas, Larysa Istomina, Frank Kauker, and Dmitrii Murashkin
The Cryosphere, 13, 2051–2073, https://doi.org/10.5194/tc-13-2051-2019, https://doi.org/10.5194/tc-13-2051-2019, 2019
Short summary
Short summary
Sea-ice concentration, the fraction of an area covered by sea ice, can be observed from satellites with different methods. We combine two methods to obtain a product which is better than either of the input measurements alone. The benefit of our product is demonstrated by observing the formation of an open water area which can now be observed with more detail. Additionally, we find that the open water area formed because the sea ice drifted in the opposite direction and faster than usual.
Meng Qu, Xiaoping Pang, Xi Zhao, Jinlun Zhang, Qing Ji, and Pei Fan
The Cryosphere, 13, 1565–1582, https://doi.org/10.5194/tc-13-1565-2019, https://doi.org/10.5194/tc-13-1565-2019, 2019
Short summary
Short summary
Can we ignore the contribution of small ice leads when estimating turbulent heat flux? Combining bulk formulae and a fetch-limited model with surface temperature from MODIS and Landsat-8 Thermal Infrared Sensor (TIRS) images, we found small leads account for 25 % of the turbulent heat flux, due to its large total area. Estimated turbulent heat flux is larger from TIRS than that from MODIS with a coarser resolution and larger using a fetch-limited model than that using bulk formulae.
Daniel Price, Iman Soltanzadeh, Wolfgang Rack, and Ethan Dale
The Cryosphere, 13, 1409–1422, https://doi.org/10.5194/tc-13-1409-2019, https://doi.org/10.5194/tc-13-1409-2019, 2019
Short summary
Short summary
Snow depth on Antarctic sea ice is poorly mapped. We examine the usefulness of various snow products to provide snow depth information over Antarctic fast ice in McMurdo Sound, with a focus on a novel approach using a high-resolution numerical snow accumulation model. We find the model performs better than existing snow products from reanalysis products. However, when combining this information with satellite data to retrieve sea ice thickness, large uncertainties in thickness remain.
Dyre Oliver Dammann, Leif E. B. Eriksson, Joshua M. Jones, Andrew R. Mahoney, Roland Romeiser, Franz J. Meyer, Hajo Eicken, and Yasushi Fukamachi
The Cryosphere, 13, 1395–1408, https://doi.org/10.5194/tc-13-1395-2019, https://doi.org/10.5194/tc-13-1395-2019, 2019
Short summary
Short summary
We evaluate single-pass synthetic aperture radar interferometry (InSAR) as a tool to assess sea ice drift and deformation. Initial validation shows that TanDEM-X phase-derived drift speed corresponds well with ground-based radar-derived motion. We further show that InSAR enables the identification of potentially important short-lived dynamic processes otherwise difficult to observe, with possible implication for engineering and sea ice modeling.
Lise Kilic, Rasmus Tage Tonboe, Catherine Prigent, and Georg Heygster
The Cryosphere, 13, 1283–1296, https://doi.org/10.5194/tc-13-1283-2019, https://doi.org/10.5194/tc-13-1283-2019, 2019
Short summary
Short summary
In this study, we develop and present simple algorithms to derive the snow depth, the snow–ice interface temperature, and the effective temperature of Arctic sea ice. This is achieved using satellite observations collocated with buoy measurements. The errors of the retrieved parameters are estimated and compared with independent data. These parameters are useful for sea ice concentration mapping, understanding sea ice properties and variability, and for atmospheric sounding applications.
Heidi Sallila, Sinéad Louise Farrell, Joshua McCurry, and Eero Rinne
The Cryosphere, 13, 1187–1213, https://doi.org/10.5194/tc-13-1187-2019, https://doi.org/10.5194/tc-13-1187-2019, 2019
Short summary
Short summary
We assess 8 years of sea ice thickness observations derived from measurements of CryoSat-2 (CS2), AVHRR and SMOS satellites, collating key details of primary interest to users. We find a number of differences among data products but find that CS2 measurements are reliable for sea ice thickness, particularly between ~ 0.5 and 4 m. Regional comparisons reveal noticeable differences in ice thickness between products, particularly in the marginal seas in areas of considerable ship traffic.
Haibo Bi, Zehua Zhang, Yunhe Wang, Xiuli Xu, Yu Liang, Jue Huang, Yilin Liu, and Min Fu
The Cryosphere, 13, 1025–1042, https://doi.org/10.5194/tc-13-1025-2019, https://doi.org/10.5194/tc-13-1025-2019, 2019
Short summary
Short summary
Baffin Bay serves as a huge reservoir of sea ice which provides solid freshwater sources for the seas downstream. Based on satellite observations, significant increasing trends are found for the annual sea ice area flux through the three gates. These trends are chiefly related to the increasing ice motion which is associated with thinner ice owing to the warmer climate (i.e., higher surface air temperature and shortened freezing period) and increased air and water drag coefficients.
Cătălin Paţilea, Georg Heygster, Marcus Huntemann, and Gunnar Spreen
The Cryosphere, 13, 675–691, https://doi.org/10.5194/tc-13-675-2019, https://doi.org/10.5194/tc-13-675-2019, 2019
Short summary
Short summary
Sea ice thickness is important for representing atmosphere–ocean interactions in climate models. A validated satellite sea ice thickness measurement algorithm is transferred to a new sensor. The results offer a better temporal and spatial coverage of satellite measurements in the polar regions. Here we describe the calibration procedure between the two sensors, taking into account their technical differences. In addition a new filter for interference from artificial radio sources is implemented.
Nils Hutter, Lorenzo Zampieri, and Martin Losch
The Cryosphere, 13, 627–645, https://doi.org/10.5194/tc-13-627-2019, https://doi.org/10.5194/tc-13-627-2019, 2019
Short summary
Short summary
Arctic sea ice is an aggregate of ice floes with various sizes. The different sizes result from constant deformation of the ice pack. If a floe breaks, open ocean is exposed in a lead. Collision of floes forms pressure ridges. Here, we present algorithms that detect and track these deformation features in satellite observations and model output. The tracked features are used to provide a comprehensive description of localized deformation of sea ice and help to understand its material properties.
Dyre O. Dammann, Leif E. B. Eriksson, Andrew R. Mahoney, Hajo Eicken, and Franz J. Meyer
The Cryosphere, 13, 557–577, https://doi.org/10.5194/tc-13-557-2019, https://doi.org/10.5194/tc-13-557-2019, 2019
Short summary
Short summary
We present an approach for mapping bottomfast sea ice and landfast sea ice stability using Synthetic Aperture Radar Interferometry. This is the first comprehensive assessment of Arctic bottomfast sea ice extent with implications for subsea permafrost and marine habitats. Our pan-Arctic analysis also provides a new understanding of sea ice dynamics in five marginal seas of the Arctic Ocean relevant for strategic planning and tactical decision-making for different uses of coastal ice.
Thomas Lavergne, Atle Macdonald Sørensen, Stefan Kern, Rasmus Tonboe, Dirk Notz, Signe Aaboe, Louisa Bell, Gorm Dybkjær, Steinar Eastwood, Carolina Gabarro, Georg Heygster, Mari Anne Killie, Matilde Brandt Kreiner, John Lavelle, Roberto Saldo, Stein Sandven, and Leif Toudal Pedersen
The Cryosphere, 13, 49–78, https://doi.org/10.5194/tc-13-49-2019, https://doi.org/10.5194/tc-13-49-2019, 2019
Short summary
Short summary
The loss of polar sea ice is an iconic indicator of Earth’s climate change. Many satellite-based algorithms and resulting data exist but they differ widely in specific sea-ice conditions. This spread hinders a robust estimate of the future evolution of sea-ice cover.
In this study, we document three new climate data records of sea-ice concentration generated using satellite data available over the last 40 years. We introduce the novel algorithms, the data records, and their uncertainties.
Robert Ricker, Fanny Girard-Ardhuin, Thomas Krumpen, and Camille Lique
The Cryosphere, 12, 3017–3032, https://doi.org/10.5194/tc-12-3017-2018, https://doi.org/10.5194/tc-12-3017-2018, 2018
Short summary
Short summary
We present ice volume flux estimates through the Fram Strait using CryoSat-2 ice thickness data. This study presents a detailed analysis of temporal and spatial variability of ice volume export through the Fram Strait and shows the impact of ice volume export on Arctic ice mass balance.
Maria Belmonte Rivas, Ines Otosaka, Ad Stoffelen, and Anton Verhoef
The Cryosphere, 12, 2941–2953, https://doi.org/10.5194/tc-12-2941-2018, https://doi.org/10.5194/tc-12-2941-2018, 2018
Short summary
Short summary
We provide a novel record of scatterometer sea ice extents and backscatter that complements the passive microwave products nicely, particularly for the correction of summer melt errors. The sea ice backscatter maps help differentiate between seasonal and perennial Arctic ice classes, and between second-year and older multiyear ice, revealing the emergence of SY ice as the dominant perennial ice type after the record loss in 2007 and attesting to its use as a proxy for ice thickness.
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...
