Articles | Volume 17, issue 3
https://doi.org/10.5194/tc-17-1411-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-17-1411-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
31 Mar 2023
Research article |
| 31 Mar 2023
| 31 Mar 2023
Linking scales of sea ice surface topography: evaluation of ICESat-2 measurements with coincident helicopter laser scanning during MOSAiC
NORCE Norwegian Research Centre, Tromsø, Norway
Steven Fons
Department of Atmospheric and Oceanic Sciences, University of Maryland, College Park, MD, USA
Cryospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Arttu Jutila
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany
Nils Hutter
Cooperative Institute for Climate, Ocean, and Ecosystem Studies, University of Washington, WA, USA
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany
Kyle Duncan
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
Sinead L. Farrell
Department of Geographical Sciences, University of Maryland, College Park, MD, USA
Department of Atmospheric and Oceanic Sciences, University of Maryland, College Park, MD, USA
Nathan T. Kurtz
Cryospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Renée Mie Fredensborg Hansen
Department of Geodesy and Earth Observation, DTU Space, Kongens Lyngby, Denmark
Department of Civil and Environmental Engineering, NTNU, Trondheim, Norway
Arctic Geophysics, University Centre in Svalbard (UNIS), Longyearbyen, Svalbard, Norway
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Short summary
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Renée Mie Fredensborg Hansen, Henriette Skourup, Eero Rinne, Arttu Jutila, Isobel R. Lawrence, Andrew Shepherd, Knut Vilhelm Høyland, Jilu Li, Fernando Rodriguez-Morales, Sebastian Bjerregaaard Simonsen, Jeremy Wilkinson, Gaelle Veyssiere, Donghui Yi, René Forsberg, and Taniâ Gil Duarte Casal
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To measure Arctic ice thickness, we often check how much ice sticks out of the water. This method depends on knowing the ice's density, which drops significantly in summer. Our study, validated with sonar and laser data, shows that these seasonal changes in density can complicate melt measurements. We stress the importance of considering these density changes for more accurate ice thickness readings.
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Revised manuscript accepted for ESSD
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Discover the latest advancements in sea ice research with our comprehensive Climate Change Initiative (CCI) sea ice thickness (SIT) Round Robin Data Package (RRDP). This pioneering collection contains reference measurements from 1960 to 2022 from airborne sensors, buoys, visual observations and sonar and covers the polar regions from 1993 to 2021, providing crucial reference measurements for validating satellite-derived sea ice thickness.
Yi Zhou, Xianwei Wang, Ruibo Lei, Arttu Jutila, Donald K. Perovich, Luisa von Albedyll, Dmitry V. Divine, Yu Zhang, and Christian Haas
EGUsphere, https://doi.org/10.5194/egusphere-2024-2821, https://doi.org/10.5194/egusphere-2024-2821, 2024
Preprint archived
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This study examines how the bulk density of Arctic sea ice varies seasonally, a factor often overlooked in satellite measurements of sea ice thickness. From October to April, we found significant seasonal variations in sea ice bulk density at different spatial scales using direct observations as well as airborne and satellite data. New models were then developed to indirectly predict sea ice bulk density. This advance can improve our ability to monitor changes in Arctic sea ice.
Lars Kaleschke, Xiangshan Tian-Kunze, Stefan Hendricks, and Robert Ricker
Earth Syst. Sci. Data, 16, 3149–3170, https://doi.org/10.5194/essd-16-3149-2024, https://doi.org/10.5194/essd-16-3149-2024, 2024
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We describe a sea ice thickness dataset based on SMOS satellite measurements, initially designed for the Arctic but adapted for Antarctica. We validated it using limited Antarctic measurements. Our findings show promising results, with a small difference in thickness estimation and a strong correlation with validation data within the valid thickness range. However, improvements and synergies with other sensors are needed, especially for sea ice thicker than 1 m.
Michael Studinger, Benjamin E. Smith, Nathan Kurtz, Alek Petty, Tyler Sutterley, and Rachel Tilling
The Cryosphere, 18, 2625–2652, https://doi.org/10.5194/tc-18-2625-2024, https://doi.org/10.5194/tc-18-2625-2024, 2024
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We use green lidar data and natural-color imagery over sea ice to quantify elevation biases potentially impacting estimates of change in ice thickness of the polar regions. We complement our analysis using a model of scattering of light in snow and ice that predicts the shape of lidar waveforms reflecting from snow and ice surfaces based on the shape of the transmitted pulse. We find that biased elevations exist in airborne and spaceborne data products from green lidars.
Karl Kortum, Suman Singha, Gunnar Spreen, Nils Hutter, Arttu Jutila, and Christian Haas
The Cryosphere, 18, 2207–2222, https://doi.org/10.5194/tc-18-2207-2024, https://doi.org/10.5194/tc-18-2207-2024, 2024
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Luisa von Albedyll, Stefan Hendricks, Nils Hutter, Dmitrii Murashkin, Lars Kaleschke, Sascha Willmes, Linda Thielke, Xiangshan Tian-Kunze, Gunnar Spreen, and Christian Haas
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Leads (openings in sea ice cover) are created by sea ice dynamics. Because they are important for many processes in the Arctic winter climate, we aim to detect them with satellites. We present two new techniques to detect lead widths of a few hundred meters at high spatial resolution (700 m) and independent of clouds or sun illumination. We use the MOSAiC drift 2019–2020 in the Arctic for our case study and compare our new products to other existing lead products.
Ellen M. Buckley, Sinéad L. Farrell, Ute C. Herzfeld, Melinda A. Webster, Thomas Trantow, Oliwia N. Baney, Kyle A. Duncan, Huilin Han, and Matthew Lawson
The Cryosphere, 17, 3695–3719, https://doi.org/10.5194/tc-17-3695-2023, https://doi.org/10.5194/tc-17-3695-2023, 2023
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In this study, we use satellite observations to investigate the evolution of melt ponds on the Arctic sea ice surface. We derive melt pond depth from ICESat-2 measurements of the pond surface and bathymetry and melt pond fraction (MPF) from the classification of Sentinel-2 imagery. MPF increases to a peak of 16 % in late June and then decreases, while depth increases steadily. This work demonstrates the ability to track evolving melt conditions in three dimensions throughout the summer.
Steven Fons, Nathan Kurtz, and Marco Bagnardi
The Cryosphere, 17, 2487–2508, https://doi.org/10.5194/tc-17-2487-2023, https://doi.org/10.5194/tc-17-2487-2023, 2023
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Antarctic sea ice thickness is an important quantity in the Earth system. Due to the thick and complex snow cover on Antarctic sea ice, estimating the thickness of the ice pack is difficult using traditional methods in radar altimetry. In this work, we use a waveform model to estimate the freeboard and snow depth of Antarctic sea ice from CryoSat-2 and use these values to calculate sea ice thickness and volume between 2010 and 2021 and showcase how the sea ice pack has changed over this time.
Vishnu Nandan, Rosemary Willatt, Robbie Mallett, Julienne Stroeve, Torsten Geldsetzer, Randall Scharien, Rasmus Tonboe, John Yackel, Jack Landy, David Clemens-Sewall, Arttu Jutila, David N. Wagner, Daniela Krampe, Marcus Huntemann, Mallik Mahmud, David Jensen, Thomas Newman, Stefan Hendricks, Gunnar Spreen, Amy Macfarlane, Martin Schneebeli, James Mead, Robert Ricker, Michael Gallagher, Claude Duguay, Ian Raphael, Chris Polashenski, Michel Tsamados, Ilkka Matero, and Mario Hoppmann
The Cryosphere, 17, 2211–2229, https://doi.org/10.5194/tc-17-2211-2023, https://doi.org/10.5194/tc-17-2211-2023, 2023
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We show that wind redistributes snow on Arctic sea ice, and Ka- and Ku-band radar measurements detect both newly deposited snow and buried snow layers that can affect the accuracy of snow depth estimates on sea ice. Radar, laser, meteorological, and snow data were collected during the MOSAiC expedition. With frequent occurrence of storms in the Arctic, our results show that
wind-redistributed snow needs to be accounted for to improve snow depth estimates on sea ice from satellite radars.
Guillaume Boutin, Einar Ólason, Pierre Rampal, Heather Regan, Camille Lique, Claude Talandier, Laurent Brodeau, and Robert Ricker
The Cryosphere, 17, 617–638, https://doi.org/10.5194/tc-17-617-2023, https://doi.org/10.5194/tc-17-617-2023, 2023
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Sea ice cover in the Arctic is full of cracks, which we call leads. We suspect that these leads play a role for atmosphere–ocean interactions in polar regions, but their importance remains challenging to estimate. We use a new ocean–sea ice model with an original way of representing sea ice dynamics to estimate their impact on winter sea ice production. This model successfully represents sea ice evolution from 2000 to 2018, and we find that about 30 % of ice production takes place in leads.
Francesca Doglioni, Robert Ricker, Benjamin Rabe, Alexander Barth, Charles Troupin, and Torsten Kanzow
Earth Syst. Sci. Data, 15, 225–263, https://doi.org/10.5194/essd-15-225-2023, https://doi.org/10.5194/essd-15-225-2023, 2023
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This paper presents a new satellite-derived gridded dataset, including 10 years of sea surface height and geostrophic velocity at monthly resolution, over the Arctic ice-covered and ice-free regions, up to 88° N. We assess the dataset by comparison to independent satellite and mooring data. Results correlate well with independent satellite data at monthly timescales, and the geostrophic velocity fields can resolve seasonal to interannual variability of boundary currents wider than about 50 km.
Jinfei Wang, Chao Min, Robert Ricker, Qian Shi, Bo Han, Stefan Hendricks, Renhao Wu, and Qinghua Yang
The Cryosphere, 16, 4473–4490, https://doi.org/10.5194/tc-16-4473-2022, https://doi.org/10.5194/tc-16-4473-2022, 2022
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The differences between Envisat and ICESat sea ice thickness (SIT) reveal significant temporal and spatial variations. Our findings suggest that both overestimation of Envisat sea ice freeboard, potentially caused by radar backscatter originating from inside the snow layer, and the AMSR-E snow depth biases and sea ice density uncertainties can possibly account for the differences between Envisat and ICESat SIT.
Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Ruzica Dadic, Philip Rostosky, Michael Gallagher, Robbie Mallett, Andrew Barrett, Stefan Hendricks, Rasmus Tonboe, Michelle McCrystall, Mark Serreze, Linda Thielke, Gunnar Spreen, Thomas Newman, John Yackel, Robert Ricker, Michel Tsamados, Amy Macfarlane, Henna-Reetta Hannula, and Martin Schneebeli
The Cryosphere, 16, 4223–4250, https://doi.org/10.5194/tc-16-4223-2022, https://doi.org/10.5194/tc-16-4223-2022, 2022
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Impacts of rain on snow (ROS) on satellite-retrieved sea ice variables remain to be fully understood. This study evaluates the impacts of ROS over sea ice on active and passive microwave data collected during the 2019–20 MOSAiC expedition. Rainfall and subsequent refreezing of the snowpack significantly altered emitted and backscattered radar energy, laying important groundwork for understanding their impacts on operational satellite retrievals of various sea ice geophysical variables.
David N. Wagner, Matthew D. Shupe, Christopher Cox, Ola G. Persson, Taneil Uttal, Markus M. Frey, Amélie Kirchgaessner, Martin Schneebeli, Matthias Jaggi, Amy R. Macfarlane, Polona Itkin, Stefanie Arndt, Stefan Hendricks, Daniela Krampe, Marcel Nicolaus, Robert Ricker, Julia Regnery, Nikolai Kolabutin, Egor Shimanshuck, Marc Oggier, Ian Raphael, Julienne Stroeve, and Michael Lehning
The Cryosphere, 16, 2373–2402, https://doi.org/10.5194/tc-16-2373-2022, https://doi.org/10.5194/tc-16-2373-2022, 2022
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Based on measurements of the snow cover over sea ice and atmospheric measurements, we estimate snowfall and snow accumulation for the MOSAiC ice floe, between November 2019 and May 2020. For this period, we estimate 98–114 mm of precipitation. We suggest that about 34 mm of snow water equivalent accumulated until the end of April 2020 and that at least about 50 % of the precipitated snow was eroded or sublimated. Further, we suggest explanations for potential snowfall overestimation.
Klaus Dethloff, Wieslaw Maslowski, Stefan Hendricks, Younjoo J. Lee, Helge F. Goessling, Thomas Krumpen, Christian Haas, Dörthe Handorf, Robert Ricker, Vladimir Bessonov, John J. Cassano, Jaclyn Clement Kinney, Robert Osinski, Markus Rex, Annette Rinke, Julia Sokolova, and Anja Sommerfeld
The Cryosphere, 16, 981–1005, https://doi.org/10.5194/tc-16-981-2022, https://doi.org/10.5194/tc-16-981-2022, 2022
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Sea ice thickness anomalies during the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) winter in January, February and March 2020 were simulated with the coupled Regional Arctic climate System Model (RASM) and compared with CryoSat-2/SMOS satellite data. Hindcast and ensemble simulations indicate that the sea ice anomalies are driven by nonlinear interactions between ice growth processes and wind-driven sea-ice transports, with dynamics playing a dominant role.
Arttu Jutila, Stefan Hendricks, Robert Ricker, Luisa von Albedyll, Thomas Krumpen, and Christian Haas
The Cryosphere, 16, 259–275, https://doi.org/10.5194/tc-16-259-2022, https://doi.org/10.5194/tc-16-259-2022, 2022
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Sea-ice thickness retrieval from satellite altimeters relies on assumed sea-ice density values because density cannot be measured from space. We derived bulk densities for different ice types using airborne laser, radar, and electromagnetic induction sounding measurements. Compared to previous studies, we found high bulk density values due to ice deformation and younger ice cover. Using sea-ice freeboard, we derived a sea-ice bulk density parameterisation that can be applied to satellite data.
Florent Garnier, Sara Fleury, Gilles Garric, Jérôme Bouffard, Michel Tsamados, Antoine Laforge, Marion Bocquet, Renée Mie Fredensborg Hansen, and Frédérique Remy
The Cryosphere, 15, 5483–5512, https://doi.org/10.5194/tc-15-5483-2021, https://doi.org/10.5194/tc-15-5483-2021, 2021
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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.
Isolde A. Glissenaar, Jack C. Landy, Alek A. Petty, Nathan T. Kurtz, and Julienne C. Stroeve
The Cryosphere, 15, 4909–4927, https://doi.org/10.5194/tc-15-4909-2021, https://doi.org/10.5194/tc-15-4909-2021, 2021
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Scientists can estimate sea ice thickness using satellites that measure surface height. To determine the sea ice thickness, we also need to know the snow depth and density. This paper shows that the chosen snow depth product has a considerable impact on the findings of sea ice thickness state and trends in Baffin Bay, showing mean thinning with some snow depth products and mean thickening with others. This shows that it is important to better understand and monitor snow depth on sea ice.
Thomas Krumpen, Luisa von Albedyll, Helge F. Goessling, Stefan Hendricks, Bennet Juhls, Gunnar Spreen, Sascha Willmes, H. Jakob Belter, Klaus Dethloff, Christian Haas, Lars Kaleschke, Christian Katlein, Xiangshan Tian-Kunze, Robert Ricker, Philip Rostosky, Janna Rückert, Suman Singha, and Julia Sokolova
The Cryosphere, 15, 3897–3920, https://doi.org/10.5194/tc-15-3897-2021, https://doi.org/10.5194/tc-15-3897-2021, 2021
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We use satellite data records collected along the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) drift to categorize ice conditions that shaped and characterized the floe and surroundings during the expedition. A comparison with previous years is made whenever possible. The aim of this analysis is to provide a basis and reference for subsequent research in the six main research areas of atmosphere, ocean, sea ice, biogeochemistry, remote sensing and ecology.
Anja Rösel, Sinead Louise Farrell, Vishnu Nandan, Jaqueline Richter-Menge, Gunnar Spreen, Dmitry V. Divine, Adam Steer, Jean-Charles Gallet, and Sebastian Gerland
The Cryosphere, 15, 2819–2833, https://doi.org/10.5194/tc-15-2819-2021, https://doi.org/10.5194/tc-15-2819-2021, 2021
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Recent observations in the Arctic suggest a significant shift towards a snow–ice regime caused by deep snow on thin sea ice which may result in a flooding of the snowpack. These conditions cause the brine wicking and saturation of the basal snow layers which lead to a subsequent underestimation of snow depth from snow radar mesurements. As a consequence the calculated sea ice thickness will be biased towards higher values.
Renée Mie Fredensborg Hansen, Eero Rinne, Sinéad Louise Farrell, and Henriette Skourup
The Cryosphere, 15, 2511–2529, https://doi.org/10.5194/tc-15-2511-2021, https://doi.org/10.5194/tc-15-2511-2021, 2021
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Ice navigators rely on timely information about ice conditions to ensure safe passage through ice-covered waters, and one parameter, the degree of ice ridging (DIR), is particularly useful. We have investigated the possibility of estimating DIR from the geolocated photons of ICESat-2 (IS2) in the Bay of Bothnia, show that IS2 retrievals from different DIR areas differ significantly, and present some of the first steps in creating sea ice applications beyond e.g. thickness retrieval.
Francesca Doglioni, Robert Ricker, Benjamin Rabe, and Torsten Kanzow
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-170, https://doi.org/10.5194/essd-2021-170, 2021
Manuscript not accepted for further review
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This paper presents a new satellite-derived gridded dataset of sea surface height and geostrophic velocity, over the Arctic ice-covered and ice-free regions up to 88° N. The dataset includes velocities north of 82° N, which were not available before. We assess the dataset by comparison to one independent satellite dataset and to independent mooring data. Results show that the geostrophic velocity fields can resolve seasonal to interannual variability of boundary currents wider than about 50 km.
Cited articles
Andersen, O. B.: DTU21 Mean Sea Surface, DTU Data [data set], https://doi.org/10.11583/DTU.19383221.v1,
2022. a
Andersen, O. B., Rose, S. K., Knudsen, P., and Stenseng, L.: The DTU18 MSS Mean
Sea Surface improvement from SAR altimetry,
https://ftp.space.dtu.dk/pub/DTU18/MSS_MATERIAL/PRESENTATIONS/DTU18MSS-V2.pdf (last access: 24 March 2023),
2018. a
Castellani, G., Lüpkes, C., Hendricks, S., and Gerdes, R.: Variability of
Arctic sea-ice topography and its impact on the atmospheric surface drag,
J. Geophys. Res.-Oceans, 119, 6743–6762,
https://doi.org/10.1002/2013JC009712, 2014. a, b
Di Bella, A., Skourup, H., Bouffard, J., and Parrinello, T.: Uncertainty
reduction of Arctic sea ice freeboard from CryoSat-2 interferometric mode,
Adv. Space Res., 62, 1251–1264, https://doi.org/10.1016/j.asr.2018.03.018,
2018. a
Duncan, K., Farrell, S. L., Connor, L. N., Richter-Menge, J., Hutchings, J. K.,
and Dominguez, R.: High-resolution airborne observations of sea-ice pressure
ridge sail height, Ann. Glaciol., 59, 137–147,
https://doi.org/10.1017/aog.2018.2, 2018. a
Farrell, S. L., Duncan, K., Buckley, E. M., Richter-Menge, J., and Li, R.:
Mapping Sea Ice Surface Topography in High Fidelity With ICESat-2,
Geophys. Res. Lett., 47, e2020GL090708,
https://doi.org/10.1029/2020GL090708, 2020. a, b, c
Fredensborg Hansen, R. M., Rinne, E., Farrell, S. L., and Skourup, H.: Estimation of degree of sea ice ridging in the Bay of Bothnia based on geolocated photon heights from ICESat-2, The Cryosphere, 15, 2511–2529, https://doi.org/10.5194/tc-15-2511-2021, 2021. a
Hibler, W. D.: Characterization of Cold-Regions Terrain Using Airborne Laser
Profilometry, Journal of Glaciology, 15, 329–347,
https://doi.org/10.3189/S0022143000034468, 1975. a
Hibler III, W. D., Weeks, W. F., and Mock, S. J.: Statistical aspects of
sea-ice ridge distributions, J. Geophys. Res., 77,
5954–5970, https://doi.org/10.1029/JC077i030p05954, 1972. a, b
Hutter, N., Hendricks, S., Jutila, A., Birnbaum, G., von Albedyll, L., Ricker,
R., and Haas, C.: Gridded segments of sea-ice or snow surface elevation and
freeboard from helicopter-borne laser scanner during the MOSAiC expedition,
version 1, PANGAEA [data set], https://doi.pangaea.de/10.1594/PANGAEA.950339,
2022. a, b, c, d
Johnson, T., Tsamados, M., Muller, J.-P., and Stroeve, J.: Mapping Arctic
Sea-Ice Surface Roughness with Multi-Angle Imaging SpectroRadiometer, Remote
Sensing, 14, 6249, https://doi.org/10.3390/rs14246249, 2022. a
Jutila, A., Hendricks, S., Birnbaum, G., von Albedyll, L., Ricker, R., Helm,
V., Hutter, N., and Haas, C.: Geolocated sea-ice or snow surface elevation
point cloud segments from helicopter-borne laser scanner during the MOSAiC
expedition, version 1, PANGAEA [data set],
https://doi.pangaea.de/10.1594/PANGAEA.950509,
2022a. a
Jutila, A., Hendricks, S., Ricker, R., von Albedyll, L., Krumpen, T., and Haas, C.: Retrieval and parameterisation of sea-ice bulk density from airborne multi-sensor measurements, The Cryosphere, 16, 259–275, https://doi.org/10.5194/tc-16-259-2022, 2022b. a
Kwok, R., Kacimi, S., Markus, T., Kurtz, N. T., Studinger, M., Sonntag, J. G.,
Manizade, S. S., Boisvert, L. N., and Harbeck, J. P.: ICESat-2 Surface Height
and Sea Ice Freeboard Assessed With ATM Lidar Acquisitions From Operation
IceBridge, Geophys. Res. Lett., 46, 11228–11236,
https://doi.org/10.1029/2019GL084976, 2019a. a, b, c, d
Kwok, R., Markus, T., Kurtz, N. T., Petty, A. A., Neumann, T. A., Farrell,
S. L., Cunningham, G. F., Hancock, D. W., Ivanoff, A., and Wimert, J. T.:
Surface Height and Sea Ice Freeboard of the Arctic Ocean From ICESat-2:
Characteristics and Early Results, J. Geophys. Res.-Oceans,
124, 6942–6959, https://doi.org/10.1029/2019JC015486, 2019b. a, b
Kwok, R., Bagnardi, M., Petty, A., and Kurtz, N.: ICESat-2 sea ice ancillary
data – Mean Sea Surface Height Grids, Zenodo [data set], https://doi.org/10.5281/zenodo.4294048, 2020. a
Kwok, R., Petty, A., Cunningham, G., Markus, T., Hancock, D., Ivanoff, A.,
Wimert, J., Bagnardi, M., Kurtz, N., and the ICESat-2 Science Team:
ATLAS/ICESat-2 L3A Sea Ice Height, Version 5, Boulder, Colorado USA, NASA
National Snow and Ice Data Center Distributed Active Archive Center [data set],
https://doi.org/10.5067/ATLAS/ATL07.005,
2021a. a, b, c
Kwok, R., Petty, A. A., Bagnardi, M., Kurtz, N. T., Cunningham, G. F., Ivanoff, A., and Kacimi, S.: Refining the sea surface identification approach for determining freeboards in the ICESat-2 sea ice products, The Cryosphere, 15, 821–833, https://doi.org/10.5194/tc-15-821-2021, 2021b. a, b
Landy, J. C., Petty, A. A., Tsamados, M., and Stroeve, J. C.: Sea Ice Roughness
Overlooked as a Key Source of Uncertainty in CryoSat-2 Ice Freeboard
Retrievals, J. Geophys. Res.-Oceans, 125, e2019JC015820,
https://doi.org/10.1029/2019JC015820, 2020. a
Lavergne, T., Eastwood, S., Teffah, Z., Schyberg, H., and Breivik, L.-A.: Sea
ice motion from low-resolution satellite sensors: An alternative method and
its validation in the Arctic, J. Geophys. Res.-Oceans, 115,
C10032, https://doi.org/10.1029/2009JC005958, 2010. a
Luthcke, S. B., Thomas, T. C., Pennington, T. A., Rebold, T. W., Nicholas,
J. B., Rowlands, D. D., Gardner, A. S., and Bae, S.: ICESat-2 Pointing
Calibration and Geolocation Performance, Earth Space Sci., 8,
e2020EA001494, https://doi.org/10.1029/2020EA001494,
2021. a
Magruder, L. A., Brunt, K. M., and Alonzo, M.: Early ICESat-2 on-orbit
Geolocation Validation Using Ground-Based Corner Cube Retro-Reflectors,
Remote Sensing, 12, 3653, https://doi.org/10.3390/rs12213653, 2020. a, b, c, d
Markus, T., Neumann, T., Martino, A., Abdalati, W., Brunt, K., Csatho, B.,
Farrell, S., Fricker, H., Gardner, A., Harding, D., Jasinski, M., Kwok, R.,
Magruder, L., Lubin, D., Luthcke, S., Morison, J., Nelson, R.,
Neuenschwander, A., Palm, S., Popescu, S., Shum, C., Schutz, B. E., Smith,
B., Yang, Y., and Zwally, J.: The Ice, Cloud, and land Elevation Satellite-2
(ICESat-2): Science requirements, concept, and implementation, Remote Sens. Environ., 190, 260–273, https://doi.org/10.1016/j.rse.2016.12.029, 2017. a, b, c
Mchedlishvili, A., Lüpkes, C., Petty, A., Tsamados, M., and Spreen, G.: New estimates of the pan-Arctic sea ice–atmosphere neutral drag coefficients from ICESat-2 elevation data, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-187, 2023. a
Neumann, T. A., Brenner, A., Hancock, D., Robbins, J., Saba, J., Harbeck, K.,
Gibbons, A., Lee, J., Luthcke, S. B., Rebold, T., et al.: ATLAS/ICESat-2 L2A
Global Geolocated Photon Data, Version 5, Boulder, Colorado USA, NASA
National Snow and Ice Data Center Distributed Active Archive Center [data set],
https://doi.org/10.5067/ATLAS/ATL03.005, 2021. a
Nicolaus, M., Perovich, D. K., Spreen, G., Granskog, M. A., von Albedyll, L.,
Angelopoulos, M., Anhaus, P., Arndt, S., Belter, H. J., Bessonov, V.,
Birnbaum, G., Brauchle, J., Calmer, R., Cardellach, E., Cheng, B.,
Clemens-Sewall, D., Dadic, R., Damm, E., de Boer, G., Demir, O., Dethloff,
K., Divine, D. V., Fong, A. A., Fons, S., Frey, M. M., Fuchs, N.,
Gabarró, C., Gerland, S., Goessling, H. F., Gradinger, R., Haapala, J.,
Haas, C., Hamilton, J., Hannula, H.-R., Hendricks, S., Herber, A., Heuzé,
C., Hoppmann, M., Høyland, K. V., Huntemann, M., Hutchings, J. K., Hwang,
B., Itkin, P., Jacobi, H.-W., Jaggi, M., Jutila, A., Kaleschke, L., Katlein,
C., Kolabutin, N., Krampe, D., Kristensen, S. S., Krumpen, T., Kurtz, N.,
Lampert, A., Lange, B. A., Lei, R., Light, B., Linhardt, F., Liston, G. E.,
Loose, B., Macfarlane, A. R., Mahmud, M., Matero, I. O., Maus, S.,
Morgenstern, A., Naderpour, R., Nandan, V., Niubom, A., Oggier, M., Oppelt,
N., Pätzold, F., Perron, C., Petrovsky, T., Pirazzini, R., Polashenski,
C., Rabe, B., Raphael, I. A., Regnery, J., Rex, M., Ricker, R.,
Riemann-Campe, K., Rinke, A., Rohde, J., Salganik, E., Scharien, R. K.,
Schiller, M., Schneebeli, M., Semmling, M., Shimanchuk, E., Shupe, M. D.,
Smith, M. M., Smolyanitsky, V., Sokolov, V., Stanton, T., Stroeve, J.,
Thielke, L., Timofeeva, A., Tonboe, R. T., Tavri, A., Tsamados, M., Wagner,
D. N., Watkins, D., Webster, M., and Wendisch, M.: Overview of the MOSAiC
expedition: Snow and sea ice, Elementa, 10, 000046,
https://doi.org/10.1525/elementa.2021.000046, 2022. a, b
Nixdorf, U., Dethloff, K., Rex, M., Shupe, M., Sommerfeld, A., Perovich, D. K.,
Nicolaus, M., Heuzé, C., Rabe, B., Loose, B., Damm, E., Gradinger, R., Fong,
A., Maslowski, W., Rinke, A., Kwok, R., Spreen, G., Wendisch, M., Herber, A.,
Hirsekorn, M., Mohaupt, V., Frickenhaus, S., Immerz, A., Weiss-Tuider, K.,
König, B., Mengedoht, D., Regnery, J., Gerchow, P., Ransby, D., Krumpen, T.,
Morgenstern, A., Haas, C., Kanzow, T., Rack, F. R., Saitzev, V., Sokolov, V.,
Makarov, A., Schwarze, S., Wunderlich, T., Wurr, K., and Boetius, A.: MOSAiC
Extended Acknowledgement, Zenodo, https://doi.org/10.5281/zenodo.5541624, 2021. a
OSI SAF: Global Sea Ice Concentration (netCDF) – DMSP, EUMETSAT SAF on
Ocean and Sea Ice [data set], https://doi.org/10.15770/EUM_SAF_OSI_NRT_2004, 2017a. a
OSI SAF: Global Low Resolution Sea Ice Drift – Multimission, EUMETSAT
SAF on Ocean and Sea Ice [data set], https://doi.org/10.15770/EUM_SAF_OSI_NRT_2007, 2017b. a
Petty, A. A., Kurtz, N. T., Kwok, R., Markus, T., and Neumann, T. A.: Winter
Arctic sea ice thickness from ICESat-2 freeboards, J. Geophys.
Res.-Oceans, 125, e2019JC015764, https://doi.org/10.1029/2019JC015764, 2020. a, b
Quartly, G. D., Rinne, E., Passaro, M., Andersen, O. B., Dinardo, S., Fleury,
S., Guillot, A., Hendricks, S., Kurekin, A. A., Müller, F. L., Ricker, R.,
Skourup, H., and Tsamados, M.: Retrieving Sea Level and Freeboard in the
Arctic: A Review of Current Radar Altimetry Methodologies and Future
Perspectives, Remote Sensing, 11, 881, https://doi.org/10.3390/rs11070881, 2019. a
Ricker, R., Hendricks, S., Helm, V., Skourup, H., and Davidson, M.: Sensitivity of CryoSat-2 Arctic sea-ice freeboard and thickness on radar-waveform interpretation, The Cryosphere, 8, 1607–1622, https://doi.org/10.5194/tc-8-1607-2014, 2014. a
Ricker, R., Hendricks, S., and Beckers, J. F.: The Impact of Geophysical
Corrections on Sea-Ice Freeboard Retrieved from Satellite Altimetry, Remote
Sensing, 8, 317, https://doi.org/10.3390/rs8040317, 2016. a
Ricker, R., Kauker, F., Schweiger, A., Hendricks, S., Zhang, J., and Paul, S.:
Evidence for an Increasing Role of Ocean Heat in Arctic Winter Sea Ice
Growth, J. Climate, 34, 5215–5227, https://doi.org/10.1175/JCLI-D-20-0848.1,
2021. a
Tan, B., Li, Z.-J., Lu, P., Haas, C., and Nicolaus, M.: Morphology of sea ice
pressure ridges in the northwestern Weddell Sea in winter, J.
Geophys. Res.-Oceans, 117, C06024,
https://doi.org/10.1029/2011JC007800, 2012. a
Tsamados, M., Feltham, D. L., Schroeder, D., Flocco, D., Farrell, S. L., Kurtz,
N., Laxon, S. W., and Bacon, S.: Impact of Variable Atmospheric and Oceanic
Form Drag on Simulations of Arctic Sea Ice, J. Phys. Oceanogr.,
44, 1329–1353, https://doi.org/10.1175/JPO-D-13-0215.1, 2014. a
von Albedyll, L., Hendricks, S., Grodofzig, R., Krumpen, T., Arndt, S., Belter,
H. J., Birnbaum, G., Cheng, B., Hoppmann, M., Hutchings, J., Itkin, P., Lei,
R., Nicolaus, M., Ricker, R., Rohde, J., Suhrhoff, M., Timofeeva, A.,
Watkins, D., Webster, M., and Haas, C.: Thermodynamic and dynamic
contributions to seasonal Arctic sea ice thickness distributions from
airborne observations, Elementa, 10, 00074,
https://doi.org/10.1525/elementa.2021.00074, 2022. a, b
Wagner, D. N., Shupe, M. D., Cox, C., Persson, O. G., Uttal, T., Frey, M. M., Kirchgaessner, A., Schneebeli, M., Jaggi, M., Macfarlane, A. R., Itkin, P., Arndt, S., Hendricks, S., Krampe, D., Nicolaus, M., Ricker, R., Regnery, J., Kolabutin, N., Shimanshuck, E., Oggier, M., Raphael, I., Stroeve, J., and Lehning, M.: Snowfall and snow accumulation during the MOSAiC winter and spring seasons, The Cryosphere, 16, 2373–2402, https://doi.org/10.5194/tc-16-2373-2022, 2022. 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., Rostan, F., Viau, P., and Wallis, D.: CryoSat: A
mission to determine the fluctuations in Earth's land and marine ice fields,
Adv. Space Res., 37, 841–871, https://doi.org/10.1016/j.asr.2005.07.027,
2006. a
Short summary
Information on sea ice surface topography is important for studies of sea ice as well as for ship navigation through ice. The ICESat-2 satellite senses the sea ice surface with six laser beams. To examine the accuracy of these measurements, we carried out a temporally coincident helicopter flight along the same ground track as the satellite and measured the sea ice surface topography with a laser scanner. This showed that ICESat-2 can see even bumps of only few meters in the sea ice cover.
Information on sea ice surface topography is important for studies of sea ice as well as for...
