Articles | Volume 17, issue 9
https://doi.org/10.5194/tc-17-4103-2023
© 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-17-4103-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
New estimates of pan-Arctic sea ice–atmosphere neutral drag coefficients from ICESat-2 elevation data
Alexander Mchedlishvili
CORRESPONDING AUTHOR
Institute of Environmental Physics, University of Bremen, Bremen, Germany
Christof Lüpkes
Alfred Wegener Institute for Polar and Marine Research, Bremerhaven, Germany
Alek Petty
Goddard Space Flight Center, National Aeronautics and Space Administration, Greenbelt, MD, USA
Earth System Science Interdisciplinary Center (ESSIC) of the University of Maryland, University of Maryland, College Park, MD, USA
Michel Tsamados
Department of Earth Sciences, University College London, London, UK
Gunnar Spreen
Institute of Environmental Physics, University of Bremen, Bremen, Germany
Related authors
Alexander Mchedlishvili, Gunnar Spreen, Christian Melsheimer, and Marcus Huntemann
The Cryosphere, 16, 471–487, https://doi.org/10.5194/tc-16-471-2022, https://doi.org/10.5194/tc-16-471-2022, 2022
Short summary
Short summary
In this paper we show that the activity leading to the open-ocean polynyas near the Maud Rise seamount that have occurred repeatedly from 1974–1976 as well as 2016–2017 does not simply stop for polynya-free years. Using apparent sea ice thickness retrieval, we have identified anomalies where there is thinning of sea ice on a scale that is comparable to that of the polynya events of 2016–2017. These anomalies took place in 2010, 2013, 2014 and 2018.
Manuel Moser, Christiane Voigt, Oliver Eppers, Johannes Lucke, Elena De La Torre Castro, Johanna Mayer, Regis Dupuy, Guillaume Mioche, Olivier Jourdan, Hans-Christian Clemen, Johannes Schneider, Philipp Joppe, Stephan Mertes, Bruno Wetzel, Stephan Borrmann, Marcus Klingebiel, Mario Mech, Christof Lüpkes, Susanne Crewell, André Ehrlich, Andreas Herber, and Manfred Wendisch
EGUsphere, https://doi.org/10.5194/egusphere-2025-3876, https://doi.org/10.5194/egusphere-2025-3876, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
In this study we analyzed Arctic mixed-phase clouds using airborne in-situ measurements in spring 2022. Based on microphysical properties, we show that within these clouds a distinction must be made between classic mixed-phase clouds and a mixed-phase haze regime. Instead of supercooled droplets, the haze regime contains large wet sea salt aerosols. These findings improve our understanding of Arctic low-level cloud processes.
Alex Cabaj, Paul J. Kushner, and Alek A. Petty
The Cryosphere, 19, 3033–3064, https://doi.org/10.5194/tc-19-3033-2025, https://doi.org/10.5194/tc-19-3033-2025, 2025
Short summary
Short summary
The output of snow-on-sea-ice models is influenced by the choice of snowfall input used. We ran such a model with different snowfall inputs and calibrated it to observations, produced a new calibrated snow product, and regionally compared the model outputs to outputs from another snow-on-sea-ice model. The two models agree best on the seasonal cycle of snow in the central Arctic Ocean. Observational comparisons highlight ongoing challenges in estimating the depth and density of snow on Arctic sea ice.
Zsófia Jurányi, Christof Lüpkes, Frank Stratmann, Jörg Hartmann, Jonas Schaefer, Anna-Marie Jörss, Alexander Schulz, Bruno Wetzel, David Simon, Eduard Gebhard, Maximilian Stöhr, Paula Hofmann, Dirk Kalmbach, Sarah Grawe, Manfred Wendisch, and Andreas Herber
Atmos. Meas. Tech., 18, 3477–3494, https://doi.org/10.5194/amt-18-3477-2025, https://doi.org/10.5194/amt-18-3477-2025, 2025
Short summary
Short summary
Understanding the lowest layers of the atmosphere is crucial for climate research, especially in the Arctic. Our study introduces the T-Bird, an aircraft-towed platform designed to measure turbulence and aerosol properties at extremely low altitudes. Traditional aircraft cannot access this region, making the T-Bird a breakthrough for capturing critical atmospheric data. Its first deployment over the Arctic demonstrated its potential to improve our understanding of polar processes.
Elie René-Bazin, Michel Tsamados, Sabrina Sofea Binti Aliff Raziuddin, Joel Perez Ferrer, Tudor Suciu, Carmen Nab, Chamkaur Ghag, Harry Heorton, Rosemary Willatt, Jack Landy, Matthew Fox, and Thomas Bodin
EGUsphere, https://doi.org/10.5194/egusphere-2025-1163, https://doi.org/10.5194/egusphere-2025-1163, 2025
Short summary
Short summary
This paper introduces a new statistical approach to retrieve ice and snow depth over the Arctic Ocean, using satellite altimeters measurements. We demonstrate the ability of this method to compute efficiently the sea ice thickness and the snow depth over the Arctic, without major assumptions on the snow. In addition to the ice and snow depth, this approach is efficient to study the penetration of radar and laser pulses, paving the way for further research in satellite altimetry.
André Ehrlich, Susanne Crewell, Andreas Herber, Marcus Klingebiel, Christof Lüpkes, Mario Mech, Sebastian Becker, Stephan Borrmann, Heiko Bozem, Matthias Buschmann, Hans-Christian Clemen, Elena De La Torre Castro, Henning Dorff, Regis Dupuy, Oliver Eppers, Florian Ewald, Geet George, Andreas Giez, Sarah Grawe, Christophe Gourbeyre, Jörg Hartmann, Evelyn Jäkel, Philipp Joppe, Olivier Jourdan, Zsófia Jurányi, Benjamin Kirbus, Johannes Lucke, Anna E. Luebke, Maximilian Maahn, Nina Maherndl, Christian Mallaun, Johanna Mayer, Stephan Mertes, Guillaume Mioche, Manuel Moser, Hanno Müller, Veronika Pörtge, Nils Risse, Greg Roberts, Sophie Rosenburg, Johannes Röttenbacher, Michael Schäfer, Jonas Schaefer, Andreas Schäfler, Imke Schirmacher, Johannes Schneider, Sabrina Schnitt, Frank Stratmann, Christian Tatzelt, Christiane Voigt, Andreas Walbröl, Anna Weber, Bruno Wetzel, Martin Wirth, and Manfred Wendisch
Earth Syst. Sci. Data, 17, 1295–1328, https://doi.org/10.5194/essd-17-1295-2025, https://doi.org/10.5194/essd-17-1295-2025, 2025
Short summary
Short summary
This paper provides an overview of the HALO–(AC)3 aircraft campaign data sets, the campaign-specific instrument operation, data processing, and data quality. The data set comprises in situ and remote sensing observations from three research aircraft: HALO, Polar 5, and Polar 6. All data are published in the PANGAEA database by instrument-separated data subsets. It is highlighted how the scientific analysis of the HALO–(AC)3 data benefits from the coordinated operation of three aircraft.
Alek Aaron Petty, Christopher Cardinale, and Madison Smith
EGUsphere, https://doi.org/10.5194/egusphere-2025-766, https://doi.org/10.5194/egusphere-2025-766, 2025
Short summary
Short summary
We put global climate models to the test against NASA’s ICESat-2 satellite to see how well they simulate global sea ice cover. By adding fancy laser data from ICESat-2, we can better assess how well the models are performing compared to the standard assessments of sea ice area. Overall the models do a good job but there’s room for improvement, especially across the Southern Ocean. We should think a bit more about sea ice density if we want more reliable freeboard comparisons.
Siqi Liu, Shiming Xu, Wenkai Guo, Yanfei Fan, Lu Zhou, Jack Landy, Malin Johansson, Weixin Zhu, and Alek Petty
EGUsphere, https://doi.org/10.5194/egusphere-2025-1069, https://doi.org/10.5194/egusphere-2025-1069, 2025
Short summary
Short summary
In this study, we explore the potential of using synthetic aperture radars (SAR) to predict the sea ice height measurements by the airborne campaign of Operation IceBridge. In particular, we predict the meter-scale sea ice height with the statistical relationship between the two, overcoming the resolution limitation of SAR images from Sentinel-1 satellites. The prediction and ice drift correction algorithms can be applied to the extrapolation of ICESat-2 measurements in the Arctic region.
Youngmin Choi, Alek Petty, Denis Felikson, and Jonathan Poterjoy
EGUsphere, https://doi.org/10.5194/egusphere-2025-301, https://doi.org/10.5194/egusphere-2025-301, 2025
Short summary
Short summary
In this study, we combined numerical models with satellite data using the ensemble Kalman filter to improve predictions of glacier states and their basal conditions. Simulations showed that adding more data enhances prediction accuracy. We also tested the effect of various data types and found that the high-resolution data improve model performance. This method could inform the design of better observation systems and refine future projections of ice sheet behavior.
Larysa Istomina, Hannah Niehaus, and Gunnar Spreen
The Cryosphere, 19, 83–105, https://doi.org/10.5194/tc-19-83-2025, https://doi.org/10.5194/tc-19-83-2025, 2025
Short summary
Short summary
Melt water puddles, or melt ponds on top of the Arctic sea ice, are a good measure of the Arctic climate state. In the context of recent climate warming, the Arctic has warmed about 4 times faster than the rest of the world, and a long-term dataset of the melt pond fraction is needed to be able to model the future development of the Arctic climate. We present such a dataset, produce 2002–2023 trends and highlight a potential melt regime shift with drastic regional trends of + 20 % per decade.
Karl Kortum, Suman Singha, and Gunnar Spreen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3351, https://doi.org/10.5194/egusphere-2024-3351, 2024
Short summary
Short summary
Improved sea ice observations are essential to understanding the processes that lead to the strong warming effect currently being observed in the Arctic. In this work, we combine complementary satellite measurement techniques and find remarkable correlations between the two observations. This allows us to expand the coverage of ice topography measurements to a scope and resolution that could not previously be observed.
Rémy Lapere, Louis Marelle, Pierre Rampal, Laurent Brodeau, Christian Melsheimer, Gunnar Spreen, and Jennie L. Thomas
Atmos. Chem. Phys., 24, 12107–12132, https://doi.org/10.5194/acp-24-12107-2024, https://doi.org/10.5194/acp-24-12107-2024, 2024
Short summary
Short summary
Elongated open-water areas in sea ice, called leads, can release marine aerosols into the atmosphere. In the Arctic, this source of atmospheric particles could play an important role for climate. However, the amount, seasonality and spatial distribution of such emissions are all mostly unknown. Here, we propose a first parameterization for sea spray aerosols emitted through leads in sea ice and quantify their impact on aerosol populations in the high Arctic.
Hannah Niehaus, Gunnar Spreen, Larysa Istomina, and Marcel Nicolaus
EGUsphere, https://doi.org/10.5194/egusphere-2024-3127, https://doi.org/10.5194/egusphere-2024-3127, 2024
Short summary
Short summary
Melt ponds on Arctic sea ice affect how much solar energy is absorbed, influencing ice melt and climate change. This study used satellite data from 2017–2023 to examine how these ponds vary across regions and seasons. The results show that the surface fraction of melt ponds is more stable in the Central Arctic, with air temperature and ice surface roughness playing key roles in their formation. Understanding these patterns can help to improve climate models and predictions for Arctic warming.
Jack C. Landy, Claude de Rijke-Thomas, Carmen Nab, Isobel Lawrence, Isolde A. Glissenaar, Robbie D. C. Mallett, Renée M. Fredensborg Hansen, Alek Petty, Michel Tsamados, Amy R. Macfarlane, and Anne Braakmann-Folgmann
EGUsphere, https://doi.org/10.5194/egusphere-2024-2904, https://doi.org/10.5194/egusphere-2024-2904, 2024
Short summary
Short summary
In this study we use three satellites to test the planned remote sensing approach of the upcoming mission CRISTAL over sea ice: that its dual radars will accurately measure the heights of the top and base of snow sitting atop floating sea ice floes. Our results suggest that CRISTAL's dual radars won’t necessarily measure the snow top and base under all conditions. We find that accurate height measurements depend much more on surface roughness than on snow properties, as is commonly assumed.
Nils Risse, Mario Mech, Catherine Prigent, Gunnar Spreen, and Susanne Crewell
The Cryosphere, 18, 4137–4163, https://doi.org/10.5194/tc-18-4137-2024, https://doi.org/10.5194/tc-18-4137-2024, 2024
Short summary
Short summary
Passive microwave observations from satellites are crucial for monitoring Arctic sea ice and atmosphere. To do this effectively, it is important to understand how sea ice emits microwaves. Through unique Arctic sea ice observations, we improved our understanding, identified four distinct emission types, and expanded current knowledge to include higher frequencies. These findings will enhance our ability to monitor the Arctic climate and provide valuable information for new satellite missions.
Manfred Wendisch, Susanne Crewell, André Ehrlich, Andreas Herber, Benjamin Kirbus, Christof Lüpkes, Mario Mech, Steven J. Abel, Elisa F. Akansu, Felix Ament, Clémantyne Aubry, Sebastian Becker, Stephan Borrmann, Heiko Bozem, Marlen Brückner, Hans-Christian Clemen, Sandro Dahlke, Georgios Dekoutsidis, Julien Delanoë, Elena De La Torre Castro, Henning Dorff, Regis Dupuy, Oliver Eppers, Florian Ewald, Geet George, Irina V. Gorodetskaya, Sarah Grawe, Silke Groß, Jörg Hartmann, Silvia Henning, Lutz Hirsch, Evelyn Jäkel, Philipp Joppe, Olivier Jourdan, Zsofia Jurányi, Michail Karalis, Mona Kellermann, Marcus Klingebiel, Michael Lonardi, Johannes Lucke, Anna E. Luebke, Maximilian Maahn, Nina Maherndl, Marion Maturilli, Bernhard Mayer, Johanna Mayer, Stephan Mertes, Janosch Michaelis, Michel Michalkov, Guillaume Mioche, Manuel Moser, Hanno Müller, Roel Neggers, Davide Ori, Daria Paul, Fiona M. Paulus, Christian Pilz, Felix Pithan, Mira Pöhlker, Veronika Pörtge, Maximilian Ringel, Nils Risse, Gregory C. Roberts, Sophie Rosenburg, Johannes Röttenbacher, Janna Rückert, Michael Schäfer, Jonas Schaefer, Vera Schemann, Imke Schirmacher, Jörg Schmidt, Sebastian Schmidt, Johannes Schneider, Sabrina Schnitt, Anja Schwarz, Holger Siebert, Harald Sodemann, Tim Sperzel, Gunnar Spreen, Bjorn Stevens, Frank Stratmann, Gunilla Svensson, Christian Tatzelt, Thomas Tuch, Timo Vihma, Christiane Voigt, Lea Volkmer, Andreas Walbröl, Anna Weber, Birgit Wehner, Bruno Wetzel, Martin Wirth, and Tobias Zinner
Atmos. Chem. Phys., 24, 8865–8892, https://doi.org/10.5194/acp-24-8865-2024, https://doi.org/10.5194/acp-24-8865-2024, 2024
Short summary
Short summary
The Arctic is warming faster than the rest of the globe. Warm-air intrusions (WAIs) into the Arctic may play an important role in explaining this phenomenon. Cold-air outbreaks (CAOs) out of the Arctic may link the Arctic climate changes to mid-latitude weather. In our article, we describe how to observe air mass transformations during CAOs and WAIs using three research aircraft instrumented with state-of-the-art remote-sensing and in situ measurement devices.
Michael Studinger, Benjamin E. Smith, Nathan Kurtz, Alek Petty, Tyler Sutterley, and Rachel Tilling
The Cryosphere, 18, 2625–2652, https://doi.org/10.5194/tc-18-2625-2024, https://doi.org/10.5194/tc-18-2625-2024, 2024
Short summary
Short summary
We use green lidar data and natural-color imagery over sea ice to quantify elevation biases potentially impacting estimates of change in ice thickness of the polar regions. We complement our analysis using a model of scattering of light in snow and ice that predicts the shape of lidar waveforms reflecting from snow and ice surfaces based on the shape of the transmitted pulse. We find that biased elevations exist in airborne and spaceborne data products from green lidars.
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
Short summary
Short summary
A dataset of 20 radar satellite acquisitions and near-simultaneous helicopter-based surveys of the ice topography during the MOSAiC expedition is constructed and used to train a variety of deep learning algorithms. The results give realistic insights into the accuracy of retrieval of measured ice classes using modern deep learning models. The models able to learn from the spatial distribution of the measured sea ice classes are shown to have a clear advantage over those that cannot.
Luisa von Albedyll, Stefan Hendricks, Nils Hutter, Dmitrii Murashkin, Lars Kaleschke, Sascha Willmes, Linda Thielke, Xiangshan Tian-Kunze, Gunnar Spreen, and Christian Haas
The Cryosphere, 18, 1259–1285, https://doi.org/10.5194/tc-18-1259-2024, https://doi.org/10.5194/tc-18-1259-2024, 2024
Short summary
Short summary
Leads (openings in sea ice cover) are created by sea ice dynamics. Because they are important for many processes in the Arctic winter climate, we aim to detect them with satellites. We present two new techniques to detect lead widths of a few hundred meters at high spatial resolution (700 m) and independent of clouds or sun illumination. We use the MOSAiC drift 2019–2020 in the Arctic for our case study and compare our new products to other existing lead products.
Evelyn Jäkel, Sebastian Becker, Tim R. Sperzel, Hannah Niehaus, Gunnar Spreen, Ran Tao, Marcel Nicolaus, Wolfgang Dorn, Annette Rinke, Jörg Brauchle, and Manfred Wendisch
The Cryosphere, 18, 1185–1205, https://doi.org/10.5194/tc-18-1185-2024, https://doi.org/10.5194/tc-18-1185-2024, 2024
Short summary
Short summary
The results of the surface albedo scheme of a coupled regional climate model were evaluated against airborne and ground-based measurements conducted in the European Arctic in different seasons between 2017 and 2022. We found a seasonally dependent bias between measured and modeled surface albedo for cloudless and cloudy situations. The strongest effects of the albedo model bias on the net irradiance were most apparent in the presence of optically thin clouds.
Hannah Niehaus, Larysa Istomina, Marcel Nicolaus, Ran Tao, Aleksey Malinka, Eleonora Zege, and Gunnar Spreen
The Cryosphere, 18, 933–956, https://doi.org/10.5194/tc-18-933-2024, https://doi.org/10.5194/tc-18-933-2024, 2024
Short summary
Short summary
Melt ponds are puddles of meltwater which form on Arctic sea ice in the summer period. They are darker than the ice cover and lead to increased absorption of solar energy. Global climate models need information about the Earth's energy budget. Thus satellite observations are used to monitor the surface fractions of melt ponds, ocean, and sea ice in the entire Arctic. We present a new physically based algorithm that can separate these three surface types with uncertainty below 10 %.
Pablo Saavedra Garfias, Heike Kalesse-Los, Luisa von Albedyll, Hannes Griesche, and Gunnar Spreen
Atmos. Chem. Phys., 23, 14521–14546, https://doi.org/10.5194/acp-23-14521-2023, https://doi.org/10.5194/acp-23-14521-2023, 2023
Short summary
Short summary
An important Arctic climate process is the release of heat fluxes from sea ice openings to the atmosphere that influence the clouds. The characterization of this process is the objective of this study. Using synergistic observations from the MOSAiC expedition, we found that single-layer cloud properties show significant differences when clouds are coupled or decoupled to the water vapour transport which is used as physical link between the upwind sea ice openings and the cloud under observation.
Alistair Duffey, Robbie Mallett, Peter J. Irvine, Michel Tsamados, and Julienne Stroeve
Earth Syst. Dynam., 14, 1165–1169, https://doi.org/10.5194/esd-14-1165-2023, https://doi.org/10.5194/esd-14-1165-2023, 2023
Short summary
Short summary
The Arctic is warming several times faster than the rest of the planet. Here, we use climate model projections to quantify for the first time how this faster warming in the Arctic impacts the timing of crossing the 1.5 °C and 2 °C thresholds defined in the Paris Agreement. We show that under plausible emissions scenarios that fail to meet the Paris 1.5 °C target, a hypothetical world without faster warming in the Arctic would breach that 1.5 °C target around 5 years later.
Olivia Linke, Johannes Quaas, Finja Baumer, Sebastian Becker, Jan Chylik, Sandro Dahlke, André Ehrlich, Dörthe Handorf, Christoph Jacobi, Heike Kalesse-Los, Luca Lelli, Sina Mehrdad, Roel A. J. Neggers, Johannes Riebold, Pablo Saavedra Garfias, Niklas Schnierstein, Matthew D. Shupe, Chris Smith, Gunnar Spreen, Baptiste Verneuil, Kameswara S. Vinjamuri, Marco Vountas, and Manfred Wendisch
Atmos. Chem. Phys., 23, 9963–9992, https://doi.org/10.5194/acp-23-9963-2023, https://doi.org/10.5194/acp-23-9963-2023, 2023
Short summary
Short summary
Lapse rate feedback (LRF) is a major driver of the Arctic amplification (AA) of climate change. It arises because the warming is stronger at the surface than aloft. Several processes can affect the LRF in the Arctic, such as the omnipresent temperature inversion. Here, we compare multimodel climate simulations to Arctic-based observations from a large research consortium to broaden our understanding of these processes, find synergy among them, and constrain the Arctic LRF and AA.
Philip Rostosky and Gunnar Spreen
The Cryosphere, 17, 3867–3881, https://doi.org/10.5194/tc-17-3867-2023, https://doi.org/10.5194/tc-17-3867-2023, 2023
Short summary
Short summary
During winter, storms entering the Arctic region can bring warm air into the cold environment. Strong increases in air temperature modify the characteristics of the Arctic snow and ice cover. The Arctic sea ice cover can be monitored by satellites observing the natural emission of the Earth's surface. In this study, we show that during warm air intrusions the change in the snow characteristics influences the satellite-derived sea ice cover, leading to a false reduction of the estimated ice area.
Manfred Wendisch, Johannes Stapf, Sebastian Becker, André Ehrlich, Evelyn Jäkel, Marcus Klingebiel, Christof Lüpkes, Michael Schäfer, and Matthew D. Shupe
Atmos. Chem. Phys., 23, 9647–9667, https://doi.org/10.5194/acp-23-9647-2023, https://doi.org/10.5194/acp-23-9647-2023, 2023
Short summary
Short summary
Atmospheric radiation measurements have been conducted during two field campaigns using research aircraft. The data are analyzed to see if the near-surface air in the Arctic is warmed or cooled if warm–humid air masses from the south enter the Arctic or cold–dry air moves from the north from the Arctic to mid-latitude areas. It is important to study these processes and to check if climate models represent them well. Otherwise it is not possible to reliably forecast the future Arctic climate.
Amelie U. Schmitt and Christof Lüpkes
The Cryosphere, 17, 3115–3136, https://doi.org/10.5194/tc-17-3115-2023, https://doi.org/10.5194/tc-17-3115-2023, 2023
Short summary
Short summary
In the last few decades, the region between Greenland and Svalbard has experienced the largest loss of Arctic sea ice in winter. We analyze how changes in air temperature, humidity and wind in this region differ for winds that originate from sea ice covered areas and from the open ocean. The largest impacts of sea ice cover are found for temperatures close to the ice edge and up to a distance of 500 km. Up to two-thirds of the observed temperature variability is related to sea ice changes.
Manuel Moser, Christiane Voigt, Tina Jurkat-Witschas, Valerian Hahn, Guillaume Mioche, Olivier Jourdan, Régis Dupuy, Christophe Gourbeyre, Alfons Schwarzenboeck, Johannes Lucke, Yvonne Boose, Mario Mech, Stephan Borrmann, André Ehrlich, Andreas Herber, Christof Lüpkes, and Manfred Wendisch
Atmos. Chem. Phys., 23, 7257–7280, https://doi.org/10.5194/acp-23-7257-2023, https://doi.org/10.5194/acp-23-7257-2023, 2023
Short summary
Short summary
This study provides a comprehensive microphysical and thermodynamic phase analysis of low-level clouds in the northern Fram Strait, above the sea ice and the open ocean, during spring and summer. Using airborne in situ cloud data, we show that the properties of Arctic low-level clouds vary significantly with seasonal meteorological situations and surface conditions. The observations presented in this study can help one to assess the role of clouds in the Arctic climate system.
Vishnu Nandan, Rosemary Willatt, Robbie Mallett, Julienne Stroeve, Torsten Geldsetzer, Randall Scharien, Rasmus Tonboe, John Yackel, Jack Landy, David Clemens-Sewall, Arttu Jutila, David N. Wagner, Daniela Krampe, Marcus Huntemann, Mallik Mahmud, David Jensen, Thomas Newman, Stefan Hendricks, Gunnar Spreen, Amy Macfarlane, Martin Schneebeli, James Mead, Robert Ricker, Michael Gallagher, Claude Duguay, Ian Raphael, Chris Polashenski, Michel Tsamados, Ilkka Matero, and Mario Hoppmann
The Cryosphere, 17, 2211–2229, https://doi.org/10.5194/tc-17-2211-2023, https://doi.org/10.5194/tc-17-2211-2023, 2023
Short summary
Short summary
We show that wind redistributes snow on Arctic sea ice, and Ka- and Ku-band radar measurements detect both newly deposited snow and buried snow layers that can affect the accuracy of snow depth estimates on sea ice. Radar, laser, meteorological, and snow data were collected during the MOSAiC expedition. With frequent occurrence of storms in the Arctic, our results show that
wind-redistributed snow needs to be accounted for to improve snow depth estimates on sea ice from satellite radars.
Jan Chylik, Dmitry Chechin, Regis Dupuy, Birte S. Kulla, Christof Lüpkes, Stephan Mertes, Mario Mech, and Roel A. J. Neggers
Atmos. Chem. Phys., 23, 4903–4929, https://doi.org/10.5194/acp-23-4903-2023, https://doi.org/10.5194/acp-23-4903-2023, 2023
Short summary
Short summary
Arctic low-level clouds play an important role in the ongoing warming of the Arctic. Unfortunately, these clouds are not properly represented in weather forecast and climate models. This study tries to cover this gap by focusing on clouds over open water during the spring, observed by research aircraft near Svalbard. The study combines the high-resolution model with sets of observational data. The results show the importance of processes that involve both ice and the liquid water in the clouds.
Dmitry G. Chechin, Christof Lüpkes, Jörg Hartmann, André Ehrlich, and Manfred Wendisch
Atmos. Chem. Phys., 23, 4685–4707, https://doi.org/10.5194/acp-23-4685-2023, https://doi.org/10.5194/acp-23-4685-2023, 2023
Short summary
Short summary
Clouds represent a very important component of the Arctic climate system, as they strongly reduce the amount of heat lost to space from the sea ice surface. Properties of clouds, as well as their persistence, strongly depend on the complex interaction of such small-scale properties as phase transitions, radiative transfer and turbulence. In this study we use airborne observations to learn more about the effect of clouds and radiative cooling on turbulence in comparison with other factors.
Wenkai Guo, Polona Itkin, Suman Singha, Anthony P. Doulgeris, Malin Johansson, and Gunnar Spreen
The Cryosphere, 17, 1279–1297, https://doi.org/10.5194/tc-17-1279-2023, https://doi.org/10.5194/tc-17-1279-2023, 2023
Short summary
Short summary
Sea ice maps are produced to cover the MOSAiC Arctic expedition (2019–2020) and divide sea ice into scientifically meaningful classes. We use a high-resolution X-band synthetic aperture radar dataset and show how image brightness and texture systematically vary across the images. We use an algorithm that reliably corrects this effect and achieve good results, as evaluated by comparisons to ground observations and other studies. The sea ice maps are useful as a basis for future MOSAiC studies.
Alek A. Petty, Nicole Keeney, Alex Cabaj, Paul Kushner, and Marco Bagnardi
The Cryosphere, 17, 127–156, https://doi.org/10.5194/tc-17-127-2023, https://doi.org/10.5194/tc-17-127-2023, 2023
Short summary
Short summary
We present upgrades to winter Arctic sea ice thickness estimates from NASA's ICESat-2. Our new thickness results show better agreement with independent data from ESA's CryoSat-2 compared to our first data release, as well as new, very strong comparisons with data collected by moorings in the Beaufort Sea. We analyse three winters of thickness data across the Arctic, including 50 cm thinning of the multiyear ice over this 3-year period.
Christian Melsheimer, Gunnar Spreen, Yufang Ye, and Mohammed Shokr
The Cryosphere, 17, 105–126, https://doi.org/10.5194/tc-17-105-2023, https://doi.org/10.5194/tc-17-105-2023, 2023
Short summary
Short summary
It is necessary to know the type of Antarctic sea ice present – first-year ice (grown in one season) or multiyear ice (survived one summer melt) – to understand and model its evolution, as the ice types behave and react differently. We have adapted and extended an existing method (originally for the Arctic), and now, for the first time, daily maps of Antarctic sea ice types can be derived from microwave satellite data. This will allow a new data set from 2002 well into the future to be built.
Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Ruzica Dadic, Philip Rostosky, Michael Gallagher, Robbie Mallett, Andrew Barrett, Stefan Hendricks, Rasmus Tonboe, Michelle McCrystall, Mark Serreze, Linda Thielke, Gunnar Spreen, Thomas Newman, John Yackel, Robert Ricker, Michel Tsamados, Amy Macfarlane, Henna-Reetta Hannula, and Martin Schneebeli
The Cryosphere, 16, 4223–4250, https://doi.org/10.5194/tc-16-4223-2022, https://doi.org/10.5194/tc-16-4223-2022, 2022
Short summary
Short summary
Impacts of rain on snow (ROS) on satellite-retrieved sea ice variables remain to be fully understood. This study evaluates the impacts of ROS over sea ice on active and passive microwave data collected during the 2019–20 MOSAiC expedition. Rainfall and subsequent refreezing of the snowpack significantly altered emitted and backscattered radar energy, laying important groundwork for understanding their impacts on operational satellite retrievals of various sea ice geophysical variables.
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.
Janosch Michaelis, Amelie U. Schmitt, Christof Lüpkes, Jörg Hartmann, Gerit Birnbaum, and Timo Vihma
Earth Syst. Sci. Data, 14, 1621–1637, https://doi.org/10.5194/essd-14-1621-2022, https://doi.org/10.5194/essd-14-1621-2022, 2022
Short summary
Short summary
A major goal of the Springtime Atmospheric Boundary Layer Experiment (STABLE) aircraft campaign was to observe atmospheric conditions during marine cold-air outbreaks (MCAOs) originating from the sea-ice-covered Arctic ocean. Quality-controlled measurements of several meteorological variables collected during 15 vertical aircraft profiles and by 22 dropsondes are presented. The comprehensive data set may be used for validating model results to improve the understanding of future trends in MCAOs.
Alexander Mchedlishvili, Gunnar Spreen, Christian Melsheimer, and Marcus Huntemann
The Cryosphere, 16, 471–487, https://doi.org/10.5194/tc-16-471-2022, https://doi.org/10.5194/tc-16-471-2022, 2022
Short summary
Short summary
In this paper we show that the activity leading to the open-ocean polynyas near the Maud Rise seamount that have occurred repeatedly from 1974–1976 as well as 2016–2017 does not simply stop for polynya-free years. Using apparent sea ice thickness retrieval, we have identified anomalies where there is thinning of sea ice on a scale that is comparable to that of the polynya events of 2016–2017. These anomalies took place in 2010, 2013, 2014 and 2018.
Anna A. Shestakova, Dmitry G. Chechin, Christof Lüpkes, Jörg Hartmann, and Marion Maturilli
Atmos. Chem. Phys., 22, 1529–1548, https://doi.org/10.5194/acp-22-1529-2022, https://doi.org/10.5194/acp-22-1529-2022, 2022
Short summary
Short summary
This article presents a comprehensive analysis of the easterly orographic wind episode which occurred over Svalbard on 30–31 May 2017. This wind caused a significant temperature rise on the lee side of the mountains and greatly intensified the snowmelt. This episode was investigated on the basis of measurements collected during the ACLOUD/PASCAL field campaigns with the help of numerical modeling.
Florent Garnier, Sara Fleury, Gilles Garric, Jérôme Bouffard, Michel Tsamados, Antoine Laforge, Marion Bocquet, Renée Mie Fredensborg Hansen, and Frédérique Remy
The Cryosphere, 15, 5483–5512, https://doi.org/10.5194/tc-15-5483-2021, https://doi.org/10.5194/tc-15-5483-2021, 2021
Short summary
Short summary
Snow depth data are essential to monitor the impacts of climate change on sea ice volume variations and their impacts on the climate system. For that purpose, we present and assess the altimetric snow depth product, computed in both hemispheres from CryoSat-2 and SARAL satellite data. The use of these data instead of the common climatology reduces the sea ice thickness by about 30 cm over the 2013–2019 period. These data are also crucial to argue for the launch of the CRISTAL satellite mission.
Thomas Krumpen, Luisa von Albedyll, Helge F. Goessling, Stefan Hendricks, Bennet Juhls, Gunnar Spreen, Sascha Willmes, H. Jakob Belter, Klaus Dethloff, Christian Haas, Lars Kaleschke, Christian Katlein, Xiangshan Tian-Kunze, Robert Ricker, Philip Rostosky, Janna Rückert, Suman Singha, and Julia Sokolova
The Cryosphere, 15, 3897–3920, https://doi.org/10.5194/tc-15-3897-2021, https://doi.org/10.5194/tc-15-3897-2021, 2021
Short summary
Short summary
We use satellite data records collected along the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) drift to categorize ice conditions that shaped and characterized the floe and surroundings during the expedition. A comparison with previous years is made whenever possible. The aim of this analysis is to provide a basis and reference for subsequent research in the six main research areas of atmosphere, ocean, sea ice, biogeochemistry, remote sensing and ecology.
Susanne Crewell, Kerstin Ebell, Patrick Konjari, Mario Mech, Tatiana Nomokonova, Ana Radovan, David Strack, Arantxa M. Triana-Gómez, Stefan Noël, Raul Scarlat, Gunnar Spreen, Marion Maturilli, Annette Rinke, Irina Gorodetskaya, Carolina Viceto, Thomas August, and Marc Schröder
Atmos. Meas. Tech., 14, 4829–4856, https://doi.org/10.5194/amt-14-4829-2021, https://doi.org/10.5194/amt-14-4829-2021, 2021
Short summary
Short summary
Water vapor (WV) is an important variable in the climate system. Satellite measurements are thus crucial to characterize the spatial and temporal variability in WV and how it changed over time. In particular with respect to the observed strong Arctic warming, the role of WV still needs to be better understood. However, as shown in this paper, a detailed understanding is still hampered by large uncertainties in the various satellite WV products, showing the need for improved methods to derive WV.
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.
Anja Rösel, Sinead Louise Farrell, Vishnu Nandan, Jaqueline Richter-Menge, Gunnar Spreen, Dmitry V. Divine, Adam Steer, Jean-Charles Gallet, and Sebastian Gerland
The Cryosphere, 15, 2819–2833, https://doi.org/10.5194/tc-15-2819-2021, https://doi.org/10.5194/tc-15-2819-2021, 2021
Short summary
Short summary
Recent observations in the Arctic suggest a significant shift towards a snow–ice regime caused by deep snow on thin sea ice which may result in a flooding of the snowpack. These conditions cause the brine wicking and saturation of the basal snow layers which lead to a subsequent underestimation of snow depth from snow radar mesurements. As a consequence the calculated sea ice thickness will be biased towards higher values.
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.
Johannes Stapf, André Ehrlich, Christof Lüpkes, and Manfred Wendisch
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-279, https://doi.org/10.5194/acp-2021-279, 2021
Preprint withdrawn
Short summary
Short summary
Airborne observations of the surface radiative energy budget in the marginal sea ice zone (the region between open ocean and closed sea ice) are presented. Atmospheric thermodynamic profiles and surface properties change on small spatial scales in this area and influence the impact of clouds on the radiative energy budget. The radiation budget over sea ice is compared to available studies in the Arctic and the influence of cold air outbreaks and warm air intrusions is illustrated.
Yu Zhang, Tingting Zhu, Gunnar Spreen, Christian Melsheimer, Marcus Huntemann, Nick Hughes, Shengkai Zhang, and Fei Li
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-85, https://doi.org/10.5194/tc-2021-85, 2021
Revised manuscript not accepted
Short summary
Short summary
We developed an algorithm for ice-water classification using Sentinel-1 data during melting seasons in the Fram Strait. The proposed algorithm has the OA of nearly 90 % with STD less than 10 %. The comparison of sea ice concentration demonstrate that it can provide detailed information of sea ice with the spatial resolution of 1km. The time series shows the average June to September sea ice area does not change so much in 2015–2017 and 2019–2020, but it has a significant decrease in 2018.
Ron Kwok, Alek A. Petty, Marco Bagnardi, Nathan T. Kurtz, Glenn F. Cunningham, Alvaro Ivanoff, and Sahra Kacimi
The Cryosphere, 15, 821–833, https://doi.org/10.5194/tc-15-821-2021, https://doi.org/10.5194/tc-15-821-2021, 2021
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.
Larysa Istomina, Henrik Marks, Marcus Huntemann, Georg Heygster, and Gunnar Spreen
Atmos. Meas. Tech., 13, 6459–6472, https://doi.org/10.5194/amt-13-6459-2020, https://doi.org/10.5194/amt-13-6459-2020, 2020
Johannes Stapf, André Ehrlich, Evelyn Jäkel, Christof Lüpkes, and Manfred Wendisch
Atmos. Chem. Phys., 20, 9895–9914, https://doi.org/10.5194/acp-20-9895-2020, https://doi.org/10.5194/acp-20-9895-2020, 2020
Cited articles
Andreas, E. L. and Cash, B. A.: Convective heat transfer over wintertime leads
and polynyas, J. Geophys. Res.-Oceans, 104, 25721–25734,
https://doi.org/10.1029/1999JC900241, 1999. a
Andreas, E. L., Horst, T. W., Grachev, A. A., Persson, P. O. G., Fairall,
C. W., Guest, P. S., and Jordan, R. E.: Parametrizing turbulent exchange over
summer sea ice and the marginal ice zone, Q. J. Roy. Meteor. Soc., 136,
927–943, https://doi.org/10.1002/qj.618, 2010. a, b, c, d
Bagnardi, M., Kurtz, N. T., Petty, A. A., and Kwok, R.: Sea Surface Height
Anomalies of the Arctic Ocean From ICESat-2: A First Examination and
Comparisons With CryoSat-2, Geophys. Res. Lett., 48, e2021GL093155,
https://doi.org/10.1029/2021GL093155, 2021. a
Banke, E. and Smith, S.: Measurement of form drag on ice ridges, Aidjex Bull.,
28, 21–27, 1975. a
Birnbaum, G. and Lüpkes, C.: A new parameterization of surface drag in the
marginal sea ice zone, Tellus A, 54, 107–123,
https://doi.org/10.3402/tellusa.v54i1.12121, 2002. a, b, c
Bourke, R. H. and Garrett, R. P.: Sea ice thickness distribution in the Arctic
Ocean, Cold Reg. Sci. Technol., 13, 259–280,
https://doi.org/10.1016/0165-232X(87)90007-3, 1987. a
Brenner, S., Rainville, L., Thomson, J., Cole, S., and Lee, C.: Comparing
Observations and Parameterizations of Ice-Ocean Drag Through an Annual Cycle
Across the Beaufort Sea, J. Geophys. Res.-Oceans, 126, e2020JC016977,
https://doi.org/10.1029/2020JC016977, 2021. a, b
Dammann, D. O., Eicken, H., Mahoney, A. R., Saiet, E., Meyer, F. J., and
George, J. C.: Traversing Sea Ice–Linking Surface Roughness and Ice
Trafficability Through SAR Polarimetry and Interferometry, IEEE. J. Sel. Top.
Appl., 11, 416–433, https://doi.org/10.1109/JSTARS.2017.2764961, 2018. a
Duncan, K. and Farrell, S. L.: Determining Variability in Arctic Sea Ice
Pressure Ridge Topography with ICESat-2, Geophys. Res. Lett., 49,
e2022GL100272, https://doi.org/10.1029/2022GL100272, 2022. a, b
Elvidge, A. D., Renfrew, I. A., Weiss, A. I., Brooks, I. M., Lachlan-Cope, T. A., and King, J. C.: Observations of surface momentum exchange over the marginal ice zone and recommendations for its parametrisation, Atmos. Chem. Phys., 16, 1545–1563, https://doi.org/10.5194/acp-16-1545-2016, 2016. a, b
Elvidge, A. D., Renfrew, I. A., Brooks, I. M., Srivastava, P., Yelland, M. J.,
and Prytherch, J.: Surface Heat and Moisture Exchange in the Marginal Ice
Zone: Observations and a New Parameterization Scheme for Weather and Climate
Models, J. Geophys. Res.-Atmos., 126, e2021JD034827,
https://doi.org/10.1029/2021JD034827, 2021. a
Garbrecht, T., Lüpkes, C., Hartmann, J., and Wolff, M.: Atmospheric drag
coefficients over sea ice–validation of a parameterisation concept, Tellus
A, 54, 205–219, https://doi.org/10.3402/tellusa.v54i2.12129, 2002. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x, y, z, aa, ab, ac
Garratt, J. R.: The atmospheric boundary layer, Chap. 18, Cambridge
University Press, p. 316, https://doi.org/10.1002/qj.49712051919, 1992. a, b
Gryanik, V. M. and Lüpkes, C.: An Efficient Non-iterative Bulk Parametrization
of Surface Fluxes for Stable Atmospheric Conditions Over Polar Sea-Ice,
Bound.-Lay. Meteorol., 166, 301–325, https://doi.org/10.1007/s10546-017-0302-x, 2018. a
Gryanik, V. M. and Lüpkes, C.: A Package of Momentum and Heat Transfer
Coefficients for the Stable Surface Layer Extended by New Coefficients over
Sea Ice, Bound.-Lay. Meteorol., 187, 41–72,
https://doi.org/10.1007/s10546-022-00730-9, 2023. a
Hibler, W. D.: Characterization of Cold-Regions Terrain Using Airborne Laser
Profilometry, J. Glaciol., 15, 329–347, https://doi.org/10.3189/S0022143000034468,
1975. a
Hopkins, M. A.: Four stages of pressure ridging, J. Geophys. Res.-Oceans, 103,
21883–21891, https://doi.org/10.1029/98JC01257, 1998. a
Huber, P. J. and Ronchetti, E. M.: Regression, Chap. 7, John Wiley &
Sons, Ltd, p. 172, https://doi.org/10.1002/9780470434697.ch7, 2009. a
Knowles, K. W.: A Mapping and Gridding Primer: Points, Pixels, Grids, and
Cells,
https://nsidc.org/data/user-resources/help-center/mapping-and-gridding-primer-points-pixels-grids-and-cells (last access: 15 October 2022),
1993. 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, e, f, g, h, i, j, k, l
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, c, d, e
Kwok, R., Petty, A., Bagnardi, M., Wimert, J. T., Cunningham, G. F., Hancock,
D. W., Ivanoff, A., and Kurtz, N.: ICESat-2 Algorithm Theoretical Basis
Document for Sea Ice Products (ATL07/ATL10), Release 005, Algorithm
theoretical basis document (atbd) for sea ice products, National Aeronautics
and Space Administration, Goddard Space Flight Center, Greenbelt, Maryland
20771,
https://nsidc.org/sites/nsidc.org/files/technical-references/ICESat2_ATL07_ATL10_ATL20_ATL21_ATBD_r005.pdf (last access: 22 January 2023),
2021a. a, b, c, d, e, f
Kwok, R., Petty, A. 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, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/ATLAS/ATL07.005,
2021b. a, b
Landy, J. C., Ehn, J. K., and Barber, D. G.: Albedo feedback enhanced by
smoother Arctic sea ice, Geophys. Res. Lett., 42, 10714–10720,
https://doi.org/10.1002/2015GL066712, 2015. a, b
Lüpkes, C., Gryanik, V. M., Hartmann, J., and Andreas, E. L.: A
parametrization, based on sea ice morphology, of the neutral atmospheric drag
coefficients for weather prediction and climate models, J. Geophys.
Res.-Atmos., 117, 205–219, https://doi.org/10.3402/tellusa.v54i2.12129, 2012. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w
Lüpkes, C., Gryanik, V. M., Rösel, A., Birnbaum, G., and Kaleschke, L.:
Effect of sea ice morphology during Arctic summer on atmospheric drag
coefficients used in climate models, Geophys. Res. Lett., 40, 446–451,
https://doi.org/10.1002/grl.50081, 2013. a
MacGregor, J. A., Boisvert, L. N., Medley, B., Petty, A. A., Harbeck, J. P.,
Bell, R. E., Blair, J. B., Blanchard-Wrigglesworth, E., Buckley, E. M.,
Christoffersen, M. S., Cochran, J. R., Csathó, B. M., De Marco, E. L.,
Dominguez, R. T., Fahnestock, M. A., Farrell, S. L., Gogineni, S. P.,
Greenbaum, J. S., Hansen, C. M., Hofton, M. A., Holt, J. W., Jezek, K. C.,
Koenig, L. S., Kurtz, N. T., Kwok, R., Larsen, C. F., Leuschen, C. J., Locke,
C. D., Manizade, S. S., Martin, S., Neumann, T. A., Nowicki, S. M., Paden,
J. D., Richter-Menge, J. A., Rignot, E. J., Rodríguez-Morales, F.,
Siegfried, M. R., Smith, B. E., Sonntag, J. G., Studinger, M., Tinto, K. J.,
Truffer, M., Wagner, T. P., Woods, J. E., Young, D. A., and Yungel, J. K.:
The Scientific Legacy of NASA’s Operation IceBridge, Rev. Geophys., 59,
e2020RG000712, https://doi.org/10.1029/2020RG000712, 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 Sens.-Basel, 12, 3653, https://doi.org/10.3390/rs12213653, 2020. a, b, c
Magruder, L. A., Brunt, K. M., Neumann, T., Klotz, B., and Alonzo, M.: Passive
Ground-Based Optical Techniques for Monitoring the On-Orbit ICESat-2
Altimeter Geolocation and Footprint Diameter, Earth Space Sci., 8,
e2020EA001414, https://doi.org/10.1029/2020EA001414, 2021. a, b
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), Remote Sens. Environ., 190, 260–273,
https://doi.org/10.1016/j.rse.2016.12.029, 2017. a
Martin, C. F., Krabill, W. B., Manizade, S. S., Russell, R. L., Sonntag, J. G.,
Swift, R. N., and Yungel, J. K.: Airborne Topographic Mapper Calibration
Procedures and Accuracy Assessment, Tech. Rep. Technical Memorandum, 215891,
National Aeronautics and Space Administration, Greenbelt, Maryland 20771,
Goddard Space Flight Center,
https://ntrs.nasa.gov/api/citations/20120008479/downloads/20120008479.pdf (last access: 10 April 2022),
2012. a
Martin, T., Tsamados, M., Schroeder, D., and Feltham, D. L.: The impact of
variable sea ice roughness on changes in Arctic Ocean surface stress: A model
study, J. Geophys. Res.-Oceans, 121, 1931–1952,
https://doi.org/10.1002/2015JC011186, 2016. a, b
Mchedlishvili, A., Spreen, G., Lüpkes, C., Tsamados, M., and Petty, A.:
Gridded pan-Arctic total neutral atmospheric 10-m drag coefficient estimates
derived from ICESat-2 ATL07 sea ice height data, PANGAEA [data set],
https://doi.org/10.1594/PANGAEA.959728, 2022. a
Melsheimer, C. and Spreen, G.: AMSR2 ASI sea ice concentration data, Arctic,
version 5.4 (NetCDF) (July 2012–December 2019), PANGAEA [data set],
https://doi.org/10.1594/PANGAEA.898399, 2019. a, b
Melsheimer, C., Spreen, G., Ye, Y., and Shokr, M.: First results of Antarctic sea ice type retrieval from active and passive microwave remote sensing data, The Cryosphere, 17, 105–126, https://doi.org/10.5194/tc-17-105-2023, 2023. a, b
Mock, S. J., Hartwell, A. D., and Hibler, W. D.: Spatial aspects of pressure
ridge statistics, J. Geophys. Res., 77, 5945–5953,
https://doi.org/10.1029/JC077i030p05945, 1972. a
Neumann, T. A., Martino, A. J., Markus, T., Bae, S., Bock, M. R., Brenner,
A. C., Brunt, K. M., Cavanaugh, J., Fernandes, S. T., Hancock, D. W.,
Harbeck, K., Lee, J., Kurtz, N. T., Luers, P. J., Luthcke, S. B., Magruder,
L., Pennington, T. A., Ramos-Izquierdo, L., Rebold, T., Skoog, J., and
Thomas, T. C.: The Ice, Cloud, and Land Elevation Satellite – 2 mission: A
global geolocated photon product derived from the Advanced Topographic Laser
Altimeter System, Remote Sens. Environ., 233, 111325,
https://doi.org/10.1016/j.rse.2019.111325, 2019. a
Petty, A. A., Tsamados, M. C., Kurtz, N. T., Farrell, S. L., Newman, T., Harbeck, J. P., Feltham, D. L., and Richter-Menge, J. A.: Characterizing Arctic sea ice topography using high-resolution IceBridge data, The Cryosphere, 10, 1161–1179, https://doi.org/10.5194/tc-10-1161-2016, 2016. a
Remote Sensing for Polar Regions working group from the Remote Sensing department at the Institute of Environmental Physics: Sea Ice Remote Sensing at the University of Bremen, https://seaice.uni-bremen.de/data-archive/, last access: 30 June 2023. a
Renfrew, I. A., Elvidge, A. D., and Edwards, J. M.: Atmospheric sensitivity to
marginal-ice-zone drag: Local and global responses, Q. J. Roy. Meteor. Soc.,
145, 1165–1179, https://doi.org/10.1002/qj.3486, 2019. a
Ricker, R., Fons, S., Jutila, A., Hutter, N., Duncan, K., Farrell, S. L., Kurtz, N. T., and Fredensborg Hansen, R. M.: Linking scales of sea ice surface topography: evaluation of ICESat-2 measurements with coincident helicopter laser scanning during MOSAiC, The Cryosphere, 17, 1411–1429, https://doi.org/10.5194/tc-17-1411-2023, 2023. a, b, c, d, e, f, g, h, i, j, k
Schneider, T., Lüpkes, C., Dorn, W., Chechin, D., Handorf, D., Khosravi, S.,
Gryanik, V. M., Makhotina, I., and Rinke, A.: Sensitivity to changes in the
surface-layer turbulence parameterization for stable conditions in winter: A
case study with a regional climate model over the Arctic, Atmos. Sci. Lett.,
23, e1066, https://doi.org/10.1002/asl.1066, 2022. a
Serreze, M. C. and Barry, R. G.: Processes and impacts of Arctic amplification:
A research synthesis, Global Planet. Change, 77, 85–96,
https://doi.org/10.1016/j.gloplacha.2011.03.004, 2011. a
Shokr, M., Lambe, A., and Agnew, T.: A New Algorithm (ECICE) to Estimate Ice
Concentration From Remote Sensing Observations: An Application to 85-GHz
Passive Microwave Data, IEEE T. Geosci. Remote, 46, 4104–4121,
https://doi.org/110.1109/TGRS.2008.2000624, 2008. a, b
Spreen, G., Kaleschke, L., and Heygster, G.: Sea ice remote sensing using
AMSR-E 89-GHz channels, J. Geophys. Res.-Oceans, 113, C02S03,
https://doi.org/10.1029/2005JC003384, 2008. a, b
Srivastava, P., Brooks, I. M., Prytherch, J., Salisbury, D. J., Elvidge, A. D., Renfrew, I. A., and Yelland, M. J.: Ship-based estimates of momentum transfer coefficient over sea ice and recommendations for its parameterization, Atmos. Chem. Phys., 22, 4763–4778, https://doi.org/10.5194/acp-22-4763-2022, 2022. a
Steele, M., Zhang, J., Rothrock, D., and Stern, H.: The force balance of sea
ice in a numerical model of the Arctic Ocean, J. Geophys. Res.-Oceans,
102, 21061–21079, https://doi.org/10.1029/97JC01454, 1997. a
Steiner, N., Harder, M., and Lemke, P.: Sea-ice roughness and drag coefficients
in a dynamic–thermodynamic sea-ice model for the Arctic, Tellus A, 51,
964–978, https://doi.org/10.3402/tellusa.v51i5.14505, 1999. a
Stroeve, J., Serreze, M., Holland, M., Kay, J., Malanik, J., and Barrett, A.:
Atmospheric drag coefficients over sea ice–validation of a parameterisation
concept, Climatic Change, 110, 1005–1027, https://doi.org/10.1007/s10584-011-0101-1,
2012. a, b
Studinger, M.: IceBridge ATM L1B Elevation and Return Strength, Version 2, National Snow and Ice Data Center [data set],
https://doi.org/10.5067/19SIM5TXKPGT, 2013. a
Studinger, M.: IceBridge ATM L1B Elevation and Return Strength, Version 2,
Tech. rep., National Aeronautics and Space Administration, Boulder, Colorado
USA, NASA National Snow and Ice Data Center Distributed Active Archive
Center [data set], https://doi.org/10.5067/19SIM5TXKPGT, 2013, updated 2020. a
Studinger, M., Manizade, S. S., Linkswiler, M. A., and Yungel, J. K.: High-resolution imaging of supraglacial hydrological features on the Greenland Ice Sheet with NASA's Airborne Topographic Mapper (ATM) instrument suite, The Cryosphere, 16, 3649–3668, https://doi.org/10.5194/tc-16-3649-2022, 2022. a, b
Thorndike, A. S. and Colony, R.: Sea ice motion in response to geostrophic
winds, J. Geophys. Res.-Oceans, 87, 5845–5852,
https://doi.org/10.1029/JC087iC08p05845, 1982. a
Thorndike, A. S., Rothrock, D. A., Maykut, G. A., and Colony, R.: The thickness
distribution of sea ice, J. Geophys. Res., 80, 4501–4513,
https://doi.org/10.1029/JC080i033p04501, 1975. a
Tilling, R., Kurtz, N. T., Bagnardi, M., Petty, A. A., and Kwok, R.: Detection
of Melt Ponds on Arctic Summer Sea Ice From ICESat-2, Geophys. Res. Lett.,
47, e2020GL090644, https://doi.org/10.1029/2020GL090644, 2020. a, b
Timco, G. W. and Burden, R. P.: An analysis of the shapes of sea ice ridges,
Cold Reg. Sci. Technol., 25, 65–77, https://doi.org/10.1016/S0165-232X(96)00017-1,
1997. a
Tin, T., Jeffries, M. O., Lensu, M., and Tuhkuri, J.: Estimating the thickness
of ridged sea ice from ship observations in the Ross Sea, Antarctic Sci., 15,
47–54, https://doi.org/10.1017/S0954102003001056, 2003. a
Tremblay, L.-B. and Mysak, L. A.: Modeling Sea Ice as a Granular Material,
Including the Dilatancy Effect, J. Phys. Oceanogr., 27, 2342–2360,
https://doi.org/10.1175/1520-0485(1997)027<2342:MSIAAG>2.0.CO;2, 1977. a
Tsamados, M., Feltham, D., Petty, A., Schroeder, D., and Flocco, D.: Processes
controlling surface, bottom and lateral melt of Arctic sea ice in a state of
the art sea ice model, Philos. T. R. Soc. A, 373, 20140167,
https://doi.org/10.1098/rsta.2014.0167, 2016. a
Vihma, T., Hartmann, J., and Lüpkes, C.: A Case Study Of An On-Ice Air Flow
Over The Arctic Marginal Sea-Ice Zone, Bound.-Lay. Meteorol., 107, 189–217,
https://doi.org/10.1023/A:1021599601948, 2003. a
Wadhams, P. and Davy, T.: On the spacing and draft distributions for pressure
ridge keels, J. Geophys. Res.-Oceans, 91, 10697–10708,
https://doi.org/10.1029/JC091iC09p10697, 1986. a
Ye, Y., Heygster, G., and Shokr, M.: Improving Multiyear Ice Concentration
Estimates With Reanalysis Air Temperatures, IEEE T. Geosci. Remote, 54,
2602–2614, https://doi.org/10.1109/TGRS.2015.2503884, 2016a. a, b
Ye, Y., Shokr, M., Heygster, G., and Spreen, G.: Improving Multiyear Sea Ice
Concentration Estimates with Sea Ice Drift, Remote Sens.-Basel, 8, 397,
https://doi.org/10.3390/rs8050397, 2016b. a, b
Yu, X., Rinke, A., Dorn, W., Spreen, G., Lüpkes, C., Sumata, H., and Gryanik, V. M.: Evaluation of Arctic sea ice drift and its dependency on near-surface wind and sea ice conditions in the coupled regional climate model HIRHAM–NAOSIM, The Cryosphere, 14, 1727–1746, https://doi.org/10.5194/tc-14-1727-2020, 2020. a, b
Short summary
In this study we looked at sea ice–atmosphere drag coefficients, quantities that help with characterizing the friction between the atmosphere and sea ice, and vice versa. Using ICESat-2, a laser altimeter that measures elevation differences by timing how long it takes for photons it sends out to return to itself, we could map the roughness, i.e., how uneven the surface is. From roughness we then estimate drag force, the frictional force between sea ice and the atmosphere, across the Arctic.
In this study we looked at sea ice–atmosphere drag coefficients, quantities that help with...