Articles | Volume 15, issue 10
https://doi.org/10.5194/tc-15-4909-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-15-4909-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Oct 2021
Research article |
| 21 Oct 2021
| 21 Oct 2021
Impacts of snow data and processing methods on the interpretation of long-term changes in Baffin Bay early spring sea ice thickness
Isolde A. Glissenaar
CORRESPONDING AUTHOR
Bristol Glaciology Centre, School of Geographical Sciences, University of Bristol, Bristol, UK
Jack C. Landy
Bristol Glaciology Centre, School of Geographical Sciences, University of Bristol, Bristol, UK
The Earth Observation Laboratory, Department of Physics and Technology, UiT The Arctic University of Norway, Tromsø, Norway
Alek A. Petty
Cryospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
Nathan T. Kurtz
Cryospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Julienne C. Stroeve
Centre for Earth Observation Science, University of Manitoba, Winnipeg, MB, Canada
Department of Earth Sciences, University College London, London, UK
National Snow and Ice Data Center, University of Colorado Boulder, Boulder, CO, USA
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Robbie D. C. Mallett, Julienne C. Stroeve, Michel Tsamados, Jack C. Landy, Rosemary Willatt, Vishnu Nandan, and Glen E. Liston
The Cryosphere, 15, 2429–2450, https://doi.org/10.5194/tc-15-2429-2021, https://doi.org/10.5194/tc-15-2429-2021, 2021
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Rasmus T. Tonboe, Vishnu Nandan, John Yackel, Stefan Kern, Leif Toudal Pedersen, and Julienne Stroeve
The Cryosphere, 15, 1811–1822, https://doi.org/10.5194/tc-15-1811-2021, https://doi.org/10.5194/tc-15-1811-2021, 2021
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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
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Stephen E. L. Howell, Randall K. Scharien, Jack Landy, and Mike Brady
The Cryosphere, 14, 4675–4686, https://doi.org/10.5194/tc-14-4675-2020, https://doi.org/10.5194/tc-14-4675-2020, 2020
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Michael Studinger, Brooke C. Medley, Kelly M. Brunt, Kimberly A. Casey, Nathan T. Kurtz, Serdar S. Manizade, Thomas A. Neumann, and Thomas B. Overly
The Cryosphere, 14, 3287–3308, https://doi.org/10.5194/tc-14-3287-2020, https://doi.org/10.5194/tc-14-3287-2020, 2020
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We use repeat airborne geophysical data consisting of laser altimetry, snow, and Ku-band radar and optical imagery to analyze the spatial and temporal variability in surface roughness, slope, wind deposition, and snow accumulation at 88° S. We find small–scale variability in snow accumulation based on the snow radar subsurface layering, indicating areas of strong wind redistribution are prevalent at 88° S. There is no slope–independent relationship between surface roughness and accumulation.
Robbie D. C. Mallett, Isobel R. Lawrence, Julienne C. Stroeve, Jack C. Landy, and Michel Tsamados
The Cryosphere, 14, 251–260, https://doi.org/10.5194/tc-14-251-2020, https://doi.org/10.5194/tc-14-251-2020, 2020
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Soils store large carbon and are important for global warming. We do not know what factors are important for soil carbon storage in the alpine Andes and how they work. We studied how rainfall affects soil carbon storage related to soil structure. We found soil structure is not important, but soil carbon storage and stability controlled by rainfall are dependent on rocks under the soils. The results indicate that we should pay attention to the rocks when studying soil carbon storage in the Andes.
Dyre O. Dammann, Leif E. B. Eriksson, Son V. Nghiem, Erin C. Pettit, Nathan T. Kurtz, John G. Sonntag, Thomas E. Busche, Franz J. Meyer, and Andrew R. Mahoney
The Cryosphere, 13, 1861–1875, https://doi.org/10.5194/tc-13-1861-2019, https://doi.org/10.5194/tc-13-1861-2019, 2019
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We validate TanDEM-X interferometry as a tool for deriving iceberg subaerial morphology using Operation IceBridge data. This approach enables a volumetric classification of icebergs, according to volume relevant to iceberg drift and decay, freshwater contribution, and potential impact on structures. We find iceberg volumes to generally match within 7 %. These results suggest that TanDEM-X could pave the way for future interferometric systems of scientific and operational iceberg classification.
Steven W. Fons and Nathan T. Kurtz
The Cryosphere, 13, 861–878, https://doi.org/10.5194/tc-13-861-2019, https://doi.org/10.5194/tc-13-861-2019, 2019
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A method to measure the snow freeboard of Antarctic sea ice from CryoSat-2 data is developed. Through comparisons with data from airborne campaigns and another satellite mission, we find that this method can reasonably retrieve snow freeboard across the Antarctic and shows promise in retrieving snow depth in certain locations. Snow freeboard data from CryoSat-2 are important because they enable the calculation of sea ice thickness and help to better understand snow depth on Antarctic sea ice.
Isobel R. Lawrence, Michel C. Tsamados, Julienne C. Stroeve, Thomas W. K. Armitage, and Andy L. Ridout
The Cryosphere, 12, 3551–3564, https://doi.org/10.5194/tc-12-3551-2018, https://doi.org/10.5194/tc-12-3551-2018, 2018
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In this paper we estimate the thickness of snow cover on Arctic sea ice from space. We use data from two radar altimeter satellites, AltiKa and CryoSat-2, that have been operating synchronously since 2013. We produce maps of monthly average snow depth for the four growth seasons (October to April): 2012–2013, 2013–2014, 2014–2015, and 2015–2016. Snow depth estimates are essential for the accurate retrieval of sea ice thickness from satellite altimetry.
Julienne C. Stroeve, David Schroder, Michel Tsamados, and Daniel Feltham
The Cryosphere, 12, 1791–1809, https://doi.org/10.5194/tc-12-1791-2018, https://doi.org/10.5194/tc-12-1791-2018, 2018
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This paper looks at the impact of the warm winter and anomalously low number of total freezing degree days during winter 2016/2017 on thermodynamic ice growth and overall thickness anomalies. The approach relies on evaluation of satellite data (CryoSat-2) and model output. While there is a negative feedback between rapid ice growth for thin ice, with thermodynamic ice growth increasing over time, since 2012 that relationship is changing, in part because the freeze-up is happening later.
Alek A. Petty, Julienne C. Stroeve, Paul R. Holland, Linette N. Boisvert, Angela C. Bliss, Noriaki Kimura, and Walter N. Meier
The Cryosphere, 12, 433–452, https://doi.org/10.5194/tc-12-433-2018, https://doi.org/10.5194/tc-12-433-2018, 2018
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There was significant scientific and media attention surrounding Arctic sea ice in 2016, due primarily to the record-warm air temperatures and low sea ice conditions observed at the start of the year. Here we quantify and assess the record-low monthly sea ice cover in winter, spring and fall, and the lack of record-low sea ice conditions in summer. We explore the primary drivers of these monthly sea ice states and explore the implications for improved summer sea ice forecasting.
Ron Kwok, Nathan T. Kurtz, Ludovic Brucker, Alvaro Ivanoff, Thomas Newman, Sinead L. Farrell, Joshua King, Stephen Howell, Melinda A. Webster, John Paden, Carl Leuschen, Joseph A. MacGregor, Jacqueline Richter-Menge, Jeremy Harbeck, and Mark Tschudi
The Cryosphere, 11, 2571–2593, https://doi.org/10.5194/tc-11-2571-2017, https://doi.org/10.5194/tc-11-2571-2017, 2017
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Since 2009, the ultra-wideband snow radar on Operation IceBridge has acquired data in annual campaigns conducted during the Arctic and Antarctic springs. Existing snow depth retrieval algorithms differ in the way the air–snow and snow–ice interfaces are detected and localized in the radar returns and in how the system limitations are addressed. Here, we assess five retrieval algorithms by comparisons with field measurements, ground-based campaigns, and analyzed fields of snow depth.
Julienne C. Stroeve, John R. Mioduszewski, Asa Rennermalm, Linette N. Boisvert, Marco Tedesco, and David Robinson
The Cryosphere, 11, 2363–2381, https://doi.org/10.5194/tc-11-2363-2017, https://doi.org/10.5194/tc-11-2363-2017, 2017
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As the sea ice has declined strongly in recent years there has been a corresponding increase in Greenland melting. While both are likely a result of changes in atmospheric circulation patterns that favor summer melt, this study evaluates whether or not sea ice reductions around the Greenland ice sheet are having an influence on Greenland summer melt through enhanced sensible and latent heat transport from open water areas onto the ice sheet.
Lars H. Smedsrud, Mari H. Halvorsen, Julienne C. Stroeve, Rong Zhang, and Kjell Kloster
The Cryosphere, 11, 65–79, https://doi.org/10.5194/tc-11-65-2017, https://doi.org/10.5194/tc-11-65-2017, 2017
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Export of Arctic sea ice area southwards through the Fram Strait from 1935 to 2014 is calculated based on satellite radar images and surface pressure observations. The annual mean export is 880 000 km2, representing 10 % of the Arctic sea ice area. In recent years the export has been above 1 million km2, and there are positive trends over the last 30 years. Increased ice export during spring and summer contributes to more open water in September, and this correlations has increased over time.
Dirk Notz, Alexandra Jahn, Marika Holland, Elizabeth Hunke, François Massonnet, Julienne Stroeve, Bruno Tremblay, and Martin Vancoppenolle
Geosci. Model Dev., 9, 3427–3446, https://doi.org/10.5194/gmd-9-3427-2016, https://doi.org/10.5194/gmd-9-3427-2016, 2016
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The large-scale evolution of sea ice is both an indicator and a driver of climate changes. Hence, a realistic simulation of sea ice is key for a realistic simulation of the climate system of our planet. To assess and to improve the realism of sea-ice simulations, we present here a new protocol for climate-model output that allows for an in-depth analysis of the simulated evolution of sea ice.
Julienne C. Stroeve, Stephanie Jenouvrier, G. Garrett Campbell, Christophe Barbraud, and Karine Delord
The Cryosphere, 10, 1823–1843, https://doi.org/10.5194/tc-10-1823-2016, https://doi.org/10.5194/tc-10-1823-2016, 2016
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Sea ice variability within the marginal ice zone and polynyas plays an important role for phytoplankton productivity and krill abundance. Therefore mapping their spatial extent as well as seasonal and interannual variability is essential for understanding how current and future changes in these biologically active regions may impact the Antarctic marine ecosystem. Assessments are complicated, however, by which sea ice algorithm is used, with impacts on interpretations on seabird populations.
Alek A. Petty, Michel C. Tsamados, Nathan T. Kurtz, Sinead L. Farrell, Thomas Newman, Jeremy P. Harbeck, Daniel L. Feltham, and Jackie A. Richter-Menge
The Cryosphere, 10, 1161–1179, https://doi.org/10.5194/tc-10-1161-2016, https://doi.org/10.5194/tc-10-1161-2016, 2016
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This study presents an analysis of Arctic sea ice topography using high-resolution, three-dimensional surface elevation data from the Airborne Topographic Mapper (ATM) laser altimeter, flown as part of NASA's Operation IceBridge mission. We describe and implement a newly developed sea ice surface feature-picking algorithm and derive novel information regarding the height, volume and geometry of surface features over the western Arctic sea ice cover.
Marco Tedesco, Sarah Doherty, Xavier Fettweis, Patrick Alexander, Jeyavinoth Jeyaratnam, and Julienne Stroeve
The Cryosphere, 10, 477–496, https://doi.org/10.5194/tc-10-477-2016, https://doi.org/10.5194/tc-10-477-2016, 2016
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Summer surface albedo over Greenland decreased at a rate of 0.02 per decade between 1996 and 2012. The decrease is due to snow grain growth, the expansion of bare ice areas, and trends in light-absorbing impurities on snow and ice surfaces. Neither aerosol models nor in situ observations indicate increasing trends in impurities in the atmosphere over Greenland. Albedo projections through to the end of the century under different warming scenarios consistently point to continued darkening.
N.-X. Geilfus, R. J. Galley, O. Crabeck, T. Papakyriakou, J. Landy, J.-L. Tison, and S. Rysgaard
Biogeosciences, 12, 2047–2061, https://doi.org/10.5194/bg-12-2047-2015, https://doi.org/10.5194/bg-12-2047-2015, 2015
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We investigated the evolution of inorganic carbon within landfast sea ice in Resolute Passage during the spring and summer melt period.
Low TA and TCO2 concentrations observed in sea ice and brine were associated with the percolation of meltwater from melt ponds. Meltwater was continuously supplied to the ponds which prevented melt ponds from fully equilibrating with the atmospheric CO2 concentration, promoting a continuous uptake of CO2 from the atmosphere.
J. Stroeve, A. Barrett, M. Serreze, and A. Schweiger
The Cryosphere, 8, 1839–1854, https://doi.org/10.5194/tc-8-1839-2014, https://doi.org/10.5194/tc-8-1839-2014, 2014
N. T. Kurtz, N. Galin, and M. Studinger
The Cryosphere, 8, 1217–1237, https://doi.org/10.5194/tc-8-1217-2014, https://doi.org/10.5194/tc-8-1217-2014, 2014
N. T. Kurtz, S. L. Farrell, M. Studinger, N. Galin, J. P. Harbeck, R. Lindsay, V. D. Onana, B. Panzer, and J. G. Sonntag
The Cryosphere, 7, 1035–1056, https://doi.org/10.5194/tc-7-1035-2013, https://doi.org/10.5194/tc-7-1035-2013, 2013
Related subject area
Discipline: Sea ice | Subject: Remote Sensing
Pan-Arctic sea ice concentration from SAR and passive microwave
Assessing sea ice microwave emissivity up to submillimeter waves from airborne and satellite observations
The AutoICE Challenge
A study of sea ice topography in the Weddell and Ross seas using dual-polarimetric TanDEM-X imagery
Estimating differential penetration of green (532 nm) laser light over sea ice with NASA's Airborne Topographic Mapper: observations and models
Estimating the uncertainty of sea-ice area and sea-ice extent from satellite retrievals
Sea ice transport and replenishment across and within the Canadian Arctic Archipelago, 2016–2022
SAR deep learning sea ice retrieval trained with airborne laser scanner measurements from the MOSAiC expedition
MMSeaIce: a collection of techniques for improving sea ice mapping with a multi-task model
Lead fractions from SAR-derived sea ice divergence during MOSAiC
Ice floe segmentation and floe size distribution in airborne and high-resolution optical satellite images: towards an automated labelling deep learning approach
Snow Depth Estimation on Lead-less Landfast ice using Cryo2Ice satellite observations
Updated Arctic melt pond fraction dataset and trends 2002–2023 using ENVISAT and Sentinel-3 remote sensing data
New estimates of pan-Arctic sea ice–atmosphere neutral drag coefficients from ICESat-2 elevation data
Relevance of warm air intrusions for Arctic satellite sea ice concentration time series
Observing the evolution of summer melt on multiyear sea ice with ICESat-2 and Sentinel-2
The Variability of CryoSat-2 derived Sea Ice Thickness introduced by modelled vs. empirical snow thickness, sea ice density and water density
Spaceborne thermal infrared observations of Arctic sea ice leads at 30 m resolution
Wind redistribution of snow impacts the Ka- and Ku-band radar signatures of Arctic sea ice
First observations of sea ice flexural–gravity waves with ground-based radar interferometry in Utqiaġvik, Alaska
Feasibility of retrieving Arctic sea ice thickness from the Chinese HY-2B Ku-band radar altimeter
Sea ice classification of TerraSAR-X ScanSAR images for the MOSAiC expedition incorporating per-class incidence angle dependency of image texture
Aerial observations of sea ice breakup by ship waves
Monitoring Arctic thin ice: a comparison between CryoSat-2 SAR altimetry data and MODIS thermal-infrared imagery
The effects of surface roughness on the calculated, spectral, conical–conical reflectance factor as an alternative to the bidirectional reflectance distribution function of bare sea ice
Inter-comparison and evaluation of Arctic sea ice type products
A simple model for daily basin-wide thermodynamic sea ice thickness growth retrieval
Ice ridge density signatures in high-resolution SAR images
Rain on snow (ROS) understudied in sea ice remote sensing: a multi-sensor analysis of ROS during MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate)
Quantifying the effects of background concentrations of crude oil pollution on sea ice albedo
Characterizing the sea-ice floe size distribution in the Canada Basin from high-resolution optical satellite imagery
Generating large-scale sea ice motion from Sentinel-1 and the RADARSAT Constellation Mission using the Environment and Climate Change Canada automated sea ice tracking system
Rotational drift in Antarctic sea ice: pronounced cyclonic features and differences between data products
Satellite passive microwave sea-ice concentration data set intercomparison using Landsat data
Cross-platform classification of level and deformed sea ice considering per-class incident angle dependency of backscatter intensity
Advances in altimetric snow depth estimates using bi-frequency SARAL and CryoSat-2 Ka–Ku measurements
Antarctic snow-covered sea ice topography derivation from TanDEM-X using polarimetric SAR interferometry
A lead-width distribution for Antarctic sea ice: a case study for the Weddell Sea with high-resolution Sentinel-2 images
Satellite altimetry detection of ice-shelf-influenced fast ice
MOSAiC drift expedition from October 2019 to July 2020: sea ice conditions from space and comparison with previous years
Towards a swath-to-swath sea-ice drift product for the Copernicus Imaging Microwave Radiometer mission
Spaceborne infrared imagery for early detection of Weddell Polynya opening
Estimating instantaneous sea-ice dynamics from space using the bi-static radar measurements of Earth Explorer 10 candidate Harmony
Estimating subpixel turbulent heat flux over leads from MODIS thermal infrared imagery with deep learning
An improved sea ice detection algorithm using MODIS: application as a new European sea ice extent indicator
Faster decline and higher variability in the sea ice thickness of the marginal Arctic seas when accounting for dynamic snow cover
Estimation of degree of sea ice ridging in the Bay of Bothnia based on geolocated photon heights from ICESat-2
Linking sea ice deformation to ice thickness redistribution using high-resolution satellite and airborne observations
Simulated Ka- and Ku-band radar altimeter height and freeboard estimation on snow-covered Arctic sea ice
Improved machine-learning-based open-water–sea-ice–cloud discrimination over wintertime Antarctic sea ice using MODIS thermal-infrared imagery
Tore Wulf, Jørgen Buus-Hinkler, Suman Singha, Hoyeon Shi, and Matilde Brandt Kreiner
The Cryosphere, 18, 5277–5300, https://doi.org/10.5194/tc-18-5277-2024, https://doi.org/10.5194/tc-18-5277-2024, 2024
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Here, we present ASIP: a new and comprehensive deep-learning-based methodology to retrieve high-resolution sea ice concentration with accompanying well-calibrated uncertainties from satellite-based active and passive microwave observations at a pan-Arctic scale for all seasons. In a comparative study against pan-Arctic ice charts and well-established passive-microwave-based sea ice products, we show that ASIP generalizes well to the pan-Arctic region.
Nils Risse, Mario Mech, Catherine Prigent, Gunnar Spreen, and Susanne Crewell
The Cryosphere, 18, 4137–4163, https://doi.org/10.5194/tc-18-4137-2024, https://doi.org/10.5194/tc-18-4137-2024, 2024
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Passive microwave observations from satellites are crucial for monitoring Arctic sea ice and atmosphere. To do this effectively, it is important to understand how sea ice emits microwaves. Through unique Arctic sea ice observations, we improved our understanding, identified four distinct emission types, and expanded current knowledge to include higher frequencies. These findings will enhance our ability to monitor the Arctic climate and provide valuable information for new satellite missions.
Andreas Stokholm, Jørgen Buus-Hinkler, Tore Wulf, Anton Korosov, Roberto Saldo, Leif Toudal Pedersen, David Arthurs, Ionut Dragan, Iacopo Modica, Juan Pedro, Annekatrien Debien, Xinwei Chen, Muhammed Patel, Fernando Jose Pena Cantu, Javier Noa Turnes, Jinman Park, Linlin Xu, Katharine Andrea Scott, David Anthony Clausi, Yuan Fang, Mingzhe Jiang, Saeid Taleghanidoozdoozan, Neil Curtis Brubacher, Armina Soleymani, Zacharie Gousseau, Michał Smaczny, Patryk Kowalski, Jacek Komorowski, David Rijlaarsdam, Jan Nicolaas van Rijn, Jens Jakobsen, Martin Samuel James Rogers, Nick Hughes, Tom Zagon, Rune Solberg, Nicolas Longépé, and Matilde Brandt Kreiner
The Cryosphere, 18, 3471–3494, https://doi.org/10.5194/tc-18-3471-2024, https://doi.org/10.5194/tc-18-3471-2024, 2024
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The AutoICE challenge encouraged the development of deep learning models to map multiple aspects of sea ice – the amount of sea ice in an area and the age and ice floe size – using multiple sources of satellite and weather data across the Canadian and Greenlandic Arctic. Professionally drawn operational sea ice charts were used as a reference. A total of 179 students and sea ice and AI specialists participated and produced maps in broad agreement with the sea ice charts.
Lanqing Huang and Irena Hajnsek
The Cryosphere, 18, 3117–3140, https://doi.org/10.5194/tc-18-3117-2024, https://doi.org/10.5194/tc-18-3117-2024, 2024
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Interferometric synthetic aperture radar can measure the total freeboard of sea ice but can be biased when radar signals penetrate snow and ice. We develop a new method to retrieve the total freeboard and analyze the regional variation of total freeboard and roughness in the Weddell and Ross seas. We also investigate the statistical behavior of the total freeboard for diverse ice types. The findings enhance the understanding of Antarctic sea ice topography and its dynamics in a changing climate.
Michael Studinger, Benjamin E. Smith, Nathan Kurtz, Alek Petty, Tyler Sutterley, and Rachel Tilling
The Cryosphere, 18, 2625–2652, https://doi.org/10.5194/tc-18-2625-2024, https://doi.org/10.5194/tc-18-2625-2024, 2024
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We use green lidar data and natural-color imagery over sea ice to quantify elevation biases potentially impacting estimates of change in ice thickness of the polar regions. We complement our analysis using a model of scattering of light in snow and ice that predicts the shape of lidar waveforms reflecting from snow and ice surfaces based on the shape of the transmitted pulse. We find that biased elevations exist in airborne and spaceborne data products from green lidars.
Andreas Wernecke, Dirk Notz, Stefan Kern, and Thomas Lavergne
The Cryosphere, 18, 2473–2486, https://doi.org/10.5194/tc-18-2473-2024, https://doi.org/10.5194/tc-18-2473-2024, 2024
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The total Arctic sea-ice area (SIA), which is an important climate indicator, is routinely monitored with the help of satellite measurements. Uncertainties in observations of sea-ice concentration (SIC) partly cancel out when summed up to the total SIA, but the degree to which this is happening has been unclear. Here we find that the uncertainty daily SIA estimates, based on uncertainties in SIC, are about 300 000 km2. The 2002 to 2017 September decline in SIA is approx. 105 000 ± 9000 km2 a−1.
Stephen E. L. Howell, David G. Babb, Jack C. Landy, Isolde A. Glissenaar, Kaitlin McNeil, Benoit Montpetit, and Mike Brady
The Cryosphere, 18, 2321–2333, https://doi.org/10.5194/tc-18-2321-2024, https://doi.org/10.5194/tc-18-2321-2024, 2024
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The CAA serves as both a source and a sink for sea ice from the Arctic Ocean, while also exporting sea ice into Baffin Bay. It is also an important region with respect to navigating the Northwest Passage. Here, we quantify sea ice transport and replenishment across and within the CAA from 2016 to 2022. We also provide the first estimates of the ice area and volume flux within the CAA from the Queen Elizabeth Islands to Parry Channel, which spans the central region of the Northwest Passage.
Karl Kortum, Suman Singha, Gunnar Spreen, Nils Hutter, Arttu Jutila, and Christian Haas
The Cryosphere, 18, 2207–2222, https://doi.org/10.5194/tc-18-2207-2024, https://doi.org/10.5194/tc-18-2207-2024, 2024
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A dataset of 20 radar satellite acquisitions and near-simultaneous helicopter-based surveys of the ice topography during the MOSAiC expedition is constructed and used to train a variety of deep learning algorithms. The results give realistic insights into the accuracy of retrieval of measured ice classes using modern deep learning models. The models able to learn from the spatial distribution of the measured sea ice classes are shown to have a clear advantage over those that cannot.
Xinwei Chen, Muhammed Patel, Fernando J. Pena Cantu, Jinman Park, Javier Noa Turnes, Linlin Xu, K. Andrea Scott, and David A. Clausi
The Cryosphere, 18, 1621–1632, https://doi.org/10.5194/tc-18-1621-2024, https://doi.org/10.5194/tc-18-1621-2024, 2024
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This paper introduces an automated sea ice mapping pipeline utilizing a multi-task U-Net architecture. It attained the top score of 86.3 % in the AutoICE challenge. Ablation studies revealed that incorporating brightness temperature data and spatial–temporal information significantly enhanced model accuracy. Accurate sea ice mapping is vital for comprehending the Arctic environment and its global climate effects, underscoring the potential of deep learning.
Luisa von Albedyll, Stefan Hendricks, Nils Hutter, Dmitrii Murashkin, Lars Kaleschke, Sascha Willmes, Linda Thielke, Xiangshan Tian-Kunze, Gunnar Spreen, and Christian Haas
The Cryosphere, 18, 1259–1285, https://doi.org/10.5194/tc-18-1259-2024, https://doi.org/10.5194/tc-18-1259-2024, 2024
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Leads (openings in sea ice cover) are created by sea ice dynamics. Because they are important for many processes in the Arctic winter climate, we aim to detect them with satellites. We present two new techniques to detect lead widths of a few hundred meters at high spatial resolution (700 m) and independent of clouds or sun illumination. We use the MOSAiC drift 2019–2020 in the Arctic for our case study and compare our new products to other existing lead products.
Qin Zhang and Nick Hughes
The Cryosphere, 17, 5519–5537, https://doi.org/10.5194/tc-17-5519-2023, https://doi.org/10.5194/tc-17-5519-2023, 2023
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To alleviate tedious manual image annotations for training deep learning (DL) models in floe instance segmentation, we employ a classical image processing technique to automatically label floes in images. We then apply a DL semantic method for fast and adaptive floe instance segmentation from high-resolution airborne and satellite images. A post-processing algorithm is also proposed to refine the segmentation and further to derive acceptable floe size distributions at local and global scales.
Monojit Saha, Julienne Stroeve, Dustin Isleifson, John Yackel, Vishnu Nandan, Jack Christopher Landy, and Hoi Ming Lam
EGUsphere, https://doi.org/10.5194/egusphere-2023-2509, https://doi.org/10.5194/egusphere-2023-2509, 2023
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Snow on sea ice is vital for near-shore sea ice geophysical and biological processes. Past studies have measured snow depths using satellite altimeters Cryosat-2 and ICESat-2 (Cryo2Ice) but estimating sea surface height from lead-less land-fast sea ice remains challenging. Snow depths from Cryo2Ice are compared to in-situ after adjusting for tides. Realistic snow depths are retrieved but difference in roughness, satellite footprints and snow geophysical properties are identified as challenges.
Larysa Istomina, Hannah Niehaus, and Gunnar Spreen
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-142, https://doi.org/10.5194/tc-2023-142, 2023
Revised manuscript accepted for TC
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Melt water puddles, or melt ponds on top of the Arctic sea ice are a good measure of the Arctic climate state. In the context of the recent climate warming, the Arctic has warmed about 4 times faster than the rest of the world, and a long-term dataset of the melt pond fraction is needed to be able to model the future development of the Arctic climate. We present such a dataset, produce 2002–2023 trends and highlight a potential melt regime shift with drastic regional trends of +20 % per decade.
Alexander Mchedlishvili, Christof Lüpkes, Alek Petty, Michel Tsamados, and Gunnar Spreen
The Cryosphere, 17, 4103–4131, https://doi.org/10.5194/tc-17-4103-2023, https://doi.org/10.5194/tc-17-4103-2023, 2023
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In this study we looked at sea ice–atmosphere drag coefficients, quantities that help with characterizing the friction between the atmosphere and sea ice, and vice versa. Using ICESat-2, a laser altimeter that measures elevation differences by timing how long it takes for photons it sends out to return to itself, we could map the roughness, i.e., how uneven the surface is. From roughness we then estimate drag force, the frictional force between sea ice and the atmosphere, across the Arctic.
Philip Rostosky and Gunnar Spreen
The Cryosphere, 17, 3867–3881, https://doi.org/10.5194/tc-17-3867-2023, https://doi.org/10.5194/tc-17-3867-2023, 2023
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During winter, storms entering the Arctic region can bring warm air into the cold environment. Strong increases in air temperature modify the characteristics of the Arctic snow and ice cover. The Arctic sea ice cover can be monitored by satellites observing the natural emission of the Earth's surface. In this study, we show that during warm air intrusions the change in the snow characteristics influences the satellite-derived sea ice cover, leading to a false reduction of the estimated ice area.
Ellen M. Buckley, Sinéad L. Farrell, Ute C. Herzfeld, Melinda A. Webster, Thomas Trantow, Oliwia N. Baney, Kyle A. Duncan, Huilin Han, and Matthew Lawson
The Cryosphere, 17, 3695–3719, https://doi.org/10.5194/tc-17-3695-2023, https://doi.org/10.5194/tc-17-3695-2023, 2023
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In this study, we use satellite observations to investigate the evolution of melt ponds on the Arctic sea ice surface. We derive melt pond depth from ICESat-2 measurements of the pond surface and bathymetry and melt pond fraction (MPF) from the classification of Sentinel-2 imagery. MPF increases to a peak of 16 % in late June and then decreases, while depth increases steadily. This work demonstrates the ability to track evolving melt conditions in three dimensions throughout the summer.
Imke Sievers, Henriette Skourup, and Till A. S. Rasmussen
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-122, https://doi.org/10.5194/tc-2023-122, 2023
Revised manuscript accepted for TC
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To derive sea ice thickness (SIT) from satellite freeboard (FB) observations, assumptions about snow thickness, snow density, sea ice density and water density are needed. These parameters are impossible to observe alongside FB, so many existing products use empirical values. In this study, modeled values are used instead. The modeled values and otherwise commonly used empirical values are evaluated against in situ observations. In a further analysis, the influence on the SIT is quantified.
Yujia Qiu, Xiao-Ming Li, and Huadong Guo
The Cryosphere, 17, 2829–2849, https://doi.org/10.5194/tc-17-2829-2023, https://doi.org/10.5194/tc-17-2829-2023, 2023
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Spaceborne thermal infrared sensors with kilometer-scale resolution cannot support adequate parameterization of Arctic leads. For the first time, we applied the 30 m resolution data from the Thermal Infrared Spectrometer (TIS) on the emerging SDGSAT-1 to detect Arctic leads. Validation with Sentinel-2 data shows high accuracy for the three TIS bands. Compared to MODIS, the TIS presents more narrow leads, demonstrating its great potential for observing previously unresolvable Arctic leads.
Vishnu Nandan, Rosemary Willatt, Robbie Mallett, Julienne Stroeve, Torsten Geldsetzer, Randall Scharien, Rasmus Tonboe, John Yackel, Jack Landy, David Clemens-Sewall, Arttu Jutila, David N. Wagner, Daniela Krampe, Marcus Huntemann, Mallik Mahmud, David Jensen, Thomas Newman, Stefan Hendricks, Gunnar Spreen, Amy Macfarlane, Martin Schneebeli, James Mead, Robert Ricker, Michael Gallagher, Claude Duguay, Ian Raphael, Chris Polashenski, Michel Tsamados, Ilkka Matero, and Mario Hoppmann
The Cryosphere, 17, 2211–2229, https://doi.org/10.5194/tc-17-2211-2023, https://doi.org/10.5194/tc-17-2211-2023, 2023
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We show that wind redistributes snow on Arctic sea ice, and Ka- and Ku-band radar measurements detect both newly deposited snow and buried snow layers that can affect the accuracy of snow depth estimates on sea ice. Radar, laser, meteorological, and snow data were collected during the MOSAiC expedition. With frequent occurrence of storms in the Arctic, our results show that
wind-redistributed snow needs to be accounted for to improve snow depth estimates on sea ice from satellite radars.
Dyre Oliver Dammann, Mark A. Johnson, Andrew R. Mahoney, and Emily R. Fedders
The Cryosphere, 17, 1609–1622, https://doi.org/10.5194/tc-17-1609-2023, https://doi.org/10.5194/tc-17-1609-2023, 2023
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We investigate the GAMMA Portable Radar Interferometer (GPRI) as a tool for evaluating flexural–gravity waves in sea ice in near real time. With a GPRI mounted on grounded ice near Utqiaġvik, Alaska, we identify 20–50 s infragravity waves in landfast ice with ~1 mm amplitude during 23–24 April 2021. Observed wave speed and periods compare well with modeled wave propagation and on-ice accelerometers, confirming the ability to track propagation and properties of waves over hundreds of meters.
Zhaoqing Dong, Lijian Shi, Mingsen Lin, Yongjun Jia, Tao Zeng, and Suhui Wu
The Cryosphere, 17, 1389–1410, https://doi.org/10.5194/tc-17-1389-2023, https://doi.org/10.5194/tc-17-1389-2023, 2023
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We try to explore the application of SGDR data in polar sea ice thickness. Through this study, we find that it seems difficult to obtain reasonable results by using conventional methods. So we use the 15 lowest points per 25 km to estimate SSHA to retrieve more reasonable Arctic radar freeboard and thickness. This study also provides reference for reprocessing L1 data. We will release products that are more reasonable and suitable for polar sea ice thickness retrieval to better evaluate HY-2B.
Wenkai Guo, Polona Itkin, Suman Singha, Anthony P. Doulgeris, Malin Johansson, and Gunnar Spreen
The Cryosphere, 17, 1279–1297, https://doi.org/10.5194/tc-17-1279-2023, https://doi.org/10.5194/tc-17-1279-2023, 2023
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Sea ice maps are produced to cover the MOSAiC Arctic expedition (2019–2020) and divide sea ice into scientifically meaningful classes. We use a high-resolution X-band synthetic aperture radar dataset and show how image brightness and texture systematically vary across the images. We use an algorithm that reliably corrects this effect and achieve good results, as evaluated by comparisons to ground observations and other studies. The sea ice maps are useful as a basis for future MOSAiC studies.
Elie Dumas-Lefebvre and Dany Dumont
The Cryosphere, 17, 827–842, https://doi.org/10.5194/tc-17-827-2023, https://doi.org/10.5194/tc-17-827-2023, 2023
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By changing the shape of ice floes, wave-induced sea ice breakup dramatically affects the large-scale dynamics of sea ice. As this process is also the trigger of multiple others, it was deemed relevant to study how breakup itself affects the ice floe size distribution. To do so, a ship sailed close to ice floes, and the breakup that it generated was recorded with a drone. The obtained data shed light on the underlying physics of wave-induced sea ice breakup.
Felix L. Müller, Stephan Paul, Stefan Hendricks, and Denise Dettmering
The Cryosphere, 17, 809–825, https://doi.org/10.5194/tc-17-809-2023, https://doi.org/10.5194/tc-17-809-2023, 2023
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Thinning sea ice has significant impacts on the energy exchange between the atmosphere and the ocean. In this study we present visual and quantitative comparisons of thin-ice detections obtained from classified Cryosat-2 radar reflections and thin-ice-thickness estimates derived from MODIS thermal-infrared imagery. In addition to good comparability, the results of the study indicate the potential for a deeper understanding of sea ice in the polar seas and improved processing of altimeter data.
Maxim L. Lamare, John D. Hedley, and Martin D. King
The Cryosphere, 17, 737–751, https://doi.org/10.5194/tc-17-737-2023, https://doi.org/10.5194/tc-17-737-2023, 2023
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The reflectivity of sea ice is crucial for modern climate change and for monitoring sea ice from satellites. The reflectivity depends on the angle at which the ice is viewed and the angle illuminated. The directional reflectivity is calculated as a function of viewing angle, illuminating angle, thickness, wavelength and surface roughness. Roughness cannot be considered independent of thickness, illumination angle and the wavelength. Remote sensors will use the data to image sea ice from space.
Yufang Ye, Yanbing Luo, Yan Sun, Mohammed Shokr, Signe Aaboe, Fanny Girard-Ardhuin, Fengming Hui, Xiao Cheng, and Zhuoqi Chen
The Cryosphere, 17, 279–308, https://doi.org/10.5194/tc-17-279-2023, https://doi.org/10.5194/tc-17-279-2023, 2023
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Arctic sea ice type (SITY) variation is a sensitive indicator of climate change. This study gives a systematic inter-comparison and evaluation of eight SITY products. Main results include differences in SITY products being significant, with average Arctic multiyear ice extent up to 1.8×106 km2; Ku-band scatterometer SITY products generally performing better; and factors such as satellite inputs, classification methods, training datasets and post-processing highly impacting their performance.
James Anheuser, Yinghui Liu, and Jeffrey R. Key
The Cryosphere, 16, 4403–4421, https://doi.org/10.5194/tc-16-4403-2022, https://doi.org/10.5194/tc-16-4403-2022, 2022
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A prominent part of the polar climate system is sea ice, a better understanding of which would lead to better understanding Earth's climate. Newly published methods for observing the temperature of sea ice have made possible a new method for estimating daily sea ice thickness growth from space using an energy balance. The method compares well with existing sea ice thickness observations.
Mikko Lensu and Markku Similä
The Cryosphere, 16, 4363–4377, https://doi.org/10.5194/tc-16-4363-2022, https://doi.org/10.5194/tc-16-4363-2022, 2022
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Ice ridges form a compressing ice cover. From above they appear as walls of up to few metres in height and extend even kilometres across the ice. Below they may reach tens of metres under the sea surface. Ridges need to be observed for the purposes of ice forecasting and ice information production. This relies mostly on ridging signatures discernible in radar satellite (SAR) images. New methods to quantify ridging from SAR have been developed and are shown to agree with field observations.
Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Ruzica Dadic, Philip Rostosky, Michael Gallagher, Robbie Mallett, Andrew Barrett, Stefan Hendricks, Rasmus Tonboe, Michelle McCrystall, Mark Serreze, Linda Thielke, Gunnar Spreen, Thomas Newman, John Yackel, Robert Ricker, Michel Tsamados, Amy Macfarlane, Henna-Reetta Hannula, and Martin Schneebeli
The Cryosphere, 16, 4223–4250, https://doi.org/10.5194/tc-16-4223-2022, https://doi.org/10.5194/tc-16-4223-2022, 2022
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Impacts of rain on snow (ROS) on satellite-retrieved sea ice variables remain to be fully understood. This study evaluates the impacts of ROS over sea ice on active and passive microwave data collected during the 2019–20 MOSAiC expedition. Rainfall and subsequent refreezing of the snowpack significantly altered emitted and backscattered radar energy, laying important groundwork for understanding their impacts on operational satellite retrievals of various sea ice geophysical variables.
Benjamin Heikki Redmond Roche and Martin D. King
The Cryosphere, 16, 3949–3970, https://doi.org/10.5194/tc-16-3949-2022, https://doi.org/10.5194/tc-16-3949-2022, 2022
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Sea ice is bright, playing an important role in reflecting incoming solar radiation. The reflectivity of sea ice is affected by the presence of pollutants, such as crude oil, even at low concentrations. Modelling how the brightness of three types of sea ice is affected by increasing concentrations of crude oils shows that the type of oil, the type of ice, the thickness of the ice, and the size of the oil droplets are important factors. This shows that sea ice is vulnerable to oil pollution.
Alexis Anne Denton and Mary-Louise Timmermans
The Cryosphere, 16, 1563–1578, https://doi.org/10.5194/tc-16-1563-2022, https://doi.org/10.5194/tc-16-1563-2022, 2022
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Arctic sea ice has a distribution of ice sizes that provides insight into the physics of the ice. We examine this distribution from satellite imagery from 1999 to 2014 in the Canada Basin. We find that it appears as a power law whose power becomes less negative with increasing ice concentrations and has a seasonality tied to that of ice concentration. Results suggest ice concentration be considered in models of this distribution and are important for understanding sea ice in a warming Arctic.
Stephen E. L. Howell, Mike Brady, and Alexander S. Komarov
The Cryosphere, 16, 1125–1139, https://doi.org/10.5194/tc-16-1125-2022, https://doi.org/10.5194/tc-16-1125-2022, 2022
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We describe, apply, and validate the Environment and Climate Change Canada automated sea ice tracking system (ECCC-ASITS) that routinely generates large-scale sea ice motion (SIM) over the pan-Arctic domain using synthetic aperture radar (SAR) images. The ECCC-ASITS was applied to the incoming image streams of Sentinel-1AB and the RADARSAT Constellation Mission from March 2020 to October 2021 using a total of 135 471 SAR images and generated new SIM datasets (i.e., 7 d 25 km and 3 d 6.25 km).
Wayne de Jager and Marcello Vichi
The Cryosphere, 16, 925–940, https://doi.org/10.5194/tc-16-925-2022, https://doi.org/10.5194/tc-16-925-2022, 2022
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Ice motion can be used to better understand how weather and climate change affect the ice. Antarctic sea ice extent has shown large variability over the observed period, and dynamical features may also have changed. Our method allows for the quantification of rotational motion caused by wind and how this may have changed with time. Cyclonic motion dominates the Atlantic sector, particularly from 2015 onwards, while anticyclonic motion has remained comparatively small and unchanged.
Stefan Kern, Thomas Lavergne, Leif Toudal Pedersen, Rasmus Tage Tonboe, Louisa Bell, Maybritt Meyer, and Luise Zeigermann
The Cryosphere, 16, 349–378, https://doi.org/10.5194/tc-16-349-2022, https://doi.org/10.5194/tc-16-349-2022, 2022
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High-resolution clear-sky optical satellite imagery has rarely been used to evaluate satellite passive microwave sea-ice concentration products beyond case-study level. By comparing 10 such products with sea-ice concentration estimated from > 350 such optical images in both hemispheres, we expand results of earlier evaluation studies for these products. Results stress the need to look beyond precision and accuracy and to discuss the evaluation data’s quality and filters applied in the products.
Wenkai Guo, Polona Itkin, Johannes Lohse, Malin Johansson, and Anthony Paul Doulgeris
The Cryosphere, 16, 237–257, https://doi.org/10.5194/tc-16-237-2022, https://doi.org/10.5194/tc-16-237-2022, 2022
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This study uses radar satellite data categorized into different sea ice types to detect ice deformation, which is significant for climate science and ship navigation. For this, we examine radar signal differences of sea ice between two similar satellite sensors and show an optimal way to apply categorization methods across sensors, so more data can be used for this purpose. This study provides a basis for future reliable and constant detection of ice deformation remotely through satellite data.
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.
Lanqing Huang, Georg Fischer, and Irena Hajnsek
The Cryosphere, 15, 5323–5344, https://doi.org/10.5194/tc-15-5323-2021, https://doi.org/10.5194/tc-15-5323-2021, 2021
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This study shows an elevation difference between the radar interferometric measurements and the optical measurements from a coordinated campaign over the snow-covered deformed sea ice in the western Weddell Sea, Antarctica. The objective is to correct the penetration bias of microwaves and to generate a precise sea ice topographic map, including the snow depth on top. Excellent performance for sea ice topographic retrieval is achieved with the proposed model and the developed retrieval scheme.
Marek Muchow, Amelie U. Schmitt, and Lars Kaleschke
The Cryosphere, 15, 4527–4537, https://doi.org/10.5194/tc-15-4527-2021, https://doi.org/10.5194/tc-15-4527-2021, 2021
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Linear-like openings in sea ice, also called leads, occur with widths from meters to kilometers. We use satellite images from Sentinel-2 with a resolution of 10 m to identify leads and measure their widths. With that we investigate the frequency of lead widths using two different statistical methods, since other studies have shown a dependency of heat exchange on the lead width. We are the first to address the sea-ice lead-width distribution in the Weddell Sea, Antarctica.
Gemma M. Brett, Daniel Price, Wolfgang Rack, and Patricia J. Langhorne
The Cryosphere, 15, 4099–4115, https://doi.org/10.5194/tc-15-4099-2021, https://doi.org/10.5194/tc-15-4099-2021, 2021
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Ice shelf meltwater in the surface ocean affects sea ice formation, causing it to be thicker and, in particular conditions, to have a loose mass of platelet ice crystals called a sub‐ice platelet layer beneath. This causes the sea ice freeboard to stand higher above sea level. In this study, we demonstrate for the first time that the signature of ice shelf meltwater in the surface ocean manifesting as higher sea ice freeboard in McMurdo Sound is detectable from space using satellite technology.
Thomas Krumpen, Luisa von Albedyll, Helge F. Goessling, Stefan Hendricks, Bennet Juhls, Gunnar Spreen, Sascha Willmes, H. Jakob Belter, Klaus Dethloff, Christian Haas, Lars Kaleschke, Christian Katlein, Xiangshan Tian-Kunze, Robert Ricker, Philip Rostosky, Janna Rückert, Suman Singha, and Julia Sokolova
The Cryosphere, 15, 3897–3920, https://doi.org/10.5194/tc-15-3897-2021, https://doi.org/10.5194/tc-15-3897-2021, 2021
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We use satellite data records collected along the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) drift to categorize ice conditions that shaped and characterized the floe and surroundings during the expedition. A comparison with previous years is made whenever possible. The aim of this analysis is to provide a basis and reference for subsequent research in the six main research areas of atmosphere, ocean, sea ice, biogeochemistry, remote sensing and ecology.
Thomas Lavergne, Montserrat Piñol Solé, Emily Down, and Craig Donlon
The Cryosphere, 15, 3681–3698, https://doi.org/10.5194/tc-15-3681-2021, https://doi.org/10.5194/tc-15-3681-2021, 2021
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Pushed by winds and ocean currents, polar sea ice is on the move. We use passive microwave satellites to observe this motion. The images from their orbits are often put together into daily images before motion is measured. In our study, we measure motion from the individual orbits directly and not from the daily images. We obtain many more motion vectors, and they are more accurate. This can be used for current and future satellites, e.g. the Copernicus Imaging Microwave Radiometer (CIMR).
Céline Heuzé, Lu Zhou, Martin Mohrmann, and Adriano Lemos
The Cryosphere, 15, 3401–3421, https://doi.org/10.5194/tc-15-3401-2021, https://doi.org/10.5194/tc-15-3401-2021, 2021
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For navigation or science planning, knowing when sea ice will open in advance is a prerequisite. Yet, to date, routine spaceborne microwave observations of sea ice are unable to do so. We present the first method based on spaceborne infrared that can forecast an opening several days ahead. We develop it specifically for the Weddell Polynya, a large hole in the Antarctic winter ice cover that unexpectedly re-opened for the first time in 40 years in 2016, and determine why the polynya opened.
Marcel Kleinherenbrink, Anton Korosov, Thomas Newman, Andreas Theodosiou, Alexander S. Komarov, Yuanhao Li, Gert Mulder, Pierre Rampal, Julienne Stroeve, and Paco Lopez-Dekker
The Cryosphere, 15, 3101–3118, https://doi.org/10.5194/tc-15-3101-2021, https://doi.org/10.5194/tc-15-3101-2021, 2021
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Harmony is one of the Earth Explorer 10 candidates that has the chance of being selected for launch in 2028. The mission consists of two satellites that fly in formation with Sentinel-1D, which carries a side-looking radar system. By receiving Sentinel-1's signals reflected from the surface, Harmony is able to observe instantaneous elevation and two-dimensional velocity at the surface. As such, Harmony's data allow the retrieval of sea-ice drift and wave spectra in sea-ice-covered regions.
Zhixiang Yin, Xiaodong Li, Yong Ge, Cheng Shang, Xinyan Li, Yun Du, and Feng Ling
The Cryosphere, 15, 2835–2856, https://doi.org/10.5194/tc-15-2835-2021, https://doi.org/10.5194/tc-15-2835-2021, 2021
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MODIS thermal infrared (TIR) imagery provides promising data to study the rapid variations in the Arctic turbulent heat flux (THF). The accuracy of estimated THF, however, is low (especially for small leads) due to the coarse resolution of the MODIS TIR data. We train a deep neural network to enhance the spatial resolution of estimated THF over leads from MODIS TIR imagery. The method is found to be effective and can generate a result which is close to that derived from Landsat-8 TIR imagery.
Joan Antoni Parera-Portell, Raquel Ubach, and Charles Gignac
The Cryosphere, 15, 2803–2818, https://doi.org/10.5194/tc-15-2803-2021, https://doi.org/10.5194/tc-15-2803-2021, 2021
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We describe a new method to map sea ice and water at 500 m resolution using data acquired by the MODIS sensors. The strength of this method is that it achieves high-accuracy results and is capable of attenuating unwanted resolution-breaking effects caused by cloud masking. Our resulting March and September monthly aggregates reflect the loss of sea ice in the European Arctic during the 2000–2019 period and show the algorithm's usefulness as a sea ice monitoring tool.
Robbie D. C. Mallett, Julienne C. Stroeve, Michel Tsamados, Jack C. Landy, Rosemary Willatt, Vishnu Nandan, and Glen E. Liston
The Cryosphere, 15, 2429–2450, https://doi.org/10.5194/tc-15-2429-2021, https://doi.org/10.5194/tc-15-2429-2021, 2021
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We re-estimate pan-Arctic sea ice thickness (SIT) values by combining data from the Envisat and CryoSat-2 missions with data from a new, reanalysis-driven snow model. Because a decreasing amount of ice is being hidden below the waterline by the weight of overlying snow, we argue that SIT may be declining faster than previously calculated in some regions. Because the snow product varies from year to year, our new SIT calculations also display much more year-to-year variability.
Renée Mie Fredensborg Hansen, Eero Rinne, Sinéad Louise Farrell, and Henriette Skourup
The Cryosphere, 15, 2511–2529, https://doi.org/10.5194/tc-15-2511-2021, https://doi.org/10.5194/tc-15-2511-2021, 2021
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Ice navigators rely on timely information about ice conditions to ensure safe passage through ice-covered waters, and one parameter, the degree of ice ridging (DIR), is particularly useful. We have investigated the possibility of estimating DIR from the geolocated photons of ICESat-2 (IS2) in the Bay of Bothnia, show that IS2 retrievals from different DIR areas differ significantly, and present some of the first steps in creating sea ice applications beyond e.g. thickness retrieval.
Luisa von Albedyll, Christian Haas, and Wolfgang Dierking
The Cryosphere, 15, 2167–2186, https://doi.org/10.5194/tc-15-2167-2021, https://doi.org/10.5194/tc-15-2167-2021, 2021
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Convergent sea ice motion produces a thick ice cover through ridging. We studied sea ice deformation derived from high-resolution satellite imagery and related it to the corresponding thickness change. We found that deformation explains the observed dynamic thickness change. We show that deformation can be used to model realistic ice thickness distributions. Our results revealed new relationships between thickness redistribution and deformation that could improve sea ice models.
Rasmus T. Tonboe, Vishnu Nandan, John Yackel, Stefan Kern, Leif Toudal Pedersen, and Julienne Stroeve
The Cryosphere, 15, 1811–1822, https://doi.org/10.5194/tc-15-1811-2021, https://doi.org/10.5194/tc-15-1811-2021, 2021
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A relationship between the Ku-band radar scattering horizon and snow depth is found using a radar scattering model. This relationship has implications for (1) the use of snow climatology in the conversion of satellite radar freeboard into sea ice thickness and (2) the impact of variability in measured snow depth on the derived ice thickness. For both 1 and 2, the impact of using a snow climatology versus the actual snow depth is relatively small.
Stephan Paul and Marcus Huntemann
The Cryosphere, 15, 1551–1565, https://doi.org/10.5194/tc-15-1551-2021, https://doi.org/10.5194/tc-15-1551-2021, 2021
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Cloud cover in the polar regions is difficult to identify at night when using only thermal-infrared data. This is due to occurrences of warm clouds over cold sea ice and cold clouds over warm sea ice. Especially the standard MODIS cloud mask frequently tends towards classifying open water and/or thin ice as cloud cover. Using a neural network, we present an improved discrimination between sea-ice, open-water and/or thin-ice, and cloud pixels in nighttime MODIS thermal-infrared satellite data.
Cited articles
Andersen, S., Breivik, L. A., Eastwood, S., Godøy, Ø., Lavergne, T., Lind, M., Procires, M., Schyberg, H., and Tonboe, R.:
Ocean and Sea Ice SAF,
Sea Ice Product Manual, version 3.8, oSI SAF document no. SAF/OSI/met.no/TEC/MA/125,
EUMETSAT, Darmstadt, Germany, 2012. a
Armstrong, R. and Brodzik, M.:
DMSP SSM/I-SSMIS Pathfinder Daily EASE-Grid Brightness Temperatures, Version 2, NASA National Snow and Ice Data Center Distributed Active Archive Center
[data set],
https://doi.org/10.5067/3EX2U1DV3434, 1994. a
Bi, H., Zhang, Z., Wang, Y., Xu, X., Liang, Y., Huang, J., Liu, Y., and Fu, M.: Baffin Bay sea ice inflow and outflow: 1978–1979 to 2016–2017, The Cryosphere, 13, 1025–1042, https://doi.org/10.5194/tc-13-1025-2019, 2019. a
Blanchard-Wrigglesworth, E., Webster, M. A., Farrell, S. L., and Bitz, C. M.:
Reconstruction of snow on Arctic sea ice,
J. Geophys. Res.-Oceans,
123, 3588–3602, https://doi.org/10.1002/2017JC013364, 2018. a
Connor, L. N., Laxon, S. W., Ridout, A. L., Krabill, W. B., and McAdoo, D. C.:
Comparison of Envisat radar and airborne laser altimeter measurements over Arctic sea ice,
Remote Sens. Environ.,
113, 563–570, https://doi.org/10.1016/j.rse.2008.10.015, 2009. a
Cuny, J., Rhines, P. B., and Kwok, R.:
Davis Strait volume, freshwater and heat fluxes,
Oceanographic Research Papers,
52, 519–542, https://doi.org/10.1016/j.dsr.2004.10.006, 2005. a
Curry, B., Lee, C. M., and Petrie, B.:
Volume, freshwater, and heat fluxes through Davis Strait, 2004-05,
J. Phys. Oceanogr.,
41, 429–436, https://doi.org/10.1175/2010JPO4536.1, 2011. a
Curry, B., Lee, C. M., Petrie, B., Moritz, R. E., and Kwok, R.:
Multiyear volume, liquid freshwater, and sea ice transport through Davis Strait, 2004-10,
J. Phys. Oceanogr.,
44, 1244–1266, https://doi.org/10.1175/JPO-D-13-0177.1, 2014. a
Day, J., Hawkins, E., and Tietsche, S.:
Will Arctic sea ice thickness initialization improve seasonal forecast skill,
Geophys. Res. Lett.,
41, 7566–7575, https://doi.org/10.1002/2014GL061694, 2014. a
Canadian Ice Service, Environment and Climate Change Canada: Canadian Ice Service Arctic Weekly Regional Ice Data, available at: https://iceweb1.cis.ec.gc.ca/Archive/page1.xhtml?lang=en
last access: April 2021. a
Fenty, I. and Heimbach, P.:
Coupled sea ice-ocean-state estimation in the Labrador Sea and Baffin Bay,
J. Phys. Oceanogr.,
43, 884–904, https://doi.org/10.1175/JPO-D-12-065.1, 2013. a
GEBCO Compilation Group:
GEBCO 2019 Grid,
https://doi.org/10.5285/836f016a-33be-6ddc-e053-6c86abc0788e, 2019. a
Geiger, C., Müller, H. R., Samluk, J. P., Bernstein, E. R., and Richter-Menge, J.:
Impact of spatial aliasing on sea-ice thickness measurements,
Ann. Glaciol.,
56, 353–362, https://doi.org/10.3189/2015AoG69A644, 2015. a
Giles, K. A., Laxon, S. W., Wingham, D. J., Wallis, D. W., Krabill, W. B., Leuschen, C. J., McAdoo, D., Manizade, S. S., and Raney, R. K.:
Combined airborne laser and radar altimeter measurements over the Fram Strait in May 2002,
Remote Sens. Environ.,
111, 182–194, https://doi.org/10.1016/j.rse.2007.02.037, 2007. a
Glissenaar, I.:
Sea ice thickness and snow depth in Baffin Bay (March 2003–2020), data.bris Research Data Repository [data set], https://doi.org/10.5523/bris.2peywz756l8182cpwlanm4ratz, 2021. a
Hendricks, S., Paul, S., and Rinne, E.:
ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):
Northern hemisphere sea ice thickness from CryoSat-2 on the satellite swath (L2P), v2.0, CEDA Archive,
Centre for Environmental Data Analysis [data set], https://doi.org/10.5285/5b6033bfb7f241e89132a83fdc3d5364, 2018a. a, b
Hendricks, S., Paul, S., and Rinne, E.:
ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):
Northern hemisphere sea ice thickness from Envisat on the satellite swath (L2P), v2.0, CEDA Archive,
Centre for Environmental Data Analysis [data set],
https://doi.org/10.5285/54e2ee0803764b4e84c906da3f16d81b,
2018b. a, b, c
Holland, M. M., Bitz, C. M., Eby, M., and Weaver, A. J.:
The role of ice–ocean interactions in the variability of the North Atlantic Thermohaline Circulation,
J. Climate,
14, 656–675, https://doi.org/10.1175/1520-0442(2001)014<0656:TROIOI>2.0.CO;2, 2001. a
Jeffries, M., Overland, J., and Perovich, D.:
The Arctic shifts to a new normal,
Phys. Today,
66, 35–40, https://doi.org/10.1063/PT.3.2147, 2013. a
Kern, S., Skourop, H., and Khvorostovsky, K.:
Sea Ice Climate Change Initiative:
Phase 2 – D4.1 Product Validation & Intercomparison Report (PVIR-SIT), Tech. rep.,
ESA, 2018. a
Kurtz, N., Markus, T., Cavalieri, D., Sparling, L. C., Krabill, W. B., Gasiewski, A. J., and Sonntag, J. G.:
Estimation of sea ice thickness distributions through the combination of snow depth and satellite laser altimetry data,
J. Geophys. Res.-Oceans,
114, C10007, https://doi.org/10.1029/2009JC005292, 2009. a, b, c, d
Kurtz, N. T. and Farrell, S. L.:
Large-scale surveys of snow depth on Arctic sea ice from Operation IceBridge,
Geophys. Res. Lett.,
38, L20505, https://doi.org/10.1029/2011GL049216, 2011. a
Kurtz, N., Studinger, M., Harbeck, J., Onana, V.-D.-P., and Yi, D.:
IceBridge L4 Sea Ice Freeboard, Snow Depth, and Thickness, Version 1,
NASA National Snow and Ice Data Center Distributed Active Archive Center
[data set],
https://doi.org/10.5067/G519SHCKWQV6, 2015. a
Kwok, R.:
Variability of Nares Strait ice flux,
Geophys. Res. Lett.,
32, L24502, https://doi.org/10.1029/2005GL024768, 2005. a
Kwok, R.:
Arctic sea ice thickness, volume, and multiyear ice coverage: Losses and coupled variability (1958–2018),
Environ. Res. Lett.,
13, 105005, https://doi.org/10.1088/1748-9326/aae3ec, 2018. a, b, c
Kwok, R. and Cunningham, G. F.:
Variability of Arctic sea ice thickness and volume from CryoSat-2,
Philos. T. R. Soc. A,
373, 2045, https://doi.org/10.1098/rsta.2014.0157, 2015. a
Kwok, R., Cunningham, G. F., Zwally, H. J., and Yi, D.:
ICESat over Arctic sea ice: Interpretation of altimetric and reflectivity profiles,
J. Geophys. Res.,
111, C06006, https://doi.org/10.1029/2005JC003175, 2006. a
Kwok, R., Cunningham, G. F., Zwally, H. J., and Yi, D.:
Ice, Cloud, and land Elevation Satellite (ICESat) over Arctic sea ice: Retrieval of freeboard,
J. Geophys. Res.,
112, C12013, https://doi.org/10.1029/2006JC003978, 2007. a
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 Freeboard, Version 4, March 2019 & March 2020, Boulder, Colorado USA, NSIDC: National Snow and Ice Data Center [data set], https://doi.org/10.5067/ATLAS/ATL10.004, 2021. 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, 1–40, https://doi.org/10.1029/2019jc015820, 2020. a, b
Laxon, S. W., Giles, K. A., Ridout, A. L., Wingham, D. J., Willatt, R., Cullen, R., Kwok, R., Schweiger, A., Zhang, J., Haas, C., Hendricks, S., Krishfield, R., Kurtz, N., Farrell, S., and Davidson, M.:
CryoSat-2 estimates of Arctic sea ice thickness and volume,
Geophys. Res. Lett.,
40, 732–737, https://doi.org/10.1002/grl.50193, 2013. a, b
Lee, C. M., Petrie, B., Gobat, J. I., Soukhovtsev, V., Abriel, J., van Thiel, K., and Scotney, M.:
An Observational Array for High-Resolution year-round measurements of volume, freshwater and ice flux variability in Davis Strait: Cruise report for R/V Knorr 179-05, 22 September–4 October 2004, Tech. Rep. APL-UW TR 0408,
Applied Physics Laboratory, University of Washington,
available at: https://iop.apl.washington.edu/assets/pdfs/APL_UW_TR_0408.pdf (last access: October 2019), 2004. a
Liston, G., Itkin, P., Stroeve, J., Tschudi, M., Stewart, J. S., Pedersan, S. H., Reinking, A. K., and Elder, K.:
A Langrangian snow-evolution system for sea-ice applications (SnowModel-LG): Part I – Model Description,
J. Geophys. Res.-Oceans,
125, e2019JC015913, https://doi.org/10.1029/2019JC015913, 2020. a, b, c, d
Mallett, R. D. C., Stroeve, J. C., Tsamados, M., Landy, J. C., Willatt, R., Nandan, V., and Liston, G. E.: Faster decline and higher variability in the sea ice thickness of the marginal Arctic seas when accounting for dynamic snow cover, The Cryosphere, 15, 2429–2450, https://doi.org/10.5194/tc-15-2429-2021, 2021. a, b
Markus, T. and Cavalieri, D. J.:
Snow depth distribution over sea ice in the Southern Ocean from satellite passive microwave data, edited by: Jeffries, M. O.,
https://doi.org/10.1029/AR074p0019, 1998. a, b, c
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
Min, C., Yang, Q., Mu, L., Kauker, F., and Ricker, R.: Ensemble-based estimation of sea-ice volume variations in the Baffin Bay, The Cryosphere, 15, 169–181, https://doi.org/10.5194/tc-15-169-2021, 2021. a, b
Moritz, R.:
Davis Strait Freshwater Flux Array, available at: http://psc.apl.uw.edu/sea_ice_cdr/Sources/Davis_Strait.html, last access:
October 2019. a
Obbard, M. E., Derocher, A. E., Lunn, N.J., Peacock, E., Stirling, I., and Thiemann, G. W.: Research on Polar Bears in Canada, 2005–2009, in: Polar Bears: Proceedings of the 15th Working Meeting of the IUCN/SSC Polar Bear Specialist Group, 29 June–3 July 2009, Copenhagen, Denmark, edited by: Obbard, M. E., Thiemann, G. W., Peacock, E., and DeBruyn, T. D., 115–132, 2010. a
Paul, S., Hendricks, S., Ricker, R., Kern, S., and Rinne, E.: Empirical parametrization of Envisat freeboard retrieval of Arctic and Antarctic sea ice based on CryoSat-2: progress in the ESA Climate Change Initiative, The Cryosphere, 12, 2437–2460, https://doi.org/10.5194/tc-12-2437-2018, 2018. a
Petty, A. A., Webster, M., Boisvert, L., and Markus, T.: The NASA Eulerian Snow on Sea Ice Model (NESOSIM) v1.0: initial model development and analysis, Geosci. Model Dev., 11, 4577–4602, https://doi.org/10.5194/gmd-11-4577-2018, 2018. a, b
Rango, A., Chang, A. T. C., and Foster, J. L.:
Utilization of spaceborne microwave radiometers for monitoring snowpack properties,
Nord. Hydrol.,
10, 25–40, https://doi.org/10.2166/nh.1979.0003, 1979. a
Ricker, R., Hendricks, S., Kaleschke, L., Tian-Kunze, X., King, J., and Haas, C.: A weekly Arctic sea-ice thickness data record from merged CryoSat-2 and SMOS satellite data, The Cryosphere, 11, 1607–1623, https://doi.org/10.5194/tc-11-1607-2017, 2017. a, b
Ricker, R., Hendricks, S., Tian-Kunze, X., and Kaleschke, L.:
CryoSat-SMOS Merged Sea Ice Thickness, available at: https://spaces.awi.de/pages/viewpage.action?pageId=291898639, last access: July 2021b. a
Rostosky, P., Spreen, G., Farrell, S. L., Frost, T., Heygster, G., and Melsheimer, C.:
Snow depth retrieval on Arctic sea ice from passive microwave radiometers – Improvements and extensions to multiyear ice using lower frequencies,
J. Geophys. Res.-Oceans,
123, 7120–7138, https://doi.org/10.1029/2018JC014028, 2018. a
Serreze, M. and Stroeve, J.:
Arctic sea ice trends, variability and implications for seasonal ice forecasting,
Philos. T. R. Soc. A,
373, 2045, https://doi.org/10.1098/rsta.2014.0159, 2015. a
Stroeve, J. and Notz, D.:
Changing state of Arctic sea ice across all seasons,
Environ. Res. Lett.,
13, 103001, https://doi.org/10.1088/1748-9326/aade56, 2018. a, b
Stroeve, J., Liston, G. E., Buzzard, S., Zhou, L., Mallett, R., Barrett, A., Tschudi, M., Tsamados, M., Itkin, P., and Scott Stewart, J.:
A Lagrangian Snow-Evolution System for Sea Ice Applications (SnowModel-LG): Part II – Analyses,
J. Geophys. Res.-Oceans,
125, e2019JC015900, https://doi.org/10.1029/2019jc015900, 2020. a
Tang, C. L., Ross, C. K., Yao, T., Petrie, B., DeTracey, B. M., and Dunlap, E.:
The circulation, water masses and sea-ice of Baffin Bay,
Prog. Oceanogr.,
63, 183–228, https://doi.org/10.1016/j.pocean.2004.09.005, 2004. a
Tilling, R., Ridout, A., and Shepherd, A.:
Assessing the Impact of Lead and Floe Sampling on Arctic Sea Ice Thickness Estimates from Envisat and CryoSat-2,
J. Geophys. Res.-Oceans,
124, 7473–7485, https://doi.org/10.1029/2019JC015232, 2019. a
Tilling, R. L., Ridout, A., and Shepherd, A.:
Estimating Arctic sea ice thickness and volume using CryoSat-2 radar altimeter data,
Adv. Space Res.,
62, 1203–1225, https://doi.org/10.1016/j.asr.2017.10.051, 2018. a
Ulaby, F., Moore, R., and Fung, A.:
Microwave remote sensing: active and passive, Volume 3 – From Theory to Applications, Artech House,
Norwood, Massachusetts, USA, equation number: E.80, 1986. a
Warren, S. G., Rigor, I. G., Untersteiner, N., Radinov, V. F., Bryazgin, N. N., Aleksandrov, Y. I., and Colony, R.:
Snow depth on Arctic sea ice,
J. Climate,
12, 1814–1829, https://doi.org/10.1175/1520-0442(1999)012<1814:SDOASI>2.0.CO;2, 1999. a, b, c, d
Webster, M. A., Rignor, I. G., Nghiem, S. V., Kurtz, N. T., Farrell, S. L., Perovich, D. K., and Sturm, M.:
Interdecadal changes in snow depth on Arctic sea ice,
J. Geophys. Res.-Oceans,
119, 5395–5406, https://doi.org/10.1002/2014JC009985, 2014. a
Wingham, D. J., Francis, C. R., Baker, S., Bouzinac, C., Brockley, D., Cullen, R., de Chateau-Thierry, P., Laxon, S. W., Mallow, U., Mavrocordatos, C., Phalippou, L., Ratier, G., Rey, L., Rostan, F., Viau, P., and Wallis, D. W.:
CryoSat: A mission to determine the fluctuations in Earth's land and marine ice fields,
Adv. Space Res.,
37, 841–871, https://doi.org/10.1016/j.asr.2005.07.027, 2006. a, b
Zhou, L., Stroeve, J., Xu, S., Petty, A., Tilling, R., Winstrup, M., Rostosky, P., Lawrence, I. R., Liston, G. E., Ridout, A., Tsamados, M., and Nandan, V.: Inter-comparison of snow depth over Arctic sea ice from reanalysis reconstructions and satellite retrieval, The Cryosphere, 15, 345–367, https://doi.org/10.5194/tc-15-345-2021, 2021. a
Zwally, H. J., Schutz, R., Hancock, D., and Dimarzio, J.:
GLAS/ICESat L2 Sea Ice Altimetry Data (HDF5), Version 34, NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, Colorado, USA, NSIDC,
https://doi.org/10.5067/ICESAT/GLAS/DATA210,
2014. a, b
Zygmuntowska, M., Rampal, P., Ivanova, N., and Smedsrud, L. H.: Uncertainties in Arctic sea ice thickness and volume: new estimates and implications for trends, The Cryosphere, 8, 705–720, https://doi.org/10.5194/tc-8-705-2014, 2014. a
Short summary
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.
Scientists can estimate sea ice thickness using satellites that measure surface height. To...
