Articles | Volume 17, issue 1
https://doi.org/10.5194/tc-17-279-2023
© 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-17-279-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Jan 2023
Research article |
| 20 Jan 2023
| 20 Jan 2023
Inter-comparison and evaluation of Arctic sea ice type products
Yufang Ye
School of Geospatial Engineering and Science, Sun Yat-sen University
& Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),
Zhuhai, China
Yanbing Luo
School of Geospatial Engineering and Science, Sun Yat-sen University
& Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),
Zhuhai, China
School of Geospatial Engineering and Science, Sun Yat-sen University
& Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),
Zhuhai, China
Mohammed Shokr
Meteorological Research Division, Environment and Climate Change
Canada, Toronto, Canada
Signe Aaboe
Division for Remote Sensing and Data Management, Norwegian
Meteorological Institute, Tromsø, Norway
Fanny Girard-Ardhuin
Laboratoire d'Océanographie Physique et Spatiale (LOPS),
Ifremer-Univ. Brest-CNRS-IRD, IUEM, Plouzané, France
Fengming Hui
School of Geospatial Engineering and Science, Sun Yat-sen University
& Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),
Zhuhai, China
Xiao Cheng
School of Geospatial Engineering and Science, Sun Yat-sen University
& Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),
Zhuhai, China
Zhuoqi Chen
CORRESPONDING AUTHOR
School of Geospatial Engineering and Science, Sun Yat-sen University
& Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),
Zhuhai, China
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Related subject area
Discipline: Sea ice | Subject: Remote Sensing
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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
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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.
Polona Itkin
EGUsphere, https://doi.org/10.5194/egusphere-2023-2626, https://doi.org/10.5194/egusphere-2023-2626, 2023
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We analyzed radar satellite images of sea ice to understand how sea ice moves and deforms. This data is noisy, especially when looking at small details. We developed a method to filter out the noise. We used the filtered data to monitor how ice plates stretch and compresses over time, revealing slow healing of ice fractures. We also studied cohesive clusters of ice plates that move together. Our methods provide climate-relevant insights into the dynamic nature of winter sea ice cover.
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.
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.
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.
Isolde A. Glissenaar, Jack C. Landy, Alek A. Petty, Nathan T. Kurtz, and Julienne C. Stroeve
The Cryosphere, 15, 4909–4927, https://doi.org/10.5194/tc-15-4909-2021, https://doi.org/10.5194/tc-15-4909-2021, 2021
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Scientists can estimate sea ice thickness using satellites that measure surface height. To determine the sea ice thickness, we also need to know the snow depth and density. This paper shows that the chosen snow depth product has a considerable impact on the findings of sea ice thickness state and trends in Baffin Bay, showing mean thinning with some snow depth products and mean thickening with others. This shows that it is important to better understand and monitor snow depth on sea ice.
Marek Muchow, Amelie U. Schmitt, and Lars Kaleschke
The Cryosphere, 15, 4527–4537, https://doi.org/10.5194/tc-15-4527-2021, https://doi.org/10.5194/tc-15-4527-2021, 2021
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Linear-like openings in sea ice, also called leads, occur with widths from meters to kilometers. We use satellite images from Sentinel-2 with a resolution of 10 m to identify leads and measure their widths. With that we investigate the frequency of lead widths using two different statistical methods, since other studies have shown a dependency of heat exchange on the lead width. We are the first to address the sea-ice lead-width distribution in the Weddell Sea, Antarctica.
Gemma M. Brett, Daniel Price, Wolfgang Rack, and Patricia J. Langhorne
The Cryosphere, 15, 4099–4115, https://doi.org/10.5194/tc-15-4099-2021, https://doi.org/10.5194/tc-15-4099-2021, 2021
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Ice shelf meltwater in the surface ocean affects sea ice formation, causing it to be thicker and, in particular conditions, to have a loose mass of platelet ice crystals called a sub‐ice platelet layer beneath. This causes the sea ice freeboard to stand higher above sea level. In this study, we demonstrate for the first time that the signature of ice shelf meltwater in the surface ocean manifesting as higher sea ice freeboard in McMurdo Sound is detectable from space using satellite technology.
Thomas Krumpen, Luisa von Albedyll, Helge F. Goessling, Stefan Hendricks, Bennet Juhls, Gunnar Spreen, Sascha Willmes, H. Jakob Belter, Klaus Dethloff, Christian Haas, Lars Kaleschke, Christian Katlein, Xiangshan Tian-Kunze, Robert Ricker, Philip Rostosky, Janna Rückert, Suman Singha, and Julia Sokolova
The Cryosphere, 15, 3897–3920, https://doi.org/10.5194/tc-15-3897-2021, https://doi.org/10.5194/tc-15-3897-2021, 2021
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We use satellite data records collected along the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) drift to categorize ice conditions that shaped and characterized the floe and surroundings during the expedition. A comparison with previous years is made whenever possible. The aim of this analysis is to provide a basis and reference for subsequent research in the six main research areas of atmosphere, ocean, sea ice, biogeochemistry, remote sensing and ecology.
Thomas Lavergne, Montserrat Piñol Solé, Emily Down, and Craig Donlon
The Cryosphere, 15, 3681–3698, https://doi.org/10.5194/tc-15-3681-2021, https://doi.org/10.5194/tc-15-3681-2021, 2021
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Pushed by winds and ocean currents, polar sea ice is on the move. We use passive microwave satellites to observe this motion. The images from their orbits are often put together into daily images before motion is measured. In our study, we measure motion from the individual orbits directly and not from the daily images. We obtain many more motion vectors, and they are more accurate. This can be used for current and future satellites, e.g. the Copernicus Imaging Microwave Radiometer (CIMR).
Céline Heuzé, Lu Zhou, Martin Mohrmann, and Adriano Lemos
The Cryosphere, 15, 3401–3421, https://doi.org/10.5194/tc-15-3401-2021, https://doi.org/10.5194/tc-15-3401-2021, 2021
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For navigation or science planning, knowing when sea ice will open in advance is a prerequisite. Yet, to date, routine spaceborne microwave observations of sea ice are unable to do so. We present the first method based on spaceborne infrared that can forecast an opening several days ahead. We develop it specifically for the Weddell Polynya, a large hole in the Antarctic winter ice cover that unexpectedly re-opened for the first time in 40 years in 2016, and determine why the polynya opened.
Marcel Kleinherenbrink, Anton Korosov, Thomas Newman, Andreas Theodosiou, Alexander S. Komarov, Yuanhao Li, Gert Mulder, Pierre Rampal, Julienne Stroeve, and Paco Lopez-Dekker
The Cryosphere, 15, 3101–3118, https://doi.org/10.5194/tc-15-3101-2021, https://doi.org/10.5194/tc-15-3101-2021, 2021
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Harmony is one of the Earth Explorer 10 candidates that has the chance of being selected for launch in 2028. The mission consists of two satellites that fly in formation with Sentinel-1D, which carries a side-looking radar system. By receiving Sentinel-1's signals reflected from the surface, Harmony is able to observe instantaneous elevation and two-dimensional velocity at the surface. As such, Harmony's data allow the retrieval of sea-ice drift and wave spectra in sea-ice-covered regions.
Zhixiang Yin, Xiaodong Li, Yong Ge, Cheng Shang, Xinyan Li, Yun Du, and Feng Ling
The Cryosphere, 15, 2835–2856, https://doi.org/10.5194/tc-15-2835-2021, https://doi.org/10.5194/tc-15-2835-2021, 2021
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MODIS thermal infrared (TIR) imagery provides promising data to study the rapid variations in the Arctic turbulent heat flux (THF). The accuracy of estimated THF, however, is low (especially for small leads) due to the coarse resolution of the MODIS TIR data. We train a deep neural network to enhance the spatial resolution of estimated THF over leads from MODIS TIR imagery. The method is found to be effective and can generate a result which is close to that derived from Landsat-8 TIR imagery.
Joan Antoni Parera-Portell, Raquel Ubach, and Charles Gignac
The Cryosphere, 15, 2803–2818, https://doi.org/10.5194/tc-15-2803-2021, https://doi.org/10.5194/tc-15-2803-2021, 2021
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We describe a new method to map sea ice and water at 500 m resolution using data acquired by the MODIS sensors. The strength of this method is that it achieves high-accuracy results and is capable of attenuating unwanted resolution-breaking effects caused by cloud masking. Our resulting March and September monthly aggregates reflect the loss of sea ice in the European Arctic during the 2000–2019 period and show the algorithm's usefulness as a sea ice monitoring tool.
Robbie D. C. Mallett, Julienne C. Stroeve, Michel Tsamados, Jack C. Landy, Rosemary Willatt, Vishnu Nandan, and Glen E. Liston
The Cryosphere, 15, 2429–2450, https://doi.org/10.5194/tc-15-2429-2021, https://doi.org/10.5194/tc-15-2429-2021, 2021
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We re-estimate pan-Arctic sea ice thickness (SIT) values by combining data from the Envisat and CryoSat-2 missions with data from a new, reanalysis-driven snow model. Because a decreasing amount of ice is being hidden below the waterline by the weight of overlying snow, we argue that SIT may be declining faster than previously calculated in some regions. Because the snow product varies from year to year, our new SIT calculations also display much more year-to-year variability.
Renée Mie Fredensborg Hansen, Eero Rinne, Sinéad Louise Farrell, and Henriette Skourup
The Cryosphere, 15, 2511–2529, https://doi.org/10.5194/tc-15-2511-2021, https://doi.org/10.5194/tc-15-2511-2021, 2021
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Ice navigators rely on timely information about ice conditions to ensure safe passage through ice-covered waters, and one parameter, the degree of ice ridging (DIR), is particularly useful. We have investigated the possibility of estimating DIR from the geolocated photons of ICESat-2 (IS2) in the Bay of Bothnia, show that IS2 retrievals from different DIR areas differ significantly, and present some of the first steps in creating sea ice applications beyond e.g. thickness retrieval.
Luisa von Albedyll, Christian Haas, and Wolfgang Dierking
The Cryosphere, 15, 2167–2186, https://doi.org/10.5194/tc-15-2167-2021, https://doi.org/10.5194/tc-15-2167-2021, 2021
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Convergent sea ice motion produces a thick ice cover through ridging. We studied sea ice deformation derived from high-resolution satellite imagery and related it to the corresponding thickness change. We found that deformation explains the observed dynamic thickness change. We show that deformation can be used to model realistic ice thickness distributions. Our results revealed new relationships between thickness redistribution and deformation that could improve sea ice models.
Rasmus T. Tonboe, Vishnu Nandan, John Yackel, Stefan Kern, Leif Toudal Pedersen, and Julienne Stroeve
The Cryosphere, 15, 1811–1822, https://doi.org/10.5194/tc-15-1811-2021, https://doi.org/10.5194/tc-15-1811-2021, 2021
Short summary
Short summary
A relationship between the Ku-band radar scattering horizon and snow depth is found using a radar scattering model. This relationship has implications for (1) the use of snow climatology in the conversion of satellite radar freeboard into sea ice thickness and (2) the impact of variability in measured snow depth on the derived ice thickness. For both 1 and 2, the impact of using a snow climatology versus the actual snow depth is relatively small.
Cited articles
Aaboe, S., Sørensen, A., Eastwood, S., and Lavergne, T.: Sea ice edge and
type daily gridded data from 1978 to present derived from satellite
observations, Climate Data Store [data set], https://doi.org/10.24381/cds.29c46d83, 2020.
Aaboe, S., Down, E., and Eastwood, S.: Global Sea Ice Edge (OSI-402-d) and
Type (OSI-403-d) Validation Report, v3.1, in:
SAF/OSI/CDOP3/MET-Norway/SCI/RP/224, EUMETSAT OSISAF – Ocean and Sea Ice
Satellite Application Facility, 2021a.
Aaboe, S., Down, E., and Eastwood, S.: Algorithm Theoretical Basis Document
for the Global Sea-Ice Edge and Type, v3.4, in:
SAF/OSI/CDOP3/MET-Norway/TEC/MA/379, EUMETSAT OSISAF: Ocean and Sea Ice
Satellite Application Facility, 2021b.
Aaboe, S., Sørensen, A., Lavergne, T., and Eastwood, S.: Sea Ice Edge and
Sea Ice Type Climate Data Records Algorithm Theoretical Basis Document,
v3.1, EU C3S-Copernicus Climate Change Service, Copernicus Climate Change
Service, https://datastore.copernicus-climate.eu/documents/satellite-sea-ice-edge-type/v2.0/D1.SIETy.2-v2.0_ATBD-of-v2.0-SeaIceEdgeType-products_v3.1_APPROVED_Ver1.pdf (last access: 1 April 2022), 2021c.
Aldenhoff, W., Heuzé, C., and Eriksson, L. E. B.: Comparison of
ice/water classification in Fram Strait from C- and L-band SAR imagery,
Ann. Glaciol., 59, 112–123, https://doi.org/10.1017/aog.2018.7, 2018.
Alexandrov, V., Sandven, S., Wahlin, J., and Johannessen, O. M.: The relation between sea ice thickness and freeboard in the Arctic, The Cryosphere, 4, 373–380, https://doi.org/10.5194/tc-4-373-2010, 2010.
Andersen, S.: Evaluation of SSM/I Sea Ice Algorithms for use in the SAF on
Ocean and Sea Ice July 2000, DANISH METEOROLOGICAL INSTITUTE, https://www.dmi.dk/fileadmin/Rapporter/SR/sr00-10.pdf (last access: 1 April 2022), 2000.
Anderson, H. S. and Long, D. G.: Sea ice mapping method for SeaWinds, IEEE
T. Geosci. Remote, 43, 647–657,
https://doi.org/10.1109/TGRS.2004.842017, 2005.
Barber, D. G. and Thomas, A.: The influence of cloud cover on the radiation
budget, physical properties, and microwave scattering coefficient (/spl
sigma//spl deg/) of first-year and multiyear sea ice, IEEE T. Geosci. Remote, 36, 38–50, https://doi.org/10.1109/36.655316,
1998.
Belchansky, G. I. and Douglas, D. C.: Classification methods for monitoring
Arctic sea ice using OKEAN passive/active two-channel microwave data, Remote
Sens. Environ., 73, 307-322,
https://doi.org/10.1016/S0034-4257(00)00107-3, 2000.
Belmonte Rivas, M., Verspeek, J., Verhoef, A., and Stoffelen, A.: Bayesian
Sea Ice Detection With the Advanced Scatterometer ASCAT, IEEE T. Geosci. Remote, 50, 2649–2657,
https://doi.org/10.1109/tgrs.2011.2182356, 2012.
Belmonte Rivas, M., Otosaka, I., Stoffelen, A., and Verhoef, A.: A scatterometer record of sea ice extents and backscatter: 1992–2016, The Cryosphere, 12, 2941–2953, https://doi.org/10.5194/tc-12-2941-2018, 2018.
Belter, H. J., Krumpen, T., von Albedyll, L., Alekseeva, T. A., Birnbaum, G., Frolov, S. V., Hendricks, S., Herber, A., Polyakov, I., Raphael, I., Ricker, R., Serovetnikov, S. S., Webster, M., and Haas, C.: Interannual variability in Transpolar Drift summer sea ice thickness and potential impact of Atlantification, The Cryosphere, 15, 2575–2591, https://doi.org/10.5194/tc-15-2575-2021, 2021.
Berg, A. and Eriksson, L. E. B.: SAR Algorithm for Sea Ice
Concentration – Evaluation for the Baltic Sea, IEEE Geosci. Remote
Sens. Lett., 9, 938–942, https://doi.org/10.1109/LGRS.2012.2186280, 2012.
Bi, H. B., Liang, Y., Wang, Y. H., Liang, X., Zhang, Z. H., Du, T. Q., Yu,
Q. L., Huang, J., Kong, M., and Huang, H. J.: Arctic multiyear sea ice
variability observed from satellites: a review, J. Oceanol. Limnol., 38,
962–984, https://doi.org/10.1007/s00343-021-0382-9, 2020.
Boisvert, L. N., Wu, D. L., and Shie, C. L.: Increasing evaporation amounts
seen in the Arctic between 2003 and 2013 from AIRS data, J.
Geophys. Res.-Atmos., 120, 6865–6881,
https://doi.org/10.1002/2015JD023258, 2015.
Breivik, L.-A., Eastwood, S., and Lavergne, T.: Use of C-band scatterometer
for sea ice edge identification, IEEE T. Geosci. Remote, 50, 2669–2677, https://doi.org/10.1109/TGRS.2012.2188898, 2012.
Carsey, F.: Summer Arctic sea ice character from satellite microwave data,
J. Geophys. Res.-Oceans, 90, 5015–5034,
https://doi.org/10.1029/JC090iC03p05015, 1985.
Cavalieri, D. J., Gloersen, P., and Campbell, W. J.: Determination of sea
ice parameters with the NIMBUS 7 SMMR, J. Geophys. Res.-Atmos., 89, 5355–5369, https://doi.org/10.1029/JD089iD04p05355, 1984.
Comiso, J. C.: Sea ice effective microwave emissivities from satellite
passive microwave and infrared observations, J. Geophys.
Res.-Oceans, 88, 7686–7704, https://doi.org/10.1029/JC088iC12p07686,
1983.
Comiso, J. C., Parkinson, C. L., Gersten, R., and Stock, L.: Accelerated
decline in the Arctic sea ice cover, Geophys. Res. Lett., 35, L01703,
https://doi.org/10.1029/2007GL031972, 2008.
Dabboor, M. and Geldsetzer, T.: Towards sea ice classification using
simulated RADARSAT Constellation Mission compact polarimetric SAR imagery,
Remote Sens. Environ., 140, 189–195,
https://doi.org/10.1016/j.rse.2013.08.035, 2014.
Early, D. S. and Long, D. G.: Image reconstruction and enhanced resolution
imaging from irregular samples, IEEE T. Geosci. Remote, 39, 291–302, https://doi.org/10.1109/36.905237, 2001.
Emmerson, C. and Lahn, G.: Arctic opening: Opportunity and risk in the high north, 55, Lloyd's, http://library.arcticportal.org/1671/1/Arctic_O (last access: 1 April 2022), 2012.
Eppler, D. T., Farmer, L. D., Lohanick, A. W., Anderson, M. R., Cavalieri,
D. J., Comiso, J., Gloersen, P., Garrity, C., Grenfell, T. C., Hallikainen,
M., Maslanik, J. A., MäTzler, C., Melloh, R. A., Rubinstein, I., and
Swift, C. T.: Passive Microwave Signatures of Sea Ice, in: Microwave Remote
Sensing of Sea Ice, Geophys. Monogr. Ser., 47–71,
https://doi.org/10.1029/GM068p0047, 1992.
Ezraty, R. and Cavanie, A.: Construction and evaluation of 12.5-km grid
NSCAT backscatter maps over Arctic sea ice, IEEE T. Geosci. Remote, 37, 1685–1697, https://doi.org/10.1109/36.763289, 1999.
Ezraty, R. and Cavanié, A.: Intercomparison of backscatter maps over
Arctic sea ice from NSCAT and the ERS scatterometer, J. Geophys.
Res.-Oceans, 104, 11471–11483, https://doi.org/10.1029/1998JC900086,
1999.
Fowler, C., Emery, W. J., and Maslanik, J.: Satellite-derived evolution of
Arctic sea ice age: October 1978 to March 2003, IEEE Geosci. Remote
Sens. Lett., 1, 71–74, https://doi.org/10.1109/LGRS.2004.824741, 2004.
Girard-Ardhuin, F.: Multi-year Arctic sea ice extent estimate from
scatterometers onboard satellite since 2000, AGU Fall meeting, San
Francisco, CA, USA, 12–16 December, C42B-07, 2016.
Gloersen, P. and Cavalieri, D. J.: Reduction of weather effects in the
calculation of sea ice concentration from microwave radiances, J.
Geophys. Res.-Oceans, 91, 3913–3919,
https://doi.org/10.1029/JC091iC03p03913, 1986.
Gray, A., Hawkins, R., Livingstone, C., Arsenault, L., and Johnstone, W.:
Simultaneous scatterometer and radiometer measurements of sea-ice microwave
signatures, IEEE J. Ocean. Eng., 7, 20–32,
https://doi.org/10.1109/JOE.1982.1145506, 1982.
Hakkinen, S., Proshutinsky, A., and Ashik, I.: Sea ice drift in the Arctic
since the 1950s, Geophys. Res. Lett., 35, L19704,
https://doi.org/10.1029/2008GL034791, 2008.
Hallikainen, M. and Winebrenner, D. P.: The physical basis for sea ice
remote sensing, in: Microwave remote sensing of sea ice, edited by: Carsey,
F., American Geophysical Union, 29–46, https://doi.org/10.1029/GM068p0029, 1992.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A.,
Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I.,
Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5
hourly data on single levels from 1979 to present, Climate Data Store [data set],
https://doi.org/10.24381/cds.adbb2d47, 2018.
Holmes, Q. A., Nuesch, D. R., and Shuchman, R. A.: Textural Analysis And
Real-Time Classification of Sea-Ice Types Using Digital SAR Data, IEEE
T. Geosci. Remote, GE-22, 113–120,
https://doi.org/10.1109/TGRS.1984.350602, 1984.
IMarEST: Safety & Sustainability of Shipping and Offshore Activities in
the Arctic, Institute of Marine Engineering, Science & Technology,
IMarEST Report, London International Shipping Week, 2015.
Jung, T., Kasper, M. A., Semmler, T., and Serrar, S.: Arctic influence on
subseasonal midlatitude prediction, Geophys. Res. Lett., 41,
3676–3680, https://doi.org/10.1002/2014GL059961, 2014.
Kern, S., Rösel, A., Pedersen, L. T., Ivanova, N., Saldo, R., and Tonboe, R. T.: The impact of melt ponds on summertime microwave brightness temperatures and sea-ice concentrations, The Cryosphere, 10, 2217–2239, https://doi.org/10.5194/tc-10-2217-2016, 2016.
Kilic, L., Prigent, C., Aires, F., Boutin, J., Heygster, G., Tonboe, R. T.,
Roquet, H., Jimenez, C., and Donlon, C.: Expected Performances of the
Copernicus Imaging Microwave Radiometer (CIMR) for an All-Weather and High
Spatial Resolution Estimation of Ocean and Sea Ice Parameters, J.
Geophys. Res.-Oceans, 123, 7564–7580,
https://doi.org/10.1029/2018JC014408, 2018.
Kim, Y. S., Moore, R. K., Onstott, R. G., and Gogineni, S.: Towards
Identification of Optimum Radar Parameters for Sea-Ice Monitoring, J. Glaciol., 31, 214–219, https://doi.org/10.3189/S0022143000006523, 1985.
Korosov, A. A., Rampal, P., Pedersen, L. T., Saldo, R., Ye, Y., Heygster, G., Lavergne, T., Aaboe, S., and Girard-Ardhuin, F.: A new tracking algorithm for sea ice age distribution estimation, The Cryosphere, 12, 2073–2085, https://doi.org/10.5194/tc-12-2073-2018, 2018.
Kuang, H., Luo, Y., Ye, Y., Shokr, M., Chen, Z., Wang, S., Hui, F., Bi, H.,
and Cheng, X.: Arctic Multiyear Ice Areal Flux and Its Connection with
Large-Scale Atmospheric Circulations in the Winters of 2002–2021, Remote
Sens., 14, 3742, https://doi.org/10.3390/rs14153742, 2022.
Kwok, R.: Annual cycles of multiyear sea ice coverage of the Arctic Ocean:
1999–2003, J. Geophys. Res., 109, C11004,
https://doi.org/10.1029/2003JC002238, 2004.
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.
Kwok, R. and Cunningham, G. F.: Variability of Arctic sea ice thickness and
volume from CryoSat-2, Philosophical Transactions of the Royal Society A:
Mathematical, Phys. Eng. Sci., 373, 20140157,
https://doi.org/10.1098/rsta.2014.0157, 2015.
Kwok, R., Cunningham, G. F., and Yueh, S.: Area balance of the Arctic Ocean
perennial ice zone: October 1996 to April 1997, J. Geophys.
Res.-Oceans, 104, 25747–25759, https://doi.org/10.1029/1999JC900234,
1999.
Kwok, R., Spreen, G., and Pang, S.: Arctic sea ice circulation and drift
speed: Decadal trends and ocean currents, J. Geophys. Res.-Oceans, 118, 2408–2425, https://doi.org/10.1002/jgrc.20191, 2013.
Lavergne, T., Sørensen, A., Tonboe, R. T., Saldo, R., Pedersen, L. T.,
Strong, C., Cherkaev, E., Golden, K. M., and Eastwood, S.: Algorithm
Theoretical Basis Document for the Global Sea-Ice Concentration Climate Data
Records, v3.0, In SAF/OSI/CDOP3/DMI_Met/SCI/MA/270, EUMETSAT
OSISAF – Ocean and Sea Ice Satellite Application Facility, https://osisaf-hl.met.no/sites/osisaf-hl/files/baseline_document/osisaf_cdop3_ss2_atbd_sea-ice-conc-climate-data-record_v3p0.pdf (last access: 1 April 2022), 2022.
Lee, S.-M., Sohn, B.-J., and Kim, S.-J.: Differentiating between first-year
and multiyear sea ice in the Arctic using microwave-retrieved ice
emissivities, J. Geophys. Res.-Atmos., 122, 5097–5112,
https://doi.org/10.1002/2016JD026275, 2017.
Lindell, D. B. and Long, D.: Multiyear Arctic Ice Classification Using ASCAT
and SSMIS, Remote Sens., 8, 294, https://doi.org/10.3390/rs8040294, 2016a.
Lindell, D. B. and Long, D. G.: Multiyear Arctic Sea Ice Classification
Using OSCAT and QuikSCAT, IEEE T. Geosci. Remote, 54, 167–175, https://doi.org/10.1109/tgrs.2015.2452215, 2016b.
Liu, J., Curry, J. A., Wang, H., Song, M., and Horton, R. M.: Impact of
declining Arctic sea ice on winter snowfall, P. Natl.
Acad. Sci. USA, 109, 4074–4079,
https://doi.org/10.1073/pnas.1114910109, 2012.
Liu, Q., Babanin, A. V., Zieger, S., Young, I. R., and Guan, C.: Wind and
Wave Climate in the Arctic Ocean as Observed by Altimeters, J.
Climate, 29, 7957–7975, https://doi.org/10.1175/JCLI-D-16-0219.1, 2016.
Lomax, A. S., Lubin, D., and Whritner, R. H.: The potential for interpreting
total and multiyear ice concentrations in SSM/I 85.5 GHz imagery, Remote
Sens. Environ., 54, 13–26,
https://doi.org/10.1016/0034-4257(95)00082-C, 1995.
Long, D. G., Hardin, P. J., and Whiting, P. T.: Resolution enhancement of
spaceborne scatterometer data, IEEE T. Geosci. Remote, 31, 700–715, https://doi.org/10.1109/36.225536, 1993.
Maaß, N. and Kaleschke, L.: Improving passive microwave sea ice
concentration algorithms for coastal areas: applications to the Baltic Sea,
Tellus A, 62, 393–410, https://doi.org/10.1111/j.1600-0870.2010.00452.x,
2010.
Maslanik, J., Stroeve, J., Fowler, C., and Emery, W.: Distribution and
trends in Arctic sea ice age through spring 2011, Geophys. Res. Lett., 38, L13502, https://doi.org/10.1029/2011GL047735, 2011.
Maslanik, J. A., Fowler, C., Stroeve, J., Drobot, S., Zwally, J., Yi, D.,
and Emery, W.: A younger, thinner Arctic ice cover: Increased potential for
rapid, extensive sea-ice loss, Geophys. Res. Lett., 34, L24501,
https://doi.org/10.1029/2007GL032043, 2007.
Meier, W. N., Hovelsrud, G. K., Van Oort, B. E. H., Key, J. R., Kovacs, K.
M., Michel, C., Haas, C., Granskog, M. A., Gerland, S., Perovich, D. K.,
Makshtas, A., and Reist, J. D.: Arctic sea ice in transformation: A review
of recent observed changes and impacts on biology and human activity,
Rev. Geophys., 52, 185–217, https://doi.org/10.1002/2013rg000431,
2014.
Onarheim, I. H., Eldevik, T., Smedsrud, L. H., and Stroeve, J. C.: Seasonal
and Regional Manifestation of Arctic Sea Ice Loss, J. Climate, 31,
4917–4932, https://doi.org/10.1175/jcli-d-17-0427.1, 2018.
Onstott, R. G.: SAR and Scatterometer Signatures of Sea Ice, in: Microwave
Remote Sensing of Sea Ice, Geophys. Monogr. Ser., 73–104,
https://doi.org/10.1029/GM068p0073, 1992.
Onstott, R. G., Moore, R. K., and Weeks, W. F.: Surface-Based Scatterometer
Results of Arctic Sea Ice, IEEE T. Geosci. Electron., 17,
78–85, https://doi.org/10.1109/TGE.1979.294616, 1979.
Perovich, D., Meier, W., Tschudi, M., Hendricks, S., Petty, A. A., Divine,
D., Farrell, S., Gerland, S., Haas, C., Kaleschke, L., Pavlova, O., Ricker,
R., Tian-Kunze, X., Webster, M., and Wood, K.: Sea ice, in: Arctic report card 2020, NOAA, https://doi.org/10.25923/n170-9h57, 2020.
Perovich, D., Meier, W., Tschudi, M., Farrell, S., Hendricks, S., Gerland,
S., Kaleschke, L., Ricker, R., Tian-Kunze, X., Webster, M., and Wood, K.:
Sea ice, in: Arctic Report Card 2021, NOAA,
https://doi.org/10.25923/y2wd-fn85, 2021.
Petty, A. A., Kurtz, N. T., Kwok, R., Markus, T., and Neumann, T. A.: Winter
Arctic sea ice thickness from ICESat-2 freeboards, J. Geophys.
Res.-Oceans, 125, e2019JC015764, https://doi.org/10.1029/2019JC015764,
2020.
Raphael, M. N. and Handcock, M. S.: A new record minimum for Antarctic sea
ice, Nat. Rev. Earth Environ., 3, 215–216,
https://doi.org/10.1038/s43017-022-00281-0, 2022.
Rostosky, P., Spreen, G., Farrell, S. L., Frost, T., Heygster, G., and
Melsheimer, C.: Snow Depth Retrieval on Arctic Sea Ice From Passive
Microwave RadiometersImprovements and Extensions to Multiyear Ice Using
Lower Frequencies, J. Geophys. Res.-Oceans, 123, 7120–7138,
https://doi.org/10.1029/2018jc014028, 2018.
Rothrock, D. A., Percival, D. B., and Wensnahan, M.: The decline in arctic
sea-ice thickness: Separating the spatial, annual, and interannual
variability in a quarter century of submarine data, J. Geophys.
Res.-Oceans, 113, C05003, https://doi.org/10.1029/2007JC004252, 2008.
Screen, J. A., Simmonds, I., Deser, C., and Tomas, R.: The atmospheric
response to three decades of observed Arctic sea ice loss, J.
Climate, 26, 1230–1248, https://doi.org/10.1175/JCLI-D-12-00063.1, 2013.
Shokr, M. E.: Field observations and model calculations of dielectric
properties of Arctic sea ice in the microwave C-band, IEEE T. Geosci. Remote, 36, 463–478,
https://doi.org/10.1109/36.662730, 1998.
Shokr, M. and Agnew, T. A.: Validation and potential applications of
Environment Canada Ice Concentration Extractor (ECICE) algorithm to Arctic
ice by combining AMSR-E and QuikSCAT observations, Remote Sens.
Environ., 128, 315–332, https://doi.org/10.1016/j.rse.2012.10.016, 2013.
Shokr, M. and Sinha, N. K.: Sea ice: physics and remote sensing, John Wiley
& Sons, American Geophysical Union, ISBN 978-1-119-02789-8, 2015.
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/10.1109/TGRS.2008.2000624, 2008.
Song, W., Li, M., Gao, W., Huang, D., Ma, Z., Liotta, A., and Perra, C.:
Automatic Sea-Ice Classification of SAR Images Based on Spatial and Temporal
Features Learning, IEEE T. Geosci. Remote, 59,
9887–9901, https://doi.org/10.1109/TGRS.2020.3049031, 2021.
Sumata, H., Lavergne, T., Girard-Ardhuin, F., Kimura, N., Tschudi, M. A.,
Kauker, F., Karcher, M., and Gerdes, R.: An intercomparison of Arctic ice
drift products to deduce uncertainty estimates, J. Geophys.
Res.-Oceans, 119, 4887–4921, https://doi.org/10.1002/2013JC009724,
2014.
Sun, Y. and Li, X.-M.: Denoising Sentinel-1 Extra-Wide Mode
Cross-Polarization Images Over Sea Ice, IEEE T. Geosci. Remote,
59, 2116–2131, https://doi.org/10.1109/TGRS.2020.3005831, 2021.
Swan, A. M. and Long, D. G.: Multiyear Arctic Sea Ice Classification Using
QuikSCAT, IEEE T. Geosci. Remote, 50, 3317–3326,
https://doi.org/10.1109/tgrs.2012.2184123, 2012.
Szanyi, S., Lukovich, J. V., Barber, D. G., and Haller, G.: Persistent
artifacts in the NSIDC ice motion data set and their implications for
analysis, Geophys. Res. Lett., 43, 10800–10807,
https://doi.org/10.1002/2016GL069799, 2016.
Tschudi, M. A., Stroeve, J. C., and Stewart, J. S.: Relating the Age of
Arctic Sea Ice to its Thickness, as Measured during NASA's ICESat and
IceBridge Campaigns, Remote Sens., 8, 457, https://doi.org/10.3390/rs8060457,
2016.
Tschudi, M. A., Meier, W. N., and Stewart, J. S.: An enhancement to sea ice motion and age products at the National Snow and Ice Data Center (NSIDC), The Cryosphere, 14, 1519–1536, https://doi.org/10.5194/tc-14-1519-2020, 2020.
Tschudi, M., Meier, W. N., Stewart, J. S., Fowler, C., and Maslanik, J.: EASE-Grid Sea Ice Age, Version 4, Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/UTAV7490FEPB, 2019.
Vant, M. R., Ramseier, R. O., and Makios, V.: The complex-dielectric
constant of sea ice at frequencies in the range 0.1–40 GHz, J.
Appl. Phys., 49, 1264–1280, https://doi.org/10.1063/1.325018, 1978.
Voss, S., Heygster, G., and Ezraty, R.: Improving sea ice type
discrimination by the simultaneous use of SSM/I and scatterometer data,
Polar Res., 22, 35–42,
https://doi.org/10.1111/j.1751-8369.2003.tb00093.x, 2003.
Walker, N. P., Partington, K. C., Woert, M. L. V., and Street, T. L. T.:
Arctic Sea Ice Type and Concentration Mapping Using Passive and Active
Microwave Sensors, IEEE T. Geosci. Remote, 44,
3574–3584, https://doi.org/10.1109/TGRS.2006.881116, 2006.
Weeks, W. F. and Ackley, S. F.: The Growth, Structure, and Properties of Sea
Ice, in: The Geophysics of Sea Ice, edited by: Untersteiner, N., Springer
US, Boston, MA, 9–164,
https://doi.org/10.1007/978-1-4899-5352-0_2, 1986.
Wentz, F. J.: A well-calibrated ocean algorithm for special sensor microwave/imager, J. Geophys. Res.-Oceans, 102, 8703–8718,
https://doi.org/10.1029/96JC01751, 1997.
Xu, R., Zhao, C., Zhai, X., and Chen, G.: Arctic Sea Ice Type Classification
by Combining CFOSCAT and AMSR-2 Data, Earth Space Sci., 9,
e2021EA002052, https://doi.org/10.1029/2021EA002052, 2022.
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.
Ye, Y., Shokr, M., Heygster, G., and Spreen, G.: Improving multiyear sea ice
concentration estimates with sea ice drift, Remote Sens., 8, 397,
https://doi.org/10.3390/rs8050397, 2016b.
Ye, Y., Shokr, M., Aaboe, S., Aldenhoff, W., Eriksson, L. E. B., Heygster, G., Melsheimer, C., and Girard-Ardhuin, F.: Inter-comparison and evaluation of sea ice type concentration algorithms, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2019-200, 2019.
Yu, P., Clausi, D. A., and Howell, S.: Fusing AMSR-E and QuikSCAT Imagery
for Improved Sea Ice Recognition, IEEE T. Geosci. Remote, 47, 1980–1989, https://doi.org/10.1109/tgrs.2009.2013632, 2009.
Zhang, Z., Yu, Y., Li, X., Hui, F., Cheng, X., and Chen, Z.: Arctic Sea Ice
Classification Using Microwave Scatterometer and Radiometer Data During
2002–2017, IEEE T. Geosci. Remote, 57,
5319-5328, https://doi.org/10.1109/TGRS.2019.2898872, 2019.
Zhang, Z., Yu, Y., Shokr, M., Li, X., Ye, Y., Cheng, X., Chen, Z., and Hui,
F.: Intercomparison of Arctic Sea Ice Backscatter and Ice Type
Classification Using Ku-Band and C-Band Scatterometers, IEEE T. Geosci. Remote, 1–18,
https://doi.org/10.1109/TGRS.2021.3099835, 2021.
Short summary
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.
Arctic sea ice type (SITY) variation is a sensitive indicator of climate change. This study...
