Articles | Volume 17, issue 2
https://doi.org/10.5194/essd-17-423-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-423-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
05 Feb 2025
Data description paper |
| 05 Feb 2025
| 05 Feb 2025
A sea ice deformation and rotation rate dataset (2017–2023) from the Environment and Climate Change Canada automated sea ice tracking system (ECCC-ASITS)
Recherche en prévision numérique environnementale, Environment and Climate Change Canada, Dorval, Quebec, Canada
Jean-François Lemieux
Recherche en prévision numérique environnementale, Environment and Climate Change Canada, Dorval, Quebec, Canada
L. Bruno Tremblay
Department of Atmospheric and Oceanic Sciences, McGill University, Montréal, Quebec, Canada
Amélie Bouchat
Department of Atmospheric and Oceanic Sciences, McGill University, Montréal, Quebec, Canada
Damien Ringeisen
Department of Atmospheric and Oceanic Sciences, McGill University, Montréal, Quebec, Canada
NASA Goddard Institute for Space Studies, Columbia University, New York, NY, USA
Philippe Blain
Service Météorologique Canadien, Environment and Climate Change Canada, Dorval, Quebec, Canada
Stephen Howell
Climate Research Division, Environment and Climate Change Canada, Toronto, Canada
Mike Brady
Climate Research Division, Environment and Climate Change Canada, Toronto, Canada
Alexander S. Komarov
Meteorological Research Division, Environment and Climate Change Canada, Ottawa, Canada
Béatrice Duval
Department of Atmospheric and Oceanic Sciences, McGill University, Montréal, Quebec, Canada
Lekima Yakuden
Department of Atmospheric and Oceanic Sciences, McGill University, Montréal, Quebec, Canada
Frédérique Labelle
Recherche en prévision numérique environnementale, Environment and Climate Change Canada, Dorval, Quebec, Canada
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Climate warming could enhance the load of terrigenous dissolved organic carbon (tDOC) of Arctic rivers. We show that tDOC concentrations simulated by an ocean–biogeochemical model in the Canadian Beaufort Sea compare favorably with their satellite counterparts. Over spring–summer, riverine tDOC contributes to 35 % of primary production and an equivalent of ~ 10 % of tDOC is exported westwards with the potential for fueling the biological production of the eastern Alaskan nearshore waters.
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.
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.
Stephen E. L. Howell, Frédéric Laliberté, Ron Kwok, Chris Derksen, and Joshua King
The Cryosphere, 10, 1463–1475, https://doi.org/10.5194/tc-10-1463-2016, https://doi.org/10.5194/tc-10-1463-2016, 2016
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The Canadian Ice Service record of observed landfast ice and snow thickness represents one of the longest in the Arctic that spans over 5 decades. We analyze this record to report on long-term trends and variability of ice and snow thickness within the Canadian Arctic Archipelago (CAA). Results indicate a thinning of ice at several sites in the CAA. State-of-the-art climate models still have difficultly capturing observed ice thickness values in the CAA and should be used with caution.
F. Dupont, S. Higginson, R. Bourdallé-Badie, Y. Lu, F. Roy, G. C. Smith, J.-F. Lemieux, G. Garric, and F. Davidson
Geosci. Model Dev., 8, 1577–1594, https://doi.org/10.5194/gmd-8-1577-2015, https://doi.org/10.5194/gmd-8-1577-2015, 2015
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1/12th degree resolution runs of Arctic--Atlantic were compared for the period 2003-2009. We found good representation of sea surface height and of its statistics; model temperature and salinity in general agreement with in situ measurements, but upper ocean properties in Beaufort Sea are challenging; distribution of concentration and volume of sea ice is improved when slowing down the ice and further improvements require better initial conditions and modifications to mixing.
S. E. L. Howell, T. Wohlleben, A. Komarov, L. Pizzolato, and C. Derksen
The Cryosphere, 7, 1753–1768, https://doi.org/10.5194/tc-7-1753-2013, https://doi.org/10.5194/tc-7-1753-2013, 2013
Related subject area
Domain: ESSD – Ice | Subject: Snow and Sea Ice
An Arctic sea ice concentration data record on a 6.25 km polar stereographic grid from 3 years of Landsat-8 imagery
Time series of alpine snow surface radiative-temperature maps from high-precision thermal-infrared imaging
Operational and experimental snow observation systems in the upper Rofental: data from 2017 to 2023
SMOS-derived Antarctic thin sea ice thickness: data description and validation in the Weddell Sea
A 12-year climate record of wintertime wave-affected marginal ice zones in the Atlantic Arctic based on CryoSat-2
MODIS daily cloud-gap-filled fractional snow cover dataset of the Asian Water Tower region (2000–2022)
Mapping of sea ice concentration using the NASA NIMBUS 5 Electrically Scanning Microwave Radiometer data from 1972–1977
A climate data record of year-round global sea-ice drift from the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF)
Snow accumulation and ablation measurements in a midlatitude mountain coniferous forest (Col de Porte, France, 1325 m altitude): the Snow Under Forest (SnoUF) field campaign data set
A new sea ice concentration product in the polar regions derived from the FengYun-3 MWRI sensors
NH-SWE: Northern Hemisphere Snow Water Equivalent dataset based on in situ snow depth time series
IT-SNOW: a snow reanalysis for Italy blending modeling, in situ data, and satellite observations (2010–2021)
HMRFS–TP: long-term daily gap-free snow cover products over the Tibetan Plateau from 2002 to 2021 based on hidden Markov random field model
Hee-Sung Jung, Sang-Moo Lee, Joo-Hong Kim, and Kyungsoo Lee
Earth Syst. Sci. Data, 17, 233–258, https://doi.org/10.5194/essd-17-233-2025, https://doi.org/10.5194/essd-17-233-2025, 2025
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This dataset consists of reference sea ice concentration (SIC) data records over the Arctic Ocean, which were derived from the 30 m resolution imagery from the Operational Land Imager (OLI) on board Landsat-8. Each SIC map is given in a 6.25 km polar stereographic grid and is catalogued into one of the 12 regions of the Arctic Ocean. This dataset was produced to be used as a reference in the validation of various SIC products.
Sara Arioli, Ghislain Picard, Laurent Arnaud, Simon Gascoin, Esteban Alonso-González, Marine Poizat, and Mark Irvine
Earth Syst. Sci. Data, 16, 3913–3934, https://doi.org/10.5194/essd-16-3913-2024, https://doi.org/10.5194/essd-16-3913-2024, 2024
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High-accuracy precision maps of the surface temperature of snow were acquired with an uncooled thermal-infrared camera during winter 2021–2022 and spring 2023. The accuracy – i.e., mean absolute error – improved from 1.28 K to 0.67 K between the seasons thanks to an improved camera setup and temperature stabilization. The dataset represents a major advance in the validation of satellite measurements and physical snow models over a complex topography.
Michael Warscher, Thomas Marke, Erwin Rottler, and Ulrich Strasser
Earth Syst. Sci. Data, 16, 3579–3599, https://doi.org/10.5194/essd-16-3579-2024, https://doi.org/10.5194/essd-16-3579-2024, 2024
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Continuous observations of snow and climate at high altitudes are still sparse. We present a unique collection of weather and snow cover data from three automatic weather stations at remote locations in the Ötztal Alps (Austria) that include continuous recordings of snow cover properties. The data are available over multiple winter seasons and enable new insights for snow hydrological research. The data are also used in operational applications, i.e., for avalanche warning and flood forecasting.
Lars Kaleschke, Xiangshan Tian-Kunze, Stefan Hendricks, and Robert Ricker
Earth Syst. Sci. Data, 16, 3149–3170, https://doi.org/10.5194/essd-16-3149-2024, https://doi.org/10.5194/essd-16-3149-2024, 2024
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We describe a sea ice thickness dataset based on SMOS satellite measurements, initially designed for the Arctic but adapted for Antarctica. We validated it using limited Antarctic measurements. Our findings show promising results, with a small difference in thickness estimation and a strong correlation with validation data within the valid thickness range. However, improvements and synergies with other sensors are needed, especially for sea ice thicker than 1 m.
Weixin Zhu, Siqi Liu, Shiming Xu, and Lu Zhou
Earth Syst. Sci. Data, 16, 2917–2940, https://doi.org/10.5194/essd-16-2917-2024, https://doi.org/10.5194/essd-16-2917-2024, 2024
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In the polar ocean, wind waves generate and propagate into the sea ice cover, forming marginal ice zones (MIZs). Using ESA's CryoSat-2, we construct a 12-year dataset of the MIZ in the Atlantic Arctic, a key region for climate change and human activities. The dataset is validated with high-resolution observations by ICESat2 and Sentinel-1. MIZs over 300 km wide are found under storms in the Barents Sea. The new dataset serves as the basis for research areas, including wave–ice interactions.
Fangbo Pan, Lingmei Jiang, Gongxue Wang, Jinmei Pan, Jinyu Huang, Cheng Zhang, Huizhen Cui, Jianwei Yang, Zhaojun Zheng, Shengli Wu, and Jiancheng Shi
Earth Syst. Sci. Data, 16, 2501–2523, https://doi.org/10.5194/essd-16-2501-2024, https://doi.org/10.5194/essd-16-2501-2024, 2024
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It is important to strengthen the continuous monitoring of snow cover as a key indicator of imbalance in the Asian Water Tower (AWT) region. We generate long-term daily gap-free fractional snow cover products over the AWT at 0.005° resolution from 2000 to 2022 based on the multiple-endmember spectral mixture analysis algorithm and the gap-filling algorithm. They can provide highly accurate, quantitative fractional snow cover information for subsequent studies on hydrology and climate.
Wiebke Margitta Kolbe, Rasmus T. Tonboe, and Julienne Stroeve
Earth Syst. Sci. Data, 16, 1247–1264, https://doi.org/10.5194/essd-16-1247-2024, https://doi.org/10.5194/essd-16-1247-2024, 2024
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Current satellite-based sea-ice climate data records (CDRs) usually begin in October 1978 with the first multichannel microwave radiometer data. Here, we present a sea ice dataset based on the single-channel Electrical Scanning Microwave Radiometer (ESMR) that operated from 1972-1977 onboard NASA’s Nimbus 5 satellite. The data were processed using modern methods and include uncertainty estimations in order to provide an important, easy-to-use reference period of good quality for current CDRs.
Thomas Lavergne and Emily Down
Earth Syst. Sci. Data, 15, 5807–5834, https://doi.org/10.5194/essd-15-5807-2023, https://doi.org/10.5194/essd-15-5807-2023, 2023
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Sea ice in the Arctic and Antarctic can move several tens of kilometers per day due to wind and ocean currents. By analysing thousands of satellite images, we measured how sea ice has been moving every single day from 1991 through to 2020. We compare our data to how buoys attached to the ice moved and find good agreement. Other scientists will now use our data to better understand if climate change has modified the way sea ice moves and in what way.
Jean Emmanuel Sicart, Victor Ramseyer, Ghislain Picard, Laurent Arnaud, Catherine Coulaud, Guilhem Freche, Damien Soubeyrand, Yves Lejeune, Marie Dumont, Isabelle Gouttevin, Erwan Le Gac, Frédéric Berger, Jean-Matthieu Monnet, Laurent Borgniet, Éric Mermin, Nick Rutter, Clare Webster, and Richard Essery
Earth Syst. Sci. Data, 15, 5121–5133, https://doi.org/10.5194/essd-15-5121-2023, https://doi.org/10.5194/essd-15-5121-2023, 2023
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Forests strongly modify the accumulation, metamorphism and melting of snow in midlatitude and high-latitude regions. Two field campaigns during the winters 2016–17 and 2017–18 were conducted in a coniferous forest in the French Alps to study interactions between snow and vegetation. This paper presents the field site, instrumentation and collection methods. The observations include forest characteristics, meteorology, snow cover and snow interception by the canopy during precipitation events.
Ying Chen, Ruibo Lei, Xi Zhao, Shengli Wu, Yue Liu, Pei Fan, Qing Ji, Peng Zhang, and Xiaoping Pang
Earth Syst. Sci. Data, 15, 3223–3242, https://doi.org/10.5194/essd-15-3223-2023, https://doi.org/10.5194/essd-15-3223-2023, 2023
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The sea ice concentration product derived from the Microwave Radiation Image sensors on board the FengYun-3 satellites can reasonably and independently identify the seasonal and long-term changes of sea ice, as well as extreme cases of annual maximum and minimum sea ice extent in polar regions. It is comparable with other sea ice concentration products and applied to the studies of climate and marine environment.
Adrià Fontrodona-Bach, Bettina Schaefli, Ross Woods, Adriaan J. Teuling, and Joshua R. Larsen
Earth Syst. Sci. Data, 15, 2577–2599, https://doi.org/10.5194/essd-15-2577-2023, https://doi.org/10.5194/essd-15-2577-2023, 2023
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We provide a dataset of snow water equivalent, the depth of liquid water that results from melting a given depth of snow. The dataset contains 11 071 sites over the Northern Hemisphere, spans the period 1950–2022, and is based on daily observations of snow depth on the ground and a model. The dataset fills a lack of accessible historical ground snow data, and it can be used for a variety of applications such as the impact of climate change on global and regional snow and water resources.
Francesco Avanzi, Simone Gabellani, Fabio Delogu, Francesco Silvestro, Flavio Pignone, Giulia Bruno, Luca Pulvirenti, Giuseppe Squicciarino, Elisabetta Fiori, Lauro Rossi, Silvia Puca, Alexander Toniazzo, Pietro Giordano, Marco Falzacappa, Sara Ratto, Hervè Stevenin, Antonio Cardillo, Matteo Fioletti, Orietta Cazzuli, Edoardo Cremonese, Umberto Morra di Cella, and Luca Ferraris
Earth Syst. Sci. Data, 15, 639–660, https://doi.org/10.5194/essd-15-639-2023, https://doi.org/10.5194/essd-15-639-2023, 2023
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Snow cover has profound implications for worldwide water supply and security, but knowledge of its amount and distribution across the landscape is still elusive. We present IT-SNOW, a reanalysis comprising daily maps of snow amount and distribution across Italy for 11 snow seasons from September 2010 to August 2021. The reanalysis was validated using satellite images and snow measurements and will provide highly needed data to manage snow water resources in a warming climate.
Yan Huang, Jiahui Xu, Jingyi Xu, Yelei Zhao, Bailang Yu, Hongxing Liu, Shujie Wang, Wanjia Xu, Jianping Wu, and Zhaojun Zheng
Earth Syst. Sci. Data, 14, 4445–4462, https://doi.org/10.5194/essd-14-4445-2022, https://doi.org/10.5194/essd-14-4445-2022, 2022
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Reliable snow cover information is important for understating climate change and hydrological cycling. We generate long-term daily gap-free snow products over the Tibetan Plateau (TP) at 500 m resolution from 2002 to 2021 based on the hidden Markov random field model. The accuracy is 91.36 %, and is especially improved during snow transitional period and over complex terrains. This dataset has great potential to study climate change and to facilitate water resource management in the TP.
Cited articles
Babb, D. G., Kirillov, S., Galley, R. J., Straneo, F., Ehn, J. K., Howell, S. E. L., Brady, M., Ridenour, N. A., and Barber, D. G.: Sea ice dynamics in Hudson Strait and its impact on winter shipping operations, J. Geophys. Res.-Oceans, 126, e2021JC018024, https://doi.org/10.1029/2021JC018024, 2021. a
Bouchat, A. and Tremblay, B.: Using sea-ice deformation fields to constrain the mechanical strength parameters of geophysical sea ice, J. Geophys. Res.-Oceans, 122, 5802–5825, https://doi.org/10.1002/2017JC013020, 2017. a, b
Bouchat, A. and Tremblay, B.: Reassessing the quality of sea-ice deformation estimates derived from the RADARSAT geophysical processor system and its impact on the spatiotemporal scaling statistics, J. Geophys. Res.-Oceans, 125, e2019JC015944, https://doi.org/10.1029/2019JC015944, 2020. a, b, c, d, e
Bouchat, A., Hutter, N., Chanut, J., Dupont, F., Dukhovskoy, D., Garric, G., Lee, Y. J., Lemieux, J.-F., Lique, C., Losch, M., Maslowski, W., Myers, P. G., Ólason, E., Rampal, P., Rasmussen, T., Talandier, C., Tremblay, B., and Wang, Q.: Sea Ice Rheology Experiment (SIREx): 1. Scaling and statistical properties of sea-ice deformation fields, J. Geophys. Res.-Oceans, 127, e2021JC017667, https://doi.org/10.1029/2021JC017667, 2022. a, b, c
Bouillon, S. and Rampal, P.: On producing sea ice deformation data sets from SAR-derived sea ice motion, The Cryosphere, 9, 663–673, https://doi.org/10.5194/tc-9-663-2015, 2015. a, b, c
Brady, M. and Howell, S. E. L.: RADARSAT Constellation Mission / Sentinel-1 Sea Ice Motion (RCMS1SIM), https://catalogue.ec.gc.ca/geonetwork/srv/api/records/09ab22c4-e211-4d23-afe6-9e7aa6831c62 (last access: December 2024), 2021. a
Chen, J., Kang, S., You, Q., Zhang, Y., and Du, W.: Projected changes in sea ice and the navigability of the Arctic passages under global warming of 2 °C and 3 °C, Anthropocene, 40, 100349, https://doi.org/10.1016/j.ancene.2022.100349, 2022. a
Dawson, J., Pizzolato, L., Howell, S. E., Copland, L., and Johnston, M. E.: Temporal and spatial patterns of ship traffic in the Canadian Arctic from 1990 to 2015, Arctic, 71, 15–26, 2018. a
Dierking, W., Stern, H. L., and Hutchings, J. K.: Estimating statistical errors in retrievals of ice velocity and deformation parameters from satellite images and buoy arrays, The Cryosphere, 14, 2999–3016, https://doi.org/10.5194/tc-14-2999-2020, 2020. a
Fetterer, F., Knowles, K., Meier, W. N., Savoie, M., and Windnagel, A. K.: Sea Ice Index. (G02135, Version 3), Boulder, Colorado USA, National Snow and Ice Data Center [data set], https://doi.org/10.7265/N5K072F8, 2017. a
Griebel, J. and Dierking, W.: Impact of sea ice drift retrieval errors, discretization and grid type on calculations of ice deformation, Remote Sens.-Basel, 10, 393, https://doi.org/10.3390/rs10030393, 2018. a, b
Heil, P., Lytle, V. I., and Allison, I.: Enhanced thermodynamic ice growth by sea-ice deformation, Ann. Glaciol., 27, 433–437, https://doi.org/10.3189/1998AoG27-1-433-437, 1998. a
Howell, S. E. L., Brady, M., and Komarov, A. S.: 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, The Cryosphere, 16, 1125–1139, https://doi.org/10.5194/tc-16-1125-2022, 2022. a, b, c, d, e, f, g
Hutchings, J. K., Roberts, A., Geiger, C. A., and Richter-Menge, J.: Spatial and temporal characterization of sea-ice deformation, Ann. Glaciol., 52, 360–368, https://doi.org/10.3189/172756411795931769, 2011. a, b
Hutchings, J. K., Heil, P., Steer, A., and Hibler III, W. D.: Subsynoptic scale spatial variability of sea ice deformation in the western Weddell Sea during early summer, J. Geophys. Res.-Oceans, 117, C01002, https://doi.org/10.1029/2011JC006961, 2012. a
Hutter, N. and Losch, M.: Feature-based comparison of sea ice deformation in lead-permitting sea ice simulations, The Cryosphere, 14, 93–113, https://doi.org/10.5194/tc-14-93-2020, 2020. a
Hutter, N., Losch, M., and Menemenlis, D.: Scaling properties of Arctic sea ice deformation in a high-resolution viscous-plastic sea ice model and in satellite observations, J. Geophys. Res.-Oceans, 123, 672–687, https://doi.org/10.1002/2017JC013119, 2018. a
Hutter, N., Bouchat, A., Dupont, F., Dukhovskoy, D., Koldunov, N., Lee, Y. J., Lemieux, J.-F., Lique, C., Losch, M., Maslowski, W., Myers, P. G., Ólason, E., Rampal, P., Rasmussen, T., Talandier, C., Tremblay, B., and Wang, Q.: Sea Ice Rheology Experiment (SIREx): 2. Evaluating linear kinematic features in high-resolution sea ice simulations, J. Geophys. Res.-Oceans, 127, e2021JC017666, https://doi.org/10.1029/2021JC017666, e2021JC017666 2021JC017666, 2022. a, b
Itkin, P., Spreen, G., Cheng, B., Doble, M., Girard-Ardhuin, F., Haapala, J., Hughes, N., Kaleschke, L., Nicolaus, M., and Wilkinson, J.: Thin ice and storms: Sea ice deformation from buoy arrays deployed during N-ICE2015, J. Geophys. Res.-Oceans, 122, 4661–4674, https://doi.org/10.1002/2016JC012403, 2017. a
Komarov, A. S. and Barber, D. G.: Sea ice motion tracking from sequential dual-polarization RADARSAT-2 Images, IEEE T. Geosci. Remote, 52, 121–136, https://doi.org/10.1109/TGRS.2012.2236845, 2014. a, b, c, d
Korosov, A. A. and Rampal, P.: A combination of feature tracking and pattern matching with optimal parametrization for sea ice drift retrieval from SAR data, Remote Sens.-Basel, 9, 258, https://doi.org/10.3390/rs9030258, 2017. a
Kwok, R.: Deformation of the Arctic Ocean Sea Kwok, R.: Deformation of the Arctic Ocean Sea Ice Cover between November 1996 and April 1997: A Qualitative Survey, in: IUTAM Symposium on Scaling Laws in Ice Mechanics and Ice Dynamics. Solid Mechanics and Its Applications, edited by: Dempsey, J. P. and Shen, H. H., vol. 94, Springer, Dordrecht, https://doi.org/10.1007/978-94-015-9735-7_26, 2001. a, b, c, d
Kwok, R. and Cunningham, G. F.: Seasonal ice area and volume production of the Arctic Ocean: November 1996 through April 1997, J. Geophys. Res.-Oceans, 107, SHE 12–1–SHE 12–17, https://doi.org/10.1029/2000JC000469, 2002. a
Kwok, R., Schweiger, A., Rothrock, D. A., Pang, S., and Kottmeier, C.: Sea ice motion from satellite passive microwave imagery assessed with ERS SAR and buoy motions, J. Geophys. Res.-Oceans, 103, 8191–8214, https://doi.org/10.1029/97JC03334, 1998. a, b, c, d
Kwok, R., Hunke, E. C., Maslowski, W., Menemenlis, D., and Zhang, J.: Variability of sea ice simulations assessed with RGPS kinematics, J. Geophys. Res.-Oceans, 113, C11012, https://doi.org/10.1029/2008JC004783, 2008. a, b
Lavergne, T. and Down, E.: A climate data record of year-round global sea-ice drift from the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF), Earth Syst. Sci. Data, 15, 5807–5834, https://doi.org/10.5194/essd-15-5807-2023, 2023. a
Lavergne, T., Eastwood, S., Teffah, Z., Schyberg, H., and Breivik, L.-A.: Sea ice motion from low-resolution satellite sensors: an alternative method and its validation in the Arctic, J. Geophys. Res.-Oceans, 115, C10032, https://doi.org/10.1029/2009JC005958, 2010. a
Lei, R., Gui, D., Hutchings, J. K., Heil, P., and Li, N.: Annual Cycles of Sea Ice Motion and Deformation Derived From Buoy Measurements in the Western Arctic Ocean Over Two Ice Seasons, J. Geophys. Res.-Oceans, 125, e2019JC015310, https://doi.org/10.1029/2019JC015310, 2020. a
Lindsay, R. W. and Stern, H. L.: The RADARSAT geophysical processor system: quality of sea ice trajectory and deformation estimates, J. Atmos. Ocean. Tech., 20, 1333–1347, https://doi.org/10.1175/1520-0426(2003)020<1333:TRGPSQ>2.0.CO;2, 2003. a, b, c
Linow, S. and Dierking, W.: Object-based detection of linear kinematic features in sea ice, Remote Sens.-Basel, 9, 493, https://doi.org/10.3390/rs9050493, 2017. a
Marsan, D., Stern, H., Lindsay, R., and Weiss, J.: Scale dependence and localization of the deformation of Arctic Sea Ice, Phys. Rev. Lett., 93, 178501, https://doi.org/10.1103/PhysRevLett.93.178501, 2004. a, b
Moore, G. W. K., Howell, S. E. L., Brady, M., Xu, X., and McNeil, K.: Anomalous collapses of Nares Strait ice arches leads to enhanced export of Arctic sea ice, Nat. Commun., 12, 1, https://doi.org/10.1038/s41467-020-20314-w, 2021. a
Mudryk, L. R., Dawson, J., Howell, S. E. L., Derksen, C., Zagon, T. A., and Brady, M.: Impact of 1, 2 and 4 °C of global warming on ship navigation in the Canadian Arctic, Nat. Clim. Change, 11, 673–679, https://doi.org/10.1038/s41558-020-0757-5, 2021. a
Oikkonen, A., Haapala, J., Lensu, M., Karvonen, J., and Itkin, P.: Small-scale sea ice deformation during N-ICE2015: from compact pack ice to marginal ice zone, J. Geophys. Res.-Oceans, 122, 5105–5120, https://doi.org/10.1002/2016JC012387, 2017. a
Ólason, E., Boutin, G., Korosov, A., Rampal, P., Williams, T., Kimmritz, M., Dansereau, V., and Samaké, A.: A new brittle rheology and numerical framework for large-scale sea-ice models, J. Adv. Model. Earth Sy., 14, e2021MS002685, https://doi.org/10.1029/2021MS002685, 2022. a
Pizzolato, L., Howell, S. E. L., Derksen, C., Dawson, J., and Copland, L.: Changing sea ice conditions and marine transportation activity in Canadian Arctic waters between 1990 and 2012, Climatic Change, 123, 161–173, https://doi.org/10.1007/s10584-013-1038-3, 2014. a
Pizzolato, L., Howell, S. E. L., Dawson, J., Laliberté, F., and Copland, L.: The influence of declining sea ice on shipping activity in the Canadian Arctic, Geophys. Res. Lett., 43, 12146–12154, https://doi.org/10.1002/2016GL071489, 2016. a
Plante, M., Lemieux, J.-F., Tremblay, L. B., Bouchat, A., Ringeisen, D., Blain, P., Howell, S., Brady, M., Alexander, K., Duval, B., Yakuden, L., and Labelle, F.: Sea ice deformation and rotation rates (SIDRR) from the ECCC-ASITS, Zenodo [data set], https://doi.org/10.5281/zenodo.13936609, 2024a. a, b
Plante, M., Yakuden, L., Duval, B., Bouchat, A., Ringeisen, D., Lemieux, J.-F., Tremblay, L. B., and Blain, P.: McGill-sea-ice/SIDRRpy: SIDRRpy v1.1, Zenodo [code], https://doi.org/10.5281/zenodo.13936712, 2024b. a
Plante, M., Yakuden, Y., Duval, B., Bouchat, A., Ringeisen, D., Lemieux, J.-F., Tremblay, L. B., Blain, P., and Labelle, F.: SIDRR production code (v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.14783107, 2025. a
Rampal, P., Dansereau, V., Olason, E., Bouillon, S., Williams, T., Korosov, A., and Samaké, A.: On the multi-fractal scaling properties of sea ice deformation, The Cryosphere, 13, 2457–2474, https://doi.org/10.5194/tc-13-2457-2019, 2019. a, b, c
Ringeisen, D., Hutter, N., and von Albedyll, L.: Deformation lines in Arctic sea ice: intersection angle distribution and mechanical properties, The Cryosphere, 17, 4047–4061, https://doi.org/10.5194/tc-17-4047-2023, 2023. a
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Short summary
Sea ice forms a thin boundary between the ocean and the atmosphere, with complex, crust-like dynamics and ever-changing networks of sea ice leads and ridges. Statistics of these dynamical features are often used to evaluate sea ice models. Here, we present a new pan-Arctic dataset of sea ice deformations derived from satellite imagery, from 1 September 2017 to 31 August 2023. We discuss the dataset coverage and some limitations associated with uncertainties in the computed values.
Sea ice forms a thin boundary between the ocean and the atmosphere, with complex, crust-like...
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