Articles | Volume 16, issue 5
https://doi.org/10.5194/tc-16-1821-2022
© Author(s) 2022. 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-16-1821-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Kara and Barents sea ice thickness estimation based on CryoSat-2 radar altimeter and Sentinel-1 dual-polarized synthetic aperture radar
Juha Karvonen
CORRESPONDING AUTHOR
Marine Research Unit, Finnish Meteorological Institute, PB 503, 00101, Helsinki, Finland
Eero Rinne
Marine Research Unit, Finnish Meteorological Institute, PB 503, 00101, Helsinki, Finland
Heidi Sallila
Marine Research Unit, Finnish Meteorological Institute, PB 503, 00101, Helsinki, Finland
Petteri Uotila
Institute for Atmospheric and Earth System Research (INAR), Faculty of Science, University of Helsinki, Helsinki, Finland
Marko Mäkynen
Marine Research Unit, Finnish Meteorological Institute, PB 503, 00101, Helsinki, Finland
Related authors
Alexandru Gegiuc, Juha Karvonen, Jouni Vainio, Eero Rinne, Roman Bednarik, and Marko Mäkynen
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-8, https://doi.org/10.5194/tc-2022-8, 2022
Publication in TC not foreseen
Short summary
Short summary
Current users of operational ice charts call for quantitative uncertainty information, which the current ice charts lack. In this work we demonstrate for the first time the use of eye tracking methodology as a non-invasive way to identify elements behind uncertainties typically introduced during the process of visual mapping of sea ice information in satellite radar imagery. Uncertainty information would increase reliability of the manually produced ice charts and increase navigation safety.
Cecilia Äijälä, Yafei Nie, Lucía Gutiérrez-Loza, Chiara De Falco, Siv Kari Lauvset, Bin Cheng, David Anthony Bailey, and Petteri Uotila
Geosci. Model Dev., 18, 4823–4853, https://doi.org/10.5194/gmd-18-4823-2025, https://doi.org/10.5194/gmd-18-4823-2025, 2025
Short summary
Short summary
The sea ice around Antarctica has experienced record lows in recent years. To understand these changes, models are needed. MetROMS-UHel is a new version of an ocean–sea ice model with updated sea ice code and the atmospheric data. We investigate the effect of our updates on different variables with a focus on sea ice and show an improved sea ice representation as compared with observations.
Robert Massom, Phillip Reid, Stephen Warren, Bonnie Light, Donald Perovich, Luke Bennetts, Petteri Uotila, Siobhan O'Farrell, Michael Meylan, Klaus Meiners, Pat Wongpan, Alexander Fraser, Alessandro Toffoli, Giulio Passerotti, Peter Strutton, Sean Chua, and Melissa Fedrigo
EGUsphere, https://doi.org/10.5194/egusphere-2025-3166, https://doi.org/10.5194/egusphere-2025-3166, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Ocean waves play a previously-neglected role in the rapid annual melting of Antarctic sea ice by flooding and pulverising floes, removing the snow cover and reducing the albedo by an estimated 0.38–0.54 – to increase solar absorption and enhance the vertical melt rate by up to 5.2 cm/day. Ice algae further decrease the albedo, to increase the melt-rate enhancement to up to 6.1 cm/day. Melting is accelerated by four previously-unconsidered wave-driven positive feedbacks.
Kristiina Verro, Cecilia Äijälä, Roberta Pirazzini, Ruzica Dadic, Damien Maure, Willem Jan van de Berg, Giacomo Traversa, Christiaan T. van Dalum, Petteri Uotila, Xavier Fettweis, Biagio Di Mauro, and Milla Johansson
EGUsphere, https://doi.org/10.5194/egusphere-2025-386, https://doi.org/10.5194/egusphere-2025-386, 2025
Short summary
Short summary
A realistic representation of Antarctic sea ice is crucial for accurate climate and ocean model predictions. We assessed how different models capture the sunlight reflectivity, snow cover, and ice thickness. Most performed well under mild weather conditions, but overestimated snow/ice reflectivity during cold, with patchy/thin snow conditions. High-resolution satellite imagery revealed spatial albedo variability that models failed to replicate.
Tereza Uhlíková, Timo Vihma, Alexey Yu Karpechko, and Petteri Uotila
The Cryosphere, 19, 1031–1046, https://doi.org/10.5194/tc-19-1031-2025, https://doi.org/10.5194/tc-19-1031-2025, 2025
Short summary
Short summary
To better understand the local, regional, and global impacts of the recent rapid sea-ice decline in the Arctic, one of the key issues is to quantify the effects of sea-ice concentration on the surface radiative fluxes. We analyse these effects utilising four data sets called atmospheric reanalyses, and we evaluate uncertainties in these effects arising from inter-reanalysis differences in the sensitivity of the surface radiative fluxes to sea-ice concentration.
Ida Birgitte Lundtorp Olsen, Henriette Skourup, Heidi Sallila, Stefan Hendricks, Renée Mie Fredensborg Hansen, Stefan Kern, Stephan Paul, Marion Bocquet, Sara Fleury, Dmitry Divine, and Eero Rinne
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-234, https://doi.org/10.5194/essd-2024-234, 2024
Revised manuscript under review for ESSD
Short summary
Short summary
Discover the latest advancements in sea ice research with our comprehensive Climate Change Initiative (CCI) sea ice thickness (SIT) Round Robin Data Package (RRDP). This pioneering collection contains reference measurements from 1960 to 2022 from airborne sensors, buoys, visual observations and sonar and covers the polar regions from 1993 to 2021, providing crucial reference measurements for validating satellite-derived sea ice thickness.
Tereza Uhlíková, Timo Vihma, Alexey Yu Karpechko, and Petteri Uotila
The Cryosphere, 18, 957–976, https://doi.org/10.5194/tc-18-957-2024, https://doi.org/10.5194/tc-18-957-2024, 2024
Short summary
Short summary
A prerequisite for understanding the local, regional, and hemispherical impacts of Arctic sea-ice decline on the atmosphere is to quantify the effects of sea-ice concentration (SIC) on the sensible and latent heat fluxes in the Arctic. We analyse these effects utilising four data sets called atmospheric reanalyses, and we evaluate uncertainties in these effects arising from inter-reanalysis differences in SIC and in the sensitivity of the latent and sensible heat fluxes to SIC.
Marion Bocquet, Sara Fleury, Fanny Piras, Eero Rinne, Heidi Sallila, Florent Garnier, and Frédérique Rémy
The Cryosphere, 17, 3013–3039, https://doi.org/10.5194/tc-17-3013-2023, https://doi.org/10.5194/tc-17-3013-2023, 2023
Short summary
Short summary
Sea ice has a large interannual variability, and studying its evolution requires long time series of observations. In this paper, we propose the first method to extend Arctic sea ice thickness time series to the ERS-2 altimeter. The developed method is based on a neural network to calibrate past missions on the current one by taking advantage of their differences during the mission-overlap periods. Data are available as monthly maps for each year during the winter period between 1995 and 2021.
Xiaoqiao Wang, Zhaoru Zhang, Michael S. Dinniman, Petteri Uotila, Xichen Li, and Meng Zhou
The Cryosphere, 17, 1107–1126, https://doi.org/10.5194/tc-17-1107-2023, https://doi.org/10.5194/tc-17-1107-2023, 2023
Short summary
Short summary
The bottom water of the global ocean originates from high-salinity water formed in polynyas in the Southern Ocean where sea ice coverage is low. This study reveals the impacts of cyclones on sea ice and water mass formation in the Ross Ice Shelf Polynya using numerical simulations. Sea ice production is rapidly increased caused by enhancement in offshore wind, promoting high-salinity water formation in the polynya. Cyclones also modulate the transport of this water mass by wind-driven currents.
Yafei Nie, Chengkun Li, Martin Vancoppenolle, Bin Cheng, Fabio Boeira Dias, Xianqing Lv, and Petteri Uotila
Geosci. Model Dev., 16, 1395–1425, https://doi.org/10.5194/gmd-16-1395-2023, https://doi.org/10.5194/gmd-16-1395-2023, 2023
Short summary
Short summary
State-of-the-art Earth system models simulate the observed sea ice extent relatively well, but this is often due to errors in the dynamic and other processes in the simulated sea ice changes cancelling each other out. We assessed the sensitivity of these processes simulated by the coupled ocean–sea ice model NEMO4.0-SI3 to 18 parameters. The performance of the model in simulating sea ice change processes was ultimately improved by adjusting the three identified key parameters.
Ralf Döscher, Mario Acosta, Andrea Alessandri, Peter Anthoni, Thomas Arsouze, Tommi Bergman, Raffaele Bernardello, Souhail Boussetta, Louis-Philippe Caron, Glenn Carver, Miguel Castrillo, Franco Catalano, Ivana Cvijanovic, Paolo Davini, Evelien Dekker, Francisco J. Doblas-Reyes, David Docquier, Pablo Echevarria, Uwe Fladrich, Ramon Fuentes-Franco, Matthias Gröger, Jost v. Hardenberg, Jenny Hieronymus, M. Pasha Karami, Jukka-Pekka Keskinen, Torben Koenigk, Risto Makkonen, François Massonnet, Martin Ménégoz, Paul A. Miller, Eduardo Moreno-Chamarro, Lars Nieradzik, Twan van Noije, Paul Nolan, Declan O'Donnell, Pirkka Ollinaho, Gijs van den Oord, Pablo Ortega, Oriol Tintó Prims, Arthur Ramos, Thomas Reerink, Clement Rousset, Yohan Ruprich-Robert, Philippe Le Sager, Torben Schmith, Roland Schrödner, Federico Serva, Valentina Sicardi, Marianne Sloth Madsen, Benjamin Smith, Tian Tian, Etienne Tourigny, Petteri Uotila, Martin Vancoppenolle, Shiyu Wang, David Wårlind, Ulrika Willén, Klaus Wyser, Shuting Yang, Xavier Yepes-Arbós, and Qiong Zhang
Geosci. Model Dev., 15, 2973–3020, https://doi.org/10.5194/gmd-15-2973-2022, https://doi.org/10.5194/gmd-15-2973-2022, 2022
Short summary
Short summary
The Earth system model EC-Earth3 is documented here. Key performance metrics show physical behavior and biases well within the frame known from recent models. With improved physical and dynamic features, new ESM components, community tools, and largely improved physical performance compared to the CMIP5 version, EC-Earth3 represents a clear step forward for the only European community ESM. We demonstrate here that EC-Earth3 is suited for a range of tasks in CMIP6 and beyond.
Hanna K. Lappalainen, Tuukka Petäjä, Timo Vihma, Jouni Räisänen, Alexander Baklanov, Sergey Chalov, Igor Esau, Ekaterina Ezhova, Matti Leppäranta, Dmitry Pozdnyakov, Jukka Pumpanen, Meinrat O. Andreae, Mikhail Arshinov, Eija Asmi, Jianhui Bai, Igor Bashmachnikov, Boris Belan, Federico Bianchi, Boris Biskaborn, Michael Boy, Jaana Bäck, Bin Cheng, Natalia Chubarova, Jonathan Duplissy, Egor Dyukarev, Konstantinos Eleftheriadis, Martin Forsius, Martin Heimann, Sirkku Juhola, Vladimir Konovalov, Igor Konovalov, Pavel Konstantinov, Kajar Köster, Elena Lapshina, Anna Lintunen, Alexander Mahura, Risto Makkonen, Svetlana Malkhazova, Ivan Mammarella, Stefano Mammola, Stephany Buenrostro Mazon, Outi Meinander, Eugene Mikhailov, Victoria Miles, Stanislav Myslenkov, Dmitry Orlov, Jean-Daniel Paris, Roberta Pirazzini, Olga Popovicheva, Jouni Pulliainen, Kimmo Rautiainen, Torsten Sachs, Vladimir Shevchenko, Andrey Skorokhod, Andreas Stohl, Elli Suhonen, Erik S. Thomson, Marina Tsidilina, Veli-Pekka Tynkkynen, Petteri Uotila, Aki Virkkula, Nadezhda Voropay, Tobias Wolf, Sayaka Yasunaka, Jiahua Zhang, Yubao Qiu, Aijun Ding, Huadong Guo, Valery Bondur, Nikolay Kasimov, Sergej Zilitinkevich, Veli-Matti Kerminen, and Markku Kulmala
Atmos. Chem. Phys., 22, 4413–4469, https://doi.org/10.5194/acp-22-4413-2022, https://doi.org/10.5194/acp-22-4413-2022, 2022
Short summary
Short summary
We summarize results during the last 5 years in the northern Eurasian region, especially from Russia, and introduce recent observations of the air quality in the urban environments in China. Although the scientific knowledge in these regions has increased, there are still gaps in our understanding of large-scale climate–Earth surface interactions and feedbacks. This arises from limitations in research infrastructures and integrative data analyses, hindering a comprehensive system analysis.
Alexandru Gegiuc, Juha Karvonen, Jouni Vainio, Eero Rinne, Roman Bednarik, and Marko Mäkynen
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-8, https://doi.org/10.5194/tc-2022-8, 2022
Publication in TC not foreseen
Short summary
Short summary
Current users of operational ice charts call for quantitative uncertainty information, which the current ice charts lack. In this work we demonstrate for the first time the use of eye tracking methodology as a non-invasive way to identify elements behind uncertainties typically introduced during the process of visual mapping of sea ice information in satellite radar imagery. Uncertainty information would increase reliability of the manually produced ice charts and increase navigation safety.
Yu Yan, Wei Gu, Andrea M. U. Gierisch, Yingjun Xu, and Petteri Uotila
Geosci. Model Dev., 15, 1269–1288, https://doi.org/10.5194/gmd-15-1269-2022, https://doi.org/10.5194/gmd-15-1269-2022, 2022
Short summary
Short summary
In this study, we developed NEMO-Bohai, an ocean–ice model for the Bohai Sea, China. This study presented the scientific design and technical choices of the parameterizations for the NEMO-Bohai model. The model was calibrated and evaluated with in situ and satellite observations of ocean and sea ice. NEMO-Bohai is intended to be a valuable tool for long-term ocean and ice simulations and climate change studies.
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
Short summary
Short summary
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.
Cited articles
AARI: AARI ice chart web page, Arctic-Antarctic Research Institute, St. Petersbug, Russia, http://wdc.aari.ru/datasets/d0004/kar/sigrid/ (last access: 10 May 2022), 2018. a
Afanasyeva, E. V., Alekseeva, T. A., Sokolova, J. V., Demchev, D. M., Chufarova, M. S., Bychenkov, Y. D., and Devyataev, O. S.:
AARI methodology for sea ice charts composition,
Russian Arctic, 7, 5–20, https://doi.org/10.24411/2658-4255-2019-10071, 2019. a, b
Armitage, T. W. K. and Ridout, A. L.: Arctic sea ice freeboard from AltiKa and comparison with CryoSat-2 and Operation IceBridge,
Geophys. Res. Lett., 42, 6724–6731, https://doi.org/10.1002/2015GL064823, 2015. a
Besag, J.: On the Statistical Analysis of Dirty Pictures, J. R. Statis. Soc. B, 48, 259–302, 1986. a
Böhme, L. and Send, U.: Objective analyses of hydrographic data for referencing profiling float salinities in highly variable environments, Deep-Sea Res. Pt. II, 52, 651–664, 2005. a
Bourbigot, M., Johnsen, H., and Piantanida, R.:
SENTINEL-1 ProductSpecification, document S1-RS-MDA-52-7441, ESA, https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-1-sar/document-library/-/asset_publisher/1dO7RF5fJMbd/content/sentinel-1-product-specification
(last access: 11 May 2022), 2016. a
Box, G. E. P. and Jenkins, G.: Time Series Analysis: Forecasting and Control, Holden-Day, ISBN 0816211043, 1976. a
Cressie, N.: Statistics for spatial data, Wiley, New York, 69–101, ISBN 9780471002550, 1993. a
Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte-Carlo methods to forecast errorstatistics, J. Geophys. Res., 99, 10143–10162, https://doi.org/10.1029/94JC00572, 1994. a
Frey, R. A., Ackerman, S. A., Liu, Y., Strabala, K. I., Zhang, H., Key, J. R., and Wang, X.: Cloud detection with MODIS. Part I: Improvements in the MODIS cloud mask for collection 5, J. Atmos. Ocean. Technol., 25, 1057–1072, 2008. a
Fukunaga, K. and Hostetler, L. D.: The Estimation of the Gradient of a Density Function, with Applications in Pattern Recognition,
IEEE T. Inform. Theory, 21, 32–40, 1975. a
Giles, K. A., Laxon, S. W., and Ridout, A. L.: Circumpolar thinning of Arctic sea ice following the 2007 record ice extent minimum, Geophys. Res. Lett., 35, L22502, https://doi.org/10.1029/2008GL035710, 2008. a
Hackett, B., Bertino, L., Ali, A., Burud, A., and Williams, T.:
Product User Manual for Arctic Ocean Physical and Bio Analysis and Forecasting Products, issue 5.10, EU Copernicus Marine Service, Public Ref: CMEMS-ARC-PUM-002-ALL, https://marine.copernicus.eu/sites/default/files/product_improvement_migrated_files/CMEMS-ARC-PUM-002-ALL.pdf (last access: 10 May 2022), 2020. a
Hendricks, S., Ricker, R. and Paul, S.: Product User Guide & Algorithm Specification: AWI CryoSat-2 Sea Ice Thickness (version 2.4), EU Copernicus Marine Service, Public Ref: CMEMS-ARC-PUM-002-ALL, https://epic.awi.de/id/eprint/54733/ (last access: 11 May 2022), 2021a. a
Hendricks, S., Sallila, H., Brockley, D., and Paul, S.: shendric/pysiral: Product update (C3S, AWI, CCI, CryoTEMPO) (v0.9.6), Zenodo [data set], https://doi.org/10.5281/zenodo.5566347, 2021b. a, b
Huntemann, M., Heygster, G., Kaleschke, L., Krumpen, T., Mäkynen, M., and Drusch, M.: Empirical sea ice thickness retrieval during the freeze-up period from SMOS high incident angle observations, The Cryosphere, 8, 439–451, https://doi.org/10.5194/tc-8-439-2014, 2014. a
IMarEST: Safety & Sustainability of Shipping and Offshore Activities in the Arctic, Institute of Marine Engineering, Science & Technology,
IMarEST Report, London International Shipping Week, https://www.imarest.org/reports/731-imarest-arctic-roundtable-report/file (last access: 11 May 2022), 2015. a
Iwamoto, K., Ohshima, K. I., and Tamura, T.: Improved mapping
of sea ice production in the Arctic Ocean using AMSR-E thin ice
thickness algorithm, J. Geophys. Res., 119, 3574–3594, 2014. a
JCOMM Expert Team on Sea Ice: Sea-ice nomenclature: Snapshot of the WMO sea ice nomenclature, WMO no. 259, Joint WMO-IOC Commission for Oceanography and Marine Meteorology, Tech. Rep., World Meteorological Organization (WMO), Geneva, Switzerland, https://doi.org/10.25607/OBP-1515, 2014a. a, b
JCOMM Expert Team on Sea Ice: SIGRID-3: a vector archive format for sea ice georeferenced information and data, Joint WMO-IOC Commission for Oceanography and Marine Meteorology, Technical Report No. 23, World Meteorological Organization (WMO), Geneva, Switzerland, https://doi.org/10.25607/OBP-1498.2, 2014b. a
Johannessen, O. M., Alexandrov, V. Y., Frolov, I. Y., Sandven, S., Pettersson, L. H., Bobylev, L. P., Kloster, K., Smirnov, V. G.,
Mironov, Y. U., and Babich, N. G.: Remote sensing of sea ice in the northern sea route: studies and applications, Springer-Praxis, Chichester, UK, 25–64, ISBN 9783540488408, 2007. a, b
Jung, T., Kasper, M. A., Semmler, T., and Serrar, S.: Arctic influence on subseasonal midlatitude prediction, Geophys. Res. Lett., 41, 3676–3680, 2014. a
Kaleschke, L., Tian-Kunze, X., Maaß, N., Makynen, M., and Drusch, M.:
Sea ice thickness retrieval from SMOS brightness temperatures during
the Arctic freeze-up period, Geophys. Res. Lett., 39, L05501, https://doi.org/10.1029/2012GL050916, 2012. a, b
Kaleschke, L., Tioan-Kunze, X., Maass, N., Beitsch, A., Wernecke, A., Miernecki, M., Muller, G., Fock, B. H., Gierischc, A. M. U., Schlunzen, K. H., Pohlmann, T., Dobrynin, M., Hendricks, S., Asseng, J., Gerdes, R., Jochmann, P., Reimer, N., Holfort, J., Melsheimer, C., Heygster, G., Spreen, G., Gerland, S., King, J., Skou, N., Søbjærg, S. S., Haas, C., Richter, F., and Casal, T.: SMOS sea ice product: Operational application and validation in the Barents Sea marginal ice zone, Remote Sens. Environ., 180, 264–273, 2016. a, b
Karvonen, J.: Virtual radar ice buoys – a method for measuring fine-scale sea ice drift, The Cryosphere, 10, 29–42, https://doi.org/10.5194/tc-10-29-2016, 2016. a
Kern, S., Khvorostovsky, K., Skourup, H., Rinne, E., Parsakhoo, Z. S., Djepa, V., Wadhams, P., and Sandven, S.: The impact of snow depth, snow density and ice density on sea ice thickness retrieval from satellite radar altimetry: results from the ESA-CCI Sea Ice ECV Project Round Robin Exercise, The Cryosphere, 9, 37–52, https://doi.org/10.5194/tc-9-37-2015, 2015. a
Kern, M., Cullen, R., Berruti, B., Bouffard, J., Casal, T., Drinkwater, M. R., Gabriele, A., Lecuyot, A., Ludwig, M., Midthassel, R., Navas Traver, I., Parrinello, T., Ressler, G., Andersson, E., Martin-Puig, C., Andersen, O., Bartsch, A., Farrell, S., Fleury, S., Gascoin, S., Guillot, A., Humbert, A., Rinne, E., Shepherd, A., van den Broeke, M. R., and Yackel, J.: The Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL) high-priority candidate mission, The Cryosphere, 14, 2235–2251, https://doi.org/10.5194/tc-14-2235-2020, 2020. a, b
Knapp, C. H. and Carter, G. C.: The Generalized Correlation Method for Estimation of Time Delay, IEEE T. Acoust. Speech, 4, 320–327, 1976. a
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
Kwok, R. and Cunningham, G. F.: ICESat over Arctic sea ice: Estimation of snow depth and ice thickness, J. Geophys. Res.-Oceans,
113, c08010, https://doi.org/10.1029/2008JC004753, 2008. a
Kwok, R., Nghiem, S. V., Yueh, S. H., and Huynh, D. D.: Retrieval of
thin ice thickness from multifrequency polarimetric SAR data,
Remote Sens. Environ., 51, 361–374, 1995. a
Kwok, K., Cunningham, G. F., Wensnahan, M., Rigor, I., Zwally, H. J., and Yi, D.: Thinning and volume loss of the Arctic Ocean sea ice cover: 2003–2008,
J. Geophys. Res.-Oceans, 114, C07005, https://doi.org/10.1029/2009JC005312, 2009. a
Laxon, S., Peacock, N., and Smith, D.: High interannual variability of sea ice thickness in the Arctic region, Nature, 425, 947–950, 2003. a
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, 2013. a
Lemieux, J.-F., Bouillon, S., Dupont, F., Flato, G., Losch, M., Rampal, P., Tremblay, L.-B., Vancoppenolle, M., and Williams, T.: Sea Ice Physics and Modelling, in: Sea Ice Analysis and Forecasting, Cambridge University Press, https://doi.org/10.1017/9781108277600.003, 2018. a
Makynen, M. and Karvonen, J.: Incidence Angle Dependence of First-Year Sea Ice Backscattering Coefficient in Sentinel-1 SAR Imagery Over the Kara Sea, IEEE T. Geosci. Remote, 55, 6170–6181, 2017b. a
Martin, S., Drucker, R., Kwok, R., and Holt, B.: Estimation of the thin
ice thickness and heat flux for the Chukchi Sea Alaskan coast polynya
from Special Sensor Microwave/Imager data, 1990–2001, J. Geophys.
Res.-Oceans, 109, C10012, https://doi.org/10.1029/2004JC002428, 2004. a
McIntosh, P. C.: Oceanographic data interpolation: Objective analysis and splines, J. Geophys. Res.-Oceans, 95, 529–13 541, 1990. a
McPhee, M.: Air-Ice Interaction, Springer, Naches, WA, USA, ISBN 9780387783352, 2008. a
Meloni, M., Bouffard, J., Parrinello, T., Dawson, G., Garnier, F., Helm, V., Di Bella, A., Hendricks, S., Ricker, R., Webb, E., Wright, B., Nielsen, K., Lee, S., Passaro, M., Scagliola, M., Simonsen, S. B., Sandberg Sørensen, L., Brockley, D., Baker, S., Fleury, S., Bamber, J., Maestri, L., Skourup, H., Forsberg, R., and Mizzi, L.: CryoSat Ice Baseline-D validation and evolutions, The Cryosphere, 14, 1889–1907, https://doi.org/10.5194/tc-14-1889-2020, 2020. a
Nakamura, K., Wakabayashi, H., Uto, S., Naoki, K., Nishio, F., and
Uratsuka, S.: Sea-ice thickness retrieval in the Sea of
Okhotsk using dual-polarization SAR data, Ann. Glaciol., 44,
261–268, 2006. a
Nakata, K., Ohshima, K. I., and Nihashi, S.: Estimation of thin-ice thickness
and discrimination of ice type from AMSR-E passive microwave
data, IEEE T. Geosci. Remote, 57, 263–276, 2019. a
Ojala, T., Pietikäinen, M., and Harwood, D.: A comparative study of texture measures with classification based on featured distributions,
Pattern Recognit., 29, 51–59, 1996. a
Onshima, K. I., Nihashi, S., and Iwamoto, K.: Global view of sea-ice
production in polynyas and its linkage to dense/bottom water formation,
Geosci. Lett., 3, 13, https://doi.org/10.1186/s40562-016-0045-4, 2016. a
Paul, S., Willmes, S., and Heinemann, G.: Long-term coastal-polynya dynamics in the southern Weddell Sea from MODIS thermal-infrared imagery, The Cryosphere, 9, 2027–2041, https://doi.org/10.5194/tc-9-2027-2015, 2015. a
Petty, A. A., Kurtz, N. T., Kwok, R., Markus, T., and Neumann, T. A.: Winter arctic sea ice thickness from ICESat-2 freeboards, J. Geophys. Res.-Oceans, 125, e2019JC015764, https://doi.org/10.1029/2019JC015764, 2020. a, b
Preußer, A., Heinemann, G., Willmes, S., and Paul, S.: Circumpolar polynya regions and ice production in the Arctic: results from MODIS thermal infrared imagery from 2002/2003 to 2014/2015 with a regional focus on the Laptev Sea, The Cryosphere, 10, 3021–3042, https://doi.org/10.5194/tc-10-3021-2016, 2016. a
Ricker, R., Hendricks, S., Helm, V., Skourup, H., and Davidson, M.: Sensitivity of CryoSat-2 Arctic sea-ice freeboard and thickness on radar-waveform interpretation, The Cryosphere, 8, 1607–1622, https://doi.org/10.5194/tc-8-1607-2014, 2014. a
Rösel, A., Itkin, P., King, J., Divine, D., Wang, C., Granskog, M. A., Krumpen, T., and Gerland, S.: Thin Sea Ice, Thick Snow, and Widespread Negative Freeboard Observed During N-ICE2015 North
of Svalbard, J. Geophys. Res.-Oceans, 123, 1156–1176, 2018. a
Rostosky, P., Spreen, G., Gunnar, S., Farrell, 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
Sakov, P., Counillon, F., Bertino, L., Lisæter, K. A., Oke, P. R., and Korablev, A.: TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic, Ocean Sci., 8, 633–656, https://doi.org/10.5194/os-8-633-2012, 2012. a
Scagliola, M.: CryoSat Footprints (Aresys Technical Note), ESA
report no. XCRY-GSEG-EOPG-TN-13-0013, ESA Scientific and Technical Branch ESTEC, Noordwijk, the Netherlands, https://earth.esa.int/eogateway/documents/20142/37627/CryoSat-Footprints-ESA-Aresys.pdf
(last access: 11 May 2022), 2013. a
Schmitt, A. and Kaleschke, L.: A consistent combination of brightness temperatures from SMOS and SMAP over Polar Oceans
for sea ice applications, Remote Sens., 10, 553, https://doi.org/10.3390/rs10040553, 2018. a
Schweiger, A., Lindsay, R., Zhang, J., Steele, M.,
Stern, H., and Kwok, R.: Uncertainty in modeled Arctic sea ice volume, J. Geophys. Res., 116, C00D06, https://doi.org/10.1029/2011JC007084, 2011. a, b
Shannon, C. E.: A Mathematical Theory of Communication,
Bell Syst. Tech. J., 27, 379–423, 623–656, 1948. a
Simila, M., Makynen, M., and Heiler, I.: Comparison between C
band synthetic aperture radar and 3-D laser scanner statistics for
the Baltic Sea ice, J. Geophys. Res., 115, C10056, https://doi.org/10.1029/2009JC005970, 2010. a, b, c
Tian-Kunze, X., Kaleschke, L., Maaß, N., Mäkynen, M., Serra, N., Drusch, M., and Krumpen, T.: SMOS-derived thin sea ice thickness: algorithm baseline, product specifications and initial verification, The Cryosphere, 8, 997–1018, https://doi.org/10.5194/tc-8-997-2014, 2014. a
Tibshirani, R.: Regression Shrinkage and Selection via the lasso,
J. Roy. Stat. Soc. B, 58, 267–288, 1996. a
Tietsche, S., Balmaseda, M. A., Zuo, H., and Mogensen, K.:
Arctic sea ice in the global eddy-permitting ocean reanalysis ORAP5,
Clim. Dynam., 49, 775–789, https://doi.org/10.1007/s00382-015-2673-3, 2017. a, b, c
Tilling, R. L., Ridout, A., Shepherd, A., and Wingham, D. J.:
Increased Arctic sea ice volume after anomalously low melting in 2013, Nat. Geosci., 8, 643–646, 2015. 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, 2018. a
Toyota, T., Ono, S., Cho, K., and Ohshima, K.: Retrieval of sea-ice thickness distribution in the Sea of Okhotsk from ALOS/PALSAR backscatter data, Ann. Glaciol., 52, 177–184, 2011. a
Wadhams, P., Aulicino, G., Parmiggiani, F., Persson, P. O. G., and Holt, B.: Pancake ice thickness mapping in the Beaufort Sea From wave dispersion observed in SAR imagery, J. Geophys. Res.-Oceans, 123,
2213–2237, 2018. a
Wakabayashi, H., Matsuoka, T., Nakamura, K., and Nishio, F.:
Polarimetric characteristics of sea ice in the Sea of Okhotsk
observed by airborne L-band SAR, IEEE T. Geosci. Remote, 42, 2412–2425, 2004. a
Warren, S. G., Rigor, I. G., Untersteiner, N., Radionov, 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
Wessel P. and Smith, W. H. F.: A Global Self-consistent, Hierarchical, High-resolution Shoreline Database, J. Geophys. Res., 101, 8741–8743, https://doi.org/10.1029/96JB00104, 1996. a
Wingham, D., Francis, C., Baker, S., Bouzinac, C., Brockley, D., Cullen, R., de Chateau-Thierry, P., Laxon, S., Mallow, U., Mavrocordatos, C., Phalippou, L., Ratier, G., Rey, L., Rostan, F., Viau, P., and Wallis, D.: CryoSat: A mission to determine the fluctuations in Earth's land and marine ice fields,
Adv. Space Res., 37, 841–871, https://doi.org/10.1016/j.asr.2005.07.027, 2006. a, b
Xia, W. and Xie, H.: Assessing three waveform retrackers on sea ice freeboard retrieval from CryoSat-2 using Operation IceBridge Airborne altimetry datasets, Remote Sens. Environ., 204, 450–471,
https://doi.org/10.1016/j.rse.2017.10.010, 2018. a
Xie, J., Counillon, F., and Bertino, L.: Impact of assimilating a merged sea-ice thickness from CryoSat-2 and SMOS in the Arctic reanalysis, The Cryosphere, 12, 3671–3691, https://doi.org/10.5194/tc-12-3671-2018, 2018. a
Xu, S., Zhou, L., and Wang, B.: Variability scaling and consistency in airborne and satellite altimetry measurements of Arctic sea ice, The Cryosphere, 14, 751–767, https://doi.org/10.5194/tc-14-751-2020, 2020. a
Yi, D., Kurtz, N., Harbeck, J., Kwok, R., Hendricks, S., and Ricker, R.: Comparing Coincident Elevation and Freeboard From IceBridge and Five Different CryoSat-2 Retrackers, IEEE T. Geosci. Remote, 57,
1219–1229, https://doi.org/10.1109/TGRS.2018.2865257, 2018. a
Yu, Y. and Rothrock, D. A.: Thin ice thickness from satellite thermal
imagery, J. Geophys. Res., 101, 25753–25766, 1996. a
Zhang, J. L. and Rothrock, D. A.: Modeling global sea ice with a thickness and enthalpy distribution model in generalized curvilinear coordinates, Mon. Weather Rev., 131, 845–861, 2003. a
Zhang, X., Dierking, W., Zhang, J., Meng, J., and Lang, H.: Retrieval of the thickness of undeformed sea ice from simulated C-band compact polarimetric SAR images, The Cryosphere, 10, 1529–1545, https://doi.org/10.5194/tc-10-1529-2016, 2016. a
Zuo, H., Balmaseda, M. A., de Boisseson, E., Hirahara, S., Chrust, M., and De Rosnay, P.: A generic ensemble generation scheme for data assimilation and ocean analysis, ECMWF Tech Memo., https://doi.org/10.21957/cub7mq0i4, 2017. a
Zuo, H., Balmaseda, M. A., Tietsche, S., Mogensen, K., and Mayer, M.: The ECMWF operational ensemble reanalysis–analysis system for ocean and sea ice: a description of the system and assessment, Ocean Sci., 15, 779–808, https://doi.org/10.5194/os-15-779-2019, 2019. a
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
We propose a method to provide sea ice thickness (SIT) estimates over a test area in the Arctic utilizing radar altimeter (RA) measurement lines and C-band SAR imagery. The RA data are from CryoSat-2, and SAR imagery is from Sentinel-1. By combining them we get a SIT grid covering the whole test area instead of only narrow measurement lines from RA. This kind of SIT estimation can be extended to cover the whole Arctic (and Antarctic) for operational SIT monitoring.
We propose a method to provide sea ice thickness (SIT) estimates over a test area in the Arctic...