Articles | Volume 17, issue 7
https://doi.org/10.5194/tc-17-3013-2023
© 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-17-3013-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Jul 2023
Research article |
| 25 Jul 2023
| 25 Jul 2023
Arctic sea ice radar freeboard retrieval from the European Remote-Sensing Satellite (ERS-2) using altimetry: toward sea ice thickness observation from 1995 to 2021
Marion Bocquet
CORRESPONDING AUTHOR
LEGOS, Université de Toulouse, CNES, CNRS, IRD, UPS, Toulouse, France
Collecte Localisation Satellites (CLS), Toulouse, France
Sara Fleury
LEGOS, Université de Toulouse, CNES, CNRS, IRD, UPS, Toulouse, France
Fanny Piras
Collecte Localisation Satellites (CLS), Toulouse, France
Eero Rinne
Marine Research, Finnish Meteorological Institute, Helsinki, Finland
University Centre in Svalbard (UNIS), P.O. Box 156, 9171 Longyearbyen, Norway
Heidi Sallila
Marine Research, Finnish Meteorological Institute, Helsinki, Finland
Florent Garnier
LEGOS, Université de Toulouse, CNES, CNRS, IRD, UPS, Toulouse, France
Frédérique Rémy
LEGOS, Université de Toulouse, CNES, CNRS, IRD, UPS, Toulouse, France
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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
Preprint under review for ESSD
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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.
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.
Maya Raghunath Suryawanshi, Malcolm McMillan, Jennifer Maddalena, Fanny Piras, Jérémie Aublanc, Jean-Alexis Daguzé, Clara Grau, and Qi Huang
EGUsphere, https://doi.org/10.5194/egusphere-2024-3446, https://doi.org/10.5194/egusphere-2024-3446, 2024
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Increasing melting rates of the polar Ice Sheets are contributing more and more to sea level rise. Due to the remoteness and expanse of ice sheets these changes are mainly observed using satellites. However, the accuracy of these measurements depends on the processing of these datasets. Here we use advanced algorithms to provide improved historical ice sheet elevation measurements, derived from satellite altimeters flying between 1991 and 2012, which will benefit to cryospheric applications.
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
Preprint under review for ESSD
Short summary
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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.
Renée M. Fredensborg Hansen, Henriette Skourup, Eero Rinne, Arttu Jutila, Isobel R. Lawrence, Andrew Shepherd, Knut V. Høyland, Jilu Li, Fernando Rodriguez-Morales, Sebastian B. Simonsen, Jeremy Wilkinson, Gaelle Veyssiere, Donghui Yi, René Forsberg, and Taniâ G. D. Casal
EGUsphere, https://doi.org/10.5194/egusphere-2024-2854, https://doi.org/10.5194/egusphere-2024-2854, 2024
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In December 2022, an airborne campaign collected unprecedented coincident multi-frequency radar and lidar data over sea ice along a CryoSat-2 and ICESat-2 (CRYO2ICE) orbit in the Weddell Sea useful for evaluating microwave penetration. We found limited snow penetration at Ka- and Ku-bands, with significant contributions from the air-snow interface, contradicting traditional assumptions. These findings challenge current methods for comparing air- and spaceborne altimeter estimates of sea ice.
Ole Baltazar Andersen, Stine Kildegaard Rose, Adili Abulaitijiang, Shengjun Zhang, and Sara Fleury
Earth Syst. Sci. Data, 15, 4065–4075, https://doi.org/10.5194/essd-15-4065-2023, https://doi.org/10.5194/essd-15-4065-2023, 2023
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The mean sea surface (MSS) is an important reference for mapping sea-level changes across the global oceans. It is widely used by space agencies in the definition of sea-level anomalies as mapped by satellite altimetry from space. Here a new fully global high-resolution mean sea surface called DTU21MSS is presented, and a suite of evaluations are performed to demonstrate its performance.
Juha Karvonen, Eero Rinne, Heidi Sallila, Petteri Uotila, and Marko Mäkynen
The Cryosphere, 16, 1821–1844, https://doi.org/10.5194/tc-16-1821-2022, https://doi.org/10.5194/tc-16-1821-2022, 2022
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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.
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
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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.
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.
Yeray Santana-Falcón, Pierre Brasseur, Jean Michel Brankart, and Florent Garnier
Ocean Sci., 16, 1297–1315, https://doi.org/10.5194/os-16-1297-2020, https://doi.org/10.5194/os-16-1297-2020, 2020
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Data assimilation is the most comprehensive strategy to estimate the biogeochemical state of the ocean. Here, surface Chl a data are daily assimilated into a 24-member NEMO–PISCES ensemble configuration to implement a complete 4D assimilation system. Results show the assimilation increases the skills of the ensemble, though a regional diagnosis suggests that the description of model and observation uncertainties needs to be refined according to the biogeochemical characteristics of each region.
Michael Kern, Robert Cullen, Bruno Berruti, Jerome Bouffard, Tania Casal, Mark R. Drinkwater, Antonio Gabriele, Arnaud Lecuyot, Michael Ludwig, Rolv Midthassel, Ignacio Navas Traver, Tommaso Parrinello, Gerhard Ressler, Erik Andersson, Cristina Martin-Puig, Ole Andersen, Annett Bartsch, Sinead Farrell, Sara Fleury, Simon Gascoin, Amandine Guillot, Angelika Humbert, Eero Rinne, Andrew Shepherd, Michiel R. van den Broeke, and John Yackel
The Cryosphere, 14, 2235–2251, https://doi.org/10.5194/tc-14-2235-2020, https://doi.org/10.5194/tc-14-2235-2020, 2020
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The Copernicus Polar Ice and Snow Topography Altimeter will provide high-resolution sea ice thickness and land ice elevation measurements and the capability to determine the properties of snow cover on ice to serve operational products and services of direct relevance to the polar regions. This paper describes the mission objectives, identifies the key contributions the CRISTAL mission will make, and presents a concept – as far as it is already defined – for the mission payload.
Marco Meloni, Jerome Bouffard, Tommaso Parrinello, Geoffrey Dawson, Florent Garnier, Veit Helm, Alessandro Di Bella, Stefan Hendricks, Robert Ricker, Erica Webb, Ben Wright, Karina Nielsen, Sanggyun Lee, Marcello Passaro, Michele Scagliola, Sebastian Bjerregaard Simonsen, Louise Sandberg Sørensen, David Brockley, Steven Baker, Sara Fleury, Jonathan Bamber, Luca Maestri, Henriette Skourup, René Forsberg, and Loretta Mizzi
The Cryosphere, 14, 1889–1907, https://doi.org/10.5194/tc-14-1889-2020, https://doi.org/10.5194/tc-14-1889-2020, 2020
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This manuscript aims to describe the evolutions which have been implemented in the new CryoSat Ice processing chain Baseline-D and the validation activities carried out in different domains such as sea ice, land ice and hydrology.
This new CryoSat processing Baseline-D will maximise the uptake and use of CryoSat data by scientific users since it offers improved capability for monitoring the complex and multiscale changes over the cryosphere.
Heidi Sallila, Sinéad Louise Farrell, Joshua McCurry, and Eero Rinne
The Cryosphere, 13, 1187–1213, https://doi.org/10.5194/tc-13-1187-2019, https://doi.org/10.5194/tc-13-1187-2019, 2019
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We assess 8 years of sea ice thickness observations derived from measurements of CryoSat-2 (CS2), AVHRR and SMOS satellites, collating key details of primary interest to users. We find a number of differences among data products but find that CS2 measurements are reliable for sea ice thickness, particularly between ~ 0.5 and 4 m. Regional comparisons reveal noticeable differences in ice thickness between products, particularly in the marginal seas in areas of considerable ship traffic.
Nicolas Champollion, Ghislain Picard, Laurent Arnaud, Éric Lefebvre, Giovanni Macelloni, Frédérique Rémy, and Michel Fily
The Cryosphere, 13, 1215–1232, https://doi.org/10.5194/tc-13-1215-2019, https://doi.org/10.5194/tc-13-1215-2019, 2019
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The snow density close to the surface has been retrieved from satellite observations at Dome C on the Antarctic Ice Sheet. It shows a marked decrease between 2002 and 2011 of about 10 kg m-3 yr-1. This trend has been confirmed by in situ measurements and other satellite observations though no long-term meteorological evolution has been found. These results have implications for surface mass balance and energy budget.
Ida Russo, Guillaume Ramillien, Frédéric Frappart, and Frédérique Rémy
The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-16, https://doi.org/10.5194/tc-2019-16, 2019
Preprint withdrawn
Florent Garnier, Pierre Brasseur, Jean-Michel Brankart, Yeray Santana-Falcon, and Emmanuel Cosme
Ocean Sci. Discuss., https://doi.org/10.5194/os-2018-153, https://doi.org/10.5194/os-2018-153, 2019
Publication in OS not foreseen
Graham D. Quartly, Eero Rinne, Marcello Passaro, Ole B. Andersen, Salvatore Dinardo, Sara Fleury, Kevin Guerreiro, Amandine Guillot, Stefan Hendricks, Andrey A. Kurekin, Felix L. Müller, Robert Ricker, Henriette Skourup, and Michel Tsamados
The Cryosphere Discuss., https://doi.org/10.5194/tc-2018-148, https://doi.org/10.5194/tc-2018-148, 2018
Revised manuscript not accepted
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Radar altimetry is a high-precision technique for measuring sea level and sea ice thickness from space, which are important for monitoring ocean circulation, sea level rise and changes in the Arctic ice cover. This paper reviews the processing techniques needed to best extract the information from complicated radar echoes, and considers the likely developments in the coming decade.
Muriel Llubes, Lucia Seoane, Sean Bruinsma, and Frédérique Rémy
Solid Earth, 9, 457–467, https://doi.org/10.5194/se-9-457-2018, https://doi.org/10.5194/se-9-457-2018, 2018
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We computed a global map of crustal thickness covering the entire Antarctic continent from GOCE satellite gravimetric data. We compare our result to seismological models: a fairly good agreement is shown. However, better resolution is obtained using the gravity observations than using only seismological ones. This research will help us to understand the geology of Antarctica.
Kevin Guerreiro, Sara Fleury, Elena Zakharova, Alexei Kouraev, Frédérique Rémy, and Philippe Maisongrande
The Cryosphere, 11, 2059–2073, https://doi.org/10.5194/tc-11-2059-2017, https://doi.org/10.5194/tc-11-2059-2017, 2017
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We analyse CryoSat-2 and Envisat freeboard height discrepancy over Arctic sea ice and we study the potential role of ice roughness.
Based on our results, we build a CryoSat-2-like version of Envisat freeboard height. The improved Envisat freeboard is converted to sea ice draught and compared to in situ mooring observations to demonstrate the potential of our methodology to produce accurate ice thickness estimates over the 2002–2012 period.
Related subject area
Discipline: Sea ice | Subject: Arctic (e.g. Greenland)
Reconstruction of Arctic sea ice thickness (1992–2010) based on a hybrid machine learning and data assimilation approach
Assessing the representation of Arctic sea ice and the marginal ice zone in ocean–sea ice reanalyses
Sea-ice conditions from 1880 to 2017 on the Northeast Greenland continental shelf: a biomarker and observational record comparison
The radiative and geometric properties of melting first-year landfast sea ice in the Arctic
Improving short-term sea ice concentration forecasts using deep learning
Retrieval of sea ice drift in the Fram Strait based on data from Chinese satellite HaiYang (HY-1D)
Sea-ice variations and trends during the Common Era in the Atlantic sector of the Arctic Ocean
Melt pond fractions on Arctic summer sea ice retrieved from Sentinel-3 satellite data with a constrained physical forward model
Extent, duration and timing of the sea ice cover in Hornsund, Svalbard, from 2014–2023
Modeled variations in the inherent optical properties of summer Arctic ice and their effects on the radiation budget: a case based on ice cores from 2008 to 2016
Comparing elevation and backscatter retrievals from CryoSat-2 and ICESat-2 over Arctic summer sea ice
Summer sea ice floe perimeter density in the Arctic: high-resolution optical satellite imagery and model evaluation
Patterns of wintertime Arctic sea-ice leads and their relation to winds and ocean currents
A long-term proxy for sea ice thickness in the Canadian Arctic: 1996–2020
Rapid sea ice changes in the future Barents Sea
Causes and evolution of winter polynyas north of Greenland
Winter Arctic sea ice thickness from ICESat-2: upgrades to freeboard and snow loading estimates and an assessment of the first three winters of data collection
Sea ice breakup and freeze-up indicators for users of the Arctic coastal environment
Improving model-satellite comparisons of sea ice melt onset with a satellite simulator
Kara and Barents sea ice thickness estimation based on CryoSat-2 radar altimeter and Sentinel-1 dual-polarized synthetic aperture radar
Contribution of warm and moist atmospheric flow to a record minimum July sea ice extent of the Arctic in 2020
Perspectives on future sea ice and navigability in the Arctic
Lasting impact of winds on Arctic sea ice through the ocean's memory
Holocene sea-ice dynamics in Petermann Fjord in relation to ice tongue stability and Nares Strait ice arch formation
Presentation and evaluation of the Arctic sea ice forecasting system neXtSIM-F
Combined influence of oceanic and atmospheric circulations on Greenland sea ice concentration
Seasonal changes in sea ice kinematics and deformation in the Pacific sector of the Arctic Ocean in 2018/19
Year-round impact of winter sea ice thickness observations on seasonal forecasts
Ensemble-based estimation of sea-ice volume variations in the Baffin Bay
Sea ice drift and arch evolution in the Robeson Channel using the daily coverage of Sentinel-1 SAR data for the 2016–2017 freezing season
Brief communication: Arctic sea ice thickness internal variability and its changes under historical and anthropogenic forcing
Seasonal transition dates can reveal biases in Arctic sea ice simulations
The Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL) high-priority candidate mission
The MOSAiC ice floe: sediment-laden survivor from the Siberian shelf
Spectral attenuation of ocean waves in pack ice and its application in calibrating viscoelastic wave-in-ice models
New observations of the distribution, morphology and dissolution dynamics of cryogenic gypsum in the Arctic Ocean
Evaluation of Arctic sea ice drift and its dependency on near-surface wind and sea ice conditions in the coupled regional climate model HIRHAM–NAOSIM
Multidecadal Arctic sea ice thickness and volume derived from ice age
Going with the floe: tracking CESM Large Ensemble sea ice in the Arctic provides context for ship-based observations
The Arctic sea ice extent change connected to Pacific decadal variability
Impact of sea ice floe size distribution on seasonal fragmentation and melt of Arctic sea ice
Induced surface fluxes: a new framework for attributing Arctic sea ice volume balance biases to specific model errors
Comparison of ERA5 and ERA-Interim near-surface air temperature, snowfall and precipitation over Arctic sea ice: effects on sea ice thermodynamics and evolution
Benchmark seasonal prediction skill estimates based on regional indices
On the timescales and length scales of the Arctic sea ice thickness anomalies: a study based on 14 reanalyses
Past and future interannual variability in Arctic sea ice in coupled climate models
Arctic sea-ice-free season projected to extend into autumn
Definition differences and internal variability affect the simulated Arctic sea ice melt season
The potential of sea ice leads as a predictor for summer Arctic sea ice extent
Arctic climate: changes in sea ice extent outweigh changes in snow cover
Léo Edel, Jiping Xie, Anton Korosov, Julien Brajard, and Laurent Bertino
The Cryosphere, 19, 731–752, https://doi.org/10.5194/tc-19-731-2025, https://doi.org/10.5194/tc-19-731-2025, 2025
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This study developed a new method to estimate Arctic sea ice thickness from 1992 to 2010 using a combination of machine learning and data assimilation. By training a machine learning model on data from 2011 to 2022, past errors in sea ice thickness can be corrected, leading to improved estimations. This approach provides insights into historical changes in sea ice thickness, showing a notable decline from 1992 to 2022, and offers a valuable resource for future studies.
Francesco Cocetta, Lorenzo Zampieri, Julia Selivanova, and Doroteaciro Iovino
The Cryosphere, 18, 4687–4702, https://doi.org/10.5194/tc-18-4687-2024, https://doi.org/10.5194/tc-18-4687-2024, 2024
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Arctic sea ice is thinning and retreating because of global warming. Thus, the region is transitioning to a new state featuring an expansion of the marginal ice zone, a region where mobile ice interacts with waves from the open ocean. By analyzing 30 years of sea ice reconstructions that combine numerical models and observations, this paper proves that an ensemble of global ocean and sea ice reanalyses is an adequate tool for investigating the changing Arctic sea ice cover.
Joanna Davies, Kirsten Fahl, Matthias Moros, Alice Carter-Champion, Henrieka Detlef, Ruediger Stein, Christof Pearce, and Marit-Solveig Seidenkrantz
The Cryosphere, 18, 3415–3431, https://doi.org/10.5194/tc-18-3415-2024, https://doi.org/10.5194/tc-18-3415-2024, 2024
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Here, we evaluate the use of biomarkers for reconstructing sea ice between 1880 and 2017 from three sediment cores located in a transect across the Northeast Greenland continental shelf. We find that key changes, specifically the decline in sea-ice cover identified in observational records between 1971 and 1984, align with our biomarker reconstructions. This outcome supports the use of biomarkers for longer reconstructions of sea-ice cover in this region.
Nathan J. M. Laxague, Christopher J. Zappa, Andrew R. Mahoney, John Goodwin, Cyrus Harris, Robert E. Schaeffer, Roswell Schaeffer Sr., Sarah Betcher, Donna D. W. Hauser, Carson R. Witte, Jessica M. Lindsay, Ajit Subramaniam, Kate E. Turner, and Alex Whiting
The Cryosphere, 18, 3297–3313, https://doi.org/10.5194/tc-18-3297-2024, https://doi.org/10.5194/tc-18-3297-2024, 2024
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The state of sea ice strongly affects its absorption of solar energy. In May 2019, we flew uncrewed aerial vehicles (UAVs) equipped with sensors designed to quantify the sunlight that is reflected by sea ice at each wavelength over the sea ice of Kotzebue Sound, Alaska. We found that snow patches get darker (up to ~ 20 %) as they get smaller, while bare patches get darker (up to ~ 20 %) as they get larger. We believe that this difference is due to melting around the edges of small features.
Cyril Palerme, Thomas Lavergne, Jozef Rusin, Arne Melsom, Julien Brajard, Are Frode Kvanum, Atle Macdonald Sørensen, Laurent Bertino, and Malte Müller
The Cryosphere, 18, 2161–2176, https://doi.org/10.5194/tc-18-2161-2024, https://doi.org/10.5194/tc-18-2161-2024, 2024
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Sea ice forecasts are operationally produced using physically based models, but these forecasts are often not accurate enough for maritime operations. In this study, we developed a statistical correction technique using machine learning in order to improve the skill of short-term (up to 10 d) sea ice concentration forecasts produced by the TOPAZ4 model. This technique allows for the reduction of errors from the TOPAZ4 sea ice concentration forecasts by 41 % on average.
Dunwang Lu, Jianqiang Liu, Lijian Shi, Tao Zeng, Bin Cheng, Suhui Wu, and Manman Wang
The Cryosphere, 18, 1419–1441, https://doi.org/10.5194/tc-18-1419-2024, https://doi.org/10.5194/tc-18-1419-2024, 2024
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We retrieved sea ice drift in Fram Strait using the Chinese HaiYang 1D Coastal Zone Imager. The dataset is has hourly and daily intervals for analysis, and validation is performed using a synthetic aperture radar (SAR)-based product and International Arctic Buoy Programme (IABP) buoys. The differences between them are explained by investigating the spatiotemporal variability in sea ice motion. The accuracy of flow direction retrieval for sea ice drift is also related to sea ice displacement.
Ana Lúcia Lindroth Dauner, Frederik Schenk, Katherine Elizabeth Power, and Maija Heikkilä
The Cryosphere, 18, 1399–1418, https://doi.org/10.5194/tc-18-1399-2024, https://doi.org/10.5194/tc-18-1399-2024, 2024
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In this study, we analysed 14 sea-ice proxy records and compared them with the results from two different climate simulations from the Atlantic sector of the Arctic Ocean over the Common Era (last 2000 years). Both proxy and model approaches demonstrated a long-term sea-ice increase. The good correspondence suggests that the state-of-the-art sea-ice proxies are able to capture large-scale climate drivers. Short-term variability, however, was less coherent due to local-to-regional scale forcings.
Hannah Niehaus, Larysa Istomina, Marcel Nicolaus, Ran Tao, Aleksey Malinka, Eleonora Zege, and Gunnar Spreen
The Cryosphere, 18, 933–956, https://doi.org/10.5194/tc-18-933-2024, https://doi.org/10.5194/tc-18-933-2024, 2024
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Melt ponds are puddles of meltwater which form on Arctic sea ice in the summer period. They are darker than the ice cover and lead to increased absorption of solar energy. Global climate models need information about the Earth's energy budget. Thus satellite observations are used to monitor the surface fractions of melt ponds, ocean, and sea ice in the entire Arctic. We present a new physically based algorithm that can separate these three surface types with uncertainty below 10 %.
Zuzanna M. Swirad, A. Malin Johansson, and Eirik Malnes
The Cryosphere, 18, 895–910, https://doi.org/10.5194/tc-18-895-2024, https://doi.org/10.5194/tc-18-895-2024, 2024
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We used satellite images to create sea ice maps of Hornsund fjord, Svalbard, for nine seasons and calculated the percentage of the fjord that was covered by ice. On average, sea ice was present in Hornsund for 158 d per year, but it varied from year to year. April was the "iciest'" month and 2019/2020, 2021/22 and 2014/15 were the "iciest'" seasons. Our data can be used to understand sea ice conditions compared with other fjords of Svalbard and in studies of wave modelling and coastal erosion.
Miao Yu, Peng Lu, Matti Leppäranta, Bin Cheng, Ruibo Lei, Bingrui Li, Qingkai Wang, and Zhijun Li
The Cryosphere, 18, 273–288, https://doi.org/10.5194/tc-18-273-2024, https://doi.org/10.5194/tc-18-273-2024, 2024
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Variations in Arctic sea ice are related not only to its macroscale properties but also to its microstructure. Arctic ice cores in the summers of 2008 to 2016 were used to analyze variations in the ice inherent optical properties related to changes in the ice microstructure. The results reveal changing ice microstructure greatly increased the amount of solar radiation transmitted to the upper ocean even when a constant ice thickness was assumed, especially in marginal ice zones.
Geoffrey J. Dawson and Jack C. Landy
The Cryosphere, 17, 4165–4178, https://doi.org/10.5194/tc-17-4165-2023, https://doi.org/10.5194/tc-17-4165-2023, 2023
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In this study, we compared measurements from CryoSat-2 and ICESat-2 over Arctic summer sea ice to understand any possible biases between the two satellites. We found that there is a difference when we measure elevation over summer sea ice using CryoSat-2 and ICESat-2, and this is likely due to surface melt ponds. The differences we found were in good agreement with theoretical predictions, and this work will be valuable for summer sea ice thickness measurements from both altimeters.
Yanan Wang, Byongjun Hwang, Adam William Bateson, Yevgeny Aksenov, and Christopher Horvat
The Cryosphere, 17, 3575–3591, https://doi.org/10.5194/tc-17-3575-2023, https://doi.org/10.5194/tc-17-3575-2023, 2023
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Sea ice is composed of small, discrete pieces of ice called floes, whose size distribution plays a critical role in the interactions between the sea ice, ocean and atmosphere. This study provides an assessment of sea ice models using new high-resolution floe size distribution observations, revealing considerable differences between them. These findings point not only to the limitations in models but also to the need for more high-resolution observations to validate and calibrate models.
Sascha Willmes, Günther Heinemann, and Frank Schnaase
The Cryosphere, 17, 3291–3308, https://doi.org/10.5194/tc-17-3291-2023, https://doi.org/10.5194/tc-17-3291-2023, 2023
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Sea ice is an important constituent of the global climate system. We here use satellite data to identify regions in the Arctic where the sea ice breaks up in so-called leads (i.e., linear cracks) regularly during winter. This information is important because leads determine, e.g., how much heat is exchanged between the ocean and the atmosphere. We here provide first insights into the reasons for the observed patterns in sea-ice leads and their relation to ocean currents and winds.
Isolde A. Glissenaar, Jack C. Landy, David G. Babb, Geoffrey J. Dawson, and Stephen E. L. Howell
The Cryosphere, 17, 3269–3289, https://doi.org/10.5194/tc-17-3269-2023, https://doi.org/10.5194/tc-17-3269-2023, 2023
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Observations of large-scale ice thickness have unfortunately only been available since 2003, a short record for researching trends and variability. We generated a proxy for sea ice thickness in the Canadian Arctic for 1996–2020. This is the longest available record for large-scale sea ice thickness available to date and the first record reliably covering the channels between the islands in northern Canada. The product shows that sea ice has thinned by 21 cm over the 25-year record in April.
Ole Rieke, Marius Årthun, and Jakob Simon Dörr
The Cryosphere, 17, 1445–1456, https://doi.org/10.5194/tc-17-1445-2023, https://doi.org/10.5194/tc-17-1445-2023, 2023
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The Barents Sea is the region of most intense winter sea ice loss, and future projections show a continued decline towards ice-free conditions by the end of this century but with large fluctuations. Here we use climate model simulations to look at the occurrence and drivers of rapid ice change events in the Barents Sea that are much stronger than the average ice loss. A better understanding of these events will contribute to improved sea ice predictions in the Barents Sea.
Younjoo J. Lee, Wieslaw Maslowski, John J. Cassano, Jaclyn Clement Kinney, Anthony P. Craig, Samy Kamal, Robert Osinski, Mark W. Seefeldt, Julienne Stroeve, and Hailong Wang
The Cryosphere, 17, 233–253, https://doi.org/10.5194/tc-17-233-2023, https://doi.org/10.5194/tc-17-233-2023, 2023
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During 1979–2020, four winter polynyas occurred in December 1986 and February 2011, 2017, and 2018 north of Greenland. Instead of ice melting due to the anomalous warm air intrusion, the extreme wind forcing resulted in greater ice transport offshore. Based on the two ensemble runs, representing a 1980s thicker ice vs. a 2010s thinner ice, a dominant cause of these winter polynyas stems from internal variability of atmospheric forcing rather than from the forced response to a warming climate.
Alek A. Petty, Nicole Keeney, Alex Cabaj, Paul Kushner, and Marco Bagnardi
The Cryosphere, 17, 127–156, https://doi.org/10.5194/tc-17-127-2023, https://doi.org/10.5194/tc-17-127-2023, 2023
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We present upgrades to winter Arctic sea ice thickness estimates from NASA's ICESat-2. Our new thickness results show better agreement with independent data from ESA's CryoSat-2 compared to our first data release, as well as new, very strong comparisons with data collected by moorings in the Beaufort Sea. We analyse three winters of thickness data across the Arctic, including 50 cm thinning of the multiyear ice over this 3-year period.
John E. Walsh, Hajo Eicken, Kyle Redilla, and Mark Johnson
The Cryosphere, 16, 4617–4635, https://doi.org/10.5194/tc-16-4617-2022, https://doi.org/10.5194/tc-16-4617-2022, 2022
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Indicators for the start and end of annual breakup and freeze-up of sea ice at various coastal locations around the Arctic are developed. Relative to broader offshore areas, some of the coastal indicators show an earlier freeze-up and later breakup, especially at locations where landfast ice is prominent. However, the trends towards earlier breakup and later freeze-up are unmistakable over the post-1979 period in synthesized metrics of the coastal breakup/freeze-up indicators.
Abigail Smith, Alexandra Jahn, Clara Burgard, and Dirk Notz
The Cryosphere, 16, 3235–3248, https://doi.org/10.5194/tc-16-3235-2022, https://doi.org/10.5194/tc-16-3235-2022, 2022
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The timing of Arctic sea ice melt each year is an important metric for assessing how sea ice in climate models compares to satellite observations. Here, we utilize a new tool for creating more direct comparisons between climate model projections and satellite observations of Arctic sea ice, such that the melt onset dates are defined the same way. This tool allows us to identify climate model biases more clearly and gain more information about what the satellites are observing.
Juha Karvonen, Eero Rinne, Heidi Sallila, Petteri Uotila, and Marko Mäkynen
The Cryosphere, 16, 1821–1844, https://doi.org/10.5194/tc-16-1821-2022, https://doi.org/10.5194/tc-16-1821-2022, 2022
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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.
Yu Liang, Haibo Bi, Haijun Huang, Ruibo Lei, Xi Liang, Bin Cheng, and Yunhe Wang
The Cryosphere, 16, 1107–1123, https://doi.org/10.5194/tc-16-1107-2022, https://doi.org/10.5194/tc-16-1107-2022, 2022
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A record minimum July sea ice extent, since 1979, was observed in 2020. Our results reveal that an anomalously high advection of energy and water vapor prevailed during spring (April to June) 2020 over regions with noticeable sea ice retreat. The large-scale atmospheric circulation and cyclones act in concert to trigger the exceptionally warm and moist flow. The convergence of the transport changed the atmospheric characteristics and the surface energy budget, thus causing a severe sea ice melt.
Jinlei Chen, Shichang Kang, Wentao Du, Junming Guo, Min Xu, Yulan Zhang, Xinyue Zhong, Wei Zhang, and Jizu Chen
The Cryosphere, 15, 5473–5482, https://doi.org/10.5194/tc-15-5473-2021, https://doi.org/10.5194/tc-15-5473-2021, 2021
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Sea ice is retreating with rapid warming in the Arctic. It will continue and approach the worst predicted pathway released by the IPCC. The irreversible tipping point might show around 2060 when the oldest ice will have completely disappeared. It has a huge impact on human production. Ordinary merchant ships will be able to pass the Northeast Passage and Northwest Passage by the midcentury, and the opening time will advance to the next 10 years for icebreakers with moderate ice strengthening.
Qiang Wang, Sergey Danilov, Longjiang Mu, Dmitry Sidorenko, and Claudia Wekerle
The Cryosphere, 15, 4703–4725, https://doi.org/10.5194/tc-15-4703-2021, https://doi.org/10.5194/tc-15-4703-2021, 2021
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Using simulations, we found that changes in ocean freshwater content induced by wind perturbations can significantly affect the Arctic sea ice drift, thickness, concentration and deformation rates years after the wind perturbations. The impact is through changes in sea surface height and surface geostrophic currents and the most pronounced in warm seasons. Such a lasting impact might become stronger in a warming climate and implies the importance of ocean initialization in sea ice prediction.
Henrieka Detlef, Brendan Reilly, Anne Jennings, Mads Mørk Jensen, Matt O'Regan, Marianne Glasius, Jesper Olsen, Martin Jakobsson, and Christof Pearce
The Cryosphere, 15, 4357–4380, https://doi.org/10.5194/tc-15-4357-2021, https://doi.org/10.5194/tc-15-4357-2021, 2021
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Here we examine the Nares Strait sea ice dynamics over the last 7000 years and their implications for the late Holocene readvance of the floating part of Petermann Glacier. We propose that the historically observed sea ice dynamics are a relatively recent feature, while most of the mid-Holocene was marked by variable sea ice conditions in Nares Strait. Nonetheless, major advances of the Petermann ice tongue were preceded by a shift towards harsher sea ice conditions in Nares Strait.
Timothy Williams, Anton Korosov, Pierre Rampal, and Einar Ólason
The Cryosphere, 15, 3207–3227, https://doi.org/10.5194/tc-15-3207-2021, https://doi.org/10.5194/tc-15-3207-2021, 2021
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neXtSIM (neXt-generation Sea Ice Model) includes a novel and extremely realistic way of modelling sea ice dynamics – i.e. how the sea ice moves and deforms in response to the drag from winds and ocean currents. It has been developed over the last few years for a variety of applications, but this paper represents its first demonstration in a forecast context. We present results for the time period from November 2018 to June 2020 and show that it agrees well with satellite observations.
Sourav Chatterjee, Roshin P. Raj, Laurent Bertino, Sebastian H. Mernild, Meethale Puthukkottu Subeesh, Nuncio Murukesh, and Muthalagu Ravichandran
The Cryosphere, 15, 1307–1319, https://doi.org/10.5194/tc-15-1307-2021, https://doi.org/10.5194/tc-15-1307-2021, 2021
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Sea ice in the Greenland Sea (GS) is important for its climatic (fresh water), economical (shipping), and ecological contribution (light availability). The study proposes a mechanism through which sea ice concentration in GS is partly governed by the atmospheric and ocean circulation in the region. The mechanism proposed in this study can be useful for assessing the sea ice variability and its future projection in the GS.
Ruibo Lei, Mario Hoppmann, Bin Cheng, Guangyu Zuo, Dawei Gui, Qiongqiong Cai, H. Jakob Belter, and Wangxiao Yang
The Cryosphere, 15, 1321–1341, https://doi.org/10.5194/tc-15-1321-2021, https://doi.org/10.5194/tc-15-1321-2021, 2021
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Quantification of ice deformation is useful for understanding of the role of ice dynamics in climate change. Using data of 32 buoys, we characterized spatiotemporal variations in ice kinematics and deformation in the Pacific sector of Arctic Ocean for autumn–winter 2018/19. Sea ice in the south and west has stronger mobility than in the east and north, which weakens from autumn to winter. An enhanced Arctic dipole and weakened Beaufort Gyre in winter lead to an obvious turning of ice drifting.
Beena Balan-Sarojini, Steffen Tietsche, Michael Mayer, Magdalena Balmaseda, Hao Zuo, Patricia de Rosnay, Tim Stockdale, and Frederic Vitart
The Cryosphere, 15, 325–344, https://doi.org/10.5194/tc-15-325-2021, https://doi.org/10.5194/tc-15-325-2021, 2021
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Our study for the first time shows the impact of measured sea ice thickness (SIT) on seasonal forecasts of all the seasons. We prove that the long-term memory present in the Arctic winter SIT is helpful to improve summer sea ice forecasts. Our findings show that realistic SIT initial conditions to start a forecast are useful in (1) improving seasonal forecasts, (2) understanding errors in the forecast model, and (3) recognizing the need for continuous monitoring of world's ice-covered oceans.
Chao Min, Qinghua Yang, Longjiang Mu, Frank Kauker, and Robert Ricker
The Cryosphere, 15, 169–181, https://doi.org/10.5194/tc-15-169-2021, https://doi.org/10.5194/tc-15-169-2021, 2021
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An ensemble of four estimates of the sea-ice volume (SIV) variations in Baffin Bay from 2011 to 2016 is generated from the locally merged satellite observations, three modeled sea ice thickness sources (CMST, NAOSIM, and PIOMAS) and NSIDC ice drift data (V4). Results show that the net increase of the ensemble mean SIV occurs from October to April with the largest SIV increase in December, and the reduction occurs from May to September with the largest SIV decline in July.
Mohammed E. Shokr, Zihan Wang, and Tingting Liu
The Cryosphere, 14, 3611–3627, https://doi.org/10.5194/tc-14-3611-2020, https://doi.org/10.5194/tc-14-3611-2020, 2020
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This paper uses sequential daily SAR images covering the Robeson Channel to quantitatively study kinematics of individual ice floes with exploration of wind influence and the evolution of the ice arch at the entry of the channel. Results show that drift of ice floes within the Robeson Channel and the arch are both significantly influenced by wind. The study highlights the advantage of using the high-resolution daily SAR coverage in monitoring sea ice cover in narrow water passages.
Guillian Van Achter, Leandro Ponsoni, François Massonnet, Thierry Fichefet, and Vincent Legat
The Cryosphere, 14, 3479–3486, https://doi.org/10.5194/tc-14-3479-2020, https://doi.org/10.5194/tc-14-3479-2020, 2020
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We document the spatio-temporal internal variability of Arctic sea ice thickness and its changes under anthropogenic forcing, which is key to understanding, and eventually predicting, the evolution of sea ice in response to climate change.
The patterns of sea ice thickness variability remain more or less stable during pre-industrial, historical and future periods, despite non-stationarity on short timescales. These patterns start to change once Arctic summer ice-free events occur, after 2050.
Abigail Smith, Alexandra Jahn, and Muyin Wang
The Cryosphere, 14, 2977–2997, https://doi.org/10.5194/tc-14-2977-2020, https://doi.org/10.5194/tc-14-2977-2020, 2020
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The annual cycle of Arctic sea ice can be used to gain more information about how climate model simulations of sea ice compare to observations. In some models, the September sea ice area agrees with observations for the wrong reasons because biases in the timing of seasonal transitions compensate for other unrealistic sea ice characteristics. This research was done to provide new process-based metrics of Arctic sea ice using satellite observations, the CESM Large Ensemble, and CMIP6 models.
Michael Kern, Robert Cullen, Bruno Berruti, Jerome Bouffard, Tania Casal, Mark R. Drinkwater, Antonio Gabriele, Arnaud Lecuyot, Michael Ludwig, Rolv Midthassel, Ignacio Navas Traver, Tommaso Parrinello, Gerhard Ressler, Erik Andersson, Cristina Martin-Puig, Ole Andersen, Annett Bartsch, Sinead Farrell, Sara Fleury, Simon Gascoin, Amandine Guillot, Angelika Humbert, Eero Rinne, Andrew Shepherd, Michiel R. van den Broeke, and John Yackel
The Cryosphere, 14, 2235–2251, https://doi.org/10.5194/tc-14-2235-2020, https://doi.org/10.5194/tc-14-2235-2020, 2020
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The Copernicus Polar Ice and Snow Topography Altimeter will provide high-resolution sea ice thickness and land ice elevation measurements and the capability to determine the properties of snow cover on ice to serve operational products and services of direct relevance to the polar regions. This paper describes the mission objectives, identifies the key contributions the CRISTAL mission will make, and presents a concept – as far as it is already defined – for the mission payload.
Thomas Krumpen, Florent Birrien, Frank Kauker, Thomas Rackow, Luisa von Albedyll, Michael Angelopoulos, H. Jakob Belter, Vladimir Bessonov, Ellen Damm, Klaus Dethloff, Jari Haapala, Christian Haas, Carolynn Harris, Stefan Hendricks, Jens Hoelemann, Mario Hoppmann, Lars Kaleschke, Michael Karcher, Nikolai Kolabutin, Ruibo Lei, Josefine Lenz, Anne Morgenstern, Marcel Nicolaus, Uwe Nixdorf, Tomash Petrovsky, Benjamin Rabe, Lasse Rabenstein, Markus Rex, Robert Ricker, Jan Rohde, Egor Shimanchuk, Suman Singha, Vasily Smolyanitsky, Vladimir Sokolov, Tim Stanton, Anna Timofeeva, Michel Tsamados, and Daniel Watkins
The Cryosphere, 14, 2173–2187, https://doi.org/10.5194/tc-14-2173-2020, https://doi.org/10.5194/tc-14-2173-2020, 2020
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In October 2019 the research vessel Polarstern was moored to an ice floe in order to travel with it on the 1-year-long MOSAiC journey through the Arctic. Here we provide historical context of the floe's evolution and initial state for upcoming studies. We show that the ice encountered on site was exceptionally thin and was formed on the shallow Siberian shelf. The analyses presented provide the initial state for the analysis and interpretation of upcoming biogeochemical and ecological studies.
Sukun Cheng, Justin Stopa, Fabrice Ardhuin, and Hayley H. Shen
The Cryosphere, 14, 2053–2069, https://doi.org/10.5194/tc-14-2053-2020, https://doi.org/10.5194/tc-14-2053-2020, 2020
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Wave states in ice in polar oceans are mostly studied near the ice edge. However, observations in the internal ice field, where ice morphology is very different from the ice edge, are rare. Recently derived wave data from satellite imagery are easier and cheaper than field studies and provide large coverage. This work presents a way of using these data to have a close view of some key features in the wave propagation over hundreds of kilometers and calibrate models for predicting wave decay.
Jutta E. Wollenburg, Morten Iversen, Christian Katlein, Thomas Krumpen, Marcel Nicolaus, Giulia Castellani, Ilka Peeken, and Hauke Flores
The Cryosphere, 14, 1795–1808, https://doi.org/10.5194/tc-14-1795-2020, https://doi.org/10.5194/tc-14-1795-2020, 2020
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Based on an observed omnipresence of gypsum crystals, we concluded that their release from melting sea ice is a general feature in the Arctic Ocean. Individual gypsum crystals sank at more than 7000 m d−1, suggesting that they are an important ballast mineral. Previous observations found gypsum inside phytoplankton aggregates at 2000 m depth, supporting gypsum as an important driver for pelagic-benthic coupling in the ice-covered Arctic Ocean.
Xiaoyong Yu, Annette Rinke, Wolfgang Dorn, Gunnar Spreen, Christof Lüpkes, Hiroshi Sumata, and Vladimir M. Gryanik
The Cryosphere, 14, 1727–1746, https://doi.org/10.5194/tc-14-1727-2020, https://doi.org/10.5194/tc-14-1727-2020, 2020
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This study presents an evaluation of Arctic sea ice drift speed for the period 2003–2014 in a state-of-the-art coupled regional model for the Arctic, called HIRHAM–NAOSIM. In particular, the dependency of the drift speed on the near-surface wind speed and sea ice conditions is presented. Effects of sea ice form drag included by an improved parameterization of the transfer coefficients for momentum and heat over sea ice are discussed.
Yinghui Liu, Jeffrey R. Key, Xuanji Wang, and Mark Tschudi
The Cryosphere, 14, 1325–1345, https://doi.org/10.5194/tc-14-1325-2020, https://doi.org/10.5194/tc-14-1325-2020, 2020
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This study provides a consistent and accurate multi-decadal product of ice thickness and ice volume from 1984 to 2018 based on satellite-derived ice age. Sea ice volume trends from this dataset are stronger than the trends from other datasets. Changes in sea ice thickness contribute more to overall sea ice volume trends than changes in sea ice area do in all months.
Alice K. DuVivier, Patricia DeRepentigny, Marika M. Holland, Melinda Webster, Jennifer E. Kay, and Donald Perovich
The Cryosphere, 14, 1259–1271, https://doi.org/10.5194/tc-14-1259-2020, https://doi.org/10.5194/tc-14-1259-2020, 2020
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In autumn 2019, a ship will be frozen into the Arctic sea ice for a year to study system changes. We analyze climate model data from a group of experiments and follow virtual sea ice floes throughout a year. The modeled sea ice conditions along possible tracks are highly variable. Observations that sample a wide range of sea ice conditions and represent the variety and diversity in possible conditions are necessary for improving climate model parameterizations over all types of sea ice.
Xiao-Yi Yang, Guihua Wang, and Noel Keenlyside
The Cryosphere, 14, 693–708, https://doi.org/10.5194/tc-14-693-2020, https://doi.org/10.5194/tc-14-693-2020, 2020
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The post-2007 Arctic sea ice cover is characterized by a remarkable increase in annual cycle amplitude, which is attributed to multiyear variability in spring Bering sea ice extent. We demonstrated that changes of NPGO mode, by anomalous wind stress curl and Ekman pumping, trigger subsurface variability in the Bering basin. This accounts for the significant decadal oscillation of spring Bering sea ice after 2007. The study helps us to better understand the recent Arctic climate regime shift.
Adam W. Bateson, Daniel L. Feltham, David Schröder, Lucia Hosekova, Jeff K. Ridley, and Yevgeny Aksenov
The Cryosphere, 14, 403–428, https://doi.org/10.5194/tc-14-403-2020, https://doi.org/10.5194/tc-14-403-2020, 2020
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The Arctic sea ice cover has been observed to be decreasing, particularly in summer. We use numerical models to gain insight into processes controlling its seasonal and decadal evolution. Sea ice is made of pieces of ice called floes. Previous models have set these floes to be the same size, which is not supported by observations. In this study we show that accounting for variable floe size reveals the importance of sea ice regions close to the open ocean in driving seasonal retreat of sea ice.
Alex West, Mat Collins, Ed Blockley, Jeff Ridley, and Alejandro Bodas-Salcedo
The Cryosphere, 13, 2001–2022, https://doi.org/10.5194/tc-13-2001-2019, https://doi.org/10.5194/tc-13-2001-2019, 2019
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This study presents a framework for examining the causes of model errors in Arctic sea ice volume, using HadGEM2-ES as a case study. Simple models are used to estimate how much of the error in energy arriving at the ice surface is due to error in key Arctic climate variables. The method quantifies how each variable affects sea ice volume balance and shows that for HadGEM2-ES an annual mean low bias in ice thickness is likely due to errors in surface melt onset.
Caixin Wang, Robert M. Graham, Keguang Wang, Sebastian Gerland, and Mats A. Granskog
The Cryosphere, 13, 1661–1679, https://doi.org/10.5194/tc-13-1661-2019, https://doi.org/10.5194/tc-13-1661-2019, 2019
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A warm bias and higher total precipitation and snowfall were found in ERA5 compared with ERA-Interim (ERA-I) over Arctic sea ice. The warm bias in ERA5 was larger in the cold season when 2 m air temperature was < −25 °C and smaller in the warm season than in ERA-I. Substantial anomalous Arctic rainfall in ERA-I was reduced in ERA5, particularly in summer and autumn. When using ERA5 and ERA-I to force a 1-D sea ice model, the effects on ice growth are very small (cm) during the freezing period.
John E. Walsh, J. Scott Stewart, and Florence Fetterer
The Cryosphere, 13, 1073–1088, https://doi.org/10.5194/tc-13-1073-2019, https://doi.org/10.5194/tc-13-1073-2019, 2019
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Persistence-based statistical forecasts of a Beaufort Sea ice severity index as well as September pan-Arctic ice extent show significant statistical skill out to several seasons when the data include the trend. However, this apparent skill largely vanishes when the trends are removed from the data. This finding is consistent with the notion of a springtime “predictability barrier” that has been found in sea ice forecasts based on more sophisticated methods.
Leandro Ponsoni, François Massonnet, Thierry Fichefet, Matthieu Chevallier, and David Docquier
The Cryosphere, 13, 521–543, https://doi.org/10.5194/tc-13-521-2019, https://doi.org/10.5194/tc-13-521-2019, 2019
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The Arctic is a main component of the Earth's climate system. It is fundamental to understand the behavior of Arctic sea ice coverage over time and in space due to many factors, e.g., shipping lanes, the travel and tourism industry, hunting and fishing activities, mineral resource extraction, and the potential impact on the weather in midlatitude regions. In this work we use observations and results from models to understand how variations in the sea ice thickness change over time and in space.
John R. Mioduszewski, Stephen Vavrus, Muyin Wang, Marika Holland, and Laura Landrum
The Cryosphere, 13, 113–124, https://doi.org/10.5194/tc-13-113-2019, https://doi.org/10.5194/tc-13-113-2019, 2019
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Arctic sea ice is projected to thin substantially in every season by the end of the 21st century with a corresponding increase in its interannual variability as the rate of ice loss peaks. This typically occurs when the mean ice thickness falls between 0.2 and 0.6 m. The high variability in both growth and melt processes is the primary factor resulting in increased ice variability. This study emphasizes the importance of short-term variations in ice cover within the mean downward trend.
Marion Lebrun, Martin Vancoppenolle, Gurvan Madec, and François Massonnet
The Cryosphere, 13, 79–96, https://doi.org/10.5194/tc-13-79-2019, https://doi.org/10.5194/tc-13-79-2019, 2019
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The present analysis shows that the increase in the Arctic ice-free season duration will be asymmetrical, with later autumn freeze-up contributing about twice as much as earlier spring retreat. This feature is robustly found in a hierarchy of climate models and is consistent with a simple mechanism: solar energy is absorbed more efficiently than it can be released in non-solar form and should emerge out of variability within the next few decades.
Abigail Smith and Alexandra Jahn
The Cryosphere, 13, 1–20, https://doi.org/10.5194/tc-13-1-2019, https://doi.org/10.5194/tc-13-1-2019, 2019
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Here we assessed how natural climate variations and different definitions impact the diagnosed and projected Arctic sea ice melt season length using model simulations. Irrespective of the definition or natural variability, the sea ice melt season is projected to lengthen, potentially by as much as 4–5 months by 2100 under the business as usual scenario. We also find that different definitions have a bigger impact on melt onset, while natural variations have a bigger impact on freeze onset.
Yuanyuan Zhang, Xiao Cheng, Jiping Liu, and Fengming Hui
The Cryosphere, 12, 3747–3757, https://doi.org/10.5194/tc-12-3747-2018, https://doi.org/10.5194/tc-12-3747-2018, 2018
Aaron Letterly, Jeffrey Key, and Yinghui Liu
The Cryosphere, 12, 3373–3382, https://doi.org/10.5194/tc-12-3373-2018, https://doi.org/10.5194/tc-12-3373-2018, 2018
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Significant reductions in Arctic sea ice and snow cover on Arctic land have led to increases in absorbed solar energy by the surface. Does one play a more important role in Arctic climate change? Using 34 years of satellite data we found that solar energy absorption increased by 10 % over the ocean, which was 3 times greater than over land. Therefore, the decreasing sea ice cover, not changes in terrestrial snow cover, has been the dominant feedback mechanism over the last few decades.
Cited articles
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. a
Belter, H. J., Janout, M. A., Hölemann, J. A., and Krumpen, T.: Daily mean sea
ice draft from moored upward-looking Acoustic Doppler Current Profilers
(ADCPs) in the Laptev Sea from 2003 to 2016, PANGAEA [data set] https://doi.org/10.1594/PANGAEA.912927,
2020. a, b
Bocquet, M. and Fleury, S.: Arctic sea ice radar freeboard from ERS-2, Envisat
and CryoSat-2, Zenodo [data set], https://doi.org/10.5281/zenodo.7712503, 2023. a
Brockley, D. J., Baker, S., Femenias, P., Martinez, B., Massmann, F.-H., Otten,
M., Paul, F., Picard, B., Prandi, P., Roca, M., Rudenko, S., Scharroo, R.,
and Visser, P.: REAPER: Reprocessing 12 Years of ERS-1 and ERS-2
Altimeters and Microwave Radiometer Data, IEEE T.
Geosci. Remote, 55, 5506–5514,
https://doi.org/10.1109/TGRS.2017.2709343, 2017. a, b
Brodzik, M. J., Billingsley, B., Haran, T., Raup, B., and Savoie, M. H.:
EASE-Grid 2.0: Incremental but Significant Improvements for
Earth-Gridded Data Sets, ISPRS Int. J.
Geo-Inf., 1, 32–45, https://doi.org/10.3390/ijgi1010032, 2012. a
Calafat, F., Cipollini, P., Bouffard, J., Snaith, H., and Féménias, P.:
Evaluation of new CryoSat-2 products over the ocean, Remote Sens.
Environ., 191, 131–144, https://doi.org/10.1016/j.rse.2017.01.009,
2017. a
Carrere, L., Lyard, F., Cancet, M., and Guillot, A.: FES 2014, a new tidal
model on the global ocean with enhanced accuracy in shallow seas and in the
Arctic region, EGU General Assembly, 17, p. 5481, Vienna, Austria,
https://meetingorganizer.copernicus.org/EGU2015/EGU2015-5481-1.pdf (last access: 19 July 2023), 2015. a
Cavalieri, D., Parkinson, C., Gloersen, P., and Zwally, H. J.: Sea Ice
Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive
Microwave Data, Version 1, National Snow and Ice Data Center [data set], https://doi.org/10.5067/8GQ8LZQVL0VL, 1996. a, b
Chelton, D. B., Walsh, E. J., and MacArthur, J. L.: Pulse Compression and
Sea Level Tracking in Satellite Altimetry, J. Atmos.
Ocean. Tech., 6, 407–438,
https://doi.org/10.1175/1520-0426(1989)006<0407:PCASLT>2.0.CO;2, 1989. a
Crameri, F.: Scientific colour maps, Zenodo, https://doi.org/10.5281/ZENODO.1243862, 2021. a
Crameri, F., Shephard, G. E., and Heron, P. J.: The misuse of colour in science
communication, Nat. Commun., 11, 5444,
https://doi.org/10.1038/s41467-020-19160-7, 2020. a
Drucker, R., Martin, S., and Moritz, R.: Observations of ice thickness and
frazil ice in the St. Lawrence Island polynya from satellite imagery, upward
looking sonar, and salinity/temperature moorings, J. Geophys. Res.-Oceans, 108, 3149,
https://doi.org/10.1029/2001JC001213, 2003. a
Environment Canada: Ice Thickness Program Collection, 1947-2002, https://open.canada.ca/data/en/dataset/054cb024-e0bc-43ae-90c7-d9e23517ab8e, last access: 28 June 2021a. a
Environment Canada: Ice Thickness Program Collection, 2002-, https://open.canada.ca/data/en/dataset/8b624b7b-2e8f-436b-b9bd-f31c2e6613cf, last access: 28 June 2021b. a
European Space Agency: 2014, ERS-1/2 Radar Altimeter REAPER Sensor Geophysical Data Record – SGDR [ERS_ALT_2S], Version 1.08, https://doi.org/10.57780/ers-07698ce, 2014. a
European Space Agency: RA-2 Geophysical Data Record, Version 3.0, ESA [data set], https://doi.org/10.5270/EN1-ajb696a, 2018. a
European Space Agency: L1b SAR Precise Orbit, Baseline D, ESA [data set], https://doi.org/10.5270/CR2-2cnblvi, 2019a. a
European Space Agency: L1b SARin Precise Orbit, Baseline D, ESA [data set], https://doi.org/10.5270/CR2-u3805kw, 2019b. a
Garnier, F., Fleury, S., Garric, G., Bouffard, J., Tsamados, M., Laforge, A., Bocquet, M., Fredensborg Hansen, R. M., and Remy, F.: Advances in altimetric snow depth estimates using bi-frequency SARAL and CryoSat-2 Ka–Ku measurements, The Cryosphere, 15, 5483–5512, https://doi.org/10.5194/tc-15-5483-2021, 2021. a
Giles, K. A., Laxon, S. W., and Worby, A. P.: Antarctic sea ice elevation from
satellite radar altimetry, Geophys. Res. Lett., 35, L03503,
https://doi.org/10.1029/2007GL031572, 2008. a
Guerreiro, K. and Fleury, S.:
Sea-Ice Thickness North Hemisphere Envisat, CTOH [data set], https://doi.org/10.6096/CTOH_SIT_NH_ENV_2017_01,
2022. a
Guerreiro, K., Fleury, S., Zakharova, E., Kouraev, A., Rémy, F., and Maisongrande, P.: Comparison of CryoSat-2 and ENVISAT radar freeboard over Arctic sea ice: toward an improved Envisat freeboard retrieval, The Cryosphere, 11, 2059–2073, https://doi.org/10.5194/tc-11-2059-2017, 2017. a, b, c, d, e, f, g, h, i, j
Haas, C., Lobach, J., Hendricks, S., Rabenstein, L., and Pfaffling, A.:
Helicopter-borne measurements of sea ice thickness, using a small and
lightweight, digital EM system, J. Appl. Geophys., 67, 234–241,
https://doi.org/10.1016/j.jappgeo.2008.05.005, 2009. a
Helm, V., Humbert, A., and Miller, H.: Elevation and elevation change of Greenland and Antarctica derived from CryoSat-2, The Cryosphere, 8, 1539–1559, https://doi.org/10.5194/tc-8-1539-2014, 2014. a
Hendricks, S., Paul, S., and Rinne, E.: ESA Sea Ice Climate Change Initiative
(Sea_Ice_cci): Northern hemisphere sea ice thickness from the Envisat
satellite on a monthly grid (L3C), v2.0, Centre for Environmental Data
Analysis (CEDA) [data set], https://doi.org/10.5285/F4C34F4F0F1D4D0DA06D771F6972F180, 2018. a, b
Kendall, M. G.: Rank correlation methods, Oxford University Press,
ISBN 10: 0195208374, ISBN 13 9780195208375, 1990. a
Kingma, D. P. and Ba, J.: Adam: A Method for Stochastic Optimization,
3rd International Conference for Learning Representations, San Diego, 2015,
https://doi.org/10.48550/arXiv.1412.6980, 2014. a
Krishfield, R. A., Proshutinsky, A., Tateyama, K., Williams, W. J., Carmack,
E. C., McLaughlin, F. A., and Timmermans, M.-L.: Deterioration of perennial
sea ice in the Beaufort Gyre from 2003 to 2012 and its impact on the
oceanic freshwater cycle: SEA ICE IN THE BG FROM 2003 TO 2012,
J. Geophys. Res.-Oceans, 119, 1271–1305,
https://doi.org/10.1002/2013JC008999, 2014. a, b, c
Kurtz, N. T., Farrell, S. L., Studinger, M., Galin, N., Harbeck, J. P., Lindsay, R., Onana, V. D., Panzer, B., and Sonntag, J. G.: Sea ice thickness, freeboard, and snow depth products from Operation IceBridge airborne data, The Cryosphere, 7, 1035–1056, https://doi.org/10.5194/tc-7-1035-2013, 2013. a, b
Kurtz, N. T., Galin, N., and Studinger, M.: An improved CryoSat-2 sea ice freeboard retrieval algorithm through the use of waveform fitting, The Cryosphere, 8, 1217–1237, https://doi.org/10.5194/tc-8-1217-2014, 2014. a, b
Kwok, R.: Simulated effects of a snow layer on retrieval of CryoSat-2 sea ice
freeboard, Geophys. Res. Lett., 41, 5014–5020,
https://doi.org/10.1002/2014GL060993, 2014. a
Kwok, R. and Haas, C.: Effects of radar side-lobes on snow depth retrievals
from Operation IceBridge, J. Glaciol., 61, 576–584,
https://doi.org/10.3189/2015JoG14J229, 2015. a
Kwok, R., Kurtz, N. T., Brucker, L., Ivanoff, A., Newman, T., Farrell, S. L., King, J., Howell, S., Webster, M. A., Paden, J., Leuschen, C., MacGregor, J. A., Richter-Menge, J., Harbeck, J., and Tschudi, M.: Intercomparison of snow depth retrievals over Arctic sea ice from radar data acquired by Operation IceBridge, The Cryosphere, 11, 2571–2593, https://doi.org/10.5194/tc-11-2571-2017, 2017. a
Laforge, A., Fleury, S., Dinardo, S., Garnier, F., Remy, F., Benveniste, J.,
Bouffard, J., and Verley, J.: Toward improved sea ice freeboard observation
with SAR altimetry using the physical retracker SAMOSA+, Adv.
Space Res., 68, 732–745, https://doi.org/10.1016/j.asr.2020.02.001, 2020. a, b
Landy, J. C., Tsamados, M., and Scharien, R. K.: A Facet-Based Numerical
Model for Simulating SAR Altimeter Echoes From Heterogeneous
Sea Ice Surfaces, IEEE T. Geosci. Remote,
57, 4164–4180, https://doi.org/10.1109/TGRS.2018.2889763, 2019. a
Landy, J. C., Petty, A. A., Tsamados, M., and Stroeve, J. C.: Sea Ice
Roughness Overlooked as a Key Source of Uncertainty in
CryoSat-2 Ice Freeboard Retrievals, J. Geophys.
Res.-Oceans, 125, e2019JC015820, https://doi.org/10.1029/2019JC015820, 2020 a, b, c, d
Landy, J. C., Dawson, G. J., Tsamados, M., Bushuk, M., Stroeve, J. C., Howell,
S. E. L., Krumpen, T., Babb, D. G., Komarov, A. S., Heorton, H. D. B. S.,
Belter, H. J., and Aksenov, Y.: A year-round satellite sea-ice thickness
record from CryoSat-2, Nature, 609, 517–522,
https://doi.org/10.1038/s41586-022-05058-5, 2022. a
Laxon, S.: Sea ice altimeter processing scheme at the EODC, Int.
J. Remote Sens., 15, 915–924, https://doi.org/10.1080/01431169408954124,
1994. a, b
Laxon, S., Peacock, N., and Smith, D.: High interannual variability of sea ice
thickness in the Arctic region, Nature,
425, 947–950, https://doi.org/10.1038/nature02050,
2003. a, b, c
Lindsay, R. and Schweiger, A.: Arctic sea ice thickness loss determined using subsurface, aircraft, and satellite observations, The Cryosphere, 9, 269–283, https://doi.org/10.5194/tc-9-269-2015, 2015. a
Liston, G., Stroeve, J., and Itkin, P.: Lagrangian Snow Distributions for
Sea-Ice Applications, National Snow and Ice Data Center [data set], https://doi.org/10.5067/27A0P5M6LZBI,
2020a. a, b
Liston, G. E., Itkin, P., Stroeve, J., Tschudi, M., Stewart, J. S., Pedersen,
S. H., Reinking, A. K., and Elder, K.: A Lagrangian Snow-Evolution System
for Sea-Ice Applications (SnowModel-LG): Part I – Model Description, J. Geophys. Res.-Oceans,
125, e2019JC015913, https://doi.org/10.1029/2019JC015913, 2020b. a
Mallett, R. D. C., Lawrence, I. R., Stroeve, J. C., Landy, J. C., and Tsamados, M.: Brief communication: Conventional assumptions involving the speed of radar waves in snow introduce systematic underestimates to sea ice thickness and seasonal growth rate estimates, The Cryosphere, 14, 251–260, https://doi.org/10.5194/tc-14-251-2020, 2020. a
Mann, H. B.: Nonparametric tests against trend, Econometrica, 13, 245–259, 1945. a
Meier, W. N., Hovelsrud, G. K., van Oort, B. E., 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:
ARCTIC SEA ICE: REVIEW OF RECENT CHANGES, Rev.
Geophys., 52, 185–217, https://doi.org/10.1002/2013RG000431, 2014. a
Meier, W. N., P. D., Farrell, S., Haas, C., Hendricks, S., Petty, A., Webster,
M., Divine, D., Gerland, S., Kaleschke, L., Ricker, R., Steer, A.,
Tian-Kunze, X., Tschudi, M., and Wood, K.: NOAA Arctic Report Card
2021: Sea Ice, United States,
National Oceanic and Atmospheric Administration, Office of Oceanic and
Atmospheric Research, Global Ocean Monitoring and Observing (GOMO) Program,https://doi.org/10.25923/Y2WD-FN85,
2021. a
Melling, H.: Ice Draft and Ice Velocity Data in the Beaufort Sea, 1990–2003,
Version 1, National Snow and Ice Data Center [data set], https://doi.org/10.7265/N58913S6, 2008. a, b, c
Nandan, V., Geldsetzer, T., Yackel, J., Mahmud, M., Scharien, R., Howell, S.,
King, J., Ricker, R., and Else, B.: Effect of Snow Salinity on
CryoSat-2 Arctic First-Year Sea Ice Freeboard Measurements:
Sea Ice Brine-Snow Effect on CryoSat-2, Geophys. Res.
Lett., 44, 10419–10426, https://doi.org/10.1002/2017GL074506, 2017. a
National Snow and Ice Data Center: Submarine Upward Looking Sonar Ice Draft
Profile Data and Statistics, Version 1, National Snow and Ice Data Center [data set], https://doi.org/10.7265/N54Q7RWK, 2006. a, b
Paul, S., Hendricks, S., Ricker, R., Kern, S., and Rinne, E.: Empirical parametrization of Envisat freeboard retrieval of Arctic and Antarctic sea ice based on CryoSat-2: progress in the ESA Climate Change Initiative, The Cryosphere, 12, 2437–2460, https://doi.org/10.5194/tc-12-2437-2018, 2018. a, b, c, d, e, f
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel,
O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J.,
Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.:
Scikit-learn: Machine Learning in Python, J. Mach. Learn.
Res., 12, 2825–2830, 2011. a, b
Poisson, J.-C., Quartly, G. D., Kurekin, A. A., Thibaut, P., Hoang, D., and
Nencioli, F.: Development of an ENVISAT Altimetry Processor Providing
Sea Level Continuity Between Open Ocean and Arctic Leads,
IEEE T. Geosci. Remote, 56, 5299–5319,
https://doi.org/10.1109/TGRS.2018.2813061, 2018. a, b
Raney, R.: A delay/Doppler radar altimeter for ice sheet monitoring, in: 1995
International Geoscience and Remote Sensing Symposium, IGARSS
'95, Quantitative Remote Sensing for Science and Applications,
IEEE, Firenze, Italy, vol. 2, 862–864, https://doi.org/10.1109/IGARSS.1995.521080,
1995. a
Rheinlænder, J. W., Davy, R., Ólason, E., Rampal, P., Spensberger, C.,
Williams, T. D., Korosov, A., and Spengler, T.: Driving Mechanisms of an
Extreme Winter Sea Ice Breakup Event in the Beaufort Sea,
Geophys. Res. Lett., 49, e2022GL099024, https://doi.org/10.1029/2022GL099024, 2022. a
Ricker, R., Hendricks, S., Perovich, D. K., Helm, V., and Gerdes, R.: Impact of
snow accumulation on CryoSat-2 range retrievals over Arctic sea ice: An
observational approach with buoy data, Geophys. Res. Lett., 42,
4447–4455, https://doi.org/10.1002/2015GL064081, 2015. a
Roca, M., Laxon, S., and Zelli, C.: The EnviSat RA-2 Instrument Design and
Tracking Performance, IEEE T. Geosci. Remote, 47,
3489–3506, https://doi.org/10.1109/TGRS.2009.2020793, 2009. a, b
Rothrock, D. A. and Wensnahan, M.: The Accuracy of Sea Ice Drafts Measured from
U.S. Navy Submarines, J. Atmos. Ocean. Tech., 24, 1936–1949, https://doi.org/10.1175/JTECH2097.1, 2007. a
Sen, P. K.: Estimates of the regression coefficient based on Kendall's tau,
J. Am. Stat. Assoc., 63, 1379–1389, 1968. a
Stammer, D.: Satellite altimetry over oceans and land surfaces, Earth
observation of global changes, 2018. a
Stroeve, J. and Notz, D.: Changing state of Arctic sea ice across all
seasons, Environ. Res. Lett., 13, 103001,
https://doi.org/10.1088/1748-9326/aade56, 2018. a, b, c
Stroeve, J., Liston, G. E., Buzzard, S., Zhou, L., Mallett, R., Barrett, A.,
Tschudi, M., Tsamados, M., Itkin, P., and Stewart, J. S.: A Lagrangian
Snow Evolution System for Sea Ice Applications
(SnowModel-LG): Part II – Analyses, J. Geophys.
Res.-Oceans, 125, e2019JC015900, https://doi.org/10.1029/2019JC015900, 2020. a
Stroeve, J. C., Serreze, M. C., Holland, M. M., Kay, J. E., Malanik, J., and
Barrett, A. P.: The Arctic’s rapidly shrinking sea ice cover: a research
synthesis, Climatic Change, 110, 1005–1027, https://doi.org/10.1007/s10584-011-0101-1,
2012. a
Theil, H.: A rank-invariant method of linear and polynomial regression
analysis, Stichting Mathematisch Centrum, Statistische Afdeling, 12, 467–482, 1950. a
Tilling, R., Ridout, A., and Shepherd, A.: Assessing the Impact of Lead and
Floe Sampling on Arctic Sea Ice Thickness Estimates from Envisat and
CryoSat-2, J. Geophys. Res.-Oceans, 124, 7473–7485,
https://doi.org/10.1029/2019JC015232, 2019.
a, b, c
Tschudi, M., Meier, W. N., Stewart, J. S., Fowler, C., and Maslanikand, J.:
EASE-Grid Sea Ice Age, National Snow and Ice Data Center [data set], https://doi.org/10.5067/UTAV7490FEPB, 2019. a, b
Ulaby, F. T., Moore, R. K., and Fung, A. K.: Microwave remote sensing: Active
and passive, Volume 3 – From theory to applications, 3, https://doi.org/10.1017/S0016756800015831, 1986. a
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T.,
Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van
der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson,
A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, İ., Feng,
Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R.,
Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro,
A. H., Pedregosa, F., van Mulbregt, P., and SciPy 1.0 Contributors:
SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python,
Nat. Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020. a
Wadhams, P.: Arctic sea ice morphology and its measurement, Arctic
Technology and Policy, edited by: Dyer, I. and Chryssostomidis,
C., Hemisphere Publishing Corp., Washington, DC, 1984. a
Wadhams, P. and Horne, R. J.: An Analysis Of Ice Profiles Obtained
By Submarine Sonar In The Beaufort Sea, J. Glaciol.,
25, 401–424, https://doi.org/10.3189/S0022143000015264, 1980. 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
Wensnahan, M.: Sea-ice draft from submarine-based sonar: Establishing a
consistent record from analog and digitally recorded data, Geophys.
Res. Lett., 32, L11502, https://doi.org/10.1029/2005GL022507, 2005. a
Wingham, D. J., Francis, C. R., Baker, S., Bouzinac, C., Brockley, D., Cullen,
R., de Chateau-Thierry, P., Laxon, S. W., Mallow, U., Mavrocordatos, C.,
Phalippou, L., Ratier, G., Rey, L., Rostan, F., Viau, P., and Wallis, D. W.:
CryoSat: A mission to determine the fluctuations in Earth’s land and
marine ice fields, Adv. Space Res., 37, 841–871, https://doi.org/10.1016/j.asr.2005.07.027, 2006. a, b, c, d, e, f, g
Witte, H. and Fahrbach, E.: AWI Moored ULS Data, Greenland Sea and Fram
Strait, 1991–2002, Version 1, National Snow and Ice Data Center [data set], https://doi.org/10.7265/N5G15XSR, 2005. a, b
Yi, D. and Zwally, H. J.: Arctic Sea Ice Freeboard and Thickness,
Version 1, National Snow and Ice Data Center [data set], https://doi.org/10.5067/SXJVJ3A2XIZT, 2009. a, b
Zwally, H. J., Yi, D., Kwok, R., and Zhao, Y.: ICESat measurements of sea ice
freeboard and estimates of sea ice thickness in the Weddell Sea, J. Geophys. Res., 113, C02S15, https://doi.org/10.1029/2007JC004284, 2008. a
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.
Sea ice has a large interannual variability, and studying its evolution requires long time...
