Articles | Volume 17, issue 8
https://doi.org/10.5194/tc-17-3291-2023
© 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-17-3291-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Aug 2023
Research article |
| 17 Aug 2023
| 17 Aug 2023
Patterns of wintertime Arctic sea-ice leads and their relation to winds and ocean currents
Department of Environmental Meteorology, Trier University, Trier, Germany
Günther Heinemann
Department of Environmental Meteorology, Trier University, Trier, Germany
Frank Schnaase
Alfred Wegener Institute for Polar and Marine Research, Bremerhaven, Germany
Related authors
Luisa von Albedyll, Stefan Hendricks, Nils Hutter, Dmitrii Murashkin, Lars Kaleschke, Sascha Willmes, Linda Thielke, Xiangshan Tian-Kunze, Gunnar Spreen, and Christian Haas
The Cryosphere, 18, 1259–1285, https://doi.org/10.5194/tc-18-1259-2024, https://doi.org/10.5194/tc-18-1259-2024, 2024
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Leads (openings in sea ice cover) are created by sea ice dynamics. Because they are important for many processes in the Arctic winter climate, we aim to detect them with satellites. We present two new techniques to detect lead widths of a few hundred meters at high spatial resolution (700 m) and independent of clouds or sun illumination. We use the MOSAiC drift 2019–2020 in the Arctic for our case study and compare our new products to other existing lead products.
Thomas Krumpen, Luisa von Albedyll, Helge F. Goessling, Stefan Hendricks, Bennet Juhls, Gunnar Spreen, Sascha Willmes, H. Jakob Belter, Klaus Dethloff, Christian Haas, Lars Kaleschke, Christian Katlein, Xiangshan Tian-Kunze, Robert Ricker, Philip Rostosky, Janna Rückert, Suman Singha, and Julia Sokolova
The Cryosphere, 15, 3897–3920, https://doi.org/10.5194/tc-15-3897-2021, https://doi.org/10.5194/tc-15-3897-2021, 2021
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We use satellite data records collected along the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) drift to categorize ice conditions that shaped and characterized the floe and surroundings during the expedition. A comparison with previous years is made whenever possible. The aim of this analysis is to provide a basis and reference for subsequent research in the six main research areas of atmosphere, ocean, sea ice, biogeochemistry, remote sensing and ecology.
Alexander D. Fraser, Robert A. Massom, Kay I. Ohshima, Sascha Willmes, Peter J. Kappes, Jessica Cartwright, and Richard Porter-Smith
Earth Syst. Sci. Data, 12, 2987–2999, https://doi.org/10.5194/essd-12-2987-2020, https://doi.org/10.5194/essd-12-2987-2020, 2020
Short summary
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Landfast ice, or
fast ice, is a form of sea ice which is mechanically fastened to stationary parts of the coast. Long-term and accurate knowledge of its extent around Antarctica is critical for understanding a number of important Antarctic coastal processes, yet no accurate, large-scale, long-term dataset of its extent has been available. We address this data gap with this new dataset compiled from satellite imagery, containing high-resolution maps of Antarctic fast ice from 2000 to 2018.
Andreas Preußer, Günther Heinemann, Sascha Willmes, and Stephan Paul
The Cryosphere, 10, 3021–3042, https://doi.org/10.5194/tc-10-3021-2016, https://doi.org/10.5194/tc-10-3021-2016, 2016
Short summary
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We present spatial and temporal characteristics of 17 Arctic polynya regions. By using an energy balance model, daily thin-ice thickness distributions are derived from TIR satellite and atmospheric reanalysis data. All polynyas combined yield an average ice production of about 1811 km3 per winter. Interestingly, we find distinct regional differences in calculated trends over the last 13 years. Finally, we set a special focus on the Laptev Sea region and its relation to the Transpolar Drift.
Oliver Gutjahr, Günther Heinemann, Andreas Preußer, Sascha Willmes, and Clemens Drüe
The Cryosphere, 10, 2999–3019, https://doi.org/10.5194/tc-10-2999-2016, https://doi.org/10.5194/tc-10-2999-2016, 2016
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We estimated the formation of new sea ice within polynyas in the Laptev Sea (Siberia) with the regional climate model COSMO-CLM at 5 km horizontal resolution. Fractional sea ice and the representation of thin ice is often neglected in atmospheric models. Our study demonstrates, however, that the way thin ice in polynyas is represented in the model considerably affects the amount of newly formed sea-ice and the air–ice–ocean heat flux. Both processes impact the Arctic sea-ice budget.
S. Paul, S. Willmes, and G. Heinemann
The Cryosphere, 9, 2027–2041, https://doi.org/10.5194/tc-9-2027-2015, https://doi.org/10.5194/tc-9-2027-2015, 2015
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We established a 13-year-long MODIS-derived thin-ice thickness data set from which we derived information about polynya dynamics in the southern Weddell Sea. In contrast to other studies, we do not focus on a single region but instead discuss polynya dynamics for the complete coastal area. The higher spatial resolution of MODIS compared to passive-microwave sensors enables us to resolve even very narrow coastal polynyas that would remain otherwise undetected.
A. Preußer, S. Willmes, G. Heinemann, and S. Paul
The Cryosphere, 9, 1063–1073, https://doi.org/10.5194/tc-9-1063-2015, https://doi.org/10.5194/tc-9-1063-2015, 2015
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The Storfjorden polynya (Svalbard) forms regularly under the influence of strong north-easterly winds. In this study, spatial and temporal characteristics for the period 2002/03-2013/14 were inferred from daily calculated thin-ice thickness distributions, based on MODIS ice surface temperatures and ERA-interim reanalysis.
With an estimated average ice production of 28.3km³/winter, this polynya system is of particular interest regarding its potential contribution to deep water formation.
S. Willmes, M. Nicolaus, and C. Haas
The Cryosphere, 8, 891–904, https://doi.org/10.5194/tc-8-891-2014, https://doi.org/10.5194/tc-8-891-2014, 2014
T. Krumpen, M. Janout, K. I. Hodges, R. Gerdes, F. Girard-Ardhuin, J. A. Hölemann, and S. Willmes
The Cryosphere, 7, 349–363, https://doi.org/10.5194/tc-7-349-2013, https://doi.org/10.5194/tc-7-349-2013, 2013
Luisa von Albedyll, Stefan Hendricks, Nils Hutter, Dmitrii Murashkin, Lars Kaleschke, Sascha Willmes, Linda Thielke, Xiangshan Tian-Kunze, Gunnar Spreen, and Christian Haas
The Cryosphere, 18, 1259–1285, https://doi.org/10.5194/tc-18-1259-2024, https://doi.org/10.5194/tc-18-1259-2024, 2024
Short summary
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Leads (openings in sea ice cover) are created by sea ice dynamics. Because they are important for many processes in the Arctic winter climate, we aim to detect them with satellites. We present two new techniques to detect lead widths of a few hundred meters at high spatial resolution (700 m) and independent of clouds or sun illumination. We use the MOSAiC drift 2019–2020 in the Arctic for our case study and compare our new products to other existing lead products.
Lukrecia Stulic, Ralph Timmermann, Stephan Paul, Rolf Zentek, Günther Heinemann, and Torsten Kanzow
Ocean Sci., 19, 1791–1808, https://doi.org/10.5194/os-19-1791-2023, https://doi.org/10.5194/os-19-1791-2023, 2023
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In the southern Weddell Sea, the strong sea ice growth in coastal polynyas drives formation of dense shelf water. By using a sea ice–ice shelf–ocean model with representation of the changing icescape based on satellite data, we find that polynya sea ice growth depends on both the regional atmospheric forcing and the icescape. Not just strength but also location of the sea ice growth in polynyas affects properties of the dense shelf water and the basal melting of the Filchner–Ronne Ice Shelf.
Thomas Krumpen, Luisa von Albedyll, Helge F. Goessling, Stefan Hendricks, Bennet Juhls, Gunnar Spreen, Sascha Willmes, H. Jakob Belter, Klaus Dethloff, Christian Haas, Lars Kaleschke, Christian Katlein, Xiangshan Tian-Kunze, Robert Ricker, Philip Rostosky, Janna Rückert, Suman Singha, and Julia Sokolova
The Cryosphere, 15, 3897–3920, https://doi.org/10.5194/tc-15-3897-2021, https://doi.org/10.5194/tc-15-3897-2021, 2021
Short summary
Short summary
We use satellite data records collected along the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) drift to categorize ice conditions that shaped and characterized the floe and surroundings during the expedition. A comparison with previous years is made whenever possible. The aim of this analysis is to provide a basis and reference for subsequent research in the six main research areas of atmosphere, ocean, sea ice, biogeochemistry, remote sensing and ecology.
Alexander D. Fraser, Robert A. Massom, Kay I. Ohshima, Sascha Willmes, Peter J. Kappes, Jessica Cartwright, and Richard Porter-Smith
Earth Syst. Sci. Data, 12, 2987–2999, https://doi.org/10.5194/essd-12-2987-2020, https://doi.org/10.5194/essd-12-2987-2020, 2020
Short summary
Short summary
Landfast ice, or
fast ice, is a form of sea ice which is mechanically fastened to stationary parts of the coast. Long-term and accurate knowledge of its extent around Antarctica is critical for understanding a number of important Antarctic coastal processes, yet no accurate, large-scale, long-term dataset of its extent has been available. We address this data gap with this new dataset compiled from satellite imagery, containing high-resolution maps of Antarctic fast ice from 2000 to 2018.
Rolf Zentek and Günther Heinemann
Geosci. Model Dev., 13, 1809–1825, https://doi.org/10.5194/gmd-13-1809-2020, https://doi.org/10.5194/gmd-13-1809-2020, 2020
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We used the climate model CCLM to simulate the Weddell Sea region with a resolutions of 15 and 5 km. By adjusting the turbulence parametrization a warm bias over the Antarctic Plateau was removed. For sea ice we found a temperature bias around +/-1 K and a wind speed bias of 1 m s-1. Comparisons of radio soundings showed a bias around zero and a RMSE of 1–2 K for temperature and 3–4 m s-1 for wind speed. Comparison with wind Doppler lidar yielded almost no bias and a RMSE of ca. 2 m s-1.
Anne Klosterhalfen, Alexander Graf, Nicolas Brüggemann, Clemens Drüe, Odilia Esser, María P. González-Dugo, Günther Heinemann, Cor M. J. Jacobs, Matthias Mauder, Arnold F. Moene, Patrizia Ney, Thomas Pütz, Corinna Rebmann, Mario Ramos Rodríguez, Todd M. Scanlon, Marius Schmidt, Rainer Steinbrecher, Christoph K. Thomas, Veronika Valler, Matthias J. Zeeman, and Harry Vereecken
Biogeosciences, 16, 1111–1132, https://doi.org/10.5194/bg-16-1111-2019, https://doi.org/10.5194/bg-16-1111-2019, 2019
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To obtain magnitudes of flux components of H2O and CO2 (e.g., transpiration, soil respiration), we applied source partitioning approaches after Scanlon and Kustas (2010) and after Thomas et al. (2008) to high-frequency eddy covariance measurements of 12 study sites covering various ecosystems (croplands, grasslands, and forests) in different climatic regions. We analyzed the interrelations among turbulence, site characteristics, and the performance of both partitioning methods.
Rolf Zentek, Svenja H. E. Kohnemann, and Günther Heinemann
Atmos. Meas. Tech., 11, 5781–5795, https://doi.org/10.5194/amt-11-5781-2018, https://doi.org/10.5194/amt-11-5781-2018, 2018
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The performance of the lidar measurements in comparison with radio soundings generally shows small RMSD (bias) for wind speed of around 1 m s−1 (0.1 m s−1) and for a wind direction of around 10° (1°). The post-processing of the non-motion-stabilized data shows comparably high quality to studies with motion stabilized systems. Ship-based doppler lidar measurements can contribute to filling the data gap over oceans, particularly in polar regions.
Andreas Preußer, Günther Heinemann, Sascha Willmes, and Stephan Paul
The Cryosphere, 10, 3021–3042, https://doi.org/10.5194/tc-10-3021-2016, https://doi.org/10.5194/tc-10-3021-2016, 2016
Short summary
Short summary
We present spatial and temporal characteristics of 17 Arctic polynya regions. By using an energy balance model, daily thin-ice thickness distributions are derived from TIR satellite and atmospheric reanalysis data. All polynyas combined yield an average ice production of about 1811 km3 per winter. Interestingly, we find distinct regional differences in calculated trends over the last 13 years. Finally, we set a special focus on the Laptev Sea region and its relation to the Transpolar Drift.
Oliver Gutjahr, Günther Heinemann, Andreas Preußer, Sascha Willmes, and Clemens Drüe
The Cryosphere, 10, 2999–3019, https://doi.org/10.5194/tc-10-2999-2016, https://doi.org/10.5194/tc-10-2999-2016, 2016
Short summary
Short summary
We estimated the formation of new sea ice within polynyas in the Laptev Sea (Siberia) with the regional climate model COSMO-CLM at 5 km horizontal resolution. Fractional sea ice and the representation of thin ice is often neglected in atmospheric models. Our study demonstrates, however, that the way thin ice in polynyas is represented in the model considerably affects the amount of newly formed sea-ice and the air–ice–ocean heat flux. Both processes impact the Arctic sea-ice budget.
S. Paul, S. Willmes, and G. Heinemann
The Cryosphere, 9, 2027–2041, https://doi.org/10.5194/tc-9-2027-2015, https://doi.org/10.5194/tc-9-2027-2015, 2015
Short summary
Short summary
We established a 13-year-long MODIS-derived thin-ice thickness data set from which we derived information about polynya dynamics in the southern Weddell Sea. In contrast to other studies, we do not focus on a single region but instead discuss polynya dynamics for the complete coastal area. The higher spatial resolution of MODIS compared to passive-microwave sensors enables us to resolve even very narrow coastal polynyas that would remain otherwise undetected.
A. Preußer, S. Willmes, G. Heinemann, and S. Paul
The Cryosphere, 9, 1063–1073, https://doi.org/10.5194/tc-9-1063-2015, https://doi.org/10.5194/tc-9-1063-2015, 2015
Short summary
Short summary
The Storfjorden polynya (Svalbard) forms regularly under the influence of strong north-easterly winds. In this study, spatial and temporal characteristics for the period 2002/03-2013/14 were inferred from daily calculated thin-ice thickness distributions, based on MODIS ice surface temperatures and ERA-interim reanalysis.
With an estimated average ice production of 28.3km³/winter, this polynya system is of particular interest regarding its potential contribution to deep water formation.
S. Willmes, M. Nicolaus, and C. Haas
The Cryosphere, 8, 891–904, https://doi.org/10.5194/tc-8-891-2014, https://doi.org/10.5194/tc-8-891-2014, 2014
T. Krumpen, M. Janout, K. I. Hodges, R. Gerdes, F. Girard-Ardhuin, J. A. Hölemann, and S. Willmes
The Cryosphere, 7, 349–363, https://doi.org/10.5194/tc-7-349-2013, https://doi.org/10.5194/tc-7-349-2013, 2013
Related subject area
Discipline: Sea ice | Subject: Arctic (e.g. Greenland)
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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
Retrieval of sea ice drift in the Fram Strait based on data from Chinese satellite HaiYang (HY1-D)
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
A long-term proxy for sea ice thickness in the Canadian Arctic: 1996–2020
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
Sea ice variations and trends during the Common Era in the Atlantic sector of the Arctic Ocean
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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
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Contribution of warm and moist atmospheric flow to a record minimum July sea ice extent of the Arctic in 2020
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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
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Arctic Mission Benefit Analysis: impact of sea ice thickness, freeboard, and snow depth products on sea ice forecast performance
Thin Arctic sea ice in L-band observations and an ocean reanalysis
Snow depth on Arctic sea ice from historical in situ data
Impacts of a lengthening open water season on Alaskan coastal communities: deriving locally relevant indices from large-scale datasets and community observations
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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.
Dunwang Lu, Jianqiang Liu, Lijian Shi, Tao Zeng, Bin Cheng, Suhui Wu, and Manman Wang
EGUsphere, https://doi.org/10.5194/egusphere-2023-1927, https://doi.org/10.5194/egusphere-2023-1927, 2023
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In this paper, we retrieve sea ice drift in the Fram Strait using China's HaiYang1-D Coastal Zone Imager. The dataset covering March to May 2021 is divided into hourly and daily intervals for analysis, and validation is performed using Copernicus Marine Environment Monitoring Service product and IABP buoys. The validation shows great accuracy. During the validation against buoys, we find that the accuracy of retrieving the SID flow direction is highly interrelated with the sea ice displacement.
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.
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.
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
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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.
Ana Lúcia Lindroth Dauner, Frederik Schenk, Katherine Elizabeth Power, and Maija Heikkilä
EGUsphere, https://doi.org/10.5194/egusphere-2023-1327, https://doi.org/10.5194/egusphere-2023-1327, 2023
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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.
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.
Thomas Kaminski, Frank Kauker, Leif Toudal Pedersen, Michael Voßbeck, Helmuth Haak, Laura Niederdrenk, Stefan Hendricks, Robert Ricker, Michael Karcher, Hajo Eicken, and Ola Gråbak
The Cryosphere, 12, 2569–2594, https://doi.org/10.5194/tc-12-2569-2018, https://doi.org/10.5194/tc-12-2569-2018, 2018
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We present mathematically rigorous assessments of the observation impact (added value) of remote-sensing products and in terms of the uncertainty reduction in a 4-week forecast of sea ice volume and snow volume for three regions along the Northern Sea Route by a coupled model of the sea-ice–ocean system. We quantify the difference in impact between rawer (freeboard) and higher-level (sea ice thickness) products, and the impact of adding a snow depth product.
Steffen Tietsche, Magdalena Alonso-Balmaseda, Patricia Rosnay, Hao Zuo, Xiangshan Tian-Kunze, and Lars Kaleschke
The Cryosphere, 12, 2051–2072, https://doi.org/10.5194/tc-12-2051-2018, https://doi.org/10.5194/tc-12-2051-2018, 2018
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We compare Arctic sea-ice thickness from L-band microwave satellite observations and an ocean–sea ice reanalysis. There is good agreement for some regions and times but systematic discrepancy in others. Errors in both the reanalysis and observational products contribute to these discrepancies. Thus, we recommend proceeding with caution when using these observations for model validation or data assimilation. At the same time we emphasise their unique value for improving sea-ice forecast models.
Elena V. Shalina and Stein Sandven
The Cryosphere, 12, 1867–1886, https://doi.org/10.5194/tc-12-1867-2018, https://doi.org/10.5194/tc-12-1867-2018, 2018
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In this paper we analyze snow data from Soviet airborne expeditions, Sever, which operated in late winter 1959-1986, in the Arctic and made snow measurements on the ice around plane landing sites. The snow measurements were made on the multiyear ice in the central Arctic and on the first-year ice in the Eurasian seas in the areas for which snow characteristics are poorly described in the literature. The main goal of this study is to produce an improved data set of snow depth on the sea ice.
Rebecca J. Rolph, Andrew R. Mahoney, John Walsh, and Philip A. Loring
The Cryosphere, 12, 1779–1790, https://doi.org/10.5194/tc-12-1779-2018, https://doi.org/10.5194/tc-12-1779-2018, 2018
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Using thresholds of physical climate variables developed from community observations, together with two large-scale datasets, we have produced local indices directly relevant to the impacts of a reduced sea ice cover on Alaska coastal communities. We demonstrate how community observations can inform use of large-scale datasets to derive these locally relevant indices.
Xianmin Hu, Jingfan Sun, Ting On Chan, and Paul G. Myers
The Cryosphere, 12, 1233–1247, https://doi.org/10.5194/tc-12-1233-2018, https://doi.org/10.5194/tc-12-1233-2018, 2018
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We evaluated the sea ice thickness simulation in the Canadian Arctic Archipelago region using 1/4 and 1/12 degree NEMO LIM2 configurations. Model resolution dose not play a significant role. Relatively smaller thermodynamic contribution in the winter season is found in the thick ice covered areas, with larger contributions in the thin ice covered regions. No significant trend in winter maximum ice volume is found in the northern CAA and Baffin Bay but a decline is simulated within Parry Channel.
Cited articles
Aksenov, Y., Ivanov, V. V., Nurser, A. J. G., Bacon, S., Polyakov, I. V.,
Coward, A. C., Naveira-Garabato, A. C., and Beszczynska-Moeller, A.: The
Arctic Circumpolar Boundary Current, J. Geophys. Res.-Oceans, 116, C09017, https://doi.org/10.1029/2010JC006637, 2011. a, b, c
Årthun, M., Eldevik, T., and Smedsrud, L. H.: The Role of Atlantic
Heat Transport in Future Arctic Winter Sea Ice Loss, J. Climate, 32, 3327–3341, https://doi.org/10.1175/JCLI-D-18-0750.1, 2019. a, b
Aue, L., Vihma, T., Uotila, P., and Rinke, A.: New Insights Into Cyclone
Impacts on Sea Ice in the Atlantic Sector of the Arctic Ocean
in Winter, Geophys. Res. Lett., 49, e2022GL100051,
https://doi.org/10.1029/2022GL100051, 2022. a
Babb, D. G., Landy, J. C., Barber, D. G., and Galley, R. J.: Winter Sea Ice
Export From the Beaufort Sea as a Preconditioning Mechanism for
Enhanced Summer Melt: A Case Study of 2016, J.
Geophys. Res.-Oceans, 124, 6575–6600, https://doi.org/10.1029/2019JC015053,
2019. a, b, c
Babb, D. G., Galley, R. J., Howell, S. E. L., Landy, J. C., Stroeve, J. C., and
Barber, D. G.: Increasing Multiyear Sea Ice Loss in the Beaufort
Sea: A New Export Pathway for the Diminishing Multiyear Ice
Cover of the Arctic Ocean, Geophys. Res. Lett., 49,
e2021GL097595, https://doi.org/10.1029/2021GL097595, 2022. a
Cassianides, A., Lique, C., Tréguier, A.-M., Meneghello, G., and De Marez,
C.: Observed Spatio-Temporal Variability of the Eddy-Sea Ice
Interactions in the Arctic Basin, J. Geophys. Res.-Oceans, 128, e2022JC019469, https://doi.org/10.1029/2022JC019469, 2023. a
Cohanim, K., Zhao, K. X., and Stewart, A. L.: Dynamics of Eddies Generated
by Sea Ice Leads, J. Phys. Oceanogr., 51, 3071–3092,
https://doi.org/10.1175/JPO-D-20-0169.1, 2021. a
Creamean, J. M., Barry, K., Hill, T. C. J., Hume, C., DeMott, P. J., Shupe,
M. D., Dahlke, S., Willmes, S., Schmale, J., Beck, I., Hoppe, C. J. M., Fong,
A., Chamberlain, E., Bowman, J., Scharien, R., and Persson, O.: Annual cycle
observations of aerosols capable of ice formation in central Arctic clouds,
Nat. Commun., 13, 3537–3537, https://doi.org/10.1038/s41467-022-31182-x,
2022. a
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi,
S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P.,
Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C.,
Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B.,
Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler,
M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J.,
Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and
Vitart, F.: The ERA-Interim reanalysis: configuration and performance of
the data assimilation system, Q. J. Roy. Meteor.
Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011. a, b
Eicken, H., Shapiro, L., Heinrichs, T., Gens, R., Meyer, F., Mahoney, A.,
Gaylord, A., and Ak, H.: Mapping and characterization of recurring spring
leads and landfast ice in the Beaufort and Chukchi Seas, U.S. Dept. of the Interior, Bureau of Ocean Energy Management, Alaska Region, Anchorage, AK, https://espis.boem.gov/technical summaries/5225.pdf (last access: 21 January 2023),
2012. a
Feltham, D. L.: Sea Ice Rheology, Annu. Rev. Fluid Mech., 40,
91–112, https://doi.org/10.1146/annurev.fluid.40.111406.102151, 2008. a
Frey, K. E., Moore, G. W. K., Cooper, L. W., and Grebmeier, J. M.: Divergent
patterns of recent sea ice cover across the Bering, Chukchi, and
Beaufort seas of the Pacific Arctic Region, Prog. Oceanogr.,
136, 32–49, https://doi.org/10.1016/j.pocean.2015.05.009, 2015. a
Graham, R. M., Itkin, P., Meyer, A., Sundfjord, A., Spreen, G., Smedsrud,
L. H., Liston, G. E., Cheng, B., Cohen, L., Divine, D., Fer, I., Fransson,
A., Gerland, S., Haapala, J., Hudson, S. R., Johansson, A. M., King, J.,
Merkouriadi, I., Peterson, A. K., Provost, C., Randelhoff, A., Rinke, A.,
Rösel, A., Sennéchael, N., Walden, V. P., Duarte, P., Assmy, P.,
Steen, H., and Granskog, M. A.: Winter storms accelerate the demise of sea
ice in the Atlantic sector of the Arctic Ocean, Sci. Rep.-UK 9,
9222, https://doi.org/10.1038/s41598-019-45574-5, 2019. a
Gutjahr, O., Heinemann, G., Preußer, A., Willmes, S., and Drüe, C.: Quantification of ice production in Laptev Sea polynyas and its sensitivity to thin-ice parameterizations in a regional climate model, The Cryosphere, 10, 2999–3019, https://doi.org/10.5194/tc-10-2999-2016, 2016. a
Harms, I. H. and Karcher, M. J.: Kara Sea freshwater dispersion and export in
the late 1990s, J. Geophys. Res.-Oceans, 110, C08007,
https://doi.org/10.1029/2004JC002744, 2005. a
Hegyi, B. M. and Taylor, P. C.: The regional influence of the Arctic
Oscillation and Arctic Dipole on the wintertime Arctic surface
radiation budget and sea ice growth, Geophys. Res. Lett., 44,
4341–4350, https://doi.org/10.1002/2017GL073281, 2017. a
Heinemann, G., Willmes, S., Schefczyk, L., Makshtas, A., Kustov, V., and
Makhotina, I.: Observations and Simulations of Meteorological
Conditions over Arctic Thick Sea Ice in Late Winter during the
Transarktika 2019 Expedition, Atmosphere, 12, 174,
https://doi.org/10.3390/atmos12020174, 2021. a
Heinemann, G., Schefczyk, L., Willmes, S., and Shupe, M. D.: Evaluation of
simulations of near-surface variables using the regional climate model CCLM
for the MOSAiC winter period, Elementa, 10, 00033,
https://doi.org/10.1525/elementa.2022.00033, 2022. a, b
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M.,
Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P.,
Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5
global reanalysis, Q. J. Roy. Meteor. Soc.,
146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b, c, d, e
Hoffman, J. P., Ackerman, S. A., Liu, Y., and Key, J. R.: A 20-Year
Climatology of Sea Ice Leads Detected in Infrared Satellite
Imagery Using a Convolutional Neural Network, Remote Sens.-Basel, 14, 22,
https://doi.org/10.3390/rs14225763, 2022. a
Holloway, G. and Proshutinsky, A.: Role of tides in Arctic ocean/ice climate,
J. Geophys. Res.-Oceans, 112, C04S06, https://doi.org/10.1029/2006JC003643,
2007. a
Hutchings, J. K. and Rigor, I. G.: Role of ice dynamics in anomalous ice
conditions in the Beaufort Sea during 2006 and 2007, J.
Geophys. Res.-Oceans, 117, C00E04, https://doi.org/10.1029/2011JC007182, 2012. a
Hwang, B., Wilkinson, J., Maksym, T., Graber, H. C., Schweiger, A., Horvat, C.,
Perovich, D. K., Arntsen, A. E., Stanton, T. P., Ren, J., and Wadhams, P.:
Winter-to-summer transition of Arctic sea ice breakup and floe size
distribution in the Beaufort Sea, Elementa,
5, 40, https://doi.org/10.1525/elementa.232, 2017. a
Jakobsson, M., Mayer, L. A., Bringensparr, C., Castro, C. F., Mohammad, R.,
Johnson, P., Ketter, T., Accettella, D., Amblas, D., An, L., Arndt, J. E.,
Canals, M., Casamor, J. L., Chauché, N., Coakley, B., Danielson, S.,
Demarte, M., Dickson, M.-L., Dorschel, B., Dowdeswell, J. A., Dreutter, S.,
Fremand, A. C., Gallant, D., Hall, J. K., Hehemann, L., Hodnesdal, H., Hong,
J., Ivaldi, R., Kane, E., Klaucke, I., Krawczyk, D. W., Kristoffersen, Y.,
Kuipers, B. R., Millan, R., Masetti, G., Morlighem, M., Noormets, R.,
Prescott, M. M., Rebesco, M., Rignot, E., Semiletov, I., Tate, A. J.,
Travaglini, P., Velicogna, I., Weatherall, P., Weinrebe, W., Willis, J. K.,
Wood, M., Zarayskaya, Y., Zhang, T., Zimmermann, M., and Zinglersen, K. B.:
The International Bathymetric Chart of the Arctic Ocean Version
4.0, Sci. Data, 7, 176, https://doi.org/10.1038/s41597-020-0520-9, 2020. a, b, c, d
Janout, M. A. and Lenn, Y.-D.: Semidiurnal Tides on the Laptev Sea
Shelf with Implications for Shear and Vertical Mixing, J.
Phys. Oceanogr., 44, 202–219, https://doi.org/10.1175/JPO-D-12-0240.1, 2014. a
Janout, M. A., Aksenov, Y., Hölemann, J. A., Rabe, B., Schauer, U.,
Polyakov, I. V., Bacon, S., Coward, A. C., Karcher, M., Lenn, Y.-D., Kassens,
H., and Timokhov, L.: Kara Sea freshwater transport through Vilkitsky
Strait: Variability, forcing, and further pathways toward the western
Arctic Ocean from a model and observations, J. Geophys.
Res.-Oceans, 120, 4925–4944, https://doi.org/10.1002/2014JC010635, 2015. a, b
Janout, M. A., Hölemann, J., Laukert, G., Smirnov, A., Krumpen, T., Bauch,
D., and Timokhov, L.: On the Variability of Stratification in the
Freshwater-Influenced Laptev Sea Region, Front. Mar.
Sci., 7, 543489, https://doi.org/10.3389/fmars.2020.543489, 2020. a
Jewell, M. E. and Hutchings, J. K.: Observational Perspectives on Beaufort
Sea Ice Breakouts, Geophys. Res. Lett., 50, e2022GL101408,
https://doi.org/10.1029/2022GL101408, 2023. a
Jewell, M. E., Hutchings, J. K., and Geiger, C. A.: Atmospheric highs drive asymmetric sea ice drift during lead opening from Point Barrow, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2023-9, in review, 2023. a, b, c
Kirchmeier-Young, M. C., Zwiers, F. W., and Gillett, N. P.: Attribution of
Extreme Events in Arctic Sea Ice Extent, J. Climate, 30,
553–571, https://doi.org/10.1175/JCLI-D-16-0412.1, 2017. a
Kohnemann, S. and Heinemann, G.: A climatology of wintertime low-level jets in
Nares Strait, Polar Res., 40, 1–16, https://doi.org/10.33265/polar.v40.3622, 2021. a
Korosov, A., Rampal, P., Ying, Y., Ólason, E., and Williams, T.: Towards improving short-term sea ice predictability using deformation observations, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2022-46, in review, 2022. a, b
Kort, E. A., Wofsy, S. C., Daube, B. C., Diao, M., Elkins, J. W., Gao, R. S.,
Hintsa, E. J., Hurst, D. F., Jimenez, R., Moore, F. L., Spackman, J. R., and
Zondlo, M. A.: Atmospheric observations of Arctic Ocean methane emissions
up to 82∘ north, Nat. Geosci., 5, 318–321,
https://doi.org/10.1038/ngeo1452, 2012. a
Kwok, R., Spreen, G., and Pang, S.: Arctic sea ice circulation and drift speed:
Decadal trends and ocean currents, J. Geophys. Res.-Oceans,
118, 2408–2425, https://doi.org/10.1002/jgrc.20191, 2013. a, b
Levitus, S., Locarnini, R. A., Boyer, T. P., Mishonov, A. V., Antonov, J. I.,
Garcia, H. E., Baranova, O. K., Zweng, M. M., Johnson, D. R., and Seidov,
1948, D.: World ocean atlas 2009, NOAA atlas NESDIS,
https://repository.library.noaa.gov/view/noaa/1259 (last access: 1 June 2020), 2010. a
Lewis, B. J. and Hutchings, J. K.: Leads and Associated Sea Ice Drift
in the Beaufort Sea in Winter, J. Geophys. Res.-Oceans,
124, 3411–3427, https://doi.org/10.1029/2018JC014898, 2019. a, b, c
Liu, Z., Risi, C., Codron, F., Jian, Z., Wei, Z., He, X., Poulsen, C. J., Wang,
Y., Chen, D., Ma, W., Cheng, Y., and Bowen, G. J.: Atmospheric forcing
dominates winter Barents-Kara sea ice variability on interannual to
decadal time scales, P. Natl. Acad. Sci. USA, 119,
e212077011, https://doi.org/10.1073/pnas.2120770119, 2022. a, b, c
Lüpkes, C., Vihma, T., Birnbaum, G., and Wacker, U.: Influence of leads in
sea ice on the temperature of the atmospheric boundary layer during polar
night, Geophys. Res. Lett., 35, L03805, https://doi.org/10.1029/2007GL032461, 2008. a
Manucharyan, G. E. and Thompson, A. F.: Heavy footprints of upper-ocean eddies
on weakened Arctic sea ice in marginal ice zones, Nat. Commun.,
13, 2147, https://doi.org/10.1038/s41467-022-29663-0, 2022. a
Marcq, S. and Weiss, J.: Influence of sea ice lead-width distribution on turbulent heat transfer between the ocean and the atmosphere, The Cryosphere, 6, 143–156, https://doi.org/10.5194/tc-6-143-2012, 2012. a
Mioduszewski, J., Vavrus, S., and Wang, M.: Diminishing Arctic Sea Ice
Promotes Stronger Surface Winds, J. Climate, 31, 8101–8119,
https://doi.org/10.1175/JCLI-D-18-0109.1, 2018. a
Moore, C. W., Obrist, D., Steffen, A., Staebler, R. M., Douglas, T. A.,
Richter, A., and Nghiem, S. V.: Convective forcing of mercury and ozone in
the Arctic boundary layer induced by leads in sea ice, Nature, 506,
81–84, https://doi.org/10.1038/nature12924, 2014. a
Nguyen, A. T., Menemenlis, D., and Kwok, R.: Improved modeling of the Arctic
halocline with a subgrid-scale brine rejection parameterization, J.
Geophys. Res.-Oceans, 114, C11014, https://doi.org/10.1029/2008JC005121, 2009. a
Notz, D. and SIMIP community: Arctic Sea Ice in CMIP6,
Geophys. Res. Lett., 47, e2019GL086749,
https://doi.org/10.1029/2019GL086749, 2020. a
Pavlova, O., Pavlov, V., and Gerland, S.: The impact of winds and sea surface
temperatures on the Barents Sea ice extent, a statistical approach,
J. Marine Syst., 130, 248–255,
https://doi.org/10.1016/j.jmarsys.2013.02.011, 2014. a, b, c
Pnyushkov, A. V., Polyakov, I. V., Ivanov, V. V., Aksenov, Y., Coward, A. C.,
Janout, M., and Rabe, B.: Structure and variability of the boundary current
in the Eurasian Basin of the Arctic Ocean, Deep-Sea Res. Pt. I, 101, 80–97, https://doi.org/10.1016/j.dsr.2015.03.001,
2015. a, b
Polyakov, I. V., Rippeth, T. P., Fer, I., Alkire, M. B., Baumann, T. M.,
Carmack, E. C., Ingvaldsen, R., Ivanov, V. V., Janout, M., Lind, S., Padman,
L., Pnyushkov, A. V., and Rember, R.: Weakening of Cold Halocline Layer
Exposes Sea Ice to Oceanic Heat in the Eastern Arctic Ocean,
J. Climate, 33, 8107–8123, https://doi.org/10.1175/JCLI-D-19-0976.1, 2020. a
Qu, M., Pang, X., Zhao, X., Lei, R., Ji, Q., Liu, Y., and Chen, Y.: Spring
leads in the Beaufort Sea and its interannual trend using Terra/MODIS
thermal imagery, Remote Sens. Environ., 256, 112342,
https://doi.org/10.1016/j.rse.2021.112342, 2021. a
Reiser, F., Willmes, S., Hausmann, U., and Heinemann, G.: Predominant Sea
Ice Fracture Zones Around Antarctica and Their Relation to
Bathymetric Features, Geophys. Res. Lett., 46, 12117–12124,
https://doi.org/10.1029/2019GL084624, 2019. a, b
Schaffer, J., Timmermann, R., Arndt, J. E., Kristensen, S. S., Mayer, C., Morlighem, M., and Steinhage, D.: A global, high-resolution data set of ice sheet topography, cavity geometry, and ocean bathymetry, Earth Syst. Sci. Data, 8, 543–557, https://doi.org/10.5194/essd-8-543-2016, 2016. a
Sidorenko, D., Goessling, H., Koldunov, N., Scholz, P., Danilov, S., Barbi, D.,
Cabos, W., Gurses, O., Harig, S., Hinrichs, C., Juricke, S., Lohmann, G.,
Losch, M., Mu, L., Rackow, T., Rakowsky, N., Sein, D., Semmler, T., Shi, X.,
Stepanek, C., Streffing, J., Wang, Q., Wekerle, C., Yang, H., and Jung, T.:
Evaluation of FESOM2.0 Coupled to ECHAM6.3: Preindustrial and
HighResMIP Simulations, J. Adv. Model. Earth Sy.,
11, 3794–3815, https://doi.org/10.1029/2019MS001696, 2019. a
Schefczyk, L. and Heinemann, G.: CATS Projekt – Uni Trier – Simulation C15_Arctic, DOKU at DKRZ [data set], https://www.wdc-climate.de/ui/entry?acronym=DKRZ_LTA_474_dsg0001 (last access: February 2023), 2023. a
Smedsrud, L. H., Sirevaag, A., Kloster, K., Sorteberg, A., and Sandven, S.: Recent wind driven high sea ice area export in the Fram Strait contributes to Arctic sea ice decline, The Cryosphere, 5, 821–829, https://doi.org/10.5194/tc-5-821-2011, 2011. a
Spreen, G., Kaleschke, L., and Heygster, G.: Sea ice remote sensing using
AMSR-E 89-GHz channels, J. Geophys. Res.-Oceans, 113, C02S03,
https://doi.org/10.1029/2005JC003384, 2008. a
Spreen, G., Kwok, R., Menemenlis, D., and Nguyen, A. T.: Sea-ice deformation in a coupled ocean–sea-ice model and in satellite remote sensing data, The Cryosphere, 11, 1553–1573, https://doi.org/10.5194/tc-11-1553-2017, 2017. a, b
Stabeno, P., Kachel, N., Ladd, C., and Woodgate, R.: Flow Patterns in the
Eastern Chukchi Sea: 2010–2015, J. Geophys.
Res.-Oceans, 123, 1177–1195, https://doi.org/10.1002/2017JC013135, 2018. a, b
Steger, C. and Bucchignani, E.: Regional Climate Modelling with
COSMO-CLM: History and Perspectives, Atmosphere, 11, 1250,
https://doi.org/10.3390/atmos11111250, 2020. a
Stirling, I.: The importance of polynyas, ice edges, and leads to marine
mammals and birds, J. Marine Syst., 10, 9–21,
https://doi.org/10.1016/S0924-7963(96)00054-1, 1997. 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
Suzuki, T., Yamazaki, D., Tsujino, H., Komuro, Y., Nakano, H., and Urakawa, S.:
A dataset of continental river discharge based on JRA-55 for use in a
global ocean circulation model, J. Oceanogr., 74, 421–429,
https://doi.org/10.1007/s10872-017-0458-5, 2018. a
Wang, Q., Danilov, S., Sidorenko, D., Timmermann, R., Wekerle, C., Wang, X., Jung, T., and Schröter, J.: The Finite Element Sea Ice-Ocean Model (FESOM) v.1.4: formulation of an ocean general circulation model, Geosci. Model Dev., 7, 663–693, https://doi.org/10.5194/gmd-7-663-2014, 2014. a
Wang, Q., Danilov, S., Jung, T., Kaleschke, L., and Wernecke, A.: Sea ice leads
in the Arctic Ocean: Model assessment, interannual variability and
trends, Geophys. Res. Lett., 43, 7019–7027,
https://doi.org/10.1002/2016GL068696, 2016. a, b, c, d
Wang, Q., Wekerle, C., Danilov, S., Wang, X., and Jung, T.: A 4.5 km resolution Arctic Ocean simulation with the global multi-resolution model FESOM 1.4, Geosci. Model Dev., 11, 1229–1255, https://doi.org/10.5194/gmd-11-1229-2018, 2018.
Warner, J. L., Screen, J. A., and Scaife, A. A.: Links Between
Barents-Kara Sea Ice and the Extratropical Atmospheric
Circulation Explained by Internal Variability and Tropical
Forcing, Geophys. Res. Lett., 47,
e2019GL085679, https://doi.org/10.1029/2019GL085679, 2020. a
Wekerle, C., Wang, Q., von Appen, W.-J., Danilov, S., Schourup-Kristensen, V.,
and Jung, T.: Eddy-Resolving Simulation of the Atlantic Water
Circulation in the Fram Strait With Focus on the Seasonal
Cycle, J. Geophys. Res.-Oceans, 122, 8385–8405,
https://doi.org/10.1002/2017JC012974, 2017. a, b
Willmes, S. and Heinemann, G.: Pan-Arctic lead detection from MODIS thermal
infrared imagery, Ann. Glaciol., 56, 29–37,
https://doi.org/10.3189/2015AoG69A615, 2015.
Willmes, S. and Heinemann, G.: Sea-Ice Wintertime Lead Frequencies and
Regional Characteristics in the Arctic, 2003–2015, Remote
Sens., 8, 4, https://doi.org/10.3390/rs8010004, 2016. a, b, c, d
Willmes, S., Heinemann, G., and Reiser, F.: ArcLeads: Daily sea-ice lead maps for the Arctic, 2002–2021, NOV-APR, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.955561, 2023. a, b
Winsor, P. and Chapman, D. C.: Pathways of Pacific water across the Chukchi
Sea: A numerical model study, J. Geophys. Res.-Oceans,
109, C03002, https://doi.org/10.1029/2003JC001962, 2004. a
Woods, C. and Caballero, R.: The Role of Moist Intrusions in Winter
Arctic Warming and Sea Ice Decline, J. Climate, 29,
4473–4485, https://doi.org/10.1175/JCLI-D-15-0773.1, 2016. a
Zhang, J. 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,
https://doi.org/10.1175/1520-0493(2003)131<0845:MGSIWA>2.0.CO;2, 2003.
a
Zhang, J., Lindsay, R., Schweiger, A., and Rigor, I.: Recent changes in the
dynamic properties of declining Arctic sea ice: A model study,
Geophys. Res. Lett., 39, L20503, https://doi.org/10.1029/2012GL053545, 2012. a, b, c
Zhang, Y., Cheng, X., Liu, J., and Hui, F.: The potential of sea ice leads as a predictor for summer Arctic sea ice extent, The Cryosphere, 12, 3747–3757, https://doi.org/10.5194/tc-12-3747-2018, 2018. a
Short summary
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.
Sea ice is an important constituent of the global climate system. We here use satellite data to...
