Articles | Volume 17, issue 11
https://doi.org/10.5194/tc-17-4609-2023
© 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-17-4609-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Nov 2023
Research article |
| 03 Nov 2023
| 03 Nov 2023
The impacts of anomalies in atmospheric circulations on Arctic sea ice outflow and sea ice conditions in the Barents and Greenland seas: case study in 2020
Fanyi Zhang
Chinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan 430079, China
Key Laboratory for Polar Science, Ministry of Natural Resources, Polar Research Institute of China, Shanghai 200136, China
Key Laboratory for Polar Science, Ministry of Natural Resources, Polar Research Institute of China, Shanghai 200136, China
Chinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan 430079, China
Mengxi Zhai
Key Laboratory for Polar Science, Ministry of Natural Resources, Polar Research Institute of China, Shanghai 200136, China
Xiaoping Pang
Chinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan 430079, China
Na Li
Key Laboratory for Polar Science, Ministry of Natural Resources, Polar Research Institute of China, Shanghai 200136, China
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Ruibo Lei, Mario Hoppmann, Bin Cheng, Marcel Nicolaus, Fanyi Zhang, Benjamin Rabe, Long Lin, Julia Regnery, and Donald K. Perovich
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-25, https://doi.org/10.5194/tc-2023-25, 2023
Manuscript not accepted for further review
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To characterize the freezing and melting of different types of sea ice, we deployed four IMBs during the MOSAiC second drift. The drifting pattern, together with a large snow accumulation, relatively warm air temperatures, and a rapid increase in oceanic heat close to Fram Strait, determined the seasonal evolution of the ice mass balance. The refreezing of ponded ice and voids within the unconsolidated ridges amplifies the anisotropy of the heat exchange between the ice and the atmosphere/ocean.
Yi Zhou, Xianwei Wang, Ruibo Lei, Arttu Jutila, Donald K. Perovich, Luisa von Albedyll, Dmitry V. Divine, Yu Zhang, and Christian Haas
EGUsphere, https://doi.org/10.5194/egusphere-2024-2821, https://doi.org/10.5194/egusphere-2024-2821, 2024
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This study examines how the bulk density of Arctic sea ice varies seasonally, a factor often overlooked in satellite measurements of sea ice thickness. From October to April, we found significant seasonal variations in sea ice bulk density at different spatial scales using direct observations as well as airborne and satellite data. New models were then developed to indirectly predict sea ice bulk density. This advance can improve our ability to monitor changes in Arctic sea ice.
Yi Zhou, Xianwei Wang, Ruibo Lei, Luisa von Albedyll, Donald K. Perovich, Yu Zhang, and Christian Haas
EGUsphere, https://doi.org/10.5194/egusphere-2024-1240, https://doi.org/10.5194/egusphere-2024-1240, 2024
Preprint archived
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This study examines how the density of Arctic sea ice varies seasonally, a factor often overlooked in satellite measurements of sea ice thickness. From October to April, using direct observations and satellite data, we found that sea ice density decreases significantly until mid-January due to increased porosity as the ice ages, and then stabilizes until April. We then developed new models to estimate sea ice density. This advance can improve our ability to monitor changes in Arctic sea ice.
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.
Ying Chen, Ruibo Lei, Xi Zhao, Shengli Wu, Yue Liu, Pei Fan, Qing Ji, Peng Zhang, and Xiaoping Pang
Earth Syst. Sci. Data, 15, 3223–3242, https://doi.org/10.5194/essd-15-3223-2023, https://doi.org/10.5194/essd-15-3223-2023, 2023
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The sea ice concentration product derived from the Microwave Radiation Image sensors on board the FengYun-3 satellites can reasonably and independently identify the seasonal and long-term changes of sea ice, as well as extreme cases of annual maximum and minimum sea ice extent in polar regions. It is comparable with other sea ice concentration products and applied to the studies of climate and marine environment.
Na Li, Ruibo Lei, Petra Heil, Bin Cheng, Minghu Ding, Zhongxiang Tian, and Bingrui Li
The Cryosphere, 17, 917–937, https://doi.org/10.5194/tc-17-917-2023, https://doi.org/10.5194/tc-17-917-2023, 2023
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The observed annual maximum landfast ice (LFI) thickness off Zhongshan (Davis) was 1.59±0.17 m (1.64±0.08 m). Larger interannual and local spatial variabilities for the seasonality of LFI were identified at Zhongshan, with the dominant influencing factors of air temperature anomaly, snow atop, local topography and wind regime, and oceanic heat flux. The variability of LFI properties across the study domain prevailed at interannual timescales, over any trend during the recent decades.
Ruibo Lei, Mario Hoppmann, Bin Cheng, Marcel Nicolaus, Fanyi Zhang, Benjamin Rabe, Long Lin, Julia Regnery, and Donald K. Perovich
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-25, https://doi.org/10.5194/tc-2023-25, 2023
Manuscript not accepted for further review
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To characterize the freezing and melting of different types of sea ice, we deployed four IMBs during the MOSAiC second drift. The drifting pattern, together with a large snow accumulation, relatively warm air temperatures, and a rapid increase in oceanic heat close to Fram Strait, determined the seasonal evolution of the ice mass balance. The refreezing of ponded ice and voids within the unconsolidated ridges amplifies the anisotropy of the heat exchange between the ice and the atmosphere/ocean.
Long Lin, Ruibo Lei, Mario Hoppmann, Donald K. Perovich, and Hailun He
The Cryosphere, 16, 4779–4796, https://doi.org/10.5194/tc-16-4779-2022, https://doi.org/10.5194/tc-16-4779-2022, 2022
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Ice mass balance observations indicated that average basal melt onset was comparable in the central Arctic Ocean and approximately 17 d earlier than surface melt in the Beaufort Gyre. The average onset of basal growth lagged behind the surface of the pan-Arctic Ocean for almost 3 months. In the Beaufort Gyre, both drifting-buoy observations and fixed-point observations exhibit a trend towards earlier basal melt onset, which can be ascribed to the earlier warming of the surface ocean.
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.
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.
Y. Chen, X. Zhao, M. Qu, Z. Cheng, X. Pang, and Q. Ji
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 861–867, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-861-2020, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-861-2020, 2020
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.
Dawei Gui, Xiaoping Pang, Ruibo Lei, Xi Zhao, and Jia Wang
Abstr. Int. Cartogr. Assoc., 1, 101, https://doi.org/10.5194/ica-abs-1-101-2019, https://doi.org/10.5194/ica-abs-1-101-2019, 2019
Haiyan Liu and Xiaoping Pang
Abstr. Int. Cartogr. Assoc., 1, 221, https://doi.org/10.5194/ica-abs-1-221-2019, https://doi.org/10.5194/ica-abs-1-221-2019, 2019
Xiaoping Pang, Pei Fan, Xi Zhao, and Qing Ji
Abstr. Int. Cartogr. Assoc., 1, 289, https://doi.org/10.5194/ica-abs-1-289-2019, https://doi.org/10.5194/ica-abs-1-289-2019, 2019
Meng Qu, Xiaoping Pang, Xi Zhao, Jinlun Zhang, Qing Ji, and Pei Fan
The Cryosphere, 13, 1565–1582, https://doi.org/10.5194/tc-13-1565-2019, https://doi.org/10.5194/tc-13-1565-2019, 2019
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Can we ignore the contribution of small ice leads when estimating turbulent heat flux? Combining bulk formulae and a fetch-limited model with surface temperature from MODIS and Landsat-8 Thermal Infrared Sensor (TIRS) images, we found small leads account for 25 % of the turbulent heat flux, due to its large total area. Estimated turbulent heat flux is larger from TIRS than that from MODIS with a coarser resolution and larger using a fetch-limited model than that using bulk formulae.
Related subject area
Discipline: Sea ice | Subject: Sea Ice
Suitability of the CICE sea ice model for seasonal prediction and positive impact of CryoSat-2 ice thickness initialization
A large-scale high-resolution numerical model for sea-ice fragmentation dynamics
Experimental modelling of the growth of tubular ice brinicles from brine flows under sea ice
Why is summertime Arctic sea ice drift speed projected to decrease?
Seasonal Evolution of the Sea Ice Floe Size Distribution from Two Decades of MODIS Data
Impact of atmospheric rivers on Arctic sea ice variations
Atmospheric highs drive asymmetric sea ice drift during lead opening from Point Barrow
Spatial characteristics of frazil streaks in the Terra Nova Bay Polynya from high-resolution visible satellite imagery
Modelling the evolution of Arctic multiyear sea ice over 2000–2018
A quasi-objective single-buoy approach for understanding Lagrangian coherent structures and sea ice dynamics
Linking scales of sea ice surface topography: evaluation of ICESat-2 measurements with coincident helicopter laser scanning during MOSAiC
Analysis of microseismicity in sea ice with deep learning and Bayesian inference: application to high-resolution thickness monitoring
A collection of wet beam models for wave–ice interaction
First results of Antarctic sea ice type retrieval from active and passive microwave remote sensing data
Probabilistic spatiotemporal seasonal sea ice presence forecasting using sequence-to-sequence learning and ERA5 data in the Hudson Bay region
Predictability of Arctic sea ice drift in coupled climate models
Recovering and monitoring the thickness, density, and elastic properties of sea ice from seismic noise recorded in Svalbard
Influences of changing sea ice and snow thicknesses on simulated Arctic winter heat fluxes
Reassessing seasonal sea ice predictability of the Pacific-Arctic sector using a Markov model
A new state-dependent parameterization for the free drift of sea ice
Arctic sea ice sensitivity to lateral melting representation in a coupled climate model
Retrieval and parameterisation of sea-ice bulk density from airborne multi-sensor measurements
A generalized stress correction scheme for the Maxwell elasto-brittle rheology: impact on the fracture angles and deformations
Wave dispersion and dissipation in landfast ice: comparison of observations against models
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Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks
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Sea ice volume variability and water temperature in the Greenland Sea
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Shan Sun and Amy Solomon
The Cryosphere, 18, 3033–3048, https://doi.org/10.5194/tc-18-3033-2024, https://doi.org/10.5194/tc-18-3033-2024, 2024
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The study brings to light the suitability of CICE for seasonal prediction being contingent on several factors, such as initial conditions like sea ice coverage and thickness, as well as atmospheric and oceanic conditions including oceanic currents and sea surface temperature. We show there is potential to improve seasonal forecasting by using a more reliable sea ice thickness initialization. Thus, data assimilation of sea ice thickness is highly relevant for advancing seasonal prediction skills.
Jan Åström, Fredrik Robertsen, Jari Haapala, Arttu Polojärvi, Rivo Uiboupin, and Ilja Maljutenko
The Cryosphere, 18, 2429–2442, https://doi.org/10.5194/tc-18-2429-2024, https://doi.org/10.5194/tc-18-2429-2024, 2024
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The HiDEM code has been developed for analyzing the fracture and fragmentation of brittle materials and has been extensively applied to glacier calving. Here, we report on the adaptation of the code to sea-ice dynamics and breakup. The code demonstrates the capability to simulate sea-ice dynamics on a 100 km scale with an unprecedented resolution. We argue that codes of this type may become useful for improving forecasts of sea-ice dynamics.
Sergio Testón-Martínez, Laura M. Barge, Jan Eichler, C. Ignacio Sainz-Díaz, and Julyan H. E. Cartwright
The Cryosphere, 18, 2195–2205, https://doi.org/10.5194/tc-18-2195-2024, https://doi.org/10.5194/tc-18-2195-2024, 2024
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Brinicles are tubular ice structures that grow under the sea ice in cold regions. This happens because the salty water going downwards from the sea ice is colder than the seawater. We have successfully recreated an analogue of these structures in our laboratory. Three methods were used, producing different results. In this paper, we explain how to use these methods and study the behaviour of the brinicles created when changing the flow of water and study the importance for natural brinicles.
Jamie L. Ward and Neil F. Tandon
The Cryosphere, 18, 995–1012, https://doi.org/10.5194/tc-18-995-2024, https://doi.org/10.5194/tc-18-995-2024, 2024
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Over the long term, the speed at which sea ice in the Arctic moves has been increasing during all seasons. However, nearly all climate models project that sea ice motion will decrease during summer. This study aims to understand the mechanisms responsible for these projected decreases in summertime sea ice motion. We find that models produce changes in winds and ocean surface tilt which cause the sea ice to slow down, and it is realistic to expect such changes to also occur in the real world.
Ellen Margaret Buckley, Leela Cañuelas, Mary-Louise Timmermans, and Monica Martinez Wilhelmus
EGUsphere, https://doi.org/10.5194/egusphere-2024-89, https://doi.org/10.5194/egusphere-2024-89, 2024
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The Arctic sea ice cover seasonally evolves from large plates separated by long, linear leads in the winter to a mosaic of smaller sea ice floes in the summer. Here, we present a new image segmentation algorithm applied to thousands of images and identifying over 9 million individual pieces of ice. We observe the characteristics of the floes and how they evolve throughout the summer as the ice breaks up.
Linghan Li, Forest Cannon, Matthew R. Mazloff, Aneesh C. Subramanian, Anna M. Wilson, and Fred Martin Ralph
The Cryosphere, 18, 121–137, https://doi.org/10.5194/tc-18-121-2024, https://doi.org/10.5194/tc-18-121-2024, 2024
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We investigate how the moisture transport through atmospheric rivers influences Arctic sea ice variations using hourly atmospheric ERA5 for 1981–2020 at 0.25° × 0.25° resolution. We show that individual atmospheric rivers initiate rapid sea ice decrease through surface heat flux and winds. We find that the rate of change in sea ice concentration has significant anticorrelation with moisture, northward wind and turbulent heat flux on weather timescales almost everywhere in the Arctic Ocean.
MacKenzie E. Jewell, Jennifer K. Hutchings, and Cathleen A. Geiger
The Cryosphere, 17, 3229–3250, https://doi.org/10.5194/tc-17-3229-2023, https://doi.org/10.5194/tc-17-3229-2023, 2023
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Sea ice repeatedly fractures near a prominent Alaskan headland as winds move ice along the coast, challenging predictions of sea ice drift. We find winds from high-pressure systems drive these fracturing events, and the Alaskan coastal boundary modifies the resultant ice drift. This observational study shows how wind patterns influence sea ice motion near coasts in winter. Identified relations between winds, ice drift, and fracturing provide effective test cases for dynamic sea ice models.
Katarzyna Bradtke and Agnieszka Herman
The Cryosphere, 17, 2073–2094, https://doi.org/10.5194/tc-17-2073-2023, https://doi.org/10.5194/tc-17-2073-2023, 2023
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The frazil streaks are one of the visible signs of complex interactions between the mixed-layer dynamics and the forming sea ice. Using high-resolution visible satellite imagery we characterize their spatial properties, relationship with the meteorological forcing, and role in modifying wind-wave growth in the Terra Nova Bay Polynya. We provide a simple statistical tool for estimating the extent and ice coverage of the region of high ice production under given wind speed and air temperature.
Heather Regan, Pierre Rampal, Einar Ólason, Guillaume Boutin, and Anton Korosov
The Cryosphere, 17, 1873–1893, https://doi.org/10.5194/tc-17-1873-2023, https://doi.org/10.5194/tc-17-1873-2023, 2023
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Multiyear ice (MYI), sea ice that survives the summer, is more resistant to changes than younger ice in the Arctic, so it is a good indicator of sea ice resilience. We use a model with a new way of tracking MYI to assess the contribution of different processes affecting MYI. We find two important years for MYI decline: 2007, when dynamics are important, and 2012, when melt is important. These affect MYI volume and area in different ways, which is important for the interpretation of observations.
Nikolas O. Aksamit, Randall K. Scharien, Jennifer K. Hutchings, and Jennifer V. Lukovich
The Cryosphere, 17, 1545–1566, https://doi.org/10.5194/tc-17-1545-2023, https://doi.org/10.5194/tc-17-1545-2023, 2023
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Coherent flow patterns in sea ice have a significant influence on sea ice fracture and refreezing. We can better understand the state of sea ice, and its influence on the atmosphere and ocean, if we understand these structures. By adapting recent developments in chaotic dynamical systems, we are able to approximate ice stretching surrounding individual ice buoys. This illuminates the state of sea ice at much higher resolution and allows us to see previously invisible ice deformation patterns.
Robert Ricker, Steven Fons, Arttu Jutila, Nils Hutter, Kyle Duncan, Sinead L. Farrell, Nathan T. Kurtz, and Renée Mie Fredensborg Hansen
The Cryosphere, 17, 1411–1429, https://doi.org/10.5194/tc-17-1411-2023, https://doi.org/10.5194/tc-17-1411-2023, 2023
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Information on sea ice surface topography is important for studies of sea ice as well as for ship navigation through ice. The ICESat-2 satellite senses the sea ice surface with six laser beams. To examine the accuracy of these measurements, we carried out a temporally coincident helicopter flight along the same ground track as the satellite and measured the sea ice surface topography with a laser scanner. This showed that ICESat-2 can see even bumps of only few meters in the sea ice cover.
Ludovic Moreau, Léonard Seydoux, Jérôme Weiss, and Michel Campillo
The Cryosphere, 17, 1327–1341, https://doi.org/10.5194/tc-17-1327-2023, https://doi.org/10.5194/tc-17-1327-2023, 2023
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In the perspective of an upcoming seasonally ice-free Arctic, understanding the dynamics of sea ice in the changing climate is a major challenge in oceanography and climatology. It is therefore essential to monitor sea ice properties with fine temporal and spatial resolution. In this paper, we show that icequakes recorded on sea ice can be processed with artificial intelligence to produce accurate maps of sea ice thickness with high temporal and spatial resolutions.
Sasan Tavakoli and Alexander V. Babanin
The Cryosphere, 17, 939–958, https://doi.org/10.5194/tc-17-939-2023, https://doi.org/10.5194/tc-17-939-2023, 2023
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We have tried to develop some new wave–ice interaction models by considering two different types of forces, one of which emerges in the ice and the other of which emerges in the water. We have checked the ability of the models in the reconstruction of wave–ice interaction in a step-wise manner. The accuracy level of the models is acceptable, and it will be interesting to check whether they can be used in wave climate models or not.
Christian Melsheimer, Gunnar Spreen, Yufang Ye, and Mohammed Shokr
The Cryosphere, 17, 105–126, https://doi.org/10.5194/tc-17-105-2023, https://doi.org/10.5194/tc-17-105-2023, 2023
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It is necessary to know the type of Antarctic sea ice present – first-year ice (grown in one season) or multiyear ice (survived one summer melt) – to understand and model its evolution, as the ice types behave and react differently. We have adapted and extended an existing method (originally for the Arctic), and now, for the first time, daily maps of Antarctic sea ice types can be derived from microwave satellite data. This will allow a new data set from 2002 well into the future to be built.
Nazanin Asadi, Philippe Lamontagne, Matthew King, Martin Richard, and K. Andrea Scott
The Cryosphere, 16, 3753–3773, https://doi.org/10.5194/tc-16-3753-2022, https://doi.org/10.5194/tc-16-3753-2022, 2022
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Machine learning approaches are deployed to provide accurate daily spatial maps of sea ice presence probability based on ERA5 data as input. Predictions are capable of predicting freeze-up/breakup dates within a 7 d period at specific locations of interest to shipping operators and communities. Forecasts of the proposed method during the breakup season have skills comparing to Climate Normal and sea ice concentration forecasts from a leading subseasonal-to-seasonal forecasting system.
Simon Felix Reifenberg and Helge Friedrich Goessling
The Cryosphere, 16, 2927–2946, https://doi.org/10.5194/tc-16-2927-2022, https://doi.org/10.5194/tc-16-2927-2022, 2022
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Using model simulations, we analyze the impact of chaotic error growth on Arctic sea ice drift predictions. Regarding forecast uncertainty, our results suggest that it matters in which season and where ice drift forecasts are initialized and that both factors vary with the model in use. We find ice velocities to be slightly more predictable than near-surface wind, a main driver of ice drift. This is relevant for future developments of ice drift forecasting systems.
Agathe Serripierri, Ludovic Moreau, Pierre Boue, Jérôme Weiss, and Philippe Roux
The Cryosphere, 16, 2527–2543, https://doi.org/10.5194/tc-16-2527-2022, https://doi.org/10.5194/tc-16-2527-2022, 2022
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As a result of global warming, the sea ice is disappearing at a much faster rate than predicted by climate models. To better understand and predict its ongoing decline, we deployed 247 geophones on the fast ice in Van Mijen Fjord in Svalbard, Norway, in March 2019. The analysis of these data provided a precise daily evolution of the sea-ice parameters at this location with high spatial and temporal resolution and accuracy. The results obtained are consistent with the observations made in situ.
Laura L. Landrum and Marika M. Holland
The Cryosphere, 16, 1483–1495, https://doi.org/10.5194/tc-16-1483-2022, https://doi.org/10.5194/tc-16-1483-2022, 2022
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High-latitude Arctic wintertime sea ice and snow insulate the relatively warmer ocean from the colder atmosphere. As the climate warms, wintertime Arctic conductive heat fluxes increase even when the sea ice concentrations remain high. Simulations from the Community Earth System Model Large Ensemble (CESM1-LE) show how sea ice and snow thicknesses, as well as the distribution of these thicknesses, significantly impact large-scale calculations of wintertime surface heat budgets in the Arctic.
Yunhe Wang, Xiaojun Yuan, Haibo Bi, Mitchell Bushuk, Yu Liang, Cuihua Li, and Haijun Huang
The Cryosphere, 16, 1141–1156, https://doi.org/10.5194/tc-16-1141-2022, https://doi.org/10.5194/tc-16-1141-2022, 2022
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We develop a regional linear Markov model consisting of four modules with seasonally dependent variables in the Pacific sector. The model retains skill for detrended sea ice extent predictions for up to 7-month lead times in the Bering Sea and the Sea of Okhotsk. The prediction skill, as measured by the percentage of grid points with significant correlations (PGS), increased by 75 % in the Bering Sea and 16 % in the Sea of Okhotsk relative to the earlier pan-Arctic model.
Charles Brunette, L. Bruno Tremblay, and Robert Newton
The Cryosphere, 16, 533–557, https://doi.org/10.5194/tc-16-533-2022, https://doi.org/10.5194/tc-16-533-2022, 2022
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Sea ice motion is a versatile parameter for monitoring the Arctic climate system. In this contribution, we use data from drifting buoys, winds, and ice thickness to parameterize the motion of sea ice in a free drift regime – i.e., flowing freely in response to the forcing from the winds and ocean currents. We show that including a dependence on sea ice thickness and taking into account a climatology of the surface ocean circulation significantly improves the accuracy of sea ice motion estimates.
Madison M. Smith, Marika Holland, and Bonnie Light
The Cryosphere, 16, 419–434, https://doi.org/10.5194/tc-16-419-2022, https://doi.org/10.5194/tc-16-419-2022, 2022
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Climate models represent the atmosphere, ocean, sea ice, and land with equations of varying complexity and are important tools for understanding changes in global climate. Here, we explore how realistic variations in the equations describing how sea ice melt occurs at the edges (called lateral melting) impact ice and climate. We find that these changes impact the progression of the sea-ice–albedo feedback in the Arctic and so make significant changes to the predicted Arctic sea ice.
Arttu Jutila, Stefan Hendricks, Robert Ricker, Luisa von Albedyll, Thomas Krumpen, and Christian Haas
The Cryosphere, 16, 259–275, https://doi.org/10.5194/tc-16-259-2022, https://doi.org/10.5194/tc-16-259-2022, 2022
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Sea-ice thickness retrieval from satellite altimeters relies on assumed sea-ice density values because density cannot be measured from space. We derived bulk densities for different ice types using airborne laser, radar, and electromagnetic induction sounding measurements. Compared to previous studies, we found high bulk density values due to ice deformation and younger ice cover. Using sea-ice freeboard, we derived a sea-ice bulk density parameterisation that can be applied to satellite data.
Mathieu Plante and L. Bruno Tremblay
The Cryosphere, 15, 5623–5638, https://doi.org/10.5194/tc-15-5623-2021, https://doi.org/10.5194/tc-15-5623-2021, 2021
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We propose a generalized form for the damage parameterization such that super-critical stresses can return to the yield with different final sub-critical stress states. In uniaxial compression simulations, the generalization improves the orientation of sea ice fractures and reduces the growth of numerical errors. Shear and convergence deformations however remain predominant along the fractures, contrary to observations, and this calls for modification of the post-fracture viscosity formulation.
Joey J. Voermans, Qingxiang Liu, Aleksey Marchenko, Jean Rabault, Kirill Filchuk, Ivan Ryzhov, Petra Heil, Takuji Waseda, Takehiko Nose, Tsubasa Kodaira, Jingkai Li, and Alexander V. Babanin
The Cryosphere, 15, 5557–5575, https://doi.org/10.5194/tc-15-5557-2021, https://doi.org/10.5194/tc-15-5557-2021, 2021
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We have shown through field experiments that the amount of wave energy dissipated in landfast ice, sea ice attached to land, is much larger than in broken ice. By comparing our measurements against predictions of contemporary wave–ice interaction models, we determined which models can explain our observations and which cannot. Our results will improve our understanding of how waves and ice interact and how we can model such interactions to better forecast waves and ice in the polar regions.
Marika M. Holland, David Clemens-Sewall, Laura Landrum, Bonnie Light, Donald Perovich, Chris Polashenski, Madison Smith, and Melinda Webster
The Cryosphere, 15, 4981–4998, https://doi.org/10.5194/tc-15-4981-2021, https://doi.org/10.5194/tc-15-4981-2021, 2021
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As the most reflective and most insulative natural material, snow has important climate effects. For snow on sea ice, its high reflectivity reduces ice melt. However, its high insulating capacity limits ice growth. These counteracting effects make its net influence on sea ice uncertain. We find that with increasing snow, sea ice in both hemispheres is thicker and more extensive. However, the drivers of this response are different in the two hemispheres due to different climate conditions.
Don Perovich, Madison Smith, Bonnie Light, and Melinda Webster
The Cryosphere, 15, 4517–4525, https://doi.org/10.5194/tc-15-4517-2021, https://doi.org/10.5194/tc-15-4517-2021, 2021
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During summer, Arctic sea ice melts on its surface and bottom and lateral edges. Some of this fresh meltwater is stored on the ice surface in features called melt ponds. The rest flows into the ocean. The meltwater flowing into the upper ocean affects ice growth and melt, upper ocean properties, and ocean ecosystems. Using field measurements, we found that the summer meltwater was equal to an 80 cm thick layer; 85 % of this meltwater flowed into the ocean and 15 % was stored in melt ponds.
Sönke Maus, Martin Schneebeli, and Andreas Wiegmann
The Cryosphere, 15, 4047–4072, https://doi.org/10.5194/tc-15-4047-2021, https://doi.org/10.5194/tc-15-4047-2021, 2021
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As the hydraulic permeability of sea ice is difficult to measure, observations are sparse. The present work presents numerical simulations of the permeability of young sea ice based on a large set of 3D X-ray tomographic images. It extends the relationship between permeability and porosity available so far down to brine porosities near the percolation threshold of a few per cent. Evaluation of pore scales and 3D connectivity provides novel insight into the percolation behaviour of sea ice.
Cyril Palerme and Malte Müller
The Cryosphere, 15, 3989–4004, https://doi.org/10.5194/tc-15-3989-2021, https://doi.org/10.5194/tc-15-3989-2021, 2021
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Methods have been developed for calibrating sea ice drift forecasts from an operational prediction system using machine learning algorithms. These algorithms use predictors from sea ice concentration observations during the initialization of the forecasts, sea ice and wind forecasts, and some geographical information. Depending on the calibration method, the mean absolute error is reduced between 3.3 % and 8.0 % for the direction and between 2.5 % and 7.1 % for the speed of sea ice drift.
Dongyang Fu, Bei Liu, Yali Qi, Guo Yu, Haoen Huang, and Lilian Qu
The Cryosphere, 15, 3797–3811, https://doi.org/10.5194/tc-15-3797-2021, https://doi.org/10.5194/tc-15-3797-2021, 2021
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Our results show three main sea ice drift patterns have different multiscale variation characteristics. The oscillation period of the third sea ice transport pattern is longer than the other two, and the ocean environment has a more significant influence on it due to the different regulatory effects of the atmosphere and ocean environment on sea ice drift patterns on various scales. Our research can provide a basis for the study of Arctic sea ice dynamics parameterization in numerical models.
Andrii Murdza, Arttu Polojärvi, Erland M. Schulson, and Carl E. Renshaw
The Cryosphere, 15, 2957–2967, https://doi.org/10.5194/tc-15-2957-2021, https://doi.org/10.5194/tc-15-2957-2021, 2021
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The strength of refrozen floes or piles of ice rubble is an important factor in assessing ice-structure interactions, as well as the integrity of an ice cover itself. The results of this paper provide unique data on the tensile strength of freeze bonds and are the first measurements to be reported. The provided information can lead to a better understanding of the behavior of refrozen ice floes and better estimates of the strength of an ice rubble pile.
H. Jakob Belter, Thomas Krumpen, Luisa von Albedyll, Tatiana A. Alekseeva, Gerit Birnbaum, Sergei V. Frolov, Stefan Hendricks, Andreas Herber, Igor Polyakov, Ian Raphael, Robert Ricker, Sergei S. Serovetnikov, Melinda Webster, and Christian Haas
The Cryosphere, 15, 2575–2591, https://doi.org/10.5194/tc-15-2575-2021, https://doi.org/10.5194/tc-15-2575-2021, 2021
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Summer sea ice thickness observations based on electromagnetic induction measurements north of Fram Strait show a 20 % reduction in mean and modal ice thickness from 2001–2020. The observed variability is caused by changes in drift speeds and consequential variations in sea ice age and number of freezing-degree days. Increased ocean heat fluxes measured upstream in the source regions of Arctic ice seem to precondition ice thickness, which is potentially still measurable more than a year later.
Ann Keen, Ed Blockley, David A. Bailey, Jens Boldingh Debernard, Mitchell Bushuk, Steve Delhaye, David Docquier, Daniel Feltham, François Massonnet, Siobhan O'Farrell, Leandro Ponsoni, José M. Rodriguez, David Schroeder, Neil Swart, Takahiro Toyoda, Hiroyuki Tsujino, Martin Vancoppenolle, and Klaus Wyser
The Cryosphere, 15, 951–982, https://doi.org/10.5194/tc-15-951-2021, https://doi.org/10.5194/tc-15-951-2021, 2021
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We compare the mass budget of the Arctic sea ice in a number of the latest climate models. New output has been defined that allows us to compare the processes of sea ice growth and loss in a more detailed way than has previously been possible. We find that that the models are strikingly similar in terms of the major processes causing the annual growth and loss of Arctic sea ice and that the budget terms respond in a broadly consistent way as the climate warms during the 21st century.
Ron Kwok, Alek A. Petty, Marco Bagnardi, Nathan T. Kurtz, Glenn F. Cunningham, Alvaro Ivanoff, and Sahra Kacimi
The Cryosphere, 15, 821–833, https://doi.org/10.5194/tc-15-821-2021, https://doi.org/10.5194/tc-15-821-2021, 2021
Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Rasmus Tonboe, Stefan Hendricks, Robert Ricker, James Mead, Robbie Mallett, Marcus Huntemann, Polona Itkin, Martin Schneebeli, Daniela Krampe, Gunnar Spreen, Jeremy Wilkinson, Ilkka Matero, Mario Hoppmann, and Michel Tsamados
The Cryosphere, 14, 4405–4426, https://doi.org/10.5194/tc-14-4405-2020, https://doi.org/10.5194/tc-14-4405-2020, 2020
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This study provides a first look at the data collected by a new dual-frequency Ka- and Ku-band in situ radar over winter sea ice in the Arctic Ocean. The instrument shows potential for using both bands to retrieve snow depth over sea ice, as well as sensitivity of the measurements to changing snow and atmospheric conditions.
Leandro Ponsoni, François Massonnet, David Docquier, Guillian Van Achter, and Thierry Fichefet
The Cryosphere, 14, 2409–2428, https://doi.org/10.5194/tc-14-2409-2020, https://doi.org/10.5194/tc-14-2409-2020, 2020
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The continuous melting of the Arctic sea ice observed in the last decades has a significant impact at global and regional scales. To understand the amplitude and consequences of this impact, the monitoring of the total sea ice volume is crucial. However, in situ monitoring in such a harsh environment is hard to perform and far too expensive. This study shows that four well-placed sampling locations are sufficient to explain about 70 % of the inter-annual changes in the pan-Arctic sea ice volume.
H. Jakob Belter, Thomas Krumpen, Stefan Hendricks, Jens Hoelemann, Markus A. Janout, Robert Ricker, and Christian Haas
The Cryosphere, 14, 2189–2203, https://doi.org/10.5194/tc-14-2189-2020, https://doi.org/10.5194/tc-14-2189-2020, 2020
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The validation of satellite sea ice thickness (SIT) climate data records with newly acquired moored sonar SIT data shows that satellite products provide modal rather than mean SIT in the Laptev Sea region. This tendency of satellite-based SIT products to underestimate mean SIT needs to be considered for investigations of sea ice volume transports. Validation of satellite SIT in the first-year-ice-dominated Laptev Sea will support algorithm development for more reliable SIT records in the Arctic.
Mark S. Handcock and Marilyn N. Raphael
The Cryosphere, 14, 2159–2172, https://doi.org/10.5194/tc-14-2159-2020, https://doi.org/10.5194/tc-14-2159-2020, 2020
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Traditional methods of calculating the annual cycle of sea ice extent disguise the variation of amplitude and timing (phase) of the advance and retreat of the ice. We present a multiscale model that explicitly allows them to vary, resulting in a much improved representation of the cycle. We show that phase is the dominant contributor to the variability in the cycle and that the anomalous decay of Antarctic sea ice in 2016 was due largely to a change of phase.
Rebecca J. Rolph, Daniel L. Feltham, and David Schröder
The Cryosphere, 14, 1971–1984, https://doi.org/10.5194/tc-14-1971-2020, https://doi.org/10.5194/tc-14-1971-2020, 2020
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It is well known that the Arctic sea ice extent is declining, and it is often assumed that the marginal ice zone (MIZ), the area of partial sea ice cover, is consequently increasing. However, we find no trend in the MIZ extent during the last 40 years from observations that is consistent with a widening of the MIZ as it moves northward. Differences of MIZ extent between different satellite retrievals are too large to provide a robust basis to verify model simulations of MIZ extent.
Mark A. Tschudi, Walter N. Meier, and J. Scott Stewart
The Cryosphere, 14, 1519–1536, https://doi.org/10.5194/tc-14-1519-2020, https://doi.org/10.5194/tc-14-1519-2020, 2020
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A new version of a set of data products that contain the velocity of sea ice and the age of this ice has been developed. We provide a history of the product development and discuss the improvements to the algorithms that create these products. We find that changes in sea ice motion and age show a significant shift in the Arctic ice cover, from a pack with a high concentration of older ice to a sea ice cover dominated by younger ice, which is more susceptible to summer melt.
Angela Cheng, Barbara Casati, Adrienne Tivy, Tom Zagon, Jean-François Lemieux, and L. Bruno Tremblay
The Cryosphere, 14, 1289–1310, https://doi.org/10.5194/tc-14-1289-2020, https://doi.org/10.5194/tc-14-1289-2020, 2020
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Sea ice charts by the Canadian Ice Service (CIS) contain visually estimated ice concentration produced by analysts. The accuracy of manually derived ice concentrations is not well understood. The subsequent uncertainty of ice charts results in downstream uncertainties for ice charts users, such as models and climatology studies, and when used as a verification source for automated sea ice classifiers. This study quantifies the level of accuracy and inter-analyst agreement for ice charts by CIS.
Young Jun Kim, Hyun-Cheol Kim, Daehyeon Han, Sanggyun Lee, and Jungho Im
The Cryosphere, 14, 1083–1104, https://doi.org/10.5194/tc-14-1083-2020, https://doi.org/10.5194/tc-14-1083-2020, 2020
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In this study, we proposed a novel 1-month sea ice concentration (SIC) prediction model with eight predictors using a deep-learning approach, convolutional neural networks (CNNs). The proposed CNN model was evaluated and compared with the two baseline approaches, random-forest and simple-regression models, resulting in better performance. This study also examined SIC predictions for two extreme cases in 2007 and 2012 in detail and the influencing factors through a sensitivity analysis.
Shiming Xu, Lu Zhou, and Bin Wang
The Cryosphere, 14, 751–767, https://doi.org/10.5194/tc-14-751-2020, https://doi.org/10.5194/tc-14-751-2020, 2020
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Sea ice thickness parameters are key to polar climate change studies and forecasts. Airborne and satellite measurements provide complementary observational capabilities. The study analyzes the variability in freeboard and snow depth measurements and its changes with scale in Operation IceBridge, CryoVEx, CryoSat-2 and ICESat. Consistency between airborne and satellite data is checked. Analysis calls for process-oriented attribution of variability and covariability features of these parameters.
Valeria Selyuzhenok, Igor Bashmachnikov, Robert Ricker, Anna Vesman, and Leonid Bobylev
The Cryosphere, 14, 477–495, https://doi.org/10.5194/tc-14-477-2020, https://doi.org/10.5194/tc-14-477-2020, 2020
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This study explores a link between the long-term variations in the integral sea ice volume in the Greenland Sea and oceanic processes. We link the changes in the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) regional sea ice volume with the mixed layer, depth and upper-ocean heat content derived using the ARMOR dataset.
Chao Min, Longjiang Mu, Qinghua Yang, Robert Ricker, Qian Shi, Bo Han, Renhao Wu, and Jiping Liu
The Cryosphere, 13, 3209–3224, https://doi.org/10.5194/tc-13-3209-2019, https://doi.org/10.5194/tc-13-3209-2019, 2019
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Sea ice volume export through the Fram Strait has been studied using varied methods, however, mostly in winter months. Here we report sea ice volume estimates that extend over summer seasons. A recent developed sea ice thickness dataset, in which CryoSat-2 and SMOS sea ice thickness together with SSMI/SSMIS sea ice concentration are assimilated, is used and evaluated in the paper. Results show our estimate is more reasonable than that calculated by satellite data only.
M. Jeffrey Mei, Ted Maksym, Blake Weissling, and Hanumant Singh
The Cryosphere, 13, 2915–2934, https://doi.org/10.5194/tc-13-2915-2019, https://doi.org/10.5194/tc-13-2915-2019, 2019
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Sea ice thickness is hard to measure directly, and current datasets are very limited to sporadically conducted drill lines. However, surface elevation is much easier to measure. Converting surface elevation to ice thickness requires making assumptions about snow depth and density, which leads to large errors (and may not generalize to new datasets). A deep learning method is presented that uses the surface morphology as a direct predictor of sea ice thickness, with testing errors of < 20 %.
Pierre Rampal, Véronique Dansereau, Einar Olason, Sylvain Bouillon, Timothy Williams, Anton Korosov, and Abdoulaye Samaké
The Cryosphere, 13, 2457–2474, https://doi.org/10.5194/tc-13-2457-2019, https://doi.org/10.5194/tc-13-2457-2019, 2019
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In this article, we look at how the Arctic sea ice cover, as a solid body, behaves on different temporal and spatial scales. We show that the numerical model neXtSIM uses a new approach to simulate the mechanics of sea ice and reproduce the characteristics of how sea ice deforms, as observed by satellite. We discuss the importance of this model performance in the context of simulating climate processes taking place in polar regions, like the exchange of energy between the ocean and atmosphere.
Alberto Alberello, Miguel Onorato, Luke Bennetts, Marcello Vichi, Clare Eayrs, Keith MacHutchon, and Alessandro Toffoli
The Cryosphere, 13, 41–48, https://doi.org/10.5194/tc-13-41-2019, https://doi.org/10.5194/tc-13-41-2019, 2019
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Existing observations do not provide quantitative descriptions of the floe size distribution for pancake ice floes. This is important during the Antarctic winter sea ice expansion, when hundreds of kilometres of ice cover around the Antarctic continent are composed of pancake floes (D = 0.3–3 m). Here, a new set of images from the Antarctic marginal ice zone is used to measure the shape of individual pancakes for the first time and to infer their size distribution.
Frédéric Laliberté, Stephen E. L. Howell, Jean-François Lemieux, Frédéric Dupont, and Ji Lei
The Cryosphere, 12, 3577–3588, https://doi.org/10.5194/tc-12-3577-2018, https://doi.org/10.5194/tc-12-3577-2018, 2018
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Ice that forms over marginal seas often gets anchored and becomes landfast. Landfast ice is fundamental to the local ecosystems, is of economic importance as it leads to hazardous seafaring conditions and is also a choice hunting ground for both the local population and large predators. Using observations and climate simulations, this study shows that, especially in the Canadian Arctic, landfast ice might be more resilient to climate change than is generally thought.
Iina Ronkainen, Jonni Lehtiranta, Mikko Lensu, Eero Rinne, Jari Haapala, and Christian Haas
The Cryosphere, 12, 3459–3476, https://doi.org/10.5194/tc-12-3459-2018, https://doi.org/10.5194/tc-12-3459-2018, 2018
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We quantify the sea ice thickness variability in the Bay of Bothnia using various observational data sets. For the first time we use helicopter and shipborne electromagnetic soundings to study changes in drift ice of the Bay of Bothnia. Our results show that the interannual variability of ice thickness is larger in the drift ice zone than in the fast ice zone. Furthermore, the mean thickness of heavily ridged ice near the coast can be several times larger than that of fast ice.
Edward W. Blockley and K. Andrew Peterson
The Cryosphere, 12, 3419–3438, https://doi.org/10.5194/tc-12-3419-2018, https://doi.org/10.5194/tc-12-3419-2018, 2018
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Arctic sea-ice prediction on seasonal time scales is becoming increasingly more relevant to society but the predictive capability of forecasting systems is low. Several studies suggest initialization of sea-ice thickness (SIT) could improve the skill of seasonal prediction systems. Here for the first time we test the impact of SIT initialization in the Met Office's GloSea coupled prediction system using CryoSat-2 data. We show significant improvements to Arctic extent and ice edge location.
Cited articles
Banzon, V., Smith., T. M., Steele, M., Huang, B., and Zhang, H.-M.: Improved estimation of proxy sea surface temperature in the Arctic, J. Atmos. Ocean. Technol., 37, 341–349, https://doi.org/10.1175/JTECH-D-19-0177.1, 2020.
Banzon, V., Reynolds, R., and National Center for Atmospheric Research Staff (Eds.): Last modified 2022-09-09, The Climate Data Guide: SST data: NOAA High-resolution (0.25x0.25) Blended Analysis of Daily SST and Ice, OISSTv2, https://climatedataguide.ucar.edu/climate-data/sst-data-noaa-high-resolution-025x025-blended-analysis-daily-sst-and-ice-oisstv2, last access: 29 October 2023.
Bi, H., Sun, K., Zhou, X., Huang, H., and Xu, X.: Arctic Sea ice area export through the Fram Strait estimated from satellite-based data: 1988–2012, IEEE J. Stars, 9, 3144–3157, https://doi.org/10.1109/jstars.2016.2584539, 2016.
Cai, L., Alexeev, V.A., and Walsh, J.E.: Arctic sea ice growth in response to synoptic- and large-scale atmospheric forcing from CMIP5 models, J. Climate, 33, 6083–6099, https://doi.org/10.1175/jcli-d-19-0326.1, 2020.
Cavalieri, D.J., Gloersen, P., and Campbell, W.J.: Determination of sea ice parameters with the Nimbus 7 SMMR, J. Geophys. Res.-Atmos., 89, 5355–5369, https://doi.org/10.1016/0198-0254(84)93205-9, 1984.
Comiso, J.C.: Characteristics of Arctic winter sea ice from satellite multispectral microwave observations, J. Geophys. Res.-Oceans, 91, 975–994, https://doi.org/10.1029/jc091ic01p00975, 1986.
Department of Fisheries and Oceans, Canada: https://iabp.apl.uw.edu/Data_Products/BUOY_DATA/, last access: 15 June 2021.
de Steur, L., Hansen, E., Mauritzen, C., Beszczynska-Moeller, A., and Fahrbach, E.: Impact of recirculation on the East Greenland Current in Fram Strait: Results from moored current meter measurements between 1997 and 2009, Deep-Sea Res. Pt. I, 92, 26–40, https://doi.org/10.1016/j.dsr.2014.05.018, 2014.
Dethloff, K., Maslowski, W., Hendricks, S., Lee, Y. J., Goessling, H. F., Krumpen, T., Haas, C., Handorf, D., Ricker, R., Bessonov, V., Cassano, J. J., Kinney, J. C., Osinski, R., Rex, M., Rinke, A., Sokolova, J., and Sommerfeld, A.: Arctic sea ice anomalies during the MOSAiC winter 2019/20, The Cryosphere, 16, 981–1005, https://doi.org/10.5194/tc-16-981-2022, 2022.
Dickson, R. R., Meincke, J., Malmberg, S. A., and Lee, A. J.: The “Great Salinity Anomaly” in the Northern North-Atlantic 1968–1982, Prog. Oceanogr., 20, 103–151, https://doi.org/10.1016/0079-6611(88)90049-3, 1988.
Dorr, J., Arthun, M., Eldevik, T., and Madonna, E.: Mechanisms of regional winter sea-ice variability in a warming Arctic, J. Climate, 34, 8635–8653, https://doi.org/10.1175/jcli-d-21-0149.1, 2021.
Grosfeld, K., Treffeisen, R., Asseng, J., Bartsch, A., Bräuer, B., Fritzsch, B., Gerdes, R., Hendricks, S., Hiller, W., Heygster, G., Krumpen, T., Lemke, P., Melsheimer, C., Nicolaus, M., Ricker, R., and Weigelt, M.: Online sea-ice knowledge and data platform <https://www.meereisportal.de>, Polarforschung, Bremerhaven, Alfred Wegener Institute for Polar and Marine Research & German Society of Polar Research, 85, 143–155, https://doi.org/10.2312/polfor.2016.011 (data available at: https://data.meereisportal.de/data/buoys/, last access: 30 October 2023), 2016.
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.
Graham, R. M., Hudson, S. R., and Maturilli, M.: Improved performance of ERA5 in Arctic gateway relative to four global atmospheric reanalyses, Geophys. Res. Lett., 46, 6138–6147, https://doi.org/10.1029/2019gl082781, 2019.
Hastenrath, S. and Greischar, L.: The North Atlantic oscillation in the NCEP-NCAR reanalysis, J. Climate, 14, 2404–2413, https://doi.org/10.1175/1520-0442(2001)014<2404:TNAOIT>2.0.CO;2, 2001.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horanyi, A., Munoz-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., Holm, E., Janiskova, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thepaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J.-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023.
Huang, B., Liu, C., Banzon, V., Freeman, E., Graham, G., Hankins, B., Smith, T., and Zhang, H.M.: Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version 2.1, J. Climate, 34, 2923–2939, https://doi.org/10.1175/JCLI-D-20-0166.1, 2021.
Hurrell, J. W.: Climate and climate change | Climate variability: North Atlantic and Arctic oscillation, in: Encyclopedia of Atmospheric Sciences, 2nd edn., edited by: North, G.R., Pyle, J., and Zhang, F., Academic Press, Oxford, 47–60, https://doi.org/10.1016/B978-0-12-382225-3.00109-2, 2015.
Krumpen, T., Birrien, F., Kauker, F., Rackow, T., von Albedyll, L., Angelopoulos, M., Belter, H. J., Bessonov, V., Damm, E., Dethloff, K., Haapala, J., Haas, C., Harris, C., Hendricks, S., Hoelemann, J., Hoppmann, M., Kaleschke, L., Karcher, M., Kolabutin, N., Lei, R., Lenz, J., Morgenstern, A., Nicolaus, M., Nixdorf, U., Petrovsky, T., Rabe, B., Rabenstein, L., Rex, M., Ricker, R., Rohde, J., Shimanchuk, E., Singha, S., Smolyanitsky, V., Sokolov, V., Stanton, T., Timofeeva, A., Tsamados, M., and Watkins, D.: The MOSAiC ice floe: sediment-laden survivor from the Siberian shelf, The Cryosphere, 14, 2173–2187, https://doi.org/10.5194/tc-14-2173-2020, 2020.
Krumpen, T., von Albedyll, L., Goessling, H. F., Hendricks, S., Juhls, B., Spreen, G., Willmes, S., Belter, H. J., Dethloff, K., Haas, C., Kaleschke, L., Katlein, C., Tian-Kunze, X., Ricker, R., Rostosky, P., Rückert, J., Singha, S., and Sokolova, J.: MOSAiC drift expedition from October 2019 to July 2020: sea ice conditions from space and comparison with previous years, The Cryosphere, 15, 3897–3920, https://doi.org/10.5194/tc-15-3897-2021, 2021.
Kwok, R.: Outflow of Arctic ocean sea ice into the Greenland and Barents Seas: 1979–2007, J. Climate, 22, 2438–2457, https://doi.org/10.1175/2008jcli2819.1, 2009.
Kwok, R., Cunningham, G., Wensnahan, M., Rigor, I., Zwally, H., and Yi, D.: Thinning and volume loss of the Arctic Ocean sea ice cover: 2003–2008, J. Geophys. Res.-Oceans, 114, https://doi.org/10.1029/2009jc005312, 2009.
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.
Lei, R., Heil, P., Wang, J., Zhang, Z., Li, Q., and Li, N.: Characterization of sea-ice kinematic in the Arctic outflow region using buoy data, Polar. Res., 35, 22658, https://doi.org/10.3402/polar.v35.22658, 2016.
Lei, R., Gui, D., Hutchings, J.K., Wang, J., and Pang, X.: Backward and forward drift trajectories of sea ice in the northwestern Arctic Ocean in response to changing atmospheric circulation, Int. J. Climatol., 39, 4372–4391, https://doi.org/10.1002/joc.6080, 2019.
Lind, S., Ingvaldsen, R. B., and Furevik, T.: Arctic warming hotspot in the northern Barents Sea linked to declining sea-ice import, Nat. Clim. Change, 8, 634–639, https://doi.org/10.1038/s41558-018-0205-y, 2018.
Mayot, N., Matrai, P. A., Arjona, A., Belanger, S., Marchese, C., Jaegler, T., Ardyna, M., and Steele, M.: Springtime export of Arctic sea ice influences phytoplankton production in the Greenland Sea, J. Geophys. Res.-Oceans, 125, https://doi.org/10.1029/2019jc015799, 2020.
Meier, W. N., Fetterer, F., Windnagel, A. K., and Stewart, J. S.: NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 4, Boulder, NSIDC: National Snow and Ice Data Center, Colorado USA [data set], https://doi.org/10.7265/efmz-2t65, 2021.
Minnett, P. J. and Key, E. L.: Meteorology and atmosphere–surface coupling in and around polynyas, Elsev. Oceanogr. Serie., 74, 127–161, https://doi.org/10.1016/S0422-9894(06)74004-1, 2007.
Mori, M., Watanabe, M., Shiogama, H., Inoue, J., and Kimoto, M.: Robust Arctic sea-ice influence on the frequent Eurasian cold winters in past decades, Nat. Geosci., 7, 869–873, https://doi.org/10.1038/ngeo2277, 2014.
Mørk, T., Bohlin, J., Fuglei, E., Asbakk, K., and Tryland, M.: Rabies in the arctic fox population, Svalbard, Norway, J. Wildlife. Dis., 47, 945–957, https://doi.org/10.7589/0090-3558-47.4.945, 2011.
National Weather Service Climate Prediction Center of the National Oceanic and Atmospheric Administration: https://www.cpc.ncep.noaa.gov/products/precip/CWlink/daily_ao_index/ao.shtml, last access: September 2023a.
National Weather Service Climate Prediction Center of the National Oceanic and Atmospheric Administration: https://www.cpc.ncep.noaa.gov/products/precip/CWlink/pna/nao.shtml, last access: September 2023b.
Nicolaus, M., Riemann-Campe, K., Bliss, A., Hutchings, J. K., Granskog, M. A., Haas, C., Hoppmann, M., Kanzow, T., Krishfield, R. A., Lei, R., Rex, M., Li, T., and Rabe, B.: Drift trajectories of the main sites of the Distributed Network of MOSAiC 2019/2020, Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, PANGAEA, https://doi.org/10.1594/PANGAEA.937204, 2021.
Onarheim, I. H., Eldevik, T., Smedsrud, L. H., and Stroeve, J. C.: Seasonal and regional manifestation of Arctic sea ice loss, J. Climate, 31, 4917–4932, https://doi.org/10.1175/jcli-d-17-0427.1, 2018.
Pabi, S., van Dijken, G. L., and Arrigo, K. R.: Primary production in the Arctic Ocean, 1998–2006, J. Geophys. Res.-Oceans, 113, C08005, https://doi.org/10.1029/2007JC004578, 2008.
Parkinson, C. L. and DiGirolamo, N. E.: Sea ice extents continue to set new records: Arctic, Antarctic, and global results, Remote Sens. Environ., 267, 112753, https://doi.org/10.1016/j.rse.2021.112753, 2021.
Parkinson, C. L. and Washington, W. M.: A large-scale numerical model of sea ice, J. Geophys. Res.-Oceans, 84, 311–337, https://doi.org/10.1029/JC084iC01p00311, 1979.
Peeken, I., Primpke, S., Beyer, B., Gutermann, J., Katlein, C., Krumpen, T., Bergmann, M., Hehemann, L., and Gerdts, G.: Arctic sea ice is an important temporal sink and means of transport for microplastic, Nat. Commun., 9, 1505, https://doi.org/10.1038/s41467-018-03825-5, 2018.
Peng, G., Meier, W. N., Scott, D. J., and Savoie, M. H.: A long-term and reproducible passive microwave sea ice concentration data record for climate studies and monitoring, Earth Syst. Sci. Data, 5, 311–318, https://doi.org/10.5194/essd-5-311-2013, 2013.
Polyakov, I. V., Ingvaldsen, R. B., Pnyushkov, A. V., Bhatt, U. S., Francis, J. A., Janout, M., Kwok, R., and Skagseth, Ø.: Fluctuating Atlantic inflows modulate Arctic atlantification, Science, 381, 972–979, https://doi.org/10.1126/science.adh5158, 2023.
Rahmstorf, S., Box, J. E., Feulner, G., Mann, M. E., Robinson, A., Rutherford, S., and Schaffernicht, E. J.: Exceptional twentieth-century slowdown in Atlantic Ocean overturning circulation, Nat. Clim. Change, 5, 475–480, https://doi.org/10.1038/NCLIMATE2554, 2015.
Reynolds, R. W., Smith, T. M., Liu, C., Chelton, D. B., Casey, K. S., and Schlax, M. G.: Daily high-resolution-blended analyses for sea surface temperature, J. Climate, 20, 5473–5496, https://doi.org/10.1175/2007jcli1824.1, 2007.
Ricker, R., Hendricks, S., Kaleschke, L., Tian-Kunze, X., King, J., and Haas, C.: A weekly Arctic sea-ice thickness data record from merged CryoSat-2 and SMOS satellite data, The Cryosphere, 11, 1607–1623, https://doi.org/10.5194/tc-11-1607-2017, 2017 (data available at: https://data.seaiceportal.de/data/cs2smos_awi/v204/, last access: 10 April 2022).
Rigor, I. G., Wallace, J. M., and Colony, R. L.: Response of sea ice to the Arctic Oscillation, J. Climate, 15, 2648–2663, https://doi.org/10.1029/1999gl002389, 2002.
Schlichtholz, P.: Subsurface ocean flywheel of coupled climate variability in the Barents Sea hotspot of global warming, Sci. Rep., 9, 1–16, https://doi.org/10.1038/s41598-019-49965-6, 2019.
Schweiger, A., Lindsay, R., Zhang, J. L., Steele, M., Stern, H., and Kwok, R.: Uncertainty in modeled Arctic sea ice volume, J. Geophys. Res.-Oceans, 116, C00D06, https://doi.org/10.1029/2011jc007084, 2011.
Shu, Q., Wang, Q., Song, Z., and Qiao, F.: The poleward enhanced Arctic Ocean cooling machine in a warming climate, Nat. Commun., 12, 2966, https://doi.org/10.1038/s41467-021-23321-7, 2021.
Siswanto, E.: Temporal variability of satellite-retrieved chlorophyll-a data in Arctic and subarctic ocean regions within the past two decades, Int. J. Remote. Sens., 41, 7427–7445, https://doi.org/10.1080/01431161.2020.1759842, 2020.
Smedsrud, L. H., Esau, I., Ingvaldsen, R. B., Eldevik, T., Haugan, P. M., Li, C., Lien, V. S., Olsen, A., Omar, A. M., and Otterå, O. H.: The role of the Barents Sea in the Arctic climate system, Rev. Geophy., 51, 415–449, https://doi.org/10.1017/cbo9780511535888.008, 2013.
Smedsrud, L. H., Halvorsen, M. H., Stroeve, J. C., Zhang, R., and Kloster, K.: Fram Strait sea ice export variability and September Arctic sea ice extent over the last 80 years, The Cryosphere, 11, 65–79, https://doi.org/10.5194/tc-11-65-2017, 2017.
Spreen, G., Kern, S., Stammer, D., and Hansen, E.: Fram Strait sea ice volume export estimated between 2003 and 2008 from satellite data, Geophys. Res. Lett., 36, L19502, https://doi.org/10.1029/2009GL039591, 2009.
Stroeve, J., Barrett, A., Serreze, M., and Schweiger, A.: Using records from submarine, aircraft and satellites to evaluate climate model simulations of Arctic sea ice thickness, The Cryosphere, 8, 1839–1854, https://doi.org/10.5194/tc-8-1839-2014, 2014.
Sumata, H., de Steur, L., Gerland, S., Divine, D. V., and Pavlova, O.: Unprecedented decline of Arctic sea ice outflow in 2018, Nat. Commun., 13, 1747, https://doi.org/10.1038/s41467-022-29470-7, 2022.
Thompson, D. W. J. and Wallace, J. M.: The Arctic Oscillation signature in the wintertime geopotential height and temperature fields, Geophys. Res. Lett., 25, 1297–1300, https://doi.org/10.1029/98gl00950, 1998.
Tschudi, M. A,, Meier, W. N., Stewart, J. S., Fowler, C., and Maslanik, J.: Polar Pathfinder Daily 25 km EASE-Grid Sea Ice Motion Vectors, Version 4, Boulder, NASA National Snow and Ice Data Center Distributed Active Archive Center, Colorado USA, [data set], https://doi.org/10.5067/INAWUWO7QH7B, 2019.
Tschudi, M. A., Meier, W. N., and Stewart, J. S.: An enhancement to sea ice motion and age products at the National Snow and Ice Data Center (NSIDC), The Cryosphere, 14, 1519–1536, https://doi.org/10.5194/tc-14-1519-2020, 2020.
Vihma, T., Tisler, P., and Uotila, P.: Atmospheric forcing on the drift of Arctic sea ice in 1989–2009, Geophys. Res. Lett., 39, L02501, https://doi.org/10.1029/2011gl050118, 2012.
Wang, X., Key, J., Kwok, R., and Zhang, J.: Comparison of Arctic sea ice thickness from satellites, aircraft, and PIOMAS data, Remote Sens., 8, 713, https://doi.org/10.3390/rs8090713, 2016.
Wang, Y., Bi, H., Huang, H., Liu, Y., Liu, Y., Liang, X., Fu, M., and Zhang, Z.: Satellite-observed trends in the Arctic sea ice concentration for the period 1979–2016, J. Oceanol. Limnol., 37, 18–37, https://doi.org/10.1007/s00343-019-7284-0, 2019.
Wassmann, P., Slagstad, D., and Ellingsen, I.: Primary production and climatic variability in the European sector of the Arctic Ocean prior to 2007: preliminary results, Polar. Biol., 33, 1641–1650, https://doi.org/10.1007/s00300-010-0839-3, 2010.
Zhang, F., Pang, X., Lei, R., Zhai, M., Zhao, X., and Cai, Q.: Arctic sea ice motion change and response to atmospheric forcing between 1979 and 2019, Int. J. Climatol., 42, 1854–1876, https://doi.org/10.1002/joc.7340, 2022.
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 (data available at: https://pscfiles.apl.uw.edu/zhang/PIOMAS/, last access: 31 December 2020).
Zhang, S., Gan, T., and Bush, A. B. G.: Variability of Arctic Sea Ice Based on Quantile Regression and the Teleconnection with Large-Scale Climate Patterns, J. Climate, 33, 4009–4025, https://doi.org/10.1175/JCLI-D-19-0375.1, 2020.
Short summary
Atmospheric circulation anomalies lead to high Arctic sea ice outflow in winter 2020, causing heavy ice conditions in the Barents–Greenland seas, subsequently impeding the sea surface temperature warming. This suggests that the winter–spring Arctic sea ice outflow can be considered a predictor of changes in sea ice and other marine environmental conditions in the Barents–Greenland seas, which could help to improve our understanding of the physical connections between them.
Atmospheric circulation anomalies lead to high Arctic sea ice outflow in winter 2020, causing...
