Articles | Volume 19, issue 8
https://doi.org/10.5194/tc-19-3065-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-19-3065-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Aug 2025
Research article |
| 15 Aug 2025
| 15 Aug 2025
Estimation of duration and its changes in Lagrangian observations relying on ice floes in the Arctic Ocean utilizing a sea ice motion product
Fanyi Zhang
Chinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan 430079, China
Key Laboratory for Polar Science of the MNR, Polar Research Institute of China, Shanghai 200136, China
Chinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan 430079, China
Key Laboratory for Polar Science of the MNR, Polar Research Institute of China, Shanghai 200136, China
Key Laboratory for Polar Science of the MNR, Polar Research Institute of China, Shanghai 200136, China
Na Li
Key Laboratory for Polar Science of the MNR, Polar Research Institute of China, Shanghai 200136, China
Ying Chen
Key Laboratory for Polar Science of the MNR, Polar Research Institute of China, Shanghai 200136, China
Xiaoping Pang
CORRESPONDING AUTHOR
Chinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan 430079, China
Related authors
Fanyi Zhang, Ruibo Lei, Mengxi Zhai, Xiaoping Pang, and Na Li
The Cryosphere, 17, 4609–4628, https://doi.org/10.5194/tc-17-4609-2023, https://doi.org/10.5194/tc-17-4609-2023, 2023
Short summary
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.
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
Short summary
Short summary
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.
Guokun Lyu, Longjiang Mu, Armin Koehl, Ruibo Lei, Xi Liang, and Chuanyu Liu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-189, https://doi.org/10.5194/gmd-2024-189, 2025
Revised manuscript under review for GMD
Short summary
Short summary
In the sea ice-ocean models, errors in the parameters and missing spatiotemporal variations contribute to the deviations between the simulations and the observations. We extended an adjoint method to optimize spatiotemporally varying parameters together with the atmosphere forcing and the initial conditions using satellite and in-situ observations. Seasonally, this scheme demonstrates a more prominent advantage in mid-autumn and show great potential for accurately reproducing the Arctic changes.
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
Preprint archived
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Fanyi Zhang, Ruibo Lei, Mengxi Zhai, Xiaoping Pang, and Na Li
The Cryosphere, 17, 4609–4628, https://doi.org/10.5194/tc-17-4609-2023, https://doi.org/10.5194/tc-17-4609-2023, 2023
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Spring 2021 sea ice transport in the southern Beaufort Sea occurred during coastal-lead opening events
National Weather Service Alaska Sea Ice Program: gridded ice concentration maps for the Alaskan Arctic
Improving Seasonal Arctic Sea Ice Predictions with the Combination of Machine Learning and Earth System Model
Seasonal evolution of the sea ice floe size distribution in the Beaufort Sea from 2 decades of MODIS data
Regional and seasonal evolution of melt ponds on Arctic sea ice
Suitability of the CICE sea ice model for seasonal prediction and positive impact of CryoSat-2 ice thickness initialization
Seasonality and scenario dependence of rapid Arctic sea ice loss events in CMIP6 simulations
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?
Developing a deep learning forecasting system for short-term and high-resolution prediction of sea ice concentration
Impact of atmospheric rivers on Arctic sea ice variations
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
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
The influence of snow on sea ice as assessed from simulations of CESM2
Meltwater sources and sinks for multiyear Arctic sea ice in summer
An X-ray micro-tomographic study of the pore space, permeability and percolation threshold of young sea ice
Calibration of sea ice drift forecasts using random forest algorithms
Multiscale variations in Arctic sea ice motion and links to atmospheric and oceanic conditions
The flexural strength of bonded ice
Interannual variability in Transpolar Drift summer sea ice thickness and potential impact of Atlantification
An inter-comparison of the mass budget of the Arctic sea ice in CMIP6 models
Refining the sea surface identification approach for determining freeboards in the ICESat-2 sea ice products
Surface-based Ku- and Ka-band polarimetric radar for sea ice studies
Statistical predictability of the Arctic sea ice volume anomaly: identifying predictors and optimal sampling locations
Satellite-based sea ice thickness changes in the Laptev Sea from 2002 to 2017: comparison to mooring observations
Modeling the annual cycle of daily Antarctic sea ice extent
Changes of the Arctic marginal ice zone during the satellite era
An enhancement to sea ice motion and age products at the National Snow and Ice Data Center (NSIDC)
Accuracy and inter-analyst agreement of visually estimated sea ice concentrations in Canadian Ice Service ice charts using single-polarization RADARSAT-2
Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks
Variability scaling and consistency in airborne and satellite altimetry measurements of Arctic sea ice
Sea ice volume variability and water temperature in the Greenland Sea
MacKenzie E. Jewell, Jennifer K. Hutchings, and Angela C. Bliss
The Cryosphere, 19, 1413–1430, https://doi.org/10.5194/tc-19-1413-2025, https://doi.org/10.5194/tc-19-1413-2025, 2025
Short summary
Short summary
We quantify the contribution of sea ice drift during coastal-lead events to seasonal ice transport in the Beaufort Sea. Data from GPS tracker buoys deployed as part of the Sea Ice Dynamic Experiment (SIDEx) in spring 2021 show that sea ice in the southern Beaufort Sea became more responsive to wind forcing during fracturing events detected from satellite observations. Nearly all the spring 2021 sea ice transport occurred during these events, highlighting their importance at seasonal timescales.
Astrid Pacini, Michael Steele, and Mary-Beth Schreck
The Cryosphere, 19, 1391–1411, https://doi.org/10.5194/tc-19-1391-2025, https://doi.org/10.5194/tc-19-1391-2025, 2025
Short summary
Short summary
While sea ice concentration data are critically important for climate research, obtaining high-resolution data remains a challenge. Here we present and validate the US National Weather Service Alaska Sea Ice Program (ASIP) ice maps. These maps are shown to be highly accurate when compared to in situ observations and to outperform a passive-microwave-based product, especially at low concentrations. Therefore, ASIP data provide an exciting new tool to study ice conditions in the Pacific Arctic.
Zikang He, Yiguo Wang, Julien Brajard, Xidong Wang, and Zheqi Shen
EGUsphere, https://doi.org/10.5194/egusphere-2024-4092, https://doi.org/10.5194/egusphere-2024-4092, 2025
Short summary
Short summary
Declining Arctic sea ice presents both risks and opportunities for ecosystems, communities, and economic activities. To address prediction errors in dynamical models, we apply machine learning for error correction during prediction (online) or post-processing (offline). Our results show both methods enhance sea ice predictions, particularly from September to January, with offline corrections outperforming online corrections.
Ellen M. Buckley, Leela Cañuelas, Mary-Louise Timmermans, and Monica M. Wilhelmus
The Cryosphere, 18, 5031–5043, https://doi.org/10.5194/tc-18-5031-2024, https://doi.org/10.5194/tc-18-5031-2024, 2024
Short summary
Short summary
Arctic sea ice cover evolves seasonally 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 identify 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.
Hannah Niehaus, Gunnar Spreen, Larysa Istomina, and Marcel Nicolaus
EGUsphere, https://doi.org/10.5194/egusphere-2024-3127, https://doi.org/10.5194/egusphere-2024-3127, 2024
Short summary
Short summary
Melt ponds on Arctic sea ice affect how much solar energy is absorbed, influencing ice melt and climate change. This study used satellite data from 2017–2023 to examine how these ponds vary across regions and seasons. The results show that the surface fraction of melt ponds is more stable in the Central Arctic, with air temperature and ice surface roughness playing key roles in their formation. Understanding these patterns can help to improve climate models and predictions for Arctic warming.
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
Short summary
Short summary
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.
Annelies Sticker, François Massonnet, Thierry Fichefet, Patricia DeRepentigny, Alexandra Jahn, David Docquier, Christopher Wyburn-Powell, Daphne Quint, Erica Shivers, and Makayla Ortiz
EGUsphere, https://doi.org/10.5194/egusphere-2024-1873, https://doi.org/10.5194/egusphere-2024-1873, 2024
Short summary
Short summary
Our study analyses rapid Arctic sea ice loss events (RILEs), which are significant reductions in sea ice extent. RILEs are expected throughout the year, varying in frequency and duration with the seasons. Our research gives a year-round analysis of their characteristics in climate models and suggests that summer RILEs could begin before the mid-century. Understanding these events is crucial as they can have profound impacts on the Arctic environment.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Are Frode Kvanum, Cyril Palerme, Malte Müller, Jean Rabault, and Nick Hughes
EGUsphere, https://doi.org/10.5194/egusphere-2023-3107, https://doi.org/10.5194/egusphere-2023-3107, 2024
Short summary
Short summary
Recent studies have shown that machine learning models are effective at predicting sea ice concentration, yet few have explored the development of such models in an operational context. In this study, we present the development of a machine learning forecasting system which can predict sea ice concentration at 1 km resolution, up to 3 days ahead using real time operational data. The developed forecasts predict the sea ice edge position with a better accuracy than physical and baseline forecasts.
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
Short summary
Short summary
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.
Fanyi Zhang, Ruibo Lei, Mengxi Zhai, Xiaoping Pang, and Na Li
The Cryosphere, 17, 4609–4628, https://doi.org/10.5194/tc-17-4609-2023, https://doi.org/10.5194/tc-17-4609-2023, 2023
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Cited articles
Babb, D. G., Galley, R. J., Kirillov, S., Landy, J. C., Howell, S. E. L., Stroeve, J. C., Meier, W., Ehn, J. K., and Barber, D. G.: The Stepwise Reduction of Multiyear Sea Ice Area in the Arctic Ocean Since 1980, J. Geophys. Res.-Ocean, 128, e2023JC020157, https://doi.org/10.1029/2023JC020157, 2023.
Batrak, Y. and Müller, M.: On the warm bias in atmospheric reanalyses induced by the missing snow over Arctic sea-ice, Nat. Commun., 10, 4170, https://doi.org/10.1038/s41467-019-11975-3, 2019.
Bigdeli, A., Nguyen, A. T., Pillar, H. R., Ocaña, V., and Heimbach, P.: Atmospheric Warming Drives Growth in Arctic Sea Ice: A Key Role for Snow, Geophys. Res. Lett., 47, e2020GL090236, https://doi.org/10.1029/2020GL090236, 2020.
Brümmer, B., Müller, G., Haller, M., Kriegsmann, A., Offermann, M. and Wetzel, C.: DAMOCLES 2007–2008 – Hamburg Arctic Ocean Buoy Drift Experiment: meteorological measurements of 16 autonomous drifting ice buoys, World Data Center for Climate (WDCC) at DKRZ [data set], https://doi.org/10.1594/WDCC/UNI_HH_MI_DAMOCLES2007, 2011.
Cabaniss, G. H., Hunkins, K. L., and Untersteiner, N.: US-IGY Drifting Station Alpha, Arctic Ocean 1957–1958, US Air Force, Bedford, MA: Air Force Cambridge Research Laboratories, Special Reports No. 38 (AFCRL-65-848), 336 pp., 1965.
Cohen, J., Zhang, X., Francis, J., Jung, T., Kwok, R., Overland, J., Ballinger, T. J., Bhatt, U. S., Chen, H. W., Coumou, D., Feldstein, S., Gu, H., Handorf, D., Henderson, G., Ionita, M., Kretschmer, M., Laliberte, F., Lee, S., Linderholm, H. W., Maslowski, W., Peings, Y., Pfeiffer, K., Rigor, I., Semmler, T., Stroeve, J., Taylor, P. C., Vavrus, S., Vihma, T., Wang, S., Wendisch, M., Wu, Y., and Yoon, J.: Divergent consensuses on Arctic amplification influence on midlatitude severe winter weather, Nat. Clim. Change, 10, 20–29, https://doi.org/10.1038/s41558-019-0662-y, 2020.
Coon, M. D.: A review of AIDJEX modeling, in: A review of AIDJEX modeling, edited by: Pritchard, R. S., Univ. of Wash. Press, Seattle, 12–27, ISBN-13: 978-0295956589, 1980.
Cox, C. J., Gallagher, M. R., Shupe, M. D., Persson, P. O. G., Solomon, A., Fairall, C. W., Ayers, T., Blomquist, B., Brooks, I. M., Costa, D., Grachev, A., Gottas, D., Hutchings, J. K., Kutchenreiter, M., Leach, J., Morris, S. M., Morris, V., Osborn, J., Pezoa, S., Preußer, A., Riihimaki, L. D., and Uttal, T.: Continuous observations of the surface energy budget and meteorology over the Arctic sea ice during MOSAiC, Sci. Data, 10, 519, https://doi.org/10.1038/s41597-023-02415-5, 2023.
Devogele, T., Etienne, L., Esnault, M., and Lardy, F.: Optimized Discrete Fréchet Distance between trajectories, in: Proceedings of the 6th ACM SIGSPATIAL Workshop on Analytics for Big Geospatial Data, Redondo Beach, CA, USA, 7–10 November 2017, 11–19, https://doi.org/10.1145/3150919.3150924, 2017.
Fang, Y., Wang, X., Li, G., Chen, Z., Hui, F., and Cheng, X.: Arctic sea ice drift fields extraction based on feature tracking to MODIS imagery, Int. J. Appl. Earth. Obs, 120, 103353, https://doi.org/10.1016/j.jag.2023.103353, 2023.
Feng, J., Zhang, Y., Cheng, Q., Wong, K., Li, Y., and Yeu Tsou, J.: Effect of melt ponds fraction on sea ice anomalies in the Arctic Ocean, Int. J. Appl. Earth. Obs, 98, 102297, https://doi.org/10.1016/j.jag.2021.102297, 2021.
Flanders Marine Institute: Maritime Boundaries Geodatabase: Maritime Boundaries and Exclusive Economic Zones (200NM), version 12, digital media, VLIZ [data set], https://doi.org/10.14284/632, 2023.
Frolov, I. E., Gudkovich, Z., Radionov, V., Shirochkov, A., and Timokhov, L.: The arctic basin : results from the Russian drifting stations, Springer, Berlin, Germany, https://doi.org/10.1007/3-540-37665-8, 2005.
Goosse, H., Kay, J. E., Armour, K. C., Bodas-Salcedo, A., Chepfer, H., Docquier, D., Jonko, A., Kushner, P. J., Lecomte, O., Massonnet, F., Park, H.-S., Pithan, F., Svensson, G., and Vancoppenolle, M.: Quantifying climate feedbacks in polar regions, Nat. Commun., 9, 1919, https://doi.org/10.1038/s41467-018-04173-0, 2018.
Graham, R. M., Rinke, A., Cohen, L., Hudson, S. R., Walden, V. P., Granskog, M. A., Dorn, W., Kayser, M., and Maturilli, M.: A comparison of the two Arctic atmospheric winter states observed during N-ICE2015 and SHEBA, J. Geophys. Res.-Atmos, 122, 5716–5737, https://doi.org/10.1002/2016JD025475, 2017.
Graham, R. M., Cohen, L., Ritzhaupt, N., Segger, B., Graversen, R. G., Rinke, A., Walden, V. P., Granskog, M. A., and Hudson, S. R.: Evaluation of Six Atmospheric Reanalyses over Arctic Sea Ice from Winter to Early Summer, J. Climate, 32, 4121–4143, https://doi.org/10.1175/JCLI-D-18-0643.1, 2019a.
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, 2019b.
Granskog, M., Assmy, P., Gerland, S., Spreen, G., Steen, H., and Smedsrud, L. J. E.: Arctic research on thin ice: Consequences of Arctic sea ice loss, Eos, 97, 22–26, https://doi.org/10.1029/2016EO044097, 2016.
Gui, D., Lei, R., Pang, X., Hutchings, J. K., Zuo, G., and Zhai, M.: Validation of remote-sensing products of sea-ice motion: a case study in the western Arctic Ocean, J. Glaciol., 66, 807–821, https://doi.org/10.1017/jog.2020.49, 2020.
Haller, M., Brümmer, B., and Müller, G.: Atmosphere–ice forcing in the transpolar drift stream: results from the DAMOCLES ice-buoy campaigns 2007–2009, The Cryosphere, 8, 275–288, https://doi.org/10.5194/tc-8-275-2014, 2014.
Herman, A. and Glowacki, O.: Variability of sea ice deformation rates in the Arctic and their relationship with basin-scale wind forcing, The Cryosphere, 6, 1553–1559, https://doi.org/10.5194/tc-6-1553-2012, 2012.
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.
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., and 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.
Holland, M. M. and Hunke, E. C.: A review of Arctic sea ice climate predictability in large-scale Earth system models, Oceanography, 35, 20–27, https://doi.org/10.5670/oceanog.2022.113, 2022.
International Arctic Buoy Programme: International Arctic Buoy Programme, digital media, International Arctic Buoy Programme [data set], https://iabp.apl.uw.edu/TABLES/ArcticTable.html, last access: August 2025.
Jackson, K., Wilkinson, J., Maksym, T., Meldrum, D., Beckers, J., Haas, C., and Mackenzie, D.: A Novel and Low-Cost Sea Ice Mass Balance Buoy, J. Atmos. Ocean. Tech., 30, 2676–2688, https://doi.org/10.1175/JTECH-D-13-00058.1, 2013.
Koo, Y., Lei, R. B., Cheng, Y. B., Cheng, B., Xie, H. J., Hoppmann, M., Kurtz, N. T., Ackley, S. F., and Mestas-Nuñez, A. M.: Estimation of thermodynamic and dynamic contributions to sea ice growth in the Central Arctic using ICESat-2 and MOSAiC SIMBA buoy data, Remote Sens. Environ., 267, 112730, https://doi.org/10.1016/j.rse.2021.112730, 2021.
Krumpen, T., Belter, H. J., Boetius, A., Damm, E., Haas, C., Hendricks, S., Nicolaus, M., Nöthig, E.-M., Paul, S., Peeken, I., Ricker, R., and Stein, R.: Arctic warming interrupts the Transpolar Drift and affects long-range transport of sea ice and ice-rafted matter, Sci. Rep., 9, 5459, https://doi.org/10.1038/s41598-019-41456-y, 2019.
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.: Arctic sea ice thickness, volume, and multiyear ice coverage: losses and coupled variability (1958–2018), Environ. Res. Lett., 13, 105005, https://doi.org/10.1088/1748-9326/aae3ec, 2018.
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.
Lavergne, T., Eastwood, S., Teffah, Z., Schyberg, H., and Breivik, L. A.: Sea ice motion from low-resolution satellite sensors: An alternative method and its validation in the Arctic, J. Geophys. Res.-Oceans, 115, C10032, https://doi.org/10.1029/2009JC005958, 2010.
Lavergne, T., Sørensen, A. M., Kern, S., Tonboe, R., Notz, D., Aaboe, S., Bell, L., Dybkjær, G., Eastwood, S., Gabarro, C., Heygster, G., Killie, M. A., Brandt Kreiner, M., Lavelle, J., Saldo, R., Sandven, S., and Pedersen, L. T.: Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records, The Cryosphere, 13, 49–78, https://doi.org/10.5194/tc-13-49-2019, 2019.
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.
Lei, R., Cheng, B., Hoppmann, M., Zhang, F., Zuo, G., Hutchings, J. K., Lin, L., Lan, M., Wang, H., Regnery, J., Krumpen, T., Haapala, J., Rabe, B., Perovich, D. K., and Nicolaus, M.: Seasonality and timing of sea ice mass balance and heat fluxes in the Arctic transpolar drift during 2019–2020, Elementa-Sci. Anthrop., 10, 000089, https://doi.org/10.1525/elementa.2021.000089, 2022.
Leppäranta, M.: A review of analytical models of sea-ice growth, Atmos. Ocean, 31, 123–138, https://doi.org/10.1080/07055900.1993.9649465, 1993.
Leppäranta, M.: The Drift of Sea Ice, Springer Berlin, Heidelberg, https://doi.org/10.1007/978-3-642-04683-4, 2011.
Li, M., Zhou, C., Chen, X., Liu, Y., Li, B., and Liu, T.: Improvement of the feature tracking and patter matching algorithm for sea ice motion retrieval from SAR and optical imagery, Int. J. Appl. Earth. Obs., 112, 102908, https://doi.org/10.1016/j.jag.2022.102908, 2022.
Lindsay, R. W.: Ice deformation near SHEBA, J. Geophys. Res.-Oceans, 107, 8042, https://doi.org/10.1029/2000JC000445, 2002.
Liu, J. P., Chen, Z. Q., Hu, Y. Y., Zhang, Y. Y., Ding, Y. F., Cheng, X., Yang, Q. H., Nerger, L., Spreen, G., Horton, R., Inoue, J., Yang, C. Y., Li, M., and Song, M. R.: Towards reliable Arctic sea ice prediction using multivariate data assimilation, Sci. Bull., 64, 63–72, https://doi.org/10.1016/j.scib.2018.11.018, 2019.
Lukovich, J. V., Babb, D. G., and Barber, D. G.: On the scaling laws derived from ice beacon trajectories in the southern Beaufort Sea during the International Polar Year – Circumpolar Flaw Lead study, 2007–2008, J. Geophys. Res.-Oceans, 116, C00G07, https://doi.org/10.1029/2011JC007049, 2011.
Lund-Hansen, L. C., Søgaard, D. H., Sorrell, B. K., Gradinger, R., and Meiners, K. M.: Autumn, development and consolidation of sea ice, in: book Autumn, development and consolidation of sea ice, edited by: Lund-Hansen, L. C., Søgaard, D. H., Sorrell, B. K., Gradinger, R., and Meiners, K. M., Springer International Publishing, Cham, 13–30, https://doi.org/10.1007/978-3-030-37472-3_2, 2020.
Markus, T., Stroeve, J. C., and Miller, J.: Recent changes in Arctic sea ice melt onset, freezeup, and melt season length, J. Geophys. Res.-Oceans, 114, C12024, https://doi.org/10.1029/2009JC005436, 2009.
Meier, W. N. and Stroeve, J.: An Updated Assessment of the Changing Arctic Sea Ice Cover, Oceanography, 35, 10–19, https://doi.org/10.5670/oceanog.2022.114, 2022.
Meier, W. N., Fetterer, F., Windnagel, A. K., and Stewart, J. S.: NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, G02202, Version 4, National Snow and Ice Data Center [data set], https://doi.org/10.7265/efmz-2t65, 2021.
Moore, G. W. K., Schweiger, A., Zhang, J., and Steele, M.: Collapse of the 2017 Winter Beaufort High: A Response to Thinning Sea Ice?, Geophys. Res. Lett., 45, 2860–2869, https://doi.org/10.1002/2017gl076446, 2018.
Morison, J., Aagaard, K., Falkner, K. K., Hatakeyama, K., Moritz, R., Overland, J. E., Perovich, D., Shimada, K., Steele, M., Takizawa, T., and Woodgate, R.: North Pole Environmental Observatory delivers early results, Eos, 83, 357–361, https://doi.org/10.1029/2002EO000259, 2002.
Nicolaus, M., Hoppmann, M., Arndt, S., Hendricks, S., Katlein, C., König-Langlo, G., Nicolaus, A., Rossmann, L., Schiller, M., Schwegmann, S., Langevin, D., and Bartsch, A.: Snow height and air temperature on sea ice from Snow Buoy measurements, Alfred Wegener Institute, Helmholtz Center for Polar and Marine Research, Bremerhaven, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.875638, 2017.
Nicolaus, M., Hoppmann, M., Arndt, S., Hendricks, S., Katlein, C., Nicolaus, A., Rossmann, L., Schiller, M., and Schwegmann, S.: Snow Depth and Air Temperature Seasonality on Sea Ice Derived From Snow Buoy Measurements, Front. Mar. Sci., 8, 655446, https://doi.org/10.3389/fmars.2021.655446, 2021.
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.
Pithan, F., Athanase, M., Dahlke, S., Sánchez-Benítez, A., Shupe, M. D., Sledd, A., Streffing, J., Svensson, G., and Jung, T.: Nudging allows direct evaluation of coupled climate models with in situ observations: a case study from the MOSAiC expedition, Geosci. Model Dev., 16, 1857–1873, https://doi.org/10.5194/gmd-16-1857-2023, 2023.
Proshutinsky, A., Bourke, R. H., and McLaughlin, F. A.: The role of the Beaufort Gyre in Arctic climate variability: Seasonal to decadal climate scales, Geophys. Res. Lett., 29, 2100, https://doi.org/10.1029/2002GL015847, 2002.
Proshutinsky, A. Y. and Johnson, M. A.: Two circulation regimes of the wind-driven Arctic Ocean, J. Geophys. Res.-Oceans, 102, 12493–12514, https://doi.org/10.1029/97JC00738, 1997.
Rabe, B., Heuzé, C., Regnery, J., Aksenov, Y., Allerholt, J., Athanase, M., Bai, Y. C., Basque, C., Bauch, D., Baumann, T. M., Chen, D. K., Cole, S. T., Craw, L., Davies, A., Damm, E., Dethloff, K., Divine, D. V., Doglioni, F., Ebert, F., Fang, Y. C., Fer, I., Fong, A. A., Gradinger, R., Granskog, M. A., Graupner, R., Haas, C., He, H., He, Y., Hoppmann, M., Janout, M., Kadko, D., Kanzow, T., Karam, S., Kawaguchi, Y., Koenig, Z., Kong, B., Krishfield, R. A., Krumpen, T., Kuhlmey, D., Kuznetsov, I., Lan, M. S., Laukert, G., Lei, R. B., Li, T., Lin, L. N., Lin, L., Liu, H. L., Liu, N., Loose, B., Ma, X. B., McKay, R., Mallet, M., Mallett, R. D. C., Maslowski, W., Mertens, C., Mohrholz, V., Muilwijk, M., Nicolaus, M., O'Brien, J. K., Perovich, D., Ren, J., Rex, M., Ribeiro, N., Rinke, A., Schaffer, J., Schuffenhauer, I., Schulz, K., Shupe, M. D., Shaw, W., Sokolov, V., Sommerfeld, A., Spreen, G., Stanton, T., Stephens, M., Su, J., Sukhikh, N., Sundfjord, A., Thomisch, K., Tippenhauer, S., Toole, J. M., Vredenborg, M., Walter, M., Wang, H. Z., Wang, L., Wang, Y. T., Wendisch, M., Zhao, J. P., Zhou, M., and Zhu, J. L.: Overview of the MOSAiC expedition: Physical oceanography, Elementa-Sci. Anthrop., 10, 00062, https://doi.org/10.1525/elementa.2021.00062, 2022.
Rabe, B., Cox, C. J., Fang, Y.-C., Goessling, H., Granskog, M. A., Hoppmann, M., Hutchings, J. K., Krumpen, T., Kuznetsov, I., Lei, R., Li, T., Maslowski, W., Nicolaus, M., Perovich, D., Persson, O., Regnery, J., Rigor, I., Shupe, M. D., Sokolov, V., Spreen, G., Stanton, T., Watkins, D. M., Blockley, E., Buenger, H. J., Cole, S., Fong, A., Haapala, J., Heuzé, C., Hoppe, C. J. M., Janout, M., Jutila, A., Katlein, C., Krishfield, R., Lin, L., Ludwig, V., Morgenstern, A., O'Brien, J., Zurita, A. Q., Rackow, T., Riemann-Campe, K., Rohde, J., Shaw, W., Smolyanitsky, V., Solomon, A., Sperling, A., Tao, R., Toole, J., Tsamados, M., Zhu, J., and Zuo, G.: The MOSAiC Distributed Network: Observing the coupled Arctic system with multidisciplinary, coordinated platforms, Elementa-Sci. Anthrop., 12, 00103, https://doi.org/10.1525/elementa.2023.00103, 2024.
Rantanen, M., Karpechko, A. Y., Lipponen, A., Nordling, K., Hyvärinen, O., Ruosteenoja, K., Vihma, T., and Laaksonen, A.: The Arctic has warmed nearly four times faster than the globe since 1979, Commun. Earth. Environ., 3, 168, https://doi.org/10.1038/s43247-022-00498-3, 2022.
Richter-Menge, J. A., Perovich, D. K., Elder, B. C., Claffey, K., Rigor, I., and Ortmeyer, M.: Ice mass-balance buoys: a tool for measuring and attributing changes in the thickness of the Arctic sea-ice cover, Ann. Glaciol., 44, 205–210, https://doi.org/10.3189/172756406781811727, 2006.
Ricker, R., Hendricks, S., Girard-Ardhuin, F., Kaleschke, L., Lique, C., Tian-Kunze, X., Nicolaus, M., and Krumpen, T.: Satellite-observed drop of Arctic sea ice growth in winter 2015–2016, Geophys. Res. Lett., 44, 3236–3245, https://doi.org/10.1002/2016GL072244, 2017a.
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, 2017b (data available at: https://data.seaiceportal.de/data/cs2smos_awi/v204/, last access: 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/10.1175/1520-0442(2002)015<2648:ROSITT>2.0.CO;2, 2002.
Rinke, A., Cassano, J. J., Cassano, E. N., Jaiser, R., and Handorf, D.: Meteorological conditions during the MOSAiC expedition: Normal or anomalous?, Elementa-Sci. Anthrop., 9, 00023, https://doi.org/10.1525/elementa.2021.00023, 2021.
Robinson, A., Lehmann, J., Barriopedro, D., Rahmstorf, S., and Coumou, D.: Increasing heat and rainfall extremes now far outside the historical climate, Npj. Clim. Atmos. Sci., 4, 45, https://doi.org/10.1038/s41612-021-00202-w, 2021.
Sagawa, G. and Yamaguchi, H.: A Semi-Lagrangian Sea Ice Model For High Resolution Simulation, in: The Sixteenth International Offshore and Polar Engineering Conference, San Francisco, California, USA, 28 May–2 June 2006, ISOPE-I-06-253, https://www.onepetro.org/conference-paper/ISOPE-I-06-253, last access: 28 May 2006.
Shaw, W. J., Stanton, T. P., McPhee, M. G., and Kikuchi, T.: Estimates of surface roughness length in heterogeneous under-ice boundary layers, J. Geophys. Res.-Oceans, 113, C08030, https://doi.org/10.1029/2007JC004550, 2008.
Smith, M. M., Holland, M., and Light, B.: Arctic sea ice sensitivity to lateral melting representation in a coupled climate model, The Cryosphere, 16, 419–434, https://doi.org/10.5194/tc-16-419-2022, 2022.
Subich, C., Pellerin, P., Smith, G., and Dupont, F.: Development of a semi-Lagrangian advection scheme for the NEMO ocean model (3.1), Geosci. Model Dev., 13, 4379–4398, https://doi.org/10.5194/gmd-13-4379-2020, 2020.
Sumata, H., de Steur, L., Divine, D. V., Granskog, M. A., and Gerland, S.: Regime shift in Arctic Ocean sea ice thickness, Nature, 615, 443–449, https://doi.org/10.1038/s41586-022-05686-x, 2023.
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.
Tian, T., Yang, S., Høyer, J. L., Nielsen-Englyst, P., and Singha, S.: Cooler Arctic surface temperatures simulated by climate models are closer to satellite-based data than the ERA5 reanalysis, Commun. Earth. Environ., 5, 111, https://doi.org/10.1038/s43247-024-01276-z, 2024.
Toole, J. M., Curry, R. G., Joyce, T. M., McCartney, M., and Peña-Molino, B.: Transport of the North Atlantic Deep Western Boundary Current about 39° N, 70° W: 2004–2008, Deep-Sea. Res. Pt. II, 58, 1768–1780, https://doi.org/10.1016/j.dsr2.2010.10.058, 2011.
Tschudi, M., Fowler, C., Maslanik, J., and Stroeve, J.: Tracking the Movement and Changing Surface Characteristics of Arctic Sea Ice, IEEE J. Sel. Top. Appl., 3, 536–540, https://doi.org/10.1109/JSTARS.2010.2048305, 2010.
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, NASA National Snow and Ice Data Center [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.
Uttal, T., Curry, J. A., Mcphee, M. G., Perovich, D. K., Moritz, R. E., Maslanik, J., Guest, P., Stern, H. L., Moore, J. A., Turenne, R., Heiberg, A., Serreze, M. C., Wylie, D. P., Persson, O. G., Paulson, C. A., Halle, C., Morison, J., Wheeler, P. A., Makshtas, A., Welch, H. E., Shupe, M. D., Intrieri, J. M., Stamnes, K., Lindsey, R. W., Pinkel, R., Pegau, W. S., Stanton, T., and Grenfeld, T. C.: Surface Heat Budget of the Arctic Ocean, B. Am. Meteorol. Soc, 83, 255–275, https://doi.org/10.1175/1520-0477(2002)083<0255:SHBOTA>2.3.CO;2, 2002.
Vautard, R., Gobiet, A., Jacob, D., Belda, M., Colette, A., Déqué, M., Fernández, J., García-Díez, M., Goergen, K., Güttler, I., Halenka, T., Karacostas, T., Katragkou, E., Keuler, K., Kotlarski, S., Mayer, S., van Meijgaard, E., Nikulin, G., Patarčić, M., Scinocca, J., Sobolowski, S., Suklitsch, M., Teichmann, C., Warrach-Sagi, K., Wulfmeyer, V., and Yiou, P.: The simulation of European heat waves from an ensemble of regional climate models within the EURO-CORDEX project, Clim. Dynam., 41, 2555–2575, https://doi.org/10.1007/s00382-013-1714-z, 2013.
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, C., Graham, R. M., Wang, K., Gerland, S., and Granskog, M. A.: Comparison of ERA5 and ERA-Interim near-surface air temperature, snowfall and precipitation over Arctic sea ice: effects on sea ice thermodynamics and evolution, The Cryosphere, 13, 1661–1679, https://doi.org/10.5194/tc-13-1661-2019, 2019.
Wang, J., Zhang, J., Watanabe, E., Ikeda, M., Mizobata, K., Walsh, J. E., Bai, X., and Wu, B.: Is the Dipole Anomaly a major driver to record lows in Arctic summer sea ice extent?, Geophys. Res. Lett., 36, https://doi.org/10.1029/2008GL036706, 2009.
Wu, B. Y., Wang, J., and Walsh, J. E.: Dipole anomaly in the winter Arctic atmosphere and its association with sea ice motion, J. Climate, 19, 210–225, https://doi.org/10.1175/JCLI3619.1, 2006.
Zhang, X., Tang, H., Zhang, J., Walsh, J. E., Roesler, E. L., Hillman, B., Ballinger, T. J., and Weijer, W.: Arctic cyclones have become more intense and longer-lived over the past seven decades, Commun. Earth. Environ., 4, 348, https://doi.org/10.1038/s43247-023-01003-0, 2023.
Short summary
We reconstructed sea ice drift trajectories and identified optimal deployment areas for Lagrangian observations in the central Arctic Ocean. The trajectories revealed a preference for ice advection towards the Transpolar Drift region over the Beaufort Gyre, with endpoints influenced by large-scale atmospheric circulation patterns. This study provides critical support for the planning and implementation of Lagrangian observations relying on ice floes in the central Arctic Ocean under changing environmental conditions.
We reconstructed sea ice drift trajectories and identified optimal deployment areas for...
