Articles | Volume 18, issue 3
https://doi.org/10.5194/tc-18-1215-2024
© Author(s) 2024. 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-18-1215-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Mar 2024
Research article |
| 12 Mar 2024
| 12 Mar 2024
Understanding the influence of ocean waves on Arctic sea ice simulation: a modeling study with an atmosphere–ocean–wave–sea ice coupled model
Chao-Yuan Yang
CORRESPONDING AUTHOR
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, Guangdong, China
Jiping Liu
CORRESPONDING AUTHOR
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, Guangdong, China
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, Guangdong, China
Dake Chen
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, Guangdong, China
Related authors
Hu Yang, Xiaoxu Shi, Xulong Wang, Qingsong Liu, Yi Zhong, Xiaodong Liu, Youbin Sun, Yanjun Cai, Fei Liu, Gerrit Lohmann, Martin Werner, Zhimin Jian, Tainã M. L. Pinho, Hai Cheng, Lijuan Lu, Jiping Liu, Chao-Yuan Yang, Qinghua Yang, Yongyun Hu, Xing Cheng, Jingyu Zhang, and Dake Chen
EGUsphere, https://doi.org/10.5194/egusphere-2024-2778, https://doi.org/10.5194/egusphere-2024-2778, 2024
Short summary
Short summary
The precession driven low-latitude hydrological cycle is not paced by hemispheric summer insolation, but shifting perihelion.
Xiaoxu Shi, Martin Werner, Hu Yang, Roberta D'Agostino, Jiping Liu, Chaoyuan Yang, and Gerrit Lohmann
Clim. Past, 19, 2157–2175, https://doi.org/10.5194/cp-19-2157-2023, https://doi.org/10.5194/cp-19-2157-2023, 2023
Short summary
Short summary
The Last Glacial Maximum (LGM) marks the most recent extremely cold and dry time period of our planet. Using AWI-ESM, we quantify the relative importance of Earth's orbit, greenhouse gases (GHG) and ice sheets (IS) in determining the LGM climate. Our results suggest that both GHG and IS play important roles in shaping the LGM temperature. Continental ice sheets exert a major control on precipitation, atmospheric dynamics, and the intensity of El Niño–Southern Oscillation.
Fengguan Gu, Qinghua Yang, Frank Kauker, Changwei Liu, Guanghua Hao, Chao-Yuan Yang, Jiping Liu, Petra Heil, Xuewei Li, and Bo Han
The Cryosphere, 16, 1873–1887, https://doi.org/10.5194/tc-16-1873-2022, https://doi.org/10.5194/tc-16-1873-2022, 2022
Short summary
Short summary
The sea ice thickness was simulated by a single-column model and compared with in situ observations obtained off Zhongshan Station in the Antarctic. It is shown that the unrealistic precipitation in the atmospheric forcing data leads to the largest bias in sea ice thickness and snow depth modeling. In addition, the increasing snow depth gradually inhibits the growth of sea ice associated with thermal blanketing by the snow.
Chao-Yuan Yang, Jiping Liu, and Dake Chen
Geosci. Model Dev., 15, 1155–1176, https://doi.org/10.5194/gmd-15-1155-2022, https://doi.org/10.5194/gmd-15-1155-2022, 2022
Short summary
Short summary
We present an improved coupled modeling system for Arctic sea ice prediction. We perform Arctic sea ice prediction experiments with improved/updated physical parameterizations, which show better skill in predicting sea ice state as well as atmospheric and oceanic state in the Arctic compared with its predecessor. The improved model also shows extended predictive skill of Arctic sea ice after the summer season. This provides an added value of this prediction system for decision-making.
Han Zhang, Dake Chen, Tongya Liu, Di Tian, Min He, Qi Li, Guofei Wei, and Jian Liu
Earth Syst. Sci. Data, 16, 5665–5679, https://doi.org/10.5194/essd-16-5665-2024, https://doi.org/10.5194/essd-16-5665-2024, 2024
Short summary
Short summary
This paper provides a cross-shaped moored array dataset (MASCS 1.0) of observations that consist of five buoys and four moorings in the northern South China Sea from 2014 to 2015. The moored array is influenced by atmospheric forcings such as tropical cyclones and monsoon as well as oceanic tides and flows. The data reveal variations of the air–sea interface and the ocean itself, which are valuable for studies of air–sea interactions and ocean dynamics in the northern South China Sea.
Hu Yang, Xiaoxu Shi, Xulong Wang, Qingsong Liu, Yi Zhong, Xiaodong Liu, Youbin Sun, Yanjun Cai, Fei Liu, Gerrit Lohmann, Martin Werner, Zhimin Jian, Tainã M. L. Pinho, Hai Cheng, Lijuan Lu, Jiping Liu, Chao-Yuan Yang, Qinghua Yang, Yongyun Hu, Xing Cheng, Jingyu Zhang, and Dake Chen
EGUsphere, https://doi.org/10.5194/egusphere-2024-2778, https://doi.org/10.5194/egusphere-2024-2778, 2024
Short summary
Short summary
The precession driven low-latitude hydrological cycle is not paced by hemispheric summer insolation, but shifting perihelion.
Chenhui Ning, Shiming Xu, Yan Zhang, Xuantong Wang, Zhihao Fan, and Jiping Liu
Geosci. Model Dev., 17, 6847–6866, https://doi.org/10.5194/gmd-17-6847-2024, https://doi.org/10.5194/gmd-17-6847-2024, 2024
Short summary
Short summary
Sea ice models are mainly based on non-moving structured grids, which is different from buoy measurements that follow the ice drift. To facilitate Lagrangian analysis, we introduce online tracking of sea ice in Community Ice CodE (CICE). We validate the sea ice tracking with buoys and evaluate the sea ice deformation in high-resolution simulations, which show multi-fractal characteristics. The source code is openly available and can be used in various scientific and operational applications.
Ziying Yang, Jiping Liu, Mirong Song, Yongyun Hu, Qinghua Yang, and Ke Fan
EGUsphere, https://doi.org/10.5194/egusphere-2024-1001, https://doi.org/10.5194/egusphere-2024-1001, 2024
Short summary
Short summary
Antarctic sea ice has changed rapidly in recent years. Here we developed a deep learning model trained by multiple climate variables for extended seasonal Antarctic sea ice prediction. Our model shows high predictive skills up to 6 months in advance, particularly in predicting extreme events. It also shows skillful predictions at the sea ice edge and year-to-year sea ice changes. Variable importance analyses suggest what variables are more important for prediction at different lead times.
Xiaoxu Shi, Martin Werner, Hu Yang, Roberta D'Agostino, Jiping Liu, Chaoyuan Yang, and Gerrit Lohmann
Clim. Past, 19, 2157–2175, https://doi.org/10.5194/cp-19-2157-2023, https://doi.org/10.5194/cp-19-2157-2023, 2023
Short summary
Short summary
The Last Glacial Maximum (LGM) marks the most recent extremely cold and dry time period of our planet. Using AWI-ESM, we quantify the relative importance of Earth's orbit, greenhouse gases (GHG) and ice sheets (IS) in determining the LGM climate. Our results suggest that both GHG and IS play important roles in shaping the LGM temperature. Continental ice sheets exert a major control on precipitation, atmospheric dynamics, and the intensity of El Niño–Southern Oscillation.
Shijie Peng, Qinghua Yang, Matthew D. Shupe, Xingya Xi, Bo Han, Dake Chen, Sandro Dahlke, and Changwei Liu
Atmos. Chem. Phys., 23, 8683–8703, https://doi.org/10.5194/acp-23-8683-2023, https://doi.org/10.5194/acp-23-8683-2023, 2023
Short summary
Short summary
Due to a lack of observations, the structure of the Arctic atmospheric boundary layer (ABL) remains to be further explored. By analyzing a year-round radiosonde dataset collected over the Arctic sea-ice surface, we found the annual cycle of the ABL height (ABLH) is primarily controlled by the evolution of ABL thermal structure, and the surface conditions also show a high correlation with ABLH variation. In addition, the Arctic ABLH is found to be decreased in summer compared with 20 years ago.
Fengguan Gu, Qinghua Yang, Frank Kauker, Changwei Liu, Guanghua Hao, Chao-Yuan Yang, Jiping Liu, Petra Heil, Xuewei Li, and Bo Han
The Cryosphere, 16, 1873–1887, https://doi.org/10.5194/tc-16-1873-2022, https://doi.org/10.5194/tc-16-1873-2022, 2022
Short summary
Short summary
The sea ice thickness was simulated by a single-column model and compared with in situ observations obtained off Zhongshan Station in the Antarctic. It is shown that the unrealistic precipitation in the atmospheric forcing data leads to the largest bias in sea ice thickness and snow depth modeling. In addition, the increasing snow depth gradually inhibits the growth of sea ice associated with thermal blanketing by the snow.
Chao-Yuan Yang, Jiping Liu, and Dake Chen
Geosci. Model Dev., 15, 1155–1176, https://doi.org/10.5194/gmd-15-1155-2022, https://doi.org/10.5194/gmd-15-1155-2022, 2022
Short summary
Short summary
We present an improved coupled modeling system for Arctic sea ice prediction. We perform Arctic sea ice prediction experiments with improved/updated physical parameterizations, which show better skill in predicting sea ice state as well as atmospheric and oceanic state in the Arctic compared with its predecessor. The improved model also shows extended predictive skill of Arctic sea ice after the summer season. This provides an added value of this prediction system for decision-making.
Xuewei Li, Qinghua Yang, Lejiang Yu, Paul R. Holland, Chao Min, Longjiang Mu, and Dake Chen
The Cryosphere Discuss., https://doi.org/10.5194/tc-2020-359, https://doi.org/10.5194/tc-2020-359, 2021
Preprint withdrawn
Short summary
Short summary
The Arctic sea ice thickness record minimum is confirmed occurring in autumn 2011. The dynamic and thermodynamic processes leading to the minimum thickness is analyzed based on a daily sea ice thickness reanalysis data covering the melting season. The results demonstrate that the dynamic transport of multiyear ice and the subsequent surface energy budget response is a critical mechanism actively contributing to the evolution of Arctic sea ice thickness in 2011.
Related subject area
Discipline: Sea ice | Subject: Numerical Modelling
A hybrid ice-mélange model based on particle and continuum methods
How many parameters are needed to represent polar sea ice surface patterns and heterogeneity?
Exploring non-Gaussian sea ice characteristics via observing system simulation experiments
Past and future of the Arctic sea ice in High-Resolution Model Intercomparison Project (HighResMIP) climate models
Data-driven surrogate modeling of high-resolution sea-ice thickness in the Arctic
Using Icepack to reproduce ice mass balance buoy observations in landfast ice: improvements from the mushy-layer thermodynamics
Sea ice cover in the Copernicus Arctic Regional Reanalysis
Smoothed particle hydrodynamics implementation of the standard viscous–plastic sea-ice model and validation in simple idealized experiments
Phase-field models of floe fracture in sea ice
The effect of partial dissolution on sea-ice chemical transport: a combined model–observational study using poly- and perfluoroalkylated substances (PFASs)
Deep learning subgrid-scale parametrisations for short-term forecasting of sea-ice dynamics with a Maxwell elasto-brittle rheology
Impact of atmospheric forcing uncertainties on Arctic and Antarctic sea ice simulations in CMIP6 OMIP models
Arctic sea ice mass balance in a new coupled ice–ocean model using a brittle rheology framework
Wave-triggered breakup in the marginal ice zone generates lognormal floe size distributions: a simulation study
Exploring the capabilities of electrical resistivity tomography to study subsea permafrost
Sea ice floe size: its impact on pan-Arctic and local ice mass and required model complexity
A probabilistic seabed–ice keel interaction model
The effect of changing sea ice on wave climate trends along Alaska's central Beaufort Sea coast
Arctic sea ice anomalies during the MOSAiC winter 2019/20
Edge displacement scores
Toward a method for downscaling sea ice pressure for navigation purposes
The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O) – Part 1: How to obtain sea ice brightness temperatures at 6.9 GHz from climate model output
The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O) – Part 2: Development and evaluation
Feature-based comparison of sea ice deformation in lead-permitting sea ice simulations
Wave energy attenuation in fields of colliding ice floes – Part 1: Discrete-element modelling of dissipation due to ice–water drag
Validation of the sea ice surface albedo scheme of the regional climate model HIRHAM–NAOSIM using aircraft measurements during the ACLOUD/PASCAL campaigns
Simulating intersection angles between conjugate faults in sea ice with different viscous–plastic rheologies
IcePAC – a probabilistic tool to study sea ice spatio-temporal dynamics: application to the Hudson Bay area
New insight from CryoSat-2 sea ice thickness for sea ice modelling
Investigating future changes in the volume budget of the Arctic sea ice in a coupled climate model
Medium-range predictability of early summer sea ice thickness distribution in the East Siberian Sea based on the TOPAZ4 ice–ocean data assimilation system
Saskia Kahl, Carolin Mehlmann, and Dirk Notz
The Cryosphere, 19, 129–141, https://doi.org/10.5194/tc-19-129-2025, https://doi.org/10.5194/tc-19-129-2025, 2025
Short summary
Short summary
Ice mélange, a mixture of sea ice and icebergs, can impact sea-ice–ocean interactions. But climate models do not yet represent it due to computational limits. To address this shortcoming and include ice mélange into climate models, we suggest representing icebergs as particles. We integrate their feedback into mathematical equations used to model the sea-ice motion in climate models. The setup is computationally efficient due to the iceberg particle usage and enables a realistic representation.
Joseph Fogarty, Elie Bou-Zeid, Mitchell Bushuk, and Linette Boisvert
The Cryosphere, 18, 4335–4354, https://doi.org/10.5194/tc-18-4335-2024, https://doi.org/10.5194/tc-18-4335-2024, 2024
Short summary
Short summary
We hypothesize that using a broad set of surface characterization metrics for polar sea ice surfaces will lead to more accurate representations in general circulation models. However, the first step is to identify the minimum set of metrics required. We show via numerical simulations that sea ice surface patterns can play a crucial role in determining boundary layer structures. We then statistically analyze a set of high-resolution sea ice surface images to obtain this minimal set of parameters.
Christopher Riedel and Jeffrey Anderson
The Cryosphere, 18, 2875–2896, https://doi.org/10.5194/tc-18-2875-2024, https://doi.org/10.5194/tc-18-2875-2024, 2024
Short summary
Short summary
Accurate sea ice conditions are crucial for quality sea ice projections, which have been connected to rapid warming over the Arctic. Knowing which observations to assimilate into models will help produce more accurate sea ice conditions. We found that not assimilating sea ice concentration led to more accurate sea ice states. The methods typically used to assimilate observations in our models apply assumptions to variables that are not well suited for sea ice because they are bounded variables.
Julia Selivanova, Doroteaciro Iovino, and Francesco Cocetta
The Cryosphere, 18, 2739–2763, https://doi.org/10.5194/tc-18-2739-2024, https://doi.org/10.5194/tc-18-2739-2024, 2024
Short summary
Short summary
Climate models show differences in sea ice representation in comparison to observations. Increasing the model resolution is a recognized way to improve model realism and obtain more reliable future projections. We find no strong impact of resolution on sea ice representation; it rather depends on the analysed variable and the model used. By 2050, the marginal ice zone (MIZ) becomes a dominant feature of the Arctic ice cover, suggesting a shift to a new regime similar to that in Antarctica.
Charlotte Durand, Tobias Sebastian Finn, Alban Farchi, Marc Bocquet, Guillaume Boutin, and Einar Ólason
The Cryosphere, 18, 1791–1815, https://doi.org/10.5194/tc-18-1791-2024, https://doi.org/10.5194/tc-18-1791-2024, 2024
Short summary
Short summary
This paper focuses on predicting Arctic-wide sea-ice thickness using surrogate modeling with deep learning. The model has a predictive power of 12 h up to 6 months. For this forecast horizon, persistence and daily climatology are systematically outperformed, a result of learned thermodynamics and advection. Consequently, surrogate modeling with deep learning proves to be effective at capturing the complex behavior of sea ice.
Mathieu Plante, Jean-François Lemieux, L. Bruno Tremblay, Adrienne Tivy, Joey Angnatok, François Roy, Gregory Smith, Frédéric Dupont, and Adrian K. Turner
The Cryosphere, 18, 1685–1708, https://doi.org/10.5194/tc-18-1685-2024, https://doi.org/10.5194/tc-18-1685-2024, 2024
Short summary
Short summary
We use a sea ice model to reproduce ice growth observations from two buoys deployed on coastal sea ice and analyze the improvements brought by new physics that represent the presence of saline liquid water in the ice interior. We find that the new physics with default parameters degrade the model performance, with overly rapid ice growth and overly early snow flooding on top of the ice. The performance is largely improved by simple modifications to the ice growth and snow-flooding algorithms.
Yurii Batrak, Bin Cheng, and Viivi Kallio-Myers
The Cryosphere, 18, 1157–1183, https://doi.org/10.5194/tc-18-1157-2024, https://doi.org/10.5194/tc-18-1157-2024, 2024
Short summary
Short summary
Atmospheric reanalyses provide consistent series of atmospheric and surface parameters in a convenient gridded form. In this paper, we study the quality of sea ice in a recently released regional reanalysis and assess its added value compared to a global reanalysis. We show that the regional reanalysis, having a more complex sea ice model, gives an improved representation of sea ice, although there are limitations indicating potential benefits in using more advanced approaches in the future.
Oreste Marquis, Bruno Tremblay, Jean-François Lemieux, and Mohammed Islam
The Cryosphere, 18, 1013–1032, https://doi.org/10.5194/tc-18-1013-2024, https://doi.org/10.5194/tc-18-1013-2024, 2024
Short summary
Short summary
We developed a standard viscous–plastic sea-ice model based on the numerical framework called smoothed particle hydrodynamics. The model conforms to the theory within an error of 1 % in an idealized ridging experiment, and it is able to simulate stable ice arches. However, the method creates a dispersive plastic wave speed. The framework is efficient to simulate fractures and can take full advantage of parallelization, making it a good candidate to investigate sea-ice material properties.
Huy Dinh, Dimitrios Giannakis, Joanna Slawinska, and Georg Stadler
The Cryosphere, 17, 3883–3893, https://doi.org/10.5194/tc-17-3883-2023, https://doi.org/10.5194/tc-17-3883-2023, 2023
Short summary
Short summary
We develop a numerical method to simulate the fracture in kilometer-sized chunks of floating ice in the ocean. Our approach uses a mathematical model that balances deformation energy against the energy required for fracture. We study the strength of ice chunks that contain random impurities due to prior damage or refreezing and what types of fractures are likely to occur. Our model shows that crack direction critically depends on the orientation of impurities relative to surrounding forces.
Max Thomas, Briana Cate, Jack Garnett, Inga J. Smith, Martin Vancoppenolle, and Crispin Halsall
The Cryosphere, 17, 3193–3201, https://doi.org/10.5194/tc-17-3193-2023, https://doi.org/10.5194/tc-17-3193-2023, 2023
Short summary
Short summary
A recent study showed that pollutants can be enriched in growing sea ice beyond what we would expect from a perfectly dissolved chemical. We hypothesise that this effect is caused by the specific properties of the pollutants working in combination with fluid moving through the sea ice. To test our hypothesis, we replicate this behaviour in a sea-ice model and show that this type of modelling can be applied to predicting the transport of chemicals with complex behaviour in sea ice.
Tobias Sebastian Finn, Charlotte Durand, Alban Farchi, Marc Bocquet, Yumeng Chen, Alberto Carrassi, and Véronique Dansereau
The Cryosphere, 17, 2965–2991, https://doi.org/10.5194/tc-17-2965-2023, https://doi.org/10.5194/tc-17-2965-2023, 2023
Short summary
Short summary
We combine deep learning with a regional sea-ice model to correct model errors in the sea-ice dynamics of low-resolution forecasts towards high-resolution simulations. The combined model improves the forecast by up to 75 % and thereby surpasses the performance of persistence. As the error connection can additionally be used to analyse the shortcomings of the forecasts, this study highlights the potential of combined modelling for short-term sea-ice forecasting.
Xia Lin, François Massonnet, Thierry Fichefet, and Martin Vancoppenolle
The Cryosphere, 17, 1935–1965, https://doi.org/10.5194/tc-17-1935-2023, https://doi.org/10.5194/tc-17-1935-2023, 2023
Short summary
Short summary
This study provides clues on how improved atmospheric reanalysis products influence sea ice simulations in ocean–sea ice models. The summer ice concentration simulation in both hemispheres can be improved with changed surface heat fluxes. The winter Antarctic ice concentration and the Arctic drift speed near the ice edge and the ice velocity direction simulations are improved with changed wind stress. The radiation fluxes and winds in atmospheric reanalyses are crucial for sea ice simulations.
Guillaume Boutin, Einar Ólason, Pierre Rampal, Heather Regan, Camille Lique, Claude Talandier, Laurent Brodeau, and Robert Ricker
The Cryosphere, 17, 617–638, https://doi.org/10.5194/tc-17-617-2023, https://doi.org/10.5194/tc-17-617-2023, 2023
Short summary
Short summary
Sea ice cover in the Arctic is full of cracks, which we call leads. We suspect that these leads play a role for atmosphere–ocean interactions in polar regions, but their importance remains challenging to estimate. We use a new ocean–sea ice model with an original way of representing sea ice dynamics to estimate their impact on winter sea ice production. This model successfully represents sea ice evolution from 2000 to 2018, and we find that about 30 % of ice production takes place in leads.
Nicolas Guillaume Alexandre Mokus and Fabien Montiel
The Cryosphere, 16, 4447–4472, https://doi.org/10.5194/tc-16-4447-2022, https://doi.org/10.5194/tc-16-4447-2022, 2022
Short summary
Short summary
On the fringes of polar oceans, sea ice is easily broken by waves. As small pieces of ice, or floes, are more easily melted by the warming waters than a continuous ice cover, it is important to incorporate these floe sizes in climate models. These models simulate climate evolution at the century scale and are built by combining specialised modules. We study the statistical distribution of floe sizes under the impact of waves to better understand how to connect sea ice modules to wave modules.
Mauricio Arboleda-Zapata, Michael Angelopoulos, Pier Paul Overduin, Guido Grosse, Benjamin M. Jones, and Jens Tronicke
The Cryosphere, 16, 4423–4445, https://doi.org/10.5194/tc-16-4423-2022, https://doi.org/10.5194/tc-16-4423-2022, 2022
Short summary
Short summary
We demonstrate how we can reliably estimate the thawed–frozen permafrost interface with its associated uncertainties in subsea permafrost environments using 2D electrical resistivity tomography (ERT) data. In addition, we show how further analyses considering 1D inversion and sensitivity assessments can help quantify and better understand 2D ERT inversion results. Our results illustrate the capabilities of the ERT method to get insights into the development of the subsea permafrost.
Adam William Bateson, Daniel L. Feltham, David Schröder, Yanan Wang, Byongjun Hwang, Jeff K. Ridley, and Yevgeny Aksenov
The Cryosphere, 16, 2565–2593, https://doi.org/10.5194/tc-16-2565-2022, https://doi.org/10.5194/tc-16-2565-2022, 2022
Short summary
Short summary
Numerical models are used to understand the mechanisms that drive the evolution of the Arctic sea ice cover. The sea ice cover is formed of pieces of ice called floes. Several recent studies have proposed variable floe size models to replace the standard model assumption of a fixed floe size. In this study we show the need to include floe fragmentation processes in these variable floe size models and demonstrate that model design can determine the impact of floe size on size ice evolution.
Frédéric Dupont, Dany Dumont, Jean-François Lemieux, Elie Dumas-Lefebvre, and Alain Caya
The Cryosphere, 16, 1963–1977, https://doi.org/10.5194/tc-16-1963-2022, https://doi.org/10.5194/tc-16-1963-2022, 2022
Short summary
Short summary
In some shallow seas, grounded ice ridges contribute to stabilizing and maintaining a landfast ice cover. A scheme has already proposed where the keel thickness varies linearly with the mean thickness. Here, we extend the approach by taking into account the ice thickness and bathymetry distributions. The probabilistic approach shows a reasonably good agreement with observations and previous grounding scheme while potentially offering more physical insights into the formation of landfast ice.
Kees Nederhoff, Li Erikson, Anita Engelstad, Peter Bieniek, and Jeremy Kasper
The Cryosphere, 16, 1609–1629, https://doi.org/10.5194/tc-16-1609-2022, https://doi.org/10.5194/tc-16-1609-2022, 2022
Short summary
Short summary
Diminishing sea ice is impacting waves across the Arctic region. Recent work shows the effect of the sea ice on offshore waves; however, effects within the nearshore are less known. This study characterizes the wave climate in the central Beaufort Sea coast of Alaska. We show that the reduction of sea ice correlates strongly with increases in the average and extreme waves. However, found trends deviate from offshore, since part of the increase in energy is dissipated before reaching the shore.
Klaus Dethloff, Wieslaw Maslowski, Stefan Hendricks, Younjoo J. Lee, Helge F. Goessling, Thomas Krumpen, Christian Haas, Dörthe Handorf, Robert Ricker, Vladimir Bessonov, John J. Cassano, Jaclyn Clement Kinney, Robert Osinski, Markus Rex, Annette Rinke, Julia Sokolova, and Anja Sommerfeld
The Cryosphere, 16, 981–1005, https://doi.org/10.5194/tc-16-981-2022, https://doi.org/10.5194/tc-16-981-2022, 2022
Short summary
Short summary
Sea ice thickness anomalies during the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) winter in January, February and March 2020 were simulated with the coupled Regional Arctic climate System Model (RASM) and compared with CryoSat-2/SMOS satellite data. Hindcast and ensemble simulations indicate that the sea ice anomalies are driven by nonlinear interactions between ice growth processes and wind-driven sea-ice transports, with dynamics playing a dominant role.
Arne Melsom
The Cryosphere, 15, 3785–3796, https://doi.org/10.5194/tc-15-3785-2021, https://doi.org/10.5194/tc-15-3785-2021, 2021
Short summary
Short summary
This study presents new methods to assess how well observations of sea ice expansion are reproduced by results from models. The aim is to provide information about the quality of forecasts for changes in the sea ice extent to operators in or near ice-infested waters. A test using 2 years of model results demonstrates the practical applicability and usefulness of the methods that are presented.
Jean-François Lemieux, L. Bruno Tremblay, and Mathieu Plante
The Cryosphere, 14, 3465–3478, https://doi.org/10.5194/tc-14-3465-2020, https://doi.org/10.5194/tc-14-3465-2020, 2020
Short summary
Short summary
Sea ice pressure poses great risk for navigation; it can lead to ship besetting and damages. Sea ice forecasting systems can predict the evolution of pressure. However, these systems have low spatial resolution (a few km) compared to the dimensions of ships. We study the downscaling of pressure from the km-scale to scales relevant for navigation. We find that the pressure applied on a ship beset in heavy ice conditions can be markedly larger than the pressure predicted by the forecasting system.
Clara Burgard, Dirk Notz, Leif T. Pedersen, and Rasmus T. Tonboe
The Cryosphere, 14, 2369–2386, https://doi.org/10.5194/tc-14-2369-2020, https://doi.org/10.5194/tc-14-2369-2020, 2020
Short summary
Short summary
The high disagreement between observations of Arctic sea ice makes it difficult to evaluate climate models with observations. We investigate the possibility of translating the model state into what a satellite could observe. We find that we do not need complex information about the vertical distribution of temperature and salinity inside the ice but instead are able to assume simplified distributions to reasonably translate the simulated sea ice into satellite
language.
Clara Burgard, Dirk Notz, Leif T. Pedersen, and Rasmus T. Tonboe
The Cryosphere, 14, 2387–2407, https://doi.org/10.5194/tc-14-2387-2020, https://doi.org/10.5194/tc-14-2387-2020, 2020
Short summary
Short summary
The high disagreement between observations of Arctic sea ice inhibits the evaluation of climate models with observations. We develop a tool that translates the simulated Arctic Ocean state into what a satellite could observe from space in the form of brightness temperatures, a measure for the radiation emitted by the surface. We find that the simulated brightness temperatures compare well with the observed brightness temperatures. This tool brings a new perspective for climate model evaluation.
Nils Hutter and Martin Losch
The Cryosphere, 14, 93–113, https://doi.org/10.5194/tc-14-93-2020, https://doi.org/10.5194/tc-14-93-2020, 2020
Short summary
Short summary
Sea ice is composed of a multitude of floes that constantly deform due to wind and ocean currents and thereby form leads and pressure ridges. These features are visible in the ice as stripes of open-ocean or high-piled ice. High-resolution sea ice models start to resolve these deformation features. In this paper we present two simulations that agree with satellite data according to a new evaluation metric that detects deformation features and compares their spatial and temporal characteristics.
Agnieszka Herman, Sukun Cheng, and Hayley H. Shen
The Cryosphere, 13, 2887–2900, https://doi.org/10.5194/tc-13-2887-2019, https://doi.org/10.5194/tc-13-2887-2019, 2019
Short summary
Short summary
Sea ice interactions with waves are extensively studied in recent years, but mechanisms leading to wave energy attenuation in sea ice remain poorly understood. Close to the ice edge, processes contributing to dissipation include collisions between ice floes and turbulence generated under the ice due to velocity differences between ice and water. This paper analyses details of those processes both theoretically and by means of a numerical model.
Evelyn Jäkel, Johannes Stapf, Manfred Wendisch, Marcel Nicolaus, Wolfgang Dorn, and Annette Rinke
The Cryosphere, 13, 1695–1708, https://doi.org/10.5194/tc-13-1695-2019, https://doi.org/10.5194/tc-13-1695-2019, 2019
Short summary
Short summary
The sea ice surface albedo parameterization of a coupled regional climate model was validated against aircraft measurements performed in May–June 2017 north of Svalbard. The albedo parameterization was run offline from the model using the measured parameters surface temperature and snow depth to calculate the surface albedo and the individual fractions of the ice surface subtypes. An adjustment of the variables and additionally accounting for cloud cover reduced the root-mean-squared error.
Damien Ringeisen, Martin Losch, L. Bruno Tremblay, and Nils Hutter
The Cryosphere, 13, 1167–1186, https://doi.org/10.5194/tc-13-1167-2019, https://doi.org/10.5194/tc-13-1167-2019, 2019
Short summary
Short summary
We study the creation of fracture in sea ice plastic models. To do this, we compress an ideal piece of ice of 8 km by 25 km. We use two different mathematical expressions defining the resistance of ice. We find that the most common one is unable to model the fracture correctly, while the other gives better results but brings instabilities. The results are often in opposition with ice granular nature (e.g., sand) and call for changes in ice modeling.
Charles Gignac, Monique Bernier, and Karem Chokmani
The Cryosphere, 13, 451–468, https://doi.org/10.5194/tc-13-451-2019, https://doi.org/10.5194/tc-13-451-2019, 2019
Short summary
Short summary
The IcePAC tool is made to estimate the probabilities of specific sea ice conditions based on historical sea ice concentration time series from the EUMETSAT OSI-409 product (12.5 km grid), modelled using the beta distribution and used to build event probability maps, which have been unavailable until now. Compared to the Canadian ice service atlas, IcePAC showed promising results in the Hudson Bay, paving the way for its usage in other regions of the cryosphere to inform stakeholders' decisions.
David Schröder, Danny L. Feltham, Michel Tsamados, Andy Ridout, and Rachel Tilling
The Cryosphere, 13, 125–139, https://doi.org/10.5194/tc-13-125-2019, https://doi.org/10.5194/tc-13-125-2019, 2019
Short summary
Short summary
This paper uses sea ice thickness data (CryoSat-2) to identify and correct shortcomings in simulating winter ice growth in the widely used sea ice model CICE. Adding a model of snow drift and using a different scheme for calculating the ice conductivity improve model results. Sensitivity studies demonstrate that atmospheric winter conditions have little impact on winter ice growth, and the fate of Arctic summer sea ice is largely controlled by atmospheric conditions during the melting season.
Ann Keen and Ed Blockley
The Cryosphere, 12, 2855–2868, https://doi.org/10.5194/tc-12-2855-2018, https://doi.org/10.5194/tc-12-2855-2018, 2018
Short summary
Short summary
As the climate warms during the 21st century, our model shows extra melting at the top and the base of the Arctic sea ice. The reducing ice cover affects the impact these processes have on the sea ice volume budget, where the largest individual change is a reduction in the amount of growth at the base of existing ice. Using different forcing scenarios we show that, for this model, changes in the volume budget depend on the evolving ice area but not on the speed at which the ice area declines.
Takuya Nakanowatari, Jun Inoue, Kazutoshi Sato, Laurent Bertino, Jiping Xie, Mio Matsueda, Akio Yamagami, Takeshi Sugimura, Hironori Yabuki, and Natsuhiko Otsuka
The Cryosphere, 12, 2005–2020, https://doi.org/10.5194/tc-12-2005-2018, https://doi.org/10.5194/tc-12-2005-2018, 2018
Short summary
Short summary
Medium-range predictability of early summer sea ice thickness in the East Siberian Sea was examined, based on TOPAZ4 forecast data. Statistical examination indicates that the estimate drops abruptly at 4 days, which is related to dynamical process controlled by synoptic-scale atmospheric fluctuations such as an Arctic cyclone. For longer lead times (> 4 days), the thermodynamic melting process takes over, which represents most of the remaining prediction.
Cited articles
Asplin, M. G., Scharien, R., Else, B., Howell, S., Barber, D. G., Papakyriakou, T., and Prinsenberg, S.: Implications of fractured Arctic perennial ice cover on thermodynamic and dynamic sea ice processes, J. Geophys. Res.-Oceans, 119, 2327–2343, https://doi.org/10.1002/2013JC009557, 2014.
Bai, Q. and Bai, Y.: 7 – Hydrodynamics around Pipes, Subsea Pipeline Design, Analysis, and Installation, Gulf Professional Publishing, 153–170, https://doi.org/10.1016/B978-0-12-386888-6.00007-9, 2014.
Bateson, A. W., Feltham, D. L., Schröder, D., Hosekova, L., Ridley, J. K., and Aksenov, Y.: Impact of sea ice floe size distribution on seasonal fragmentation and melt of Arctic sea ice, The Cryosphere, 14, 403–428, https://doi.org/10.5194/tc-14-403-2020, 2020.
Bateson, A. W., Feltham, D. L., Schröder, D., Wang, Y., Hwang, B., Ridley, J. K., and Aksenov, Y.: Sea ice floe size: its impact on pan-Arctic and local ice mass and required model complexity, The Cryosphere, 16, 2565–2593, https://doi.org/10.5194/tc-16-2565-2022, 2022.
Battjes, J. A. and Janssen, J. P. F. M.: Energy loss and set-up due to breaking of random waves, Coastal Engineering Proceedings, 1, 32, https://doi.org/10.9753/icce.v16.32, 1978.
Bennetts, L. G., O'Farrell, S., and Uotila, P.: Brief communication: Impacts of ocean-wave-induced breakup of Antarctic sea ice via thermodynamics in a stand-alone version of the CICE sea-ice model, The Cryosphere, 11, 1035–1040, https://doi.org/10.5194/tc-11-1035-2017, 2017.
Bitz, C. M. and Lipscomb, W. H.: An energy-conserving thermodynamic sea ice model for climate study. J. Geophys. Res.-Oceans, 104, 15669–15677, https://doi.org/10.1029/1999JC900100, 1999.
Blanchard-Wrigglesworth, E., Donohoe, A., Roach, L. A., DuVivier, A., and Bitz, C. M.:. High-frequency sea ice variability in observations and models, Geophys. Res. Lett., 48, e2020GL092356, https://doi.org/10.1029/2020GL092356, 2021.
Booij, N., Ris, R. C., and Holthuijsen, L. H.: A third-generation wave model for coastal regions. Part I: Model description and validation, J. Geophys. Res., 104, 7649–7666, https://doi.org/10.1029/98JC02622, 1999.
Boutin, G., Lique, C., Ardhuin, F., Rousset, C., Talandier, C., Accensi, M., and Girard-Ardhuin, F.: Towards a coupled model to investigate wave–sea ice interactions in the Arctic marginal ice zone, The Cryosphere, 14, 709–735, https://doi.org/10.5194/tc-14-709-2020, 2020.
Bretschneider, C. L.: Wave variability and wave spectra for wind-generated gravity waves, Technical Memorandum No. 118, Beach Erosion Board, Corps of Engineers, 196 pp., https://apps.dtic.mil/sti/tr/pdf/AD0227467.pdf (last access: 20 May 2023) 1959.
Briegleb, B. P. and Light, B.: A Delta-Eddington multiple scattering parameterization for solar radiation in the sea ice component of the Community Climate System Model, No. NCAR/TN-472+STR, University Corporation for Atmospheric Research, https://doi.org/10.5065/D6B27S71, 2007.
Casas-Prat, M. and Wang, X.: Sea ice retreat contributes to projected increases in extreme Arctic ocean surface waves, Geophys. Res. Lett., 47, e2020GL088100, https://doi.org/10.1029/2020GL088100, 2020.
Cassano, J. J., Higgins, M. E., and Seefeldt, M. W.: Performance of the Weather Research and Forecasting Model for Month-Long Pan-Arctic Simulations, Mon. Weather Rev., 139, 3469–3488, https://doi.org/10.1175/MWR-D-10-05065.1, 2011.
Chen, F. and Dudhia, J.: Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part I: Model implementation and sensitivity, Mon. Weather Rev., 129, 569–585, https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2, 2001.
Cole, S. T., Toole, J. M., Lele, R., Timmermans, M.-L., Gallagher, S. G., Stanton, T. P., Shaw, W. J., Hwang, B., Maksym, T., Wilkinson, J. P., Ortiz, M., Graber, H., Rainville, L., Petty, A. A., Farrell, S. L., Richter-Menge, J. A., and Haas, C.: Ice and ocean velocity in the Arctic marginal ice zone: Ice roughness and momentum transfer, Elementa: Science of the Anthropocene, 5, 55, https://doi.org/10.1525/elementa.241, 2017.
Collins III, C. O., Rogers, W. E., Marchenko, A., and Babanin, A. V.: In situ measurements of an energetic wave event in the Arctic marginal ice zone, Geophys. Res. Lett., 42, 1863–1870, https://doi.org/10.1002/2015GL063063, 2015.
Collins III, C. O. and Rogers, W. E.: A Source Term for Wave Attenuation by Sea ice in WAVEWATCH III: IC4, NRL Report NRL/MR/7320–17-9726, 25 pp., https://www7320.nrlssc.navy.mil/pubs/2017/rogers-2017.pdf (last access: 20 May 2023), 2017.
Collins, W. D., Rasch, P. J., Boville, B. A., McCaa, J., Williamson, D. L., Kiehl, J. T., Briegleb, B. P., Bitz, C., Lin, S.-J., Zhang, M., and Dai, Y.: Description of the NCAR Community Atmosphere Model (CAM 3.0), No. NCAR/TN-464+STR, University Corporation for Atmospheric Research. https://doi.org/10.5065/D63N21CH, 2004.
Cooper, V. T., Roach, L. A., Thomson, J., Brenner, S. D., Smith, M. M., Meylan, M. H., and Bitz, C.M.: Wind waves in sea ice of the western Arctic and a global coupled wave-ice model, Philos. T. R. Soc. A, 380, 20210258, https://doi.org/10.1098/rsta.2021.0258, 2022.
Courtois, P., Hu, X., Pennelly, C., Spence, P., and Myers, P. G.: Mixed layer depth calculation in deep convection regions in ocean numerical models, Ocean Model., 120, 67–78, https://doi.org/10.1016/j.ocemod.2017.10.007, 2017.
Curry, J. A., Schramm, J. L., and Ebert, E. E.: Sea ice-albedo climate feedback mechanism, J. Climate, 8, 240–247, https://doi.org/10.1175/1520-0442(1995)008<0240:SIACFM>2.0.CO;2, 1995.
Dobrynin, M., Murawsky, J., and Yang, S.: Evolution of the global wind wave climate in CMIP5 experiments, Geophys. Res. Lett., 39, L18606, https://doi.org/10.1029/2012GL052843, 2012.
Dumont, D., Kohout, A., and Bertino, L.: A wave-based model for the marginal ice zone including a floe breaking parameterization, J. Geophys. Res., 116, C04001, https://doi.org/10.1029/2010JC006682, 2011.
Freitas, S. R., Grell, G. A., Molod, A., Thompson, M. A., Putman, W. M., Santos e Silva, C. M., and Souza, E. P.: Assessing the Grell–Freitas convection parameterization in the NASA GEOS modeling system, J. Adv. Model. Earth Sy., 10, 1266–1289, https://doi.org/10.1029/2017MS001251, 2018.
Gupta, M. and Thompson, A. F.: Regimes of sea-ice floe melt: Ice-ocean coupling at the submesoscales, J. Geophys. Res.-Oceans, 127, e2022JC018894, https://doi.org/10.1029/2022JC018894, 2022.
Hasselmann, S., Hasselmann, K., Allender, J. H., and Barnett, T. P.: Computations and parameterizations of the nonlinear energy transfer in a gravity wave spectrum. Part II: Parameterizations of the nonlinear transfer for application in wave models, J. Phys. Oceanogr., 15, 1378–1391, https://doi.org/10.1175/1520-0485(1985)015<1378:CAPOTN>2.0.CO;2, 1985.
Horvat, C.: Marginal ice zone fraction benchmarks sea ice climate model skill, Nat. Commun., 12, 2221, https://doi.org/10.1038/s41467-021-22004-7, 2021.
Horvat, C. and Tziperman, E.: A prognostic model of the sea-ice floe size and thickness distribution, The Cryosphere, 9, 2119–2134, https://doi.org/10.5194/tc-9-2119-2015, 2015.
Horvat, C., Tziperman, E., and Campin, J.-M.: Interaction of sea ice floe size, ocean eddies, and sea ice melting, Geophys. Res. Lett., 43, 8083–8090, https://doi.org/10.1002/2016GL069742, 2016.
Horvat, C., Roach, L. A., Tilling, R., Bitz, C. M., Fox-Kemper, B., Guider, C., Hill, K., Ridout, A., and Shepherd, A.: Estimating the sea ice floe size distribution using satellite altimetry: theory, climatology, and model comparison, The Cryosphere, 13, 2869–2885, https://doi.org/10.5194/tc-13-2869-2019, 2019.
Horvat, C., Blanchard-Wrigglesworth, E., and Petty, A. A.: Observing waves in sea ice with ICESat-2, Geophys. Res. Lett., 47, e2020GL087629, https://doi.org/10.1029/2020GL087629, 2020.
Hunke, E. C. and Dukowicz, J. K.: An elastic-viscous-plastic model for sea ice dynamics, J. Phys. Oceanogr., 27, 1849–67, https://doi.org/10.1175/1520-0485(1997)027<1849:AEVPMF>2.0.CO;2, 1997.
Josberger, E. G. and Martin, S.: A laboratory and theoretical study of the boundary layer adjacent to a vertical melting ice wall in salt water, J. Fluid Mech., 111, 439–473, https://doi.org/10.1017/S0022112081002450, 1981.
Kirby, J. T. and Chen, T. M.: Surface waves on vertically sheared flows: approximate dispersion relations, J. Geophys. Res., 94, 1013–1027, https://doi.org/10.1029/JC094iC01p01013, 1989.
Kohout, A., Williams, M. J., Dean, S. M., and Meylan, M.: Storm-induced sea-ice breakup and the implications for ice extent, Nature, 509, 604–607, https://doi.org/10.1038/nature13262, 2014.
Komen, G. J., Hasselmann, S., and Hasselmann, K.: On the existence of a fully developed wind-sea spectrum, J. Phys. Oceanogr., 14, 1271–1285, https://doi.org/10.1175/1520-0485(1984)014<1271:OTEOAF>2.0.CO;2, 1984.
Kumar, N., Voulgaris, G., Warner, J. C., and Olabarrieta, M.: Implementation of the vortex force formalism in the coupled ocean-atmosphere-wave-sediment transport (COAWST) modeling system for inner shelf and surf zone applications, Ocean Model., 47, 65–95, https://doi.org/10.1016/j.ocemod.2012.01.003, 2012.
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.
Langhorne, P. J., Squire, V. A., Fox, C., and Haskell, T. G.: Break-up of sea ice by ocean waves, Ann. Glaciol., 27, 438–442. https://doi.org/10.3189/S0260305500017869, 1998.
Leonard, B. and Mokhtari, S.: ULTRA-SHARP nonoscillatory convection schemes for high-speed steady multidimensional flow, NASA Technical Memorandum 102568, ICOMP-90-12, 54 pp., https://ntrs.nasa.gov/citations/19900012254 (last access: 20 May 2023), 1990.
Li, Z., Wang, Q., Li, G., Lu. P., Wang, Z., and Xie, F.: Laboratory Studies on the Parametrization Scheme of the Melting Rate of Ice–Air and Ice–Water Interfaces, Water, 14, 1775, https://doi.org/10.3390/w14111775, 2022.
Liang, X., Losch, M., Nerger, L., Mu, L., Yang, Q., and Liu, C.: Using sea surface temperature observations to constrain upper ocean properties in an Arctic sea ice-ocean data assimilation system, J. Geophys. Res.-Oceans, 124, 4727–4743, https://doi.org/10.1029/2019JC015073, 2019.
Lipscomb, W. H., Hunke, E. C., Maslowski, W., and Jakacki. J.: Ridging, strength, and stability in high-resolution sea ice models, J. Geophys. Res., 112, C03S91, https://doi.org/10.1029/2005JC003355, 2007.
Liu, Q., Babanin, A. V., Zieger, S., Young, I. R., and Guan, C.: Wind and Wave Climate in the Arctic Ocean as Observed by Altimeters, J. Climate 29, 7957–7975, https://doi.org/10.1175/JCLI-D-16-0219.1, 2016.
Longuet-Higgins, M. S. and Stewart, R. W.: Radiation stresses and mass transport in surface gravity waves with application to “surf beats”, J. Fluid Mech., 13, 481–504, https://doi.org/10.1017/S0022112062000877, 1962.
Loose, B., McGillis, W. R., Perovich, D., Zappa, C. J., and Schlosser, P.: A parameter model of gas exchange for the seasonal sea ice zone, Ocean Sci., 10, 17–28, https://doi.org/10.5194/os-10-17-2014, 2014.
Liu, A. K., Holt, B., and Vachon, P. W.: Wave propagation in the marginal ice zone: Model predictions and comparisons with buoy and synthetic aperture radar data, J. Geophys. Res., 96, 4605-4621, https://doi.org/10.1029/90JC02267, 1991.
Liu, D., Tsarau, A., Guan, C., and Shen, H. H.: Comparison of ice and wind-wave modules in WAVEWATCH III® in the Barents Sea, Cold Reg. Sci. Tech., 172, 103008, https://doi.org/10.1016/j.coldregions.2020.103008, 2020.
Lu, P., Li, Z., Cheng, B., and Leppäranta, M.: A parameterization of the ice-ocean drag coefficient, J. Geophys. Res., 116, C07019, https://doi.org/10.1029/2010JC006878, 2011.
Lukovich, J. V., Stroeve, J. C., Crawford, A., Hamilton, L., Tsamados, M., Heorton, H., and Massonnet, F.: Summer Extreme Cyclone Impacts on Arctic Sea Ice, J. Climate, 34, 4817–4834, https://doi.org/10.1175/JCLI-D-19-0925.1, 2021.
Madsen, O. S., Poon, Y.-K., and Graber, H. C.: Spectral wave attenuation by bottom friction: Theory, Coastal Engineering 1988, 492–504, https://doi.org/10.1061/9780872626874.035, 1988.
Martin, T., Tsamados, M., Schroeder, D., and Feltham, D. L.: The impact of variable sea ice roughness on changes in Arctic Ocean surface stress: A model study, J. Geophys. Res.-Oceans, 121, 1931–1952, https://doi.org/10.1002/2015JC011186, 2016.
Maykut, G. A. and McPhee, M. G.: Solar heating of the Arctic mixed layer, J. Geophys. Res.-Oceans, 100, 24691–24703, https://doi.org/10.1029/95JC02554, 1995.
Maykut, G. A. and Perovich, D. K.: The role of shortwave radiation in the summer decay of a sea ice cover, J. Geophys. Res.-Ocean., 92, 7032–7044, https://doi.org/10.1029/JC092iC07p07032, 1987.
Meylan, M. and Squire, V. A.: The response of ice floes to ocean waves, J. Geophys. Res., 99, 891–900, https://doi.org/10.1029/93JC02695, 1994.
Meylan, M. H., Bennetts, L. G., and A. L. Kohout: In situ measurements and analysis of ocean waves in the Antarctic marginal ice zone, Geophys. Res. Lett., 41, 5046–5051, https://doi.org/10.1002/2014GL060809, 2014.
Montiel, F., Squire, V., and Bennetts, L.: Attenuation and directional spreading of ocean wave spectra in the marginal ice zone, J. Fluid Mech., 790, 492-522, https://doi.org/10.1017/jfm.2016.21, 2016.
Morrison, H., Thompson, G., and Tatarskii, V.: Impact of Cloud Microphysics on the Development of Trailing Stratiform Precipitation in a Simulated Squall Line: Comparison of One- and Two-Moment Schemes, Mon. Weather Rev., 137, 991–1007, https://doi.org/10.1175/2008MWR2556.1, 2009.
Nakanishi, M. and Niino, H.: Development of an improved turbulence closure model for the atmospheric boundary layer, J. Meteor. Soc. Japan, 87, 895–912, https://doi.org/10.2151/jmsj.87.895, 2009.
National Centers for Environmental Prediction, National Weather Service, NOAA, and U.S. Department of Commerce: Climate Forecast System Version 2 (CFSv2) Operational Analaysis, NOAA National Centers for Environmental Information [data set], https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00878, last access: 20 May 2023.
Naughten, K. A., Galton-Fenzi, B. K., Meissner, K. J., England, M. H., Brassington, G. B., Colberg, F., Hattermann, T., and Debernard, J. B.: Spurious sea ice formation caused by oscillatory ocean tracer advection schemes, Ocean Model., 116, 108–117, https://doi.org/10.1016/j.ocemod.2017.06.010, 2017.
Nicolaus, M., Perovich, D., Spreen, G., Granskog, M., Albedyll, L., Angelopoulos, M., Anhaus, P., Arndt, S., Belter, H., Bessonov, V., Birnbaum, G., Brauchle, J., Calmer, R., Cardellach, E., Cheng, B., Clemens-Sewall, D., Dadic, R., Damm, E., Boer, G., Demir, O., Dethloff, K., Divine, D., Fong, A., Fons, S., Frey, M., Fuchs, N., Gabarró, C., Gerland, S., Goessling, H., Gradinger, R., Haapala, J., Haas, C., Hamilton, J., Hannula, H.-R., Hendricks, S., Herber, A., Heuzé, C., Hoppmann, M., Høyland, K., Huntemann, M., Hutchings, J., Hwang, B., Itkin, P., Jacobi, H.-W., Jaggi, M., Jutila, A., Kaleschke, L., Katlein, C., Kolabutin, N., Krampe, D., Kristensen, S., Krumpen, T., Kurtz, N., Lampert, A., Lange, B., Lei, R., Light, B., Linhardt, F., Liston, G., Loose, B., Macfarlane, A., Mahmud, M., Matero, I., Maus, S., Morgenstern, A., Naderpour, R., Nandan, V., Niubom, A., Oggier, M., Oppelt, N., Pätzold, F., Perron, C., Petrovsky, T., Pirazzini, R., Polashenski, C., Rabe, B., Raphael, I., Regnery, J., Rex, M., Ricker, R., Riemann-Campe, K., Rinke, A., Rohde, J., Salganik, E., Scharien, R., Schiller, M., Schneebeli, M., Semmling, M., Shimanchuk, E., Shupe, M., Smith, M., Smolyanitsky, V., Sokolov, V., Stanton, T., Stroeve, J., Thielke, L., Timofeeva, A., Tonboe, R., Tavri, A., Tsamados, M., Wagner, D., Watkins, D., Webster, M., and Wendisch, M.: Overview of the MOSAiC expedition – Snow and Sea Ice, Elementa: Science of the Anthropocene, 10, 000046, https://doi.org/10.1525/elementa.2021.000046, 2021.
Notz, D., McPhee, M. G., Worster, M. G., Maykut, G. A., Schlünzen, K. H., and Eicken, H.: Impact of underwater-ice evolution on Arctic summer sea ice, J. Geophys. Res.-Oceans, 108, 3223, https://doi.org/10.1029/2001JC001173, 2003.
Notz, D., Jahn, A., Holland, M., Hunke, E., Massonnet, F., Stroeve, J., Tremblay, B., and Vancoppenolle, M.: The CMIP6 Sea-Ice Model Intercomparison Project (SIMIP): understanding sea ice through climate-model simulations, Geosci. Model Dev., 9, 3427–3446, https://doi.org/10.5194/gmd-9-3427-2016, 2016.
Parkinson, C. L. and Comiso, J. C.: On the 2012 record low Arctic sea ice cover: Combined impact of preconditioning and an August storm, Geophys. Res. Lett., 40, 1356–1361, https://doi.org/10.1002/grl.50349, 2013.
Peng, L., Zhang, X., Kim, J.-H., Cho, K.-H., Kim, B.-M., Wang, Z., and Tang, H.: Role of intense Arctic storm in accelerating summer sea ice melt: An in situ observational study, Geophys. Res. Lett., 48, e2021GL092714, https://doi.org/10.1029/2021GL092714, 2021.
Perovich, D.: On the summer decay of a sea ice cover, PhD dissertation, University of Washington, Seattle, 176 pp., 1983.
Perovich, D., Meier, W., Tshudi, M., Hendricks, S., Petty, A. A., Divine, D., Farrell, S., Gerland, S., Haas, C., Kaleschke, L., Pavlova, O., Ricker, R., Tian-Kunze, X., Webster, M., and Wood, K.: Sea Ice, Arctic Report Card 2020, edited by: Thoman, R. L., Richter-Menge, J., and Druckenmiller, M. L., https://doi.org/10.25923/n170-9h57, 2020.
Rampal, P., Weiss, J., and Marsan, D.: Positive trend in the mean speed and deformation rate of Arctic sea ice, 1979–2007, J. Geophys. Res., 114, C05013, https://doi.org/10.1029/2008JC005066, 2009.
Roach, L. A., Horvat, C., Dean, S. M., and Bitz, C. M.: An emergent sea ice floe size distribution in a global coupled ocean-sea ice model, J. Geophys. Res.-Oceans, 123, 4322–4337, https://doi.org/10.1029/2017JC013692, 2018a.
Roach, L. A., Smith, M. M., and Dean, S. M.: Quantifying growth of pancake sea ice floes using images from drifting buoys, J. Geophys. Res.-Oceans, 123, 2851–2866, https://doi.org/10.1002/2017JC013693, 2018b.
Roach, L. A., Bitz, C. M., Horvat, C., and Dean, S. M.: Advances in Modeling Interactions Between Sea Ice and Ocean Surface Waves, J. Adv. Model. Earth Sy., 11, 4167–4181, https://doi.org/10.1029/2019MS001836, 2019.
Rogers, W. E., Meylan, M. H., and Kohout, A. L.: Frequency Distribution of Dissipation of Energy of Ocean Waves by Sea Ice Using Data from Wave Array 3 of the ONR ”Sea State” Field Experiment, NRL Report NRL/MR/7322–18-9801, 25 pp., https://www7320.nrlssc.navy.mil/pubs/2018/rogers2-2018.pdf (last access: 20 May 2023), 2018.
Rogers, W. E.: Implementation of sea ice in the wave model SWAN, NRL Memorandum Report NRL/MR/7322–19-9874, 25 pp., https://www7320.nrlssc.navy.mil/pubs/2019/rogers2-2019.pdf (last access: 20 May 2023), 2019.
Rothrock, D. A. and Thorndike, A. S.: Measuring the sea ice floe size distribution, J. Geophys. Res., 89, 6477–6486, https://doi.org/10.1029/JC089iC04p06477, 1984.
Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer, D., Hou, Y., Chuang, H., Iredell, M., Ek, M., Meng, J., Yang, R., Mendez, M. P., van den Dool, H., Zhang, Q., Wang, W., Chen, M., and Becker, E.: The NCEP climate forecast system version 2, J. Climate, 27, 2185–2208, https://doi.org/10.1175/JCLI-D-12-00823.1, 2014.
Schäfer, M., Bierwirth, E., Ehrlich, A., Jäkel, E., and Wendisch, M.: Airborne observations and simulations of three-dimensional radiative interactions between Arctic boundary layer clouds and ice floes, Atmos. Chem. Phys., 15, 8147–8163, https://doi.org/10.5194/acp-15-8147-2015, 2015.
Schmidt, G. A., Bitz, C. M., Mikolajewicz, U., and Tremblay, L.-B.: Ice-ocean boundary conditions for coupled models, Ocean Model., 7, 59–74, https://doi.org/10.1016/S1463-5003(03)00030-1, 2004.
Sepp, M. and Jaagus, J.: Changes in the activity and tracks of Arctic cyclones, Climatic Change, 105, 577–595, https://doi.org/10.1007/s10584-010-9893-7, 2011.
Shchepetkin, A. F. and McWilliams, J. C.: The Regional Ocean Modeling System: A split-explicit, free-surface, topography following coordinates ocean model, Ocean Model., 9, 347–404, https://doi.org/10.1016/j.ocemod.2004.08.002, 2005.
Simmonds, I. and Rudeva, I.: The great Arctic cyclone of August 2012, Geophys. Res. Lett., 39, L23709, https://doi.org/10.1029/2012GL054259, 2012.
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.
Spreen, G., Kwok, R., and Menemenlis, D.: Trends in Arctic sea ice drift and role of wind forcing: 1992–2009, Geophys. Res. Lett., 38, L19501, https://doi.org/10.1029/2011GL048970, 2011.
Squire, V. A.: Ocean Wave Interactions with Sea Ice: A Reappraisal, Annu. Rev. Fluid Mech., 52, 37–60, https://doi.org/10.1146/annurev-fluid-010719-060301, 2020.
Squire, V. A. and Montiel, F.: Evolution of Directional Wave Spectra in the Marginal Ice Zone: A New Model Tested with Legacy Data, J. Phys. Oceanogr., 46, 3121–3137, https://doi.org/10.1175/JPO-D-16-0118.1, 2016.
Steele, M.: Sea ice melting and floe geometry in a simple ice-ocean model, J. Geophys. Res., 97, 17729–17738, https://doi.org/10.1029/92JC01755, 1992.
Steele, M., Morison, J. H., and Untersteiner, N.: The partition of air-ice-ocean momentum exchange as a function of ice concentration, floe size, and draft, J. Geophys. Res., 94, 12739–12750, https://doi.org/10.1029/JC094iC09p12739, 1989.
Steer, A., Worby, A., and Heil, P.: Observed changes in sea-ice floe size distribution during early summer in the western Weddell Sea, Deep Sea Res. Pt. II, 55, 933–942, https://doi.org/10.1016/j.dsr2.2007.12.016, 2008.
Stern, D., P, Doyle, J. D., Barton, N. P., Finocchio, P. M., Komaromi, W. A., and Metzger, E. J.: The impact of an intense cyclone on short-term sea ice loss in a fully coupled atmosphere-ocean-ice model, Geophys. Res. Lett., 47, e2019GL085580, https://doi.org/10.1029/2019GL085580, 2020.
Stopa, J. E., Ardhuin, F., and Girard-Ardhuin, F.: Wave climate in the Arctic 1992–2014: seasonality and trends, The Cryosphere, 10, 1605–1629, https://doi.org/10.5194/tc-10-1605-2016, 2016.
Taylor, P. K. and Yelland, M. J.: The dependence of sea surface roughness on the height and steepness of the waves, J. Phys. Oceanogr., 31, 572–590, https://doi.org/10.1175/1520-0485(2001)031<0572:TDOSSR>2.0.CO;2, 2001.
Thomson, J. and Rogers, W. E.: Swell and sea in the emerging Arctic Ocean, Geophys. Res. Lett., 41, 3136–3140, https://doi.org/10.1002/2014GL059983, 2014.
Thorndike, A. S., Rothrock, D. A., Maykut, G. A., and Colony, R.: The thickness distribution of sea ice, J. Geophys. Res., 80, 4501–4513, https://doi.org/10.1029/JC080i033p04501, 1975.
Toyota, T., Takatsuji, S., and Nakayama, M.: Characteristics of sea ice floe size distribution in the seasonal ice zone, Geophys. Res. Lett., 33, L02616, https://doi.org/10.1029/2005GL024556, 2006.
Toyota, T., Haas, C., and Tamura, T.: Size distribution and shape properties of relatively small sea-ice floes in the Antarctic marginal ice zone in late winter, Deep Sea Res. Pt. II, 58, 1182–1193, https://doi.org/10.1016/j.dsr2.2010.10.034, 2011.
Tsamados, M., Feltham, D. L., Schroeder, D., Flocco, D., Farrell, S. L., Kurtz, N., Laxon, S. W., and Bacon, S.: Impact of Variable Atmospheric and Oceanic Form Drag on Simulations of Arctic Sea Ice, J. Phys. Oceanogr., 44, 5, 1329–1353, https://doi.org/10.1175/JPO-D-13-0215.1, 2014.
Tsamados, M., Feltham, D., Petty, A., Schroeder, D., and Flocco, D.: Processes controlling surface, bottom and lateral melt of Arctic sea ice in a state of the art sea ice model, Philos. T. R. Soc. A, 373, 20140167, https://doi.org/10.1098/rsta.2014.0167, 2015.
Tschudi, M. A., Stroeve, J. C., and Stewart, J. S.: Relating the age of Arctic sea ice to its thickness, as measured during NASA's ICESat and IceBridge campaigns, Remote Sensing, 8, 457, https://doi.org/10.3390/rs8060457, 2016.
Uchiyama, Y., McWilliams, J. C., and Shchepetkin, A. F.: Wave–current interaction in an oceanic circulation model with a vortex-force formalism: Application to the surf zone, Ocean Model., 34, 16–35, https://doi.org/10.1016/j.ocemod.2010.04.002, 2010.
Umlauf, L. and Burchard, H.: A generic length-scale equation for geophysical turbulence models, J. Marine Res., 61, 235–265, https://elischolar.library.yale.edu/journal_of_marine_research/9/ (last access: 20 May 2023), 2003.
Valkonen, E., Cassano, J., and Cassano, E.: Arctic cyclones and their interactions with the declining sea ice: A recent climatology, J. Geophys. Res.-Atmos., 126, e2020JD034366, https://doi.org/10.1029/2020JD034366, 2021.
Vella, D. and Wettlaufer, J. S.: Explaining the patterns formed by ice floe interactions, J. Geophys. Res., 113, C11011, https://doi.org/10.1029/2008JC004781, 2008.
Wang, R. and Shen, H. H.: Gravity waves propagating into an ice-covered ocean: A viscoelastic model, J. Geophys. Res., 115, C06024, https://doi.org/10.1029/2009JC005591, 2010.
Warner, J. C., Armstrong, B., He, R., and Zambon, J.: Development of a coupled ocean-atmosphere-wave-sediment transport (COAWST) modeling system, Ocean Modell., 35, 230–244, https://doi.org/10.1016/j.ocemod.2010.07.010, 2010.
Waseda, T., Webb, A., Sato, K., Inoue, J., Kohout, A., Penrose, B., and Penrose, S.: Correlated Increase of High Ocean Waves and Winds in the Ice-Free Waters of the Arctic Ocean, Sci. Rep., 8, 4489, https://doi.org/10.1038/s41598-018-22500-9, 2018.
Waseda, T., Nose, T., Kodaira, T., Sasmal, K., and Webb, A.: Climatic trends of extreme wave events caused by Arctic cyclones in the western Arctic Ocean, Polar Sci., 27, 100625, https://doi.org/10.1016/j.polar.2020.100625, 2021.
Weiss J. and Dansereau V.: Linking scales in sea ice mechanics. Philos. T. R. Soc. A, 375, 20150352, https://doi.org/10.1098/rsta.2015.0352, 2017.
Wenta, M. and Herman, A.: Area-Averaged Surface Moisture Flux over Fragmented Sea Ice: Floe Size Distribution Effects and the Associated Convection Structure within the Atmospheric Boundary Layer, Atmosphere, 10, 654, https://doi.org/10.3390/atmos10110654, 2019.
Wilchinsky, A. V., Feltham, D. L., and Hopkins, M. A.: Effect of shear rupture on aggregate scale formation in sea ice, J. Geophys. Res., 115, C10002, https://doi.org/10.1029/2009JC006043, 2010.
Yang, C.-Y., Liu, J., and Xu, S.: Seasonal Arctic sea ice prediction using a newly developed fully coupled regional model with the assimilation of satellite sea ice observations, J. Adv. Model. Earth Sy., 12, e2019MS001938, https://doi.org/10.1029/2019MS001938, 2020.
Yang, C.-Y., Liu, J., and Chen, D.: An improved regional coupled modeling system for Arctic sea ice simulation and prediction: a case study for 2018, Geosci. Model Dev., 15, 1155–1176, https://doi.org/10.5194/gmd-15-1155-2022, 2022.
Yang, C.-Y., Liu, J., and Chen, D.: The simulated outputs analyzed in the article: “Understanding the influences of ocean waves on Arctic sea ice simulation: a modeling study with an atmosphere-ocean-wave-sea ice coupled model”, Zenodo [data set], https://doi.org/10.5281/zenodo.7922725, 2023.
Zahn, M., Akperov, M., Rinke, A., Feser, F., and Mokhov, I. I.: Trends of cyclone characteristics in the Arctic and their patterns from different reanalysis data, J. Geophys. Res.-Atmos., 123, 2737–2751, https://doi.org/10.1002/2017JD027439, 2018.
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., Lindsay, R., Schweiger, A., and Steele, M.: The impact of an intense summer cyclone on 2012 Arctic sea ice retreat, Geophys. Res. Lett., 40, 720–726, https://doi.org/10.1002/grl.50190, 2013.
Zhang, J., Schweiger, A., Steele, M., and Stern, H.: Sea ice floe size distribution in the marginal ice zone: Theory and numerical experiments, J. Geophys. Res.-Oceans, 120, 3484–3498, https://doi.org/10.1002/2015JC010770, 2015.
Zhang, J., Stern, H., Hwang, B., Schweiger, A., Steele, M., Stark, M., and Graber, H. C.: Modeling the seasonal evolution of the Arctic sea ice floe size distribution, Elementa: Science of the Anthropocene, 4, 000126, https://doi.org/10.12952/journal.elementa.000126, 2016.
Short summary
We present a new atmosphere–ocean–wave–sea ice coupled model to study the influences of ocean waves on Arctic sea ice simulation. Our results show (1) smaller ice-floe size with wave breaking increases ice melt, (2) the responses in the atmosphere and ocean to smaller floe size partially reduce the effect of the enhanced ice melt, (3) the limited oceanic energy is a strong constraint for ice melt enhancement, and (4) ocean waves can indirectly affect sea ice through the atmosphere and the ocean.
We present a new atmosphere–ocean–wave–sea ice coupled model to study the influences of ocean...
