Articles | Volume 15, issue 2
https://doi.org/10.5194/tc-15-1053-2021
© Author(s) 2021. 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-15-1053-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Feb 2021
Research article |
| 26 Feb 2021
| 26 Feb 2021
On the statistical properties of sea-ice lead fraction and heat fluxes in the Arctic
Nansen Environmental Remote Sensing Center and Bjerknes Centre for Climate Research, Bergen, Norway
Pierre Rampal
Nansen Environmental Remote Sensing Center and Bjerknes Centre for Climate Research, Bergen, Norway
now at: Institut de Géophysique de l'Environnement, CNRS, Grenoble, France
Véronique Dansereau
Nansen Environmental Remote Sensing Center and Bjerknes Centre for Climate Research, Bergen, Norway
now at: Institut des Sciences de la Terre, CNRS, Grenoble, France
Related authors
Simon Driscoll, Alberto Carrassi, Julien Brajard, Laurent Bertino, Einar Ólason, Marc Bocquet, and Amos Lawless
EGUsphere, https://doi.org/10.5194/egusphere-2024-2476, https://doi.org/10.5194/egusphere-2024-2476, 2024
Short summary
Short summary
The formation and evolution of sea ice melt ponds (ponds of melted water) are complex, insufficiently understood and represented in models with considerable uncertainty. These uncertain representations are not traditionally included in climate models potentially causing the known underestimation of sea ice loss in climate models. Our work creates the first observationally based machine learning model of melt ponds that is also a ready and viable candidate to be included in climate models.
Laurent Bertino, Patrick Heimbach, Ed Blockley, and Einar Ólason
State Planet Discuss., https://doi.org/10.5194/sp-2024-24, https://doi.org/10.5194/sp-2024-24, 2024
Preprint under review for SP
Short summary
Short summary
Forecasts of sea ice are in high demand in the polar regions, they are also quickly improving and becoming more easily accessible to non-experts. We provide here a brief status of the short-term forecasting services – typically 10 days ahead – and an outlook of their upcoming developments.
Anton Korosov, Yue Ying, and Einar Olason
EGUsphere, https://doi.org/10.5194/egusphere-2024-2527, https://doi.org/10.5194/egusphere-2024-2527, 2024
Short summary
Short summary
We have developed a new method to improve the accuracy of sea ice models, which predict how ice moves and deforms due to wind and ocean currents. Traditional models use parameters that are often poorly defined. The new approach uses machine learning to fine-tune these parameters by comparing simulated ice drift with satellite data. The method identifies optimal settings for the model by analysing patterns in ice deformation. This results in more accurate simulations of sea ice drift forecasting.
Laurent Brodeau, Pierre Rampal, Einar Ólason, and Véronique Dansereau
Geosci. Model Dev., 17, 6051–6082, https://doi.org/10.5194/gmd-17-6051-2024, https://doi.org/10.5194/gmd-17-6051-2024, 2024
Short summary
Short summary
A new brittle sea ice rheology, BBM, has been implemented into the sea ice component of NEMO. We describe how a new spatial discretization framework was introduced to achieve this. A set of idealized and realistic ocean and sea ice simulations of the Arctic have been performed using BBM and the standard viscous–plastic rheology of NEMO. When compared to satellite data, our simulations show that our implementation of BBM leads to a fairly good representation of sea ice deformations.
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.
Anton Korosov, Pierre Rampal, Yue Ying, Einar Ólason, and Timothy Williams
The Cryosphere, 17, 4223–4240, https://doi.org/10.5194/tc-17-4223-2023, https://doi.org/10.5194/tc-17-4223-2023, 2023
Short summary
Short summary
It is possible to compute sea ice motion from satellite observations and detect areas where ice converges (moves together), forms ice ridges or diverges (moves apart) and opens leads. However, it is difficult to predict the exact motion of sea ice and position of ice ridges or leads using numerical models. We propose a new method to initialise a numerical model from satellite observations to improve the accuracy of the forecasted position of leads and ridges for safer navigation.
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.
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.
Timothy Williams, Anton Korosov, Pierre Rampal, and Einar Ólason
The Cryosphere, 15, 3207–3227, https://doi.org/10.5194/tc-15-3207-2021, https://doi.org/10.5194/tc-15-3207-2021, 2021
Short summary
Short summary
neXtSIM (neXt-generation Sea Ice Model) includes a novel and extremely realistic way of modelling sea ice dynamics – i.e. how the sea ice moves and deforms in response to the drag from winds and ocean currents. It has been developed over the last few years for a variety of applications, but this paper represents its first demonstration in a forecast context. We present results for the time period from November 2018 to June 2020 and show that it agrees well with satellite observations.
Guillaume Boutin, Timothy Williams, Pierre Rampal, Einar Olason, and Camille Lique
The Cryosphere, 15, 431–457, https://doi.org/10.5194/tc-15-431-2021, https://doi.org/10.5194/tc-15-431-2021, 2021
Short summary
Short summary
In this study, we investigate the interactions of surface ocean waves with sea ice. We focus on the evolution of sea ice after it has been fragmented by the waves. Fragmented sea ice is expected to experience less resistance to deformation. We reproduce this evolution using a new coupling framework between a wave model and the recently developed sea ice model neXtSIM. We find that waves can significantly increase the mobility of compact sea ice over wide areas in the wake of storm events.
Pierre Rampal, Véronique Dansereau, Einar Olason, Sylvain Bouillon, Timothy Williams, Anton Korosov, and Abdoulaye Samaké
The Cryosphere, 13, 2457–2474, https://doi.org/10.5194/tc-13-2457-2019, https://doi.org/10.5194/tc-13-2457-2019, 2019
Short summary
Short summary
In this article, we look at how the Arctic sea ice cover, as a solid body, behaves on different temporal and spatial scales. We show that the numerical model neXtSIM uses a new approach to simulate the mechanics of sea ice and reproduce the characteristics of how sea ice deforms, as observed by satellite. We discuss the importance of this model performance in the context of simulating climate processes taking place in polar regions, like the exchange of energy between the ocean and atmosphere.
Pierre Rampal, Sylvain Bouillon, Jon Bergh, and Einar Ólason
The Cryosphere, 10, 1513–1527, https://doi.org/10.5194/tc-10-1513-2016, https://doi.org/10.5194/tc-10-1513-2016, 2016
Short summary
Short summary
Due to the increasing activity in Arctic, sea-ice–ocean models are now frequently used to produce operational forecasts, for oil spill trajectory modelling and to assist in offshore operations planning. In this study we evaluate the performance of two models with respect to their capability to reproduce observed sea ice diffusion properties by using metrics based on Lagrangian statistics. This paper presents a new and useful evaluation metric for current coupled sea ice–ocean models.
Pierre Rampal, Sylvain Bouillon, Einar Ólason, and Mathieu Morlighem
The Cryosphere, 10, 1055–1073, https://doi.org/10.5194/tc-10-1055-2016, https://doi.org/10.5194/tc-10-1055-2016, 2016
Short summary
Short summary
The Arctic sea ice cover has changed drastically over the last decades and undergone a shift in its dynamical regime, as seen by the increase of extreme fracturing events and the acceleration of sea ice drift. In this paper we present a new sea ice model, neXtSIM, that is capable of simulating both sea ice drift and deformation as observed from satellites, with similar spatial and temporal scaling properties. At the same time, the model reproduces sea ice area, extent, and volume correctly.
Rémy Lapere, Louis Marelle, Pierre Rampal, Laurent Brodeau, Christian Melsheimer, Gunnar Spreen, and Jennie L. Thomas
Atmos. Chem. Phys., 24, 12107–12132, https://doi.org/10.5194/acp-24-12107-2024, https://doi.org/10.5194/acp-24-12107-2024, 2024
Short summary
Short summary
Elongated open-water areas in sea ice, called leads, can release marine aerosols into the atmosphere. In the Arctic, this source of atmospheric particles could play an important role for climate. However, the amount, seasonality and spatial distribution of such emissions are all mostly unknown. Here, we propose a first parameterization for sea spray aerosols emitted through leads in sea ice and quantify their impact on aerosol populations in the high Arctic.
Simon Driscoll, Alberto Carrassi, Julien Brajard, Laurent Bertino, Einar Ólason, Marc Bocquet, and Amos Lawless
EGUsphere, https://doi.org/10.5194/egusphere-2024-2476, https://doi.org/10.5194/egusphere-2024-2476, 2024
Short summary
Short summary
The formation and evolution of sea ice melt ponds (ponds of melted water) are complex, insufficiently understood and represented in models with considerable uncertainty. These uncertain representations are not traditionally included in climate models potentially causing the known underestimation of sea ice loss in climate models. Our work creates the first observationally based machine learning model of melt ponds that is also a ready and viable candidate to be included in climate models.
Laurent Bertino, Patrick Heimbach, Ed Blockley, and Einar Ólason
State Planet Discuss., https://doi.org/10.5194/sp-2024-24, https://doi.org/10.5194/sp-2024-24, 2024
Preprint under review for SP
Short summary
Short summary
Forecasts of sea ice are in high demand in the polar regions, they are also quickly improving and becoming more easily accessible to non-experts. We provide here a brief status of the short-term forecasting services – typically 10 days ahead – and an outlook of their upcoming developments.
Anton Korosov, Yue Ying, and Einar Olason
EGUsphere, https://doi.org/10.5194/egusphere-2024-2527, https://doi.org/10.5194/egusphere-2024-2527, 2024
Short summary
Short summary
We have developed a new method to improve the accuracy of sea ice models, which predict how ice moves and deforms due to wind and ocean currents. Traditional models use parameters that are often poorly defined. The new approach uses machine learning to fine-tune these parameters by comparing simulated ice drift with satellite data. The method identifies optimal settings for the model by analysing patterns in ice deformation. This results in more accurate simulations of sea ice drift forecasting.
Laurent Brodeau, Pierre Rampal, Einar Ólason, and Véronique Dansereau
Geosci. Model Dev., 17, 6051–6082, https://doi.org/10.5194/gmd-17-6051-2024, https://doi.org/10.5194/gmd-17-6051-2024, 2024
Short summary
Short summary
A new brittle sea ice rheology, BBM, has been implemented into the sea ice component of NEMO. We describe how a new spatial discretization framework was introduced to achieve this. A set of idealized and realistic ocean and sea ice simulations of the Arctic have been performed using BBM and the standard viscous–plastic rheology of NEMO. When compared to satellite data, our simulations show that our implementation of BBM leads to a fairly good representation of sea ice deformations.
Yumeng Chen, Polly Smith, Alberto Carrassi, Ivo Pasmans, Laurent Bertino, Marc Bocquet, Tobias Sebastian Finn, Pierre Rampal, and Véronique Dansereau
The Cryosphere, 18, 2381–2406, https://doi.org/10.5194/tc-18-2381-2024, https://doi.org/10.5194/tc-18-2381-2024, 2024
Short summary
Short summary
We explore multivariate state and parameter estimation using a data assimilation approach through idealised simulations in a dynamics-only sea-ice model based on novel rheology. We identify various potential issues that can arise in complex operational sea-ice models when model parameters are estimated. Even though further investigation will be needed for such complex sea-ice models, we show possibilities of improving the observed and the unobserved model state forecast and parameter accuracy.
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.
Anton Korosov, Pierre Rampal, Yue Ying, Einar Ólason, and Timothy Williams
The Cryosphere, 17, 4223–4240, https://doi.org/10.5194/tc-17-4223-2023, https://doi.org/10.5194/tc-17-4223-2023, 2023
Short summary
Short summary
It is possible to compute sea ice motion from satellite observations and detect areas where ice converges (moves together), forms ice ridges or diverges (moves apart) and opens leads. However, it is difficult to predict the exact motion of sea ice and position of ice ridges or leads using numerical models. We propose a new method to initialise a numerical model from satellite observations to improve the accuracy of the forecasted position of leads and ridges for safer navigation.
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.
Thomas Richter, Véronique Dansereau, Christian Lessig, and Piotr Minakowski
Geosci. Model Dev., 16, 3907–3926, https://doi.org/10.5194/gmd-16-3907-2023, https://doi.org/10.5194/gmd-16-3907-2023, 2023
Short summary
Short summary
Sea ice covers not only the pole regions but affects the weather and climate globally. For example, its white surface reflects more sunlight than land. The oceans around the poles are therefore kept cool, which affects the circulation in the oceans worldwide. Simulating the behavior and changes in sea ice on a computer is, however, very difficult. We propose a new computer simulation that better models how cracks in the ice change over time and show this by comparing to other simulations.
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.
Sukun Cheng, Yumeng Chen, Ali Aydoğdu, Laurent Bertino, Alberto Carrassi, Pierre Rampal, and Christopher K. R. T. Jones
The Cryosphere, 17, 1735–1754, https://doi.org/10.5194/tc-17-1735-2023, https://doi.org/10.5194/tc-17-1735-2023, 2023
Short summary
Short summary
This work studies a novel application of combining a Lagrangian sea ice model, neXtSIM, and data assimilation. It uses a deterministic ensemble Kalman filter to incorporate satellite-observed ice concentration and thickness in simulations. The neXtSIM Lagrangian nature is handled using a remapping strategy on a common homogeneous mesh. The ensemble is formed by perturbing air–ocean boundary conditions and ice cohesion. Thanks to data assimilation, winter Arctic sea ice forecasting is enhanced.
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.
Timothy Williams, Anton Korosov, Pierre Rampal, and Einar Ólason
The Cryosphere, 15, 3207–3227, https://doi.org/10.5194/tc-15-3207-2021, https://doi.org/10.5194/tc-15-3207-2021, 2021
Short summary
Short summary
neXtSIM (neXt-generation Sea Ice Model) includes a novel and extremely realistic way of modelling sea ice dynamics – i.e. how the sea ice moves and deforms in response to the drag from winds and ocean currents. It has been developed over the last few years for a variety of applications, but this paper represents its first demonstration in a forecast context. We present results for the time period from November 2018 to June 2020 and show that it agrees well with satellite observations.
Marcel Kleinherenbrink, Anton Korosov, Thomas Newman, Andreas Theodosiou, Alexander S. Komarov, Yuanhao Li, Gert Mulder, Pierre Rampal, Julienne Stroeve, and Paco Lopez-Dekker
The Cryosphere, 15, 3101–3118, https://doi.org/10.5194/tc-15-3101-2021, https://doi.org/10.5194/tc-15-3101-2021, 2021
Short summary
Short summary
Harmony is one of the Earth Explorer 10 candidates that has the chance of being selected for launch in 2028. The mission consists of two satellites that fly in formation with Sentinel-1D, which carries a side-looking radar system. By receiving Sentinel-1's signals reflected from the surface, Harmony is able to observe instantaneous elevation and two-dimensional velocity at the surface. As such, Harmony's data allow the retrieval of sea-ice drift and wave spectra in sea-ice-covered regions.
Guillaume Boutin, Timothy Williams, Pierre Rampal, Einar Olason, and Camille Lique
The Cryosphere, 15, 431–457, https://doi.org/10.5194/tc-15-431-2021, https://doi.org/10.5194/tc-15-431-2021, 2021
Short summary
Short summary
In this study, we investigate the interactions of surface ocean waves with sea ice. We focus on the evolution of sea ice after it has been fragmented by the waves. Fragmented sea ice is expected to experience less resistance to deformation. We reproduce this evolution using a new coupling framework between a wave model and the recently developed sea ice model neXtSIM. We find that waves can significantly increase the mobility of compact sea ice over wide areas in the wake of storm events.
Pierre Rampal, Véronique Dansereau, Einar Olason, Sylvain Bouillon, Timothy Williams, Anton Korosov, and Abdoulaye Samaké
The Cryosphere, 13, 2457–2474, https://doi.org/10.5194/tc-13-2457-2019, https://doi.org/10.5194/tc-13-2457-2019, 2019
Short summary
Short summary
In this article, we look at how the Arctic sea ice cover, as a solid body, behaves on different temporal and spatial scales. We show that the numerical model neXtSIM uses a new approach to simulate the mechanics of sea ice and reproduce the characteristics of how sea ice deforms, as observed by satellite. We discuss the importance of this model performance in the context of simulating climate processes taking place in polar regions, like the exchange of energy between the ocean and atmosphere.
Ali Aydoğdu, Alberto Carrassi, Colin T. Guider, Chris K. R. T Jones, and Pierre Rampal
Nonlin. Processes Geophys., 26, 175–193, https://doi.org/10.5194/npg-26-175-2019, https://doi.org/10.5194/npg-26-175-2019, 2019
Short summary
Short summary
Computational models involving adaptive meshes can both evolve dynamically and be remeshed. Remeshing means that the state vector dimension changes in time and across ensemble members, making the ensemble Kalman filter (EnKF) unsuitable for assimilation of observational data. We develop a modification in which analysis is performed on a fixed uniform grid onto which the ensemble is mapped, with resolution relating to the remeshing criteria. The approach is successfully tested on two 1-D models.
Anton Andreevich Korosov, Pierre Rampal, Leif Toudal Pedersen, Roberto Saldo, Yufang Ye, Georg Heygster, Thomas Lavergne, Signe Aaboe, and Fanny Girard-Ardhuin
The Cryosphere, 12, 2073–2085, https://doi.org/10.5194/tc-12-2073-2018, https://doi.org/10.5194/tc-12-2073-2018, 2018
Short summary
Short summary
A new algorithm for estimating sea ice age in the Arctic is presented. The algorithm accounts for motion, deformation, melting and freezing of sea ice and uses daily sea ice drift and sea ice concentration products. The major advantage of the new algorithm is the ability to generate individual ice age fractions in each pixel or, in other words, to provide a frequency distribution of the ice age. Multi-year ice concentration can be computed as a sum of all ice fractions older than 1 year.
Fabrice Ardhuin, Yevgueny Aksenov, Alvise Benetazzo, Laurent Bertino, Peter Brandt, Eric Caubet, Bertrand Chapron, Fabrice Collard, Sophie Cravatte, Jean-Marc Delouis, Frederic Dias, Gérald Dibarboure, Lucile Gaultier, Johnny Johannessen, Anton Korosov, Georgy Manucharyan, Dimitris Menemenlis, Melisa Menendez, Goulven Monnier, Alexis Mouche, Frédéric Nouguier, George Nurser, Pierre Rampal, Ad Reniers, Ernesto Rodriguez, Justin Stopa, Céline Tison, Clément Ubelmann, Erik van Sebille, and Jiping Xie
Ocean Sci., 14, 337–354, https://doi.org/10.5194/os-14-337-2018, https://doi.org/10.5194/os-14-337-2018, 2018
Short summary
Short summary
The Sea surface KInematics Multiscale (SKIM) monitoring mission is a proposal for a future satellite that is designed to measure ocean currents and waves. Using a Doppler radar, the accurate measurement of currents requires the removal of the mean velocity due to ocean wave motions. This paper describes the main processing steps needed to produce currents and wave data from the radar measurements. With this technique, SKIM can provide unprecedented coverage and resolution, over the global ocean.
Matthias Rabatel, Pierre Rampal, Alberto Carrassi, Laurent Bertino, and Christopher K. R. T. Jones
The Cryosphere, 12, 935–953, https://doi.org/10.5194/tc-12-935-2018, https://doi.org/10.5194/tc-12-935-2018, 2018
Short summary
Short summary
Large deviations still exist between sea ice forecasts and observations because of both missing physics in models and uncertainties on model inputs. We investigate how the new sea ice model neXtSIM is sensitive to uncertainties in the winds. We highlight and quantify the role of the internal forces in the ice on this sensitivity and show that neXtSIM is better at predicting sea ice drift than a free-drift (without internal forces) ice model and is a skilful tool for search and rescue operations.
Timothy D. Williams, Pierre Rampal, and Sylvain Bouillon
The Cryosphere, 11, 2117–2135, https://doi.org/10.5194/tc-11-2117-2017, https://doi.org/10.5194/tc-11-2117-2017, 2017
Short summary
Short summary
As the Arctic sea ice extent drops, more ship traffic seeks to take advantage of this, and a need for better wave and sea ice forecasts arises. One aspect of this is the location of the sea ice edge. The waves here can be quite large, but they die away as they travel into the ice. This causes momentum to be transferred from the waves to the ice, causing ice drift. However, our study found that the effect of the wind drag had more impact on the ice edge position than the waves.
Véronique Dansereau, Jérôme Weiss, Pierre Saramito, Philippe Lattes, and Edmond Coche
The Cryosphere, 11, 2033–2058, https://doi.org/10.5194/tc-11-2033-2017, https://doi.org/10.5194/tc-11-2033-2017, 2017
Short summary
Short summary
A new mechanical framework is used to model the drift of sea ice in a narrow channel between Greenland and Ellesmere Island. It is able to reproduce its main features : curved cracks, ice “bridges” that stop the flow of ice for several months of the year and some thick, strongly localized ridged ice. The simulations suggest that a mechanical weakening of the sea ice cover can shorten the lifespan of ice bridges and result in an increased export of ice through the narrow channels of the Arctic.
Kirill Khvorostovsky and Pierre Rampal
The Cryosphere, 10, 2329–2346, https://doi.org/10.5194/tc-10-2329-2016, https://doi.org/10.5194/tc-10-2329-2016, 2016
Short summary
Short summary
We analyse two methods of freeboard retrieval from ICESat satellite data that were used to derive the two widely used Arctic sea ice thickness products. We show that although different factors result in significant local differences between freeboards, they roughly compensate each other with respect to overall freeboard estimation. Thus the difference found between the sea ice thickness datasets should be attributed to different parameters used in the freeboard-to-thickness conversion.
Pierre Rampal, Sylvain Bouillon, Jon Bergh, and Einar Ólason
The Cryosphere, 10, 1513–1527, https://doi.org/10.5194/tc-10-1513-2016, https://doi.org/10.5194/tc-10-1513-2016, 2016
Short summary
Short summary
Due to the increasing activity in Arctic, sea-ice–ocean models are now frequently used to produce operational forecasts, for oil spill trajectory modelling and to assist in offshore operations planning. In this study we evaluate the performance of two models with respect to their capability to reproduce observed sea ice diffusion properties by using metrics based on Lagrangian statistics. This paper presents a new and useful evaluation metric for current coupled sea ice–ocean models.
Véronique Dansereau, Jérôme Weiss, Pierre Saramito, and Philippe Lattes
The Cryosphere, 10, 1339–1359, https://doi.org/10.5194/tc-10-1339-2016, https://doi.org/10.5194/tc-10-1339-2016, 2016
Short summary
Short summary
In this paper we present a new mechanical modelling framework for the deformation of sea ice on regional and larger scales named Maxwell elasto-brittle. The model successfully reproduces the formation of narrow, oriented leads which concentrate the deformation within the damaged, i.e., fractured, ice as well as the intermittency of the damaging process, and hence represents a relevant contribution to the ongoing development of operational modelling platforms, regional and global climate models.
Pierre Rampal, Sylvain Bouillon, Einar Ólason, and Mathieu Morlighem
The Cryosphere, 10, 1055–1073, https://doi.org/10.5194/tc-10-1055-2016, https://doi.org/10.5194/tc-10-1055-2016, 2016
Short summary
Short summary
The Arctic sea ice cover has changed drastically over the last decades and undergone a shift in its dynamical regime, as seen by the increase of extreme fracturing events and the acceleration of sea ice drift. In this paper we present a new sea ice model, neXtSIM, that is capable of simulating both sea ice drift and deformation as observed from satellites, with similar spatial and temporal scaling properties. At the same time, the model reproduces sea ice area, extent, and volume correctly.
Natalia Ivanova, Pierre Rampal, and Sylvain Bouillon
The Cryosphere, 10, 585–595, https://doi.org/10.5194/tc-10-585-2016, https://doi.org/10.5194/tc-10-585-2016, 2016
Short summary
Short summary
Accurate observations of lead fraction are of high importance for model evaluation and/or assimilation into models. In this work, consistent quantitative error estimation of an existing lead fraction data set obtained from passive microwave observations is completed using Synthetic Aperture Radar data. A significant bias in the data set is found, and possible improvement in the methodology is suggested, so that the pixel-wise error is reduced by a factor of 2 on average.
S. Bouillon and P. Rampal
The Cryosphere, 9, 663–673, https://doi.org/10.5194/tc-9-663-2015, https://doi.org/10.5194/tc-9-663-2015, 2015
Short summary
Short summary
We present a new method to compute sea ice deformation fields from satellite-derived motion. The method particularly reduces the artificial noise that arises along discontinuities in the sea ice motion field. We estimate that this artificial noise may cause an overestimation of about 60% of sea ice opening and closing. The constant overestimation of the opening and closing could have led in previous studies to a large overestimation of freezing in leads, salt rejection and sea ice ridging.
M. Zygmuntowska, P. Rampal, N. Ivanova, and L. H. Smedsrud
The Cryosphere, 8, 705–720, https://doi.org/10.5194/tc-8-705-2014, https://doi.org/10.5194/tc-8-705-2014, 2014
Related subject area
Discipline: Sea ice | Subject: Energy Balance Obs/Modelling
A sensor-agnostic albedo retrieval method for realistic sea ice surfaces: model and validation
Understanding model spread in sea ice volume by attribution of model differences in seasonal ice growth and melt
New insights into radiative transfer within sea ice derived from autonomous optical propagation measurements
Sunlight, clouds, sea ice, albedo, and the radiative budget: the umbrella versus the blanket
Yingzhen Zhou, Wei Li, Nan Chen, Yongzhen Fan, and Knut Stamnes
The Cryosphere, 17, 1053–1087, https://doi.org/10.5194/tc-17-1053-2023, https://doi.org/10.5194/tc-17-1053-2023, 2023
Short summary
Short summary
We present a method to compute albedo (percentage of the light reflected) of the cryosphere surface using observations from optical satellite sensors. This method can be applied to sea ice, snow-covered ice, melt pond, open ocean, and mixtures thereof. Evaluation of the albedo values calculated using this approach demonstrated excellent agreement with observations. In addition, we have included a statistical comparison of the proposed method's results with those derived from other approaches.
Alex West, Edward Blockley, and Matthew Collins
The Cryosphere, 16, 4013–4032, https://doi.org/10.5194/tc-16-4013-2022, https://doi.org/10.5194/tc-16-4013-2022, 2022
Short summary
Short summary
In this study we explore a method of examining model differences in ice volume by looking at the seasonal ice growth and melt. We use simple physical relationships to judge how model differences in key variables affect ice growth and melt and apply these to three case study models with ice volume ranging from very thin to very thick. Results suggest that differences in snow and melt pond cover in early summer are most important in causing the sea ice differences for these models.
Christian Katlein, Lovro Valcic, Simon Lambert-Girard, and Mario Hoppmann
The Cryosphere, 15, 183–198, https://doi.org/10.5194/tc-15-183-2021, https://doi.org/10.5194/tc-15-183-2021, 2021
Short summary
Short summary
To improve autonomous investigations of sea ice optical properties, we designed a chain of multispectral light sensors, providing autonomous in-ice light measurements. Here we describe the system and the data acquired from a first prototype deployment. We show that sideward-looking planar irradiance sensors basically measure scalar irradiance and demonstrate the use of this sensor chain to derive light transmittance and inherent optical properties of sea ice.
Donald K. Perovich
The Cryosphere, 12, 2159–2165, https://doi.org/10.5194/tc-12-2159-2018, https://doi.org/10.5194/tc-12-2159-2018, 2018
Short summary
Short summary
The balance of longwave and shortwave radiation plays a central role in the summer melt of Arctic sea ice. It is governed by clouds and surface albedo. The basic question is what causes more melting, sunny skies or cloudy skies. It depends on the albedo of the ice surface. For snow-covered or bare ice, sunny skies always result in less radiative heat input. In contrast, the open ocean always has, and melt ponds usually have, more radiative input under sunny skies than cloudy skies.
Cited articles
Aagaard, K., Coachman, L., and Carmack, E.: On the halocline of the Arctic
Ocean, Deep-Sea Res. Pt. I, 28, 529–545, https://doi.org/10.1016/0198-0149(81)90115-1, 1981. a
Andreas, E. and Murphy, B.: Bulk transfer coefficients for heat and momentum
over leads and polynyas, J. Phys. Ocean., 16, 1875–1883, 1986. a
Andreas, E. L. and Cash, B. A.: Convective heat transfer over wintertime leads
and polynyas, J. Geophys. Res.-Oceans, 104, 25721–25734,
https://doi.org/10.1029/1999JC900241, 1999. a, b, c
Andreas, E. L., Paulson, C. A., William, R. M., Lindsay, R. W., and
Businger, J. A.: The turbulent heat flux from arctic leads,
Bound.-Lay. Meteorol., 17, 57–91, https://doi.org/10.1007/BF00121937, 1979. a
Barthélemy, A., Fichefet, T., Goosse, H., and Madec, G.: Modeling the
interplay between sea ice formation and the oceanic mixed layer: Limitations
of simple brine rejection parameterizations, Ocean Modell., 86, 141–152,
https://doi.org/10.1016/j.ocemod.2014.12.009, 2015. a
Bouillon, S. and Rampal, P.: On producing sea ice deformation data sets from SAR-derived sea ice motion, The Cryosphere, 9, 663–673, https://doi.org/10.5194/tc-9-663-2015, 2015a. a, b, c
Bouillon, S. and Rampal, P.: Presentation of the dynamical core of neXtSIM, a
new sea ice model, Ocean Modell., 91, 23–37, 2015b. a
Bröhan, D. and Kaleschke, L.: A Nine-Year Climatology of Arctic Sea Ice
Lead Orientation and Frequency from AMSR-E, Remote Sens., 6, 1451–1475,
2014. a
Bromwich, D. H., Wilson, A. B., Bai, L.-S., Moore, G. W. K., and Bauer, P.: A
comparison of the regional Arctic System Reanalysis and the global
ERA-Interim Reanalysis for the Arctic, Q. J. Roy. Meteor. Soc., 142, 644–658, https://doi.org/10.1002/qj.2527, 2016. a
Chechin, D. G., Makhotina, I. A., Lüpkes, C., and Makshtas, A. P.: Effect of
Wind Speed and Leads on Clear-Sky Cooling over Arctic Sea Ice during Polar
Night, J. Atmos. Sci., 76, 2481–2503,
https://doi.org/10.1175/JAS-D-18-0277.1, 2019. a
Dansereau, V., Weiss, J., Saramito, P., and Lattes, P.: A Maxwell elasto-brittle rheology for sea ice modelling, The Cryosphere, 10, 1339–1359, https://doi.org/10.5194/tc-10-1339-2016, 2016. a
Esau, I. N.: Amplification of turbulent exchange over wide Arctic leads:
Large-eddy simulation study, J. Geophys. Res.-Atmos.,
112, d08109, https://doi.org/10.1029/2006JD007225, 2007. a, b, c
Girard, L., Bouillon, S., Weiss, J., Amitrano, D., Fichefet, T., and Legat, V.:
A new modelling framework for sea ice mechanics based on elasto-brittle
rheology, Ann. Glaciol., 52, 123–132, 2011. a
Hibler, W. D.: A dynamic thermodynamic sea ice model, J. Phys. Ocean., 9,
817–846, 1979. a
Hutchings, J. K., Roberts, A., Geiger, C. A., and Richter-Menge, J.: Spatial
and temporal characterization of sea-ice deformation, Ann. Glaciol., 52,
360–368, 2011. a
Hutter, N., Losch, M., and Menemenlis, D.: Scaling Properties of Arctic Sea Ice
Deformation in a High-Resolution Viscous-Plastic Sea Ice Model and in
Satellite Observations, J. Geophys. Res.-Oceans, 123,
672–687, https://doi.org/10.1002/2017JC013119, 2018. a
Ivanova, N., Rampal, P., and Bouillon, S.: Error assessment of satellite-derived lead fraction in the Arctic, The Cryosphere, 10, 585–595, https://doi.org/10.5194/tc-10-585-2016, 2016. a, b
Kagan, Y. Y.: Fractal dimension of brittle fracture, J. Nonlinear
Sci., 1, 1–16, 1991. a
Koldunov, N. V., Danilov, S., Sidorenko, D., Hutter, N., Losch, M., Goessling,
H., Rakowsky, N., Scholz, P., Sein, D., Wang, Q., and Jung, T.: Fast EVP
Solutions in a High-Resolution Sea Ice Model, J. Adv. Model. Earth Sy., 11, 1269–1284, https://doi.org/10.1029/2018MS001485, 2019. a
Kozo, T. L.: Initial model results for Arctic mixed layer circulation under a
refreezing lead, J. Geophys. Res.-Oceans, 88, 2926–2934,
https://doi.org/10.1029/JC088iC05p02926, 1983. a
Kwok, R., Cunningham, G. F., Wensnahan, M., Rigor, I., Zwally, H. J., and Yi,
D.: Thinning and volume loss of the Arctic Ocean sea ice cover: 2003–2008,
J. Geophys. Res.-Oceans, 114, c07005, https://doi.org/10.1029/2009JC005312,
2009. a
Leppäranta, M.: The Drift of Sea Ice, Springer, Helsinki, 2005. a
Li, X., Krueger, S. K., Strong, C., Mace, G. G., and Benson, S.: Midwinter
Arctic leads form and dissipate low clouds, Nat. Commun., 11, 206,
https://doi.org/10.1038/s41467-019-14074-5, 2020. a
Lüpkes, C., Gryanik, V. M., Witha, B., Gryschka, M., Raasch, S., and
Gollnik, T.: Modeling convection over arctic leads with LES and a
non-eddy-resolving microscale model, J. Geophys. Res., 113, C09028,
https://doi.org/10.1029/2007JC004099, 2008a. a, b
Lüpkes, C., Vihma, T., Birnbaum, G., and Wacker, U.: Influence of leads in
sea ice on the temperature of the atmospheric boundary layer during polar
night, Geophys. Res. Lett., 35, L03805, https://doi.org/10.1029/2007GL032461,
2008b. a, b
Matsushita, M.: Fractal Viewpoint of Fracture and Accretion, J. Phys. Soc. Jpn., 54, 857–860, https://doi.org/10.1143/JPSJ.54.857, 1985. a
Morales Maqueda, M. A., Willmott, A. J., and Biggs, N. R. T.: Polynya Dynamics:
a Review of Observations and Modeling, Rev. Geophys., 42, RG1004,
https://doi.org/10.1029/2002RG000116, 2004. a
Morison, J. H. and McPhee, M. G.: Lead convection measured with an autonomous
underwater vehicle, J. Geophys. Res.-Oceans, 103, 3257–3281,
https://doi.org/10.1029/97JC02264, 1998. a
Morison, J. H., McPhee, M. G., Curtin, T. B., and Paulson, C. A.: The
oceanography of winter leads, J. Geophys. Res.-Oceans, 97,
11199–11218, https://doi.org/10.1029/92JC00684, 1992. a
Nguyen, A. T., Menemenlis, D., and Kwok, R.: Improved modeling of the Arctic
halocline with a subgrid-scale brine rejection parameterization, J. Geophys. Res.-Oceans, 114, c11014, https://doi.org/10.1029/2008JC005121, 2009. a
Rampal, P., Weiss, J., Marsan, D., Lindsay, R., and Stern, H.: Scaling
properties of sea ice deformation from buoy dispersion analysis, J. Geophys. Res.-Oceans, 113, c03002, https://doi.org/10.1029/2007JC004143, 2008. a
Rampal, P., Bouillon, S., Ólason, E., and Morlighem, M.: neXtSIM: a new Lagrangian sea ice model, The Cryosphere, 10, 1055–1073, https://doi.org/10.5194/tc-10-1055-2016, 2016. a, b, c
Röhrs, J. and Kaleschke, L.: An algorithm to detect sea ice leads by using AMSR-E passive microwave imagery, The Cryosphere, 6, 343–352, https://doi.org/10.5194/tc-6-343-2012, 2012. a, b, c
Rothrock, D. A. and Thorndike, A. S.: Measuring the Sea Ice Floe Size
Distribution, J. Geophys. Res., 89, 6477–6486, 1984. a
Sakov, P., Counillon, F., Bertino, L., Lisæter, K. A., Oke, P. R., and Korablev, A.: TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic, Ocean Sci., 8, 633–656, https://doi.org/10.5194/os-8-633-2012, 2012. a
Schertzer, D. and Lovejoy, S.: Physical Modeling and Analysis of Rain and
Clouds by Anisotropic Scaling Multiplicative Processes, J. Geophys. Res., 92,
9693–9714, 1987. a
Smith, D. C. and Morison, J. H.: A numerical study of haline convection beneath
leads in sea ice, J. Geophys. Res.-Oceans, 98,
10069–10083, https://doi.org/10.1029/93JC00137, 1993. a
Smith, D. C., Lavelle, J. W., and Fernando, H. J. S.: Arctic Ocean mixed-layer
eddy generation under leads in sea ice, J. Geophys. Res.-Oceans, 107, 1–17, https://doi.org/10.1029/2001JC000822, 2002. a
Smith, J.: Oceanographic investigations during the AIDJEX lead experiment,
AIDJEX Bull., 27, 125–133, 1974. a
Spreen, G., Kwok, R., Menemenlis, D., and Nguyen, A. T.: Sea-ice deformation in a coupled ocean–sea-ice model and in satellite remote sensing data, The Cryosphere, 11, 1553–1573, https://doi.org/10.5194/tc-11-1553-2017, 2017. a
Stern, H. L. and Lindsay, R. W.: Spatial scaling of Arctic sea ice deformation,
J. Geophys. Res.-Oceans, 114, c10017, https://doi.org/10.1029/2009JC005380,
2009. a
Stern, H. L., Stark, A. J., Zhang, J., Steele, M., and Hwang, B.: Seasonal
evolution of the sea-ice floe size distribution in the Beaufort and Chukchi
seas, Elementa Sci. Anthro., 6, 48, https://doi.org/10.1525/elementa.305,
2018. a
Tamura, T. and Ohshima, K. I.: Mapping of sea ice production in the Arctic
coastal polynyas, J. Geophys. Res., 116, C07030, https://doi.org/10.1029/2010JC006586, 2011. a
Tetzlaff, A., Lüpkes, C., and Hartmann, J.: Aircraft-based observations of
atmospheric boundary-layer modification over Arctic leads, Q. J. Roy. Meteor. Soc., 141, 2839–2856, https://doi.org/10.1002/qj.2568,
2015. a
Tonboe, R. T., Eastwood, S., Lavergne, T., Sørensen, A. M., Rathmann, N., Dybkjær, G., Pedersen, L. T., Høyer, J. L., and Kern, S.: The EUMETSAT sea ice concentration climate data record, The Cryosphere, 10, 2275–2290, https://doi.org/10.5194/tc-10-2275-2016, 2016. a
Vihma, T., Pirazzini, R., Fer, I., Renfrew, I. A., Sedlar, J., Tjernström, M., Lüpkes, C., Nygård, T., Notz, D., Weiss, J., Marsan, D., Cheng, B., Birnbaum, G., Gerland, S., Chechin, D., and Gascard, J. C.: Advances in understanding and parameterization of small-scale physical processes in the marine Arctic climate system: a review, Atmos. Chem. Phys., 14, 9403–9450, https://doi.org/10.5194/acp-14-9403-2014, 2014. a
Wang, Q., Danilov, S., Jung, T., Kaleschke, L., and Wernecke, A.: Sea ice leads
in the Arctic Ocean: Model assessment, interannual variability and trends,
Geophys. Res. Lett., 43, 7019–7027, https://doi.org/10.1002/2016GL068696,
2016. a
Warren, S. G., Rigor, I. G., Untersteiner, N., Radionov, V. F., Bryazgin,
N. N., Aleksandrov, Y. I., and Colony, R.: Snow Depth on Arctic Sea Ice,
J. Climate, 12, 1814–1829,
https://doi.org/10.1175/1520-0442(1999)012<1814:SDOASI>2.0.CO;2, 1999. a
Weiss, J.: Scaling of Fracture and Faulting of Ice on Earth, Surv. Geophys., 24, 185–227, https://doi.org/10.1023/A:1023293117309, 2003. a
Weiss, J. and Dansereau, V.: Linking scales in sea ice mechanics,
Philos. T. Roy. Soc. A, 375, 20150352, https://doi.org/10.1098/rsta.2015.0352, 2017.
a
Willmes, S. and Heinemann, G.: Pan-Arctic lead detection from MODIS thermal
infrared imagery, Ann. Glaciol., 56, 29–37,
https://doi.org/10.3189/2015AoG69A615, 2015. a
Winsor, P. and Björk, G.: Polynya activity in the Arctic Ocean from 1958 to
1997, J. Geophys. Res.-Oceans, 105, 8789–8803,
https://doi.org/10.1029/1999JC900305, 2000. a
Winton, M.: A reformulated three-layer sea ice model, J. Atmos. Ocean. Tech.,
17, 525–531, 2000. a
Short summary
We analyse the fractal properties observed in the pattern of the long, narrow openings that form in Arctic sea ice known as leads. We use statistical tools to explore the fractal properties of the lead fraction observed in satellite data and show that our sea-ice model neXtSIM displays the same behaviour. Building on this result we then show that the pattern of heat loss from ocean to atmosphere in the model displays similar fractal properties, stemming from the fractal properties of the leads.
We analyse the fractal properties observed in the pattern of the long, narrow openings that form...
