Search

Multiyear sea ice (MYI) cover in the Arctic has been monitored for decades using increasingly sophisticated remote sensing techniques, and these have documented a significant decline in MYI over time. However, such techniques are unable to differentiate between the processes affecting the evolution of the MYI. Further, estimating the thickness, and thus the volume of MYI remains challenging. Here we use the neXtSIM sea ice model, coupled to the ocean component of NEMO, to investigate the changes to MYI over the period 2000-2018. We exploit the Lagrangian framework of the sea ice model to introduce a new method of tracking MYI area and volume, which is based on identifying MYI during freeze onset each autumn. The model is found to successfully reproduce the spatial distribution and evolution of observed MYI extent. We discuss the balance of the processes (melt, ridging, export, and replenishment) linked to the general decline in MYI cover. The model suggests that rather than one process dominating the losses, there is an episodic imbalance between the different sources and sinks of MYI. We identify those key to the significant observed declines of 2007 and 2012; while melt and replenishment are important in 2012, sea ice dynamics play a significant role in 2007. Notably, the model suggests that convergence of the ice, through ridging, can result in large reductions of MYI area without a corresponding loss of MYI volume. This highlights the benefit of using models alongside satellite observations to aid interpretation of the observed MYI evolution in the Arctic. Based on the MYI tracking method here, we demonstrate how MYI is now implemented in the neXtSIM-F forecasts distributed by the CMEMS platform.

Sea ice is a key component of the Earth's climate system as it modulates the energy exchanges and associated feedback processes at the air–sea interface in polar regions. These exchanges have been suggested to strongly depend on openings in the sea ice cover, which are associated with fine-scale sea ice deformations, but the importance of these processes remains poorly understood as most numerical models struggle to represent these deformations without using very costly horizontal resolutions (≃ 5 km). In this study, we present results from a 12 km resolution ocean–sea ice coupled model, the first that uses a brittle rheology to represent the mechanical behaviour of sea ice. This rheology has been shown to reproduce observed characteristics and complexity of fine-scale sea ice deformations at relatively coarse resolutions. We evaluate and discuss the Arctic sea ice mass balance of this coupled model for the period 2000–2018. We first assess sea ice quantities relevant for climate (volume, extent, and drift) and find that they are consistent with satellite observations. We evaluate components of the mass balance for which observations are available, i.e. sea ice volume export through Fram Strait and winter mass balance in the Arctic marginal seas for the period 2003–2018. Model values show a good match with observations, remaining within the estimated uncertainty, and the interannual variability of the dynamic contribution to the winter mass balance is generally well captured. We discuss the relative contributions of dynamics and thermodynamics to the sea ice mass balance in the Arctic Basin for 2000–2018. Using the ability of the model to represent divergence motions at different scales, we investigate the role of leads and polynyas in ice production. We suggest a way to estimate the contribution of leads and polynyas to ice growth in winter, and we estimate this contribution to add up to 25 %–35 % of the total ice growth in pack ice from January to March. This contribution shows a significant increase over 2000–2018. This coupled framework opens up new opportunities to understand and quantify the interplay between small-scale sea ice dynamics and ocean properties.

The loss of thick multiyear sea ice in the Arctic leads to weaker sea ice that is more easily broken up by strong winds. As a consequence, extreme sea ice breakup events may become more frequent, even during the middle of winter when the sea ice cover is frozen solid. This can lead to an earlier onset of the melt season and potentially accelerate Arctic sea ice loss. Such extreme breakup events are generally not captured by climate models, potentially limiting our confidence in projections of Arctic sea ice. We investigated the driving forces behind sea ice breakup events during winter and how they change in a future climate. Our sea ice model is the first to reproduce such breakup events and reveals that the combination of strong winds and thin sea ice is a key factor for these breakups. We found that winter breakups have a large effect on local heat and moisture transfer and cause enhanced sea ice production, but also increase the overall movement of the sea ice cover, making it more vulnerable. Finally, we show that if the Arctic sea ice continues to thin, these extreme breakup events could become even more frequent.

Advanced data assimilation (DA) methods, widely used in geophysical and climate studies to merge observations with numerical models, can improve state estimates and consequent forecasts. We interface the deterministic ensemble Kalman filter (DEnKF) to the Lagrangian neXt generation Sea Ice Model, neXtSIM. The ensemble is generated by perturbing the atmospheric and oceanic forcing throughout the simulations and randomly initialized ice cohesion. Our ensemble–DA system assimilates sea ice concentration (SIC) from the Ocean and Sea Ice Satellite Application Facility (OSI-SAF) and sea ice thickness (SIT) from the merged CryoSat-2 and SMOS datasets (CS2SMOS). Because neXtSIM is computationally solved on a time-dependent evolving mesh, it is a challenging application for ensemble–DA. As a solution, we perform the DEnKF analysis on a fixed and regular reference mesh, on which model variables are interpolated before the DA and then back to each member's mesh after the DA. We evaluate the impact of assimilating different types of sea ice observations on the model's forecast skills of the Arctic sea ice by comparing satellite observations and a free-run ensemble in an Arctic winter period, 2019–2020. Significant improvements in modeled SIT indicate the importance of assimilating weekly CS2SMOS SIT, while the improvements of SIC and ice extent are moderate but benefit from daily ingestion of the OSI-SAF SIC. For most of the winter, the correlation between SIT and SIC is weaker, which results in little cross-inference between the two variables in the assimilation step. Overall, the ensemble–DA system based on the stand-alone sea ice model demonstrates the feasibility of winter Arctic sea ice prediction with good computational efficiency. These results open the path toward operational implementation and the extension to multi-year assimilation.

Advanced data assimilation (DA) methods, widely used in geophysical and climate studies to merge observations with numerical models, can improve state estimates and consequent forecasts. We interface the deterministic ensemble Kalman filter (DEnKF) to the Lagrangian neXt generation Sea Ice Model, neXtSIM. The ensemble is generated by perturbing the atmospheric and oceanic forcing throughout the simulations and randomly initialized ice cohesion. Our ensemble–DA system assimilates sea ice concentration (SIC) from the Ocean and Sea Ice Satellite Application Facility (OSI-SAF) and sea ice thickness (SIT) from the merged CryoSat-2 and SMOS datasets (CS2SMOS). Because neXtSIM is computationally solved on a time-dependent evolving mesh, it is a challenging application for ensemble–DA. As a solution, we perform the DEnKF analysis on a fixed and regular reference mesh, on which model variables are interpolated before the DA and then back to each member's mesh after the DA. We evaluate the impact of assimilating different types of sea ice observations on the model's forecast skills of the Arctic sea ice by comparing satellite observations and a free-run ensemble in an Arctic winter period, 2019–2020. Significant improvements in modeled SIT indicate the importance of assimilating weekly CS2SMOS SIT, while the improvements of SIC and ice extent are moderate but benefit from daily ingestion of the OSI-SAF SIC. For most of the winter, the correlation between SIT and SIC is weaker, which results in little cross-inference between the two variables in the assimilation step. Overall, the ensemble–DA system based on the stand-alone sea ice model demonstrates the feasibility of winter Arctic sea ice prediction with good computational efficiency. These results open the path toward operational implementation and the extension to multi-year assimilation.

Sea ice is a key component of the Earth’s climate system as it modulates the energy exchanges and associated feedback processes at the air-sea interface in polar regions. These exchanges strongly depend on openings in the sea ice cover, which are associated with fine-scale sea ice deformations, but the importance of these processes remains poorly understood as most numerical models struggle to represent these deformations without using very costly horizontal resolutions ( 2 km). In this study, we present results from a 12 km resolution ocean–sea-ice coupled model, the first that uses a brittle rheology to represent the mechanical behaviour of sea ice. Using this rheology enables the reproduction of the observed characteristics and complexity of fine-scale sea ice deformations with little dependency on the mesh resolution. We evaluate and discuss the Arctic sea ice mass balance of this coupled model for the period 2000–2018. We first assess sea ice quantities relevant for climate (volume, extent and drift) and find that they are consistent with satellite observations. We evaluate components of the mass balance for which observations are available, i.e. sea ice volume export through Fram Strait and winter mass balance in the Arctic marginal seas for the period 2003–2018. The model performs well, particularly for the dynamic contribution to the winter mass balance. We discuss the relative contributions of dynamics and thermodynamics to the sea ice mass balance in the Arctic Basin for 2000–2018. Benefitting from the model's ability to reproduce fine-scale sea ice deformations, we estimate that the formation of sea ice in leads and polynyas contributes to 25 %–35 % of the total ice growth in pack ice from January to March, with a significant increase over 2000–2018. This coupled framework opens up new opportunities to understand and quantify the interplay between small-scale sea ice dynamics and ocean properties.

International audience; The drift and deformation of sea ice is a key aspect of the overall state of the ice cover. Large-scale drift redistributes ice, affecting where it forms, melts, and is collected, while small scale deformation opens up leads and builds ridges, which influence virtually all interactions between the atmosphere, ocean, and ice in ice-covered areas.

International audience; Arctic sea ice is thinning (Meier, 2017) in conjunction with the decrease in the area covered by thick multiyear ice (MYI) (Kwok, 2018), which is replaced by thinner first-year ice (FYI) that is more mobile and less dynamically stable (Rampal et al., 2009; J. Zhang et al., 2012). This makes the ice cover more vulnerable to intense winds breaking up the sea ice. In the Beaufort Sea in particular, the loss of MYI may contribute to the earlier onset of the melt season in recent years (Johnson & Eicken, 2016). When sea ice breaks up, it exposes the underlying warmer ocean within narrow, linear openings in the ice cover known as leads. This has important consequences for air-sea exchange, ocean eddy generation and dynamics, sea ice production, and Arctic Ocean properties in general (Cohanim et al., 2021; Graham et al., 2019; Nguyen et al., 2009), especially during the winter months when heat fluxes over sea ice are generally small (Andreas & Cash, 1999). In addition, breakup in winter weakens the ice cover, potentially preconditioning the minimum ice extent in summer (Y. Zhang et al., 2018; Babb et al., 2019) and thus creating a positive feedback to Arctic amplification (Dai et al., 2019). Therefore, extreme breakup events are of crucial interest for understanding the seasonal and long-term evolution of Arctic sea ice, which in turn affect weather, ecosystems, and local communities in polar regions and beyond (Forbes et al., 2016; Vihma, 2014).

Advanced data assimilation methods can improve the forecast of Arctic sea ice, which has been widely used in climate modeling systems to merge observations into simulations. We apply the deterministic Ensemble Kalman filter (DEnKF) to a Lagrangian sea ice model, neXtSIM for sea ice forecast. neXtSIM is computationally solved on a time-dependent evolving mesh, causing a key challenge for applying the EnKF since the mesh grid number and positions are generally different in each ensemble member. The DEnKF analysis is performed on a fixed reference mesh, where model variables are interpolated between the reference mesh and the individual ensemble meshes before and after the assimilation. An ensemble-DA forecasting system for Arctic sea ice forecast based on neXtSIM is built by assimilating the OSI-SAF sea ice concentration (SIC) and the CS2SMOS sea ice thickness (SIT). The ensemble is generated by perturbing atmospheric and oceanic forcing online throughout the forecast. We evaluate the impact of sea-ice assimilation on the Arctic winter sea-ice forecast skills against the satellite observations and a free run during the 2019-2020 Arctic winter. Significant improvements in modeled SIT indicate the importance of assimilating CS2SMOS thickness. While the improvement of SIC and ice extend are clearly observed only in the case with daily assimilating OSI-SAF SIC, which avoids the constraint of daily loaded ocean variables. We found that assimilating a special observation gives the best forecast skill of the relevant variables. With a proper assimilation strategy, neXtSIM as a stand-alone sea ice model could perform computationally efficiently and maintain good forecast skills compared with coupled models.

Short-term sea ice predictability is challenging due to the lack of constraints on ice deformation features (open leads and ridges) at kilometre scale. Deformation observations capture these small-scale features and have the potential to improve the predictability. A new method for assimilation of satellite-derived sea ice deformation into the neXt generation Sea Ice Model (neXtSIM) is presented. Ice deformation provided by the Copernicus Marine Environmental Monitoring Service is computed from sea ice drift derived from Synthetic Aperture Radar at a spatio-temporal resolution of 10 km and 24 hours. We show that high values of ice deformation can be interpreted as reduced ice concentration and increased ice damage – scalar variables of neXtSIM. The proof-of-concept assimilation scheme uses a data nudging approach and deterministic forecasting with one member. Assimilation and forecasting experiments are run on example observations from January 2021 and show improvement of neXtSIM skills to predict sea ice deformation in 3–5 days horizon. It is demonstrated that neXtSIM is also capable of extrapolating the assimilated information in space — gaps in spatially discontinuous satellite observations of deformation are filled with a realistic pattern of ice cracks, confirmed by later satellite observations. The experiments also indicate that reduction in sea ice concentration plays a bigger role in improving ice deformation forecast on synoptic scales. Limitations and usefulness of the proposed assimilation approach are discussed in a context of ensemble forecasts. Pathways to estimate intrinsic predictability of sea ice deformation are proposed.

We present a new brittle rheology and an accompanying numerical framework for large-scale sea-ice modelling. We have based this rheology on a Bingham-Maxwell constitutive model and the Maxwell-Elasto-Brittle (MEB) rheology for sea ice. The key strength of the MEB rheology is its ability to represent the scaling properties of simulated sea-ice deformation in space and time. The new rheology we propose here, which we refer to as the brittle Bingham-Maxwell rheology (BBM), represents a further evolution of the MEB rheology. We developed BBM to address two main shortcomings of the MEB rheology and numerical implementation: excessive thickening of the ice in model runs longer than about one winter and a relatively high computational cost. The BBM addresses these shortcomings by demanding that the ice deforms under convergence in a purely elastic manner when internal stresses lie below a given compressive threshold. It also improves numerical performance by introducing an explicit scheme to solve the ice momentum equation. We show that using an implementation of BBM in the neXtSIM sea-ice model, the model gives reasonable long-term evolution of the Arctic sea-ice cover. It also gives very good deformation fields and statistics compared to satellite observations.

As the sea‐ice modeling community is shifting to advanced numerical frameworks, developing new sea‐ice rheologies, and increasing model spatial resolution, ubiquitous deformation features in the Arctic sea ice are now being resolved by sea‐ice models. Initiated at the Forum for Arctic Modeling and Observational Synthesis, the Sea Ice Rheology Experiment (SIREx) aims at evaluating state‐of‐the‐art sea‐ice models using existing and new metrics to understand how the simulated deformation fields are affected by different representations of sea‐ice physics (rheology) and by model configuration. Part 1 of the SIREx analysis is concerned with evaluation of the statistical distribution and scaling properties of sea‐ice deformation fields from 35 different simulations against those from the RADARSAT Geophysical Processor System (RGPS). For the first time, the viscous‐plastic (and the elastic‐viscous‐plastic variant), elastic‐anisotropic‐plastic, and Maxwell‐elasto‐brittle rheologies are compared in a single study. We find that both plastic and brittle sea‐ice rheologies have the potential to reproduce the observed RGPS deformation statistics, including multi‐fractality. Model configuration (e.g., numerical convergence, atmospheric representation, spatial resolution) and physical parameterizations (e.g., ice strength parameters and ice thickness distribution) both have effects as important as the choice of sea‐ice rheology on the deformation statistics. It is therefore not straightforward to attribute model performance to a specific rheological framework using current deformation metrics. In light of these results, we further evaluate the statistical properties of simulated Linear Kinematic Features in a SIREx Part 2 companion paper. Plain Language Summary: The ice in the Arctic Ocean is not continuous: it is broken into individual pieces of ice (floes). As the winds and ocean currents continually move these ice floes, they get piled up together or pushed away from each other, forming regions of increased ice thickness (ridges) or regions of open water (leads). These leads and ridges (ice deformations) are important features of the Arctic pack ice because they control the amount of energy that can be exchanged between the atmosphere and the ocean. Current climate models cannot simulate individual ice floes and their deformations. Instead, various methods are used to represent the movement and deformation of the Arctic sea‐ice cover. The goal of the Sea Ice Rheology Experiment (SIREx) is to compare these different methods and evaluate the ability of a large number of sea‐ice models to reproduce observed sea‐ice deformations from satellite imagery. SIREx is divided in two parts. In Part 1 (this study), we evaluate how the intensity of ice deformations varies in space and time. In Part 2 (companion paper), we track and evaluate the occurrence of specific deformation features. With this work, we show how to improve sea‐ice models for realistic simulations of sea‐ice deformations. Key Points: Power law scaling and multi‐fractality of deformations in space and time can be achieved by both plastic and brittle sea‐ice rheologies Scaling statistics of simulated sea‐ice deformation fields depend on the model configuration and physical parameterizations Finite‐difference plastic models need to be run at higher resolution than observations to agree with the observed deformation statistics

The thick multi-year sea ice that once covered large parts of the Arctic Ocean is being replaced by thinner and weaker first-year ice, making it increasingly vulnerable to breakup by storms. Here we use a sea ice model to investigate the driving mechanisms behind a large sea-ice breakup event in the Beaufort Sea in response to a series of storms during February–March 2013.These simulations are the first to successfully reproduce the timing, location and propagation of sea-ice leads associated with storm-induced breakup. We found that rheology in the sea-ice model and horizontal resolution in the atmospheric model are both crucial in accurately simulating such breakup events. The sensitivity of the breakup to the initial sea-ice thickness indicates that large breakup events will become more frequent as Arctic sea ice continues to thin. Here we show that large breakup events during winter have a significant impact on ice growth through enhanced air-sea fluxes in open leads, and enhanced drift speeds which increase the export of old, thick ice out of the Beaufort Sea. Overall, this results in a thinner and weaker ice cover that may precondition earlier breakup in spring and accelerate sea-ice loss.

We study the response of the Lagrangian sea ice model neXtSIM to the uncertainty in the sea surface wind and sea ice cohesion. The ice mechanics in neXtSIM is based on a brittle-like rheological framework. The study considers short-term ensemble forecasts of the Arctic sea ice from January to April 2008. Ensembles are generated by perturbing the wind inputs and ice cohesion field both separately and jointly. The resulting uncertainty in the probabilistic forecasts is evaluated statistically based on the analysis of Lagrangian sea ice trajectories as sampled by virtual drifters seeded in the model to cover the Arctic Ocean and using metrics borrowed from the search-and-rescue literature. The comparison among the different ensembles indicates that wind perturbations dominate the forecast uncertainty i.e. the absolute spread of the ensemble, while the inhomogeneities in the ice cohesion field significantly increase the degree of anisotropy in the spread i.e. trajectories drift differently in different directions. We suggest that in order to get a full flavor of uncertainties in a sea ice model with brittle-like rheologies, to predict sea ice drift and trajectories, one should consider using ensemble-based simulations where both wind forcing and sea ice cohesion are perturbed., Comment: 18 pages, 10 figures

Simulating sea-ice drift and deformation in the Arctic Ocean is still a challenge because of the multi-scale interaction of sea-ice floes that compose the Arctic sea ice cover. The Sea Ice Rheology...

As the sea-ice modeling community is shifting to advanced numerical frameworks, developing new sea-ice rheologies, and increasing model spatial resolution, ubiquitous deformation features in the Ar...

Sea ice is a key component of the earth’s climate system as it modulates air-sea interactions in polar regions. These interactions strongly depend on openings in the sea ice cover, which are associated with fine-scale sea ice deformations. Visco-plastic sea ice rheologies used in most numerical models struggle at representing these fine-scale sea ice dynamics without going to very costly horizontal resolutions (~1km). A solution is to use damage propagation sea ice models, which were shown to reproduce well sea ice deformations with little dependency on the mesh resolution. Here we present results from the first ocean--sea-ice coupled model using a rheology with damage propagation. The ocean component is the NEMO-OPA model. The sea ice component is neXtSIM, introducing the newly developed Brittle Bingham-Maxwell rheology. Results show that sea ice dynamics are very well represented from large scales (sea ice drift) to small-scales (sea ice deformation). Sea ice properties relevant for climate, i.e volume and area, also show a remarkable match with satellite observations. This coupled framework opens new opportunities to quantify the impact of small-scale sea ice dynamics on ice-ocean interactions.

Simulating sea-ice drift and deformation in the Arctic Ocean is still a challenge because of the multi-scale interaction of sea-ice floes that compose the Arctic sea ice cover. The Sea Ice Rheology Experiment (SIREx) is a model intercomparison project formed within the Forum of Arctic Modeling and Observational Synthesis (FAMOS) to collect and design skill metrics to evaluate different recently suggested approaches for modeling linear kinematic features (LKFs) and provide guidance for modeling small-scale deformation. In this contribution, spatial and temporal properties of LKFs are assessed in 33 simulations of state-of-the-art sea ice models (VP/EVP,EAP, and MEB) and compared to deformation features derived from RADARSAT Geophysical Processor System (RGPS).All simulations produce LKFs, but only very few models realistically simulate at least some statistics of LKF properties such as densities, lengths, lifetimes, or growth rates. All SIREx models overestimate the angle of fracture between conjugate pairs of LKFs pointing to inaccurate model physics. The temporal and spatial resolution of a simulation and the spatial resolution of atmospheric forcing affect simulated LKFs as much as the model's sea ice rheology and numerics. Only in very high resolution simulations (≤2km) the concentration and thickness anomalies along LKFs are large enough to affect air-ice-ocean interaction processes.

The neXtSIM-F forecast platform entered into service as part of CMEMS (as product ARCTIC_ANALYSISFORECAST_PHY_ICE_002_011) in July 2020, using the neXtSIM sea ice model . It is a stand-alone sea ice model, forced with atmospheric fields from ECMWF and with ocean fields from TOPAZ4. At that time (July 2021) the model was using the Maxwell Elasto Brittle (MEB) sea ice rheology in its dynamical core. In December 2020, the forecast was upgraded to use the Brittle Bingham Maxwell (BBM) rheology, result in significant improvements to the physical results and in numerical performance and stability. We will present results obtained using this new rheology.

As sea ice extent decreases in the Arctic, surface ocean waves have more time and space to develop and grow, exposing the marginal ice zone (MIZ) to more frequent and more energetic wave events. Waves can fragment the ice cover over tens of kilometres, and the prospect of increasing wave activity has sparked recent interest in the interactions between wave-induced sea ice fragmentation and lateral melting. The impact of this fragmentation on sea ice dynamics, however, remains mostly unknown, although it is thought that fragmented sea ice experiences less resistance to deformation than pack ice. Here, we introduce a new coupled framework involving the spectral wave model WAVEWATCH III and the sea ice model neXtSIM, which includes a Maxwell elasto-brittle rheology. This rheological framework enables the model to efficiently track and keep a “memory” of the level of sea ice damage. We propose that the level of sea ice damage increases when wave-induced fragmentation occurs. We used this coupled modelling system to investigate the potential impact of such a local mechanism on sea ice kinematics. Focusing on the Barents Sea, we found that the internal stress decrease of sea ice resulting from its fragmentation by waves resulted in a more dynamical MIZ, particularly in areas where sea ice is compact. Sea ice drift is enhanced for both on-ice and off-ice wind conditions. Our results stress the importance of considering wave–sea-ice interactions for forecast applications. They also suggest that waves likely modulate the area of sea ice that is advected away from the pack by the ocean, potentially contributing to the observed past, current and future sea ice cover decline in the Arctic.

International audience; Using a combination of multi-directional SAR and TIR measurements, the Harmony Earth Explorer 10 mission candidate will provide high resolution simultaneous measurements of surface stress, surface currents SST and wave spectra over oceans, 3-D deformation vectors over solid Earth, and time-series of surface elevation changes over volcanic areas and land ice masses. This will serve a series of science objectives aimed at better understating multi-scale processed and feedbacks in the Earth System.

This article describes the observation techniques and suggests processing methods to estimate dynamical sea-ice parameters from data of the Earth Explorer 10 candidate Harmony. The two Harmony satellites will fly in a reconfigurable formation with Sentinel-1D. Both will be equipped with a multi-angle thermal infrared sensor and a passive radar receiver, which receives the reflected Sentinel-1D signals using two antennas. During the lifetime of the mission, two different formations will be flown. In the stereo formation, the Harmony satellites will fly approximately 300 km in front and behind Sentinel-1, which allows for the estimation of instantaneous sea-ice drift vectors. We demonstrate that the addition of instantaneous sea-ice drift estimates on top of the daily integrated values from feature tracking have benefits in terms of interpretation, sampling and resolution. The wide-swath instantaneous drift observations of Harmony also help to put high-temporal-resolution instantaneous buoy observations into a spatial context. Additionally, it allows for the extraction of deformation parameters, such as shear and divergence. As a result, Harmony's data will help to improve sea-ice statistics and parametrizations to constrain sea-ice models. In the cross-track interferometry (XTI) mode, Harmony's satellites will fly in close formation with an XTI baseline to be able to estimate surface elevations. This will allow for improved estimates of sea-ice volume and also enables the retrieval of full, two-dimensional swell-wave spectra in sea-ice-covered regions without any gaps. In stereo formation, the line-of-sight diversity allows the inference of swell properties in both directions using traditional velocity bunching approaches. In XTI mode, Harmony's phase differences are only sensitive to the ground-range direction swell. To fully recover two-dimensional swell-wave spectra, a synergy between XTI height spectra and intensity spectra is required. If selected, the Harmony mission will be launched in 2028.

This article describes the observation techniques and processing methods to estimate dynamical sea-ice parameters from data of the Earth Explorer 10 candidate Harmony. The two Harmony satellites will fly in a reconfigurable formation with Sentinel-1D. Both will be equipped with a multi-angle thermal infra-red sensor and a passive radar receiver, which receive the reflected Sentinel-1D signals using two antennas. During the lifetime of the mission, two different formations will be flown. In the stereo formation, the Harmony satellites will fly approximately 300 km in front and behind Sentinel-1, which allows the estimation of instantaneous sea-ice drift vectors. We demonstrate that the addition of instantaneous sea-ice drift estimates on top of the daily integrated values from speckle tracking have benefits in terms of interpretation, sampling and resolution. Additionally, it allows for the extraction of deformation parameters, such as shear and divergence. As a result, Harmony's data will help improve sea-ice statistics and parametrizations to constrain sea-ice models. In the cross-track interferometry (XTI) mode, Harmony's satellites will fly in close formation with an XTI baseline to be able to estimate surface elevations. This will allow for improved estimates of sea-ice volume, and also enable for the first time to retrieve full two-dimensional swell-wave spectra in sea-ice covered regions without any gap. In stereo formation, the line-of-sight diversity allows to get swell in both directions using traditional velocity bunching approaches. In XTI mode, Harmony's phase differences are only sensitive to the ground-range direction swell. To fully recover two-dimensional swell-wave spectra, a synergy between XTI height spectra and intensity spectra is required. If selected, the Harmony mission will be launched in 2028.

The neXtSIM-F forecast system consists of a stand-alone sea ice model, neXtSIM, forced by the TOPAZ ocean forecast and the ECMWF atmospheric forecast, combined with daily data assimilation.neXtSIM is a novel sea ice model which is able to reproduce sea ice deformation properties and statistics, such as spatial localisation and temporal intermittency,even at relatively low resolutions. For our forecast we run it at 10km resolution, over a pan-Arctic domain. We assimilate OSISAF SSMI and AMSR2 sea ice concentration products and the SMOS sea ice thickness product by modifying the initial conditions daily and adding a compensating heat flux to prevent removed ice growing back too quickly. We present an evaluation of the platform over the period from November 2018 to present, looking at sea ice drift and concentration and extent, and thin ice thickness.neXtSIM-F is scheduled to become part of the CMEMS Arctic Marine Forecast Center in June 2020.

The decrease in Arctic sea ice extent is associated with an increase of the area where sea ice and open ocean interact, commonly referred to as the Marginal Ice Zone (MIZ). In this area, sea ice is particularly exposed to waves that can penetrate over tens to hundreds of kilometres into the ice cover. Waves are known to play a major role in the fragmentation of sea ice in the MIZ, and the interactions between wave-induced sea ice fragmentation and lateral melting have received particular attention in recent years. The impact of this fragmentation on sea ice dynamics, however, remains mostly unknown, although it is thought that fragmented sea ice experiences less resistance to deformation than pack ice. In this presentation, we will introduce a new coupled framework involving the spectral wave model WAVEWATCH III and the sea ice model neXtSIM, which includes a Maxwell-Elasto Brittle rheology. We use this coupled modelling system to investigate the potential impact of wave-induced sea ice fragmentation on sea ice dynamics. Focusing on the Barents Sea, we find that the decrease of the internal stress of sea ice resulting from its fragmentation by waves results in a more dynamical MIZ, in particular in areas where sea ice is compact. Sea ice drift is enhanced for both on-ice and off-ice wind conditions. Our results stress the importance of considering wave–sea-ice interactions for forecast applications. They also suggest that waves likely modulate the area of sea ice that is advected away from the pack by ocean (sub-)mesoscale eddies near the ice edge, potentially contributing to the observed past, current and future sea ice cover decline in the Arctic.

In this paper we explore several statistical properties of the observed and simulated Arctic sea-ice lead-fraction, as well as the statistics of simulated Arctic ocean-atmosphere heat fluxes. We first show that the probability density function (PDF) and the monofractal spatial scaling of the observed lead fraction in the Central Arctic are both well represented by our model, neXtSIM. Given that the heat flux through leads may be up to two orders of magnitude larger than that through unbroken ice we then explore the statistical properties (PDF and spatial scaling) of the heat fluxes simulated by neXtSIM. We demonstrate that the modelled heat fluxes present a multifractal scaling in the Central Arctic, where heat fluxes through leads dominate the high-flux tail of the PDF. In the Central Arctic, the high-flux tail of the PDF is dominated by an exponential decay, which we attribute to the presence of coastal polynyas. Finally, we show that the scaling of simulated lead fraction and heat fluxes depend weakly on the model resolution and discuss the role sub-grid scale parameterisations of the ice heterogeneity may have in improving this result.

Continuum sea ice models have been applied close to the presumed limits of their validity for many years, yet they remain compatible with current observations. The resolution requirements for sea ice models varies considerably depending on the application (e.g., large ensembles, paleoclimate simulations, short-range forecasting), and therefore continuum models will likely remain useful for many years to come. Meanwhile, it is highly desirable to explore the potential of DEMs. These models are expected to be more physically faithful at the highest resolutions envisioned for sea ice in ESMs, provided they incorporate all the required processes. DEMs may also prove more efficient for some new computer architectures. Such perspectives highlight the need for the sea ice modeling community to have a clear and consistent vision of the future evolution of HPC systems. Sea ice models are used for many different purposes and therefore benefit from modularity, which allows the activation or exclusion of parameterizations and code features. Thus, users can adjust model complexity to fit their specific application. Considering limited human resources among core sea ice modeling groups, engagement of the wider community has proven a very efficient way to advance large-scale sea ice models. However, there is still scope for further integration of the wider community in model development activities. An important feature of the Laugarvatn sea ice modeling workshop was the open minded atmosphere in which very different views were exchanged. The workshop successfully brought together model developers and users of sea ice models for Earth system modeling, operational forecasting and (re)analyses. International sea ice modeling workshops such as this foster collaboration and community engagement in the field of sea ice modeling. A recommendation from this workshop is that the exercise should be repeated every 2-3 years to maintain community engagement, exchange cutting-edge ideas, and reinforce collaborative momentum. © 2020 American Meteorological Society.

We present a sensitivity analysis and discuss the probabilistic forecast capabilities of the novel sea ice model neXtSIM used in hindcast mode. The study pertains to the response of the model to the uncertainty on winds using probabilistic forecasts of ice trajectories. neXtSIM is a continuous Lagrangian numerical model that uses an elasto-brittle rheology to simulate the ice response to external forces. The sensitivity analysis is based on a Monte Carlo sampling of 12 members. The response of the model to the uncertainties is evaluated in terms of simulated ice drift distances from their initial positions, and from the mean position of the ensemble, over the mid-term forecast horizon of 10 days. The simulated ice drift is decomposed into advective and diffusive parts that are characterised separately both spatially and temporally and compared to what is obtained with a free-drift model, that is, when the ice rheology does not play any role in the modelled physics of the ice. The seasonal variability of the model sensitivity is presented and shows the role of the ice compactness and rheology in the ice drift response at both local and regional scales in the Arctic. Indeed, the ice drift simulated by neXtSIM in summer is close to the one obtained with the free-drift model, while the more compact and solid ice pack shows a significantly different mechanical and drift behaviour in winter. For the winter period analysed in this study, we also show that, in contrast to the free-drift model, neXtSIM reproduces the sea ice Lagrangian diffusion regimes as found from observed trajectories. The forecast capability of neXtSIM is also evaluated using a large set of real buoy's trajectories and compared to the capability of the free-drift model. We found that neXtSIM performs significantly better in simulating sea ice drift, both in terms of forecast error and as a tool to assist search and rescue operations, although the sources of uncertainties assumed for the present experiment are not sufficient for complete coverage of the observed IABP positions.

Numerical models solved on adaptive moving meshes have become increasingly prevalent in recent years. Motivating problems include the study of fluids in a Lagrangian frame and the presence of highly localized structures such as shock waves or interfaces. In the former case, Lagrangian solvers move the nodes of the mesh with the dynamical flow; in the latter, mesh resolution is increased in the proximity of the localized structure. Mesh adaptation can include remeshing, a procedure that adds or removes mesh nodes according to specific rules reflecting constraints in the numerical solver. In this case, the number of mesh nodes will change during the integration and, as a result, the dimension of the model's state vector will not be conserved. This work presents a novel approach to the formulation of ensemble data assimilation (DA) for models with this underlying computational structure. The challenge lies in the fact that remeshing entails a different state space dimension across members of the ensemble, thus impeding the usual computation of consistent ensemble-based statistics. Our methodology adds one forward and one backward mapping step before and after the ensemble Kalman filter (EnKF) analysis, respectively. This mapping takes all the ensemble members onto a fixed, uniform reference mesh where the EnKF analysis can be performed. We consider a high-resolution (HR) and a low-resolution (LR) fixed uniform reference mesh, whose resolutions are determined by the remeshing tolerances. This way the reference meshes embed the model numerical constraints and are also upper and lower uniform meshes bounding the resolutions of the individual ensemble meshes. Numerical experiments are carried out using 1-D prototypical models: Burgers and Kuramoto–Sivashinsky equations and both Eulerian and Lagrangian synthetic observations. While the HR strategy generally outperforms that of LR, their skill difference can be reduced substantially by an optimal tuning of the data assimilation parameters. The LR case is appealing in high dimensions because of its lower computational burden. Lagrangian observations are shown to be very effective in that fewer of them are able to keep the analysis error at a level comparable to the more numerous observers for the Eulerian case. This study is motivated by the development of suitable EnKF strategies for 2-D models of the sea ice that are numerically solved on a Lagrangian mesh with remeshing.

In this paper, we evaluate the neXtSIM sea ice model with respect to the observed scaling invariance properties of sea ice deformation in the spatial and temporal domains. Using an Arctic set-up with realistic initial conditions, state-of-the-art atmospheric reanalysis forcing and geostrophic currents retrieved from satellite data, we show that the model is able to reproduce the observed properties of these scaling in both the spatial and temporal do- mains over a wide range of scales and, for the first time, their multi-fractality. The variability of these properties during the winter season are also captured by the model. We also show that the simulated scaling exhibit a space-time coupling, a suggested property of brittle deformation at geophysical scales. The ability to reproduce the multi-fractality of these scaling is crucial in the context of downscaling model simulation outputs to infer sea ice variables at the sub-grid scale, and also has implication in modeling the statistical properties of deformation-related quantities such as lead fractions, and heat and salt fluxes.

In this paper, we evaluate the neXtSIM sea ice model with respect to the observed scaling invariance properties of sea ice deformation in the spatial and temporal domains. Using an Arctic setup with realistic initial conditions, state-of-the-art atmospheric reanalysis forcing and geostrophic currents retrieved from satellite data, we show that the model is able to reproduce the observed properties of this scaling in both the spatial and temporal domains over a wide range of scales, as well as their multi-fractality. The variability of these properties during the winter season is also captured by the model. We also show that the simulated scaling exhibits a space–time coupling, a suggested property of brittle deformation at geophysical scales. The ability to reproduce the multi-fractality of this scaling is crucial in the context of downscaling model simulation outputs to infer sea ice variables at the sub-grid scale and also has implications for modeling the statistical properties of deformation-related quantities, such as lead fractions and heat and salt fluxes.

We characterize sea-ice drift by applying a Lagrangian diffusion analysis to buoy trajectories from the International Arctic Buoy Programme (IABP) dataset and from two different models: the standalone Lagrangian sea-ice model neXtSIM and the Eulerian coupled ice–ocean model used for the TOPAZ reanalysis. By applying the diffusion analysis to the IABP buoy trajectories over the period 1979–2011, we confirm that sea-ice diffusion follows two distinct regimes (ballistic and Brownian) and we provide accurate values for the diffusivity and integral timescale that could be used in Eulerian or Lagrangian passive tracers models to simulate the transport and diffusion of particles moving with the ice. We discuss how these values are linked to the evolution of the fluctuating displacements variance and how this information could be used to define the size of the search area around the position predicted by the mean drift. By comparing observed and simulated sea-ice trajectories for three consecutive winter seasons (2007–2011), we show how the characteristics of the simulated motion may differ from or agree well with observations. This comparison illustrates the usefulness of first applying a diffusion analysis to evaluate the output of modeling systems that include a sea-ice model before using these in, e.g., oil spill trajectory models or, more generally, to simulate the transport of passive tracers in sea ice.

We present a sensitivity analysis, and discuss the probabilistic forecast capabilities, of the novel sea ice model neXtSIM. The study pertains to the response of the model to the uncertainty on winds using probabilistic forecasts of ice trajectories. neXtSIM is a continuous Lagrangian numerical model, and uses an elasto-brittle rheology to simulate the ice response to external forces. The sensitivity analysis is based on a Monte Carlo sampling of 12 members. The response of the model to the uncertainties is evaluated in terms of simulated ice drift distances from their initial positions, and from the mean position of the ensemble, over the mid-term forecast horizon of 10-days. The simulated ice drift is decomposed into advective and diffusive parts that are characterised separately both spatially and temporally and compared to what is obtained with a free-drift model, that is, when the ice rheology does not play any role on the modelled physics of the ice. The seasonal variability of the model sensitivity is presented, and shows the role of the ice compactness and rheology in the ice drift response at both local and regional scales in Arctic. Indeed, the ice drift simulated by neXtSIM in summer is close to the one obtained with the free-drift model, while the more compact and solid ice pack shows a significantly different mechanical and drift behaviour in winter. For the winter period analysed in this study, we also show that, in contrast to of free-drift model, neXtSIM reproduces the sea ice Lagrangian diffusion regimes as found from observed trajectories. The forecast capability of neXtSIM is also evaluated using a large set of real buoys' trajectories, and compared to the capability of the free-drift model. We found that neXtSIM performs significantly better in simulating sea ice drift, both in terms of forecast error and as a tool to assist search-and-rescue operations, although the sources of uncertainties assumed for the present experiment are not sufficient for a complete coverage of the observed IABP positions.

International audience; We present a parallel implementation framework for a new dynamic/thermodynamic seaice model, called neXtSIM, based on the Elasto-Brittle rheology and using an adaptive mesh. The spatial discretisation of the model is done using the finite-element method. The temporal discretisation is semi-implicit and the advection is achieved using either a pure Lagrangian scheme or an Arbitrary Lagrangian Eulerian scheme (ALE). The parallel implementation presented here focuses on the distributed-memory approach using the message-passing library MPI. The efficiency and the scalability of the parallel algorithms are illustrated by the numerical experiments performed using up to 500 processor cores of a cluster computing system. The performance obtained by the proposed parallel implementation of the neXtSIM code is shown being sufficient to perform simulations for state-of-the-art sea ice forecasting and geophysical process studies over geographical domain of several millions squared kilometers like the Arctic region.

We propose a new satellite mission that uses a near-nadir Ka-band Doppler radar to measure surface currents, ice drift and ocean waves at spatial scales of 40 km and more, with snapshots at least every day for latitudes 75 to 82, and every few days otherwise. The use of incidence angles at 6 and 12 degrees allows a measurement of the directional wave spectrum which yields accurate corrections of the wave-induced bias in the current measurements. The instrument principle, algorithm for current velocity and mission performance are presented here. The proposed instrument can reveal features on tropical ocean and marginal ice zone dynamics that are inaccessible to other measurement systems, as well as a global monitoring of the ocean mesoscale that surpasses the capability of today’s nadir altimeters. Measuring ocean wave properties facilitates many applications, from wave-current interactions and air-sea fluxes to the transport and convergence of marine plastic debris and assessment of marine and coastal hazards.

International audience; Sea ice drift strongly influences sea ice thickness distribution and indirectly controls air-sea ice-ocean interactions. Estimating sea ice drift over a large range of spatial and temporal scales is therefore needed to characterize the properties of sea ice dynamics and to better understand the ongoing changes of the climate in the polar regions. An efficient algorithm is developed for processing SAR data based on the combination of feature tracking (FT) and pattern matching (PM) techniques. The main advantage of the combination is that the FT rapidly provides the first guess estimate of ice drift in a few unevenly distributed keypoints, and PM accurately provides drift vectors on a regular or irregular grid. Thorough sensitivity analysis of the algorithm is performed, and optimal sets of parameters are suggested for retrieval of sea ice drift on various spatial and temporal scales. The algorithm has rather high accuracy (error is below 300 m) and high speed (the time for one image pair is 1 min), which opens new opportunities for studying sea ice kinematic processes. The ice drift can now be efficiently observed in the Lagrangian coordinate system on an irregular grid and, therefore, used for pointwise evaluation of the models running on unstructured meshes or for assimilation into Lagrangian models.

In this paper we describe a waves-in-ice model which calculates ice breakage and the wave radiation stress (WRS) that is coupled to the new sea ice model neXtSIM, which is based on the Elasto-Brittle (EB) rheology. We highlight some numerical issues involved in the coupling, and investigate the impact of the WRS, and of modifying the EB to lower the stiffness of the ice in the area where the ice has broken up (the marginal ice zone, or MIZ). In experiments in the absence of wind, we find that wind waves can produce noticeable movement in loose ice (concentration around 70 %) – up to 36 km, depending on the material parameters of the ice that are used, and the dynamical model used for the broken ice. Swell waves do not produce any movement, as they are attenuated too little to induce a very large WRS. In the presence of wind, we find that the wind stress dominates the WRS, which while large near the ice edge, decays exponentially away from it. This is in contrast to the wind stress which is applied over a much larger ice area. In this case (when wind is present) the dynamical model for the MIZ has more impact than the WRS, although that effect too is relatively modest. When the stiffness in the MIZ is lowered due to ice breakage, we find that on-ice winds produce more compression in the MIZ than in the pack, while off-ice winds can cause the MIZ to be separated from the pack ice.

In this paper we describe a waves-in-ice model (WIM), which calculates ice breakage and the wave radiation stress (WRS). This WIM is then coupled to the new sea-ice model neXtSIM, which is based on the elasto-brittle (EB) rheology. We highlight some numerical issues involved in the coupling and investigate the impact of the WRS, and of modifying the EB rheology to lower the stiffness of the ice in the area where the ice has broken up (the marginal ice zone or MIZ). In experiments in the absence of wind, we find that wind waves can produce noticeable movement of the ice edge in loose ice (concentration around 70 %) – up to 36 km, depending on the material parameters of the ice that are used and the dynamical model used for the broken ice. The ice edge position is unaffected by the WRS if the initial concentration is higher (≳ 0.9). Swell waves (monochromatic waves with low frequency) do not affect the ice edge location (even for loose ice), as they are attenuated much less than the higher-frequency components of a wind wave spectrum, and so consequently produce a much lower WRS (by about an order of magnitude at least).In the presence of wind, we find that the wind stress dominates the WRS, which, while large near the ice edge, decays exponentially away from it. This is in contrast to the wind stress, which is applied over a much larger ice area. In this case (when wind is present) the dynamical model for the MIZ has more impact than the WRS, although that effect too is relatively modest. When the stiffness in the MIZ is lowered due to ice breakage, we find that on-ice winds produce more compression in the MIZ than in the pack, while off-ice winds can cause the MIZ to be separated from the pack ice.

The Arctic sea ice cover has changed drastically over the last decades. Associated with these changes is a shift in dynamical regime seen by an increase of extreme fracturing events and an acceleration of sea ice drift. The highly non-linear dynamical response of sea ice to external forcing makes modelling these changes and the future evolution of Arctic sea ice a challenge for current models. It is, however, increasingly important that this challenge be better met, both because of the important role of sea ice in the climate system and because of the steady increase of industrial operations in the Arctic. In this paper we present a new dynamical/thermodynamical sea ice model called neXtSIM that is designed to address this challenge. neXtSIM is a continuous and fully Lagrangian model, whose momentum equation is discretised with the finite-element method. In this model, sea ice physics are driven by the combination of two core components: a model for sea ice dynamics built on a mechanical framework using an elasto-brittle rheology, and a model for sea ice thermodynamics providing damage healing for the mechanical framework. The evaluation of the model performance for the Arctic is presented for the period September 2007 to October 2008 and shows that observed multi-scale statistical properties of sea ice drift and deformation are well captured as well as the seasonal cycles of ice volume, area, and extent. These results show that neXtSIM is an appropriate tool for simulating sea ice over a wide range of spatial and temporal scales.