Assimilating sea ice observations into numerical sea ice and climate models has garnered increasing interest, driven by a demand for more comprehensive sea ice records and forecasts in response to a rapidly changing cryosphere. The development of data assimilation (DA) techniques targeted specifically for sea ice, however, has been comparatively limited. The computational requirements and structure of many modern sea ice models, the physical characteristics of key sea ice variables, and the uncertainty and relatively limited scope of assimilated sea ice observations all pose significant challenges for the development and tuning of sea ice DA systems. This work presents a new, lightweight framework for sea ice DA development that couples a flexible ensemble DA software to a single-column, multi-category sea ice model, and reviews several recent applications. Key results include the variable impact of common sea ice observation kinds across different sea ice regime types; the benefits of tailoring DA algorithms to the physical and modeled characteristics of sea ice; and the efficacy of assimilating new kinds of observations, including the ice thickness distribution and sea ice albedo. Collectively, these results highlight the ease of experimentation proffered by this new framework, which enables both novel research and more accessible development in sea ice state estimation and forecasting contexts.
How to cite: Wieringa, M., Rotondo, J., Riedel, C., Anderson, J., and Bitz, C.: Fast, flexible, focused: the case for a single-column sea ice data assimilation framework, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12835, https://doi.org/10.5194/egusphere-egu25-12835, 2025.
The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition was implemented with one year observation of atmosphere, ocean and sea ice, giving us an opportunity to understand the sea ice processes. Due to the missing observation during the expedition, ERA5 atmospheric reanalysis along the MOSAiC drift trajectory, after its validation, is used to force a column sea ice model Icepack, commonly used in coupled climate models. We compare sea ice thickness (SIT) simulations against MOSAiC observation to understand the reasons of SIT simulation misfits fordifferent combinations of two melt pond schemes and three snow redistribution configurations. The three snow redistribution configurations are bulk scheme, snwITDrag scheme and one simulation selection without snow redistribution. In both bulk and snwITDrdg snow redistribution schemes, snow can be lost to leads and open water. In the bulk scheme, snow from level ice can be lost to leads or open water. In snwITDrdg scheme, snow is distributed to different sea ice categories and the scheme also allows wind-driven snow compaction and erosion. The two melt pond schemes are TOPO scheme and LVL scheme, which differ in the distribution of melt water. The results show that Icepack can reproduce sea ice growth in the winter and spring periods of MOSAiC expedition. Icepack without snow redistribution scheme simulates excessive snow ice formation and its contribution to sea ice mass balance, resulting in thicker SIT simulation than the observation in spring. Applying snow redistribution schemes in Icepack reduces snow-ice formation while enhancing congelation rate. The bulk snow redistribution scheme improves the SIT simulation in winter and spring, while the bias is larger in simulations using the snwITDrdg scheme. During summer time, Icepack underestimates the sea ice surface albedo, resulting in an underestimation of SIT at the end of simulation. The simulations with TOPO scheme present a more reasonable melt pond evolution than the LVL scheme, resulting in a smaller bias in SIT simulation. Sensitivity analysis and parameter estimation are required to improve sea ice thickness simulation. Some earlier results using adjoint model to improve sea ice simulation will also be presented.
How to cite: Lu, Y., Zhao, J., Wang, X., and Giering, R.: Influence of Snow Redistribution and Melt Pond Schemes on Sea Ice Thickness Simulation during MOSAiC Expedition, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21072, https://doi.org/10.5194/egusphere-egu25-21072, 2025.
In the sea ice model Finite-Element Sea Ice Model (FESIM), a part of the Finite-Element Sea ice Ocean Model (FESOM), sea ice albedo is treated as a tuning parameter defined by four constant values depending on snow cover and surface temperature. This parametrisation is too simple to capture the spatiotemporal variability in sea ice albedo observed via satellites. Our work aims to improve this parametrisation by discovering an interpretable, physically-consistent equation for sea ice albedo using symbolic regression, an interpretable machine learning technique, combined with physical constraints. Leveraging pan-Arctic satellite and reanalyses data from 2013 to 2020, we apply sequential feature selection to identify the most informative input variables for sea ice albedo. With sequential feature selection, we develop parsimonious models that perform well with as few input variables as possible. To understand how additional model complexity reduces error, we evaluate our discovered equations against baseline models with different complexities, such as multilayer perceptron neural networks and polynomials on an error-complexity plane, identifying the models on the Pareto front. Our results indicate that parsimonious models demonstrate better generalisation to unseen data than models using the full set of input variables. Compared to the current FESIM parametrisation, our best equation reduces the mean squared error by about 51%, while excelling in balancing error and complexity. Unlike neural networks, our equation allows for further regional and seasonal analyses due to its inherent interpretability by fine-tuning the coefficients representing the weights of each term and input variable. Through the synergy of observations with machine learning, we aim to deepen the process-level understanding of the Arctic Ocean’s surface radiative budget and reduce uncertainty in climate projections.
How to cite: Atmojo, D., Weigel, K., Grundner, A., Holland, M., and Eyring, V.: Data-driven equation discovery of a sea ice albedo parametrisation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4127, https://doi.org/10.5194/egusphere-egu25-4127, 2025.
The rapid changes occurring in Arctic sea ice influence climate and marine ecosystems, mid-latitude weather on timescales from weeks to months, and human activities, further motivating the need for accurate forecasts. A novel generation of sea ice models based on Elasto-Brittle rheologies, such as neXtSIM (Rampal et al, 2016), successfully represents sea-ice processes, with a remarkable accuracy at the mesoscale, for resolutions of about 10 km. However, these models are computationally expensive, limiting their practical application for long-term forecasting. To address this challenge, we leverage deep learning techniques to build an accurate and computationally affordable surrogate of the physical model.
Following up from the initial work by Durand et al., 2024 on univariate surrogate of the sea-ice thickness (SIT) in neXtSIM, we present here a multivariate surrogate model designed to emulate simultaneously SIT, sea-ice concentration (SIC), and sea-ice velocities (SIU and SIV) in the Arctic region. As its core, our deterministic neural-network-based surrogate model uses a U-Net architecture, tailored to the sea-ice forecasting problem. The model is trained on reforecast-like data generated from neXtSIM and atmospheric forcings from ERA5, which help the model to better represent advective and thermodynamic processes. The neural network is trained to predict sea-ice fields with a 12-hour lead time, and it can iteratively be applied to extend predictions for up to a year.
We thoroughly investigate the learning process, providing a detailed analysis of our choice of customized loss function and its optimal parameter values. In particular, we investigate the importance of each predicted variable and perform a feature sensitivity analysis. The forecast skills of our model have been successfully evaluated for lead times of up to one year, using both statistical and physical-dynamical metrics. Our preliminary results indicate that the model demonstrates good prediction capabilities at much lower computational costs than the original physical model. The application of a supervised deep learning approach to sea-ice modeling offers a promising alternative to traditional, computationally intensive methods. The positive results from our model's predictions underscore its potential as a reliable tool for seasonal sea ice forecasting.
Rampal P. et al. “neXtSIM: a new Lagrangian sea ice model”. In: The Cryosphere 10.3 (2016), pp. 1055–1073
Durand C. et al. “Data-driven surrogate modeling of high-resolution sea-ice thickness in the Arctic”. In: The Cryosphere 18.4 (2024), pp 1791-1815
How to cite: Porro, F., Durand, C., Finn, T. S., Bocquet, M., Carrassi, A., and Rampal, P.: Multivariate surrogate model of sea ice in the Arctic region , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16681, https://doi.org/10.5194/egusphere-egu25-16681, 2025.
Arctic sea ice has undergone dramatic changes in recent decades. The decline in sea ice thickness has resulted in more brittle ice, which is increasingly susceptible to deformation by wind and ocean currents. Small-scale features such as sea ice leads and ridges are frequently observed in the field but remain poorly understood. Accurately forecasting these features requires high-resolution sea ice modeling with a horizontal resolution of several kilometers. To address this, a pan-Arctic ultra-high-resolution (~500 m) sea ice-ocean coupled model has been developed. This model is based on the Massachusetts Institute of Technology General Circulation Model (MITgcm) but has been substantially refactored and enhanced to adapt to the heterogeneous many-core architecture of the computing system. The model's Pacific open boundary is positioned north of the Okhotsk Sea, away from the Aleutian Islands, while the Atlantic open boundary is set north of the Strait of Gibraltar to avoid the influence of deep convection processes. The model operates on a three-dimensional grid comprising approximately 15.1 billion points, with around 9 billion wet points. The sea ice component shares the same grid as the ocean model, enabling direct coupling between the two at each grid point. For sea ice thermodynamics, a zero-heat-capacity, one-layer model is employed, while sea ice dynamics are governed by viscous-plastic rheology. The highly nonlinear sea ice momentum equations are solved using a tridiagonal solver combined with a line successive relaxation method, achieving an accuracy of 1.0×10⁻⁵. The nonlinear integration is iterated 10 times, with each iteration allowing a maximum of 500 steps to ensure convergence of the high-resolution solutions. The model demonstrates significant improvements in simulating sea ice ridges compared to lower-resolution models. Validation against IceSAT-2 along-track data reveals strong agreement in both spatial distribution and probability density function, underscoring the model's enhanced capability to capture small-scale sea ice features.
How to cite: Mu, L., Chen, Y., Wang, H., Song, R., Zheng, L., and Chen, X.: Ultra-high resolution pan-Arctic sea ice-ocean coupled simulation on a heterogeneous many-core supercomputer, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7735, https://doi.org/10.5194/egusphere-egu25-7735, 2025.
Arctic sea ice is a mosaic of ice floes whose distribution and thicknesses greatly impact the interaction of sea ice with the atmosphere and the ocean. However, we are still lacking knowledge of the physics to describe the complex interplay of ice floes that are a key characteristic of sea ice. In our contribution, we outline a framework to characterize sea-ice deformation at the floe-scale from observational data by studying the mechanical interaction of multiple identifiable floes. We use Sentinel SAR imagery and ICESat-2 data acquired during the MOSAiC expedition to map ice floes and their thickness in the larger area around Polarstern. This combination of data products allows us to describe the floe-size distribution of floe diameters from tens of kilometers down to tens of meters. With the repeated coverage of SAR imagery, ice motion is tracked and deformation estimates are derived. By combining both floe-size estimates and deformation rates we provide insights into how the floe composition changes in regions that were exposed to deformation and highlight ice fracture as a major source of the power-law distribution of floe sizes. Finally, we present a parameterization of this relationship between floe sizes and ice fracture for large-scale continuum sea-ice models.
How to cite: Hutter, N., Bitz, C., and von Albedyl, L.: Linking the evolution of floe-scale ice characteristics to its deformation history using satellite observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9952, https://doi.org/10.5194/egusphere-egu25-9952, 2025.
Interactions between ocean surface waves and sea ice dictate the width of the marginal ice zone, where new ice formation and increased sea ice melt are present in the winter and summer (respectively). Existing sea ice wave fracture models predict fracture when one of two limits is reached: (i) a maximum strain failure criterion assuming that the ice is a perfectly flexible plate that follows the ocean surface, and (ii) a maximum stress failure criterion assuming that the ice is a perfectly rigid plate that does not deform under the action of buoyancy and gravity forces. The perfectly rigid sea ice plate model is valid for small wavelengths that have a short lever arm but systematically predicts fracture for long wavelengths irrespective of the amplitude because of the long lever arm. Conversely, the flexible plate model is valid for long wavelengths but systematically predicts fracture for short wavelengths because of the unrealistically large strain. In this work, we present a unified sea ice fracture model based on elastic beam theory for the bending of a sea ice plate (or floe) that is valid for all wavelengths. Our approach reduces to the rigid plate and fully flexible model for short and long incoming ocean wavelength limits, respectively. Results using a fully-developed ocean wave field show much smaller strain within the ice plate and a resulting floe size distribution after fracture with a higher mean and no floes in the smallest size categories. This distribution also aligns with correct ice thickness and Young's Modulus dependencies, matching observational evidence, and contrasts with results from perfectly rigid or flexible sea ice plate models.
How to cite: Tremblay, B. and Roach, L.: A unified sea ice fracture model for climate applications, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12255, https://doi.org/10.5194/egusphere-egu25-12255, 2025.
We study at the laboratory scale the rupture of thin floating sheets made of a brittle material under wave induced mechanical forcing. We show that the rupture occurs where the curvature is maximum, and the break up threshold strongly depends on the wave properties. We observe that the corresponding critical stress for fracture depends on the forcing wavelength: our observations are thus incompatible with a critical stress criteria for fracture. Our measurements can rather be rationalized using an energy criteria: a fracture propagates when the material surface energy is lower than the released elastic energy, which depends on the forcing geometry. I will eventually discuss the possible implication for sea ice fracture criterion by ocean waves.
How to cite: Auvity, B., Duchemin, L., Eddi, A., and Perrard, S.: An analog experiment of sea-ice fracture by waves at the laboratory scale., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20343, https://doi.org/10.5194/egusphere-egu25-20343, 2025.
Continuum sea-ice models are increasingly being applied to high-resolution settings, while there are still open questions about the physics governing sea-ice deformation on these resolutions. Simultaneously, discrete element method (DEM) models are now starting to be used to address questions regarding specific processes within sea-ice deformation. A direct comparison of both methods has not been done yet, as the spatial resolution differs on several orders of magnitude and the computational costs of high-resolution DEM simulations over large areas of sea ice are high. Here, we will present a comparison of idealized simulations of sea-ice convergence utilizing both methods. We used the neXtSIM sea-ice model as the continuum model and HiDEM as the DEM model. Sea-ice deformation in neXtSIM is determined by a brittle rheology with Lagrangian sea-ice advection. In HiDEM, the ice is described by spherical particles connected by beams, which can fail as the ice cover locally reaches a critical stress state. In both cases, we simulate the same sea-ice area and use the same forcing, yet the spatial resolution differs. This setup enables us to investigate the sea-ice deformation yielding from both methods. We compare the resulting ice thickness distributions and ice ridge formation patterns and highlight the similarities and differences between both methods.
How to cite: Muchow, M., Ólason, E., and Polojärvi, A.: Exploratory sea-ice simulations: Comparing idealized sea-ice compression simulations using a continuum and discrete element method models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-130, https://doi.org/10.5194/egusphere-egu25-130, 2025.
The continuum viscous-plastic sea-ice model is widely used in climate models for simulating large-scale sea-ice dynamics, usually on grids of several kilometres (> 10km). Recently, there is an increasing interest in modelling small-scale processes that have the potential to impact large-scale dynamics, such as sea-ice iceberg interactions in the context of ice mélange. Ice mélange has not yet been studied in the context of climate models as efficient numerical realizations are missing. To close this gap, we present a hybrid ice-mélange model. In this approach, icebergs in form of particles are coupled to the viscous-plastic sea-ice model by modifying the tensile strength in the presence of icebergs. The icebergs, in the size of several hundreds of meters, are tracked on a subgrid scale, which makes the approach numerically efficient. Based on a series of idealised test cases, we demonstrate that this approach captures relevant small-scale physics such as polynya formation caused by grounded icebergs.
How to cite: Kahl, S. and Mehlmann, C.: A multi-scale approach to model ice mélange, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13483, https://doi.org/10.5194/egusphere-egu25-13483, 2025.
Posters on site: Fri, 2 May, 10:45–12:30 | Hall X4
The PCAPS ORCAS task team is part of the WMO's World Weather Research Programme's PCAPS (Polar Coupled Analysis and Prediction for Services) project. PCAPS builds upon the foundational work of the Polar Prediction Project and its flagship activity, the Year of Polar Prediction, to improve the actionability, impact, and fidelity of environmental forecasting for human and environmental well-being in the Arctic and Antarctic regions. PCAPS ORCAS is a community effort that aims to enhance forecasting capabilities by exploring the potential of new AI techniques. Outcomes from this initiative will contribute to strengthening observing systems, including satellite and field campaign data, to provide better initialisation and validation for sea-ice forecasts.
Recent advances in artificial intelligence are transforming sea-ice forecasting, with AI models demonstrating comparable or superior performance to traditional physics-based approaches while requiring significantly fewer computing resources. These advantages could enable more frequent and timely predictions, benefiting stakeholders. However, the effective development and validation of these AI systems depend heavily on high-quality observational data. AI models are generally trained on reanalysis datasets, and data from observational campaigns--though vital for process understanding--has seen limited integration into these products. Such observations are essential to evaluate the physical realism of AI models and build trust in their predictive capabilities.
The PCAPS ORCAS task team systematically evaluates the observational requirements necessary for next-generation AI-based sea-ice prediction systems. This effort combines historical campaign data analysis with collaborative AI model assessments, focusing particularly on extreme events captured during major observational campaigns such as MOSAiC. We examine how different types of observational data contribute to model initialisation and validation while assessing the physical consistency of AI predictions compared to traditional forecasting systems.
This approach identifies critical gaps in current observing systems and will inform the design of future field campaigns and observation networks, including those proposed for Antarctica InSync and the upcoming fifth International Polar Year. Our recommendations for strengthening polar observing systems specifically address the unique requirements of AI-based prediction systems while maintaining physical consistency in forecasts. These insights are essential for the polar science community as we work to improve the accuracy and reliability of sea-ice predictions in a rapidly changing Arctic and Antarctic environment.
How to cite: Eayrs, C. and Zampieri, L.: Observational Requirements in the Context of AI prediction Systems - a PCAPS ORCAS Task Team, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14272, https://doi.org/10.5194/egusphere-egu25-14272, 2025.
Reliable prediction of short-term Arctic sea ice variation is crucial for ensuring the safety of navigation on Arctic shipping routes. While deep-learning models have demonstrated potential in improving the accuracy of sea ice predictions, many data-driven approaches focus solely on individual aspects of sea ice without considering the interrelationships and underlying physical laws governing various sea ice factors. To address this limitation, we introduce a dual-task prediction model that simultaneously targets sea ice concentration (SIC) and sea ice motion (SIM). Our approach incorporates a novel loss function that enforces dynamic constraints derived from the sea ice control equation, ensuring that predictions of both SIC and SIM are consistent with physical dynamics. We conduct comprehensive comparative experiments to identify the optimal model structure for predicting SIC and SIM. Our findings reveal that a dual-task branching architecture is particularly effective for this purpose, with a post-decoder branch network structure exhibiting the best performance in predicting both SIC and SIM. By integrating the sea ice dynamics equation into the loss function, our models demonstrate enhanced alignment with physical laws, leading to improved predictability and accuracy in SIC and SIM prediction.
How to cite: Wang, Y. and Liu, Q.: Physics-Embedded Deep Convolutional Network: A Novel Approach for Prediction of Sea Ice Concentration and Motion, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3643, https://doi.org/10.5194/egusphere-egu25-3643, 2025.
The relationship between Arctic sea ice and tropical climate variability is a crucial aspect of global climate dynamics. While numerous studies have explored potential links between sea ice concentration (SIC) or sea ice thickness (SIT) and teleconnection indices such as AMO, AO, NAO, ENSO, and PDO, these investigations often faced challenges in fully capturing the complexity of these interactions. For instance, most analyses relied on linear, non-causal methods such as trend matching (although the underlying processes are likely highly nonlinear), or focused on single indices (thus potentially missing more complex interactions when more than one index is considered at once), or analyzed the relationship in aggregate over the entire Arctic region, rather than considering subtle regional differences. Additionally, these teleconnections were often assessed in only one “direction” (e.g., how much ENSO influences SIC), but there is evidence to suggest that there may be two-way interactions at play.
In this study, we address these challenges by proposing a bi-directional, causal, and spatially distributed approach to analyze the relationships between SIC/SIT and eight teleconnection indices. Using transfer entropy (TE), a non-parametric measure of information flow, we quantify the influence of these indices on SIC/SIT and vice versa across multiple lead times. This approach lets us understand how these causal relationships vary at different lead times and over different Arctic regions, to verify whether the various teleconnection indices provide information that is complementary or redundant, and to detect preferential directions in the causal relationship between indices and ice (thus answering the question “who influences whom?”). For instance, our results indicate that the North Atlantic Oscillation is influenced by the Arctic ice more than it itself affects the ice, whereas the relationship is inverted for the Atlantic Multidecadal Oscillation.
Although we focus our analysis on understanding the spatial and temporal variability of Arctic-teleconnection interactions, the proposed framework is highly flexible and can be adapted to consider other indices and lead times, and entirely different domains altogether.
How to cite: Ascenso, G., Sangiorgio, M., Baxter, I., and Castelletti, A.: Who causes whom? A spatially distributed causal analysis of the relationship between Arctic sea ice and teleconnection indices, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8384, https://doi.org/10.5194/egusphere-egu25-8384, 2025.
Capturing all sea ice dynamical aspects in a model is notoriously challenging due to the complex interplay of granular and fracture-dominated processes. In the central Arctic, linear kinematic features (LKFs) dominate deformation patterns, while the marginal ice zone (MIZ) is characterized by fragmented floes where the collisional mode is dominant. The rheological properties of sea ice in these region differ significantly, and a rheological model that could be used in all regimes is desirable. Continuum models, commonly used for large-scale sea ice simulations, rely on parameterizations to approximate subgrid-scale processes such as floe interactions, wave attenuation, and dilation. Although high-resolution (<2 km) continuum models improve the representation of LKFs and deformation statistics, they remain fundamentally limited by their reliance on simplified, or ill-posed rheologies and the continuum assumption, which cannot reconcile velocity discontinuities inherent in granular materials like sea ice. Discrete element models (DEMs), on the other hand, explicitly resolve particle-scale interactions and naturally capture fracture and granular behaviour, but their computational cost has historically restricted their application to small-scale scenarios.
We addressed this gap by developing the granular floes for discrete Arctic rheology (GODAR) model, a DEM specifically designed to simulate the mesoscale evolution of sea ice mechanics. GODAR tracks the time evolution of contact normals between floes, enabling us to derive generalized equations that relate dilation to prognostic variables such as shear and normal stress, open water fraction, and floe size distribution. These results demonstrate that GODAR effectively captures both the granular physics and fracture-driven dynamics underpinning LKFs. By seamlessly integrating microscale processes into macroscale behaviour, GODAR offers a powerful framework for bridging the limitations of continuum models. Its insights provide a pathway to improved parameterizations, advancing both the scientific understanding of sea ice dynamics and the operational forecasting capabilities necessary for safe navigation and climate modeling.
How to cite: Savard, A., Tremblay, B., and Polojärvi, A.: A New Parameterization of Dilation Using GODAR, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1585, https://doi.org/10.5194/egusphere-egu25-1585, 2025.
Forecasts of sea-ice motion and deformation are crucial for maritime operations including winter navigation and offshore wind energy harvesting. Further, sea-ice models have a key role in predictions on long-term effects of climate change. In this study we utilize the Helsinki Discrete Element Model (HiDEM) to simulate sea-ice breakup and dynamics. HiDEM code is optimized for high-performance supercomputers and achieves superior temporal and spatial resolutions when compared to conventionally used continuum models. We compare simulated fracture patterns and ice motion with satellite images from the Kvarken region of the Baltic Sea and show that HiDEM reproduces observed ice deformation patterns, which formed over a period of few days in nature. The results closely match the observed ice fracture and motion patterns, floe sizes, ridge structures, and fast-ice regions. The simulations cover an area of about 100 km × 100 km with 8 m resolution and they completed in about 10 hours of wall clock time.
How to cite: Polojärvi, A., Åström, J., and Haapala, J.: High-resolution large-scale model for sea ice dynamics, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4696, https://doi.org/10.5194/egusphere-egu25-4696, 2025.
Landfast ice, i.e., sea ice that is mechanically immobilized for several weeks along the coasts, significantly influences the underlying ocean by controlling the occurrence of coastal polynyas and the formation of dense water within. However, it is usually poorly represented in numerical models. In the Arctic, the accurate simulation of landfast ice relies on parameterizing sea ice grounding in shallow water areas and on the sea ice rheology capability to form ice arches in regions with restricted geometry. In this study, we compare a brittle rheology (i.e., the Brittle Bingham-Maxwell or BBM one), newly implemented in the ocean-sea ice model NEMO-SI3, with a standard viscous-plastic rheology (i.e., the aEVP), which is widely used in sea ice models. The performance of the two rheologies in forming ice arches and landfast ice is evaluated at the scale of the Arctic at a 0.25° horizontal resolution. For the grounding parameterization, we apply a probabilistic grounding scheme based on the ice thickness distribution and investigate how leveraging subgrid-scale bathymetry statistics can enhance its performance.
How to cite: Lambotte, A., Fichefet, T., Massonnet, F., Brodeau, L., Rampal, P., Lemieux, J.-F., and Dupont, F.: Arctic landfast ice simulation with brittle rheology and probabilistic grounding, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12073, https://doi.org/10.5194/egusphere-egu25-12073, 2025.
Approximately 4% to 13% of sea ice remains stationary, forming a narrow band around Antarctica. This contrasts with the majority of sea ice, which drifts with winds and ocean currents as "pack ice." This stationary landfast sea ice, known as "fast ice," is anchored to the coastline or grounded by icebergs and has significant implications for the global climate. However, current global climate models poorly represent fast ice, casting doubt on their ability to make accurate future projections for this critical component.
To address this limitation, we have developed a prognostic fast-ice representation suitable for coupled climate models. Our approach introduces a novel coupling mechanism between sea ice and grounded icebergs. This mechanism incorporates feedback from subgrid-scale grounded iceberg particles into the sea ice rheology. Idealized test cases demonstrate that this method successfully simulates fast ice as well as coastal polynyas due to subgrid-scale iceberg grounding.
How to cite: Mehlmann, C.: Modeling Fast Ice in the Southern Ocean Using a Particle-Continuum Approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11839, https://doi.org/10.5194/egusphere-egu25-11839, 2025.
Posters virtual: Wed, 30 Apr, 14:00–15:45 | vPoster spot 4
EGU25-1264 | Posters virtual | VPS18
Directed percolation threshold of sea ice permeability and electrical conductivityWed, 30 Apr, 14:00–15:45 (CEST) | vP4.5