Rapid changes in sea ice: processes and implications 

Recent years have seen significant reductions in Arctic sea ice extent, and a redistribution of sea ice in the Antarctic. Climate projections suggest a reduction of the sea ice cover in both poles, with the Arctic becoming seasonally ice free in the latter half of this century.

The scientific community is investing considerable effort in organising our current knowledge of the physical and biogeochemical properties of sea ice, exploring poorly understood sea ice processes, and forecasting future changes of the sea ice cover.

In this session, we invite contributions regarding all aspects of sea ice science and sea ice-climate interactions, including snow and sea ice thermodynamics and dynamics, sea ice-atmosphere and sea ice-ocean interactions, sea ice biological and chemical processes, and sea ice models. A focus on emerging processes and implications is particularly welcome.

Co-organized by OS4
Convener: Daniel Feltham | Co-conveners: Daniela Flocco, Andrew Wells
vPICO presentations
| Fri, 30 Apr, 13:30–15:00 (CEST)

vPICO presentations: Fri, 30 Apr

Chairpersons: Andrew Wells, Daniela Flocco, Daniel Feltham
Observations and Data Assimilation
Wolfgang Rack, Daniel Price, Christian Haas, Patricia J. Langhorne, and Greg H. Leonard

Sea ice cover is arguably the longest and best observed climate variable from space, with over four decades of highly reliable daily records of extent in both hemispheres. In Antarctica, a slight positive decadal trend in sea ice cover is driven by changes in the western Ross Sea, where a variation in weather patterns over the wider region forced a change in meridional winds. The distinguishing wind driven sea ice process in the western Ross Sea is the regular occurrence of the Ross Sea, McMurdo Sound, and Terra Nova Bay polynyas. Trends in sea ice volume and mass in this area unknown, because ice thickness and dynamics are particularly hard to measure.

Here we present the first comprehensive and direct assessment of large-scale sea-ice thickness distribution in the western Ross Sea. Using an airborne electromagnetic induction (AEM) ice thickness sensor towed by a fixed wing aircraft (Basler BT-67), we observed in November 2017 over a distance of 800 km significantly thicker ice than expected from thermodynamic growth alone. By means of time series of satellite images and wind data we relate the observed thickness distribution to satellite derived ice dynamics and wind data. Strong southerly winds with speeds of up to 25 ms-1 in early October deformed the pack ice, which was surveyed more than a month later.

We found strongly deformed ice with a mean and maximum thickness of 2.0 and 15.6 m, respectively. Sea-ice thickness gradients are highest within 100-200 km of polynyas, where the mean thickness of the thickest 10% of ice is 7.6 m. From comparison with aerial photographs and satellite images we conclude that ice preferentially grows in deformational ridges; about 43% of the sea ice volume in the area between McMurdo Sound and Terra Nova Bay is concentrated in more than 3 m thick ridges which cover about 15% of the surveyed area. Overall, 80% of the ice was found to be heavily deformed and concentrated in ridges up to 11.8 m thick.

Our observations hold a link between wind driven ice dynamics and the ice mass exported from the western Ross Sea. The sea ice statistics highlighted in this contribution forms a basis for improved satellite derived mass balance assessments and the evaluation of sea ice simulations.

How to cite: Rack, W., Price, D., Haas, C., Langhorne, P. J., and Leonard, G. H.: Sea ice deformation and thickness in the Western Ross Sea, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10607,, 2021.

Christoph Herbert, Joan Francisc Munoz-Martin, David LLaveria, Miriam Pablos, and Adriano Camps

Several approaches have been developed to yield Arctic sea ice thickness based on satellite observations. Microwave radiometry operating at L-band is sensitive to sea ice properties and allows to retrieve thin sea ice up to ~ 0.5 m. Sea ice thickness retrievals above 1 m can be successfully derived using sea ice freeboard data from satellite altimeters. Current inference models are build upon empirically determined assumptions on the physical composition of sea ice and are validated against regionally available data. However, sea ice can exhibit time-dependent non-linear relations between sea ice properties during the process of formation and melting, which cannot be fully addressed by simple inversion models. Until now, an accurate estimation of sea ice thickness requires specific conditions and is only viable during Arctic freeze up from mid-October to mid-April. Neural networks are an efficient model-based learning technique capable of resolving complex systems while recognizing hidden links among large amounts of data. Models have the advantage to be adaptive to new data and can therefore reflect seasonally changing sea ice conditions to make predictions based on the relationship between a set of input features. FSSCat is a two 6-unit CubeSat mission launched on September 3, 2020, which carries the FMPL-2 payload on board the 3Cat-5/A, one out of two spacecrafts. FMPL-2 encompasses the first L-band radiometer and GNSS-Reflectometer on a CubeSat, designed to provide global brightness temperature data suitable for soil moisture retrieval on land and sea ice applications.

In this work a predictive regression neural network was built to predict thin sea ice thickness up to 0.6 m at Arctic scale based on FMPL-2 observations and ancillary data including sea ice concentration and surface temperature. The network was trained based on CubeSat acquisitions during early Arctic freeze up from October 15 to December 4, 2020, using existing maps of daily estimated sea ice thickness derived from the Soil Moisture and Ocean Salinity (SMOS) mission as ground truth data. Hyperparameters were optimized to prevent the model from overfitting and overgeneralization with the best fit resulting in an overall mean absolute error of 6.5 cm. Preliminary results reveal good performance up to 0.5 m, whereas predicted values are slightly underestimated for higher thickness. The thin ice model allows to produce weekly composites of Arctic sea ice thickness maps. Future work involves the complementation of the input features with sea ice freeboard observations from the Cryosat-2 mission to extend the sensitivity range of the current network and to validate the findings with on-site data. Aim is to apply the model trained on Arctic data to retrieve full-range Arctic and Antarctic sea ice thickness maps. The presented approach demonstrated the potential of neural networks for sea ice parameter retrieval and indicated that data acquired by moderate-cost CubeSat missions can offer scientifically valuable contributions to applications in Earth observation.

How to cite: Herbert, C., Munoz-Martin, J. F., LLaveria, D., Pablos, M., and Camps, A.: Sea Ice Thickness Retrieval based on Predictive Regression Neural Networks using L-band Microwave Radiometry Data from the FSSCat mission, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12946,, 2021.

Harry Heorton, Michel Tsamados, Paul Holland, and Jack Landy

We combine satellite-derived observations of sea ice concentration, drift, and thickness to provide the first observational decomposition of the dynamic (advection/divergence) and thermodynamic (melt/growth) drivers of wintertime Arctic sea ice volume change. Ten winter growth seasons are analyzed over the CryoSat-2 period between October 2010 and April 2020. Sensitivity to several observational products is performed to provide an estimated uncertainty of the budget calculations. The total thermodynamic ice volume growth and dynamic ice losses are calculated with marked seasonal, inter-annual and regional variations. Ice growth is fastest during Autumn, in the Marginal Seas and over first year ice. Our budget decomposition methodology can help diagnose the processes confounding climate model predictions of sea ice. We make our product and code available to the community in monthly pan-Arctic netcdft files for the entire October 2010 to April 2020 period.

How to cite: Heorton, H., Tsamados, M., Holland, P., and Landy, J.: Arctic sea ice volume budget decomposition satellite product for the CryoSat-2 (2010-2020) period , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12079,, 2021.

Ashleigh Womack and Marcello Vichi

Sea-ice drift in the Antarctic marginal ice zone (MIZ) was investigated by using an ice buoy (buoy U1), deployed during the winter sea-ice expansion in July 2017, and drifted for approximately four months from the South Atlantic sector to the Indian Ocean sector of the Southern Ocean. The analysis of this buoy revealed that it remained within the MIZ even during the winter ice expansion, as the mixed pancake-frazil field was maintained. This allowed for a continued assumption of free drift conditions for buoy U1’s full drift, where it continued to respond linearly to the momentum transfer from surface winds. The analysis of buoy U1 also indicated a strong inertial signature at a period of 13.47 hours however, the wavelet analysis indicated majority of the power remained within the lower frequencies. This strong influence at the lower (multi-day) frequencies has therefore been identified as the primary effect of atmospheric forcing. When these lower frequencies were filtered out using the Butterworth high-pass filter it allowed the inertial oscillations to become more significant within the wavelet power spectrum, where it can be seen that these inertial oscillations were often triggered by the passage of cyclones. The initiation of inertial oscillations of sea ice has therefore been identified as the secondary effect of atmospheric forcing, which dominates ice drift at sub-daily timescales and results in the deviation of ice drift from a straight-line path. This comprehensive analysis suggests that the general concentration-based definition of the MIZ is not enough to describe the sea-ice cover, and that the MIZ, where sea ice is in free drift and under the influence of cyclone induced inertial motion, and presumably waves, extends up to »200 km.

How to cite: Womack, A. and Vichi, M.: Atmospheric drivers of a 4-month drift of an ice buoy in the Antarctic marginal ice zone, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14246,, 2021.

Nicholas Williams, Nicholas Byrne, Daniel Feltham, Peter Jan Van Leeuwen, Ross Bannister, David Schroeder, and Andrew Shepherd
A modified, standalone version of the Los Alamos Sea Ice Model (CICE) has been coupled to the Parallelized Data Assimilation Framework (PDAF) to produce a new Arctic sea ice data assimilation system CICE-PDAF, with routines for assimilating many types of recently developed sea ice observations. In this study we explore the effects of assimilating a sub-grid scale sea ice thickness distribution derived from Cryosat-2 Arctic sea ice estimates into CICE-PDAF. The true state of the sub-grid scale ice thickness distribution is not well established, and yet it plays a key role in large scale sea ice models and is vital to the dynamical and thermodynamical processes necessary to produce a good representation of the Arctic sea ice state. We examine how assimilating sub-grid scale sea ice thickness distributions can affect the evolution of the sea ice state in CICE-PDAF and better our understanding of the Arctic sea ice system.

How to cite: Williams, N., Byrne, N., Feltham, D., Van Leeuwen, P. J., Bannister, R., Schroeder, D., and Shepherd, A.: The effects of assimilating a sub-grid scale sea ice thickness distribution in a new Arctic sea ice data assimilation system, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2657,, 2021.

Imke Sievers, Till Rasmussen, and Lars Stenseng

With the presented work we aim to improve sea ice forecasts and our understanding of Arcitc sea ice formation though freeboard assimilation. Over the last years understanding Arctic sea ice changes and being able to make a reliable sea ice forecast has gained in importance. The central roll of Arctic sea ice extent in climate warming makes it a highly discussed topic in the climate research community. However a reliable Arctic sea ice forecast both on short term to seasonal time scales remains a challenge to be mastered, hinting that there are still many processes at play to be better understood.
One promising approach to improve forecasts has been to assimilate satellite sea ice data into numerical sea ice models. Mainly two parameters measured by satellites have been used for assimilation: Sea ice concentration, which is competitively easy to obtain from satellites measuring passive microwave emissions as for example obtained by the SMOS satellite, and sea ice thickness, which is not directly measured, but has to be calculated from surface elevation measurements, as for example obtained by Cryosat 2. Compering the skill, of assimilation products using sea ice thickness and sea ice concentration shows that sea ice thickness has a longer memory and is over all leading to a better performance then sea ice concentration assimilation. Knowing this, sea ice thickness assimilation is far from being straight forward. Surface elevation measurements, obtained from satellite altemitry measurements, have to be separated into snow and ice freeborad, by assuming a snow thickness, to derive sea ice thickness from. Most of the time this is done using a snow thickness climatology obtained from Soviet drift stations measuring snow over multi year ice during the period 1954-1991 with adaption over first year sea ice, where this climatology has proven to be overestimating snow thickness. The technique is widely used jet known to introduce an error.
To avoid errors caused by wrongly assumed snow covers the DMI and Aalborg University and DTU are at the moment collaborating on assimilating freebord instead of sea ice thickness into the CICE-NEMO modeling frame work using LARS NGen (LARS the Advanced Retracking System, Next Generation) sate of the art retracing software. In the presented work we will show first results of freeboard assimilation with a focus how this assimilation influences winter sea ice formation as well as the upper Arctic Ocean dynamics.

How to cite: Sievers, I., Rasmussen, T., and Stenseng, L.: Improving Arctic Sea Ice Prediction Though Freeboard Assimilation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2943,, 2021.

Simulating sea ice
Melinda Webster, Alice DuVivier, Marika Holland, and David Bailey

Snow on Arctic sea ice is important for several reasons: it creates a habitat for microorganisms and mammals, it changes sea-ice growth and melt, and it affects the speed at which ships and people can travel through sea ice. Therefore, investigating how snow on Arctic sea ice may change in a warming climate is useful for anticipating its potential effects on ecosystems, sea ice, and socioeconomic activities. Here, we use experiments from two versions of the Community Earth System Model (CESM) to study how snow conditions change over time. Comparison with observations indicates that CESM2 produces an overly-thin, overly-uniform snow distribution, while CESM1-LE produces a variable, excessively-thick snow cover. The 1950-2050 snow depth trend in CESM2 is 75% smaller than in CESM1-LE due to CESM2 having less snow. In CESM1-LE, long-lasting, thick sea ice, cool summers, and excessive summer snowfall facilitate a thicker, longer-lasting snow cover. In a warming climate, CESM2 shows that snow on Arctic sea ice will: (1) have greater, earlier spring melt, (2) accumulate less in summer-autumn, (3) sublimate more, and (4) cause marginally more snow-ice formation. CESM2 reveals that snow-free summers can occur ~30-60 years before an ice-free central Arctic, which may promote faster sea-ice melt.

How to cite: Webster, M., DuVivier, A., Holland, M., and Bailey, D.: Snow on Arctic sea ice in a warming climate as simulated in CESM, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3253,, 2021.

Sukun Cheng, Ali Aydoğdu, Pierre Rampal, Alberto Carrassi, and Laurent Bertino

We evaluate the impact of uncertainties in surface wind and sea ice cohesion on sea ice forecasts by the neXtSIM sea ice model. neXtSIM includes the Maxwell-elasto-brittle rheology describing the ice dynamics. Ensemble forecasts are done every 10 days from January to April 2008. The ensembles are generated by perturbing the wind forcing and ice cohesion field both separately and jointly. The wind forcing, an external forcing of the model, is perturbed continuously during the forecast. While the sea-ice cohesion, an internal parameter of the model, is randomized on the initial field of each sea ice forecast. The model uncertainties are assessed statistically using ensemble forecasts, in which virtual drifters are seeded over the Arctic Ocean. We analyze the spread of Lagrangian sea ice trajectories of the ensemble of virtual drifters and compare them with the IABP buoys. We demonstrate that the wind perturbations usually contribute more to the forecast uncertainty, but the ice cohesion perturbations significantly increase the degree of anisotropy in the spread and become occasionally important during strong wind events.

How to cite: Cheng, S., Aydoğdu, A., Rampal, P., Carrassi, A., and Bertino, L.: Probabilistic Forecasts of Sea Ice Trajectories in the Arctic: Impact of Uncertainties in Surface Wind and Ice Cohesion, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5141,, 2021.

David Schroeder and Danny Feltham

The decrease of Arctic sea ice affects the future climate in the Arctic and beyond. Therefore, it is important to understand the drivers of sea ice variability and trend. Previous model studies found that the summer sea ice is mainly driven by atmospheric processes (incoming radiation and albedo feedback) and the winter sea ice extent by ocean processes (ocean heat transport from Atlantic into Arctic Ocean, e.g. applying Community Earth System Model large ensemble simulation). In our study, we analyse a historical simulation with the UK Earth System Model (UKESM1) performed for CMIP6 from 1850 to 2014 and ocean – sea ice simulations forced by atmospheric reanalysis data with the same ocean model NEMOv3.6 and sea ice model CICEv5.1. The UKESM simulation confirms previous findings showing that the ocean heat transport between Norway and Svalbard (Barents Sea Opening; BSO) is strongly correlated with the winter (and annual) sea ice extent in the Barents Sea and the whole Arctic. However, there is no correlation in the atmospheric-forced simulations suggesting that the interaction between atmosphere and ocean is crucial. We will present sensitivity simulations showing the impact of atmospheric forcing data on the BSO heat flux and analyse the role of atmospheric processes (large scale circulation, cloud formation) on winter sea ice conditions.

How to cite: Schroeder, D. and Feltham, D.: The role of ocean heat transport from the Atlantic into the Arctic Ocean on sea ice variability, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3280,, 2021.

Jake Aylmer, David Ferreira, and Daniel Feltham

Estimating long-term projections of sea ice extent is a key part of understanding the possible future climate state. This is hampered by uncertainties within and across comprehensive climate models, and the relative importance and nature of contributing factors are not fully understood. Here, we investigate the role of ocean and atmospheric forcing on sea ice on multidecadal time scales.

Pre-industrial control simulations of 19 CMIP6 models are analysed. Sea ice extent is negatively correlated with ocean heat transport (OHT), and positively correlated with atmospheric heat (moist-static energy) transport (AHT), in both hemispheres. In most models, increased OHT into the Arctic enhances surface fluxes in the Atlantic sector just south of the sea ice edge, which in turn increases the AHT convergence at higher latitudes. In the southern ocean, increased OHT directly increases the mean ocean–ice heat flux while AHT plays no direct role. Sensitivities of the sea ice cover to OHT are consistent with predictions from an idealised energy balance model (EBM), which is fitted to each model in turn. This shows that the sensitivities are constrained by atmospheric radiation parameters and the mean surface temperature response, with no explicit dependence on ocean parameters. These results are a step towards quantifying the effect of ocean biases on sea ice uncertainty in climate projections.

How to cite: Aylmer, J., Ferreira, D., and Feltham, D.: Ocean heat transport as a driver of sea ice extent in CMIP6 models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2170,, 2021.

Yuqing Liu and Martin Losch

Sea ice is regarded as a significant indicator of climate change in the Arctic Ocean. Landfastice is sea ice that is immobile or almost immobile in coastal regions, decreasing the transfer of heat, moisture, and momentum. As an extension of the land for travel and hunting, landfast ice also influences the construction of ice roads and arctic shipping routes in the summertime. Despite the important role of landfast ice in the climate system, the formation and maintenance of landfast ice are not well simulated by current sea ice models. Lemieux (2015) came up with the grounding scheme, by adding a basal stress term according to the water depth, improving landfast ice representation in shallow regions while underestimating in deep regions especially in the Kara Sea. The two different resolution model configurations with the MIT General Circulation Model (MITgcm) sea ice package is compared in landfast ice simulation in the arctic region. Preliminary results show that a higher resolution model better represents landfast ice in deep regions. The proper illustration of coastlines, which serve as pinning points for sea ice arches, in the high-resolution model can improve the representation of landfast ice. We also apply a new parameterization lateral drag term, a function with sea ice thickness, drift velocity, and coastline intricacy, in the model to better simulate landfast ice. The results suggest a combination of lateral drag and basal stress terms successfully simulates fast ice in most regions

How to cite: Liu, Y. and Losch, M.: Numerical modeling on landfast ice in Arctic region, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-461,, 2021.

Adam Bateson, Daniel Feltham, David Schröder, Yanan Wang, Byongjun Hwang, Jeff Ridley, and Yevgeny Aksenov

The Arctic sea ice cover is not a continuous expanse of ice but is instead composed of individual sea ice floes. These floes can range in size from just a few metres to tens of kilometres. Floe size can influence a variety of processes, including lateral melt rates, momentum transfer within the sea ice-ocean-atmosphere system, surface moisture flux, and sea ice rheology. Sea ice models have traditionally defined floe size using a single parameter, if floe size is explicitly treated at all. There have been several recent efforts to incorporate models of the Floe Size Distribution (FSD) into sea ice models in order to explore both how the shape of the FSD emerges and evolves and its impact on the sea ice cover, including the seasonal retreat. Existing models have generally focused on ocean surface wave-floe interactions and thermodynamic melting and growth processes. However, in-situ observations have indicated the presence of mechanisms other than wave fracture involved in the fragmentation of floes, including brittle failure and melt-induced break up.

In this study we consider two alternative FSD models within the CICE sea ice model: the first assumes the FSD follows a power law with a fixed exponent, with parameterisations of individual processes characterised using a variable FSD tracer; the second uses a prognostic approach, with the shape of the FSD an emergent characteristic of the model rather than imposed. We firstly use case studies to understand how similarities and differences in the impacts of the two FSD models on the sea ice emerge, including the different spatial and temporal variability of these impacts. We also consider whether the inclusion of FSD processes in sea ice models can enhance seasonal predictability. We will also demonstrate the need to include in-plane brittle fracture processes in FSD models and discuss the requirements needed within any parameterisation of the brittle failure mechanism.

How to cite: Bateson, A., Feltham, D., Schröder, D., Wang, Y., Hwang, B., Ridley, J., and Aksenov, Y.: Sea ice fragmentation and its role in the evolution of the Arctic sea ice cover. , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9875,, 2021.

Arctic MIZ Sensitivity to Atmosphere- Ice-Ocean Feedbacks
Rebecca Frew, Daniel Feltham, David Schroeder, and Adam Bateson
Sea ice processes
Nils Hutter, Amélie Bouchat, Frédéric Dupont, Dmitry Dukhovskoy, Nikolay Koldunov, Younjoo Lee, Jean-François Lemieux, Camille Lique, Martin Losch, Wieslaw Maslowski, Paul G. Myers, Einar Olason, Pierre Rampal, Till Rasmussen, Claude Talandier, Bruno Tremblay, and Qiang Wang

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.

How to cite: Hutter, N., Bouchat, A., Dupont, F., Dukhovskoy, D., Koldunov, N., Lee, Y., Lemieux, J.-F., Lique, C., Losch, M., Maslowski, W., Myers, P. G., Olason, E., Rampal, P., Rasmussen, T., Talandier, C., Tremblay, B., and Wang, Q.: Evaluating simulated linear kinematic features in high-resolution sea-ice simulations of the FAMOS Sea Ice rheology experiments (SIREx), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9739,, 2021.

Damien Ringeisen, Martin Losch, and L. Bruno Tremblay

Fracture lines dominate the dynamics of sea ice. They affect the ice mass balance and the heat transfer between the atmosphere and the ocean. Therefore, climate modeling and sea ice prediction require an accurate fracture representation. Most sea ice models use viscous-plastic (VP) rheologies to simulate sea ice internal stresses. One of the issues with these rheologies is that they overestimate the intersection angles between fracture lines, with consequences for the subsequent sea ice drift. In idealized experiments, we investigate the mechanisms linking VP rheologies and fracture angles and assess alternative rheologies for high-resolution modeling. Results show that the definition of the transition between viscous and plastic states is essential for the creation of sharp fracture lines. The fracture angles with Mohr-Coulomb yield curves agree with the Arthur fault orientation theory. Further, rheologies with Mohr-Coulomb yield curves or teardrop yield curves appear to reduce intersection angles. Finally, experiments show that these results are reproduced for different sea ice initial conditions. With rheologies that favor smaller intersection angles, sea ice models move a step closer to accurate sea ice dynamics at high-resolution.

How to cite: Ringeisen, D., Losch, M., and Tremblay, L. B.: Alternative viscous-plastic rheologies for the representation of fracture lines in high-resolution sea ice models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1373,, 2021.

Jean Sterlin, Thierry Fichefet, Francois Massonnet, and Michel Tsamados

Sea ice features a variety of obstacles to the flow of air and seawater at its top and bottom surfaces. Sea ice ridges, floe edges, ice surface roughness and melt ponds, lead to a form drag that interacts dynamically with the air-ice and ocean-ice fluxes of heat and momentum. In most climate models, surface fluxes of heat and momentum are calculated by bulk formulas using constant drag coefficients over sea ice, to reflect the mean surface roughness of the interfaces with the atmosphere and ocean. However, such constant drag coefficients do not account for the subgrid-scale variability of the sea ice surface roughness. To study the effect of form drag over sea ice on air-ice-ocean fluxes, we have implemented a formulation that estimates drag coefficients in ice-covered areas comprising the effect of sea ice ridges, floe edges and melt ponds, and ice surface skin (Tsamados et al., 2013) into the NEMO3.6-LIM3 global coupled ice-ocean model. In this work, we thoroughly analyse the impacts of this improvement on the model performance in both the Arctic and Antarctic. A particular attention is paid to the influence of this modification on the air-ice-ocean fluxes of heat and momentum, and the characteristics of the oceanic surface layers. We also formulate an assessment of the importance of variable drag coefficients over sea ice for the climate modelling community.

How to cite: Sterlin, J., Fichefet, T., Massonnet, F., and Tsamados, M.: Importance of variable neutral drag coefficients for ocean-ice and air-ice fluxes in polar regions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12274,, 2021.

Rachel Diamond, Louise Sime, David Schroeder, and Maria-Vittoria Guarino

HadGEM3 is the first coupled climate model to simulate an ice-free Arctic during the Last Interglacial (LIG), 127 000 years ago. This simulation appears to yield accurate Arctic surface temperatures during the summer season. Here, we investigate the causes and impacts of this extreme simulated ice loss. We find that the summer ice melt is predominantly driven by thermodynamic processes: atmospheric and ocean circulation changes do not significantly contribute to the ice loss. We demonstrate these thermodynamic processes are significantly impacted by melt ponds, which form on average 8 days earlier during the LIG than during the pre-industrial control (PI) simulation. This relatively small difference significantly changes the LIG surface energy balance, and strengthens the albedo feedback. Compared to the PI simulation: in mid-June, of the absorbed flux at the surface over ice-covered cells (ice concentration>0.15), ponds account for 45-50%, open water 45%, and bare ice and snow 5-10%. We show that the simulated ice loss leads to large Arctic sea surface salinity and temperature changes. The sea surface temperature and salinity signals we identify here provide a means to verify, in marine observations, if and when an ice-free Arctic occurred during the LIG. Strong LIG correlations between spring melt pond and summer ice area indicate that, as Arctic ice continues to thin in future, the spring melt pond area will likely become an increasingly reliable predictor of the September sea-ice area. Finally, we note that models with explicitly modelled melt ponds seem to simulate particularly low LIG sea-ice extent. These results show that models with explicit (as opposed to parameterised) melt ponds can simulate very different sea-ice behaviour under forcings other than the present-day. This is of concern for future projections of sea-ice loss.

How to cite: Diamond, R., Sime, L., Schroeder, D., and Guarino, M.-V.: The contribution of melt ponds to enhanced Arctic sea-ice melt during the Last Interglacial, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9239,, 2021.

David Livings, Danny Feltham, and David Schroeder

SI3 (Sea Ice modelling Integrated Initiative) is the sea ice engine of the NEMO ocean model. It incorporates elements of three sea ice models that have been used with NEMO in the past: CICE, GELATO, and LIM. It takes account of sea ice dynamics, thermodynamics, brine inclusions, and subgrid-scale thickness variations.

A process that has historically been poorly represented in sea ice models is the formation and evolution of melt ponds. These ponds accumulate on the surface of sea ice during the melt season and affect the heat and mass balance in various ways, the most important of which is a reduction in albedo. A melt pond scheme that has a significant impact on surface albedo has recently been added to SI3, based on the ideas of Flocco et al (JGR, 2010). This scheme attempts to represent the influence of ice topography on lateral meltwater transport. We present the results of tests of the grid-level conservation of heat and fresh water in this new scheme. To perform these tests we have incorporated a basic mixed-layer ocean model into SI3 as an intermediate complexity alternative to running with the full ocean model or forcing with saved ocean fields.

We also present a comparison of SI3 with the Los Alamos sea ice model (CICE) in multi-decadal simulations. These comparisons cover the sea ice mass balance (sea ice concentration, extent, and thickness) and the sea ice motion.

How to cite: Livings, D., Feltham, D., and Schroeder, D.: Testing a Sea Ice Model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3518,, 2021.

Sönke Maus

The permeability of sea ice is an important property with regard to the role of sea ice in the earth system. It controls fluid flow within sea ice, and thus affects processes like desalination and melt pond drainage. It also impacts the role of sea ice in hosting sea ice algae and organisms, and the uptake and release of nutrients and pollutants from Arctic surface waters. However, as sea ice permeability is difficult to measure in the field, observations are sparse and vary, even for similar porosity, over orders of magnitude. This range is related to the evolution of the sea ice pore space during aging from young ice to thick first year ice. In young ice, the pore network is dominated by primary pores constrained by brine layers and the near-interface microstructure. In older sea ice, the ongoing desalination and thermal fluctuations have created wider secondary brine channels, implying a several orders of magnitude higher permeability. It is a challenge to understand and model these changes in pore space and permeability. Here a directed percolation model for the permeability of young sea ice is proposed. The model describes the dependence of sea ice permeability and electrical conductivity on brine porosity, and its critical behaviour and percolation transition due to necking of pores, and related disconnection of pore networks. Its parameters are based on 3D X-ray micro-tomographic imaging of young sea ice and direct numerical simulation of its transport properties, that strongly support the application of directed percolation theory to young sea ice, with a threshold porosity (impermeable ice) of 2 to 3 percent. Combined to an approach to predict the crystal structure at the ice-ocean interface, the model also the growth-velocity dependence and evolution of permeability near the ice-ocean interface. As the model is strictly valid for growing and cooling sea ice, without present of wider secondary brine channels, it is mostly relevant for sea ice desalination processes during winter. Modelling permeability of older and summer ice (and melt pond drainage) will require more observations of the pore space evolution in warming sea ice, for which the present results can be considered as a starting point.

How to cite: Maus, S.: A directed percolation model for the permeability of young sea ice , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3510,, 2021.

Michael Coughlan

I present a physically-based network model for systems of ponds which accounts for both the individual and collective behaviour of ponds, and allows us to investigate the behaviour of both. Each pond initially occupies a distinct catchment basin and evolves according to a mass-conserving differential equation representing the melting dynamics for bare and water-covered ice. Ponds can later connect together to form a network with fluxes of water between catchment areas, constrained by the ice topography and pond water levels. 

I use the model to explore how the evolution of pond area and hence melting depends on the governing parameters, and to explore how the connections between ponds develop over the melt season. Comparisons with observations are made to demonstrate the ways in which the model qualitatively replicates properties of pond systems, including fractal dimension of pond areas and two distinct regimes of pond complexity that are observed during their development cycle. The network structure, and tools from percolation theory also allows us to probe how the connectivity of pond systems affect the system at each stage of development.

How to cite: Coughlan, M.: A network model for ponding on sea ice, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13270,, 2021.