Session CR3.3

CR3.3 | Rapid changes in sea ice: processes and implications

Rapid changes in sea ice: processes and implications

Convener: Adam BatesonECSECS | Co-conveners: Daniela Flocco, Srikanth Toppaladoddi, Gaelle VeyssiereECSECS, Daniel Feltham

Orals

| Thu, 01 May, 10:45–12:30 (CEST)

Room L2

Posters on site

| Attendance Fri, 02 May, 10:45–12:30 (CEST) | Display Fri, 02 May, 08:30–12:30

Hall X4

Significant reductions in Arctic sea ice extent, concentration and thickness have been consistently witnessed during the last decades. Whilst Antarctic sea ice extent was remarkably stable until 2016/2017, this has changed over recent years with 2022 to 2024 producing the lowest three minimum Antarctic sea ice extents on record. 2023 and 2024 have been particularly stark due to the lack of recovery of the sea ice cover, raising concerns for the future of Antarctic sea ice. Climate projections suggest a continued reduction of the sea ice cover for 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, such as in CMIP6.

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

Orals: Thu, 1 May, 10:45–12:30 | Room L2

Chairpersons: Adam Bateson, Daniel Feltham

10:45–10:55

EGU25-11156

ECS

solicited

Highlight

On-site presentation

Future sea ice weakening amplifies wind-driven trends in surface stress and Arctic Ocean spin-up

Morven Muilwijk, Tore Hattermann, Torge Martin, and Mats Granskog

Arctic sea ice mediates atmosphere-ocean momentum transfer, which drives upper ocean circulation. How Arctic Ocean surface stress and velocity respond to sea ice decline and changing winds under global warming is unclear. Here we show that state-of-the-art climate models consistently predict an increase in future (2015-2100) ocean surface stress in response to increased surface wind speed, declining sea ice area, and a weaker ice pack. While wind speeds increase most during fall (+2.2% per decade), surface stress rises most in winter (+5.1% per decade) being amplified by reduced internal ice stress. This is because, as sea ice concentration decreases in a warming climate, less energy is dissipated by the weaker ice pack, resulting in more momentum transfer to the ocean. The increased momentum transfer accelerates Arctic Ocean surface velocity (+31-47% by 2100), leading to elevated ocean kinetic energy and enhanced vertical mixing. The enhanced surface stress also increases the Beaufort Gyre Ekman convergence and freshwater content, impacting Arctic marine ecosystems and the downstream ocean circulation. The impacts of projected changes are profound, but different and simplified model formulations of atmosphere-ice-ocean momentum transfer introduce considerable uncertainty, highlighting the need for improved coupling in climate models.

How to cite: Muilwijk, M., Hattermann, T., Martin, T., and Granskog, M.: Future sea ice weakening amplifies wind-driven trends in surface stress and Arctic Ocean spin-up, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11156, https://doi.org/10.5194/egusphere-egu25-11156, 2025.

10:55–11:05

EGU25-7248

On-site presentation

A surprising, but not unexpected, multi-decadal hiatus in Arctic sea ice decline

Lorenzo Polvani, Mark England, and James Screen

Since the beginning of the satellite era in 1979, September sea ice area over the Arctic has nearly halved, and the rapid sea ice decline in the early 2000s fueled concerns that the first ice-free Arctic summer could occur before 2020. In light of these dire forecasts, therefore, it is remarkable that no statistically significant decline in September Arctic sea ice coverage has occurred over past two decades, as we demonstrate here.

This 20-year-long hiatus in pan-Arctic sea ice decline is robust across observational datasets, and also robust to the choice metric (sea ice area or extent). In fact, the present hiatus is seen in all months of the year, not only in September.

One is immediately led to ask whether climate models are able to capture multi-decadal-long periods with no Arctic sea ice decline in the presence of strong anthropogenic radiative forcing. To answer this, we analyze multiple large ensembles of CMIP5 and CMIP6 simulations over the first half of the 21st century. We find that 20-year-long periods with no significant Arctic in sea ice loss occur rather frequently in climate models, even under high emissions scenarios. Roughly 20-30% of the hundreds of model runs analyzed here show 2005-2024 trends smaller than the small observed trend, although the intermodel spread is considerable. Sub-selecting models based on their ability of simulating observed climatological sea ice, or on their equilibrium climate sensitivity, or on the specific scenario forcings, does not alter the key result.

Our analysis also reveals that the simulated forced response, in most models, is considerably larger than the observed trends. This highlights the important role of internal variability in enhancing and, more recently, in reducing Arctic sea ice decline. In fact, our analysis of model output reveals that a different configuration of internal climate variability could have caused an overall growth in Arctic sea ice over the past 20 years, surprisingly enough.

Looking at the coming decades, and focusing on those climate simulation which are consistent with the present observed trends, we find the hiatus is 50% likely to persist for another five years, and could plausibly last another full decade (with roughly 30% chance).

Taken together, our findings lead us to conclude that the current hiatus in Arctic sea ice decline, even as emissions of greenhouse gases continue to rise, is not an unexpected event that questions our basic understanding of the Arctic climate system. Climate models show that it can occur rather frequently, and that it could plausibly persist for years to come.

How to cite: Polvani, L., England, M., and Screen, J.: A surprising, but not unexpected, multi-decadal hiatus in Arctic sea ice decline, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7248, https://doi.org/10.5194/egusphere-egu25-7248, 2025.

11:05–11:15

EGU25-8393

On-site presentation

New perspectives on the skill of modelled sea ice trends in light of recent Antarctic sea ice loss

Caroline Holmes, Thomas J. Bracegirdle, Paul R. Holland, Julienne Stroeve, and Jeremy Wilkinson

Most climate models do not reproduce the 1979–2014 increase in Antarctic sea ice area (SIA). This was a contributing factor in successive Intergovernmental Panel on Climate Change reports allocating low confidence to model projections of sea ice over the 21st century. However, due to the rapid declines in Antarctic sea ice since 2016, the linear trend in annual mean Antarctic SIA is no longer positive. We therefore investigate what impact this has on the evaluation of trends from the CMIP6 multi-model ensemble and show that the recent rapid declines bring observed SIA trends back into line with the models. More generally, the level of agreement between observed and modelled linear trends depends both on the length of the time series examined ('timescale') and the exact years ('time period'). Our novel result that trends over the full satellite era 1979–2023 do not disagree between observations and models could imply that models are better able to represent changes over longer timescales than previously thought. However, this is not the only interpretation. One confounding aspect is the abrupt nature of recent change, as a result of which the full time series does not appear particularly linear. This presentation will discuss these aspects and the implications for future research priorities.

How to cite: Holmes, C., Bracegirdle, T. J., Holland, P. R., Stroeve, J., and Wilkinson, J.: New perspectives on the skill of modelled sea ice trends in light of recent Antarctic sea ice loss, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8393, https://doi.org/10.5194/egusphere-egu25-8393, 2025.

11:15–11:25

EGU25-11201

ECS

On-site presentation

CMIP6 Models Rarely Simulate Antarctic Winter Sea-Ice Anomalies as Large as Observed in 2023

Rachel Diamond, Louise Sime, Caroline Holmes, and David Schroeder

ABSTRACT:

In 2023, Antarctic sea-ice extent (SIE) reached record lows, with winter SIE falling to 2.5Mkm² below the satellite era average. With this multi-model study, we investigate the occurrence of anomalies of this magnitude in latest-generation global climate models. When these anomalies occur, SIE takes decades to recover: this indicates that SIE may transition to a new, lower, state over the next few decades. Under internal variability alone, models are extremely unlikely to simulate these anomalies, with return period >1000 years for most models. The only models with return period <1000 years for these anomalies have likely unrealistically large interannual variability. Based on extreme value theory, the return period is reduced from 2650 years under internal variability to 580 years under a strong climate change forcing scenario.

REFERENCE:

Diamond, Rachel, et al. "CMIP6 models rarely simulate Antarctic winter sea‐ice anomalies as large as observed in 2023." Geophysical Research Letters 51.10 (2024): e2024GL109265.

PLAIN LANGUAGE SUMMARY:

In 2023, the area of winter Antarctic sea ice fell to the lowest measured since satellite records began in late 1978. It is still under debate how far this low can be explained by natural variations, and how much can be explained by climate change. Global climate models are tools used to study past and predict future global change. We show that, without climate change, the latest generation of these models are extremely unlikely to simulate a sea-ice reduction from the mean as large as observed in winter 2023. Including strong climate change quadruples the chance of such a reduction, but the chance is still very low. When these rare reductions are simulated, sea ice takes around 10 years to recover to a new, lower, area: this indicates that Antarctic sea ice may transition to a new, lower, state over the next few decades.

How to cite: Diamond, R., Sime, L., Holmes, C., and Schroeder, D.: CMIP6 Models Rarely Simulate Antarctic Winter Sea-Ice Anomalies as Large as Observed in 2023, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11201, https://doi.org/10.5194/egusphere-egu25-11201, 2025.

11:25–11:35

EGU25-7366

ECS

On-site presentation

Interannual Variability of the Arctic Marginal Ice Zone Over Four Decades: A Comparison of Two Definitions

Armina Soleymani, Alex Crawford, and K. Andrea Scott

The Marginal Ice Zone (MIZ) is a dynamic region between open water
and consolidated ice, crucial for heat and moisture exchange and support-
ing diverse marine ecosystems. With Arctic sea ice thinning and the melt
season lengthening, monitoring the MIZ has become increasingly impor-
tant. This study analyzed Arctic MIZ trends over 40 years (1983–2022)
using Bootstrap SIC data and two definitions: one based on the SIC
threshold (MIZ_t) and another on the SIC anomaly (MIZ_σ ). MIZt was de-
fined as 0.15 0.15 ≤ SIC < 0.80, while MIZσ used grid cells with a median
standard deviation of SIC anomaly above 0.11, derived from the probabil-
ity density function. This research represents a novel exploration of the
Arctic MIZ using a SIC anomaly-based approach. Both definitions showed
similar seasonal trends, but MIZ_σ peaked during freeze-up (October) and
break-up (July), while MIZ_t peaked in summer (August). MIZ_σ fractions
were consistently higher than those from MIZ_t across all seasons. Finally,
October and August exhibit the most rapid increases in both MIZ_t and
MIZ_σ fractions, coinciding with accelerated sea ice decline. These results
highlight the importance of selecting an MIZ definition tailored to specific
research or applications.

How to cite: Soleymani, A., Crawford, A., and Scott, K. A.: Interannual Variability of the Arctic Marginal Ice Zone Over Four Decades: A Comparison of Two Definitions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7366, https://doi.org/10.5194/egusphere-egu25-7366, 2025.

11:35–11:45

EGU25-4117

On-site presentation

Arctic regional changes revealed by clustering of sea-ice observations

Amelie Simon, Pierre Tandeo, Florian Sévellec, and Camille Lique

Understanding the evolution of Arctic sea-ice is crucial due to its socio-economic impacts. Usual descriptors (e.g., sea-ice extent, sea-ice age and ice-free duration) quantify changes but do not account for the full seasonal cycle. Here, using satellite observations of sea-ice concentration (1979-2023), we perform a k-means clustering of the Arctic sea-ice seasonal cycle, initializing with equal quantile separation and using Mahalanobis distance. We identify four optimal seasonal cycle clusters: ocean-only (no ice year-round), permanent sea-ice (full coverage with a minimum of 0.7 sea-ice concentration), and two clusters showing ice-free conditions, namely partial and full winter freezing. The latter has larger sea-ice concentration in winter, more abrupt melting and freezing, and shorter ice-free season than the former. Hence, the starting dates for melting are good precursors of ice-free duration. The probability of belonging to the open-ocean cluster increased by 1.6% per decade mostly due to cluster spatial expansion in the Atlantic side. The permanent sea-ice decreased by 1.5% per decade with a likelihood reduction in the Pacific side. The last two clusters do not exhibit any trend but spatial shifts occur. We further diagnose cluster transitions and subsequently infer regions of stabilization and destabilization. The East Siberian and Laptev Seas are destabilized (losing their typical permanent sea-ice seasonal cycle) while the Kara and Chukchi Seas have stabilized (experiencing a new typical seasonal cycle, the partial winter-freezing cluster). This work provides a new way to describe Arctic regional changes using a statistical framework based on physical behaviours of sea-ice.

How to cite: Simon, A., Tandeo, P., Sévellec, F., and Lique, C.: Arctic regional changes revealed by clustering of sea-ice observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4117, https://doi.org/10.5194/egusphere-egu25-4117, 2025.

11:45–11:55

EGU25-1736

ECS

On-site presentation

Seasonal change in floe size distribution at the northern entrance of the Arctic Nares Strait derived from Sentinel-1 images: retrieval method and case study in 2019

Fan Yang, Tingting Liu, and Ruibo Lei

Floe size distribution (FSD) is a parameter that describes the geometric features of discrete sea ice floes. It plays a crucial role in the momentum, energy and mass exchanges between atmosphere and ocean over the frozen waters, through regulating the ice field consolidation and lead distribution. Previous studies on characterizing the Arctic FSD mainly focused on the marginal ice zone (MIZ) in the peripheral waters. However, continuous ice thinning and enhanced response of ice motion to wind forcing lead to more intense sea ice fragmentation in the Arctic pack ice zone (PIZ). This, in turn, further strengthens sea ice mobility and contributes to the Arctic sea ice outflow. Therefore, this study focuses on the FSD at the northern entrance of the Nares Strait, a region characterized by severe ice conditions and high ice dynamics, which is essential for the outflow of Arctic multi-year ice. We firstly developed a parameter-free method based on a deep convolutional neural network and graph partitioning algorithm to retrieve floes geometry in this specific region from Sentinel-1 synthetic aperture radar images. Using manual produced ground truth data as the benchmark, the proposed method outperformed four conventional algorithms, including K-means, dynamic local thresholding, watershed, and kernel graph cut, in visual assessment and quantitative evaluation. It achieved an overall accuracy and F1-score of 97.0% and 97.6%, respectively. Subsequently, the method was applied to obtain the FSD at the northern entrance of the Nares Strait in 2019. Result showed that the FSD exhibited a power-law distribution for floes with mean caliper diameter ranging from approximately 3 to 53 km for most time. Exceptions occurred in early autumn at the onset of ice freezing, where invalid power law exponent α emerged due to the finite image size and/or abnormal ice advection. We further found that the variations of α were primarily regulated by sea ice thickness, ice advection, and events of floe breakup and welding. Compared with that identified in the Arctic MIZ, seasonal change in FSD in this region appeared relatively moderate, owing to high sea ice concentration and different regulating mechanisms.This work provides a practical algorithm for floe geometry retrieval and a rare case study on the seasonal change in FSD in the Arctic PIZ.

How to cite: Yang, F., Liu, T., and Lei, R.: Seasonal change in floe size distribution at the northern entrance of the Arctic Nares Strait derived from Sentinel-1 images: retrieval method and case study in 2019, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1736, https://doi.org/10.5194/egusphere-egu25-1736, 2025.

11:55–12:05

EGU25-16126

On-site presentation

Hydrodynamic interactions significantly effect frazil ice crystal collisions in the ocean

Deborah Rhee, Andrew Wells, and Ian Hewitt

The early stages of sea ice formation often involve frazil ice, formed as a crystal suspension in supercooled turbulent water. Frazil ice formation is known to play a significant role in the development of Antarctic Bottom Water, and is thus a key component of the global ocean circulation. However, despite its significance, frazil ice formation is still a poorly understood sea-ice process.

Understanding how frazil ice impacts the water column requires a model for the number, size and velocities of frazil ice crystals. All of these quantities change as the crystals collide and potentially fracture or flocculate. However, the conditions required for crystals to fracture or flocculate remain uncertain.

We develop a model for frazil ice collisions which includes the effect of hydrodynamic interactions between the crystals. This model allows us to determine the collision efficiency of crystal interactions, a quantity previously not included in frazil ice models. We find that the collision efficiency strongly depends on the level of turbulence and in more quiescent flow, hydrodynamic interactions significantly reduce the number of crystal collisions.

Our model also suggests that nucleation is more likely to occur by small dendritic structures breaking off from the crystal surface, rather than from crystals snapping. This has implications for the number and size distribution of the resulting crystals.

We discuss the utility of our model for parameterising frazil ice population models and discuss the implications for understanding the role of frazil ice in the development of Antarctic Bottom Water.

How to cite: Rhee, D., Wells, A., and Hewitt, I.: Hydrodynamic interactions significantly effect frazil ice crystal collisions in the ocean, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16126, https://doi.org/10.5194/egusphere-egu25-16126, 2025.

12:05–12:15

EGU25-6431

ECS

On-site presentation

Sea-ice turbulent dynamics derived from Lagrangian buoys tracks

Romain Caneill, Pierre Rampal, and Mickaël Bourgoin

The Arctic, a central place to our global climate, is mainly covered by sea ice. The motion of sea ice is important as it exports freshwater from the central Arctic to the North Atlantic. The motion itself is on a large scale driven by atmospheric and oceanic patterns. Yet, fluctuating velocities are superimposed on this mean circulation. These fluctuating velocities represent atmospheric and oceanic turbulence as well as internal sea ice dynamics. One main property of the sea-ice fluctuating velocity is its high intermittency induced by the fracturing of sea ice. To understand the dynamical properties of the fluctuating velocities, we study the kinematic energy using Lagrangian statistics derived from observed trajectories, inspired by classical multi-scale analysis from fluid turbulence. We use trajectories from the International Arctic Buoy Program (IABP), covering a 40-years period of the sea-ice covered central Arctic, both in summer and winter seasons. The energy spectra is found to follow the Kolmogorov -5/3 scaling, which is classically observed for fluid turbulence. The cross-correlated acceleration-velocity structure function (Sau) provides information on the energy cascade between spatial scales. While noisy, the computed Sau is negative in the 10-1000 km scales, which would imply a direct energy cascade (from large to small scales). The imprint of the atmospheric and oceanic turbulence must present a direct energy cascade. However, as the characteristics of the sea-ice fluctuating velocity fields are also impacted by the sea ice internal physics, such a negative cascade could result from the energy transfer from large-scale forcing fields down to the smallest fractures in the sea ice. While the statistics of the sea-ice turbulence are similar over the whole 40-years period, differences are found between the old and modern data. These changes point towards a transition of dynamical regime that the Arctic sea ice is undergoing.

How to cite: Caneill, R., Rampal, P., and Bourgoin, M.: Sea-ice turbulent dynamics derived from Lagrangian buoys tracks, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6431, https://doi.org/10.5194/egusphere-egu25-6431, 2025.

12:15–12:25

EGU25-4593

ECS

Highlight

On-site presentation

How Flat is Flat? Investigating the Spatial Variability of Snow Surface Temperature and Topography on Landfast Sea Ice Using Drone-Based Mapping

Julia Martin, Ruzica Dadic, Brian Anderson, Roberta Pirazzini, Lauren Vargo, and Oliver Wigmore

How do snow distribution patterns influence the surface temperature of snow on sea ice? Despite its crucial role in influencing sea-ice energy balance, snow on Antarctic sea ice remains poorly understood.

To address this knowledge gap, we used an Uncrewed Aerial Vehicle (UAV) and ground-based measurements to produce a Digital Elevation Model (DEM) of the snow topography and map the snow surface temperature over relatively uniform landfast sea ice in McMurdo Sound, Ross Sea, Antarctica during our field season in November-December 2022.

A key methodological innovation in this study is an algorithm that corrects thermal drift caused by Non-Uniformity Correction (NUC) events in the DJI Matrice 30T thermal camera. The new algorithm minimizes temperature jumps and distortion in the imagery, ensuring consistent and accurate high-resolution (9 cm/px) snow surface temperature maps.

As expected, largest surface temperature anomalies were associated with sediment deposition on the snow surface, which was identified by low red band values in UAV optical imagery. Additionally, we found that the small-scale topography on a seemingly flat snow field significantly influences the incoming solar radiation (insolation) at the point scale. Using a model that accounts for topographical effects on insolation, we found that assuming uniform insolation over our study area (200x200 m) underestimated insolation variability due to relatively small-scale surface topography. The modeled mean insolation for the overflown study site, which accounts for surface topography, is 592 ± 90 Wm^-2(2 Standard Deviations), whereas the mean measured insolation at the point scale is 593 ± 20 Wm^-2. This shows that assuming a flat surface fails to represent the full range of insolation and may impact non-linear energy balance processes.

Our results improve our understanding of snow's spatial distribution, how it influences snow surface temperatures and how it may influence the sea-ice energy balance.

How to cite: Martin, J., Dadic, R., Anderson, B., Pirazzini, R., Vargo, L., and Wigmore, O.: How Flat is Flat? Investigating the Spatial Variability of Snow Surface Temperature and Topography on Landfast Sea Ice Using Drone-Based Mapping , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4593, https://doi.org/10.5194/egusphere-egu25-4593, 2025.

12:25–12:30

Poster Preview

Posters on site: Fri, 2 May, 10:45–12:30 | Hall X4

Display time: Fri, 2 May, 08:30–12:30

Chairpersons: Adam Bateson, Daniel Feltham

X4.26

EGU25-11669

ECS

Predictability of Rapid Sea Ice Loss Events in CESM2 model

Annelies Sticker, Francois Massonnet, Thierry Fichefet, and Alexandra Jahn

The decline in summer Arctic sea ice extent that has been underway for several decades is set to continue until summer Arctic sea ice disappears completely by the middle of the century, according to the latest climate projections. Based on observations and these climate model projections, the rate at which sea ice is retreating is not linear: the decrease in the Arctic sea ice cover is marked by periods of abrupt sea ice decline.
Specifically, it has been suggested that these rapid ice loss events (RILEs) will become a frequent phenomenon in the coming decades. The causes of such events remain poorly understood and we are still unable to reliably predict their evolution. By running sensitivity simulations with the CESM2 model, we aim to investigate the predictability of RILEs and the factors contributing to their onset. Simulations initialized in the year of the event demonstrate predictive skill, whereas those initialized one to two years prior exhibit limited predictive capability. Additional simulations are designed to explore the role of the sea ice mean state and previous oceanic conditions in driving these events.

How to cite: Sticker, A., Massonnet, F., Fichefet, T., and Jahn, A.: Predictability of Rapid Sea Ice Loss Events in CESM2 model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11669, https://doi.org/10.5194/egusphere-egu25-11669, 2025.

X4.27

EGU25-12243

Barents Sea ice volume budget in a 40-member historical ensemble of a global climate model

David Schroeder, Danny Feltham, and Jake Aylmer

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 trends. The variability of the Arctic-wide winter sea ice extent is largely determined by that in the Barents Sea. The relative impact of oceanic and atmospheric processes has been discussed controversially in the literature for different time scales. Here, we provide a volume budget analysis over the period from 1960 to 2014 based on a 40-member ensemble with the Hadley Centre global climate model HadGEM3 using a horizontal resolution of around 10km in the Arctic. Sea ice gain is Arctic-wide dominated by sea ice growth with a net contribution from sea ice advection and divergence generally below 10%. However, in the Barents Sea this contribution reaches 100% with significant ice growth only taking place on the northern edge of the Barents Sea. Locally up to 5m of annual sea ice are advected in certain place of the Barents Sea. Consequences for the importance of atmospheric and oceanic drivers on variability and trend will be analysed.

How to cite: Schroeder, D., Feltham, D., and Aylmer, J.: Barents Sea ice volume budget in a 40-member historical ensemble of a global climate model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12243, https://doi.org/10.5194/egusphere-egu25-12243, 2025.

X4.28

EGU25-20855

Surface energy budget and its impact on the sea ice evolution in the Barents Sea

Caixin Wang and Keguang Wang

X4.29

EGU25-15695

ECS

Linking observations of Arctic summer sea ice thickness and melt ponds to model simulations

Lena Buth, Gerit Birnbaum, Thomas Krumpen, Niklas Neckel, Melinda Webster, and Christian Haas

Melt ponds on Arctic sea ice play a critical role in the ice-albedo feedback, influencing the Arctic energy budget and climate. While satellite-derived products provide broad-scale estimates of melt pond fraction, detailed information on individual pond properties and direct high-resolution comparisons with other sea ice properties remain limited. In this study, we combine airborne observations of melt ponds and sea ice thickness with model output to evaluate the representation of melt ponds in Arctic sea ice simulations.

Melt pond properties, in particular pond fraction, along flight tracks are derived using high-resolution RGB imagery, while coincident ice thickness measurements are obtained using the EM-Bird, a tethered electromagnetic sensor. These observations are analyzed to explore the relationship between melt pond fraction, ice thickness, and surface morphology, providing a comprehensive observational perspective. This relationship is then compared to simulations from the Los Alamos Sea Ice Model (CICE) and its column physics module, Icepack, which include melt pond parameterizations based on sea ice thickness categories and the distribution of level and deformed ice within these categories. By examining how observed relationships between melt pond fraction and ice thickness agree with model results, this study aims to evaluate models in realistically simulating melt pond properties and processes on Arctic sea ice.

How to cite: Buth, L., Birnbaum, G., Krumpen, T., Neckel, N., Webster, M., and Haas, C.: Linking observations of Arctic summer sea ice thickness and melt ponds to model simulations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15695, https://doi.org/10.5194/egusphere-egu25-15695, 2025.

X4.30

EGU25-15728

ECS

Persistent Antarctic sea ice biases in spite of recent observed sharp decline

(withdrawn)

Lettie A. Roach and Lorenzo M. Polvani

X4.31

EGU25-5747

Tropospheric BrO and sea ice in the surroundings of the Weddell Sea (Antarctica)

Cristina Prados-Roman, Laura Gómez-Martín, Olga Puentedura, Jose Antonio Adame, Mónica Navarro-Comas, Héctor Ochoa, and Margarita Yela

Sea ice plays a crucial role in atmospheric chemistry. In the polar regions, particularly during spring, large amounts of bromine migrate from reservoir state in sea ice to gas phase (Br₂, BrCl) and subsequently to reactive bromine (Br, BrO). This involves autocatalytic processes which deplete surface ozone and oxidizes toxic mercury, facilitating its entry into the trophic chain.

During the last years, in collaboration with the Argentinian Antarctic Directorate/Argentine Antarctic Institute, INTA has been performing long-term observations of relevant tropospheric trace gases aiming at characterizing the polar photochemistry and its link to environmental conditions. These observations are performed from Ushuaia (USH, 54˚S, 68˚S) co-operating with SMN (Argentine National Meteorological Service), and from the Antarctic sites of Marambio (MAR, 64˚S, 56˚W) and Belgrano (BEL, 78˚S, 34˚W). One of the main objectives of the current nationally funded research project GARDENIA (Gases and aerosols in Antarctica: distribution, context and variability) is to investigate the role that sea ice plays on tropospheric halogens in Antarctica.

The work presented herein focuses on a long-term (2015-2022) study of reactive bromine in the context of the sea ice surrounding the Weddell Sea sector. This will be assessed by combining long-term observations of tropospheric BrO from the Antarctic sites of MAR and BEL, with back trajectories computed using the HYSPLIT model (Stein et al., 2015) and the sea ice state of development facilitated by the U.S. National Snow and Ice Data Center (NSIDC, 2020). This pluriannual study suggests that not only sea ice extent but also sea ice state of development should be further investigated in the framework of cryosphere-atmosphere interactions in a context of global warming.

References:

Stein et al., (2015): NOAA’s HYSPLIT atmospheric transport and dispersion modeling system. Bull. Am. Meteorol. Soc. 96, 2059–2077.

NSIDC (2020): U.S. National Ice Center Arctic and Antarctic Sea Ice Concentration and Climatologies in Gridded Format. (G10033, Version 1). Fetterer, F. & Stewart, J. S. (Comps.).

How to cite: Prados-Roman, C., Gómez-Martín, L., Puentedura, O., Adame, J. A., Navarro-Comas, M., Ochoa, H., and Yela, M.: Tropospheric BrO and sea ice in the surroundings of the Weddell Sea (Antarctica), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5747, https://doi.org/10.5194/egusphere-egu25-5747, 2025.

X4.32

EGU25-8158

ECS

Arctic Sea Ice Lead Detection From China FY-3D MERSI-II Quality Enhanced Images

Lu Zhang, Fengming Hui, Xiao Cheng, Gang Li, Xiaopo Zheng, Zhaohui Chi, Hang Yu, Ling Sun, and Shengli Wu

The 250-m resolution Chinese Fengyun-3D (FY-3D) Medium Resolution Spectral Imager-II (MERSI-II) thermal infrared (TIR) data can help us understand the rapid variations of Arctic sea ice leads, which are key features within the sea ice. The challenges of utilizing the 250-m FY-3D MERSI-II TIR data are bowtie effect and nonuniform brightness stripe noise.

While previous solutions have addressed these issues separately, this study introduces a more integrated two-step image quality enhancement strategy for MERSI-II TIR images. It considered the interactions between the two issues and overcame the excessive or inadequate destriping in existing models due to the ideal stripe-type assumption. Specifically, for the bowtie effect, a rigorous geometric model suitable for MERSI-II was constructed. For the nonuniform brightness stripe noise, a novel adaptive multiscale frequential (AMSF) algorithm was developed. The proposed strategy was outperforming existing methods in quantitative and qualitative assessments with higher efficiency on both bowtie effect and stripe noise removal. To validate the effectiveness of proposed strategy in sea ice lead detection, the temperature anomaly method was used to extract leads in winter Arctic Baffin Bay from images of varying quality. The results show that the overall accuracy improved from 0.88 to 0.95.

However, the sea ice lead extraction results from the traditional temperature anomaly method on TIR images is affected by the subjective selection of window sizes and is prone to misclassification caused by clouds. To address these issues, a novel deep learning method is applied to quality-enhanced FY-3D MERSI-II TIR images, which adaptively extracts sea ice leads and reduces cloud interference. Several commonly image segmentation networks: PSP Net, U-Net, and Deeplabv3, were compared to identify the most suitable network for extracting sea ice leads from TIR images, with the U-Net architecture providing the best segmentation results.

Nevertheless, the segmentation results of U-Net network still exist some misclassification caused by clouds. Therefore, the network was further optimized by introducing frequency domain filtering modules, which eliminate the interference of low-frequency clouds and enhance the model's focus on the high-frequency regions like linear features of sea ice leads, thereby improving the extraction accuracy of sea ice leads from thermal infrared images. Results show that the novel network effectively reduces misclassification caused by clouds, thereby providing more accurate and reliable sea ice lead data.

How to cite: Zhang, L., Hui, F., Cheng, X., Li, G., Zheng, X., Chi, Z., Yu, H., Sun, L., and Wu, S.: Arctic Sea Ice Lead Detection From China FY-3D MERSI-II Quality Enhanced Images , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8158, https://doi.org/10.5194/egusphere-egu25-8158, 2025.

X4.33

EGU25-6280

ECS

Exploring Nonlinear Dynamics of Sea Ice Deformation Using Deep Learning-Based Optical Flow

Matias Uusinoka, Arttu Polojärvi, Jari Haapala, and Jan Åström

Recent advances in high-resolution deep learning-based optical flow and radar imaging have opened new opportunities to analyze sea ice deformation at unprecedented spatiotemporal scales. Building on our novel deep neural network-based motion-tracking method we can now provide insights into the fundamental processes driving sea-ice deformation. For the ship radar data from the MOSAiC expedition, our approach resolves deformation events at scales down to 10 meters and 10-minute intervals across a 10 km × 10 km domain, generating on the order of 10^8 deformation-rate estimates per day with accuracy comparable to, or exceeding, that of traditional measurements. By quantifying the presence of nonlinear dynamics in intermediate-scale ice dynamics, our analysis refines established scaling laws and reveals emergent behaviors in deformation processes. Our findings emphasize the importance of seasonality and spatial heterogeneity in determining the mechanical response of Arctic sea ice under changing climate conditions.

How to cite: Uusinoka, M., Polojärvi, A., Haapala, J., and Åström, J.: Exploring Nonlinear Dynamics of Sea Ice Deformation Using Deep Learning-Based Optical Flow, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6280, https://doi.org/10.5194/egusphere-egu25-6280, 2025.

X4.34

EGU25-8605

ECS

Assessing Arctic Marginal Ice Zone Dynamics from 1979 to 2023: Insights into Long-Term Variability and Morphological Changes

Lijuan Song and Xi Zhao

Against the backdrop of global climate change, the continued decline in Arctic sea ice extent and thickness has intensified the dynamic evolution of the marginal ice zone (MIZ). As a critical transitional region between the open ocean and pack ice, the MIZ plays a pivotal role in mediating ocean-atmosphere interactions, influencing sea ice dynamics, and supporting polar ecosystems. Therefore, we investigated the dynamic variability and morphological evolution of the Arctic MIZ from 1979 to 2023 using the Bootstrap sea ice concentration (SIC) product. Results reveal that while the overall MIZ extent has remained relatively stable over the long term, the MIZ fraction (i.e., the ratio of MIZ extent to Arctic sea ice extent) has increased significantly, particularly during summer. The MIZ has experienced a northward shift over the past four decades, with an accelerated rate of migration post-2000. This shift is accompanied by morphological changes, characterized by a smoother ice edge and more compact ice during late summer. A significant change point was detected in 2006, signaling a structural shift in MIZ dynamics. Post-2006, the frequency of MIZ occurrence increased in high-latitude regions, particularly across the Beaufort, Chukchi, East Siberian, and Laptev Seas. Building on these findings, we are currently employing deep learning techniques combined with optical data and Sentinel-1 SAR data to invert the distribution of sea ice floes within the Arctic MIZ during the melt season (May to August), with a focus on analyzing the evolution of these floes during the retreat of Arctic sea ice. These findings provide critical insights into Arctic sea ice dynamics, highlighting the evolving nature of the MIZ and its role in shaping the future Arctic ice regime under continued climate change.

How to cite: Song, L. and Zhao, X.: Assessing Arctic Marginal Ice Zone Dynamics from 1979 to 2023: Insights into Long-Term Variability and Morphological Changes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8605, https://doi.org/10.5194/egusphere-egu25-8605, 2025.

X4.35

EGU25-5195

ECS

Simulating in-plane failure of sea ice floes in the Arctic using discrete element methods

Adam Bateson, Daniel Feltham, David Schröder, Scott Durski, Jennifer Hutchings, Rajlaxmi Basu, and Byongjun Hwang

Sea ice is made up of individual pieces of ice called floes, and these floes can vary in size from scales of just metres to tens of kilometres. There has been much recent interest in simulating variable floe size in continuum models of sea ice, since floe size can impact the evolution of the sea ice cover via several mechanisms including lateral melt volume, rheology, and momentum exchange. These simulations usually only account for the breakup of floes driven by waves. Observations of the Arctic sea ice cover show that there also exists several mechanisms of in-plane floe failure resulting from processes including wind forcing, interactions between neighbouring floes, and thermal weakening. The limited availability of in-situ observations of these in-plane failure mechanisms inhibits the development of accurate parameterisations for use in continuum models. Discrete element models (DEMs) are able to resolve relevant properties such as shear and normal stress and sea ice strength at the sub-floe scale, and they can therefore directly simulate crack formation and propagation. DEMs can thus be applied as a virtual laboratory of floe breakup and be used to supplement observations to develop a more complete understanding of floe failure mechanisms.

In this study, we use well-characterised case studies of floe breakup events to test sea ice DEM capability in simulating the mechanical breakup of floes. We will present results from a series of DEM simulations to explore these observed case studies of floe breakup and identify important parameters and processes that impact whether the floe fails and the resulting floe sizes. We will also discuss the challenges that have emerged in applying a sea ice DEM to floe fragmentation at smaller scales.

How to cite: Bateson, A., Feltham, D., Schröder, D., Durski, S., Hutchings, J., Basu, R., and Hwang, B.: Simulating in-plane failure of sea ice floes in the Arctic using discrete element methods, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5195, https://doi.org/10.5194/egusphere-egu25-5195, 2025.