Sea ice is both a key witness and driver of climate change. While sea ice extent and area is well described with observations during the last four decades, sea ice thickness and volumes changes remain poorly known. However, thickness is a mandatory variable to fully understand the past, present and future changes of sea ice. Despite improvements in sea ice thickness estimation from altimetry during the past few years thanks to SAR and laser altimetry, former radar altimetry missions such as Envisat and especially ERS-1 and ERS-2 have remained under exploited so far. ERS-2 arctic sea ice thickness has been recently retrieved thanks to a machine learning approach aiming at calibrating ERS-2 and Envisat against CryoSat-2. We are now able to extend the time series from ERS-1 for both polar oceans, allowing to propose a 29 years-long sea ice thickness and volume time series. Estimates are combined with uncertainties derived from a Monte Carlo methodology. Nearly 30 years of sea ice volume time series reveals that Arctic sea ice is melting by 120 +/- 45 km³/year up to 81.5 °N (-13.1  +/- 5.1 %/decade). Antarctic sea ice evolution has no significant trends along the whole period, but a volume drop is observed since 2016. For both hemispheres, prominent regional changes have been identified with a strong heterogeneity of trends across regions. Finally, comparisons between observations and models show increasing negative bias while going back in time.

How to cite: Bocquet, M., Fleury, S., Rémy, F., Garnier, F., and Moreau, T.: Arctic and Antarctic sea ice thickness and volume changes during the last 29 years from satellites, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8780, https://doi.org/10.5194/egusphere-egu23-8780, 2023.

Changes in the timing of Antarctic sea ice retreat and advance have been analyzed over 1979-2012, based on satellite sea ice concentration retrievals. The Ross Sea showed large trends towards earlier sea ice advance and later retreat whereas the Bellingshausen and Amundsen Seas showed opposite trends.

Since 2016, however, we find the occurrence of anomalously late advance and early retreat in the Ross and Weddell Seas and anomalously early advance and late retreat, west of the Peninsula. Trends in the timing of sea ice retreat and advance are consequently weaker over 1979-2022, than over 1979-2012. Here, we investigate the possible role of ocean thermodynamics and wind-driven ice drift in causing such anomalies and resulting trend weakening, using satellite and reanalysis data.

In most of the seasonal zone, anomalies in the date of advance strongly correlate with anomalies in the previous seasonal maximum sea surface temperature (SST) and in the previous date of retreat. This suggests that anomalies in the date of advance are caused by summer ocean heat uptake anomalies, themselves constrained by anomalies in the previous date of retreat. In a large outer band of the seasonal ice zone, however, anomalies in the timing of sea ice advance seem linked to anomalies in the magnitude of winter southerlies, suggesting a possible role for ice drift anomalies there.

By contrast, we find no clear correspondence between anomalies in the date of retreat and anomalies in winds or SST. We will provide more analysis to disentangle the thermodynamic and dynamic mechanisms causing anomalies in the date of retreat, based on a sea ice concentration budget decomposition.

How to cite: Himmich, K., Stammerjohn, S., Vancoppenolle, M., and Madec, G.: Understanding recent changes in Antarctic sea ice seasonality, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16687, https://doi.org/10.5194/egusphere-egu23-16687, 2023.

This work presents a new method for quantifying the Antarctic marginal ice zone (MIZ). The MIZ acts as an interface between the open Southern Ocean and the consolidated inner pack, and is generally described as area of sea ice affected by ocean surface waves. We use standalone CICE6, which includes a floe size distribution with atmospheric, oceanic and wave forcing, to simulate the evolution of Antarctic sea ice from 2010 – 2020. CICE output variables were categorised as static (sea ice concentration, age, thickness, etc.), thermodynamic, and dynamic. Unsupervised statistical methods were used to classify distinct sea ice regions and then to identify the dominant processes which contribute to the spatial and temporal variance of Antarctic sea ice cover. The unsupervised sea ice classification agrees with recent MIZ extent estimations using altimetry observations of wave attenuation. The addition of floe size information enhanced our MIZ classification to include high concentration pancake fields (which are promoted by waves). These results support the inclusion of floe size within sea ice modelling, and the importance of multi-variate approaches to describe sea ice.

How to cite: Day, N., Bennetts, L., and O'Farrell, S.: Unsupervised statistical classification of the Antarctic marginal ice zone., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11652, https://doi.org/10.5194/egusphere-egu23-11652, 2023.

Very rapid ice loss events (VRILEs) are extreme, local reductions in Arctic sea ice extent on timescales of days to weeks. They are poorly captured in operational forecasts that are used, for instance, to inform shipping through the Arctic Ocean. A better understanding of the drivers and underlying processes is thus critical to a range of stakeholders. We analyse summertime (May–September) VRILEs occurring in a simulation (1980–2022) with the sea ice model CICE forced by atmospheric reanalyses. Our configuration includes novel marginal ice physics such as a prognostic floe size distribution and an explicit form drag scheme. Most VRILEs are dominated by thermodynamic processes. However, many events occurring near the start or end of the melt season are driven by advective redistribution, often associated with the presence of a cyclone. We illustrate this with key case studies and generalise the results to all simulated VRILEs using simple metrics quantifying the dominant contributions to the sea ice concentration tendencies and atmospheric conditions in each event. Finally, a suite of parameter sensitivity studies highlights factors with potential to improve forecasting of VRILEs.

How to cite: Aylmer, J., Feltham, D., Methven, J., and Volonté, A.: Drivers of very rapid sea ice loss on sub-seasonal timescales, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4483, https://doi.org/10.5194/egusphere-egu23-4483, 2023.

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

How to cite: Davy, R., Rheinlaender, J., Rampal, P., Spensburger, C., Korosov, A., Williams, T., and Spengler, T.: Driving Mechanisms of an Extreme Winter Sea Ice Breakup Event in the Beaufort Sea, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14000, https://doi.org/10.5194/egusphere-egu23-14000, 2023.

Seasonal evolution of Arctic sea ice floe is caused by various fragmentation and melt processes. Those include melt fragmentation in summer due to weaker part of floe by melt ponds, legacy re-frozen leads or cracks, as well as mechanical breakup in spring due to ice deformation forcing. Understanding these fragmentation processes is important not only to evaluate recent Arctic sea ice decline, but also to improve climate models for the Arctic. The objective of this study is to investigate those fragmentation processes at individual floe scales, with hypothesis that fractal properties may differ between melt fragmentation and mechanical breakup. With that in mind, we collected the “floe-scale” data set of 1-m MEDEA images that contain floe-scale imagery before and after fragmentation, and calculated the floe size, perimeter and fractal properties at the floe scale. In this presentation, we will share preliminary results of those analysis, including the role of melt ponds and legacy refrozen leads or cracks in melt fragmentation and difference in fractal properties between melt fragmentation and mechanical breakup.

How to cite: Basu, R. and Hwang, B.: Fractal properties of Arctic sea ice floe fragmentation processes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14770, https://doi.org/10.5194/egusphere-egu23-14770, 2023.

The thermodynamic growth of sea ice is governed by heat transfer through the ice together with appropriate boundary conditions at the interfaces with the atmosphere and ocean. Several different representations of this process have been used in climate modelling, including the simplest zero-layer models (Semtner, 1976) and more complex partial-differential-equation-based models (Maykut & Untersteiner, 1971; Bitz & Lipscomb, 1999). Recently, these latter models have been extended to include a representation of the dynamic evolution of the salinity of sea ice based on mushy-layer theory (Turner et al., 2013; Griewank & Notz, 2013; Rees Jones and Worster, 2014). Salinity variation might be expected to have a significant effect on ice growth given that it controls the relative proportions of solid ice and liquid brine, and these materials have different thermal properties. 

In this study, we develop a simplified framework to investigate the effects of variations in the thermal properties of sea ice. We develop and test a quasi-static simplification. In this simplification, we apply a transformation to the underlying heat equation such that the spatial coordinate scales with the ice thickness. We then neglect the explicit time dependence. This procedure reduces the full partial differential equation to an ordinary differential equation. The solution is exact for constant forcing conditions. 

We show that ice salinity has only a modest effect on the growth rate, notwithstanding its large effect on the thermal properties of sea ice. The model allows us to unpick the physical causes, which are related to the trade-off between the effect of salinity on thermal conductivity and latent heat release. We calculate the growth of ice under steady and time-dependent forcing. Under steady forcing, the ice growth equation admits an analytical approximate solution, which compares well to numerical solutions. We show that saltier ice initially grows slightly faster but subsequently grows slightly slower, a further trade-off explaining the relatively weak sensitivity of ice growth to salinity.

Under time-dependent forcing, we show that the quasi-static model compares well to full partial-differential-equation-based models. So our approach offers intermediate complexity between zero-layer Semtner models and full models based on partial differential equations such as Maykut-Untersteiner/Bitz-Lipscomb/mushy-layer models.

References:
Semtner, A. J. (1976) J. Phys. Oceanogr. 6 (3), 379–389.
Maykut, G. A. & Untersteiner, N. (1971) J. Geophys. Res. 76 (6), 1550–1575.
Bitz, C. M. & Lipscomb, W. H. (1999) J. Geophys. Res. – Oceans 104 (C7), 15669–15677.
Turner, A. K., Hunke, E. C. & Bitz, C. M. (2013) J. Geophys. Res. – Oceans 118 (5), 2279–2294.
Griewank, P. J. & Notz, D. (2013) J. Geophys. Res. – Oceans 118 (7), 3370–3386.
Rees Jones, D. W. & Worster, M. G. (2014) J. Geophys. Res. – Oceans 119 (9), 5599–5621.

How to cite: Rees Jones, D.: Modelling the thermodynamic growth of sea ice: insights from quasi-static models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11105, https://doi.org/10.5194/egusphere-egu23-11105, 2023.