Antarctic ice shelves border c. 75% of the Antarctic coastline, and often act to buffer grounded ice contributions to sea level rise. The mass balance and stability of ice shelves is partly determined by surface melting, which can lead to the formation of extensive surface meltwater systems that may result in hydrofracturing and eventual ice shelf disintegration. It is crucial, therefore, that we better understand the extent, onset, and duration of surface meltwater systems across Antarctic ice shelves. Surface meltwater systems comprise slush (saturated firn), ponded meltwater (lakes) and streams. Meltwater ponding may increase an ice shelf’s vulnerability to hydrofracture whereas runoff in streams may reduce it. Where meltwater re-freezes within the firn pack over successive seasons, it can drive firn densification, encouraging further surface ponding and therefore increased vulnerability to hydrofracture.

Here we use two random forest classifiers, trained separately for application to Sentinel-2 and Landsat 8 optical images, to map both slush and ponded water across Antarctic ice shelves from 2013 to 2021. Early results across the Antarctic Peninsula (AP) show marked spatial and inter-annual variability, with peaks in surface meltwater extent observed predominantly during the 2017/2018 and 2019/2020 melt seasons on the western AP ice shelves, but during the 2016/2017 melt season on the eastern AP ice shelves. Intra-annually, the extent of ponded water typically peaks in January-March, and the maximum extent of slush typically co-insides with or precedes this. Across most ice shelves, surface meltwater is observed towards the grounding line, close to areas of exposed bedrock and blue ice. However, on George VI Ice Shelf, surface meltwater extent is more extensive, and is observed across much of the northern portion of the ice shelf in the 2019/2020 melt season.

Icebergs impact the physical and biological properties of the ocean along their drift trajectory by releasing cold fresh meltwater and nutrients. This facilitates sea ice formation, fosters biological production and influences the local ocean circulation. The intensity of the impact depends on the amount of meltwater. A68 was the sixth largest iceberg ever recorded in satellite observations, and hence had a significant potential to impact its environment. It calved from the Larsen-C Ice Shelf in July 2017, drifted through the Weddell and Scotia Sea and approached South Georgia at the end of 2020. Finally, it disintegrated near South Georgia in early 2021. Although this is a common trajectory for Antarctic icebergs, the sheer size of A68A elevates its potential to impact ecosystems around South Georgia through release of fresh water and nutrients, through blockage and through collision with the benthic habitat.

In this study we combine satellite imagery data from Sentinel 1, Sentinel 3 and MODIS and satellite altimetry from CryoSat-2 and ICESat-2 to chart changes in the A68A iceberg’s area, freeboard, thickness, volume and mass over its lifetime to assess its disintegration and melt rate in different environments. We find that A68A thinned from 235 ± 9 to 168 ± 10 m, on average, and lost 802 ± 35 Gt of ice in 3.5 years. While the majority of this loss is due to fragmentation into smaller icebergs, which do not melt instantly, 254 ± 17 billion tons are released through melting at the iceberg’s base - a lower bound estimate for the fresh water input into the ocean. Basal melting peaked at 7.2 ± 2.3 m/month in the Northern Scotia Sea. In the vicinity of South Georgia we estimate that 152 ± 61 Gt of freshwater were released over 96 days, potentially altering the local ocean properties, plankton occurrence and conditions for predators. The iceberg may also have scoured the sea floor briefly. Our detailed maps of the A68A iceberg thickness change will be useful to investigate the impact on the Larsen-C Ice Shelf, and for more detailed studies on the effects of meltwater and nutrients released off South Georgia. Our results could also help to model the disintegration of other large tabular icebergs that take a similar path and to include their impact in ocean models.

The quantification of the sea ice mass balance as the marine part of the cryosphere depends on sea ice thickness satellite observations for the entire ice-covered oceans. The challenges to this task are numerous. Sea ice itself is a highly dynamic medium with a significant variability at meter scale and a strong seasonal cycle which significantly impacts it remote sensing signature. Satellite sensors must therefore provide precise observations at high spatial resolution to observe the full spread of the sea ice thickness distribution and its governing processes. Average thickness values for larger areas are sufficient for mass balance estimates, but available methods such as satellite altimetry and passive microwave remote sensing rely on indirect methods and auxiliary information. Even more challenging is the estimation of sea ice thickness during the presence of surface melt. In addition, suitable satellite sensors in orbits that enabling sea ice thickness retrieval in the inner Arctic Ocean have been in service only until recently in comparison to satellites capable of observing sea ice area. Thus, the assessment of the sea ice mass balance for longer time series is often based on reanalysis models and not Earth Observation data since satellite thickness data records observing the full sea ice cover are short and more than often do not provide information during the minimum of the annual sea ice cycle.

But when available the parameter for the mass budget is traditionally sea ice volume and mass. We will therefore present an available sea ice volume data record that is derived by data fusion of CryoSat-2 radar altimeter and SMOS L-Band passive microwave-based sea ice thickness information. Both methods have a complementary sensitivity to different thickness classes and optimal interpolation is employed for gap-less sea ice thickness information in the northern hemisphere since November 2010. The data record is generated for the ESA funded SMOS & CryoSat-2 Sea Ice Data Product Processing and Dissemination Service (CS2SMOS-PDS).

We discuss the characteristics of the data set and provide an overview of intended evolutions of the data set, specifically improvements to the spatial resolutions, a potential extension to the southern hemisphere and the addition of other available satellite sensors to the optimal interpolation. Within the context of the mass balance of the cryosphere we will share our thoughts on the significance of the CryoSat-2/SMOS based sea ice volume time series for climate applications in the context of its comparable short temporal and how this information can be presented more consistently to other components of the cryosphere.

Ice shelf calving is an important mass loss component of the Antarctic Ice Sheet (AIS). The discharge of the AIS is sensitive to changes in ice shelf extent and thickness due to ice shelf buttressing effects. Up to now, it has been challenging to continuously monitor calving front change as the manual delineation of ice shelf fronts is very time-consuming and suitable satellite data was missing. Since the launch of Sentinel-1, plenty of imagery over the Antarctic coastline is freely available. To overcome the tedious manual work, we present a novel ice shelf monitoring service called “IceLines” which enables the near-real-time tracking of front positions based on Sentinel-1 SAR data. IceLines is based on a Deep Learning (DL) algorithm to automatically extract the border between ocean and ice sheet from high volumes of Sentinel-1 data. Further processing creates a line shapefile for each front position based on suitable Sentinel-1 acquisitions to create a time series of calving front movement. A truncation algorithm secures that erroneously extracted fronts due to surface melt, wind roughening sea or dry snow are excluded from the time series. For validation, manually delineated fronts for randomly selected dates (outside the training data time period) are created. The accuracy is measured as the mean and median distance between DL-extracted and manually delineated fronts. Finally, the continuously extracted calving fronts are made available to the public via the DLR GeoService (geoservice.dlr.de). The webservice will provide researchers with up-to-date front positions on major Antarctic ice shelf extents. This will allow to easily include ice shelf front fluctuations in future analysis in order to provide more accurate estimates on the mass balance of the AIS.

Ice shelves play a crucial role in controlling rates of ice discharge across Antarctica’s grounding lines. Mass loss from ice shelves, predominately due to basal melting and calving, can reduce the buttressing force provided by ice shelves, leading to increased grounded ice discharge. Despite the importance of ice shelves, existing estimates of calving and freshwater fluxes from ice shelves have utilised disparate datasets valid for inconsistent time periods or have relied on invalid assumptions, resulting in a limited account of the health of many ice shelves and little indication of processes driving ice shelf mass imbalance. Here, we quantify these fluxes at annual temporal resolution during 2010 to 2019. On average during the study period, a calving flux of 1283±109 Gt yr-1 balances a melt flux of 1247±149 Gt yr-1. Inter-annual variations in the fluxes of both basal meltwater and calving mean that the melt contribution to ice shelf mass loss varies between 35% and 62%, with the lowest contributions in years with large calving events. These large (>100 Gt) calving events are rare (8 events during 2010-2019), yet account for 35% of the total ice shelf calving flux, highlighting the importance of large calving events for ice shelf mass balance. Eighty percent of ice shelves, including many in East Antarctica, are melting at or faster than their balance rates, indicating that ocean-driven erosion of ice shelf grounding lines is widespread around Antarctica. Furthermore, we find a significant and strong positive correlation (R=0.68) between basal melt flux and grounding line discharge, implying that ocean-driven melt may pace grounded ice loss from Antarctica.

Sea ice is a key component of the earth’s climate system as it modulates the energy exchanges and associated feedback processes at the air-sea interface in polar regions. These exchanges strongly depend on openings in the sea-ice cover, which are associated with fine-scale sea-ice deformations. Viscous-plastic sea-ice rheologies, used in most numerical models, struggle to represent these fine-scale sea-ice dynamics without going to very costly horizontal resolutions (~1km). One solution is to use a rheological framework based on brittle mechanics associated with damage propagation to simulate such deformations. This approach enables to reproduce the characteristics of observed sea-ice deformations with little dependency on the mesh resolution. Here we present results from the first ocean--sea-ice coupled model that uses such a rheological framework. The sea-ice component is given by the neXt generation Sea-Ice Model (neXtSIM), while the ocean component is adopted from the Nucleus for European Modelling of the Ocean (NEMO).

Results for the period 2000-2018 are evaluated using remote sensing observation datasets for each metric of interest: the OSI-SAF drift and concentration datasets for ice drift and extent, the CS2SMOS ice thickness dataset for ice volume and the RGPS dataset for sea-ice deformations. We also evaluate the winter ice mass balance of the model using a recent dataset of sea-ice volume changes estimated using version 2.0 of the ESA CCI sea-ice thickness dataset combined to the Centre ERS d’Archivage et de Traitement (CERSAT) sea-ice motion dataset. We find that sea-ice dynamics are well represented in the model, showing a remarkable match with satellite observations from large scales (sea-ice drift) to small scales (sea-ice deformation). Other sea-ice properties relevant for climate, i.e., volume and extent, also show a good match with satellite observations. We assess the relative contribution of dynamical vs. thermodynamic processes to the sea-ice mass balance in the Arctic Basin and find a good agreement with ice volume changes estimated from the ESA CCI sea-ice thickness dataset in the winter, especially for the dynamical contribution.

Using the unique capability of the model to reproduce sea-ice deformations, we estimate the contribution of leads and polynyas to winter ice production. We find that ice formation in leads and polynyas adds up 25% to 40% of the total ice growth in pack ice in winter, showing a significant increase over the 18 years covered by the model simulation. This coupled framework opens new opportunities to understand and quantify the interplay between small-scale sea-ice dynamics and ocean properties that cannot be inferred from satellite observations.