Revisiting Ice Flux and Mass Balance of the Lambert Glacier–Amery Ice Shelf System Using Multi-Remote-Sensing Datasets, East Antarctica

Due to rapid global warming, the relationship between the mass loss of the Antarctic ice sheet and rising sea levels are attracting widespread attention. The Lambert–Amery glacial system is the largest drainage system in East Antarctica, and its mass balance has an important influence on the stability of the Antarctic ice sheet. In this paper, the recent ice flux in the Lambert Glacier of the Lambert–Amery system was systematically analyzed based on recently updated remote sensing data. According to Landsat-8 ice velocity data from 2018 to April 2019 and the updated Bedmachine v2 ice thickness dataset in 2021, the contribution of ice flux approximately 140 km downstream from Dome A in the Lambert Glacier area to downstream from the glacier is 8.5 ± 1.9, and the ice flux in the middle of the convergence region is 18.9 ± 2.9. The ice mass input into the Amery ice shelf through the grounding line of the whole glacier is 19.9 ± 1.3. The ice flux output from the mainstream area of the grounding line is 19.3 ± 1.0. Using the annual SMB data of the regional atmospheric climate model (RACMO v2.3) as the quality input, the mass balance of the upper, middle, and lower reaches of the Lambert Glacier was analyzed. The results show that recent positive accumulation appears in the middle region of the glacier (about 74–78°S, 67–85°E) and the net accumulation of the whole glacier is 2.4 ± 3.5. Although the mass balance of the Lambert Glacier continues to show a positive accumulation, and the positive value in the region is decreasing compared with values obtained in early 2000.

Summertime turbulent heat fluxes at East Antarctica over neighbouring rocky and ice shelf sites

During the ninth Indian Scientific Expedition to Antarctica, in the year 1990, a micro-meteorological tower was installed at Maitri (70° S, 12° E) on the rocky terrain and on an experimental basis, on the nearby ice-shelf at the location of Dakshin Gangotri (70° 7¢ S, 11° 7¢ E). The synoptic features strongly influencing over the sites are the southeasterly katabatic winds from the polar cap and/or northerly low level warm and humid winds from the sea. These flows are responsible for the formation of strong surface based inversion and strong convective conditions at the ice shelf and rocky terrain during the minimum and maximum insolation periods, respectively. This paper presents a study of surface layer structure over two contrasting surfaces on near-coastal Antarctica and deals with the significance of the energy exchange processes over the rocky Antarctic region and the parameterization of turbulent fluxes over surfaces having strong inversion/convection conditions. The paper makes use of turbulence structure functions which are different from the conventional one given by Panofsky and Dutton (1984).

Predicting ocean-induced ice-shelf melt rates using a machine learning image segmentation approach

Through their role in buttressing upstream ice flow, Antarctic ice shelves play an important part in regulating future sea level change. Reduction in ice-shelf buttressing caused by increased ocean-induced melt along their undersides is now understood to be one of the key drivers of ice loss from the Antarctic Ice Sheet. However, despite the importance of this forcing mechanism most ice-sheet simulations currently rely on simple melt-parametrisations of this ocean-driven process, since a fully coupled ice-ocean modelling framework is prohibitively computationally expensive. Here, we provide an alternative approach that is able to capture the greatly improved physical description of this process provided by large-scale ocean-circulation models over currently employed melt-parameterisations but with trivial computational expense. We introduce a new approach that brings together deep learning and physical modelling to develop a deep neural network framework, MELTNET, that can emulate ocean model predictions of sub-ice shelf melt rates. We train MELTNET on synthetic geometries, using the NEMO ocean model as a ground-truth in lieu of observations to provide melt rates both for training and to evaluate the performance of the trained network. We show that MELTNET can accurately predict melt rates for a wide range of complex synthetic geometries and outperforms more traditional parameterisations for > 95 % of geometries tested. Furthermore, we find MELTNET's melt rate estimates show sensitivity to established physical relationships such as a changes in thermal forcing and ice shelf slope. This study demonstrates the potential for a deep learning framework to calculate melt rates with almost no computational expense, that could in the future be used in conjunction with an ice sheet model to provide predictions for large-scale ice sheet models.

Phylogenetically and functionally diverse microorganisms reside under the Ross Ice Shelf

Throughout coastal Antarctica, ice shelves separate oceanic waters from sunlight by hundreds of meters of ice. Historical studies have detected activity of nitrifying microorganisms in oceanic cavities below permanent ice shelves. However, little is known about the microbial composition and pathways that mediate these activities. In this study, we profiled the microbial communities beneath the Ross Ice Shelf using a multi-omics approach. Overall, beneath-shelf microorganisms are of comparable abundance and diversity, though distinct composition, relative to those in the open meso- and bathypelagic ocean. Production of new organic carbon is likely driven by aerobic lithoautotrophic archaea and bacteria that can use ammonium, nitrite, and sulfur compounds as electron donors. Also enriched were aerobic organoheterotrophic bacteria capable of degrading complex organic carbon substrates, likely derived from in situ fixed carbon and potentially refractory organic matter laterally advected by the below-shelf waters. Altogether, these findings uncover a taxonomically distinct microbial community potentially adapted to a highly oligotrophic marine environment and suggest that ocean cavity waters are primarily chemosynthetically-driven systems.

Grounding Event of Iceberg D28 and Its Interactions with Seabed Topography

Iceberg D28, a giant tabular iceberg that calved from Amery Ice Shelf in September 2019, grounded off Kemp Coast, East Antarctica, from August to September of 2020. The motion of the iceberg is characterized herein by time-series images captured by synthetic aperture radar (SAR) on Sentinel-1 and the moderate resolution imaging spectroradiometer (MODIS) boarded on Terra from 6 August to 15 September 2020. The thickness of iceberg D28 was estimated by utilizing data from altimeters on Cryosat-2, Sentinel-3, and ICESat-2. By using the iceberg draft and grounding point locations inferred from its motion, the maximum water depths at grounding points were determined, varying from 221.72 ± 21.77 m to 269.42 ± 25.66 m. The largest disagreements in seabed elevation inferred from the grounded iceberg and terrain models from the Bedmap2 and BedMachine datasets were over 570 m and 350 m, respectively, indicating a more complicated submarine topography in the study area than that presented by the existing seabed terrain models. Wind and sea water velocities from reanalysis products imply that the driving force from sea water is a more dominant factor than the wind in propelling iceberg D28 during its grounding, which is consistent with previous findings on iceberg dynamics.

Automated mapping of the seasonal evolution of surface meltwater and its links to climate on the Amery Ice Shelf, Antarctica

Surface meltwater is widespread around the Antarctic Ice Sheet margin and has the potential to influence ice shelf stability, ice flow and ice–albedo feedbacks. Our understanding of the seasonal and multi-year evolution of Antarctic surface meltwater is limited. Attempts to generate robust meltwater cover time series have largely been constrained by computational expense or limited ice surface visibility associated with mapping from optical satellite imagery. Here, we add a novel method for calculating visibility metrics to an existing meltwater detection method within Google Earth Engine. This enables us to quantify uncertainty induced by cloud cover and variable image data coverage, allowing time series of surface meltwater area to be automatically generated over large spatial and temporal scales. We demonstrate our method on the Amery Ice Shelf region of East Antarctica, analysing 4164 Landsat 7 and 8 optical images between 2005 and 2020. Results show high interannual variability in surface meltwater cover, with mapped cumulative lake area totals ranging from 384 to 3898 km2 per melt season. By incorporating image visibility assessments, however, we estimate that cumulative total lake areas are on average 42 % higher than minimum mapped values. We show that modelled melt predictions from a regional climate model provide a good indication of lake cover in the Amery region and that annual lake coverage is typically highest in years with a negative austral summer SAM index. Our results demonstrate that our method could be scaled up to generate a multi-year time series record of surface water extent from optical imagery at a continent-wide scale.

The Antarctic contribution to 21st century sea-level rise predicted by the UK Earth System Model with an interactive ice sheet

The Antarctic Ice Sheet will play a crucial role in the evolution of global mean sea-level as the climate warms. An interactively coupled climate and ice sheet model is needed to understand the impacts of ice—climate feedbacks during this evolution. Here we use a two-way coupling between the U.K. Earth System Model and the BISICLES dynamic ice sheet model to investigate Antarctic ice—climate interactions under two climate change scenarios. We perform ensembles of SSP1-1.9 and SSP5-8.5 scenario simulations to 2100, which we believe are the first such simulations with a climate model with two-way coupling between both atmosphere and ocean models to dynamic models of the Greenland and Antarctic ice sheets. In SSP1-1.9 simulations, ice shelf basal melting and grounded ice mass loss are generally lower than present rates during the entire simulation period. In contrast, the responses to SSP5-8.5 forcing are strong. By the end of 21st century, these simulations feature order-of-magnitude increases in basal melting of the Ross and Filchner-Ronne ice shelves, caused by intrusions of warm ocean water masses. Due to the slow response of ice sheet drawdown, this strong melting does not cause a substantial increase in ice discharge during the simulations. The surface mass balance in SSP5-8.5 simulations shows a pattern of strong decrease on ice shelves, caused by increased melting, and strong increase on grounded ice, caused by increased snowfall. Despite strong surface and basal melting of the ice shelves, increased snowfall dominates the mass budget of the grounded ice, leading to an ensemble-mean Antarctic contribution to global mean sea level of a fall of 22 mm by 2100 in the SSP5-8.5 scenario. We hypothesise that this signal would revert to sea-level rise on longer timescales, caused by the ice sheet dynamic response to ice shelf thinning. These results demonstrate the need for fully coupled ice—climate models in reducing the substantial uncertainty in sea-level rise from the Antarctic Ice Sheet.