This dataset accompanies the paper 'Antarctic Marine Viruses: A Review and Future Perspectives' and compiles quantitative data on Antarctic marine viruses, including sampling locations, viral abundance, lytic viral production, carbon release by lysis, associated environmental factors and others. The primary aim of this dataset is to consolidate existing measurements on Antarctic marine viruses, facilitating future research on viral ecology, biogeochemical cycling, ecosystem dynamics, climate modelling in polar marine environments.

This project used marine benthic imagery and fine-scale oceanographic data to refine existing models of the contemporary circumpolar distribution of Antarctic benthic biodiversity, and used oceanographic models developed in the ACEAS program to generate spatial predictions of the distribution of various aspects on benthic biodiversity and blue carbon under future climate scenarios. ***Access to data is currently embargoed, to be made available ~June 2026***

The Antarctic Slope and Coastal Currents are key ocean circulation features around the Antarctic margin, driving the transport of heat, salt and nutrients around the continent. They are also coupled to the hydrography of the region, where in certain locations around Antarctica, the weakening of these currents facilitate the cross-slope transport of warm water onto the continental slope, inducing basal melt of ice shelves and increasing glacial and ice sheet flow, resulting in global sea level rise. However, a lack of observations in this remote region limits our understanding of these current systems. This work examined the dynamics of the Antarctic Slope and Coastal Currents, which shed new light on their temporal and spatial variability and has implications on their roles in the climate system. In Part I of this work we examined an intrinsic variability in warm water intrusions on the Antarctic continental slope through canyons, with narrower canyons resulting in more irregular intrusions in an idealised channel ocean model. Using dynamical systems theory we found that this intrinsic variability arises from the Antarctic Slope Current, driven by feedbacks between eddy generation and surface wind stress input. In Part 2 of this work we compared eddy and current velocities across a 1/10 degree and 1/20 degree regional ocean-sea ice model, showing that eddy activity is more than doubled in the 1/20 degree model than the 1/10 degree model, with minimal differences in current velocities. Eddy activity and coastal current velocities were found to exhibit a hysteresis loop with sea ice, with sea ice growth leading a dampening of eddy activity and current velocities, a feature more strongly represented in the higher resolution model. In Part III of this work modelled Antarctic Slope Current changes under a transient meltwater perturbation were investigated, representative of projected meltwater inputs under climate change. The Antarctic Slope Current increases non-linearly over time as more meltwater is added around Antarctica, with an increased acceleration towards the middle of the 21st century. The non-linear acceleration is attributed to a strengthened salinity gradient across the continental slope, driven by poleward shifting warm waters. This work provides new dynamical insight into the variability of these circulation features, but also motivates further investigation into these emergent phenomena to better understand their impact on our changing Antarctic climate.

The Whole-Antarctic Ocean Model (WAOM) is based on the Regional Ocean Modelling System (ROMS) with thermodynamic ice shelf interactions following Galton-Fenzi et al. (2012)¹. WAOM simulations of the present-day (year 2007) were used to investigate the physical drivers of Antarctic ice shelf basal melting via an ocean heat budget in a longitudinal framework. Ice shelves buttress the Antarctic Ice Sheet and therefore are critical in the dynamics of ice sheet instability and its contribution to sea level rise. New insights into the seasonal melting mechanisms and its importance at circum-Antarctic scale were investigated in Dias et al (2025)². 1) Galton-Fenzi B, Hunter JR, Coleman R, Marsland SJ, & Warner RC. (2012) Modeling the basal melting and marine ice accretion of the Amery Ice Shelf, Journal of Geophysical Research: Oceans, 117 (C9). https://doi.org/10.1029/2012JC008214 2) Boeira Dias F, England MH, Morrison AK, & Galton-Fenzi B. (2025).: On the seasonal variability of ocean heat transport and ice shelf melt around Antarctica, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-3905

Data pertain to the analysis of Antarctic Ice Sheet (AIS) mass variability during different ENSO phases, using GRACE‑observed mass changes and modelled surface mass balance from RACMO2.4p1 over the period 2002–2022, together with associated atmospheric circulation patterns. The gridded datasets include derived quantities from the regression analyses for the defined ENSO periods and AIS mass‑change trends, provided in NetCDF format, along with the accompanying MATLAB code.

Data pertain to analysis of GRACE time series of mass change in both gridded and basin format. The GRACE data are based on the COST-G solution obtained from http://gravis.gfz-potsdam.de/home. Both 50km regular gridded data and basin-level time series are included, as well as the SAM and ENSO climate indices on which the regressions were based. The gridded datasets provided here include derived quantities from the regressions such as trends, uncertainties, and regression coefficients in NetCDF format.

The data result from an analysis of gridded satellite altimetry time series of ice surface elevation for the Antarctic Ice Sheet. Multiple linear regression is performed, including parameters associated with the cumulative sum of each of the Southern Annular Mode (SAM) and El Nino/Southern Oscillation (ENSO). The gridded data include the original data and the derived parameters and statistical values. Four grids are provided. Two grids are from separate regressions performed on data after applying one of two different spatial smoothing filters (10 km and 200 km Gaussian functions). Two further grids are from regressions after subtracting one of two firn densification models from the 10 km-smoothed altimetry data.