Outline This is the Southern Ocean Monthly Climatology of Yamazaki et al. "Unlocking Southern Ocean Under-ice Seasonality with a New Monthly Climatology". The interpolation method follows Barth et al. (2014) available via DIVAnd Julia package (https://github.com/gher-uliege/DIVAnd.jl). CTD data sourced from Argo, MEOP, and World Ocean Database (including low resolution ocean station data). The dataset covers south of 40S and above 2000 dbar (above 1000 dbar for "_minimal"). The horizontal grid is 1/4 and 1/2 degrees in latitude and longitude, and the vertical grid is the 66 WOA layers. Mixed layer depth, temperature, salinity, crudely derived from max("Δσθ_10m=0.03kg/m³", "Holte&Talley"), are also provided in "_MLD". The following variables are included (* are excluded in "_minimal"): In-situ temperature (°C) in ITS-90 Practical salinity (psu) *Standard deviation of temperature (°C), inferred by the spread of observations *Standard deviation of practical salinity (psu), inferred by the spread of observations *Interpolation error of temperature (°C), inferred by the sparsity of observations *Interpolation error of practical salinity (psu), inferred by the sparsity of observations *Cabbeling correction for temperature (°C) *Cabbeling correction for practical salinity (psu) *Density stabilization factor for temperature (°C) *Density stabilization factor for practical salinity (psu) Project Description The advent of under-ice profiling float and biologging techniques has enabled year-round observation of the Southern Ocean and its Antarctic margin. These under-ice data are often overlooked in widely used oceanographic datasets, despite their importance in understanding seasonality and its role in sea ice changes, water mass formation, and glacial melt. We develop a monthly climatology of the Southern using Data Interpolating Variational Analysis, which excels in multi-dimensional interpolation and consistent handling of topography and horizontal advection. The dataset will be instrumental in investigating the seasonality and improving ocean models, thereby making valuable under-ice observations more accessible.

This work aimed to understand the influences of tropical to high-latitude Southern Hemisphere teleconnections on Southern Ocean atmospheric circulation, the air-sea-sea ice system, and Antarctic sea ice variability. It also sought to investigate how the Southern Annular Mode (SAM) interacts with tropically-forced climate patterns such as Zonal Wave 3 (ZW3) to affect high-latitude atmospheric circulation and impact sea-ice.

Globally, terrestrially-breeding marine predators have experienced shifts in species distribution, prey availability, breeding phenology, and population dynamics due to climate change. These central-place foragers are restricted within proximity of their breeding colonies during the breeding season, making them highly susceptible to any changes in both marine and terrestrial environments. While ecologists have developed risk assessments to assess likely climate risk in various contexts, these often overlook critical breeding biology data. To address this knowledge gap, we developed a trait-based risk assessment framework, focusing on the breeding season and applying it to marine predators breeding in parts of Australian territory and Antarctica. Our objectives were to quantify climate change risk, identify specific threats, and establish an adaptable framework. The assessment considered 25 criteria related to three risk components: vulnerability, exposure, and hazard, while accounting for uncertainty. We employed a scoring system that integrated a systematic literature review and expert elicitation for the hazard criteria. Monte Carlo sensitivity analysis was conducted to identify key factors contributing to overall risk. Our results identified shy albatross (Thalassarche cauta), southern rockhopper penguins (Eudyptes chrysocome), Australian fur seals (Arctocephalus pusillus doriferus), and Australian sea lions (Neophoca cinerea) with high climate urgency. Species breeding in lower latitudes as well as certain eared seal, albatross, and penguin species were particularly at risk. Hazard and exposure explained the most variation in relative risk, outweighing vulnerability. Key climate hazards affecting most species include extreme weather events, changes in habitat suitability, and prey availability. We emphasise the need for further research, focusing on at-risk species, and filling knowledge gaps (less-studied hazard criteria, and/or species) to provide a more accurate and robust climate change risk assessment. Our findings offer valuable insights for conservation efforts, given monitoring and implementing climate adaptation strategies for land-dependent marine predators is more feasible during their breeding season.