Climate change could irreversibly modify Southern Ocean ecosystems. Marine ecosystem model (MEM) ensembles can assist policy making by projecting future changes and allowing the evaluation and assessment of alternative management approaches. However, projected future changes in total consumer biomass from the Fisheries and Marine Ecosystem Model Intercomparison Project (FishMIP) global MEM ensemble highlight an uncertain future for the Southern Ocean, indicating the need for a region-specific ensemble. A large source of model uncertainty originates from the Earth system models (ESMs) used to force FishMIP models, particularly future changes to lower trophic level biomass and sea ice coverage. To build confidence in regional MEMs as ecosystem-based management tools in a changing climate that can better account for uncertainty, we propose the development of a Southern Ocean Marine Ecosystem Model Ensemble (SOMEME) contributing to the FishMIP 2.0 regional model intercomparison initiative. One of the challenges hampering progress of regional MEM ensembles is achieving the balance of global standardised inputs with regional relevance. As a first step, we design a SOMEME simulation protocol, that builds on and extends the existing FishMIP framework, in stages that include: detailed skill assessment of climate forcing variables for Southern Ocean regions, extension of fishing forcing data to include whaling, and new simulations that assess ecological links to sea-ice processes in an ensemble of candidate regional MEMs. These extensions will help advance assessments of urgently needed climate change impacts on Southern Ocean ecosystems.

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 Ocean (south of 40°S and above 2,000 m) using Data Interpolating Variational Analysis, which excels in multi-dimensional interpolation and consistent handling of topography and horizontal advection. The climatology successfully captures thermohaline variability under sea ice, previously hard to obtain, and outperforms other observation-based products and state estimate simulations in data fidelity, with smaller root-mean-square errors and biases. To demonstrate its multi-purpose capability, we present a qualitative description of the seasonal variation, including 1) the surface mixed layer, 2) the water mass volume census, 3) the Antarctic Slope Front, and 4) shelf bottom waters. Particularly, the circumpolar variation in the extent of dense shelf water and the annual volume overturning rate of water masses are revealed for the first time. The present work offers a new monthly climatology of the Southern Ocean and the Antarctic margin, which will be instrumental in investigating the seasonality and improving ocean models, thereby making valuable winter observations more accessible. We further highlight the quantitative significance of under-ice data in reproducing ocean conditions, advocating for their increased use to achieve a better Southern Ocean observing system.

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 the seasonality and its role in sea ice changes, bottom water formation, and glacial melt. We develop a four-dimensional climatology of the Southern Ocean (south of 40°S and above 2,000 m) using Data Interpolating Variational Analysis, which excels in multi-dimensional interpolation and consistent handling of topography and advection. The climatology captures thermohaline variability under sea ice, previously hard to obtain, and outperforms other products in data fidelity with smaller root-mean-square errors and biases. Our dataset will be instrumental for investigating seasonality and for improving ocean models. This work further highlights the quantitative significance of under-ice data in reproducing ocean conditions, advocating for their increased use to achieve a better Southern Ocean observing system.