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.

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.

Abstract: In climate-ocean models, eddy mixing coefficients are often constant in space and time. However, observations, advances in theory, and high-resolution-eddy-resolving ocean models have revealed significant variability in the strength of isopycnal mixing both spatially and temporally. A theory that includes the effect of mean flow suppression has been developed by [1] it has a strong impact on tracer uptake and ventilation. This theory is used in offline calculations by [1, 2, 4] but has not been implemented as a parameterization in an ocean model and in this study we implement it for the first time in an ocean model [3]. The parameterization is tested in a non-eddy-resolving ocean model, demonstrating passive ventilation of a tracer as a function of depth and latitude. The first main result is this parameterization improves the mean state of the ocean. The other result is this parameterization improves the sensitivity of tracer uptake to changing winds.