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.