Data

Southern Ocean Monthly Climatology

University of Tasmania, Australia
Yamazaki, Kaihe ; Bindoff, Nathan ; Phillips, Helen ; Nikurashin, Maxim ; Herraiz-Borreguero, Laura ; Spence, Paul
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=https://metadata.imas.utas.edu.au/geonetwork/srv/eng/catalog.search#/metadata/ae0d2fd6-62c9-4cb1-9d93-44ae8af8af69&rft.title=Southern Ocean Monthly Climatology&rft.identifier=https://metadata.imas.utas.edu.au/geonetwork/srv/eng/catalog.search#/metadata/ae0d2fd6-62c9-4cb1-9d93-44ae8af8af69&rft.description=OutlineThis 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-90Practical 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 DescriptionThe 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.Maintenance and Update Frequency: notPlannedStatement: A diverse set of observational data was collected from multiple platforms, including Argo profiling floats, biologging instruments attached to marine mammals, and Conductivity-Temperature-Depth (CTD) profiles obtained from ship-based measurements. This dataset spans more than a century, with a heavier reliance on recent data collected after 2005 by Argo float and biologging. The gridding covers the Southern Ocean from 75°S to 40°S with horizontal grid intervals of 1/2° longitude and 1/4° latitude, resulting in an approximate 25 km resolution at 60°S. The vertical grid consists of 66 WOA layers, ranging from 5 to 2,000 meters in depth, with monthly temporal resolution. The initial climatology field was constructed using Data Interpolating Variational Analysis (DIVA), a sophisticated method designed to handle multidimensional data interpolation. This method efficiently integrates residual data from an existing climatology (WOA23) and applies topographic and advection constraints to improve the spatial correlation of the data, particularly in regions with complex topography, such as the Antarctic margin. The DIVA model underwent a cross-validation process to optimize interpolation parameters, including spatial and temporal correlation scales and the signal-to-noise ratio. Additionally, calibration tests of advection weighting and correlation time scales was carried out to further refine the model's physical consistency. Post-processing involved generating error maps to assess the accuracy of the interpolated fields. Corrections for non-linearities in seawater properties (cabbeling) were applied, along with density stabilization techniques to ensure that the final data output remained physically consistent.&rft.creator=Yamazaki, Kaihe &rft.creator=Bindoff, Nathan &rft.creator=Phillips, Helen &rft.creator=Nikurashin, Maxim &rft.creator=Herraiz-Borreguero, Laura &rft.creator=Spence, Paul &rft.date=2025&rft.coverage=westlimit=-180.00; southlimit=-78.00; eastlimit=180.00; northlimit=-40.00&rft.coverage=westlimit=-180.00; southlimit=-78.00; eastlimit=180.00; northlimit=-40.00&rft.coverage=uplimit=2000; downlimit=10&rft.coverage=uplimit=2000; downlimit=10&rft_rights=Creative Commons Attribution 4.0 International License https://creativecommons.org/licenses/by/4.0/&rft_rights=Cite data as: Yamazaki K, Bindoff NL, Phillips HE, Nikurashin M, Herraiz-Borreguero L, & Spence P. (2025) Southern Ocean Monthly Climatology [Data set]. Institute for Marine and Antarctic Studies. https://metadata.imas.utas.edu.au/geonetwork/srv/eng/catalog.search#/metadata/ae0d2fd6-62c9-4cb1-9d93-44ae8af8af69&rft.type=dataset&rft.language=English Access the data
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Creative Commons Attribution 4.0 International License
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Cite data as: Yamazaki K, Bindoff NL, Phillips HE, Nikurashin M, Herraiz-Borreguero L, & Spence P. (2025) Southern Ocean Monthly Climatology [Data set]. Institute for Marine and Antarctic Studies. https://metadata.imas.utas.edu.au/geonetwork/srv/eng/catalog.search#/metadata/ae0d2fd6-62c9-4cb1-9d93-44ae8af8af69

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Full description

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.

Lineage

Maintenance and Update Frequency: notPlanned
Statement: A diverse set of observational data was collected from multiple platforms, including Argo profiling floats, biologging instruments attached to marine mammals, and Conductivity-Temperature-Depth (CTD) profiles obtained from ship-based measurements. This dataset spans more than a century, with a heavier reliance on recent data collected after 2005 by Argo float and biologging. The gridding covers the Southern Ocean from 75°S to 40°S with horizontal grid intervals of 1/2° longitude and 1/4° latitude, resulting in an approximate 25 km resolution at 60°S. The vertical grid consists of 66 WOA layers, ranging from 5 to 2,000 meters in depth, with monthly temporal resolution. The initial climatology field was constructed using Data Interpolating Variational Analysis (DIVA), a sophisticated method designed to handle multidimensional data interpolation. This method efficiently integrates residual data from an existing climatology (WOA23) and applies topographic and advection constraints to improve the spatial correlation of the data, particularly in regions with complex topography, such as the Antarctic margin. The DIVA model underwent a cross-validation process to optimize interpolation parameters, including spatial and temporal correlation scales and the signal-to-noise ratio. Additionally, calibration tests of advection weighting and correlation time scales was carried out to further refine the model's physical consistency. Post-processing involved generating error maps to assess the accuracy of the interpolated fields. Corrections for non-linearities in seawater properties (cabbeling) were applied, along with density stabilization techniques to ensure that the final data output remained physically consistent.

Notes

Credit
This research was supported by the Australian Research Council Special Research Initiative, Australian Centre for Excellence in Antarctic Science (ACEAS) (Project Number SR200100008).

Issued: 05 09 2025

Data time period: 2023-01-03 to 2024-06-09

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text: westlimit=-180.00; southlimit=-78.00; eastlimit=180.00; northlimit=-40.00

text: uplimit=2000; downlimit=10

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Other Information
(Data access via Zenodo)

url : https://zenodo.org/records/13148501

(Data access via IMAS storage)

url : https://data.imas.utas.edu.au/attachments/ae0d2fd6-62c9-4cb1-9d93-44ae8af8af69

Yamazaki, K., Bindoff, N. L., Phillips, H. E., Nikurashin, M., Herraiz-Borreguero, L., & Spence, P. (2025). Unlocking southern ocean under-ice seasonality with a new monthly climatology. Journal of Geophysical Research: Oceans, 130, e2024JC020920. doi:10.1029/2024JC020920 (Associated Publication)

doi : https://doi.org/10.1029/2024JC020920

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