DOI: 10.5281/zenodo.13920233
title: A global four-dimensional gridded dataset of ocean dissolved oxygen concentrations retrieval from Argo profiles

description: Based on temperature and salinity observations from Argo floats, this dataset uses machine learning methods to reconstruct and invert global ocean dissolved oxygen concentrations.

Data Product Characteristics Description

The Global Ocean Dissolved Oxygen Concentration Dataset is a high spatiotemporal resolution product generated based on global ocean observation data and machine learning models. This dataset provides information on global ocean dissolved oxygen concentrations at annual, monthly, and seasonal scales, covering the global marine environment from 2005 to 2022. The significance of dissolved oxygen data is reflected in its role in studying marine ecosystems, global biogeochemical cycles, and climate change. It serves as a crucial basis for assessing ocean health and monitoring changes in oxygen minimum zones (OMZ).

Time Range: The "Global Ocean Dissolved Oxygen Concentration Dataset" spans annual scale (monthly averages), monthly scale (monthly data), and seasonal scale (spring, summer, autumn, winter), with a time range from 2005 to 2022.

 

Geographic Range:The Global Ocean Dissolved Oxygen Concentration Dataset covers the global oceans (excluding the Arctic Ocean), spanning from 90°S to 84°N latitude and from 180°W to 180°E longitude. The spatial resolution is 1°×1°, and it includes 26 depth layers (ranging from 10, 20, 30, 40, 50, 75, 100, 125, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1750, to 2000 dbar).

 

Product Usage Instructions

The Global Ocean Dissolved Oxygen Concentration Dataset provides dissolved oxygen data on annual, monthly, and seasonal scales. Users can select global ocean dissolved oxygen data based on specific needs for different time scales and depth levels. Common GIS tools such as ArcGIS, QGIS, as well as programming tools like MATLAB and Python can read and process this data. The unit of dissolved oxygen concentration in the files is μmol/kg.

Users can retrieve data based on the required year, month, season, and depth layer. The dataset offers three time scales (annual, monthly, seasonal) and 26 depth levels (ranging from 10 dbar to 2000 dbar).

File Naming Conventions:

1)     Monthly Scale Naming Format: DOC_MTH_yyyy_Mmm_Ddddd.tif

yyyy: Represents the year (e.g., 2005)

Mmm: Represents the month (e.g., M05 for May)

Ddddd: Represents the depth layer (e.g., D0100 for 100 dbar)

Example: DOC_MTH_2005_M05_D0100.tif indicates dissolved oxygen data for the 100 dbar depth layer in May 2005.

2)     Annual Scale Naming Format: DOC_yyyy_Ddddd.tif

yyyy: Represents the year (e.g., 2005)

Ddddd: Represents the depth layer (e.g., D0010 for 10 dbar)

Example: DOC_2005_D0010.tif indicates dissolved oxygen data for the 10 dbar depth layer in 2005.

3)     Seasonal Scale Naming Format: DOC_MTH_yyyy_ssss_Ddddd.tif

 

yyyy: Represents the year (e.g., 2005)

ssss: Represents the season (e.g., Spring, Summer, Autumn, Winter)

Ddddd: Represents the depth layer (e.g., D0010 for 10 dbar)

Example: DOC_MTH_2005_Autumn_D0010.tif indicates dissolved oxygen data for the 10 dbar depth layer in Autumn 2005.



Global Ocean Dissolved Oxygen Concentration Distribution Map at 1000 dbar Depth for July 2022

 

Data Usage Notice: Citation and Disclaimer

Users of the data product must clearly state the source and authorship of the "Global Ocean Dissolved Oxygen Concentration Dataset" in any research outcomes generated using this data (including but not limited to published papers, reports, data products, and unpublished research reports). They should also cite the relevant literature. The data producers are not responsible for any losses incurred by users due to the use of this data; the boundaries or mask data used in the maps do not represent any official views or endorsements by the data producers regarding the related information.

 

Contacts and Contact Information

Cunjin Xue,xuecj@aircas.ac.cn

Zhenguo Wang,zgwang24@m.fudan.edu.cn