03 Jan 2024
 | 03 Jan 2024
Status: a revised version of this preprint is currently under review for the journal ESSD.

A consistent ocean oxygen profile dataset with new quality control and bias assessment

Viktor Gouretski, Lijing Cheng, Juan Du, Xiaogang Xing, and Fei Chai

Abstract. The global ocean oxygen levels have declined in the past decades, posing threats to marine life and human society. High-quality and bias-free observations are crucial to understanding the ocean oxygen changes and assessing their impact. Here, we propose a new automated quality control procedure for ocean profile oxygen data. This procedure consists of a suite of nine quality checks, with outlier rejection thresholds being defined based on underlying statistics of the data. The procedure is applied to three main instrumentation types: bottle casts, CTD (Conductivity-Temperature-Depth) casts, and Argo profiling floats. Application of the quality control procedure to several manually quality-controlled datasets of good quality suggests the ability of the scheme to successfully identify outliers in the data. Collocated quality-controlled oxygen profiles obtained utilizing the Winkler titration method are used as unbiased references to estimate possible residual biases in the oxygen sensor data. The residual bias is negligible for electrochemical sensors typically used on CTD casts. We explain this as the consequence of adjusting to the concurrent sample Winkler data. However, our analysis finds a prevailing negative residual bias for the delayed-mode quality-controlled adjusted Argo profiling floats varying from -4 to -1 µmol kg-1 among the data adjusted by different Argo data assembly centers (DACs). The respective overall DAC-specific corrections are suggested. Applying the new QC procedure and bias adjustment resulted in a new global ocean oxygen dataset from 1920 to 2022 with consistent data quality across bottle samples, CTD casts, and Argo floats. The adjusted Argo profile data is available at the Marine Science Data Center of the Chinese Academy of Sciences (Gouretski et al., 2023,

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Viktor Gouretski, Lijing Cheng, Juan Du, Xiaogang Xing, and Fei Chai

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-518', Anonymous Referee #1, 31 Jan 2024
    • AC1: 'Reply on RC1', Lijing Cheng, 14 Apr 2024
  • RC2: 'Comment on essd-2023-518', Anonymous Referee #2, 01 Mar 2024
    • AC2: 'Reply on RC2', Lijing Cheng, 14 Apr 2024
Viktor Gouretski, Lijing Cheng, Juan Du, Xiaogang Xing, and Fei Chai

Data sets

A quality-controlled and bias-adjusted global ocean oxygen profile dataset V. Gouretski et al.

Viktor Gouretski, Lijing Cheng, Juan Du, Xiaogang Xing, and Fei Chai


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Short summary
High-quality observations are crucial to understanding ocean oxygen changes and their impact on marine biota. We developed a quality control procedure to ensure the high quality of the heterogeneous ocean oxygen data archive and to prove data consistency. Oxygen data obtained by means of oxygen sensors on autonomous Argo floats were compared with reference data based on the chemical analysis and estimates of the residual offsets were obtained.