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

Abstract. Global ocean oxygen concentrations have declined in the past decades, posing threats to marine life and human society. High-quality and bias-free observations are crucial to understanding ocean oxygen changes and assessing their impact. Here, we propose a new automated quality control (QC) procedure for ocean profile oxygen data. This procedure consists of a suite of 10 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 by means of the Winkler titration method are used as unbiased references to estimate possible residual biases in the oxygen sensor data. The residual bias is found to be negligible for electrochemical sensors typically used on CTD casts. We explain this as the consequence of adjusting to the concurrent sample Winkler data. Our analysis finds a prevailing negative residual bias with the magnitude of several µmol kg−1 for the delayed-mode quality-controlled and adjusted profiles from Argo floats varying among the data subsets adjusted by different Argo Data Assembly Centers (DACs). The respective overall DAC- and sensor-specific corrections are suggested. We also find the bias dependence on pressure, a feature common to both AANDERAA optodes and SBE43-series sensors. Applying the new QC procedure and bias adjustments resulted in a new global ocean oxygen dataset from 1920 to 2023 with consistent data quality across bottle samples, CTD casts, and Argo floats. The adjusted Argo profile data are available at the Marine Science Data Center of the Chinese Academy of Sciences (https://doi.org/10.12157/IOCAS.20231208.001, Gouretski et al., 2024).