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

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, http://dx.doi.org/10.12157/IOCAS.20231208.001)