A new automatic quality control system for ocean profile observations and impact on ocean warming estimate. (April 2023)

• Record Type: Journal Article
• Authors: Tan, Zhetao; Cheng, Lijing; Gouretski, Viktor; Zhang, Bin; Wang, Yanjun; Li, Fuchao; Liu, Zenghong; Zhu, Jiang
• Is Part Of: Deep sea research. Volume 194(2023)
• Publication Date: 2023-04
• Subjects: Quality control -- Ocean observations -- Temperature -- Ocean heat content -- Outlier -- Climate; Oceanography -- Periodicals; Océanographie -- Périodiques; 551.4605
• DOI: 10.1016/j.dsr.2022.103961
• Languages: English
• ISSNs: 0967-0637

Abstract

Abstract: The rapidly growing global archive of hydrographic in-situ observations is characterized by a high degree of the data quality heterogeneity. Different data applications (e.g., ocean warming studies) require an internally consistent dataset and therefore the development of an automated quality control (QC) system permitting to reliably identify outliers in profile data obtained by different instrumentation types. In this study, we present a new automatic QC procedure (CAS-Ocean Data Center (CODC) Quality Control system; CODC-QC) for ocean in-situ temperature observations, which includes a suite of distinct quality checks (14 in total) to identify temperature outliers. Unlike many existing QC procedures, no assumption is made of a Gaussian distribution law in the new approach as the oceanic variables (e.g., temperature and salinity) are typically skewed. Instead, the time-varying 0.5% and 99.5% quantiles are used as thresholds in CODC-QC to define the local climatological parameter ranges. In addition to temperature ranges, we constructed the local climatological ranges for the vertical temperature gradient which increased the ability of the scheme to identify spurious profiles. The performance of CODC-QC procedure was evaluated using two expert/manual QCed benchmark datasets. This evaluation demonstrated the effectiveness of the proposed scheme in removing spurious data and minimizing the percentage of mistakenly flagged good data. Additionally, the CODC-QC was applied to the global World Ocean Database (WOD) amounting to 16, 804, 361 temperature profiles from 1940 to 2021. Based on the statistics of temperature outliers, we suggest a significant dependency of the quality of temperature observations on instrumentation type. Finally, as ocean heat content (OHC) is a fundamental indicator of climate change, the impact of different QC systems on OHC estimates is examined. Preliminary results based on an existing mapping approach indicate that the application of the CODC-QC system leads to a 3.33% (15.09%) difference for linear trend of the global 0–2000m OHC changes within 1955–1990 (1991–2021) compared to the WOD-QC, implying a non-negligible source of error in OHC estimates. The new AutoQC system could support further improvement of the oceanic climate records and other applications.