Abstract. Argo salinity is a key set of in-situ ocean measurements for many scientific applications. However, use of the raw, unadjusted salinity data should be done with caution as they may contain bias from various instrument problems, most significant being from sensor calibration drift in the conductivity cells. For example, inclusion of raw, unadjusted Argo salinity has been shown to lead to spurious results in the global sea level estimates. Argo delayed-mode salinity data are data that have been evaluated and, if needed, adjusted for sensor drift. These delayed-mode data represent an improvement over the raw data because of the reduced bias, the detailed quality control flags, and the provision of uncertainty estimates. Such improvement may help researchers in scientific applications that are sensitive to salinity errors. Both the raw data and the delayed-mode data can be accessed via https://doi.org/10.17882/42182 (Argo, 2022). In this paper, we first describe the Argo delayed-mode process. The bias in the raw salinity data is then analyzed by using the adjustments that have been applied in delayed-mode. There was an increase in salty bias in the raw Argo data beginning around 2015 and peaked in 2017–2018. This salty bias is expected to decrease in the coming years as the underlying manufacturer problem has likely been resolved. The best ways to use Argo data in order to ensure that the instrument bias is filtered out are then described. Finally, a validation of the Argo delayed-mode salinity dataset is carried out to quantify residual errors and regional variations in uncertainty. These results reinforce the need for continual re-evaluation of this global dataset.

中文翻译：

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Argo 盐度：偏差和不确定性评估

摘要。Argo 盐度是用于许多科学应用的一组关键的原位海洋测量。但是，应谨慎使用未经调整的原始盐度数据，因为它们可能包含来自各种仪器问题的偏差，最重要的是来自电导池中的传感器校准漂移。例如，包含原始的、未经调整的 Argo 盐度已被证明会导致全球海平面估计的虚假结果。Argo 延迟模式盐度数据是经过评估并在需要时针对传感器漂移进行调整的数据。由于减少了偏差、详细的质量控制标志和提供了不确定性估计，这些延迟模式数据代表了对原始数据的改进。这种改进可能有助于对盐度误差敏感的科学应用中的研究人员。原始数据和延迟模式数据都可以通过 https://doi.org/10.17882/42182 (Argo, 2022) 访问。在本文中，我们首先描述了 Argo 延迟模式过程。然后通过使用在延迟模式中应用的调整来分析原始盐度数据中的偏差。从 2015 年左右开始，原始 Argo 数据中的咸味偏差有所增加，并在 2017-2018 年达到顶峰。由于潜在的制造商问题可能已经解决，这种咸味偏见预计将在未来几年减少。然后描述了使用 Argo 数据以确保过滤掉仪器偏差的最佳方法。最后，对 Argo 延迟模式盐度数据集进行了验证，以量化残差和不确定性的区域变化。