Preprints
https://doi.org/10.5194/essd-2022-323
https://doi.org/10.5194/essd-2022-323
 
29 Sep 2022
29 Sep 2022
Status: this preprint is currently under review for the journal ESSD.

Argo salinity: bias and uncertainty evaluation

Annie P. S. Wong1, John Gilson2, and Cecile Cabanes3,4 Annie P. S. Wong et al.
  • 1School of Oceanography, University of Washington, Seattle, WA, United States
  • 2Scripps Institution of Oceanography, La Jolla, CA, United States
  • 3University of Brest, CNRS, Ifremer, IRD, Laboratoire d'Océanographie Physique et Spatiale (LOPS), IUEM, Brest, France
  • 4University of Brest, CNRS, IRD, UAR 3113, IUEM, Brest, France

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.

Annie P. S. Wong et al.

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-2022-323', Birgit Klein, 27 Oct 2022
    • AC1: 'Reply on RC1', Annie P. S. Wong, 06 Nov 2022
  • RC2: 'Comment on essd-2022-323', Mathieu Dever, 31 Oct 2022
    • AC2: 'Reply on RC2', Annie P. S. Wong, 07 Nov 2022

Annie P. S. Wong et al.

Data sets

Argo float data and metadata from Global Data Assembly Centre (Argo GDAC) Argo https://www.seanoe.org/data/00311/42182

Annie P. S. Wong et al.

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Short summary
This article describes the instrument bias in the raw Argo salinity data from 2000 to 2021. The main cause of this bias is sensor drift. Using Argo data without filtering out this instrument bias has been shown to lead to spurious results in various scientific applications. We describe the Argo delayed-mode process that evaluates and adjusts such instrument bias, and estimate the uncertainty of the Argo delayed-mode salinity dataset. The best ways to use Argo data are illustrated.