Articles | Volume 15, issue 1
Data description paper
20 Jan 2023
Data description paper |  | 20 Jan 2023

Argo salinity: bias and uncertainty evaluation

Annie P. S. Wong, John Gilson, and Cécile Cabanes


Interactive discussion

Status: closed

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

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Annie P. S. Wong on behalf of the Authors (20 Dec 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (27 Dec 2022) by Dagmar Hainbucher
AR by Annie P. S. Wong on behalf of the Authors (29 Dec 2022)
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 we estimate the uncertainty of the Argo delayed-mode salinity dataset. The best ways to use Argo data are illustrated.
Final-revised paper