Argo salinity: bias and uncertainty evaluation

[摘要] Argo salinity is a key set of in situ ocean measurements for manyscientific applications. However, use of the raw, unadjusted salinity datashould be done with caution as they may contain bias from various instrumentproblems, most significant being from sensor calibration drift in theconductivity cells. For example, inclusion of biased but unadjusted Argosalinity has been shown to lead to spurious results in the global sea levelestimates. Argo delayed-mode salinity data are data that have been evaluatedand, if needed, adjusted for sensor drift. These delayed-mode data representan improvement over the raw data because of the reduced bias, the detailedquality control flags, and the provision of uncertainty estimates. Suchimprovement may help researchers in scientific applications that aresensitive to salinity errors. Both the raw data and the delayed-mode datacan be accessed via https://doi.org/10.17882/42182 (Argo, 2022). In thispaper, we first describe the Argo delayed-mode process. The bias in the rawsalinity data is then analyzed by using the adjustments that have beenapplied in delayed mode. There was an increase in salty bias in the raw Argodata beginning around 2015 and peaking during 2017–2018. This salty bias isexpected to decrease in the coming years as the underlying manufacturerproblem has likely been resolved. The best ways to use Argo data to ensurethat the instrument bias is filtered out are then described. Finally, avalidation of the Argo delayed-mode salinity dataset is carried out toquantify residual errors and regional variations in uncertainty. Theseresults reinforce the need for continual re-evaluation of this globaldataset.

[学科分类] 眼科学