FN Archimer Export Format
PT J
TI Argo salinity: bias and uncertainty evaluation
BT 
AF Wong, Annie P. S.
   Gilson, John
   Cabanes, Cecile
AS 1:1;2:2;3:3,4;
FF 1:;2:;3:;
C1 School of Oceanography, University of Washington, Seattle, WA, USA
   Scripps Institution of Oceanography, La Jolla, CA, USA
   Laboratoire d'Océanographie Physique et Spatiale (LOPS), University of Brest, CNRS, Ifremer, IRD, IUEM, Brest, France
   UAR 3113, University of Brest, CNRS, IRD, IUEM, Brest, France
C2 UNIV WASHINGTON, USA
   UNIV CALIF SAN DIEGO, USA
   CNRS, FRANCE
   CNRS, FRANCE
UM LOPS
IN WOS Cotutelle UMR
   DOAJ
   copubli-int-hors-europe
IF 11.4
TC 7
UR https://archimer.ifremer.fr/doc/00820/93191/99790.pdf
   https://archimer.ifremer.fr/doc/00820/93191/99791.pdf
LA English
DT Article
AB 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 biased but 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 peaking during 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 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. 
PY 2023
PD JAN
SO Earth System Science Data
SN 1866-3508
PU Copernicus GmbH
VL 15
IS 1
UT 000918214300001
BP 383
EP 393
DI 10.5194/essd-15-383-2023
ID 93191
ER

EF