Argo salinity: bias and uncertainty evaluation
<p>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 con...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2023-01-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/15/383/2023/essd-15-383-2023.pdf |
| _version_ | 1797946837722726400 |
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| author | A. P. S. Wong J. Gilson C. Cabanes C. Cabanes |
| author_facet | A. P. S. Wong J. Gilson C. Cabanes C. Cabanes |
| author_sort | A. P. S. Wong |
| collection | DOAJ |
| description | <p>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 <a href="https://doi.org/10.17882/42182">https://doi.org/10.17882/42182</a> (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.</p> |
| first_indexed | 2024-04-10T21:17:21Z |
| format | Article |
| id | doaj.art-565e88ef3c974ec4933bbe2d7681221d |
| institution | Directory Open Access Journal |
| issn | 1866-3508 1866-3516 |
| language | English |
| last_indexed | 2024-04-10T21:17:21Z |
| publishDate | 2023-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Earth System Science Data |
| spelling | doaj.art-565e88ef3c974ec4933bbe2d7681221d2023-01-20T10:59:13ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162023-01-011538339310.5194/essd-15-383-2023Argo salinity: bias and uncertainty evaluationA. P. S. Wong0J. Gilson1C. Cabanes2C. Cabanes3School of Oceanography, University of Washington, Seattle, WA, USAScripps Institution of Oceanography, La Jolla, CA, USALaboratoire d'Océanographie Physique et Spatiale (LOPS), University of Brest, CNRS, Ifremer, IRD, IUEM, Brest, FranceUAR 3113, University of Brest, CNRS, IRD, IUEM, Brest, France<p>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 <a href="https://doi.org/10.17882/42182">https://doi.org/10.17882/42182</a> (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.</p>https://essd.copernicus.org/articles/15/383/2023/essd-15-383-2023.pdf |
| spellingShingle | A. P. S. Wong J. Gilson C. Cabanes C. Cabanes Argo salinity: bias and uncertainty evaluation Earth System Science Data |
| title | Argo salinity: bias and uncertainty evaluation |
| title_full | Argo salinity: bias and uncertainty evaluation |
| title_fullStr | Argo salinity: bias and uncertainty evaluation |
| title_full_unstemmed | Argo salinity: bias and uncertainty evaluation |
| title_short | Argo salinity: bias and uncertainty evaluation |
| title_sort | argo salinity bias and uncertainty evaluation |
| url | https://essd.copernicus.org/articles/15/383/2023/essd-15-383-2023.pdf |
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