Articles | Volume 15, issue 1
https://doi.org/10.5194/essd-15-383-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-383-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
20 Jan 2023
Data description article |
| 20 Jan 2023
| 20 Jan 2023
Argo salinity: bias and uncertainty evaluation
School of Oceanography, University of Washington, Seattle, WA, USA
John Gilson
Scripps Institution of Oceanography, La Jolla, CA, USA
Cécile Cabanes
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
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Cited
22 citations as recorded by crossref.
- Near real time processing of underway salinity data from ships of opportunity G. Alory et al. https://doi.org/10.1080/1755876X.2025.2541460
- Global Mean Sea Level Rise Inferred From Ocean Salinity and Temperature Changes A. Bagnell & T. DeVries https://doi.org/10.1029/2022GL101004
- Barystatic sea level change observed by satellite gravimetry: 1993–2022 Y. Nie et al. https://doi.org/10.1073/pnas.2425248122
- On the global reconstruction of ocean interior variables: a feasibility data-driven study with simulated surface and water column observations A. Garcia-Espriu et al. https://doi.org/10.5194/os-21-2579-2025
- Regional sea level trend budget over 2004–2022 M. Bouih et al. https://doi.org/10.5194/os-21-1425-2025
- Capability of the Mediterranean Argo network to monitor sub-regional climate change indicators C. Chevillard et al. https://doi.org/10.3389/fmars.2024.1416486
- Assessment of the Representativeness and Uncertainties of CTD Temperature Profiles M. Le Menn et al. https://doi.org/10.3390/jmse13020213
- Twenty-first century thermohaline trends and abrupt shifts in the Ionian Sea E. Terzić & I. Vilibić https://doi.org/10.3389/fmars.2025.1718186
- Global and regional ocean mass budget closure since 2003 C. Ludwigsen et al. https://doi.org/10.1038/s41467-024-45726-w
- Poleward migration of warm Circumpolar Deep Water towards Antarctica J. Lanham et al. https://doi.org/10.1038/s43247-026-03426-x
- Best practices for Core Argo floats - part 1: getting started and data considerations T. Morris et al. https://doi.org/10.3389/fmars.2024.1358042
- Cause of Substantial Global Mean Sea Level Rise Over 2014–2016 W. Llovel et al. https://doi.org/10.1029/2023GL104709
- Long-wavelength steric sea level and heat storage anomaly maps to 2000 m by combining Argo temperature and salinity profiles with satellite altimetry and gravimetry D. Chambers & S. Reinelt https://doi.org/10.5194/essd-18-741-2026
- Dense Water Formation in the North–Central Aegean Sea during Winter 2021–2022 M. Potiris et al. https://doi.org/10.3390/jmse12020221
- Evaluation of the effects of Argo data quality control on global ocean data assimilation systems I. Ishikawa et al. https://doi.org/10.3389/fmars.2024.1496409
- A new approach to inferring the threshold range for quality control of ocean T/S profiles based on probability distribution of historical data L. Conghao et al. https://doi.org/10.1007/s13131-025-2504-2
- A Method for Sea Surface Temperature Retrieval Based on XGBoost Optimized by the Improved Sparrow Search Algorithm K. Li et al. https://doi.org/10.1109/TGRS.2026.3668416
- Research Progress on the Detection of Deep-Sea Microorganisms and the Significance of Measurement Standards Z. Cheng et al. https://doi.org/10.3390/chemosensors14040094
- Improved Automated Quality Control Method based on the Signature for Argo profiles S. Kouketsu et al. https://doi.org/10.1007/s10872-026-00791-1
- Technical note: Determining Arctic Ocean halocline and cold halostad depths based on vertical stability E. Metzner & M. Salzmann https://doi.org/10.5194/os-19-1453-2023
- Reconstructing monthly 20$$^\textrm{th}$$ century salinity fields using a data-driven method and Argo data E. Oulhen et al. https://doi.org/10.1007/s10236-025-01690-7
- DSE-NN: Discretized Spatial Encoding Neural Network for Ocean Temperature and Salinity Interpolation in the North Atlantic S. Liu et al. https://doi.org/10.3390/jmse12061013
22 citations as recorded by crossref.
- Near real time processing of underway salinity data from ships of opportunity G. Alory et al. https://doi.org/10.1080/1755876X.2025.2541460
- Global Mean Sea Level Rise Inferred From Ocean Salinity and Temperature Changes A. Bagnell & T. DeVries https://doi.org/10.1029/2022GL101004
- Barystatic sea level change observed by satellite gravimetry: 1993–2022 Y. Nie et al. https://doi.org/10.1073/pnas.2425248122
- On the global reconstruction of ocean interior variables: a feasibility data-driven study with simulated surface and water column observations A. Garcia-Espriu et al. https://doi.org/10.5194/os-21-2579-2025
- Regional sea level trend budget over 2004–2022 M. Bouih et al. https://doi.org/10.5194/os-21-1425-2025
- Capability of the Mediterranean Argo network to monitor sub-regional climate change indicators C. Chevillard et al. https://doi.org/10.3389/fmars.2024.1416486
- Assessment of the Representativeness and Uncertainties of CTD Temperature Profiles M. Le Menn et al. https://doi.org/10.3390/jmse13020213
- Twenty-first century thermohaline trends and abrupt shifts in the Ionian Sea E. Terzić & I. Vilibić https://doi.org/10.3389/fmars.2025.1718186
- Global and regional ocean mass budget closure since 2003 C. Ludwigsen et al. https://doi.org/10.1038/s41467-024-45726-w
- Poleward migration of warm Circumpolar Deep Water towards Antarctica J. Lanham et al. https://doi.org/10.1038/s43247-026-03426-x
- Best practices for Core Argo floats - part 1: getting started and data considerations T. Morris et al. https://doi.org/10.3389/fmars.2024.1358042
- Cause of Substantial Global Mean Sea Level Rise Over 2014–2016 W. Llovel et al. https://doi.org/10.1029/2023GL104709
- Long-wavelength steric sea level and heat storage anomaly maps to 2000 m by combining Argo temperature and salinity profiles with satellite altimetry and gravimetry D. Chambers & S. Reinelt https://doi.org/10.5194/essd-18-741-2026
- Dense Water Formation in the North–Central Aegean Sea during Winter 2021–2022 M. Potiris et al. https://doi.org/10.3390/jmse12020221
- Evaluation of the effects of Argo data quality control on global ocean data assimilation systems I. Ishikawa et al. https://doi.org/10.3389/fmars.2024.1496409
- A new approach to inferring the threshold range for quality control of ocean T/S profiles based on probability distribution of historical data L. Conghao et al. https://doi.org/10.1007/s13131-025-2504-2
- A Method for Sea Surface Temperature Retrieval Based on XGBoost Optimized by the Improved Sparrow Search Algorithm K. Li et al. https://doi.org/10.1109/TGRS.2026.3668416
- Research Progress on the Detection of Deep-Sea Microorganisms and the Significance of Measurement Standards Z. Cheng et al. https://doi.org/10.3390/chemosensors14040094
- Improved Automated Quality Control Method based on the Signature for Argo profiles S. Kouketsu et al. https://doi.org/10.1007/s10872-026-00791-1
- Technical note: Determining Arctic Ocean halocline and cold halostad depths based on vertical stability E. Metzner & M. Salzmann https://doi.org/10.5194/os-19-1453-2023
- Reconstructing monthly 20$$^\textrm{th}$$ century salinity fields using a data-driven method and Argo data E. Oulhen et al. https://doi.org/10.1007/s10236-025-01690-7
- DSE-NN: Discretized Spatial Encoding Neural Network for Ocean Temperature and Salinity Interpolation in the North Atlantic S. Liu et al. https://doi.org/10.3390/jmse12061013
Saved (final revised paper)
Latest update: 09 Jun 2026
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.
This article describes the instrument bias in the raw Argo salinity data from 2000 to 2021. The...
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