Spatial Scales of Sea Surface Salinity Subfootprint Variability in the SPURS Regions

Bingham, Frederick M.; Li, Zhijin

doi:10.1002/essoar.10504559.3

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…Strong dependencies of SFV on region, season and footprint size have been found. SFV are particularly high in rainy regions [11], along the continental shelves where strong river outflow is present [12], and near frontal zones such as the western boundary currents [13]. The variabilities obtained range from less than 0.1 to 1 pss.…”

Section: Introductionmentioning

confidence: 99%

Satellite and in situ Sampling Mismatches: Consequences for the Estimate of Satellite Sea Surface Salinity Uncertainties

Thouvenin-Masson¹,

Boutin²,

Vergely³

et al. 2022

Preprint

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Validation of satellite sea surface salinity (SSS) products is typically based on comparisons with in-situ measurements at a few meters depth, that are mostly done at a single location and time. The difference in term of spatio-temporal resolution between the in-situ near-surface salinity and the two-dimensional satellite SSS results in a sampling mismatch uncertainty. The Climate Change Initiative (CCI) project has merged SSS from three satellite missions. Using an optimal interpolation, weekly and monthly SSS and their uncertainties are estimated at a 50 km spatial resolution over the global ocean. Over the 2016-2018 period the mean uncertainty on weekly CCI SSS is 0.13, whereas the standard deviation of weekly CCI minus in-situ Argo salinities is 0.24. Using high resolution SSS simulations, we estimate the expected uncertainty due to the CCI versus Argo sampling mismatch. Most of the largest spatial variability of the satellite minus Argo salinity are observed in regions with large mismatch. A quantitative validation is performed by considering the statistical distribution of the CCI minus Argo salinity normalized by the sampling and retrieval uncertainties. This quantity should follow a Gaussian distribution with a standard deviation of 1, if all uncertainty contributions are properly considered. We find that 1) the sampling mismatch can explain most of the observed differences between Argo and CCI data, especially for monthly products and in dynamical regions (river plumes, fronts), 2) overall, the uncertainties are well estimated in CCI version 3, much better compared to CCI version 2. There are a few dynamical regions where discrepancies remain, and where the satellite SSS, their associated uncertainties and the sampling mismatch estimates should be further validated.

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Section: Introductionmentioning

confidence: 99%

Satellite and in situ Sampling Mismatches: Consequences for the Estimate of Satellite Sea Surface Salinity Uncertainties

Thouvenin-Masson¹,

Boutin²,

Vergely³

et al. 2022

Preprint

View full text Add to dashboard Cite

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Matchup Characteristics of Sea Surface Salinity Using a High-Resolution Ocean Model

et al. 2021

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Sea surface salinity (SSS) satellite measurements are validated using in situ observations usually made by surfacing Argo floats. Validation statistics are computed using matched values of SSS from satellites and floats. This study explores how the matchup process is done using a high-resolution numerical ocean model, the MITgcm. One year of model output is sampled as if the Aquarius and Soil Moisture Active Passive (SMAP) satellites flew over it and Argo floats popped up into it. Statistical measures of mismatch between satellite and float are computed, RMS difference (RMSD) and bias. The bias is small, less than 0.002 in absolute value, but negative with float values being greater than satellites. RMSD is computed using an “all salinity difference” method that averages level 2 satellite observations within a given time and space window for comparison with Argo floats. RMSD values range from 0.08 to 0.18 depending on the space–time window and the satellite. This range gives an estimate of the representation error inherent in comparing single point Argo floats to area-average satellite values. The study has implications for future SSS satellite missions and the need to specify how errors are computed to gauge the total accuracy of retrieved SSS values.

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Spatial Scales of Sea Surface Salinity Subfootprint Variability in the SPURS Regions

Cited by 2 publications

References 26 publications

Satellite and in situ Sampling Mismatches: Consequences for the Estimate of Satellite Sea Surface Salinity Uncertainties

Satellite and in situ Sampling Mismatches: Consequences for the Estimate of Satellite Sea Surface Salinity Uncertainties

Matchup Characteristics of Sea Surface Salinity Using a High-Resolution Ocean Model

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