Sea Surface Salinity Short Term Variability in the Tropics

Bingham, Frederick M.; Brodnitz, Susannah

doi:10.5194/os-2021-51

Cited by 2 publications

(7 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It likely has to do with the characteristically negatively skewed distribution of SSS [34]. The magnitudes given by [20] computed from tropical mooring data are similar to the ones found here.…”

Section: Median Sfv At Random Box Locations In Septembersupporting

confidence: 82%

“…The representation error for 100 km (40 km) footprint has a mode at around 0.1 (0.06). The fact that the representation error is larger than the SFV was also observed by [20] for the tropics. It likely has to do with the characteristically negatively skewed distribution of SSS [34].…”

Section: Median Sfv At Random Box Locations In Septembersupporting

confidence: 63%

“…This dependence has been hinted at by [10 and [19] for two specific locations in the tropical Pacific and subtropical North Atlantic. [20] also found strong seasonal variability in SFV (using a proxy measurement) for the global tropics, though they did not look at variation by footprint size.…”

Section: Discussionmentioning

confidence: 93%

“…In addition, we examine RE. This is different from SFV [20] as will be described below, and better quantifies the sampling error that is expected in satellite measurement of SSS. Looking at SFV and RE for different footprint sizes can help in the design of potential future SSS satellite missions, by informing the details of the expected error budget.…”

Section: Introductionmentioning

confidence: 94%

“…SSS SFV has been quantified in a few publications using models and in situ observations [10,[16][17][18][19][20]. Most relevant to the present investigation is that of D'Addezio et al [9], who looked at SFV in a high-resolution model in two specific regions: the western Pacific and Arabian Sea.…”

Section: Introductionmentioning

confidence: 97%

See 4 more Smart Citations

Sea surface salinity subfootprint variability from a global high-resolution model

Bingham¹,

Brodnitz²,

Fournier³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Subfootprint variability (SFV) is variability at a spatial scale smaller than the footprint of a sat-ellite, and cannot be resolved by satellite observations. It is important to quantify and understand as it contributes to the error budget for satellite data. The purpose of this study is to estimate the SFV for sea surface salinity (SSS) satellite observations. This is done using a high-resolution (1/48°) numerical model, the MITgcm, from which one year of output has recently become availa-ble. SFV, defined as the weighted standard deviation of SSS within the satellite footprint, was computed from the model for a 2°X2° grid of points for the one model year. We present maps of SFV for 40 and 100 km footprint size, display histograms of its distribution for a range of foot-print sizes and quantify its seasonality. At 100 km (40 km) footprint size, SFV has a mode of 0.06 (0.04). It is found to vary strongly by location and season. It has larger values in western bound-ary and eastern equatorial regions, and a few other areas. SFV has strong variability throughout the year, with generally largest values in the fall season. We also quantify representation error, the degree of mismatch between random samples within a footprint and the footprint average. Our estimates of SFV and representation error can be used in understanding errors in satellite obser-vation of SSS.

show abstract

Section: Median Sfv At Random Box Locations In Septembersupporting

confidence: 82%

Section: Median Sfv At Random Box Locations In Septembersupporting

confidence: 63%

Section: Discussionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 97%

See 3 more Smart Citations

Sea surface salinity subfootprint variability from a global high-resolution model

Bingham¹,

Brodnitz²,

Fournier³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Satellite-based Sea Surface Salinity designed for Ocean and Climate Studies

Boutin

Reul

Kohler³

et al. 2021

Preprint

View full text Add to dashboard Cite

Sea Surface Salinity (SSS) is an increasingly used Essential Ocean and Climate Variable. The Soil Moisture and Ocean Salinity (SMOS), Aquarius, and Soil Moisture Active Passive (SMAP) satellite missions all provide SSS measurements, with very different instrumental features leading to specific measurement characteristics. The Climate Change Initiative Salinity project (CCI + SSS) aims to produce a SSS Climate Data Record (CDR) that addresses well-established user needs based on those satellite measurements. To generate a homogeneous CDR, instrumental differences are carefully adjusted based on in-depth analysis of the measurements themselves, together with some limited use of independent reference data. An optimal interpolation in the time domain without temporal relaxation to reference data or spatial smoothing is applied. This allows preserving the original datasets variability. SSS CCI fields are well suited for monitoring weekly to interannual signals, at spatial scales ranging from 50 km to the basin scale. They display large year-to-year seasonal variations over the 2010-2019 decade, sometimes by more than ±0.4 over large regions. The robust standard deviation of the monthly CCI SSS minus in situ Argo salinities is 0.15 globally, while it is at least 0.20 with individual satellite SSS fields. r 2 is 0.97, similar or better than with original datasets. The correlation with independent ship thermosalinographs SSS further highlights the CCI data set excellent performance, especially near land areas. During the SMOS-Aquarius period, when the representativity uncertainties are the largest, r 2 is 0.84 with CCI while it is 0.48 with the Aquarius original data set. SSS CCI data are freely available and will be updated and extended as more satellite data become available.

show abstract

Sea Surface Salinity Short Term Variability in the Tropics

Cited by 2 publications

References 31 publications

Sea surface salinity subfootprint variability from a global high-resolution model

Sea surface salinity subfootprint variability from a global high-resolution model

Satellite-based Sea Surface Salinity designed for Ocean and Climate Studies

Contact Info

Product

Resources

About