Revisiting the Global Patterns of Seasonal Cycle in Sea Surface Salinity

We investigate the spatial and temporal patterns of satellite‐derived sea surface temperature, salinity and chlorophyll‐a concentration along the eastern South American coast. Two decade‐long time series (2002–2019; except for the salinity) allowed us to investigate changes from seasonal to interannual time scales on an array that stretches from the south (42°S) to the north (10°N) of the continent and from isobaths ranging from −50 to −1000 m away from the coast. The novelty of our approach is to assess comparatively the magnitude, variability, and spectrum of these variables following the same isobaths and using the same methodology. This allowed us to examine the influence of large scale ocean circulation patterns entangled with the local forcing systems such as river discharge and coastal currents, and to quantify to what extent these patterns are spatially and temporally consistent. The seasonal cycle of the temperature explains more variance than that of salinity and chlorophyll on average. Comparatively, salinity has a weak seasonal signal, except near major rivers. Significant long‐term trends were observed in specific regions in the salinity time series. Our study revealed distinct interannual changes at 2‐ and 4‐year period in the whole array with the largest spectral peaks near the La Plata and the Amazon Rivers. Within this period band, thermal signals propagate northward along the whole array. Statistical correlations between satellite‐derived variables and several climate indices suggest remote forcing.

show abstract

“…This result is in agreement with Yu et al. (2021)'s study using satellite‐ and in situ‐derived products. The broad maxima in

{\sigma }_{f}^{2}

between (A) and (B), and between (G) and (H) can be understood as a consequence of seasonal changes in the rivers discharge.…”

Section: Space‐time Diagrams and The Seasonal Cyclesupporting

confidence: 93%

Evolution of Physical and Biological Patterns Along the Tropical and South Atlantic Western Boundary: A Satellite Perspective

et al. 2022

View full text Add to dashboard Cite

show abstract

“…This is a result of the unique nature of the SSS field. It has a short spatial decorrelation scale due to the influence of rainfall and submesoscale variability [23], but is dominated in time scale by the seasonal cycle in many areas [35,38,39]. This can give those doing validation studies ways to formulate their space/time search criteria for optimum results (i.e.…”

Section: Discussionmentioning

confidence: 99%

Matchup Characteristics of Sea Surface Salinity using a High-resolution Ocean Model

Bingham¹,

Fournier²,

Brodnitz³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Sea surface salinity (SSS) satellite measurements are validated using in situ observations 8 usually made by surfacing Argo floats. Validation statistics are computed using matched values of 9 SSS from satellites and floats. This study explores how the matchup process is done using a high- 10 resolution numerical ocean model, the MITgcm. One year of model output is sampled as if the 11 Aquarius and Soil Moisture Active Passive (SMAP) satellites flew over it and Argo floats popped 12 up into it. Statistical measures of mismatch between satellite and float are computed, RMS difference 13 (RMSD) and bias. The bias is small, less than 0.002 in absolute value, but negative with float values 14 being greater than satellites. RMSD is computed using an “all salinity difference” method that av- 15 erages level 2 satellite observations within a given time and space window for comparison with 16 Argo floats. RMSD values range from 0.08 to 0.18 depending on the space-time window and the 17 satellite. This range gives an estimate of the representation error inherent in comparing single point 18 Argo floats to area-average satellite values. The study has implications for future SSS satellite mis- 19 sions and the need to specify how errors are computed to gauge the total accuracy of retrieved SSS 20 values.

show abstract

“…Yu et al. (2021) and Bingham et al. (2021) found that SSS annual and semiannual amplitudes are quite well represented over the open ocean by the satellite SSS products considered for building the CCI L4 SSS.…”

Section: Discussion and Perspectivesmentioning

confidence: 99%

“…The improved accuracy of the CCI L4 SSS with respect to individual sensor SSS accuracy has been made possible because individual sensor SSS products were already shown to detect consistent variabilities. For instance, D'Addezio and Subrahmanyam (2016) showed that SMOS and Aquarius detect high SSS variability associated with the Agulhas current consistent with Argo SSS, whereas Fournier and Lee (2021) showed very consistent SMOS and SMAP SSS in river plumes areas Yu et al (2021). andBingham et al (2021) found that SSS annual and semiannual amplitudes are quite well represented over the open ocean by the satellite SSS products considered for building the CCI L4 SSS.…”

mentioning

confidence: 99%

Satellite‐Based Sea Surface Salinity Designed for Ocean and Climate Studies

et al. 2021

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. r2 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, r2 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

Revisiting the Global Patterns of Seasonal Cycle in Sea Surface Salinity

Cited by 15 publications

References 101 publications

Evolution of Physical and Biological Patterns Along the Tropical and South Atlantic Western Boundary: A Satellite Perspective

Evolution of Physical and Biological Patterns Along the Tropical and South Atlantic Western Boundary: A Satellite Perspective

Matchup Characteristics of Sea Surface Salinity using a High-resolution Ocean Model

Satellite‐Based Sea Surface Salinity Designed for Ocean and Climate Studies

Contact Info

Product

Resources

About