Sea Surface Salinity Remote Sensing

Tridacnine oxygen isotope thermometers (TOITs) commonly utilize gridded sea surface temperature (SST) and sea surface salinity (SSS) data without carefully checking whether they were appropriate substitutes for in situ monitoring records at the tridacnine growth site. Many SST and SSS datasets are available, but those best suited for establishing TOITs have yet to be examined sufficiently. In this study, we have established TOITs for shells collected at two islands (Okinoerabu Jima in the north and Ishigaki Jima in the south) in the northwestern Pacific using six combinations of SST (Extended Reconstructed Sea Surface Temperature [ERSST], Sea Surface Temperature data of Integrated Global Ocean Services System [IGOSS SST], and Hadley Centre Sea Ice and Sea Surface Temperature dataset [HadISST]) and SSS (Simple Ocean Data Assimilation ocean/sea ice reanalysis [SODA3.15.2] and Met Office Hadley Centre “EN” series [EN4.2.2]) datasets and evaluated the differences among the TOITs. Consequently, the slopes and intercepts of all TOITs fell within or near the range of TOITs reported in the previous studies, regardless of the combination of datasets. However, a significant difference in the coldest month SST at Okinoerabu Jima (~ 1.0 °C) exists between IGOSS SST and ERSST, caused by that an SST drop by the southward migration of the Kuroshio Front during winter is detected not in IGOSS SST (1° gridded data) but in ERSST (2° gridded data). Thus, the choice of SST dataset can lead to up to 1.0 °C difference in reconstructed winter SST. SSS differences exist between SODA SSS and EN SSS around the two islands up to 0.8 (equivalent to ~ 0.25 ‰ in oxygen isotope composition of seawater), practically leading to insignificant variations of the slopes and intercepts of the TOITs. To establish TOITs using the gridded SST and SSS datasets, we should select datasets with SSTs and SSSs similar to those at the growth sites of the studied tridacnine shells. Even if a gridded SST (SSS) dataset and an in situ monitoring SST (SSS) record show good correspondence, it could be a coincidence. We propose an averaged Ryukyu TOIT: SST = − 3.95 ± 1.02 × (δ 18 O shell [VPDB] − δ 18 O sw [VSMOW]) + 20.50 ± 2.75 (2σ).

Deviations of satellite-measured sea surface salinity caused by environmental factors and their regional dependence

Variability at large to meso-scale in sea surface salinity (SSS) and sea surface temperature (SST) is investigated in the subtropical North Atlantic Ocean during the Subtropical Atlantic Surface Salinity Experiment Strasse/SPURS in August 2012—August 2013. The products of the Soil Moisture and Ocean Salinity (SMOS) mission corrected from large scale systematic errors are tested and used to retrieve meso-scale salinity features, while OSTIA products, resolving meso-scale temperature features are used for SST. The comparison of corrected SMOS SSS data with drifter's in situ measurements from SPURS experiment shows a reasonable agreement, especially during winter time with RMS differences on the order of 0.15 pss (for 10 days, 75 km resolution SMOS product). The analysis of SSS (SST) variability reveals that the meso-scale eddies contribute to a substantial freshening (cooling) in the central high salinity region of the subtropical gyre, albeit smaller than Ekman and atmospheric freshwater (heat) seasonal flux, which are the leading terms in SSS (SST) budget. An error is estimated along with SSS and SST budgets; as well as sensitivity to the different products in use and residuals are discussed. The residuals in the SSS budget are large and can arise from errors in the advection fields and freshwater flux, from neglected small scale or unresolved local processes (salt fingering, vertical mixing, and small scale subduction, etc.). However, their magnitude is similar to what is often parameterized as eddy horizontal diffusion to close large scale budgets.\n

<p>The Western Tropical Atlantic is a crucial region when it comes to understanding the CO<sub>2</sub> dynamics in the tropics, as it is subject to large inputs of freshwater from the Amazon River and the ITCZ rainfall, as well as the input of CO<sub>2</sub>-rich waters from upwelling of subsurface water. This study aims to reconstruct the surface marine carbonate system from 1998 to 2018 using sea surface temperature (SST) and sea surface salinity (SSS) data from the PIRATA buoy at 8°N 38°W and describe its variability in time. Two empirical models were used to calculate total alkalinity (TA) and dissolved inorganic carbon (DIC) from SSS. From these two parameters and SST data, it was possible to calculate pH and CO<sub>2</sub> fugacity (<em>f</em>CO<sub>2</sub>) values. Only DIC, pH and <em>f</em>CO<sub>2</sub> showed a statistically significant trend in time, where DIC showed an increase of 0.717 µmol kg<sup>-1</sup> year<sup>-1</sup>, pH decreased 0.001394 pH units year<sup>-1</sup>, and <em>f</em>CO<sub>2</sub> had an increase of 1.539 µatm year<sup>-1</sup>. Two different seasons were observed when data were analyzed: a dry season from January to June, when SSTs were lower (around 27°C) and SSS was stable around 36, matching the period when the ITCZ is over the South American continent, Amazon river plume is restricted to western shelf areas and Equatorial upwelling is more active, and a rainy season from July to December, when SSTs were higher (around 28.5°C) and SSS had higher variability (from 31 to 36), matching the period when the ITCZ is at its northern range, the Amazon plume is spread eastwards through the North Brazil Current’s retroflection and the Equatorial upwelling is less intense. Along with that, TA, DIC and pH varied positively with SSS, with higher values (TA around 2350 µmol kg<sup>-1</sup>, DIC around 2025 µmol kg<sup>-1</sup> and pH around 8.060 pH units) during dry season and lower values (TA around 2300 µmol kg<sup>-1</sup>, DIC around 1990 µmol kg<sup>-1</sup> and pH around 8.050 pH units) during rainy season. On the other hand, <em>f</em>CO<sub>2</sub> varied positively with SST, with lower values (around 385 µatm) during dry, upwelling season and higher values (around 390 µatm) during rainy season, showing that both SSS and SST variability play an important role in the CO<sub>2</sub> solubility in the region.</p>

The Indo‐Pacific coral Diploastrea heliopora reveals regional multidecadal‐ to centennial‐ scale climate variability using coral carbonate δ18O (δ18Oc) as a combined proxy for sea surface temperature (SST) and sea surface salinity (SSS). However, to assess the coral's full potential in resolving climatic events, an independent SST proxy would be more advantageous. We examined both Sr/Ca and δ18O of Diploastrea against an adjacent Porites lobata core collected from northeast Luzon, Philippines. Winter Sr/Ca data from Diploastrea show a significant correlation to SST (r = −0.41, p < 0.05, (root‐mean‐square of the residual) RMSR = 0.81°C) and provide a proxy with similar sensitivity as Porites (r = −0.57, p < 0.05, RMSR = 0.62°C). An interspecies SST record is shown to be robust and used for a reconstruction of the Pacific Decadal Oscillation during boreal winter (r = −0.70, p = 0.02). While we were unable to generate a robust Diploastrea δ18O‐SSS calibration at interannual timescale, the freshening trend toward the present, commonly observed in the region, is qualitatively captured in Diploastrea δ18O. Comparison with Porites δ18O and instrumental SSS records shows that the magnitude of freshening is consistent between coral species. Wet and dry season Porites δ18O provide support for the relative influence of El Niño–Southern Oscillation events and local precipitation to SSS variability at our site. The multiproxy, multispecies approach of this study further strengthens the evidence for Diploastrea as an alternate climate archive in the Indo‐Pacific region and seals its potential in helping resolve less understood global‐scale climate phenomena.

Distributions and variations in the partial pressure of CO2 in surface waters (pCO2w) of the central and western equatorial Pacific during the 1997/1998 El Niño event

Abrupt and short climate changes, such as the Younger Dryas, punctuated the last glacial-to-interglacial transition (Ruddiman and McIntyre, 1981 doi:10.1016/0031-0182(81)90097-3; Duplessy et al., 1981 doi:10.1016/0031-0182(81)90096-1; Oeschger et al. 1984; Broecker et al., 1985 doi:10.1038/315021a0). Broecker et al. (1988 doi:10.1029/PA003i001p00001) proposed that these may have been caused by an interruption of thermohaline circulation as inputs of glacial meltwater freshened the surface waters of the North Atlantic. The finding (Fairbanks, 1989 doi:10.1038/342637a0) that meltwater discharge was minimal during the Younger Dryas, however, led to the suggestion that the surface-water salinity drop might have been caused instead by changes in the freshwater budget (the difference between precipitation and evaporation), accompanied by a reduction in poleward advection of saline subtropical water. Here we use micropalaeontological and stable-isotope records from foraminifera in two cores from the North Atlantic to generate two continuous, high-resolution records of sea surface temperature and salinity changes over the past 18,000 years. Despite the injection of glacial meltwater during warm episodes, we find that sea surface salinity and temperature remain positively correlated during deglaciation. Cold, low-salinity events occurred during the early stages of deglaciation (14,500-13,000 years ago) and the Younger Dryas, but the minor injections of meltwater at high latitudes during these events are insufficient to account for the observed salinity changes. We conclude that an additional feedback from changes in the hydrological cycle and in advection was necessary to trigger changes in thermohaline circulation and thus in climate. This feedback did not act when the meltwater injection occurred at low latitude.

Seasonal variability in sea surface oceanographic conditions in the Aegean Sea (Eastern Mediterranean): an overview

The extraction of physical information about the subsurface ocean from surface information obtained from satellite measurements is both important and challenging. We introduce a back-propagation neural network (BPNN) method to determine the subsurface temperature of the North Pacific Ocean by selecting the optimum input combination of sea surface parameters obtained from satellite measurements. In addition to sea surface height (SSH), sea surface temperature (SST), sea surface salinity (SSS) and sea surface wind (SSW), we also included the sea surface velocity (SSV) as a new component in our study. This allowed us to partially resolve the non-linear subsurface dynamics associated with advection, which improved the estimated results, especially in regions with strong currents. The accuracy of the estimated results was verified with reprocessed observational datasets. Our results show that the BPNN model can accurately estimate the subsurface (upper 1000 m) temperature of the North Pacific Ocean. The corresponding mean square errors were 0.868 and 0.802 using four (SSH, SST, SSS and SSW) and five (SSH, SST, SSS, SSW and SSV) input parameters and the average coefficients of determination were 0.952 and 0.967, respectively. The input of the SSV in addition to the SSH, SST, SSS and SSW therefore has a positive impact on the BPNN model and helps to improve the accuracy of the estimation. This study provides important technical support for retrieving thermal information about the ocean interior from surface satellite remote sensing observations, which will help to expand the scope of satellite measurements of the ocean.

The Mediterranean Sea is considered a hot spot of global warming because it has been changing faster than the global ocean, creating a strong impact on the marine environment. Recent studies agree on the increase in the sea level, in the sea surface temperature, and in the sea surface salinity in the Mediterranean Sea over the last two decades. In this research, the possible interconnection between these and other parameters that contribute to the regulatory effect of the sea on the climate are identified and discussed. Spatio-temporal variability of four oceanographic and air–sea interaction parameters (sea-level, sea surface temperature, sea surface salinity, and freshwater flux) are estimated over the last 27 years by performing the empirical orthogonal function analysis. Climatic trends, and interannual and decadal variability of the different datasets are delineated and described in the whole Mediterranean and in its sub-basins. On the climatic scale, the Mediterranean and its sub-basins behave in a coherent way, showing the seal level, temperature, salinity, and freshwater flux rise. On the interannual scale, the temporal evolution of the sea level and sea surface temperature are highly correlated, whereas freshwater flux affects the variability of sea level, temperature, and the salinity field mainly in the Western and Central Mediterranean. The decadal signal associated with the Northern Ionian Gyre circulation reversals is clearly identified in three of the four parameters considered, with different intensities and geographical extents. This signal also affects the intermediate layer of the Eastern Mediterranean, from where it is advected to the other sub-basins. Decadal signal not associated with the Northern Ionian Gyre reversals is strongly related to the variability of main sub-basin scale local structures.

The role of ocean dynamics for Atlantic climate variability and predictability is often studied through the lens of sea surface temperature (SST). Unlike SST, sea surface salinity (SSS) is not directly damped by surface fluxes, and its low-frequency variability responds primarily to oceanic processes. This study investigates the drivers of SSS variability using a stochastic model hierarchy to disentangle oceanic and atmospheric contributions to Atlantic climate variability, in particular the role of local vertical processes. Representation of SST and SSS persistence and variance is especially improved by the introduction of damping of anomalies below the mixed-layer, though SSS anomalies remain too persistent. The effect of SST-evaporation feedback on SSS is comparatively smaller except in regions with strong SST-SSS correlation. Despite the lack of representation of geostrophic advection, the stochastic model successfully reproduces spatial patterns of recurring SST/SSS anomalies in the Community Earth System Model 1 (CESM1) Large Ensemble at monthly to interannual timescales. At multidecadal timescales, the stochastic model is unable to simulate the amplitude of SST/SSS variability, but their spatial patterns are broadly reproduced, suggesting that direct atmospheric forcing and local vertical processes are important for capturing these features. Further analysis of the processes missing from the stochastic model suggest that the lack of geostrophic advection is largely responsible for too persistent SSS in the stochastic model, while lack of interannual mixed-layer depth variability explains the underestimated persistence and variance in some regions for both SST and SSS.

Abstract. Eurasian rivers provide a quarter of total fresh water to the Arctic, maintaining a persistent fresh layer that covers the surface Arctic Ocean. This freshwater export controls Arctic Ocean stratification, circulation, and basin-wide sea ice concentration. The Lena River supplies the largest volume of runoff and plays a key role in this system, as runoff outflows into the Laptev Sea as a particularly shallow plume. Previous in situ and modelling studies suggest that local wind forcing is a driver of variability in Laptev sea surface salinity (SSS) but there is no consensus on the roles of Lena River discharge and sea ice cover in contributing to this variability or on the dominant driver of variability. Until recently, satellite SSS retrievals were insufficiently accurate for use in the Arctic. However, retreating sea ice cover and continuous progress in satellite product development have significantly improved SSS retrievals, giving satellite SSS data true potential in the Arctic. In this region, satellite-based SSS is found to agree well with in situ data (r>0.8) and provides notable improvements compared to the reanalysis product used in this study (r>0.7) in capturing patterns and variability observed in in situ data. This study demonstrates a novel method of identifying the dominant drivers of interannual variability in Laptev Sea dynamics within reanalysis products and testing if these relationships appear to hold in satellite-based SSS, sea surface temperature (SST) data, and in situ observations. The satellite SSS data firmly establish what is suggested by reanalysis products and what has previously been subject to debate due to the limited years and locations analysed with in situ data; the zonal wind is the dominant driver of offshore or onshore Lena River plume transport. The eastward wind confines the plume to the southern Laptev Sea and drives alongshore transport into the East Siberian Sea, and westward wind drives offshore plume transport into the northern Laptev Sea. This finding is affirmed by the strong agreement in SSS pattern under eastward and westward wind regimes in all reanalyses and satellite products used in this study, as well as with in situ data. The pattern of SST also varies with the zonal wind component and drives spatial variability in sea ice concentration.

Climatic variability and trends in the surface waters of coastal British Columbia

Sea surface temperature (SST) serves as a crucial ancillary input field to the retrieval algorithm for sea surface salinity (SSS) from L-band satellite radiometers, such as Soil Moisture and Ocean Salinity mission, Aquarius, and Soil Moisture Active Passive mission. It is needed for inverting the radiative transfer model equation of the ocean surface emissivity, which depends both on ocean surface salinity and ocean surface temperature. Our analysis studies the sensitivity of the performance of the Aquarius salinity retrieval algorithm to the ancillary SST that is used in the algorithm. We have retrieved Aquarius salinities using four different SST fields as ancillary input, namely, the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR)-only Optimum Interpolation SST (OISST), the SST from WindSat, the SST from the Canadian Meteorological Center (CMC), and the Multi-scale Ultra-high Resolution (MUR) SST from the National Aeronautics and Space Administration's Jet Propulsion Laboratory. The retrieved Aquarius SSS is compared with ground truth data; thus, the performance of the salinity retrieval algorithm in all four cases can be evaluated. The WindSat, CMC, and MUR SST products, which are all based on or are assimilating SST measurements from the passive microwave (MW) sensors, give better performance than the NOAA AVHRR-only OISST, which does not use any MW SST data, but which is solely based on the in situ data and observations from the infrared AVHRR sensor. The CMC SST gives the best overall performance for the retrieved SSS. The sensitivity of the SSS retrievals and therefore the performance differences between the various ancillary input fields increases in cold water.

Variation of δ18O in otoliths of Stellifer lanceolatus and Eucinostomus gula environmental change indicator in Terminos Lagoon, Mexico