<p>We introduce a new version of the multi-mission sea surface salinity (SSS) optimum interpolation analysis (OISSS) which combines observations from NASA’s AQUARIUS/SAC-D and SMAP (Soil Moisture Active-Passive) satellite missions into continuous and consistent SSS data record. The dataset covers the period from September 2011 to present. Measurements from ESA’s SMOS (Soil Moisture and Ocean Salinity) satellite are used to fill gaps in SMAP observations during June-July 2019 and August-September 2022, when the SMAP satellite was in a safe mode and did not deliver scientific data. The analysis is based on Optimum Interpolation (OI), utilizes Level-2 (swath) data, and uses satellite-specific bias-correction algorithms to correct the satellite retrievals for large-scale biases.  The dataset includes uncertainty estimates, both formal and empirical. We use this dataset as an example to discuss requirements for the multi-mission SSS data products.</p> <p>To demonstrate its utility, the new dataset is used to characterize spatial patterns of SSS variability in the global ocean and on different time scales. The spatial pattern of the regional SSS trends show that the subtropical North Pacific is becoming fresher while the subtropical South Indian Ocean is becoming saltier. This is seemingly a part of a longer term oscillation as the trends are reversed compared to the preceding decade (2005-2015) estimated from Argo data. In particular, abrupt changes occurred during 2015, related, presumably, to a strong El Nino event of 2015-2016. The annual cycle is a dominant signal globally and can nicely be described by two leading empirical orthogonal functions (EOFs) explaining more than 35% of the total SSS variance. Except for the Indian Ocean, the oscillations are out of phase in the Northern and Southern Hemispheres and describe poleward propagation away from the Equator driven, presumably, by Ekman dynamics. The intra-seasonal signal is strongest in the tropics, particularly in the quasi-zonal bands associated with the Inter-tropical convergence zone (ITCZ) and South Pacific convergence zone (SPCZ), but also near outflows of major rivers, including the Amazon, Congo, Mississippi, Plata, Ganges and Brahmaputra.  Another region of interest is the northern North Atlantic, where satellite observations during the last decade have provided an unprecedented resource to study the spatial distribution and temporal evolution of SSS, allowing to observe areas typically not available by in-situ components of the ocean observing system. Here, the multi-mission SSS dataset is examined in its accuracy and appropriateness for studying SSS variability in high latitudes and marginal seas.</p> <p> </p>

Sea surface salinity (SSS) observations from Aquarius, Soil Moisture and Ocean Salinity (SMOS), and Soil Moisture Active Passive (SMAP) satellite missions are compared to characterize the time and length scales of SSS variability globally. Overall, there is general agreement between the global patterns of the time and length scales of SSS variability estimated from the three satellite missions. The temporal scales of SSS variability vary from more than 90 days in the tropics to ~15 days in the Southern Ocean. The very short temporal scales (close to the Nyquist period) in some parts of the ocean are probably due to the high level of noise in the satellite data or the high noise-to-signal ratio. The longest temporal scales are observed along the South Pacific Convergence Zone (SPCZ) and in the central and western tropical Pacific. These areas are also related to the strongest ENSO-related signal in SSS. The processes governing the SSS variability and distribution are also non-stationary, such that the scales determined over different observation periods may differ. Dominant spatial scales of SSS variability are generally the longest (up to 150 km) in the tropics and the shortest (<60 km) in the subpolar regions. The distribution of the dominant spatial scales is not simply latitudinal but exhibits a more complex spatial pattern. In the tropics, there is slight east-west and inter-hemispheric asymmetry observed in the Pacific but absent in the other two oceans. The analysis also reveals that the length scales of SSS variability are highly anisotropic in the tropics (the zonal scales are generally shorter than the meridional ones) and become more isotropic towards higher latitudes. Regional differences in the estimates of the scales from the three satellite SSS datasets may arise due to differences in the observation duration, spatial resolution and/or different level of noise.

Observations of sea surface salinity (SSS) from NASA's Soil Moisture Active-Passive (SMAP) and ESA's Soil Moisture and Ocean Salinity (SMOS) satellite missions are used to characterize and quantify the contribution of mesoscale eddies to the ocean transport of salt. Given large errors in satellite retrievals and, consequently, SSS maps, we evaluate two products from the two missions and also use two different methods to assess the eddy transport of salt. Comparing the two missions, we find that the estimates of the eddy transport of salt agree very well, particularly in the tropics and subtropics. The transport is divergent in the subtropical gyres (eddies pump salt out of the gyres) and convergent in the tropics. The estimates from the two satellites start to differ regionally at higher latitudes, particularly in the Southern Ocean and along the Antarctic Circumpolar Current (ACC), resulting, presumably, from a considerable increase in the level of noise in satellite retrievals (because of poor sensitivity of the satellite radiometer to SSS in cold water), or they can be due to insufficient spatial resolution. Overall, our study demonstrates that the possibility of characterizing and quantifying the eddy transport of salt in the ocean surface mixed layer can rely on the use of satellite observations of SSS. Yet, new technologies are required to improve the resolution capabilities of future satellite missions in order to observe mesoscale and sub-mesoscale variability, improve the signal-to-noise ratio, and extend these capabilities to the polar oceans.

Using newly available satellite observations of sea surface salinity (SSS), we provide, for the first time, a detailed and synoptic view of the spatiotemporal variability of SSS in the South China Sea (SCS). The results depict the SCS as a very dynamic region exhibiting variability over a broad range of time scales, from intraseasonal to interannual, with the seasonal cycle dominating (similar to 47% of the total SSS variance). The seasonal distribution of SSS has considerable latitudinal variations: the strongest variance across the southern SCS (similar to 5-12 degrees N), weaker in the northern part of the sea (north of similar to 18 degrees N), with the weakest seasonal SSS variability in between. The factors controlling patterns of seasonal SSS distribution are closely related to both the external freshwater forcing and ocean processes over the entire SCS monsoon system. The most active interannual SSS variability is found in the northeastern and eastern parts of the SCS, as well as the adjacent western Pacific. A significant basin-wide salinification began in summer of 2015, peaked in spring of 2016 with the averaged amplitude of up to 0.5 PSU, and maintained until the fall of 2016. Such persistent salinification during 2015-2016 following a strong El Nino event can be largely modulated by El Nino-related atmospheric and oceanic dynamics. The intraseasonal variability was found to be surprisingly weak throughout the SCS (the standard deviation <0.2 PSU), except for a few regions near the coast where it is likely related to the intraseasonal variability in the monsoon rainfall and subsequent variations in river runoff.

Sea surface salinity (SSS) observations from NASA’s satellite missions, Aquarius/SAC-D and Soil Moisture Active Passive (SMAP), are used to describe spatial patterns of the seasonal cycle, as well as intraseasonal and interannual variability, in the eastern tropical Pacific, the location of the second Salinity Processes in the Upper-ocean Regional Study (SPURS-2) field experiment. The results indicate that the distribution of SSS variance is highly inhomogeneous in both space and time. The seasonal signal is largest in the core of the Eastern Pacific Fresh Pool and in the Gulf of Panama. The interannual signal is highest in a relatively narrow zonal band along approximately 5°N, while the intraseasonal signal appears to be a dominant mode of variability in the zonally stretched near-equatorial region. Located right in the middle of a hotspot of high SSS variance, the SPURS-2 site appears to be at the crossroads of many different processes that shape the distribution of SSS in the eastern tropical Pacific and beyond.

Aquarius was the first NASA satellite to observe the sea surface salinity (SSS) over the global ocean. The mission successfully collected data from 25 August 2011 to 7 June 2015. The Aquarius project released its final version (Version-5) of the SSS data product in December 2017. The purpose of this paper is to summarize the validation results from the Aquarius Validation Data System (AVDS) and other statistical methods, and to provide a general view of the Aquarius SSS quality to the users. The results demonstrate that Aquarius has met the mission target measurement accuracy requirement of 0.2 psu on monthly averages on 150 km scale. From the triple point analysis using Aquarius, in situ field and Hybrid Coordinate Ocean Model (HYCOM) products, the root mean square errors of Aquarius Level-2 and Level-3 data are estimated to be 0.17 psu and 0.13 psu, respectively. It is important that caution should be exercised when using Aquarius salinity data in areas with high radio frequency interference (RFI) and heavy rainfall, close to the coast lines where leakage of land signals may significantly affect the quality of the SSS data, and at high-latitude oceans where the L-band radiometer has poor sensitivity to SSS.

A persistent signature of coherent mesoscale eddies in sea surface salinity (SSS) is revealed by analyzing the relationship between satellite SSS and sea surface height (SSH) variability in an eddy-following reference frame. Our analysis focuses on mid-ocean eddies in two representative regions, the southern Indian Ocean and the North Atlantic subtropical gyre. The resulting composite averages reveal a clear signature of mesoscale eddies in satellite SSS with typical SSS anomalies of 0.03-0.05 psu. The spatial structure of eddy-induced SSS perturbations can be characterized as a superposition of a dipole structure, arising from horizontal advection of the background SSS gradient by eddy velocity field, and a monopole structure related to the eddy core. The observed relationships between SSS and SSH anomalies are used to provide a regional assessment of the role of mesoscale eddies in the ocean freshwater transport in the North Atlantic subtropical gyre.

The straits in Indonesia allow for low-latitude exchange of water between the Pacific and Indian Oceans. Collectively known as the Indonesian Throughflow (ITF), this exchange is thought to occur primarily via the Makassar Strait and downstream via Lombok Strait, Ombai Strait, and Timor Passage. The Sunda Strait, between the islands of Sumatra and Java, is a very narrow ( approximate to 10km) and shallow ( approximate to 20m) gap, but it connects the Java Sea directly to the Indian Ocean. Flow through this strait is presumed to be small, given the size of the passage; however, recent observations from the Aquarius satellite indicate periods of significant freshwater transport, suggesting the Sunda Strait may play a more important role in Pacific to Indian Ocean exchange. The nature of this exchange is short-duration (several days) bursts of freshwater injected into the eastern Indian Ocean superimposed on a mean seasonal cycle. The mean volume transport is small averaging about 0.1 Sv toward the Indian Ocean, but the freshwater transport is nonnegligible (estimated at 5.8 mSv). Transport through the strait is hydraulically controlled and directly correlates to the along-strait pressure difference. The episodic low-salinity plumes observed by Aquarius do not, however, appear to be forced by this same mechanism but are instead controlled by convergence of flow at the exit of the Strait in the Indian Ocean. Numerical model results show the fate of this freshwater plume varies with season and is either advected to the northwest along the coast of Sumatra or southerly into the ITF pathway.

A new high-resolution sea surface salinity (SSS) analysis has been produced using Aquarius satellite observations from September 2011 to June 2015. The motivation for the new product is twofold: to produce Level-4 SSS analysis that is consistent with existing in situ observations such as from Argo profile data, and to reduce the large-scale satellite biases that have existed in all versions of the standard Level-3 Aquarius products. The new product is a weekly SSS analysis on a nearly global 0.5 degrees grid. The analysis method is optimum interpolation (OI) that takes into account analyzed errors of the observations, specific to the Aquarius instrument. The method also includes a large-scale correction for satellite biases, filtering of along-track SSS data prior to OI, and the use of realistic correlation scales of SSS anomalies. All these features of the analysis are shown to result in more accurate SSS maps. In particular, the method reduces the effects of relative biases between the Aquarius beams and eliminates most of the large-scale, space-varying, and time-varying satellite biases relative to in situ data, including spurious annual signals. Statistical comparison between the weekly OI SSS maps and concurrent buoy data demonstrates that the global root-mean-square error of the analysis is smaller than 0.2 pss for nearly all weeks over the similar to 4 year period of comparison. The utility of the OI SSS analysis is also exemplified by the derived patterns of regional SSS variability.

A method is presented for mapping sea surface salinity (SSS) from Aquarius level-2 along-track data in order to improve the utility of the SSS fields at short length [O(150 km)] and time [O(1 week)] scales. The method is based on optimal interpolation (OI) and derives an SSS estimate at a grid point as a weighted sum of nearby satellite observations. The weights are optimized to minimize the estimation error variance. As an initial demonstration, the method is applied to Aquarius data in the North Atlantic. The key element of the method is that it takes into account the so-called long-wavelength errors (by analogy with altimeter applications), referred to here as interbeam and ascending/descending biases, which appear to correlate over long distances along the satellite tracks. The developed technique also includes filtering of along-track SSS data prior to OI and the use of realistic correlation scales of mesoscale SSS anomalies. All these features are shown to result in more accurate SSS maps, free from spurious structures. A trial SSS analysis is produced in the North Atlantic on a uniform grid with 0.25 degrees resolution and a temporal resolution of one week, encompassing the period from September 2011 through August 2013. A brief statistical description, based on the comparison between SSS maps and concurrent in situ data, is used to demonstrate the utility of the OI analysis and the potential of Aquarius SSS products to document salinity structure at similar to 150-km length and weekly time scales.

[1] Interannual-to-decadal time scale eddy variability in the Hawaiian Lee Countercurrent (HLCC) band is investigated using the available sea surface height, sea surface temperature, and surface wind stress data sets. In the HLCC band of 17°N–21.7°N and 170E°–160°W, the prevailing interannual eddy kinetic energy (EKE) signals show enhanced eddy activities in 1993–1998 and 2002–2006, and subpar eddy activities in 1999–2001 and 2007–2009. These interannual EKE signals exhibit little connection to the zonal HLCC velocity changes generated by the dipolar wind stress curl forcing in the immediate lee of the island of Hawaii. Instead, they are highly correlated to the time series of the Pacific Decadal Oscillation (PDO) index. Through a budget analysis for the meridional temperature gradient along the HLCC, we find that during the positive phase of the PDO index, the surface heat flux forcing induces cold (warm) sea surface temperature (SST) anomalies to the north (south) of the HLCC, intensifying the vertical shear between the surface, eastward-flowing HLCC and the subsurface, westward-flowing North Equatorial Current (NEC). This increased vertical shear enhances the baroclinic instability of the HLCC-NEC system and leads to a higher regional EKE level. The opposite processes occur when the PDO switches to a negative phase with the resulting lowered EKE level along the HLCC band. Compared to the surface heat flux forcing, the Ekman flux convergence forcing is found to play a minor role in modifying the meridional SST changes along the HLCC band.

Weekly satellite sea surface height (SSH) anomaly data are used to clarify the mesoscale eddy characteristics in the lee of the island of Hawaii, the largest island in the Hawaiian Island chain. The lee eddy variability can be separated into two geographical regions. In the immediate lee southwest of Hawaii (Region E), eddy signals have a predominant 60 day period and a short life‐span, whereas in the region along 19°N west of ∼160°W (Region W), the eddy variability is dominated by 100 day signals and extends over a broad region. By applying a linear Ekman pumping model forced by the weekly QuikSCAT wind data, we find that the observed 60 day eddy signals originate in the southwest corner of Hawaii and are induced by the local 60 day wind stress curl variability associated with the blocking of the trade wind by the island of Hawaii. The relationship between the wind forcing and the observed SSH signals demonstrates the role of the ocean as an integrator that responds more effectively to the low‐frequency synoptic atmospheric forcing (∼60 days) than to the higher‐frequency forcing (∼30 days). Since the large‐amplitude 60 day SSH anomalies take 1–2 weeks to fully develop, it is possible that real‐time observed wind stress data can be used for the prediction of these anomalies. In contrast to the wind‐induced 60 day eddy signals in the lee of the island of Hawaii, the 100 day eddy signals in Region W are likely generated by the instability of the sheared North Equatorial Current and Hawaii Lee Countercurrent.

Satellites view the world oceans in days to weeks, and can repeat such measurements for many years.Among the Essential Climate Variables (ECVs), those that can be measured from space are sea surface temperature, height, vector winds, colour, sea state and sea ice.In addition, there are emerging ECVs: ocean mass and sea surface salinity.Our Recommendations can be summarized as follows: measurement.Improve the time-averaged geoid using GOCE, aided by GRACE, CHAMP, and historical laser-tracked geodetic satellites.The previous decade saw these ECVs be used primarily on their own.We fully expect interdisciplinary use of two or more ECVs to become the norm in the next decade.