Validation of the multi-satellite merged sea surface salinity in the South China Sea

Sea surface salinity(SSS)is an essential variable of ocean dynamics and climate research.The Soil Moisture and Ocean Salinity(SMOS),Aquarius,and Soil Moisture Active Passive(SMAP)satellite missions all provide SSS measurements.The European Space Agency(ESA)Climate Change Initiative Sea Surface Salinity(CCI-SSS)project merged these three satellite SSS data to produce CCI L4SSS products.We validated the accuracy of the four satellite products(CCI,SMOS,Aquarius,and SMAP)using in-situ gridded data and Argo floats in the South China Sea(SCS).Compared with in-situ gridded data,it shows that the CCI achieved the best performance(RMSD:0.365)on monthly time scales.The RMSD of SMOS,Aquarius,and SMAP(SMOS:0.389;Aquarius:0.409;SMAP:0.391)are close,and the SMOS takes a slight advantage in contrast with Aquarius and SMAP.Large discrepancies can be found near the coastline and in the shelf seas.Meanwhile,CCI with lower RMSD(0.295)perform better than single satellite data(SMOS:0.517;SMAP:0.297)on weekly time scales compared with Argo floats.Overall,the merged CCI have the smallest RMSD among the four satellite products in the SCS on both weekly time scales and monthly time scales,which illustrates the improved accuracy of merged CCI compared with the individual satellite data.

Characteristics and mechanisms of the intraseasonal variability of sea surface salinity in the southeastern Arabian Sea during 2015-2020

Based on Soil Moisture Active Passive sea surface salinity(SSS)data from April 2015 to August 2020,combined with Objectively Analyzed Air-Sea Heat Flux and other observational data and Hybrid Coordinate Ocean Model(HYCOM)data,this work explores the characteristics and mechanisms of the intraseasonal variability of SSS in the southeastern Arabian Sea(SEAS).The results show that the intraseasonal variability of SSS in the SEAS is very significant,especially the strongest intraseasonal signal in SSS,which is located along the northeast monsoon current(NMC)path south of the Indian Peninsula.There are remarkable seasonal differences in intraseasonal SSS variability,which is very weak in spring and summer and much stronger in autumn and winter.This strong intraseasonal variability in autumn and winter is closely related to the Madden-Julian Oscillation(MJO)event during this period.The northeast wind anomaly in the Bay of Bengal(BOB)associated with the active MJO phase strengthens the East India Coastal Current and NMC and consequently induces more BOB low-salinity water to enter the SEAS,causing strong SSS fluctuations.In addition,MJO-related precipitation further amplifies the intraseasonal variability of SSS in SEAS.Based on budget analysis of the mixed layer salinity using HYCOM data,it is shown that horizontal salinity advection(especially zonal advection)dominates the intraseasonal variability of mixed layer salinity and that surface freshwater flux has a secondary role.

A new method to retrieve salinity profiles from sea surface salinity observed by SMOS satellite

This paper proposes a new method to retrieve salinity profiles from the sea surface salinity （SSS） observed by the Soil Moisture and Ocean Salinity （SMOS） satellite. The main vertical patterns of the salinity profiles are firstly extracted from the salinity profiles measured by Argo using the empirical orthogonal function. To determine the time coefficients for each vertical pattern, two statistical models are developed. In the linear model, a transfer function is proposed to relate the SSS observed by SMOS （SMOS_SSS） with that measured by Argo, and then a linear relationship between the SMOS_SSS and the time coefficient is established. In the nonlinear model, the neural network is utilized to estimate the time coefficients from SMOS_SSS, months and positions of the salinity profiles. The two models are validated by comparing the salinity profiles retrieved from SMOS with those measured by Argo and the climatological salinities. The root-mean-square error （RMSE） of the linear and nonlinear model are 0.08-0.16 and 0.08-0.14 for the upper 400 m, which are 0.01-0.07 and 0.01-0.09 smaller than the RMSE of climatology. The error sources of the method are also discussed.

An analysis on the error structure and mechanism of soil moisture and ocean salinity remotely sensed sea surface salinity products

For the application of soil moisture and ocean salinity（SMOS） remotely sensed sea surface salinity（SSS） products,SMOS SSS global maps and error characteristics have been investigated based on quality control information.The results show that the errors of SMOS SSS products are distributed zonally,i.e.,relatively small in the tropical oceans,but much greater in the southern oceans in the Southern Hemisphere（negative bias） and along the southern,northern and some other oceanic margins（positive or negative bias）.The physical elements responsible for these errors include wind,temperature,and coastal terrain and so on.Errors in the southern oceans are due to the bias in an SSS retrieval algorithm caused by the coexisting high wind speed and low temperature; errors along the oceanic margins are due to the bias in a brightness temperature（TB） reconstruction caused by the high contrast between L-band emissivities from ice or land and from ocean; in addition,some other systematic errors are due to the bias in TB observation caused by a radio frequency interference and a radiometer receivers drift,etc.The findings will contribute to the scientific correction and appropriate application of the SMOS SSS products.

Assessment of Interannual Sea Surface Salinity Variability and Its Effects on the Barrier Layer in the Equatorial Pacific Using BNU-ESM

As salinity stratification is necessary to form the barrier layer （BL）, the quantification of its role in BL interannual variability is crucial. This study assessed salinity variability and its effect on the BL in the equatorial Pacific using outputs from Beijing Normal University Earth System Model （BNU-ESM） simulations. A comparison between observations and the BNU-ESM simulations demonstrated that BNU-ESM has good capability in reproducing most of the interannual features observed in nature. Despite some discrepancies in both magnitude and location of the interannual variability centers, the displacements of sea surface salinity （SSS）, barrier layer thickness （BLT）, and SST simulated by BNU-ESM in the equatorial Pacific are realistic. During E1 Nifio, for example, the modeled interannual anomalies of BLT, mixed layer depth, and isothermal layer depth, exhibit good correspondence with observations, including the development and decay of E1 Nifio in the central Pacific, whereas the intensity of the interannual variabilities is weaker relative to observations. Due to the bias in salinity simulations, the SSS front extends farther west along the equator, whereas BLT variability is weaker in the central Pacific than in observations. Further, the BNU-ESM simulations were examined to assess the relative effects of salinity and temperature variability on BLT. Consistent with previous observation-based analyses, the interannual salinity variability can make a significant contribution to BLT relative to temperature in the western-central equatorial Pacific.

Validation and application of soil moisture active passive sea surface salinity observation over the Changjiang River Estuary

Using sea surface salinity(SSS)observation from the soil moisture active passive(SMAP)mission,we analyzed the spatial distribution and seasonal variation of SSS around Changjiang River(Yangtze River)Estuary for the period of September 2015 to August 2018.First,we found that the SSS from SMAP is more accurate than soil moisture and ocean salinity(SMOS)mission observation when comparing with the in situ observations.Then,the SSS signature of the Changjiang River freshwater was analyzed using SMAP data and the river discharge data from the Datong hydrological station.The results show that the SSS around the Changjiang River Estuary is significantly lower than that of the open ocean,and shows significant seasonal variation.The minimum value of SSS appears in July and maximum SSS in December.The root mean square difference of daily SSS between SMAP observation and in situ observation is around 3 in both summer and winter,which is much lower than the annual range of SSS variation.In summer,the diffusion direction of the Changjiang River freshwater depicted by SSS from SMAP is consistent with the path of freshwater from in situ observation,suggesting that SMAP observation may be used in coastal seas in monitoring the diffusion and advection of freshwater discharge.

Subtropical sea surface salinity maxima in the South Indian Ocean

Subtropical sea surface salinity(SSS)maximum is formed in the subtropical South Indian Ocean(SIO)by excessive evaporation over precipitation and serves as the primary salt source of the SIO.Spaceborne SSS measurements by Aquarius satellite during September 2011-May 2015 detect three disconnected SSS maximum regions(>35.6)in the eastern(105°E-115°E,38°S-28°S),central(60°E-100°E,35°S-25°S),and western(25°E-40°E,38°S-20°S)parts of the subtropical SIO,respectively.Such structure is however not seen in gridded Argo data.Analysis of Argo profile data confirms the existence of the eastern maximum patch and also reveals SSS overestimations of Aquarius near the western and eastern boundaries.Although subjected to large uncertainties,a mixed-layer budget analysis is employed to explain the seasonal cycle of SSS.The eastern and central regions reach the highest salinity in February-March and lowest salinity in August-September,which can be well explained by surface freshwater forcing(SFF)term.SFF is however not controlled by evaporation(E)or precipitation(P).Instead,the large seasonal undulations of mixed layer depth(MLD)is the key factor.The shallow(deep)MLD in austral summer(winter)amplifies(attenuates)the forcing effect of local positive E-P and causes SSS rising(decreasing).Ocean dynamics also play a role.Particularly,activity of mesoscale eddies is a critical factor regulating SSS variability in the eastern and western regions.

A performance evaluation of remotely sensed sea surface salinity products in combination with other surface measurements in reconstructing three-dimensional salinity fields

Several remotely sensed sea surface salinity（SSS） retrievals with various resolutions from the soil moisture and ocean salinity（SMOS） and Aquarius/SAC-D missions are applied as inputs for retrieving salinity profiles（S） using multilinear regressions. The performance is evaluated using a total root mean square（RMS） error, different error sources, and the feature resolutions of the retrieved S fields. In the mixed layer of the salinity, the SSS-S regression coefficients are uniformly large. The SSS inputs yield smaller RMS errors in the retrieved S with respect to Argo profiles as their spatial or temporal resolution decreases. The projected SSS errors are dominant, and the retrieved S values are more accurate than those of climatology in the tropics except for the tropical Atlantic, where the regression errors are abnormally large. Below that level, because of the influence of a sea level anomaly, the areas of high-accuracy S values shift to higher latitudes except in the high-latitude southern oceans, where the projected SSS errors are abnormally large. A spectral analysis suggests that the CATDS-0.25° results are much noisier and that the BEC-L4-0.25° results are much smoother than those of the other retrievals. Aquarius-CAP-1° generates the smallest RMS errors, and Aquarius-V2-1° performs well in depicting large-scale phenomena. BEC-L3-0.25°,which has small RMS errors and remarkable mesoscale energy, is the best fit for portraying mesoscale features in the SSS and retrieved S fields. The current priority for retrieving S is to improve the reliability of satellite SSS especially at middle and high latitudes, by developing advanced algorithms, combining both sensors, or weighing between accuracy and resolutions.

Assessment and adjustment of sea surface salinity products from Aquarius in the southeast Indian Ocean based on in situ measurement and My Ocean modeled data

The in situ sea surface salinity（SSS） measurements from a scientific cruise to the western zone of the southeast Indian Ocean covering 30°-60°S, 80°-120°E are used to assess the SSS retrieved from Aquarius（Aquarius SSS）.Wind speed and sea surface temperature（SST） affect the SSS estimates based on passive microwave radiation within the mid- to low-latitude southeast Indian Ocean. The relationships among the in situ, Aquarius SSS and wind-SST corrections are used to adjust the Aquarius SSS. The adjusted Aquarius SSS are compared with the SSS data from My Ocean model. Results show that：（1） Before adjustment： compared with My Ocean SSS, the Aquarius SSS in most of the sea areas is higher; but lower in the low-temperature sea areas located at the south of 55°S and west of 98°E. The Aquarius SSS is generally higher by 0.42 on average for the southeast Indian Ocean.（2） After adjustment： the adjustment greatly counteracts the impact of high wind speeds and improves the overall accuracy of the retrieved salinity（the mean absolute error of the Zonal mean is improved by 0.06, and the mean error is-0.05 compared with My Ocean SSS）. Near the latitude 42°S, the adjusted SSS is well consistent with the My Ocean and the difference is approximately 0.004.

Sea surface salinity-derived indexes for distinguishing two types of El Niño events in the tropical Pacific

In this study,sea surface salinity(SSS)indexes are derived from reanalysis and observational datasets to distinguish the two types of(Central Pacific(CP)and Eastern Pacific(EP))El Niño events in the tropical Pacific.Based on the SSS anomalous spatial and temporal pointwise correlations with sea surface temperature(SST)indexes of two types of El Niño events,the key areas with SSS variations for EP and CP El Niño events are identified.For EP El Niño events,the key areas are located over an arcuate area centered at(0°,130°E)and in the central equatorial Pacific covering(5°S–5°N,175°W–158°W).For CP El Niño events,the key areas are located in the northeastern western Pacific covering(2°N,142°E–170°E)and in the southeastern Pacific covering(20°S–10°S,135°W–95°W).The key areas for EP and CP El Niño events in this study are not located near the dateline in the equatorial Pacific and differ from those obtained from the regression or composite methods.Accordingly,these key areas are used to construct SSS indexes,termed as the CP/EP El Niño SSS index(CSI/ESI),to distinguish EP and CP El Niño events independently.The SSS indexes are verified by different datasets over varying time periods and they can be adequately used to identify the two types of El Niño events and serve as another useful tool for monitoring ENSO.These analyses offer novel insight into how to represent the diversity of El Niño events.

Modulation of El Nio-Southern Oscillation by Freshwater Flux and Salinity Variability in the Tropical Pacific

The E1 Nifio-Southern Oscillation （ENSO） is emphasized the roles of wind stress and heat flux environmental forcing to the ocean; its effect and modulated by many factors; most previous studies have in the tropical Pacific. Freshwater flux （FWF） is another the related ocean salinity variability in the ENSO region have been of increased interest recently. Currently, accurate quantifications of the FWF roles in the climate remain challenging; the related observations and coupled ocean-atmosphere modeling involve large elements of uncertainty. In this study, we utilized satellite-based data to represent FWF-induced feedback in the tropical Pacific climate system; we then incorporated these data into a hybrid coupled ocean-atmosphere model （HCM） to quantify its effects on ENSO. A new mechanism was revealed by which interannual FWF forcing modulates ENSO in a significant way. As a direct forcing, FWF exerts a significant influence on the ocean through sea surface salinity （SSS） and buoyancy flux （QB） in the western-central tropical Pacific. The SSS perturbations directly induced by ENSO-related interannual FWF variability affect the stability and mixing in the upper ocean. At the same time, the ENSO-induced FWF has a compensating effect on heat flux, acting to reduce interannual Qs variability during ENSO cycles. These FWF-induced processes in the ocean tend to modulate the vertical mixing and entrainment in the upper ocean, enhancing cooling during La Nifia and enhancing warming during E1 Nifio, respectively. The interannual FWF forcing-induced positive feedback acts to enhance ENSO amplitude and lengthen its time scales in the tropical Pacific coupled climate system.