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.

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.

Preliminary validation of SMOS sea surface salinity measurements in the South China Sea

The SMOS(soil moisture and ocean salinity) mission undertaken by the European Space Agency(ESA) has provided sea surface salinity(SSS) measurements at global scale since 2009.Validation of SSS values retrieved from SMOS data has been done globally and regionally.However,the accuracy of SSS measurements by SMOS in the China seas has not been examined in detail.In this study,we compared retrieved SSS values from SMOS data with in situ measurements from a South China Sea(SCS) expedition during autumn 2011.The comparison shows that the retrieved SSS values using ascending pass data have much better agreement with in situ measurements than the result derived from descending pass data.Accuracy in terms of bias and root mean square error(RMS) of the SSS retrieved using three different sea surface roughness models is very consistent,regardless of ascending or descending orbits.When ascending and descending measurements are combined for comparison,the retrieved SSS using a semi-empirical model shows the best agreement with in situ measurements,with bias-0.33 practical salinity units and RMS 0.74.We also investigated the impact of environmental conditions of sea surface wind and sea surface temperature on accuracy of the retrieved SSS.The SCS is a semi-closed basin where radio frequencies transmitted from the mainland strongly interfere with SMOS measurements.Therefore,accuracy of retrieved SSS shows a relationship with distance between the validation sites and land.

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.

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.

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.

Retrieve Sea Surface Salinity Using Principal Component Regression Model Based on SMOS Satellite Data

The sea surface salinity(SSS) is a key parameter in monitoring ocean states. Observing SSS can promote the understanding of global water cycle. This paper provides a new approach for retrieving sea surface salinity from Soil Moisture and Ocean Salinity(SMOS) satellite data. Based on the principal component regression(PCR) model, SSS can also be retrieved from the brightness temperature data of SMOS L2 measurements and Auxiliary data. 26 pair matchup data is used in model validation for the South China Sea(in the area of 4?–25?N, 105?–125?E). The RMSE value of PCR model retrieved SSS reaches 0.37 psu(practical salinity units) and the RMSE of SMOS SSS1 is 1.65 psu when compared with in-situ SSS. The corresponding Argo daily salinity data during April to June 2013 is also used in our validation with RMSE value 0.46 psu compared to 1.82 psu for daily averaged SMOS L2 products. This indicates that the PCR model is valid and may provide us with a good approach for retrieving SSS from SMOS satellite data.

Analysis of Cost Functions for Retrieving Sea Surface Salinity

Two kinds of Bayesian-based cost functions (i.e., the unconstrained cost function and parameter-constrained cost function) are investigated for retrieving the sea surface salinity (SSS). In low SSS regions, we have analyzed the sensitivity of the two cost functions to geophysical parameters. The results show that the unconstrained cost function is valid for retrieving several parameters (including SSS, wind speed and significant wave height), and the constrained cost function, which largely depends on the accuracy of reference values, may lead to large retrieval biases. Furthermore, as a retrieval parameter, the sea surface temperature (SST) can re-sult in the divergence of other geophysical parameters in an unconstrained cost function due to the strong sensitivity of brightness temperature to SST. By using the unconstrained cost function and the simulated brightness temperature TB with white noises, the retrieval biases of SSS are discussed with the following two procedures. Procedure a): the simulated TB values are first averaged, and then SSS is retrieved. Procedure b): the SSS is directly retrieved from the simulated TB , and then the retrieved SSS values are aver-aged. The results indicate that, for low SSS and SST distributions, the SSS retrieval by procedure a) has less biases compared with that by procedure b), while the two procedures give almost the same retrieval results for high SSS and SST sea regions.

Validation and correction of sea surface salinity retrieval from SMAP

In this study, sea surface salinity(SSS) Level 3(L3) daily product derived from soil moisture active passive(SMAP)during the year 2016, was validated and compared with SSS daily products derived from soil Moisture and ocean salinity(SMOS) and in-situ measurements. Generally, the root mean square error(RMSE) of the daily SSS products is larger along the coastal areas and at high latitudes and is smaller in the tropical regions and open oceans. Comparisons between the two types of daily satellite SSS product revealed that the RMSE was higher in the daily SMOS product than in the SMAP, whereas the bias of the daily SMOS was observed to be less than that of the SMAP when compared with Argo floats data. In addition, the latitude-dependent bias and RMSE of the SMAP SSS were found to be primarily influenced by the precipitation and the sea surface temperature(SST). Then, a regression analysis method which has adopted the precipitation and SST data was used to correct the larger bias of the daily SMAP product. It was confirmed that the corrected daily SMAP product could be used for assimilation in high-resolution forecast models, due to the fact that it was demonstrated to be unbiased and much closer to the in-situ measurements than the original uncorrected SMAP product.

Interannual variability of the sea surface salinity and its related freshwater flux in the tropical Pacific:A comparison of CMIP5 and CMIP6

This paper assesses the interannual variabilities of simulated sea surface salinity(SSS)and freshwater flux(FWF)in the tropical Pacific from phases 5 and 6 of the Coupled Model Intercomparison Project(CMIP5 and CMIP6).The authors focus on comparing the simulated SSS and FWF responses to El Nino–Southern Oscillation(ENSO)from two generations of models developed by the same group.The results show that CMIP5 and CMIP6 models can perform well in simulating the spatial distributions of the SSS and FWF responses associated with ENSO,as well as their relationship.It is found that most CMIP6 models have improved in simulating the geographical distribution of the SSS and FWF interannual variability in the tropical Pacific compared to CMIP5 models.In particular,CMIP6 models have corrected the underestimation of the spatial relationship of the FWF and SSS variability with ENSO in the central-western Pacific.In addition,CMIP6 models outperform CMIP5 models in simulating the FWF interannual variability(spatial distribution and intensity)in the tropical Pacific.However,as a whole,CMIP6 models do not show improved skill scores for SSS interannual variability,which is due to their overestimation of the intensity in some models.Large uncertainties exist in simulating the interannual variability of SSS among CMIP5 and CMIP6 models and some improvements with respect to physical processes are needed.

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.

Synthesizing high-resolution satellite salinity data based on multi-fractal fusion

The spaceborne platform has unprecedently provided the global eddy-permitting(typically about 0.25°)products of sea surface salinity(SSS),however the existing SSS products can hardly resolve mesoscale motions due to the heavy noises therein and the over-smoothing in denoising processes.By means of the multi-fractal fusion(MFF),the high-resolution SSS product is synthesized with the template of sea surface temperature(SST).Two low-resolution SSS products and four SST products are considered as the source data and the templates respectively to determine the best combination.The fused products are validated by the in situ observations and intercompared via SSS maps,Singularity Exponent maps and wavenumber spectra.The results demonstrate that the MFF can perform a good work in mitigating the noises and improving the resolution.The combination of the climate change initiative SSS and the remote sensing system SST can produce the 0.1°denoised product whose global mean standard derivation of salinity against Argo is 0.21 and the feature resolution can reach 30−40 km.

Observed Relationship between Surface Freshwater Flux and Salinity in The North Indian Ocean

Using 10-year (2001 10) monthly evaporation, precipitation, and sea surface salinity (SSS) datasets, the relationship between local freshwater flux and SSS in the north Indian Ocean (NIO) is evaluated quantitatively. The results suggest a highly positive linear correlation between freshwater flux and SSS in the Arabian Sea (correlation coefficient, R=0.74) and the western equatorial Indian Ocean (R=0.73), whereas the linear relationships are relatively weaker in the Bay of Bengal (R=0.50) and the eastern equatorial Indian Ocean (R=0.40). Additionally, the interannual variations of freshwater flux and SSS and their mutual relationship are investigated in four sub- regions for pre-monsoon, monsoon, and post-monsoon seasons separately. The satellite retrievals of SSS from the Soil Moisture and Ocean Salinity (SMOS) and Aquarius missions can provide continuous and consistent SSS fields for a better understanding of its variability and the differences between the freshwater flux and SSS signals, which are commonly thought to be linearly related.

Evaluating CMIP6 models in simulating the North Pacific decadal variability in sea surface salinity

The North Pacific sea surface salinity(SSS)decadal variability(NPSDV)and its potential forcing were evaluated from 25 coupled models of the Coupled Model Intercomparison Project phase 6(CMIP6)considering the prospects for decadal climate predictions.The results indicated that the CMIP6 models generally reproduced the spatial patterns of NPSDV.The large standard deviation of the SSS anomaly over the strong current regions,such as the Kuroshio-Oyashio Extension(KOE),North Pacific Current(NPC),California Current System(CCS),and Alaskan Coastal Current(ACC),is reflected in the two leading modes of NPSDV:a dipole with out-of-phase loadings in the KOE-NPC versus CCS-ACC and a monopole with positive loading over the KOE-NPC.The order of modes is sensitive to individual models that exhibit discrepancies,especially in temporal phases and power spectra.An autoregressive model of order-1 was used to reconstruct the NPSDV with several forcing terms.The generally weaker influence of forcings in an autoregressive model of order-1 is partly related to the overestimated response time of NPSDV relative to forcings.Most NPSDV variances originate from the persistence of SSS anomalies,but the dominant forcing factors are diverse among models.The model diversity for the NPSDV simulation mainly arises from the influence of the tropical El Ni?o-Southern Oscillation through teleconnection on the North Pacific Oscillation or Aleutian Low with timescale dependence.Conversely,models that can reproduce the NPSDV well are not dependent on those with larger impacts from the North Pacific oceanic processes.

Surface sediment diatoms from the western Pacific marginal seas and their correlation to environmental variables

Diatom data of 192 surface sediment samples from the marginal seas in the western Pacific together with modern summer and winter sea surface temperature and salinity data were analyzed.The results of canonical correspondence analysis show that summer sea-surface salinity(SSS) is highly positively correlated with winter SSS and so is summer sea-surface temperature(SST) with winter SST.The correlations between SSSs and SSTs are less positively correlated,which may be due to interactions of regional current pattern and monsoon climate.The correlations between diatom species,sample sites and environmental variables concur with known diatom ecology and regional oceanographic characters.The results of forward selection of the environmental variables and associated Monte Carlo permutation tests of the statistical significance of each variable suggest that summer SSS and winter SST are the main environmental factors affecting the diatom distribution in the area and therefore preserved diatom data from down core could be used for reconstructions of summer SSS and winter SST in the region.

Diatoms as indicators of environmental change in coastal areas:a case study in Lianjiang coast of East China Sea

Owing to the significant differences in environmental characteristics and explanatory factors among estuarine and coastal regions,research on diatom transfer functions and database establishment remains incomplete.This study analysed diatoms in surface sediment samples and a sediment core from the Lianjiang coast of the East China Sea,together with environmental variables.Principal component analysis of the environmental variables showed that sea surface salinity(SSS)and sea surface temperature were the most important factors controlling hydrological conditions in the Lianjiang coastal area,whereas canonical correspondence analysis indicated that SSS and pH were the main environmental factors affecting diatom distribution.Based on the modern diatom species–environmental variable database,we developed a diatom-based SSS transfer function to quantitatively reconstruct the variability in SSS between 1984 and 2021 for sediment core HK3 from the Lianjiang coastal area.The agreement between the reconstructed SSS and instrument SSS data from 1984 to 2021 suggests that diatombased SSS reconstruction is reliable for studying past SSS variability in the Lianjiang coastal area.Three low SSS events in AD 2019,2013,and 1999,together with an increased relative concentration of freshwater diatom species and coarser sediment grain sizes,corresponded to two super-typhoon events and a catastrophic flooding event in Lianjiang County.Thus,a diatom-based SSS transfer function for reconstructing past SSS variability in the estuarine and coastal areas of the East China Sea can be further used to reflect the paleoenvironmental events in this region.

Comparisons of Several Evaporation/Precipitation Datasets for the Bohai Sea Based on Salinity Simulation

Evaporation （E） rate and precipitation （P） rate are two significant meteorological elements required in the ocean baro- clinic modeling as external forcings. However, there are some uncertainties in the currently used E/P rates datasets, especially in terms of the data quality. In this study, we collected E/P rates data from ERA-40, NCEP/NCAR Reanalysis, HOAPS for the Bohai Sea and nine routine stations around Laizhou Bay, and made comparisons among them. It was found that the differences in E/P rates between land and sea are remarkable, which was due to the difference in underlying surfaces. Therefore, the traditional way of using E/P rates acquired on land directly at sea is not correct. Since no final conclusion has been reached concerning the net water transport between the Bohai Sea and the Yellow Sea, it is unfeasible to judge the adequacy of the four kinds of data by using the water budget equation. However, the E/P rates at ERA-40 sea points were considered to he the optimal in terms of temporal/spatial coverage and resolution for the hindcast of salinity variation in the Bohai Sea. Besides, using the 3-D hydrodynamic model HAMSOM （HAMburg Shelf Ocean Model）, we performed numerical experiments with different E/P datasets and found that the E/P rates at sea points from ERA-40 dataset are better than those from NCEP/NCAR Reanalysis dataset. If NCEP/NCAR Reanalysis E/P rates are to be used, they need to be adjusted and tested prior to simulation so that more close-to-reality salinity values can be reproduced.