Characteristics and triggering mechanisms of early negative Indian Ocean Dipole

Negative Indian Ocean Dipole(nIOD)can exert great impacts on global climate and can also strongly influence the climate in China.Early nIOD is a major type of nIOD,which can induce more pronounced climate anomalies in summer than La Niña-related nIOD.However,the characteristics and triggering mechanisms of early nIOD are unclear.Our results based on reanalysis datasets indicate that the early nIOD and La Niña-related nIOD are the two major types of nIOD,and the former accounts for over one third of all the nIOD events in the past six decades.These two types of nIODs are similar in their intensities,but are different in their spatial patterns and seasonal cycles.The early nIOD,which develops in spring and peaks in summer,is one season earlier than the La Niña-related nIOD.The spatial pattern of the wind anomaly associated with early nIOD exhibits a winter monsoon-like pattern,with strong westerly anomalies in the equatorial Indian Ocean and eastly anomalies in the northern Indian Ocean.Opposite to the triggering mechanism of early positve IOD,the early nIOD is induced by delayed Indian summer monsoon onset.The results of this study are helpful for improving the prediction skill of IOD and its climate impacts.

Current observation and analysis based on mooring systems in the Andaman Sea

The basic structure and intraseasonal evolution of currents in the southeastern Andaman Sea was analyzed based on data collected in 2017 from two subsurface moorings(C1 and C5).Periodic variation in the upper ocean currents of the Andaman Sea was investigated by combining observational and satellite data.Mooring observations show that rapid changes of current speed and direction occurred in May and June,with a significant increase in current velocity at the C1 mooring.In the second half of the year,southward flow dominated at the C1 mooring,and alternating northward and southward flows were evident at the C5 mooring during the same period but the northward flow prevailed in boreal winter.In addition,analysis of the power spectra of the upper currents revealed that the tidal period at both moorings is primarily semidiurnal with weaker energy than that of the low-frequency currents.The upper ocean currents at the C1 and C5 moorings exhibited intraseasonal variation of 30-60 d and 120 d,while the zonal current at the C1 mooring exhibited a notable period of approximately 180 d.Further analysis indicated that the variability of currents in the Andaman Sea is influenced primarily by equatorial Kelvin waves and Rossby wave packets.Moreover,our results suggest that equatorial Kelvin waves from the eastern Indian Ocean entered the Andaman Sea in the form of Wyrtki Jets and propagated primarily along two distinct pathways during the observation period.In addition to coastal boundary Kelvin waves,it was found that a branch of the Wyrtki Jet that directly enters the Andaman Sea and flows northward along the slope of the continental shelf,and reflected Rossby wave packets by topography.

Correcting Climate Model Sea Surface Temperature Simulations with Generative Adversarial Networks:Climatology,Interannual Variability,and Extremes

Climate models are vital for understanding and projecting global climate change and its associated impacts.However,these models suffer from biases that limit their accuracy in historical simulations and the trustworthiness of future projections.Addressing these challenges requires addressing internal variability,hindering the direct alignment between model simulations and observations,and thwarting conventional supervised learning methods.Here,we employ an unsupervised Cycle-consistent Generative Adversarial Network(CycleGAN),to correct daily Sea Surface Temperature(SST)simulations from the Community Earth System Model 2(CESM2).Our results reveal that the CycleGAN not only corrects climatological biases but also improves the simulation of major dynamic modes including the El Niño-Southern Oscillation(ENSO)and the Indian Ocean Dipole mode,as well as SST extremes.Notably,it substantially corrects climatological SST biases,decreasing the globally averaged Root-Mean-Square Error(RMSE)by 58%.Intriguingly,the CycleGAN effectively addresses the well-known excessive westward bias in ENSO SST anomalies,a common issue in climate models that traditional methods,like quantile mapping,struggle to rectify.Additionally,it substantially improves the simulation of SST extremes,raising the pattern correlation coefficient(PCC)from 0.56 to 0.88 and lowering the RMSE from 0.5 to 0.32.This enhancement is attributed to better representations of interannual,intraseasonal,and synoptic scales variabilities.Our study offers a novel approach to correct global SST simulations and underscores its effectiveness across different time scales and primary dynamical modes.

Seasonal characteristics and formation mechanism of the thermohaline structure of mesoscale eddy in the South China Sea

The seasonal characteristics and formation mechanism of the thermohaline structure of mesoscale eddy in the South China Sea are investigated using the latest eddy dataset and ARMOR3D data. Eddy-centric composites reveal that the horizontal distribution of temperature anomaly associated with eddy in winter is more of a dipole pattern in upper 50 m and tends to be centrosymmetric below 50 m, while in summer the distribution pattern is centrosymmetric in the entire water column. The horizontal distribution of eddy-induced salinity anomaly exhibits similar seasonal characteristics, except that the asymmetry of the salinity anomaly is weaker. The vertical distribution of temperature anomaly associated with eddy shows a monolayer structure, while the salinity anomaly demonstrates a triple-layer structure. Further analysis indicates that the vertical distribution of the anomalies is related to the vertical structure of background temperature and salinity fields, and the asymmetry of the anomalies in upper 50 m is mainly caused by the horizontal advection of background temperature and salinity.

Development of a fine-resolution atmosphere-wave-ocean coupled forecasting model for the South China Sea and its adjacent seas

A 72-h fine-resolution atmosphere-wave-ocean coupled forecasting system was developed for the South China Sea and its adjacent seas. The forecasting model domain covers from from 15°S to 45°N in latitude and 99°E to135°E in longitude including the Bohai Sea, the Yellow Sea, the East China Sea, the South China Sea and the Indonesian seas. To get precise initial conditions for the coupled forecasting model, the forecasting system conducts a 24-h hindcast simulation with data assimilation before forecasting. The Ensemble Adjustment Kalman Filter(EAKF) data assimilation method was adopted for the wave model MASNUM with assimilating Jason-2 significant wave height(SWH) data. The EAKF data assimilation method was also introduced to the ROMS model with assimilating sea surface temperature(SST), mean absolute dynamic topography(MADT) and Argo profiles data. To improve simulation of the structure of temperature and salinity, the vertical mixing scheme of the ocean model was improved by considering the surface wave induced vertical mixing and internal wave induced vertical mixing. The wave and current models were integrated from January 2014 to October 2015 driven by the ECMWF reanalysis 6 hourly mean dataset with data assimilation. Then the coupled atmosphere-wave-ocean forecasting system was carried out 14 months operational running since November 2015. The forecasting outputs include atmospheric forecast products, wave forecast products and ocean forecast products. A series of observation data are used to evaluate the coupled forecasting results, including the wind, SHW, ocean temperature and velocity.The forecasting results are in good agreement with observation data. The prediction practice for more than one year indicates that the coupled forecasting system performs stably and predict relatively accurate, which can support the shipping safety, the fisheries and the oil exploitation.

A model for the transportation and distribution of jellyfish Rhopilema esculentum for stock enhancement in the Liaodong Bay, China

A numerical model for jellyfish Rhopilema esculentum stock enhancement is developed for the first time. The model is based on an operational ocean circulation-surface wave coupled forecasting system for the seas off China and adjacent areas and uses a Lagrangian particle-tracking scheme to track the trajectories of released jellyfish. The Jellyfish are modeled as particles with diel vertical migration and are passively drifted by the current and dispersion due to the sub-grid processes. A comparison between the simulation and survey results demonstrate that the model can capture the primary distribution patterns of the released jellyfish. The model results show that the ocean current and indirect wind impact are the main drivers controlling the jellyfish transport. A connectivity matrix between the release sites and fishing grounds indicates the top of the bay is better than the eastern and western coasts for jellyfish fishing. The matrix also shows that only 45% and 27% of the jellyfish released from Wafangdian(WFD) can enter the fishing ground in 2008 and 2010; thus, the site near WFD is not an advisable location for jellyfish release. A Lagrangian probability density function based on a nine-year tracing experiment validates the results and further provides a "climatology" distribution of the released jellyfish.Several experiments are conducted to examine the sensitivity of the model to random walk schemes and to release conditions. The model requires a random walk but is insensitive to the random walk scheme. The experiments with different habitat depths show that if the jellyfish are fixed on the bottom of the water, most of them will be transported to the center, or even out of the bay, by the bottom circulation.

Vertical development of a Prorocentrum donghaiense bloom in the coastal waters of the East China Sea: coupled biophysical numerical modeling

Algal blooms caused by Prorocentrum donghaiense occurred frequently in the East China Sea （ECS） during spring in recent years. In this study, a coupled biophysical model was used to hindcast a massive P. donghaiense bloom that occurred in 2005 and to determine the factors influencing bloom initiation and development. The model comprised the Regional Ocean Modeling System tailored for the ECS that utilized a multi-nested configuration and a population dynamics model for 19. donghaiense. Comparisons between simulations and observations revealed that the biological model is capable of reproducing the characteristics of 19. donghaiense growth under different irradiances and phosphorus limitation scenarios. The variation of intracellular phosphorus and the effects of 19. donghaiense on ambient nutrients conditions were also reproduced. The biophysical model hindcasted the hydrodynamics and spatiotemporal distributions of the P. donghaiense bloom reasonably well. Bloom development was consistent with observations reported in earlier studies. The results demonstrate the capability of the model in capturing subsurface incubation during bloom initiation. Then model＇s hindcast solutions were further used to diagnose the factors controlling the vertical distribution. Phosphate appeared to be one of the factors controlling the subsurface incubation, whereas surface wind fields played an important role in determining P. donghaiense distribution. The results highlight the importance of nutrient-limitation as a mechanism in the formation of P. donghaiense subsurface layers and the dispersing of P. donghaiense blooms. This coupled biophysical model should be improved and used to investigate 19. donghaiense blooms occurring in different scenarios.

Determination of the Backward Predictability Limit and Its Relationship with the Forward Predictability Limit

In this work, two types of predictability are proposed—forward and backward predictability—and then applied in the nonlinear local Lyapunov exponent approach to the Lorenz63 and Lorenz96 models to quantitatively estimate the local forward and backward predictability limits of states in phase space. The forward predictability mainly focuses on the forward evolution of initial errors superposed on the initial state over time, while the backward predictability is mainly concerned with when the given state can be predicted before this state happens. From the results, there is a negative correlation between the local forward and backward predictability limits. That is, the forward predictability limits are higher when the backward predictability limits are lower, and vice versa. We also find that the sum of forward and backward predictability limits of each state tends to fluctuate around the average value of sums of the forward and backward predictability limits of sufficient states.Furthermore, the average value is constant when the states are sufficient. For different chaotic systems, the average value is dependent on the chaotic systems and more complex chaotic systems get a lower average value. For a single chaotic system,the average value depends on the magnitude of initial perturbations. The average values decrease as the magnitudes of initial perturbations increase.

Seasonal characteristics and forcing mechanisms of the surface Kuroshio branch intrusion into the South China Sea

Using observational data of Argos satellite-tracked drifters from 1988 to 2012, we analyzed seasonal characteristics of the surface Kuroshio branch(KB) intrusion into the South China Sea(SCS). The analysis results are as follows.The surface KB originates from the southern Balintang Channel(BLTC) and Babuyan Channel(BBYC). It begins in late September, reaches peak strength in November–December, and declines at the end of March. The mean speed of drifters along the KB path during their traverse of the Luzon Strait(LS) was 43% faster than during the two days before entering the LS for the flow originating from the southern BLTC, but there was a 24% increase in speed for the flow from the BBYC. The observations show that in winter, monthly-mean sea-level anomalies(SLAs) were positive southwest of Taiwan Island and extended to the northern LS. The SLAs were negative northwest of Luzon Island and extended to the southern LS, which acted like a pump, forcing a part of Kuroshio water westward into the SCS. The condition under which the KB forms was solved by a set of simplified motion equations. The results indicate that whether the KB can form depends upon the sea-level gradient at the central LS and region to the west, as well as the location, speed and direction of Kuroshio surface water when it enters the LS.

Succession of causative species during spring blooms in the EastChina Sea： coupled biophysical numerical modeling

In the East China Sea(ECS), the succession of causative species responsible for blooms is a recurrent phenomenon during the spring, which changes from diatoms to dinoflagellates. Observations from space and in situ cruises captured this pattern of succession during spring of 2005. In this study, we coupled two biological models, which were developed previously for Skeletonema costatum and Prorocentrum donghaiense,into a circulation model tailored for the ECS. The coupled biophysical model was used to hindcast the blooms and to test the hypothesis proposed in earlier studies that phosphate(PO4 3–) is the first-order decider of the succession. The coupled model successfully reproduced the hydrodynamics(as described in a companion paper by Sun et al.(1), the spatiotemporal distribution of the chlorophyll a(Chl a) concentration, and the species succession reasonably well. By analyzing the effects of different factors on the surface Chl a distribution, we confirmed that the offshore boundaries of the blooms were confined by PO4 3–. In addition, we suggest that surface wind fields may modulate the horizontal distribution of blooms. Thus, during the dispersal of blooms, surface winds coupled with PO4 3– may control the succession of blooms in the ECS. The proposed coupled model provides a benchmark to facilitate future improvements by including more size classes for organisms, multiple nutrient schemes, and additional processes.

The sensitivity of numerical simulation to vertical mixing parameterization schemes: a case study for the Yellow Sea Cold Water Mass

The vertical mixing parameterization scheme,by providing the eff ects of some explicitly missed physical processes and more importantly closing the energy budgets,is a critical model component and therefore imposes signifi cant impacts on model performance.The Yellow Sea Cold Water Mass(YSCWM),as the most striking and unique phenomenon in the Yellow Sea during summer,is dramatically aff ected by vertical mixing process during its each stage and therefore seriously sensitive to the proper choice of parameterization scheme.In this paper,a hindcast of YSCWM in winter of 2006 was implemented by using the Regional Ocean Modeling System(ROMS).Three popular parameterization schemes,including the level 2.5 Mellor-Yamada closure(M-Y 2.5),Generic Length Scale closure(GLS)and K-Profi le Parameterization(KPP),were tested and compared with each other by conducting a series of sensitivity model experiments.The infl uence of diff erent parameterization schemes on modeling the YSCWM was then carefully examined and assessed based on these model experiments.Although reasonable thermal structure and its seasonal variation were well reproduced by all schemes,considerable diff erences could still be found among all experiments.A warmer and spatially smaller simulation of YSCWM,with very strong thermocline,appeared in M-Y 2.5 experiment,while a spatially larger YSCWM with shallow mixed layer was found in GLS and KPP schemes.Among all the experiments,the discrepancy,indicated by core temperature,appeared since spring,and grew gradually by the end of November.Additional experiments also confi rmed that the increase of background diff usivity could eff ectively weaken the YSCWM,in either strength or coverage.Surface wave,another contributor in upper layer,was found responsible for the shrinkage of YSCWM coverage.The treatment of wave eff ect as an additional turbulence production term in prognostic equation was shown to be more superior to the strategy of directly increasing diff usivity for a coastal region.

Intraseasonal oscillation of the southwest monsoon over Sri Lanka and evaluation of its subseasonal forecast skill

斯里兰卡的雨季发生于5–9月间,主要受西南季风的控制.本文发现该地区的西南季风降水存在很强的次季节变率,主导周期为10–35天.降水的季节内变化与西传的异常气旋有关.进一步,利用S2S比较计划中欧洲中心的数值预报模式(ECMWF)提供的回报试验数据,评估了当今动力模式对斯里兰卡西南季风次季节变化的预报技巧.结果显示,对季风指数的预测技巧超过30天,而对降水指数的预测技巧大约两周,且模式的预报技巧具有明显的年际差异.分析表明,能否正确模拟出大尺度环流对热带对流的响应是影响斯里兰卡降水预测的重要因子.

Arctic sea ice concentration and thickness data assimilation in the FIO-ESM climate forecast system

To improve the Arctic sea ice forecast skill of the First Institute of Oceanography-Earth System Model(FIO-ESM)climate forecast system,satellite-derived sea ice concentration and sea ice thickness from the Pan-Arctic IceOcean Modeling and Assimilation System(PIOMAS)are assimilated into this system,using the method of localized error subspace transform ensemble Kalman filter(LESTKF).Five-year(2014–2018)Arctic sea ice assimilation experiments and a 2-month near-real-time forecast in August 2018 were conducted to study the roles of ice data assimilation.Assimilation experiment results show that ice concentration assimilation can help to get better modeled ice concentration and ice extent.All the biases of ice concentration,ice cover,ice volume,and ice thickness can be reduced dramatically through ice concentration and thickness assimilation.The near-real-time forecast results indicate that ice data assimilation can improve the forecast skill significantly in the FIO-ESM climate forecast system.The forecasted Arctic integrated ice edge error is reduced by around 1/3 by sea ice data assimilation.Compared with the six near-real-time Arctic sea ice forecast results from the subseasonal-toseasonal(S2 S)Prediction Project,FIO-ESM climate forecast system with LESTKF ice data assimilation has relatively high Arctic sea ice forecast skill in 2018 summer sea ice forecast.Since sea ice thickness in the PIOMAS is updated in time,it is a good choice for data assimilation to improve sea ice prediction skills in the near-realtime Arctic sea ice seasonal prediction.

Seasonal prediction skills of FIO-ESM for North Pacific sea surface temperature and precipitation

The seasonal prediction of sea surface temperature(SST) and precipitation in the North Pacific based on the hindcast results of The First Institute of Oceanography Earth System Model(FIO-ESM) is assessed in this study.The Ensemble Adjusted Kalman Filter assimilation scheme is used to generate initial conditions, which are shown to be reliable by comparison with the observations. Based on this comparison, we analyze the FIO-ESM 6-month hindcast results starting from each month of 1993–2013. The model exhibits high SST prediction skills over most of the North Pacific for two seasons in advance. Furthermore, it remains skillful at long lead times for midlatitudes. The reliable prediction of SST can transfer fairly well to precipitation prediction via air-sea interactions.The average skill of the North Pacific variability(NPV) index from 1 to 6 months lead is as high as 0.72(0.55) when El Ni?o-Southern Oscillation and NPV are in phase(out of phase) at initial conditions. The prediction skill of the NPV index of FIO-ESM is improved by 11.6%(23.6%) over the Climate Forecast System, Version 2. For seasonal dependence, the skill of FIO-ESM is higher than the skill of persistence prediction in the later period of prediction.

Northern-Hemisphere Snow Cover Patterns and Formation Conditions in Winter 2007 and 2012

The Arctic sea ice minimum records appeared in the Septembers of 2007 and 2012, followed by high snow cover areas in the Northern Hemisphere winters. The snow cover distributions show different spatial patterns in these two years: increased snow cover in Central Asia and Central North America in 2007, while increased snow cover in East Asia and northwestern Europe in 2012. The high snow cover anomaly shifted to higher latitudes in winter of 2012 compared to 2007. It is noticed that the snow cover had positive anomaly in 2007 and 2012 with the following conditions: the negative geopotential height and the related cyclonic wind anomaly were favorable for upwelling, and, with the above conditions, the low troposphere and surface air temperature anomaly and water vapor anomaly were favorable for the formation and maintenance of snowfalls. The negative geopotential height, cyclonic wind and low air temperature conditions were satisfied in different locations in 2007 and 2012, resulting in different spatial snow cover patterns. The cross section of lower air temperature move to higher latitudes in winter of 2012 compared to 2007.

Objective array design for three-dimensional temperature and salinity observation:Application to the South China Sea

In this study,a moored array optimization tool(MAOT)was developed and applied to the South China Sea(SCS)with a focus on three-dimensional temperature and salinity observations.Application of the MAOT involves two steps:(1)deriving a set of optimal arrays that are independent of each other for different variables at different depths based on an empirical orthogonal function method,and(2)consolidating these arrays using a K-center clustering algorithm.Compared with the assumed initial array consisting of 17 mooring sites located on a 3°×3°horizontal grid,the consolidated array improved the observing ability for three-dimensional temperature and salinity in the SCS with optimization efficiencies of 19.03%and 21.38%,respectively.Experiments with an increased number of moored sites showed that the most cost-effective option is a total of 20 moorings,improving the observing ability with optimization efficiencies up to 26.54%for temperature and 27.25%for salinity.The design of an objective array relies on the ocean phenomenon of interest and its spatial and temporal scales.In this study,we focus on basin-scale variations in temperature and salinity in the SCS,and thus our consolidated array may not well resolve mesoscale processes.The MAOT can be extended to include other variables and multi-scale variability and can be applied to other regions.

The Relationship between Deterministic and Ensemble Mean Forecast Errors Revealed by Global and Local Attractor Radii

It has been demonstrated that ensemble mean forecasts, in the context of the sample mean, have higher forecasting skill than deterministic(or single) forecasts. However, few studies have focused on quantifying the relationship between their forecast errors, especially in individual prediction cases. Clarification of the characteristics of deterministic and ensemble mean forecasts from the perspective of attractors of dynamical systems has also rarely been involved. In this paper, two attractor statistics—namely, the global and local attractor radii(GAR and LAR, respectively)—are applied to reveal the relationship between deterministic and ensemble mean forecast errors. The practical forecast experiments are implemented in a perfect model scenario with the Lorenz96 model as the numerical results for verification. The sample mean errors of deterministic and ensemble mean forecasts can be expressed by GAR and LAR, respectively, and their ratio is found to approach2^(1/2) with lead time. Meanwhile, the LAR can provide the expected ratio of the ensemble mean and deterministic forecast errors in individual cases.

Geographical Information Visualization on a Panoramic Sphere in an Immersive Environment

In this paper,we propose a novel approach to visualizing global geographical information:a panoramic sphere in an immersive environment.The whole geographical surface can be observed through the rotating of heads as the viewpoint of the panoramic sphere is inside the sphere.We compared three approaches to visualizing the earth for rendering the geographical information in a virtual reality environment.On the tasks of terrestrial and marine geographical information,we compare the visualization effects on a)a globe,b)a flat map and c)a panoramic sphere.Terrestrial geographical information tasks include the area comparison and direction determination.Marine geographical information tasks contain the visualization of sea surface temperature and sea surface currents.For terrestrial geographical information tasks,the experimental results show that the panoramic sphere outperforms the globe and the flat map,with a higher average accuracy and a shorter time.On marine geographical information task,the panoramic sphere visualization is also superior to the flat map and the globe in an immersive environment for the sea surface temperature data and the sea surface current fields.In all three visualization experiments,the panoramic sphere is most preferred by the participants,particularly for global,transcontinental and transoceanic needs.

Three-Dimensional Climatological Structures of the Arabian Sea Eddies and Eddy-Induced Flux

This study explores the spatial structure and transport characteristics of eddies in the Arabian Sea(AS)using Argo profiles and satellite measurements.The majority of eddies occur in the northern AS,especially along its northeastern boundary.In contrast,the western AS had a relatively higher eddy kinetic energy compared to the eastern part.Particularly,the strongest energetic eddies were present in the Somali Current system.The composite results revealed the evident thermohaline anomalies caused by cyclonic eddies(CEs)and anticyclonic eddies(AEs)in the upper 300m layers.The anomalous temperature structure within CEs and AEs showed a dominant dipole structure in the near-surface layer and a monopole structure below,with maximum temperature anomalies of approximately−0.8℃and+1.0℃located at depths of 100–150m,respectively.The composited salinity structures for CEs and AEs exhibited monopole vertical structures and sandwich-like patterns.For AEs,large positive salinity anomalies occurred at subsurface layers of 60–180 m with a peak value of about 0.07,and weak negative values were observed above 60m and below 180 m.A similar vertical structure but with an opposite sign operates for CEs.The composited CE and AE caused an equatorward salt flux with values of−8.1×10^(4)and−2.2×10^(4)kg s^(−1),respectively.CEs caused an equatorward heat flux of−7.7×10^(11)W,and AEs induced a poleward flux of 1.5×1011 W.

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.