集合同化方法在黄海海域海表面温度同化中的应用

The effects of sea surface temperature(SST) data assimilation in two regional ocean modeling systems were examined for the Yellow Sea(YS). The SST data from the Operational Sea Surface Temperature and Sea Ice Analysis(OSTIA) were assimilated. The National Marine Environmental Forecasting Center(NMEFC) modeling system uses the ensemble optimal interpolation method for ocean data assimilation and the Kunsan National University(KNU) modeling system uses the ensemble Kalman filter. Without data assimilation, the NMEFC modeling system was better in simulating the subsurface temperature while the KNU modeling system was better in simulating SST. The disparity between both modeling systems might be related to differences in calculating the surface heat flux, horizontal grid spacing, and atmospheric forcing data. The data assimilation reduced the root mean square error(RMSE) of the SST from 1.78°C(1.46°C) to 1.30°C(1.21°C) for the NMEFC(KNU) modeling system when the simulated temperature was compared to Optimum Interpolation Sea Surface Temperature(OISST) SST dataset. A comparison with the buoy SST data indicated a 41%(31%) decrease in the SST error for the NMEFC(KNU) modeling system by the data assimilation. In both data assimilative systems, the RMSE of the temperature was less than 1.5°C in the upper 20 m and approximately 3.1°C in the lower layer in October. In contrast, it was less than 1.0°C throughout the water column in February. This study suggests that assimilations of the observed temperature profiles are necessary in order to correct the lower layer temperature during the stratified season and an ocean modeling system with small grid spacing and optimal data assimilation method is preferable to ensure accurate predictions of the coastal ocean in the YS.     

Assimilation of sea surface temperature in the Northwest Pacific Ocean and its marginal seas using the ensemble Kalman filter

Satellite-borne sea surface temperature (SST) data were assimilated with the ensemble Kalman filter (EnKF) in a Northwest Pacific Ocean circulation model to examine the effect of data assimilation. The model domain included the northwestern part of the Pacific Ocean and its marginal seas, such as the Yellow Sea and East/Japan Sea. The performance of the data assimilation was evaluated by comparing the simulated ocean state with that observed. Spatially averaged root-mean-squared errors in the SST and sea surface height (SSH) decreased by 0.44 °C and 4 cm, respectively, by the assimilation. The results of the numerical experiments substantiated the effectiveness of the SST assimilation via the EnKF for all marginal seas, as well as the Kuroshio region. The benefit of the data assimilation depended on the characteristics of each marginal sea. The variation of the SST in the East/Japan Sea and the Kuroshio extension (KE) region were improved 34% and those in the Yellow Sea 12.5%. The variation of the SSH was improved approximately 36% in the KE region. This large improvement was achieved in the deep-water regions because assimilation of SST data corrected the separation point of the western boundary currents, such as the Kuroshio and the East Korea Warm Current, and the associated horizontal surface currents. The SST assimilation via the EnKF also improved the subsurface temperature profiles. The effectiveness of SST assimilation was seasonally dependent, with the improvement being relatively larger in winter than in summer, which was related to the seasonal variation of the vertical mixing and stratification in the ocean surface layer.     

利用卫星资料分析黄海海表温度的年际与年代际变化

海表温度长期变化在一定程度上反映了海域的气候变化信号,卫星遥感资料是获取高时空分辨率水温长期变化的有效手段。基于国家海洋局1982—1999年黄海断面监测器测数据的2 954组水温数据对时空匹配的卫星(NOAA/AVHRR)反演海表温度(SST)进行校验,计算得到卫星反演SST系统偏差为(0.18±1.00)℃。卫星反演的水温空间分布以及长期变化趋势与器测趋势较为一致,可以用来研究海域SST长期变化规律。利用校验后1982-01～2011-08NOAA/AVHRR的SST数据,分析了该时段黄海冬夏季代表月2、8月海表水温的变化规律。结果显示:(1)近30a,黄海冬季水温有2次跃迁:1989—1990年由冷至暖的状态跃迁,2000-2001年出现由暖至冷的状态转变;1990年代冬季水温达最高,相比1880年代,水温升高1.07℃,新世纪水温稍有降低,水温较1990年代下降了0.53℃,温度变化较大区域位于北黄海、山东半岛沿岸,苏北浅滩毗邻海区,该区SST与局地经向风场存在显著正相关,且北极涛动通过影响冬季风间接影响黄海水温变化;(2)夏季海表水温在1994—1995年呈现由冷至暖的状态跃迁,冷、暖期水温相差0.57℃,水温变化较显著的区域为黄东海分界处,其具体变化机制需深入研究。     

The impact of ocean data assimilation on seasonal predictions based on the National Climate Center climate system model

An ensemble optimal interpolation (EnOI) data assimilation method is applied in the BCC_CSM1.1 to investigate the impact of ocean data assimilations on seasonal forecasts in an idealized twin experiment framework. Pseudo-observations of sea surface temperature (SST), sea surface height (SSH), sea surface salinity (SSS), temperature and salinity (T/S) profiles were first generated in a free model run. Then, a series of sensitivity tests initialized with predefined bias were conducted for a one-year period; this involved a free run (CTR) and seven assimilation runs. These tests allowed us to check the analysis field accuracy against the “truth”. As expected, data assimilation improved all investigated quantities; the joint assimilation of all variables gave more improved results than assimilating them separately. One-year predictions initialized from the seven runs and CTR were then conducted and compared. The forecasts initialized from joint assimilation of surface data produced comparable SST root mean square errors to that from assimilation of T/S profiles, but the assimilation of T/S profiles is crucial to reduce subsurface deficiencies. The ocean surface currents in the tropics were better predicted when initial conditions produced by assimilating T/S profiles, while surface data assimilation became more important at higher latitudes, particularly near the western boundary currents. The predictions of ocean heat content and mixed layer depth are significantly improved initialized from the joint assimilation of all the variables. Finally, a central Pacific El Ni?o was well predicted from the joint assimilation of surface data, indicating the importance of joint assimilation of SST, SSH, and SSS for ENSO predictions.     

发展基于集合样本估计的SST张驰逼近方法用于气候模式的次表层海温场订正

An ensemble-based assimilation method is proposed for correcting the subsurface temperature field when nudging the sea surface temperature(SST) observations into the Max Planck Institute(MPI) climate model,ECHAM5/MPI-OM. This method can project SST directly to subsurface according to model ensemble-based correlations between SST and subsurface temperature. Results from a 50 year(1960–2009) assimilation experiment show the method can improve the subsurface temperature field up to 300 m compared to the qualitycontrolled subsurface ocean temperature objective analyses(EN4), through reducing the biases of the thermal states, improving the thermocline structure, and reducing the root mean square(RMS) errors. Moreover, as most of the improvements concentrate over the upper 100 m, the ocean heat content in the upper 100 m(OHT100 m)is further adopted as a property to validate the performance of the ensemble-based correction method. The results show that RMS errors of the global OHT100 m convergent to one value after several times iteration,indicating this method can represent the relationship between SST and subsurface temperature fields well, and then improve the accuracy of the simulation in the subsurface temperature of the climate model.     

中国海及邻近海域卫星观测资料同化试验

利用1个基于POMgcs海洋模式和多重网格三维变分同化方法建立的中国海及邻近海域海面高与三维温盐流数值预报模型,通过一系列数值试验,研究了同化卫星测高和卫星遥感海面温度观测资料对该模型预报能力的影响。试验结果表明,同化卫星测高资料可明显改善海面高度与三维温度和盐度的分析预报效果,使1 200 m以上的温度预报误差减小0.16℃,并能有效提高对海洋中尺度现象的预报能力;同化卫星遥感海面温度对100 m以上的温度和盐度的预报效果有所改善,可使海面温度的预报误差减小10%。     

Assimilating satellite SST/SSH and in-situ T/S profiles with the Localized Weighted Ensemble Kalman Filter

The Localized Weighted Ensemble Kalman Filter(LWEnKF) is a new nonlinear/non-Gaussian data assimilation(DA) method that can effectively alleviate the filter degradation problem faced by particle filtering, and it has great prospects for applications in geophysical models. In terms of operational applications, along-track sea surface height(AT-SSH), swath sea surface temperature(S-SST) and in-situ temperature and salinity(T/S) profiles are assimilated using the LWEnKF in the northern South China ...     

海洋资料同化对气候季节-年际预测技巧及初始场的影响试验

集合卡尔曼滤波(Ensemble Kalman filter, EnKF)是一种国内外广泛使用的海洋资料同化方案, 用集合成员的状态集合表征模式的背景误差协方差, 结合观测误差协方差, 计算卡尔曼增益矩阵, 有效地将观测信息添加到模式初始场中。由于季节、年际预测很大程度上受到初始场的影响, 因此资料同化可以提高模式的预测性能。本文在NUIST-CFS1.0预测系统逐日SST nudging的初始化方案上, 利用EnKF在每个月末将全场(full field)海表温度(sea surface temperature, SST)、温盐廓线(in-situ temperature and salinity profiles, T-S profiles)以及卫星观测海平面高度异常(sea level anomalies, SLA)观测资料同化到模式初始场中, 对比分析了无海洋资料同化以及加入同化后初始场的区别、加入海洋资料同化后模式提前1~24个月预测性能的差异以及对于厄尔尼诺-南方涛动(El Niño-southern oscillation, ENSO)预测技巧的影响。结果表明, 加入海洋资料同化能有效地改进初始场, 并且呈现随深度增加初始场改进越显著的特征。加入同化后, 对全球SST、次表层海水温度的平均预测技巧均有一定的提高, 也表现出随深度增加预测技巧改进越明显的特征。但加入海洋资料同化后, 模式对ENSO的预测技巧有所下降, 可能是由于模式误差的存在, 使得同化后的预测初始场从接近观测的状态又逐渐恢复到与模式动力相匹配的状态, 加剧了赤道太平洋冷舌偏西、中东部偏暖的气候平均态漂移。     

夏季北冰洋无冰海域次表层暖水结构的形成机理

在夏季北冰洋的无冰海域,经常可以观测到次表层暖水现象,即在水深20～50m的范围内发生海水温度的极大值。建立了一个一维的热力学解析模式,用于研究夏季北冰洋次表层暖水的形成机制。模式的计算结果表明,太阳辐射作用是形成次表层暖水的关键因素。在北冰洋的开阔水域,大气吸收海洋热量的过程导致了海面温度下降,使温度极大值出现在次表层。海洋垂向湍流热扩散对次表层暖水温度有显著影响;当湍流热扩散较弱时,热扩散的范围较小,有利于形成次表层暖水。次表层暖水的位置随着时间的推移不断加深,温度不断增高。在北极,大气温度低于海面温度是普遍现象,次表层暖水经常发生。虽然当海面气温发生变化时,次表层海水温度结构会发生相应的变化,但次表层暖水结构形成之后,如果不受强烈天气过程的破坏,则会一直存在下去。按照本文的结论,随着北极气候变暖,海冰将进一步减少,次表层暖水现象还会明显增加,海洋对气候变化将有更加强烈的响应和反馈,对全球气候变化产生意义深远的影响。     

The influence of thermohaline fronts on chlorophyll a concentrations during spring and summer in the southeastern Yellow Sea

A spatial and temporal variation in physiochemical parameters in the southeastern Yellow Sea(YS) is investigated in the spring and summer of 2009 to 2011.Nutrient show a strong negative relationship with chlorophyll a(Chl a) concentration in spring,and the subsurface chlorophyll a maxima(SCM) layer was associated with the nitracline in summer.In summer,the SCM was usually found within or above the pycnocline and at the depths of shoals from the open sea to the coastal sea due to tidal and/or topographical fronts in the southernmost study area.High Chl a concentrations were found in the central southern YS,where the YS cold water layer expanded under the pycnocline and encountered water masses during spring and summer.After a typhoon in the summer of 2011,Chl a concentration increased,especially in the central southern YS,where cold waters occurred below the pycnocline.The results suggest that the development of thermohaline fronts may play an important role in the growth and accumulation of phytoplankton biomass in the upper layer of the southeastern YS during spring and summer.     

Reconstruction of subsurface DIC and alkalinity fields in the North Pacific using assimilation of upper ocean data

The subsurface water beneath the summer mixed layer is important to air–sea carbon flux, while its geochemical properties are not frequently observed. A data assimilation method is applied to determine the geochemical fields in the subsurface (i.e., 100 m) from the data collected at the surface in the North Pacific. This method, in the family of the inverse methods, is constructed on a one-dimensional bulk mixed layer model. In addition to temperature and salinity, dissolved inorganic carbon (DIC) and alkalinity are also considered as model variables, whereas biological productivity is omitted. The geochemical properties increase from the fall to the winter, which is the period simulated by the model, as the mixed layer develops and entrains subsurface water rich in DIC and alkalinity. Consequently, the geochemical fields in the mixed layer must have extremely sharp north–south gradients in the western region of the North Pacific and can be reproduced only by enhancing the north–south gradients in the subsurface. The fields reconstructed by the data assimilation provide useful information about the biogeochemical cycles. It is suggested that the large difference in DIC between the surface and the subsurface in the northwestern region is produced by transporting DIC from the mixed layer to the subsurface in the summer, implying extremely high biological productivity. Furthermore, it is suggested that high DIC in the ambient water is maintained by the upwelling of lower layer water.     

夏季琼州海峡表层海水二氧化碳分压时空变化

采用船载海?气CO₂连续观测系统于2011年和2014年夏季在琼州海峡开展了现场观测,分析研究了表层海水二氧化碳分压(pCO₂)时空变化及其影响因子。2011年和2014年夏季pCO₂分别为(516±29) μatm和(533±15) μatm,海?气CO₂交换通量分别为(8.4±1.7) mmol/(m2·d)和(4.5±0.4) mmol/(m2·d),均是大气CO₂的强源,高于相邻及相似海域,主要受控于东口海域上升流和海峡中部狭管效应。2011年夏季东口上升流增大pCO₂的同时也促进了浮游植物繁殖,光合作用吸收水体CO₂,降低了pCO₂,而且受其影响,西口口门附近叶绿素a和溶解氧含量陡增,pCO₂突降。2014年夏季东口海域上升流较弱,且观测海域垂直混合作用显著,pCO₂和溶解氧分布特征与2001年夏季明显不同。海峡中部狭管效应造成水体输运速率大、混合作用强,浮游植物“来不及”生长,pCO₂较高。     

OSTIA数据在中国近海业务化环流模型中的同化应用

The prediction of sea surface temperature(SST) is an essential task for an operational ocean circulation model. A sea surface heat flux, an initial temperature field, and boundary conditions directly affect the accuracy of a SST simulation. Here two quick and convenient data assimilation methods are employed to improve the SST simulation in the domain of the Bohai Sea, the Yellow Sea and the East China Sea(BYECS). One is based on a surface net heat flux correction, named as Qcorrection(QC), which nudges the flux correction to the model equation; the other is ensemble optimal interpolation(En OI), which optimizes the model initial field. Based on such two methods, the SST data obtained from the operational SST and sea ice analysis(OSTIA) system are assimilated into an operational circulation model for the coastal seas of China. The results of the simulated SST based on four experiments, in 2011, have been analyzed. By comparing with the OSTIA SST, the domain averaged root mean square error(RMSE) of the four experiments is 1.74, 1.16, 1.30 and 0.91°C, respectively; the improvements of assimilation experiments Exps 2, 3 and 4 are about 33.3%, 25.3%, and 47.7%, respectively.Although both two methods are effective in assimilating the SST, the En OI shows more advantages than the QC,and the best result is achieved when the two methods are combined. Comparing with the observational data from coastal buoy stations, show that assimilating the high-resolution satellite SST products can effectively improve the SST prediction skill in coastal regions.     

Winter-to-winter recurrence of sea surface temperature, salinity and mixed layer depth anomalies

The mean seasonal cycle of mixed layer depth (MLD) in the extratropical oceans has the potential to influence temperature, salinity and mixed layer depth anomalies from one winter to the next. Temperature and salinity anomalies that form at the surface and spread throughout the deep winter mixed layer are sequestered beneath the mixed layer when it shoals in spring, and are then re-entrained into the surface layer in the subsequent fall and winter. Here we document this ‘re-emergence mechanism’ in the North Pacific Ocean using observed SSTs, subsurface temperature fields from a data assimilation system, and coupled atmosphere–ocean model simulations. Observations indicate that the dominant large-scale SST anomaly pattern that forms in the North Pacific during winter recurs in the following winter. The model simulation with mixed layer ocean physics reproduced the winter-to-winter recurrence, while model simulations with observed SSTs specified in the tropical Pacific and a 50 m slab in the North Pacific did not. This difference between the model results indicates that the winter-to-winter SST correlations are the result of the re-emergence mechanism, and not of similar atmospheric forcing of the ocean in consecutive winters. The model experiments also indicate that SST anomalies in the tropical Pacific associated with El Niño are not essential for re-emergence to occur.The recurrence of observed SST and simulated SST and SSS anomalies are found in several regions in the central North Pacific, and are quite strong in the northern (>50°N) part of the basin. The winter-to-winter autocorrelation of SSS anomalies exceed those of SST, since only the latter are strongly damped by surface fluxes. The re-emergence mechanism also has a modest influence on MLD through changes in the vertical stratification in the seasonal thermocline.     

Production and transport of long-chain alkenones and alkyl alkenoates in a sea water column in the northwestern Pacific off central Japan

Sinking particles collected from year-long time-series sediment traps at 1674, 4180, 5687 and 8688 m depths, the underlying bottom sediment at 9200 m depth, and suspended particles from surface and subsurface waters in the northwestern North Pacific off Japan were analyzed for long-chain alkenones and alkyl alkenoates (A&A) which are derived mainly from Gephyrocapsacean algae, especially Emiliania huxleyi and Gephyrocapsa oceanica. Alkenone temperature records in sediment trap samples at 1674 m were almost similar to observed sea surface temperatures (SST) with a time delay of one half to one full month. However, alkenone temperatures in trap samples were about slightly lower than measured SST in late spring to early fall. The lowering might be caused by formation of the seasonal thermocline. Nevertheless, these temperature drops observed in trap samples were smaller than those actually observed in a subsurface layer off central Japan. Vertical profiles of A&A concentrations and alkenone temperatures in suspended particles collected from the subsurface waters in early fall indicated that these compounds were produced mostly in a surface mixed layer above the depth of the chlorophyll maximum even in warm seasons. These results suggested that alkenone temperatures strongly reflected SST rather than the temperatures of thermocline waters in these study areas even in such a warm season. Pronounced maxima in A&A fluxes found in sediment trap samples at 1674 m in late spring to summer showed that A&A productions were highest during the periods of spring bloom, according to a time delay between alkenone temperatures and observed SST. Seasonal patterns of alkenone records in trap samples at 4180 and 5687 m could also preserve SST signals well, suggesting that A&A in deep sea waters were mainly derived from primary products in the surface layer. A&A fluxes tended to decrease with water depth, and the ratios of A&A to particulate organic carbon (POC) rapidly decreased in underlying bottom sediment. This clearly indicates that A&A were decomposed and diluted by other refractory organic materials in either the water column or the sediment–water interface. However, A&A compositions were consistently uniform between the trap samples and the underlying bottom sediments, so that A&A could not qualitatively alter during early diagenetic processes.     

Satellite-derived SST validation based on in-situ data during summer in the East China Sea and western North Pacific

Satellite-derived sea surface temperature (SST) is validated based on in-situ data from the East China Sea (ECS) and western North Pacific where most typhoons, which make landfall on the Korean peninsula, are formed and pass. While forecasting typhoons in terms of intensity and track, coupled ocean-typhoon models are significantly influenced by initial ocean condition. Potentially, satellite-derived SST is a very useful dataset to obtain initial ocean field because of its wide spatial coverage and high temporal resolution. In this study, satellite-derived SST from various sources such as Tropical Rainfall Measuring Mission Microwave Imager (TMI), Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and New Generation Sea Surface Temperature for Open Ocean (NGSST-O) datasets from merged SSTs were compared with in-situ observation data using an indirect method which is using near surface temperature for validation of satellite derived SST. In-situ observation data included shipboard measurements such as Expendable Bathythermograph (XBT), and Conductivity, Temperature, Depth (CTD), and Argo buoy data. This study shows that in-situ data can be used for microwave derived SST validation because homogeneous features of seawater prevail at water depths of 2 m to 10 m under favorable wind conditions during the summer season in the East China Sea. As a result of validation, root-mean-square errors (RMSEs) are shown to be 0.55 °C between microwave SST and XBT/CTD data mostly under weak wind conditions, and 0.7 °C between XBT/CTD measurement and NGSST-O data. Microwave SST RMSE of 0.55 °C is a potentially valuable data source for general application. Change of SST before and after typhoon passing may imply strength of ocean mixing due to upwelling and turbulent mixing driven by the typhoon. Based on SST change, ocean mixing, driven by Typhoon Nari, was examined. Satellite-derived SST reveals a significant SST drop around the track immediately following the passing of Typhoon Nari in October, 2007.     

Assimilation of historical SST data for long-term ENSO retrospective forecasts

In this study, the assimilation of historic SST (sea surface temperature) data was performed for long-term ENSO hindcasts. The emphasis was placed on the design of background error covariance (BEC) that dominates the transfer of SST information to the subsurface. Four different data-assimilation schemes, based on Optimal Interpolation (OI) algorithm, were proposed, and compared in terms of ENSO simulation and prediction skills for the period from 1876 to 2000.It was found that the data-assimilation scheme that has a three-dimensional BEC constructed from model simulations forced by observed wind stress can effectively correct the second-layer temperature in the SST assimilation and lead to the best ENSO prediction skill. Further analysis for the long-term hindcasts shows that the prediction skills have a striking decadal/interdecadal variability similar to that found in other models. These results provide a fundamental basis for the further study of ENSO predictability.     

Argos表面漂流浮标在黑潮区的若干观测结果

利用近几年国家海洋局第二海洋研究所及国家海洋技术中心在南海和西北太平洋海域布放的部分卫星跟踪表面漂流浮标所取得的观测资料,分析了浮标流经海域的表层海流特征及浮标漂移路径上水温的变化。结果表明:2003年1月,黑潮表层水有入侵南海的趋势,夏季南海表层水经吕宋海峡流出,汇入黑潮主干;夏末冬初,黑潮主干经过东海时明显呈弯曲流动;2003年春季,日本以南海域黑潮弯曲不明显;台湾东北部海域存在一个强反气旋涡;表层海水的温度日变化和季节变化明显,在浮标漂移路径呈反气旋或气旋式转动的区域,对应出现了表层水温的高、低温区。     

Generation of high resolution sea surface temperature using multi-satellite data for operational oceanography

In the present article, we introduce a high resolution sea surface temperature(SST) product generated daily by Korea Institute of Ocean Science and Technology(KIOST). The SST product is comprised of four sets of data including eight-hour and daily average SST data of 1 km resolution, and is based on the four infrared(IR) satellite SST data acquired by advanced very high resolution radiometer(AVHRR), Moderate Resolution Imaging Spectroradiometer(MODIS), Multifunctional Transport Satellites-2(MTSAT-2) Imager and Meteorological Imager(MI), two microwave radiometer SSTs acquired by Advanced Microwave Scanning Radiometer 2(AMSR2), and Wind SAT with in-situ temperature data. These input satellite and in-situ SST data are merged by using the optimal interpolation(OI) algorithm. The root-mean-square-errors(RMSEs) of satellite and in-situ data are used as a weighting value in the OI algorithm. As a pilot product, four SST data sets were generated daily from January to December 2013. In the comparison between the SSTs measured by moored buoys and the daily mean KIOST SSTs, the estimated RMSE was 0.71°C and the bias value was –0.08°C. The largest RMSE and bias were 0.86 and –0.26°C respectively, observed at a buoy site in the boundary region of warm and cold waters with increased physical variability in the Sea of Japan/East Sea. Other site near the coasts shows a lower RMSE value of 0.60°C than those at the open waters. To investigate the spatial distributions of SST, the Group for High Resolution Sea Surface Temperature(GHRSST) product was used in the comparison of temperature gradients, and it was shown that the KIOST SST product represents well the water mass structures around the Korean Peninsula. The KIOST SST product generated from both satellite and buoy data is expected to make substantial contribution to the Korea Operational Oceanographic System(KOOS) as an input parameter for data assimilation.     

Improving Ocean State by Assimilating SARAL/AltiKa Derived Sea Level and Other Satellite-Derived Data in MITGCM

Assimilation of satellite-derived surface datasets has been explored in the study. Three types of surface data, namely sea level anomaly, sea surface temperature and sea surface salinity, have been used in various data assimilation experiments. The emphasis has been on the extra benefit arising out of the additional sea level assimilation and hence there are two parallel runs, in one of which sea level assimilation has been withheld. The model used is a state-of-the art ocean general circulation model (OGCM) and the assimilation method is the widely used singular evolutive extended Kalman filter (SEEK). Evaluation of the assimilation skill has been carried out by comparing the simulated depth of the 20°C isotherm with the same quantity measured by buoys and Argo floats. Simulated subsurface temperature and salinity profiles have also been compared with the same profiles measured by Argo floats. Finally, surface currents in the assimilation runs have been compared with currents measured by several off-equatorial buoys. Addition of sea level has been found to substantially improve the quality of simulation. An important feature that has been effectively simulated by the addition of sea level in the assimilation scheme is the near-surface temperature inversion (2-3°C) in the northern Bay of Bengal.