Coupled assimilation for an intermediated coupled ENSO prediction model

The value of coupled assimilation is discussed using an intermediate coupled model in which the wind stress is the only atmospheric state which is slavery to model sea surface temperature (SST). In the coupled assimilation analysis, based on the coupled wind–ocean state covariance calculated from the coupled state ensemble, the ocean state is adjusted by assimilating wind data using the ensemble Kalman filter. As revealed by a series of assimilation experiments using simulated observations, the coupled assimilation of wind observations yields better results than the assimilation of SST observations. Specifically, the coupled assimilation of wind observations can help to improve the accuracy of the surface and subsurface currents because the correlation between the wind and ocean currents is stronger than that between SST and ocean currents in the equatorial Pacific. Thus, the coupled assimilation of wind data can decrease the initial condition errors in the surface/subsurface currents that can significantly contribute to SST forecast errors. The value of the coupled assimilation of wind observations is further demonstrated by comparing the prediction skills of three 12-year (1997–2008) hindcast experiments initialized by the ocean-only assimilation scheme that assimilates SST observations, the coupled assimilation scheme that assimilates wind observations, and a nudging scheme that nudges the observed wind stress data, respectively. The prediction skills of two assimilation schemes are significantly better than those of the nudging scheme. The prediction skills of assimilating wind observations are better than assimilating SST observations. Assimilating wind observations for the 2007/2008 La Niña event triggers better predictions, while assimilating SST observations fails to provide an early warning for that event.     

Processes of India’s offshore summer intraseasonal sea surface temperature variability

Active and break phases of the Indian summer monsoon are associated with sea surface temperature (SST) fluctuations at 30–90 days timescale in the Arabian Sea and Bay of Bengal. Mechanisms responsible for basin-scale intraseasonal SST variations have previously been discussed, but the maxima of SST variability are actually located in three specific offshore regions: the South-Eastern Arabian Sea (SEAS), the Southern Tip of India (STI) and the North-Western Bay of Bengal (NWBoB). In the present study, we use an eddy-permitting 0.25° regional ocean model to investigate mechanisms of this offshore intraseasonal SST variability. Modelled climatological mixed layer and upper thermocline depth are in very good agreement with estimates from three repeated expendable bathythermograph transects perpendicular to the Indian Coast. The model intraseasonal forcing and SST variability agree well with observed estimates, although modelled intraseasonal offshore SST amplitude is undere-stimated by 20–30 %. Our analysis reveals that surface heat flux variations drive a large part of the intraseasonal SST variations along the Indian coastline while oceanic processes have contrasted contributions depending of the region considered. In the SEAS, this contribution is very small because intraseasonal wind variations are essentially cross-shore, and thus not associated with significant upwelling intraseasonal fluctuations. In the STI, vertical advection associated with Ekman pumping contributes to ～30 % of the SST fluctuations. In the NWBoB, vertical mixing diminishes the SST variations driven by the atmospheric heat flux perturbations by 40 %. Simple slab ocean model integrations show that the amplitude of these intraseasonal SST signals is not very sensitive to the heat flux dataset used, but more sensitive to mixed layer depth.     

Summertime sea surface temperature fronts associated with upwelling around the Taiwan Bank

It is well known that upwelling of subsurface water is dominant around the Taiwan Bank (TB) and the Penghu (PH) Islands in the southern Taiwan Strait in summertime. Sea surface temperature (SST) frontal features and related phenomena around the TB upwelling and the PH upwelling were investigated using long-term AVHRR (1996–2005) and SeaWiFS (1998–2005) data received at the station of National Taiwan Ocean University. SST and chlorophyll-a (Chl-a) images with a spatial resolution of 0.01° were generated and used for the monthly SST and Chl-a maps. SST fronts were extracted from each SST images and gradient magnitudes (GMs); the orientations were derived for the SST fronts. Monthly maps of cold fronts where the cooler SSTs were over a shallower bottom were produced from the orientation.     

Assimilation of sea surface temperature into CECOM by flux correction

Sea surface temperature (SST) from a near real-time data set produced from satellites data has been assimilated into a coupled ice–ocean forecasting model (Canadian East Coast Ocean Model) using an efficient data assimilation method. The method is based on an optimal interpolation scheme by which SST is melded into the model through the adjustment of surface heat flux. The magnitude and space–time variation of the adjustment depend on the depth of heat diffusion into the water column in response to changes in surface flux, the correlation time scale of the data, and model and data errors. The diffusion depth is scaled by the eddy diffusivity for temperature. The ratio of the model and data errors is treated as an adjustable parameter. To evaluate the quality of the assimilation, the results from the model with and without assimilation are compared to independent ship data from the Atlantic Zone Monitoring Program and the World Ocean Circulation Experiment. It is shown that the assimilation has a significant impact on the modeled SST, reducing the root mean square difference (RMSD) between the model SST and the ship SST by 0.63°C or 37%. The RMSD of the assimilated SST is smaller than that of the satellite SST by 0.23°C. This suggests that model simulations or predictions with data assimilation can provide the best estimate of the true SST. A sensitivity study is performed to examine the change of the model RMSD with the adjustable parameter in the assimilation equation. The results show that there is an optimal value of the parameter and the model SST is not very sensitive to the parameter.     

Coastal upwelling along the north coast of Papua New Guinea and El Niño events during 1981–2005

We investigate the relationship between sea surface temperature (SST) cooling and upwelling along Papua New Guinea’s (PNG) north coast before the onset of El Niño events using a hindcast experiment with a high-resolution ocean general circulation model. Coastal upwelling and related SST cooling appear along PNG north coast during the boreal winter before the onsets of six El Niño events occurring during 1981–2005. Relatively cool SSTs appear along PNG north coast during that time, when anomalous northwesterly surface wind stress, which can cause coastal upwelling by offshore Ekman transport appearing over the region. In addition, anomalous cooling tendencies of SST are observed, accompanying anomalous upward velocities at the base of the mixed layer and shallow anomalies of 27°C isotherm depth. It is also shown that entrainment cooling plays an important role in the cooling of the mixed layer temperature in this region.     

Change of Indonesian Throughflow outflow in response to East Asian monsoon and ENSO activities since the Last Glacial

The Indonesian Throughflow (ITF) links upper ocean waters of the west Pacific and Indian Ocean, modulates heat and fresh water budgets between these oceans, and in turn plays an important role in global climate change. The climatic phenomena such as the East Asian monsoon and El Niño-Southern Oscillation (ENSO) exert a strong influence on flux, water properties and vertical stratification of the ITF. This work studied sediments of Core SO18462 that was retrieved from the outflow side of the ITF in the Timor Sea in order to investigate response of the ITF to monsoon and ENSO activities since the last glacial. Based on Mg/Ca ratios and oxygen isotopes in shells of planktonic foraminiferal surface and thermocline species, seawater temperatures and salinity of both surface and thermocline waters and vertical thermal gradient of the ITF outflow were reconstructed. Records of Core SO18462 were then compared with those from Core 3cBX that was recovered from the western Pacific warm pool (WPWP). The results displayed that similar surface waters occurred in the Timor Sea and the WPWP during the last glacial. Since ～16 ka, an apparent difference in surface waters between these two regions exists in salinity, indicated by much fresher waters in the Timor Sea than in the WPWP. In contrast, there is little change in difference of sea surface temperatures (SSTs). With regard to thermocline temperature (TT), it increased until ～11.5 ka since the last glacial, and then remained an overall unchanged trend in the WPWP but continuously decreased in the Timor Sea towards the late Holocene. Since ～6 ka, thermocline waters have tended to be close to each other in between the Timor Sea and the WPWP. It is indicated that intensified precipitation due to East Asian monsoon and possible ENSO cold phase significantly freshened surface waters over the Indonesian Seas, impeding the ITF surface flow and in turn having enhanced thermocline flow during the Holocene. Consequently, thermocline water of the ITF outflow was cooling and thermocline was shoaling towards the late Holocene. It is speculated that, in addition to strengthening of East Asian winter monsoon, increasing ENSO events during the late Holocene likely played an important role in influencing thermocline depth of the ITF outflow.     

Ocean satellite data assimilation experiments in FIO-ESM using ensemble adjustment Kalman filter

Using Ensemble Adjustment Kalman Filter (EAKF), two types of ocean satellite datasets were assimilated into the First Institute of Oceanography Earth System Model (FIO-ESM), v1.0. One control experiment without data assimilation and four assimilation experiments were conducted. All the experiments were ensemble runs for 1-year period and each ensemble started from different initial conditions. One assimilation experiment was designed to assimilate sea level anomaly (SLA); another, to assimilate sea surface temperature (SST); and the other two assimilation experiments were designed to assimilate both SLA and SST but in different orders. To examine the effects of data assimilation, all the results were compared with an objective analysis dataset of EN3. Different from the ocean model without coupling, the momentum and heat fluxes were calculated via air-sea coupling in FIO-ESM, which makes the relations among variables closer to the reality. The outputs after the assimilation of satellite data were improved on the whole, especially at depth shallower than 1000 m. The effects due to the assimilation of different kinds of satellite datasets were somewhat different. The improvement due to SST assimilation was greater near the surface, while the improvement due to SLA assimilation was relatively great in the subsurface. The results after the assimilation of both SLA and SST were much better than those only assimilated one kind of dataset, but the difference due to the assimilation order of the two kinds of datasets was not significant.     

Seasonality and anomalies of sea surface temperature off the coast of Nayarit, Mexico

Sea surface temperature (SST) harmonic and empirical orthogonal function (EOF) analyses covering 18 years were performed for the area located from 114° to 105° W and from 18° to 25° N. The results indicate that the influence of the annual signal predominates over the semi-annual signal, and the closer to the coast, the stronger the annual harmonic. Several interannual anomalies arose that are connected with the main global indexes, especially the Oceanic Niño Index. Pearson correlations between the first temporal mode of the SST and regional rainfalls in Nayarit indicate that maximum correlations (r?>?0.7) are observed when there is a +1-month lag between the series. However, this result indicates that SST is delayed with 1 month after rainfall occurrence, which shows that the dominant influence in this relationship is not the SST forcing.     

Validation of AVHRR and TMI-derived sea surface temperature in the northern South China Sea

Satellite-derived SSTs are validated in the northern South China Sea (NSCS) using in situ SSTs from the drifting buoys and well-calibrated sensors installed on Research/Vessel(R/V) Shiyan 3. The satellite SSTs are Advanced Very High Resolution Radiometer (AVHRR) daytime SST, AVHRR nighttime SST, Tropical rainfall Measuring Mission Microwave Imager (TMI) daytime SST and TMI nighttime SST. Availability of satellite SST, which is the ratio that the number of available satellite SST to the total ocean pixels in NSCS is calculated; annual average SST availabilities of AVHRR daytime SST, AVHRR nighttime SST, TMI daytime SST and TMI nighttime SST are 68.42%, 69.99%, 56.57% and 52.80%, respectively. Though the TMI SST availability is nearly constant throughout the year, the variations of the AVHRR SST availability are much larger because of seasonal variations of cloud cover in NSCS. Validation of the satellite-derived SSTs shows that bias±standard deviation (STD) of AVHRR SST is −0.43±0.76 and −0.33±0.79 °C for daytime and nighttime, respectively, and bias±STD of TMI SSTs is 0.07±1.11 and 0.00±0.97 °C for daytime and nighttime, respectively. It is clear that AVHRR SSTs have significant regional biases of about −0.4 °C against the drifting buoy SSTs. Differences between satellite-derived−in situ SSTs are investigated in terms of the diurnal SST cycle. When satellite-derived wind speeds decrease down below 6 m/s, the satellite SSTs become higher than the corresponding in situ SSTs, which means that the SST difference (satellite SST−Buoy SST) is positive. This wind-speed dependence of the SST difference is consistent with the previous results, which have mentioned that low wind speed coupled with clear sky conditions (high surface solar radiation) enhance the diurnal SST amplitude and the bulk-skin temperature difference.     

Occurrence and distribution of hydroxylated isoprenoid glycerol dialkyl glycerol tetraethers(OH-GDGTs) in the Han River system,South Korea

We investigated the occurrence and distribution of terrestrial-derived hydroxylated isoprenoid glycerol dialkyl glycerol tetraethers(OH-GDGTs) in the Han River system and their potential impact on the application of the ring index of OH-GDGTs(RI-OH) as a sea surface temperature(SST) proxy in the eastern Yellow Sea. Thereby, we analyzed various samples collected along the Han River and from its surrounding areas(South Korea, n = 34). The OHGDGTs were found in all samples investigated. OH-GDGT-0 was the dominant OH-GDGT component in the estuary and marine samples while OH-GDGT-2 was generally dominant in the soils, the lake sediments and the river suspended particulate matter(SPM). Our results thus suggests a possible warm bias of the RI-OH-derived summer SSTs in the coastal zone to which a large amount of terrestrial organic matter is being supplied. Further studies are necessary to better assess the applicability of the RI-OH proxy in the eastern Yellow Sea.     

Comparison between satellite and in situ sea surface temperature data in the Western Mediterranean Sea

A comparison between in situ and satellite sea surface temperature (SST) is presented for the Western Mediterranean Sea during 1999. Several international databases are used to extract in situ data (World Ocean Database, MEDAR/Medatlas, Coriolis Data Center, International Council for the Exploration of the Sea and International Comprehensive Ocean-Atmosphere Data Set). The in situ data are classified into different platforms or sensors (conductivity–temperature–depth, expendable bathythermographs, drifters, bottles, and ships), in order to assess the relative accuracy of these type of data with respect to Advanced Very High Resolution Radiometer SST satellite data. It is shown that the results of the error assessment vary with the sensor type, the depth of the in situ measurements, and the database used. Ship data are the most heterogeneous data set, and therefore present the largest differences with respect to in situ data. A cold bias is detected in drifter data. The differences between satellite and in situ data are not normally distributed. However, several analysis techniques, as merging and data assimilation, usually require Gaussian-distributed errors. The statistics obtained during this study will be used in future work to merge the in situ and satellite data sets into one unique estimation of the SST.     

Decadal scale variability of sea surface temperature in the Mediterranean Sea in relation to atmospheric variability

Twenty-four years of AVHRR-derived sea surface temperature (SST) data (1985–2008) and 35 years of NOCS (V.2) in situ-based SST data (1973–2008) were used to investigate the decadal scale variability of this parameter in the Mediterranean Sea in relation to local air–sea interaction and large-scale atmospheric variability. Satellite and in situ-derived data indicate a strong eastward increasing sea surface warming trend from the early 1990s onwards. The satellite-derived mean annual warming rate is about 0.037°C year^–1 for the whole basin, about 0.026°C year^–1 for the western sub-basin and about 0.042°C year^–1 for the eastern sub-basin over 1985–2008. NOCS-derived data indicate similar variability but with lower warming trends for both sub-basins over the same period. The long-term Mediterranean SST spatiotemporal variability is mainly associated with horizontal heat advection variations and an increasing warming of the Atlantic inflow. Analysis of SST and net heat flux inter-annual variations indicates a negative correlation, with the long-term SST increase, driving a net air–sea heat flux decrease in the Mediterranean Sea through a large increase in the latent heat loss. Empirical orthogonal function (EOF) analysis of the monthly average anomaly satellite-derived time series showed that the first EOF mode is associated with a long-term warming trend throughout the whole Mediterranean surface and it is highly correlated with both the Eastern Atlantic (EA) pattern and the Atlantic Multidecadal Oscillation (AMO) index. On the other hand, SST basin-average yearly anomaly and NAO variations show low and not statistically significant correlations of opposite sign for the eastern (negative correlation) and western (positive correlation) sub-basins. However, there seems to be a link between NAO and SST decadal-scale variations that is particularly evidenced in the second EOF mode of SST anomalies. NOCS SST time series show a significant SST rise in the western basin from 1973 to the late 1980s following a large warming of the inflowing surface Atlantic waters and a long-term increase of the NAO index, whereas SST slowly increased in the eastern basin. In the early 1990s, there is an abrupt change from a very high positive to a low NAO phase which coincides with a large change in the SST spatiotemporal variability pattern. This pronounced variability shift is followed by an acceleration of the warming rate in the Mediterranean Sea and a change in the direction (from westward to eastward) of its spatial increasing tendency.     

Planktonic foraminifera and sea surface temperature (SST) of the Xisha Trough, South China Sea since Last Glaciation

South China Sea is the largest marginal sea of the Western Pacific between the Pacific Ocean and Asia Continent. It has been influenced by both the Pacific Ocean and continental climate. Its continental margins are broad in north and south, narrow in west. There are many islands in east. A large amount of siliciclastic sediments derived from peri-continents and islands were trans-ported into the sea[1], in which significant information of paleoceanography and paleoclimate and paleoenvironm…     

新安江水库(千岛湖)热力学状况及热力分层研究

利用2012年1-12月在新安江水库(千岛湖)6个点位的每月一次的水温及其他环境因子的周年观测资料,分析了水库水温逐月变化、季节变化、垂直分布及温跃层的形成与变化,探讨了温跃层特征量(温跃层深度、厚度、强度)与表层水温、水体透明度的关系.新安江水库表层和中层水温与气温存在显著的线性相关,又以表层水温线性关系最好,而下层水温与气温没有显著相关性,说明下层水温受气温的影响很小,全年处于相对恒温状态.水库表层和中层水温逐月变化明显,呈现夏季最高、春秋季次之、冬季最低的变化趋势,其中中层水温最高值出现的季节较表层水温明显后延,下层水温没有明显的逐月变化和季节变化.水温垂直分布显示,4个季节均存在不同程度的温跃层和温度分层现象,其中水深最深的大坝前水温分层最明显.小金山、三潭岛和大坝前3个典型点位从春季的4月份到冬季的2月份温跃层深度由1.61±0.47 m逐渐增加至39.37±5.35 m,而温跃层厚度和强度则在夏季最高、冬季最低,温跃层随着季节的变化呈现增强稳定减弱消失的周期变化.温跃层深度与水体透明度存在显著正相关,与表层水温存在显著负相关,并基于透明度和表层水温建立温跃层深度的多元线性回归模型.     

Radium-228 profile at the second Geosecs intercalibration station, 1970, in the North Atlantic

²²⁸Ra concentrations were measured in 12 subsurface water samples collected during August–September 1970 in the North Atlantic at the Second Geosecs Intercalibration Station.²²⁸Ra concentrations ranged from 1.5 to 2.1 dpm/100 kg in the water layer between 100 and 600 m depth, above the main thermocline, but decreased with increasing depth to less than 0.5 dpm/100 kg at 1.5 km, below the main thermocline. The two deepest samples, collected below 4 km depth, indicated that the²²⁸Ra concentration increased again to at least 1.2 dpm/100 kg as the interface with the sea floor sediments was approached. Above the 4 km level, the²²⁸Ra profile was roughly similar to the tritium profile measured by Roether and Münnich.     

Identification of best predictors for forecasting seasonal rainfall and runoff in Australia

The first step towards developing a reliable seasonal runoff forecast is identifying the key predictors that drive rainfall and runoff. This paper investigates the lag relationships between rainfall across Australia and runoff across southeast Australia versus 12 atmospheric‐oceanic predictors, and how the relationships change over time. The analysis of rainfall data indicates that the relationship is greatest in spring and summer in northeast Australia and in spring in southeast Australia. The best predictors for spring rainfall in eastern Australia are NINO4 [sea surface temperature (SST) in western Pacific] and thermocline (20 °C isotherm of the Pacific) and those for summer rainfall in northeast Australia are NINO4 and Southern Oscillation Index (SOI) (pressure difference between Tahiti and Darwin). The relationship in northern Australia is greatest in spring and autumn with NINO4 being the best predictor. In western Australia, the relationship is significant in summer, where SST2 (SST over the Indian Ocean) and II (SST over the Indonesian region) is the best predictor in the southwest and northwest, respectively. The analysis of runoff across southeast Australia indicates that the runoff predictability in the southern parts is greatest in winter and spring, with antecedent runoff being the best predictor. The relationship between spring runoff and NINO4, thermocline and SOI is also relatively high and can be used together with antecedent runoff to forecast spring runoff. In the northern parts of southeast Australia, the atmospheric‐oceanic variables are better predictors of runoff than antecedent runoff, and have significant correlation with winter, spring and summer runoff. For longer lead times, the runoff serial correlation is reduced, especially over the northern parts, and the atmospheric‐oceanic variables are likely to be better predictors for forecasting runoff. The correlations between runoff versus the predictors vary with time, and this has implications for the development of forecast relationship that assumes stationarity in the historical data. Copyright © 2010 John Wiley & Sons, Ltd.     

High-resolution records of thermocline in the Okinawa Trough since about 10000 aBP

The present paper uses planktonic foraminifera and their stable isotopes to study the changes in the depth of thermocline (DOT) in the Okinawa Trough since the last 10000 a based on the analysis of Core B-3GC in the northern Okinawa Trough, together with that of the core in the southern Okinawa Trough. As results show, the thermocline was shallow before 6400 aBP, and deepened afterward, then became shallow again from 4000 to 2000 aBP. The DOT fluctuations display a positive correlation with those of sea surface temperature (SST). In addition, the changes in the northern Okinawa Trough are similar to those in the southern trough, implying a possible connection with the variation of the Kuroshio Current. The changes of SST and DOT suggest that the Kuroshio Current changed its intensity or main axis from 4000 to 2000 aBP and around about 6400 aBP respectively. Moreover, the changes of DOT from 8200 to 6400 aBP may indicate a gradual intensification of the Kuroshio Current.     

Air-sea Coupling During the Tropical Cyclones in the Indian Ocean: A Case Study Using Satellite Observations

In the years 1999 and 2001, three intense tropical cyclones formed over the northern Indian Ocean—two over the Bay of Bengal during 15–19 and 25–29 October, 1999 and one over the Arabian Sea during 21–28 May, 2001. We examined the thermal, salinity and circulation responses at the sea surface due to these severe cyclones in order to understand the air-sea coupling using data from satellite measurements and model simulations. It is found that the Sea Surface Temperature (SST) cooled by about 0.5 °–0.8 °C in the Bay of Bengal and 2 °C in the Arabian Sea. In the Bay of Bengal, this cooling took place beneath the cyclone center whereas in the Arabian Sea, the cooling occurred behind the cyclone only a few days later. This contrasting oceanic response resulted mainly from the salinity stratification in the Bay of Bengal and thermal stratification in the Arabian Sea and the associated mixing processes. In particular, the cyclones moved over the region of low salinity and smaller mixed layer depth with a distinct mixed layer deepening to the left side of the cyclone track. It is envisaged that daily satellite estimates of SST and Sea Surface Salinity (SSS) using Outgoing Longwave Radiation (OLR) and model simulated mixed layer depth would be useful for the study of tropical cyclones and prediction of their path over the northern Indian Ocean.     

Mechanisms controlling the seasonal mixed-layer temperature and salinity of the Indonesian seas

We examine the seasonal mixed-layer temperature (MLT) and salinity (MLS) budgets in the Banda–Arafura Seas region (120–138° E, 8–3° S) using an ECCO ocean-state estimation product. MLT in these seas is relatively high during November–May (austral spring through fall) and relatively low during June–September (austral winter and the period associated with the Asian summer monsoon). Surface heat flux makes the largest contribution to the seasonal MLT tendency, with significant reinforcement by subsurface processes, especially turbulent vertical mixing. Temperature declines (the MLT tendency is negative) in May–August when seasonal insolation is smallest and local winds are strong due to the southeast monsoon, which causes surface heat loss and cooling by vertical processes. In particular, Ekman suction induced by local wind stress curl raises the thermocline in the Arafura Sea, bringing cooler subsurface water closer to the base of the mixed layer where it is subsequently incorporated into the mixed layer through turbulent vertical mixing; this has a cooling effect. The MLT budget also has a small, but non-negligible, semi-annual component since insolation increases and winds weaken during the spring and fall monsoon transitions near the equator. This causes warming via solar heating, reduced surface heat loss, and weakened turbulent mixing compared to austral winter and, to a lesser extent, compared to austral summer. Seasonal MLS is dominated by ocean processes rather than by local freshwater flux. The contributions by horizontal advection and subsurface processes have comparable magnitudes. The results suggest that ocean dynamics play a significant part in determining both seasonal MLT and MLS in the region, such that coupled model studies of the region should use a full ocean model rather than a slab ocean mixed-layer model.     

Toward a global ocean data assimilation system based on ensemble optimum interpolation: altimetry data assimilation experiment

A global ocean data assimilation system based on the ensemble optimum interpolation (EnOI) has been under development as the Chinese contribution to the Global Ocean Data Assimilation Experiment. The system uses a global ocean general circulation model, which is eddy permitting, developed by the Institute of Atmospheric Physics of the Chinese Academy of Sciences. In this paper, the implementation of the system is described in detail. We describe the sampling strategy to generate the stationary ensembles for EnOI. In addition, technical methods are introduced to deal with the requirement of massive memory space to hold the stationary ensembles of the global ocean. The system can assimilate observations such as satellite altimetry, sea surface temperature (SST), in situ temperature and salinity from Argo, XBT, Tropical Atmosphere Ocean (TAO), and other sources in a straightforward way. As a first step, an assimilation experiment from 1997 to 2001 is carried out by assimilating the sea level anomaly (SLA) data from TOPEX/Poseidon. We evaluate the performance of the system by comparing the results with various types of observations. We find that SLA assimilation shows very positive impact on the modeled fields. The SST and sea surface height fields are clearly improved in terms of both the standard deviation and the root mean square difference. In addition, the assimilation produces some improvements in regions where mesoscale processes cannot be resolved with the horizontal resolution of this model. Comparisons with TAO profiles in the Pacific show that the temperature and salinity fields have been improved to varying degrees in the upper ocean. The biases with respect to the independent TAO profiles are reduced with a maximum magnitude of about 0.25°C and 0.1 psu for the time-averaged temperature and salinity. The improvements on temperature and salinity also lead to positive impact on the subsurface currents. The equatorial under current is enhanced in the Pacific although it is still underestimated after the assimilation.