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A meteotsunami with a wave height of 0.1–0.9 m and a period of 60 min was observed at tide gauges along the Korea Strait on 7 April 2019, while a train of two to four atmospheric pressure disturbances with disturbance heights of 1.5–3.9 hPa moved eastward from the Yellow Sea to the Korea Strait. Analysis of observational data indicated that isobar lines of the atmospheric pressure disturbances had angles of 75–83° counterclockwise due east and propagated with a velocity of 26.5–31.0 m/s. The generation and propagation process of the meteotsunami was investigated using the Regional Ocean Modeling System. The long ocean waves were amplified due to Proudman resonance in the southwestern Yellow Sea, where the water is deeper than 75 m, here, the long ocean waves were refracted toward the coast on the shallow coastal region of the northern Korea Strait. Refraction and reflection by offshore islands significantly affect the wave heights at the coast. To investigate the effects of an eastward-moving velocity and angle of atmospheric pressure disturbance on the height of a long ocean wave, sensitivity simulations were performed. This result will be useful for the real-time prediction system of meteotsunamis in the Korea Strait.

The Yellow Sea is recognized as a meteotsunami “hot-spot”, with a relatively high rate of events’ occurrence. The March 2007 and May 2008 meteotsunami events attracted large attention due to their deadly and high impact on the west coast of the Korean Peninsula. However, other small size meteotsunamis remain less known because of their insignificant coastal effect. Yet, a better understanding of meteotsunami hazard in the Yellow Sea requires thorough investigation of both large and small events. This paper reveals the occurrence of a meteotsunami on 11–12 June 2009 in the eastern Yellow Sea. It addresses the analyses of both the recorded sea-level and air-pressure data, the correlation between the atmospheric forcing and the meteotsunami formation, the numerical modeling of meteotsunami propagation, and the resonance effects on the recorded waves. Analysis results evidence a moving air-pressure jump of about 3 hPa that disturbed the sea surface and caused a meteotsunami with wave height up to 0.45 m (crest-to-trough). Both meteorological observations and numerical modeling suggest a speed of 11 to 13 m/s for the atmospheric disturbance propagation, which is much smaller than the optimal condition for Proudman resonance of meteotsunamis in the eastern Yellow Sea. Here, we demonstrate that the Greenspan resonance was responsible for amplifying the incident waves. Despite the insignificant coastal impact of the 11 June 2009 event, its investigation unravels new insights into the formation, amplification, and hazard extent of small size meteotsunamis in the Yellow Sea.

Kwon, K.; Choi, B.-J., and Lee, S.-H., 2018. Assimilation of Different SST Datasets to a Coastal Ocean Modeling System in the Yellow and East China Sea. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 1041–1045. Coconut Creek (Florida), ISSN 0749-0208.Global gridded gap-free sea surface temperature (SST) datasets were quantitatively evaluated using independent insitu datasets in the Yellow and East China Sea from September 2011 to February 2012. The assimilation effect of the four different SST datasets on a regional ocean modeling system was examined. The SST from the gridded SST datasets was compared with two ocean buoy SST data and bi-monthly routine hydrographic observation data in the Yellow Sea (YS). The root-mean-square differences (RMSDs) of the OISST, MGDSST, OSTIA, and MWIR SST datasets relative to the in-situ SST were 0.64, 0.53, 0.62, and 0.91°C, respectively. The RMSDs of SST were higher than 1°C for all datasets in the southeastern corner of the YS. Ocean circulation was simulated with a regional ocean model, and the SST data from the four SST datasets were assimilated. Assimilation of the SST to the ocean model improved the ocean current as well as temperature distribution. The daily mean simulated temperature was compared to the observed SST and vertical profiles of the temperature at the ocean buoys and hydrographic observation stations. After the assimilation of OISST, MGDSST, OSTIA and MWIR datasets, RMSDs of the simulated SST were 0.78, 0.92, 0.66 and 0.86°C, respectively, at the buoy stations. The RMSDs of the simulated subsurface temperature were 1.60, 1.67, 1.56 and 1.70°C, respectively, at the hydrographic observation stations. The simulated SSTs from the model with assimilation of the OSTIA and OISST datasets had relatively smaller RMSDs.

Kim, M.-J.; Kim, C.-S.; Choi, B.-J., and Lee, S.-H., 2018. Plume Current Change by Seawall Construction for a Harbor Development in South Korea. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 126–130. Coconut Creek (Florida), ISSN 0749-0208.The present study investigates the effects of a seawall construction on the nearfield plume dispersal and surface circulation in the west coast of Korea where the Saemangeum tide dike (33 km long) had been constructed from 1993 to 2006 to reclaim the wide tidal flat in the riverine estuary. The plume water has since flowed out by the gate operation from the two sluices (11 km apart) in the southern dike to the offshore, which altered source location of the plume in the coastal sea. Recently a 3.5 km long seawall was constructed at 4 km offshore from the northern sluice. The changes of the plume current and surface circulation near the newly constructed seawall were examined using data set of High Frequency Radar-derived current (HFR-current), wind and sluice-outflow velocity in summer 2010 (before the seawall construction) and summer 2014 (under the seawall construction). It was found that construction of seawall evidently changed tidal plume dispersal pattern and nearfield distribution of monthly-mean plume current. Complex correlation and Empirical Orthogonal Function (EOF) analyses on the subinertial surface current, wind, and sluice-outflow velocity in summer 2010 and summer 2014 revealed that wind and outflow velocity are dominant factors controlling variations of the plume extension and coastal current.

Kim, J.-K.; Choi, B.-J., and Lee S.-H., 2018. Non-seasonal sea level variations in the Korea Strait and regional atmospheric conditions. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 581–585. Coconut Creek (Florida), ISSN 0749-0208.To investigate dominant sea level variations in the Korea Strait (KS) and to find their responsible factors, Empirical Orthogonal Function (EOF) analysis was performed on the non-seasonal components of sea level, wind, and sea level pressure data from 1993 to 2014. The non-seasonal sea level variation accounts for 20% of the total sea level variations in the KS. The first EOF mode of the sea level explains 63.8% of the total variance and represents strait-wide simultaneous sea level oscillations. EOF analysis on the surface wind data over the KS and the East China Sea was performed and the first EOF of winds, explaining 49.5% of the total variance, represents the strength of the northeasterly wind. The amplitude time series of the first EOFs from wind and sea level data has correlation coefficients (r) of 0.48. The sea level and wind data have common spectral peaks at periods of 5.1 and 10.2 months. Variance and the first EOF of the steric height in the KS are relatively high around Jeju Island while they are small near the south coast of Korea. Correlation between the amplitude time series of sea level and steric height first modes is low (r = 0.20). The basin-wide simultaneous sea level variations with intra-seasonal and interannual periods in the KS are found to be modulated by variations in the regional surface winds and sea level pressure patterns over the East China Sea and the KS rather than by changes in local steric height.

Seasonal changes in the distribution of submesoscale (SM) flow features were examined using a fine-resolution numerical simulation. The SM flows are expected to be strong where mesoscale (MS) eddies actively develop and also when the mixed layer depth (MLD) is deep due to enhanced baroclinic instability. In the East Sea (ES), MS eddies more actively develop in summer while the MLD is deeper in winter, which provided the motivation to conduct this study to test the effects of MLD and MS eddies on the SM activity in this region. Finite-scale Liapunov exponents and the vertical velocity components were employed to analyze the SM activities. It was found that the SM intensity was marked by seasonality: it is stronger in winter when the mixed layer is deep but weaker in summer - despite the greater eddy kinetic energy. This is because in summer the mixed layer is so thin that there is not enough available potential energy. When the SM activity was quantified based on parameterization, (MLD × density gradient), it was determined that the seasonal variation of MLD plays a more important role than the lateral density gradient variation on SM flow motion in the ES.

The interannual variation of surface ocean currents can be as large as seasonal variation in the Japan/East Sea (JES). To identify the major factors that cause such interannual variability of surface ocean circulation in the JES, surface circulation was simulated from 1998 to 2009 using a three-dimensional model. Contributions of atmospheric forcing (ATM), open boundary data (OBC), and intrinsic variability (ITV) of the surface flow in the JES on the interannual variability of surface ocean circulation were separately examined using numerical simulations. Variability in surface circulation was quantified in terms of variance in sea surface height, 100-m depth water temperature, and surface currents. ITV was found to be the dominant factor that induced interannual variabilities of surface circulation, the main path of the East Korea Warm Current (EKWC), and surface kinetic energy on a time scale of 2–4 years. OBC and ATM were secondary factors contributing to the interannual variation of surface circulation. Interannual variation of ATM changed the separation latitude of EKWC and increased the variability of surface circulation in the Ulleung Basin. Interannual variation of OBC enhanced low-frequency changes in surface circulation and eddies in the Yamato Basin. It also modulated basin-wide uniform oscillations of sea level. This study suggests that precise estimation of initial conditions using data assimilation is essential for long-term prediction of surface circulation in the JES.

To examine the surface circulation and vertical structure of currents in the region of the Keum River (KR) plume, we analyzed the subinertial surface currents obtained by high frequency radar and the vertical profiles of currents measured at a station (M1) located 10 km distance from the estuary mouth for one month in late spring 2008. Monthly-mean surface circulation is composed of the westward flow from the estuary mouth and the northward flow in the offshore. These surface mean currents are a gradient (geostrophic) current around the monthly-mean plume bulge. Dominant variabilities of the surface currents, winds, and KR-outflow are decomposed by the Empirical Orthogonal Functions (EOF). The first current EOF mode, explaining 39% of total variation, is primarily related to the first wind EOF mode varying along the coast and the second current mode, explaining 33% of total variation, is mainly related to the first KR-outflow EOF mode varying along the mean KR-outflow direction. Meanwhile, vertical profile of the monthly-mean current at M1 shows a two-layer structure of the current flowing offshore (onshore) in the upper (lower) layer because the water column is divided by a pycnocline at 7-9 m depths below the plume water. This two layer structure is a background persisting current structure, at least in spring, maintained by the geostrophic balance induced by the sea level slope and density gradient along the line normal to the westward mean surface current direction due to monthly-mean plume bulge off the KR estuary. EOF analysis of vertical current profiles reveals that the first mode, explaining 43% of total variation, represents the two-layer structure of the current variability. The upper-layer current varies along a line normal to the mainland coastline and the low-layer one varies approximately along a line parallel to the coastline, with direction difference of about 115° between the upper-and low-layer. From the correlation analysis it is found that 60% of the first mode variation is influenced by the first mode of KR-outflow and 36% by the first mode of wind. Any forcing modes of KR-outflow and wind influencing the other current vertical modes could not be found in the present study.

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.

Son, Y.B.; Kim, Y.H.; Choi, B.-J., and Park, Y.-G., 2016. Lagrangian Coherence Structures and the dispersion of green algal bloom in the Yellow and East China Sea. In: Vila-Concejo, A.; Bruce, E.; Kennedy, D.M., and McCarroll, R.J. (eds.), Proceedings of the 14th International Coastal Symposium (Sydney, Australia). Journal of Coastal Research, Special Issue, No. 75, pp. 1237 - `1241. Coconut Creek (Florida), ISSN 0749-0208. During the summer 2011, the Geostationary Ocean Colour Imager (GOCI) detected patches of the recurrent massive floating green algae in the central part of the Yellow Sea (YS) and East China Sea (ECS). This study utilizes the Lagrangian Coherent Structures (LCSs) analysis to demonstrate the horizontal dispersion pattern of algal bloom patches. This approach is based on the assumption that unstable manifolds such as ridges in the finite-time Lyapunov exponent (FTLE) fields coincide with material transport barriers. To calculate the FTLE fields, flow fields derived from the Regio...

To examine the evolution of the wind-driven flows in the Yellow Sea (YS) during winter, ocean circulation was simulated using a three-dimensional ocean model with realistic topography and atmospheric forcing. The simulated sea surface temperature, ocean currents, and path of the Yellow Sea Warm Current (YSWC) agreed with observations. Southward currents along the Korean coast and the Chinese coast in winter were also effectively identified. Spectra of the daily mean winds and the YSWC velocities in the subsurface layer had dominant peaks at 12 and 20 day periods. Time-lagged correlation analysis suggested that the downwind flow in the surface layer reacts concurrently to the northwesterly wind in winter whereas the subsurface layer responds with a delay. One day after the wind burst, an upwind current in the subsurface layer appeared in the center of the trough, whereas the downwind flow in the surface layer decreased significantly. Two days later, the upwind flow in the subsurface layer shifted to the west of the trough while the downwind flow along the Korean coast strengthened. These flow responses to the wind variations resulted in a clockwise circulation in the YS during winter.

To increase the accuracy of ocean predictions in the Yellow and East China Sea (YES), the satellite-borne sea surface temperature (SST) data have been assimilated to an operational ocean modeling system by applying an ensemble Kalman filter (EnKF). As the observed SST was assimilated continuously into the model with time, the ensemble spread decreased and the efficiency of data assimilation degenerated. To increase liability of the system and the ensemble spread, model uncertainties were represented stochastically by perturbing the model tendency parameters such as eddy viscosity, bottom drag coefficient, light attenuation depth, as well as atmospheric forcing. Data assimilation experiments were performed with forcing from a regional atmospheric model from September 2011 to February 2012. The assimilation results with and without the stochastically perturbed model parameters and atmospheric forcing were compared. The ensemble with the perturbations has larger spread and smaller root-mean-square deviation (RMSD) in temperature compared with the ensemble without the perturbations. The SST RMSD relative to anther supplementary SST dataset was reduced from 0.91 to 0.81 °C over the YES. The assimilation of the SST data improved the simulated SST compared with the observation at the ocean buoy stations, and also made the subsurface temperature profiles closer to the observed ones. The assimilation experiments showed that a stochastic representation of the model errors by the perturbations of the model parameters and atmospheric forcing increases the spread of the ensemble and improves the structure of background error covariance, which enhances the performance of the ensemble modeling system in the YES.

The sea surface temperature (SST) is essential data for the ocean and atmospheric prediction systems and climate change studies. Five global gridded sea surface temperature products were evaluated with independent in situ SST data of the Yellow Sea (YS) from 2010 to 2013 and the sources of SST error were identified. On average, SST from the gridded optimally interpolated level 4 (L4) datasets had a root mean square difference (RMSD) of less than 1 °C compared to the in situ observation data of the YS. However, the RMSD was relatively high (2.3 °C) in the shallow coastal region in June and July and this RMSD was mostly attributed to the large warm bias (>2 °C). The level 3 (L3) SST data were frequently missing in early summer because of frequent sea fog formation and a strong (>1.2 °C/12 km) spatial temperature gradient across the tidal mixing front in the eastern YS. The missing data were optimally interpolated from the SST observation in offshore warm water and warm biased SST climatology in the region. To fundamentally improve the accuracy of the L4 gridded SST data, it is necessary to increase the number of SST observation data in the tidally well mixed region. As an interim solution to the warm bias in the gridded SST datasets in the eastern YS, the SST climatology for the optimal interpolation can be improved based on long-term in situ observation data. To reduce the warm bias in the gridded SST products, two bias correction methods were suggested and compared. Bias correction methods using a simple analytical function and using climatological observation data reduced the RMSD by 19−29% and 37−49%, respectively, in June.

This paper presents the surface flashover characteristics to simulate the poor contact between an anode and a solid dielectric in a mixture gas (8/2) under a non-uniform field. The surface flashover voltage of the mixture gas revealed the irregular tendency that was not in accordance with the Paschen's law with an increasing gap of the poor contact. In addition, the insulation performance of the mixture gas at 0.6MPa was comparable to that of gas of 0.1MPa based on the insulation performance on the poor contact. These results are able to apply the insulation design of eco-friendly gas insulation switchgear considering the internal faults.

This paper presents the surface flashover mechanism of a laminated solid dielectric and describes the surface flashover characteristics with the inherent capacitance of the laminated solid dielectric in a mixture gas (8:2) under an quasi uniform field. It was found that the electron emission at a cathode and the high-local electric field region around an anode were important factors to reasonably describe the surface flashover mechanism. The surface flashover voltage by the mechanism decreased with the inherent capacitance increase of the laminated solid dielectric. In addition to the surface flashover mechanism and its characteristics, the surface flashover voltage equations as a function of the inherent capacitance were derived by considering a gas pressure used in future eco-friendly GIS and the factors influencing the surface flashover.

A data assimilation system has been developed to apply to a fully coupled climate model, CM2.1, in the Korea Institute of Ocean Science and Technology (KIOST). While the ocean observation data are assimilated into the ocean component model through the data assimilation system of the KIOST (DASK), the other component models are freely integrated. Here, we evaluated the variability of the ocean climate in the climate reanalysis by the DASK from 1947 to 2012. To assess oceanic processes and ocean climate variability as modeled by the DASK, we examined the North Pacific Intermediate Water, El Nino–Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), Indian Ocean Dipole (IOD), upper 300 m heat content (HC300), Sea Surface Height (SSH), meridional heat transport, mean global temperature and salinity, and temperature and zonal velocity in the tropical Pacific. Furthermore, we compared these modeled features with various in-situ observations and with various other global reanalyses, such as the Simple Ocean Data Assimilation (SODA) and the GFDL Ensemble Coupled Data Assimilation systems (ECDA). The DASK represents global temperature and salinity well, not only at the surface but also at intermediate depths in the ocean. In addition, the DASK closely models the features of North Pacific Intermediate Water, a typical water mass in the North Pacific characterized by the salinity minimum layer. The DASK’s ocean climate variability also matches well with observations of the ENSO, PDO and IOD. The HC300 of the DASK shows as good or better correlations with real-world observations compared to other reanalyses systems except in the tropics. In addition, the SSH variability of the DASK shows lower correlation in the tropics and north Atlantic rather than its HC300 correlation. It is suggested that the poor performance of the DASK in the tropics and north Atlantic is resulted from the bias of its low-resolution atmospheric model. Nevertheless, it is noteworthy that the DASK performs better or comparable particularly in the North Pacific and North Atlantic even though the DASK applies the Ensemble Optimal Interpolation, which is numerically more efficient than the ensemble Kalman filter applied by the ECDA, and a lower horizontal model resolution than that of the SODA. The validation suggests the high performance of the DASK in terms of ocean climate estimation and variability at a lower computational cost than the other two reanalyses systems we studied.

The purpose of this study was to analyze the change of secondary earth science teachers` knowledge about the currents of the East Sea through drawing of a schematic map of oceanic currents. For this purpose, thirty two earth science teachers participated in the six-hour long training of learning and practice related to ocean current schematic map. The teacher participants performed drawing of the ocean current schematic map of the East Sea in three different phases, i.e.; pre-, post-, and delayed-post phase. In addition, all the maps conducted by participants were converted to digitalized image data. Detailed analysis were performed to investigate participating teachers` knowledge about the currents of the East Sea. Findings are as follows: First, the teacher participants have background knowledge about the ocean current map, but it reveals an incorrect knowledge about some concepts. Second, after teacher training, teachers` knowledge increased about the East Sea`s currents, while a decrease was found in the differences between individual teachers` knowledge. This pattern was more evident in the delayed-post phase of drawing than in the post-phase occurred immediately after training. Third, the teacher participants were strongly aware of the need to improve the ocean current schematic map of the East Sea in science textbook in terms of scientific knowledge. In addition, they showed a high level of satisfaction about teacher training because they perceived that it was meaningful in various aspects; recognizing the importance of content knowledge and conjunction with instructional strategies, the needs of secondary science curriculum, and recognition of the nature of scientific knowledge. The results imply that teachers` subject matter knowledge plays a significant role to make science teaching effective.

The Korea Operational Oceanographic System (KOOS) was developed at the Korea Institute of Ocean Science and Technology (KIOST) to produce real-time forecasting and simulation of interdisciplinary multi-scale oceanic fields. This offers valuable information to better mitigate coastal disasters, such as oil spills and other marine accidents, and provides the necessary ocean predictions to support the marine activities of government agencies, marine industries, and public users. The KOOS became operational in March 2012, and consists of several operational modules and realtime observations, including satellite remote sensing, coastal remote monitoring stations using high-frequency radar, and ocean observatories. The basic forecasting system includes weather, regional and high-resolution coastal circulation and wave prediction models; the practical application system includes storm surges, oil spills, and search and rescue prediction models. An integrated maritime port prediction system and data information and skill assessment systems are also part of the KOOS. In this work, the performance of the numerical models was evaluated by the skill assessment systems. From the monthly and yearly skill assessments, the models showed reasonable skill in predicting atmospheric and oceanic states except for the regional ocean circulation models. The ongoing development and improvement of the KOOS includes improvement of the model skills through the upgrade of the satellite-based sea surface temperature algorithm, the enhancement of the ocean monitoring ability, the upgrade of the forecasting models for higher spatial resolutions and the application of data assimilation techniques improved with the feedback from the skill assessment report.

To observe vertical structure and temporal variations of currents in the Tsushima Warm Current region of the Korea Strait, a moored buoy system was deployed in autumn 2009. The moored buoy system measured vertical profiles of current, temperature, and salinity for 24 days and a background hydrographic survey was performed. Along-strait northeastward currents were dominant in the upper layer (8–35 m). The mean current veers counterclockwise from 48 m to 74 m as much as 50°, and its speed is reduced with depth. There were distinct northward onshore currents near the bottom (65–80 m). It was demonstrated that thermal wind relation holds in the inclined pycnocline layer, which generates the counterclockwise veering current structure. Density gradient along the strait is a main factor producing the cross-strait onshore current component below the upper-layer and the cross-strait density gradient reduces the along-strait current component with depth. Previous studies have never focused on the effect of the along-strait density structure on current structure. The first Empirical Orthogonal Function mode (CM1) of current variability explains 70% of local current variations and its vertical structure is close to the mean current structure. The correlation analysis among variations of CM1 current, slope of sea level anomaly (SSLA) and local wind anomaly revealed that the variation of CM1 current is mainly related to the variation of SSLA across the strait (c-SSLA), which is known to be controlled by remote and local wind forcing. Similarity between vertical structures of mean and CM1 current suggests that thermal wind relation is the main dynamics maintaining the counterclockwise turning of CM1 current below the upper layer although the upperlayer CM1 current is controlled by c-SSLA through barotropic geostrophic relation. Time series of temperature and salinity indicate that the thermohaline front between Korean Coastal Water and Tsushima Warm Current Water meanders in time and migrates over the mooring station back and forth. The front meandering and migration also affect the local SSLA and CM1 current variations in autumn in the Korea Strait.

This study introduces the East Asian Marginal Seas (EAMS) reanalysis system and evaluates its products from 1982 to 2006. The EAMS reanalysis system consists of an ocean circulation model with 0.25° horizontal grid spacing and a data assimilation module. Temperature profiles taken by ship and Argo floats as well as satellite-borne sea surface temperature (SST) were assimilated into the model by applying the ensemble Kalman filter every 7 days. The reanalyzed oceanic fields were compared with ones by the control run without data assimilation to assess the impact of the data assimilation. The assimilative model significantly improved the horizontal structures of SST, sea surface height (SSH), vertical structure of temperature, and volume transports through the major straits. Root-mean-square error (RMSE) of SST decreased from 1.0 to 0.6°C. Horizontal and vertical distribution of subsurface temperature was corrected close to the observed values. High SSH variability in the south of Japan and in the Kuroshio extension region was partially restored. Overshooting of the Kuroshio Current and the East Korea Warm Current was suppressed to the south by the data assimilation. SSH and surface circulation in the Oyashio region and in the East Sea (also known as the Sea of Japan) recovered, which corrected the sea level difference between the East Sea and the Pacific Ocean and produced realistic transport through the Korea Strait. The reanalysis well resolved transport through the Korea Strait. The correlation coefficient with the transport based on an Acoustic Doppler Current Profiler measurement was 0.70 and the RMSE was 0.37 Sv (106 m3/s), whereas those were 0.59 and 0.43 Sv in the control run, respectively. The EAMS reanalysis dataset may help us to understand circulation variations in the marginal seas and to investigate factors controlling volume and heat transport.

The Yellow Sea is a shallow (average depth of 44 m), epi-continental sea bounded by China and Korea. The region is subject to highly variable environmental conditions and processes that combine along with the basin characteristics to impact the oceanographic conditions and coastal morphology. Furthermore anthropogenic developments of relatively large scale are common in the region, and not only directly impact the coastline in development areas but also alter the physical processes and therefore indirectly impact additional coastal areas. The physical processes at work include solar radiation, wind forcing, river discharge, tides, and water exchange with adjacent seas. This paper provides a literature review of the physical processes in the Yellow Sea as well as a review of the anthropogenic interaction and impact to the coastal area and these physical processes. The Yellow Sea is a macro-tidal environment with the maximum tidal amplitude of approximately 10 m in the coastal regions. Semidiurnal and diurnal harmonic components dominate and the associated tidal currents play a large role in the coastal morphology in the region. Secondary to tides the most influential environmental factor is the seasonal variability associated with the Asian monsoon system that is present in this region. This climatological system accounts for strong northwesterly wind and dry air during the winter and mild, warm southeasterly winds that are associated with relatively high precipitation during the summer. The region is also subject to seasonal variability of storm activity including occasional typhoons in the region during late summer and early fall. The seasonal variability plays a role in the development of seasonal circulation patterns, where wind, precipitation and solar radiation all play a role. During summer the strongly stratified water column circulates cyclonically and the Korean Coastal Current (KCC) flows northward along the Korean coast and the Yellow Sea Coastal Current (YSCC) flows southward along the Chinese coast and the Yangtze Bank. During winter the strong northwesterly wind mixes water column and both KCC and YSCC flow southward in the shallower regions while the Yellow Sea Warm Current (YSWC) episodically penetrates into the Yellow Sea and transport swarm, saline water to the central Yellow Sea. The seasonal conditions combine to control water and sediment transport; high freshwater input during summer causes strong stratification and inhibits resuspension while strong wind during winter reduces stratification and promotes resuspension. The temporal variability of the sediment loading and physical processes combined with anthropogenic coastal developments serve to continuously reshape the coastal landscape.

While an air pressure jump was moving southeastward over the shallow water region of the eastern Yellow Sea in March 2007, a long ocean wave (meteotsunami) was generated and amplified due to the Proudman resonance. The long wave arrived at the coast during high tide with wave amplitude of 1.4 m and seawater overflew seawalls and inundated the land. High-frequency sea level oscillations continued for 8–9 h after the long wave hit a local coast. The Moon's age was 12 days, and the tidal range was about 4 m between neap and spring tides. Two-dimensional numerical simulations were performed, to reproduce amplification of the long ocean wave in offshore and oscillations of sea level at the coast. Both tidal elevation and tidal currents were found to affect the growth of the long wave amplitude by the interactions between tides and the long wave. Long wave-tides interactions are important processes for the accurate prediction of long wave arrival time and maximum height and for the reduction of coastal hazards in the macrotidal region. After the long wave hit the coast of remote regions, reflected waves propagated radially from remote regions to a local coast. The high-frequency sea level oscillations at a local observation station continued, until all of the reflected waves from remote regions had passed by. It was concluded that high-frequency oscillations of sea level are generated not only by local reflection of the long wave, but also by propagation of the reflected waves from remote regions.

This study presents future climate change projections in the Northwest Pacific (NWP) marginal seas using dynamic downscaling from global climate models (GCMs). A regional climate model (RCM) for the Northwest Pacific Ocean was setup and integrated over the period from 2001 to 2100. The model used forcing fields from three different GCM simulations to downscale the effect of global climate change. MIROC, ECHAM, and HADCM were selected to provide climate change signals for the RCM. These signals were calculated from the GCMs using Cyclostationary Empirical Orthogonal Function analysis and added to the present lateral open boundary and the surface forcing. The RCM was validated by comparing hindcast result with the observation. It was able to project detailed regional climate change processes that GCMs were not able to resolve. A relatively large increases of water temperature were found in the marginal seas. However, only a marginal change was found along the Kuroshio path. Heat supply to the atmosphere decreases in most study areas due to a slower warming of the sea surface compared to the atmosphere. The RCM projection suggests that the temperature of the Yellow Sea Bottom Cold Water will gradually increase by 2100. Volume transports through major straits except the Taiwan Strait in the marginal seas are projected to increase slightly in future. Increased northeasterly wind stress in the East China Sea may also result in the transport change.

The variability of freshwater transport in the Korea Strait (FTKS) affects the circulation and ecosystem of the East/Japan Sea. Numerical simulations using realistic surface forcing, Changjiang River discharge (CRD), and open boundary values were performed to quantify the interannual variation of FTKS and to investigate its underlying physical processes. The simulated salinity and volume transport, which determine the variability of FTKS, were verified by comparing with observations. Salinity played a more important role than volume transport in inducing the interannual variation of FTKS. FTKS has a positive correlation with CRD, difference between precipitation and evaporation (P–E), southeasterly wind, and freshwater transport in the Taiwan Strait (FTTS). FTKS has its best correlation (0.62) with FTTS. The correlations with CRD (0.25) and P–E (0.37) are weaker, probably due to wind stress. The southeasterly wind that drives Changjiang diluted water toward the Korea Strait by Ekman flow in the East China Sea has good correlation (0.51) with FTKS. The vertical structures of FTKS and its variability are more effectively affected by CRD and P–E in the surface layer, FTTS in the middle layer, and the wind in the subsurface layer.

High-resolution reanalysis of heat transport in the northwestern Pacific marginal seas was conducted for the period January 1980–December 2009 using ensemble Kalman filter. An ocean circulation model with a grid of 0.1 × 0.1° horizontal resolution and 20 vertical levels was used. Atmospheric forcing data from daily European Centre for Medium-Range Weather Forecasts were used in the ocean model. The assimilated data for the reanalysis were based on available observations of hydrographic profiles, including field surveys and Argo float and satellite-observed sea-surface temperature data. This study focused on mean and temporal variations in oceanic heat transport within the major straits among the marginal seas over 30 years. The mean heat transport in the Korea/Tsushima Strait and onshore transport across the shelf break in the East China Sea (ECS), Taiwan Strait, Tsugaru Strait, and Soya Strait were 182, 123, 82, 100, and 34 × 1012 W, respectively. The long-term trends in heat transport through the Korea/Tsushima Strait and Tsugaru Strait and onshore transport across the shelf break of the ECS were increasing, whereas the trend in heat transport through the Taiwan Strait was decreasing. There was little long-term change in heat transport in the Soya Strait. These long-term changes in heat transport through the Korea/Tsushima Strait, across the shelf of the ECS, and through the Taiwan Strait may be related to increased northeasterly wind stress in the ECS, which drives Ekman transport onto the shelf across the shelf break.

Accurate prediction of sea water temperature has been emphasized to make precise local weather forecast and to understand change of ecosystem. The Yellow Sea, which has turbid water and strong tidal current, is an unique shallow marginal sea. It is essential to include the effects of the turbidity and the strong tidal mixing for the realistic simulation of temperature distribution in the Yellow Sea. Evaluation of ocean circulation model response to vertical mixing scheme and turbidity is primary objective of this study. Three-dimensional ocean circulation model(Regional Ocean Modeling System) was used to perform numerical simulations. Mellor- Yamada level 2.5 closure (M-Y) and K-Profile Parameterization (KPP) scheme were selected for vertical mixing parameterization in this study. Effect of Jerlov water type 1, 3 and 5 was also evaluated. The simulated temperature distribution was compared with the observed data by National Fisheries Research and Development Institute to estimate model`s response to turbidity and vertical mixing schemes in the Yellow Sea. Simulations with M-Y vertical mixing scheme produced relatively stronger vertical mixing and warmer bottom temperature than the observation. KPP scheme produced weaker vertical mixing and did not well reproduce tidal mixing front along the coast. However, KPP scheme keeps bottom temperature closer to the observation. Consequently, numerical ocean circulation simulations with M-Y vertical mixing scheme tends to produce well mixed vertical temperature structure and that with KPP vertical mixing scheme tends to make stratified vertical temperature structure. When Jerlov water type is higher, sea surface temperature is high and sea bottom temperature is low because downward shortwave radiation is almost absorbed near the sea surface.