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Surface seawater temperature in the southwestern coast of Korea suddenly rose in the summer of 2017. This rapid temperature rise event occurred simultaneously with a change in wind direction in the Korea Strait from northwesterly to southeasterly due to the approach of typhoon Noru. To identify the causes of the abrupt rise in surface temperature, the variations of the surface currents and temperature were investigated using a three-dimensional ocean circulation model. Warm and less saline surface water, a mixed shelf water of the Changjiang Diluted Water and saline water from an onshore branch of the Kuroshio in the East China Sea (ECS), flowed northeastward to the west and south of Jeju Island, proceeding eastward through the Jeju and Korea Straits. While westerly winds prevailed, wind-driven ageostrophic currents flowed southeastward, moving away from the south coast of Korea, due to Ekman transport. The shallow coastal region was occupied by cool and saline surface water (T < 22 °C, S > 32.5 psu). However, after the wind shifted to an easterly direction, the surface ageostrophic currents realigned northwestward, and the warm and less saline water moved into the shallow coastal region. In a passive tracer dispersal experiment, dyes injected from the ECS flowed to the west of Jeju Island and through the Jeju Strait via geostrophic currents. These dyes did not affect the shallow southern coastal region of Korea while the westerly winds dominated. However, during the easterly wind event, the dyes were advected toward the coast by the coastward Ekman transport. An analysis of temperature data observed at Cheongsando over 16 years and the tracer experiment revealed that the abrupt temperature rise in the summer of 2017 was a marine heatwave event generated by the advection of warm and less saline surface water from the ECS to the southwestern coast of Korea through the Jeju Strait. • Warm and less saline surface water flows east through the Jeju Strait in summer. • This surface current is primarily in geostrophic balance. • Wind-driven ageostrophic current drifted warm surface water toward the coast. • Surface temperature increased and salinity decreased rapidly in the coastal region. • The warm and less saline water originated from the surface of the East China Sea. [ABSTRACT FROM AUTHOR]

ABSTRACT 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Variations in seawater temperature have a critical influence on marine ecosystems and aquaculture industries in coastal regions. From July 25 to August 5, 2017, the sea surface temperature off the southwestern coast of Korea rose rapidly from 17.1 °C to 27.3 °C, which induced environmental stress in marine organisms such as farmed abalone. This study investigated the cause of this abrupt sea temperature rise. Westerly winds from July 1 to 25 were favorable for the upwelling of cold subsurface water along the bottom slope in this coastal region, helping maintain cool surface water temperatures. As the wind changed to easterly, the cold subsurface water moved away and warmer, fresher surface water moved into the coastal region, where the surface currents changed from a southeastward flow to a northeastward flow from July 25 to August 5. Thus, to maintain stable, cool seawater temperatures during summer, both cold water supply and upwelling wind conditions are essential. The analysis of water masses indicated that the cold water was formed by the mixing of the Tsushima Warm Current Water and Yellow Sea Bottom Water in the northern East China Sea. The cold water advected eastward and formed a cold water pool in the intermediate depths (30–80 m) of the northern Jeju Strait, providing cold water to the coastal region during the upwelling period. The westerly winds were disrupted by the approach of a typhoon, which discontinued the supply of bottom cold water. The physical processes identified in this study will help predict short-term increases in water temperature and can assist in the development of countermeasures for the aquaculture industry against the negative effects of abrupt environmental changes. [Display omitted] • Subsurface cold water pool (CWP) advects to the northern Jeju Strait in summer. • The CWP on the northern slope of Jeju Strait upwells due to westerly winds. • Coastal upwelling of the CWP maintains surface cool water in the coastal region. • An easterly wind event causes a retreat of CWP and an abrupt rise of temperature. • The CWP forms by subsurface mixing of cold and warm water masses in the East China Sea. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 Niño–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. [ABSTRACT FROM AUTHOR]

The North Pacific is a crucial region in decadal climate changes like the El-Ninõ Southern Oscillation (ENSO) and the Pacific decadal oscillation (PDO), which influence critically not only oceans but also atmosphere. Despite of the importance of ocean data in the North Pacific, available data have been rare due to limitation of accessibility. Data assimilation have been used to produce more reliable ocean data. The North Pacific is comprised of several distinctive areas, including the equatorial and tropical regions, subtropical regions, and the boreal Bering Sea region. Each region demonstrates various ocean current characteristics. Appropriate assimilation method considering the characteristics of the sea area might be selected. In this study, we compared the performance of Ensemble Optimal Interpolation (EnOI), a simplified version of Ensemble Kalman Filter (EnKF), according to ensemble composition and assimilation time interval and compared the regional performance difference between EnOI and EnKF in each region. Ocean circulation model in the North Pacific was conducted using ROMS from 2006 to 2015. Assimilation of sea surface temperature for 2015 using EnKF and EnOI methods with satellite data from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) was applied respectively. The RMSE of the Sea Surface Temperature of EnKF was lower than that of EnOI in the Northwest Pacific where the spatial SST correlation structure was complex. But, the RMSE of EnOI was lower than EnKF in the Equator area with simple SST correlation structure in SST. The efficiency of each method in terms of accuracy, resources and time was also compared. EnOI was faster by about 7 times than EnKF. [ABSTRACT FROM AUTHOR]