• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.953-968
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• 2018

In order to understand the change of surface water temperature in the East China Sea (ECS), this study analyzed the relationship between sea surface temperature (SST), air temperature (AT) and heat flux using satellite and model reanalysis data from 2003 to 2017. SST in the ECS showed the lowest (average : $13.72^{\circ}C$) in March and the highest (average : $28.12^{\circ}C$) in August. AT is highly correlated with SST and shows a similar seasonal change. In August, SST is higher than AT and then continuously higher than AT until winter. To analyze the change of the summer SST in the ECS, we used the SST anomaly value in August to classify the periods with positive (04', 06', 07', 13', 16', 17') and negative (03', 05', 08', 09', 10', 11', 12', 14', 15') values. Spatial similarity between the two periods indicates that SSTs are relatively larger variations in the northern part than in the southern part, and in the western part than in the eastern part in the study area. AT and net heat flux values also show similar changes with SST. However, the periods of the positive SST anomaly have the relatively increasing SST, AT and heat flux values compared to the periods of the negative SST anomaly in the summer season of the ECS. Although the change of SST in the summer season generally well correlates with AT, there were the periods when it was different from general trends between SST and AT (10', 12', 15', 16'). SST in August 2010 and 2012 decreased by $0.5^{\circ}C$ from AT. It suggests that the decreasing SST was considered to be caused by the effects of the typhoon passing through the study area. In August 2015, AT was relatively lower than SST (> $0.5^{\circ}C$), which is might be weakening of the East Asian Summer Monsoon. In August 2016, SST and AT show the highest values during the whole study periods, but SST is higher than AT (> $1^{\circ}C$). From satellite and heat flux data, the variations of SST have been shown to be relatively higher in the area of the expansion Changjiang Diluted Water (CDW) originated from the China coast. More research is needed to analyze this phenomenon, it is believed as not only the effect of rising AT but also the expansion of the low-salinity water.

• Journal of the Korean Society for Marine Environment & Energy
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• v.12 no.3
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• pp.133-142
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• 2009

In order to understand the present environmental condition and future impingement of Changjiang(Yangtze River) outflow upon the adjacent seas after the scheduled completion of the Sanxia (Three Gorges) Dam in 2009, we tried to estimate the mixing ratios among surface waters of three end-members: Changjiang Water (CW), Kuroshio Water (KW), and East China Sea Water (ECSW) using $^{228}Ra/^{226}Ra$ activity ratio and salinity as tracers. Water samples were collected from 32 stations in November 2005 (R/V Tamgu 3), from 20 stations in July 2006 (R/V Ocean 2000) and from 17 stations in August 2006 (R/V Ieodo) in the northern part of the East China Sea. Radium isotopes in ~300 liters of surface seawater were extracted onboard by filtering through manganese impregnated acrylic fibers and following coprecipitation as $Ba(Ra)SO_4$. Activities of radium isotopes were determined by a high purity germanium detector. Results show that the fraction of CW was in the range of 1-23% in the study area, while KW was in the range of 0-30 % and ECSW 58-100 %. The eastward plume of Changjiang outflow, commonly observed in satellite images during summer and also displayed by the eastward-decreasing CW fraction in this study, could be attributed to Ekman transport caused by the SE monsoon prevailing in this region during summer. Results of this study showed that in the drought season, there was a little or no fraction of CW in the study area. Concentration of dissolved inorganic nitrogen (DIN) showed strong positive relationship with the fraction of CW, suggesting Changjiang as the major source of nitrogen. The mixing curve of DIN indicates the removal of nitrate by biological uptake during the mixing of CW with ambient seawater in the study area.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.24 no.3
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• pp.375-388
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• 2019

Accurate evaluation of sea-to-air $CO_2$ flux and its variability is crucial information to the understanding of global carbon cycle and the prediction of atmospheric $CO_2$ concentration. $fCO_2$ observations are sparse in space and time in the East Sea. In this study, we derived high resolution time series of surface $fCO_2$ values in the southwest East Sea, by feeding sea surface temperature (SST), salinity (SSS), chlorophyll-a (CHL), and mixed layer depth (MLD) values, from either satellite-observations or numerical model outputs, to three machine learning models. The root mean square error of the best performing model, a Random Forest (RF) model, was $7.1{\mu}atm$. Important parameters in predicting $fCO_2$ in the RF model were SST and SSS along with time information; CHL and MLD were much less important than the other parameters. The net $CO_2$ flux in the southwest East Sea, calculated from the $fCO_2$ predicted by the RF model, was $-0.76{\pm}1.15mol\;m^{-2}yr^{-1}$, close to the lower bound of the previous estimates in the range of $-0.66{\sim}-2.47mol\;m^{-2}yr^{-1}$. The time series of $fCO_2$ predicted by the RF model showed a significant variation even in a short time interval of a week. For accurate evaluation of the $CO_2$ flux in the Ulleung Basin, it is necessary to conduct high resolution in situ observations in spring when $fCO_2$ changes rapidly.

• Atmosphere
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• v.31 no.2
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• pp.229-240
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• 2021

The Global Ocean Data Assimilation and Prediction System (GODAPS) in operation at the KMA (Korea Meteorological Administration) is introduced. GODAPS consists of ocean model, ice model, and 3-d variational ocean data assimilation system. GODAPS assimilates conventional and satellite observations for sea surface temperature and height, observations of sea-ice concentration, as well as temperature and salinity profiles for the ocean using a 24-hour data assimilation window. It finally produces ocean analysis fields with a resolution of 0.25 ORCA (tripolar) grid and 75-layer in depth. This analysis is used for providing a boundary condition for the atmospheric model of the KMA Global Seasonal Forecasting System version 5 (GloSea5) in addition to monitoring on the global ocean and ice. For the purpose of evaluating the quality of ocean analysis produced by GODAPS, a one-year data assimilation experiment was performed. Assimilation of global observing system in GODAPS results in producing improved analysis and forecast fields with reduced error in terms of RMSE of innovation and analysis increment. In addition, comparison with an unassimilated experiment shows a mostly positive impact, especially over the region with large oceanic variability.