• Korean Journal of Remote Sensing
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• v.34 no.3
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• pp.519-533
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• 2018

The sensible heat flux (SHF)and latent heat flux (LHF) over Korean Peninsula ocean during recent 4 years were calculated using Coupled Ocean-Atmosphere Response Experiment (COARE) 3.5 bulk algorithm and satellite-based atmospheric-ocean variables. Among the four input variables (10-m wind speed; U, sea surface temperature; $T_s$, air temperature; $T_a$, and air humidity; $Q_a$) required for heat flux calculation, Ta and $Q_a$, which are not observed directly by satellites, were estimated from empirical relations developed using satellite-based columnar atmospheric water vapor (W) and $T_s$. The estimated satellite-based $T_a$ and $Q_a$ show high correlation coefficients above 0.96 with the buoy observations. The temporal and spatial variability of monthly ocean heat fluxes were analyzed for the Korean Peninsula ocean. The SHF showed low values of $20W/m^2$ over the entire areas from March to August. Particularly, in July, SHF from the atmosphere to the ocean, which is less than $0W/m^2$, has been shown in some areas. The SHF gradually increased from September and reached the maximum value in December. Similarly, The LHF showed low values of $40W/m^2$ from April to July, but it increased rapidly from autumn and was highest in December. The analysis of monthly characteristics of the meteorological variables affecting the heat fluxes revealed that the variation in differences of temperature and humidity between air and sea modulate the SHF and LHF, respectively. In addition, as the sensitivity of SHF and LHF to U increase in winter, it contributed to the highest values of ocean heat fluxes in this season.

• Korean Journal of Environmental Biology
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• v.40 no.4
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• pp.464-476
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• 2022

In recent years, macroalgal bloom occurs frequently in coastal oceans worldwide. It might be attributed to accelerating climate change. "Green tide" events caused by proliferation of green macroalgae (Ulva spp.) not only damage the local economy, but also harm coastal environments. These nuisance events have become common across several coastal regions of continents. In Korea, green tide incidences are readily seen throughout the year along the coastlines of Jeju Island, particularly the northeastern coast, since the 2000s. Ulva species are notorious to be difficult for morphology-based species identification due to their high degrees of phenotypic plasticity. In this study, to investigate temporal variation in Ulva community structure on Jeju Island between 2015 and 2020, chloroplast barcode tufA gene was sequenced and phylogenetically analyzed for 152 specimens from 24 sites. We found that Ulva ohnoi and Ulva pertusa known to be originated from subtropical regions were the most predominant all year round, suggesting that these two species contributed the most to local green tides in this region. While U. pertusa was relatively stable in frequency during 2015 to 2020, U. ohnoi increased 16% in frequency in 2020 (36.84%), which might be associated with rising sea surface temperature from which U. ohnoi could benefit. Two species (Ulva flexuosa, Ulva procera) of origins of Europe should be continuously monitored. The findings of this study provide valuable information and molecular genetic data of genus Ulva occurring in southern coasts of Korea, which will help mitigate negative influences of green tide events on Korea coast.

• Journal of Korea Water Resources Association
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• v.44 no.5
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• pp.339-350
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• 2011

This study developed a climate informed Bayesian nonstationary frequency model which allows us to forecast seasonal summer rainfall at Nakdong River. We constructed a 37-year summer rainfall data set from 10 weather stations within Nakdong river basin, and two climate indices from sea surface temperature (SST) and outgoing longwave radiation (OLR) were derived through correlation analysis. The selected SST and OLR have been widely acknowledged as a climate driver for summer rainfall. The developed model was applied first to the 2010-year summer rainfall (888.1 mm) in order to assure ourself. We demonstrated model performance by comparing posterior distributions. It was confirmed that the proposed model is able to produce a reasonable forecast. The forecasted value is about 858.2 mm, and the difference between forecast and observation is about 30 mm. As the second case study, 2011-year summer rainfall forecast was made using an observed winter SSTs and an assumed 50% value of OLRs. The forecasted value is 967.7 mm and associated exceedance probability over average summer rainfall 680 mm is 92.9%. In addition, 50-year return period for summer rainfall was projected through the nonstationary frequency model. An exceedance probability over 1,400 mm corresponding to the 50-year return level is about 73.7%.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.21 no.1
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• pp.1-10
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• 2016

Currently available surface seawater partial pressure carbon dioxide ($pCO_2$) data sets in the East Sea are not enough to quantify statistically the carbon dioxide flux through the air-sea interface. To complement the scarcity of the $pCO_2$ measurements, we construct a neural network (NN) model based on satellite data to map $pCO_2$ for the areas, which were not observed. The NN model is constructed for the Ulleung Basin, where $pCO_2$ data are best available, to map and estimate the variability of $pCO_2$ based on in situ $pCO_2$ for the years from 2003 to 2012, and the sea surface temperature (SST) and chlorophyll data from the MODIS (Moderate-resolution Imaging Spectroradiometer) sensor of the Aqua satellite along with geographic information. The NN model was trained to achieve higher than 95% of a correlation between in situ and predicted $pCO_2$ values. The RMSE (root mean square error) of the NN model output was $19.2{\mu}atm$ and much less than the variability of in situ $pCO_2$. The variability of $pCO_2$ with respect to SST and chlorophyll shows a strong negative correlation with SST than chlorophyll. As SST decreases the variability of $pCO_2$ increases. When SST is lower than $15^{\circ}C$, $pCO_2$ variability is clearly affected by both SST and chlorophyll. In contrast when SST is higher than $15^{\circ}C$, the variability of $pCO_2$ is less sensitive to changes in SST and chlorophyll. The mean rate of the annual $pCO_2$ increase estimated by the NN model output in the Ulleung Basin is $0.8{\mu}atm\;yr^{-1}$ from 2003 to 2014. As NN model can successfully map $pCO_2$ data for the whole study area with a higher resolution and less RMSE compared to the previous studies, the NN model can be a potentially useful tool for the understanding of the carbon cycle in the East Sea, where accessibility is limited by the international affairs.