• Proceedings of the Korean Quaternary Association Conference
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• 2007.06a
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• pp.16-16
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• 2007

• Proceedings of the Korea Society of Environmental Biology Conference
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• 2004.05a
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• pp.65-65
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• 2004

• Korean Journal of Environmental Biology
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• v.23 no.3 s.59
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• pp.257-268
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• 2005

Meiofauna community was surveyed in the Arctic Ocean. Sediment samples were collected from six stations in the east Barents Sea and from five stations in Kongsfjorden, Svalbard during summer 2002. Eight taxa of meiofauna were identified in the Barents Sea. Meiofauna abundance ranged from 245 to 906 indiv.10 $cm^{-2}$ (mean 580 indiv.10 $cm^{-2}$) and total biomass varied from 23 and 404 ${\mu}gC10cm^{-2}$ (mean 184 ${\mug}C10cm^{-2}$) in the Barent Sea. Nematode predominated in meiofauna comprising $95.2\%$ of total abundance and $66.4\%$ of biomass. Copepods, polycheats and sarcomastigophonans were also dominant in the study area. Nine taxa of meiofauna were identified in Kongsfiorden. Meiofauna abundance ranged from 103 to 513 indiv.10 $cm^{-2}$ (mean 292 indiv.10 $cm^{-2}$) and biomass varied from 13 and 196{\mu}gC10\;cm^{-2}$ (mean 94{\mu}gC10\;cm^{-2}$) in the Kongsfiorden. Nematodes predominated in meiofauna, comprising $64.1\%$ of abundance and $64.3\%$ biomass. Copepods, polychaets, and kinorhyncha were also dominant in the study area. The meiofauna abundances from both the study areas well match with the previous reports from the various regions including the temperate areas. However the occurred taxa in the present study are only a half comparing with the reports from temperate zone. Meiofauna abundance, biomass, diversity index and species richness were much higher than in the coastal which were strongly affected by fresh water run off in the Barents Sea. The stations affected by chlorophyll had high abundance and biomass, but low diversity index and spices richness in Kongsfiorden.

• Ocean and Polar Research
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• v.25 no.3
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• pp.315-329
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• 2003

During the 1st Korea-Russia Arctic Expedition from 3 to 26 August, 2000 phytoplankton biomass and nutrient concentration were measured in the Barents and Kara Seas. Total of 57 surface samples were collected f3r the phytoplankton related measurements. Chlorophyll a (chi a) concentraitons were measured to investigate the relations between physico-chemical factors and phytoplankton biomass distribution. Chl a values ranged from 0.14 to $2.34mg\;m^{-3}$ (mean of $0.65{\pm}0.42mg\;m^{-3}$) over the surface stations. The elevated values of the chi a concentrations $(1.49{\sim}2.34mg\;m^{-3})$ were found in the southeastern Barents Sea near the Pechora River. Nanoplanktonic $(<20{\mu}m)$ phytoflagellates were the important contributors for the increase of the chi a. The nano-sized phytoflagellates accounted for more than 80% of the total chi a biomass in the study area. Mean chi a concentration in the Barents Sea $(0.72{\pm}0.57 mg\;m^{-3})$ was higher than in the Kan Sea $(0.52{\pm}0.45mg\;m^{-3})$, but there was no big difference between two areas. Surface temperatures and salinities ranged from 4.1 to $11.7^{\circ}C$ (mean of $8.8{\pm}1.9^{\circ}C$) and from 23.8 to 32.5psu (mean of $30.3{\pm}1.9^{\circ}C$ psu), respectively. The physical factors were not highly correlated with phytoplankton distribution. It is speculated that the insignificant correlation between phytoplankton biomass and physical factor was due to the same current which introduced similar water mass with higher water temperature and lower salinity into the study area. The mean values of major nutrients such as ammonia, nitrite, nitrate, phosphate, and silicate were $0.42{\pm}0.31{\mu}M,\;0.10{\pm}0.03{\mu}M,\;1.44{\pm}1.03{\mu}M,\;0.35{\pm}0.12{\mu}M,\;10.99{\pm}3.45{\pm}M$, respectively. The relations between phytoplankton biomass and nutrient concentration were not close, indicating that the surface nutrient concentrations during the study seem to be controlled by other physical factors such as input of fresh water (i.e. dilution effects).

• Ocean and Polar Research
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• v.26 no.4
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• pp.567-579
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• 2004

To investigate the spatial distribution and community structure of heterotrophic protists, we collected water samples at 23 stations of central Barents Sea in August, 2003. This study area was divided into three area with physico-chemical and chi-a distribution characteristics: Area I of warm Atlantic water mass, Area III of cold Arctic water mass and Area II of mixed water mass. Chl-a concentration ranged from 0.18 to $1.04{\mu}g\;l^{-1}$ and was highest in Area I. The nano-sized chi-a accounted fur more than 80% of the total chi-a biomass in this study area. The contribution of nano-sized chi-a to total chi-a was higher in Area I than in Area II. Communities of heterotrophic protists were classified into three groups such as heterotrophic nanoflagellates (HNF), ciliates and heterotrophic dinoflagellates (HDF). During the study periods, carbon biomass of heterotrophic protists range from 11.3 to $38.7{\mu}gC\;l^{-1}$ (average $21.0{\mu}gC\;l^{-1}$), and were highest in Area I and were lowest in Area III. The biomass of ciliates ranged from 4.2 to $19.3{\mu}gC\;l^{-1}$ and contributed 31.5-66.9% (average 48.1%) to the biomass of heterotrophic protists. Ciliates to heterotrophic protists biomass accounted fur more than 50% in Area I. Heterotrophic dinoflagellates biomass ranged from 5.7 to $18.4{\mu}gC\;l^{-1}$ and contributed 27.1 to 56.3% (average 42.8%) of heterotrophic protists. Heterotrophic dinoflakellates to heterotrophic protists biomass accounted fur about 50% in Area III. Heterotrophic nanoflageltate biomass ranged from 0.5 to $3.4{\mu}gC\;l^{-1}$ and contributed 3.2 to 19.6% (average 9.2%) of heterotrophic protists. Heterotrophic nanoflagellates to heterotrophic protists biomass accounted fur more than 10% in Area III. These results indicate that the relative importance and structure of heterotrophic protists may vary according to water mass. Heterotrophic protists and phytoplankton biomass showed strong positive correlation in the study area The results suggest that heterotrophic protists are important consumers of phytoplankton, and protists might play a pivotal role in organic carbon cycling In the pelagic ecosystem of this study area during the study period.

• Ocean and Polar Research
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• v.27 no.3
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• pp.265-276
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• 2005

In order to grasp the structure and dynamics of phytoplankton communities, chlorophyll-a (Chl-a) and cell abundance were measured at 20 stations during the period from August 9 to August 21, 2003 in the southeastern Barents Sea on surface and subsurface chlorophyll maximum depth (SCM). Surface temperatures were varied from minimum $-0.7^{\circ}C(st. 18)$ to maximum $10.4^{\circ}C(st.1)$. Salinities were varied from minimum 29.9 psu(st. 18) to maximum 35.8 psu(st.2). The maximum nutrient(phosphate, nitrate, silicate) concentrations were $0.12{\mu}M,\;0.11{\mu}M,\;7.53{\mu}M$ and minimum concentrations were $0.01{\mu}M,\;0.03{\mu}M,\;1.43{\mu}M$, respectively. On SCM physical environmental factor were almost similar. Chl-a concentrations ranged from 0.23 to $2.13{\mu}g\;chi-a\;l^{-1}$ at SCM. Nano- and pico phytoplankton were the important contributors for increase of the Chl-a. It was about seven times difference between highest concentration to lowest. Phytoplankton communities were composed of diatoms, dinoflagellates, cryptophyceae, silicoflagellate, and prymnesiophyceae showing 37 taxa at surface and 38 taxa at SCM. Picophytoplankton was the most dominant in all stations and all layers, but the second groups were 2 and/or 3 taxa. Phytoplankton abundance ranged from minimum $4.3{\times}10^5\;cells\;l^{-1}$ (st. 20) to maximum $2.4{\times}10^6\;cells\;l^{\-1}$. (st. 17) at surface water. As a result, phytoplankton might be controlled by physical factors such as North Atlantic ocean currents and northern melt water among environmental factors in Barents Set h addition the dominant species were nano- and pico phytoplankton such as Phaeocystis, Cryptomonas and Dinobryon in the study area.

• Journal of the Mineralogical Society of Korea
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• v.30 no.4
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• pp.219-227
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• 2017

Manganese (Mn) nodules in the Arctic Sea have been founded in the Kara Sea and Barents Sea, but mineral and chemical compositions have been rarely investigated. In this study, mineralogical and geochemical characteristics of Mn nodules obtained during the Arctic Expedition ARA07C in northern East Siberian Sea were identified, and then genesis of Mn nodules were estimated by using these characteristics. Main manganese oxide minerals constituting the manganese nodule were buserite, birnessite, and vernadite. The Mn nodules generally represent radiated and massive texture, and the layered texture was developed restrictively. The radiated texture, main feature of the manganese nodule in the East Siberian Sea, is mainly composed of cuspate-globular microstructure. Compared with the Mn nodules in Pacific and Indian Oceans, Mn nodules of the East Siberian Sea are abundant in Mn, but Fe is too scarce. There was no difference in the chemical composition and microstructures between outer and inner part of nodule. Therefore, nodules are most likely to have only one genesis during their growth, and all of nodules indicate the diagenetic in $Mn-Fe-(Cu+Ni+Co){\times}10$ ternary diagram. It is considered that the manganese nodules in the East Siberian Sea are characterized by high Mn contents because manganese contents in the Arctic Ocean were mainly resulted from river or coastal erosion and most of them are trapped in the Arctic Ocean.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1261-1272
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• 2018

It is important to measure the Arctic surface air temperature because it plays a key-role in the exchange of energy between the ocean, sea ice, and the atmosphere. Although in-situ observations provide accurate measurements of air temperature, they are spatially limited to show the distribution of Arctic surface air temperature. In this study, we proposed machine learning-based models to estimate the Arctic surface air temperature in summer based on buoy data and Advanced Microwave Scanning Radiometer 2 (AMSR2)satellite data. Two machine learning approaches-random forest (RF) and support vector machine (SVM)-were used to estimate the air temperature twice a day according to AMSR2 observation time. Both RF and SVM showed $R^2$ of 0.84-0.88 and RMSE of $1.31-1.53^{\circ}C$. The results were compared to the surface air temperature and spatial distribution of the ERA-Interim reanalysis data from the European Center for Medium-Range Weather Forecasts (ECMWF). They tended to underestimate the Barents Sea, the Kara Sea, and the Baffin Bay region where no IABP buoy observations exist. This study showed both possibility and limitations of the empirical estimation of Arctic surface temperature using AMSR2 data.

• Journal of the Korean Geophysical Society
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• v.8 no.4
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• pp.187-194
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• 2005

As a study of Arctic marine survey project, Side-scan sonar survey was carried out in the Pechora Sea belonging to the southeaster part of Barents Sea. The study area is a shallow sea 11 m-16 m deep with recent sediments of rich organic carbon. Side-scan sonar profiles show large-scale marine plant communities 2-3 m wide covering the southeastern area. A lot of lineaments are traced on the seafloor in the central and northern area. The major trends of the lineaments are 220°and 290°(WSW-ENE and WNW-ESE). This trends is thought to be a main path of icebergs. Pockmarks on the seafloor are locally distributed in the area, which are formed by fluid and/or gas discharge. These would be related with petroleum/gas system well developed around the study area. Dut to weak appearances and limited distribution of the pockmarks, more detailed studies are necessary to examine their nature and structure.

• Korean Journal of Remote Sensing
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• v.25 no.5
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• pp.423-434
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• 2009

The Arctic environment is sensitive to change of sea-ice distribution. The increase and decrease of sea ice work to an index of globe warming progress. In order to predict the progress of hereafter earth global warming, continuous monitoring regarding a change of the sea ice area in the Arctic should be performed. The remote sensing based on an artificial satellite is most effective on the North Pole. The sea ice observation using a passive microwave sensor has been continued from 1970's. The determination of sea ice extent and ice type is one of the great successes of the passive microwave imagers. In this paper, to investigate the seasonal and inter-annual variation of sea-ice distribution we used here the sea ice data from July 2002 to May 2009 around the Arctic within $60^{\circ}N$ for the AMSR-E 12.5km sea-ice concentration, a passive microwave sensor. From an early analysis of these data, the arctic sea-ice extent has been steadily decreasing at a rate of about 3.1%, accounting for about $2{\times}10^5\;km^2$, which was calculated for the sea-ice cover reaching its minimum extent at the end of each summer. It is also revealed that this trend corresponds to a decline in the multi-year ice that is affected mainly by summer sea surface and air temperature increases. The extent of younger and thinner (first-year) ice decreased to the 2007 minimum, but rapidly recovered in 2008 and 2009 due to the dramatic loss in 2007. Seasonal variations of the sea-ice extent show significant year-to-year variation in the seasons of January-March in the Barents and Labrador seas and August-October in the region from the East Siberian and Chukchi seas to the North Pole. The spatial distribution of multi-year ice (7-year old) indicates that the perennial ice fraction has rapidly shrunk recently out of the East Siberian, Laptev, and Kara seas to the high region of the Arctic within the last seven years and the Northeast Passage could become open year-round in near future.