• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1047-1056
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• 2022

Sea ice, frozen sea water, in the Artic is a primary indicator of global warming. Due to its importance to the climate system, shipping-route navigation, and fisheries, Arctic sea ice prediction has gained increased attention in various disciplines. Recent advances in artificial intelligence (AI), motivated by a desire to develop more autonomous and efficient future predictions, have led to the development of new sea ice prediction models as alternatives to conventional numerical and statistical prediction models. This study aims to evaluate the performance of the two-stream convolutional long-and short-term memory (TS-ConvLSTM) AI model, which is designed for learning both global and local characteristics of the Arctic sea ice changes, for the minimum September Arctic sea ice from 2001 to 2021, and to show the possibility for an operational prediction system. Although the TS-ConvLSTM model generally increased the prediction performance as training data increased, predictability for the marginal ice zone, 5-50% concentration, showed a negative trend due to increasing first-year sea ice and warming. Additionally, a comparison of sea ice extent predicted by the TS-ConvLSTM with the median Sea Ice Outlooks (SIOs) submitted to the Sea Ice Prediction Network has been carried out. Unlike the TS-ConvLSTM, the median SIOs did not show notable improvements as time passed (i.e., the amount of training data increased). Although the TS-ConvLSTM model has shown the potential for the operational sea ice prediction system, learning more spatio-temporal patterns in the difficult-to-predict natural environment for the robust prediction system should be considered in future work.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2013.10a
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• pp.69-70
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• 2013

IPCC (International Panel on Climate Change) reported that the arctic sea-ice extent has been decreased by 2.7% per decades since satellite observations in 1978. The decreased sea-ice extent has gained an international attention due to its economical benefits from the NSR (Northen Sea Route). The NRS - not a clearly defined single route, but a number of alternative routes across the top of Russiahas a 37 % reduction in sailing distance, comparing to the SSR (Southen Sea Route) passing thourgh the Suez Canal. Sailing days are consequently reduced from 30 days to 20 days. Also, it is estimated that the Northen Sea has 20 to 25% of world's oil resources and occupies 40% of the world's fishery production. As Republic of Korea was admitted as an observer to the Arctic Council on May 15, 2013, there has been increasing needs to explore new route in the Northen Sea. In this situation, Korea Institute of Ocean Science & Technology (KIOST) is preparing a plan for the development of Arctic-circle Ocean Environmental Information System to support the ice navigation and resource exploration in the Arctic. We will introduce a conceptual design of a satellite-based ice navigation supporting system for the northern sea route.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1273-1282
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• 2018

Recently, active research on the Arctic Ocean has been conducted due to the influence of global warming and new Arctic ship route. Although previous studies already calculated quantitative extent of sea ice using passive microwave radiometers, melting at the edge of sea ice and surface roughness were hardly considered due to low spatial resolution. Since Sentienl-1A/B data in Extended Wide (EW) mode are being distributed as free of charge and bulk data for Arctic sea can be generated during a short period, the entire Arctic sea ice data can be covered in high spatial resolution by mosaicking bulk data. However, Sentinel-1A/B data in EW mode, especially in HV polarization, needs significant radiometric correction for further classification. Thus, in this study, we developed algorithms that can correct thermal noise and scalloping effects, and confirmed that Arctic sea ice and open-water were well classified using the corrected dual-polarization SAR data.

• The Korean Journal of Quaternary Research
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• v.32 no.1_2
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• pp.30-40
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• 2018

In response to the increase in anthropogenic greenhouse gases, the Arctic temperature is increasing rapidly by 2-3 times other regions. This larger Arctic warming than lower latitudes is called 'Arctic Amplification'(Overland et al., 2017; Goose et al., 2018). Associated with the Arctic Amplification, the Arctic sea ice is declining rapidly and Greenland ice sheet is melting rapidly, especially around the coastal margins (State of Climate, 2018). However, Antarctic climate change appears to be different from the Arctic. In the western part of Antarctica, surface temperature is rising rapidly with large sea and land ice melting, but in the eastern part, there is little temperature change with slight increase in sea ice extent. The contrasting east-west temperature response is illustrated by the deepening of the Amundsen Sea Low whose upstream brings warm maritime air to the Antarctic peninsula and Amundsen-Bellingshausen Seas, but downstream air provides cold air to the Ross Sea, increasing sea ice. Besides, the increase in Southern Annular Mode (SAM) phase due to stratospheric ozone reduction enhances westerly winds, pushing sea ice northward by Ekman divergence and cooling east Antarctica. In this study, we review the recent Antarctic climate change and its possible causes.

• Atmosphere
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• v.28 no.2
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• pp.153-162
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• 2018

The statistical prediction model for wintertime surface air temperature, that is based on snow cover extent and Arctic sea ice concentration, is updated by considering $El-Ni{\tilde{n}}o$ Southern Oscillation (ENSO) and Quasi-Biennial Oscillation (QBO). These additional factors, representing leading modes of interannual variability in the troposphere and stratosphere, enhance the seasonal prediction over the Northern Hemispheric surface air temperature, even though their impacts are dependent on the predicted month and region. In particular, the prediction of Korean surface air temperature in midwinter is substantially improved. In December, ENSO improved about 10% of prediction skill compared without it. In January, ENSO and QBO jointly helped to enhance prediction skill up to 36%. These results suggest that wintertime surface air temperature in Korea can be better predicted by considering not only high-latitude surface conditions (i.e., Eurasian snow cover extent and Arctic sea ice concentration) but also equatorial sea surface temperature and stratospheric circulation.

• Korean Journal of Remote Sensing
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• v.33 no.3
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• pp.333-338
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• 2017

We compared sea ice concentration(SIC) and sea ice extent(SIE) using EUMETSAT Ocean and Sea Ice Satellite Application Facilities(OSI SAF) and NASA Team(NT) sea ice algorithm in the Arctic during 1980-2010 to investigate the difference between sea ice data applied different algorithms. SIC and SIE of the two data showed different consistency by season and by sea area. Seasonally, SIC of OSI SAF was 0.85 % overall, 0.48 % in spring, 0.97 % in summer, 1.38 % in autumn and 0.66 % in winter higher than NT SIC. By sea area, OSI SAF SIC was 2.7 %, SIE was $198,000km^2$ higher than NT in Arctic Ocean, but in Lincoln Sea, OSI SAF SIC was 2.3 %, SIE was $20,000km^2$ lower than NT.

• Environmental and Resource Economics Review
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• v.25 no.2
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• pp.321-349
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• 2016

According to the recent observation by NOAA(US National Oceanic and Atmospheric Administration), 2015 is the warmest year based on global average temperature since 1880. The air temperatures in the Arctic have been rising at almost twice the global average and the extent and thickness of sea ice in the Arctic have declined. And the warming process in the Arctic is accelerating rapidly. These impacts of drastic change in sea ice caused by climate change in the Arctic threaten the eco-system service and biodiversity in the Arctic. This study intends to estimate the economic value on changes in eco-system services and biodiversity of the Arctic caused by climate change. The result of the valuation indicates that the total benefit from improvement of ecosystem in the Arctic ranges from 318.6 billion won to 715.9 billion won per annum. Replication scenarios can be explored into two broad categories in future studies: scenarios in consideration of conflicts of different stakeholders and scenarios based on wider or narrower definition of biodiversity in the Arctic.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2014.06a
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• pp.8-10
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• 2014

정부의 북극항로 시험운항이 시행된 2013년 NSR횡단 선박 척수는 전년도에 비해 54%가 늘어난 71척에 이르렀으나 실제 화물수송량은 당초 예상량인 150만톤에도 못 미치는 1,355,897톤으로 전년도에 비해 7.5% 증가에 그치고 말았다. 이는 2013년 하절기 북극해의 얼음이 2012년도에 비해 훨씬 적게 녹았기 때문으로 분석되고 있다. 그러나 지구온난화로 인한 전체적인 해빙 크기의 감소추세는 큰 틀에서 벗어나지 않고 있으므로 NSR의 선점을 위해서는 화물의 경제성 평가와 더불어 고효율선의 운항에 대비할 필요가 있다.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1177-1186
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• 2021

As the Arctic melt ponds play an important role in determining the interannual variation of the sea ice extent and changes in the Arctic environment, it is crucial to monitor the Arctic melt ponds with high accuracy. Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), which is the NASA's latest altimeter satellite based on the green laser (532 nm), observes the global surface elevation. When compared to the CryoSat-2 altimetry satellite whose along-track resolution is 250 m, ICESat-2 is highly expected to provide much more detailed information about Arctic melt ponds thanks to its high along-track resolution of 70 cm. The basic products of ICESat-2 are the surface height and the number of reflected photons. To aggregate the neighboring information of a specific ICESat-2 photon, the segments of photons with 10 m length were used. The standard deviation of the height and the total number of photons were calculated for each segment. As the melt ponds have the smoother surface than the sea ice, the lower variation of the height over melt ponds can make the melt ponds distinguished from the sea ice. When the melt ponds were extracted, the number of photons per segment was used to classify the melt ponds covered with open-water and specular ice. As photons are much more absorbed in the water-covered melt pondsthan the melt ponds with the specular ice, the number of photons persegment can distinguish the water- and ice-covered ponds. As a result, the suggested melt pond detection method was able to classify the sea ice, water-covered melt ponds, and ice-covered melt ponds. A qualitative analysis was conducted using the Sentinel-2 optical imagery. The suggested method successfully classified the water- and ice-covered ponds which were difficult to distinguish with Sentinel-2 optical images. Lastly, the pros and cons of the melt pond detection using satellite altimetry and optical images were discussed.