• 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 Climate Change Research
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• v.6 no.2
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• pp.95-104
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• 2015

To discuss whether we have credible estimations about historical surface temperature evolution since industrial revolution or not, present study investigates consistencies and differences of averaged surface air temperature since 1900 between the multiple data sources: Hadley Center Climate Research Unit (HadCRU4) surface air temperature data, ECMWF 20 Century Reanalysis data (ERA20CR), and NCEP 20 Century Reanalysis data (NCEP20CR). Averaged surface temperatures are obtained for the global, polar (90S~60S, 60N~0N), midlatitude (60S~30S, 30N~60N), tropical (30S~30N) region, separately. From the analysis, we show that: 1) spatio-temporal inhomogenity and scarcity of HadCRU4 data are not major obstacles in the reliable estimation of global surface air temperature. 2) Globally averaged temperature variability is largely contributed by those of tropical and midlatitude, which occupy more than 70% of earth surface in area. 3) Both data show consistent temperature variability in tropical region. 4) ERA20CR does not capture warm period over Arctic region in early 1900s, which is obvious feature in HadCRU4 data. Discrepancies among datasets suggest that high-level caution is needed especially in the interpretation of large Arctic warming in the early 1900s, which is often regarded as a natural variability in the Arctic region.

• 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.

• Atmosphere
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• v.32 no.4
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• pp.297-306
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• 2022

We examined potential seasonal prediction of the Korean surface temperature using the relationships between the Arctic Sea Ice Area (SIA) in autumn and the temperature in the following July and February at 850 hPa in East Asia (EA). The Surface Air Temperature (SAT) over Korea shows a similar relationship to that for EA. Since 2007, reduction of autumn SIA has been followed by warming in Korea in July. The regional distribution shows strong correlations in the southern and eastern coastal areas of Korea. The correlations in the sea surface temperature shows the maximum values in July around the Korean Peninsula, consistent with the coastal regions in which the maximum correlations in the Korean SAT are seen. In February, the response of the SAT to the SIA is the opposite of that for the July temperature. The autumn sea ice reduction is followed by cooling over Korea in February, although the magnitude is small. Cooling in the Korean Peninsula in February may be related to planetary wave-like features. Examining the autumn Arctic sea ice variation would be helpful for seasonal prediction of the Korean surface temperature, mostly in July and somewhat in February. Particularly in July, the regression line would be useful as supplementary information for seasonal temperature prediction.

• Atmosphere
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• v.31 no.1
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• pp.61-71
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• 2021

The relationship between the Arctic Oscillation (AO) and surface air temperature (SAT) over Korea is re-examined using the long-term observation and reanalysis datasets for the period of December 1958 to February 2020. Over the entire period, Korean SAT is positively correlated with the AO index with a statistically significant correlation coefficient, greater than 0.4, only in the boreal winter. It is found that this correlation is not static but changes on the decadal time scale. While the 15-year moving correlations are as high as 0.6 in 1980s and 1990s, they are smaller than 0.3 in the other decades. It is revealed that this decadal variation is partly due to the AO structure change over the North Pacific. In the period of 1980s-1990s, the AO-related sea level pressure fluctuation is strong and well defined over the western North Pacific and the related temperature advection effectively changes the winter SAT over Korea. In the other periods, the AO-related circulation anomaly is either weak or mostly confined within the central North Pacific. This result suggests that Korean SAT-AO index relationship, which becomes insignificant in recent decades is highly dependent on mean flow change in the North Pacific.

• The Korean Journal of Quaternary Research
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• v.33 no.1_2
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• pp.1-23
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• 2021

In response to the increase in atmospheric carbon dioxide and greenhouse gases, the global mean temperature is rising rapidly. In particular, the warming of the Arctic is two to three times faster than the rest. Associated with the rapid Arctic warming, the sea ice shows decreasing trends in all seasons. The faster Arctic warming is due to ice-albedo feedback by the presence of snow and ice in polar regions, which have higher reflectivity than the ocean, the bare land, or vegetation, higher long-wave heat loss to space than lower latitudes by lower surface temperature in the Arctic than lower latitudes, different stability of atmosphere between the Arctic and lower latitudes, where low stability leads to larger heat losses to atmosphere from surface by larger latent heat fluxes than the Arctic, where high stability, especially in winter, prohibits losing heat to atmosphere, increase in clouds and water vapor in the Arctic atmosphere that subsequently act as green house gases, and finally due to the increase in sensible heat fluxes from low latitudes to the Arctic via lower troposphere. In contrast to the rapid Arctic warming, in midlatitudes, especially in eastern Asia and eastern North America, cold air outbreaks occur more frequently and last longer in recent decades. Two pathways have been suggested to link the Arctic warming to cold air outbreaks over midlatitudes. The first is through troposphere in synoptic-scales by enhancing the Siberian high via a development of Rossby wave trains initiated from the Arctic, especially the Barents-Kara Seas. The second is via stratosphere by activating planetary waves to stratosphere and beyond, that leads to warming in the Arctic stratosphere and increase in geopotential height that subsequently weakens the polar vortex and results in cold air outbreaks in midlatitudes for several months. There exists lags between the Arctic warming and cold events in midlatitudes. Thus, understanding chain reactions from the Arctic warming to midlatitude cooling could help improve a predictability of seasonal winter weather in midlatitudes. This study reviews the results on the Arctic warming and its connection to midlatitudes and examines the trends in surface temperature and the Arctic sea ice.

• Journal of Astronomy and Space Sciences
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• v.33 no.4
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• pp.305-311
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• 2016

The spatial size and variation of Arctic sea ice play an important role in Earth's climate system. These are affected by conditions in the polar atmosphere and Arctic sea temperatures. The Arctic sea ice concentration is calculated from brightness temperature data derived from the Defense Meteorological Satellite program (DMSP) F13 Special Sensor Microwave/Imagers (SSMI) and the DMSP F17 Special Sensor Microwave Imager/Sounder (SSMIS) sensors. Many previous studies point to significant reductions in sea ice and their causes. We investigated the variability of Arctic sea ice using the daily sea ice concentration data from passive microwave observations to identify the sea ice melting regions near the Arctic polar ice cap. We discovered the abnormal melting of the Arctic sea ice near the North Pole during the summer and the winter. This phenomenon is hard to explain only surface air temperature or solar heating as suggested by recent studies. We propose a hypothesis explaining this phenomenon. The heat from the deep sea in Arctic Ocean ridges and/or the hydrothermal vents might be contributing to the melting of Arctic sea ice. This hypothesis could be verified by the observation of warm water column structure below the melting or thinning arctic sea ice through the project such as Coriolis dataset for reanalysis (CORA).

• Korean Journal of Remote Sensing
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• v.33 no.6_1
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• pp.901-915
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• 2017

Recently, global climate change has caused a catastrophic event in the Arctic Ocean, directly and indirectly. The air-sea interaction has caused the significant sea-ice reduction in the Arctic Ocean, and has been accelerating the Arctic warming. Many scientists are worried about the Arctic environment change, suggesting that many of anomalous events will produce direct or indirect biophysical effects on the Arctic. The aim of this study is to understand the inter-annual variability of the Arctic Ocean in wide-view using multi-satellite-derived measurements. Sea surface temperature (SST) and sea ice concentration (SIC) data were obtained from Optimum Interpolation Sea Surface Temperature (OISST) and ECMWF ERA-Interim, respectively. Chlorophyll-a concentration (CHL) was obtained from Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) and Aqua sensor from MODerate resolution Imaging Spectroradiometer (MODIS-Aqua) sensor which has continuously observed since 1998. From 1998 to 2016 summer in the Arctic Ocean which was defined as regions over $60^{\circ}N$ in this study, there were three consequences that CHL increase ($0.15mg\;m^{-3}\;decade^{-1}$), SST warming ($0.43^{\circ}C\;decade^{-1}$) and SIC decrease ($-5.37%\;decade^{-1}$). While SST and SIC highly correlated each other (r = -0.76), a relationship between CHL and SIC was very low ($r={\pm}0.1$) because of data limitations. And a relationship between CHL and SST shows meaningful results ($r={\pm}0.66$) with regional differences.

• The Korean Journal of Quaternary Research
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• v.33 no.1_2
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• pp.37-48
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• 2021

The cold surge is an important extreme weather in East Asia during winter, and is largely affected by behavior of the Siberian high Arctic Oscillation, which represents undulation of large-scale pressure pattern in the Arctic region. Recent studies also revealed that the synoptic low pressure system developing in the eastern boundary of the Asian continent is sensitive to sea surface temperature (SST) and plays an important role in strengthening the cold advection over the Korean Peninsula during cold surges. In this study, we analyzed the Arctic Oscillation affecting the large-scale background of cold surge in East Asia, and the sea surface temperature in the coast of East Asia is examined focusing on its role on synoptic low-inducing cold advection. For the analysis, the days with the bottom 3% of the average daily temperature, measured at five surface stations in Korean Peninsula during 49 years (1969/70-2017/18), were used for the cold surge cases. During the negative phase of the Arctic Oscillation, a strong trough is observed over East Asia, and the inflow of cold air from the polar region is strengthened, which lead to frequent cold surges. In addition, anomalously high SST in the eastern coast of Asia increases sensible and latent heat release from the ocean, therefore, it enlarges the likelihood of synoptic low-inducing extreme cold surges.

• 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.