• 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.

• 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.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.294-298
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• 2008

Snow cover is a potential water resource for later spring and summer seasons as well as a thermal mirror with high reflectivity causing decreases of surface air temperature during cold winter seasons. In this study, current and future changes in Northern Hemisphere snow extent and their potential linkages with atmospheric circulation are examined. The NOAA AVHRR visible snow extent (1967-2006) data as well as observational (NCEP-DOE 1979-2006) and modeled (GFDL 2.1 2081-2100) pressure and surface air temperature data are used. Analyses of observational data demonstrate that the snow extent in meteorological spring (March to April) and summer (June to August) has significantly decreased since the late 1980s. The offset of snow seasons (the timing of snow melt in spring) have also significantly advanced particularly in Europe, East Asia, and northwestern North America. Analyses of pressure fields reveal that the spatial patterns of the earlier snow melt are associated with changes in atmospheric circulation such as the Arctic Oscillation (AO). In the positive winter AO years, multiple positive pressure departure cores in the upper troposphere (200hPa) are observed over the mid-latitude regions from March to mid-April, while a negative pressure departure core (70hPa) prevails over the Arctic Ocean. The reversed anomaly patterns related to later snow melt occur in negative winter AO years. The comparison between current and future thermal spring onsets suggest that snow melt patterns will intensify with larger greenhouse gas emissions, indicating earlier hydrological spring onset.

• Journal of the Korean Geographical Society
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• v.43 no.1
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• pp.1-19
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• 2008

This study intends to clarify the characteristics and causes of current changes in wintertime precipitation in Korea and to predict the future directions based on surface observational $(1973/04\sim2006/07)$ and modeled (GFDL 2.1) climate data. Analyses of surface observation data demonstrate that without changes in the total amount of precipitation, snowfall in winter (November-April) has reduced by 4.3cm/decade over the $1973\sim2007$ period. Moreover, the frequency and intensity of snowfall have decreased; the duration of snow season has shortened; and the snow-to-rain day ratio (STDR) has decreased. These patterns indicate that the type of wintertime precipitation has changed from snow to rain in recent decades. The snow-to-rain change in winter is associated with the increases of air temperature (AT) over South Korea. Analyses of synoptic charts reveal that the warming pattern is associated with the formation of a positive pressure anomaly core over northeast Asia by a hemispheric positive winter Arctic Oscillation (AO) mode. Moreover, the differentiated warming of AT versus sea surface temperature (SST) under the high pressure anomaly core reduces the air-sea temperature gradient, and subsequently it increases the atmospheric stability above oceans, which is associated with less formation of snow cloud. Comparisons of modeled data between torrent $(1981\sim2000)$ and future $(2081\sim2100)$ periods suggest that the intensified warming with larger anthropogenic greenhouse gas emission in the $21^{st}$ century will amplify the magnitude of these changes. More reduction of snow impossible days as well as more abbreviation of snow seasons is predicted in the $21^{st}$ century.

• Journal of the Korean earth science society
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• v.27 no.3
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• pp.302-312
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• 2006

This study has investigated the effect of Arctic Oscillation (AO) on cold surge through atmospheric circulation and heat source analysis for the past winters from 1979 to 2004. The number of occurrence of cold surge in the negative AO phase is about 14.3% larger than that in the positive AO phase. The number of occurrence of cold surge per a month in the negative (positive) AO phase is about 1.33 (1.05), respectively, indicating that the negative AO phase has about 26.6% larger occurrence than the positive AO phase. It means that the cold surge has occurred frequently in particular months with the negative AO phase. And it also shows that surface temperature in the negative AO phase is about $0.6^{\circ}C$ lower than in positive AO phase. As a result of the analysis for the difference of heat source according to the intensity of AO, it shows that surface air temperature around the Korean peninsula in the negative AO phase is more lower than in positive AO phase by the intensification of cold advection term. However, heat source term cancels out the cooling effect by cold advection term, indicating that it suppresses the decrease in surface air temperature. It results in a small difference of $0.6^{\circ}C$ in surface air temperature between the positive and negative AO phase in spite of the significance of atmospheric circulation change.