대기 (Atmosphere)

  • 제28권2호
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  • Pages.153-162
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  • 2018
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  • 1598-3560(pISSN)
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  • 2288-3266(eISSN)
한국기상학회 (Korean Meteorological Society)
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한반도 겨울철 기온의 월별 통계 예측 모형 구축 및 검증

Development and Evaluation of Statistical Prediction Model of Monthly-Mean Winter Surface Air Temperature in Korea

  • 한보름 (서울대학교 지구환경과학부) ;
  • 임유나 (서울대학교 지구환경과학부) ;
  • 김혜진 (서울대학교 지구환경과학부) ;
  • 손석우 (서울대학교 지구환경과학부)
  • Han, Bo-Reum (School of Earth and Environmental Sciences, Seoul National University) ;
  • Lim, Yuna (School of Earth and Environmental Sciences, Seoul National University) ;
  • Kim, Hye-Jin (School of Earth and Environmental Sciences, Seoul National University) ;
  • Son, Seok-Woo (School of Earth and Environmental Sciences, Seoul National University)
  • 투고 : 2018.03.07
  • 심사 : 2018.05.29
  • 발행 : 2018.06.30
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초록

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.

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참고문헌

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