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Prediction Skill for East Asian Summer Monsoon Indices in a KMA Global Seasonal Forecasting System (GloSea5)

기상청 기후예측시스템(GloSea5)의 여름철 동아시아 몬순 지수 예측 성능 평가

  • Lee, So-Jeong (Operational Systems Development Department, National Institute of Meteorological Sciences) ;
  • Hyun, Yu-Kyung (Operational Systems Development Department, National Institute of Meteorological Sciences) ;
  • Lee, Sang-Min (Operational Systems Development Department, National Institute of Meteorological Sciences) ;
  • Hwang, Seung-On (Operational Systems Development Department, National Institute of Meteorological Sciences) ;
  • Lee, Johan (Operational Systems Development Department, National Institute of Meteorological Sciences) ;
  • Boo, Kyung-On (Operational Systems Development Department, National Institute of Meteorological Sciences)
  • 이소정 (국립기상과학원 현업운영개발부) ;
  • 현유경 (국립기상과학원 현업운영개발부) ;
  • 이상민 (국립기상과학원 현업운영개발부) ;
  • 황승언 (국립기상과학원 현업운영개발부) ;
  • 이조한 (국립기상과학원 현업운영개발부) ;
  • 부경온 (국립기상과학원 현업운영개발부)
  • Received : 2020.07.29
  • Accepted : 2020.09.21
  • Published : 2020.09.30
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Abstract

There are lots of indices that define the intensity of East Asian summer monsoon (EASM) in climate systems. This paper assesses the prediction skill for EASM indices in a Global Seasonal Forecasting System (GloSea5) that is currently operating at KMA. Total 5 different types of EASM indices (WNPMI, EAMI, WYI, GUOI, and SAHI) are selected to investigate how well GloSea5 reproduces them using hindcasts with 12 ensemble members with 1~3 lead months. Each index from GloSea5 is compared to that from ERA-Interim. Hindcast results for the period 1991~2010 show the highest prediction skill for WNPMI which is defined as the difference between the zonal winds at 850 hPa over East China Sea and South China Sea. WYI, defined as the difference between the zonal winds of upper and lower level over the Indian Ocean far from East Asia, is comparatively well captured by GloSea5. Though the prediction skill for EAMI which is defined by using meridional winds over areas of East Asia and Korea directly affected by EASM is comparatively low, it seems that EAMI is useful for predicting the variability of precipitation by EASM over East Asia. The regressed atmospheric fields with EASM index and the correlation with precipitation also show that GloSea5 best predicts the synoptic environment of East Asia for WNPMI among 5 EASM indices. Note that the result in this study is limited to interpret only for GloSea5 since the prediction skill for EASM index depends greatly on climate forecast model systems.

Keywords

References

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