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Korean Meteorological Society (한국기상학회)
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Comparative Assessment of the Seasonal Prediction Skill of Climate Prediction Systems (GloSea6) Using WMO LC-LRF Verification

WMO LC-LRF 검증 지수를 활용한 기후예측시스템(GloSea6)의 계절예측 성능 비교 평가

  • Yu-Kyung Hyun (Climate Research Department, National Institute of Meteorological Sciences) ;
  • Jinkyung Park (Climate Research Department, National Institute of Meteorological Sciences) ;
  • Hee-Sook Ji (Climate Research Department, National Institute of Meteorological Sciences) ;
  • Johan Lee (Climate Research Department, National Institute of Meteorological Sciences) ;
  • Beomcheol Shin (Climate Research Department, National Institute of Meteorological Sciences) ;
  • Sang-Min Lee (Climate Research Department, National Institute of Meteorological Sciences) ;
  • Hyun-Ju Lee (Climate Services and Research Division, APEC Climate Center) ;
  • Hyung-Jin Kim (Climate Services and Research Division, APEC Climate Center) ;
  • Yeon-Hee Park (Climate Research Department, National Institute of Meteorological Sciences) ;
  • Ji-Yeong Kim (Climate Research Department, National Institute of Meteorological Sciences) ;
  • Kyung-On Boo (Climate Research Department, National Institute of Meteorological Sciences)
  • 현유경 (국립기상과학원 기후연구부) ;
  • 박진경 (국립기상과학원 기후연구부) ;
  • 지희숙 (국립기상과학원 기후연구부) ;
  • 이조한 (국립기상과학원 기후연구부) ;
  • 신범철 (국립기상과학원 기후연구부) ;
  • 이상민 (국립기상과학원 기후연구부) ;
  • 이현주 (APEC 기후센터 기후사업본부) ;
  • 김형진 (APEC 기후센터 기후사업본부) ;
  • 박연희 (국립기상과학원 기후연구부) ;
  • 김지영 (국립기상과학원 기후연구부) ;
  • 부경온 (국립기상과학원 기후연구부)
  • Received : 2024.10.16
  • Accepted : 2024.10.27
  • Published : 2024.11.30
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Abstract

This study aims to assess the performance of climate prediction systems around the world, and understand objective seasonal prediction skill of KMA's GloSea6. Using the 2023 hindcast verification values provided by the WMO Lead Centre for Long-Range Forecast (LC-LRF), we analyzed the skill in the global, East Asia, and European regions. The differences in prediction skill and RMSE between GPC (Global Producing Centers) were very small in this challenging area. Overall, GloSea6 showed the best ACC across variables and periods. Operating this outstanding climate prediction system not only ensures the provision of the best forecasting services but also offers excellent research and development tools. This result also suggests that seasonal forecasting requires different strategies against short- to medium-range forecast to account for climate prediction sources and reduce uncertainties. The skill differences between GloSea6-Seoul and GloSea6-Exeter, especially in high latitude, could be due to differences in snow and soil temperature initialization. Understanding these differences is important for future prediction system development. GPCs that use atmospheric only models instead of coupled, showed the limitations for seasonal predictions. Systems developed a relatively long time ago tended to perform low, suggesting that continuous improvements and upgrades are important. Among variables, SST showed the best prediction skill with the lowest RMSE. Temperature and pressure variables showed practical skill levels, around 0.5. We aimed to quantitatively assess the skills of climate prediction systems, and this assessment can guide the improvement and development of future systems and serve as a reference.

Keywords

Acknowledgement

이 연구는 기상청 국립기상과학원 「기후예측 현업 시스템 개발」(KMA2018-00322)과 「아·태 기후정보서비스 및 연구개발 사업」(KMA2013-07510)의 지원으로 수행되었습니다.

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