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Korean Meteorological Society (한국기상학회)
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Prediction Skill of Intraseasonal Monthly Temperature and Precipitation Variations for APCC Multi-Models

APCC 다중 모형 자료 기반 계절 내 월 기온 및 강수 변동 예측성

  • Song, Chan-Yeong (Division of Earth Environmental System, Pusan National University) ;
  • Ahn, Joong-Bae (Division of Earth Environmental System, Pusan National University)
  • 송찬영 (부산대학교 지구환경시스템학부) ;
  • 안중배 (부산대학교 지구환경시스템학부)
  • Received : 2020.08.27
  • Accepted : 2020.11.02
  • Published : 2020.12.31
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Abstract

In this study, we investigate the predictability of intraseasonal monthly temperature and precipitation variations using hindcast datasets from eight global circulation models participating in the operational multi-model ensemble (MME) seasonal prediction system of the Asia-Pacific Economic Cooperation Climate Center for the 1983~2010 period. These intraseasonal monthly variations are defined by categorical deterministic analysis. The monthly temperature and precipitation are categorized into above normal (AN), near normal (NN), and below normal (BN) based on the σ-value ± 0.43 after standardization. The nine patterns of intraseasonal monthly variation are defined by considering the changing pattern of the monthly categories for the three consecutive months. A deterministic and a probabilistic analysis are used to define intraseasonal monthly variation for the multi-model consisting of numerous ensemble members. The results show that a pattern (pattern 7), which has the same monthly categories in three consecutive months, is the most frequently occurring pattern in observation regardless of the seasons and variables. Meanwhile, the patterns (e.g., patterns 8 and 9) that have consistently increasing or decreasing trends in three consecutive months, such as BN-NN-AN or AN-NN-BN, occur rarely in observation. The MME and eight individual models generally capture pattern 7 well but rarely capture patterns 8 and 9.

Keywords

Acknowledgement

이 논문은 부산대학교 기본연구지원사업(2년)에 의하여 연구되었음.

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