Journal of the Korean earth science society (한국지구과학회지)

The Korean Earth Science Society (한국지구과학회)
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Multivariable Integrated Evaluation of GloSea5 Ocean Hindcasting

  • Lee, Hyomee (Division of Science Education & Institute of Fusion Science, Jeonbuk National University) ;
  • Moon, Byung-Kwon (Division of Science Education & Institute of Fusion Science, Jeonbuk National University) ;
  • Kim, Han-Kyoung (Division of Science Education & Institute of Fusion Science, Jeonbuk National University) ;
  • Wie, Jieun (Division of Science Education & Institute of Fusion Science, Jeonbuk National University) ;
  • Park, Hyo Jin (Jeonju Jungang Middle School) ;
  • Chang, Pil-Hun (Operational Systems Development Department, National Institute of Meteorological Sciences) ;
  • Lee, Johan (Operational Systems Development Department, National Institute of Meteorological Sciences) ;
  • Kim, Yoonjae (Korean National Meteorological Satellite Center)
  • Received : 2021.09.04
  • Accepted : 2021.11.28
  • Published : 2021.12.31
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Abstract

Seasonal forecasting has numerous socioeconomic benefits because it can be used for disaster mitigation. Therefore, it is necessary to diagnose and improve the seasonal forecast model. Moreover, the model performance is partly related to the ocean model. This study evaluated the hindcast performance in the upper ocean of the Global Seasonal Forecasting System version 5-Global Couple Configuration 2 (GloSea5-GC2) using a multivariable integrated evaluation method. The normalized potential temperature, salinity, zonal and meridional currents, and sea surface height anomalies were evaluated. Model performance was affected by the target month and was found to be better in the Pacific than in the Atlantic. An increase in lead time led to a decrease in overall model performance, along with decreases in interannual variability, pattern similarity, and root mean square vector deviation. Improving the performance for ocean currents is a more critical than enhancing the performance for other evaluated variables. The tropical Pacific showed the best accuracy in the surface layer, but a spring predictability barrier was present. At the depth of 301 m, the north Pacific and tropical Atlantic exhibited the best and worst accuracies, respectively. These findings provide fundamental evidence for the ocean forecasting performance of GloSea5.

Keywords

Acknowledgement

This work was supported by a National Research Foundation of Korea (NRF) grant funded by the government of Korea (MSIT; No. 2019R1A2C100 8549) and the Korea Meteorological Administration Research and Development Program "Development of Climate Prediction System" under Grant (1365003054). The authors would like to thank Prof. Zhongfeng Xu for generously offering the scripts used to calculate the statistical metrics.

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