Volume 41 Issue 5
May  2022
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Article Contents
Yanjie Cheng, Youmin Tang, Tongwen Wu, Xiaoge Xin, Xiangwen Liu, Jianglong Li, Xiaoyun Liang, Qiaoping Li, Junchen Yao, Jinghui Yan. Investigating the ENSO prediction skills of the Beijing Climate Center climate prediction system version 2[J]. Acta Oceanologica Sinica, 2022, 41(5): 99-109. doi: 10.1007/s13131-021-1951-7
Citation: Yanjie Cheng, Youmin Tang, Tongwen Wu, Xiaoge Xin, Xiangwen Liu, Jianglong Li, Xiaoyun Liang, Qiaoping Li, Junchen Yao, Jinghui Yan. Investigating the ENSO prediction skills of the Beijing Climate Center climate prediction system version 2[J]. Acta Oceanologica Sinica, 2022, 41(5): 99-109. doi: 10.1007/s13131-021-1951-7

Investigating the ENSO prediction skills of the Beijing Climate Center climate prediction system version 2

doi: 10.1007/s13131-021-1951-7
Funds:  The National Key Research and Development Program under contract No. 2017YFA0604200; the National Program on Global Change and Air-Sea Interaction under contract No. GASI-IPOVAI-06; the National Natural Science Foundation of China under contract No. 41530961.
More Information
  • Corresponding author: E-mail: ytang@unbc.ca
  • Received Date: 2021-07-01
  • Accepted Date: 2021-08-10
  • Available Online: 2022-03-31
  • Publish Date: 2022-05-31
  • The El Niño-Southern Oscillation (ENSO) ensemble prediction skills of the Beijing Climate Center (BCC) climate prediction system version 2 (BCC-CPS2) are examined for the period from 1991 to 2018. The upper-limit ENSO predictability of this system is quantified by measuring its “potential” predictability using information-based metrics, whereas the actual prediction skill is evaluated using deterministic and probabilistic skill measures. Results show that: (1) In general, the current operational BCC model achieves an effective 10-month lead predictability for ENSO. Moreover, prediction skills are up to 10–11 months for the warm and cold ENSO phases, while the normal phase has a prediction skill of just 6 months. (2) Similar to previous results of the intermediate coupled models, the relative entropy (RE) with a dominating ENSO signal component can more effectively quantify correlation-based prediction skills compared to the predictive information (PI) and the predictive power (PP). (3) An evaluation of the signal-dependent feature of the prediction skill scores suggests the relationship between the “Spring predictability barrier (SPB)” of ENSO prediction and the weak ENSO signal phase during boreal spring and early summer.
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