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

Yanjie Cheng Youmin Tang Tongwen Wu Xiaoge Xin Xiangwen Liu Jianglong Li Xiaoyun Liang Qiaoping Li Junchen Yao Jinghui Yan

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. 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. doi: 10.1007/s13131-021-1951-7

doi: 10.1007/s13131-021-1951-7

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

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.
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  • Figure  1.  The correlation skill, RMSE and ensemble spread (SPREAD) of the BCC-CPS2 model as function of lead month.

    Figure  2.  The probabilistic skill measures of the BCC-CPS2 model at three ENSO phases as a function of lead month. BSS (a), REL (b), and RES (c).

    Figure  3.  Ensemble-base potential predication skill metrics of the BCC-CPS2 model, |EM| (a), ES (b), and ER (c).

    Figure  4.  Information-based potential prediction skill metrics of the BCC-CPS2 model. RE (a), PI (b), and PP (c) as a function of lead time and initial condition.

    Figure  5.  Seasonal dependent characteristics of prediction skill in the BCC-CPS2 model. Actual prediction skill: Correlation and RMSE (upper two panels, a−d) as a function of starting time vs lead time (left) or target time vs lead time (right). As comparisons, the seasonal variations of the correlation and the RMSE for the Zebiak-Cane (ZC) model ensemble hindcasts are given in the lower two panels (e−h).

    Figure  6.  Seasonal dependent characteristics of Prediction skill in the BCC-CPS2 model. Potential prediction skill ensemble mean (|EM|), ensemble spread (ES), and Relative entropy (RE)

    Figure  7.  EM (b), ES (c), and RE (d) as a function of the background ENSO phase, and the bar curves which indicate the Ni$\tilde{{\rm{n}}} $o3.4 index of the composite background ENSO cycle (a).

    Figure  8.  EM (b), ES (c), and RE (d) as a function of the background ENSO phase from the Zebiak-Cane model hindcast data, and the bar curves which indicate the Ni$\tilde{{\rm{n}}}$o3.4 index of the composite background ENSO cycle (a).

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出版历程
  • 收稿日期:  2021-07-01
  • 录用日期:  2021-08-10
  • 网络出版日期:  2022-03-31

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