Investigating the ENSO prediction skills of the Beijing Climate Center climate prediction system version 2
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Abstract: 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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Figure 1. The correlation skill, RMSE and ensemble spread (ES) 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. a. BSS, b. REL, and c. RES.
Figure 3. Ensemble-base potential predication skill metrics of the BCC-CPS2 model. a. |EM|, b. ES, and c. ER.
Figure 4. Information-based potential prediction skill metrics of the BCC-CPS2 model. RE (a), PI (b), and PP (c) are 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 skills include 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). -
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