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

• Published in: Acta oceanologica Sinica Vol. 41; no. 5; pp. 99 - 109
• Main Authors: Cheng, Yanjie, Tang, Youmin, Wu, Tongwen, Xin, Xiaoge, Liu, Xiangwen, Li, Jianglong, Liang, Xiaoyun, Li, Qiaoping, Yao, Junchen, Yan, Jinghui
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2022; Springer Nature B.V; State Key Laboratory of Severe Weather,China Meteorological Administration,Beijing 100081,China%College of Environmental Science and Engineering,University of Northern British Columbia,Prince George V2N4Z9,Canada; CMA Earth System Modeling and Prediction Centre,China Meteorological Administration(CMA),Beijing 100081,China
• Subjects: Climate; Climate prediction; Climate system; Climatology; Earth and Environmental Science; Earth Sciences; Ecology; El Nino; El Nino phenomena; El Nino-Southern Oscillation event; Engineering Fluid Dynamics; Entropy; Environmental Chemistry; Evaluation; Marine & Freshwater Sciences; Oceanography; Predictions; Skills; Southern Oscillation; Spring; Spring (season); Beijing China; China; BCC-CPS2 climate model; ENSO; ensemble prediction skill; potential predictability measure
• ISSN: 0253-505X; 1869-1099
• DOI: 10.1007/s13131-021-1951-7

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.