Verification of Seasonal Prediction by the Upgraded China Multi-Model Ensemble Prediction System (CMMEv2.0)

• Published in: Journal of Meteorological Research 2024-10, Vol.38 (5), p.880-900
• Main Authors: Wu, Jie, Ren, Hong-Li, Wan, Jianghua, Liu, Jingpeng, Zuo, Jinqing, Liu, Changzheng, Liu, Ying, Nie, Yu, Zhao, Chongbo, Guo, Li, Lu, Bo, Chen, Lijuan, Bao, Qing, Su, Jingzhi, Wang, Lin, Luo, Jing-Jia, Jia, Xiaolong, Chao, Qingchen
• Format: Article
• Language: English
• Subjects: Atmospheric Protection/Air Quality Control/Air Pollution; Atmospheric Sciences; Earth and Environmental Science; Earth Sciences; Geophysics and Environmental Physics; Meteorology; Original Paper
• ISSN: 2095-6037; 2198-0934
• DOI: 10.1007/s13351-024-4001-5

Based on a combination of six Chinese climate models and three international operational models, the China multi-model ensemble (CMME) prediction system has been upgraded into its version 2 (CMMEv2.0) at the National Climate Centre (NCC) of the China Meteorological Administration (CMA) by including new model members and expanding prediction products. A comprehensive assessment of the performance of the upgraded CMME during its hindcast (1993–2016) and real-time prediction (2021–present) periods is conducted in this study. The results demonstrate that CMMEv2.0 outperforms all the individual models by capturing more realistic equatorial sea surface temperature (SST) variability. It exhibits better prediction skills for precipitation and 2-m temperature anomalies, and the improvements in prediction skill of CMMEv2.0 are significant over East Asia. The superiority of CMMEv2.0 can be attributed to its better projection of El Niño–Southern Oscillation (ENSO; with the temporal correlation coefficient score for Niño3.4 index reaching 0.87 at 6-month lead) and ENSO-related teleconnections. As for the real-time prediction in recent three years, CMMEv2.0 has also yielded relatively stable skills; it successfully predicted the primary rainbelt over northern China in summers of 2021–2023 and the warm conditions in winters of 2022/2023. Beyond that, ensemble sampling experiments indicate that the CMMEv2.0 skills become saturated after the ensemble model number increased to 5–6, indicating that selection of only an optimal subgroup of ensemble models could benefit the prediction performance, especially over the extratropics, yet the underlying reasons await future investigation.