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

作　　者：Jie WU Hong-Li REN Jianghua WAN Jingpeng LIU Jinqing ZUO Changzheng LIU Ying LIU Yu NIE Chongbo ZHAO Li GUO Bo LU Lijuan CHEN Qing BAO Jingzhi SU Lin WANG Jing-Jia LUO Xiaolong JIA Qingchen CHAO 

机构地区：[1]CMA Key Laboratory of Climate Prediction Studies,National Climate Centre,China Meteorological Administration(CMA),Beijing 100081 [2]Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters,Nanjing University of Information Science&Technology,Nanjing 210044 [3]State Key Laboratory of Severe Weather and Institute of Tibetan Plateau Meteorology,Chinese Academy of Meteorological Sciences,China Meteorological Administration,Beijing 100081 [4]State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029 [5]CMA Earth System Modeling and Prediction Centre,China Meteorological Administration,Beijing 100081

出　　处：《Journal of Meteorological Research》2024年第5期880-900,共21页气象学报（英文版）

基　　金：Supported by the National Natural Science Foundation of China (U2242206 and 42175052);National Key Research and Development Program of China (2021YFA071800 and 2023YFC3007700);Innovative Development Special Project of China Meteorological Administration (CXFZ2023J002 and CXFZ2023J050);China Meteorological Administration (CMA) Joint Research Project for Meteorological Capacity Improvement (23NLTSZ003);Special Operating Expenses of Scientific Research Institutions for “Key Technology Development of Numerical Forecasting” of Chinese Academy of Meteorological Sciences;CMA Youth Innovation Team(CMA2024QN06)。

摘　　要：Based on a combination of six Chinese climate models and three international operational models,the China multimodel 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.

关 键 词：China multi-model ensemble(CMME)prediction system predictability source El Niño-Southern Oscillation(ENSO) real-time forecast VERIFICATION 

分 类 号：P456.7[天文地球—大气科学及气象学]