大气科学学报2026，Vol.49Issue(3)：459-471,13.DOI:10.13878/j.cnki.dqkxxb.20250107002
CAMS-CSM次季节预报系统的表面气温高技巧预报窗口分析
Analysis of high forecast skill windows for surface air temperature in the CAMS-CSM sub-seasonal forecast system
摘要
Abstract
Using hindcast experiments from the Chinese Academy of Meteorological Sciences Climate System Model(CAMS-CSM)subseasonal-to-seasonal(S2S)forecasting system,this study evaluates the forecast skill of surface air temperature(SAT)over global land regions during 2000-2020.Subseasonal forecasting,which bridges the gap between weather prediction and seasonal climate outlooks,is increasingly important for early warning of extreme events such as heatwaves,which have substantial societal impacts.However,S2S prediction remains challenging because of limited predictability arising from atmospheric chaos and interactions with slowly varying boundary forcings such as sea surface temperatures(SST). The CAMS-CSM S2S forecasting system,with a horizontal resolution of 1°×1°,provides hindcasts issued six times per month(on the 1st,6th,11th,16th,21st,and 26th)with an 8-member ensemble.For comparison,multi-model hindcast data from the S2S database,including 12 operational models(e.g.,ECMWF),were ana-lyzed,and ERA-5 reanalysis data were used as observations.Global land areas were divided into eight regions(A-sia,East Asia,Africa,Europe,Australia,North America,South America,and global land as a whole)to assess re-gional differences in forecast performance. Forecast skill was evaluated using the pattern correlation coefficient(PCC)of the third week(days 15th—21st)mean SAT,representing subseasonal predictability beyond the dominant influence of initial atmospheric conditions.To reduce sampling variability associated with the forecast frequency,a 7-point running mean was ap-plied to the CAMS-CSM results.The CAMS-CSM system exhibits regionally varying skill,with PCC values gen-erally ranging from 0.11 to 0.18.The global land mean PCC reaches 0.18,while Europe shows the lowest skill(PCC＜0.12),likely due in part to limited spatial coverage.Relatively higher skill is found over East Asia and North America.Compared with other models,ECMWF generally demonstrates superior performance,while CAMS-CSM shows intermediate skill,outperforming some systems(e.g.,BoM)over global land and exhibiting competitive performance over East Asia. A key feature identified in this study is the presence of"forecast skill windows",defined as intermittent peri-ods of enhanced prediction skill.These windows were objectively identified using region-specific PCC thresholds based on the 25th percentile of the skill distribution,periods with seven consecutive forecasts exceeding this threshold were classified as high-skill windows.This approach ensures that skill windows account for approximate-ly 10%—20%of the total time series.For CAMS-CSM S2S,threshold values range from 0.24(Africa)to 0.28(East Asia and Australia).Globally,20 skill windows were identified,totaling 1 129 days(approximately 15%of the full record). Seasonal analysis reveals that forecast skill windows occur more frequently during boreal winter in most re-gions.For example,winter windows account for more than 700 days in East Asia(62%of total window dura-tion),whereas Australia,skill windows are more prevalent during boreal summer(315 days;31%),consistent with its Southern Hemisphere location.These seasonal differences suggest modulation by large-scale circulation a-nomalies and more stable teleconnection patterns during winter. Further analysis indicates a clear relationship between skill windows and ENSO phases.During El Niño e-vents,the frequency of skill windows increases substantially over North America in winter and spring(January-April),exceeding 50%.In contrast,during La Niña events,Africa exhibits higher skill-window frequencies(ex-ceeding 30%)compared with other models.These results highlight the role of ENSO-related SST anomalies in en-hancing predictability over specific regions. In summary,the CAMS-CSM S2S system demonstrates meaningful subseasonal forecast skill for SAT,char-acterized by identifiable high-skill windows that vary seasonally and are modulated by ENSO.These windows pro-vide practical opportunities for targeted forecast applications.Nevertheless,the dynamical mechanisms underlying their occurrence remain complex and warrant further investigation,particularly with respect to large-scale atmos-pheric oscillations and land-atmosphere coupling processes.关键词
CAMS-CSM S2S预测系统/表面气温/预报技巧窗口/ENSOKey words
CAMS-CSM S2S forecasting system/surface air temperature/forecast skill windows/ENSO引用本文复制引用
刘晓蕾,苏京志,彭一豪,刘欣莉..CAMS-CSM次季节预报系统的表面气温高技巧预报窗口分析[J].大气科学学报,2026,49(3):459-471,13.基金项目
国家重点研发计划项目(2022YFC3004203) （2022YFC3004203）
中国气象科学研究院科技发展基金项目(2024KJ013) （2024KJ013）
