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Intercomparison of multi-model ensemble-processing strategies within a consistent framework for climate projection in China

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Science China Earth Sciences 2023年第9期66卷 2125-2141页

作者： Huanhuan ZHU Zhihong JIANG Laurent LI Wei LI Sheng JIANG Panyu ZHOU Weihao ZHAO Tong LI Key Laboratory of Meteorological Disaster of Ministry of Education Collaborative Innovation Center on Forecast and Evaluation of Meteorological DisasterNanjing University of Information Science and TechnologyNanjing 210044China Laboratoire de Météorologie Dynamique CNRSSorbonne UniversitéEcole Normale SupérieureEcole PolytechniqueParis 75005France School of Mathematics and Statistics Nanjing University of Information Science and TechnologyNanjing 210044China

climate change adaptation and relevant policy-making need reliable projections of future *** based on multi-model ensemble are generally considered as the most efficient way to achieve the ***,their efficiency varies and inter-comparison is a challenging task,as they use a variety of target variables,geographic regions,time periods,or model ***,we construct and use a consistent framework to evaluate the performance of five ensemble-processing methods,i.e.,multimodel ensemble mean(MME),rank-based weighting(RANK),reliability ensemble averaging(REA),climate model weighting by independence and performance(ClimWIP),and Bayesian model averaging(BMA).We investigate the annual mean temperature(Tav)and total precipitation(Prcptot)changes(relative to 1995–2014)over China and its seven subregions at 1.5 and 2℃warming levels(relative to pre-industrial).All ensemble-processing methods perform better than MME,and achieve generally consistent results in terms of median *** they show different results in terms of inter-model spread,served as a measure of uncertainty,and signal-to-noise ratio(SNR).ClimWIP is the most optimal method with its good performance in simulating current climate and in providing credible future *** uncertainty,measured by the range of 10th–90th percentiles,is reduced by about 30%for Tav,and 15%for Prcptot in China,with a certain variation among *** on ClimWIP,and averaged over whole China under 1.5/2℃global warming levels,Tav increases by about 1.1/1.8℃(relative to 1995–2014),while Prcptot increases by about 5.4%/11.2%,*** of projections is found dependent on investigated regions and *** projection for Tav is credible across all regions,as its SNR is generally larger than 2,while the SNR is lower than 1 for Prcptot over most regions under 1.5℃*** largest warming is found in northeastern China,with increase of 1.3(0.6–1.7)/2.0(1.4–2.6)℃(ensemble’s median and range

关键词： Multi-model ensemble simulation Ensemble-processing strategy Global warming targets climate projection uncertainty assessment Regional climate change in China

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