摘要
利用 1986-2005 年18 个CMIP6气候模式及其同源的CMIP5 气候模式和贵州省 84个气象台站逐日平均气温、最高气温和最低气温资料,基于色度图和泰勒图,系统评估CMIP6 模式对贵州省气温的模拟能力,并采用最佳模拟结果预估 2023-2100 年SSP1-2.6、SSP2-4.5和SSP5-8.5未来情景下贵州省气温的变化特征.结果表明:CMIP6-MME(MME,多模式集合平均)对 1986-2005 年贵州省霜冻日数(FD)、生长季长度(GSL)、夏日日数(SU)、最低气温的最低值(TNN)及平均气温(Tav)的总体模拟能力最优;相对于参照期(1995-2014年),2023-2100 年贵州省在SSP1-2.6、SSP2-4.5 和SSP5-8.5 情景下FD值呈现显著下降趋势,GSL、SU、TNN和Tav值均呈现显著上升趋势.另外,21世纪各个阶段各情景下贵州省FD值(GSL、SU、TNN和Tav值)相对于参照期均表现为偏少(多)的特征,且排放越高,偏少(多)幅度越大,从空间分布来看,FD(GSL)值偏少(多)幅度自南向北增加,SU值偏多幅度自东北向西南增加,TNN和Tav值偏多幅度自西南向东北增加.
Abstract
Based on the daily average temperature,daily maximum temperature and daily minimum temperature from the 18 global climate models that participated in the phase 6 of the Coupled Model Intercomparison Project(CMIP6)and their precedent phase project(CMIP5),and the 84 observational stations during 1986-2005,the capabilities of CMIP6 on simulating the temperature over Guizhou by Portrait and Taylor chat were evaluated.The best simulation results were selected to project the characteristics of temperature in Guizhou under three scenarios(SSP1-2.6,SSP2-4.5 and SSP5-8.5)from 2023 to 2100.The results showed that the CMIP6-MME(multi-model ensemble)generally had better performance in simulating the indices including the numbers of forest days(FD),the length of the growing season(GSL),the numbers of summer days(SU),the lowest value of the minimum temperature(TNN)and the mean temperature(Tav),respectively during 1986-2005.Compared to the reference period(1995-2014),FD decreased significantly,but GSL,SU,TNN and Tav increased obviously from 2023 to 2100 under three scenarios.Furthermore,it was indicated that FD(GSL,SU,TNN and Tav)were less(more)than that in 1995-2014 during the 21st century under three scenarios over Guizhou,and the decrease(increase)was proportional to the emission scenario,with the amplitude of FD(GSL)reducing(growing)from south to north,SU rising from northeast to southwest,TNN/Tav going up from southwest to northeast.
基金项目
中国气象局复盘总结专项(FPZJ2023-118)
云贵准静止锋研究攻关团队项目(QHLSSLJ[2022]-11)
贵州省企业气象联合基金(QHLQLJ[2022]-05)
NETL
NSTL
万方数据