摘要
利用全球模式(BCC_CSM1.1)驱动区域气候模式Reg CM4,模拟分析了RCP 4.5和RCP 8.5温室气体排放情景下未来2010—2099年长白山区的气候变化特征。结果表明:Reg CM4模式对长白山区气候特征具有较好的模拟能力,未来RCPs情景下长白山区气温明显升高。与参照时段(1986—2005年)相比,RCP 4.5和RCP 8.5情景下长白山区的年平均气温在21世纪20年代分别增加了0.7℃和1.0℃,21世纪50年代年平均气温分别增加了1.6℃和2.2℃,21世纪80年代年平均气温分别增加了1.9℃和3.8℃。RCP 4.5和RCP 8.5情景下,未来长白山区降水均呈略增多的趋势,21世纪20年代降水分别增加了6.5%和2.8%,21世纪50年代降水分别增加了6.6%和7.9%,21世纪80年代降水分别增加了11.0%和6.7%。此外,两种排放情景下未来长白山区日平均气温的统计特征发生改变,偏度系数的负值减小,峰度系数的负值增加,说明未来高温事件发生的可能性增加;同时,中雨以上级别降水的发生频率增加,说明未来极端降水事件发生的可能性增加。
Change trends of temperature and precipitation during 2010-2099 in Changbai Mountain area under RCPs scenarios were analyzed based on projections over China simulated by a RegCM4 （ a high resolution regional climate model） that was driven by a GCM （BCC_CSM1.1） in its boundary. The results show that the RegCM4 can well simulate local climate characteristics,judging from comparisons of the simulation with in situ observation. The projected temperature is in a significantly increasing trend in Changbal Mountain area under RCPS scenarios. Compared to that in the reference period （ 1986-2005 ）, annual average temperature will increase by 0.7 ℃ and 1.0 ℃ in 2020s, 1.6 ℃and 2.2 ℃ in 2050s, 1.9 ℃ and 3.8 ℃ in 2080s under RCP 4.5 and RCP 8.5 scenarios respectively. The projected precipitation in most areas of Changbal Mountain increases slightly. Compared to that in the reference period （1986-2005）, annual precipitation will increase by 6.5% and 2.8% in 2020s, 6.6% and 7.9% in 2050s, 11.0% and 6.7 % in 2080s under RCP 4.5 and RCP 8.5 scenarios respectively. In addition, sta- tistic characteristics of daily mean temperature change in Changbal Mountain area under two scenarios, i. e. , the negative value of skewness coefficient decreases, while that of kurtosis coefficient increases, and it suggests that the potential possibility of high temperature event might rise. The frequency of moderate rain and heavy rain enhances, which means possibly increasing extreme precipitation events.
出处
《气象与环境学报》
2015年第4期65-73,共9页
Journal of Meteorology and Environment
基金
中国气象局气候变化专项(CCSF201317)资助
