摘要
基于24个CMIP5全球耦合模式模拟结果,分析了中国区域年平均降水和ETCCDI强降水量(R95p)、极端强降水量(R99p)对增暖的响应.定量分析结果显示,CMIP5集合模拟的当代中国区域平均降水对增温的响应较观测偏弱,而极端降水的响应则偏强.对各子区域气温与平均降水、极端降水的关系均有一定的模拟能力,并且极端降水的模拟好于平均降水.RCP4.5和RCP8.5情景下,随着气温的升高,中国区域平均降水和极端降水均呈现一致增加的趋势,中国区域平均气温每升高1℃,平均降水增加的百分率分别为3.5%和2.4%,R95p增加百分率为11.9%和11.0%,R99p更加敏感,分别增加21.6%和22.4%.就各分区来看,当代的区域性差异较大,未来则普遍增强,并且区域性差异减小,在持续增暖背景下,中国及各分区极端降水对增暖的响应比平均降水更强,并且越强的极端降水敏感性越大.未来北方地区平均降水对增暖的响应比南方地区的要大,青藏高原和西南地区的R95p和R99p增加最显著,表明未来这些区域发生暴雨和洪涝的风险将增大.
The relationship between regional precipitation change and warming is an important open issue in climate change physical science.Because precipitation in China has strong sensitivity to warming,quantitative assessment and projection on the responses of precipitation and its extremes in a warming world are crucial for better understanding of regional climate change and helpful for regional adaption to climate change.For this reason,based on simulations of 24 models from Coupled Model Intercomparison Project Phase 5（CMIP5）,this study assesses the ability of the models in simulating the responses of annual mean precipitation and its extremes to warming over China and its subregions,and then projects their change under the RCP4.5and RCP8.5scenarios that represent respectively a medium-low and high radiative forcing.The annual mean precipitation is defined as the total amount of precipitation from January to December.The precipitation extremes are measured by the R95p（very wet days）and R99p（extremely wet days）indices,which are defined by the Expert Team on Climate Change Detection and Indices（ETCCDI）.According to the definition of ETCCDI,the R95 p and R99 p refer to annual total precipitation when the daily precipitation exceeds the 95 th and the 99 th percentile of the wet day precipitation,respectively.Eight subregions determined by administrative boundaries and societal and geographical conditions,i.e.,NEC（Northeast China）,NC（North China）,EC（East China）,CC（Central China）,SC（South China）,SWC1（Tibetan Plateau）,SWC2（Southwest China）,and NWC（Northwest China）,are used in this study.The model performance is validated through the comparison for the time period from 1961 to 2005between the historical simulation and the gridding observation dataset with a horizontal resolution of0.25°×0.25°in latitude and longitude.Quantitative analysis shows that the CMIP5 multi-model ensemble（MME）can generally capture the spatial features of the temperature,mean precipitation and precipitation extremes as well as the relationship of precipitation and its extremes with temperature over China.However,it underestimates the response of mean precipitation while overestimates the response of precipitation extremes over China region in historical period.The CMIP5 MME also has some abilities in reproducing the responses of the mean precipitation and its extremes to the warming over the subregions of China,and better performance can be found for the precipitation extremes.Under the RCP4.5and RCP8.5scenarios,concurrent with the temperature rising,the mean precipitation and precipitation extremes are projected to increase consistently over China.As the regional mean temperature rises by 1℃,the mean precipitation will increase by 3.5%and 2.4%,and the R95 p will increase by 8.0% and 11.9%,respectively.The response of R99 p is much more sensitive,respectively with an increase of 15.3%and 21.6%.For the subregions of China,they all show positive response and the regional difference will decrease in the future.Moreover,the sensitivity of the precipitation extremes to the warming is higher than that of the mean precipitation.The stronger the precipitation extreme is,the higher sensitivity it will have.Besides,the response of the mean precipitation to the warming is larger in Northern China than in Southern China.The largest increases in R95 p and R99 p are projected in the Tibetan Plateau and Southwest China,indicating an increasing risk of heavy rainfall and floods.
出处
《地球物理学报》
SCIE
EI
CAS
CSCD
北大核心
2015年第9期3048-3060,共13页
Chinese Journal of Geophysics
基金
公益性行业(气象)科研专项(GYHY201306019)
国家自然科学基金(41275078)
气候变化专项(CCSF201329)联合资助
关键词
全球变暖
CMIP5
降水
极端降水
区域响应
Warming
CMIP5
Precipitation
Precipitation extremes
Regional response
