基于统计降尺度方法的长江中下游气温的模拟与预估
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摘要
目前大部分全球气候模式(GCM)空间分辨率比较低,很难对区域尺度气候变化做出合理预测。降尺度方法的广泛运用弥补了GCM在这方面的不足。采用主成分分析和逐步回归相结合的统计降尺度方法对1980—2011年1月和7月长江中下游地区气温变化进行统计降尺度处理,并对该地区未来温度的变化进行预估。首先采用ECMWF的ERA-Interim再分析资料和实测资料建立逐月的统计降尺度模型,然后将建立的统计降尺度模型运用到CMIP5资料中,从而生成长江中下游地区各个测站未来气温变化序列。研究结果表明:(1)统计降尺度方法模拟1月和7月的温度与实测温度一致性都很好;(2)在21世纪末的时候气温在不同排放情景下都高于目前温度2～3℃,并且7月份的增温幅度要大于1月份。
It is difficult to make reasonable predictions of climate change on regional scale by Global Climate Models(GCM) due to low spatial resolution. Now downscaling method has been widely used to make up for these defects of GCM. The temperature changes in the future in Yangtze River middle and lower reaches were predicted based on the temperature data in January and July from 1980 to 2011 statistic treatments with downscaling method combining stepwise linear regression(SLR) and principal component analysis(PCA). A monthly statistical downscaling model was formulated based on gridded data of ERA_interim from ECMWF reanalysis data and observed data, then apply it to the CMIP5 data to generate the series of temperature changes in future in the middle and lower reaches of the Yangtze River. The results showed that:(1) the simulated January and July temperature with statistical downscaling method was in good agreement with the observed temperature;(2) By the end of the 21 st century, temperatures in January and July will both increase by 2-3℃ and the latter warm more intense under the different scenarios, and the increment of temperature of July will greater than that of January.
It is difficult to make reasonable predictions of climate change on regional scale by Global Climate Models(GCM) due to low spatial resolution. Now downscaling method has been widely used to make up for these defects of GCM. The temperature changes in the future in Yangtze River middle and lower reaches were predicted based on the temperature data in January and July from 1980 to 2011 statistic treatments with downscaling method combining stepwise linear regression(SLR) and principal component analysis(PCA). A monthly statistical downscaling model was formulated based on gridded data of ERA_interim from ECMWF reanalysis data and observed data, then apply it to the CMIP5 data to generate the series of temperature changes in future in the middle and lower reaches of the Yangtze River. The results showed that:(1) the simulated January and July temperature with statistical downscaling method was in good agreement with the observed temperature;(2) By the end of the 21 st century, temperatures in January and July will both increase by 2-3℃ and the latter warm more intense under the different scenarios, and the increment of temperature of July will greater than that of January.
引文
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[2]陈铁喜,陈星.近50年中国气温日较差的变化趋势分析[J].高原气象,2007,26(1):150-157.
[3]高晓荻,江志红,杨金虎.全球变暖情景下中国气温分区的未来变化[J].气象与环境学报,2009,25(5):1-6.
[4]刘燕强.长江中下游盛夏气温升高对农业生产的影响[J].中国农业信息,2015(21):110.
[5]管兆勇,蔡佳熙,唐卫亚,等.长江中下游夏季气温变化型与西太平洋副高活动异常的联系[J].气象科学,2010,30(5):666-675.
[6]蔡佳熙,管兆勇,高庆九,等.近50年长江中下游地区夏季气温变化与东半球环流异常[J].地理学报,2009,64(3):289-302.
[7]丁斌,顾显跃,缪启龙.长江流域近50年来的气温变化特征[J].长江流域资源与环境,2006,15(4):531-536.
[8]王冀.长江中下游地区最高气温的统计降尺度方法模拟研究[C]//中国气象学会.中国气象学会2007年年会气候变化分会场论文集.北京:中国气象出版社,2007.
[9]范丽军.统计降尺度方法集合预估华东气温的初步研究[J].高原气象,2010,29(2):392-402.
[10]赵立龙,徐建军.7个CMIP5模式的平流层、对流层温度趋势与SSU/MSU观测资料的对比[J].大气科学学报,2015,38(1):101-110.
[11]刘文茹,居辉,陈国庆,等.典型浓度路径(RCP)情景下长江中下游地区气温变化预估[J].中国农业气象,2017,38(2):65-75.
[12]邹海波,吴珊珊,单九生,等.2013年盛夏中国中东部高温天气的成因分析[J].气象学报,2015,73(3):481-495.
[13]Boville B A.Sensitivity of simulated climate to model resolution[J].J Climate,1991,4(5):469-485.
[14]WU J,ZHA J L,ZHAO D M.Evaluating the effects of land use and cover change on the decrease of surface wind speed over China in recent 30 years using a statistical downscaling method[J].Climate Dynamics,2017,48(1/2):131-149.
[15]高红霞,汤剑平.华东地区月平均气温统计降尺度方法比较[J].气象科学,2015,35(6):760-768.
[16]徐振亚,任福民,杨修群,等.日最高温度统计降尺度方法的比较研究[J].气象科学,2012,32(4):395-402.
[17]刘敏,王冀,刘文军.SDSM统计降尺度方法对江淮地区地面气温模拟能力评估及其未来情景预估[J].气象科学,2012,32(5):500-507.
[18]范丽军,符淙斌,陈德亮.统计降尺度法对华北地区未来区域气温变化情景的预估[J].大气科学,2007,31(5):887-897.
[19]DEE D P,UPPALA S M,SIMMONS A J,et al.The ERA-Interim reanalysis:Configuration and performance of the data assimilation system[J].Q J Roy Meteor Soc,2011,137(656):553-597.
[20]BERRISFORD P,K?LLBERG P,KOBAYASHI S,et al.Atmospheric conservation properties in ERA-Interim[J].Q J Roy Meteor Soc,2011,137(659):1381-1399.
[21]POLI P.List of observations assimilated in ERA-40 and ERA-Interim.ERA Report Series 4[R/OL].[2010-01-01].https://www.ecmwf.int/en/elibrary/11692-list-observations-assimilated-era-40-and-era-interim-v10.
[22]SIMMONS A J,POLI P,DEE D P,et al.Estimating low-frequency variability and trends in atmospheric temperature using ERA-Interim[J].Q J Roy Meteor Soc,2014,140(679):329-353.
[23]SIMMONS A.ERA-Interim:New ECMWF reanalysis products from 1989 onwards[J].ECMWF newsletter,2006,110:25-36.
[24]SIMMONS A J,WILLETT K M,JONES P D,et al.Low-frequency variations in surface atmospheric humidity,temperature,and precipitation:Inferences from reanalyses and monthly gridded observational data sets[J].J Geophys Res-Atmos,2010,115(D1):D01110.
[25]张艳武,张莉,徐影.CMIP5模式对中国地区气温模拟能力评估与预估[J].气候变化研究进展,2016,12(1):10-19.
[26]黄嘉佑.气象统计分析与预报方法[M].北京:气象出版社,1990.
[27]HANSSEN-BAUER I,ACHBERGER C,BENESTAD RE,et al.Statistical downscaling of climate scenarios over Scandinavia[J].Clim Res,2005,29(3):255-268.
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