我国未来温度变化的统计降尺度预估*

南京大学学报(自然科学版) ›› 2010, Vol. 46 ›› Issue (6): 631–642.

我国未来温度变化的统计降尺度预估*

高红霞, 汤剑平**

出版日期:2015-04-03 发布日期:2015-04-03
作者简介: ( 南京大学大气科学学院, 南京, 210093)
基金资助:
国家自然科学基金( 40705029, 40830639) , 国家重点基础研究发展计划( 2006CB400500)

Statistical downscaling estimate of the future temperature variations in China

Gao Hong X ia, T ang Jian Ping

Online:2015-04-03 Published:2015-04-03
About author: ( School of Atmospheric Sciences, Nanjing University, Nanjing, 210093, China)

摘要/Abstract

摘要： 利用我国近 600 个地面测站的逐日温度观测资料、美国国家环境预报中心和美国大气科学研究中心再分析资料的大尺度气候变量, 采用基于多元线形回归的统计降尺度方法, 对 1970- 1999 年我
国 1月和 7月的月平均地面温度进行了统计降尺度分析, 并将统计降尺度模型应用于未来气候情景下, 对 21 世纪 3 个时段(2010- 2039, 2040- 2069, 2070- 2099)的地面温度变化进行预估, 对比分析了 A2
排放情景下多个模式的降尺度效果. 结果表明, 基于多元线性回归的统计降尺度方法在我国地面温度的模拟分析中是合理可用的. 对于当前时段的模拟, 降尺度方法能够明显改善模式对地面温度的模拟效
果, 并且东部好于西部, 平原好于山地, 尤其是 7 月份, 当各模式对地面温度的模拟结果与观测资料相差较大的情况下, 降尺度方法仍然能得到较好的结果; 对于未来气候情景的预估方面, 大部分站点温度都
表现为明显的上升趋势, 并且升温幅度北方高于南方, 西部高于东部.

Abstract: Based on the daily temperature data obtained at nearly 600 observations in China and the sea level pressure field and aerial variants from the National Centers for Environmental Prediction and the National Center for
Atmospheric Research ( NCEP/ NCAR) re analyses, we study the surface air temperature of January and July from 1970 to 1999, using the statistical downscaling method based on multiple linear regression. T hen, we compare the
results derived from the statistical downscaling method with those from observations. We also apply the statistical downscaling method to the future climate condition, in attempt to predict the surface temperature of three periods
( 2010- 2039, 2040- 2069, 2070- 2099) of the 21 st century. Furthermore, we analyze the results based on the statistical downscaling method under several various Global Climate Model (GCM) from Intergovernmental Panel on
Climate Change ( IPCC) Special Report on Emissions Scenarios ( SRES) A2 scenario. Our results show that the statistical downscaling method based on multivariate linear regression is fine in the stimulation of the surface
temperature in China. For the present climate condition, the statistical downscaling method can improve the model-sability of simulating temperature, with better results in the eastern part of China than its west, and in the plane than
in the mountainous region, especially in July. When the results differ a lot from the observed results, we can still get good results through the statistical downscaling, and thus all the models match better with each other. For the 21 st century, the
monthly mean temperature has a significant increase at almost all the stations in both January and July. The estimated mean temperature increase is larger in the north than in the south, and in the west than in the east.

高红霞, 汤剑平** . 我国未来温度变化的统计降尺度预估*

[J]. 南京大学学报(自然科学版), 2010, 46(6): 631–642.

Gao Hong X ia, T ang Jian Ping . Statistical downscaling estimate of the future temperature variations in China

[J]. Journal of Nanjing University(Natural Sciences), 2010, 46(6): 631–642.

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