| 统计降尺度法对华北地区未来区域气温变化情景的预估 |
| |
| 引用本文： | 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. |
| |
| 作者姓名： | 范丽军 符淙斌 陈德亮 |
| |
| 作者单位： | 1.中国科学院大气物理研究所东亚区域气候-环境重点实验室, 北京,100029;兰州大学大气科学学院, 兰州,730000 |
| |
| 基金项目： | 国家重点基础研究发展规划项目2006CB400500，中国科学院海外杰出学者基金项目2001-2-10，中国气象局气候变化专项项目CCSF2006-6-1，瑞典STINT基金会和Sida资助项目 |
| |
| 摘 要： | 迄今为止，大部分海气耦合气候模式（AOGCM）的空间分辨率还较低，很难对区域尺度的气候变化情景做合理的预测。降尺度法已广泛用于弥补AOGCM在这方面的不足。作者采用统计降尺度方法对1月和7月华北地区49个气象观测站的未来月平均温度变化情景进行预估。采用的统计降尺度方法是主分量分析与逐步回归分析相结合的多元线性回归模型。首先，采用1961～2000年的 NCEP再分析资料和49个台站的观测资料建立月平均温度的统计降尺度模型，然后把建立的统计降尺度模型应用于HadCM3 SRES A2 和 B2 两种排放情景， 从而生成各个台站1950～2099年1月份和7月份温度变化情景。结果表明:在当前气候条件下，无论1月还是7月，统计降尺度方法模拟的温度与观测的温度有很好的一致性，而且在大多数台站，统计降尺度模拟气温与观测值相比略微偏低。对于未来气候情景的预估方面，无论1月还是7月，也无论是HadCM3 SRES A2 还是B2排放情景驱动统计模型，结果表明大多数的站点都存在温度的明显上升趋势，同时7月的上升趋势与1月相比偏低。
|
| 关 键 词： | 统计降尺度 月平均温度 华北 交叉检验 气候变化情景 |
| 文章编号： | 1006-9895（2007）05-0887-11 |
| 修稿时间： | 2006-02-222006-05-22 |
Estimation of Local Temperature Change Scenarios in North China Using Statistical Downscaling Method |
| |
| FAN Li-Jun,FU Cong-Bin and CHEN De-Liang.Estimation of Local Temperature Change Scenarios in North China Using Statistical Downscaling Method[J].Chinese Journal of Atmospheric Sciences,2007,31(5):887-897. |
| |
| Authors: | FAN Li-Jun FU Cong-Bin and CHEN De-Liang |
| |
| Institution: | 1. Key Laboratory of Regional Climate -Environment Research for Temperate East Asia, Institute of Atmospheric Physics, Chi nese Academy of Sciences, Beijing 100029 ;2. Earth Sciences Centre, Goteborg University, 40530 Goteborg, Sweden ;3. Laboratory for Climate Studies/National Climate Center, China Meteorological Administration, Beijing 100081 ;4. College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000 |
| |
| Abstract: | Coupled Atmosphere-Ocean General Circulation models(AOGCMs) are widely used as an important tool for projecting global climate change.However,their resolution is too coarse to provide the regional scale information required for regional impact assessments.Therefore,downscaling methods for extracting regional scale information from output of AOGCMs have been developed.Regional climate models nested in AOGCMs,and statistical downscaling are usually used for downscaling.In this paper,the focus is placed on estimating local temperature changes at the 49 meteorological stations of North China using a statistical method to derive local scale monthly mean temperatures from large-scale atmospheric predictors.Empirical relationships are derived among selected variables from the NCEP re-analyses and observed data,tested by using cross-validation method.Statistical downscaling technique based on Multiple Linear Regression(MLR) of predictor principal components(PCs) is applied.A stepwise screening procedure is adopted for selecting skilful PCs as predictors used in the regression equation.For the January temperature of North China,the best predictor is the combination of sea level pressure and 850 hPa temperature and the best predictor for the temperature in July is the combination of 850 hPa height and 850 hPa temperature.Subsequently the statistical models are applied to the HadCM3 output under present climate.Finally,the statistical downscaling model is applied to HadCM3 SRES A2 and B2 to construct local future climate change scenarios.For the present-day climate simulation,it is shown that in both January and July,the downscaled temperatures match the observations well,though the estimated values are slightly underestimated at almost all the stations.For future climate change scenarios at the local scale,the monthly mean temperature has a significant increase at almost all the stations in both January and July.The estimated mean temperature increase is found to be smaller in July than in January;the estimated mean temperature increase using HadCM3 SRES A2 is found to be larger than HadCM3 SRES B2. |
| |
| Keywords: | statistical downscaling monthly mean temperature North China cross-validation climate change scenarios |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
| 点击此处可从《大气科学》浏览原始摘要信息 |
| 点击此处可从《大气科学》下载免费的PDF全文 |
