globalchange  > 气候变化与战略
CSCD记录号: CSCD:5379401
论文题名:
天山地区未来日极端气温变化的统计降尺度分析
其他题名: Statistical downscaling of future daily extreme temperature change in Tianshan Mountains
作者: 李成1; 王让会1; 黄进2; 管延龙2
刊名: 干旱区资源与环境
ISSN: 1003-7578
出版年: 2015
卷: 29, 期:3, 页码:1429-1438
语种: 中文
中文关键词: 气候变化 ; 日极端气温 ; 统计降尺度 ; 情景分析 ; 天山地区
英文关键词: climate change ; daily extreme temperature ; statistical downscaling ; scenario analysis ; Tianshan Mountains
WOS学科分类: METEOROLOGY ATMOSPHERIC SCIENCES
WOS研究方向: Meteorology & Atmospheric Sciences
中文摘要: 基于天山地区27个气象站日最高、最低气温资料及NCEP再分析资料对统计降尺度模型(SDSM)进行率定和验证,确定模型应用的预报因子,并将HadCM3输出的A2、B2情景分别输入率定后的SDSM中,生成未来3个时期(2020s、2050s和2080s)日最高、最低气温变化情景。结果表明:SDSM模型对于天山地区日最高、最低气温的模拟效果较好;未来日最高、最低气温总体呈增温趋势,其增幅在A2情景下比B2情景下高,且日最高气温增幅普遍高于日最低气温;春季增温最大,而冬季增幅最小;日最高、最低气温在未来3个时期的空间变化趋势较为一致,两者的空间变化在A2和B2情景下大体呈现出由北向南逐渐减弱的趋势。
英文摘要: The SDSM was applied to develop the quantitative statistical relationships between the predictands and the predictors based on daily maximum and minimum temperature data observed and the NCEP re - analysis data in Tianshan Mountains during the period of 1961 - 2000. The daily temperature for future periods (2020s,2050s and 2080s) was estimated by using the validated transfer function from output of the HadCM3 SERSA2 and B2 at 27 meteorological stations. Results show that SDSM is efficient for reproducing observed daily temperature at each station. There is an obvious increasing trend for daily temperature in the future. Both of them show a greater increasing range in A2 scenario that in B2 scenario,while daily maximum temperature shows a greater increasing range than and minimum temperature in both scenarios. The broadest range was found in spring while the lowest was in winter. The spatial distributions of daily temperature are similar in the future periods. The daily increments of temperature in A2 and B2 scenarios become smaller from the north to the south of Tianshan Mountains.
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/149376
Appears in Collections:气候变化与战略

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作者单位: 1.中国气象局乌鲁木齐沙漠气象研究所, 乌鲁木齐, 新疆 830002, 中国
2.南京信息工程大学环境科学与工程学院, 南京, 江苏 210044, 中国

Recommended Citation:
李成,王让会,黄进,等. 天山地区未来日极端气温变化的统计降尺度分析[J]. 干旱区资源与环境,2015-01-01,29(3):1429-1438
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