干旱区研究

干旱区研究 ›› 2013, Vol. 30 ›› Issue (2): 322-328.

• 气候及气候变化 • 上一篇    下一篇

秦岭地区气温变化统计降尺度研究

章杰，白红英，袁博，马新萍   

  1. 西北大学城市与环境学院，陕西 西安710127
  • 收稿日期:2012-08-06 修回日期:2012-09-25 出版日期:2013-03-15 发布日期:2013-03-29
  • 作者简介:章杰(1986-)，男，硕士研究生，主要从事气候变化对植被影响研究.E-mail： nwu_zhj@126.com
  • 基金资助:

    国家林业公益性行业科研专项（201304309）

Statistical Downscaling of Air Temperature Change in the Qinling Mountains

ZHANG Jie ,BAI Hong-ying ,YUAN Bo ,MA Xin-ping   

  1. College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, Shaanxi,China
  • Received:2012-08-06 Revised:2012-09-25 Published:2013-03-15 Online:2013-03-29

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被引次数

24

摘要： 运用统计降尺度模型（SDSM）及秦岭地区7个站点1961—2011年的日平均气温、日最低气温和日最高气温资料，采用逐步线性回归方法（SMLR）选取NCEP大气环流预报因子中的最优因子，建立了预报量与预报因子之间的定量统计关系，对秦岭地区未来气温变化进行适用性分析，并对未来3个不同时期（2011—2040年、2041—2070年、2071—2099年）气温变化趋势进行预测。结果表明：SDSM模型对秦岭地区气温模拟效果良好，秦岭地区未来气温增幅明显，不同时间尺度增幅呈现明显时间差异，3个预报量在月尺度、季尺度呈现相似的时间变化特征：月尺度呈现8月增温最大，12月增温最小；季尺度呈现冬<春<秋<夏的趋势。2011—2040年气温的空间分布呈现秦岭北坡增幅大于秦岭南坡。

关键词: 气温, 变化趋势, 降尺度, SDSM, 未来情景, 秦岭地区

Abstract: A statistical downscaling model（SDSM）was applied to develop the quantitative statistical relationships between the predictands and the predictors based on the mean daily, maximum and minimum temperature data observed by 7 meteorological stations in the Qinling Mountains during the period of 1961-2011. The gradual linear regression method was used in the study, the optimal predictors in NCEP atmospheric circulation were selected, the applicability of air temperature change in the Qinling Mountains in the future was analyzed, and the change trends of air temperature in three periods of 2011-2040, 2041-2070 and 2071-2099 were predicted. The results showed that the air temperature results simulated with SDSM for the situation in the Qinling Mountains were significantly good. The increase of air temperature in the study area in the future would be remarkable, its difference, however, would be quite different from different time scales. The temporal change of three predictands was similar at monthly and seasonal scales. At monthly scale, the temperature increase would be the highest in August but the lowest in December. At seasonal scale, the temperature increase would be in an order of winter < spring < autumn < summer. Spatially, the increase of temperature in 2011-2040 would be higher in the northern slope of the Qinling Mountains than that in the southern slope.

Key words: air temperature, change trend, downscaling, SDSM, future scenario, Qinling Mountains