地理研究 ›› 2016, Vol. 35 ›› Issue (4): 639-652.doi: 10.11821/dlyj201604004

• 研究论文 • 上一篇    下一篇

1965-2013年黄土高原地区极端气温趋势变化及空间差异

赵安周1(), 刘宪锋2,3, 朱秀芳2,3(), 潘耀忠2,3, 赵玉玲1, 王冬利1   

  1. 1. 河北工程大学资源学院,邯郸 056038
    2. 北京师范大学地表过程与资源生态国家重点实验室,北京 100875
    3. 北京师范大学资源学院,北京 100875
  • 收稿日期:2015-11-14 修回日期:2016-02-19 出版日期:2016-04-20 发布日期:2016-04-27
  • 作者简介:

    作者简介:赵安周(1985- ),男,河北邯郸人,博士,研究方向为水资源对气候变化和土地利用的响应及其干旱评价。E-mail: zhaoanzhou@126.com

  • 基金资助:
    国家“高分辨率对地观测系统”重大专项(民用部分);河北工程大学博士专项基金(20120157);邯郸市科学技术研究与发展计划项目(1434201078);邯郸市科技局项目(1423109059-6)

Trend variations and spatial difference of extreme air temperature events in the Loess Plateau from 1965 to 2013

Anzhou ZHAO1(), Xianfeng LIU2,3, Xiufang ZHU2,3(), Yaozhong PAN2,3, Yuling ZHAO1, Dongli WANG1   

  1. 1. College of Resources, Hebei University of Engineering, Handan 056038, Hebei, China
    2. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
    3. College of Resources Science & Technology, Beijing Normal University, Beijing 100875, China
  • Received:2015-11-14 Revised:2016-02-19 Online:2016-04-20 Published:2016-04-27

摘要:

基于黄土高原地区52个气象站点逐日平均气温、最高和最低气温数据,采用一元线性趋势分析、相关分析等方法,分析该地区极端气温趋势变化及空间差异。结果表明:① 日最高(低)气温极低值、日最高(低)气温极高值、热夜日数、暖昼(夜)日数、热持续日数、夏季日数和生物生长季日数呈增加的趋势,其余极端气温指数呈减小的趋势。② 空间分布上,表征低温事件的冰冻日数、霜冻日数、冷昼(夜)日数和冷持续日数下降最显著的区域位于黄土高原北部;表征高温事件的热夜日数、夏季日数、暖昼(夜)日数和热持续日数上升最显著的区域主要位于黄土高原西北部;生物生长季日数上升最显著的区域主要位于黄土高原中部地区。③ 相关分析表明除了极值指数和气温日较差与其余极端气温指数相关性较差外,其余各极端气温指数之间均具有较好的相关性。④ 多数极端气温指数的变化趋势与平均气温关系密切,平均气温突变前后极端气温指数存在明显差异。⑤ Hurst指数结果表明黄土高原地区极端气温变化均呈同向变化特征。

关键词: 极端气温指数, 趋势变化, 空间差异, 黄土高原地区

Abstract:

Based on daily temperature (average, maximum, and minimum) data from 52 meteorological stations, this study analyzed the spatiotemporal variation of 16 extreme air temperature events in the Loess Plateau from 1965 to 2013, using the methods of linear regression, Mann-Kendall test, correlation analysis, and Hurst index. The results are as follows: (1) From the view of temporal variation, the occurrence of ice days (ID0), frost days (FD0), cold days (TX10p), cold nights (TN10p), as well as the cold spell duration indicator (CSDI) and diurnal temperature range (DTR), indicated statistically significant decreasing trends (P < 0.05). In particular TX10p clearly decreased (4.63d/10a, P < 0.001), while the occurrence of tropical nights (TR20), summer days (SU25), warm days (TX90p), warm nights (TN90p), warm spell duration days (WSDI), and growing season length (GSL) had significantly increasing trends (P < 0.05), of which TX90p had an especially clear increase (5.68d/10a, P < 0.001). The monthly minimum value of daily maximum temperature (TXn), monthly minimum value of daily minimum temperature (TNn), monthly maximum value of daily maximum temperature (TXx), and monthly maximum value of daily minimum temperature (TNx) showed increasing trends of 0.30°C/10a, 0.40°C/10a, 0.20°C/10a, and 0.30°C/10a, respectively; (2) In terms of spatial distribution, the extreme warm indices (TR20, SU25, TX90p, TN90p, and GSL) indicated that extremely high temperatures had the most significant increasing trends in the north Loess Plateau region, and the cold indices (ID0, FD0, TX10p, TN10p, and CSDI) indicated that extremely low temperatures had the most significant decreasing trends in the northwest Loess Plateau region. GSL increased most significantly in areas located in the central Loess Plateau region; (3) Pearson correlation indicated that all the extreme air temperature indices had good correlation, except for the extremal indices (TXn, TNn, TXx, and TNx) and the diurnal temperature range; (4) There was a close correlation between the trends of the most extreme air temperature indices and average temperature, and poor correlation between the trends of the most extreme air temperatures and elevation. It was also shown that the abrupt change points of average temperature mainly occurred in 1991, and ID0, FD0, TX10p, TN10p, CSDI, and DTR showed a decreasing trend; (5) Results of Hurst index indicated that the warm indices would keep increasing, while the cold indices would continue to decrease.

Key words: extreme temperature indices, trend variations, spatial difference, Loess Plateau region