利用墨西哥帽小波对达日县1956~2004年共588个月的气温和降水数据进行多尺度分析,揭示了达日县气候变化多时间尺度的复杂结构,分析了不同时间尺度下降水和气温序列的变化周期和突变点,并确定了各序列中存在的主周期。结果表明:达日县气温和降水的变化趋势与青藏高原以及江河源区气候总体变化基本一致,局部存在较明显的滞后反应,小尺度的变化嵌套在较大尺度的复杂背景之中,不同时间尺度下突变的年份有所差异,小波分析在揭示气候变化的多尺度构型和主周期方面具有明显的优势。
陈克龙, 李双成, 周巧富, 肖景义
. 江河源区达日县近50年气候变化的多尺度分析[J]. 地理研究, 2007
, 26(3)
: 526
-533
.
DOI: 10.11821/yj2007030013
Wavelet analysis, known as "Mathematical Microscope", has become a new mathematical approach in recent 20 years, being applied widely in many fields. Especially, with the significant advantage of characteristic of variable scale, it is suitable and effective enough to demonstrate the multi-scale traits of climate change, such as temporal series of temperature and precipitation. Accordingly, in this paper, Mexican Hat Wavelet is used to analyze the multi-scale characteristics of the climate change in Dari County, based on the temperature and precipitation of instrument data for 588 months during 1956~2004. Consequently, the periods and turning points of temperature and precipitation series in different time-scale are revealed, and the primary periods determined are 3 years or 4 years in a small scale and 16 years in a large scale. Meanwhile, the wavelet coefficients patterns exhibit a probable increasing trend in temperature and humidity in the following years. In the past 50 years, the temperature climbed to peak values for 3 times with the maximum at the beginning of the 21st century, and the precipitation is 2 times with the maximum in the early years of the 1980s. In general, the tendency of temperature and precipitation in Dari County is approximately consistent with that on the Tibetan Plateau and the source regions of the Yangtze and Yellow rivers except for some local lags in several periods. Changes in underdecadal scales are nested into complicated background in larger scales. The turning points varied along with the time-scale. According to the above analysis, we can conclude that there is a significant advantage of wavelet analysis in revealing the multi-scale characteristics, the primary period, and forecasting the trend of climate change in the following years.
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