经验模态分解下中国气温变化趋势的区域特征

用经验模态分解(EMD) 方法对中国700 多个站(1951-2001 年) 月平均气温进行了分析, 提取气温变化趋势项, 作空间分型, 并计算各站气温变化率, 以地理信息系统为数据处理平台, 以1km×1km 分辨率的DEM 数据作为地形的综合反映, 得到了中国平均气温空间分型和变化率精细化分布图。结果表明: 近50 年来中国北方大部分地区气温变化率多在0.4 ^oC/10a 以上, 西南和长江中下游部分地区气温变化率较小, 气温变化率为负值区零星状散落 在西南等地区。同时, 1881-2001 年中国9 个区域的气温资料分析表明, 近百年来中国气温变 化趋势以东北、华北、华东、华南、西北和新疆区是持续上升, 西南区呈下降型； 华中区呈倒"V" 型变化, 西藏区趋势不明显。

The Regional Features of Temper ature Variation Trends over China by Empirical Mode Decomposition Method

In this paper, a new and advanced time series analysis method--the empirical mode decomposition (EMD) method is presented into climate analysis field. It is a method developed from analyzing nonlinear and non-stationary data. The oscillations of different scales or trend in the signal are decomposed into a number of characteristic intrinsic mode function components. This decomposition method is adaptive, and, therefore, highly efficient. Thus, we could extract the variation trend from the data. The testing results indicate that EMD method is the best one for extracting data trend at the present time. The climatic trend is very important in temperature change. Therefore, how to eliminate periodic oscillation in temperature change and obtain variation trend is the important process for estimating and comprehending global climate warming. In this study, temperature variation trends of monthly mean temperature data for 740 stations over China from 1951 to 2001 are diagnosed by EMD method, and there are three types of variation trends: ascending trend, descending trend, and fluctuation. Hereby, several temperature change regions have been divided in China and fields have been spatially classified. Simultaneously, the temperature variability of every station is calculated by this method, and classification chart of long term trend and temperature variability distribution chart of China are obtained, supported by GIS, 1km×1km resolution. The results show a large scale warming trend in China, especially Northeast China, Inner Mongolia, Gansu, the west of Qinghai and Tibet, and the north of Xinjiang witnessing marked warming, as opposing to the descending trend patches distributed over Southwest China and the middle and lower Changjiang valley.