Trend analysis of the temperature variation in China based on detrending fluctuation analysis

• 1、School of Mathematics and Computer Science, Xiangtan University

Abstract：As globing warming has raised increasing concern in these years, research on trends in climate has become a hot topic. Usually, the linear regression is employed for trend analysis. This detection method works on the basis of an assumption that the series is just first-order correlated or uncorrelated at all. However, in the real-life data, especially in the climatic data, e.g. the temperature data, there generally exists long-term correlation. Therefore, the direct application of the linear regression analysis is inappropriate. This study employs Detrended Fluctuation Analysis to investigate trends in temperature data in 590 meteorological stations of China in recent 46 years. The result shows that 555 stations out of 590 have insignificant trends. In contrast, only 49 stations display insignificant trends if using the traditional linear regression. Such difference should be due to the effect of long-term correlation. Furthermore, we try to calculate the most possible trend in the long-term correlated temperature data. Our anlaysis demonstrates that the traditional linear regression method, which does not take long-term correlations into account, would significantly overestimate the trend in observed data. Therefore, the effect of long-term correlation has to be taken into consideration in trend analysis of the real-life data. Our analysis provides a general framework for the trend analysis of long-term correlated data.

Keywords： Long-term correlation ~Detrended fluctuation analysis ~temperature series ~trend ~significance.