干旱区生态环境知识资源中心

Climate systems have both nonlinear and non-stationary characteristics, and it is important to develop methods to reveal the nonlinear trends in these systems. Based on synthetic data with known signals and a precipitation anomaly time series from 74 meteorological stations in an arid region of northwest China (ARNC) from 1960 to 2015, the latest extreme-point symmetric mode decomposition (ESMD) and ensemble empirical mode decomposition (EEMD) methods were employed to conduct multi-scale mode decomposition. The results showed that both the ESMD and EEMD methods can separate synthetic signals and extract the nonlinear trends of precipitation in ARNC and can thus provide reliable decomposition results. This article employed the ESMD method to analyse the spatial and temporal variation features of nonlinear precipitation trends in ARNC from 1960 to 2015. The results indicated that over the past 50+ years, the overall precipitation in ARNC has exhibited an apparent nonlinear upwards trend. In addition, its changes have exhibited oscillation periods of 3, 6, and 11 years. The periods of the three components are significant (p < 0.05), and the variance contribution rate of the first intrinsic mode function (IMF1) is the largest, reaching 58%. Furthermore, there are obvious spatial differences in the nonlinear trends of average annual precipitation: northern Xinjiang has had a mainly rising trend; southern Xinjiang has had mainly rising and decreasing-rising trends; and the precipitation changes in the Hexi Corridor have been quite complicated. The ESMD method can reflect integrated changes in the nonlinear trends of precipitation over different time scales and has important practical significance and scientific value for revealing the complicated structural features of climate systems.