Spatio‐temporal variations of nonlinear trends of precipitation over an arid region of northwest China according to the extreme‐point symmetric mode decomposition method

Abstract

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.

Relationships between Spatial and Temporal Variations in Precipitation, Climatic Indices, and the Normalized Differential Vegetation Index in the Upper and Middle Reaches of the Heihe River Basin, Northwest China

“…In the UHRB and MHRB, the annual average ATP was 368.8 and 130.9 mm from 1961 to 2016, and changed at rates of 1.04 mm year −1 (Z = 3.09) and 0.43 mm year −1 (Z = 2.16), with abrupt change points in 2004 and 1981 in the UHRB and MHRB, respectively. The significant increases in the ATP are consistent with those reported for northwestern Gansu Province, the Tianshan Mountains, and northwestern China [37][38][39][40]42], but differ from reported decreases on the Chinese Mongolian Plateau [41] (Figure 6a,e). The long-term annual average ATD in the UHRB was 70.5 days and changed at a rate of 0.05 day year −1 (Z = 0.93), and in the MHRB it was 29.2 days and changed at a rate of 0.086 day year −1 (Z = 2.24), with an abrupt change point in 2000 (Figure 6b,f).…”

Effects of Climate Change on Land Cover Change and Vegetation Dynamics in Xinjiang, China

“…[ 14 ]. The ESMD could separate the interannual and general climate trends [ 36 ]. The ESMD was implemented with the Java-based ESMD4j v1.8 software (Qingdao University of Technology, Qingdao, PRC).…”

“…Several studies have tried to reveal the trends in climate indicators and NDVI based on linear trend regression [ 1 , 34 ]. However, this approach might be insufficient to reveal variations in nonlinear and non-stationary trends, so Extreme-Point Symmetric Mode Decomposition (ESMD) was proposed, which has been proven to be effective in revealing nonlinear trends such as those of climate and vegetation [ 36 , 37 ].…”

Spatiotemporal Characteristics of Drought in the Heihe River Basin Based on the Extreme-Point Symmetric Mode Decomposition Method

“…In this research, the ESMD method was chosen to study the cyclic characteristics of drought in the HRB. The specific steps are (Wang and Li 2013;Qin et al 2017):…”

“…The oscillatory components of different scales and trend components of the original time sequence can be gradually decomposed through ESMD, which considerably improves the assessment accuracy of drought cycles and trends. In recent years, ESMD has been widely utilized in the study of time series under climate change in China (Lei et al 2016;Lin et al 2017;Qin et al 2017). These studies suggest that the ESMD method can effectively reveal variations in long-term time sequences and can be used for the diagnosis of complex nonlinear and nonstationary signal changes.…”