Evolution mechanism of principal modes in climate dynamics

Eigen analysis has been a powerful tool to distinguish multiple processes into different simple principal modes in complex systems. For a non-equilibrium system, the principal modes corresponding to the non-equilibrium processes are usually evolving with time. Here, we apply the eigen analysis into the complex climate systems. In particular, based on the daily surface air temperature in the tropics (30? S–30? N, 0? E–360? E) between 1979-01-01 and 2016-12-31, we uncover that the strength of two dominated intra-annual principal modes represented by the eigenvalues significantly changes with the El Niño/southern oscillation from year to year. Specifically, according to the ‘regional correlation’ introduced for the first intra-annual principal mode, we find that a sharp positive peak of the correlation between the El Niño region and the northern (southern) hemisphere usually signals the beginning (end) of the El Niño. We discuss the underlying physical mechanism and suppose that the evolution of the first intra-annual principal mode is related to the meridional circulations; the evolution of the second intra-annual principal mode responds positively to the Walker circulation. Our framework presented here not only facilitates the understanding of climate systems but also can potentially be used to study the dynamical evolution of other natural or engineering complex systems. © 2020 The Author(s)

Correlation and scaling behaviors of fine particulate matter (PM2.5) concentration in China

Air pollution has become a major issue and caused widespread environmental and health problems. Aerosols or particulate matters are an important component of the atmosphere and can transport under complex meteorological conditions. Based on the data of PM2.5 observations, we develop a network approach to study and quantify their spreading and diffusion patterns. We calculate cross-correlation functions of the time lag between sites within different seasons. The probability distribution of correlation changes with season. It is found that the probability distributions in four seasons can be scaled into one scaling function with averages and standard deviations of correlation. This seasonal scaling behavior indicates that there is the same mechanism behind correlations of PM2.5 concentration in different seasons. Further, the weighted degrees reveal the strongest correlations of PM2.5 concentration in winter and in the North China Plain for the positive correlation pattern that is mainly caused by the transport of PM2.5. These directional degrees show net influences of PM2.5 along Gobi and inner Mongolia, the North China Plain, Central China, and Yangtze River Delta. The negative correlation pattern could be related to the large-scale atmospheric waves. Copyright (C) EPLA, 2018Peer reviewe