Case studies on driving factor with different scales: a modified Lorenz system and 500-hPa geopotential height

How to extract external forcing information from continuous nonstationary systems is important for studying the variability of the climate system. Based on the slow feature analysis (SFA), in this paper, two independent driving forces with different periods are given to affect the modified Lorenz model to discuss whether the SFA approach can reconstruct the forcing signals. The results show that, to some extent, SFA can dig them out, and the result is related to the embedding dimension m , the analyzed variables, and the position of the forcing signal acting on the system. At the same time, combined with wavelet analysis, the two original forcing signals can be separated from the SFA extracted signal. In addition, we apply the above method to the climatic data through the 500-hPa geopotential height field and focus on the region where SFA-extracted signal have a significant scale of about 4 years over East Asia and speculate on the possible physical factors based on the distribution characteristics of the region. The results show this scale may be derived from the East Asian trough. This work may open up the possibility to improve the monthly prediction of 500-hPa geopotential height field over East Asia from the perspective of causality.