Multi-scale event synchronization analysis for unravelling climate processes: A wavelet-based approach

Abstract

Abstract. The temporal dynamics of climate processes are spread across different time scales and, as such, the study of these processes only at one selected time scale might not reveal the complete mechanisms and interactions within and between the (sub-) processes. For capturing the nonlinear interactions between climatic events, the method of event synchronization has found increasing attention recently. The main drawback with the present estimation of event synchronization is its restriction to analyse the time series at one reference time scale only. The study of event synchronization at multiple scales would be of great interest to comprehend the dynamics of the investigated climate processes. In this paper, wavelet based multi-scale event synchronization (MSES) method is proposed by combining the wavelet transform and event synchronization. Wavelets are used extensively to comprehend multi-scale processes and the dynamics of processes across various time scales. The proposed method allows the study of spatio-temporal patterns across different time scales. The method is tested on synthetic and real-world time series in order to check its replicability and applicability. The results indicate that MSES is able to capture relationships that exist between processes at different time scales.

Coupled network analysis revealing global monthly scale co-variability patterns between sea-surface temperatures and precipitation in dependence on the ENSO state

“…On the one hand, one possible subject of future research could be extending the present approach to more than two variables, or considering two variables but focusing on two or more distinct key regions to capture their specific interactions. On the other hand, for studying climate dynamics one should also consider the effect of different timescales, i.e., how the interactions among different climate variables evolve across time and scale and how this is related with the network topology [74][75][76], including the community structure. For the latter purpose, one could use different methods (e.g., wavelet analysis) to decompose the time series into different timescales and study the interaction between two or more climate variables at separate timescales or between different scales to capture cross-scale variability [77].…”

Effect of hydroclimatological teleconnections on the watershed‐scale drought predictability in the southeastern United States

“…In order to address the inherent nonlinearity in the relationship of the climate indices and hydrologic variables, several studies have used nonlinear approaches like mutual information (Knuth et al ., ; Yoon and Lee, ), cross‐wavelet analysis (Labat, ; Agarwal et al ., ) or PC analysis (Bethere et al ., ), etc. However, the objective of this study is only to highlight the spatial variability in the strength of interrelationship between watershed‐scale drought and climate indices; the inferences are derived from linear correlation analysis and R ‐square of the least‐square model fit between hydrologic variables and DIs.…”

Spatial organization of connectivity in functional climate networks describing event synchrony of heavy precipitation

“…The effort of understanding the associated mechanisms has led to the development of a comprehensive onset and withdrawal prediction scheme for the ISM [15], which has performed impressively well since then. To investigate the interplay of the ISM with other large-scale climate variability modes such as the North Atlantic Oscillation (NAO) or the Pacific Decadal Oscillation (PDO), a wavelet-based multi-scale approach [48] has unraveled and characterized the respective spatial interdependency patterns across time-scales [49]. In addition to the ISM, some authors have also investigated other branches of the Asian monsoon system such as the East Asian monsoon system (EASM) [50].…”