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The oceans and atmosphere interact via a multiplicity of feedback mechanisms, shaping to a large extent the global climate and its variability. To deepen our knowledge of the global climate system, characterizing and investigating this interdependence is an important task of contemporary research. However, our present understanding of the underlying large-scale processes is greatly limited due to the manifold interactions between essential climatic variables at different temporal scales. To address this problem, we here propose to extend the application of complex network techniques to capture the interdependence between global fields of sea-surface temperature (SST) and precipitation (P) at multiple temporal scales. For this purpose, we combine time-scale decomposition by means of a discrete wavelet transform with the concept of coupled climate network analysis. Our results demonstrate the potential of the proposed approach to unravel the scale-specific interdependences between atmosphere and ocean and, thus, shed light on the emerging multiscale processes inherent to the climate system, which traditionally remain undiscovered when investigating the system only at the native resolution of existing climate data sets. Moreover, we show how the relevant spatial interdependence structures between SST and P evolve across time-scales. Most notably, the strongest mutual correlations between SST and P at annual scale (8–16 months) concentrate mainly over the Pacific Ocean, while the corresponding spatial patterns progressively disappear when moving toward longer time-scales.
The study of the climate system using complex networks provides new insights into spatiotemporal climate dynamics. Most previous studies have focused on a single climate variable only. Accounting for the multivariate and multiscale nature of climate variability introduces a new challenging perspective that could help improve our understanding of the underlying physical mechanisms. In this study, we focus on the aforementioned two aspects of multiple variables and time-scales contributing to the variability of the climate system and show that cross-variable statistical relations evolve differently at different time-scales. Consideration of this previously widely disregarded factor provides a more explicit picture of scale-dependent covariability patterns among climate variables and their temporal evolution, which might be overlooked when focusing only at the native resolution of existing climate data sets
