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

Abstract

The Earth’s climate is a complex system characterized by multi-scale nonlinear interrelationships between different subsystems like atmosphere and ocean. Among others, the mutual interdependence between sea surface temperatures (SST) and precipitation (PCP) has important implications for ecosystems and societies in vast parts of the globe but is still far from being completely understood. In this context, the globally most relevant coupled ocean–atmosphere phenomenon is the El Niño–Southern Oscillation (ENSO), which strongly affects large-scale SST variability as well as PCP patterns all around the globe. Although significant achievements have been made to foster our understanding of ENSO’s global teleconnections and climate impacts, there are many processes associated with ocean–atmosphere interactions in the tropics and extratropics, as well as remote effects of SST changes on PCP patterns that have not yet been unveiled or fully understood. In this work, we employ coupled climate network analysis for characterizing dominating global co-variability patterns between SST and PCP at monthly timescales. Our analysis uncovers characteristic seasonal patterns associated with both local and remote statistical linkages and demonstrates their dependence on the type of the current ENSO phase (El Niño, La Niña or neutral phase). Thereby, our results allow identifying local interactions as well as teleconnections between SST variations and global precipitation patterns.

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“…A second important recent research line addresses linkages between different regions and/or climate variables in terms of coupled network representations (Donges et al 2011). Prominent examples include studies on the co-variability of sea surface temperature (SST) and continental precipitation at global and regional scales (e.g., Ekhtiari et al 2019Ekhtiari et al , 2021. By exploiting the resulting network connectivity properties, Ciemer et al (2020) recently identified skilful SST predictors of Amazon basin rainfall deficits in both the tropical Pacific and Atlantic oceans.…”

Evaluation of global teleconnections in CMIP6 climate projections using complex networks

“…Moreover, as the number of identified domains within a climatological field is drastically smaller than the number of original grid cells, this also opens up the possibility of investigating the causal relationships between them (Nowack et al, 2020), although the basic assumption of causal network inference that the dependence structure can be represented by a directed acyclic graph is questionable in the climate context. The construction of both dependencebased and causal networks can naturally be extended to cross-networks, which include multiple fields (Feng et al, 2012;Ekhtiari et al, 2021).…”

“…Network methods allow the investigation of interactions between different climatological fields in a straightforward way, constructing cross-networks between (in our case) SST and Z500 domains that describe the coupled oceanatmosphere variability (Liu and Alexander, 2007). We notice that the inference of links between the domains of two unipartite networks is different from the construction of bipartite communities in multi-layer networks as in Ekhtiari et al (2021). Here, we just calculate the distance correlations between pairs of one SST and one Z500 domain.…”

Dynamical phenomena in complex networks: fundamentals and applications

“…Ekhtiari et al [24] employ a coupled climate network analysis for characterizing dominating global covariability patterns between sea surface temperatures (SST) and precipitation (PCP) at monthly timescales. Their analysis uncovers characteristic seasonal patterns associated with both local and remote statistical linkages and demonstrates their dependence on the type of the current ENSO phase.…”

“…Special emphasis has been placed on functional climate networks [21,26,28]. Also, here it is shown how complex climate networks can be used as a tool to study various processes and phenomena, such as equatorial wave interactions associated with the Madden-Julian oscillation [23], dominant global covariability patterns between sea surface temperatures and precipitation [24], fires in tropical forests over space and time [25], Amazon rainfalls [27], South American low-level circulation [29], climate-induced hysteresis in pan-tropical forests [32], and others.…”