https://doi.org/10.1140/epjs/s11734-021-00168-z
Regular Article
Coupled network analysis revealing global monthly scale co-variability patterns between sea-surface temperatures and precipitation in dependence on the ENSO state
1
Research Domain IV - Complexity Science, Potsdam Institute for Climate Impact Research (PIK) – Member of the Leibniz Association, Potsdam, Germany
2
Department of Physics, Humboldt University, Berlin, Germany
3
Department of Water, Environment, Construction and Safety, Magdeburg–Stendal University of Applied Sciences, Magdeburg, Germany
Received:
30
November
2020
Accepted:
23
April
2021
Published online:
10
June
2021
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.
© The Author(s) 2021
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