An interaction network perspective on the relation between patterns of sea surface temperature variability and global mean surface temperature.

On interannual-to-multidecadal time scales variability in sea surface temperature appears to be organized in large-scale spatiotemporal patterns. In this paper, we investigate these patterns by studying the community structure of interaction networks con- structed from sea surface temperature observations. Much of the community structure as well as the first neighbour maps can be interpreted using known dominant patterns of variability, such as the El Niño/Southern Oscillation and the Atlantic Multidecadal Oscillation and teleconnections. The community detection method allows to overcome some shortcomings of Empirical Orthogonal Function analysis or composite analysis and hence provides additional information with respect to these classical analysis tools. The community analysis provides also new insight into the relationship between patterns of sea surface temperature and the global mean surface temperature (GMST). On the decadal-to-multidecadal time scale, we show that only two communities (Indian Ocean and North Atlantic) determine most of the GMST variability. [ABSTRACT FROM AUTHOR]