Reconstructing Late Holocene North Atlantic atmospheric circulation changes using functional paleoclimate networks

• Main Authors: Franke, Jasper G., Werner, Johannes P., Donner, Reik V.
• Format: Article in Journal/Newspaper
• Language: English
• Published: München : European Geopyhsical Union 2017
• Subjects: ddc:550; atmospheric circulation; Holocene; network analysis; North Atlantic Oscillation; numerical model; paleoclimate; proxy climate record; qualitative analysis; reconstruction; regression analysis; Fennoscandia; Greenland; North Atlantic; North Atlantic oscillation; Svalbard
• Online Access: https://oa.tib.eu/renate/handle/123456789/654; https://doi.org/10.34657/1224

Obtaining reliable reconstructions of long-term atmospheric circulation changes in the North Atlantic region presents a persistent challenge to contemporary paleoclimate research, which has been addressed by a multitude of recent studies. In order to contribute a novel methodological aspect to this active field, we apply here evolving functional network analysis, a recently developed tool for studying temporal changes of the spatial co-variability structure of the Earth's climate system, to a set of Late Holocene paleoclimate proxy records covering the last two millennia. The emerging patterns obtained by our analysis are related to long-term changes in the dominant mode of atmospheric circulation in the region, the North Atlantic Oscillation (NAO). By comparing the time-dependent inter-regional linkage structures of the obtained functional paleoclimate network representations to a recent multi-centennial NAO reconstruction, we identify co-variability between southern Greenland, Svalbard, and Fennoscandia as being indicative of a positive NAO phase, while connections from Greenland and Fennoscandia to central Europe are more pronounced during negative NAO phases. By drawing upon this correspondence, we use some key parameters of the evolving network structure to obtain a qualitative reconstruction of the NAO long-term variability over the entire Common Era (last 2000 years) using a linear regression model trained upon the existing shorter reconstruction.