A new type of climate network based on probabilistic graphical models: Results of boreal winter versus summer



[1] In this paper we introduce a new type of climate network based on temporal probabilistic graphical models. This new method is able to distinguish between direct and indirectconnections and thus can eliminate indirect connections in the network. Furthermore, while correlation-based climate networks focus onsimilarity between nodes, this new method provides an alternative viewpoint by focusing on information flowwithin the network over time. We build a prototype of this new network utilizing daily values of 500 mb geopotential height over the entire globe during the period 1948 to 2011. The basic network features are presented and compared between boreal winter and summer in terms of intra-location properties that measurelocal memoryat a grid point and inter-location properties that quantifyremote impactof a grid point. Results suggest that synoptic-scale, sub-weekly disturbances act as the main information carrier in this network and their intrinsic timescale limits the extent to which a grid point can influence its nearby locations. The frequent passage of these disturbances over storm track regions also uniquely determines the timescale of height fluctuations thuslocal memoryat a grid point. The poleward retreat of synoptic-scale disturbances in boreal summer is largely responsible for a corresponding poleward shift of local maxima inlocal memory and remote impact, which is most evident in the North Pacific sector. For the NH as a whole, both local memory and remote impactstrengthen from winter to summer leading to intensified information flow and more tightly-coupled network nodes during the latter period.