Complex networks as a unified framework for descriptive analysis and predictive modeling in climate science

Abstract

Abstract:The analysis of climate data has relied heavily on hypothesis-driven statistical methods, while projections of future climate are based primarily on physics-based computational models. However, in recent years a wealth of new datasets has become available. Therefore, we take a more data-centric approach and propose a unified framework for studying climate, with an aim toward characterizing observed phenomena as well as discovering new knowledge in climate science. Specifically, we posit that complex networks are well suited for both descriptive analysis and predictive modeling tasks. We show that the structural properties of 'climate networks' have useful interpretation within the domain. Further, we extract clusters from these networks and demonstrate their predictive power as climate indices. Our experimental results establish that the network clusters are statistically significantly better predictors than clusters derived using a more traditional clustering approach. Using complex networks as data representation thus enables the unique opportunity for descriptive and predictive modeling to inform each other. 

Edge anisotropy and the geometric perspective on flow networks

“…Examples for such spatial networks can be found in diverse fields such as infrastructures (e.g., road networks, power grids), [6][7][8] neuronal (brain) networks, 9 or network representations of the dynamical similarity between climate variations observed at distant points on the globe commonly referred to as (functional) climate networks. [10][11][12][13][14][15][16] Due to their embedding in some metric space, spatial networks are not completely described by their topological characteristics. By contrast, the geometric structure of these a) N. Molkenthin and H. Kutza contributed equally to this work.…”

Fast Uncovering of Graph Communities on a Chip: Toward Scalable Community Detection on Multicore and Manycore Platforms

“…Since then, such community-level organization has been discovered and studied in a wide variety of real world networks, both within nature-built and human-built systems. These include (but not limited to) the Internet, social networks such as Facebook and Twitter (Papadopoulos et al, 2012), C. Elegans neural system (Watts and Strogatz, 1998), protein interaction networks (Girvan and Newman, 2002), electric power grid (Newman, 2003), dolphin communication network (Connor et al, 1999), collaboration networks (Newman, 2003), customer preference databases (Reddy et al, 2002), and climate variability networks (Steinhaeuser et al, 2011). In these systems, discovering the community structures within networks have proved to be a critical step in advancing the knowledge and understanding of the underlying system and its functions.…”

Global terrestrial water storage connectivity revealed using complex climate network analyses

“…A similar method uses the probability value (i.e., p value) of test statistics directly: a pair of nodes is considered connected if the p value is less than a critical value; for instance, Steinhaeuser et al (2011) set the critical value to 10 −10 . Yet another method defines τ from an edge-density function ρ(τ ) defined as…”

“…In typical CCN applications, cells of a gridded data set are deemed as nodes of a complex network, and links (or edges) between nodes are established on the basis of statistical similarity of the time series associated with the cells. After a climate network is constructed, various descriptive measures derived from the classical complex network theory are then applied to quantify network topologies (Donges et al, 2009b;Tsonis et al, 2006;Steinhaeuser et al, 2011). One of the main findings from the previous CCN studies is that climate networks manifest a "small-world" network property, akin to networks appear in many other fields (e.g., social networks).…”