COMPLEX NETWORKS IN CLIMATE SCIENCE: PROGRESS, OPPORTUNITIES AND CHALLENGES
- ORNL
- University of Notre Dame, IN
Networks have been used to describe and model a wide range of complex systems, both natural as well as man-made. One particularly interesting application in the earth sciences is the use of complex networks to represent and study the global climate system. In this paper, we motivate this general approach, explain the basic methodology, report on the state of the art (including our contributions), and outline open questions and opportunities for future research. Datasets and systems that can be represented as interaction networks (or graphs), broadly defined as any collection of interrelated objects or entities, have received considerable attention both from a theoretical viewpoint as well as various application domains; examples include the analysis of social networks, chemical interactions between proteins, the behavior of financial markets, and many others. Recently, the study of complex networks - that is, networks which exhibit non-trivial topological properties - has permeated numerous fields and disciplines spanning the physical, social, and computational sciences. So why do networks enjoy such broad appeal? Briefly, it is their ability to serve at once as a data representation, as an analysis framework, and as a visualization tool. The analytic capabilities in particular are quite powerful, as networks can uncover structure and patterns at multiple scales, ranging from local properties to global phenomena, and thus help better understand the characteristics of complex systems. We focus on one particular application of networks in the earth sciences, namely, the construction and analysis of climate networks. Identifying and analyzing patterns in global climate is an important task of growing scientific, social, and political interest, with the goal of deepening our understanding of the complex processes underlying observed phenomena. To this end, we make the case that complex networks offer a compelling perspective for capturing the dynamics of the climate system. Moreover, the computational sciences - specifically data mining and machine learning - are able to contribute a valuable set of methods and tools ranging from pattern recognition to predictive models. Thus, in this paper we expand upon the general approach to climate networks and motivate a promising area of interdisciplinary research. Indeed, we believe that this marriage of analytic methods, computational tools and domain science has the long-term potential for a transformative impact on our understanding of the earth's climate system.
- Research Organization:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). National Center for Computational Sciences (NCCS)
- Sponsoring Organization:
- USDOE Laboratory Directed Research and Development (LDRD) Program
- DOE Contract Number:
- DE-AC05-00OR22725
- OSTI ID:
- 1023819
- Resource Relation:
- Conference: NASA Conference on Intelligent Data Understanding (CIDU), Mountain View, CA, USA, 20101005, 20101007
- Country of Publication:
- United States
- Language:
- English
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