Multivariate and multiscale dependence in the global climate system revealed through complex networks
- 749 Downloads
A systematic characterization of multivariate dependence at multiple spatio-temporal scales is critical to understanding climate system dynamics and improving predictive ability from models and data. However, dependence structures in climate are complex due to nonlinear dynamical generating processes, long-range spatial and long-memory temporal relationships, as well as low-frequency variability. Here we utilize complex networks to explore dependence in climate data. Specifically, networks constructed from reanalysis-based atmospheric variables over oceans and partitioned with community detection methods demonstrate the potential to capture regional and global dependence structures within and among climate variables. Proximity-based dependence as well as long-range spatial relationships are examined along with their evolution over time, yielding new insights on ocean meteorology. The tools are implicitly validated by confirming conceptual understanding about aggregate correlations and teleconnections. Our results also suggest a close similarity of observed dependence patterns in relative humidity and horizontal wind speed over oceans. In addition, updraft velocity, which relates to convective activity over the oceans, exhibits short spatiotemporal decorrelation scales but long-range dependence over time. The multivariate and multi-scale dependence patterns broadly persist over multiple time windows. Our findings motivate further investigations of dependence structures among observations, reanalysis and model-simulated data to enhance process understanding, assess model reliability and improve regional climate predictions.
KeywordsComplex networks Correlation Teleconnections Reanalysis data Ocean meteorology
This research was performed as part of a project titled “Uncertainty Assessment and Reduction for Climate Extremes and Climate Change Impacts”, which in turn was funded by the initiative called “Understanding Climate Change Impact: Energy, Carbon, and Water Initiative”, within the LDRD Program of the Oak Ridge National Laboratory, managed by UT-Battelle, LLC for the U.S. Department of Energy under Contract DE-AC05-00OR22725. Nitesh Chawla was supported in part by the National Science Foundation under Grants OCI-1029584 and BCS-0826958. The United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes.
- Ahrens CD (2008) Meteorology today. Brooks Cole, Belmont, CAGoogle Scholar
- Emanuel KA (1992) Atmospheric convection. Oxford University Press, OxfordGoogle Scholar
- Steinbach M, Tan PN, Kumar V, Klooster S, Potter C (2003) Discovery of climate indices using clustering. In: Proceedings of the ACM SIGKDD conference on knowledge discovery and data mining, pp 446–455Google Scholar
- Steinhaeuser K, Chawla NV, Ganguly AR (2010b) Complex networks as a unified framework for descriptive analysis and predictive modeling in climate science. Stat Anal Data Mining (in press)Google Scholar
- von Storch H, Zwiers FW (2002) Statistical analysis in climate research. Cambridge University Press, Cambridge, UKGoogle Scholar
- Yamasaki K, Gozolchiani A, Havlin S (2008) Climate networks around the globe are significantly affected by El Niño. Phys Rev Lett 100:157–179Google Scholar