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Multivariate and multiscale dependence in the global climate system revealed through complex networks

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Abstract

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.

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Acknowledgments

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.

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Authors and Affiliations

  1. Geographic Information Science and Technology Group, Computational Sciences and Engineering Division, Oak Ridge National Laboratory, 1 Bethel Valley Rd, PO Box 2008, MS-6017, Oak Ridge, TN, 37831, USA

    Karsten Steinhaeuser & Auroop R. Ganguly

  2. Department of Computer Science and Engineering and Interdisciplinary Center for Network Science and Applications, University of Notre Dame, 384 Fitzpatrick Hall, Notre Dame, IN, 46556, USA

    Karsten Steinhaeuser & Nitesh V. Chawla

  3. Department of Civil and Environmental Engineering, University of Tennessee at Knoxville, 223 Perkins Hall, Knoxville, TN, 37996, USA

    Auroop R. Ganguly

Authors
  1. Karsten Steinhaeuser
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  2. Auroop R. Ganguly
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  3. Nitesh V. Chawla
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Corresponding author

Correspondence to Auroop R. Ganguly.

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Steinhaeuser, K., Ganguly, A.R. & Chawla, N.V. Multivariate and multiscale dependence in the global climate system revealed through complex networks. Clim Dyn 39, 889–895 (2012). https://doi.org/10.1007/s00382-011-1135-9

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