On the data-driven inference of modulatory networks in climate science: An application to West African rainfall

Journal Article · Tue Jan 13 00:00:00 EST 2015 · Nonlinear Processes in Geophysics (Online)

DOI:https://doi.org/10.5194/npg-22-33-2015· OSTI ID:1333075

Gonzalez, II, D. L. ^[1]; Angus, M. P. ^[2]; Tetteh, I. K. ^[2]; Bello, G. A. ^[1]; Padmanabhan, K. ^[1]; Pendse, S. V. ^[1]; Srinivas, S. ^[1]; Yu, J. ^[2]; Semazzi, Fred ^[2]; Kumar, Vipin ^[3]; Samatova, Nagiza F. ^[1]

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1. North Carolina State Univ., Raleigh, NC (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
2. North Carolina State Univ., Raleigh, NC (United States)
3. Univ. of Minnesota, Minneapolis, MN (United States)

Decades of hypothesis-driven and/or first-principles research have been applied towards the discovery and explanation of the mechanisms that drive climate phenomena, such as western African Sahel summer rainfall~variability. Although connections between various climate factors have been theorized, not all of the key relationships are fully understood. We propose a data-driven approach to identify candidate players in this climate system, which can help explain underlying mechanisms and/or even suggest new relationships, to facilitate building a more comprehensive and predictive model of the modulatory relationships influencing a climate phenomenon of interest. We applied coupled heterogeneous association rule mining (CHARM), Lasso multivariate regression, and dynamic Bayesian networks to find relationships within a complex system, and explored means with which to obtain a consensus result from the application of such varied methodologies. Using this fusion of approaches, we identified relationships among climate factors that modulate Sahel rainfall. As a result, these relationships fall into two categories: well-known associations from prior climate knowledge, such as the relationship with the El Niño–Southern Oscillation (ENSO) and putative links, such as North Atlantic Oscillation, that invite further research.

• Research Organization: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 1333075
• Journal Information: Nonlinear Processes in Geophysics (Online), Vol. 22, Issue 1; ISSN 1607-7946
• Publisher: European Geosciences Union - Copernicus
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES