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

• Published in: Nonlinear Processes in Geophysics
• Main Authors: D. L. González II, M. P. Angus, I. K. Tetteh, G. A. Bello, K. Padmanabhan, S. V. Pendse, S. Srinivas, J. Yu, F. Semazzi, V. Kumar, N. F. Samatova
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2015
• Subjects: Science; Q; Physics; QC1-999; Geophysics. Cosmic physics; QC801-809; North Atlantic; North Atlantic oscillation
• Online Access: https://doi.org/10.5194/npg-22-33-2015; https://doaj.org/article/04925f2be6a24c7a804f169137ddf957

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. 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.