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

• Published in: Nonlinear processes in geophysics 2015-01, Vol.22 (1), p.33-46
• Main Authors: González II, D. L., Angus, M. P., Tetteh, I. K., Bello, G. A., Padmanabhan, K., Pendse, S. V., Srinivas, S., Yu, J., Semazzi, F., Kumar, V., Samatova, N. F.
• Format: Article
• Language: English
• Subjects: Bayesian analysis; Climate science; Complex systems; ENVIRONMENTAL SCIENCES; Mathematical models; Rain; Regression analysis; Weather forecasting
• ISSN: 1607-7946; 1023-5809; 1607-7946
• DOI: 10.5194/npg-22-33-2015

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.