2015 journal article
On the data-driven inference of modulatory networks in climate science: an application to West African rainfall
NONLINEAR PROCESSES IN GEOPHYSICS, 22(1), 33–46.
open access via www.nonlin-processes-geophys.net (publisher PDF)
UN Sustainable Development Goal Categories
13. Climate Action
(Web of Science; OpenAlex)
Source: Web Of Science
Added: August 6, 2018
Abstract. 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.