on the data-driven inference of modulatory networks in climate science: an application to west african rainfall
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ID: 215103
2015
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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.
| Reference Key |
ii2015nonlinearon
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| 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 |
| Journal | BMC research notes |
| Year | 2015 |
| DOI |
10.5194/npg-22-33-2015
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| URL | |
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