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

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. 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.

Authors:
 [1];  [2];  [2];  [1];  [1];  [1];  [1];  [2];  [2];  [3];  [1]
  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)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1333075
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Nonlinear Processes in Geophysics (Online)
Additional Journal Information:
Journal Name: Nonlinear Processes in Geophysics (Online); Journal Volume: 22; Journal Issue: 1; Journal ID: ISSN 1607-7946
Publisher:
European Geosciences Union - Copernicus
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES

Citation Formats

Gonzalez, II, D. L., Angus, M. P., Tetteh, I. K., Bello, G. A., Padmanabhan, K., Pendse, S. V., Srinivas, S., Yu, J., Semazzi, Fred, Kumar, Vipin, and Samatova, Nagiza F. On the data-driven inference of modulatory networks in climate science: An application to West African rainfall. United States: N. p., 2015. Web. doi:10.5194/npg-22-33-2015.
Gonzalez, II, D. L., Angus, M. P., Tetteh, I. K., Bello, G. A., Padmanabhan, K., Pendse, S. V., Srinivas, S., Yu, J., Semazzi, Fred, Kumar, Vipin, & Samatova, Nagiza F. On the data-driven inference of modulatory networks in climate science: An application to West African rainfall. United States. doi:10.5194/npg-22-33-2015.
Gonzalez, II, D. L., Angus, M. P., Tetteh, I. K., Bello, G. A., Padmanabhan, K., Pendse, S. V., Srinivas, S., Yu, J., Semazzi, Fred, Kumar, Vipin, and Samatova, Nagiza F. Tue . "On the data-driven inference of modulatory networks in climate science: An application to West African rainfall". United States. doi:10.5194/npg-22-33-2015. https://www.osti.gov/servlets/purl/1333075.
@article{osti_1333075,
title = {On the data-driven inference of modulatory networks in climate science: An application to West African rainfall},
author = {Gonzalez, II, D. L. and Angus, M. P. and Tetteh, I. K. and Bello, G. A. and Padmanabhan, K. and Pendse, S. V. and Srinivas, S. and Yu, J. and Semazzi, Fred and Kumar, Vipin and Samatova, Nagiza F.},
abstractNote = {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.},
doi = {10.5194/npg-22-33-2015},
journal = {Nonlinear Processes in Geophysics (Online)},
number = 1,
volume = 22,
place = {United States},
year = {2015},
month = {1}
}

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