On the data-driven inference of modulatory networks in climate science: an application to West African rainfall
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, n...
Published in: | Nonlinear Processes in Geophysics |
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ftcopernicus:oai:publications.copernicus.org:npg24094 2023-05-15T17:33:28+02:00 On the data-driven inference of modulatory networks in climate science: an application to West African rainfall 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. 2018-09-09 application/pdf https://doi.org/10.5194/npg-22-33-2015 https://npg.copernicus.org/articles/22/33/2015/ eng eng doi:10.5194/npg-22-33-2015 https://npg.copernicus.org/articles/22/33/2015/ eISSN: 1607-7946 Text 2018 ftcopernicus https://doi.org/10.5194/npg-22-33-2015 2020-07-20T16:24:48Z 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. Text North Atlantic North Atlantic oscillation Copernicus Publications: E-Journals Nonlinear Processes in Geophysics 22 1 33 46 |
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Open Polar |
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Copernicus Publications: E-Journals |
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ftcopernicus |
language |
English |
description |
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. |
format |
Text |
author |
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. |
spellingShingle |
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. On the data-driven inference of modulatory networks in climate science: an application to West African rainfall |
author_facet |
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. |
author_sort |
González II, D. L. |
title |
On the data-driven inference of modulatory networks in climate science: an application to West African rainfall |
title_short |
On the data-driven inference of modulatory networks in climate science: an application to West African rainfall |
title_full |
On the data-driven inference of modulatory networks in climate science: an application to West African rainfall |
title_fullStr |
On the data-driven inference of modulatory networks in climate science: an application to West African rainfall |
title_full_unstemmed |
On the data-driven inference of modulatory networks in climate science: an application to West African rainfall |
title_sort |
on the data-driven inference of modulatory networks in climate science: an application to west african rainfall |
publishDate |
2018 |
url |
https://doi.org/10.5194/npg-22-33-2015 https://npg.copernicus.org/articles/22/33/2015/ |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_source |
eISSN: 1607-7946 |
op_relation |
doi:10.5194/npg-22-33-2015 https://npg.copernicus.org/articles/22/33/2015/ |
op_doi |
https://doi.org/10.5194/npg-22-33-2015 |
container_title |
Nonlinear Processes in Geophysics |
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22 |
container_issue |
1 |
container_start_page |
33 |
op_container_end_page |
46 |
_version_ |
1766131993262686208 |