Dynamic Bayesian Networks for Evaluation of Granger Causal Relationships in Climate Reanalyses

Abstract We apply a Bayesian structure learning approach to study interactions between global climate modes, so illustrating its use as a framework for developing process‐based diagnostics with which to evaluate climate models. Homogeneous dynamic Bayesian network models are constructed for time ser...

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Autores principales: Dylan Harries, Terence J. O'Kane
Formato: article
Lenguaje:EN
Publicado: American Geophysical Union (AGU) 2021
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Acceso en línea:https://doaj.org/article/720c584245b34176b886e6888ef0141d
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