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
Materias:	Bayesian graphical models teleconnections Physical geography GB3-5030 Oceanography GC1-1581
Acceso en línea:	https://doaj.org/article/720c584245b34176b886e6888ef0141d
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spelling	oai:doaj.org-article:720c584245b34176b886e6888ef0141d2021-11-24T08:11:41ZDynamic Bayesian Networks for Evaluation of Granger Causal Relationships in Climate Reanalyses1942-246610.1029/2020MS002442https://doaj.org/article/720c584245b34176b886e6888ef0141d2021-05-01T00:00:00Zhttps://doi.org/10.1029/2020MS002442https://doaj.org/toc/1942-2466Abstract 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 series of empirical indices diagnosing the activity of major tropical, Northern and Southern Hemisphere modes of climate variability in the NCEP/NCAR and JRA‐55 reanalyses. The resulting probabilistic graphical models are comparable to Granger causal analyses that have recently been advocated. Reversible jump Markov Chain Monte Carlo is employed to provide a quantification of the uncertainty associated with the selection of a single network structure. In general, the models fitted from the NCEP/NCAR reanalysis and the JRA‐55 reanalysis are found to exhibit broad agreement in terms of associations for which there is high posterior confidence. Differences between the two reanalyses are found that involve modes for which known biases are present or that may be attributed to seasonal effects, as well as for features that, while present in point estimates, have low overall posterior mass. We argue that the ability to incorporate such measures of confidence in structural features is a significant advantage provided by the Bayesian approach, as point estimates alone may understate the relevant uncertainties and yield less informative measures of differences between products when network‐based approaches are used for model evaluation.Dylan HarriesTerence J. O'KaneAmerican Geophysical Union (AGU)articleBayesiangraphical modelsteleconnectionsPhysical geographyGB3-5030OceanographyGC1-1581ENJournal of Advances in Modeling Earth Systems, Vol 13, Iss 5, Pp n/a-n/a (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Bayesian graphical models teleconnections Physical geography GB3-5030 Oceanography GC1-1581
spellingShingle	Bayesian graphical models teleconnections Physical geography GB3-5030 Oceanography GC1-1581 Dylan Harries Terence J. O'Kane Dynamic Bayesian Networks for Evaluation of Granger Causal Relationships in Climate Reanalyses
description	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 series of empirical indices diagnosing the activity of major tropical, Northern and Southern Hemisphere modes of climate variability in the NCEP/NCAR and JRA‐55 reanalyses. The resulting probabilistic graphical models are comparable to Granger causal analyses that have recently been advocated. Reversible jump Markov Chain Monte Carlo is employed to provide a quantification of the uncertainty associated with the selection of a single network structure. In general, the models fitted from the NCEP/NCAR reanalysis and the JRA‐55 reanalysis are found to exhibit broad agreement in terms of associations for which there is high posterior confidence. Differences between the two reanalyses are found that involve modes for which known biases are present or that may be attributed to seasonal effects, as well as for features that, while present in point estimates, have low overall posterior mass. We argue that the ability to incorporate such measures of confidence in structural features is a significant advantage provided by the Bayesian approach, as point estimates alone may understate the relevant uncertainties and yield less informative measures of differences between products when network‐based approaches are used for model evaluation.
format	article
author	Dylan Harries Terence J. O'Kane
author_facet	Dylan Harries Terence J. O'Kane
author_sort	Dylan Harries
title	Dynamic Bayesian Networks for Evaluation of Granger Causal Relationships in Climate Reanalyses
title_short	Dynamic Bayesian Networks for Evaluation of Granger Causal Relationships in Climate Reanalyses
title_full	Dynamic Bayesian Networks for Evaluation of Granger Causal Relationships in Climate Reanalyses
title_fullStr	Dynamic Bayesian Networks for Evaluation of Granger Causal Relationships in Climate Reanalyses
title_full_unstemmed	Dynamic Bayesian Networks for Evaluation of Granger Causal Relationships in Climate Reanalyses
title_sort	dynamic bayesian networks for evaluation of granger causal relationships in climate reanalyses
publisher	American Geophysical Union (AGU)
publishDate	2021
url	https://doaj.org/article/720c584245b34176b886e6888ef0141d
work_keys_str_mv	AT dylanharries dynamicbayesiannetworksforevaluationofgrangercausalrelationshipsinclimatereanalyses AT terencejokane dynamicbayesiannetworksforevaluationofgrangercausalrelationshipsinclimatereanalyses
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Dynamic Bayesian Networks for Evaluation of Granger Causal Relationships in Climate Reanalyses

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