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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
American Geophysical Union (AGU)
2021-05-01
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| Series: | Journal of Advances in Modeling Earth Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2020MS002442 |