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.

中文翻译：

---

气候再分析中格兰杰因果关系评估的动态贝叶斯网络

我们使用贝叶斯结构学习方法来研究全球气候模式之间的相互作用，因此说明了它作为开发基于过程的诊断程序以评估气候模型的框架的用途。建立均质动态贝叶斯网络模型以建立经验指标的时间序列，以诊断NCEP / NCAR和JRA-55再分析中主要的热带，北半球和南半球气候变异模式的活动。由此产生的概率图形模型可与最近提倡的格兰杰因果分析进行比较。可逆跳马尔可夫链蒙特卡罗用于对与选择单个网络结构相关的不确定性进行量化。一般来说，从NCEP / NCAR再分析和JRA-55再分析中拟合出的模型在后验置信度高的关联方面被发现表现出广泛的一致性。发现两次重新分析之间的差异涉及存在已知偏差或可能归因于季节性影响的模式以及点估计中存在的总后验质量低的特征。我们认为，将这种置信度度量纳入结构特征的能力是贝叶斯方法所提供的显着优势，因为当使用基于网络的方法用于点之间的估计时，仅点估计就可能低估了相关的不确定性，并且得出的产品间差异的信息较少。模型评估。发现两次重新分析之间的差异涉及存在已知偏差或可能归因于季节性影响的模式以及点估计中存在的总后验质量低的特征。我们认为，将这种置信度度量纳入结构特征的能力是贝叶斯方法所提供的显着优势，因为当使用基于网络的方法来评估产品之间的差异时，仅点估计就可能低估了相关的不确定性，并且产生的信息量较少。模型评估。发现两次重新分析之间的差异涉及存在已知偏差或可能归因于季节性影响的模式以及点估计中存在的总后验质量低的特征。我们认为将这种置信度度量结合到结构特征中的能力是贝叶斯方法提供的显着优势，因为当使用基于网络的方法用于点之间的估计时，仅点估计就可能低估了相关的不确定性，并且得出的产品之间差异的信息较少。模型评估。