Causal Discovery for Climate Research Using Graphical Models

Abstract

ausal discovery seeks to recover cause?effect relationships from statistical data using graphical models. One goal of this paper is to provide an accessible introduction to causal discovery methods for climate scientists, with a focus on constraint-based structure learning. Second, in a detailed case study constraint-based structure learning is applied to derive hypotheses of causal relationships between four prominent modes of atmospheric low-frequency variability in boreal winter including the Western Pacific Oscillation (WPO), Eastern Pacific Oscillation (EPO), Pacific?North America (PNA) pattern, and North Atlantic Oscillation (NAO). The results are shown in the form of static and temporal independence graphs also known as Bayesian Networks. It is found that WPO and EPO are nearly indistinguishable from the cause?effect perspective as strong simultaneous coupling is identified between the two. In addition, changes in the state of EPO (NAO) may cause changes in the state of NAO (PNA) approximately 18 (3?6) days later. These results are not only consistent with previous findings on dynamical processes connecting different low-frequency modes (e.g., interaction between synoptic and low-frequency eddies) but also provide the basis for formulating new hypotheses regarding the time scale and temporal sequencing of dynamical processes responsible for these connections. Last, the authors propose to use structure learning for climate networks, which are currently based primarily on correlation analysis. While correlation-based climate networks focus on similarity between nodes, independence graphs would provide an alternative viewpoint by focusing on information flow in the network.