Using Causal Discovery Algorithms to Learn About Our Planet’s Climate

Abstract

Causal discovery is the process of identifying potential cause-and-effect relationships from observed data. We use causal discovery to construct networks that track interactions around the globe based on time series data of atmospheric fields, such as daily geopotential height data. The key idea is to interpret large-scale atmospheric dynamical processes as information flow around the globe and to identify the pathways of this information flow using causal discovery and graphical models. We first review the basic concepts of using causal discovery, specifically constraint-based structure learning of probabilistic graphical models. Then we report on our recent progress, including some results on anticipated changes in the climate’s network structure for a warming climate and computational advances that allow us to move to three-dimensional networks.