What Can We Learn about Climate Model Runs from Their Causal Signatures?

This study applies methods from causal discovery theory to the output data of climate models. Causal discovery seeks to identify potential cause-effect relationships from data and is used here to learn so-called causal signatures from the data that indicate interactions between the different atmospheric variables. We hope that these causal signatures can act like finger prints for the underlying dynamics, and can as such be used in a variety of applications. Sample applications include (1) distinguishing correct model runs from incorrect ones, i.e. providing an additional error check for climate model runs and (2) assessing the impact of data compression on the causal signatures, as a means to determine which type and amount of compression is acceptable. Still being in the early stages of this project, we primarily describe work in progress and future work.