Performance of Spatiotemporal Causal Effect Estimation in Coupled Climate Models

Climate scientists are increasingly exploring the possible applications of artificial intelligence to climate modeling, whether for use inside the model to replace parameterized model components, or for use separately as an emulator of observed or simulated climate. However, a major limitation of standard artificial intelligence techniques is that they cannot distinguish between statistical association and causality. While this is not a drawback for the purpose of statistical prediction in an unchanging system, it can pose a problem for generalization of parameterizations and emulators under climate change, and furthermore, it means that it is not sound to use such techniques to predict the response of the climate system to unobserved interventions, including proposed climate engineering initiatives. The framework of causal inference attempts to address this limitation, providing techniques for discovering qualitative (“discovery”) and quantitative (“effect estimation”) information about the system’s response to interventions from purely observational data (or imperfect experiments) using causal reasoning. However, it was not originally developed for application to spatiotemporal dynamical systems such as the climate system.

In previous work, we develop a unified framework for causal effect estimation in spatiotemporal dynamical systems. In contrast to the hard interventions on univariate representations of coupled climate phenomena that until now have been more commonly used, our framework allows the user to investigate the effect of a spatiotemporal perturbation on a climate variable in one finite region on another variable in a different finite region at another time after specifying the qualitative causal relationships between the regions as a whole. This framework advances causal effect estimation for climate science because spatiotemporal perturbations are better defined, more actionable, and more interpretable than hard interventions on conceptual climate phenomena.

Here, we evaluate its performance using CMIP6-class models, focusing initially on the effect of the El Niño Southern Oscillation (ENSO) on the North Atlantic Oscillation as an example query. We assess the robustness of the method to data sample size, resolution, and other methodology choices by comparing the causal effect for a given model calculated from different subsets of its pre-Industrial control simulation using various amounts of spatial data and various values of other parameters of the algorithm. We use these results to assess the expected uncertainty on any inferences made using this technique from the short observational record or CMIP6 historical simulations, and make recommendations for best practices in different circumstances. Finally, we evaluate the accuracy of the predictions by using a causal model trained on historical simulations to predict the output of Tropical Basin Interaction Model Intercomparison Project experiments from the same climate model that nudge Pacific Sea Surface Temperature in the ENSO region in a manner comparable to our perturbation intervention.