Modeling and Prediction of Large‐Scale Climate Variability by Inferring Causal Structure

Abstract

Abstract This study addresses how to model and predict large‐scale climate variability, such as the El Niño–Southern Oscillation (ENSO). We introduce a framework for inferring the macroscale causal structure of the climate system using a spatial‐dimension reduction and high‐dimensional variable selection. The framework encodes the causal structure into a structural causal model, which captures the mechanisms and diversity of ENSO. It thus has a potential to reveal other physical processes within the climate system. The model predicts ENSO at a 1‐month lead time with high accuracy, and the recursive predictions at multi‐month leads are still reliable, even in a different climate state. The stand‐alone oceanic experiments capture the observed oceanic response, proving the model's capability to predict large‐scale climate variability using fragmentary information. This study demonstrates the potential for inferring causal structures to explain, model, and predict large‐scale climate variability such as ENSO.

Keywords

• causal inference
• large‐scale climate variability
• high‐dimensional statistics