Machine learning models predict climate effects with causal insights
Learning Hierarchical Causal Representations of the Effects of Forcings on Temperature in Climate Models
Machine Learning
Summary
Climate models are complex and slow to run, so people use machine learning to simulate future temperature changes faster. The authors developed a new machine learning method that not only predicts temperature changes but also explains how greenhouse gases and aerosols cause these changes. Their method works well on data it hasn’t seen before and matches expected physical behaviors. This approach could help improve trust and understanding of climate model predictions.
What this means in practice
- •For climate modelers: Create faster, interpretable climate simulations that distinguish natural variability from causes of temperature change.
- •For environmental policy teams: Evaluate the specific impacts of greenhouse gases and aerosols on temperature to inform regulatory decisions.
Authors
Shan Zhao, Ilija Trajkovic, Julia Kaltenborn, Yaniv Gurwicz, Peer Nowack, David Rolnick, Julien Boussard
Abstract
Machine learning (ML) emulators provide a fast and cost-effective method to simulate climate change scenarios after being trained on Earth System Models projections. However, the black-box nature of those data-driven approaches limit the usability and trustworthiness of their outputs and in particular their use as causal attribution tools. Here, we develop a hierarchical causal representation learning framework applied to sea surface temperature fields from a state-of-the-art global climate model. As a key advance over previous work, our framework explicitly models both atmospheric dynamical interactions arising from internal climate variability and forced responses due to changes in atmospheric greenhouse gas and aerosol concentrations. When trained on future climate change scenarios, our method accurately predicts the long-term global mean and regional temperature evolution and shows physically realistic responses to perturbations in greenhouse gas and aerosol concentrations when evaluated on unseen scenarios. Our results underline the potential of causal representation learning frameworks for advancing climate model emulation.