Portrait de Ilija Trajković

Ilija Trajković

ilija.trajkovic@mila.quebec
Collaborateur·rice de recherche - Karlsruhe Institute of Technology
Superviseur⋅e principal⋅e
David Rolnick
Sujets de recherche
Causalité
LinkedIn
GitHub

Publications

Learning Hierarchical Causal Representations of the Effects of Forcings on Temperature in Climate Models
Shan Zhao
Ilija Trajković
Julia Kaltenborn
Yaniv Gurwicz
Peer Nowack
David Rolnick
Julien Boussard
Machine learning (ML) emulators provide a fast and cost-effective method to simulate climate change scenarios after being trained on Earth S… (voir plus)ystem 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.
2026-09-24
arXiv (prépublication)
doi.org
arxiv.org
Causal Climate Emulation with Bayesian Filtering
Sebastian Hickman
Ilija Trajković
Julia Kaltenborn
Francis Pelletier
Alex Archibald
Yaniv Gurwicz
Peer Nowack
David Rolnick
Julien Boussard
Traditional models of climate change use complex systems of coupled equations to simulate physical processes across the Earth system. These … (voir plus)simulations are highly computationally expensive, limiting our predictions of climate change and analyses of its causes and effects. Machine learning has the potential to quickly emulate data from climate models, but current approaches are not able to incorporate physically-based causal relationships. Here, we develop an interpretable climate model emulator based on causal representation learning. We derive a novel approach including a Bayesian filter for stable long-term autoregressive emulation. We demonstrate that our emulator learns accurate climate dynamics, and we show the importance of each one of its components on a realistic synthetic dataset and data from two widely deployed climate models.
2025-12-02
Neural Information Processing Systems (Accept (poster))
doi.org
openreview.net