Research

My work spans scientific machine learning, Earth system science, and the cryosphere. Publications are grouped below by the research themes they contribute to; for the most current and complete list, see my Google Scholar profile.

Emulating Numerical Models

My work on emulators focuses on future climate projections, using machine learning to make knowledge encoded in expensive numerical models more accessible across different Earth system models and forcing scenarios.

ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning

Julia Kaltenborn, Charlotte E. E. Lange, Venkatesh Ramesh, Philippe Brouillard, Yaniv Gurwicz, Chandni Nagda, Jakob Runge, Peer Nowack, David Rolnick

NeurIPS Datasets and Benchmarks Track · 2023

Emulating the Forced Response of Climate Models with Generative Machine Learning

Graham Clyne, Julia Kaltenborn, Peer Nowack, Claire Monteleoni, Anastase Alexandre Charantonis

arXiv preprint · 2026 · under review

Integrating Partial Observations

My work explores how machine learning can integrate partial observations scattered across sources, such as different sensors, to build a more complete picture of cryospheric systems.

SnowGalileo: A Pre-trained Earth Observation Transformer for Daily, 100 m Fractional Snow Cover Mapping

Marlena Reil*, Julia Kaltenborn*, Donovan J. M. Allum, Francis Pelletier, Sebastian Roessler, Samip Shrestha, Zhibang Lv, John Truckenbrodt, Gabriele Schwaizer, Thomas Nagler, John W. Pomeroy, Christopher B. Marsh, Benoit Montpetit, Tobias Jonas, Gabriel Tseng, David Rolnick, Andreas J. Dietz, Celia A. Baumhoer

EGUsphere preprint (submitted to The Cryosphere) · 2026 · under review

Interrogating ML Models

My work explores causal representation learning as a path toward scientifically interrogating learned models of Earth systems, for example by enabling counterfactual questions about what they have learned.

Learning a Spatial Partitioning and its Causal Relations from Temporal Data

Philippe Brouillard, Sébastien Lachapelle, Julia Kaltenborn, Yaniv Gurwicz, Dhanya Sridhar, Alexandre Drouin, Peer Nowack, Jakob Runge, David Rolnick

Conference on Causal Learning and Reasoning (CLeaR) · 2026

Causal Climate Emulation with Bayesian Filtering

Sebastian H. M. Hickman, Ilija Trajković, Julia Kaltenborn, Francis Pelletier, Alex Archibald, Yaniv Gurwicz, Peer Nowack, David Rolnick, Julien Boussard

NeurIPS · 2025

Towards Causal Representations of Climate Model Data

Julien Boussard, Chandni Nagda, Julia Kaltenborn, Charlotte Emilie Elektra Lange, Philippe Brouillard, Yaniv Gurwicz, Peer Nowack, David Rolnick

NeurIPS Workshop on Tackling Climate Change with Machine Learning · 2023