Accepted (peer reviewed), NeurIPS 2026 Workshop on Tackling Climate Change with Machine Learning, 2026
Recommended citation: Lin, J., and Barnes, E. A. (2026). Run It Back: Autoregression vs. Video Diffusion for End-Conditioned Weather Trajectories. NeurIPS 2026 Workshop on Tackling Climate Change with Machine Learning. Accepted, peer reviewed.
Accepted (peer reviewed), Bay Area Machine Learning Symposium (BayLearn), 2026
Recommended citation: Lin, J., and Barnes, E. A. (2026). Signoff: Bringing Human Accountability Back to Agent-Powered Research. Bay Area Machine Learning Symposium (BayLearn). Accepted symposium paper, peer reviewed.
Under review · Preprint, arXiv, 2026
Recommended citation: Lin, J., Chien, M.-T., Sakarvadia, M., and Barnes, E. A. (2026). Extremes on Rewind: Generating 1,000-Member Ensembles Initialized at a Final Condition. arXiv:2608.19008. Preprint, under review. https://arxiv.org/abs/2608.19008
Published in Environmental Data Science, 2026
Published as part of the Climate Informatics 2026 proceedings, following the conference’s peer-review process.
Recommended citation: Ferretti, S. L., Lin, J., Shamekh, S., Baldwin, J. W., Pritchard, M. S., and Beucler, T. (2026). Data-driven integration kernels for interpretable nonlocal operator learning. Environmental Data Science, 5, e31. https://doi.org/10.1017/eds.2026.10056
Published in Journal of Advances in Modeling Earth Systems (JAMES), 2026
This paper takes ideas from the winning teams of the 2024 LEAP ClimSim Kaggle competition and systematically tests them offline and online to reveal SOTA performance, universal failure modes, and architecture-dependent idiosyncracies.
Recommended citation: Lin, J., Hu, Z., Beucler, T., Frields, K., Christensen, H., Hannah, W., et al. (2026). Crowdsourcing the frontier: Advancing hybrid physics-ML climate simulation via a $50,000 Kaggle competition. Journal of Advances in Modeling Earth Systems, 18, e2025MS005643. https://doi.org/10.1029/2025MS005643
Published in Journal of Machine Learning Research (JMLR), 2025
This paper builds on ClimSim and introduces a containerized version of E3SM-MMF and PyTorch-Fortran binding to allow for standardized online testing across institutions going forward.
Published in Journal of Advances in Modeling Earth Systems (JAMES), 2025
Building off ClimSim, this paper is the first to yield a stable and accurate machine-learning parameterization of subgrid processes (including explicit cloud condensate coupling) in a comprehensive atmospheric model learned from embedded convection-permitting simulations.
Published in Journal of Advances in Modeling Earth Systems (JAMES), 2025
This paper traverses the offline-online testing gap in hybrid physics-ML climate simulation using statistically meaningful ensembles and identifies the design decisions that durably contribute to better online performance.
Published in Science Advances, 2024
This paper explores feature transformations that can make machine learned convective parameterizations invariant to distribution-shift in climates.
Published in NeurIPS 2023 Climate Change AI Workshop, 2023
This paper samples the coupled behavior of neural network convective parameterizations at scale on an unseen, warmer climate to see if design decisions conducive to better online performance in-distribution do the same out-of-distribution.
Published in NeurIPS 2023 Datasets and Benchmarks Track, 2023
This paper establishes a comprehensive set of datasets and benchmarks for machine learned subgrid parameterizations of convection and radiation in E3SM-MMF. It received the Outstanding Paper award for the Datasets and Benchmarks track at NeurIPS 2023.