Exploring Design Choices for Autoregressive Deep Learning Climate Models

Published as a workshop paper at "Tackling Climate Change with Machine Learning", ICLR 2025 Deep Learning (DL) models have achieved state-of-the-art performance in medium-range weather prediction (MWP) but often fail to maintain physically consistent rollouts beyond 14 days. In contrast, a few atmospheric models demonstrate stability over decades, though the key design choices enabling this remain unclear. This study quantitatively compares the long-term stability of three prominent DL-MWP architectures -- FourCastNet, SFNO, and ClimaX -- trained on ERA5 reanalysis data at 5. 625 We systematically assess the impact of autoregressive training steps, model capacity, and choice of prognostic variables, identifying configurations that enable stable 10-year rollouts while preserving the statistical properties of the reference dataset. Notably, rollouts with SFNO exhibit the greatest robustness to hyperparameter choices, yet all models can experience instability depending on the random seed and the set of prognostic variables. Over the past few years autoregressive Deep Learning ( DL) models have emerged that are en par with physics-based state-of-the-art medium range weather prediction systems while only requiring a fraction of the computational costs for inference (Lam et al., 2023; Bi et al., 2023; Price et al., 2025).