The AIGS Pod

Climate simulations, at all grid resolutions, rely on approximations that encapsulate the forcing due to unresolved processes on resolved variables, known as parameterizations. Parameterizations often lead to inaccuracies in climate models, with significant biases in the physics of key climate phenomena. Advances in artificial intelligence (AI) are now directly enabling the learning of unresolved processes from data to improve the physics of climate simulations. This research introduces a flexible framework for developing and implementing physics- and scale-aware machine learning parameterizations within climate models, focusing on the ocean and sea-ice components of a state-of-the-art climate model by implementing a spectrum of data-driven parameterizations, ranging from complex deep learning models to more interpretable equation-based models. The results showcase the viability of AI-driven parameterizations in operational models, advancing the capabilities of a new generation of hybrid simulations, and include prototypes of fully coupled atmosphere-ocean-sea-ice hybrid simulations. The tools developed are open source, accessible, and available to all. Paper: https://arxiv.org/abs/2510.22676

Coarse resolution, imperfect parameterizations, and uncertain initial states and forcings limit Earth-system model (ESM) predictions. Traditional bias correction via data assimilation improves constrained simulations but offers limited benefit once models run freely. This research introduces an operator-learning framework that maps instantaneous model states to bias-correction tendencies and applies them online during integration. Building on a U-Net backbone, two operator architectures—Inception U-Net (IUNet) and a multi-scale network (M&M)—combine diverse upsampling and receptive fields to capture multiscale nonlinear features under Energy Exascale Earth System Model (E3SM) runtime constraints. Trained on two years of E3SM simulations nudged toward ERA5 reanalysis, the operators generalize across height levels and seasons. Both architectures outperform standard U-Net baselines in offline tests, indicating that functional richness rather than parameter count drives performance. In online hybrid E3SM runs, M&M delivers the most consistent bias reductions across variables and vertical levels. The ML-augmented configurations remain stable and computationally feasible in multi-year simulations, providing a practical pathway for scalable hybrid modeling. Paper: https://arxiv.org/abs/2512.03309v1

This research introduces a modified machine-learning (ML) weather emulator designed to accurately predict fast radiative feedbacks in response to varying CO2 levels. While traditional emulators often struggle with global perturbations, the authors developed a column-local architecture for the Allen Institute for Artificial Intelligence Climate Emulator (ACE) to better represent atmospheric physics. By coupling this ML model with a physics-based radiative transfer scheme (RRTMG), the researchers successfully replicated the hydrological and thermal responses found in complex Earth System Models (ESMs). The study demonstrates that emulators trained only on historical climate data can still simulate unprecedented greenhouse gas scenarios by focusing on rapid atmospheric processes. These findings suggest that hybrid ML-physics models can significantly reduce the computational cost of climate projections while maintaining physical reliability. Consequently, this framework offers a powerful new tool for sampling internal atmospheric variability and conducting extensive climate sensitivity experiments. Paper: https://arxiv.org/abs/2602.16090

This research investigates a significant cold bias in modern AI weather and climate models, such as FourCastNet, Pangu, and ACE2, which stems from their reliance on historical training data. By evaluating these models on recent time periods outside of their training sets, the authors discovered that the predicted temperatures often reflect climatic conditions from 15 to 30 years ago rather than current warming trends. The study highlights a "pull" toward the past: weather models struggle to predict extreme heat events due to a lack of modern examples, while the climate model shows the greatest inaccuracies in regions where global warming has been most rapid. Ultimately, the paper argues that even with the inclusion of CO2 data, these data-driven models remain anchored to their training-set history, necessitating new strategies to ensure they can accurately forecast an increasingly hot and unprecedented future. Paper: https://doi.org/10.1029/2025GL119740