Representing atmospheric turbulence in climate models requires estimating unresolved small-scale forces. This study , led by Alex Connolly, uses a deep neural network (DNN) to improve turbulence modeling by predicting these forces in large-eddy simulations (LES) of the atmospheric boundary layer. The DNN is trained on high-resolution data and tested across different conditions. Results show that models using physics-based scaling perform better than those relying only on statistical normalization. Embedding physical symmetries into the model further improves accuracy. This research highlights the importance of physics-informed machine learning for improving turbulence representation in Earth system models for more reliable climate projections.