Explainable Quantum Machine Learning for cloud cover parameterization

Kurzfassung

Cloud cover parameterizations play an important role in climate models, influencing radiative transfer, atmospheric dynamics, and hydrological cycles. However, their correct formulation remains a major challenge in climate modeling, as subgrid-scale cloud processes must be approximated. Classical and quantum Neural Networks have been proposed as potential tools for improving these parameterizations. While previous studies suggest that classical neural networks can produce physically meaningful results, it remains unclear whether quantum neural networks (QNNs) exhibit similar capabilities or rely on spurious correlations. This study begins by comparing a classical neural network and a quantum neural network to assess whether they capture comparable physical dependencies when predicting cloud cover, before exploring alternative architectures for each approach. Using explainable AI (XAI) techniques, specifically SHapley Additive exPlanations (SHAP), the learned feature dependencies in both types of models are analyzed. This approach enables us to evaluate not only predictive performance but also the extent to which each model captures the underlying physics of cloud cover. Our results show that both classical and quantum models exhibit similar learning patterns, extracting comparable relationships from the data. While the QNN does not outperform the classical network, it achieves comparable results, suggesting that quantum machine learning (QML) could be a viable approach in this domain. These findings contribute to the ongoing exploration of QML in climate science and highlight the potential of quantum methods for atmospheric modeling. More broadly, this study supports the integration of machine learning into climate science while ensuring physical consistency and interpretability.