Extending Two Explainable Artificial Intelligence Methods for Deep Climate Emulators

Climate change presents a complex and critical challenge that spans forecasting across various temporal horizons to reconstruction using data from sparsely distributed sensors. Recent advancements in deep learning have shown promising results in emulating complex climate dynamics and reconstructing physical fields using real-time measurements. Given their data-driven nature, it is crucial to investigate how these deep emulators learn and represent the underlying physics. This paper aims to address these concerns by employing Explainable Artificial Intelligence (XAI) techniques, focusing specifically on two methods--feature attribution and influence functions and demonstrating their explainability of a cutting-edge implicit neural network that learns a continuous and reliable representation from sparse sampling climate data.