Following Pedram’s invitation to focus on the interpretability of Earth System Models, we will present a few examples from our recent work. First, we will illustrate the important distinction between interpretability at the algorithmic level and interpretability of emergent properties in natural systems, using recent work on land versus ocean warming rates. Then, we will discuss how we are using emergent properties to interpret the skill of machine learning emulators. If time permits, we will conclude by emphasizing the importance of training and validation datasets when interpreting Earth System models, whether physics- or AI-based.