Updates on Model Hierarchies for Understanding and Simulating the Climate System: A Focus on Data-Informed Methods and Climate Change Impacts

The climate modeling community often describes the variety of models used to understand and simulate climate processes as a hierarchy in complexity. Simple idealized models exist at the bottom of the hierarchy and are useful for explaining underlying physics, while fully coupled Earth system models exist at the top of the hierarchy and aim to provide useable climate projections. We present perspectives on how the model hierarchy field is evolving, focusing on two noticeable changes in recent years. Firstly, models are increasingly using machine learning, and secondly, there has been a growing interest in the usability of climate models, for instance, for estimating risks associated with climate change. Here, we discuss the implications of these growing areas of interest and how we expect them to become integrated into the model hierarchies framework.