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

Laura Mansfield, Aman Gupta, Adam C. Burnett, Brian Green, Catherine Wilka, Aditi Sheshadri

Journal of Advances in Modeling Earth Systems · 01 October 2023 · Stanford University

In one sentence. Perspective on climate model hierarchies since 2016, highlighting the rapid growth of machine learning in climate modelling and model use for climate risk decisions.

Abstract

Abstract The climate model hierarchy encompasses models of varying complexity along different axes, ranging from idealized models that elegantly describe isolated mechanisms to fully coupled Earth system models that aspire to provide useable climate projections. Based on the second Model Hierarchies Workshop, which took place in 2022, we present perspectives on how this field has evolved since the first Model Hierarchies Workshop in 2016. In this period, we have witnessed a dramatic increase in the use of (a) machine learning in climate modeling and (b) climate models to estimate risks and influence decision making under climate change. Here, we discuss the implications of these growing areas of research and how we expect them to become integrated into the model hierarchies framework.

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