Articles | Volume 18, issue 4
https://doi.org/10.5194/gmd-18-1041-2025
https://doi.org/10.5194/gmd-18-1041-2025
Development and technical paper
 | 
24 Feb 2025
Development and technical paper |  | 24 Feb 2025

Using feature importance as an exploratory data analysis tool on Earth system models

Daniel Ries, Katherine Goode, Kellie McClernon, and Benjamin Hillman

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Latest update: 23 Apr 2025
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Short summary
Machine learning has advanced research in the climate science domain, but its models are difficult to understand. In order to understand the impacts and consequences of climate interventions such as stratospheric aerosol injection, complex models are often necessary. We use a case study to illustrate how we can understand the inner workings of a complex model. We present this technique as an exploratory tool that can be used to quickly discover and assess relationships in complex climate data.

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