Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 3
Articles | Volume 18, issue 3
https://doi.org/10.5194/gmd-18-787-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-787-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 3
Review and perspective paper
 | Highlight paper
 | 
11 Feb 2025
Review and perspective paper | Highlight paper |  | 11 Feb 2025

Moving beyond post hoc explainable artificial intelligence: a perspective paper on lessons learned from dynamical climate modeling

Ryan J. O'Loughlin, Dan Li, Richard Neale, and Travis A. O'Brien

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Editorial statement
This perspective paper examines in detail the concept of explicability in a climate model, whether conventional physics-based dynamical models, or those incorporating components based on machine learning. Everyone with an interest in climate models or their outputs would benefit from understanding the processes by which we can understand the importance and accuracy of these models and the methods by which it is possible to make sense of those outputs. This paper is a major contribution to that understanding. It is also very well written and should be widely read in the field.
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Short summary
We draw from traditional climate modeling practices to make recommendations for machine-learning (ML)-driven climate science. Our intended audience is climate modelers who are relatively new to ML. We show how component-level understanding – obtained when scientists can link model behavior to parts within the overall model – should guide the development and evaluation of ML models. Better understanding yields a stronger basis for trust in the models. We highlight several examples to demonstrate.
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