Articles | Volume 25, issue 21
https://doi.org/10.5194/acp-25-15033-2025
https://doi.org/10.5194/acp-25-15033-2025
Research article
 | 
07 Nov 2025
Research article |  | 07 Nov 2025

Emulating chemistry-climate dynamics with a linear inverse model

Eric J. Mei, Gregory J. Hakim, Max Taniguchi-King, Dominik Stiller, and Alexander J. Turner

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Short summary
Chemistry-climate models are used to investigate how physical climate influences the composition of the atmosphere but are slow and expensive to run. We train a linear inverse model that can replicate the behavior of chemistry-climate models at low computational cost. It captures how large-scale climate features like El Niño affect atmospheric composition and can make accurate forecasts up to a year ahead. This model enables fast hypothesis testing and estimates of past atmospheric composition.
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