Articles | Volume 17, issue 4
https://doi.org/10.5194/esd-17-1061-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-17-1061-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
07 Aug 2026
Research article | Highlight paper |
| 07 Aug 2026
| 07 Aug 2026
New insights into decadal climate variability in the North Atlantic revealed by data-driven dynamical models
Andrew J. Nicoll
CORRESPONDING AUTHOR
Department of Physics, University of Oxford, Oxford, UK
Hannah M. Christensen
Department of Physics, University of Oxford, Oxford, UK
Chris Huntingford
Centre for Ecology and Hydrology, Wallingford, UK
Doug Smith
Met Office Hadley Centre, Exeter, UK
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Editorial statement
This paper presents data-driven equation discovery as a powerful scientific method to study climate variability, and thereby exemplifies how machine-learning methods do not only work as black boxes, but can also help us better understand complex Earth system dynamics.
This paper presents data-driven equation discovery as a powerful scientific method to study...
Short summary
We use artificial intelligence to learn simple equations from historical climate data that describe how North Atlantic ocean temperature, air pressure and rainfall vary and influence each other over decades. Analysing the model's behaviour and equation terms, we find rainfall strongly feeds back on both the ocean and the atmosphere. These interactions are well captured by the models and allow rainfall to be predicted over the ocean, and nearby regions such as Europe over coming decades.
We use artificial intelligence to learn simple equations from historical climate data that...
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