New insights into decadal climate variability in the North Atlantic revealed by data-driven dynamical models

Nicoll, Andrew J.; Christensen, Hannah M.; Huntingford, Chris; Smith, Doug

doi:10.5194/esd-17-1061-2026

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.

https://doi.org/10.5194/esd-17-1061-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 17, issue 4

Research article

| Highlight paper

|

07 Aug 2026

Research article | Highlight paper |

| 07 Aug 2026

New insights into decadal climate variability in the North Atlantic revealed by data-driven dynamical models

Andrew J. Nicoll, Hannah M. Christensen, Chris Huntingford, and Doug Smith

Data sets

ERA5 monthly averaged data on single levels from 1940 to present H. Hersbach et al. https://doi.org/10.24381/cds.f17050d7

ERA-20C Project (ECMWF Atmospheric Reanalysis of the 20th Century) European Centre for Medium-Range Weather Forecasts https://doi.org/10.5065/D6VQ30QG

Interactive computing environment

Data-driven dynamical models of the North Atlantic Andrew Nicoll https://doi.org/10.5281/zenodo.17856484

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.