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Journal Article

Enhancing reproducibility in hybrid Earth system models

Authors

Chen,  Min
External Organizations;

Zhu,  Zhiyi
External Organizations;

Wagener,  Thorsten
External Organizations;

/persons/resource/Niklas.Boers

Boers,  Niklas       
Potsdam Institute for Climate Impact Research;

Müller,  R. Dietmar
External Organizations;

Strobl,  Josef
External Organizations;

Camps-Valls,  Gustau
External Organizations;

Batty,  Michael
External Organizations;

Jakeman,  Anthony J.
External Organizations;

Kolditz,  Olaf
External Organizations;

Nativi,  Stefano
External Organizations;

Brovelli,  Maria Antonia
External Organizations;

/persons/resource/Felix.Creutzig

Creutzig,  Felix       
Potsdam Institute for Climate Impact Research;

Kumar,  Pankaj
External Organizations;

Whitehead,  Paul
External Organizations;

Barton,  C. Michael
External Organizations;

Liu,  Dichen
External Organizations;

Ma,  Peilong
External Organizations;

Ma,  Zaiyang
External Organizations;

Zhang,  Fengyuan
External Organizations;

Zhang,  Bo
External Organizations;

Hou,  Peng
External Organizations;

Lü,  Guonian
External Organizations;

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Citation

Chen, M., Zhu, Z., Wagener, T., Boers, N., Müller, R. D., Strobl, J., Camps-Valls, G., Batty, M., Jakeman, A. J., Kolditz, O., Nativi, S., Brovelli, M. A., Creutzig, F., Kumar, P., Whitehead, P., Barton, C. M., Liu, D., Ma, P., Ma, Z., Zhang, F., Zhang, B., Hou, P., Lü, G. (2026 online): Enhancing reproducibility in hybrid Earth system models. - Nature Machine Intelligence.
https://doi.org/10.1038/s42256-026-01299-5


Cite as: https://publications.pik-potsdam.de/pubman/item/item_34876
Abstract
The integration of artificial intelligence into Earth system models (ESMs) has revolutionized the simulation and prediction of complex environmental dynamics. However, this shift introduces substantial challenges for reproducibility, a cornerstone of scientific progress. In particular, artificial intelligence-infused hybrid ESMs face amplified issues of numerical instability, procedural opacity and asymmetric access to computational resources. If left unaddressed, these challenges risk turning hybrid ESMs into opaque and weakly verifiable systems, reducing model traceability, weakening cumulative knowledge building and narrowing the evidential basis for climate risk assessment and policy guidance. This Perspective argues that reproducibility should be reframed to reflect the epistemological and operational realities of hybrid ESMs. We propose an integrated roadmap that couples a theory of reproducibility assessment with practical pathways for implementation in modelling practices. Within this context, we introduce Reproducibility in hybrid Earth system models (RHEM) as a reference guideline for governing transparent, trustworthy, and reproducible hybrid ESMs. Building on this foundation, the pathways operationalize the framework’s criteria into actionable measures that embed transparency and trustworthiness in the modelling process itself. By linking conceptual structure with operational guidance, reproducibility is repositioned from a post hoc requirement to a structural property of hybrid ESMs and established as a foundational principle for Earth system science in the artificial intelligence era.