Enhancing reproducibility in hybrid Earth system models

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. AI integration in Earth system models enhances prediction and modelling capabilities but also amplifies challenges for reproducibility. This Perspective introduces a framework for assessing reproducibility and provides practical ways to strengthen reproducibility in hybrid Earth system models.