Ocean Digital Twins, Hybrid Architectures, and Reproducible Frameworks for Marine Ecosystems

This article explores the concept and architecture of ocean digital twins, focusing on reproducible frameworks linking observational data, AI, and simulation models. It defines core layers data ingestion, preprocessing, modeling, orchestration, and visualization emphasizing hybrid approaches combining physics-based simulations and machine learning. It highlights modularity, interoperability, and scalability for ecosystem understanding and decision support. Key aspects include managing large datasets, predictive modeling, anomaly detection, reproducibility, and experiment tracking. The work also examines dashboards and web apps for visualization and early warnings. A case study presents a minimal viable twin for sea surface temperature forecasting. Overall, ocean digital twins emerge as transformative tools for science, climate policy, education, and marine conservation.All the code can be consulted at: https://github.com/JAIME6609/WATER-MODELS/blob/main/GEMELOS-DIGITALES-01-ENGLISH.py