Understanding Uncertainty in Ocean Transport Inferred from Multiple Data Sources

The growing availability of multiple operational ocean data services provides unprecedented opportunities for applications such as environmental incident response, search and rescue operations, and maritime management. At the same time, despite their widespread use, most ocean datasets offer limited information regarding their performance and consistency with real-world observations.

In this presentation, I address this gap by introducing a methodology to assess uncertainty in ocean transport predictions derived from different ocean data products. Building on recent work that links transport uncertainty—understood here as deviations from ground truth—to invariant dynamical structures in the ocean [1–3], the proposed approach, discussed in [4], exploits these links to guide statistical averaging strategies. We examine how well model-predicted material transport aligns with observational evidence across different dynamical scales, including scales above the mesoscale, the mesoscale, and the submesoscale. This perspective provides a systematic pathway for quantifying the performance of different data sources and assessing their overall quality and reliability.

References:

[1] G. García-Sánchez, A. M. Mancho, A. G. Ramos, J. Coca, B. Pérez-Gómez, E. Alvarez-Fanjul, M. G. Sotillo, M. García-León, V. J. García-Garrido, S. Wiggins. Very High Resolution Tools for the Monitoring and Assessment of Environmental Hazards in Coastal Areas. Frontiers in Marine 7, 605804 (2021).

[2] G. García-Sánchez, A. M. Mancho, S. Wiggins. A bridge between invariant dynamical structures and uncertainty quantification. Commun. Nonlinear Sci. Numer. Simul. 104, 106016 (2022).

[3] G. García-Sánchez, A. M. Mancho, M. Agaoglou, S. Wiggins. New links between invariant dynamical structures and uncertainty quantification. Physica D 453 133826 (2023).

[4] G. García-Sánchez, M. Agaoglou, E.M.C Smith, A. M. Mancho. A Lagrangian uncertainty quantification approach to validate ocean model datasets. Physica D 475 134690 (2025).

Acknowledgments:

Support from PIE project Ref. 202250E001 funded by CSIC, from grant PID2021-123348OB-I00 funded by MCIN/ AEI /10.13039/501100011033/ and by FEDER A way for making Europe.