Lagrangian evaluation of surface transport around the Canary Islands using drifter observations and OpenDrift simulations

The prediction of ocean surface trajectories remains a key challenge in coastal and island-influenced regions, were strong spatial variability limits model skill. Previous Lagrangian studies have shown the usefulness of drifter observations to assess trajectory predictability and to compare different sources of surface currents (e.g. Dagestad and Röhrs, 2019). In this context, Lagrangian approaches provide a direct and observation-based framework to evaluate surface transport.
This study assesses surface transport predictability around the Canary Islands using trajectories from two surface drifters (CODE/Davis type, drogued at 1 m depth) and numerical simulations performed with the OpenDrift framework (Dagestad et al., 2018). Simulations are forced with surface currents from the Iberia–Biscay–Ireland (IBI) regional ocean model distributed by the Copernicus Marine Environment Monitoring Service (CMEMS), and, where available, from the high-resolution coastal forecasting system SAMOA (Sotillo et al., 2019), operationally implemented for Spanish ports. Wind forcing is provided by ERA5 atmospheric fields, and wave-induced Stokes drift is included using IBI wave products from CMEMS.
From each observed drifter position, short-term forward simulations are performed to predict the subsequent drifter location. Model performance is quantified through the separation distance between simulated and observed positions, allowing a direct comparison of transport skill between different current products and forcing configurations.
The oceanic and atmospheric datasets used in this study correspond to operational or near-real-time products rather than fully consolidated reanalysis, reflecting realistic conditions for trajectory forecasting applications. The results reveal pronounced spatial and temporal variability in the separation between modeled and observed positions, with the relative performance of SAMOA and IBI depending on location and conditions, and neither consistently outperforming the other. While further improvements in transport predictability are expected once consolidated reanalysis products become available, the present results already provide a robust assessment of Lagrangian model skill under operational conditions.

Acknowledgments:
This work was supported by the projects SIRENA and SIRENA 2, funded by the collaboration of the Biodiversity Foundation of the Ministry for the Ecological Transition and the Demographic Challenge, through the Pleamar Program, and are co-financed by the European Union through the EMFAF (European Maritime, Fisheries and Aquaculture Fund).

References:
Dagestad, K.-F., Röhrs, J., Breivik, Ø., & Ådlandsvik, B. (2018): OpenDrift v1.0: a generic framework for trajectory modelling, Geoscientific Model Development, 11, 1405–1420, https://doi.org/10.5194/gmd-11-1405-2018
Dagestad, K.-F., & Röhrs, J. (2011): Prediction of ocean surface trajectories using satellite derived vs. modeled ocean currents, Ocean Modelling. https://doi.org/10.1016/j.rse.2019.01.001
Sotillo, M. G., Cerralbo, P., Lorente, P., Grifoll, M., Espino, M., Sanchez-Arcilla, A., & Álvarez-Fanjul, E. (2019): Coastal ocean forecasting in Spanish ports: the SAMOA operational service, Journal of Operational Oceanography, 13, 37–54, https://doi.org/10.1080/1755876X.2019.1606765
Copernicus Marine Environment Monitoring Service (CMEMS): IBI Ocean Currents Product, https://doi.org/10.48670/moi-00027
Copernicus Marine Environment Monitoring Service (CMEMS): IBI Stokes Drift Product, https://doi.org/10.48670/moi-00025
Hersbach, H. et al. (2020): ERA5 global reanalysis, Copernicus Climate Change Service (C3S), https://doi.org/10.24381/cds.adbb2d47