On the global reconstruction of ocean interior variables: a feasibility data-driven study with simulated surface and water column observations

Garcia-Espriu, Aina; González-Haro, Cristina; Aguilar-Gómez, Fernando

doi:https://doi.org/10.5194/os-21-2579-2025

Articles | Volume 21, issue 5

https://doi.org/10.5194/os-21-2579-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/os-21-2579-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 21, issue 5

Research article

|

24 Oct 2025

Research article |

| 24 Oct 2025

On the global reconstruction of ocean interior variables: a feasibility data-driven study with simulated surface and water column observations

Aina Garcia-Espriu, Cristina González-Haro, and Fernando Aguilar-Gómez

Data sets

Global Ocean Ensemble Physics Reanalysis Mercator Océan International https://doi.org/10.48670/moi-00024

Argo float data and metadata from Global Data Assembly Centre (Argo GDAC) Argo https://doi.org/10.17882/42182

Mixed layer depth climatology computed with a density threshold criterion of 0.03kg/m3 from 10 m depth value C. de Boyer Montégut https://doi.org/10.17882/91774

Model code and software

ainagarciaes/SSS-SST-4D-Reconstruction: v1 (Version v1) Aina García-Espriu https://doi.org/10.5281/zenodo.11487678

Short summary

Ocean measurements currently rely on buoys for depth data and satellites for surface observations. We investigated combining these using data-driven approaches to reconstruct full 4D ocean profiles. Using an ocean model as ground truth, we simulated satellite surface data and ARGO profiles and then applied machine learning to predict complete temperature and salinity profiles. Results showed accurate predictions that matched simulation data and captured seasonal patterns.