Estimating the mixed layer depth of the global ocean by combining multisource remote sensing and spatiotemporal deep learning

ABSTRACT

Estimating the ocean mixed layer depth (MLD) is crucial for studying the atmosphere-ocean interaction and global climate change. Satellite observations can accurately estimate the MLD over large scales, effectively overcoming the limitation of sparse in situ observations and reducing uncertainty caused by estimation based on in situ and reanalysis data. However, combining multisource satellite observations to accurately estimate the global MLD is still extremely challenging. This study proposed a novel Residual Convolutional Gate Recurrent Unit (ResConvGRU) neural networks, to accurately estimate global MLD along with multisource remote sensing data and Argo gridded data. With the inherent spatiotemporal nonlinearity and dependence of the ocean dynamic process, the proposed method is effective in spatiotemporal feature learning by considering temporal dependence and capturing more spatial features of the ocean observation data. The performance metrics show that the proposed ResConvGRU outperforms other well-used machine learning models, with a global determination coefficient (R²) and a global root mean squared error (RMSE) of 0.886 and 17.83 m, respectively. Overall, the new deep learning approach proposed is more robust and advantageous in data-driven spatiotemporal modeling for retrieving ocean MLD at the global scale, and significantly improves the estimation accuracy of MLD from remote sensing observations.

KEYWORDS:

• Mixed layer depth
• remote sensing observations
• residual convolutional gate recurrent unit
• global ocean

Acknowledgments

We thank the International Pacific Research Center (IPRC) for the Argo gridded data (http://apdrc.soest.hawaii.edu/datadoc/argo_iprc_gridded.php), the Copernicus Marine Environment Monitoring Service (CMEMS) for the SSS and SSD data (https://resources.marine.copernicus.eu/product-detail/MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013/INFORMATION), the AVISO altimetry for the SWH and ADT data (https://www.aviso.altimetry.fr/en/data/products/wind/wave-products/mswh/mwind.html; https://www.aviso.altimetry.fr/en/data/products/sea-surface-height-products/global/gridded-sea-level-heights-and-derived-variables.html), the National Oceanic and Atmospheric Administration (NOAA) for the OISST SST data (https://www.ncei.noaa.gov/products/optimum-interpolation-sst), and the Research Data Archive at the NCAR for the CCMP SSW data (https://rda.ucar.edu/datasets/ds745.1/), which are freely accessible for the public.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data supporting the findings of this study are available upon request from the corresponding author.

Additional information

Funding

This study was supported by the National Natural Science Foundation of China [grant no 41971384], and the Natural Science Foundation for Distinguished Young Scholars of Fujian Province of China [grant no 2021J06014].