Preprints
https://doi.org/10.5194/egusphere-2022-25
https://doi.org/10.5194/egusphere-2022-25
 
18 Mar 2022
18 Mar 2022

Four-dimensional temperature, salinity and mixed layer depth in the Gulf Stream, reconstructed from remote sensing and in situ observations with neural networks

Etienne Pauthenet1, Loïc Bachelot2, Kevin Balem1, Guillaume Maze1, Anne-Marie Tréguier1, Fabien Roquet3, Ronan Fablet4, and Pierre Tandeo4 Etienne Pauthenet et al.
  • 1Ifremer, Univ. Brest, CNRS, IRD, Laboratoire d’Océanographie Physique et Spatiale (LOPS), IUEM, 29280, Plouzané, France
  • 2Ifremer, Univ. Brest, CNRS, IRD, Service Ingénierie des Systèmes d’Information (PDG-IRSI-ISI), IUEM, 29280, Plouzané, France
  • 3Department of Marine Sciences, University of Gothenburg, Gothenburg, Sweden
  • 4IMT Atlantique, CNRS UMR Lab-STICC, Brest, France

Abstract. Despite the ever-growing amount of ocean’s data, the interior of the ocean remains under sampled in regions of high variability such as the Gulf Stream. In this context, neural networks have been shown to be effective for interpolating properties and understanding ocean processes. We introduce OSnet (Ocean Stratification network), a new ocean reconstruction system aimed at providing a physically consistent analysis of the upper ocean stratification. The proposed scheme is a bootstrapped multilayer perceptron trained to predict simultaneously temperature and salinity (T-S) profiles down to 1000 m and the Mixed Layer Depth (MLD) from surface data covering 1993 to 2019. OSnet is trained to fit sea surface temperature and sea level anomalies onto all historical in-situ profiles in the Gulf Stream region. To achieve vertical coherence of the profiles, the MLD prediction is used to adjust a posteriori the vertical gradients of predicted T-S profiles, thus increasing the accuracy of the solution and removing vertical density inversions. The prediction is generalized on a 1/4◦ daily grid, producing four-dimensional fields of temperature and salinity, with their associated confidence interval issued from the bootstrap. OSnet profiles have root mean square error comparable with the observation-based Armor3D weekly product and the physics-based ocean reanalysis Glorys12. The maximum of uncertainty is located north of the Gulf Stream, between the shelf and the current, where the thermohaline variability is large. The OSnet reconstructed field is coherent even in the pre-ARGO years, demonstrating the good generalization properties of the network. It reproduces the warming trend of surface temperature, the seasonal cycle of surface salinity and mesoscale structures of temperature, salinity and MLD. While OSnet delivers an accurate interpolation of the ocean’s stratification, it is also a tool to study how the interior of the ocean’s behaviour reflects on surface data. We can compute the relative importance of each input for each T-S prediction and analyse how the network learns which surface feature influences most which property and at which depth. Our results are promising and demonstrate the power of machine learning methods to improve the prediction of ocean interior properties from observations of the ocean surface.

Journal article(s) based on this preprint

25 Aug 2022
Four-dimensional temperature, salinity and mixed-layer depth in the Gulf Stream, reconstructed from remote-sensing and in situ observations with neural networks
Etienne Pauthenet, Loïc Bachelot, Kevin Balem, Guillaume Maze, Anne-Marie Tréguier, Fabien Roquet, Ronan Fablet, and Pierre Tandeo
Ocean Sci., 18, 1221–1244, https://doi.org/10.5194/os-18-1221-2022,https://doi.org/10.5194/os-18-1221-2022, 2022
Short summary

Etienne Pauthenet et al.

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-25', Anonymous Referee #1, 26 Apr 2022
    • AC1: 'Reply on RC1', Etienne Pauthenet, 22 Jun 2022
  • RC2: 'Comment on egusphere-2022-25', Michel CREPON, 29 May 2022
    • AC2: 'Reply on RC2', Etienne Pauthenet, 22 Jun 2022

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-25', Anonymous Referee #1, 26 Apr 2022
    • AC1: 'Reply on RC1', Etienne Pauthenet, 22 Jun 2022
  • RC2: 'Comment on egusphere-2022-25', Michel CREPON, 29 May 2022
    • AC2: 'Reply on RC2', Etienne Pauthenet, 22 Jun 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Etienne Pauthenet on behalf of the Authors (22 Jun 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (11 Jul 2022) by Anna Rubio

Journal article(s) based on this preprint

25 Aug 2022
Four-dimensional temperature, salinity and mixed-layer depth in the Gulf Stream, reconstructed from remote-sensing and in situ observations with neural networks
Etienne Pauthenet, Loïc Bachelot, Kevin Balem, Guillaume Maze, Anne-Marie Tréguier, Fabien Roquet, Ronan Fablet, and Pierre Tandeo
Ocean Sci., 18, 1221–1244, https://doi.org/10.5194/os-18-1221-2022,https://doi.org/10.5194/os-18-1221-2022, 2022
Short summary

Etienne Pauthenet et al.

Data sets

Gulf Stream Daily Temperature, Salinity and Mixed Layer Depth fields from Ocean Stratification network (OSnet). Pauthenet, Etienne; Bachelot, Loïc; Tréguier, Anne-Marie; Balem, Kevin; Maze, Guillaume; Roquet, Fabien; Fablet, Ronan; Tandeo, Pierre https://doi.org/10.5281/zenodo.6011144

Model code and software

OSnet Gulf Stream Etienne Pauthenet, Loïc Bachelot, Guillaume Maze, Kevin Balem https://github.com/euroargodev/OSnet-GulfStream

Etienne Pauthenet et al.

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Short summary
Temperature and salinity profiles are essential to study the ocean’s stratification, but there is not enough of this data. Satellites are able to measure daily maps of the surface ocean. We train a machine to learn the link between the satellite data and the profiles, in the Gulf Stream region. We can then use this link to predict profiles at the high resolution of the satellite maps. Our prediction is fast to compute and allows to get profiles at any locations, only from surface data.