Articles | Volume 12, issue 5
Geosci. Model Dev., 12, 2091–2105, 2019
https://doi.org/10.5194/gmd-12-2091-2019
Geosci. Model Dev., 12, 2091–2105, 2019
https://doi.org/10.5194/gmd-12-2091-2019

Model description paper 29 May 2019

Model description paper | 29 May 2019

LSCE-FFNN-v1: a two-step neural network model for the reconstruction of surface ocean pCO2 over the global ocean

Anna Denvil-Sommer et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Anna Denvil-Sommer on behalf of the Authors (17 Apr 2019)  Author's response    Manuscript
ED: Publish as is (23 Apr 2019) by James Annan
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Short summary
This work is dedicated to a new model that reconstructs the surface ocean partial pressure of carbon dioxide (pCO2) over the global ocean on a monthly 1°×1° grid. The model is based on a feed-forward neural network and represents the nonlinear relationships between pCO2 and the ocean drivers. Reconstructed pCO2 has a satisfying accuracy compared to independent observational data and shows a good agreement in seasonal and interannual variability with three existing mapping methods.