Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 12, issue 5
Articles | Volume 12, issue 5
https://doi.org/10.5194/gmd-12-2091-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-12-2091-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 12, issue 5
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, Marion Gehlen, Mathieu Vrac, and Carlos Mejia

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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.
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