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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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.