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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Cited articles

Amari, S., Murata, N., Müller, K.-R., Finke, M., and Yang, H. H.: Asymptotic Statistical Theory of Overtraining and Cross-Validation, IEEE T. Neural Networ., 8, 985–996, 1997. 
Aumont, O. and Bopp, L.: Globalizing results from ocean in situ iron fertilization studies, Global Biogeochem. Cy., 20, GB2017, https://doi.org/10.1029/2005GB002591, 2006. 
Bishop, C. M.: Neural Networks for Pattern Recognition, Oxford University Press, Cambridge, UK, 1995. 
Bishop, C. M.: Pattern Recognition and Machine Learning, Springer, Berlin, 2006. 
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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.