Articles | Volume 17, issue 4
Ocean Sci., 17, 1011–1030, 2021
https://doi.org/10.5194/os-17-1011-2021
Ocean Sci., 17, 1011–1030, 2021
https://doi.org/10.5194/os-17-1011-2021

Research article 02 Aug 2021

Research article | 02 Aug 2021

Observation system simulation experiments in the Atlantic Ocean for enhanced surface ocean pCO2 reconstructions

Anna Denvil-Sommer et al.

Related authors

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
Geosci. Model Dev., 12, 2091–2105, https://doi.org/10.5194/gmd-12-2091-2019,https://doi.org/10.5194/gmd-12-2091-2019, 2019
Short summary

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. 
Argo: Argo float data and metadata from Global Data Assembly Centre (Argo GDAC), SEANOE [data set], https://doi.org/10.17882/42182, 2000. 
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. 
Biogeochemical-Argo Planning Group: The scientific rationale, design and Implementation Plan for a Biogeochemical-Argo float array, edited by: Johnson, K. and Claustre, H., Ifremer, https://doi.org/10.13155/46601, 2016. 
Download
Short summary
In this work we explored design options for a future Atlantic-scale observational network enabling the release of carbon system estimates by combining data streams from various platforms. We used outputs of a physical–biogeochemical global ocean model at sites of real-world observations to reconstruct surface ocean pCO2 by applying a non-linear feed-forward neural network. The results provide important information for future BGC-Argo deployment, i.e. important regions and the number of floats.