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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers | Copyright
https://doi.org/10.5194/gmd-2018-247
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Model description paper 07 Nov 2018

Model description paper | 07 Nov 2018

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This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

FFNN-LSCE: A two-step neural network model for the reconstruction of surface ocean pCO2 over the Global Ocean

Anna Denvil-Sommer1, Marion Gehlen1, Mathieu Vrac1, and Carlos Mejia2 Anna Denvil-Sommer et al.
  • 1Laboratoire des Sciences du Climat et de l’Environnement (LSCE), Institut Pierre Simon Laplace (IPSL), CNRS/CEA/UVSQ/Univ. Paris-Saclay, Orme des Merisiers, Gif-Sur-Yvette, 91191, France
  • 2Sorbonne Université, CNRS, IRD, MNHN, Institut Pierre Simon Laplace (IPSL), Paris, 75005, France

Abstract. A new Feed-Forward Neural Network (FFNN) model is presented to reconstruct surface ocean partial pressure of carbon dioxide (pCO2) over the global ocean. The model consists of two steps: (1) reconstruction of pCO2 climatology and (2) reconstruction of pCO2 anomalies with respect to the climatology. For the first step, a gridded climatology was used as the target, along with sea surface salinity and temperature (SSS and SST), sea surface height (SSH), chlorophyll a (Chl), mixed layer depth (MLD), as well as latitude and longitude as predictors. For the second step, data from the Surface Ocean CO2 Atlas (SOCAT) provided the target. The same set of predictors was used during step 2 augmented by their anomalies. During each step, the FFNN model reconstructs the non-linear relations between pCO2 and the ocean predictors. It provides monthly surface ocean pCO2 distributions on a 1ºx1º grid for the period 2001–2016. Global ocean pCO2 was reconstructed with a satisfying accuracy compared to independent observational data from SOCAT. However, errors are larger in regions with poor data coverage (e.g. Indian Ocean, Southern Ocean, subpolar Pacific). The model captured the strong interannual variability of surface ocean pCO2 with reasonable skills over the Equatorial Pacific associated with ENSO (El Niño Southern Oscillation). Our model was compared to three pCO2 mapping methods that participated in the Surface Ocean pCO2 Mapping intercomparison (SOCOM) initiative. We found a good agreement in seasonal and interannual variabilty between the models over the global ocean. However, important differences still exist at the regional scale, especially in the Southern hemisphere and in particular, the Southern Pacific and the Indian Ocean, as these regions suffer from poor data-coverage. Large regional uncertainties in reconstructed surface ocean pCO2 and sea-air CO2 fluxes have a strong influence on global estimates of CO2 fluxes and trends.

Anna Denvil-Sommer et al.
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Status: open (until 02 Jan 2019)
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Short summary
The work is dedicated to a new model that reconstructs surface ocean partial pressure of carbon dioxide (pCO2) over the global ocean on a monthly 1º x 1º grid. The model is based on Feed-Forward Neural Network and represents the non-linear relations 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.
The work is dedicated to a new model that reconstructs surface ocean partial pressure of carbon...
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