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Abstract. A new feed-forward neural network (FFNN) model is presented to reconstruct surface ocean partial pressure of carbon dioxide ( pCO2 ) over theglobal ocean. The model consists of two steps: (1) the reconstruction of pCO2 climatology, and (2) the reconstruction of pCO2 anomalies with respect to the climatology. For thefirst step, a gridded climatology was used as the target, along with seasurface salinity (SSS), sea surface temperature (SST), sea surface height(SSH), chlorophyll  a (Chl  a ), mixed layer depth (MLD), as well as latitude and longitude aspredictors. For the second step, data from the Surface Ocean CO2 Atlas (SOCAT) provided the target. The same set of predictors was used duringstep (2) augmented by their anomalies. During each step, the FFNN modelreconstructs the nonlinear relationships between pCO2 andthe ocean predictors. It provides monthly surface ocean pCO2 distributions on a 1 ∘ × 1 ∘ grid for the period from2001 to 2016. Global ocean pCO2 was reconstructed withsatisfying accuracy compared with independent observational data from SOCAT.However, errors were larger in regions with poor data coverage (e.g., theIndian Ocean, the Southern Ocean and the subpolar Pacific). The modelcaptured the strong interannual variability of surface ocean pCO2 with reasonable skill over the equatorial Pacificassociated with ENSO (the El Nino–Southern Oscillation). Our model wascompared to three pCO2 mapping methods that participated inthe Surface Ocean pCO2 Mapping intercomparison (SOCOM)initiative. We found a good agreement in seasonal and interannual variabilitybetween the models over the global ocean. However, important differencesstill exist at the regional scale, especially in the Southern Hemisphere and,in particular, in the southern Pacific and the Indian Ocean, as these regionssuffer from poor data coverage. Large regional uncertainties in reconstructedsurface ocean pCO2 and sea–air CO2 fluxes have astrong influence on global estimates of CO2 fluxes and trends.

geoscientific model development

LSCE-FFNN-v1: a two-step neural network model for the reconstruction of surface ocean &amp;lt;i&amp;gt;p&amp;lt;/i&amp;gt;CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; over the global ocean