FFNN-LSCE: A two-step neural network model for thereconstruction of surface ocean pCO 2 over the GlobalOcean

Abstract. A new Feed-Forward Neural Network (FFNN) model is presented to reconstruct surface ocean partial pressure of carbon dioxide (pCO 2 ) over the global ocean. The model consists of two steps: (1) reconstruction of pCO 2 climatology and (2) reconstruction of pCO 2 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 CO 2 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 pCO 2 and the ocean predictors. It provides monthly surface ocean pCO 2 distributions on a 1o x 1o grid for the period 2001–2016. Global ocean pCO 2 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 Nino Southern Oscillation). Our model was compared to three pCO 2 mapping methods that participated in the Surface Ocean pCO 2 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 pCO 2 and sea-air CO 2 fluxes have a strong influence on global estimates of CO 2 fluxes and trends.