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) the reconstruction of
pCO2 climatology, and (2) the 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 (SSS), sea surface temperature (SST), sea surface height
(SSH), chlorophyll a (Chl a), 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 nonlinear relationships between pCO2 and
the ocean predictors. It provides monthly surface ocean pCO2
distributions on a 1∘×1∘ grid for the period from
2001 to 2016. Global ocean pCO2 was reconstructed with
satisfying accuracy compared with independent observational data from SOCAT.
However, errors were larger in regions with poor data coverage (e.g., the
Indian Ocean, the Southern Ocean and the subpolar Pacific). The model
captured the strong interannual variability of surface ocean
pCO2 with reasonable skill over the equatorial Pacific
associated with ENSO (the 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 variability
between the models over the global ocean. However, important differences
still exist at the regional scale, especially in the Southern Hemisphere and,
in particular, in 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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.