Construction of a High Spatiotemporal Resolution Dataset of Satellite-Derived pCO₂ and Air–Sea CO₂ Flux in the South China Sea (2003–2019)

The South China Sea (SCS) is one of the largest marginal seas in the world. It includes a river-dominated, highly productive ocean margin on the northern shelf and an oligotrophic ocean-dominated basin along with other subregions with various features. It was a challenge to estimate the air–sea CO2 flux in this area. We developed a retrieval algorithm for sea surface partial pressure of CO2 ( ${p}$ CO2) by a combination of our previously established mechanistic semianalytical method (MeSAA) and machine learning (ML) method, named MeSAA-ML-SCS, built upon a large dataset of sea surface ${p}$ CO2 collected from in situ measurements during 44 cruises /legs to the SCS in the last two decades. We set several semianalytical parameters, including ${p}$ CO2_therm represented the combined effect of thermodynamics and the atmospheric CO2 forcing on seawater ${p}$ CO 2; upwelling index (UISST) and mixing layer depth (MLD) to characterize the mixing processes; and chlorophyll-a concentration (Chl-a) with remote sensing reflectance at 443 and 555 nm [Rrs(443) and Rrs(555)], which were proxies of biological effects and other characteristics for distinguishing shelf, basin, and subregions. We set the difference between seawater ${p}$ CO2 and atmospheric ${p}$ CO2( $\boldsymbol {\Delta p}\mathbf {C}\mathbf {O}_{\mathbf {2}}^{\mathbf {Sea{-}Air}}$ ) as the output, and the seawater ${p}$ CO2 was finally obtained by summing atmospheric ${p}$ CO2 and $\boldsymbol {\Delta p}\mathbf {C}\mathbf {O}_{\mathbf {2}}^{\mathbf {Sea{-}Air}}$ . We compared several ML models, and the XGBoost model was confirmed as the best. Independent cruise-based datasets that are not involved in the model training were used to validate the satellite products, with low root-mean-square error (RMSE = $11.69~\mu $ atm) and mean absolute percentage deviation (APD = 1.59%). The increasing trend of time-series satellite-derived ${p}$ CO2 (2.44 ± $0.24~\mu$ atm/year) was validated by the in situ data at the Southeastern Asia Time-series Study (SEATS) station, showing good consistency. Results indicate that the SCS as a whole is a source of atmospheric CO2, releasing an average of 12.34 ± 3.11 Tg C/year from a total area of $2.87\times106$ km2, while the northern shelf acts as a sink (2.02 ± 0.64 Tg C/year). With the forcing of increasing atmospheric CO2, the area-integrated CO2 efflux over the entire SCS is decreasing with a rate of 0.41 Tg C/year during 2003–2019. This shared long time series, high-accuracy dataset (1 km) can be helpful to further improve our understanding of the air–sea CO2 exchange dynamics in the SCS.