Construction of a high spatiotemporal resolution dataset of satellite-derived pCO2 and air-sea CO2 flux in the South China Sea (2003-2019)

• Published in: IEEE transactions on geoscience and remote sensing p. 1
• Main Authors: Song, Zigeng, Yu, Shujie, Bai, Yan, Guo, Xianghui, He, Xianqiang, Zhai, Weidong, Dai, Minhan
• Format: Journal Article
• Language: English
• Published: IEEE 17.08.2023
• Subjects: Air-sea CO<sub xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">2 flux; Atmospheric modeling; Machine learning; Oceans; Satellite retrieval; Satellites; Sea measurements; Sea surface; Seawater <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">p CO<sub xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">2; Semi-analytical algorithm; South China Sea; Spatial resolution; Spatiotemporal phenomena
• ISSN: 0196-2892
• DOI: 10.1109/TGRS.2023.3306389

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 sub-regions with various features. It was challenge to estimate the air-sea CO2 flux in this area. We developed a retrieval algorithm for sea surface p CO 2 by a combination of our previously established semi-mechanistic approach (MeSAA) and machine learning (ML) method, named MeSAA-ML-SCS, built upon a large dataset of sea surface partial pressure of CO 2 ( p CO 2 ) collected from in situ measurements during 44 cruises/legs to the SCS in the last two decades. We set several semi-analytical parameters, includes: p CO 2_ therm represented the combined effect of thermodynamics and the atmospheric CO 2 forcing on seawater p CO 2 ; upwelling index (UISST) and mixing layer depth (MLD) to characterize the mixing processes; 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 sub-regions. We set the difference between seawater p CO 2 and atmospheric p CO 2 (Δ p CO 2 Sea-Air ) as the output, and the seawater p CO 2 was finally obtained by summing atmospheric p CO 2 and Δ p CO 2 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 μatm) and mean absolute percentage deviation (APD = 1.59%). The increasing trend of time-series satellite-derived p CO 2 (2.44 ± 0.24 μatm/yr) were 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 CO 2 , releasing an average of 12.34 ± 3.11 Tg C/yr from a total area of 2.87 × 10 6 km 2 , while the northern shelf act as a sink (2.02 ± 0.64 Tg C/yr). With the forcing of increasing atmospheric CO 2 , the area-integrated CO 2 efflux over the entire SCS is decreasing with a rate of 0.41 Tg C/yr 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 CO 2 exchange dynamics in the SCS.