A machine-learning reconstruction of sea surface pCO2 in the North American Atlantic Coastal Ocean Margin from 1993 to 2021.

Insufficient spatiotemporal coverage of partial pressure of CO₂ (p CO₂) observations has hindered precise studies of the coastal carbon cycle along the North American Atlantic Coastal Ocean Margin (NAACOM). Earlier p CO₂-products have encountered difficulties in accurately capturing the heterogeneity of regional variations and decadal trends of p CO₂ in the NAACOM. This study developed a regional reconstructed p CO₂-product for the NAACOM (Reconstructed Coastal Acidification Database- p CO₂, or ReCAD-NAACOM- p CO₂) using a two-step approach combining random forest regression and linear regression. The product provides monthly p CO₂ data at 0.25° spatial resolution from 1993 to 2021, enabling investigation of regional spatial differences, seasonal cycles, and decadal changes in p CO₂. The observation-based reconstruction was trained using Surface Ocean CO₂ Atlas (SOCAT) observations as ground-truth values, with various satellite-derived and reanalysis environmental variables known to control sea surface p CO₂ as model inputs. The product shows high accuracy during the model training, validation, and independent test phases, demonstrating robustness and capability to accurately reconstruct p CO₂ in regions or periods lacking direct observational data in the NAACOM. Compared with all the observation samples from SOCAT, the p CO₂-product yields a determination coefficient of 0.83, a root-mean-square error of 18.64 µatm, and an accumulative uncertainty of 23.83 µatm. The ReCAD-NAACOM- p CO₂ product demonstrates its capability to resolve seasonal cycles, regional-scale variations, and decadal linear trends of p CO₂ along the NAACOM. This new product provides reliable p CO₂ data for more precise studies of coastal carbon dynamics in the NAACOM region. The dataset is publicly accessible at https://doi.org/10.5281/zenodo.11500974 (Wu et al., 2024a) and will be updated regularly. [ABSTRACT FROM AUTHOR]