Reconstructed Coastal Acidification Database (ReCAD)

Insufficient spatiotemporal coverage of the partial pressure of CO₂ (pCO₂) observations has hindered precise carbon cycle studies in coastal oceans and justifies the development of spatially and temporally continuous pCO₂ data products. Earlier pCO₂ products have difficulties in capturing the heterogeneity of regional variations and decadal trends of pCO₂ in the North American Atlantic Coastal Ocean Margin (NAACOM). This study developed a regional reconstructed pCO₂ product for the NAACOM (Reconstructed Coastal Acidification Database-pCO₂, or ReCAD-NAACOM-pCO₂) using a two-step approach combining random forest regression and linear regression. The product provides monthly pCO₂ data at 0.25° spatial resolution from 1993 to 2021, enabling investigation of regional spatial differences, seasonal cycles, and decadal changes in pCO₂. The observation-based reconstruction was trained using Surface Ocean CO₂ Atlas (SOCAT) observations as observational values, with various satellite-derived and reanalysis environmental variables known to control sea surface pCO₂ as model inputs. The product shows high accuracy during the model training, validation, and independent test phases, demonstrating robustness and capability to accurately reconstruct pCO₂ in regions or periods lacking direct observational data. Compared with all the observation samples from SOCAT, the pCO₂ product yields a determination coefficient of 0.92, a root-mean-square error of 12.70 µatm, and an accumulative uncertainty of 23.25 µatm. The ReCAD-NAACOM-pCO₂ product demonstrates its capability to resolve seasonal cycles, regional-scale variations, and decadal trends of pCO₂ along the NAACOM. This new product provides reliable pCO₂ 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.

Version 1.1: Update the training set output as the direct model outputs instead of being the 10-fold cross-validation output in v1.0.

Data description paper: Wu, Z., Lu, W., Roobaert, A., Song, L., Yan, X.-H., and Cai, W.-J.: A machine-learning reconstruction of sea surface pCO₂ in the North American Atlantic Coastal Ocean Margin from 1993 to 2021, Earth Syst. Sci. Data, 17, 43–63, https://doi.org/10.5194/essd-17-43-2025, 2025.