Modelling seawater pCO₂ and pH in the Canary Islands region based on satellite measurements and machine learning techniques

Abstract. The improvement of remote sensing systems, together with the emergence of new model-fitting algorithms based on machine-learning techniques, has allowed the determination of the partial pressure of carbon dioxide (pCO_2,sw) and pH (pH_T,sw) in the waters of the Canary Islands. Among all the fitted models, the most powerful one was the bootstrap aggregation (bagging), giving a RMSE of 2.0 µatm (R² > 0.99) for pCO_2,sw and RMSE of 0.002 for pH_T,is, although the multilinear regression (MLR), neural network (NN) and categorical boosting (catBoost) also have a good predictive performance, with RMSE ranging from 5.4 to 10 µatm for 360 < pCO_2,sw < 481 µatm and from 0.004 and 0.008 for 7.97 < pH_T,is < 8.07. Using the most reliable model, it was determined that there is an interannual trend of 3.51 ± 0.31 µatm yr^-1 for pCO_2,sw (which surpasses the rate of increase for atmospheric CO₂ of 2.3 µatm yr^-1) and an increase in acidity of -0.003 ± 0.001 pH units yr^-1. The increase in both, the atmospheric CO₂ and the sea surface temperature of 0.2 °C yr^-1 observed in the 6-year period, influenced by the unprecedented 2023 marine heat wave, contribute to this important rate. Considering the Canary Islands between 13°–19° W and 27°–30° N, the region has moved from a slight CO₂ source of 0.90 Tg CO₂ yr^-1 in 2019 to 4.5 Tg CO₂ yr^-1 in 2024. After 2022, eastern locations that acted as an annual sink of CO₂ switched to acting as a source.