An improved long-term high-resolution surface pCO2 data product for the Indian Ocean using machine learning

Accurate estimation of surface ocean pCO₂ is crucial for understanding the ocean's role in the global carbon cycle and its response to climate change. In this study, we employ a machine learning algorithm to correct the deviations in high-resolution (1/12°) model simulations of surface pCO₂ from the INCOIS-BIO-ROMS model (pCO₂^model) for the period 1980-2019, using available observations (pCO₂^obs). We train the XGBoost model to generate spatio-temporal deviations (pCO₂^obs - pCO₂^model) of pCO₂^model. The interannually and climatologically varying deviations are then added back to the original model separately, which results in an improved surface pCO₂ data product. A comparison of our surface pCO₂ data product with moored observations, gridded SOCAT, CMEMS-LSCE-FFNN, and OceanSODA demonstrates an improvement by approximately 40% ± 3.31% in RMSE. Further analysis reveals that adding climatological deviations to pCO₂^model results in greater improvements than adding interannual deviations. This analysis underscores the ability of machine learning algorithms to enhance the accuracy of model-simulated surface pCO₂ outputs.