Increasingly severe ocean acidification (OA) disrupts the balance of marine ecosystems. Seawater pH is a key indicator of OA but remains challenging to characterize due to sparse and limited in situ observations. In this study, we propose a spatiotemporal inversion method for surface pH based on interpretable machine learning. By applying carbonate system calculations, we construct an expanded pH observational dataset and obtain spatiotemporal distributions of pH and its influencing factors across the Pacific Ocean from 2003 to 2021. The interpretability analysis reveals that physical, biological, and optical factors contribute 53.9%, 23.9%, and 22.2%, respectively, to pH variability. Sea-surface temperature is the dominant driver, contributing 15.9% of all factors by regulating CO2 solubility and biological activity. Particulate inorganic carbon (PIC) and particulate organic carbon (POC) show relative contributions of 12.6% and 9.4%, respectively, quantitatively reflecting the important roles of biogenic calcification and the biological carbon pump. Furthermore, the analysis focusing on the Niño 3.4 region reveals a potential pathway through which the ENSO disturbances may affect pH by influencing PIC and POC. Therefore, this study provides a data-driven approach to gain deeper insights into the spatiotemporal patterns of pH and its influencing factors.
Huang M., Qi J., Zhang C., Wang Y., Chen Y., Shao J. & Wu S., 2025. Spatiotemporal analysis of sea-surface pH in the Pacific Ocean based on interpretable machine learning. Journal of Marine Science and Engineering 13(7): 1220. doi: 10.3390/jmse13071220. Article.
