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March 27, 2025 This article has been reviewed according to Science X's editorial process and policies . Editors have highlightedthe following attributes while ensuring the content's credibility: fact-checked peer-reviewed publication trusted source proofread by Li Yali, Chinese Academy of Sciences Ocean acidification, caused by the ongoing absorption of atmospheric CO2, poses threats to marine ecosystems and biodiversity. Accurately assessing variations in seawater pH is crucial for evaluating biological responses to acidification and predicting the ocean's capacity for carbon sequestration.
However, global ocean acidification has not been thoroughly studied due to sparse observations of seawater pH and inconsistent spatial coverage, especially at depths below the ocean's surface. To address these challenges, a research team from the Institute of Oceanology of the Chinese Academy of Sciences (IOCAS) utilized a Stepwise Feed-Forward Neural Network (Stepwise FFNN) algorithm to identify the predictors that yielded the lowest reconstruction errors for seawater pH. Additionally, they integrated observational data from the Global Ocean Data Analysis Project (GLODAP) to create a global monthly 3D gridded pH dataset spanning the past 30 years.
"Our 3D gridded pH dataset extends to a depth of 2,000 meters and improves in both accuracy and reliability," said Dr. Zhong Guorong, the first author of the study published in Earth System Science Data . By categorizing global oceans into biogeochemical provinces based on pH drivers, the researchers optimized the selection of environmental variables, which enhanced the dataset's accuracy.
In addition, the use of cross-boundary optimal interpolation technology improved the accuracy of reconstructing marine chemical parameters. Moreover, the pH dataset has been validated using a cross-validation method that reduces the risk of model overfitting, ensuring its reliability. The dataset is available to the public via the IOCAS Data Center, making it an essential resource for global climate modeling and marine conservation efforts.
More information: Guorong Zhong et al, A global monthly 3D field of seawater pH over 3 decades: a machine learning approach, Earth System Science Data (2025). DOI: 10.5194/essd-17-719-2025 Journal information: Earth System Science Data Provided by Chinese Academy of Sciences.
