Articles | Volume 17, issue 2
https://doi.org/10.5194/essd-17-719-2025
https://doi.org/10.5194/essd-17-719-2025
Data description paper
 | 
24 Feb 2025
Data description paper |  | 24 Feb 2025

A global monthly 3D field of seawater pH over 3 decades: a machine learning approach

Guorong Zhong, Xuegang Li, Jinming Song, Baoxiao Qu, Fan Wang, Yanjun Wang, Bin Zhang, Lijing Cheng, Jun Ma, Huamao Yuan, Liqin Duan, Ning Li, Qidong Wang, Jianwei Xing, and Jiajia Dai

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Revised manuscript accepted for ESSD
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Cited articles

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Broullón, D., Pérez, F. F., Velo, A., Hoppema, M., Olsen, A., Takahashi, T., Key, R. M., Tanhua, T., González-Dávila, M., Jeansson, E., Kozyr, A., and van Heuven, S. M. A. C.: A global monthly climatology of total alkalinity: a neural network approach, Earth Syst. Sci. Data, 11, 1109–1127, https://doi.org/10.5194/essd-11-1109-2019, 2019. 
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The continuous uptake of atmospheric CO2 by the ocean leads to decreasing seawater pH, which is an ongoing threat to the marine ecosystem. This pH change has been globally documented in the surface ocean, but information is limited below the surface. Here, we present a monthly 1° gridded product of global seawater pH based on a machine learning method and real pH observations. The pH product covers the years from 1992 to 2020 and depths from 0 to 2000 m.
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