Articles | Volume 13, issue 10
https://doi.org/10.5194/gmd-13-5103-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-5103-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Retrieving monthly and interannual total-scale pH (pHT) on the East China Sea shelf using an artificial neural network: ANN-pHT-v1
Xiaoshuang Li
State Key Laboratory of Estuarine and Coastal Research, East China
Normal University, Shanghai, 200241, China
Norwegian Institute for Water Research, Bergen, 5006, Norway
Richard Garth James Bellerby
CORRESPONDING AUTHOR
State Key Laboratory of Estuarine and Coastal Research, East China
Normal University, Shanghai, 200241, China
Norwegian Institute for Water Research, Bergen, 5006, Norway
Jianzhong Ge
State Key Laboratory of Estuarine and Coastal Research, East China
Normal University, Shanghai, 200241, China
Philip Wallhead
Norwegian Institute for Water Research, Bergen, 5006, Norway
Jing Liu
State Key Laboratory of Estuarine and Coastal Research, East China
Normal University, Shanghai, 200241, China
Anqiang Yang
State Key Laboratory of Estuarine and Coastal Research, East China
Normal University, Shanghai, 200241, China
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Short summary
We have developed an ANN model to predict pH using 11 cruise datasets from 2013 to 2017,
demonstrated its reliability using three cruise datasets during 2018 and applied it to
retrieve monthly pH for the period 2000 to 2016 on the East China Sea shelf using the
ANN model in combination with input variables from the Changjiang biology Finite-Volume
Coastal Ocean Model. This approach may be a valuable tool for understanding the seasonal
variation of pH in poorly observed regions.
We have developed an ANN model to predict pH using 11 cruise datasets from 2013 to 2017,...