Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 13, issue 10
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.
https://doi.org/10.5194/gmd-13-5103-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 13, issue 10
Model description paper
 | 
27 Oct 2020
Model description paper |  | 27 Oct 2020

Retrieving monthly and interannual total-scale pH (pHT) on the East China Sea shelf using an artificial neural network: ANN-pHT-v1

Xiaoshuang Li, Richard Garth James Bellerby, Jianzhong Ge, Philip Wallhead, Jing Liu, and Anqiang Yang

Supplement

https://doi.org/10.5194/gmd-13-5103-2020-supplement

Data sets

The monthly-average input variables (T, S, DO, N, P, Si) and retrieved pH Xiaoshuang Li https://doi.org/10.5281/zenodo.3519236

The application performance of the ANN model in the ECS shelf Xiaoshuang Li https://doi.org/10.5281/zenodo.3491747

Model code and software

source code of the ANN model for pH estimation Xiaoshuang Li https://doi.org/10.5281/zenodo.3519219

Interactive computing environment

source code of the ANN model for pH estimation Xiaoshuang Li https://doi.org/10.5281/zenodo.3519219

Video supplement

Monthly distribution of surface pH in the East China Sea Shelf from 2000 to 2016 year Xiaoshuang Li https://doi.org/10.5281/zenodo.2672943

Profile distribution of pH at 31N in the East China Sea Shelf from 2000 to 2016 year Xiaoshuang Li https://doi.org/10.5281/zenodo.2672929

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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.
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