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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/gmd-2019-236
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-236
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: model description paper 28 Oct 2019

Submitted as: model description paper | 28 Oct 2019

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A revised version of this preprint was accepted for the journal GMD and is expected to appear here in due course.

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

Xiaoshuang Li1,2, Richard Bellerby1,2, Jianzhong Ge1, Philip Wallhead2, Jing Liu1, and Anqiang Yang1 Xiaoshuang Li et al.
  • 1State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai, 200241, China
  • 2Norwegian Institute for Water Research, Bergen, 5006, Norway

Abstract. While our understanding of pH dynamics has strongly progressed for open ocean regions, for marginal seas such as the East China Sea (ECS) progress has been constrained by limited observations and complex interactions between biological, physical, and chemical processes. Seawater pH is a very valuable oceanographic variable but not always measured using high quality instrumentation and according to standard practices. In order to predict water column total scale pH (pHT) and enhance our understanding of the seasonal variability of pHT on the ECS shelf, an artificial neural network (ANN) model was developed using 11 cruise datasets from 2013 to 2017 with coincident observations of pHT, temperature (T), salinity (S), dissolved oxygen (DO), nitrate (N), phosphate (P) and silicate (Si) together with sampling position and time. The reliability of the ANN model was evaluated using independent observations from 3 cruises in 2018, and showed a root mean square error accuracy of 0.04. A weight analysis of the ANN model variables suggested that DO, S, T were the most important predictor variables. Monthly water column pHT for the period 2000-2016 was retrieved using T, S, DO, N, P, and Si from the Changjiang Biology Finite-Volume Coastal Ocean Model (FVCOM).

Xiaoshuang Li et al.

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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Xiaoshuang Li et al.

Data sets

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

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

Model code and software

source code of the ANN model for pH estimation 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 X. 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 X. Li https://doi.org/10.5281/zenodo.2672929

Xiaoshuang Li et al.

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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 3 cruise datasets during 2018, and applied it to retrieve monthly pH for the period 2000–2016 on the East China Sea shelf using T, S, DO, N, P, and Si 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 and can be applied to other regions to predict pH.
We have developed an ANN model to predict pH using 11 cruise datasets from 2013 to 2017,...
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