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

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).

Li X., Bellerby R., Ge J., Wallhead P., Liu J. & Yang A., 2019. Retrieving monthly and interannual pHT on the East China Sea shelf using an artificial neural network: ANN-pHT-v1. Geoscientific Model Development (GMD) Discussions. Article.

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