Abstract. While our understanding of pH dynamics has strongly
progressed for open-ocean regions, for marginal seas such as the East China
Sea (ECS) shelf 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 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 three cruises in 2018,
and it showed a root mean square error accuracy of 0.04. The ANN model
responded to T and DO errors in a positive way and S errors in a negative way,
and the ANN model was most sensitive to S errors, followed by DO and T
errors. 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). The agreement is good here in winter, while the
reduced performance in summer can be attributed in large part to limitations
of the Changjiang biology FVCOM in simulating summertime input variables.
An estimate of the cross-frontal transport at the shelf break of the East China Sea with the Finite Volume Coastal Ocean Model
