Abstract
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.
Highlights
Atmospheric carbon dioxide (CO2) levels have increased by nearly 46 %, from approximately 278 ppm in 1750 (Ciais et al, 2014) to 405 ppm in 2017 (Le Quéré et al, 2018)
With 5 % T errors added, the artificial neural network (ANN) model showed slight overestimation in pHT, with a mean bias (MB) of 0.0059, root mean square error (RMSE) of 0.0079 and R2 of 0.9949 (Fig. 9a); with 5 % dissolved oxygen (DO) errors added, the ANN model showed slight pHT overestimation, with a MB of 0.0050, RMSE of 0.0090 and R2 of 0.9934 (Fig. 9c); and with 5 % S errors added, the ANN model showed an overestimation in pHT, with a MB of −0.0111, RMSE of 0.0162 and R2 of 0.9789 (Fig. 9b)
We have developed an artificial neural network (ANN) model, demonstrated its reliability and used it to retrieve monthly pHT for the period 2000–2016 on the East China Sea shelf
Summary
Atmospheric carbon dioxide (CO2) levels have increased by nearly 46 %, from approximately 278 ppm (parts per million) in 1750 (Ciais et al, 2014) to 405 ppm in 2017 (Le Quéré et al, 2018). While a gradual decrease in pH is a predictable open-ocean response to elevated anthropogenic CO2 emissions, the seasonal changes and long-term trends in pH in coastal seas have not been fully understood due to the lack of long-term pH data and complexity of coastal systems In this context, the development of approaches to predict carbonate chemistry parameters in coastal regions may assist both the management of local water quality and our wider understanding of the ocean carbon cycle. A summary is given and conclusions are drawn in the last section
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