A Neural Network-Based Analysis of the Seasonal Variability of Surface Total Alkalinity on the East China Sea Shelf

Abstract

Total alkalinity (AT) is an important variable in the regulation of the seawater carbonate chemistry system, determining the capacity to buffer changes in pH. In the coastal oceans, carbonate system dynamics are controlled by numerous processes such as land-derived inputs, biological activity, and coastal water dynamics, and seasonal alkalinity variations can play an important role in the regional carbon cycle. However, our understanding of these variations on the East China Sea (ECS) shelf remains poor due to limited observations. In order to estimate and investigate the seasonal variability of AT on the ECS shelf, an artificial neural network (ANN) model was developed using five cruise datasets from 2008 to 2018. The model used temperature, salinity, and dissolved oxygen to estimate AT with a root-mean-square error (RMSE) of ∼7 umol kg–1, and was applied to calculate AT for eight cruises during 2013–2016. In addition, monthly water column AT for the period 2000–2016 was obtained using temperature, salinity, and dissolved oxygen from the Changjiang Biology Finite-Volume Coastal Ocean Model (FVCOM) Data. Spatial distributions, seasonal cycles and correlations of surface AT indicated that the seasonal fluctuation of the Changjiang River discharge is the major factor affecting seasonal variation of surface AT on the ECS shelf. The largest seasonal fluctuations of surface AT were found on the inner shelf near the Changjiang Estuary, which is under the influence of the Changjiang River discharge.