A neural network-based estimate of the seasonal variability of total alkalinity in the East China Sea shelf

In order to estimate the seasonal variability of total alkalinity in the ECS shelf, an artificial neural network (ANN) model was developed using 5 cruise datasets from 2008 to 2018. The model used temperature, salinity, and dissolved oxygen to estimate A_T with a root-mean-square error of ~7 umol kg^-1, and was applied to fill missing alkalinity data for 8 cruises during 2013-2016. In addition, monthly water column A_T for the period 2000-2016 was also obtained passing temperature, salinity, and dissolved oxygen from the Changjiang Biology Finite-Volume Coastal Ocean Model (FVCOM) Data. Spatial distributions, seasonal cycles and correlations of surface A_T indicated that the seasonal fluctuation of the Changjiang River discharge is the major factor affecting seasonal variation of surface total alkalinity in the ECS shelf. The largest seasonal fluctuation of surface total alkalinity was found on the inner shelf near the Changjiang Estuary.