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 total alkalinity on 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 AT with a root-mean-square error (RMSE) of ~7 umol kg-1, and was applied to fill missing AT data for 8 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.
Highlights
Despite occupying a small proportion of the global surface area, coastal seas play an important role in the global carbon cycle because they receive a large amount of terrestrial materials and nutrients from rivers, rapidly transform different forms of carbon, and exchange large fluxes with the open ocean and atmosphere (Gattuso et al, 1998)
Numerous processes in the coastal seas lead to the complexity of carbonate system dynamics, which means that each specific region may have different variation characteristics of AT in different seasons and separate, regional algorithms may be required (e.g., Juranek et al, 2009; Kim et al, 2010)
We developed an artificial neural network (ANN) to predict AT on the East China Sea (ECS) shelf and used it to investigate seasonal variability
Summary
Despite occupying a small proportion of the global surface area, coastal seas play an important role in the global carbon cycle because they receive a large amount of terrestrial materials and nutrients from rivers, rapidly transform different forms of carbon, and exchange large fluxes with the open ocean and atmosphere (Gattuso et al, 1998). Several studies have attempted to develop multiple linear regression (MLR) relationships to predict total alkalinity (AT) from more commonly observed variables such as temperature and salinity (e.g., Millero et al, 1998; Lee et al, 2006; Carter et al, 2016, 2018; Fine et al, 2017). A new method of self-organizing multiple linear output (SOMLO) was developed by Sasse et al (2013), and showed a 19% improvement in predictive accuracy for dissolved inorganic carbon compared to a traditional MLR approach. Alin et al (2012) developed an MLR model for AT in the southern California Current System, while Gemayel et al (2015) derived polynomial fits to estimate AT in the Mediterranean Sea. As discussed by Friis et al (2003), simple linear regressions between salinity and AT may not be suitable for broader coastal ocean regions. Numerous processes in the coastal seas lead to the complexity of carbonate system dynamics, which means that each specific region may have different variation characteristics of AT in different seasons and separate, regional algorithms may be required (e.g., Juranek et al, 2009; Kim et al, 2010)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
