Total alkalinity estimation using MLR and neural network techniques

Abstract

During the last decade, two important collections of carbon relevant hydrochemical data have become available: GLODAP and CARINA. These collections comprise a synthesis of bottle data for all ocean depths from many cruises collected over several decades. For a majority of the cruises at least two carbon parameters were measured. However, for a large number of stations, samples or even cruises, the carbonate system is under-determined (i.e., only one or no carbonate parameter was measured) resulting in data gaps for the carbonate system in these collections. A method for filling these gaps would be very useful, as it would help with estimations of the anthropogenic carbon (Cant) content or quantification of oceanic acidification. The aim of this work is to apply and describe, a 3D moving window multilinear regression algorithm (MLR) to fill gaps in total alkalinity (AT) of the CARINA and GLODAP data collections for the Atlantic. In addition to filling data gaps, the estimated AT values derived from the MLR are useful in quality control of the measurements of the carbonate system, as they can aid in the identification of outliers. For comparison, a neural network algorithm able to perform non-linear predictions was also designed. The goal here was to design an alternative approach to accomplish the same task of filling AT gaps. Both methods return internally consistent results, thereby giving confidence in our approach.