Investigating the complex relationship between in situ Southern Ocean pCO2 and its ocean physics and biogeochemical drivers using a nonparametric regression approach

• Main Authors: Pretorius, W, Das, Sonali, Monteiro, Pedro MS
• Format: Article in Journal/Newspaper
• Language: English
• Published: Springer link 2014
• Subjects: Oceanic carbon dioxide; pCO2; Carbon Flux; Nonparametric Regression; SANAE49; Southern Ocean; Spatial sparseness; Antarctic; Antarc*
• Online Access: http://hdl.handle.net/10204/7492; http://download.springer.com/static/pdf/609/art%253A10.1007%252Fs10651-014-0276-5.pdf?auth66=1403764949_a57aa77d146a3749519bd389a7551975&ext=.pdf

Copyright: 2014 Springer link. This is the post print version of the work. The definitive version is published in Environmental and Ecological Statistics, pp 1-18 The objective in this paper is to investigate the use of a non-parametric model approach to model the relationship between oceanic carbon dioxide (pCO(sub2)) and a range of biogeochemical in situ variables in the Southern Ocean, which influence its in situ variability. The need for this stems from the need to obtain reliable estimates of carbon dioxide concentrations in the Southern Ocean which plays an important role in the global carbon flux cycle. The main challenge involved in this objective is the spatial sparseness and seasonal bias of the in situ data. Moreover, studies have also reported that the relationship between pCO(sub2) and its drivers is complex. As such, in this paper, we use the nonparametric kernel regression approach since it is able to accurately represent the complex relationships between the response and predictor variables using the in situ data obtained from the SANAE49 return leg journey between Antarctic to Cape Town. To the best of our knowledge, this is the first time this data set has been subjected to such analysis. The model variants were developed on a training data subset, and the `goodness' of the models were assessed on an "unseen" testing subset. Results indicate that the nonparametric approach consistently captures the relationship more accurately in terms of MSE, RMSE and MAE, than a standard parametric approach (multiple linear regression). These results provide a platform for using the developed nonparametric regression model based on in situ measurements to predict pCO(sub2) for a larger spatial region in the Southern Ocean based on satellite biogeochemical measurements of predictor variables, given that satellite measurements do not measure pCO(sub2).