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dc.contributor.author Pretorius, W
dc.contributor.author Das, Sonali
dc.contributor.author Monteiro, Pedro MS
dc.date.accessioned 2014-07-18T09:15:23Z
dc.date.available 2014-07-18T09:15:23Z
dc.date.issued 2014-01
dc.identifier.citation Pretorius, W, Das, S and Monteiro, P.M.S. 2014. Investigating the complex relationship between in situ Southern Ocean pCO2 and its ocean physics and biogeochemical drivers using a nonparametric regression approach. Environmental and Ecological Statistics, pp 1-18 en_US
dc.identifier.issn 1352-8505
dc.identifier.uri http://download.springer.com/static/pdf/609/art%253A10.1007%252Fs10651-014-0276-5.pdf?auth66=1403764949_a57aa77d146a3749519bd389a7551975&ext=.pdf
dc.identifier.uri http://hdl.handle.net/10204/7492
dc.description 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 en_US
dc.description.abstract 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). en_US
dc.language.iso en en_US
dc.publisher Springer link en_US
dc.relation.ispartofseries Workflow;12742
dc.subject Oceanic carbon dioxide en_US
dc.subject pCO2 en_US
dc.subject Carbon Flux en_US
dc.subject Nonparametric Regression en_US
dc.subject SANAE49 en_US
dc.subject Southern Ocean en_US
dc.subject Spatial sparseness en_US
dc.title Investigating the complex relationship between in situ Southern Ocean pCO2 and its ocean physics and biogeochemical drivers using a nonparametric regression approach en_US
dc.type Article en_US


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