Publication Date:
2017-06-12
Description:
The Southern Ocean accounts for 40 % of oceanic CO2 uptake, but the estimates are bound by large uncertainties due to a paucity in observations. Gap filling empirical methods have been used to good effect to approximate pCO2 from satellite observable variables in other parts of the ocean, but many of these methods are not in agreement in the Southern Ocean. In this study we propose two additional methods that perform well in the Southern Ocean: Support Vector Regression (SVR) and Random Forest Regression (RFR). The methods are used to estimate ∆pCO2 in the Southern Ocean, achieving similar results to the SOM-FFN method by Landschützer et al. (2014a). The RFR as able to achieve better RMSE (12.26 µatm) compared the SVR (16.04 µatm) and SOM-FFN (12.97 µatm). To assess the efficacy of the methods and the limits of the training dataset (SOCAT v3), SVR and RFR are applied in a modelled environment. Again the RFR method outperformed the SVR by a substantial margin. However, both methods achieved higher out-of-sample than in-sample errors, indicating that the SOCAT v3 dataset is not yet fully representative of the Southern Ocean. The SVR was able to generalise better to the training dataset than the RFR with lower ratio between the out-of-sample and in-sample errors, but not enough to compensate for its poorer performance. The ensemble of the estimates show that interannual variability of the Southern Ocean CO2 sink is dominated by the Polar Frontal Zone, while the Sub-Antarctic Zone is the dominant sink.
Print ISSN:
1810-6277
Electronic ISSN:
1810-6285
Topics:
Biology
,
Geosciences
