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| Gregor, Luke; Kok, Schalk; Monteiro, Pedro M. S.. |
| 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 pCO(2) 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 Delta pCO(2) in the Southern Ocean based on SOCAT v3, achieving similar trends to the SOM-FFN method by Landschitzer et al. (2014). Results show that the SOM-FFN and RFR approaches have RMSEs of similar magnitude... |
| Tipo: Text |
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| Ano: 2017 |
URL: https://archimer.ifremer.fr/doc/00662/77388/79018.pdf |
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| Gregor, Luke; Kok, Schalk; Monteiro, Pedro M. S.. |
| Resolving and understanding the drivers of variability of CO2 in the Southern Ocean and its potential climate feedback is one of the major scientific challenges of the ocean-climate community. Here we use a regional approach on empirical estimates of pCO(2) to understand the role that seasonal variability has in long-term CO2 changes in the Southern Ocean. Machine learning has become the preferred empirical modelling tool to interpolate time- and location-restricted ship measurements of pCO(2). In this study we use an ensemble of three machine-learning products: support vector regression (SVR) and random forest regression (RFR) from Gregor et al. (2017), and the self-organising-map feed-forward neural network (SOM-FFN) method from Land-schutzer et al.... |
| Tipo: Text |
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| Ano: 2018 |
URL: https://archimer.ifremer.fr/doc/00673/78492/80822.pdf |
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| Gregor, Luke; Lebehot, Alice D.; Kok, Schalk; Monteiro, Pedro M. Scheel. |
| Over the last decade, advanced statistical inference and machine learning have been used to fill the gaps in sparse surface ocean CO2 measurements (Rodenbeck et al., 2015). The estimates from these methods have been used to constrain seasonal, interannual and decadal variability in sea-air CO2 fluxes and the drivers of these changes (Landschutzer et al., 2015, 2016; Gregor et al., 2018). However, it is also becoming clear that these methods are converging towards a common bias and root mean square error (RMSE) boundary: "the wall", which suggests that pCO(2) estimates are now limited by both data gaps and scale-sensitive observations. Here, we analyse this problem by introducing a new gap-filling method, an ensemble average of six machine-learning models... |
| Tipo: Text |
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| Ano: 2019 |
URL: https://archimer.ifremer.fr/doc/00676/78797/81035.pdf |
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