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| Lebehot, Alice D.; Halloran, Paul Richard; Watson, Andrew J.; Mcneall, Doug; Ford, David A.; Landschuetzer, Peter; Lauvset, Siv K.; Schuster, Ute. |
| The North Atlantic Ocean is a region of intense uptake of atmospheric CO2. To assess how this CO2 sink has evolved over recent decades, various approaches have been used to estimate basin-wide uptake from the irregularly sampled in situ CO2 observations. Until now, the lack of robust uncertainties associated with observation-based gap-filling methods required to produce these estimates has limited the capacity to validate climate model simulated surface ocean CO2 concentrations. After robustly quantifying basin-wide and annually varying interpolation uncertainties using both observational and model data, we show that the North Atlantic surface ocean fugacity of CO2 (fCO(2-ocean)) increased at a significantly slower rate than that simulated by the latest... |
| Tipo: Text |
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| Ano: 2019 |
URL: https://archimer.ifremer.fr/doc/00675/78721/81005.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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