| 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... |
