Abstract. Over the last decade, advanced statistical inference and
machine learning have been used to fill the gaps in sparse surface ocean
CO2 measurements (Rödenbeck 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
(Landschützer 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 pCO2 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 (CSIR-ML6 version 2019a, Council for Scientific and Industrial Research – Machine Learning ensemble with Six members), where each model is
constructed with a two-step clustering-regression approach. The ensemble
average is then statistically compared to well-established methods. The
ensemble average, CSIR-ML6, has an RMSE of 17.16 µatm and bias of
0.89 µatm when compared to a test dataset kept separate from training procedures. However, when validating our estimates with independent datasets, we find that our method improves only incrementally on other gap-filling methods. We investigate the differences between the methods to
understand the extent of the limitations of gap-filling estimates of
pCO2. We show that disagreement between methods in the South Atlantic,
southeastern Pacific and parts of the Southern Ocean is too large to
interpret the interannual variability with confidence. We conclude that
improvements in surface ocean pCO2 estimates will likely be incremental
with the optimisation of gap-filling methods by (1) the inclusion of
additional clustering and regression variables (e.g. eddy kinetic energy), (2) increasing the sampling resolution and (3) successfully incorporating
pCO2 estimates from alternate platforms (e.g. floats, gliders) into existing
machine-learning approaches.
A statistical gap-filling method to interpolate global monthly surface ocean carbon dioxide data
