A comparative assessment of the uncertainties of global surface ocean CO2 estimates using a machine-learning ensemble (CSIR-ML6 version 2019a) - have we hit the wall?

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 (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 mu atm and bias of 0.89 mu atm 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 pCO(2). 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 pCO(2) 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 pCO(2) estimates from alternate platforms (e.g. floats, gliders) into existing machine-learning approaches

Reconciling Observation and Model Trends in North Atlantic Surface CO2

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 generation of Earth System Models during the period 1992-2014. We further show, with initialized model simulations, that the inability of these models to capture the observed trend in surface fCO(2-ocean) is primarily due to biases in the models' ocean biogeochemistry. Our results imply that current projections may underestimate the contribution of the North Atlantic to mitigating increasing future atmospheric CO2 concentrations

Global Carbon and other Biogeochemical Cycles and Feedbacks

International audienc