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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: development and technical paper 05 Apr 2019

Submitted as: development and technical paper | 05 Apr 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

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?

Luke Gregor1,2, Alice D. Lebehot1,2, Schalk Kok3, and Pedro M. Scheel Monteiro1 Luke Gregor et al.
  • 1SOCCO, Council for Scientific and Industrial Research, Cape Town, 7700, South Africa
  • 2MaRe, Marine Research Institute, University of Cape Town, Cape Town, 7700, South Africa
  • 3Department of Mechanical & Aeronautical Engineering, University of Pretoria, Pretoria, 0028, South Africa

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 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 of six machine learning models (CSIR-ML6 version 2019a), where each model is constructed with a two-step clustering-regression approach. The ensemble is then statistically compared to well-established methods. The ensemble, 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 are 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. Larger improvements will only be realised with an increase in CO2 observational coverage, particularly in today's poorly sampled areas.

Luke Gregor et al.
Luke Gregor et al.
Data sets

Global surface ocean pCO2 from CSIR-ML6 (version 2019a) L. Gregor

Luke Gregor et al.
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Publications Copernicus
Short summary
The ocean plays a vital role in mitigating climate change by taking up atmospheric carbon dioxide (CO2). Historically-sparse ship-based measurements of surface ocean CO2 make direct estimates of CO2 exchange changes unreliable. We introduce a machine learning ensemble approach to fill these observational gaps. Our method performs incrementally better relative to past methods, leading to our hypothesis that we are perhaps reaching the limitation of machine learning algorithms' capability.
The ocean plays a vital role in mitigating climate change by taking up atmospheric carbon...