Articles | Volume 12, issue 12
Geosci. Model Dev., 12, 5113–5136, 2019
Geosci. Model Dev., 12, 5113–5136, 2019

Development and technical paper 10 Dec 2019

Development and technical paper | 10 Dec 2019

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 Gregor et al.

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Cited articles

Bakker, D. C. E., Hoppema, M., Schröder, M., Geibert, W., and de Baar, H. J. W.: A rapid transition from ice covered CO2-rich waters to a biologically mediated CO2 sink in the eastern Weddell Gyre, Biogeosciences, 5, 1373–1386,, 2008. 
Banzon, V., Smith, T. M., Chin, T. M., Liu, C., and Hankins, W.: A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies, Earth Syst. Sci. Data, 8, 165–176,, 2016. 
Bates, N. R.: Interannual variability of oceanic CO2 and biogeochemical properties in the Western North Atlantic subtropical gyre, Deep-Res. Pt. II, 48, 1507–1528,, 2001. 
Bockmon, E. E. and Dickson, A. G.: An inter-laboratory comparison assessing the quality of seawater carbon dioxide measurements, Mar. Chem., 171, 36–43,, 2015. 
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.