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.