Abstract
Oceanic emissions represent a highly uncertain term in the natural atmospheric methane (CH(4)) budget, due to the sparse sampling of dissolved CH(4) in the marine environment. Here we overcome this limitation by training machine-learning models to map the surface distribution of methane disequilibrium (∆CH(4)). Our approach yields a global diffusive CH(4) flux of 2-6TgCH(4)yr(-1) from the ocean to the atmosphere, after propagating uncertainties in ∆CH(4) and gas transfer velocity. Combined with constraints on bubble-driven ebullitive fluxes, we place total oceanic CH(4) emissions between 6-12TgCH(4)yr(-1), narrowing the range adopted by recent atmospheric budgets (5-25TgCH(4)yr(-1)) by a factor of three. The global flux is dominated by shallow near-shore environments, where CH(4) released from the seafloor can escape to the atmosphere before oxidation. In the open ocean, our models reveal a significant relationship between ∆CH(4) and primary production that is consistent with hypothesized pathways of in situ methane production during organic matter cycling.