Global ocean methane emissions dominated by shallow coastal waters

Oceanic emissions represent a highly uncertain term in the natural atmospheric methane (CH4) budget, due to the sparse sampling of dissolved CH4 in the marine environment. Here we overcome this limitation by training machine-learning models to map the surface distribution of methane disequilibrium (∆CH4). Our approach yields a global diffusive CH4 flux of 2–6TgCH4yr−1 from the ocean to the atmosphere, after propagating uncertainties in ∆CH4 and gas transfer velocity. Combined with constraints on bubble-driven ebullitive fluxes, we place total oceanic CH4 emissions between 6–12TgCH4yr−1, narrowing the range adopted by recent atmospheric budgets (5–25TgCH4yr−1) by a factor of three. The global flux is dominated by shallow near-shore environments, where CH4 released from the seafloor can escape to the atmosphere before oxidation. In the open ocean, our models reveal a significant relationship between ∆CH4 and primary production that is consistent with hypothesized pathways of in situ methane production during organic matter cycling. The ocean emits the greenhouse gas methane, but its vastness renders estimations challenging. Here the authors use machine learning to map global ocean methane fluxes, finding a disproportionate contribution from shallow coastal waters, and a link between primary production and methane cycling.