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.
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Weber et al. (2019) studied this question.
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