Advancements and opportunities to improve bottom-up estimates of global wetland methane emissions

Wetlands are the single largest natural source of atmospheric methane (CH4), contributing approximately 30% of total surface CH4 emissions, and they have been identified as the largest source of uncertainty in the global CH4 budget based on the most recent Global Carbon Project (GCP) CH4 report. High uncertainties in the bottom-up estimates of wetland CH4 emissions pose significant challenges for accurately understanding their spatiotemporal variations, and for the scientific community to monitor wetland CH4 emissions from space. In fact, there are large disagreements between bottom-up estimates versus top-down estimates inferred from inversion of atmospheric CH4 concentrations. To address these critical gaps, we review recent development, validation, and applications of bottom-up estimates of global wetland CH4 emissions, as well as how they are used in top-down inversions. These bottom-up estimates, using (1) empirical biogeochemical modeling (e.g., WetCHARTs: 125 - 208 TgCH4 yr-1); (2) process-based biogeochemical modeling (e.g., WETCHIMP: 190  39 TgCH4 yr-1); (3) data-driven machine learning approach (e.g., UpCH4: 146  43 TgCH4 yr-1), were all subject to large uncertainties (~ 80 TgCH4 yr-1). Recent advances in surface measurements of CH4 fluxes (e.g., FLUXNET-CH4) across a wide range of ecosystems including bogs, fens, marshes, and forest swamps provide an unprecedented opportunity to improve existing bottom-up estimates of wetland CH4 estimates. We suggest that continuous long-term surface measurements at representative wetlands, high fidelity wetland mapping, combined with an appropriate modeling framework, will be needed to significantly improve global estimates of wetland CH4 emissions. There is also a pressing unmet need for fine-resolution and high-precision satellite CH4 observations directed at wetlands.