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Multi-model ensemble does not fill the gaps in sparse wetland methane observations

Presentation Date
Wednesday, December 15, 2021 at 4:00pm
Location
Convention Center - Poster Hall, D-F
Authors

Author

Abstract

Methane (CH4) is a strong greenhouse gas that accelerates climate change and offsets mitigation efforts. Although wetlands are considered to be the largest natural CH4 source, estimates of global wetland CH4 emissions vary widely among approaches taken by bottom-up process-based biogeochemical models (102–182 TgCH4 yr−1) and top-down atmospheric-inversion methods (159–200 TgCH4 yr−1). Importantly, the discrepancy between bottom-up and top-down wetland CH4 emission estimates has remained wide, ~17 TgCH4 yr−1 in 2016 and ~30 TgCH4 yr−1 in 2020, despite recent advances in CH4 observations and simulations. Here, we use ecosystem-scale measurements, multi-model ensemble from bottom-up and top-down approaches, and machine-learning upscaling estimates with ILAMB (the International Land Model Benchmarking System) to constrain uncertainty in modeled global wetland CH4 emissions. We find the discrepancy between bottom-up and top-down global wetland CH4 emission estimates increased by 49%, 2%, and decreased by 65% with constraints posed by ecosystem-scale measurements, multi-model ensemble, and machine-learning upscaling estimates, respectively. Our analyses highlight the importance of improving wetland heterogeneity representation in current observations and reveal the large sensitivity of the best-available multi-model ensemble to underlying approaches. Our results also demonstrate that traditional model benchmarking is strongly limited by data representativeness, and motivate the use of machine-learning methods to reduce uncertainty in sparse observations to facilitate model development. We acknowledge the FLUXNET-CH4 contributors and Global Carbon Project CH4 modeling groups for the data provided in these analyses.

Funding Program Area(s)