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A top-down evaluation of bottom-up estimates to reduce uncertainty in methane emissions from Arctic wetlands
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A top-down evaluation of bottom-up estimates to reduce uncertainty in methane emissions from Arctic wetlands
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Journal Article
A top-down evaluation of bottom-up estimates to reduce uncertainty in methane emissions from Arctic wetlands
2026
Overview
Wetlands are a major natural source of atmospheric CH.sub.4, however, accurately estimating their emissions is difficult due to the complex biogeochemical interactions and spatial heterogeneity of wetland environments. This study explores how a combination of atmospheric inverse and process-based modelling can reduce the discrepancy in Arctic wetland estimates between bottom-up and top-down approaches. We employed the Jena CarboScope global inversion system, incorporating prior wetland fluxes simulated by the JSBACH land surface model, which is part of the Max Planck Institute Earth System Model (MPI-ESM). We conducted a series of inversion experiments, each incorporating JSBACH-generated CH.sub.4 fluxes based on different CH.sub.4 production Q.sub.10 values, which represents the temperature dependence of CH.sub.4 production. Additionally, we examined the impact of changing the baseline fCH4 fraction value, which defines the fraction of anaerobically mineralized carbon converted to CH.sub.4, while keeping all other JSBACH and inversion settings constant. Our findings show that, at a pan-Arctic scale, using a CH.sub.4 Q.sub.10 value of 1.8 produces the best agreement between the two approaches. However, no single Q.sub.10 value yielded optimal agreement between the simulated fluxes and the fluxes inferred from atmospheric observations across all subregions. Instead, the best performance varied spatially, with different CH.sub.4 production Q.sub.10 values and baseline fCH4 fraction leading to a better flux agreement in specific areas. These results highlight the importance of using regionally specific parameters to more accurately estimate wetland CH.sub.4 emissions, and the potential of employing atmospheric inversions to guide bottom-up process models towards regionally representative parameter settings.
