Partitioning anthropogenic and natural methane emissions in Finland during 2000–2021 by combining bottom-up and top-down estimates

Accurate national methane (CH4) emission estimates are essential for tracking progress towards climate goals. This study investigated Finnish CH4 emissions from 2000–2021 using bottom-up and top-down approaches. We evaluated a global atmospheric inversion model’s ability to estimate CH4 emissions within a single country, focusing on how the choice of priors and uncertainties affected optimised emissions. The optimised anthropogenic and natural CH4 emissions strongly depended on the prior emissions. While the range of CH4 estimates was large, the optimised emissions were more constrained than the bottom-up estimates. Further analysis of CarbonTracker Europe - CH4 results showed that optimisation aligned the trends of anthropogenic and natural CH4 emission and improved modelled seasonal cycles of natural emissions. Comparison of atmospheric CH4 observations with model results showed no clear preference between anthropogenic inventories (EDGAR v6 and CAMS-REG), but results using the largest natural prior (JSBACH-HIMMELI) best agreed with observations, suggesting that process-based models may underestimate CH4 emissions from Finnish peatlands or unaccounted sources such as freshwater emissions. Additionally, using a process-model spread-based uncertainty estimate for natural CH4 emissions seemed advantageous compared to the standard constant estimate. The average total posterior emission of the ensemble from one inversion model with different priors was similar to the average of the ensemble including different inversion models but similar priors. Thus, a range of priors can be used to reliably estimate CH4 emissions when an ensemble of different models is unavailable.