Partitioning anthropogenic and natural methane emissions in Finland during 2000–2021 by combining bottom-up and top-down estimates
2025
Tenkanen, Maria K. | Tsuruta, Aki | Denier van der Gon, Hugo | Höglund-Isaksson, Lena | Leppänen, Antti | Markkanen, Tiina | Petrescu, Ana Maria Roxana | Raivonen, Maarit | Aaltonen, Hermanni | Aalto, Tuula | Ilmatieteen laitos | Finnish Meteorological Institute | 0000-0002-5867-6295 | 0000-0002-9197-3005 | 0000-0001-7257-6402 | 0000-0003-4169-8225 | 0000-0002-8672-0433 | 0000-0002-3264-7947
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 the ability of a global atmospheric inverse model CarbonTracker Europe – CH4 to estimate CH4 emissions within a single country. We focused on how different priors and their uncertainties affect the optimised emissions and showed that the optimised anthropogenic and natural CH4 emissions were strongly dependent on the prior emissions. However, while the range of CH4 estimates was large, the optimised emissions were more constrained than the bottom-up estimates. Further analysis showed that the optimisation aligned the trends of anthropogenic and natural CH4 emissions and improved the 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 highest natural prior (JSBACH–HIMMELI) agreed best with observations, suggesting that process-based models may underestimate CH4 emissions from Finnish peatlands or unaccounted sources such as freshwater emissions. Additionally, using an uncertainty estimate based on a process-based model ensemble for natural CH4 emissions seemed to be advantageous compared to the standard uncertainty definition. The average total posterior emission of the ensemble from one inverse model with different priors was similar to the average of the ensemble including different inverse models but similar priors. Thus, a single inverse model using a range of priors can be used to reliably estimate CH4 emissions when an ensemble of different models is unavailable.
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