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Methane fluxes from Arctic & boreal North America: comparisons between process-based estimates and atmospheric observations
Methane fluxes from Arctic & boreal North America: comparisons between process-based estimates and atmospheric observations
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Methane fluxes from Arctic & boreal North America: comparisons between process-based estimates and atmospheric observations
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Methane fluxes from Arctic & boreal North America: comparisons between process-based estimates and atmospheric observations
Methane fluxes from Arctic & boreal North America: comparisons between process-based estimates and atmospheric observations

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Methane fluxes from Arctic & boreal North America: comparisons between process-based estimates and atmospheric observations
Methane fluxes from Arctic & boreal North America: comparisons between process-based estimates and atmospheric observations
Journal Article

Methane fluxes from Arctic & boreal North America: comparisons between process-based estimates and atmospheric observations

2026
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Overview
Methane (CH4) flux estimates from high-latitude North American wetlands remain highly uncertain in magnitude, seasonality, and spatial distribution. In this study, we evaluate a decade (2007–2017) of CH4 flux estimates by comparing 16 process-based models with atmospheric CH4 observations collected from in situ towers. We compare the Global Carbon Project (GCP) process-based models with a model inter-comparison from a decade earlier called The Wetland and Wetland CH4 Intercomparison of Models Project (WETCHIMP). Our analysis reveals that the GCP models have a much smaller inter-model uncertainty and have an average magnitude that is a factor of 1.5 smaller across Canada and Alaska. However, current GCP models likely overestimate wetland fluxes by a factor of two or more across Canada and Alaska based on tower-based atmospheric CH4 observations. The differences in flux magnitudes among GCP models are more likely driven by uncertainties in the amount of soil carbon or spatial extent of inundation than in temperature relationships, such as Q10 factors. The GCP models do not agree on the timing and amplitude of the seasonal cycle, and we find that models with a seasonal peak in July and August show the best agreement with atmospheric observations. Models that exhibit the best fit to atmospheric observation also have a similar spatial distribution; these models concentrate fluxes near Canada's Hudson Bay Lowlands. Current, state-of-the-art process-based models are much more consistent with atmospheric observations than models from a decade ago, but our analysis shows that there are still numerous opportunities for improvement.