Wetlands of northern high latitudes are ecosystems highly vulnerable to climate change. Some degradation effects include soil hydrologic changes due to permafrost thaw, formation of deeper active layers, and rising topsoil temperatures that accelerate the degradation of permafrost carbon and increase in CO2 and CH4 emissions. In this work we present 2 years of modeled year-round CH4 emissions into the atmosphere from a Northeast Siberian region in the Russian Far East. We use a revisited version of the process-based JSBACH-methane model that includes four CH4 transport pathways: plant-mediated transport, ebullition and molecular diffusion in the presence or absence of snow. The gas is emitted through wetlands represented by grid cell inundated areas simulated with a TOPMODEL approach. The magnitude of the summertime modeled CH4 emissions is comparable to ground-based CH4 fluxes measured with the eddy covariance technique and flux chambers in the same area of study, whereas wintertime modeled values are underestimated by 1 order of magnitude. In an annual balance, the most important mechanism for transport of methane into the atmosphere is through plants (61 %). This is followed by ebullition (similar to 35 %), while summertime molecular diffusion is negligible (0.02 %) compared to the diffusion through the snow during winter (similar to 4 %). We investigate the relationship between temporal changes in the CH4 fluxes, soil temperature, and soil moisture content. Our results highlight the heterogeneity in CH4 emissions at landscape scale and suggest that further improvements to the representation of large-scale hydrological conditions in the model will facilitate a more process-oriented land surface scheme and better simulate CH4 emissions under climate change. This is especially necessary at regional scales in Arctic ecosystems influenced by permafrost thaw.

The use of dynamic global vegetation models (DGVMs) to estimate CO2 emissions from land-use and land-cover change (LULCC) offers a new window to account for spatial and temporal details of emissions and for ecosystem processes affected by LULCC. One drawback of LULCC emissions from DGVMs, however, is lack of observation constraint. Here, we propose a new method of using satellite-and inventory-based biomass observations to constrain historical cumulative LULCC emissions (E-LUC(c)) from an ensemble of nine DGVMs based on emerging relationships between simulated vegetation biomass and E-LUC(c). This method is applicable on the global and regional scale. The original DGVM estimates of E-LUC(c) range from 94 to 273 PgC during 1901-2012. After constraining by current biomass observations, we derive a best estimate of 155 +/- 50 PgC (1 sigma Gaussian error). The constrained LULCC emissions are higher than prior DGVM values in tropical regions but significantly lower in North America. Our emergent constraint approach independently verifies the median model estimate by biomass observations, giving support to the use of this estimate in carbon budget assessments. The uncertainty in the constrained Ec LUC is still relatively large because of the uncertainty in the biomass observations, and thus reduced uncertainty in addition to increased accuracy in biomass observations in the future will help improve the constraint. This constraint method can also be applied to evaluate the impact of land-based mitigation activities.