Climate change is expected to have significant and uncertain impacts on methane (CH4) emissions from northern peatlands. Biogeochemical models can extrapolate site-specificCH4 measurements to larger scales and predict responses of CH4 emissions to environmental changes. However, these models include considerable uncertainties and limitations in representing CH4 production, consumption, and transport processes. To improve predictions of CH4 transformations, we incorporated acetate and stable carbon (C) isotopic dynamics associated with CH4 cycling into a biogeochemistry model, DNDC. By including these new features, DNDC explicitly simulates acetate dynamics and the relative contribution of acetotrophic and hydrogenotrophic methanogenesis (AM and HM) to CH4 production, and predicts the C isotopic signature (δ13C) in soil C pools and emitted gases. When tested against biogeochemical and microbial community observations at two sites in a zone of thawing permafrost in a subarctic peatland in Sweden, the new formulation substantially improved agreement with CH4 production pathways and δ13C in emitted CH4 (δ13C-CH4), a measure of the integrated effects of microbial production and consumption, and of physical transport. We also investigated the sensitivity of simulated δ13C-CH4 to C isotopic composition of substrates and, to fractionation factors for CH4 production (αAM and αHM), CH4 oxidation (αMO), and plant-mediated CH4 transport (αTP). The sensitivity analysis indicated that the δ13C-CH4 is highly sensitive to the factors associated with microbial metabolism (αAM, αHM, and αMO). The model framework simulating stable C isotopic dynamics provides a robust basis for better constraining and testing microbial mechanisms in predicting CH4 cycling in peatlands.

The Arctic climate system is host to many processes which interact vertically over the tightly coupled atmosphere, sea ice and ocean. The coupled Atmosphere-Ocean Single-Column Model (AOSCM) allows to decouple local small-scale and large-scale processes to investigate the model performance in an idealized setting. Here, an observed Arctic warm air intrusion event is used to show how to identify model deficiencies using the AOSCM. The AOSCM allows us to effectively produce a large number of perturbation simulations, around 1,000, to map sensitivities of the model results due to changes in physical and model properties as well as to the large-scale tendencies. The analysis of the summary diagnostics, that is, aggregated results from sensitivity experiments evaluated against modeled physical properties, such as surface energy budget and mean sea ice thickness, reveals sensitivities to the chosen parameters. Further, we discuss how the conclusions can be used to understand the behavior of the global host model. The simulations confirm that the horizontal advection of heat and moisture plays an important role for maintaining a low-level cloud cover, as in earlier studies. The combined cloud layers increase the energy input to the surface, which in turn enhances the ongoing melt. The clouds present an additional sensitivity in terms of how they are represented but also their interaction with the large-scale advection and the model time step. The methodology can be used for a variety of other regions, where the coupling to the ocean is important.