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Abstract Microbial processes are crucial in producing and oxidizing biological methane (CH₄) in natural wetlands. Therefore, modeling methanogenesis and methanotrophy is advantageous for accurately projecting CH₄cycling. Utilizing the CLM‐Microbe model, which explicitly represents the growth and death of methanogens and methanotrophs, we demonstrate that genome‐enabled model parameterization improves model performance in four natural wetlands. Compared to the default model parameterization against CH₄flux, genomic‐enabled model parameterization added another contain on microbial biomass, notably enhancing the precision of simulated CH₄flux. Specifically, the coefficient of determination (R²) increased from 0.45 to 0.74 for Sanjiang Plain, from 0.78 to 0.89 for Changbai Mountain, and from 0.35 to 0.54 for Sallie's Fen, respectively. A drop inR²was observed for the Dajiuhu nature wetland, primarily caused by scatter data points. Theil's coefficient (U) and model efficiency (ME) confirmed the model performance from default parameterization to genome‐enabled model parameterization. Compared with the model solely calibrated to surface CH₄flux, additional constraints of functional gene data led to better CH₄seasonality; meanwhile, genome‐enabled model parameterization established more robust associations between simulated CH₄production rates and environmental factors. Sensitivity analysis underscored the pivotal role of microbial physiology in governing CH₄flux. This genome‐enabled model parameterization offers a valuable promise to integrate fast‐cumulating genomic data with CH₄models to better understand microbial roles in CH₄in the era of climate change. 

The positive Arctic–methane (CH₄) feedback forms when more CH₄is released from the Arctic tundra to warm the climate, further stimulating the Arctic to emit CH₄. This study utilized the CLM-Microbe model to project CH₄emissions across five distinct Arctic tundra ecosystems on the Alaska North Slope, considering three Shared Socioeconomic Pathway (SSP) scenarios using climate data from three climate models from 2016 to 2100. Employing a hyper-resolution of 5 m × 5 m within 40,000 m²domains accounted for the Arctic tundra’s high spatial heterogeneity; three sites were near Utqiaġvik (US-Beo, US-Bes, and US-Brw), with one each in Atqasuk (US-Atq) and Ivotuk (US-Ivo). Simulated CH₄emissions substantially increased by a factor of 5.3 to 7.5 under the SSP5–8.5 scenario compared to the SSP1–2.6 and SSP2–4.5 scenarios. The projected CH₄emissions exhibited a stronger response to rising temperature under the SSP5–8.5 scenario than under the SSP1–2.6 and SSP2–4.5 scenarios, primarily due to strong temperature dependence and the enhanced precipitation-induced expansion of anoxic conditions that promoted methanogenesis. The CH₄transport via ebullition and plant-mediated transport is projected to increase under all three SSP scenarios, and ebullition dominated CH₄transport by 2100 across five sites. Projected CH₄emissions varied in temperature sensitivity, with a Q₁₀range of 2.7 to 60.9 under SSP1–2.6, 3.8 to 17.6 under SSP2–4.5, and 5.7 to 17.2 under SSP5–8.5. Compared with the other three sites, US-Atq and US-Ivo were estimated to have greater increases in CH₄emissions due to warmer temperatures and higher precipitation. The fact that warmer sites and warmer climate scenarios had higher CH₄emissions suggests an intensified positive Arctic–CH₄feedback in the 21st century. Microbial physiology and substrate availability dominated the enhanced CH₄production. The simulated intensified positive feedback underscores the urgent need for a more mechanistic understanding of CH₄dynamics and the development of strategies to mitigate CH₄across the Arctic. 

Abstract The theory of microbial stoichiometry can predict the proportional coupling of microbial assimilation of carbon (C), nitrogen (N), and phosphorus (P). The proportional coupling is quantified by the homeostasis value (H). Covariation of H values for C, N, and P indicates that microbial C, N, and P assimilation are coupled. Here, we used a global dataset to investigate the spatiotemporal dynamics of H values of microbial C, N, and P across biomes. We found that land use and management led to the decoupling of P from C and N metabolism over time and across space. Results from structural equation modeling revealed that edaphic factors dominate the microbial homeostasis of P, while soil elemental concentrations dominate the homeostasis of C and N. This result was further confirmed using the contrasting factors on microbial P vs. microbial C and N derived from a machine-learning algorithm. Overall, our study highlights the impacts of management on shifting microbial roles in nutrient cycling. 

Spatial heterogeneity in methane (CH 4 ) flux requires a reliable upscaling approach to reach accurate regional CH 4 budgets in the Arctic tundra. In this study, we combined the CLM-Microbe model with three footprint algorithms to scale up CH 4 flux from a plot level to eddy covariance (EC) tower domains (200 m × 200 m) in the Alaska North Slope, for three sites in Utqiaġvik (US-Beo, US-Bes, and US-Brw), one in Atqasuk (US-Atq) and one in Ivotuk (US-Ivo), for a period of 2013–2015. Three footprint algorithms were the homogenous footprint (HF) that assumes even contribution of all grid cells, the gradient footprint (GF) that assumes gradually declining contribution from center grid cells to edges, and the dynamic footprint (DF) that considers the impacts of wind and heterogeneity of land surface. Simulated annual CH 4 flux was highly consistent with the EC measurements at US-Beo and US-Bes. In contrast, flux was overestimated at US-Brw, US-Atq, and US-Ivo due to the higher simulated CH 4 flux in early growing seasons. The simulated monthly CH 4 flux was consistent with EC measurements but with different accuracies among footprint algorithms. At US-Bes in September 2013, RMSE and NNSE were 0.002 μmol m −2  s −1 and 0.782 using the DF algorithm, but 0.007 μmol m −2  s −1 and 0.758 using HF and 0.007 μmol m −2  s −1 and 0.765 using GF, respectively. DF algorithm performed better than the HF and GF algorithms in capturing the temporal variation in daily CH 4 flux each month, while the model accuracy was similar among the three algorithms due to flat landscapes. Temporal variations in CH 4 flux during 2013–2015 were predominately explained by air temperature (67–74%), followed by precipitation (22–36%). Spatial heterogeneities in vegetation fraction and elevation dominated the spatial variations in CH 4 flux for all five tower domains despite relatively weak differences in simulated CH 4 flux among three footprint algorithms. The CLM-Microbe model can simulate CH 4 flux at both plot and landscape scales at a high temporal resolution, which should be applied to other landscapes. Integrating land surface models with an appropriate algorithm provides a powerful tool for upscaling CH 4 flux in terrestrial ecosystems. 

Abstract Spatial heterogeneities in soil hydrology have been confirmed as a key control on CO₂and CH₄fluxes in the Arctic tundra ecosystem. In this study, we applied a mechanistic ecosystem model, CLM‐Microbe, to examine the microtopographic impacts on CO₂and CH₄fluxes across seven landscape types in Utqiaġvik, Alaska: trough, low‐centered polygon (LCP) center, LCP transition, LCP rim, high‐centered polygon (HCP) center, HCP transition, and HCP rim. We first validated the CLM‐Microbe model against static‐chamber measured CO₂and CH₄fluxes in 2013 for three landscape types: trough, LCP center, and LCP rim. Model application showed that low‐elevation and thus wetter landscape types (i.e., trough, transitions, and LCP center) had larger CH₄emissions rates with greater seasonal variations than high‐elevation and drier landscape types (rims and HCP center). Sensitivity analysis indicated that substrate availability for methanogenesis (acetate, CO₂ + H₂) is the most important factor determining CH₄emission, and vegetation physiological properties largely affect the net ecosystem carbon exchange and ecosystem respiration in Arctic tundra ecosystems. Modeled CH₄emissions for different microtopographic features were upscaled to the eddy covariance (EC) domain with an area‐weighted approach before validation against EC‐measured CH₄fluxes. The model underestimated the EC‐measured CH₄flux by 20% and 25% at daily and hourly time steps, suggesting the importance of the time step in reporting CH₄flux. The strong microtopographic impacts on CO₂and CH₄fluxes call for a model‐data integration framework for better understanding and predicting carbon flux in the highly heterogeneous Arctic landscape.