Microbes, and their traits, play key roles in terrestrial biogeochemical cycling, yet a systematic understanding of microbial trait distributions and their implications for system functioning (greenhouse gas, GHG) are lacking. Here we built a modeling framework, genome-to-ecosystem (G2E), to integrate genome-inferred microbial kinetic traits into a mechanistic ecosystem model (ecosys), and benchmarked it against observed GHG emissions at a thawing permafrost site. We find large differences in simulated CH₄ emissions due to microbial kinetic trait variation, indicating the importance of accurate microbial trait parameterization. Community-aggregated traits with genome-resolved relative abundance information reduce this variability, improve simulated CH4 emissions, and reduce the number of required simulations. We found that trait-optimization using field observations resulted in large parametric equifinality, which we interpret as an indicator of microbial kinetic biodiversity, motivating its inclusion in biogeochemical models. Our G2E framework allows integration of emerging genomic measurements and microbial ecophysiological modeling, thereby potentially improving ecosystem predictions, and points the way to integrate these approaches for a wide range of microbiome associated questions.