Moisture–Microbial Interaction Amplifies N2O Emission Hot Moments Under Deepened Snow in Grasslands

Freeze–thaw-induced N₂O pulses could account for nearly half of annual N₂O fluxes in cold climates, but their episodic nature, sensitivity to snow cover dynamics, and the challenges of cold-season monitoring complicate their accurate estimation and representation in global models. To address these challenges, we combined in situ automated high-frequency flux measurements with cross-ecoregion soil core incubations to investigate the mechanisms driving freeze–thaw-induced N₂O emissions. We found that deepened snow significantly amplified freeze–thaw N₂O pulses, with these ~50-day episodes contributing over 50% of annual fluxes. Additionally, freeze–thaw-induced N₂O pulses exhibited significant spatial heterogeneity, ranging from 3.4 to 1184.1 μg N m⁻² h⁻¹ depending on site conditions. Despite significant spatiotemporal variation, our results indicated that 68%–86% of this variation can be explained by shifts in controlling factors: from water-filled pore space (WFPS), which drove anaerobic conditions, to microbial constraints as snow depth increases. Below 43% WFPS, soil moisture was the overwhelmingly dominant driver of emissions; between 43% and 66% WFPS, moisture and microbial attributes (including denitrifying gene abundance, nitrogen enzyme kinetics, and microbial biomass) jointly triggered N₂O emissions pulses; above 66% WFPS, microbial attributes, particularly nitrogen enzyme kinetics, prevailed. These findings suggested that maintaining higher soil moisture served as a trigger for activating microbial activity, particularly enhancing nitrogen cycling. Furthermore, we showed that hotspots of freeze–thaw-induced N₂O emissions were linked to high root production and microbial activity in cold and humid grasslands. Overall, our study highlighted the hierarchical control of WFPS and microbial processes in driving freeze–thaw-induced N₂O emission pulses. The easily measurable WFPS and microbial attributes predictable from plant and soil properties could forecast the magnitude and spatial distribution of N₂O emission “hot moments” under changing climate. Integrating these hot moments, particularly the dynamics of WFPS, into process-based models could refine N₂O emission modeling and enhance the accuracy of global N₂O budget prediction.