Microbial physiology plays a critical role in the biogeochemical cycles of the Earth system. However, most traditional soil carbon models are lacking in terms of the representation of key microbial processes that control the soil carbon response to global climate change. In this study, the improved process‐based model TRIPLEX‐GHG was developed by coupling it with the new MEND (Microbial‐ENzyme‐mediated Decomposition) model to estimate total global soil organic carbon (SOC) and global soil microbial carbon. The new model (TRIPLEX‐MICROBE) shows considerable improvement over the previous version (TRIPLEX‐GHG) in simulating SOC. We estimated the global soil carbon stock to be approximately 1195 Pg C, with 348 Pg C located in the high northern latitudes, which is in good agreement with the well‐regarded Harmonized World Soil Database (HWSD) and the Northern Circumpolar Soil Carbon Database (NCSCD). We also estimated the global soil microbial carbon to be 21 Pg C, similar to the 23 Pg C estimated by Xu et al. (2014). We found that the microbial carbon quantity in the latitudinal direction showed reversions at approximately 30°N, near the equator and at 25°S. A sensitivity analysis suggested that the tundra ecosystem exhibited the highest sensitivity to a 1°C increase or decrease in temperature in terms of dissolved organic carbon (DOC), microbial biomass carbon (MBC), and mineral‐associated organic carbon (MOC). However, our work represents the first step toward a new generation of ecosystem process models capable of integrating key microbial processes into soil carbon cycles.

Potential changes in both the intensity and frequency of extreme drought events are vital aspects of regional climate change that can alter the distribution and dynamics of water availability and subsequently affect carbon cycles at the ecosystem level. The effects of extreme drought events on the carbon budget of peatland in the Zoige plateau and its response mechanisms were studied using an in-field controlled experimental method. The results indicated that the peatland ecosystem of the Zoige plateau functioned as a carbon sink while under the control (CK) or extreme drought (D) treatment throughout the entire growing season. Maximum fluxes of methane (CH₄) emissions and the weakest carbon sink activity from this ecosystem were in the early growth stage, the most powerful carbon sink activity was during the peak growth stage, while the absorption sink activity of carbon dioxide (CO₂) and CH₄ was present during the senescence stage. Extreme drought reduced the gross primary productivity (GPP) and ecosystem respiration (R_e) of the peatland ecosystem by 14.5% and 12.6%, respectively (p < 0.05) and the net ability to store carbon was reduced by 11.3%. Overall, the GPP was highly sensitive to extreme drought. Moreover, extreme drought significantly reduced the CH₄ fluxes of the ecosystem and even changed the peatland from a CH₄ emission source to a CH₄ sink. Subsequent to drought treatment, extreme drought was also shown to have a carry-over effect on the carbon budget of this ecosystem. Soil water content and soil temperature were the main driving factors of carbon budget change in the peatland of the Zoige plateau, but with the increase in soil depth, these driving forces were decreased. The findings indicated that frequent extreme drought events in the future might reduce the net carbon sink function of peatland areas, with an especially strong influence on CO₂.

Alpine wetlands degrade rapidly due to climate change and human activities. Studying degradation effects on flora, soil, and microbes, and their mechanisms, can aid wetland management and global carbon dynamic insights. Here, we conducted transect surveys across various levels of degradation in the Qinghai-Tibet Plateau, ranging from non-degraded to severely degraded alpine wetlands. Severe degradation reduced aboveground biomass by 72.5%. As degradation intensified, the abundance of high-quality forage plants, especially Cyperaceae, gradually declined. Degradation resulted in soil nutrient deficiencies and stoichiometric imbalances, which significantly affected plant growth and soil microbial diversity. These changes ultimately led to a decline in carbon sequestration. The diversity of microbial and plant communities’ response to degradation aligned with the “intermediate interference hypothesis.” The altered bacterial community composition, which favors oligotrophic dominance, and its nonlinear response to soil stoichiometry and pH, could explain the maintenance of diversity and species richness of microbial communities under intermediate disturbance.

Forests and grasslands play a significant role in global nitrous oxide (N₂O) emission, while a sophisticated model is missing to interpret the microbial processes involved in N₂O production and consumption in these ecosystems. A new N₂O emission model, MicN model, was developed for natural forest and grassland ecosystems by integrating with Integrated BIosphere Simulator (IBIS). This new model includes four major N₂O correlated processes: autotrophic nitrification, heterotrophic nitrification, nitrifier denitrification and denitrifier denitrification. Thirty‐nine observation sites, including various forest and grassland ecosystem types, were selected to calibrate and validate IBIS‐MicN model. The IBIS‐MicN model explained 89% and 85% of observed daily variability in N₂O emission for all calibration and validation sites, respectively. In addition, the model successfully predicted the concentrations of two important intermediates (i.e., ammonium and nitrate) for N₂O production at nine validation sites. Compared with Forest‐DNDC model, IBIS‐MicN model performed better in simulating N₂O emission, soil NH₄⁺ and NO₃⁻concentrations. At site scale, the simulated N₂O emissions by IBIS‐MicN model positively correlated with annual mean soil temperature and annual precipitation, but negatively correlated with soil pH. This study provides an alternative model to estimate global N₂O emission from natural forest and grassland ecosystems and to investigate corresponding environmental regulations.

The development of the new process‐based TRIPLEX‐GHG model derives from the Integrated Biosphere Simulator (IBIS), which couples nitrification and denitrification processes to quantify nitrous oxide (N₂O) emissions from natural forests and grasslands. Sensitivity analysis indicates that the nitrification rate coefficient (COE_NR) is the most sensitive parameter to simulate N₂O emissions. Accordingly, we calibrated this parameter using data from 29 global forest sites (across different latitudes) and grassland sites. The average nitrification rate coefficient gradually increases in the order of tropical forest to grassland to temperate forest to boreal forest, and giving means of 0.009, 0.03, 0.04, and 0.09, respectively. This study validated the mean value for each ecosystem at 52 sites globally. Calibration results both indicate the good performance of the model and its suitability in capturing seasonal variation and magnitude of N₂O flux; however, it is limited in modeling N₂O uptake and increments during periods of snowmelt. Additionally, validation results indicate that simulated and observed annual or seasonal N₂O fluxes are highly correlated (R² = 0.75; P < 0.01). Consequently, our results suggest that the model is suitable in simulating N₂O emissions from different forest and grassland land types under varying environmental conditions on a global scale.