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Updated: Feb 26, 2026

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Published on: October 28, 2022
Terrestrial ecosystem nitrogen cycling in response to field warming: Global patterns and future trends
Xudong Wang1,2, Chenrui Ni2, Ziyi Fan2
1Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau, Inner Mongolia Key Laboratory of Grassland Ecology, and School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China.
None:
Nitrogen cycling regulates terrestrial ecosystem productivity and carbon sequestration, yet its response to climate warming remains uncertain. Here, we compiled the most comprehensive dataset to date, integrating 7,941 observations from 413 field warming experiments worldwide with random forest regression and Community Land Model (CLM) simulations. Field warming significantly accelerated nitrogen cycling, increasing N2O emissions (+24.7%), mineralization (+25.8%), nitrification (+51.7%), and denitrification (+41.1%). Soil inorganic nitrogen also increased, while plant nitrogen remained largely unchanged. Elevated natural abundance of 15N indicated that warming alleviates nitrogen limitation and promotes more open nitrogen cycles. Soil moisture, ecosystem type, and warming magnitude were key drivers. N2O emission and nitrification further intensified with increased warming magnitude in random forest analyses. In contrast, CLM5-BGC simulated weak responses in N2O emissions and nitrification and negative changes in nitrogen mineralization, substantially diverging from field observations. These discrepancies highlight the omission of microbial processes and the oversimplification of large-scale ecosystem feedbacks, respectively. Uniquely, this study provides a direct comparison among empirical data, random forest regression, and CLM simulations, revealing discrepancies and their potential causes. Collectively, our findings demonstrate that terrestrial nitrogen cycling is more responsive to climate warming than previously recognized and underscore the importance of integrating multiple analytical approaches to synthesize cross-scale ecological data.
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