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Soil moisture-atmosphere feedback dominates land N 2 O nitrification emissions and denitrification reduction.

Jiayuan LiaoQiqi LuoAng HuWenkai WanDian TianJingwei MaTian MaHao LuoSheng Lu
Published in: Global change biology (2022)
Soil moisture (SM) is essential to microbial nitrogen (N)-cycling networks in terrestrial ecosystems. Studies have found that SM-atmosphere feedbacks dominate the changes in land carbon fluxes. However, the influence of SM-atmosphere feedbacks on the N fluxes changes, and the underlying mechanisms remain highly unsure, leading to uncertainties in climate projections. To fill this gap, we used in situ observation coupled with gridded and remote sensing data to analyze N 2 O fluxes emissions globally. Here, we investigated the synergistic effects of temperature, hydroclimate on global N 2 O fluxes, as the result of SM-atmosphere feedback impact on N fluxes. We found that SM-temperature feedback dominates land N 2 O emissions by controlling the balance between nitrifier and denitrifier genes. The mechanism is that atmospheric water demand increases with temperature and thereby reduces SM, which increases the dominant N 2 O production nitrifier (containing amoA AOB gene) and decreases the N 2 O consumption denitrifier (containing the nosZ gene), consequently will potential increasing N 2 O emissions. However, we find that the spatial variations of soil-water availability as a result of the nonlinear response of SM to vapor pressure deficit caused by temperature are some of the greatest challenges in predicting future N 2 O emissions. Our data-driven assessment deepens the understanding of the impact of SM-atmosphere interactions on the soil N cycle, which remains uncertain in earth system models. We suggest that the model needs to account for feedback between SM and atmospheric temperature when estimating the response of the N 2 O emissions to climatic change globally, as well as when conducting field-scale investigations of the response of the ecosystem to warming.
Keyphrases
  • climate change
  • municipal solid waste
  • life cycle
  • microbial community
  • copy number
  • particulate matter
  • machine learning
  • gene expression
  • human health
  • genome wide identification
  • high resolution
  • heavy metals
  • amino acid