Restricted power: Can microorganisms maintain soil organic matter stability under warming exceeding 2 degrees?

AbstractAimThe Paris Climate Agreement is pursuing efforts to limit global warming to less than 2 °C in this century, but increasing evidence shows that temperatures are likely to rise up to 4.8 °C by 2100. This points to an urgent need to investigate how the temperature impact on microbial regulation will endanger soil organic matter stability under warming approaching or exceeding 2 °C.LocationSouthern Germany and the globe.Time Period2010–2018.Major Taxa StudiedSoil organic matter stability and microbial adaptations.MethodsWe analyzed soil properties and enzyme activities within (a) a long-term field experiment with soil warming to below and above 2 °C (+1.6 vs. +3.2 °C), and (b) a literature review of 213 comparable studies globally (+0–2 vs. +2–4 °C).ResultsThe soil organic C (SOC) stock remained unchanged after 8 years under both warming magnitudes, whereas the labile C pool increased by 10% under >2 °C. Unlike the SOC pool, total N (TN) content increased by 20% under >2 °C as compared to ambient. A potential explanation for the increased TN content is linked to unbalanced processes of necromass formation and enzymatic decomposition. Warming induced faster microbial growth and turnover, but reduced catalytic efficiency and the enzyme-mediated decomposition of oligosaccharides and polypeptides. This consequently caused N accumulation in microbial necromass. Although microbial regulation can maintain SOC at stable levels, warming exceeding 2 °C will change the projected effects of temperature on soil TN pools in the future. Early action to accomplish the 2 °C temperature goal can therefore markedly reduce the likelihood that large regions will face substantial increase of SOC availability, N accumulation and related climate impacts on C and N cycling.Main ConclusionsIt is crucial to include microbial metabolic responses (i.e., faster microbial growth and turnover) to warming in global C and N cycle models to improve the prediction of climate warming scenarios.