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Representing microbial communities in Earth system models

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Feedbacks between climate warming and carbon cycling on land are a major source of uncertainty in future climate projections. Soils play a key role in this uncertainty, and current biogeochemical models diverge widely in their soil carbon projections. For example, current models simulate global carbon stocks of 510 to 3040 Pg C compared to observations of 890 to 1660 Pg C. Going forward, these models project changes in global soil carbon ranging from losses of 72 Pg C to gains of 253 Pg C over the 21st century. Importantly, all of these model omit microbial processes. New models that account for microbial physiology and enzyme kinetics can explain up to half of the spatial variation in contemporary soil carbon stocks, roughly double the variance explained by the best conventional models. Still, these microbial models are relatively unproven. They project a wide range of soil carbon responses to 21st century global change, and they are surprisingly insensitive to changes in carbon inputs. New analyses are required to parameterize microbial models and scale up microbial physiology. Small-scale models that account for microbial diversity and functional traits are promising tools for addressing this challenge.

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