Insights from intercomparison of microbial and conventional soil models

Abstract:

Changing the structure of soil biogeochemical models to represent coupling between microbial biomass and carbon substrate pools could improve predictions of carbon-climate feedbacks. So-called “microbial models” with this structure make very different predictions from conventional models based on first-order decay of carbon substrate pools. Still, the value of microbial models is uncertain because microbial physiological parameters are poorly constrained and model behaviors have not been fully explored. To address these issues, we developed an approach for inter-comparing microbial and conventional models. We initially focused on soil carbon responses to microbial carbon use efficiency (CUE) and temperature. Three scenarios were implemented in all models at a common reference temperature (20°C): constant CUE (held at 0.31), varied CUE (-0.016°C^-1), and 50% acclimated CUE (-0.008°C^-1). Whereas the conventional model always showed soil carbon losses with increasing temperature, the microbial models each predicted a temperature threshold above which warming led to soil carbon gain. The location of this threshold depended on CUE scenario, with higher temperature thresholds under the acclimated and constant scenarios. This result suggests that the temperature sensitivity of CUE and the structure of the soil carbon model together regulate the long-term soil carbon response to warming. Compared to the conventional model, all microbial models showed oscillatory behavior in response to perturbations and were much less sensitive to changing inputs. Oscillations were weakest in the most complex model with explicit enzyme pools, suggesting that multi-pool coupling might be a more realistic representation of the soil system. This study suggests that model structure and CUE parameterization should be carefully evaluated when scaling up microbial models to ecosystems and the globe.