Effect of explicit microbial dynamics on the performance of soil carbon cycle models
Terrestrial carbon cycle feedbacks to climate strongly depend on the dynamics of soil organic carbon (SOC). Current carbon cycle models show low predictive accuracy at simulating soil carbon pools, which can be improved through parameter calibration and/or changing modelling paradigm. To date, the role of microbes in soil carbon dynamics has been implicitly described by decay rate constants in most conventional global carbon cycle models. Explicitly including microbial biomass dynamics into carbon cycle models has shown potential to improve model predictive performance when assessed against global SOC databases. We aimed to data-constrain parameters in two soil microbial models; evaluate the improvements in performance of the calibrated models in predicting contemporary soil carbon stocks; and compare the responses of SOC to climate change and their uncertainties between microbial and conventional models.
Calibrated microbial models explained 51% of variability in the observed total soil organic carbon, whereas a calibrated conventional model explained 41%. We observed unrealistic oscillatory SOC dynamics in microbial models, which could be avoided by increasing the number of SOC pools and changing parameter values. When forced with climate and soil carbon input predictions from the 5th Coupled Model Intercomparison Project (CMIP5), the data-constrained microbial models produced stronger SOC responses to 95 years of climate change than any of the 11 CMIP5 models.