Abstract. The role of soil microorganisms in regulating soil
organic matter (SOM) decomposition is of primary importance in the carbon
cycle, in particular in the context of global change. Modeling soil
microbial community dynamics to simulate its impact on soil gaseous carbon
(C) emissions and nitrogen (N) mineralization at large spatial scales is a
recent research field with the potential to improve predictions of SOM
responses to global climate change. In this study we present a SOM model called
ORCHIMIC, which utilizes input data that are consistent with those of global
vegetation models. ORCHIMIC simulates the decomposition of SOM by explicitly
accounting for enzyme production and distinguishing three different
microbial functional groups: fresh organic matter (FOM) specialists, SOM
specialists, and generalists, while also implicitly accounting for microbes
that do not produce extracellular enzymes, i.e., cheaters. ORCHIMIC and two other organic matter decomposition models,
CENTURY (based on first-order kinetics and representative of the structure of most current global soil carbon models)
and PRIM (with FOM accelerating the decomposition rate of SOM), were calibrated
to reproduce the observed respiration fluxes of FOM and SOM from the incubation experiments of
Blagodatskaya et al. (2014).
Among the three models, ORCHIMIC was the only
one that effectively captured both the temporal dynamics of the respiratory fluxes
and the magnitude of the priming effect observed during the incubation
experiment. ORCHIMIC also effectively reproduced the temporal dynamics of microbial
biomass. We then applied different idealized changes to the model input
data, i.e., a 5 K stepwise increase of temperature and/or a doubling of plant
litter inputs. Under 5 K warming conditions, ORCHIMIC predicted a 0.002 K−1
decrease in the C use efficiency (defined as the ratio of C allocated to
microbial growth to the sum of C allocated to growth and respiration) and a
3 % loss of SOC. Under the double litter input scenario, ORCHIMIC
predicted a doubling of microbial biomass, while SOC stock increased by less
than 1 % due to the priming effect. This limited increase in SOC stock
contrasted with the proportional increase in SOC stock as modeled by the
conventional SOC decomposition model (CENTURY), which can not reproduce the
priming effect. If temperature increased by 5 K and litter input was doubled,
ORCHIMIC predicted almost the same loss of SOC as when only temperature
was increased. These tests suggest that the responses of SOC stock to
warming and increasing input may differ considerably from those simulated by
conventional SOC decomposition models when microbial dynamics are included.
The next step is to incorporate the ORCHIMIC model into a global vegetation
model to perform simulations for representative sites and future scenarios.
ORCHIMIC (v1.0), a microbe-driven model for soil organic matter
decomposition designed for large-scale applications