Informing Soil Carbon Models with Data from Global Change Experiments: Challenges and Opportunities

Abstract:

The soil carbon (C) pool may play an important role in determining the rate of climate change, but its response to future atmospheric conditions is uncertain. Elevated atmospheric CO₂ concentrations, warming and nitrogen enrichment are known to affect plant productivity and soil microbial communities, with possible consequences for the turnover rate of soil C pools. Can we improve predictions of soil C dynamics under global change scenarios by informing soil C models with data from manipulative experiments? In a previous analysis, we combined meta-analysis with data assimlation and showed how elevated CO₂ increases decomposition rates in a one-pool soil C model. Two-pool models may better represent long-term soil C dynamics, but when we refit our data to a two-pool soil C model, we arrived at similar conclusions. In addition, elevated CO₂ decreased the carbon use efficiency of soil microbes, thereby further reducing the potential for soil C storage. We also present preliminary results from a two-pool analysis on the effect of warming on soil C dynamics. We will discuss the so-called “false priming effect” (i.e., the idea that a step increase in soil C input increases the size of the labile soil C pool, giving the impression of an increase in overall decomposition rates), and possible approaches to correct for this effect. To rule out the possibility of artifacts associated with simplified model structures, we suggest that future data-assimilation efforts on soil C dynamics be done using multi-pool models. Models that explicitly represent microbial dynamics may yield important insights as well. However, as models become more complicated, they must also be more constrained by empirical data. We will outline a few approaches on how to do this.