Predictions of how the Earth will respond to climate change, including positive feedbacks caused by accelerated rates of decomposition of soil organic matter, are based in large part on projections of Earth system models (ESMs). However, one of the biggest ecosystem fluxes of CO2 in those models is based on very simplistic temperature functions, called Q10 functions. Sometimes the Q10 temperature function is accompanied by an empirical soil moisture modifier. More explicit model representation of the effects of soil moisture, substrate supply, and their interactions with temperature on microbial decomposition of soil organic matter has been part of my research for a long time, with the goal of disentangling the confounding factors of apparent temperature sensitivity of heterotrophic respiration in soils.
My post-doc, Debjani Sihi (who is now at the Oak Ridge National Laboratory), is first author of a new paper showing how our Dual Arrhenius Michaelis-Menten (DAMM) model can easily be incorporated into a larger ecosystem model, replacing its overly simplistic Q10 functions, and thereby improve the performance of the ecosystem model. We used high-frequency soil flux data from automated soil chambers and landscape-scale ecosystem fluxes from eddy covariance towers at two AmeriFlux sites (Harvard Forest, MA and Howland Forest, ME) in the northeastern USA to estimate model parameters, validate the merged model, and to quantify the uncertainties in a multiple constraints approach. While the DAMM functions require a few more parameters than a simple Q10 function, we have demonstrated that they can be included in an ecosystem model and reduce the model-data mismatch. Moreover, the mechanistic structure of the soil moisture effects using DAMM functions should be more generalizable than the wide variety of empirical functions that are commonly used, and these DAMM functions could be readily incorporated into other ecosystem models and ESMs.