December 04, 2017
Soil Carbon Cycle Confidence and Uncertainty
Developing a testbed for global soil carbon modeling.
Soils represent the largest terrestrial carbon pool on Earth. Yet, emerging theories regarding stabilization of soil organic matter remain poorly represented in global-scale models; thus, underestimating the true uncertainty associated with potential terrestrial carbon cycle–climate feedbacks.
This work builds the capacity to test emerging ecological theories in global-scale models, informs future research needs, and affords avenues to test soil biogeochemical theory, refine model features, and accelerate advancements across scientific disciplines.
Models presented in this work are some of the first to begin explicitly considering biotic activity in global-scale biogeochemical models. By forcing them under a common land model, these results are some of the first to begin quantifying the uncertainty associated with potential soil carbon responses to changes in plant productivity, temperature, and moisture and global scales. Notably, the models made divergent projections about the fate of these soil carbon stocks over the 20th century, with models either gaining or losing over 20 petagrams of carbon (Pg C) globally between 1901 and 2010.
University of Colorado Boulder
U.S. Department of Energy, Biological and Environmental Research (SC-33)
Environmental System Science
This work was supported by the Officer of Biological and Environmental Research (BER), with the U.S. Department of Energy (DOE) Office of Science, under award numbers: Terrestrial Ecosystem Science (TES) DE-SC0014374, BSS DE-SC0016364, and Environmental Research RUBISCO SFA. Other support was from the U.S. Department of Agriculture National Institute of Food and Agriculture (NIFA) 2015-67003- 23485, National Oceanic and Atmospheric Administration NA14OAR4320106, and U.S. Department of Commerce.
Wieder, W. R., M. D. Hartman, B. N. Sulman, and Y-P Wang, et al. "Carbon cycle confidence and uncertainty: Exploring variation among soil biogeochemical models." Global Change Biology 24 (4), 1563–1579 (2017). https://doi.org/10.1111/gcb.13979.