Articles | Volume 12, issue 14
Biogeosciences, 12, 4373–4383, 2015
Biogeosciences, 12, 4373–4383, 2015

Research article 28 Jul 2015

Research article | 28 Jul 2015

Convergent modelling of past soil organic carbon stocks but divergent projections

Z. Luo1, E. Wang1, H. Zheng2, J. A. Baldock3, O. J. Sun4, and Q. Shao5 Z. Luo et al.
  • 1CSIRO Agriculture Flagship, GPO Box 1666, Canberra, ACT 2601, Australia
  • 2CSIRO Land and Water Flagship, GPO Box 1666, Canberra, ACT 2601, Australia
  • 3CSIRO Agriculture Flagship, PMB 2, Glen Osmond, SA 5064, Australia
  • 4Institute of Forestry and Climate Change Research, Beijing Forestry University, Beijing 100083, China
  • 5CSIRO Digital Productivity & Services Flagship, Private Bag 5, Wembley, WA 6913, Australia

Abstract. Soil carbon (C) models are important tools for understanding soil C balance and projecting C stocks in terrestrial ecosystems, particularly under global change. The initialization and/or parameterization of soil C models can vary among studies even when the same model and data set are used, causing potential uncertainties in projections. Although a few studies have assessed such uncertainties, it is yet unclear what these uncertainties are correlated with and how they change across varying environmental and management conditions. Here, applying a process-based biogeochemical model to 90 individual field experiments (ranging from 5 to 82 years of experimental duration) across the Australian cereal-growing regions, we demonstrated that well-designed optimization procedures enabled the model to accurately simulate changes in measured C stocks, but did not guarantee convergent forward projections (100 years). Major causes of the projection uncertainty were due to insufficient understanding of how microbial processes and soil C pool change to modulate C turnover. For a given site, the uncertainty significantly increased with the magnitude of future C input and years of the projection. Across sites, the uncertainty correlated positively with temperature but negatively with rainfall. On average, a 331 % uncertainty in projected C sequestration ability can be inferred in Australian agricultural soils. This uncertainty would increase further if projections were made for future warming and drying conditions. Future improvement in soil C modelling should focus on how the microbial community and its C use efficiency change in response to environmental changes, and better conceptualization of heterogeneous soil C pools and the C transformation among those pools.

Short summary
Soil carbon models are primary tools for projecting soil carbon balance under changing environment and management. This study shows that the carbon model produces divergent projections but accurate reproduction of measured soil carbon. This projection uncertainty is mainly due to an insufficient understanding of microbial processes and soil carbon composition. Climate conditions and land management in terms of carbon input also have significant effects.
Final-revised paper