Model structures amplify uncertainty in predicted soil carbon responses to climate change

Zheng Shi (), Sean Crowell (), Yiqi Luo and Berrien Moore
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Zheng Shi: Nanjing Forestry University
Sean Crowell: University of Oklahoma
Yiqi Luo: Northern Arizona University
Berrien Moore: University of Oklahoma

Nature Communications, 2018, vol. 9, issue 1, 1-11

Abstract: Abstract Large model uncertainty in projected future soil carbon (C) dynamics has been well documented. However, our understanding of the sources of this uncertainty is limited. Here we quantify the uncertainties arising from model parameters, structures and their interactions, and how those uncertainties propagate through different models to projections of future soil carbon stocks. Both the vertically resolved model and the microbial explicit model project much greater uncertainties to climate change than the conventional soil C model, with both positive and negative C-climate feedbacks, whereas the conventional model consistently predicts positive soil C-climate feedback. Our findings suggest that diverse model structures are necessary to increase confidence in soil C projection. However, the larger uncertainty in the complex models also suggests that we need to strike a balance between model complexity and the need to include diverse model structures in order to forecast soil C dynamics with high confidence and low uncertainty.

Date: 2018
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DOI: 10.1038/s41467-018-04526-9

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