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09:44
Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
Published on: October 16, 2018
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Model structures amplify uncertainty in predicted soil carbon responses to climate change
Zheng Shi1,2, Sean Crowell3, Yiqi Luo4,5
1Co-Innovation Center for Sustainable Forestry in Southern China, College of Biology and the Environment, Nanjing Forestry University, 210037, Nanjing, China. zheng_shi_ecology@outlook.com.
Nature Communications
|June 6, 2018
Summary
Understanding soil carbon (C) projection uncertainty is key. Complex models show greater uncertainty but diverse structures are vital for reliable future soil C stock forecasts.
Area of Science:
- Earth System Science
- Climate Modeling
- Soil Science
Background:
- Projected future soil carbon (C) dynamics exhibit significant model uncertainty.
- The sources of this uncertainty remain incompletely understood.
Purpose of the Study:
- To quantify uncertainties in soil C projections stemming from model parameters, structures, and their interactions.
- To analyze how these uncertainties propagate across different soil C models.
Main Methods:
- Comparison of uncertainty propagation in a conventional soil C model, a vertically resolved model, and a microbial explicit model.
- Analysis of model parameter, structure, and interaction uncertainties.
Main Results:
- Vertically resolved and microbial explicit models project greater uncertainty in soil C stocks under climate change compared to conventional models.
- Complex models exhibit both positive and negative carbon-climate feedbacks, while conventional models consistently predict positive feedbacks.
- Model structure diversity is crucial for increasing confidence in soil C projections.
Conclusions:
- Balancing model complexity with the inclusion of diverse structures is essential for accurate soil C dynamics forecasting.
- Increased model complexity leads to greater uncertainty, necessitating a strategic approach to model selection and development.
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