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Published on: August 30, 2018
Towards quantifying uncertainty in predictions of Amazon 'dieback'
Chris Huntingford1, Rosie A Fisher, Lina Mercado
1Centre for Ecology and Hydrology, Benson Lane, Wallingford, Oxon OX10 8BB, UK. chg@ceh.ac.uk
Summary
Human-induced climate change may cause rapid Amazon rainforest loss this century. This projection remains robust despite uncertainties in climate models and land surface simulations, indicating a significant risk of forest dieback.
Area of Science:
- Climate Science
- Ecology
- Earth System Science
Background:
- General circulation models (GCMs) predict Amazon rainforest loss under business-as-usual emissions.
- The robustness of these predictions to model uncertainties requires further investigation.
Purpose of the Study:
- To assess the robustness of Amazon rainforest loss projections to climate model uncertainties.
- To investigate the impact of refined land surface schemes on rainforest dieback simulations.
- To evaluate the effect of advanced vegetation dynamics models on future forest loss predictions.
Main Methods:
- Simulations using the Hadley Centre general circulation model (HadCM3) with a carbon cycle model.
- Analysis of vegetation response to perturbed physics in the atmosphere component of HadCM3.
- Implementation of a multilayer canopy light interception model and comparison with a 'big-leaf' approach.
- Comparison of an area-based vegetation model (TRIFFID) with a size- and age-structured ecosystem demography model.
Main Results:
- Amazonian rainforest loss is robust across explored climate uncertainties, including global climate sensitivity variations.
- A refined light interception model increased carbon uptake but decreased net primary productivity, without altering future carbon loss due to soil moisture depletion.
- A more sophisticated dynamic vegetation model reduced but did not stop the rate of forest dieback.
Conclusions:
- The potential for human-induced climate change to trigger Amazon rainforest loss is robust within the explored uncertainties.
- Refined land surface and dynamic vegetation models confirm the vulnerability of the Amazon rainforest to climate change.
- Further research on factors like rooting depth representation is needed for more accurate projections.
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