Huntingford, Chris
ORCID: https://orcid.org/0000-0002-5941-7770; Fisher, Rosie A.; Mercado, Lina
ORCID: https://orcid.org/0000-0003-4069-0838; Booth, Ben B. B.; Sitch, Stephen; Harris, Phil P.; Cox, Peter M.; Jones, Chris D.; Betts, Richard A.; Malhi, Yadvinder; Harris, Glen R.; Collins, Mat; Moorcroft, Paul.
2008
Towards quantifying uncertainty in predictions of Amazon "dieback".
Philosophical Transactions of the Royal Society (B), 363 (1498).
1857-1864.
10.1098/rstb.2007.0028
Abstract
Simulations with the Hadley Centre general circulation model (HadCM3), including carbon cycle
model and forced by a ‘business-as-usual’ emissions scenario, predict a rapid loss of Amazonian
rainforest from the middle of this century onwards. The robustness of this projection to both
uncertainty in physical climate drivers and the formulation of the land surface scheme is investigated.
We analyse how the modelled vegetation cover in Amazonia responds to (i) uncertainty in the
parameters specified in the atmosphere component of HadCM3 and their associated influence on
predicted surface climate. We then enhance the land surface description and (ii) implement a
multilayer canopy light interception model and compare with the simple ‘big-leaf ’ approach used in
the original simulations. Finally, (iii) we investigate the effect of changing the method of simulating
vegetation dynamics from an area-based model (TRIFFID) to a more complex size- and agestructured
approximation of an individual-based model (ecosystem demography).
We find that the loss of Amazonian rainforest is robust across the climate uncertainty explored by
perturbed physics simulations covering a wide range of global climate sensitivity. The introduction of
the refined light interception model leads to an increase in simulated gross plant carbon uptake for
the present day, but, with altered respiration, the net effect is a decrease in net primary productivity.
However, this does not significantly affect the carbon loss from vegetation and soil as a consequence
of future simulated depletion in soil moisture; the Amazon forest is still lost. The introduction of the
more sophisticated dynamic vegetation model reduces but does not halt the rate of forest dieback.
The potential for human-induced climate change to trigger the loss of Amazon rainforest appears
robust within the context of the uncertainties explored in this paper. Some further uncertainties
should be explored, particularly with respect to the representation of rooting depth.
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