Towards Quantifying Uncertainty in Predictions of Amazon 'Dieback'

DSpace/Manakin Repository

Towards Quantifying Uncertainty in Predictions of Amazon 'Dieback'

Citable link to this page


Title: Towards Quantifying Uncertainty in Predictions of Amazon 'Dieback'
Author: Moorcroft, Paul; Collins, Mat; Harris, Glen R.; Malhi, Yadvinder; Betts, Richard A.; Jones, Chris D.; Cox, Peter M.; Harris, Phil P.; Sitch, Stephen; Booth, Ben B. B.; Mercado, Lina; Fisher, Rosie A.; Huntingford, Chris

Note: Order does not necessarily reflect citation order of authors.

Citation: Huntingford, Chris, Rosie A. Fisher, Lina Mercado, Ben B. B. Booth, Stephen Sitch, Phil P. Harris, Peter M. Cox, Chris D. Jones, Richard A. Betts, Yadvinder Malhi, Glen R. Harris, Mat Collins, Paul Moorcroft. 2008. Towards quantifying uncertainty in predictions of Amazon 'dieback'. Philosophical Transactions of the Royal Society B-Biological Sciences 363(1498): 1857-1864.
Access Status: Full text of the requested work is not available in DASH at this time (“dark deposit”). For more information on dark deposits, see our FAQ.
Full Text & Related Files:
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 age-structured 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.
Published Version:
Citable link to this page:
Downloads of this work:

Show full Dublin Core record

This item appears in the following Collection(s)


Search DASH

Advanced Search