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Title: Reducing uncertainty in predictions of the response of Amazonian forests to climate change
Author: Rowland, Lucy Miranda
ISNI:       0000 0004 2741 0966
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2013
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Amazonia contains the largest expanse of tropical forest in the world and is globally significant as a store of carbon, a regulator of climate and an area of high species diversity. The ability of the Amazonian forests to maintain these important ecological functions is however, increasingly under question in light of recent predictions of climate change. There is currently significant uncertainty in model predictions of how Amazonian forests will respond to predicted future climate change. This thesis reports the finding of two field studies, targeted at understanding the responses of two tropical forest carbon fluxes which are poorly simulated in vegetation models, and two modelling studies, which aim to better quantify uncertainty on model predictions of the effects of current and future climate change on the ecological function of Amazonian forests. The responses of forests to varying magnitudes of seasonal changes in climate which occur across Amazonia can give an important insight into the sensitivity of these forests to climate perturbations and changes. Testing the sensitivity of an Amazonian forest in Tambopata, Peru, to seasonal variations in precipitation and temperature, I find that the stem diameter growth of tropical trees is more sensitive to water availability than temperature changes. The vulnerability of trees to reduced soil water varied between tree classes with different functional traits, including wood density, tree height, tree diameter and tree growth rate. Similarly, I find that the respiration flux from tropical dead wood, at a second site in French Guiana, is highly sensitive to variations in water content. I show that these variations in respiration fluxes can be modelled successfully using seasonal variations in soil water content. To date there are few studies which have comprehensively tested vegetation models using ecological data from Amazon forests. Using data assimilation and nine sources of ecological data I estimate the certainty with which we can parameterise a carbon cycle model to represent the effects of a strong dry season on tropical forests. Using this technique I find, that the carbon balance of Amazonian forests can be very sensitive to reductions in water availability, and that these seasonal changes need to be accurately simulated across models to correctly predict annual carbon budgets. The variability in model responses caused by differences in the way processes are structured and parameterised in vegetation models requires better quantification. Using a model inter-comparison I demonstrate that the relative sensitivity of modelled climate-vegetation feedbacks to changes in ambient air temperature and precipitation is highly variable. I find that although the models showed similar directional responses at both the leaf and canopy scale some models showed a greater sensitivity to temperature and others to drought. I therefore demonstrate the need for greater constraint on modelled responses of Amazonian forests to changes in temperature and precipitation. The impact of climate change on Amazonian forests is an important global issue, yet our knowledge is reliant on our ability to understand the uncertainties on our predictions. Using field data to evaluate and to develop model predictions is a valuable way to reduce the uncertainty associated with modelling future change. This thesis presents an investigation of how tropical forests respond to changes in climate and with what certainty we can model these changes in order to predict the response of Amazon forests to predicted future climate change.
Supervisor: Meir, Patrick; Williams, Mathew Sponsor: Natural Environment Research Council (NERC)
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Amazon ; drought ; climate change