“Unravelling the role of lags and legacies in explaining the response of grasslands to elevated CO2”
The rising concentration of CO2 in the atmosphere stimulates plant growth; however, in grassland ecosystems, the observed growth responses are highly variable and often depart markedly from our theoretical predictions. Our inability to explain the reasons for this variability prevents us from predicting changes in agricultural productivity and ultimately, the future grassland carbon sink. This project will apply a state of the art, hierarchical Bayesian modelling framework to determine how past climatic conditions influence current responses to high CO2concentrations. The project will use data from a new, specially-designed experiment, as well as results from past Free-Air Carbon dioxide Enrichment (FACE) and Open-top chamber (OTC) experiments, to close this knowledge gap. In particular, the student will aim to identify the mechanisms and timescales over which past water and nutrient availability affect photosynthesis and growth responses to CO2 in grasslands.
The project is based at the Climate Change Research Centre (CCRC) at the University of New South Wales (UNSW), Australia, under the supervision of Dr Martin De Kauwe, Professor Kiona Ogle at Northern Arizona University (NAU) and Associate Professor Mark Hovenden at the University of Tasmania (UTAS).
The successful candidate will become part of the Australian Research Council Centre of Excellence for Climate Extremes, an international research consortium of five Australian universities (The University of New South Wales, Monash University, The University of Melbourne, The University of Tasmania and The Australian National University) and a suite of outstanding national and international Partner Organizations. The Centre provides excellent opportunities for travel and graduate student development. In particular, there are funds available to travel to Arizona and work closely with Professor Ogle during the PhD scholarship.
We are looking for expressions of interest from outstanding graduates with a strong academic record including Honours Class I or equivalent. Graduates with a strong background in plant ecophysiology, mathematics, physics, atmospheric science, engineering or similar quantitative sciences are particularly encouraged to apply. Programming experience with R or Python is desirable but not essential.
Questions should be directed to Martin De Kauwe (firstname.lastname@example.org). Expressions of interest including a CV, full academic transcript, and the names of up to three academic referees should be sent to email@example.com by the 1st of April. Note: this is not an official application, if your expression of interest is accepted, we will guide you through the application process.