NERC GW4+ DTP PhD studentship: How seasonal leaf dynamics in Amazonian forests affect their ability to withstand 21st century climate change

Projections of climate change indicate that tropical South America will experience more severe dry seasons in the future. Severe seasonal droughts have already become more common, with strong droughts in 2005, 2010, and 2015 contributing to a decline in biomass and increase in mortality in Amazonian forests. Although the mechanisms controlling tree mortality are not fully understood, drought-induced leaf loss is one mechanism that may help alleviate seasonal drought stress. If seasonal drought stress increases across Amazonia, as is predicted, drought-induced leaf shedding will exert a greater control over seasonal cycles of leaf biomass and photosynthesis. However this process is not represented in many models used in climate change projections.

This project will seek to understand the processes controlling seasonal leaf dynamics in the Amazon forest, and their role in the drought resilience of the forest. The student will gain key skills in measurements and modelling, working alongside experts at both the University of Exeter and the UK Met Office, where the Newton Project for Brazil provides further avenues for international collaboration. There is an opportunity for field work in the Amazon. The project has three components:

1. Data-analysis of measurements based at the world’s longest-running tropical forest drought experiment in northeast Amazonia (Caxiuan). The student will use both existing and novel measurements that will be collected during a short field campaign. The student will determine the role of seasonal nutrient cycles on carbon uptake capacity of the trees. The aim will be a mathematical representation of the processes that can be included in a process-based model of plant photosynthesis and respiration.

2. Inclusion of key processes into JULES (Joint UK Land Environment Simulator). JULES is the land surface model in the UK’s Earth System Model (used for simulations of weather and climate). Currently JULES is unable to accurately simulate the seasonal cycles of CO2 in the Amazon. The student will determine the role of the missing processes of leaf dynamics in this data-model mismatch. Testing of new model processes will be carried out at other sites in the Amazon.

3. Implications for long-term drought resilience of the Amazon: With the model improvements made earlier in the project, the student will conduct simulations for the 21st century with a variety of climate change scenarios. The inability of land surface models (like JULES) to represent seasonal cycles is a long-standing problem in the field and the student has the opportunity to make a very significant contribution by increasing scientific understanding of seasonal dynamics in the Amazon forest.

This project is one of three that are in competition for funding from the NERC Great Western Four+ Doctoral Training Partnership (GW4+ DTP). The GW4+ DTP consists of the Great Western Four alliance of the University of Bath, University of Bristol, Cardiff University and the University of Exeter plus six Research Organisation partners: British Antarctic Survey, British Geological Survey, Centre for Ecology and Hydrology, the Met Office, the Natural History Museum and Plymouth Marine Laboratory. The partnership aims to provide a broad training in earth and environmental sciences, designed to train tomorrow’s leaders in earth and environmental science. For further details about the programme please see http://nercgw4plus.ac.uk/

We are seeking to appoint 1 student in Mathematics to start in September 2017 at Exeter. Three projects are being advertised which are in competition with each other. The studentships will provide funding for a stipend of £14,553 per annum, research costs and UK/EU tuition fees at Research Council UK rates for 42 months (3.5 years) for full-time students, pro rata for part-time students.

See please http://www.exeter.ac.uk/studying/funding/award/?id=2612 for more details on how to apply.