Prof S Sitch, Dr L Mercado, Prof P M Cox
No more applications being accepted
Competition Funded PhD Project (Students Worldwide)
About the Project
Project Background;
Amazon forest dieback has long been identified as a possible tipping element in the Earth System, which would have huge negative consequences for human well-being, biodiversity, biogeochemical cycling, and climate. However, the level of global warming that could trigger dieback is not known, and neither is the extent to which this is affected by the state of the forest ecosystem itself. In the first climate model projections to include a vegetation and the carbon cycle as interactive elements, a cascade of biogeochemical and biophysical feedbacks amplified a reduction in Amazonian rainfall, causing abrupt forest loss. Later models were less pessimistic, however, no study has yet considered the interacting effects of detailed ecosystem demography, disturbance, through degradation (fire, logging) and deforestation, and climate. In this project we aim to address a key environmental question of our time (i.e. the future fate of the Amazon forest).
Project Aims and Methods:
The aim of this project is to assess the fate of the Amazon forest under scenarios of climate change and disturbance through co-occurring drought & fires and/or deforestation, providing a mechanism for forest transitions to alternative non-forest or low biomass states.
The project will address the following questions: How will climate change and level of disturbance impact the Amazon forest? Does biodiversity afford Amazon forest resilience/resistance to large-scale environment stress? how does forest loss impact Amazon climate? What is the fate of the Amazon to future climate change and disturbance?
These questions will be addressed using the novel Robust Ecosystem Demography model (RED) recently developed by our team (Prof. Cox), which represents the next-generation in global vegetation models. In this project RED will be developed further to represent the functional diversity of the Amazon forest by incorporating new Plant Functional Types into RED based on empirical datasets. RED mortality and growth parameterizations will be calibrated against field inventory and remotely sensed data on forest dynamics to reproduce observed growth rates in primary and human modified tropical forests (HMTF). A second task will be to synthesise our current understanding of how forest loss impacts Amazon climate, e.g. determine how level of deforestation impacts the spatial pattern of rainfall across the basin. This will involve analysing existing Earth System Model simulations that include land-use change (LUC) from IPCC CMIP6 experiments including dedicated Met Office UKESM LUC simulations. The RED model will then be applied for a range of future climate and disturbance (deforestation, degradation through, fires and logging) scenarios to address the research questions on the future fate of the forest and ecosystem service provision, and the role of forest biodiversity in affording forest resilience/resistance. The project offers excellent opportunities for generating new understanding and high-impact publications. There is flexibility for the candidate to decide on research direction within the topic of the project, and to be in charge of data analysis and modelling.
Candidate Requirements:
The candidate will possess a strong BSc (and ideally MSc level) degree in Mathematics, Physics, Geography, Natural Sciences or a similar discipline. The candidate will be numerate, a strong problem solver and preferably have experience in numerical modelling / computer coding in R/python/FORTRAN or equivalent.
Collaborative Partner:
The new RED model will feed directly into the next generation of the MO-NERC community Earth system model UKESM. The collaborative partner will help guide the RED applications to include the impact of biophysical feedbacks based on climate model data from UKESM and other CMIP6 model output. The student will gain experience at working alongside scientists at a research institute, key to delivering the most up to date science on climate change to the IPCC.
Training:
The student will receive expert training in ecosystem and land carbon cycle science, developing and applying ecosystem demography and land-surface models, and analysing large climate model datasets. We aim for the student to visit collaborators in South America to gain experience in monitoring HMTF, e.g. based on the candidate’s interest possible chance to participate in field campaign. Additional training will include scientific writing, science communication, project management and field-based health and safety.
Useful links:
For information relating to the research project please contact the lead Supervisor:
https://stephensitch.wordpress.com
Prospective applicants:
For information about the application process please contact the Admissions team via [Email Address Removed].
Funding Notes
NERC GW4+ funded studentship available for September 2021 entry. For eligible students, the studentship will provide funding of fees and a stipend which is currently £15,285 per annum for 2020-21.
References
Background reading and references:
Aragao et al., 21st Century drought-related fire counteract the decline of Amazon deforestation carbon emissions, Nature Communications, 9:536, 2018.
Argles et al., Robust Ecosystem Demography (RED): a parsimonious approach to modelling vegetation dynamics in Earth System Models, Biogeosciences, …
Burton et al., Representation of fire, land-use change and vegetation dynamics in the Joint UK Land Environment Simulator v4.9 (JULES), Geosci. Model Dev., 12, 179-193, 2019.
Cox et al., Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model, Nature, 408, 184-187.
Moore et al., Equilibrium forest demography explains the distribution of tree sizes across North America, Environ. Res. Letts, 13, 2018.
Moore et al., Validation of demographic equilibrium theory against tree-size distributions and biomass density in Amazonia, Biogeosciences, 17, 1013-1032, 2020.
Fyllas et al.,Deriving Plant Functional Types for Amazonian forests for use in vegetation dynamics models, Perspectives in Plant Ecology, Evolution and Systematics, 14, 97-110, 2012.
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