PhD Studentship: PhD Opportunity Title Modelling tropical vegetation to transform carbon budgets, (PhD funded)

Around one quarter of anthropogenic carbon dioxide emissions are taken up by land ecosystems, mitigating climate change (Friedlingstein et al., ESSD 2025). The largest land sink is located in tropical ecosystems with their high productivity and biomass (Ahlstrom et al., Science 2015). Yet these ecosystems are also heavily impacted by deforestation, degradation through fire, climate change and extremes (Aragao et al., Nat comms 2018).

The Global Carbon Budget (GCB) is the most comprehensive source of data and information about regional-to-global scale carbon fluxes; it is updated annually and published at the UNFCCC COP meetings every year. The largest uncertainty in GCB is in emissions from land-use and land-cover change, which has an uncertainty of approximately 60%. This is mainly because, until now, we have lacked the tools and data to quantify emissions from deforestation and degradation in the tropics with sufficient accuracy and temporal consistency. To reduce uncertainty in the Global Carbon Budget, better quantification and understanding of the complex carbon flows involved in the land carbon cycle is paramount.

Our international consortium project entitled ‘Combining LAnd-use, modeling and Remote-sensing to Transform carbon budgets, CLARiTy’ (https://www.schmidtsciences.org/vicc/) will reduce the persistently high land flux uncertainties in GCB by an order of magnitude. To achieve this goal, we will combine new datasets, a deeper process-understanding, sophisticated models and improved experimental design.

As part of CLARiTy, this PhD will focus improving the representation of tropical forest and savanna vegetation and carbon cycling in process-based Dynamic Global Vegetation Models (DGVMs), incorporating novel datasets on vegetation function and recovery from disturbance (Heinrich et al., Nature 2023).

The PhD would entail developing and running the Joint UK Land Environment Simulator (JULES) DGVM, land surface model of the UK ESM. The student will benefit from access to empirical and remote-sensing data from complementary projects funded through the UK’s Natural Environment Research Council.

This fully funded PhD project is suited to candidates with a related research background with strong numerical, analytical and coding skills with undergraduate and masters degree in Mathematics, Earth System Sciences, Natural Sciences and related subject areas.

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