Applied Mathematics PhD Studentship in Climate-Carbon Cycle Research Constraining future land carbon cycle feedbacks with in situ and EO observations using ADJULES

Ref: 1235
Location: Streatham Campus, University of Exeter, EX4 4QJ
Primary supervisor: Dr Tim Jupp, University of Exeter
Secondary supervisors:  Professor Peter Cox, University of Exeter; Chris Jones, Met Office, Exeter

The evolution of the land carbon sink is a key unknown in future climate projection. Despite the increasing number and complexity of the climate-carbon cycle models developed for the IPCC 5th Assessment Report (AR5), there is as yet no evidence that the spread amongst models has been reduced since the last IPCC report.

The different model projections of the global ocean carbon sink remain well-clustered, but the evolution of the land carbon sink, and its regional patterns, vary widely. There is an urgent need to reduce this uncertainty, and this proposed PhD aims to contribute to such an advance by objective calibration of the JULES land-surface scheme against in situ and Earth Observation data using the adjoint of JULES (ADJULES) – as developed within the NCEO programme at the University of Exeter.

ADJULES provides an efficient means to calibrate uncertain internal JULES parameters (e.g. leaf nitrogen concentration, and parameters affecting the soil moisture and temperature-sensitivity of photosynthesis) against latent, sensible and CO2 eddy-fluxes measured at FluxNet sites and vegetation greenness trends from EO (e.g. FAPAR from MODIS). Most importantly, the latest version of ADJULES provides estimates of uncertainties in the best-fit parameters (in the form of probability density functions, PDFs).

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The PDFs of the critical JULES parameters will be sampled to create a parameter ensemble which can be used in global land carbon cycle simulations within the IMOGEN system. The resulting ensemble of land carbon cycle projections can be weighted by the probability of each of the ensemble members, to create a PDF of future land carbon storage for each location.

This will enable observationally-constrained probabilistic statements to be made about the possibility of key transitions and tipping-points in the land biosphere, such as Amazon forest dieback, a global carbon sink to source transition, and greening of the boreal forests.

This project would suit a candidate with experience of, or an interest in, applied mathematics/computing/applied statistics.

Application criteria
Applicants should have or expect to achieve at least a 2:1 Honours degree, or equivalent, in Maths, Physics, Computer Science, Natural Sciences or other numerate discipline.

Application deadline: 19th July 2013
Number of awards: 1
Value: 3.5-year studentship: University fees and an annual stipend of £13726 (for academic year 2013/14). International students can apply and pay the difference
Duration of award: per year
Contact: Liz Roberts,

How to apply
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