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Climate and the carbon cycle: identifying responses and impacts using satellite remote sensing and machine learning

Project Description

Large-scale climate variations, e.g. El Nino, can have immediate effects on the terrestrial carbon cycle via anomalous periods of drought, fire, and storm damage to forests. These immediate effects have longer-term implications for plant functioning and mortality rates, which subsequently have implications for net primary production and carbon storage that are often more difficult to observe without in situ measurements.

Space-borne instruments now observe a wide range of land surface and vegetation properties and associated atmospheric variables that can help us to understand the lagged effects of climate on terrestrial vegetation. The challenge lies in developing an infrastructure that can integrate the corresponding volume of heterogeneous data.

In this project, you will use machine learning to explore relationships between environmental variables that are measured from satellite instruments (e.g., phenology, hydrology, evapotranspiration, photosynthetically active radiation, biomass burning, solar induced fluorescence and atmospheric CO2). These relationships will be interpreted within the context of current scientific knowledge and used to test and develop existing land surface models, e.g. the Joint UK Land Environment Simulator (JULES) community land surface model. The resulting model predictions will be evaluated using sparse but detailed decadal-scale measurements collected at plot sites around the world. The long history of El Nino events will be a particular focus, taking advantage of available ground-based ecological data and satellite measurements.

For further details see

Funding Notes

E4 DTP studentships are fully-funded for a minimum of 3.5 years. They include:

* Stipend based on RCUK minima (currently £15,009 for 2019/2020)
* Fees (Home/EU Fees)
* Research Costs (Standard Research Costs plus, depending on the projects requirements, Additional Research Costs can also be allocated)

The stipend can be extended to up to another 5 months through our two optional schemes - see View Website


* Clark, et al.:, 2011.
* Lary et al.:
* Xiao et al.:

How good is research at University of Edinburgh in Earth Systems and Environmental Sciences?

FTE Category A staff submitted: 104.98

Research output data provided by the Research Excellence Framework (REF)

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