Project highlights

  • Will evaluate state-of-the-art developments within the JULES land-surface model (the land surface scheme within the UK Earth System Model) related to fire using the latest Earth Observation satellite data.
  • Will develop machine-learning based methods to investigate the drivers and processes relevant to fire activity, both over historical periods and over future potential climate scenarios.
  • Provides an opportunity to contribute to a large multi-disciplinary international project ( and collaborate very closely with scientists within the UK Met Office and UKCEH on the JULES land surface model.


Extreme wildfire events, like those making global headlines over the last few years, are becoming more frequent worldwide. These fires cause enormous ecosystem damage and release vast amounts of carbon, affecting society, the climate and the wider Earth System. However, how much these fire events are exacerbated by climate change and human landscape management is difficult to determine due to the complex and highly non-linear interactions of multiple fire drivers. Attributing direct human involvement in forest loss due to fire is particularly important, given the vital role deforestation reduction financing will have in mitigating future climate change.

We are entering a period with an unprecedented wealth of satellite observations and alongside significant advancements in modelling within fire-enabled models such as JULES-INFERNO and ConFire this means we can now start to tackle these challenges. The growth in the quantity and capability of satellite observations offers an excellent opportunity to evaluate and constrain fire processes and feedbacks on the climate, both from observations of fires themselves, as well as many associated parameters (land surface temperature, soil moisture, biomass) that influence their behaviour.

There has also been a recent step-change in the areas of machine learning and data assimilation. This project will explore these state-of-the-art methods for bringing together large volumes of satellite observations with highly complex model output, leading to opportunities for developing novel analysis. In particular, we will explore machine learning emulation to allow additional explainability of the model outputs and a better understanding of the uncertainty associated with the model predictions.

The PhD outputs will inform deforestation and emission reduction efforts by assessing forest vulnerability to recent and future fires associated with climate change, and attributing near-real-time forest loss to natural, climate change-driven and direct human-caused burning.

This project would suit a highly motivated candidate interested in contributing to a high-profile, exciting and growing area of climate science with the opportunity to work closely with national and international collaborators.


University of Leicester


  • Climate and Environmental Sustainability
  • Dynamic Earth


Project investigator

  • Dr Robert Parker, NCEO, University of Leicester


  • Dr Chantelle Burton, UK Met Office
  • Dr Doug Kelley, UKCEH
  • Prof. Hartmut Boesch, NCEO, University of Leicester

How to apply


The student will be able to take advantage of recent advances in fire modelling and novel data-model fusion techniques to investigate new methods for analysing satellite observations in order to evaluate and constrain present-day fire climate feedbacks.

Modern numerical techniques such as machine learning/data assimilation will be adopted to confront the model with satellite data.

The student will develop an explainable machine-learning based emulator for the JULES-INFERNO model, allowing the importance of the different driving factors to be analysed. Incorporating ConFire’s Bayesian approach will account for uncertainty in fire drivers and biases in simulated fire impacts – allowing assessment of the confidence in attributing the cause of fire events to particular drivers.

The student will perform state-of-the-art simulations with JULES carried out using the ISIMIP framework to evaluate the ability to represent fires in future climate scenarios and utilise the emulator to examine how any climate response evolves over time.

Training and skills

NCEO will provide access to its Researcher Forum, staff conferences/workshops and national-level training.

There will be the opportunity to receive training at the UK Met Office and UKCEH related to using and analysing the JULES land-surface model. Training by UKCEH will cover Bayesian optimization methods and probabilistic programming, including using the ConFire attribution system.

Training will be provided to the student on the Earth System Model Evaluation Tool (ESMValTool) and processing on the ALICE (Leicester) and JASMIN (NERC) HPC facilities.

The student will take the MSc module GY7709 (Satellite Data Analysis in Python) at UoL and other modules deemed suitable.

Partners and collaboration

This project has been developed in collaboration with the UK Met Office and UK Centre for Ecology and Hydrology who will co-supervise the project.

In addition, a variety of other collaborations include:

  • European Space Agency – Climate Change Initiative (via Prof Boesch, ESA Greenhouse Gas CCI and Dr Parker, ESA CCI Climate Modelling User Group)
  • NCEO collaborators working on JULES analysis and machine-learning emulation (at University of Leicester and University of Reading)
  • JULES land surface modelling (via HEIs and NERC Centres)
  • Digital Twin modelling activities (via NERC and ESA)

Further details

We would very strongly encourage anyone considering an application to get in touch with us for an informal chat about the project at an early stage. This will help the candidate in the preparation of their application.

Please contact us at: Dr Robert Parker – NCEO University of Leicester – [email protected]

To apply to this project please visit:

Possible timeline

Year 1

Literature review and refinement of research questions. Training and familiarisation with JULES land surface model, initial JULES simulations using the ISMIP framework. Training regarding techniques for incorporating observations and model output. Potential placements (COVID-dependent) at UKCEH (Wallingford) and/or UK Met Office.

Year 2

Development of machine-learning based emulator to contribute to explainability of JULES output. Analysis to incorporate new satellite observations. Journal publication aimed at describing emulator development and performance.

Year 3

Analysis of JULES driven with ISIMIP future climate scenarios. Journal publication aimed at climate impacts related to key science questions around fire activity and the key climate drivers as identified by emulator analysis.

Further reading



There is no expectation that future COVID-19 restrictions would adversely affect this PhD in any significant way. Ideally, training would be delivered in-person (e.g. at the UK Met Office and UKCEH) but we have substantial experience of delivering this training remotely. Furthermore, this project will work closely with a wide team of collaborators (e.g. UoL Greenhouse-Gas Remote Sensing Group, UKESM Core Group, UK JULES Science Community, ISIMIP Community) who have been successfully collaborating remotely for the past two years. We are confident that the arrangements in place would fully support the student throughout their PhD, regardless of any future restrictions.