Land based mitigation for climate change: modelling mitigation options and trade-offs

Summary
This project will investigate land based mitigation options for climate change and their associated trade-offs using a state-of-the-art global land use model.

Project background
Climate change is expected to have profound effects on the earth system and human wellbeing. A recent IPCC special report emphasised that ambitious mitigation actions are necessary to limit global warming to 1.5°C. Land use change has been highlighted as an essential component of mitigation; low-warming pathways tend to rely on industrial-scale deployment of land-based mitigation such as reforestation. However, land is a finite resource. Around 40% of the terrestrial land surface is already dominated by agriculture for food production, and overall nearly 80% is impacted by various forms of human activity (Foley et al., 2005). Achieving and maintaining land-based mitigation requirements, while feeding a growing population with changing dietary demands and ensuring biodiversity and ecosystem services are not degraded, is a pressing global issue. It is critical to understand how land-based mitigation can be achieved sustainably. Such an understanding would allow polices and mechanisms to be evaluated, making climate targets more achievable. The potential effectiveness and trade-offs of land-based mitigation measures can be explored with modelling. To date, global land use and integrated assessment models have typically selected changes in land uses, both for agricultural production or land-based mitigation, using a relatively aggregate spatial representations, e.g. at a regional level, and consider only economic drivers. These existing approaches are, therefore, not able to characterise the details of highly spatially differentiated responses or behavioural aspects of land use decisions. The project will address this gap by using and further developing a state-of-the-art process-based modelling framework, PLUM-LPJ-GUESS (Alexander et al., 2018), which simulates land-use change, socio-economic drivers, ecosystem greenhouse gas emission and uptake and climate.

Research questions
What are the best options for land based mitigation to achieve long term climate goals?
What are the trade-offs between land based mitigation measures and food security?
What are the consequences for biodiversity and ecosystem services of land based mitigation?

Methodology
PLUM-LPJ-GUESS is a coupled global land use model developed at the University of Edinburgh and Karlsruhe Institute of Technology in Germany. Both LPJ-GUESS (Smith et al., 2001) and PLUM (Alexander et al., 2018) are models at the forefront of their respective fields and their integration provides a novel platform for addressing questions regarding land use and climate. The initial focus of the PhD studentship will be to further develop this framework to ensure questions related to land based mitigation can be addressed appropriately. This will involve primarily further developing the forestry sector within the model. In order to do this the student will have the opportunity to spend time with the co-supervisor in Karlsruhe Institute of Technology. The coupled model will then be used to simulate scenarios of land based mitigation. These scenarios will be evaluated with regards to their potential for reducing greenhouse gas emissions and associated trade-offs with food security and biodiversity.

Outline timetable:
Year 1: Familiarisation with land based mitigation and climate change concepts. Research training in land use modelling and computer programming.
Year 2: Further model development as required. Time spent in KIT, Germany. Simulating scenarios of potential land based mitigation measures.
Year 3: Analysis of scenarios, evaluation of land based mitigation and trade-offs.

Training
A comprehensive training programme will be provided comprising both specialist scientific training and generic transferable and professional skills. Advanced training in scientific programming and numerical modelling will be tailored to address the needs of the student, but may involve specific courses, e.g. on agent-based modelling, advanced programming, or use of high-performance computing infrastructures.

Requirements
The ideal student will have good quantitative skills and a background in computing and/or environmental science would be preferred. Experience with the programming languages Java and R would be advantageous and a demonstrable ability to acquire programming skills will be essential.