The overall aim of this research project will be to assess the impacts of climate change due increasing CO2 concentration on future renewable energy systems. Changes in environment conditions would be quantified by analysing Earth System Model
simulations driven by radiative forcing conditions derived through a range of CO2 emission scenarios. Models from the latest Coupled Model Inter-comparison Project (CMIP) will be selected based on their ability to reproduce current and past climates, teleconnections, variability on intra-seasonal to multidecadal timescales. Using these models, an assessment of
changes in parameters relevant to Solar, Wind and Hydrogen energy systems would be made on global scales.

Using these results regional hotspots will be identified. That is, for example, locations suitable for future renewable energy installations. The global climate change information will then be downscaled using novel techniques to some of the hotspots to derive relevant local meteorological parameters.

The downscaled meteorological parameters would then be used as an input to weather-to-power models to assess future renewable energy generation. From computational science point of view a part of the problem can be viewed as an opportunity to develop statistical emulators to populate a large ensemble of climate change simulations to asses uncertainty in model predictions. Another part of the problem would require applications of machine learning algorithms to localize global climate change information. Finally, Data Science techniques would be required to convert weather information to power generation in a warmer atmosphere. A parallel strand to the development of improved prediction models will be to apply these techniques to understand how changes to external forcing conditions (e.g. various levels of CO2
increase) impact the energy infrastructure and revenue potential for large multi-national corporations, such as Shell, with a wide variety of major assets spread around the globe. This in turn will lead to better informed business decision making and support Shell's goal to become a net-zero emissions energy business over the next few decades. This project represents a good opportunity for a student to be trained in advanced data science, machine learning and optimisation methods, as well as being exposed to renewable energy technologies and climate change impacts.