The Fagradalsfjall eruption in Iceland began on 19th March 2021 and until the 27th of April was characterised by low-altitude continuous degassing of mainly sulfur dioxide. On 27th April, the nature of the eruption changed to continuous lava fountaining, then changed again on 2nd May from continuous to pulsed fountains up to 300m high with a doubled lava discharge rate. Since 27th April, long-range transport of the volcanic plume off the coast of Iceland has occurred and satellite imagery shows that the eruption has been influencing clouds in the North Atlantic. The eruption presents a rare opportunity to make the first ever aircraft measurements of cloud properties perturbed by volcanic activity. Volcanic eruptions that emit gases such as sulfur dioxide into the lowermost part of our atmosphere have been recognised in the last decade as a perfect natural lab to study how emissions affect cloud amount and the physical properties of clouds, which includes the size of the tiny droplets that make up clouds. Clouds have a net cooling effect on climate because they reflect some of the incoming sunlight back to space. It is also known that emissions of gases such as sulfur dioxide (be they man-made or natural) cause changes to cloud properties once the gas-phase species are converted to airborne particles, but the details of the interplay of clouds, particles and the amount of sunlight reflected back to space are extremely complex and challenging to represent in climate models despite decades of research efforts. This project would deliver the very first measurements of cloud characteristics including changes in the vertical in areas that have been affected by the volcanic emissions. This can then be contrasted to areas that have not been affected by volcanic emissions. When combined with satellite data, our dataset will enable a new understanding of cloud and aerosol particle interactions, which in turn will help to improve model representation of these climate-relevant processes. Better models will provide a more accurate estimate of climate change, which will help to better prepare and mitigate climate change hazards.

Fluid dynamics underpins large areas of engineering, environmental and scientific research, and is becoming increasingly important in medical science. At Leeds, we possess research expertise across each of these domains and we have an established record of working across disciplinary boundaries. This proposal builds upon this record through the establishment of a multidisciplinary CDT in Fluid Dynamics. Research techniques that will be applied, and developed, will encompass: mathematical modelling & theory; numerical methods, CFD & high performance computing (HPC); and measurement & experimentation. Engineering application areas to be addressed include: reacting flows; carbon capture, transport & storage; flow of polymer melts; mixing problems; particulate flows; coating & deposition; lubrication; medical devices; pathogen control; heat transport; wind turbines; fluid-structure interaction; and nuclear safety. Environmental application areas will consist of: groundwater flow; river/estuary flows; tidal flows; oceanography; atmospheric pollution; weather forecasting; climate modelling; dynamics of the Earth's interior; and solar & planetary flow problems. Facilities available to undertake this research include: the University's HPC system which, combined with the N8 regional facility that is hosted at Leeds, provides ~10000 computational cores, an extensive suite of licensed software and dedicated support staff; flow measurement techniques (including Particle Imaging Velocimetry (PIV), 2-component Laser Doppler Anemometry (LDA), Phase Doppler Anemometry (PDA) and Ultrasonic Doppler Velocity Profiling (UDVP)); techniques for measuring fluid concentration (Ultrasonic High Concentration Meter (UHCM) and Optical Backscatter Probes (OBS)) and a range of optical metrology systems (e.g. pulsed and continuous wave lasers). The UK has a substantial requirement for doctoral scientists and engineers who have a deep understanding of all aspects of fluid dynamics from theory through to experimental methods and numerical simulation. In manufacturing and process engineering, for example, many processes depend critically on fluid flows (e.g. extrusion of polymer melts, deposition of coatings, spray drying, etc.) and it is essential to understand and control these processes in order to optimize production efficiency and reliability (see letter of support from P&G for example). In large-scale mechanical engineering there is a demand for expertise in reacting turbulent flows in order to optimize fuel efficiency and engine performance, and in wetting and surface flows for the design and manufacture of pumps and filters. There is also a need for a wide variety of skilled experts in environmental fluid flows to support the growing need to understand and predict local pollution and threats to safety (atmospheric, surface water, ocean and sub-surface flows), and to predict weather, climate and space weather for satellite technology. We will train a new generation of researchers who will have a broad range of skills to transfer into industry and environmental agencies, hence our approach will be multi-disciplinary throughout. All students will undertake both modelling and experimental training before embarking on their PhD project - which will be co-supervised by academics from different Schools. The MSc component of the programmee will be specifically tailored to develop expertise in the mathematical background of fluid dynamics, in CFD/HPC, and in experimental techniques. Team-based projects will be used to develop the teamwork and communication skills we believe are essential. Finally, engagement with industry will be a key feature of this CDT: all students will undertake an industrial placement, a large number of projects will be industrially sponsored, and our non-academic partners will contribute actively to our management, implementation and strategic development.

Developing scientific software, for example for climate modeling or medical research, is a highly challenging task. Domain scientists are often deeply involved in low-level programming details just to make their code run sufficiently fast. These tedious, but important, optimization steps significantly reduce the productivity of scientists. Domain specific languages (DSLs) revolutionize the productivity of domain scientists by enabling them to focus on scientific questions rather than making their code run fast. Sophisticated DSL compilers automatically generate high-performance code from domain-specific high-level problem descriptions. While there are individual successes, the existing landscape of DSLs is scattered and the reuse of software components in DSL compiler implementations is limited as traditionally DSL compilers are built in isolation. This results in high development costs of new DSLs and prevents many DSLs from ever achieving a level of maturity and sustainability that enables uptake by the scientific community. This project revolutionizes the design of DSL compiler implementations by leveraging the breadth and cross-industry support of the MLIR compiler and Python ecosystems. Python is the tool of choice for application developers in many domains, such as machine learning, data science, and - we believe - an important component of the future of High Performance Computing software. This project establishes MLIR as a common representation for code at multiple levels of abstraction in DSL compiler development. DSLs embedded in various host languages, including Python and Fortran, will be easily built on top of MLIR. Instead of building DSL compilers as isolated monolithic towers, our research will build a toolbox that enables developers to build DSLs using a rich ecosystem of shared intermediate representations IRs and optimizations. This project evaluates, drives, and demonstrates the DSL design toolbox to build the next generation of DSLs for Seismic and Climate Modelling as well as Medical imaging. These will share common software components and make them available for other DSLs. An extensive evaluation will show the scalability of DSL software towards exascale. Finally, this project investigates how future disruptors, including artificial intelligence, data science, and on-demand HPC-as-a-service, will shape and influence the next generations of high performance software. This project will work towards deeply integrating modern interactive data analytics and machine learning methods from the Python ecosystem with high-performance scientific code.

The largest threat of future rapid sea level rise is from the collapse of ice sheets due to instability and runaway ice loss. It could lead to more than 1 m of sea level rise by 2100, submerging land currently home to 100 million people and causing further destruction in higher-elevation coastal regions through enhanced storm and flood risk. Predicting the future possibility of such instabilities and the resulting plausible 'worst case' sea level change is critical for adequately planning coastal defences and long term infrastructures (e.g. 150 years planning horizon), especially those for which a rare event could have devastating consequences (e.g. nuclear power plants, the Thames barrier, transport networks). Yet, this is extremely challenging, because ice sheet instabilities have not occurred since we started observing ice sheets (the record is too short and ice sheets have been stable in the recent past) and they depend on poorly understood mechanisms (e.g. sliding of ice) that occur in inaccessible areas, such as under kilometres of ice. There is a solution: ice sheet instabilities have occurred in the geological past, for example in North America, 14,500 years ago (the time of mammoths and modern humans), producing ~7 m sea level rise in 340 years. Ancient ice sheets have left fingerprints of their activity and retreat on the landscape, which have been reconstructed in great detail in places such as the UK, Northern Europe and North America. These records of past ice sheet evolution provide an untapped goldmine of data that could be used to test and improve numerical models, informing future projections. This concept was demonstrated by DeConto and Pollard (2016), who projected Antarctic melting resulting in 15 m of sea level rise by 2500 based on constraints from 3 million years ago (the last time levels of atmospheric carbon dioxide were as high as today). However, there is an important missing piece to this work. In order to reliably translate knowledge from the past into confident future projections, the most important and complex source of uncertainty in modelling past ice sheets needs to be accounted for: the climate. This requires new statistical methods and a person with a unique combination of expertise in statistics, climate and ice sheet instabilities to lead their development. The ambition for this fellowship, is to make that person me . I will lead an interdisciplinary team of researchers to develop and apply new statistical and physically-based tools to accurately quantify uncertainties in past, present and future climate and ice sheet evolution, thus unlocking the key potential of geological records to constrain future ice sheet instability. This will produce the first robust projection of future ice sheet instability and the resulting sea level change. It will unite and grow the three leading strands of my research: mechanisms of ice sheet instability, climate change, and uncertainty quantification, establishing me as a world leader in using geological data to constrain ice sheet behaviour and future sea level change.

Anthropogenic disturbance and land-use change in the tropics is leading to irrevocable changes in biodiversity and substantial shifts in ecosystem biogeochemistry. Yet, we still have a poor understanding of how human-driven changes in biodiversity feed back to alter biogeochemical processes. This knowledge gap substantially restricts our ability to model and predict the response of tropical ecosystems to current and future environmental change. There are a number of critical challenges to our understanding of how changes in biodiversity may alter ecosystem processes in the tropics; namely: (i) how the high taxonomic diversity of the tropics is linked to ecosystem functioning, (ii) how changes in the interactions among trophic levels and taxonomic groups following disturbance impacts upon functional diversity and biogeochemistry, and (iii) how plot-level measurements can be used to scale to whole landscapes. We have formed a consortium to address these critical challenges to launch a large-scale, replicated, and fully integrated study that brings together a multi-disciplinary team with the skills and expertise to study the necessary taxonomic and trophic groups, different biogeochemical processes, and the complex interactions amongst them. To understand and quantify the effects of land-use change on the activity of focal biodiversity groups and how this impacts biogeochemistry, we will: (i) analyse pre-existing data on distributions of focal biodiversity groups; (ii) sample the landscape-scale treatments at the Stability of Altered Forest Ecosystems (SAFE) Project site (treatments include forest degradation, fragmentation, oil palm conversion) and key auxiliary sites (Maliau Basin - old growth on infertile soils, Lambir Hills - old growth on fertile soils, Sabah Biodiversity Experiment - rehabilitated forest, INFAPRO-FACE - rehabilitated forest); and (iii) implement new experiments that manipulate key components of biodiversity and pathways of belowground carbon flux. The manipulations will focus on trees and lianas, mycorrhizal fungi, termites and ants, because these organisms are the likely agents of change for biogeochemical cycling in human-modified tropical forests. We will use a combination of cutting-edge techniques to test how these target groups of organisms interact each other to affect biogeochemical cycling. We will additionally collate and analyse archived data on other taxa, including vertebrates of conservation concern. The key unifying concept is the recognition that so-called 'functional traits' play a key role in linking taxonomic diversity to ecosystem function. We will focus on identifying key functional traits associated with plants, and how they vary in abundance along the disturbance gradient at SAFE. In particular, we propose that leaf functional traits (e.g. physical and chemical recalcitrance, nitrogen content, etc.) play a pivotal role in determining key ecosystem processes and also strongly influence atmospheric composition. Critically, cutting-edge airborne remote sensing techniques suggest it is possible to map leaf functional traits, chemistry and physiology at landscape-scales, and so we will use these novel airborne methods to quantify landscape-scale patterns of forest degradation, canopy structure, biogeochemical cycling and tree distributions. Process-based mathematical models will then be linked to the remote sensing imagery and ground-based measurements of functional diversity and biogeochemical cycling to upscale our findings over disturbance gradients.