Volatile organic compounds (VOCs) are trace gases that play an important role in many atmospheric and biogeochemical processes. They are a major component of air pollution, being emitted directly to the atmosphere by natural and anthropogenic sources, e.g. transport, industrial processes, biomass burning, solvent use, etc., and also formed as secondary products by chemical reactions in the atmosphere. VOCs contribute to the formation of ozone and particulate matter in the lower atmosphere. Ozone is a respiratory irritant, a greenhouse gas and can decrease crop yields, leading to substantial economic losses. Fine particulate matter, such as PM2.5 (particles less than 2.5 um in diameter), is linked to numerous human health conditions (e.g. asthma, heart disease) and affects the radiation balance at the Earth's surface (links to climate). VOCs also play a key role in determining the oxidising capacity of the atmosphere (the Earth's ability to cleanse pollutants from the atmosphere), and it is becoming increasingly apparent that VOCs contribute significantly to indoor air pollution. In polar regions, biological and photochemical production of VOCs occurs at the snow-ice interface and in the surface ocean. However, the biogeochemical cycles of VOC formation in Arctic atmosphere remain poorly described, with unknown feedback responses to Arctic sea ice decline. As our understanding of atmospheric processes increases and computer models become more sophisticated, there is a requirement for ever-better measurements, in terms of analytical sensitivity (measuring smaller amounts), chemical speciation (identifying and measuring a larger range of compounds) and speed (faster response times to enable us to study, e.g., from a moving aircraft or to measure fast fluxes). This will help us to understand the fundamentals which are controlling these often-complex atmospheric interactions. For VOC measurements the Proton Transfer Reaction Time of Flight Mass Spectrometer (PTR-ToF-MS) will be of great benefit in this respect and will be deployable across a broad range of measurement platforms (aircraft, lab/chamber studies, observatories) and topical research areas (global atmospheric composition, indoor/outdoor air quality, mechanistic studies, fluxes). The PTR-QMS currently used on the FAAM aircraft was bought 17 years ago. It is based on a quadrupole detection system which limits the capability of the powerful PTR technique. These deficiencies include low mass resolution (inability to resolve compounds of equal mass), the necessity to pre-select a limited number of compounds (typically ~10), low sensitivity, particularly at higher masses (>100 Da). We therefore propose to replace the current PTRMS with a state-of-the-art PTR-ToF instrument which will help keep the FAAM aircraft at the cutting edge of global atmospheric research. Advantages of the PTR-ToF include: (1) Large increase in the number of compounds measured (ToF records all masses over a wide mass range, whereas the quadrupole only records a small number of pre-selected compounds); (2) Improved mass range (1-1000 Da) without loss of sensitivity at masses >100 Da; (3) Improved sensitivity (lower limit of detection); (4) Higher mass resolution (e.g. quadrupole cannot distinguish between isoprene (mass 68.117), an important biogenic compound, and furan (mass 68.075), a product of biomass burning; (5) Faster scanning; (6) Selective reagent ionisation (the use of different ion source reagents allows for improved specificity of the instrument, e.g. separation of aldehydes and ketones). The deployment of the PTR-ToF-MS in the unique RVG-Air-Sea-Ice chamber will allow the study of VOC production processes in a controlled environment, enhancing our understanding of the key parameters of VOC cycles in sea ice areas. Beyond the RvG-ASIC, the PTR-ToF-MS will open new avenues for field campaigns in polar seas, e.g. the impact of increasing shipping activity on air quality in the Arctic.

Advances in artificial intelligence (AI) are revolutionising how we search for information. Large language models (LLMs), such as OpenAI's 'Chat-GPT' or Google's 'Bard', are good at understanding what we say and the meaning behind our words. Through conversations with these tools, they are helping to improve the accuracy of what information we want to find. While existing search tools focus on using 'keywords', this may not always give good answers. LLMs help people who might not know the exact words to say, because they know the context and relationships behind our language. They can adapt to different ways of asking questions, as well as provide explanations about why they found such information. We believe that these maturing technologies can help researchers search for data. Through training existing LLMs to learn what UKRI-supported research data exist, we can make the most of their existing abilities to understand human language to create a powerful data search tool. Their potential to be used as a data search tool is unknown and we are not aware of any existing tools for UK research datasets. Our proposal will develop, pilot and evaluate the effectiveness of LLMs to this end. The main output of this work will be a fully deployable 'chat box' search tool that researchers will be able to use to discover research datasets. To achieve this, we will collate the metadata of data catalogues across a range of UKRI research investments including the Consumer Data Research Centre, NERC Environmental Data Service, Administrative Data Research UK and UK Data Service. Through combining data catalogues across these unconnected services, we provide a new single 'port of call' for searching research data. We will design our project so that it can easily adapt to integrate new datasets. These data will then be used to develop a new AI derived search tool based on LLMs. We want to understand how these technologies can be used effectively by researchers and whether they will give more useful searches. Our mixed methods approach will test and evaluate the acceptability, suitability, and performance of our new search tool in comparison to existing UKRI search tools. This will include focus groups to qualitatively examine the acceptability of LLMs for data discovery, a quantitative comparison of how our new tool performs against existing keyword search tools, and by running tests that task participants with searching for data. We will report the strengths and limitations of LLMs to examine how useful they are. We will make recommendations for how they can be deployed, refined and sustain the changing ways in how researchers search for data. Our project will bring added value to existing UKRI data discovery resources through creating a new tool that will know the context and meaning of search queries, providing a broader and more accurate list of datasets based on what is searched for. We hope that this will help researchers to find exactly the data they need for their research.

If CO2 emissions continue to rise, climate change will adversely affect global food and water availability, ecosystems, cities, and coastal communities. While reduction of fossil fuels will be an essential step for reducing atmospheric CO2, Negative Emission Technologies (NETs) can help meet emission targets. During combustion, CO2 can be extracted, transported, and stored in geologic repositories - this is the process of Carbon Capture and Storage (CCS). Combining bioenergy with CCS (BECCS) could result in negative emissions of CO2. BECCS is attractive since it results in a net removal of CO2 from the atmosphere while also providing a renewable source of energy. However, BECCS requires a large commitment of land and will have impacts on food and water availability. This work focuses on BECCS and addresses the challenges for planning a global and nationwide distribution of bioenergy crops. The vast majority of IPCC scenarios that remain below 2 degrees C makes use of NET in the 21st century. Although bioenergy crops and BECCS are an essential component of the scenarios (produced by Integrated Assessment Models, or IAMs), the crops in even the most sophisticated IAMs only respond to mean changes in climate. This results in an inconsistency in the modelling framework: the IAMs can assume bioenergy crops are effective at drawing down CO2 and producing energy in a region where actually climate change will reduce their effectiveness. Earth System Models (ESMs) represent the dynamics of the atmosphere, oceans, sea ice, and land surface. They can account for biophysical (i.e. changes to albedo and latent heat fluxes) and biogeochemical (i.e. uptake or release of greenhouse gases) feedbacks due to land use change. They are the only tool available to investigate future impacts of spatial and temporal variability in climate on the food, energy, and water nexus. However, the ESMs used in the last IPCC report only accounted for a generic crop type at best, not differentiating between bioenergy and food crops. Without an explicit representation of bioenergy crops in ESMs, the effects of climate change do not feedback to affect the food, energy, and water resources assumed to be true in the IAMs. There is an urgent need for predicting the productivity of bioenergy crops in a coupled climate simulation, to see the impact of a range of climate change on the productivity, and associated impacts on food crop productivity, energy production, and water availability. In this project, I will include representations of first and second generation bioenergy crops in the UK ESM, and investigate the impacts of climate change on the productivity at the global and regional (for the UK) level. This work will assess the viability of negative emissions of CO2 through bioenergy crops as an effective climate mitigation strategy under a changing climate, and provide data to support decisions that will minimize the impacts of both climate change and climate change mitigation on bioenergy production, food, and water availability. The outcomes of this project will enhance the resilience of the food/water/energy nexus to climate change and climate variability through better planning, and providing socially responsible recommendations for balancing the challenges of reducing climate change with feeding our growing global population.

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 ocean circulation is dominated by an energetic mesoscale eddy field on spatial scales of 10-100 km, analogous to weather systems in the atmosphere. These eddies are unresolved, or at best inadequately resolved, in the ocean models used for long-range climate projections. Thus it is necessary to parameterise the impacts of the missing mesoscale eddies on the large-scale circulation. The vast majority of numerical ocean circulation models employ the Gent and McWilliams "eddy parameterisation" which acts to flatten density surfaces, mimicking the release of potential energy to fuel the growth of the mesoscale eddies. A key parameter in this eddy parameterisation is the "eddy diffusivity", which is critical as it plays a leading order role in setting global ocean circulation, stratification and heat content, the adjustment time scale of the global circulation, and potentially atmospheric CO2. In this project, we will implement a new closure for the Gent and McWilliams eddy diffusivity, derived from first principles, which depends only on the ocean stratification, the eddy energy and a non-dimensional parameter that is less than or equal to 1. If the eddy energy is known, then there is no freedom to specify explicitly any additional dimensional parameters, such as an eddy diffusivity. For this reason, we argue that existing approaches to parameterising eddies in ocean climate models are fundamentally flawed. Our new approach requires solving an equation for the depth-integrated eddy energy. This is a significant challenge and will form a major component of the present project. However, we believe that solving for the eddy energy is tractable as we have some understanding of the key physical ingredients. These key ingredients include the source of eddy energy through instability of the large-scale flow, westward propagation of eddies, diffusion of eddy energy, dissipation of eddy energy in western boundary "eddy graveyards", and dissipation of eddy energy through bottom drag and lee wave generation. Once a consistent eddy energy budget is incorporated, our new eddy parameterisation leads to three highly desirable results, which serve as important proofs of concept: 1. It reproduces the correct dimensional growth rate for eddies in a simple model of instability of atmospheric and oceanic flows for which there is an exact mathematical solution. 2. Assuming perfect knowledge of the eddy energy, it reproduces the eddy diffusivity diagnosed from high-resolution computer simulations of fully turbulent instabilities. 3. It predicts and explains the physics of "eddy saturation", the remarkable insensitivity of the size of the Antarctic Circumpolar Current to surface wind forcing, and a long standing challenge and known deficiency of current eddy parameterisations. The work plan will consist of four inter-related work packages: 1. Implementation and validation of the new eddy parameterisation framework in the NEMO ocean model, used by NERC and the UK Met Office, along with other European partners. 2. Development and refinement of the parameterised eddy energy budget. 3. Quantifying the impact of the new parameterisation on the oceanic uptake of heat and passive tracers in the UK Earth System Model, used for the UK contribution to the Intergovernmental Panel for Climate Change (IPCC) climate projections. 4. Project management, to ensure that the work is delivered fully and in a timely manner.