According to DEFRA poor air quality costs the UK ~£15billion per year, and is governed by the chemical composition of the atmosphere. Knowledge of the gas phase oxidation of hydrocarbons (HCs) and volatile organic compounds (VOCs) emitted into the atmosphere as a result of biogenic or anthropogenic processes is central to the impacts of emissions on NOx (NOx = NO + NO2), ozone, methane lifetimes, and formation of secondary organic aerosol (SOA), and thus on air quality and climate change. An important class of oxidation reactions are initiated by ozone, and involve the oxidation of unsaturated VOCs (including both anthropogenic and biogenic sources) in ozonolysis reactions. These reactions have long been postulated to produce reactive Criegee intermediates (CIs), and have been shown to dominate atmospheric radical production at night and in low light conditions. In 2012, the first direct kinetic measurements of CI reactions were made, using photolytic sources of CIs in the laboratory, with results indicating much higher reactivity than previously expected on the basis of indirect measurements. Experiments using the newly identified photolytic sources have cast doubt on our understanding of the role of CI species in the atmosphere, with initial results indicating an enhanced role in the oxidation of SO2 and NO2. However, the competing reaction with water vapour is critical to the atmospheric impacts of CIs. The simplest CI species, CH2OO, has been shown to react rapidly with the water dimer, but the reactions of larger CIs with water (both monomers and dimers) have received relatively little attention, and no temperature dependent kinetics are available for the larger species for use in atmospheric models. Products of the reactions with water will determine the ultimate atmospheric impacts of these reactions, and are highly uncertain. Unimolecular decomposition reactions have also been highlighted as potentially significant loss mechanisms for large CI species, with little information available regarding the kinetics or products of these reactions. This work will address the uncertainties in the kinetics and products of CI decomposition and reactions with water. Moreover, we will also develop capabilities for monitoring of CI species directly in ozonolysis reactions using UV/vis absorption spectroscopy, enabling the direct determination of CI yields from ozonolysis reactions and the investigation of CI chemistry under more realistic atmospheric conditions. This study will therefore address concerns regarding the applicability of kinetic results obtained in experiments in which CI are produced photolytically. This work will reduce the significant uncertainties in the atmospheric fate and impact of Criegee intermediates, leading to improvements in capabilities for numerical modelling of atmospheric composition, air quality and climate.

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.

Global climate change threatens our future. Urgent societal action is demanded. However, crucial uncertainties regarding the future climate still need to be addressed. Extreme climate events are wreaking enormous environmental, societal, and economic tolls and they are becoming increasingly common and intense. The huge number of uncertainties related to our future climate combine with the sensitivity of the Earth's climate system to create extremely demanding challenges. Extending our understanding for deriving effective solutions demands interdisciplinary collaboration to determine the dominant factors in climate change. Currently, there is a lack of highly qualified mathematicians with the necessary training and experience to address the diverse problems and urgent challenges posed by climate change using computational and data-driven research. Our Centre for Doctoral Training (CDT) will train new cohorts of PhD students and build a scientific community to address the grand mathematical challenges raised by the significant levels of uncertainty in our future climate. The mission of our CDT will be to prepare graduates with strong mathematics, physics and engineering backgrounds to apply their skills in mathematical modelling, scientific computing, statistics and machine learning to key climate-related problems in oceanic, atmospheric and engineering contexts. By bringing together leading experts from Imperial College London, the University of Reading and the University of Southampton along with a wide range of external partners, our CDT will be uniquely placed to equip future mathematicians with the tools required to address global climate uncertainties. Our CDT will achieve its goals by developing the mathematics and its applications that are required to understand, better predict and, ultimately, respond to impending changes in the Earth's climate and the associated risks. A particular emphasis will be the creation of a vibrant environment to integrate strong cross-disciplinary engagement and collaboration, both within and between cohorts and disciplines, in advancing the range of scientific techniques, fundamental theories, approaches and applications. This will include engaging with academics, government organisations, industry and the public. As a result, the development of outstanding skills in mathematics and science communication will be a priority. The collaborative and peer-to-peer interactions will help develop the complementary techniques and approaches that will underpin essential technical research and innovation and will be coupled with exciting opportunities to discover and advance fundamental mathematics to provide practical solutions in climate science and beyond. Our CDT will act as a seed for growing the capability and capacity to inform decisions and efforts related to climate change on a rapid timescale. The technical focus of our CDT will be enhanced by activities to appreciate the social, political and economic dimensions of societal response to climate change. Furthermore, sustained efforts to mitigate and adapt to climate change will be required during the coming decades. For this reason, along with building a professional community of graduates, the CDT will invest in imaginative outreach programmes involving school pupils and undergraduates, building on opportunities through the institutions partnering with the CDT, including the Grantham Institute for Climate Change and the Environment, the National Oceanography Centre, the National Centre for Earth Observations, the UK Meteorological Office, the European Centre for Medium-Range Weather Forecasts, and the Natural History Museum.

Volcanic eruptions are an important driver of climate variability and climate change, yet climate model simulations do not agree with data on the magnitude of temperature changes caused by large-magnitude volcanic eruptions. The Vol-Clim project will resolve this discrepancy by deriving new and improved estimates of volcanic forcing using a state-of-the-art Earth System Model developed in the UK (UKESM1), which will allow us to quantify and better understand how large explosive volcanic eruptions affected the climate system since 1250 CE. The model explicitly accounts for the interaction of chemical, dynamical and aerosol microphysical processes during volcanic eruptions, all of which affect the magnitude of the climate response. However, these processes have not been taken into account in previous assessments of climate change and natural climate variability caused by volcanic eruptions since 1250 CE. In detail, at least 60 volcanic eruptions have been detected based on volcanic deposits in polar ice-cores since 1250 CE. Large-magnitude eruptions emit sulphur dioxide high into the stratosphere where it is oxidized to form sulphuric acid vapour, which nucleates and condenses to form sulphate aerosol particles. These aerosol particles scatter and absorb energy from the Sun thereby cooling the Earth's surface. In terms of the magnitude of this surface cooling, tree-rings (and other data) appear to show a smaller hemispheric temperature response (of up to 1 degree Celsius) to volcanic eruptions than simulated by current climate models. This mismatch means that at present we do not fully understand how the climate system including clouds responds after volcanic eruptions. We also do not fully understand how tree growth and subsequently tree-rings respond as a consequence of the cooling induced by a volcanic eruption. Overall, these uncertainties affect our ability to use climate models to simulate past, present and future changes of climate. Current climate models have simple implementations of volcanic effects, ignoring many key chemical and physical processes relevant after volcanic eruptions. Using UKESM1 we will be able to simulate the evolution of volcanic aerosol particles with unprecedented sophistication, which has the potential to greatly improve the fidelity of predicted climatic effects and reconcile model-simulated and observational records of climate change after volcanic eruptions. Our simulations in UKESM1 will cover the period 1250 CE to present, which will enable us to characterize and evaluate annual to centennial-scale effects on global and hemispheric surface temperatures, climate variability and impacts on surface ocean temperatures for eruptions of different frequencies and intensities. Vol-Clim is an ambitious project that aligns closely with international initiatives and NERC's main goals. Quantifying the contribution of volcanic eruptions to climate variability over the past millennium is key to understanding present day and future decadal-scale climate variability; this is in line with NERC's main goal 'to understand and predict how the planet works'. Vol-Clim will also help prepare society for the effects of future eruptions. Vol-Clim is also strongly aligned to international activities such as the new Past Global Changes (PAGES) working group "Volcanic Impacts on Climate and Society (VICS)" and the PMIP (Paleoclimate Modelling Intercomparison Project) and CMIP (Coupled Model Inter-comparison Project) communities. We will generate a volcanic aerosol forcing time-series (1250 CE to present) for use in those models that do not account for the chemical and physical aerosol processes in the stratosphere. These deliverables are relevant for CMIP6-endorsed activities such as VolMIP (Model Inter-comparison Project on the Climatic Response to Volcanic Forcing) and RFMIP (Radiative Forcing Model Inter-comparison Project), and also the IPCC.

GOTHAM represents an ambitious research programme to gain robust, relevant and transferable knowledge of past and present day patterns and trends of regional climate extremes and variability of vulnerable areas identified by the IPCC, including the tropics and high-latitudes. It will achieve this by identifying the influence of remote drivers, or teleconnections, on regional climate variability, and assessing their relative impact. It will also assess the potential for improved season-decadal prediction using a combination of contemporary climate models, citizen-science computing and advanced statistical analysis tools. GOTHAM has the direct backing of many international weather and climate research centres, and will lead to the improved development of seasonal-decadal forecasts at the regional level. The improved knowledge and understanding of dynamical factors that influence regional weather and climate in the tropics/sub-tropics, and polar regions, will directly feed through to weather and climate forecast services to assist in their decisions on which priority areas of their model development to target in order to improve forecast skills. For example, GOTHAM will advise whether a model is missing or misrepresenting important global teleconnections that significantly influence regional climate in identified vulnerable regions. These impacts will be achieved through regular meetings with GOTHAM investigator groups and their extended collaborative networks, and extensive involvement in wider science and science-policy programmes with co-aligned strategies, such as the core projects within the WCRP. Improved seasonal to decadal scale forecasts will improve predictions of extreme events and natural hazard risks such as flooding that can have devastating impact on society. There is real potential for project results feeding through to impacts-related research, such as those involved in hydrological and flood forecast modeling, and these will be explored in liaison with identified partners in Asia and Europe.