Extreme climatic events, environmental degradation and socio-economic inequalities exacerbate the risk of infectious disease epidemics. We lack the evidence-base to understand and predict the impacts of extreme events and landscape changes on disease risk, leaving communities in climate change hotspots vulnerable to increasing health threats. This is in part due to a lack of ‘ground truth’ data describing environmental change in remote and under-resourced areas, as well as a lack of trained research software engineers and data scientists. HARMONIZE will convene a transdisciplinary community of stakeholders, software engineers and data scientists to develop cost-effective and reproducible digital infrastructure for stakeholders in climate change hotspots, including cities, small islands, highlands and the Amazon rainforest. We will strategically undertake one-off longitudinal ground truth data collection using drone technology and low-cost weather sensors, to improve classification algorithms and downscaling of coarser-resolution environmental datasets (e.g., satellite images, climate reanalysis and forecasts). We will then harmonize this post-processed data with socio- economic and health data in an automated workflow packaged for users in bespoke hotspot-specific toolkits. These sustainable tools will facilitate generation of actionable knowledge to inform local risk mapping and build robust early warning and response systems to build resilience in low-resource settings.

The Atlantic Meridional Overturning Circulation (AMOC) is a crucial component of the climate system due to its role in heat and salt transports, as well as its role in transporting and storing carbon. Variability in the strength of AMOC has been linked to important climate impacts, for instance, the number of Atlantic Hurricanes, anomalous Sahel precipitation, and European weather. Therefore, improved predictions of the AMOC would have important societal benefits. Despite its importance, the predictability of the AMOC remains relatively unexplored on timescales from one season to 10 years ahead, and many uncertainties persist in our understanding of AMOC variability. For example, we are unsure of the relative importance of different processes in driving AMOC variability on different timescales and latitudes, nor how predictable they are in state-of-the-art forecasting systems. Recent studies have provided considerable evidence that the atmospheric circulation in the North Atlantic is much more predictable than previously thought on these timescales. However, the predicted signals are far too small (the so-called signal-to-noise paradox) and predictions need to be calibrated to provide credible forecasts of society relevant variables, such as surface temperature. Given that atmospheric circulation is a key driver of AMOC, then it follows that AMOC predictions on these timescales may also suffer from similar signal-to-noise issues. Furthermore, predictions of AMOC, and its climate impact, could be improved by extending the published statistical calibrations to the ocean circulation. ALPACA will utilise AMOC observations (RAPID and OSNAP) and observation-based AMOC reconstructions to assess the quality of current AMOC forecasts in state-of-the-art seasonal and decadal prediction systems. Furthermore, we will evaluate the processes that contribute to skill and assess their consistency across models. We will also use new simulations to better understand the relative roles of different processes in driving observed variability on different timescales, and we will leverage new large ensemble simulations to quantify the role of external forcing in driving AMOC variability and change. Finally, by exploiting this new understanding, we will determine whether seasonal-to-decadal predictions of AMOC and its climate impacts can be improved through physically-consistent statistical calibrations that reduce the signal-to-noise errors in predictions. ALPACA is a collaboration between the National Centre for Atmospheric Science at the University of Reading, The National Oceanography Centre Southampton, The University of Exeter, and the Met Office Hadley Centre from the U.K., and The National Center for Atmospheric Research and the University of Miami, from the U.S, and the Barcelona Supercomputing Center from Spain.

Decarbonising the transport sector is a top priority worldwide. The difficult-to-decarbonise transport applications (including mainly shipping, road freight and aviation) emit more than 50% CO2 of the entire transport sector. Among efforts on developing low-emission fuels, liquid synthetic fuels that can massively reduce pollutant emissions are drawing increasing attention, as they can be integrated into the current transportation system using existing infrastructure and combusted in existing engines (such as diesel engines for optimal fuel economy) with minor adjustments as drop-in fuels. Liquid synthetic fuels such as oxymethylene ethers (OMEx, which possess liquid properties similar to diesel when x=3-5) can be produced from a range of waste feedstocks and biomass, thereby avoiding new fossil carbon from entering the supply chain. OMEx can also be produced as an electrofuel (or e-fuel), thereby used as a sustainable energy carrier. However, due to the lack of complete knowledge of the physicochemical properties associated with the fuel composition variability, i.e. variation in the oligomer length (the x value of OMEx) and the composition variation of OMEx-diesel blends in real engine environment, there are challenges in utilising OMEx in practical engines, mainly in engine and its operation adjustments for optimal performance and minimal pollutant emissions. To address the technical issues of OMEx utilisation, accurate information on physicochemical properties and pollutant emissions of the synthetic fuels over the engine operational ranges is mandatory, but this is not readily available. This project is intended to obtain a thorough understanding on liquid synthetic fuel utilisation. The project will address the fundamental challenges in utilising renewable synthetic fuels, in particular OMEx and the associated OMEx-diesel fuel blends. The study will follow a combined modelling / simulation - experimentation approach, predicting the physicochemical properties including emission characteristics of the alternative fuels using molecular dynamics simulations, tailor-made experimentation for first-hand information on fuel utilisation, and establishing a database / mapping to guide the synthetic fuel utilisation in real engines over a wide range of conditions using machine learning.

The Subpolar North Atlantic (SNA), which is the region of the Atlantic Ocean between 45-65N latitude, is a highly variable region. Surface temperatures and surface salinity here have varied on a range of time-scales, but the changes are dominated by large and slow changes on decadal or longer timescales. This decadal timescale variability appears to form a key component of a larger climate mode, the Atlantic Multidecadal Variability, which has been linked to a broad range of important climate impacts, including rainfall in the North African and south Asian monsoons, floods and droughts over Europe and North America, and the number of hurricanes. The SNA is also one of the most predictable places on Earth at decadal timescales, which suggests the potential for improved predictions of regional climate and high-impact weather years ahead. However, the origins of this variability in the SNA, and the processes controlling its impacts, are far from fully understood. There is significant evidence to suggest that anomalous heat loss from the subpolar North Atlantic Ocean to the atmosphere can instigate a cascade of changes across the North Atlantic basin in both the ocean and atmosphere. For example, changes in the SNA can change the strength of the ocean circulation to the south, affect the northward transport of heat and freshwater in the North Atlantic, and subsequently affect the upper ocean temperatures and salinity across the whole North Atlantic basin, and into the Arctic. Changes in the subpolar North Atlantic surface temperature are also thought to affect the atmospheric circulation - i.e. wind patterns - in both summer and winter. However, observational records are very short, and so there are significant problems with understanding causality, and considerable uncertainty about how well many of the important processes are represented in current climate models. WISHBONE will make use of new advanced climate simulations and forecast systems to make progress in understanding the impact of the subpolar North Atlantic on the wider North Atlantic basin. It will also test specific hypotheses related to understanding the specific role of heat loss over the subpolar North Atlantic in driving changes throughout the basin including the role of surface anomalies in driving wind patterns. WISHBONE is a collaboration between the National Centre for Atmospheric Science at the University of Reading, The National Oceanography Centre Southampton, The University of Oxford, and The University of Southampton from the U.K., and The National Center for Atmospheric Research, from the U.S.

We live in an era of multi-cores: computing processors are no longer marketed by their clock speeds, they are marked by the number of cores. The fundamental limits of energy and power density of processors will soon push us further into an age of dark-silicon where only a small portion of the chip can be powered at any time. In such a setting, putting more of the same processing cores on a chip (i.e. homogeneity) gives no advantage. This has forced computer architects to introduce heterogeneous many-core systems built around distinct processors -- which have different energy and performance characteristics and each is specialised for a certain class of applications. Computer architects now hope that software will find ways to unlock the potential of heterogeneous many-cores. Software developers, however, are struggling to cope with this dramatic increase in complexity; and the current compiler tools, whose role is to enable software makes effective use of the underlying hardware, are simply inadequate to the task. It is already a daunting task to build optimising compilers for homogeneous multi-cores consisting of identical cores, even just targeting performance (i.e. to make programs faster). It typically takes several generations of a compiler to start to effectively exploit the processor's potential, by which time a new processor appears and the process starts again. It will be a fundamentally more difficult task to design efficient compiler heuristics for optimising energy (i.e. to reduce energy consumption) and performance on heterogeneous many-cores, especially given the subtle interactions of different cores and inter-connections. Even if successfully achieved, the task of compiler design must likely to be started again when moving to a new released processor. This never ending game of catch-up inevitably delays time to market, meaning that we rarely fully exploit the hardware in its lifetime. If no solution is found, we will be faced with software stagnation and will be unable to offer scalable computing performance -- a driving force that has dramatically changed our society over the past 50 years. What is needed is an approach that evolves and adapts to the future hardware architectural change and delivers scalable performance over hardware generations. This project offers precisely that. It will achieve this by bringing together two distinct areas of computer science: parallel compiler design and machine learning to develop a new paradigm for energy and performance optimisation. Our key insight is that the best optimisation strategies can be learned from similar software/hardware settings; and the learnt knowledge can be constantly refreshed without human involvement. This project will deliver such a smart, adaptive compilation system. We will use machine learning to acquire knowledge of workloads, applications and the underlying hardware, testing new compilation strategies, learning how each individual program should be optimised for each specific computing environment, and constantly improving the optimisation heuristics over time. As knowledge of the application environment grows, our system will make programs faster and more energy efficient; for example, software will respond quicker and the battery life will last longer on mobile phones. It will reduce time to market for software products and deliver scalable performance as hardware advances. If successful, such as programme of work will help to the looming software crisis of dark silicon, which will be of benefit to academics and UK industry, and system software researchers and developers worldwide.