As a defining challenge of our time, climate change has led to the 2015 Paris Agreement whose central policy goal is to keep global warming well below 2 degrees Celsius. The substantial remaining uncertainty in physical climate change projections, however, means that there is a very wide window of the dates within which this threshold might be passed. Assuming continuous greenhouse gas emissions, it could be within the next decade, or it might not be until well into the second half of this century. To inform their decision-making, policymakers urgently need this uncertainty reduced. Our research proposal, ML4CLOUDS, addresses the leading role of clouds in this uncertainty, and the coupled implications for climate variability. Clouds are ubiquitous phenomena covering around two thirds of Earth's surface at any time and, as such, play key roles in our climate system. Crucially, clouds are the single most important uncertainty factor in global warming projections under increasing atmospheric carbon dioxide (CO2) concentrations. Clouds are also key modulators of the main modes of climate variability, such as the El Niño Southern Oscillation (ENSO), which in turn drive regional climate and weather extremes. A better understanding of the response of clouds and their interactions with the atmospheric circulation and global warming has therefore been highlighted as one of the 7 Grand Challenges by the World Climate Research Programme. Constraining cloud-related uncertainties, and understanding the underlying physical drivers, would consequently be invaluable to society. The fundamental role of clouds primarily arises from their interaction with Earth's energy budget. Low-altitude clouds are highly reflective for sunlight (having a cooling effect on climate), while upper tropospheric clouds trap radiation emitted from the Earth (having a warming effect). Cloud formation itself releases latent heat to the atmosphere. It is the overall impacts of these processes on atmospheric temperature and the hydrological cycle that make clouds so important for the behaviour and evolution of the climate system. ML4CLOUDS aims to provide a better understanding of the complex physical control mechanisms driving cloud formation. This will improve our ability to predict how Earth's cloud cover will change under human influences such as increasing atmospheric CO2 and aerosol pollution, and thus reduce uncertainty in global warming. This reduction in cloud-related uncertainty will also feed back on our ability to model and comprehend present-day climate variability, and on how we expect the main climate modes, such as ENSO, to change in the future. We will achieve these goals through a novel approach incorporating artificial intelligence (or machine learning) methods, paired with targeted climate feedback analyses and state-of-the-art climate model simulations run on supercomputers. Specifically, our project will: 1. Use machine learning to derive cloud-controlling relationships from large climate model datasets and from space-based observations. These relationships will provide improved estimates of the cloud response and significantly reduced uncertainty in physical climate change projections. They will further provide new insights into the relative importance of distinct physical mechanisms behind the cloud response. Cloud-controlling relationships learned from observations will also be helpful to inform future climate model development, e.g. of the new UK Earth System Model (UK-ESM). 2. Improve our understanding of the role of clouds in modulating the main modes of climate variability. Next to its importance for extreme weather, climate variability is superimposed on long-term trends due to man-made climate change. A better understanding of the role of clouds in climate variability will therefore enhance our ability to detect and attribute historical climate change, and to predict future changes in climate and its extremes.

The march of technological progress has given us devices that are ever smaller and more complex: today's smart phones for example are almost unrecognizable in their size and their range of functions from the models of 25 years ago. This progress has taken us to the point where devices must now be understood in terms of the quantum behaviour of their constituent particles, a new frontier in technology that furthermore will lead to completely new applications. However, building fully quantum mechanical models of devices is notoriously difficult: the amount of information needed to describe a quantum system scales exponentially with its size. The situation is even worse when one must consider how the environment interacts with the device, and yet this is a crucial consideration for real devices. However, we have recently developed a new quantum simulation technique with remarkable efficiency: by keeping just the most important information we are able to track the behaviour of a single particle even when it is interacting very strongly with all of the other particles in its environment. In this project, we will exploit this new technique to design, simulate, and optimize four types of nanoscale devices with various technological applications. The functioning of all these devices relies on similar physics, namely how the device interacts with the environment. As such, our new method is ideally suited to all these areas. First, we will model solid state single photon sources. These produce quanta of light - photons - one at a time, and underpin future ideas for secure communication and quantum computing. We will find how the coupling between the photons and the vibrations of the solid determines affects their performance. Understanding this will allow us to determine how devices, either machined as thin wires or membranes or drawn as nanometre patterns in a solid matrix, could create more effective photon sources. Second, solar panels need to first absorb light energy from the sun, and then to transport it to electrodes. We will investigate the quantum mechanics of this energy transport problem, in particular for solar cells made of organic materials. Here, vibrations are very strongly coupled to the excited electrons that transport the energy, and our new technique is ideal for studying how this process works and how it might be improved by informed selection of component organic molecules. Third, a new frontier in electronics will be enabled if we can build circuits using molecules. Electric current is then a consequence of how electrons can tunnel quantum mechanically from one molecule to the next; this depends both on electronic coupling between molecules and how the molecules vibrate. We will use our technique to build models of molecular junctions, and explore how strong electronic and vibrational coupling changes the quantum transport properties of these materials. Fourth, diamonds have recently been at the forefront of a whole new kind of imaging technology. In particular, single electrons in diamond have a tiny magnetic moment, a 'spin', whose motion depends on how strong the magnetic field is at the position of the electron. Remarkably, the spin of a single electron can be measured in diamond, and so magnetic imaging with nanometre accuracy is a possibility. The limit of how well these 'nano-magnetometers' can work is set by how well they can be isolated from their environment. In this project, we will first use our novel approach to understand the dynamics of a spin coupled to its environment, and then show how to isolate spins more effectively. The project will advance several different nanotechnologies, and at the same time we will develop a unique and freely available tool that can be applied to a huge variety of new systems in future.

The goal of ACRoBEAR is to predict and understand health risks from wildfire air pollution and natural-focal disease at high latitudes, under rapid Arctic climate change, and resilience and adaptability of communities across the region to these risks. This will be achieved through integrating satellite and in-situ observations, modelling, health data and knowledge, and community knowledge and stakeholder dialogue.The Arctic has warmed rapidly over recent decades, at around twice the rate of global mean temperature increases, resulting in rapid changes to the high latitude Earth system. Changes in the high latitude terrestrial environment include observed increases in temperature extremes and precipitation patterns, which are leading to increasing trends in boreal wildfire and changes in the distribution of disease-carrying vectors, with evidence for emerging interactions between these changing risks. Recent years (including 2019) have seen unprecedented fire activity at Arctic latitudes, leading to unhealthy air quality in high latitude towns and cities. Vector-borne disease occurrence in these regions is also changing in response to rapid changes in temperature and moisture. Moreover, fire activity is intrinsically linked to changes in vector-borne disease risk through changing the habitat conditions for vectors and their hosts. Environmental, social, and governance factors specific to high latitudes hamper our current ability to understand community resilience and response to these changing risks. ACRoBEAR will tackle these urgent issues in the most rapidly warming region of the planet. To address these research challenges, ACRoBEAR brings together a diverse, international, interdisciplinary team of world-leading research groups and collaborators. The project will benefit from two-way dialogue with community groups and stakeholders throughout, across three key regions (Alaska, Eastern Siberia, Sweden). These groups will take an active part in co-design of specific research deliverables, and contribute local and indigenous knowledge to the development of new understanding within the project. ACRoBEAR aims to connect natural science with local community and stakeholder priorities, and to integrate natural science with local community knowledge and understanding. The ACRoBEAR team comprises world-leading experts in air pollution, climate science, natural-focal disease, social science and governance, landscape fire science, and health science, from across four European countries, Russia, and the United States. The unique interdisciplinary team will allow an end-to-end state-of-the art assessment of community resilience to changes in risk due to wildfire and natural-focal disease at high latitudes as a result of rapid Arctic warming. The planned workflow exploits cross-disciplinary collaboration and knowledge transfer to deliver integrated outcomes. ACRoBEAR will benefit a broad range of local and national-level stakeholders, including local communities, government, health and forestry agencies, and local and national policy makers. ACRoBEAR will deliver substantial impact on local communities, policy makers and health agencies in Arctic nations. Impact will result from providing new understanding to enable implementation of robust measures for mitigating harmful health impacts due to changes in high latitude wildfire and natural-focal disease and development of policy options to enable adaptation and increase resilience, tailored to regional communities and governance structures. The key legacy impact will be a series of web-based data tools and resources, carefully tailored to community and stakeholder needs via continual two-way dialogue throughout the project.