The land can contribute to climate mitigation through absorbing more carbon dioxide and reducing other greenhouse gas emissions by growing more trees and re-wetting the peatlands. But as the climate warms and more demands are made of the land to feed a growing population, there is less space for these land-based climate mitigation activities and less for nature and biodiversity. Meanwhile, the changing climate is bringing more extreme weather which impacts on our safety. To grow a green future that is safe and resilient to these changes, we need to understand the linkages between the land and water systems of the earth. We need to have clear evidence of how changes we make on land and water management impact on the other aspects of the land-system, including how they will respond to increasing temperatures and extreme weather systems. This programme of work will bring together scientists from many different disciplines to work together to understand three key questions: What is limited our ability to reduce our greenhouse gas emissions from the land? What are the options for reducing our greenhouse gas emissions and what impacts do they have on the environment? How can we improve our resilience to climate change through improved forecasting and prediction of extreme events? By bringing together scientists in disciplines from soils, water, air and ecosystem dynamics, we will improve our understanding of the complex system that lies at the heart of the problem. We will use novel downscaling techniques and uncertainty framework to link global models to regional and national scale simulations. This will enable us to reality check the assumptions made in the global analysis against local knowledge. Using the downscaled data as a base-line, we will develop new knowledge of how the land-system interacts with the climate system at the local scale. Case studies around ecosystem restoration in sub-saharan Africa and gradients of intensity of agriculture in Southeast Asia will be used to quantify the impact of ecosystem management on climate mitigation metrics. Results of these case-studies will be used to inform the global assessment of land-management potential to contribute to Net Zero. We will create a global network of scientists bringing their knowledge of the environmental and socio-political system and how it interacts. Global and regional data will be made available to the national (UK) and international community of scientists to address these urgent issues

Some of the most pressing questions in atmospheric and climate science today focus on how thunderstorms will respond to changes in the atmospheric environment. How will extreme rainfall change with climate change? And how do internal storm processes and dynamics affect these changes? Nowhere is the challenge more urgent than in (sub-)tropical regions where large thunderstorm clusters, so-called Mesoscale Convective Systems (MCSs) frequently cause severe weather and flooding, but population resilience is low due to poverty and staggering economies. To estimate and plan for future storm impacts, we need to understand and model how storm dynamics will respond (and are already responding) to atmospheric changes, and whether there are internal, dynamical mechanisms that may intensify rainfall extremes beyond purely thermodynamical considerations linked to increased moisture in a warmer atmosphere. In most affected regions, MCSs provide crucial water supplies for crops, livestock and people, contributing 50-90% to total rainfall but are likewise associated with severe weather that affects millions around the globe. A situation that will only worsen as temperatures continue to rise. And yet, in spite of the societal importance of MCSs, we still do not know why in particular their sub-daily rainfall extremes can frequently surpass expected intensities. The fact that the relative importance of external (e.g. atmospheric humidity, wind shear, temperature) and internal drivers (storm circulations, updraught speeds and size) of rainfall maxima remain unclear also hampers our ability to estimate global warming effects. Climate model assessments of driver contributions so far do not exist as conventional global climate models with coarse resolutions ~100km have major difficulties representing processes in the MCS scale range, which they can neither explicitly resolve nor satisfactorily parametrise, i.e. they do not 'see' MCSs. Over the last decade however, there have been rapid advances in the use of high-resolution (<10 km) regional convection-permitting (CP) models for climate prediction. Not having to rely on convective parametrisations, CP models produce more realistic peak rainfall intensities even compared to medium-resolution models, and can simulate realistic MCSs. However, even state-of-the-art CP models still operate in the "grey-zone" of 1-10km where internal storm circulations are only partly resolved. Consequences of the neglect of sub-grid processes are still under investigation and shortcomings need to be put under scrutiny. By combining earth observation data with emerging state-of-the-art CP climate model simulations, my project investigates how the scale of convection (contiguous cloud shields, embedded convective core scales, updraught size) affects MCS rainfall extremes and lifetimes over land. Based on earth observation data, my work will discover whether scales of continental convective organisation have changed within the last 20-30 years, and what processes are key to determining such trends. This will also explore whether MCS interactions with land features and atmospheric environments change as a function of convective scale. I will furthermore challenge CP models with the identified processes and develop process-based model benchmarking approaches, testing how trustworthy CP models are in capturing rainfall intensification mechanisms in a future climate. The findings will be used to trial methods for improved storm nowcasting and for improved estimates of future MCS rainfall extremes based on multiple lines of evidence that will crucially include convective scales. Thus, my project will bring a step-change in our understanding of how global warming drives convective scale changes, how rainfall and scales are linked, and whether scale information can improve extreme rainfall predictions on weather to climate timescales.

Planetary boundaries of river water pollution are at risk of being breached, with dangerous consequences for human and environmental health, economic prosperity, and water security. The current paradigm for environmental management is predicated on understanding of average conditions. However, we know environmental pollution ca vary markedly in space and time. This interdisciplinary Large Grant (co-created with non-academic partners and as NERC-NSF collaboration) will pioneer innovations in experimental analytics, data science and mathematical modelling to yield new mechanistic understanding of the dynamic drivers of multi-contaminant pollution hotspots (spaces) and hot moments (times) in a changing water world. The diagnosis of the impact of these locations and periods when average pollution conditions are far exceeded on large scale and long-term river basin water quality is critical to inform local and global adaptation and mitigation strategies for river pollution and develop interventions to keep within a safe(r) 'operating space' and improve water quality for people and the environment. SMARTWATER will therefore integrate environmental sensing, network and data science innovations, and mathematical modelling with stakeholders' catchment knowledge to transform the way we diagnose, understand, predict, and manage water pollution hotspots and hot moments. We will: 1. Pioneer the application of scalable field diagnostic technologies for water quality sensing and sampling for identifying and characterising multi-pollution hotspots and hot moments for emerging (e.g., wastewater indicators, pharmaceuticals, pesticides) and legacy (e.g., nutrients) contaminants. 2. Develop smart water quality monitoring network solutions at river basin scale based on integrating high-resolution networks of proxy water pollution indicators with multivariate UAV boat-based longitudinal river network sampling to understand the footprint, propagation and persistence of pollution hotspots and hot moments in river basins. 3. Develop and apply data science innovations integrating deep machine learning and artificial intelligence approaches for pollution source attribution and to identify how hotspots and hot moments of multi-pollutions dynamics results from pollution source activation, connectivity and river network transport and transformation. 4. Demonstrate the utility of the new generation of smart pollution data to improve the capacity of integrated river basin scale water quality models to adequately present and predict the emergence of pollution hotspots and hot moments including their large-scale footprint and longer-term relevance for catchment water pollution. 5. Co-create with our stakeholder community pathways for successfully implementing practical and policy relevant changes in water quality management practice and use the interdisciplinary and inter-sectoral expertise of our broad stakeholder base to inform knowledge generation and dissemination pipelines in SMARTWATER. The mechanistic process understanding and integrated technological and management solutions that will be developed in SMARTWATER will allow a step change in the diagnostics, prediction and management of water pollution and transform our ability to understand and tackle pollution pressures of increasing complexity in a rapidly changing environment.

With numerous governments, cities, and organisations declaring climate emergencies and net-zero emissions targets, greenhouse gases (GHGs) are now the focus of international geopolitics and UK domestic policies. Furthermore, with the recent identification of violations of the Montreal Protocol, ozone depleting substances (ODS), are receiving renewed attention. It is therefore critically important to be able to analyse GHG and ODS emissions trends, examine spatial patterns, estimate future trajectories, and explore mitigation options in an open, transparent and publicly accessible way. Our proposed project will enable this, using state-of-the-art computing technology to create a platform, "OpenGHG". The estimation of GHG and ODS emissions requires close collaboration between a diverse group of scientists and stakeholders: "bottom-up" methods rely on statistical information collected by governments and industries, combined with scientific studies of the emissions intensity of particular activities, or the development of computer models that describe how human or natural processes produce or absorb GHGs. Complementary "top-down" techniques rely on instruments developed by spectroscopists and analytical chemists, the data from which are analysed along with outputs from meteorological models using advanced statistical methods. The data that is being generated by these diverse research and stakeholder communities is growing rapidly. However, the development of computational tools to help researchers aggregate data from such a wide range of sources and carry out and share analyses has not kept pace. Furthermore, given the sensitive nature of, for example, the inference of national GHG or ODS emissions, these communities must urgently take steps to make their analyses more transparent and reproducible. OpenGHG meets these needs, by providing an open, cloud-based, platform for researchers to share data and analysis methods and publish workflows. Furthermore, we have co-designed with our stakeholders, a range of tools that will facilitate the sharing of research outputs with governments, private companies and the public. The OpenGHG platform will: - Continuously incorporate and standardise up to date GHG and ODS measurements, bottom-up emission estimates, and a range of ancillary information related to GHG and ODS emissions. This data will be pulled automatically, or on demand, from a range of public archives, or pushed to the platform by data providers seeking to analyse or share their own data - Provide a wide range of analysis options, including the ability to design, publish and share custom workflows - Allow production of new top-down and bottom-up emissions estimates by accessing pre-existing and newly developed models and methods incorporated into the platform - Provide users with lower levels of computational expertise an easy-to-use interface for the most useful data analysis and visualisation. This will include comparisons of top-down and bottom-up estimates of emissions from different sectors of the economy, and potential future warming from different emissions scenarios.

This project aims to use new digital solutions to create 0 to 6 hour predictions - nowcasting - for tropical storms using satellite data. The methods will be developed and rolled-out for Africa, where people urgently need information about storm hazards, through our existing online platforms and smartphone apps. In this way the results of the research will be used to deliver information on storm hazards to users within minutes. The project very closely addresses the NERC Digital Strategy. Tropical storms are very unpredictable, changing very rapidly - explosively - over timescales of an hour or so. For this reason, predictions are naturally very uncertain. Very often, the most important information people need regarding a storm hazard is what is happening now, and some information about how the storm likely to move and develop in the next couple of hours. This process is called "nowcasting" and in the USA, nowcasting of tornados saves many lives every year. The lack of weather radars in most African countries means that nowcasting is almost completely absent, but we have recently shown that satellite methods can provide useful nowcasting of storms too. The new Meteosat Third Generation (MTG) satellite will provide even better data coverage, from about 2024, at higher frequency and finer spatial scale. There is a tremendous opportunity to innovate in the creation of new nowcasting methods and communicate them to weather services, organisations and the public across Africa. While existing satellite nowcasting methods have some skill, they also have major shortcomings. They work by extrapolating observed patterns forward in time, but are not constrained to obey the laws of physics, and unphysical predictions commonly occur. The most challenging problem in storm nowcasting is to predict the initiation and subsequent development of new storms in future: there is no accepted way to do this, and our considerable knowledge of the physics of initiation is not being exploited. It takes about 30 minutes to generate these nowcasts, and when their accuracy is degrading after an hour or two, their use becomes limited. We aim to create useful 6-hour nowcasts. Nowcasting is an obvious application where new data-science methods, in particular machine-learning (ML), have the potential to make a massive impact, and a number of groups have begun to propose practical solutions. We need fundamental research to understand and improve the performance of these data-driven solutions, on the basis of the underlying physics and fluid-dynamics of storms. For instance, existing methods can extrapolate an image of a storm forward in time using ML to predict its future movement or growth, but the result may grow and be distorted in shape in a way which is incompatible with the laws of physics. These unrealistic predictions are obvious to an experienced forecaster but ordinary users of the data will be vulnerable to the consequences of inaccurate nowcasts. When nowcasts are used to predict hazards such as floods, unphysical solutions could lead to bad decisions. In this project, we aim to combine machine-learning, theoretical fluid dynamics, operational prediction and meteorology, to create innovative approaches to nowcasting of tropical storms. We will develop ML methods which are fast, and which obey physical laws, like the weather prediction models. Our solutions will include statistical forecasts of rainfall probabilities, as well as ensembles of forecast realisations, and an automated evaluation system will be created. Recent advances in physical understanding and the new data offered by MTG, will be used to create statistical nowcasts of storm initiation and its subsequent evolution. We will apply these methods through our existing web-based and mobile-phone communication portals delivering information to Africa, and support colleagues in Africa to exploit the methods locally.