Flooding is the deadliest and most costly natural hazard on the planet, affecting societies across the globe. Nearly one billion people are exposed to the risk of flooding in their lifetimes and around 300 million are impacted by floods in any given year. The impacts on individuals and societies are extreme: each year there are over 6,000 fatalities and economic losses exceed US$60 billion. These problems will become much worse in the future. There is now clear consensus that climate change will, in many parts of the globe, cause substantial increases in the frequency of occurrence of extreme rainfall events, which in turn will generate increases in peak flood flows and therefore flood vast areas of land. Meanwhile, societal exposure to this hazard is compounded still further as a result of population growth and encroachment of people and key infrastructure onto floodplains. Faced with this pressing challenge, reliable tools are required to predict how flood hazard and exposure will change in the future. Existing state-of-the-art Global Flood Models (GFMs) are used to simulate the probability of flooding across the Earth, but unfortunately they are highly constrained by two fundamental limitations. First, current GFMs represent the topography and roughness of river channels and floodplains in highly simplified ways, and their relatively low resolution inadequately represents the natural connectivity between channels and floodplains. This restricts severely their ability to predict flood inundation extent and frequency, how it varies in space, and how it depends on flood magnitude. The second limitation is that current GFMs treat rivers and their floodplains essentially as 'static pipes' that remain unchanged over time. In reality, river channels evolve through processes of erosion and sedimentation, driven by the impacts of diverse environmental changes (e.g., climate and land use change, dam construction), and leading to changes in channel flow conveyance capacity and floodplain connectivity. Until GFMs are able to account for these changes they will remain fundamentally unsuitable for predicting the evolution of future flood hazard, understanding its underlying causes, or quantifying associated uncertainties. To address these issues we will develop an entirely new generation of Global Flood Models by: (i) using Big Data sets and novel methods to enhance substantially their representation of channel and floodplain morphology and roughness, thereby making GFMs more morphologically aware; (ii) including new approaches to representing the evolution of channel morphology and channel-floodplain connectivity; and (iii) combining these developments with tools for projecting changes in catchment flow and sediment supply regimes over the 21st century. These advances will enable us to deliver new understanding on how the feedbacks between climate, hydrology, and channel morphodynamics drive changes in flood conveyance and future flooding. Moreover, we will also connect our next generation GFM with innovative population models that are based on the integration of satellite, survey, cell phone and census data. We will apply the coupled model system under a range of future climate, environmental and societal change scenarios, enabling us to fully interrogate and assess the extent to which people are exposed, and dynamically respond, to evolving flood hazard and risk. Overall, the project will deliver a fundamental change in the quantification, mapping and prediction of the interactions between channel-floodplain morphology and connectivity, and flood hazard across the world's river basins. We will share models and data on open source platforms. Project outcomes will be embedded with scientists, global numerical modelling groups, policy-makers, humanitarian agencies, river basin stakeholders, communities prone to regular or extreme flooding, the general public and school children.

Overview: A three-year extension is proposed to the Integrated Characterization of Energy, Clouds, Atmospheric state, and Precipitation at Summit (ICECAPS) project. This project is an international collaboration that funds the original ICECAPS researchers through the U.S. National Science Foundation's Arctic Observing Network and a team of researchers at the University of Leeds through the U.K. Natural Environment Research Council. The ICECAPS project has continuously operated a sophisticated suite of ground-based instruments at Summit Station, Greenland since 2010 for observation of clouds, precipitation, and atmospheric structure. The project has significantly advanced the understanding of cloud properties, radiation and surface energy, and precipitation processes over the Greenland Ice Sheet (GrIS) during a period of rapid climate change. The project has supported numerous national and international collaborations in process-based model evaluation, development of new measurement techniques, ground comparisons for multiple satellite measurements and aircraft missions, and operational radiosonde data for weather forecast models. We propose to complement the ICECAPS Summit observatory by building, testing and deploying an additional observatory for measuring parameters of the surface mass and energy budgets of the GrIS. The observatory takes a novel approach for unattended, autonomous operations by supporting a suite of instruments that require moderate power and manageable internet bandwidth. The new observatory will be deployed in successive summers at Summit Station in the dry-snow zone and at Dye-2 in the percolation zone. If this pilot project is successful, a network of these observatories will be proposed for future deployment that will observe the processes of air parcels as they move along Lagrangian trajectories in southwestern Greenland.

The research in this proposal tries to answer questions about how our solar system and its reservoirs first formed. We are proposing to focus our efforts on the issue of how terrestrial planets acquired their metallic cores (the same cores that drive planetary magnetic fields). In the process we will also determine how the silicate Earth's budgets of metal loving elements like Ni originated. We will make comparisons between the differentiated planets and asteroids, Earth, Mars, Vesta and the angrite parent body on the one hand and the Moon on the other and use these data to better constrain lunar origins. We use a combination of isotopic measurements using mass spectrometry, and experimental simulation at high pressures and temperatures. We have isotopic evidence that metallic cores of planets started forming very early; within the first million years or so of the Solar System's earliest objects, calcium aluminium refractory inclusions. These cores formed from molten rock created from accretional energy and, initially, radioactive decay. As metallic cores form they partition a variety of elements into the dense segregating metallic liquids partially removing these elements from the residual silicate planet. The degree of depletion and the magnitude of any associated isotopic fractionation will depend on the conditions under which these cores formed, in particular the pressure, temperature, oxygen fugacity and sulphur content. Therefore, by measuring the isotopic compositions of primitive meteorites and comparing them with those of samples of the silicate and metal portions of asteroids, Mars, Earth and the Moon one can deduce the environment under which different planetary objects first developed. To quantify these environments it is necessary to calibrate the isotopic and chemical effects with experimental determinations. We will focus our attention on vanadium, chromium, nickel, molybdenum, tungsten and, if time permits, ruthenium.

The atmosphere and oceans control the Earth's climate. The ocean surface boundary layer (OSBL) is the upper 300m or so of the oceans and it is the part of the ocean that is directly affected by the atmosphere. So the OSBL acts to couple the atmosphere and deeper oceans together: it mediates the transfer of heat, momentum and important greenhouse gases such as carbon dioxide, and controls the supply of nutrients to the plankton that grow in the ocean surface boundary layer. In addition the temperature of the sea surface has an impact on weather forecasts for timescales of days to seasons. The sea surface temperature is largely set by the OSBL. It is clear then that the ocean surface boundary layer it truly at the heart of weather and climate. But our knowledge of the OSBL is very incomplete, and this means our quantitative models are not accurate. Nevertheless, it is a very exciting time to be doing research into the OSBL because we have new ideas about the fundamental processes that control the its evolution from timescales of days to years, and we also have exciting new tools to measure the ocean surface boundary layer. OSMOSIS will bring together a team of meteorologists and oceanographers with backgrounds in theory, computer modelling and observations, with the aim of making a step change to our understanding and our predictive power of the OSBL. We shall do this with a combination of new theory and new measurements. We will develop theory into the fundamental physics of the OSBL using the powerful new computational tools, which allow us to simulation the three-dimensional, time varying motions of the water in the OSBL. By careful analysis of the results of these computations we shall develop simpler representations of the OSBL that can be used in weather and forecast models. Will plan two research cruises and a range of instruments attached to fixed moorings to measure the OSBL at a level of detail never previously attempted. The cruises will enable us to observe at close quarters how the OSBL evolves under different weather regimes. The moorings will be left to gather data over a whole year, which will show us the seasonal evolution of the OSBL. These data will provide stringent tests for our new theoretical ideas, and our simpler representations. Finally, we are doing this research in conjunction with the Met Office and the European Centre for Medium Range Weather forecasting. We shall work closely with them doing the project, and their involvement will ensure that our results make a difference to the practise of weather and climate forecasting.

Poor air quality damages the lives and livelihoods of millions of people and is predicted by the World Health Organisation (WHO) to become the world's largest cause of preventable death by 2030. Those living in Low- and Middle-Income Countries (LMICs) and cities are particularly affected, both through short-term acute effects and an accumulated life-long reduction in quality of life and health. There is a major opportunity to co-design and co-produce a highly fault-tolerant system for air pollution measurement, that is fully open-source, and built from easily available low cost and off-the-shelf components. The ambition is that this approach would be scale-able and could be sustained in LMICs by in-country practitioners at modest cost, long-term. New measurements can then be coupled to integrated assessment models developed by in-country agencies with our support to enhance their decision-making capacity on air pollution mitigation. This modelling will use a tool developed by project partners in the University. This new innovation for monitoring and modelling, can catalyse action and support long-term beneficial change, initially in our early adopter partner countries, and then applied to other LMICs. Recent research from the University of York's Wolfson Atmospheric Chemistry Laboratories (WACL) has developed a low power, highly fault tolerant technology based on the clustering of multiple low-cost air pollution sensors to provide high quality measurements of target air pollutants. This approach exploits the simplicity, modest cost and high reliability of state-of-the-art sensors and electronics, but significantly improves the quality of data collected. The real-world use of sensor technologies has been slowed due to issues relating to poor individual sensor data quality. York have developed a technology that uses multiple sensors of the same type to solve the two key outstanding barriers to application in LMICs, that of sensor-to-sensor variability and unexpected sensor failure. The aim is to enable a self-supporting user community that can build and fix its own instruments and help improve on our initial designs. This approach differs fundamentally from the prevailing paradigm of a top-down commercial services model which has for many years failed to function in LMICs. The Stockholm Environment Institute centre (SEI) in the Department of Environment and Geography at the University of York has been working with the Ministries of Environment in Togo and Cote d'Ivoire and the Ghana Environment Protection Agency, and the University of Lomé, Togo and Université Félix Houphouët-Boigny in Cote D'Ivoire to develop national models using LEAP-IBC (developed by SEI), to support national low-emission planning. We will build on this work applying LEAP-IBC to Lomé, Abidjan, Accra, and another Ghanaian city (e.g. Kumasi) where no such tool is available, and there is limited or no regular monitoring. This will allow them to develop emission inventories of key air pollutants, baseline and mitigation emission projections, and to estimate the resulting concentrations of PM2.5 and the associated human health impacts. We will work with local academics and planners to support the development of the analysis, guiding them through the data collection, model design, model validation and extraction of results. Working with the University of Colorado and WACL, we will further develop the GEOS-Chem Adjoint model inputs to LEAP-IBC that converts emissions in LEAP-IBC to concentrations of PM2.5 and ozone in these cities. The inclusion of this modelling, developed by planners in Ghana, Cote d'Ivoire and Togo will also allow for an understanding of how the monitoring and modelling can be mutually beneficial to provide the evidence needed for the further development, implementation and monitoring of air quality plans in these cities and opportunities to achieve ambient air quality standards in cities.