Large volcanic eruptions can have a major impact on climate, due to the emission of sulfur gases, which form small droplets (aerosols) that reflect incoming sunlight and cool the Earth's surface. When these aerosols form in the upper levels of the atmosphere (the stratosphere, 15-50 km altitude) they remain there for several years, resulting in pronounced global cooling. Indeed, this phenomenon has inspired controversial proposals to cool the planet to combat global warming through artificial stratospheric sulfur injections. However, despite its scientific and societal significance, understanding of volcanic impacts on climate is highly uncertain, due to the limited observational record of large explosive volcanism: only two eruptions, Pinatubo in 1991 and El Chichón in 1982, have impacted global climate within the satellite era. These eruptions are at least an order of magnitude smaller than the largest eruptions in the historical record, and so are not representative of the scope of how volcanoes can impact our climate. This makes it challenging to understand, and prepare for, the climatic and societal impact of large eruptions in the future. The limited observational record of volcanic sulfur emissions also creates a major issue for climate models, which need to know how much sulfur to add to their computerised stratospheres in order to mimic historical climate change events. To address these challenges, we are proposing a new way to reconstruct the amount of stratospheric sulfate from large eruptions over the last 2000 years, based on the record of volcanic sulfate found in polar ice cores. Although this approach is widely used, at present there are major uncertainties in how to convert the amount of sulfate found in ice cores into the original amount of sulfate that was in the stratosphere. This project will substantially improve this conversion - known as the "transfer function" - by using new ice cores, new measurement techniques, and new modelling approaches. First, we will make detailed comparisons of the amount of sulfate in the ice to measurements of the amount of sulfur that went into the stratosphere for eruptions during the last 150 years, a time period in which direct observations of the atmosphere (either by satellites or instruments that measure sunlight) exist. Compared to the last time this calibration was done, the number of available ice cores has grown from 11 to 90, allowing for much better spatial coverage and more representative data. We also have a new technique that measures sulfur isotopes to allow us to distinguish the climatically-important stratospheric sulfate from other sources of sulfate to the ice sheets, further improving the accuracy of the calibration. A new computer modelling approach will also be used to make sure that the transfer function is applicable to a broad range of different eruption characteristics (such as the size, season, and latitude of the eruption), and to help us characterise the transfer function's uncertainty. The insights from the ice core calibration and the modelling will be combined to generate a new record of stratospheric sulfate from volcanic eruptions over the last 2000 years. This record will be used widely in climate model simulations, including those used to inform the International Panel on Climate Change (IPCC). Indeed this work may lead to improvements in climate modelling, as if the amount of sulfate to be added to the models for historical eruptions is better known, we should be able to make better assessments of which models most accurately match the associated changes in climate. Looking forward, our work will also be valuable for policy makers and insurance companies interested in natural hazards, as it will allow them to better understand the frequency and potential impacts of the major eruptions that will occur in our future.

Ice-core observations of methane sulfonic acid (MSA) are used as a proxy for past oceanic biogenic productivity because MSA originates solely from the oxidation of dimethyl sulfide (DMS) emitted by ocean phytoplankton. The use of MSA as a proxy for biogenic productivity relies on the assumption that the branching ratio of production of MSA versus sulfur dioxide (SO2) from DMS oxidation remains constant over time. However, recent Greenland ice-core observations of MSA and biogenic sulfate over the last 800 years show that the ratio of MSA-to-biogenic sulfate (MSA/bioSO4) has not remained constant. We hypothesize that recent trends in MSA are driven by changes in oxidant abundances (e.g., NOx) that lead to a reduced yield of MSA and increased yield of SO2 during oxidation of DMS. We propose to drill shallow ice cores at Summit, Greenland covering the last 30 years of snow accumulation and measure ion and MSA concentrations and sulfur isotopes of sulfate. The last 30 years will cover the time when anthropogenic NOx emissions from North America and Europe began to decline (after the mid-1990s). This will yield an additional 16 years of data compared to our current record extending from 1200 C.E. through 2006. We hypothesize that the MSA/bioSO4 ratio continues to increase from the mid-1990s to the present day due to decreases in NOx emissions in North America and Europe. To assist data interpretation, we will utilize a global chemical transport model GEOS-Chem in order to quantify the role of different oxidants on DMS oxidation as these oxidants have changed due to anthropogenic emissions. We will measure sulfate isotopes at sub-seasonal resolution over the last 30 years of snow accumulation from the proposed shallow ice cores in addition to select, discrete samples from archived ice in the preindustrial. We hypothesize that DMS emissions peak earlier in in the year today than in the preindustrial due to the earlier sea-ice melt resulting from Arctic warming. Measuring biogenic sulfate at seasonal resolution since the preindustrial will allow us to investigate changes in the seasonality of biogenic sulfur aerosol in the Arctic resulting from changes in Arctic climate. This is a collaborative project between PIs in the US and the UK, and the UK part of the project will be supported by NERC.

All living cells and many viruses are coated with specific sugars, allowing them to interact with partners bearing specific sugar binding proteins (lectins). While each lectin-sugar interaction is often weak and biologically inactive, by coating their surfaces with arrays of specific sugars, viruses can interact with multiple cell surface lectins to strengthen the interaction, allowing them to gain cell entry which ultimately leads to infection. Despite new anti-viral and vaccine treatments, disease caused by virus infection remains high. For example, ~37 and 150 million people are living with HIV and HCV infections in 2015, causing annual global deaths of ~1.1 and 0.5 million, respectively. Fortunately, virus mimics with specific sugar coatings can block such interactions, thereby preventing infection. The inhibition potency depends critically on matching the spacing and orientation of individual interactions between the binding partners. Hence understanding how a lectin's multiple sugar binding sites (CRDs) are arranged is vital to design effective virus inhibitors. However, the advances in research have been hampered by the inability of current methods to reveal key structural information (e.g. binding site orientation, spacing and flexibility) of important cell surface lectins. For example, despite 20 years of extensive research worldwide, the structure of two critically important lectins, DC-SIGN and DC-SIGNR, remain unknown. They both contain four CRDs and bind to multiple sugars on the HIV and Ebola surface to enhance virus infection. However, why they have different binding preferences to multiple sugars and virus remain poorly understood. We will address the capability gap of current methods by developing sugar coated tiny fluorescent particles called quantum dots (QDs) as virus mimics and study their interactions with DC-SIGN/R with single lectins in solution and multiple lectins on cell surface. We plan to achieve this goal by fully exploiting QD's unique properties: strong fluorescence for binding measurement; high contrast in electron microscopy for visualising binding induced particle arrangement to reveal binding site orientation; solid core for decorating with multiple sugars to enhance binding strength, and for adjusting sugar number and inter-sugar distance to probe lectin's CRD arrangement. We have assembled a team with extensive expertise in QD, sugar synthesis, electron microscopy and lectin biochemistry who will work together to address this significant challenge, each member contributing an essential expertise to this project. We will first prepare a series of sugar-coated QDs with varying number and structure of sugars, inter-sugar distance and flexibility. We will then measure their interactions by fluorescence with individual DC-SIGN/R molecules in solution to find out how strong and how fast the molecules interact, what binding preference is for each QD-sugar-lectin partner. We will measure the particle arrangement after binding to different lectins by electron microscopy, and monitor their size changes upon each interaction. We will combine these results to find out how DC-SIGN/R CRDs are arranged and oriented, and how far apart their binding sites are spaced. We will also study why DC-SIGN/R CRDs are arranged in this particular way, which parts of the protein control such arrangement. We will further test the ability of the sugar-coated QDs to block Ebola virus infection of target cells and find out the link between individual QD-sugar-DC-SIGN/R binding strength and its virus blocking efficiency. This study is extremely timely and important because it will develop a novel method to reveal key structural mechanisms of DC-SIGN/R-virus interactions, addressing an unmet technical challenge currently facing this important research area. It will also help to reveal the link between ligand binding strength and virus inhibition potency, and so guide the development new anti-viral strategies.

Fire is the most important disturbance agent worldwide in terms of area and variety of biomes affected, a major mechanism by which carbon is transferred from from the land to the atmosphere, and a globally significant source of aerosols and many trace gas species. Forecasting of fire risk is undertaken in many fire-prone environments to aid dry season pre-planning, and appropriate consideration of fire is also required within dynamic vegetation models that aim to examine vegetation-climate interactions in the past, present and future. Current methods of mapping fire 'risk', 'susceptabilty' or 'danger' use empirical fire danger indexes calibrated against past weather conditions and fire events. As such, they provide little information on process, are appropriate to deal only with current climate, land use and land cover change (LULCC), and are limited in their ability to be tested and constrained by EO products or other observational data (e.g. ignition 'hotspots', burned area, pyrogenic C release etc). The objective of FireMAFS is to resolve these limitations by developing a robust method to forecast fire activity (fire danger indices, ignition probabilities, burnt area, fire intensity etc) via a process-based model of fire-vegetation interactions, tested, improved, and constrained using state-of-the-art EO data products and driven by seasonal weather forecasts issued with many months lead-time. Specific aims are to: (i) develop the methodology for using EO and other observational data on vegetation (fuel) condition, fire activity and fire effects to test, improve and constrain sub-components and end-to-end predictions of a forward model of fire-vegetation interactions and to inform, test and restrict the model when used in forecast mode to ensure it is nudged along the optimum trajectory, and is furthermore reset when the observation period catches up with the prior period of prediction; (ii) drive the improved forward model by seasonal weather forecast ensembles, predicting spatio-temporal variability in fire 'danger' indices, fire occurrence and a range of subsequent fire behaviour and fire effects (intensity, rate of spread, burned area, above/below ground C stock change, and trace gas/aerosol emissions) and evaluate their usefulness for seasonal fire prediction at 1 / 6 months lead time and for prognostic studies run under future projected climate and LULCC scenarios.

Fire is the most important disturbance agent worldwide in terms of area and variety of biomes affected, a major mechanism by which carbon is transferred from from the land to the atmosphere, and a globally significant source of aerosols and many trace gas species. Forecasting of fire risk is undertaken in many fire-prone environments to aid dry season pre-planning, and appropriate consideration of fire is also required within dynamic vegetation models that aim to examine vegetation-climate interactions in the past, present and future. Current methods of mapping fire 'risk', 'susceptabilty' or 'danger' use empirical fire danger indexes calibrated against past weather conditions and fire events. As such, they provide little information on process, are appropriate to deal only with current climate, land use and land cover change (LULCC), and are limited in their ability to be tested and constrained by EO products or other observational data (e.g. ignition 'hotspots', burned area, pyrogenic C release etc). The objective of FireMAFS is to resolve these limitations by developing a robust method to forecast fire activity (fire danger indices, ignition probabilities, burnt area, fire intensity etc) via a process-based model of fire-vegetation interactions, tested, improved, and constrained using state-of-the-art EO data products and driven by seasonal weather forecasts issued with many months lead-time. Specific aims are to: (i) develop the methodology for using EO and other observational data on vegetation (fuel) condition, fire activity and fire effects to test, improve and constrain sub-components and end-to-end predictions of a forward model of fire-vegetation interactions and to inform, test and restrict the model when used in forecast mode to ensure it is nudged along the optimum trajectory, and is furthermore reset when the observation period catches up with the prior period of prediction; (ii) drive the improved forward model by seasonal weather forecast ensembles, predicting spatio-temporal variability in fire 'danger' indices, fire occurrence and a range of subsequent fire behaviour and fire effects (intensity, rate of spread, burned area, above/below ground C stock change, and trace gas/aerosol emissions) and evaluate their usefulness for seasonal fire prediction at 1 / 6 months lead time and for prognostic studies run under future projected climate and LULCC scenarios.