Globally, human-induced climate change and biodiversity loss threaten ecosystem function and the services the biosphere provides for humans. Forests are carbon-dense ecosystems and are home to the majority of terrestrial biodiversity, so are crucial tools to mitigate adverse impacts. Indeed, many countries, including many in Europe, have ambitious policies to restore and replant forests to restore carbon and habitats. However, forests are themselves threatened by climate change and biodiversity loss, so understanding and predicting their future in the face of global change is a priority. In order to understand how forests are changing, and how they will change in the future, we need large monitoring networks collecting data, to embrace new measurement techniques, to fuse data from multiple sources, and to create robust, data-driven, predictive models. Traditional forest data is severely limited in both its spatiotemporal coverage and what it can measure, and whilst existing ecological models are tailored to such data, these focus on the small scale and cannot predict the future of forests at large enough scales to help understand the impacts of climate change. New approaches are needed. This fellowship will use cutting-edge remote sensing data and modern data science techniques to generate new understanding of current and future forest functioning. Active and passive remote sensors, including terrestrial and drone laser scanning and structure from motion photogrammetry, are able to capture the full three-dimensional structure of a forest to sub-cm scale within three-dimensional point clouds. This fellowship will collect and collate such data from tens of thousands of trees across hundreds of forest plots in Europe, creating a massive new dataset of tree and forest structure. Such data are extremely complex to analyse, and the project will use specially developed and tailored deep learning techniques to extract ecological information from noisy point clouds. Some plots that have already been measured will be re-measured, to capture three dimensional tree growth and forest structural change. The fellowship will analyse these data to determine how trees and forests are structured across Europe, and how their three-dimensional structure affects and is affected by their productivity, carbon storage, and the diversity of both the trees and other species living in forests. New insights into how biodiversity is related to three-dimensional structure will bring help develop approaches to co-monitoring biodiversity and biomass, crucial for demonstrating the value of ecosystems towards tackling both climate change and biodiversity loss. Using newly developed software, the fellowship will scale local, single-measurement plot-scale information to continental scale and continuous monitoring by fusing ground and Earth Observation (satellite) data. Using deep learning to link the structural and diversity information from hundreds of thousands of plot locations across Europe with the spectral properties measured by satellite sensors, the fellowship will bring new understanding on how forests are structured and how they are changing across Europe. Finally, using findings from all parts of the fellowship, a new modelling framework which can predict ecological change on the ground at local scale but which can ingest satellite data will be developed. This data-driven approach will enable robust and updatable predictions of climate change impacts on forest diversity and dynamics across Europe. It will be constructed to be flexible to incorporate future data streams, so informing inform climate change mitigation policy across the continent.

The volcanic plume from the Eyjafjallajökull eruption has caused significant disruption to air transport across Europe. The regulatory response, ensuring aviation safety, depends on dispersion models. The accuracy of the dispersion predictions depend on the intensity of the eruption, on the model representation of the plume dynamics and the physical properties of the ash and gases in the plume. Better characterisation of these processes and properties will require improved understanding of the near-source plume region. This project will bring to bear observations and modelling in order to achieve more accurate and validated dispersion predictions. The investigation will seek to integrate the volcanological and atmospheric science methods in order to initiate a complete system model of the near-field atmospheric processes. This study will integrate new modelling and insights into the dynamics of the volcanic plume and its gravitational equilibration in the stratified atmosphere, effects of meteorological conditions, physical and chemical behaviour of ash particles and gases, physical and chemical in situ measurements, ground-based remote sensing and satellite remote sensing of the plume with very high resolution numerical computational modelling. When integrated with characterisations of the emissions themselves, the research will lead to enhanced predictive capability. The Eyjafjallajökull eruption has now paused. However, all three previous historical eruptions of Eyjafjallajökull were followed by eruptions of the much larger Katla volcano. At least two other volcanic systems in Iceland are 'primed' ready to erupt. This project will ensure that the science and organisational lessons learned from the April/May 2010 response to Eyjafjallajökull are translated fully into preparedness for a further eruption of any other volcano over the coming years. Overall, the project will (a) complete the analysis of atmospheric data from the April/May eruption, (b) prepare for future observations and forecasting and (c) make additional observations if there is another eruption during within the forthcoming few years.

Methane (CH4) is the second (after CO2) most important greenhouse gas. Sources of CH4 to the atmosphere, both natural and human-driven, have been intensively studied and are now well established; however, their global and regional estimates still suffer from large uncertainties. The region above the Arctic Circle is very important from this perspective because of a unique combination of CH4 emission sources which are active now, e.g. wetlands and forest fires, and those which may become active in the future owing to regional climate change. Potentially important future sources include thawing permafrost soils and CH4-rich oceanic sediments (clathrates). Since the Arctic has been warming much faster compared to the rest of the world, this may trigger various changes in the active CH4 sources as well as those that represent large pools of carbon (permafrost soil) or gaseous CH4 (clathrates). The goal of the proposed project is thus to locate and quantify major sources of Arctic CH4 emissions to the atmosphere and contribute to understanding how these emissions may change with further regional climate warming. At present, the number of Arctic CH4 measurements is simply not sufficient to either make reliable estimates of regional CH4 sources or to understand recent trends in atmospheric CH4 concentrations. In addition to scarce measurements, most Arctic CH4 studies have been supported by campaign-based observations of the local processes responsible for CH4 emissions, mostly in summer when the region is most accessible. But owing to the episodic, and in some instances seasonal, nature of most CH4 source emissions paired against sporadic campaign-based sampling, it has not been possible to produce reliable emission estimates of different Arctic CH4 sources. To address this problem, we propose to establish year-round continuous measurements of CH4 concentration and isotopic composition in ambient air, and to synchronise campaign-based studies with the expected seasonality and location of the CH4 source emissions. Since CH4 emitted from different sources has distinct isotopic 'signatures', it is possible to attribute the observed emissions to the particular sources. This approach requires a retrospective analysis of the air mass trajectories to establish the origin of air with the observed isotopic signature. To be more specific, we propose to establish continuous CH4 measurements at Teriberka, Russia (69.2N, 35.1E; NW Russian Arctic coast), which will provide new insight into central Eurasian Arctic processes. In addition, we plan to carry out detailed isotopic studies of ambient air from several locations in the European and Russian Arctic. These will be compared with records of Arctic air reaching the UK at measurement stations at Barra (Scotland) and Weybourne (Norfolk). Combining our datasets with those from the small number of other Arctic stations of our international colleagues, we will determine whether ongoing changes in the Arctic regional climate are resulting in increased CH4 emissions. Specifically, we will use these concentration and isotopic data with the p-TOMCAT chemical transport and Met Office NAME models to locate Arctic CH4 sources and quantify any interannual changes in emissions. In addition to these main objectives, we plan to make regular measurements of atmospheric concentrations of other gases (CO2, CO, N2O, SF6, H2, O2/N2 and Ar/N2) from glass bottles collected at several Arctic locations. Such measurements will not require additional collections or costs as they will be made in parallel to the CH4 measurements, improving cost efficiency. Measurement of other gas species will help to assess the linked Arctic processes and source emissions of these gases, both on land and at sea, e.g. fire emissions (increased CO), ocean warming, expansion of oceanic 'dead zone' (due to decreased amounts of dissolved O2) and thawing permafrost soils and wetlands.

The greenhouse gases carbon dioxide (CO2) and methane (CH4) are by far the biggest contributors to recent and ongoing climate change. Of all the known greenhouse gases (excluding water vapour), CO2 and CH4 have the highest concentrations in the atmosphere and they are rising rapidly. CO2 is particularly problematic because there is so much of it (about 200 times more than CH4) and because once emitted to the atmosphere, much of it will stay there for several hundred years. Whereas, by comparison, CH4 has a lifetime in the atmosphere of about a decade, but it is a much more potent greenhouse gas than CO2 - that is, for equal amounts of CO2 and CH4 in the atmosphere, CH4 will trap heat radiation about 70 times more effectively than CO2 (over a 20-year time period). With the ratification of the Paris Agreement, the world has committed to avoiding dangerous climate change and the most obvious way to do this is by reducing emissions of CO2 and CH4. How will we know if emission mitigation policies are effective? Which nations or regions are meeting their emissions reduction targets? How will natural CO2 and CH4 fluxes respond to extreme weather events? And which aspects of the carbon cycle remain unsolved? For example, despite decades of study, scientists are still not sure why CH4 emissions are currently rising. To answer these questions we need to be able to measure and quantify CO2 and CH4 emissions and concentrations, and have the ability to separately quantify natural and manmade sources. Our current abilities to do so are severely limited, especially for CH4, which has a diverse array of natural and manmade sources. If we cannot determine the effectiveness of mitigation policies, then our ability to predict climate change impacts will be compromised by large uncertainties. 'Polyisotopologues' are one very promising new tool for distinguishing between different source emissions. The chemical elements that make up CO2 and CH4 molecules (carbon (C), oxygen (O) and hydrogen (H)) can have different masses, called isotopes. Different sources can have different isotopic 'fingerprints' or 'signatures' (because source reaction processes may favour a lighter or heavier molecule), thus measuring isotopic signatures is a useful way to gain insight into sources. Isotopic measurements have been made routinely for several decades; whereas the state-of-the-art technology developed in this project would allow us to measure molecules with more than one rare isotope. For example, most C has a relative atomic mass of 12 and H a mass of 1. The rarer isotopes of C and H have masses of 13 and 2, respectively. Isotopologues of CH4, which are measured routinely, include 12CH4, 13CH4 and 12CH3D (where 'D' represents the heavy H atom with mass 2). Whereas polyisotopologues of CH4 include 13CH3D and 12CH2D2 - these are far more challenging to measure, yet could provide invaluable insight into source emissions and sinks. POLYGRAM (POLYisotopologues of GReenhouse gases: Analysis and Modelling) will push the frontiers for both CO2 and CH4 polyisotopologue measurement capability using the latest advances in laser spectroscopic analysis and very high-resolution isotope ratio mass spectrometry. In addition to these challenging technological developments, we will establish a small global atmospheric sampling network to examine latitudinal and longitudinal variations in polyisotopologues, which will help us to constrain overall global budgets of CO2 and CH4. We will carry out field campaigns to determine polyisotopologue source signatures, for example, of CH4 from wetlands, cattle and landfills, and of CO2 from plant photosynthesis and respiration, and from fossil fuel burning. We will conduct laboratory experiments to estimate the reaction rates for CH4 isotopologues when they are oxidised and destroyed in the atmosphere. Finally, we will carry out atmospheric transport modelling for both gases to better interpret and understand the measurements.

AEROS is a collaboration of the University of Leeds, Oxford University, the UK Met Office and EMEP to comprehensively assess the performance, quantify the uncertainties and develop strategies for improvements of the latest generation of global aerosol models. Aerosols have an important but very uncertain impact on climate (IPCC, 2007). The uncertainty derives primarily from inter-model differences, the necessary simplification of aerosol processes for computational cost reasons, and uncertainties in the observations used for model evaluation. Complex 'next generation' aerosol microphysics schemes have recently been developed for several climate models that are intended to enhance model realism and improve the reliability of predictions. The models resolve particle sizes and various chemical components, and use a full microphysics scheme including nucleation, coagulation, size-resolved deposition, cloud processing, etc. The development of such advanced aerosol models creates new and substantial challenges that this proposal aims to address. Firstly, the computational demands of complex aerosol models mean that techniques of uncertainty analysis have not been routinely used, so we have very little information to guide model improvement (uncertainty importance of model factors, relative importance of structural versus parameter uncertainty, etc). We will use sensitivity and uncertainty analysis techniques to identify the most important model improvements required. Secondly, because aerosol models already consume a large fraction of climate model run-time, it is vital to assess the level of model complexity objectively so as to prioritise and optimise future development. Previous model assessments have not answered the question of whether models are more or less complex than required or where development effort should be invested. An important aspect of this proposal is the quantification of model explanatory power versus complexity, which may be scale-dependent. The benefits of finding an appropriate level of complexity in an already expensive part of the model will be enormous: more and longer model runs, more climate sensitivity tests, etc. Thirdly, more complex models require evaluation against equally information-rich datasets. But most microphysical quantitites (such as particle number, size-resolved composition, etc) can only be measured with fairly localised in situ techniques from aircraft and from ground stations. The sparse measurements restrict many aspects of model evaluation to case studies rather than long-term average measurements used in previous evaluations such as AeroCom. So the present generation of aerosol models have been evaluated against a tiny fraction of available microphysics observations. In this project we aim to overcome this problem by exploiting observations from the EUCAARI and EMEP intensive campaigns conducted in May 2008. By synthesising intensive observations we will aim for consistency among predicted quantities and avoid the problem of compensating model factors that arises when single datasets are used. The AeroCom international aerosol intercomparison project has been very successful in documenting the state-of-the-art of the simulated aerosol. It has assembled observations and results from the majority of global aerosol models to assess our understanding of global aerosol effects. However, the difficulty of establishing comparable diagnostics across a wide range of models has made it difficult to attribute differences in the results to specific processes. Our approach will assess the models at the processes level and evaluate their performance against microphysics observations for the first time. The overall outcome of this proposal will be improvement in predictions of aerosol properties, variability and spatial distribution that are fundamental requirements for accurate prediction of aerosol climate and air quality effects.