The burning of biomass (e.g., shrubs, grasslands, trees) has an ongoing role in determining the composition of Earth's surface and atmosphere, and in some regions subsequent emissions of trace gases to the atmosphere rival those from fossil fuel burning. For nearly 40 years the scientific community has studied rates of emissions of trace gases from different types of biomass and associated amospheric gaseous concentrations but our knowledge remains incomplete, reflecting the heterogeneous and stochastic nature of this Earth System process. The advent of space-borne observations of land-surface and tropospheric chemistry provided the first glimpse of the large-scale nature and impact of burning in the global troposphere. These data remain key to scaling-up detailed point- or regional-scale measurements related to burning emissions or associated atmospheric concentrations. However, Earth Observation (EO) data products are difficult to interpret without the aid of computer models of atmospheric chemistry and transport and in situ measurements. In this proposal we have assembled an integrative programme of measurements and modelling of biomass burning that encompasses ground-based and aircraft in situ data, space-borne observations of tropospheric trace gases and particles, and a hierarchy of computer models of atmospheric chemistry (detailed point models to state-of-the-art global 3-D models). Here, we focus on biomass burning over northern boreal regions, with the aircraft missions sampling outflow from North America. Our research focus is to better understand atmospheric chemistry within air masses originating from regions of biomass burning. In particular, we follow up and expand upon surprising results from a recent NERC-funded aircraft campaign (Intercontinental Transport of Ozone and Precursors, ITOP) over the North Atlantic that measured and characterised outflow from the North American boundary layer as it travelled over the North Atlantic towards Europe. During ITOP the aircraft unintentionally sampled outflow from biomass burning and found that models analysing those data were unable to reproduce the large concentrations of organic molecules and the speciation of nitrogen species. As part of this proposal we plan to fly over the North Atlantic specifically to sample outflow from North American biomass burning equipped with a more suitable suite of aircraft instruments that will help to understand and resolve this unexplained discovery in atmospheric chemistry. The resulting data will be analysed by the gold standard Master Chemical Mechanism, an explicit model description of the degradation of relevant atmospheric compounds. One of the biggest challenges that atmospheric scientists typically face is the scaling-up from detailed in situ measurements to regional and larger spatial scales. Here, we address this challenge by using global 3-D models of atmospheric chemistry and transport and data from space-borne sensors by using the model as an intermediary between the aircraft data and the relatively coarse satellite data. By statistically 'tuning' the model using the detailed aircraft data (data assimilation) we can better estimate the magnitude and 3-D distribution of outflow from North American biomass burning and its resulting effects on atmospheric composition over the northern hemisphere. The proposal will provide us with a better fundamental understanding of the evolving atmospheric chemistry within biomass burning, an improved understanding of how to combine data from in situ and space-borne sensors to relate detailed small-scale data to larger spatial scales, and a better quantitative understanding of the impact of boreal forest fires on the atmospheric composition of the northern hemisphere.

Environmental change is happening on a global scale. Freshwater ecosystems represent some of the most endangered habitats in the world, with declines in diversity (83% in the period 1970-2014) far exceeding that of terrestrial counterparts. One of the primary causes of reduced riverine ecosystem health is a loss of habitat associated with excessive fine sediment deposition (typically referred to as particles <2mm). Fine sediment is a natural part of river systems, however alterations to land use (e.g. intensive farming) and channelization / impoundment (via dams and reservoirs) have altered the quantity of fine sediment such that inputs now far exceed historic levels. Additionally, increasing hydrological extremes associated with climatic change, such as intense rainfall events, are likely to further increase the delivery of fine sediment to river channels. Fine sediment deposition alters and degrades instream habitats making rivers unsuitable for flora and fauna to live in. Such changes lead to reductions in the biodiversity of riverine ecosystems and affects all components of the food web from fish and insects through to algae. Understanding the ecological implications of fine sediment is therefore imperative to be able to manage our rivers so that they can support and sustain healthy ecosystem functioning and support anthropogenic activities (e.g., fisheries, recreational activities). This is however challenging because a number of environmental factors control the consequences of fine sediment for flora and fauna. The proposed Fellowship aims to understand and quantify which environmental factors (e.g. land use, size of fine sediment and of the gravels within the river, time of year) influence the severity of fine sediment deposition for river communities. Specific objectives are to (i) quantify the trends between fine sediment loading and ecological responses in the UK and internationally; (ii) determine if there is a threshold of fine sediment loading before ecological degradation occurs and how this varies within individual rivers, (iii) develop understanding of how environmental controls (e.g. grain size, hydrological exchange) structure the effects of fine sediment and; (iv) outline a future research agenda to tackle the management of fine sediment in rivers. In achieving these objectives, my Fellowship will provide a framework to determine when and which river types (e.g. highland or lowland, geology) are most at threat from fine sediment pressures internationally. The Fellowship will focus on macroinvertebrates (river invertebrates such as snails, insects and crustaceans) as a target organisms being abundant, diverse and occurring across the globe. The Fellowship represents a novel and exciting research programme with international reach and applicability that combines global datasets with multi-country field and artificial stream channel experiments (alpine and lowland) and laboratory experiments over different spatial scales to develop and validate theories spanning different environmental settings. The fellowship will lead to an exciting step-change in our understanding and will address unique fundamental research questions whilst working synergistically with UK statutory regulatory agencies and end-users such as the Environment Agency of England, Natural Resources Wales and Scottish Environmental Protection Agency. The research generated will have important ramifications for how stakeholders allocate resources to monitor and manage UK riverine ecosystems and will enable more efficient and targeted conservation and restoration plans.

PAGODA will focus on the global dimensions of changes in the water cycle in the atmosphere, land, and oceans. The overarching aim is to increase confidence in projections of the changing water cycle on global-to-regional scales through a process-based detection, attribution and prediction. The scientific scope prioritises themes 2,1,3,4 in the AO, adopting a focus on climate processes to extend our understanding of the causes of water source/sink uncertainty at the regional scale, which is where GCMs show huge variations concerning projected changes in precipitation, evaporation, and other water related variables. This model uncertainty is closely linked to shifts in large-scale circulation patterns and surface feedback processes, which differ between models. Furthermore, even where models agree with each other (for example, the suggested trend towards wetter winters and drier summers in Europe, connected to storm tracks and land surface processes), consistency with the real world cannot be taken for granted. The importance of quantitative comparisons between models and observations cannot be overstated: there is opportunity and urgent need for research to understand the processes that are driving changes in the water cycle, on spatial scales that range from global to microscopic, and to establish whether apparent discrepancies are attributable to observational uncertainties, to errors in the specification of forcings, or to model limitations. PAGODA will achieve its scientific objectives by confronting models with observations and reconciling observations, which possess inherent uncertainty and heterogeneity, with robust chains of physical mechanisms - employing model analysis and experiments in an integral way. Detection and attribution is applied throughout, in an iterative fashion, to merge the understanding from observations and models consistently, in order to isolate processes and identify causality. PAGODA is designed to focus specifically on the processes that govern global-to-regional scale changes in the water cycle, particularly on decadal timescales (the timescale of anthropogenic climate change). It addresses processes in the atmosphere, land and oceans, and brings together experts in climate observations, climate models, and detection and attribution. It seeks to exploit important new opportunities for research progress, including new observational data sets (e.g. ocean salinity reanalysis, TRMM and SSMIS satellite products, long precipitation records), new models (HadGEM3 & new capabilities for high resolution simulations), and the new CMIP5 model inter-comparison and to develop new methodologies for process-based detection, attribution and prediction.

The oldest, thickest sea ice in the 'last ice area' of the Arctic - a region thought to be most resilient to climate warming - unexpectedly broke up twice in the past year. Our current theories assume that the end-of-summer ice-covered area will steadily retreat into the Central Arctic Basin as global warming accelerates over coming decades. However, the dynamic break-up events witnessed in 2018 challenge this prevailing view. Here we hypothesise that a weaker, increasingly mobile Central Arctic ice pack is now susceptible to dynamic episodes of fragmentation which can precondition the ice for rapid summer melt. This mechanism of dynamic seasonal preconditioning is unaccounted for in global climate models, so our best current projections are overlooking the possibility for rapid disintegration of the Arctic's last ice area. Our team has demonstrated that seasonal preconditioning is already responsible for the neighbouring Beaufort Sea becoming ice-free twice in the past five years. Even ten years ago this region contained thick perennial sea ice, mirroring the Central Arctic Ocean, but it has now transitioned to a marginal Arctic sea. Could the processes responsible for the decline of the Beaufort Sea ice pack start to manifest themselves in the Central Arctic? Currently, a shortfall in satellite observations of the Arctic pack ice in summer prevents us from testing our hypothesis. We desperately require pan-Arctic observations of ice melting rates, but so far satellite observations of sea ice thickness are only available during winter months. Our project will therefore deliver the first measurements of Arctic sea ice thickness during summer months, from twin satellites: ESA's Cryosat-2 & NASA's ICESat-2. We have designed a new classification algorithm for separating ice and ocean radar altimeter echoes, regardless of surface melting state, providing the breakthrough required to fill the existing summer observation 'gap'. Exploiting the recent launch of multiple SAR missions for polar reconnaissance, our project will integrate information on ice-pack ablation, motion and deformation to generate a unique year-round sea ice volume budget in the High Arctic. This record will inform high-resolution ice dynamics simulations, performed with a suite of state-of-the-art sea ice models from stand alone (CICE), ocean-sea ice (NEMO/CICE), to fully coupled regional high resolution (RASM), and global coarser resolution (HadGEM) models, all now equipped with the anisotropic (EAP) sea ice rheology developed by our team. Using the regional and stand-alone models we will analyse the role of mechanics in this keystone region north of Greenland to scrutinise the coupling and preconditioning of winter breakup events - such as those witnessed in 2018 - to summer melting rates. Using the coupled models, we will quantify the likelihood of the Arctic's last ice area breaking up much sooner than expected due to oceanic and atmospheric feedbacks and how this will affect the flushing of ice and freshwater into the North Atlantic.

Until recently, awareness of the importance of winter carbon dioxide emissions from arctic soils was highly limited, resulting from incorrect assumptions that emissions from frozen soils beneath snow were insignificant compared to other sources. Consequently, carbon dioxide emissions during arctic winter months are frequently omitted from global carbon cycling budgets and our capacity to measure atmosphere-snow-soil processes controlling carbon dioxide emission and simulate them in climate models are under-developed. This limits our ability to make future climate projections, especially in arctic tundra and forested regions, which characterise about 27% of the Earth's land surface and are warming more than twice as fast as the global average since the late twentieth century. Carbon dioxide, a gas which causes the Earth's atmosphere to trap heat causing the planet to warm, is emitted by microbes decomposing organic material in soil. Decomposition can occur when the soil is frozen, but rates of carbon dioxide emission decrease as soil temperatures decrease, down to -20 degrees Celsius when carbon dioxide emissions become negligible. Winter snow cover has an important impact on arctic soil temperatures, acting like a duvet covering a bed. A thick duvet with lots of air trapped between the feathers provides insulation. Air trapped between the snow crystals within a snowpack acts in a similar manner, limiting the loss of heat from soils warmed in the summer to the cold atmosphere during long arctic winters. As the ground is often snow covered for at least half of the year in Arctic regions, it is vital that we understand processes that control the impact of snow cover on soil temperatures and carbon dioxide emissions, and accurately represent these processes in climate models. Here we ask, how sensitive are measured carbon dioxide concentrations within arctic snowpacks to the variability of snowpack physical properties (e.g. size of the snow crystals)? Can more realistic simulations of snowpack density and thermal conductivity in climate models reduce the underprediction in carbon dioxide emissions from arctic snowpacks? And, how may future changes in winter soil temperatures and snow cover affect future carbon dioxide emissions? In order to answer these questions, we will create a new field measurement database of arctic meteorology, soil and snow properties, and carbon dioxide concentrations. We will use this database to develop more realistic representations of processes controlling winter carbon dioxide emissions in climate models, which will lead to confident model projections of future winter carbon dioxide emissions from the wider Arctic region. By combining field and laboratory measurements with climate modelling, this partnership between Canadian, Finnish and UK scientists will increase our predictive understanding of Arctic environmental change resulting from, and contributing to, our warming planet.