The Eurasian Boreal region is warming 3 times faster than the global mean making it a priority region for identifying and understanding land-atmosphere-climate interactions and feedbacks. Much of the warming at these high latitudes is driven by short-lived climate pollutants (SLCPs) such as black carbon and ozone. There is a severe paucity of observations of SLCPs over the Eurasian Boreal region presenting a major challenge to improving our understanding. This proposal aims build capability for improving understanding of SLCP budgets and climate feedbacks in the Eurasian high latitudes. This will be achieved through the establishment of a network of European and Russian scientists, and exploitation of existing observations and global model simulations to motivate future large-scale field observation experiments in the Eurasian boreal region. We will leverage existing NERC investment in our ongoing research into European pollution export and evaluation of atmospheric composition in global models to provide scientific impetus and direction for new research priorities. We will build new, and strengthen existing collaborations between a network of researchers across Europe and Russia. By synthesising existing observations and multi-model simulations, we will motivate the requirements for future observations and prioritise processes that require improved understanding. Within our network we will hold a number of workshops to discuss a) existing data synthesis, b) model-observation comparisons, c) future data needs in the Eurasian boreal region, and d) plans for future aircraft field campaigns. In addition, we will make a scoping visit to the Central Aerological Observatory near Moscow, Russia, in order to evaluate the feasibility of different strategies for a joint aircraft campaign, considering issues such as logistics for joint operations and communication, geo-political constraints on aircraft operations, and experimental strategies for joint flying. The overall end-result of this project will be a coherent community of international scientists with a robust knowledge base of deficiencies in our understanding of short-lived climate pollutants, priorities for observations, and a strategy for developing future large-scale observation networks and large-scale field projects in the Eurasian boreal regions.

Forecasts of climate rely on model projections, but derivation of sophisticated climate models from first principles is not currently feasible. Therefore, evaluating climate models with observations is essential. The development and improvement of global climate models is currently only based on comparison with and tuning to historical observations of climate (the instrumental record). Model simulations of the present climate are well-tuned and are in general agreement with each other. However, there is no clear relationship between model performance for present day and model behaviour for projections. Models show a range of sensitivities when predicting the future climate response to the emission of greenhouse gases. This indicates that the evaluation of models using observations of historical climate is insufficient. It is very difficult to reduce uncertainties on projections based on the instrumental period only and the use data from earlier periods is critical. A wide variety of different climate states are recorded in the geological record (spanning greenhouse to icehouse scenarios). The modelling of past climates, in combination with data from the geological record, provides a unique laboratory to evaluate the ability of models to forecast global change. While data is available from numerous intervals in Earth history, analysis is often constrained by the availability of material of the correct age and data collection is often very time consuming and expensive (e.g. for marine sediment cores). For this reasons, it is important that data on past climate and environments is utilised optimally and that challenges resulting from sparsity of the data as well as from temporal and spatial uncertainties are addressed in the best way possible. The earth system modelling and proxy reconstruction communities often have little contact with professional statisticians. Even in publications, ad-hoc methods are used instead of established statistical "best practice". If inappropriate statistical methods are used, inference about models and the earth system will be weakly supportable or plainly wrong. To avoid these problems and to realise the opportunity of improved earth system forecasting, sound statistical methods as advised by statisticians must be used. On the other hand, use of appropriate statistical methodology is often made difficult due to sparsity of data or lack of resources, and statisticians are not always aware of the resulting restrictions on the applicability of methods. Statisticians need to develop awareness of the restrictions and requirements caused by the sparsity of palaeoclimate data and the high complexity or climate models. The Past Earth Network will develop a shared, multi-disciplinary vision for addressing the challenges encompassed by the following four network themes. (1) Quantification of error and uncertainty of data: The uncertainties inherent in different forms of climate data must be well-understood. This is particularly challenging for palaeoclimate data, since uncertainties are often large and varied. (2) Quantification of uncertainty in complex models: The uncertainties in the output of the (complex and high-dimensional) models in use must be well-understood. (3) Methodologies which enable robust model-data comparison: Appropriate methods for model-data comparison must be used, taking into account the nature and sparsity of data. (4) Forecasting and future climate projections: This theme synthesizes the results from the first three themes in order to assess and ultimately improve the ability of climate models to forecast climate change. By addressing these four challenges, results produced by the Past Earth Network will help to better understand and reduce the uncertainties in climate forecasts and ultimately will contribute to the development of better climate forecasts.

The proposed project will test the hypothesis that gradual changes in Atlantic Meridional Overturning Circulation (AMOC) -a system of surface and deep ocean currents that exerts a primary control on Earth's climate, led to abrupt shifts in North Atlantic climate during the transition out of the last ice age and into the present warm interglacial (~20,000-10,000 years ago). Greenlandic ice-core records show clear evidence that this period was characterised by major abrupt climate shifts in less than a decade, which have been attributed to changes in the AMOC regime associated with reduced northward surface heat transport in the high-latitude North Atlantic and its deep southward return flow. Critically, the anomalous weakening of the AMOC in the last decades caused by enhanced fluxes of meltwater and ice export from the Arctic in response to Arctic change prompts the question: Is the current decline in AMOC heralding a new phase of abrupt change similar to those recorded in ice cores and ocean sediments, and what is the response time of North Atlantic climate to changes in high-latitude surface and deep ocean circulation? Resolving and quantifying asynchronous changes within the coupled ocean-atmosphere system is hence essential to improve our theoretical understanding of climate processes and predictive capacity of climate models, as well as identifying under which conditions abrupt climate change occurs. ASYNC is an international collaborative project led by the University of Cambridge that will tackle this fundamental problem. The project will avail of unique North Atlantic Ocean sediment records to generate a suite of precisely dated and multidecadally-resolved proxy records of ocean circulation and climate change. ASYNC represents the first targeted effort to compare high resolution North Atlantic proxy records by precisely integrating the underlying timescales in a continuous fashion. The marine records will be synchronised to the Greenland ice-core chronology via independent and continuous reconstructions of globally synchronous variations in the incoming cosmic ray flux using multidecadally-resolved cosmogenic 10Be records from seafloor sediments and published ice cores. The proposed project will result in new cosmogenic 10Be, sea ice, meltwater discharge, and bottom- and surface-water ventilation reconstructions from three North Atlantic marine sediment cores. The palaeoceanographic reconstructions, and in particular the bottom-water ventilation records, which reflect the southward deep component of AMOC, will be directly compared to events recorded in ice-core climate reconstructions from Greenland. Together, ASYNC will result in the first network of continuously synchronised records of atmospheric, oceanic and sea ice change that will resolve the temporal and spatial propagation of North Atlantic ocean perturbations on the climate system across the major climatic transitions that punctuated the last deglaciation (~20,000-10,000 years ago). Results from ASYNC will advance the current understanding of i) the nature and timing of abrupt climate shifts across climate archives, ii) nonlinear responses of AMOC and climate to gradual Greenland Ice Sheet and Arctic sea ice meltwater forcing, and iii) ocean precursors of rapid climate change in the North Atlantic region.

The role of external drivers of climate change in mid-latitude weather events, particularly that of human influence on climate, arouses intense scientific, policy and public interest. In February 2014, the UK Prime Minister stated he "suspected a link" between the flooding at the time and anthropogenic climate change, but the scientific community was, and remains, frustratingly unable to provide a more quantitative assessment. Quantifying the role of climate change in extreme weather events has financial significance as well: at present, impact-relevant climate change will be primarily felt through changes in extreme events. While slow-onset processes can exacerbate (or ameliorate) the impact of individual weather events, any change in the probability of occurrence of these events themselves could overwhelm this effect. While this is known to be a problem, very little is known about the magnitude of such changes in occurrence probabilities, an important knowledge gap this project aims to address. The 2015 Paris Agreement of the UNFCCC has given renewed urgency to understanding relatively subtle changes in extreme weather through its call for research into the impacts of a 1.5oC versus 2oC increase in global temperatures, to contribute to an IPCC Special Report in 2018. Few, if any, mid-latitude weather events can be unambiguously attributed to external climate drivers in the sense that these events would not have happened at all without those drivers. Hence any comprehensive assessment of the cost of anthropogenic climate change and different levels of warming in the future must quantify the impact of changing risks of extreme weather, including subtle changes in the risks of relatively 'ordinary' events. The potential, and significance, of human influence on climate affecting the occupancy of the dynamical regimes that give rise to extreme weather in mid-latitudes has long been noted, but only recently have the first tentative reports of an attributable change in regime occupancy begun to emerge. A recent example is the 2014 floods in the Southern UK, which are thought to have occurred not because of individually heavy downpours, but because of a more persistent jet. Quantifying such changes presents a challenge because high atmospheric resolution is required for realistic simulation of the processes that give rise to weather regimes, while large ensembles are required to quantify subtle but potentially important changes in regime occupancy statistics and event frequency. Under this project we propose, for the first time, to apply a well-established large-ensemble methodology that allows explicit simulation of changing event probabilities to a global seasonal-forecast-resolution model. We aim to answer the following question: over Europe, does the dynamical response to human influence on climate, manifest through changing occupancy of circulation regimes and event frequency, exacerbate or counteract the thermodynamic response, which is primarily manifest through increased available moisture and energy in individual events? Our focus is on comparing present-day conditions with the counterfactual "world that might have been" without human influence on climate, and comparing 1.5 degree and 2 degree future scenarios. While higher forcing provides higher signal-to-noise, interpretation is complicated by changing drivers and the potential for a non-linear response. We compensate for a lower signal with unprecedentedly large ensembles. Event attribution has been recognised by the WCRP as a key component of any comprehensive package of climate services. NERC science has been instrumental in its development so far: this project will provide a long-overdue integration of attribution research into the broader agenda of understanding the dynamics of mid-latitude weather.

The UK is committed to quantifying and managing its emissions of greenhouse gases (GHG, i.e. CO2, CH4, N2O) to reduce the threat of dangerous climate change. Sinks and sources of GHGs vary in space and time across the UK because of the landscape's mosaic of managed and semi-natural ecosystems, and the varying temporal sensitivities of each GHG's emissions to meteorology and management. Understanding spatio-temporal patterns of biogenic GHG emissions will lead to improvements in flux estimates, allow creation of inventories with greater sensitivity to management and climate, and advance the modelling of feedbacks between climate, land use and GHG emissions. Addressing Deliverable C of the NERC Greenhouse Gas Emissions and Feedbacks Research Programme, we will use extensive existing UK field data on GHG emissions, supplemented with targeted new measurements at a range of scales, to build accurate GHG inventories and improve the capabilities of two land surface models (LSMs) to estimate GHG emissions. Our measurements will underpin state-of-the-art temporal and spatial upscaling frameworks. The temporal framework will evaluate diurnal, seasonal and inter-annual variation in emissions of CO2, CH4 and N2O over dominant UK land-covers, resolving management interventions such as ploughing, fertilizing and harvesting, and the effects of weather and climate variability. The spatial framework will evaluate landscape heterogeneity at patch (m), field (ha) and landscape (km2) scales, in two campaigns combining chambers, tower and airborne flux measurements in arable croplands of eastern England, and grazing and forest landscapes of northern Britain. For modelling, we will update two LSMs - JULES and CTESSEL- so that each generates estimates of CO2, CH4 and N2O fluxes from managed landscapes. The models will be updated to include the capabilities to represent changes in land use over time, to represent changes in land management over time (crop sowing, fertilizing, harvesting, ploughing etc), and the capacity to simulate forest rotations. With these changes in place, we will determine parameterisations for dominant UK land-covers and management interventions, using our spatio-temporal data. The work is organized in five science work-packages (WP). WP1: Data assembly and preliminary analysis. We will create a database of GHG flux data and ancillary data for major UK landcovers/landuses in order to calibrate and evaluate the LSMs' capabilities, and generate spatial databases of environmental and management drivers for the models. WP2. GHG measurement at multiple scales. We will deploy advanced technology to generate new information on spatial GHG processes from simultaneous measurement from chamber (<1 m) to landscape (40 km) length scales, and on temporal flux variation from minutes to years. WP3. Earth observation (EO) to support upscaling. EO data will provide: i) driving data for LSM upscaling, from flux tower to aircraft campaign scales; and ii) spatial data for testing LSM outputs at these larger scales. WP4 Upscaling GHG processes. Firstly, the two LSMs will be updated to allow the impacts of management activities on GHG emissions to be simulated, with calibration against an array of temporal flux data. Then, we will use the LSMs to model the fluxes of GHGs at larger spatial scales, based on a rigorous understanding of how the nonlinearity of responses and the non-Gaussian distribution of environmental input variables interact, for each GHG, using all available field data at finer scales. WP5 Application at the regional scale. The LSMs will upscale GHG emissions for both campaign regions (E. England, N. Britain) using 1-km2 resolution simulations with a focus on the airborne campaign periods of 4 weeks. We will determine how regional upscaling error can be reduced with intensive spatial soil and land management data.