Understanding the exchange of energy and gases between the earth's surface and the lower atmosphere is essential for answering many questions related to, e.g., the global carbon budget, ecosystem functioning, air pollution mitigation, greenhouse gas emissions, weather forecasting, and projections of climate change. However, uncertainties in carbon dioxide (CO2) and water vapour (H2O) budgets limit our ability to reproduce and project these exchange processes. Exchange processes are usually analysed based on micrometeorological measurements from tall flux towers, thought to be representative of large area averages. A limitation of this approach is that the actual source areas of these fluxes are not always known and that the impact of land-surface heterogeneity (at small or large scale) on the fluxes is not yet completely understood. The micrometeorological measurements of the major carbon flux networks around the world, such as Ameriflux, Canadian Carbon Program, CarboEurope (in which the UK plays a prominent role) and Oz-Net, are essential to validate global estimates of CO2 sources and sinks, to develop and validate land surface models and to understand the sensitivity of CO2 fluxes under changing climate conditions. Unfortunately, flux tower measurements currently suffer from substantial uncertainty, which is primarily due to the indeterminate relationship of fluxes and their source areas; at present our current understanding can explain 60-80% of the variance of the fluxes. The overall goal of this project is to incorporate information on topography and structure of vegetation (tree height, canopy depth, and foliage density) in footprint estimates and thereby substantially reducing the potential errors in the calculation of the CO2 and H2O budgets. The selected forested sites consist of the very few long-term flux stations within the boreal forest biomes and represent the three dominant species of the boreal forest (jack pine, black spruce, aspen). The combination of these three forest stands will provide data that is sufficiently representative to allow for upscaling to the boreal forest biome scale. The boreal forest constitutes the world's second largest forested biome (after the tropical forest) and plays an important role in regulating the climate of the northern hemisphere and in the global carbon cycle. The footprint model developed by the PI and widely used by the international community will be applied on long-term data sets to estimate the size and location of the area containing the sources or sinks (footprint) of CO2 and H2O fluxes measured at the three sites. The footprints will account for, and depend on, atmospheric conditions, such as wind speed and boundary layer stability, and surface characteristics, e.g. roughness. This footprint model is one of very few models that are valid over a huge range of stratifications and receptor heights. The major improvement of the footprint model will incorporate three-dimensional information on the structure of the forest, derived from airborne scanning LiDAR measurements, leading to exceptionally detailed high temporal resolution source information. Unlike data from passive sensors, the unique LiDAR data set provides information from within the tree canopy. The results will be used to analyse impacts of structure of vegetation and small changes in elevation on the net CO2 and H2O fluxes. The new understanding will assist future studies of upscaling from flux towers to the spatially heterogeneous boreal forest landscape and will reduce the uncertainty in the modelling of carbon budgets at local, regional and continental scale. It will lead to a greater understanding of local structural effects on carbon sources and sinks and thus the dynamics of carbon cycling and to major improvements of the description of these exchange processes in land surface models. Hence, the new insights will help reducing uncertainty in projections of climate change.

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.