Interpreting CO2 Fluxes Over a Suburban Lawn: The Influence of Traffic Emissions

Turf-grass lawns are ubiquitous in the United States. However direct measurements of land-atmosphere fluxes using the eddy-covariance method above lawn ecosystems are challenging due to the typically small dimensions of lawns and the heterogeneity of land use in an urbanised landscape. Given their typically small patch sizes, there is the potential that CO2 fluxes measured above turf-grass lawns may be influenced by nearby CO2 sources such as passing traffic. In this study, we report on twoyears of eddy-covariance flux measurements above a 1.5ha turf-grass lawn in which we assess the contribution of nearby traffic emissions to the measured CO2 flux. We use winter data when the vegetation was dormant to develop an empirical estimate of the traffic effect on the measured CO2 fluxes, based on a parametrised version of a three-dimensional Lagrangian footprint model and continuous traffic count data. The CO2 budget of the ecosystem was adjusted by 135gCm−2 in 2007 and by 134gCm−2 in 2008 to determine the natural flux, even though the road crossed the footprint only at its far edge. We show that bottom-up flux estimates based on CO2 emission factors of the passing vehicles, combined with the crosswind-integrated footprint at the distance of the road, agreed very well with the empirical estimate of the traffic contribution that we derived from the eddy-covariance measurements. The approach we developed may be useful for other sites where investigators plan to make eddy-covariance measurements on small patches within heterogeneous landscapes where there are significant contrasts in flux rates. However, we caution that the modelling approach is empirical and will need to be adapted individually to each sit

Comparison of conventional Lagrangian stochastic footprint models against LES driven footprint estimates

In this study we introduce a comparison method for footprint model results by evaluating the performance of conventional Lagrangian stochastic (LS) footprint models that use parameterised flow field characteristics with results of a Lagrangian trajectory model embedded in a large eddy simulation (LES) framework. The two conventional models follow the particles backward and forward in time while the trajectories in LES only evolve forward in time. We assess their performance in two unstably stratified boundary layers at observation levels covering the whole depth of the atmospheric boundary layer. We present a concept for footprint model comparison that can be applied for 2-D footprints and demonstrate that comparison of only cross wind integrated footprints is not sufficient for purposes facilitating two dimensional footprint information. Because the flow field description among the three models is most realistic in LES we use those results as the reference in the comparison. We found that the agreement of the two conventional models against the LES is generally better for intermediate measurement heights and for the more unstable case, whereas the two conventional flux footprint models agree best under less unstable conditions. The model comparison in 2-D was found quite sensitive to the grid resolution

Spatial representativeness and uncertainty of eddy covariance carbon flux measurements for upscaling net ecosystem productivity to the grid scale

Eddy covariance (EC) measurements are often used to validate net ecosystem productivity (NEP) estimated from satellite remote sensing data and biogeochemical models. However, EC measurements represent an integrated flux over their footprint area, which usually differs from respective model grids or remote sensing pixels. Quantifying the uncertainties of scale mismatch associated with gridded flux estimates by upscaling single EC tower NEP measurements to the grid scale is an important but not yet fully investigated issue due to limited data availability as well as knowledge of flux variability at the grid scale. The Heihe Watershed Allied Telemetry Experimental Research (HiWATER) Multi-Scale Observation Experiment on Evapotranspiration (MUSOEXE) built a flux observation matrix that includes 17 EC towers within a 5 km × 5 km area in a heterogeneous agricultural landscape in northwestern China, providing an unprecedented opportunity to evaluate the uncertainty of upscaling due to spatial representative differences at the grid scale. Based on the HiWATER-MUSOEXE data, this study evaluated the spatial representativeness and uncertainty of EC CO2 flux measurements for upscaling to the grid scale using a scheme that combines a footprint model and a model-data fusion method. The results revealed the large spatial variability of gross primary productivity (GPP), ecosystem respiration (Re), and NEP within the study site during the growing season from 10 June to 14 September 2012. The variability of fluxes led to high variability in the representativeness of single EC towers for grid-scale NEP. The systematic underestimations of a single EC tower may reach 92(±11)%, 30(±11)%, and 165(±150)% and the overestimations may reach 25(±14)%, 20(±13)%, and 40(±33)% for GPP, Re, and NEP, respectively. This finding suggests that remotely sensed NEP at the global scale (e.g., MODIS products) should not be validated against single EC tower data in the case of heterogeneous surfaces. Any systematic bias should be addressed before upscaling EC data to grid scale. Otherwise, most of the systematic bias may be propagated to grid scale due to the scale dependence of model parameters. A systematic bias greater than 20% of the EC measurements can be corrected effectively using four indicators proposed in this study. These results will contribute to the understanding of spatial representativeness of EC towers within a heterogeneous landscape, to upscaling carbon fluxes from the footprint to the grid scale, to the selection of the location of EC towers, and to the reduction in the bias of NEP products by using an improved parameterization scheme of remote-sensing driven models, such as VPRM

The positive net radiative greenhouse gas forcing of increasing methane emissions from a thawing boreal forest-wetland landscape

At the southern margin of permafrost in North America, climate change causes widespread permafrost thaw. In boreal lowlands, thawing forested permafrost peat plateaus (‘forest’) lead to expansion of permafrost‐free wetlands (‘wetland’). Expanding wetland area with saturated and warmer organic soils is expected to increase landscape methane (CH4) emissions. Here, we quantify the thaw‐induced increase in CH4 emissions for a boreal forest‐wetland landscape in the southern Taiga Plains, Canada, and evaluate its impact on net radiative forcing relative to potential long‐term net carbon dioxide (CO2) exchange. Using nested wetland and landscape eddy covariance net CH4 flux measurements in combination with flux footprint modeling, we find that landscape CH4 emissions increase with increasing wetland‐to‐forest ratio. Landscape CH4 emissions are most sensitive to this ratio during peak emission periods, when wetland soils are up to 10 °C warmer than forest soils. The cumulative growing season (May–October) wetland CH4 emission of ~13 g CH4 m−2 is the dominating contribution to the landscape CH4 emission of ~7 g CH4 m−2. In contrast, forest contributions to landscape CH4 emissions appear to be negligible. The rapid wetland expansion of 0.26 ± 0.05% yr−1 in this region causes an estimated growing season increase of 0.034 ± 0.007 g CH4 m−2 yr−1 in landscape CH4 emissions. A long‐term net CO2 uptake of >200 g CO2 m−2 yr−1 is required to offset the positive radiative forcing of increasing CH4 emissions until the end of the 21st century as indicated by an atmospheric CH4 and CO2 concentration model. However, long‐term apparent carbon accumulation rates in similar boreal forest‐wetland landscapes and eddy covariance landscape net CO2 flux measurements suggest a long‐term net CO2 uptake between 49 and 157 g CO2 m−2 yr−1. Thus, thaw‐induced CH4 emission increases likely exert a positive net radiative greenhouse gas forcing through the 21st century