Abstract/Summary

Carbon dioxide (CO2) and methane (CH4) mole fractions were measured at four near-ground sites located in and around London during the summer of 2012 with a view to investigating the potential of assimilating such measurements in an atmospheric inversion system for the monitoring of the CO2 and CH4 emissions in the London area. These data were analysed and compared with simulations using a modelling framework suited to building an inversion system: a 2 km horizontal resolution south of England configuration of the transport model CHIMERE driven by European Centre for Medium-Range Weather Forecasts (ECMWF) meteorological forcing, coupled to a 1 km horizontal resolution emission inventory (the UK National Atmospheric Emission Inventory). First comparisons reveal that local sources, which cannot be represented in the model at a 2 km resolution, have a large impact on measurements. We evaluate methods to filter out the impact of some of the other critical sources of discrepancies between the measurements and the model simulation except that of the errors in the emission inventory, which we attempt to isolate. Such a separation of the impact of errors in the emission inventory should make it easier to identify the corrections that should be applied to the inventory. Analysis is supported by observations from meteorological sites around the city and a 3-week period of atmospheric mixing layer height estimations from lidar measurements. The difficulties of modelling the mixing layer depth and thus CO2 and CH4 concentrations during the night, morning and late afternoon lead to focusing on the afternoon period for all further analyses. The discrepancies between observations and model simulations are high for both CO2 and CH4 (i.e. their root mean square (RMS) is between 8 and 12 parts per million (ppm) for CO2 and between 30 and 55 parts per billion (ppb) for CH4 at a given site). By analysing the gradients between the urban sites and a suburban or rural reference site, we are able to decrease the impact of uncertainties in the fluxes and transport outside the London area and in the model domain boundary conditions. We are thus able to better focus attention on the signature of London urban CO2 and CH4 emissions in the atmospheric CO2 and CH4 concentrations. This considerably improves the statistical agreement between the model and observations for CO2 (with model–data RMS discrepancies that are between 3 and 7 ppm) and to a lesser degree for CH4 (with model–data RMS discrepancies that are between 29 and 38 ppb). Between one of the urban sites and either the rural or suburban reference site, selecting the gradients during periods wherein the reference site is upwind of the urban site further decreases the statistics of the discrepancies in general, though not systematically. In a further attempt to focus on the signature of the city anthropogenic emission in the mole fraction measurements, we use a theoretical ratio of gradients of carbon monoxide (CO) to gradients of CO2 from fossil fuel emissions in the London area to diagnose observation-based fossil fuel CO2 gradients, and compare them with the fossil fuel CO2 gradients simulated with CHIMERE. This estimate increases the consistency between the model and the measurements when considering only one of the two urban sites, even though the two sites are relatively close to each other within the city. While this study evaluates and highlights the merit of different approaches for increasing the consistency between the mesoscale model and the near-ground data, and while it manages to decrease the random component of the analysed model–data discrepancies to an extent that should not be prohibitive to extracting the signal from the London urban emissions, large biases, the sign of which depends on the measurement sites, remain in the final model–data discrepancies. Such biases are likely related to local emissions to which the urban near-ground sites are highly sensitive. This questions our current ability to exploit urban near-ground data for the atmospheric inversion of city emissions based on models at spatial resolution coarser than 2 km. Several measurement and modelling concepts are discussed to overcome this challenge.