The turbulent future brings a breath of fresh air.

Ventilation of health hazardous aerosol pollution within the planetary boundary layer (PBL) - the lowest layer of the atmosphere - is dependent upon turbulent mixing, which again is closely linked to the height of the PBL. Here we show that emissions of both CO2 and absorbing aerosols such as black carbon influence the number of severe air pollution episodes through impacts on turbulence and PBL height. While absorbing aerosols cause increased boundary layer stability and reduced turbulence through atmospheric heating, CO2 has the opposite effect over land through surface warming. In future scenarios with increasing CO2 concentrations and reduced aerosol emissions, we find that around 10% of the world's population currently living in regions with high pollution levels are likely to experience a particularly strong increase in turbulence and PBL height, and thus a reduction in intense pollution events. Our results highlight how these boundary layer processes provide an added positive impact of black carbon mitigation to human health.

Revised historical Northern Hemisphere black carbon emissions based on inverse modeling of ice core records.

Black carbon emitted by incomplete combustion of fossil fuels and biomass has a net warming effect in the atmosphere and reduces the albedo when deposited on ice and snow; accurate knowledge of past emissions is essential to quantify and model associated global climate forcing. Although bottom-up inventories provide historical Black Carbon emission estimates that are widely used in Earth System Models, they are poorly constrained by observations prior to the late 20th century. Here we use an objective inversion technique based on detailed atmospheric transport and deposition modeling to reconstruct 1850 to 2000 emissions from thirteen Northern Hemisphere ice-core records. We find substantial discrepancies between reconstructed Black Carbon emissions and existing bottom-up inventories which do not fully capture the complex spatial-temporal emission patterns. Our findings imply changes to existing historical Black Carbon radiative forcing estimates are necessary, with potential implications for observation-constrained climate sensitivity.

On the Convergence and Capability of the Large-Eddy Simulation of Concentration Fluctuations in Passive Plumes for a Neutral Boundary Layer at Infinite Reynolds Number.

Large-eddy simulation (LES) experiments have been performed using the Parallelized LES Model (PALM). A methodology for validating and understanding LES results for plume dispersion and concentration fluctuations in an atmospheric-like flow is presented. A wide range of grid resolutions is shown to be necessary for investigating the convergence of statistical characteristics of velocity and scalar fields. For the scalar, the statistical moments up to the fourth order and the shape of the concentration probability density function (p.d.f.) are examined. The mean concentration is influenced by grid resolution, with the highest resolution simulation showing a lower mean concentration, linked to larger turbulent structures. However, a clear tendency to convergence of the concentration variance is observed at the two higher resolutions. This behaviour is explained by showing that the mechanisms driving the mean and the variance are differently influenced by the grid resolution. The analysis of skewness and kurtosis allows also the obtaining of general results on plume concentration fluctuations. Irrespective of grid resolution, a family of Gamma p.d.f.s well represents the shape of the concentration p.d.f. but only beyond the peak of the concentration fluctuation intensity. In the early plume dispersion phases, the moments of the p.d.f. are in good agreement with those generated by a fluctuating plume model. To the best of our knowledge, our study demonstrates for the first time that, if resolution and averaging time are adequate, atmospheric LES provides a trustworthy representation of the high order moments of the concentration field, up to the fourth order, for a dispersing plume.

Assessing Lagrangian inverse modelling of urban anthropogenic CO2 fluxes using in situ aircraft and ground-based measurements in the Tokyo area.

BACKGROUND: In order to use in situ measurements to constrain urban anthropogenic emissions of carbon dioxide (CO2), we use a Lagrangian methodology based on diffusive backward trajectory tracer reconstructions and Bayesian inversion. The observations of atmospheric CO2 were collected within the Tokyo Bay Area during the Comprehensive Observation Network for TRace gases by AIrLiner (CONTRAIL) flights, from the Tsukuba tall tower of the Meteorological Research Institute (MRI) of the Japan Meteorological Agency and at two surface sites (Dodaira and Kisai) from the World Data Center for Greenhouse Gases (WDCGG). RESULTS: We produce gridded estimates of the CO2 emissions and calculate the averages for different areas within the Kanto plain where Tokyo is located. Using these inversions as reference we investigate the impact of perturbing different elements in the inversion system. We modified the observations amount and location (surface only sparse vs. including aircraft CO2 observations), the background representation, the wind data used to drive the transport model, the prior emissions magnitude and time resolution and error parameters of the inverse model. CONCLUSIONS: Optimized fluxes were consistent with other estimates for the unperturbed simulations. Inclusion of CONTRAIL measurements resulted in significant differences in the magnitude of the retrieved fluxes, 13% on average for the whole domain and of up to 21% for the spatiotemporal cells with the highest fluxes. Changes in the background yielded differences in the retrieved fluxes of up to 50% and more. Simulated biases in the modelled transport cause differences in the retrieved fluxes of up to 30% similar to those obtained using different meteorological winds to advect the Lagrangian trajectories. Perturbations to the prior inventory can impact the fluxes by ~ 10% or more depending on the assumptions on the error covariances. All of these factors can cause significant differences in the estimated flux, and highlight the challenges in estimating regional CO2 fluxes from atmospheric observations.