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This study reports measurements of deposition-mode ice-nucleating particle (INP) concentrations at ground level during the period July–December 2014 in Córdoba, Argentina. Ambient air was sampled into a cloud chamber where the INP concentration was measured at a temperature of −25 °C and a 15 % supersaturation over ice. Measurements were performed on days with different thermodynamic conditions, including rainy days. The effect of the relative humidity at ground level (RH<sub>amb</sub>) on the INP concentration was analyzed. The number of INPs activated varied from 1 L<sup>−1</sup> at RH<sub>amb</sub> of 25 % to 30 L<sup>−1</sup> at RH<sub>amb</sub> of 90 %. In general, a linear trend between the INP concentration and the RH<sub>amb</sub> was found, suggesting that this variability must be related to the effectiveness of the aerosols acting as INPs. From the backward trajectories analysis, it was found that the link between INP concentration and RH<sub>amb</sub> is independent of the origin of the air masses. The role of biological INPs and nucleation occurring in pores and cavities was discussed as a possible mechanism to explain the increase of the INP concentration during high ambient relative humidity events. This work provides valuable measurements of deposition-mode INP concentrations from the Southern Hemisphere where INP data are sparse so far.
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Landscape fires produce smoke containing a very wide variety of chemical species, both gases and aerosols. For larger, more intense fires that produce the greatest amounts of emissions per unit time, the smoke tends initially to be transported vertically or semi-vertically close by the source region, driven by the intense heat and convective energy released by the burning vegetation. The column of hot smoke rapidly entrains cooler ambient air, forming a rising plume within which the fire emissions are transported. The characteristics of this plume, and in particular the height to which it rises before releasing the majority of the smoke burden into the wider atmosphere, are important in terms of how the fire emissions are ultimately transported, since for example winds at different altitudes may be quite different. This difference in atmospheric transport then may also affect the longevity, chemical conversion, and fate of the plumes chemical constituents, with for example very high plume injection heights being associated with extreme long-range atmospheric transport. Here we review how such landscape-scale fire smoke plume injection heights are represented in larger-scale atmospheric transport models aiming to represent the impacts of wildfire emissions on component of the Earth system. In particular we detail (i) satellite Earth observation data sets capable of being used to remotely assess wildfire plume height distributions and (ii) the driving characteristics of the causal fires. We also discuss both the physical mechanisms and dynamics taking place in fire plumes and investigate the efficiency and limitations of currently available injection height parameterizations. Finally, we conclude by suggesting some future parameterization developments and ideas on Earth observation data selection that may be relevant to the instigation of enhanced methodologies aimed at injection height representation.
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The decision to close airspace in the event of a volcanic eruption is based on hazard maps of predicted ash extent. These are produced using output from volcanic ash transport and dispersion (VATD) models. In this paper the fractions skill score has been used for the first time to evaluate the spatial accuracy of VATD simulations relative to satellite retrievals of volcanic ash. This objective measure of skill provides more information than traditional point-by-point metrics, such as success index and Pearson correlation coefficient, as it takes into the account spatial scale over which skill is being assessed. The FSS determines the scale over which a simulation has skill and can differentiate between a "near miss" and a forecast that is badly misplaced. The idealized scenarios presented show that even simulations with considerable displacement errors have useful skill when evaluated over neighbourhood scales of 200–700 (km)<sup>2</sup>. This method could be used to compare forecasts produced by different VATDs or using different model parameters, assess the impact of assimilating satellite-retrieved ash data and evaluate VATD forecasts over a long time period.
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Cloud properties from both ground-based as well as from geostationary passive satellite observations have been used previously for diagnosing aerosol–cloud interactions. In this investigation, a 2-year data set together with four selected case studies are analyzed with the aim of evaluating the consistency and limitations of current ground-based and satellite-retrieved cloud property data sets. The typically applied adiabatic cloud profile is modified using a sub-adiabatic factor to account for entrainment within the cloud. Based on the adiabatic factor obtained from the combination of ground-based cloud radar, ceilometer and microwave radiometer, we demonstrate that neither the assumption of a completely adiabatic cloud nor the assumption of a constant sub-adiabatic factor is fulfilled (mean adiabatic factor 0.63 ± 0.22). As cloud adiabaticity is required to estimate the cloud droplet number concentration but is not available from passive satellite observations, an independent method to estimate the adiabatic factor, and thus the influence of mixing, would be highly desirable for global-scale analyses. Considering the radiative effect of a cloud described by the sub-adiabatic model, we focus on cloud optical depth and its sensitivities. Ground-based estimates are here compared vs. cloud optical depth retrieved from the Meteosat SEVIRI satellite instrument resulting in a bias of −4 and a root mean square difference of 16. While a synergistic approach based on the combination of ceilometer, cloud radar and microwave radiometer enables an estimate of the cloud droplet concentration, it is highly sensitive to radar calibration and to assumptions about the moments of the droplet size distribution. Similarly, satellite-based estimates of cloud droplet concentration are uncertain. We conclude that neither the ground-based nor satellite-based cloud retrievals applied here allow a robust estimate of cloud droplet concentration, which complicates its use for the study of aerosol–cloud interactions.
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Combustion of fuels in the residential sector for cooking and heating results in the emission of aerosol and aerosol precursors impacting air quality, human health, and climate. Residential emissions are dominated by the combustion of solid fuels. We use a global aerosol microphysics model to simulate the impact of residential fuel combustion on atmospheric aerosol for the year 2000. The model underestimates black carbon (BC) and organic carbon (OC) mass concentrations observed over Asia, Eastern Europe, and Africa, with better prediction when carbonaceous emissions from the residential sector are doubled. Observed seasonal variability of BC and OC concentrations are better simulated when residential emissions include a seasonal cycle. The largest contributions of residential emissions to annual surface mean particulate matter (PM<sub>2.5</sub>) concentrations are simulated for East Asia, South Asia, and Eastern Europe. We use a concentration response function to estimate the human health impact due to long-term exposure to ambient PM<sub>2.5</sub> from residential emissions. We estimate global annual excess adult (> 30 years of age) premature mortality (due to both cardiopulmonary disease and lung cancer) to be 308 000 (113 300–497 000, 5th to 95th percentile uncertainty range) for monthly varying residential emissions and 517 000 (192 000–827 000) when residential carbonaceous emissions are doubled. Mortality due to residential emissions is greatest in Asia, with China and India accounting for 50 % of simulated global excess mortality. Using an offline radiative transfer model we estimate that residential emissions exert a global annual mean direct radiative effect between −66 and +21 mW m<sup>−2</sup>, with sensitivity to the residential emission flux and the assumed ratio of BC, OC, and SO<sub>2</sub> emissions. Residential emissions exert a global annual mean first aerosol indirect effect of between −52 and −16 mW m<sup>−2</sup>, which is sensitive to the assumed size distribution of carbonaceous emissions. Overall, our results demonstrate that reducing residential combustion emissions would have substantial benefits for human health through reductions in ambient PM<sub>2.5</sub> concentrations.
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This paper provides a quantification of the temporal evolution of physical variables in the atmospheric boundary layer (ABL) relevant for wind energy applications. For this purpose, we use the unique dataset gathered during the BLLAST (Boundary Layer Late Afternoon and Sunset Turbulence) field experiment to validate a large-eddy simulation (LES) model by simulating the complete diurnal cycle of the ABL. In this way, this contribution to the special issue of ACP "The Boundary-Layer Late Afternoon and Sunset Turbulence project" satisfies the purpose of the BLLAST experiment: to provide a dataset for the validation of numerical simulations aiming to study transient BL processes. For wind energy applications, we are investigating the behaviour of different physical parameters which are relevant in the height region where a wind turbine operates. This results in a quantification of the diurnal cycle influence on the vertical wind shear, the stratification and the turbulence intensity in the atmosphere. Further, the impact of different heterogeneous surface conditions on shear near the surface layer of the ABL is investigated.
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Primary emissions from anthropogenic and biogenic sources as well as secondary formation are responsible for the pollution levels of ambient air in major urban areas. These sources release fine particles into the air that negatively impact human health and the environment. Organic molecular markers, which are compounds that are unique to specific PM<sub>2.5</sub> sources, can be utilized to identify the major emission sources in urban areas. In this study, 43 representative PM<sub>2.5</sub> samples, for both daytime and nighttime periods, were built from individual samples collected in an urban site of the Monterrey metropolitan area (MMA) during the spring and fall of 2011 and 2012. The samples were analyzed for organic carbon, elemental carbon, and organic molecular markers. Several diagnostic tools were employed for the preliminary identification of emission sources. Organic compounds for eight compound classes were quantified. The <i>n</i>-alkanoic acids were the most abundant, followed by <i>n</i>-alkanes, wood smoke markers, and levoglucosan/alkenoic acids. Polycyclic aromatic hydrocarbons (PAHs) and hopanes were less abundant. The carbon preference index (0.7–2.6) for <i>n</i>-alkanes indicates a major contribution of anthropogenic and mixed sources during the fall and the spring, respectively. Hopanes levels confirmed the contribution from gasoline and diesel engines. In addition, the contribution of gasoline and diesel vehicle exhaust was confirmed and identified by the PAH concentrations in PM<sub>2.5</sub>. Diagnostic ratios of PAHs showed emissions from burning coal, wood, biomass, and other fossil fuels. The total PAHs and elemental carbon were correlated (<i>r</i><sup>2</sup> = 0.39–0.70) across the monitoring periods, reinforcing that motor vehicles are the major contributors of PAHs. Cholesterol levels remained constant during the spring and fall, showing evidence of the contribution of meat-cooking operations, while the isolated concentrations of levoglucosan suggested occasional biomass burning events. Finally, source attribution results obtained using the CMB (chemical mass balance) model indicate that emissions from motor vehicle exhausts are the most important, accounting for the 64 % of the PM<sub>2.5</sub>, followed by meat-cooking operations with 31 % The vegetative detritus and biomass burning had the smallest contribution (2.2 % of the PM<sub>2.5</sub>). To our knowledge, this is only the second study to explore the organic composition and source apportionment of fine organic aerosol based on molecular markers in Mexico and the first for the MMA. Particularly molecular marker were quantified by solvent extraction with dichloromethane, derivatization, and gas chromatography with mass spectrometry (GC/MS).
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Emissions inventories of black carbon (BC), which are traditionally constructed using a <i>bottom-up</i> approach based on activity data and emissions factors, are considered to contain a large level of uncertainty. In this paper, an ensemble optimal interpolation (EnOI) data assimilation technique is used to investigate the possibility of optimally recovering the spatially resolved emissions bias of BC. An inverse modeling system for emissions is established for an atmospheric chemistry aerosol model and two key problems related to ensemble data assimilation in the <i>top-down</i> emissions estimation are discussed: (1) how to obtain reasonable ensembles of prior emissions and (2) establishing a scheme to localize the background-error matrix. An experiment involving 1-year-long simulation cycle with EnOI inversion of BC emissions is performed for 2008. The bias of the BC emissions intensity in China at each grid point is corrected by this inverse system. The inverse emission over China in January is 240.1 Gg, and annual emission is about 2539.3 Gg, which is about 1.8 times of bottom-up emission inventory. The results show that, even though only monthly mean BC measurements are employed to inverse the emissions, the accuracy of the daily model simulation improves. Using top-down emissions, the average root mean square error of simulated daily BC is decreased by nearly 30 %. These results are valuable and promising for a better understanding of aerosol emissions and distributions, as well as aerosol forecasting.
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Cloud physics data collected during the NSF/NCAR High-performance Instrumented Airborne Platform for Environmental Research (HIAPER) Pole-to-Pole Observations (HIPPO) campaigns provide a snapshot of unusual wintertime microphysical conditions in the boundary layer over the Southern Ocean. On 29 June 2011, the HIAPER sampled the boundary layer in a region of pre-frontal warm air advection between 58 and 48° S to the south of Tasmania. Cloud droplet number concentrations were consistent with climatological values in the northernmost profiles but were exceptionally high for wintertime in the Southern Ocean at 100–200 cm<sup>−3</sup> in the southernmost profiles. Sub-micron (0.06 < <i>D</i> < 1 µm) aerosol concentrations for the southern profiles were up to 400 cm<sup>−3</sup>. <br><br> Analysis of back trajectories and atmospheric chemistry observations revealed that while conditions in the troposphere were more typical of a clean remote ocean airmass, there was some evidence of continental or anthropogenic influence. However, the hypothesis of long-range transport of continental aerosol fails to explain the magnitude of the aerosol and cloud droplet concentration in the boundary layer. Instead, the gale force surface winds in this case (wind speed at 167 m above sea level was > 25 m s<sup>−1</sup>) were most likely responsible for production of sea spray aerosol which influenced the microphysical properties of the boundary layer clouds. The smaller size and higher number concentration of cloud droplets is inferred to increase the albedo of these clouds, and these conditions occur regularly, and are expected to increase in frequency, over windy parts of the Southern Ocean.
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The global environment is changing rapidly due to anthropogenic emissions and actions. Such activities modify aerosol and greenhouse gas concentrations in the atmosphere, leading to regional and global climate change and affecting, e.g., food and fresh-water security, sustainable use of natural resources and even demography. Here we present a conceptual design of a global, hierarchical observation network that can provide tools and increased understanding to tackle the inter-connected environmental and societal challenges that we will face in the coming decades. The philosophy behind the conceptual design relies on physical conservation laws of mass, energy and momentum, as well as on concentration gradients that act as driving forces for the atmosphere-biosphere exchange. The network is composed of standard, flux and/or advanced and flagship stations, each of which having specific and identified tasks. Each ecosystem type on the globe has its own characteristic features that have to be taken into consideration. The hierarchical network as a whole is able to tackle problems related to large spatial scales, heterogeneity of ecosystems and their complexity. The most comprehensive observations are envisioned to occur in flagship stations, with which the process-level understanding can be expanded to continental and global scales together with advanced data analysis, Earth system modelling and satellite remote sensing. The denser network of the flux and standard stations allows application and up-scaling of the results obtained from flagship stations to the global level.
