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Water uptake by aerosol particles controls their ability to form cloud droplets, and reconciliation between different techniques for examining cloud condensation nuclei (CCN) properties is important to our understanding of these processes and our ability to measure and predict them. Reconciliation between measurements of sub-saturated and supersaturated aerosol particle water uptake was attempted at a wide range of locations between 2007 and 2013. The agreement in derived number of CCN (<i>N</i><sub>CCN</sub> or particle hygroscopicity was mixed across the projects, with some data sets showing poor agreement across all supersaturations and others agreeing within errors for at least some of the supersaturation range. The degree of reconciliation did not seem to depend on the environment in which the measurements were taken. The discrepancies can only be attributable to differences in the chemical behaviour of aerosols and gases in each instrument, leading to under- or overestimated growth factors and/or CCN counts, though poorer reconciliation at lower supersaturations can be attributed to uncertainties in the size distribution at the threshold diameter found at these supersaturations. From a single instrument, the variability in <i>N</i><sub>CCN</sub> calculated using particle hygroscopicity or size distribution averaged across a project demonstrates a greater sensitivity to variation in the size distribution than chemical composition in most of the experiments. However, the discrepancies between instruments indicate a strong requirement for reliable quantification of CCN in line with an improved understanding of the physical processes involved in their measurement. Without understanding the reason for discrepancies in the measurements, it is questionable whether quantification of CCN behaviour is meaningful.
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Climate models continue to exhibit strong sensitivity to the representation of aerosol effects on cloud reflectance and cloud amount. This paper evaluates a proposed method to constrain modeled cloud liquid water path (LWP) adjustments in response to changes in aerosol concentration <i>N</i><sub>a</sub> using observations of precipitation susceptibility. Recent climate modeling has suggested a linear relationship between relative LWP responses to relative changes in <i>N</i><sub>a</sub>, i.e., dln LWP / dln <i>N</i><sub>a</sub>, and the precipitation frequency susceptibility <i>S</i><sub>pop</sub>, which is defined as the relative change in the probability of precipitation for a relative change in <i>N</i><sub>a</sub>. Using large-eddy simulations (LES) of marine stratocumulus and trade wind cumulus clouds, we show that these two cloud regimes exhibit qualitatively different relationships between λ and <i>S</i><sub>pop</sub>; in stratocumulus clouds, λ increases with <i>S</i><sub>pop</sub>, while in trade wind cumulus, λ decreases with <i>S</i><sub>pop</sub>. The LES-derived relationship for marine stratocumulus is qualitatively similar but quantitatively different than that derived from climate model simulations of oceanic clouds aggregated over much larger spatial scales. We explore possible reasons for variability in these relationships, including the selected precipitation threshold and the various definitions of precipitation susceptibility that are currently in use. Because aerosol–cloud–precipitation interactions are inherently small-scale processes, we recommend that when deriving the relationship between λ and <i>S</i><sub>pop</sub>, careful attention be given to the cloud regime, the scale, and the extent of aggregation of the model output or the observed data.
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More than 2 years of columnar atmospheric aerosol measurements (2006–2009) at the Tamanrasset site (22.79° N, 5.53° E, 1377 m a.s.l.), in the heart of the Sahara, are analysed. Aerosol Robotic Network (AERONET) level 2.0 data were used. The KCICLO (K is the name of a constant and <i>ciclo</i> means cycle in Spanish) method was applied to a part of the level 1.5 data series to improve the quality of the results. The annual variability of aerosol optical depth (AOD) and Ångström exponent (AE) has been found to be strongly linked to the convective boundary layer (CBL) thermodynamic features. The dry-cool season (autumn and winter) is characterized by a shallow CBL and very low mean turbidity (AOD ~ 0.09 at 440 nm, AE ~ 0.62). The wet-hot season (spring and summer) is dominated by high turbidity of coarse dust particles (AE ~ 0.28, AOD ~ 0.39 at 440 nm) and a deep CBL. The aerosol-type characterization shows desert mineral dust as the prevailing aerosol. Both pure Saharan dust and very clear sky conditions are observed depending on the season. However, several case studies indicate an anthropogenic fine mode contribution from the industrial areas in Libya and Algeria. The concentration weighted trajectory (CWT) source apportionment method was used to identify potential sources of air masses arriving at Tamanrasset at several heights for each season. Microphysical and optical properties and precipitable water vapour were also investigated.
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Ensembles of air quality models have been formally and empirically shown to outperform single models in many cases. Evidence suggests that ensemble error is reduced when the members form a diverse and accurate ensemble. Diversity and accuracy are hence two factors that should be taken care of while designing ensembles in order for them to provide better predictions. Theoretical aspects like the bias–variance–covariance decomposition and the accuracy–diversity decomposition are linked together and support the importance of creating ensemble that incorporates both these elements. Hence, the common practice of unconditional averaging of models without prior manipulation limits the advantages of ensemble averaging. We demonstrate the importance of ensemble accuracy and diversity through an inter-comparison of ensemble products for which a sound mathematical framework exists, and provide specific recommendations for model selection and weighting <i>for multi-model ensembles</i>. The sophisticated ensemble averaging techniques, following proper training, were shown to have higher skill across all distribution bins compared to solely ensemble averaging forecasts.
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Biogenic volatile organic compounds (BVOCs) emitted by vegetation play an important role for aerosol mass loadings since the oxidation products of these compounds can take part in the formation and growth of secondary organic aerosols (SOA). The concentrations and properties of BVOCs and their oxidation products in the atmosphere are poorly characterized, which leads to high uncertainties in modeled SOA mass and properties. In this study, the formation of SOA has been modeled along an air-mass trajectory over northern European boreal forest using two aerosol dynamics box models where the prediction of the condensable organics from the gas-phase oxidation of BVOC is handled with schemes of varying complexity. The use of box model simulations along an air-mass trajectory allows us to compare, under atmospheric relevant conditions, different model parameterizations and their effect on SOA formation. The result of the study shows that the modeled mass concentration of SOA is highly dependent on the organic oxidation scheme used to predict oxidation products. A near-explicit treatment of organic gas-phase oxidation (Master Chemical Mechanism version 3.2) was compared to oxidation schemes that use the volatility basis set (VBS) approach. The resulting SOA mass modeled with different VBS schemes varies by a factor of about 7 depending on how the first-generation oxidation products are parameterized and how they subsequently age (e.g., how fast the gas-phase oxidation products react with the OH radical, how they respond to temperature changes, and if they are allowed to fragment during the aging process). Since the VBS approach is frequently used in regional and global climate models due to its relatively simple treatment of the oxidation products compared to near-explicit oxidation schemes, a better understanding of the above-mentioned processes is needed. Based on the results of this study, fragmentation should be included in order to obtain a realistic SOA formation. Furthermore, compared to the most commonly used VBS schemes, the near-explicit method produces less – but more oxidized – SOA.
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A daytime underflight of CALIPSO with the Facility for Airborne Atmospheric Measurements was performed on 20 September 2012 in the Amazon region of Brazil, during the biomass burning season. The scene is dominated by a thin elevated layer (aerosol optical depth (AOD) 0.03 at 532 nm) and a moderately turbid boundary layer (AOD ~ 0.2 at 532 nm). The boundary layer is topped with small broken stratocumulus clouds. In this complex scene, a comparison of observations from the airborne and spaceborne lidars reveals a few discrepancies. The CALIPSO detection scheme tends to miss the elevated thin layer, and also shows several gaps (~ 30%) in the boundary layer. The small clouds are not correctly removed from the signals; this can cause the CALIPSO aerosol subtype to oscillate between smoke and polluted dust and may introduce distortion in the aerosol retrieval scheme. The magnitude of the average extinction coefficient estimated from CALIPSO Level 2 data in the boundary layer is as expected, when compared to the aircraft lidar and accounting for wavelength scaling. However, when the gaps in aerosol detection mentioned above are accounted for, we are left with an overall estimate of AOD for this particular scene that is of the order of two thirds of that determined with the airborne lidar.
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Long-term measurements of particle number size distribution (PNSD) produce a very large number of observations and their analysis requires an efficient approach in order to produce results in the least possible time and with maximum accuracy. Clustering techniques are a family of sophisticated methods that have been recently employed to analyse PNSD data; however, very little information is available comparing the performance of different clustering techniques on PNSD data. This study aims to apply several clustering techniques (i.e. <i>K</i> means, PAM, CLARA and SOM) to PNSD data, in order to identify and apply the optimum technique to PNSD data measured at 25 sites across Brisbane, Australia. A new method, based on the Generalised Additive Model (GAM) with a basis of penalised B-splines, was proposed to parameterise the PNSD data and the temporal weight of each cluster was also estimated using the GAM. In addition, each cluster was associated with its possible source based on the results of this parameterisation, together with the characteristics of each cluster. The performances of four clustering techniques were compared using the Dunn index and Silhouette width validation values and the <i>K</i> means technique was found to have the highest performance, with five clusters being the optimum. Therefore, five clusters were found within the data using the <i>K</i> means technique. The diurnal occurrence of each cluster was used together with other air quality parameters, temporal trends and the physical properties of each cluster, in order to attribute each cluster to its source and origin. The five clusters were attributed to three major sources and origins, including regional background particles, photochemically induced nucleated particles and vehicle generated particles. Overall, clustering was found to be an effective technique for attributing each particle size spectrum to its source and the GAM was suitable to parameterise the PNSD data. These two techniques can help researchers immensely in analysing PNSD data for characterisation and source apportionment purposes.
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Observations of peroxynitric acid (HO<sub>2</sub>NO<sub>2</sub>) and nitric acid (HNO<sub>3</sub>) were made during a 4 month period of Antarctic winter darkness at the coastal Antarctic research station, Halley. Mixing ratios of HNO<sub>3</sub> ranged from instrumental detection limits to ~8 parts per trillion by volume (pptv), and of HO<sub>2</sub>NO<sub>2</sub> from detection limits to ~5 pptv; the average ratio of HNO<sub>3</sub> : HO<sub>2</sub>NO<sub>2</sub> was 2.0(± 0.6) : 1, with HNO<sub>3</sub> always present at greater mixing ratios than HO<sub>2</sub>NO<sub>2</sub> during the winter darkness. An extremely strong association existed for the entire measurement period between mixing ratios of the respective trace gases and temperature: for HO<sub>2</sub>NO<sub>2</sub>, <i>R</i><sup>2</sup> = 0.72, and for HNO<sub>3</sub>, <i>R</i><sup>2</sup> = 0.70. We focus on three cases with considerable variation in temperature, where wind speeds were low and constant, such that, with the lack of photochemistry, changes in mixing ratio were likely to be driven by physical mechanisms alone. We derived enthalpies of adsorption (Δ<i>H</i><sub>ads</sub>) for these three cases. The average Δ<i>H</i><sub>ads</sub> for HNO<sub>3</sub> was −42 ± 2 kJ mol<sup>−1</sup> and for HO<sub>2</sub>NO<sub>2</sub> was −56 ± 1 kJ mol<sup>−1</sup>; these values are extremely close to those derived in laboratory studies. This exercise demonstrates (i) that adsorption to/desorption from the snow pack should be taken into account when addressing budgets of boundary layer HO<sub>2</sub>NO<sub>2</sub> and HNO<sub>3</sub> at any snow-covered site, and (ii) that Antarctic winter can be used as a natural "laboratory in the field" for testing data on physical exchange mechanisms.
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A series of experiments (the Southern Oxidant and Aerosol Study – SOAS) took place in central Alabama in June–July, 2013 as part of the broader Southern Atmosphere Study (SAS). These projects were aimed at studying oxidant photochemistry and formation and impacts of aerosols at a detailed process level in a location where high biogenic organic vapor emissions interact with anthropogenic emissions, and the atmospheric chemistry occurs in a subtropical climate in North America. The majority of the ground-based experiments were located at the Southeastern Aerosol Research and Characterization (SEARCH) Centreville (CTR) site near Brent, Alabama, where extensive, unique aerometric measurements of trace gases and particles and meteorology were made beginning in the early 1990s through 2013. The SEARCH network data permits a characterization of the temporal and spatial context of the SOAS findings. Our earlier analyses of emissions and air quality trends are extended through 2013 to provide a perspective for continued decline in ambient concentrations, and the implications of these changes to regional sulfur oxide, nitrogen–ozone, and carbon chemistry. The narrative supports the SAS program in terms of long-term average chemistry (chemical climatology) and short-term comparisons of early summer average spatial variability across the southeastern US at high temporal (hourly) resolution. The long-term measurements show that the SOAS experiments took place during the second wettest and coolest year in the 2000–2013 period, with lower than average solar radiation. The pollution levels at CTR and other SEARCH sites were the lowest since full measurements began in 1999. Changes in anthropogenic gas and particle emissions between 1999 and 2013 account for the decline in pollutant concentrations at the monitoring sites in the region. The data provide an opportunity to contrast SOAS results with temporally and spatially variable conditions in support of the development of tests for the robustness of SOAS findings.
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Frequent low visibility, haze and fog events were found in the North China Plain (NCP). Data throughout the NCP during the past 30 years were examined to determine the horizontal distribution and decadal trends of low visibility, haze and fog events. The impact of meteorological factors such as wind and relative humidity (RH) on those events was investigated. Results reveal distinct distributions of haze and fog days, due to their different formation mechanisms. Low visibility, haze and fog days all display increasing trends of before 1995, a steady stage during the period 1995–2003 and a drastically drop thereafter. All three events occurred most frequently during the heating season. Benefiting from emission control measures, haze and fog both show decreasing trends in winter during the past 3 decades, while summertime haze displays continuous increasing trends. The distribution of wind speed and wind direction as well as the topography within the NCP has determinative impacts on the distribution of haze and fog. Weakened south-easterly winds in the southern part of the NCP have resulted in high pollutant concentrations and frequent haze events along the foot of the Taihang Mountains. The orographically generated boundary layer wind convergence line in the central area of the southern NCP is responsible for the frequent fog events in this region. Wind speed has been decreasing throughout the entire southern NCP, resulting in more stable atmospheric conditions and weaker dispersion abilities, calling for harder efforts to control emissions to prevent haze events. Haze events are strongly influenced by the ambient RH. RH values associated with haze days are evidently increasing, suggesting that an increasing fraction of haze events are caused by the hygroscopic growth of aerosols, rather than simply by high aerosol loadings.
