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Organic compounds represent a significant fraction of submicrometer atmospheric aerosol mass. Even if most of these compounds are semi-volatile in atmospheric concentrations, the ambient organic aerosol volatility is quite uncertain. The most common volatility measurement method relies on the use of a thermodenuder (TD). The aerosol passes through a heated tube where its more volatile components evaporate, leaving the less volatile components behind in the particulate phase. The typical result of a thermodenuder measurement is the mass fraction remaining (MFR), which depends, among other factors, on the organic aerosol (OA) vaporization enthalpy and the accommodation coefficient. We use a new method combining forward modeling, introduction of "experimental" error, and inverse modeling with error minimization for the interpretation of TD measurements. The OA volatility distribution, its effective vaporization enthalpy, the mass accommodation coefficient and the corresponding uncertainty ranges are calculated. Our results indicate that existing TD-based approaches quite often cannot estimate reliably the OA volatility distribution, leading to large uncertainties, since there are many different combinations of the three properties that can lead to similar thermograms. We propose an improved experimental approach combining TD and isothermal dilution measurements. We evaluate this experimental approach using the same model, and show that it is suitable for studies of OA volatility in the lab and the field.

Bias correction of climate variables is a standard practice in climate change impact (CCI) studies. Various methodologies have been developed within the framework of quantile mapping. However, it is well known that quantile mapping may significantly modify the long-term statistics due to the time dependency of the temperature bias. Here, a method to overcome this issue without compromising the day-to-day correction statistics is presented. The methodology separates the modeled temperature signal into a normalized and a residual component relative to the modeled reference period climatology, in order to adjust the biases only for the former and preserve the signal of the later. The results show that this method allows for the preservation of the originally modeled long-term signal in the mean, the standard deviation and higher and lower percentiles of temperature. To illustrate the improvements, the methodology is tested on daily time series obtained from five Euro CORDEX regional climate models (RCMs).

A total of 16 global chemistry transport models and general circulation models have participated in this study; 14 models have been evaluated with regard to their ability to reproduce the near-surface observed number concentration of aerosol particles and cloud condensation nuclei (CCN), as well as derived cloud droplet number concentration (CDNC). Model results for the period 2011–2015 are compared with aerosol measurements (aerosol particle number, CCN and aerosol particle composition in the submicron fraction) from nine surface stations located in Europe and Japan. The evaluation focuses on the ability of models to simulate the average across time state in diverse environments and on the seasonal and short-term variability in the aerosol properties. There is no single model that systematically performs best across all environments represented by the observations. Models tend to underestimate the observed aerosol particle and CCN number concentrations, with average normalized mean bias (NMB) of all models and for all stations, where data are available, of −24 % and −35 % for particles with dry diameters >50 and >120 nm, as well as −36 % and −34 % for CCN at supersaturations of 0.2 % and 1.0 %, respectively. However, they seem to behave differently for particles activating at very low supersaturations (<0.1 %) than at higher ones. A total of 15 models have been used to produce ensemble annual median distributions of relevant parameters. The model diversity (defined as the ratio of standard deviation to mean) is up to about 3 for simulated N3 (number concentration of particles with dry diameters larger than 3 nm) and up to about 1 for simulated CCN in the extra-polar regions. A global mean reduction of a factor of about 2 is found in the model diversity for CCN at a supersaturation of 0.2 % (CCN0.2) compared to that for N3, maximizing over regions where new particle formation is important. An additional model has been used to investigate potential causes of model diversity in CCN and bias compared to the observations by performing a perturbed parameter ensemble (PPE) accounting for uncertainties in 26 aerosol-related model input parameters. This PPE suggests that biogenic secondary organic aerosol formation and the hygroscopic properties of the organic material are likely to be the major sources of CCN uncertainty in summer, with dry deposition and cloud processing being dominant in winter. Models capture the relative amplitude of the seasonal variability of the aerosol particle number concentration for all studied particle sizes with available observations (dry diameters larger than 50, 80 and 120 nm). The short-term persistence time (on the order of a few days) of CCN concentrations, which is a measure of aerosol dynamic behavior in the models, is underestimated on average by the models by 40 % during winter and 20 % in summer. In contrast to the large spread in simulated aerosol particle and CCN number concentrations, the CDNC derived from simulated CCN spectra is less diverse and in better agreement with CDNC estimates consistently derived from the observations (average NMB −13 % and −22 % for updraft velocities 0.3 and 0.6 m s−1, respectively). In addition, simulated CDNC is in slightly better agreement with observationally derived values at lower than at higher updraft velocities (index of agreement 0.64 vs. 0.65). The reduced spread of CDNC compared to that of CCN is attributed to the sublinear response of CDNC to aerosol particle number variations and the negative correlation between the sensitivities of CDNC to aerosol particle number concentration (∂Nd/∂Na) and to updraft velocity (∂Nd/∂w). Overall, we find that while CCN is controlled by both aerosol particle number and composition, CDNC is sensitive to CCN at low and moderate CCN concentrations and to the updraft velocity when CCN levels are high. Discrepancies are found in sensitivities ∂Nd/∂Na and ∂Nd/∂w; models may be predisposed to be too “aerosol sensitive” or “aerosol insensitive” in aerosol–cloud–climate interaction studies, even if they may capture average droplet numbers well. This is a subtle but profound finding that only the sensitivities can clearly reveal and may explain inter-model biases on the aerosol indirect effect.

The possibility of measuring aerosol optical absorption properties in the UV spectral range such as single scattering albedo (SSA), using remote sensing techniques, is currently an open scientific issue. We investigate the limitations on calculating column average SSA using a combination of global UV spectral measurements (that are comon in various UV monitoring stations worldwide) with radiative transfer modeling. To point out the difficulties in such a retrieval we have used the travelling reference spectroradiometer QASUME (Quality Assurance of Spectral Ultraviolet Measurements in Europe) results from 27 visits to UV monitoring stations around Europe. We have used the QASUME instrument as relative reference, analyzing absolute differences and also temporal and spectral deviations of UV irraidances, that are used as basic input for the SSA retrieval. The results comparing the mean SSA derived by all instruments, measuring synchronous UV spectra, showed that 5 were within ± 0.02 difference from the SSA calculated from the QASUME instrument, while 17 were within ± 0.04, for the Solar zenith angle of 60 degrees. As for the uncertainty that has been calculated using the 2σ standard deviation of the spectral measurements, a mean 0.072 and 0.10 (2σ) uncertainties have been calculated for 60° and 30°, respectively. Based on the fact that additional uncertainties would be introduced in the SSA retrieval from AOD model input accuracy, assymetry parameter assumptions, we show that only very few instrumnents could be able to detect long term SSA changes. However, such measurements/results ar useful in order to retrieve SSA at UV wavelengths, a product needed for various applications such as, inputs for modeling radiative forcing studies and satellite retrieval algorithms.

High frequency of agricultural fires is observed every year during the summer months over SW Russia and Eastern Europe. This study investigates the initial injection height of aerosol generated by the fires over these regions during the biomass burning season, which determines the potential for long-range transport of the smoke. This information is critical for aerosol transport modeling, as it determines the smoke plume evolution. The study focuses on the period 2006–2008, and is based on observations made by the CALIOP instrument on board the NASA CALIPSO satellite. MODIS data are synergistically used for the detection of the fires and the characterization of their intensity. CALIPSO aerosol vertical distributions generated by the active fires are analyzed to investigate the aerosol top height which is considered dependent on the heat generated by the fires and can be associated with the initial injection height. Aerosol top heights of the vertically homogenous smoke layers are found to range between 1.6 and 5.9 km. Smoke injection heights from CALIPSO are compared with mixing layer heights taken by the European Centre for Medium-range Weather Forecast (ECMWF), to investigate the direct injection of smoke particles into the free troposphere. Our results indicate that the aerosol plumes are observed within the boundary layer for the 50% of the cases examined. For the rest of the cases, the strong updrafts generated by the fires resulted to smoke injection heights greater than the ECMWF estimated mixing layer by 0.5 to 3.0 km, indicating a direct smoke injection into the free troposphere. The smoke injection height showed a dependence on the MODIS-Land Fire Radiative Power product which is indicative of the fire intensity, especially in the cases of lower static stability in the upper part of the boundary layer and the free troposphere.

Global climate model (GCM) outputs feature systematic biases that render them unsuitable for direct use by impact models, especially for hydrological studies. To deal with this issue, many bias correction techniques have been developed to adjust the modelled variables against observations, focusing mainly on precipitation and temperature. However, most state-of-the-art hydrological models require more forcing variables, in addition to precipitation and temperature, such as radiation, humidity, air pressure, and wind speed. The biases in these additional variables can hinder hydrological simulations, but the effect of the bias of each variable is unexplored. Here we examine the effect of GCM biases on historical runoff simulations for each forcing variable individually, using the JULES land surface model set up at the global scale. Based on the quantified effect, we assess which variables should be included in bias correction procedures. To this end, a partial correction bias assessment experiment is conducted, to test the effect of the biases of six climate variables from a set of three GCMs. The effect of the bias of each climate variable individually is quantified by comparing the changes in simulated runoff that correspond to the bias of each tested variable. A methodology for the classification of the effect of biases in four effect categories (ECs), based on the magnitude and sensitivity of runoff changes, is developed and applied. Our results show that, while globally the largest changes in modelled runoff are caused by precipitation and temperature biases, there are regions where runoff is substantially affected by and/or more sensitive to radiation and humidity. Global maps of bias ECs reveal the regions mostly affected by the bias of each variable. Based on our findings, for global-scale applications, bias correction of radiation and humidity, in addition to that of precipitation and temperature, is advised. Finer spatial-scale information is also provided, to suggest bias correction of variables beyond precipitation and temperature for regional studies.

Smog chamber experiments using as a starting point ambient air can improve our understanding of the evolution of atmospheric particulate matter at timescales longer than those achieved by traditional laboratory experiments. These types of studies can take place under more realistic environmental conditions addressing the interactions among multiple pollutants. The use of two identical smog chambers, with the first serving as the baseline chamber and the second as the perturbation chamber (in which addition or removal of pollutants, addition of oxidants, change in the relative humidity, etc.), can facilitate the interpretation of the results in such inherently complex experiments. The differences of the measurements in the two chambers can be used as the basis for the analysis of the corresponding chemical or physical processes of ambient air. A portable dual smog chamber system was developed using two identical pillow-shaped smog chambers (1.5 m3 each). The two chambers are surrounded by UV lamps in a hexagonal arrangement yielding a total JNO2 of 0.1 min−1. The system can be easily disassembled and transported enabling the study of various atmospheric environments. Moreover, it can be used with natural sunlight. The results of test experiments using ambient air as starting point are discussed as examples of applications of this system.

This study characterizes the spatiotemporal variability and relative contribution of different types of aerosols to the aerosol optical depth (AOD) over the Eastern Mediterranean as derived from MODIS (Moderate Resolution Imaging Spectroradiometer) Terra (March 2000–December 2012) and Aqua (July 2002–December 2012) satellite instruments. For this purpose, a 0.1° × 0.1° gridded MODIS dataset was compiled and validated against sun photometric observations from the AErosol RObotic NETwork (AERONET). The high spatial resolution and long temporal coverage of the dataset allows for the determination of local hot spots like megacities, medium-sized cities, industrial zones and power plant complexes, seasonal variabilities and decadal averages. The average AOD at 550 nm (AOD550) for the entire region is ∼ 0.22 ± 0.19, with maximum values in summer and seasonal variabilities that can be attributed to precipitation, photochemical production of secondary organic aerosols, transport of pollution and smoke from biomass burning in central and eastern Europe and transport of dust from the Sahara and the Middle East. The MODIS data were analyzed together with data from other satellite sensors, reanalysis projects and a chemistry–aerosol-transport model using an optimized algorithm tailored for the region and capable of estimating the contribution of different aerosol types to the total AOD550. The spatial and temporal variability of anthropogenic, dust and fine-mode natural aerosols over land and anthropogenic, dust and marine aerosols over the sea is examined. The relative contribution of the different aerosol types to the total AOD550 exhibits a low/high seasonal variability over land/sea areas, respectively. Overall, anthropogenic aerosols, dust and fine-mode natural aerosols account for ∼ 51, ∼ 34 and ∼ 15 % of the total AOD550 over land, while, anthropogenic aerosols, dust and marine aerosols account ∼ 40, ∼ 34 and ∼ 26 % of the total AOD550 over the sea, based on MODIS Terra and Aqua observations.

This paper synthesizes the available scientific information connecting atmospheric nucleation with subsequent cloud condensation nuclei (CCN) formation. We review both observations and model studies related to this topic, and discuss the potential climatic implications. We conclude that CCN production associated with atmospheric nucleation is both frequent and widespread phenomenon in many types of continental boundary layers, and probably also over a large fraction of the free troposphere. The contribution of nucleation to the global CCN budget spans a relatively large uncertainty range, which, together with our poor understanding of aerosol-cloud interactions, results in major uncertainties in the radiative forcing by atmospheric aerosols. In order to better quantify the role of atmospheric nucleation in CCN formation and Earth System behavior, more information is needed on (i) the factors controlling atmospheric CCN production and (ii) the properties of both primary and secondary CCN and their interconnections. In future investigations, more emphasis should be put on combining field measurements with regional and large-scale model studies.

We present a 3-D climatology of the desert dust distribution over South and East Asia derived using CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) data. To distinguish desert dust from total aerosol load we apply a methodology developed in the framework of EARLINET (European Aerosol Research Lidar Network). The method involves the use of the particle linear depolarization ratio and updated lidar ratio values suitable for Asian dust, applied to multiyear CALIPSO observations (January 2007–December 2015). The resulting dust product provides information on the horizontal and vertical distribution of dust aerosols over South and East Asia along with the seasonal transition of dust transport pathways. Persistent high D_AOD (dust aerosol optical depth) values at 532 nm, of the order of 0.6, are present over the arid and semi-arid desert regions. Dust aerosol transport (range, height and intensity) is subject to high seasonality, with the highest values observed during spring for northern China (Taklimakan and Gobi deserts) and during summer over the Indian subcontinent (Thar Desert). Additionally, we decompose the CALIPSO AOD (aerosol optical depth) into dust and non-dust aerosol components to reveal the non-dust AOD over the highly industrialized and densely populated regions of South and East Asia, where the non-dust aerosols yield AOD values of the order of 0.5. Furthermore, the CALIPSO-based short-term AOD and D_AOD time series and trends between January 2007 and December 2015 are calculated over South and East Asia and over selected subregions. Positive trends are observed over northwest and east China and the Indian subcontinent, whereas over southeast China trends are mostly negative. The calculated AOD trends agree well with the trends derived from Aqua MODIS (Moderate Resolution Imaging Spectroradiometer), although significant differences are observed over specific regions.