Figure 4.18 NO2 and aerosol (total aerosol (open circles) and ash (filled squares)) vertical profiles for selected European locations, on the 18th of April 2010.
Table 4.4 Optical characteristics of aerosol layers and results, for different locations, on the 16th of April, 2010: total aerosol optical depth (AOD) (all aerosol types considered in the mixture) and for ash alone; single scattering albedo (SSA) for the case with all aerosol types and that without ash; airmass factor (AMF) for the three scenarios considered, no aerosol (noaer), all aerosol (totaer), all aerosol except for ash (noash); and ratios of the different AMFs.
Location AOD SSA AMF Ratios
Total Ash totaer noash noaer totaer noash noaer/totaer noaer/noash noash/totaer Bremen 0.15 0.11 0.83 0.82 1.021 0.953 1.001 1.07 1.02 1.05 Hamburg 0.07 0.03 0.82 0.82 1.019 0.979 1.000 1.04 1.02 1.02 Berlin 0.07 0.01 0.73 0.72 1.016 0.993 1.004 1.02 1.01 1.01 Düsseldorf 0.12 0.04 0.86 0.87 0.951 0.924 0.936 1.03 1.02 1.01 Cabauw 0.23 0.17 0.85 0.85 0.982 0.891 0.961 1.10 1.02 1.08 London 0.05 0.00 0.79 0.79 0.935 0.916 0.916 1.02 1.02 1.00 Paris 0.15 0.00 0.71 0.71 0.923 0.897 0.897 1.03 1.03 1.00
Table 4.5 Optical characteristics of aerosol layers and results, for different locations, on the 17th of April, 2010: total aerosol optical depth (AOD) (all species considered in the mixture) and for ash alone; single scattering albedo (SSA) for the case with all aerosol types and that without ash; airmass factor (AMF) for the three scenarios considered, no aerosol (noaer), all aerosol (totaer), all aerosol except for ash (noash); and ratios of the different AMFs.
Location
AOD SSA AMF Ratios
Total Ash totaer noash noaer totaer noash noaer/totaer noaer/noash noash/totaer Bremen 0.05 0.00 0.86 0.86 0.955 0.946 0.947 1.01 1.01 1.00
Hamburg 0.05 0.00 0.81 0.81 0.935 0.920 0.921 1.02 1.02 1.00 Berlin 0.04 0.00 0.77 0.77 0.975 0.964 0.965 1.01 1.01 1.00 Düsseldorf 0.08 0.01 0.87 0.87 0.957 0.950 0.953 1.01 1.00 1.00 Cabauw 0.05 0.00 0.86 0.87 0.958 0.956 0.958 1.00 1.00 1.00 London 0.05 0.00 0.85 0.85 0.962 0.937 0.937 1.03 1.03 1.00 Paris 0.16 0.04 0.70 0.69 0.990 0.965 0.981 1.03 1.01 1.02
Table 4.6 Optical characteristics of aerosol layers and results, for different locations, on the 18th of April, 2010: total aerosol optical depth (AOD) (all species considered in the mixture) and for ash alone; single scattering albedo (SSA) for the case with all aerosol types and that without ash; airmass factor (AMF) for the three scenarios considered, no aerosol (noaer), all aerosol (totaer), all aerosol except for ash (noash); and ratios of the different AMFs.
Location
AOD SSA AMF Ratios
Total Ash totaer noash noaer totaer noash noaer/totaer noaer/noash noash/totaer Bremen 0.13 0.01 0.90 0.90 1.061 1.084 1.087 0.98 0.98 1.00
Hamburg 0.12 0.00 0.90 0.90 1.077 1.110 1.111 0.97 0.97 1.00 Berlin 0.07 0.00 0.84 0.84 1.024 1.027 1.027 1.00 1.00 1.00 Düsseldorf 0.13 0.01 0.88 0.88 0.987 0.982 0.989 1.01 1.00 1.01 Cabauw 0.18 0.01 0.79 0.79 0.980 1.008 1.010 0.97 0.97 1.00 London 0.20 0.04 0.83 0.83 1.018 0.997 1.034 1.02 0.98 1.04 Paris 0.15 0.03 0.85 0.86 0.979 0.906 0.920 1.08 1.06 1.02
However, a homogenous internal mixing state does not correspond to reality either, as the level of mixture between the different species is not identical, but rather involving complex processes that change the chemical composition of the aerosol (e.g., reaction between NH4+, NO3− and SO42−).
Furthermore, the interaction between soluble and insoluble species (e.g., black carbon or dust particles) is still not well understood. A study presented by Lesins and colleagues (2002) has revealed that considering pure aerosol modes as an external mixture can result in high overestimations of both AOD and SSA. On the other hand, recently, Pére et al. (2010) shows that the AOD values are not highly affected by the approach taken, whereas the SSA is very sensitive to mixing state assumption.
Thus, it is difficult to understand how the results of this study are influenced by the method selected to represent the mixture of several aerosol species.
In the EURAD model, a combination of external and internal mixing is assumed, defining sulphates, nitrates, ammonium, and sea-salt, in the Aitken and accumulation modes, as soluble species (E.
Friese, personal communication, Jun. 2011). The remaining aerosol types are insoluble and in a state of external mixing. For the internally mixed particles, the refractive indices and densities of the pure species, in each size bin, are weighted (usually by volume). In this way, the new the refractive index and density reflect the chemical and optical properties of a mixture. These values would then be used to determine phase functions, extinction and scattering coefficients of an aerosol mixture. A pure external mixture was assumed in the analysis described above because the application of this method is highly demanding at computational level. It is important to highlight that several approaches have been found in the literature consulted (Fassi-Fihri et al., 1997; Tombette et al., 2008; Péré et al., 2010;
2011d), with general recommendations for an application of internal mixture, but no agreement on the error induced by the external mixing state. Furthermore, it is also not clear which species should be mixed in each state and what is the best method to determine the properties of the mixture. From the species considered in the analysis performed, volcanic ash is among the hydrophobic aerosol.
Since, the main goal of this study was to determine the impact of this specific aerosol type on the retrieval of tropospheric NO2, a large impact on the conclusions achieved is not expected.
Nevertheless, it is important to stress that other aerosol species are potentially more affected by the treatment of particle-mixing, which may induce more significant changes in the results.
As it was described above, by assuming an external mixing state, the phase functions, the extinction and scattering coefficients were determined separately for each aerosol type. In this way, the water content of aerosol would have to be considered as an extra aerosol type, assuming atmospheric water particles added to the total aerosol. A test was performed, for small parts of the domain, assuming a water refractive index of 1.34 – i2.0E-9 and density 1.0 g/cm3 (Péré et al., 2010). As it can be expected, the addition of more particles (water in this case) will increase the total extinction, i.e., leading to higher AOD. The difference for pure sulphate and pure water particles was verified and
found to be less than a factor of 2 in extinction, with a smaller value for the latter (N. Schutgens, personal communication, Aug. 2011). Hence, it was surprising to notice that for the new mixture, in most of the region analysed, the AOD obtained was often above 0.5, reaching values as high as 3.5 (see Figure 4.5 with previous total AOD without water particles). These are quite unrealistic values caused by very high mass concentrations of water in accumulation mode at some locations. It can only be speculated that such high values correspond to clouds rather than aerosol, and that further investigations are required to understand exactly how the water content provided in the model output should be accounted for.
The diameter of dry particles is another parameter that will also be affected by the commonly denoted relative humidity effect, i.e., growth of the particle due to water uptake. In general, in the present analysis, the temporal and spatial variation of the aerosol size distribution was not considered, once more because of the time required for the calculations. In addition, the CTM provides only median diameters for each aerosol mode, instead of size distribution for different aerosol types. Using such values would, in part, denote that the size of all species changes similarly, whether they are soluble or not. In the calculations carried out, the size distribution of each aerosol mode was defined according to the initial conditions of the model. Examining the radii obtained from the model output, it is possible to identify several profiles where, for the same location, the size of particles was overestimated at certain altitudes, but also underestimated in different layers. The analysis of the equations introduced in section 2.4 helps to understand how the modification of the size distribution will affect the extinction and scattering coefficients. Both the number density and cross section values are affected by the radius of the particle. In the former, the variation can be easily predicted: an increase in size results in a decrease of the number density. The more complex situation occurs with the extinction and scattering cross section values, because these are not only dependent on the geometrical area of the particle, but also on the respective efficiencies. The dependency of these two parameters to the particle’s size is neither linear, nor regular and, in addition, it is based on the refractive index (several examples are provided in the literature consulted (van de Hulst, 1981; Liou, 2002; Kokhanovsky, 2008)). The extinction and scattering coefficients might vary greatly for a certain model point, but, in the total column, small changes could be verified due to a compensation of over- and underestimation of radii at different altitudes. In addition, for the radiative transfer calculations, the prediction of impact in the tropospheric NO2 AMFs is even more complex. As it was demonstrated in chapter 3, the AMF is dependent on the relative vertical distribution of NO2 and aerosol. This means that errors at some layers can be more significant than at other altitudes where, for example, few aerosol and/or no NO2 exist. Additionally, the size of the aerosol affects the extinction and single scattering albedo differently, and these two properties do not affect the radiative transfer in the same manner. Therefore, a deep study is required to truly understand the influence of the aerosol size distribution in the results. This would involve the calculation of new extinction and
scattering coefficients, for each aerosol type, at each model point, which, in practice, is a similar method to the internal mixing approach, requiring long calculations.
Finally, it is important to highlight, that independently of the approach taken, uncertainties exist in the aerosol chemical composition, as well as in the mass and size distribution simulated by the CTM and these are more relevant for the conclusions achieved. Furthermore, the refractive indices used for secondary organic aerosol and primary aerosol are coarse approximations, as these categories include many different aerosol species with distinct optical properties. The approach taken here was necessary because the time required for more precise calculations was a significant limitation. However, the assumption of external mixture might not be the most relevant source of uncertainty in the analysis presented. This study highlights how, currently, it is still difficult and complex to consider the application of model data in the retrieval of vertical columns from satellite measurements. In the future, priority should be set in understanding and defining a best methodology to derive aerosol optical properties from simulated mass concentrations, and understand the uncertainties involved in each assumption.