Supplement of Measurement report: Comparison of airborne, in situ measured, lidar- based, and modeled aerosol optical properties in the central European background – identifying sources of deviations

A unique data set derived from remote sensing, airborne, and ground-based in situ measurements is presented. This measurement report highlights the known complexity of comparing multiple aerosol optical parameters examined with different approaches considering different states of humidification and atmospheric aerosol concentrations. Mie-theory-based modeled aerosol optical properties are compared with the respective results of airborne and ground-based in situ measurements and remote sensing (lidar and photometer) performed at the rural central European observatory at Melpitz, Germany. Calculated extinction-tobackscatter ratios (lidar ratios) were in the range of previously reported values. However, the lidar ratio is a function of the aerosol type and the relative humidity. The particle lidar ratio (LR) dependence on relative humidity was quantified and followed the trend found in previous studies. We present a fit function for the lidar wavelengths of 355, 532, and 1064 nm with an underlying equation of fLR(RH, γ (λ))= fLR(RH= 0,λ)× (1−RH) , with the derived estimates of γ (355 nm)= 0.29 (±0.01), γ (532 nm)= 0.48 (±0.01), and γ (1064 nm)= 0.31 (±0.01) for central European aerosol. This parameterization might be used in the data analysis of elastic-backscatter lidar observations or lidar-ratio-based aerosol typing efforts. Our study shows that the used aerosol model could reproduce the in situ measurements of the aerosol particle light extinction coefficients (measured at dry conditions) within 13 %. Although the model reproduced the in situ measured aerosol particle light absorption coefficients within a reasonable range, we identified many sources for significant uncertainties in the simulations, such as the unknown aerosol mixing state, brown carbon (organic material) fraction, and the unknown aerosol mixing state wavelength-dependent refractive index. The modeled ambient-state aerosol particle light extinction and backscatter coefficients were smaller than the measured ones. However, depending on the prevailing aerosol conditions, an overlap of the uncertainty ranges of both approaches was achieved.


Supplementary text
This supplementary text provides deeper insights on the methodology and measurements described in the manuscript "Measurement report: Comparison of airborne in situ measured, lidar-based, and modeled aerosol optical properties in the Central European backgroundidentifying sources of deviations".

Measurement strategy with the airborne cloud and turbulence observation system (ACTOS) 5
A typical flight pattern for one of the conducted measurement flights is displayed exemplarily for 28 June 2015, in Fig. S1 as a red line. Typically, a measurement flight lasted around two hours and started with a profile to characterize the atmosphere vertically up to altitudes of 2700 m and to identify atmospheric layers of interest.
Afterward, sections of constant flight height, so-called "legs" were flown with at least 10 minutes duration to realize measurements within on altitude level and to increase the counting statistics for other measurements, such 10 as the PNSD with a lower time resolution and such as the aerosol particle absorption coefficient deployed on ACTOS. Figure S1 also displays color-coded the attenuated σbsc(λ) at 1064 nm in Mm -1 sr -1 measured by Polly XT lidar on 28 June 2015, between 06:00 UTC and noon. Bright white color represents a strong backscatter signal and indicates clouds. The development of the planetary boundary layer is visible with the increasing cloud bottom height of 500 m at 06:00 UTC and around 1600 m altitude at noon. Also, the residual layer containing some aerosol 15 layer aloft the top of the planetary boundary layer (PBL) between 1250 m and 2300 m is visibly indicated by greenish colors. Therefore, the payload was sampling in the free troposphere and within the planetary boundary layer and was sampling different aerosol populations. A short period at around 09:30 UTC of low-level clouds interfered with the lidar measurements during the flight. 20 Figure S1. Attenuated aerosol particle light backscatter coefficient (σbsc(λ), color-coded) measured with Polly XT lidar at 1064 nm on 28 June 2015, between 6:00 UTC and 12:00 UTC is displayed. White colors indicate values larger than 2.0 Mm -1 sr -1 . The red line represents the flight pattern of ACTOS in terms of altitude in m above ground.

Experiments in the context of aerosol load and air mass
In the following, both campaigns' measurement days of interest will be set in context to aerosol load and air mass 25 origin of the shown period. Each day in both campaigns was assigned to its corresponding air-mass back-trajectory from a pool of in total 15 clusters following Sun et al. (2020). These clusters are appointed by the season (cold season, CS; transition season, TS; and warm season, WS) and the prevalent synoptic pattern. The abbreviation ST indicates a stagnant pattern; A indicates anticyclonic patterns with air masses originating in eastern (1) and western (2) Europe. C represents a cyclonic pattern with air masses originating from the south (1) and north (2). The  Color codes represent clean (blue) and polluted (red) trajectory clusters with the given keys for each day following the trajectory clustering presented by Sun et al. (2020). Clustering is explained below. Panel b) shows the total number concentration of all aerosol particles between 5 and 800 nm in diameter (N800nm) and c) displays the aerosol particle light scattering (σsca) and absorption 60 coefficient (σabs). Grey shaded areas show the measurement days investigated in more detail. pollution over Melpitz. The mass concentration of aerosol particles with an aerodynamic diameter lower than 2.5 µm (PM2.5) on 9 February exceeds typical annual average PM2.5 aerosol particle mass concentrations (e.g., Spindler et al., 2013;20.1 ± 18 µg m -3 ) by a factor of two and illustrates the unusually high pollution during this period.

Quality of used Mie-Model
For the investigations within the manuscript, a Mie-model is utilized. The underlying assumptions within the Mie-Model are validated using a correlation of the measured and Mie-based aerosol optical properties in the dried state (see Fig. S4) and with the in-situ measured σext(630 nm) derived on ACTOS (see Fig. S5).
Considering the correlation with the ground-based in-situ measurements of σsca(450 nm), the result of the 85 Mie-model agrees within 3 % during the summer campaign (underestimation; see Fig. S4a) and within 13 % (overestimation; Fig. S4b) during the winter period. Based on the correlation in Fig. S4, the Mie-model reproduces the σabs(λ) derived with the MAAP at 637 nm within 8 % (Fig. S4b) during winter, and within 18 % (Fig. S4a) during the summer period overestimating the measured σabs(λ) in both cases. Figure S4. Time series (upper panels) and scatter plot (lower panels) of modeled and measured aerosol particle light scattering (σsca(λ), left panels) and absorption (σabs(λ), right panels) coefficients derived with the Mie-model and the Nephelometer and MAAP for different wavelength (color-coded) at Melpitz Observatory during the summer (a) and winter campaign (b).
In the summer case, two distinct clusters in the σabs(λ), one above and one below the fitting line, indicate 95 different aerosol types and that the model constraints might better represent the prevalent aerosol type of lower cluster better since the data points are close to the 1:1 line. The aerosol represented by the lower cluster was prevalent at Melpitz from 13 June 2015 on, and the comparison of the modeled and measured σext(λ) (σsca(λ)) shows an agreement within 4 % (2 %). Therefore, the mixing approach within the model is a good representation of the aerosol present during the intensive period of the measurement campaign in the summer between 15 June and 28 100 June 2015. However, the model utilized rough assumptions to represent the aerosol. Besides the assumption of a wavelength-independent complex aerosol refractive index, the assumption of a constant volume fraction of eBC underestimates the BC content in the smaller aerosol particles. It leads to an overestimation in the larger aerosol particles because BC usually is primarily found in the aerosol accumulation and Aitken mode (Bond et al., 2013) 105 with a mass peak at around 250 nm of BC core diameter. Also, the coating thickness of same-sized soot cores is not constant, and the size of BC cores covers only a specific size range, as shown by Ditas et al. (2018). During the summer campaign, no size-resolved BC mass concentration measurements have been available and would also be limited to a specific size range. Therefore, implementing a constant eBC volume fraction within an optical model is a handy approach and is often used in other studies (e.g., Düsing et al., 2018;Ma et al., 2014Ma et al., , 2012. Furthermore, the model validation in terms of absorption is based on the MAC(637 nm) estimates based on the MAAP measurements and hence most representative at this wavelength. Modeled σabs(λ) at lower or larger wavelengths could deviate from measurements because of a different value of MAC(λ). However, considering the airborne in-situ correlation, the model agrees to measured σext(630 nm) within 13 % (slope = 1.13 with R 2 = 0.98; p = 0) averaged over all available data points. Nevertheless, the modeled σext(630 nm) overestimates the measured one, especially on 25 June (light blue data points). Excluding that day 120 from the correlation, the model would overestimate the measured σext(630 nm) by 2.2 % (R 2 = 0.98), which is within the measurement uncertainty of the CAPS. Note that for the airborne in-situ correlation, the underlying airborne PNSD used in the Mie-model is not corrected for diffusional and aspirational loss because both systems were sampling through the same inlet system. In winter, the altitude corrected PNSD measured at the ground, which is used to replace the missing aerosol particle size range (up to 300 nm), was corrected for the diffusional 125 losses inside the tubing. Diffusional losses inside the tubing of the balloon platform lower the in-situ measured σabs(λ). Therefore, the in-situ measured σabs(λ) would have been smaller than modeled ones by default. To which extent, however, remains unclear. Figure S6. Scatter plot of the aerosol hygroscopicity derived with VH-TDMA (y-axis) and the ZSR-mixing approach combining ACSM and MAAP measurements (x-axis). Colors represent the size-bins of the VH-TDMA. Lines with shaded areas represent a linear fit and the uncertainty of fit. Fitting parameters are displayed with corresponding colors.   Figure   S7B shows the profiles of the LR at 355, 532, and 1064 nm calculated with the Mie model in ambient (triangles) and dry (circles) state. While the LR changes only slightly, if at all, in the dry state, compared to the ambient state in which the change is more pronounced. Hence a direct relationship between LR and RH is visible. 140