Supplementary Instruments

schemes can be directly compared. Figure 3.2.20 contrasts the values of ∆nv1 and

∆n_v2.2 in relation to ∆n_max (= ∆n_v2.1 for the small aperture orifice). It is apparent that the ∆n_v1 neglecting the diffusional losses in the tubing upstream the CPCCPS

significantly overestimate the true orifice losses for most of the cases. Particularly for the large aperture orifice at lowerp_inlet the ∆n_v1 exceed the upper loss bound ∆n_maxby up to a factor of 2. In contrast, the ∆n_v2.2 values show the expected (correct) behavior.

For the small aperture orifice the ∆nv2.2 calculated via Eq. (3.2.20) almost perfectly agree with the ∆n_max = ∆n_v2.1 calculated via Eq. (3.2.19), that are assumed to be accurate following the previous considerations. For the large aperture orifice the ∆n_v2.2 on average fall below the ∆nmax and the fraction ∆nv2.2/∆nmax shows a decreasing trend with decreasing p_inlet, conform with what is expected from the reduction in Stk (cf. Fig. 3.2.19).

For clarity, Fig. 3.2.20 only features the uncertainty ranges for ∆nmaxresulting from a propagation of the uncertainties inη_os(D) orη_ol(D) and the NSD. For the data points (∆n_vi/∆n_max) only the mean solutions are shown, as the systematic uncertainties for measured ∆ncan be as large as 50%, thus disturbing the visualization. This is because the ∆nvalues represent a small difference of two large CPC concentration values, each subject to an (assumed) systematic uncertainty of 5% (cf. Sect. 3.2.2.1). Although on average agreeing with the expectations, the ∆nv2.2 values exhibit some scatter. This is, to a large extent, ascribable to small time-dependent variations of the CPC counting efficiencies and/or sample flow rates. Such variations (falling within the range of the 5% accuracy) are regularly observed during CPC laboratory measurements. Especially for the large aperture orifice, where the measured ∆n are smallest compared to the absolute concentrations (cf. Fig. 3.2.20), such fluctuations can easily explain the observed scatter. To minimize the uncertainty in the ∆nv2.2 values, the ∆nmax fraught with less uncertainty are, hence, used as an upper constraint for the derivation of the (v2) corrected CCN concentrationsn_CCN,v2, as outlined in Sect. 3.3.4.

In relation to nCCN,v2 the uncorrected nCCN,uncorr underestimate the CCN concen-trations (at SS = 0.2%) on average by 27% and 9% for the small and large aperture orifice, respectively. The n_CCN,v1, on the other hand, overestimate the CCN concen-trations on average by 6% and 17% for the two orifices respectively. The complete comparison between the different CCN concentrations is given in Tab. A.2.1. On bal-ance, from the two options available on a time step basis, i.e. n_CCN,uncorr and n_CCN,v1, the latter still offers a better approximation of the (correct) nCCN,v2 values with re-gard to the full altitude range. In Ch. 4, both n_CCN,v1 and n_CCN,v2 values are used to present CCN concentrations. Unless otherwise specified, n_CCN is used as a synonym fornCCN,v2.

In addition, the aircraft is routinely equipped with GPS sensors determining its altitude z above mean sea level (AMSL) and geographic coordinates (longitude and latitude).

Detailed information about the aircraft and the standard sensors can be found on the DLR flight experiments homepage¹⁰. For SALTRACE, the Falcon standard data were processed and provided by A. Giez, V. Dreiling, M. Zöger and Ch. Mallaun, DLR.

Complementary meteorological investigations were carried out by means of drop-sondes (Govind, 1975; Hock and Franklin, 1999). A dropsonde is a compact device that is released from the aircraft at high altitudes. While descending through the atmospheric column in a moderate speed (about 5−10 m/s), measurements of pres-sure, temperature, humidity, horizontal wind velocity and direction are conducted and transmitted to the aircraft for onboard data processing.

Micro Inertial Impactors (MINIs)

In situ sampling for offline single particle analysis was performed with cascade particle impactors, termed MINIs (Kandler et al., 2007). The sample acquisition was not continuous but manually started and stopped for specific time intervals (typically 5−10 min) by opening and reclosing the lock valves connecting the MINIs to the sampling lines (cf. Fig. 3.2.1). The MINIs consisted of two main impactor stages (equipped with different substrates), i.e. a primary stage A for particle diameters D ? 500 nm and a secondary stage B for particle diameters D > 500 nm. The large particles in stage A were analyzed by scanning electron microscopy (SEM) and energy-dispersive X-ray diffraction (EDX) for size and composition, small particles in stage B by transmission electron microscopy (TEM) and EDX for size, volatility and composition. Whereas the SEM analysis was automated, the TEM analysis for the small particles required manual support, leading to reduced statistical ensemble sizes for the stage B samples. The classification of particle composition follows Kandler et al. (2009) with the exception of quartz being classified as silicate. Volatility for the stage B particles was determined as outlined in Sect. 2.4, and described in detail in Kandler et al. (2011) and Kristensen et al. (2016). It should be noted that these measurements do not yield information on potential organic components. The MINI data set was made available by K. Kandler, Technical University of Darmstadt.

Single Particle Soot Photometer (SP2)

The in-cabin setup used in this study is complemented by an SP2 which is utilized to measure the aerosol refractory black carbon (rBC) mass concentration (Stephens et al., 2003; Laborde et al., 2012). Like for the OPCs, the SP2 guides the aerosol sample through the focus of a laser beam. Particles containing a rBC core absorb the high irradiance laser light, thereby heating up, loosing non-refractory coatings and finally incandesce. The peak intensity of the thermal emission from an incandescent rBC core is proportional to its mass. Detecting the distribution of thermal emission signal peaks, thus, allows to derive the rBC mass distribution¹¹. Here, only the integral rBC mass

10http://www.dlr.de/fb/en/desktopdefault.aspx/tabid-3714/

11Strictly speaking, the inferred rBC mass refers to the calibration material. For the presented data, this reference material is fullerene soot.

concentration mrBC within the SP2 size range (80> D > 480 nm) is presented. The SP2 data were processed by K. Heimerl and B. Weinzierl, DLR¹².

Cloud and Aerosol Spectrometer (CAS)

Like the UHSAS, the CAS (Baumgardner et al., 2001) was mounted in one of the air-craft’s under-wing pods. The centerpiece of the CAS is an OPC having its sampling volume located in an open (forward pointing) large-diameter pipe through which the ambient air flows through passively during flight. The open design has some disadvan-tages. For instance, as the aerosol sample is not focused to the center of the sampling volume it is necessary to qualify signal pulses by their point of origin in the latter, which complicates data evaluation. However, a major advantage of the open design over inlet-based OPC architectures is that it does not limit the sampling efficiency for larger particles. As a result, the CAS permits the detection of cloud droplets and aerosol particles too large to be efficiently transmitted to the in-cabin instruments (e.g.

large mineral dust particles). Apart from utilizing the data for identifying cloud pas-sages, the integral number concentration of particles with D ? 1.5 µm measured by the CASn_giant is presented as an optimistic estimate¹³ for particles beyond the Falcon inlet size cutoff. The CAS data were processed by D. Sauer, DLR.

3.2.3.2 Ground-Based Instruments

Mobility Particle Size Spectrometer (MPSS) and Aerodynamic Particle Sizer (APS)

The ground-based particle NSD measurements at Ragged Point, Barbados (see Fig.

3.1.2) involved a MPSS (Wiedensohler et al., 2012), covering a particle size range of about 10 to 800 nm, and a (TSI model 3321) APS (e.g. Pfeifer et al., 2016) for par-ticle diameters D ≥ 500 nm. The MPSS, also commonly referred to as a differential mobility particle sizer (DMPS), consist of an initial section where the aerosol parti-cles become electrically charged (with a defined charge distribution), a DMA used to extract a defined size sub-range from the sample, and a CPC to measure the integral particle number concentration of the transmitted sub-sample. Scanning different size sub-ranges allows to finally infer a NSD by inversion techniques. An APS sizes particles based on their relaxation times. It first accelerates the aerosol sample and then deter-mines the time individual particles need to pass the distance between two subsequent laser beams. This time increases with increasing particle relaxation time and is, hence, longer for larger particles with higher inertia. Calibrating the time-to-size relationship facilitates the derivation of the NSD from the measured distribution of transit times.

For the ground-level NSDs the MPSS data were merged with the APS data for sizes larger 800 nm (Kristensen et al., 2016). The ground-level NSD data are provided by T. Müller, TROPOS.

12K. Heimerl and B. Weinzierl now at: UNIVIE

13In this context, “optimistic” means that the true number concentration of particles that are not efficiently transmitted to the in-cabin instruments can be considered to be≤n_giant.

Cloud Condensation Nuclei Counter (CCNC)

In parallel to the NSD measurement devices, a CCNC was operated at Ragged Point to determine near-ground CCN concentrations for a range of supersaturations. The ground-based CCNC was identical to the one used on the Falcon and calibrated in the same way as the latter. Moreover, direct side-by-side comparison measurements in the laboratory previous to the campaign demonstrated good agreement between the two instruments (Dollner, 2015). In a similar manner to the method presented in Sect.

3.3.4, the Ragged Point NSDs and CCN concentrations were used to derive effective particle hygroscopicities (κ values) for the boundary layer aerosol. Details about the ground-based CCN measurements during SALTRACE are given in Kristensen et al.

(2016). T. Kristensen¹⁴, TROPOS made the data available for this study.

Spectral Optical Absorption Photometer (SOAP)

The presented subset of ground-based in situ data is complemented by the time series of mineral dust mass concentration at Ragged Point, which was inferred from spectral at-tenuation measurements by means of the SOAP (Müller et al., 2009, 2011). The SOAP monitors the transmittance and reflectance of fiber filters being continuously loaded with particles while the sample air is sucked through the device. After correcting the measured attenuation for the light scattering contribution, these measurements yield the spectral run of the absorption coefficient (between 300 and 950 nm). As described in Niedermeier et al. (2014), from these (multi-wavelength) absorption coefficients the mineral dust mass concentration can be derived utilizing estimates for the mineral dust mass absorption coefficients at the corresponding wavelengths. Values for the latter coefficients were obtained following a reanalysis of data from Müller et al. (2009) and Schladitz et al. (2009). The resulting dust mass concentrationsm_dust were provided by T. Müller, TROPOS.

Sun Photometers

In addition to the airborne and ground-based in situ measurements, the aerosol was studied by remote sensing instruments including a number of sun photometers (e.g.

Shaw, 1983). The sun photometer setup comprised AERONET instruments at Cabo Verde, Barbados and Puerto Rico and ancillary instruments located at the CIMH (see Fig. 3.1.2). A detailed overview of this setup is given in Weinzierl et al. (2017).

The principal output parameter of sun photometers is the spectral aerosol optical depth (AOD), which is calculated from the extinction of solar radiation based on the Lambert-Beer law. To describe the attenuation of solar radiation in the atmosphere,τo

in Eq. (2.2.1) is commonly replaced by the productτ_o⁰m_air, whereτ_o⁰ refers to the optical depth of the vertical path and m_air is called the relative air mass, which is defined as mair = 1/cosθz with the solar zenith angle θz. Contributions to τ_o⁰ from Rayleigh scattering of molecules, and absorption by ozone and other gases are estimated and removed to isolate the aerosol component, i.e. the AOD. Here, only AOD data from the Cabo Verde AERONET station and the CIMH measurements at a wavelength of

14T. Kristensen now at: Lund University

500 nm are presented. The sun photometer data were extracted from the AERONET database and provided by C. Toledano, University of Valladolid.

Lidar

Height-resolved remote sensing measurements were carried out at CIMH by means of lidar instruments, including the portable lidar system (POLIS) (Freudenthaler et al., 2009) of the LMU. A lidar, short for light detection and ranging, basically consist of a laser emitting light pulses into the atmospheric column and a receiver collecting the backscattered signal power together with information on the signal time delay.

Consequently, the fundamental output is a profile of atmospheric scattering versus distance from the instrument. More precisely, the total power received at a given time delay — corresponding to a certain distance to the scattering volume — is a function of the so-called backscatter coefficient including scattering by molecules and aerosol particles, the signal attenuation on the way from the lidar to the scattering volume and back, and the distance itself. Polarization lidars (e.g. Sassen, 1991) extent the basic lidar technique by splitting the backscatter signals into two components, parallel- and cross-polarized with respect to the plane of a linearly polarized laser output. The ratio of the two components, called the linear volume depolarization ratio δv is sensitive to the concentration and type (shape) of scatterers —δ_v ≈0 for spherical particles — and is, hence, among others suitable to trace the vertical structure of mineral dust layers.

Here, POLIS vertical δv (quicklook) data at a wavelength of 532 nm are presented.

These data are made available by V. Freudenthaler, LMU. Periods with low-level clouds interfering the lidar measurements by shielding the overlying dust layer are excluded from the data set.